Vehicle clustering improves communication efficiency, safety, and comfort in connected vehicular networks. Cluster heads (CHs) manage critical tasks such as handoff (HO), coordination, and resource allocation. However, in overlapping regions, CH-based group HO handling is hindered by high traffic and limited channel access, causing delays, signaling overhead, and reduced bandwidth efficiency. These issues increase HO latency and risk congestion and HO failures. To address HO loading, latency in overlapping regions, and poor convergence in dynamic environments, this paper proposes an evolutionary game-theoretic framework that selects multiple HO agents (HOAs) in overlapping areas. These agents form HO clusters to distribute load and mitigate channel contention, achieving up to 72.21% lower delay compared to conventional CH-based HO. The buffering of HO data at HOAs, necessitated by the distinct variability of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) links, is addressed through a queueing-theoretic model. This is followed by optimal resource block (RB) allocation, minimizing total waiting time to 2.063 ms at optimal RB values for two HO-clusters and enhancing network stability. At an average vehicle speed of 140 km/h, the proposed HOA-based HO achieves a total average HO delay of 0.91 ms, corresponding to an 83.8% reduction compared with direct V2I HO. Analytical proof of equilibrium and Lyapunov-based stability validation ensure robustness in dynamic environments. It is observed that, under high incentive, a 12.5% increase in vehicle velocity slows the convergence rate by approximately 60%, primarily due to increased channel variability at higher speeds.

Tripping of inverter-based resources (IBRs) powered by renewables poses a key challenge for reliable grid integration, especially at point of common coupling (PCC). Intermittency of solar and wind leads to power fluctuations, causing dynamic variations in PCC voltage amplitude and phase. As a result, voltage parameters sensed by incoming sources become time-varying functions of real-time power exchange. During IBR synchronization, rapid power shifts may induce fictitious frequency deviations estimated by phase-locked loops (PLLs), triggering false tripping during phase angle jumps (PAJs). This article tackles such synchronization and tripping issues for a microgrid comprising a wind-driven doubly fed induction generator (DFIG), solar photovoltaic array, and battery energy storage. A multifunctional control enables seamless operation across grid-tied and off-grid modes, even with or without DFIG stator-PCC connection. A multiple delay signal cancellation (MDSC115) prefilter and enhanced PLL (αβ-EPLLVI) ensure robust synchronization are validated as per the IEEE Std. 1547 via simulation and hardware results.

Four-wire configurations are commonly used in residential microgrids, where single-phase and nonlinear loads introduce harmonics, third-harmonic currents, and negative-sequence components. These harmonics often circulate within delta loops formed by line conductors, while negative-sequence currents cause power imbalance. In wind energy-based microgrids with transformers, such harmonic and unbalanced currents are often reflected into transformer or grid, leading to overheating, increased losses, reduced active power transfer ability, and possible failure. Processing these harmonic currents through transformers to prevent grid contamination demands a higher-rated neutral conductor, adding cost and thermal stress. To address these challenges, this work proposes a three-phase four-wire topology with an auxiliary converter at load point of common coupling (PCC), supported by a solar photovoltaic (SPV) array and battery energy storage (BES). Four-leg auxiliary converter compensates for load harmonics, neutral currents, negative-sequence components, and reactive power, while enabling power exchange from SPV array and BES units. It operates in both grid-forming and grid-following modes to support seamless transitions between islanded and grid-connected states. Integrated multi-mode control ensures coordinated source operation and compliance with IEEE Std. 1547 during mode transitions. Simulation results validate system effectiveness across various modes, demonstrating substantial improvements in power quality and operational reliability in residential microgrids.

Cluster Head (CH)-based group handoff (HO) management in VANETs faces challenges in overlapping regions, where high traffic and limited channel access exacerbate HO delays. These delays, along with loading problems, signaling overhead, and reduced bandwidth (BW) efficiency, lead to congestion and potential failures, further complicating HO management. CHs in VANETs are responsible for various essential functions beyond HO management, including coordination and communication, resource management, and data aggregation. To address the challenges of loading and delay during HO in the overlapping area and the problem of low convergence in dynamic environments, we propose an evolutionary game-theoretic (EGT) framework. This framework selects multiple handoff agents (HOAs) in overlapping regions, effectively distributing the load and reducing HO delays. Given VANETs’ dynamic topology, frequent cluster reformation can disrupt stability; however, the proposed EGT model ensures an evolutionarily stable strategy (ESS), maintaining efficient HO performance despite sudden network changes. The equilibrium point is analytically proven, and stability is further validated using the Lyapunov function, ensuring robustness against disruptions. This work offers valuable insights into achieving stable and efficient HO clustering in VANETs, especially in highly dynamic environments where traditional CH-based HO management approaches struggle. Simulation results confirm that the system quickly stabilizes, preventing failures caused by selfish user behavior.

ABSTRACT Obstructive sleep apnea (OSA) is a common and serious sleep disorder characterized by periodic interruptions in breathing lasting more than 10 s (apnea episodes) during sleep. OSA significantly affects quality of life and overall health, highlighting the critical need for an accurate and timely diagnosis. Polysomnography (PSG) is the standard diagnostic technique for OSA, involving the collection of respiratory, oxygen saturation, biometric, and physiological signals. However, manual analysis of these extensive sleep recordings by medical professionals is labor‐intensive and time‐consuming. To address this challenge, we propose a Siamese Network‐based Self‐Supervised Learning (SSSL) model for the automatic identification of SA episodes from single‐channel electrocardiogram (ECG) signals. Unlike conventional self‐supervised methods, our approach does not require a momentum encoder, large batch sizes, or negative‐positive pair construction. The model is evaluated using the PhysioNet Apnea‐ECG database and employs a two‐stage training strategy. In the first stage, the encoder is trained on unlabeled data to learn robust signal representations. In the second stage, the pre‐trained encoder and classifier are fine‐tuned using labelled data for optimal classification performance. The proposed model achieved high accuracy of , , and when fine‐tuned with , , and of the labelled training data, respectively, for the classification per segment. These results demonstrate the model's effectiveness in both offline and online diagnostic settings, outperforming state‐of‐the‐art methods.

Inverter-based generations (IBGs) in distribution networks require robust anti-islanding protection systems (AIPS) to ensure operational integrity. However, conventional AIPS often struggle to detect accidental islanding events in scenarios where loads and generation are perfectly matched. Therefore, to get over this challenge, a novel hybrid anti-islanding protection scheme using Sandia frequency shift (SFS) phase angle transformation and RMS value of absolute frequency deviation at the point of common coupling (PCC) has been proposed in this paper. The proposed approach triggers the Sandia phase angle transformation solely upon suspicion of an accidental islanding occurrence, utilizing specific parameters monitored at the Point of Common Coupling (PCC). The approach outlined in this methodology is expected to enhance the power quality and stability of IBGs substantially. The performance analysis of the proposed hybrid method for the time domain has been carried out in MATLAB/Simulink. Experimental validation was conducted using a power hardware-in-the-loop (PHIL) testbed setup. The effectiveness of the proposed anti-islanding protection system (AIPS) was tested across more than 200 diverse islanding and non-islanding events. The results demonstrate its robustness and cost-effectiveness, with an islanding detection time as low as 180 milliseconds. Note to Practitioners—This paper introduces a new method for detecting unintentional islanding, a potentially hazardous scenario if left undetected. The proposed methodology offers improved robustness, ease of implementation, and higher accuracy than existing techniques. It aims to address power quality issues associated with active islanding detection methods and achieve zero non-detection zones. With a rapid detection time of only 180 milliseconds, the proposed method ensures swift and reliable identification of islanding events. Experimentally tested across more than 200 islanding and non-islanding scenarios, it proves to be highly accurate, making it a promising solution for real-time islanding detection applications.

This article presents a new control for multirenewable energy sourced microgrid (MG) that performs operational mode change seamlessly with added power quality improvement features. This MG comprises of a wind turbine driven doubly fed induction generator and a solar photovoltaic array. This control presents an adaptive current control based on proportionate robust diffusion recursive least exponential hyperbolic cosine method utilized for fundamental weight extraction. This adaptive control offers a superior convergence rate and noise-free weight estimates, thereby providing significant harmonics reduction from injected grid currents. A change of MG operational mode is due to check imposed by islanding scheme, which functions reliably due to accurate calculation of phase angles provided by multiple delayed signal cancellation (MDSC) frequency-locked loop method. MDSC prefilter mitigates issues of harmonics distortion and dc-offset from input voltage signal, providing accurate estimates. This new control is tested on a developed prototype in the laboratory using a dSPACE MicroLabBox DS1202, under adverse operational conditions.

