Gearboxes are essential mechanical components for power transmission. Among them, planetary gearboxes stand out for their compact design and high reduction ratio, making them particularly advantageous in various sectors such as energy generation, transportation, and robotics. Like all mechanical components under load, they are susceptible to different types of degradation, including wear, cracks, chipping, and even broken teeth. Such defects can negatively impact transmission quality, potentially leading to system shutdowns and endangering operators. This necessitates an autonomous monitoring solution that can respond in real-time. This paper presents a robust monitoring methodology for diagnosing faults in planetary gearboxes. The approach begins with filtering the monitoring signals using Variational Mode Decomposition (VMD). The filtered signal is then transformed into a time–frequency image using Wavelet Transform (WT). These images are subsequently used to train a transfer learning network. To ensure the robustness of the proposed intelligent solution, a data augmentation step is included to address issues of data scarcity and imbalanced datasets. Experimental validation demonstrates the effectiveness of the proposed methodology under different operating conditions. • Autonomous, intelligent fault diagnosis methodology for planetary gearboxes. • Signal filtering using variational mode decomposition for noisy environments. • Solutions to data scarcity and dataset imbalance in intelligent diagnosis.

The growing need for sustainable and efficient power sources in underwater vehicles has led to increased exploration of alternative energy solutions. In this work, fuel cells valued for their high energy density and low environmental impact are evaluated for their potential to deliver continuous power for extended underwater missions. Additionally, two energy storage system configurations, namely batteries and supercapacitors, are analyzed based on their storage capacity and overall efficiency. To enhance energy utilization in underwater vehicles, a comparative study of various rule-based energy management strategies is carried out, incorporating experimental power consumption data and high-fidelity simulation models. This analysis aims to identify the strengths and limitations of fuel cells and storage systems in meeting the demands of underwater operations. Ultimately, the findings contribute to the ongoing development of underwater vehicle technology, with a focus on improving energy efficiency and mission endurance.

This paper presents the design and optimization of a hybrid microgrid system that integrates offshore wind energy, water-fed electrolyzers, hydrogen storage, fuel cells, and battery storage to support marine applications. The proposed system is intended to supply a sustainable and economically viable energy source for docking stations servicing both surface and underwater electric vehicles. Offshore wind power is utilized to drive the electrolysis of seawater, producing hydrogen that is stored and later converted into electricity through fuel cells. This approach ensures stable energy availability despite the intermittent nature of wind, while leveraging clean water resources. Battery storage complements the system by addressing short-term load variations. An intelligent energy management strategy coordinates the various subsystems to enhance reliability and efficiency. Simulation results confirm the system’s effectiveness as a robust, green power solution for marine infrastructure.

Lithium-ion battery diagnosis is critical for ensuring optimal performance and mitigating safety risks; however, challenges remain due to overlapping electrochemical processes and dynamic operating conditions. This study introduces the dynamic impedance spectra to enable a comprehensive diagnosis of LIBs’ behaviors with active battery charging. For starters, the voltage drifts caused by the state of charge (SOC) shifts and polarization effects are eliminated by applying a moving average filter. The time-resolved impedance spectra and the corresponding equivalent circuit model (ECM) parameters are extracted and continuously updated to decouple and monitor electrochemical processes across timescales. Kramers-Kronig (K-K) validation confirms the dynamic impedance measurement validity, with residuals below 0.5%. The analysis of ECM parameter evolution provides in-situ insights into the electrochemical dynamics and material properties during the battery charging process.

Bearing monitoring plays an important role in the condition monitoring of wind turbines. Failures in this critical component can lead to unexpected shutdowns, significantly reducing the efficiency of the energy system. Therefore, the implementation of advanced monitoring tools is essential to ensure reliability, availability, and cost-effective operation. Digital Twin (DT) technology enables the achievement of these objectives and helps address the issue of limited data. This paper presents a monitoring methodology based on Digital Twin technology for bearing fault diagnosis. It relies on a four degrees of freedom (4-DOF) bearing model and the simulation of faults that can affect this component. A machine learning-based adaptation is proposed, starting with dimensionality reduction through an encoder network, followed by a hybrid network combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). This network extracts physical information from the model and adapts it to the noise in the experimental fault signatures. The results were obtained through experimental validation using a dataset from the Machinery Failure Prevention Technology (MFPT) demonstrate the effectiveness of this methodology for bearing fault diagnosis under various operating conditions.

Solar photovoltaic (PV) systems are crucial for reducing dependence on fossil fuels and carbon emissions, but they face challenges in terms of failures that can impact their efficiency. To effectively monitor and diagnose these failures, advanced tools are needed. Current monitoring systems collect data efficiently but struggle to analyze and diagnose it accurately. This leads to delayed fault detection and increased maintenance costs. The complexity of faults further complicates the diagnostic process. In this context, deep learning techniques offer a promising solution. The YOLOv12 (You Only Look Once, Version 12) framework, a state-of-the-art deep learning model based on Convolutional Neural Networks (CNN), is proposed as an advanced tool for PV system fault diagnosis. Compared to previous models, YOLOv12 demonstrates superior accuracy and speed, enabling more effective fault detection and classification. This tool improves the identification of fault types and evaluation of PV panel condition, ensuring timely maintenance and maintaining system reliability. AI approaches, particularly deep learning techniques, show great potential in enhancing monitoring and diagnostics, ultimately improving production efficiency and ensuring the long-term sustainability of photovoltaic systems.

