Cyberattacks on transmitted signals are the most critical threats to modern microgrid (MG) systems and should be accurately addressed to ensure safe and reliable operation. This article investigates the cybersecurity of dc MGs against false data injection attacks and develops a novel model-based observer system. The proposed projection operator (PO)-based unknown input observer is uniquely designed to detect attacks on the transmitted data from other distributed generation units. For this purpose, a bank of PO observers is developed in each distributed generation unit to estimate all neighbor units' dynamic states. Cyberattack reconstruction compares the remotely observed state values with the measured ones. Afterwards, the detected attack signal values are utilized to restore the integrity of the compromised signals, effectively mitigating the harmful effects of cyberattacks. The most important feature of this scheme, which distinguishes it from similar methods, is that there is no need for a secure channel or additional information transfer in this method. Extensive real-time numerical simulations are performed to assess the practicality and efficiency of the proposed approach when subjected to various attack scenarios, demonstrating its advantages over other methods.

The rapid increase in the use of electric vehicles (EVs) leads to a critical need to address range anxiety in the drivers by accurately predicting the energy consumption of the vehicle. This research presents an innovative approach to enhancing the prediction of EV energy consumption by integrating modern advanced deep learning models, including TabTransformer and tabular neural network (TabNet), with traditional machine learning techniques, including light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), and light gradient boosting machine (CatBoost). The study introduces bidirectional encoder representations from transformers (BERT) embeddings for text feature extraction in EV energy consumption prediction, showing their usefulness in enhancing model performance. Utilizing the Volkswagen e-Golf dataset with 19 attributes, TabTransformer emerged as the most effective model, achieving an R$^{2}$of 0.9812, a significant improvement over traditional models such as LightGBM. TabNet offered a competitive R$^{2}$of 0.9758 while maintaining reduced computational complexity. In addition to that, BERT-enhanced XGBoost exhibited a solid performance, particularly in terms of R$^{2}$and MAE. Among given traditional approaches, Bayesian-optimized LightGBM showed outstanding efficiency, with a training time of only 0.476 s. These results highlight how well TabTransformer can make predictions and its potential to optimize energy consumption predictions and efficiency of Bayesian optimization for LightGBM. The findings contribute valuable insights into model selection and optimization, fostering advances in EV range estimation and promoting the broader adoption of sustainable transportation solutions.

The increasing integration of renewable energies, while beneficial for environmental and economic sustainability through decarbonization, poses challenges to frequency stability due to the intermittent nature of renewable power supply. To facilitate smoother integration into the main grid, this study proposes a resilient distributed load frequency control (RDLFC) strategy with a hierarchical structure. At the lower level, wind energy integration is managed using a model predictive control framework enhanced by an improved event-triggered scheme, which can effectively trigger key feedback signals at critical points and tolerates imperfect event modeling and generator dysfunctions. Plug-in electric vehicles are also utilized for fast frequency regulation. At the higher level, the linearized model is improved with an uncertain parameter matrix to account for variations in steady-state operating points due to renewable integration. A robust performance index is incorporated to derive stability conditions, even in the presence of temporary faults in phasor measurement units (PMUs). Validation results confirm the effectiveness of the proposed RDLFC strategy in handling temporary PMU faults.

The electricity generated from the present-day large capacity doubly fed induction generator (DFIG) installed wind farm is generally transmitted to utility grid via medium or high voltage transmission line (TL). Due to the restriction of building new TLs, series compensated TLs are some cases preferred for such applications. But, the nonlinear output power versus wind speed relation, control strategies of power electronic interfaced DFIG-wind turbine generators and the nonlinear operation of the thyristor-controlled series capacitor (TCSC) during fault impose adverse impact on the performance of the conventionally used distance relaying-based TL protection schemes. In this article, an improved fault detection and classification technique is proposed to assist distance relay in ensuring fast and reliable protection to TCSC compensated TL linked to DFIG-installed wind farm. In this method, a feature called transient monitoring indexed (TMI) is derived from the measured three-phase currents at the relay location for fault detection and TMI-assisted support vector machine is employed further for fault classification. Performance of the proposed scheme is validated on various fault and nonfault transients simulated on a test power system through MATLAB/Simulink. This protective scheme is farther validated throughout real-time assembled dSPACE DS 1104 control prototype hardware. The superiority of the proposed method is also demonstrated through comparative assessment results with few existing techniques. The overall results justify the merits of the proposed method for fast and accurate detection and classification of faults in such crucial TLs.