Community and residential loads connected to low voltage AC distribution networks require a neutral point connection. Neutral point forms return path for single-phase loads. Presence of neutral wire reduces effect of unbalanced loading of one phase to get reflected on other load phases. It helps during fault conditions to minimize effect of circulating current by providing low impedance path. However, in case of harmonically polluted load currents, rating of neutral conductor increases, leading to additional losses and unwanted heating effecting other components of network. Wind energy conversion systems (WECS) use transformer for connection to AC network to reduce requirement of higher rating and size of DC link capacitor. Neutral current and harmonic currents of load are processed through the transformer, which leads to additional heating of transformer ands which is detrimental for connected loads. Such conditions can lead to failure of WECS operation and damage transformer and connected sensitive loads due to flow of circulating currents. Hence, this work presents design and control of a novel four wire topology of a WECS comprising of a doubly fed induction generator (DFIG) and a battery energy storage (BES). A grid forming converter (GFC) is deployed to provide independent compensation for load neutral and harmonic currents without affecting the WECS transformer, reducing its failure and heating rate. WECS converters are controlled to improve power quality of stator current and point of common coupling (PCC) voltage as per the IEEE 519-2022 std. Simulation results present MPPT operation of WECS with neutral and harmonic compensation even during dynamic conditions.

Accurate battery health prediction is vital for ensuring the reliability and longevity of electric vehicles and energy storage systems, leading to optimal performance and dependability. This study explores the potential of Electrochemical Impedance Spectroscopy (EIS) data in predicting battery health through the application of machine learning techniques. By uncovering the intricate relationship between EIS signatures and capacity, this work endeavors to enable monitoring and proactive maintenance strategies for batteries. Through the application of a Bayesian Neural Networks (BNNs) based probabilistic framework, this work aims to provide reliable battery health estimates. The validation has been carried out over diverse charging/discharging and temperature conditions. With a confidence interval of 95%, the predictive model is found to predict capacities with RMSE below 0.98%.

Abstract The Induction Motor (IM) is one of the most frequently used prime movers in most industrial and transportation systems. The motor's stable and safe operation directly influences the secure and reliable operation of such prime movers. Developing an intelligent fault diagnosis system for such motors is very significant. This paper presents an intelligent fault diagnosis method based on the improved functionality of a Convolutional Neural Network (CNN) through its hyperparameter optimization using a nature‐inspired Artificial Bee Colony Optimization (ABCO) algorithm. The proposed diagnostic method introduces and analyses various possible mechanical and electrical faults in the IM. The validation of the proposed method is presented with three different modalities, including vibration, acoustic, and infrared thermography, with their comparative performance analysis. Vibration and acoustic‐based detection are done with time‐frequency scalograms using Constant Q Transform (CQT), which provides enhanced time resolution for lower and higher frequencies. The obtained result indicates that the infrared thermography‐based anomaly detection outperforms the vibration and acoustic‐based diagnosis with 100% classification accuracy. The results signify the potential to diagnose different mechanical and electrical faults in IM with substantial reliability and robustness.

The inherent tendency of single-phase grid-connected ac–dc converters is to produce double-line-frequency ripple at the dc link. A buck-based active power decoupling (APD) converter is employed to solve this issue of double-line-frequency ripple. By operating the APD converter with variable switching frequency, a critical condition mode (CRM) is achieved, which ensures soft switching of both the switches of APD converter. However, high-attenuation-based digital filters in both voltage and current loops are necessary in variable-switching-frequency-based control, which introduces new dynamics in the control loop. This limits the ability of the controller to rapidly adjust the decoupling capacitor voltage with change in output power. In this article, a novel control method is proposed for the APD converter, which entitles the design of a faster decoupling control, while ensuring the system stability and efficiency during dynamic periods. Although the proposed control results in higher robustness and improved transient response, the original dynamics of the system remain unaffected. The steady-state and dynamic performance improvement results with the proposed control of the CRM-operated APD converter are validated through simulation and experimental results.

The faults in photovoltaic (PV) array lead to increased system losses and even fire hazards. The most frequent faults in PV strings are line-to-line (LL) and line-to-ground (LG) faults. Many efforts have been made to develop machine learning-based methods that are capable of detecting faults. However, these methods do not consider low mismatch faults, high impedance faults, active MPPT control, the effect of blocking diodes, step changes in irradiation levels and partial shading conditions in a single window. In this article, a novel and efficient modified binary genetic algorithm (MBGA) based on the weighted K-nearest neighbor method, which incorporates all the abovementioned constraints, has been proposed to identify and classify faults. In addition, it also gives information about the severity of faults. Unlike other machine learning (ML)-based methods, the developed technique considers features based on both frequency and time domain and employs MBGA to extract the optimal set of features, which further improves the accuracy of the algorithm and reduces the size of the dataset. The proposed method efficiently distinguishes faults from sudden shading conditions as both have similar characteristics and prevent false detection. Moreover, it has been verified that the developed method detects faults with an accuracy of 97.3% and classifies LL and LG faults with a precision of 99.25%.

This article presents real-time Pareto-optimal scheduling for bidirectional electric vehicle (EV) charging in a commercial charging station with on-site renewable energy and battery energy storage to optimize several objectives. To incorporate the inherent uncertainty in the model, mixture density neural networks are presented to estimate the parameters of the probability distribution of demands and deadlines using a negative-log-likelihood loss function. From the joint distribution of demands and deadlines, future EV charging requests are estimated. Furthermore, we formulate the control problem as a multiobjective stochastic convex optimization problem from the perspective of the charging station operator, which simultaneously aims to minimize the total cost of charging, frequent change in charging rates, maximum demand of the charging station and battery degradation costs subject to various system constraints. We empirically evaluate the proposed scheduling policy for optimality gap, competitive ratio, and robustness, and show that the proposed scheduling policy reduces cost by about$30 \%$over the benchmark scheduling policies.

This article presents an adaptive current control for wind-fed microgrid (MG) based on doubly-fed induction generator (DFIG). Generally, such wind generation based microgrid is installed in remote locations, where power quality is a concern in the grid. In this aspect, this work proposes an adaptive current control for the stator-side converter of DFIG, which injects MG power with total harmonic distortion less than 5%, as prescribed by the IEEE-519 standard. The adaptive filtering scheme is based on Amari-Alpha least mean square method, which performs good for weight estimation under dynamic operating conditions for such MG. MG possess the ability to transfer between grid-tied and islanded mode based on an islanding scheme, which is grounded on the IEEE-1547 standard. The synchronization process to the grid is critical and requires accurate phase and frequency estimation, thereby forward compensated type-2 phase locked loop (FCT2PLL) is utilized, which is fast tracking due to the forward compensation provided. FCT2PLL is tested here in this scheme, which validates its utility in practical scenarios. Detailed performance is achieved with MATLAB simulation and tested in real-time scenario on a laboratory setup using dSPACE-DS1202 as the controller.

In this study, we jointly investigate the connectivity probability and the number of required resource blocks (RBs) for load reduction during handoff (HO) decision for hybrid vehicle-to-everything (V2X) communication. Frequent HOs is a major issue in vehicular communication, causing various concerns such as loading problems in the overlapping area, signalling overhead, and decreasing the bandwidth efficiency of the network. To combat the loading problem due to HO in the overlapping area and the low convergence issues, we propose a game-theoretic approach to resolve the imbalance of resource allocation amongst the clusters at the edge of the coverage area. The proposed game-based Flexible Resource Allocation (FRA) approach optimally redistributes the resources to all clusters within a g-NodeB (g-NB). We obtain a closed-form expression of the transmit power for a cellular-vehicle-to-everything (C-V2X) standard of 5G network, besides analyzing the effects of connectivity probability and required resources for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) during HO. We explore the implications of non-uniform speeds among the clusters and perform an analysis of time complexity. Additionally, we compare two types of cost functions. A linear cost function links a player’s cost to their action proportionally, while a nonlinear cost function shows a non-proportional relationship between a player’s cost and their action. The simulation results indicate that, with an increasing number of clusters, the linear cost case outperforms the non-linear case in both connectivity probability (which increases by 30.18%) and the number of required resources (which is reduced by 39%) in the HO region. The analytical derivation has been verified through simulation results.

This article introduces a novel shallow 3-D self-supervised tensor neural network in quantum formalism for volumetric segmentation of medical images with merits of obviating training and supervision. The proposed network is referred to as the 3-D quantum-inspired self-supervised tensor neural network (3-D-QNet). The underlying architecture of 3-D-QNet is composed of a trinity of volumetric layers, viz., input, intermediate, and output layers interconnected using an S -connected third-order neighborhood-based topology for voxelwise processing of 3-D medical image data, suitable for semantic segmentation. Each of the volumetric layers contains quantum neurons designated by qubits or quantum bits. The incorporation of tensor decomposition in quantum formalism leads to faster convergence of network operations to preclude the inherent slow convergence problems faced by the classical supervised and self-supervised networks. The segmented volumes are obtained once the network converges. The suggested 3-D-QNet is tailored and tested on the BRATS 2019 Brain MR image dataset and the Liver Tumor Segmentation Challenge (LiTS17) dataset extensively in our experiments. The 3-D-QNet has achieved promising dice similarity (DS) as compared with the time-intensive supervised convolutional neural network (CNN)-based models, such as 3-D-UNet, voxelwise residual network (VoxResNet), Dense-Res-Inception Net (DRINet), and 3-D-ESPNet, thereby showing a potential advantage of our self-supervised shallow network on facilitating semantic segmentation.

Computer-aided diagnosis and prediction of the severity of lung diseases is a promising way to help overburdened medical experts in accelerating and improving their diagnosis. The objective of this work is to investigate the use of deep learning techniques to design a framework for the automatic diagnosis of lung diseases along with the prediction of severity using chest radiographs. We identified input chest radiographs as healthy or belonging to patients with lung disease along with the confidence score of prediction. The unhealthy chest radiograph is further examined to calculate clinical parameters considered in the severity prediction of lung diseases. We calculate clinical parameters such as the extent of lung involvement in disease manifestation, the type of abnormalities present in chest radiographs, and their location in terms of lung zones. We conduct experiments with our in-house Indian database and achieved an accuracy of 95.65% in the classification between healthy and unhealthy chest radiographs. We obtained average precision scores of 0.8128, 1.00, 0.8214, and 0.9650 for the detection of effusion, cavity, lymphadenopathy, and opacity respectively. Experimental results indicated that the proposed framework can be used to provide rapid and cost-effective screening in places where massive traditional testing is not feasible.

The nonlinear loads demean the system power quality leading to increased ohmic losses and shorter equipment lifespan. To mitigate power quality challenges, this work proposes a multiple delayed signal cancellation filter with 15 layers and unit delay factor (MDSC115) for improved power quality of a grid-tied solar power conversion system (SPCS) equipped with a battery energy storage (BES). The control attenuates all integral positive sequence harmonic components from 2ndto 30thharmonics excluding positive sequence 16thharmonic. It also eliminates integral negative sequence harmonic components from 1stto 28thharmonics excluding negative sequence 14th harmonic, including 0 Hz DC component, without the need of additional prefilters cascaded to the filter. A modified frequency locked loop (MFLL) is incorporated to make the MDSC115 adaptive to the varying grid frequency and make it immune to phase angle jump and frequency fluctuations in the grid voltages. The MDSC115-MFLL improves the power quality of SPCS during grid adversities and load current irregularities. Performance of MDSC115-MFLL is validated in MATLAB during several dynamic test cases and compared with other delay-based controls to ensure better performance and commitment to the IEEE std. 519 during operation of SPCS.

In this study, we jointly investigate the connectivity probability and the number of required resource blocks (RBs) for load reduction during handoff (HO) decision for hybrid vehicle-to-everything (V2X) communication. Frequent handoffs (HOs) is a major issue in vehicular communication, causing various concerns such as loading problems in the overlapping area, signalling overhead, and decreasing the bandwidth efficiency of the network. To combat the loading problem due to HO in the overlapping area and the low convergence issues, we propose a game-theoretic approach to resolve the imbalance of resource allocation amongst the edge clusters. The proposed game-based Flexible Resource Allocation (FRA) approach optimally redistributes the resources to all clusters within a base station (BS). We obtain an approximate closed-form expression of the transmit power for a 5G network, besides analyzing the effects of connectivity probability and required resources for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) during HO. The analytical derivation has been verified through simulation results.

As a consequence of limb loss, unilateral lower limb amputees (LLAs) exert additional flexion torque in the intact limb during postural balance, leading to other secondary complications. A prosthetic device with torque delivery is required to avoid such complications. However, estimating desired torque is an acknowledged challenge in this domain. Motivated by the above, this paper focuses on estimating the corrective torque required from an assistive device to balance the individual while performing weight-shifting exercises. Further, a healthy individual’s inverted pendulum (IP) model is explored to model LLA’s dynamics during weight-shifting. The model and the control strategy are validated using forward and backward body lean angles recorded from healthy and LLA individuals. Finally, a robust Proportional Integral Derivative controller is designed to estimate the corrective torque for maintaining the postural balance during weight-shifting. Along with the IP dynamics, a suitable control law based on Coefficient Diagram Method achieves the experimentally recorded body lean angle. The proposed control scheme is validated through simulation studies to evaluate the tracking performance in two different biomechanically relevant IP postural models. The findings assist in determining the external torque required to maintain the postural balance during gait initiation and compensatory measures for pathologically reduced torque. Note to Practitioners—Gait initiation is a transition between vertical posture and gait. Since vertical stance is inherently unstable, a postural adjustment occurs before gait initiates, propelling the center of mass forward and towards the first stance leg. The absence of flexor torque in the amputated limb causes a loss of postural stability during weight shifting activities. In the case of powered exoskeletons and prosthetic devices, a prior estimation of corrective torque through a computational approach helps to select a suitable actuator that provides an appropriate joint torque while maintaining the postural balance. As a result, an efficient computational model of gait initiation that accounts for neuromuscular system components before designing assistive devices is necessary. The controllers further provide corrective action to the assistive device to obtain robust and optimal coordination between the prosthetic element and gait. Therefore, this study demonstrates the significance of neuromuscular controller and assists in determining how much corrective torque from an external device is required to maintain postural stability and adaptability during weight shifting exercises. The effectiveness of the neural controller is also tested for real-time data to compensate for the effect of external disturbance and noise, thereby achieving a proper and controlled degree of movement in amputee users. These findings could provide one of the fundamental aspects of gait initiation model paradigms of compensatory measures for pathologically diminished torque and contribute to developing low-cost prosthetic solutions for the benefit of the rehabilitation society in developing countries.

Wind power forecasting is very crucial for power system planning and scheduling. Deep neural networks (DNNs) are widely used in forecasting applications due to their exceptional performance. However, the DNNs’ architectural configuration has a significant impact on their performance, and the selection of proper hyper-parameters determines the success or failure of these models. Therefore, one of the challenging issues in DNNs is how to assess their hyper-parameter values effectively. Most of the previous researches in the literature have tuned the DNNs’ hyper-parameters manually, which is a weak and time-consuming task. Using optimization/evolutionary algorithms is an effective way to obtain the optimal values of DNNs’ hyper-parameters automatically. In this article, we propose a novel evolutionary algorithm that is based on the grasshopper optimization algorithm (GOA) improved by adding two evolutionary operators, opposition-based learning and chaos theory, to the optimization process. Overall, a novel probabilistic wind power forecasting model named neural GOA deep auto-regressive (NGOA-DeepAr) is proposed based on an auto-regressive recurrent neural network in which the proposed evolutionary algorithm has optimized its hyper-parameters. The performance of the proposed NGOA-DeepAr model is tested on two different datasets: One is the publicly available GEFCom-2014 dataset and the other is the Australian Energy Market Operator dataset. The prediction interval coverage probability and pinball loss for the two datasets are$[0.902, 0.320]$and$[0.933, 1.4885]$, respectively. According to the experimental findings, our proposed NGOA-DeepAr is much faster in learning and outperforms the benchmark DNNs and the other neuroevolutionary models.

Vulnerability of the wide area measurement system (WAMS) to denial-of-service (DoS) attacks can hinder the real-time situational awareness of the grid. In this regard, a DoS cyber-attack resilient WAMS framework is presented in this article, which utilizes deep-learning-based overcomplete denoising autoencoder architecture. The proposed framework is able to reconstruct phasor measurement unit (PMU) data during DoS attacks on PMU-phasor data concentrator (PDC) connections for the entire period of power system contingencies, which helps the operator to be aware of the occurrence of any such disturbance and assist them in decision-making related to the stability of the grid. The performance of the proposed framework has been validated on western system coordinating council (WSCC) 9-bus system, which is simulated on the developed cyber-physical WAMS testbed involving real time digital simulator, hardware PMUs, hardware PDC, network switches, etc. Subsequently, the response of the suggested framework has also been evaluated on the real field PDC data gathered from the Northern Region of the Indian Power Grid. The experimental results indicate that the reconstructed PMU measurements during DoS attacks help in maintaining the dynamic visualization of system-wide information thereby ensuring the resiliency of wide area monitoring applications against DoS attacks.

The state of health (SOH) of lithium-ion battery is very crucial in accessing the performance of electric vehicle (EV) as it is the indicator of degraded battery capacity or increased internal resistance over time. In the recent years, the machine learning based SOH estimation has garnered much attention due to the complex and nonlinear nature of battery ageing process. In this paper, five Health Indicators (HIs) are extracted from the battery data, which are both convenient and feasible to be extracted in real-time driving conditions. Based on the utmost practicality, a novel HI ‘Deviational Voltage over Relaxation Time (DVR)’ fed to Gaussian Process Regression (GPR) network is used to evaluate the estimation performance in potential real usage using NASA battery dataset. The results show that DVR correctly captured the battery ageing phenomena and provides superior estimation performance in terms of computational time and accuracy.