In this article, a data-driven adaptive control (DDAC) technique is proposed for the automatic generation control (AGC) problem of an interconnected power grid subject to deception attack (DA). The emergence of the Internet of Things (IoT) and the advancement of communication technologies have provided an opportunity for power system operators and designers to compensate for the lack of an appropriate model using a huge amount of data. However, they have also caused security challenges in the grid due to malicious attackers. This article focuses on the attack to the control network, which carries the AGC signals between the secondary and local primary frequency controllers. Intentional modifications of AGC signals during an attack may result in frequency instability because of saturation in governor signals. To counteract such an attack, a DDAC is suggested for a multiarea power system in which the system model is dynamically updated using real-time input and output signals. The model includes the attacker’s behavior, thus empowering the control system to act against it. The stability of the proposed controller is proved using the Lyapunov stability theory when the DA causes input saturation. Simulation results show that it can successfully tolerate a class of DAs and keep the multiarea power grid stable.

The microgrid (MG) is a complicated cyber-physical system that operates based on interactions between physical processes and computational components, which make it vulnerable to varied cyber-attacks. In this article, the impact of data integrity attack (DIA) has been considered, as one of the most dangerous cyber threats to MGs, on the steady-state operation of hybrid microgrids (HMGs). Additionally, a novel method based on the sequential hypothesis testing approach is proposed to detect DIA on the renewable energy sources’ metering infrastructure and improve the data security within the HMGs. The proposed method generates a binary sample, which is used to compute a test statistic that is further used against two thresholds to decide among three alternatives. The performance of the suggested method is examined using an IEEE standard test system. The results illustrated the acceptable performance of the proposed methodology in detection of DIAs. In addition, to evaluate the effect of DIA on the operation of the HMGs, DIAs with different severities are launched on the measured power generation of renewable energy resources like wind turbines. The results showed that a successful DIA on renewable units can severely affect the operation of electric grids and cause serious damage.

Abstract Cloud computing is an environment where everything is provided as a service based on demand. It follows pay as per the used model in which the service consumer needs to pay for what they have consumed. Due to the increased dependence on digitalization, the number of consumers and providers tends to grow tremendously. The consumer who needs the service from the provider is not sure about the specified service outcome, and it is too hard for them to monitor and manage the service. Hence, a trusted third party called a trusted cloud broker (TCB) is introduced for managing the services. The service level agreements (SLA) management and reputation estimation framework is proposed, which includes three phases such as (i) SLA establishment between the three parties, (ii) violation detection by comparing the observed value of the TCB and (iii) the reputation and penalty estimation of the service. The novel TCB is created to monitor the deployed services, ensuring the achievement of SLA. The TCB observes the values and estimates the reputation value for each service. It is compared with the provider log‐based reputation value and found that the proposed model provides a more precise reputation value for the service providers.

Cyberattacks against measured variables and faulty metering devices are among the most important threats to modern systems that should be detected and isolated, and the control actions based on the measured variables should be appropriate in order to keep the power system security. This article proposes a dynamic-state-estimation-based cyberattack-tolerant control method for modern power systems. The proposed method involves two new schemes: one is for dynamically detecting the cyberattack and the other isolates the location of the attack. These schemes are based on dynamic observer designs that can eliminate the effects of unknown inputs. This article also proposes a fault-tolerant control technique using these observer-based detection and isolation schemes. The proposed method can accurately track dynamic states, detecting both cyberattack against measured variables and faulty measuring devices, and isolating the cyberattack and fault locations. The results verify its superiority in comparison with other techniques.