A data-driven approach for improving wide-area situational awareness (SA) in modern power systems for building a more resilient grid is proposed in this article. SA is of paramount importance when it comes to accurate sensing of events for better visualization of the power system dynamics. Various events uniquely excite particular states of the power systems resulting in a maximum change in the entropy for certain power system components. A new conceptual framework utilizing structure preserving energy function (SPEF) for identifying the specific component of the energy function and correlating it with the appropriate event has been suggested. Cubature Kalman filter-unknown input based dynamic state estimation technique has been employed for the extraction of states and unknown inputs from the phasor measurement unit (PMU) measurements for the derivation of the SPEF-based event indicators. In order to demonstrate the efficacy of the proposed technique, it has been validated on the IEEE 39-bus system and also on actual PMU data obtained from the Indian power grid. The results indicate the suggested technique to be accurate and robust to noise and thus can be a promising tool for real-time power system monitoring.

Classical self-supervised networks suffer from convergence problems and reduced segmentation accuracy due to forceful termination. Qubits or bilevel quantum bits often describe quantum neural network models. In this article, a novel self-supervised shallow learning network model exploiting the sophisticated three-level qutrit-inspired quantum information system, referred to as quantum fully self-supervised neural network (QFS-Net), is presented for automated segmentation of brain magnetic resonance (MR) images. The QFS-Net model comprises a trinity of a layered structure of qutrits interconnected through parametric Hadamard gates using an eight-connected second-order neighborhood-based topology. The nonlinear transformation of the qutrit states allows the underlying quantum neural network model to encode the quantum states, thereby enabling a faster self-organized counterpropagation of these states between the layers without supervision. The suggested QFS-Net model is tailored and extensively validated on the Cancer Imaging Archive (TCIA) dataset collected from the Nature repository. The experimental results are also compared with state-of-the-art supervised (U-Net and URes-Net architectures) and the self-supervised QIS-Net model and its classical counterpart. Results shed promising segmented outcomes in detecting tumors in terms of dice similarity and accuracy with minimum human intervention and computational resources. The proposed QFS-Net is also investigated on natural gray-scale images from the Berkeley segmentation dataset and yields promising outcomes in segmentation, thereby demonstrating the robustness of the QFS-Net model.

A unique way evolutionary algorithms (EAs) are different from other search and optimization methods is their recombination operator. For real-parameter problems, it takes two or more high-performing population members and blends them to create one or more new solutions. Many real-parameter recombination operators have been proposed in the literature. Each operator involves at least a parameter that controls the extent of exploration (diversity) of the generated offspring population. It has been observed that different recombination operators and specific parameters produce the best performance for different problems. This fact imposes the user to use different operator and parameter combinations for every new problem. While an automated algorithm configuration method can be applied to find the best combination, in this paper, we propose an Ensembled Crossover based Evolutionary Algorithm (EnXEA), which considers a number of recombination operators simultaneously. Their parameter values and applies them with a probability updated adaptively in proportion to their success in creating better offspring solutions. Results on single-objective and multi-objective, constrained, and unconstrained problems indicate that EnXEA's performance is close to the best individual recombination operation for each problem. This alleviates the use of expensive parameter tuning either adaptively or manually for solving a new problem.

On-board charger (OBC) power handling capability has been increasing over the past few years for reducing the charging time. This requires a much detailed analysis of the losses incurred in OBCs for better volumetric design and improved effi-ciency. This paper presents a comprehensive loss model of a single stage (1-S) single phase (1-ϕ) OBC. The OBC is realized using a current-fed half-bridge converter at grid side with swinging boost inductor and a full-bridge converter at the battery side, galvanically isolated by a high frequency transformer (HFT). Swinging boost inductor maintains high power factor and keeps the inductor current in continuous conduction mode (CCM) for a wide load range. The effect of swinging boost inductor on the losses of the converter is presented. A laboratory prototype connected to 230 V/50 Hz mains voltage with the output voltage range of 300-400 V is also developed to validate the theoretical analysis.

The perturb & observe (P&O) algorithm is very popular for maximum power point tracking (MPPT) for solar photovoltaic (PV) systems. However, it has tracking problems during varying irradiations as well as the nuisance of oscillations around the maximum power point (MPP). This work introduces a circle center-line concept based P&O (CCCP&O) algorithm for MPPT, where, the concept of circle and its center are combined with the P&O algorithm. This algorithm tends to reduce the number of iterations taken to reach the MPP, which reduces settling time. Moreover, the problem of large oscillations around the MPP is eliminated by using the concept of flexible step size. The algorithm initializes with standard P&O, but utilizes a approach of diameter equivalence of a circle as a procedure to reach next operating point on the power-voltage plot. Therefore, the iterations required to get to the MPP are reduced substantially. The MPP changes with change in solar irradiance, therefore, a concept of artificial envelope around the P-V curve is used to improve tracking of the algorithm during varying irradiances. The overall performance of the algorithm is demonstrated and compared in simulation using SIMULINK MATLAB as well as also shown experimentally in a developed hardware prototype.

In this paper, a cyber-physical system (CPS) is considered, whose state estimation is done by a central controller (CC) using the measurements received from a wireless powered sensor network (WPSN) over fading channels. An adversary injects false data in this system by compromising some of the idle sensor nodes (SNs) of the WPSN. Using the WPSN for transmitting supervision and control data, in the aforementioned setting, makes the CPS vulnerable to both error and false data injection (FDI). The existing techniques of launching stealthy FDI attack are not applicable to the aforementioned network due to the random nature of wireless channels, which is used for both transmitting control and false data. The objectives of the adversary and the CC to launch stealthy FDI attack and to detect the same, respectively, are found to be depending on the powers they use for transmitting data over wireless channels. The transmit powers of the CC, and the adversary that fulfill their respective objectives are derived by modeling their interaction as a Bayesian Stackelberg game. Based on their objectives, novel utility functions are defined for the CC and the adversary. Subsequently, the equilibrium of the proposed game is obtained by solving a non-convex bi-level quadratic-quadratic program. Finally, the analytical results are verified and compared with other state-of-art techniques by applying them in a realistic smart grid simulations.

Social media conveys a reachable platform for users to share information. The inescapable practice of social media has produced remarkable volumes of social data. Social media gathers the data in both structured-unstructured and formal-informal ways as users are not concerned with the exact grammatical structure and spelling when interacting with each other by means of various social networking websites (Twitter, Facebook, YouTube, LinkedIn, etc.). People are increasingly involved in and dependent on social media networks for data, news and opinions of other handlers on a variety of topics. The strong dependence on social media network sites contributes to enormous data generation characterized by three issues: scale, noise, and variety. Such problems also hinder social network data to be evaluated manually, resulting in the correct use of statistical analytical methods. Mining social media data can extract significant patterns that can be advantageous for consumers, users, and business. Pattern mining offers a wide variety of methods to detect valuable knowledge from huge datasets, such as patterns, trends, and rules. In this work, data was collected comprised of users’ opinions and sentiments and then processed using a significant number of pattern mining methods. The results were then further analyzed to attain meaningful information. The aim of this paper is to deliver a summary and a set of strategies for utilizing the ubiquitous pattern mining approaches, and to recognize the challenges and future research guidelines of dealing out social media data.

Hazy environment attenuates the scene radiance and causes difficulty in distinguishing the color and texture of the scene. A crucial step in dehazing is the recovery of the global air-light vector. Traditional methods usually interpret the RGB value of the brightest region in haze images as the air-light. In this letter, a new prior called `color constancy prior' has been proposed to improve the robustness of air-light estimation when varicolored illumination exists. The prior utilizes the statistical observation that distant scenery objects become the most haze-opaque due to the pixel escalation towards the higher intensity side. The comparative evaluation on a variety of haze images manifests that the proposed prior perform better than existing air-light recovery methods and can be used for subsequent dehazing applications.

In this article, the presented work proposes a power management scheme in a dc microgrid by utilizing integrated device level control designs. Dynamics of nondispatchable energy sources, energy storage systems, and critical loads while synthesizing the control scheme are extensively studied. The presented work focuses on the integrated operation of local converter controls and power control unit during load fluctuations, environmental changes, accidental islanding, state of charge (SOC) breach, etc., without any time-critical information sharing. Low bandwidth communication (LBC) assisted control actions are enabled only in response to specific events such as battery management system (BMS) operation in autonomy, onset of peak hours, and load shedding. This article examines small signal analysis of individual interfacing converters to conduct eigenvalue studies of the closed-loop control systems. Successful control transitions are achieved in real-time digital simulator to validate the proposed management strategy under various system conditions. Hardware-in-the-loop setup validates the implement-ability of the control strategy in dc microgrids.

A quantum-inspired self-supervised neural network framework titled Quantum-Inspired Optimized Bi-Directional Self-Organizing Neural Network (Opti-QIBDS Net) suitable for fully automated MR image segmentation is suggested in this article. The suggested Opti-QIBDS Net is characterized by Otsu's multi-class level thresholding scheme based optimized Quantum Inspired Multi-level Sigmoidal (Opti-QIMUSIG) activation function. The network layers of the Opti-QIBDS Net architecture are inter-connected through second order neighborhood based topology and constituted by quantum neurons. The intermediate and output layers of the Opti-QIBDS Net framework are inter-connected through counter propagation of quantum states and pixel intensities are self-organized in counter-propagation fashion obviating any external supervision. Quantum observation is carried out at the end to obtain the segmented tumor from the superposition of quantum states. The proposed optimized self-supervised network architecture has been tested on T1 CE-weighted MR images and found to be very efficient while compared with other supervised and unsupervised approaches.

In this work, the authors consider the communication network of a power substation, where multiple intelligent electronic devices (IEDs) transmit their delay sensitive control and monitoring messages to a common receiver over a wireless network; in the presence of a broadband jammer, which is capable of jamming multiple channels simultaneously. The objective of the IEDs is to successfully transmit their messages within a specified time, whereas the jammer wants to obstruct IED's transmission. A novel utility function is designed for the players, that addresses the time‐critical nature of communication. Due to the conflicting interest of the IEDs and the jammer, they model the interaction between them as a repeated Bayesian zero‐sum game, which also addresses the repeated interaction among the IEDs and the jammer, and the unavailability of exact information about the jammer. The equilibrium strategies for both the scenarios of perfect and imperfect monitoring are derived and verified through simulation results. Further, the performance of the proposed game model in various scenarios is thoroughly compared in the result section. Finally, the efficacy of the proposed defence strategies is tested in a practical communication network of a power substation under jamming, which is simulated in Optimised Network Engineering Tool (OPNET).

Demand response exchange (DRX) presents a pool-based approach for optimal clearing/scheduling of demand response (DR) services through bid clearing. The same is intended to minimize the limitations of price-based and incentive-based mechanisms. However, unlike the price-based or incentive-based DR schemes, the DR sellers in DRX do not have a clear motivation to formulate the bid offers. Similarly, the DR buyers' bid formulation should also include optimal benefit analysis. This paper formulates and analyzes the bid formulation for DR sellers and buyers considering respective operation objectives. The DR seller bid formulation is derived considering load behavior through utilization index and availability index obtained from load profile of respective loads. On the other hand, DR buyer bid formulation is derived from power exchange operation attributes/cost of generation. Therefore, the DR clearing ultimately affects the operational cost of operation which, in turn, is reflected in DR buying bid and DR clearing finally. This paper develops an iterative approach in which the continuous interactions between ISO/power exchange would finally converge to a stable operation. The load profile-based strategic bid formulation is modeled using mathematical as well as fuzzy inference system (FIS). In addition, an adaptive FIS has been devised to improve the performance of DR scheduling. The simulation results illustrate the impact of customer behavior, intelligent decision-making, and penetration level on the performance and convergence of power markets (DRX and power exchange).

In this paper, a novel power normalized kernel least mean fourth algorithm based neural network (NN) control (PNKLMF-NN) technique and learning-based hill climbing (L-HC) maximum power point tracking (MPPT) algorithm are proposed for grid-integrated solar photovoltaic (PV) system. Here three-phase single-stage topology of a grid-integrated PV system is used for feeding the nonlinear/linear load at the point of common coupling. A single layer neuron structure is used for active load component (ALC) extraction from distorted load current. During ALC extraction, PNKLMF-NN control very precisely attenuates harmonics components, noise, dc offset, bias, notches, and distortions from the nonlinear current, which improves the power quality under normal as well as under abnormal conditions. This single layer PNKLMF-NN control has a very simple architecture, which reduces the computational burden and complexity. Therefore, it is easy in implementation. Moreover, proposed L-HC is the improved form of hill climbing (HC) algorithm, where inherent problems of traditional HC algorithm, such as steady-state oscillation, slow dynamic responses, and fixed step size issues, are successfully mitigated. The prime objective of proposed PNKLMF-NN control is to meet the active power requirement of the loads from generated solar PV power and excess power fed into the grid. However, when generated PV power is less than the required load power, then PNKLMF-NN control meets the load by taking extra required power from the grid. During these processes, power quality is maintained at the grid. Moreover, when solar irradiation is zero, voltage source converter (VSC) acts as distribution static compensator (DSTATCOM), which enhances the utilization factor of the system. The proposed techniques are modeled and their performances are verified experimentally on a developed prototype in adverse conditions, which test results have satisfied the objectives of the proposed system and the IEEE-519 standard.

This paper proposes a multitimescale coordinated building energy management system (BEMS) for multienergy buildings integrated with renewable energy sources (RES). It aims to dispatch active building components in two different timescales to counteract uncertain variations in RES generation and load. In the longer timescale (hourly), fuel-cell-based microcombined heat and power and energy storage system (ESS) are dispatched before uncertainty is realized. In the 15 min timescale, ESS is redispatched to supplement the first stage decision, once the uncertainty is realized. The multitimescale coordination is achieved through a two-stage stochastic programming model. The BEMS has been developed as nonlinear receding horizon and nonlinear quadratic programming models in the first and second stages, respectively. In order to minimize carbon footprint of the building, carbon tax has been incorporated in the system operation cost. Furthermore, to prolong battery lifetime in uncertain environment, battery degradation cost has been included. Case studies depict appropriateness of the proposed method in achieving lower carbon emissions while simultaneously improving battery performance in comparison to traditional systems. Extensive simulation results demonstrate robustness and effectiveness of the proposed scheme to account for uncertainties in generation and load.

Images captured in underwater (UW) are often disturbed with several kind of degradation such as low visibility, non-uniform color cast, haze, and blurriness. To date, most UW image restoration methods have ignored the effects of sensor blur and noise. Therefore, in this paper, we propose a novel three stage algorithm for visibility recovery in UW images by considering both sensor blur and noise. In the first stage, blind deconvolution is used for the estimation of an unknown point spread function (PSF). In the second stage, a new prior called weighted median channel prior (WMCP) is used for the estimation of scene depth and background light. In the third stage, a color balancing (CB) module is adopted to minimize the effect of non-uniform color cast. Experimental results manifest that the proposed algorithm is effective and has the character of visibility improvement, and color correction than previous state-of-the-art methods.

With the growth of electric vehicles across the globe, it is essential to have a corresponding charging infrastructure developed. Several research works highlight the benefits of aggregator-based scheduling, where all aggregators operate in similar conditions with the same amount of purchased energy. However, there are unscheduled EVs left due to unavailability of sufficient energy and/or charging slots. This paper presents a multiaggregator-based charge scheduling scheme that incorporates collaborative charging and realistic situations with variable energy purchase (VEP) and cancelation charges. VEP reflects a practical situation where the aggregator purchases energy based on average scheduling requests per day. Similarly, customers are penalized for not arriving at the scheduled slot. The charge scheduling problem is addressed from the aggregator point of view, wherein the total profit and the number of scheduled EVs are maximized. With VEP, a greater number of EVs were scheduled through collaboration and overall profits were observed to be higher than with equal energy purchased. The implementation of cancelation charges on the customer further resulted in an increase in the profits.

This paper presents an empirical wavelet transform (EWT) based approach to perform postmortem analysis of wide-area measurement (WAM) based signals. The commonly used empirical mode decomposition (EMD) has limitations such as mode mixing, sensitivity to noise, and sampling rate. The decomposition provided by EWT is more consistent as compared to EMD. The modes revealed by the EWT help in extracting dynamic patterns of different power system disturbances. The dynamic patterns extracted through EWT-based decomposition are further used as inputs to a data-mining tool known as random forest, to build a wide-area disturbance classifier (WADC) model. The efficient mode extraction quality of the EWT-based signal processing tool is analyzed for WAM data recorded on Northern Grid of Indian Power System. The performance of the WADC is validated on IEEE 39-bus New England test system. The results provide improved performance in terms of decomposition quality and classification accuracy.