This article proposes a novel, disturbance observer-based decentralized frequency control method for interconnected power systems. The method employs extended Kalman filter (EKF) as an observer to estimate inaccessible dynamic states of the system, including the total disturbance as one of the state variables. An optimal decentralized disturbance observer based controller is suggested for multiarea power systems that compensates the estimated disturbance and further based on minimizing the joint error energies of state estimation error and state tracking error provides its value to the controller for regulating the frequency variation. The proposed method is mathematically designed to be robust against parametric and nonparametric uncertainties. The efficacy and accuracy of the proposed control method is verified considering different types of practical operation scenarios. The results confirm the brilliant and superiority of the proposed method in controlling the frequency in power systems with high renewable shares.

Feeder reconfiguration is an effective way to reduce power losses of distribution network. In this way, configuration of distribution system is changed in order to achieve possible minimum losses, while electricity demand of consumers has to be provided. Consumers’ power demand has an important role in feeder reconfiguration because any change in demand affects power losses directly. Whereas load demand has a variable and stochastic nature because of its dependence on consumption pattern and accuracy of forecasted load amounts. Accordingly, reconfiguration models should be robust enough against load uncertainty and variations. Thus, this article presents an efficient robust model for reconfiguration of distribution feeders under uncertain and variable loads. The proposed reconfiguration model is robust enough and efficient, in which its implementation is relatively simple. The results show higher efficiency and lower complexity of the proposed model compared to existing robust reconfiguration approaches.

This article presents a k-means clustering and weighted k-nearest neighbor (k-NN) regression-based algorithm for the protection of transmission line. Three-phase current signals of both the terminals are synchronized and sampled with a sampling frequency of 3.84 kHz. Cumulative differential sum (CDS) is computed by subtracting the samples of current cycle from the previous cycle at both the terminals of transmission line. k-means clustering is applied on CDS to compute two centroids using moving window of width, equal to one cycle. Difference between the absolute values of centroids is computed at both the terminals and represented by the centroid difference (CD). The CD of both the terminals is added to compute the fault index. The computed fault index is used to detect and classify the types of faults. The location of the fault is estimated by the weighted k-NN regression method. Various case studies are performed to validate the robustness of the algorithm for different fault parameters such as fault impedance and fault location. The effect of noise is also considered to check the accuracy of the proposed algorithm in the noisy environment.

With the rapid evolution of the industrial Internet, cloud service has emerged as a next-generation industrial standard that has the potential to revolutionize and transform the enterprise industry. In recent years, numerous enterprises have acknowledged the benefits of cloud-based service models. However, the security issues are a major concern, such as stealthy malware attacks against virtual domains. In this article, we propose an introspection based security approach, called VMShield for securing virtual domains in a cloud based service platform, which is designed to detect malware in cloud infrastructure. VMShield performs virtual memory introspection from the hypervisor (trusted-domain) to collect the run-time behavior of processes, making it impossible for the malware to evade the security tool. The use of introspection makes the proposed approach a better choice over traditional static and dynamic state-of-the-art techniques which fail to detect stealthy attacks. The VMShield extracts the system call features using Bag of n-gram approach and selects important features using the meta-heuristic algorithm, binary particle swarm optimization. Random Forest (RF) classifier is used to classify the monitored programs into benign and malign processes, making it capable of detecting the variants of malware thus, an advantage over the typical signature-matching approach. The University of New Mexico (UNM) Dataset and Bare cloud Dataset (University of California) has been used for the demonstration and validation of VMShield. The results prove that VMShield achieves a higher attack detection rate and reduced storage compared to previously proposed techniques.

The custom power devices play important role for enhancing the power transfer capacity of transmission system. However, these devices introduce challenges of under reach or over reach, in the protection of transmission system. This article introduces a novel, protection algorithm based on wavelet-alienation-neural technique for STATCOM-compensated transmission system. For detecting and classifying faults, approximate coefficients are computed from the postfault quarter cycle current waveforms. Fault index, which is summation of alienation coefficients (computed by approximate coefficients) of both the buses, is computed and compared with the threshold magnitude for detecting and classifying the different faults. For the determination of fault location, artificial neural network is applied, with input as three-phase approximate coefficients, evaluated from the voltage and current signals over a time duration of a quarter cycle. Robustness of the developed scheme has been validated for various faults at different locations with varying fault impedances and angles of fault incidence.