This paper investigates the combined price-based scheduling/participation of generation company (GENCO) and demand response aggregation company (DRACO) in energy and reserve markets. The temporally coupled customer behavior can be better represented using the load profile attributes, when compared to the traditional approach with random willingness assignment. The proposed cost models for energy and reserve offerings consider the effect of load type, load pattern consumption, and availability/flexibility patterns of each type of load with time of use constraints. The load curtailment (LC) cost model accounts for criticality and willingness of the responsive loads via utilization factor and availability factors, respectively. The proposed cost models present a realistic picture of LC cost by eliminating the random willingness factor of the existing LC cost models. Thereafter, various cases of market participation with different reserve payment policies are formulated for combined participation of GENCO and DRACO. In addition, the sensitivity of participation decision of various entities to the seasonal load variation is examined for summer and winter loading profiles. The proposed cost models and scheduling framework is simulated using GENCO with ten thermal units and DRACO with various load types, profiles distributed across different load sectors comprising of commercial, residential, industrial, municipal, and agricultural loads. The combined participation resulted in improved market surplus with reduced GENCO surplus. Also, the energy and reserve market surplus dependence on seasonal load patterns is observed across different test cases and payment policies.

This paper investigates the impact of load profiling attributes on the demand response exchange (DRX) mechanism. Modeling of demand response (DR) seller bids in traditional approach/model includes a customer willingness factor assigned in a random/arbitrary manner. The proposed model of DR bids takes into account, the load attributes such as utilization and availability factors. The criticality and willingness of the DR load/customer are embedded into seller market bids through utilization and availability factor, respectively. Various cost models have been developed to emulate the possible customer behavior in DR cost modeling. However, modeling of indistinct and uncertain nature of customer behavior is a complex issue in real-time consideration. Therefore, this paper also presents a fuzzy inference system (FIS) for considering the customer load profile attributes in DR bids. In addition, parameter tuning of fuzzy membership functions is also carried out in this paper using heuristic optimization techniques to improve the performance of FIS in DRX clearing. The proposed methodology with load profile attributes is simulated considering various load types across different load sectors. Simulation results of proposed tuned FIS-based DRX clearing are compared with other nonfuzzy models, conventional models without load attributes and untuned FIS system to demonstrate the effectiveness of tuned FIS with load profile attribute consideration in DRX clearing.

This paper deals with a new version of an incremental conductance algorithm for maximum power harvesting (MPH) from the solar photovoltaic array, which has inherent decision taking and self-adaptive ability. The working principle of a self-adaptive incremental conductance (SAInC) algorithm is based on three consecutive operating points on the power–voltage characteristic. These points smartly detect the dynamic condition, as well as under normal condition, search the maximum power peak (MPP) zone. Moreover, using triangular analogy, it decides the optimum operating position for next iteration, which is responsible for quick MPP tracking as well as good dynamic performance. Here, in every new iteration, the step-size is reduced by 90% from the previous step-size, which provides an oscillation-free steady-state performance. The effectiveness of the proposed technique is validated by MATLAB simulation as well as tested on an experimental system. Moreover, performance of an SAInC algorithm is compared with the popular and recent state-of-the-art methods. The satisfactory dynamic and steady-state performances with low complexity as well as low computational burden of the SAInC algorithm show the superiority over state-of-the-art methods.

This paper presents an adaptive control approach based on a novel multilayer fifth-order generalized integrator (MFOGI) and single-input fuzzy-tuned proportional-integral (PI) controller for single-phase two-stage grid-connected partially shaded solar photovoltaic (PV) system, where the global maximum power point (GMPP) is tracked by using a novel human psychology optimization (HPO) algorithm. The MFOGI is used to extract the fundamental component from the grid voltage, even when the grid voltage is characterized by undervoltage, overvoltage, severe harmonics distortion, frequency variations, direct current (dc) offset, etc., on a wide range. Moreover, the single-input fuzzy-tuned PI controller is used for online PI controller gains tuning during different disturbances and dynamic conditions. To ensure a fast dynamic response, a PV feed-forward component is also included in the control algorithm as well as HPO is developed for quick GMPP tracking (GMPPT). The proposed control is modeled and simulated in MATLAB platform, as well as tested on a developed prototype in the laboratory. During simulation and testing, overvoltage, undervoltage, severe harmonics distortion, and dc offset in grid voltage, as well as insolation and temperature variations on PV array are considered. The total harmonic distortions in grid current, during the different complex disturbances and dynamic situations, are found very less, in comparison to the state-of-the-art techniques and IEEE-519 standard, as well as due to the HPO algorithm, the GMPPT time is very less, which shows the superiority over state-of-the-art techniques.

For reliable operation and control of smart grid, estimating the correct states is of utmost importance to the system operator. With recent incorporation of information technology and advanced metering infrastructure, the futuristic grid is more prone to cyber-threats. The false data injection (FDI) attack is one of the most thoroughly researched cyber-attacks. Intelligently crafted, it can cause false estimation of states, which further seriously affects the entire power system operation. In this paper, we propose joint-transformation-based scheme to detect FDI attacks in real time. The proposed method is built on the dynamics of measurement variations. Kullback–Leibler distance is used to find out the difference between probability distributions obtained from measurement variations. The proposed method is tested using IEEE 14 bus system considering attack on different state variables. The results shows that the proposed scheme detects FDI attacks with high detection probability.

A switched-parameter Differential Evolution (DE) enforced with equiprobable switching between two alternative mutation strategies, an optional blending crossover, and a threshold-based selection mechanism is proposed for optimization of complex functions corrupted with additive noise. In order to handle the noisy optimization problems, the DE framework is coupled with three new algorithmic components. Each individual is subjected to one of the two well known mutation strategies namely DE/best/1 and DE/rand/1 with equal chances. In the recombination stage, binomial and blending crossovers are opted in the same switchable strategy as done for mutation. A novel threshold-based selection mechanism is used to allow less fit offspring to survive occasionally, thus countering the noisy function behavior. Additive Gaussian noise is used to simulate the noisy behavior of functions defined over continuous search spaces. A benchmark suite comprising of 21 well-known numerical functions is considered to compare and contrast the proposed method with other state-of-the-art evolutionary algorithms specifically tailored for noisy optimization scenario. The proposed method shows very competitive performance indicating highly robust behavior against the noisy functional landscapes.

Coal mill is an essential component of a coal-fired power plant that affects the performance, reliability, and downtime of the plant. The availability of the milling system is influenced by poor controls and faults occurring inside the mills. There is a need for automated systems, which can provide early information about the condition of the mill and help operators to take informed decisions. In this paper, a model-based residual evaluation approach, which is capable of online fault detection and diagnosis of major faults occurring in the milling system, is proposed. A dynamic mathematical model of mill, which can authentically replicate the mill behavior under different conditions, is selected for residual generation. Fuzzy logic is employed for residual evaluation to determine the type and magnitude of the fault, while Bayesian network is used for troubleshooting the root cause. The proposed technique is validated using historical data of coal mills obtained from an actual coal-fired power plant in India. Two case studies are presented to demonstrate the effectiveness of the approach. The results indicate that the proposed approach has potential to provide useful information regarding the condition of the mills and can help operators to take appropriate control action timely. This application also shows that how fuzzy logic and Bayesian networks (probability theory) can complement each other and can be used appropriately to solve parts of the problem.

In photovoltaic (PV) array, the output power and the power–voltage (P–V) characteristic of PV array are totally dependent on the temperature and solar insolation. Therefore, if these atmospheric parameters fluctuate rapidly, then the maximum power point (MPP) of theP–Vcurve of PV array also fluctuates very rapidly. This rapid fluctuation of the MPP may be in accordance with the uniform shading of the PV panel or may be in accordance to the partially shaded due to the clouds, tall building, trees, and raindrops. However, in both cases, the MPP tracking (MPPT) is not only a nonlinear problem, this becomes a highly nonlinear problem, which solution is time bounded. Because the highly fluctuating atmospheric conditions change theP–Vcharacteristic after every small time duration. This paper introduces a hybrid of “Jaya” and “differential evolution (DE)” (JayaDE) technique for MPPT in the highly fluctuating atmospheric conditions. This JayaDE algorithm is tested on MATLAB simulator and is verified on a developed hardware of the solar PV system, which consists of a single peak and many multiple peaks in the voltage–power curve. Moreover, the tracking ability is compared with the recent state of the art methods. The satisfactory steady-state and dynamic performances of this new hybrid technique under variable irradiance and temperature levels show the superiority over the state-of-the-art control methods.

The electroencephalogram (EEG) signals are commonly used for diagnosis of epilepsy. In this paper, we present a new methodology for EEG-based automated diagnosis of epilepsy. Our method involves detection of key points at multiple scales in EEG signals using a pyramid of difference of Gaussian filtered signals. Local binary patterns (LBPs) are computed at these key points and the histogram of these patterns are considered as the feature set, which is fed to the support vector machine (SVM) for the classification of EEG signals. The proposed methodology has been investigated for the four well-known classification problems namely, 1) normal and epileptic seizure, 2) epileptic seizure and seizure free, 3) normal, epileptic seizure, and seizure free, and 4) epileptic seizure and nonseizure EEG signals using publically available university of Bonn EEG database. Our experimental results in terms of classification accuracies have been compared with existing methods for the classification of the aforementioned problems. Further, performance evaluation on another EEG dataset shows that our approach is effective for classification of seizure and seizure-free EEG signals. The proposed methodology based on the LBP computed at key points is simple and easy to implement for real-time epileptic seizure detection.

In this paper, an underlay based cognitive radio network (CRN) consisting of a secondary base station (SBS), a secondary receiver (SR), an energy harvesting node (EHN), and a primary transmitter-receiver (PT-PR) pair, is considered. The EHN is assumed to have a power splitter (PS) at the receiver to decode information and harvest energy simultaneously from the SBS transmission, thus reducing the secrecy capacity of SBS. Since both the SBS and the malicious EHN are selfish with conflicting interests, we model the interaction between them as a non-cooperative Stackelberg game. The objective of the SBS is to maximize the secrecy rate and minimize its interference at the PR, whereas the EHN aims at maximizing both the energy it can harvest and the information it can decode from the received SBS signal. The existence of a unique equilibrium point is proved through mathematical analysis. Further, a distributed algorithm is proposed, following which the players will attain the equilibrium of the game as predicted by analytical results. Analytical and simulation results demonstrate that both SBS and EHN can achieve their optimal utilities by adopting the equilibrium strategy.

Accurate prediction of wind ramp events is critical for ensuring the reliability and stability of the power systems with high penetration of wind energy. This paper proposes a classification based approach for estimating the future class of wind ramp event based on certain thresholds. A parallelized gradient boosted regression tree based technique has been proposed to accurately classify the normal as well as rare extreme wind power ramp events. The model has been validated using wind power data obtained from the National Renewable Energy Laboratory database. Performance comparison with several benchmark techniques indicates the superiority of the proposed technique in terms of superior classification accuracy.

Power line communication (PLC) has recently grabbed the attention of the researchers owing to its huge potential to provide high speed access to video and data. Since power lines were not initially designed for communication purposes, they offer a difficult communication environment in the form of impulsive noise and multiplicative noise, in addition to the background noise. This elucidates the need for evaluating the performance of PLC systems by taking into account all these factors. In this paper, we study the performance of a PLC system in the presence of Rayleigh channel gain under the combined effects of Nakagami-m distributed background noise with non- uniformly distributed phase and Middleton class A distributed impulsive noise. We evaluate closed- form expressions of the analytical average bit error rate for different cases depending on the presence or absence of impulsive noise and channel gain. We provide a further insight into the system by obtaining the diversity of the PLC system. Our analysis is validated by a close matching with the simulation results.

This paper deals with brushless generators-based isolated wind-diesel microgrid for rural areas. Here, a permanent magnet brushless dc generator (PMBLDCG) is used to convert renewable wind power into electrical energy. A diesel engine-driven generator based on the squirrel-cage induction generator (SCIG) and a battery storage system (BSS) with a voltage source converter (VSC) deliver power to feed necessary loads. BSS provides load leveling during load variations and wind fluctuations. For such microgrid, control schemes must be accurate and robust to overcome any discrepancy, and able to provide voltage and frequency regulation to the microgrid. Here, voltage and frequency control at the point of common coupling (PCC) is achieved with affine projection-like (APL) algorithm by proper switching of a VSC. This control algorithm is also able to provide reactive power compensation, load balancing, and harmonics suppression, and therefore provides sinusoidal voltage supply. Performance of the algorithm is demonstrated with wide range of test results of developed prototype of proposed microgrid.

Accurate forecasting of wind power generation is quite an important as well as challenging task for the system operators and market participants due to its high uncertainty. It is essential to quantify uncertainties associated with wind power generation forecasts for their efficient application in optimal management of wind farms and integration into power systems. Prediction intervals (PIs) are well known statistical tools which are used to quantify the uncertainty related to forecasts by estimating the ranges of the future target variables. This paper investigates the application of a novel support vector machine based methodology to directly estimate the lower and upper bounds of the PIs without expensive computational burden and inaccurate assumptions about the distribution of the data. The efficiency of the method for uncertainty quantification is examined using monthly data from a wind farm in Australia. PIs for short term application are generated with a confidence level of 90%. Experimental results confirm the ability of the method in constructing reliable PIs without resorting to complex computational methods.

Power line communication (PLC) utilizes power lines for the purpose of electronic data transmission. The performance of a PLC system is significantly affected by the additive and multiplicative power line noises; the additive noises are of two types, namely background noise and impulsive noise. Whereas, the multiplicative PLC noise leads to fading in the received signal strength. In this paper, we evaluate the performance of a PLC system over log-normal fading channel under Nakagami-m distributed additive background noise assuming binary phase shift keying modulation scheme. The analysis involving log-normal fading is very complicated. Hence we use a novel gamma approximation to log-normal distribution for our analysis. We evaluate the probability density function of the decision variable. A closed-form expression of the analytical average bit error rate of the considered system is derived. We also compute the diversity order of the considered PLC system. The validity of the derived analytical expressions is closely verified by the simulation results.

Power line communication (PLC) deals with the transmission of data through the use of power lines. It is an emerging field of communication for the home area network of smart grid. The presence of the additive and multiplicative power line noises significantly affects the performance of PLC systems. There are two types of additive noises in PLC systems, namely background noise and impulsive noise. The multiplicative PLC noise results in fading in the received signal strength. The Rician fading model has been experimentally found to be applicable to the PLC systems and has been widely used in conventional and current literature on PLC. In this paper, we provide the performance analysis of a PLC system over Rician fading channel under Nakagami-m distributed additive background noise assuming binary phase shift keying modulation scheme. We derive the probability density function of the decision variable and the instantaneous signal-to- noise ratio (SNR). A closed-form expression of the outage probability of the considered system is obtained. The validity of the derived analytical expressions is closely verified by the simulated results.

Uncertainty of the electricity prices makes the task of accurate forecasting quite difficult for the electricity market participants. Prediction intervals (PIs) are statistical tools which quantify the uncertainty related to forecasts by estimating the ranges of the future electricity prices. Traditional approaches based on neural networks (NNs) generate PIs at the cost of high computational burden and doubtful assumptions about data distributions. In this work, we propose a novel technique that is not plagued with the above limitations and it generates high-quality PIs in a short time. The proposed method directly generates the lower and upper bounds of the future electricity prices using support vector machines (SVM). Optimal model parameters are obtained by the minimization of a modified PI-based objective function using a particle swarm optimization (PSO) technique. The efficiency of the proposed method is illustrated using data from Ontario, Pennsylvania-New Jersey-Maryland (PJM) interconnection day-ahead and real-time markets.

Uncertainty is known to be a concomitant factor of almost all the real world commodities such as oil prices, stock prices, sales and demand of products. As a consequence, forecasting problems are becoming more and more challenging and ridden with uncertainty. Such uncertainties are generally quantified by statistical tools such as prediction intervals (Pis). Pis quantify the uncertainty related to forecasts by estimating the ranges of the targeted quantities. Pis generated by traditional neural network based approaches are limited by high computational burden and impractical assumptions about the distribution of the data. A novel technique for constructing high quality Pis using support vector machines (SVMs) is being proposed in this paper. The proposed technique directly estimates the upper and lower bounds of the PI in a short time and without any assumptions about the data distribution. The SVM parameters are tuned using particle swarm optimization technique by minimization of a modified Pi-based objective function. Electricity price and demand data of the Ontario electricity market is used to validate the performance of the proposed technique. Several case studies for different months indicate the superior performance of the proposed method in terms of high quality PI generation and shorter computational times.

This paper presents a novel search metaheuristic inspired from the physical interpretation of the optic flow of information in honeybees about the spatial surroundings that help them orient themselves and navigate through search space while foraging. The interpreted behavior combined with the minimal foraging is simulated by the artificial bee colony algorithm to develop a robust search technique that exhibits elevated performance in multidimensional objective space. Through detailed experimental study and rigorous analysis, we highlight the statistical superiority enjoyed by our algorithm over a wide variety of functions as compared to some highly competitive state-of-the-art methods.

A vibration analysis based interturn fault diagnosis of induction machines is proposed in this paper, using a neural-network-based scheme, constituting of two parts. The first part finds out the optimum network size of the probabilistic neural network (PNN) using the Orthogonal Least Squares Regression algorithm. This judges the size of the PNN, with an effort to reduce the computation. The feature extraction to model the PNN is made meaningful using dual tree complex wavelet transform (DTCWT), which is nearly shift invariant analytical wavelet transform, giving a true representation of the input space. In the second part, preprocessing using principal component analysis is suggested as an effective way to further reduce the dimension of the feature set and size of the PNN without compromising the performance. The sensitivity, specificity, and accuracy show that the vibration signatures capture the fault more effectively (especially by the axial and radial ones), under varying supply-frequency and load conditions. A comparison with traditional discrete wavelet transform proves the applicability of the proposed scheme. A comparative evaluation with feedforward neural network and naïve Bayes scheme brings out the advantage of the proposed optimized DTCWT-PNN based technique over other machine learning approaches.