This paper evaluates performances of Second Order Generalized Integrator-Quadrature (SOGI-Q) supported adaptive control algorithm for distributed static compensator (DSTATCOM) to enhance power quality (PQ) in a non-ideal grid in the presence of unbalanced non-linear load. SOGI-Q makes it possible to identify the changes associated with the strength of the grid using the tracking of voltage unit templates. The proposed algorithm is used for extraction of average power consumption corresponding to load current component for estimating the reference signals. Reference signals are compared with sensed non-ideal grid signals for generating gating signals for the DSTATCOM to mitigate the various PQ issues. Proposed SOGI-Q control algorithm has been successfully designed for non-ideal grid scenario to mitigate current harmonics, voltage harmonics, reactive power, maintain load balancing and power factor of the grid in the presence of non-linear load. The study is performed using MATLAB software in Simulink environment.

Load restoration procedure alongside synchronization of parallel subsystems taking into account the impacts of cold loads has attracted significant attention particularly from system operators' point of view to perform the restoration process in a short time. This paper presents a novel framework to coordinate the load restoration and standing phase angle (SPA) reduction problems with the aim of determining the optimal restorable load amount at each load point considering effect of temperature on the load pickup process. The objective function includes three terms including minimization of energy not supplied (ENS), SPA and the total recovered energy in all stages of load restoration process. Due to conflict of the objectives, a multi-objective model has been developed and the Pareto frontier has been constructed through craw search algorithm (CSA). To assess the effectiveness of the model, a 32 bus power system has been selected. The obtained results confirms the model validity.

This paper presents a technique using hybrid features extracted from current signals using Stockwell and Hilbert Transforms for detecting the islanding events and operational events of renewable energy generators and loads. The study is performed on a hybrid power system test network incorporating wind and solar power generators. Results are computed using MATLAB/Simulink software for a variety of case studies. Through the applied technique islanding events are successfully identified and discriminated from operational events.

In this paper, a multiobjective optimization method for the placement of phasor measurement units (PMUs) is proposed. The proposed method simultaneously considers different objectives including the power system small signal stability, the probability of system observability, and the total cost of PMUs. In the proposed optimal PMUs placement (OPP) model, wide area measurement system (WAMS) uncertainties and network configuration changes are also considered, while the variable cost of PMUs, based on the number of PMU channels is also observed. The elitist nondominated sorting genetic algorithm (NSGA-II) is used to obtain the Pareto optimal solutions and the best compromise final solution is selected by the decision maker on the basis of his priorities. Some simulation results on different test systems in different scenarios are presented and discussed. They confirmed that the proposed multiobjective model provides effective information for small signal stability assessment with minimum PMU cost in the presence of uncertainties.

Power quality (PQ) is a vital issue in the present power systems integrated with large renewable energy sources since more power electronics devices are incorporated in the system. This article proposes a novel method for assessing PQ associated with wind energy integration. This method is effective to recognize PQ issues in power systems with high penetration of wind energy with a low computational burden. Furthermore, it detects different operational issues in the distribution network. Stockwell transform (S-transform) is utilized to decompose the voltage signal and calculate the S-matrix. To assess the PQ, a plot is developed from this matrix. The features of this matrix such as mean, standard deviation, and maximum deviation are further utilized for detecting the operational issues such as wind speed variation, islanding, synchronization, and outage of the wind generation by using clustering with fuzzy C-means. A modified IEEE 13-bus test system is utilized to validate the proposed method, which is also supported by hardware and real-time digital simulator results. The quality of power is graded with the help of a proposed PQ index under various operational events with different levels of wind energy penetration. The proposed method is effective for the identification and grading of different operational events in terms of PQ and recognizing a wide range of PQ issues with a high share of wind energy. The performance of the proposed scheme is established by comparing its results with other approaches.