Swarm Intelligent algorithms focus on imbibing the collective intelligence of a group of simple agents that can work together as a unit. This research article focus on a recently proposed swarm-based metaheuristic called the Artificial Bee Colony (ABC) algorithm and suggests modifications to the algorithmic framework in order to enhance its performance. The proposed ABC variant shall be referred to as MsABC_Fm (Multi swarm Artificial Bee Colony with Forager migration). MsABC_Fm maintains multiple swarm populations that apply different perturbation strategies and gradually migration of the population from worse performing strategy to the better mode of perturbation is promoted. To evaluate the performance of the algorithm, we conduct comparative study involving 8 algorithms and test the problems on 25 benchmark problems proposed in the Special Session on IEEE Congress on Evolutionary Competition 2005. The superiority of the MsABC_Fm approach is also highlighted statistically.

This paper proposes an informative differential evolution with self adaptive re-clustering (IDE-SAR) technique to solve the optimal energy and spinning reserve scheduling problem of a wind-thermal power system. The goal of the paper is to solve an economic dispatch problem, and to find optimal allocation of energy and spinning reserves among the thermal and wind generators available to serve the demand. The stochastic behavior of wind speed and wind power is represented by Weibull probability density function. The total cost minimization objective includes cost of energy provided by conventional thermal generators and wind generators, cost of reserves provided by conventional thermal generators. It also includes costs due to over-estimation and under-estimation of available wind power. In order to show the effectiveness and feasibility of the proposed frame work, various case studies are presented for conventional and wind-thermal power system considering the provision of spinning reserves.

Connection of distributed generation resources in distribution system enhances the availability and reliability of electric power during peak load. However, increasing penetration of distributed generation resources causes protection coordination failure in distribution system. An optimization problem is proposed to determine relay coordination under maximum penetration level of distributed generation by optimally selecting location, parameters and size of distributed generation. The proposed optimization problem is implemented on IEEE 15 node radial system. A meta-heuristic approach based on covariance matrix adaptation evolution strategy directed target to best perturbation algorithm is applied for optimization of relay coordination problem under maximum penetration of distributed generation.

Lack of exploration capability of biogeography-based optimization (BBO) leads to slow convergence. To address this limitation, this paper presents a memetic algorithm (MA), namely, aBBOmDE, which is a new version of BBO to solve both complex and noncomplex economic load dispatch (ELD) problems of thermal plant. In aBBOmDE, the performance of BBO is accelerated by using a modified mutation and clear duplicate operators. Then, modified DE (mDE) is embedded as a neighborhood search operator to improve their fitness after a predefined threshold. mDE is used with mutation operator DE/best/1/bin to explore the search near the best solution. The length of local search is set to achieve a balance between the search capability and the excess computational cost. In aBBOmDE, migration mechanism is kept same as that of BBO to maintain its exploitation ability. Modified operators are utilized to enhance the exploration ability while a neighborhood search operator, further, enhances the search capability of the algorithm. This combination significantly improves the convergence characteristics of the original algorithm. The effectiveness of the proposed algorithm has been verified on five different test systems with varying degree of complexity. The results have been compared with other existing techniques. The results indicate that the proposed approach can efficiently solve practical ELD problems.

The theoretical analysis of evolutionary algorithms is believed to be very important for understanding their internal search mechanism and thus to develop more efficient algorithms. This paper presents a simple mathematical analysis of the explorative search behavior of a recently developed metaheuristic algorithm called harmony search (HS). HS is a derivative-free real parameter optimization algorithm, and it draws inspiration from the musical improvisation process of searching for a perfect state of harmony. This paper analyzes the evolution of the population-variance over successive generations in HS and thereby draws some important conclusions regarding the explorative power of HS. A simple but very useful modification to the classical HS has been proposed in light of the mathematical analysis undertaken here. A comparison with the most recently published variants of HS and four other state-of-the-art optimization algorithms over 15 unconstrained and five constrained benchmark functions reflects the efficiency of the modified HS in terms of final accuracy, convergence speed, and robustness.

Biogeography deals with the study of the distribution of biodiversity over space and time and has been well studied by naturists and biologists for over the last five decades. Recently, the theory of biogeography has been applied to solve difficult engineering optimization problems in the form of a nature-inspired metaheuristic, known as biogeography-based optimization (BBO) algorithm. In this correspondence paper, we present an in-depth analysis of the linear time-invariant (LTI) system model of immigration and emigration of organisms in an island biogeography system that forms the basis of BBO. We find the bound of the eigenvalues of the general LTI system matrix using the Perron-Frobenius theorem from linear algebra. Based on the bounds of the eigenvalues, we further investigate four important properties of the LTI biogeography system, including the system reasonability with probability distribution vectors, stability, convergence, and nature of the equilibrium state. Our analysis gives a better insight into the dynamics of migration in actual biogeography systems and also helps in the understanding of the search mechanism of BBO on multimodal fitness landscapes.

In this paper, we describe a multi-objective approach to determine an effective mitigation strategy during an outbreak of foot and mouth disease in cattle. The goal of the approach is to minimize the total number of infections. In addition, the direct costs must also be minimized as a secondary objective. The animals culled and vaccinations may also be minimized, as long as the number of infections is not compromised. Thus a three-tier hierarchy of objectives exists. A framework that considers objective hierarchies during a multi-objective optimization algorithm is suggested. Results show that this produces better results than a conventional multi-objective optimization approach.

In this paper, the nonlinear constrained multi-objective environmental economic dispatch (EED) problem is solved using fast multi-objective evolutionary programming (FMOEP). Due to the global warming by fossil fuel, environmental concern becomes more and more important in recent years. The purpose of multi-objective optimization algorithm is minimizing all the different objectives simultaneously and finds the best tradeoff solution for this environmental/economic dispatch problem. In order to evaluate the performance of FMOEP on EED problems, the standard IEEE 30-bus six-generator test system is studied. The performance is compared against NSGAH and a number of results reported in literature. The results show that the FMOEP is effective in solving EED problems.

Very recently bacterial foraging has emerged as a powerful technique for solving optimization problems. In this paper, we introduce a micro-bacterial foraging optimization algorithm, which evolves with a very small population compared to its classical version. In this modified bacterial foraging algorithm, the best bacterium is kept unaltered, whereas the other population members are reinitialized. This new small population mu-BFOA is tested over a number of numerical benchmark problems for high dimensions and we find this to outperform the normal bacterial foraging with a larger population as well as with a smaller population.

The paper provides a novel approach to represent cooperative/competitive interactions among coexisting emotions by a recurrent neural dynamics, and proposes a scheme for parameter estimation of the dynamics from the facial expressions of the subjects, psychologically excited by audio-visual stimulus taken from select commercial movies. Conditions for chaotic and stable behavior of the neural dynamics have been derived, and the same parametric conditions are used to predict the fluctuating dynamic behavior of emotions by testing the satisfiability of the conditions over the measured range of parameters.

Economic dispatch is a highly constrained optimization problem encompassing interaction among decision variables. Environmental concerns that arise due to the operation of fossil fuel fired electric generators, transforms the classical problem into multiobjective environmental/economic dispatch (EED). In this paper, a fuzzy clustering-based particle swarm (FCPSO) algorithm has been proposed to solve the highly constrained EED problem involving conflicting objectives. FCPSO uses an external repository to preserve nondominated particles found along the search process. The proposed fuzzy clustering technique, manages the size of the repository within limits without destroying the characteristics of the Pareto front. Niching mechanism has been incorporated to direct the particles towards lesser explored regions of the Pareto front. To avoid entrapment into local optima and enhance the exploratory capability of the particles, a self-adaptive mutation operator has been proposed. In addition, the algorithm incorporates a fuzzy-based feedback mechanism and iteratively uses the information to determine the compromise solution. The algorithm's performance has been examined over the standard IEEE 30 bus six-generator test system, whereby it generated a uniformly distributed Pareto front whose optimality has been authenticated by benchmarking against the epsiv -constraint method. Results also revealed that the proposed approach obtained high-quality solutions and was able to provide a satisfactory compromise solution in almost all the trials, thereby validating the efficacy and applicability of the proposed approach over the real-world multiobjective optimization problems.

IEEE Std 421.5, revised by the IEEE excitation system subcommittee introduced a new type of power system stabilizer model, the multiband power system stabilizers (IEEE PSS4B). Although it requires two input signals, like the widely used IEEE PSS2B, the underlying principle of the new IEEE PSS4B makes it sharply different. This paper presents a method based on Bacterial Foraging Algorithm (BFA) to simultaneously tune these modern power system stabilizers (PSSs) in multimachine power system. Simulation results of multi-machine power system validate the efficiency of this approach. The proposed method is effective for the tuning of multi-controllers in large power systems.