Accurate prediction of Remaining Useful Life (RUL) is critical for ensuring the reliability and safety of Lithium-ion Batteries (LiBs) in applications ranging from portable electronics to electric vehicles. However, capacity fading and complex aging mechanisms make RUL estimation challenging. This work presents a hybrid model based on Particle Swarm Optimization (PSO) and Variational Mode Decomposition (VMD) to evaluate the RUL of LiBs. The optimal hyperparameters of the VMD are determined with PSO, before the decomposition of the capacity data into Intrinsic Mode Functions (IMFs). Then, each IMF is input to a Transformer model for time-series forecasting and RUL estimation. The performance of the methodology is assessed with the NASA B0018 and CALCE C37 datasets with a 50%-50% training–testing split. The results show that this proposed hybrid model achieves superior accuracy and robustness compared to other state-of-the-art models.

Permanent Magnet Synchronous Machines (PMSMs) have revolutionized motor design by replacing traditional components like rotor windings, brushes, and sliding contacts with permanent magnets. This innovation has significantly improved operational efficiency and reduced maintenance needs. However, controlling PMSMs remains challenging due to the changing dynamics of the machine over time and its sensitivity to different environmental conditions. To tackle these challenges, this study presents a novel nonlinear control approach called passivity-based control (PBC). Unlike conventional methods, PBC manages both the electrical and mechanical dynamics of the system, focusing on energy flow and dissipation to maintain stability. To make the control more robust, the approach combines a nonlinear observer and a high-order sliding mode controller (HSMC), which enhance the system's ability to handle disturbances and parameter changes. Additionally, the study uses Genetic Algorithm (GA) optimization to fine-tune the parameters of the PBC, observer, and HSMC. This optimization improves the motor's tracking accuracy and robustness against external disruptions. The result is a control framework that preserves the natural dynamics of PMSMs while improving their stability and performance. Experimental validation using the platform for real-time simulation (OPAL-RT) and real world on a PMSM using dSPACE DS1202 board demonstrates that this method outperforms existing techniques under a variety of operating conditions, highlighting its effectiveness and reliability. • Novel passivity-based control (PBC) approach, integrating a nonlinear observer and high-order sliding mode controller (HSMC) for permanent magnets in synchronous machines. • Artificial intelligence (GA) optimizes PBC and controller parameters for enhanced tracking and disturbance resistance. • Experimental validation with OPAL-RT and dSPACE test bench shows the proposed method outperforms existing techniques in various operating conditions.

Tidal stream turbines (TSTs) are crucial for renewable energy generation but face challenges from marine biofouling , significantly impacting their efficiency. Traditional methods for predicting performance and detecting biofouling rely on empirical models and manual inspections, which are often time-consuming and less accurate. This study introduces RegStack, a novel machine learning-based ensemble model, to enhance the prediction of power and thrust coefficients ( C P and C T ) and accurately classify biofouling levels in TSTs. Unlike conventional models, RegStack integrates L1 and L2 regularization into a stacking framework, improving robustness, generalization, and interpretability. The model dynamically balances the strengths of multiple regression and classification algorithms , optimizing predictive accuracy while mitigating overfitting. Comprehensive experiments were conducted using an extensive dataset of tidal stream turbine performance metrics under varying operational and environmental conditions. The RegStack model outperformed conventional approaches, achieving a coefficient of determination ( R 2 ) of 0.989 for performance predictions, with minimal mean absolute error (MAE) and mean squared error (MSE). Additionally, the model achieved 98.39% classification accuracy , with precision and recall of 0.97, and an F1-score of 0.97 in biofouling detection, demonstrating its effectiveness in real-time turbine health monitoring. By providing an automated, data-driven alternative to traditional methods, this study underscores the potential of advanced machine learning techniques in optimizing TST operations, reducing maintenance costs, and enhancing the reliability of marine renewable energy systems. The proposed RegStack model offers a scalable framework applicable to other renewable energy technologies , supporting sustainable energy advancements.

MOSFETs are used in several industrial applications, such as power electronics to supply switching loads, motor power supply, switching power supply, audio amplifier, inverter etc. The estimation of the RUL (Remaining Useful Life) of these devices is very interesting for the industry. Indeed, it allows gaining availability as well as an improvement of the safety of the system. The modern industry is focused on PHM (Prognostics and Health Management) to estimate the RUL and to design modern expert systems that ensure a good reliability to the industry.In this work, authors have developed a new methodology based on the deep learning ANN (Artificial Neural Network) in order to predict the RUL of MOSFETs. To validate the proposed Long Short-Term Memory (LSTM) algorithm, the data of the Ames PCOE (Prognostics Center of Excellence) was used. In addition, the evaluation was performed by comparing the errors between the real MOSFET RUL and the predicted MOSFET RUL. Besides, a comparison with different algorithms of prediction existing in the literature and using the same PCOE data set was done to present a solid evaluation of the proposed LSTM Recurrent Neural Network (LSTM-RNN).

This work addresses the important and yet relatively unexplored topic of degradation analysis of wave energy converters and more specifically Oscillating Water Column Turbines (OWCTs). This is achieved by developing mathematical models to simulate turbine degradation factors over time while considering environmental variability. Specifically, these factors are then combined multiplicatively to portray the overall health parameters of an OWCT system. Additionally, sensor measurements are generated based on these health parameters, encompassing various turbine-related and environmental variables such as vibration, temperature, pressure, acceleration, strain, flow, torque, rotation, wind speed, wind direction, air temperature, humidity, water temperature, solar radiation, wave height, wave frequency, wave direction, current velocity, water depth, salinity, biofouling, and seabed conditions. The generated dataset facilitates degradation analysis, enabling prognostic under uncertainty quantification. A set of adaptive time series along with small-scale learning algorithms are employed to analyze the dataset. Additionally, a wide range of metrics, and coefficient of determination, are utilized in a cross-validation scheme for generalizability evaluation. Moreover, confidence interval-based uncertainty quantification is integrated. This in-depth analysis sheds light on degradation patterns and their complexity, laying the basis for future studies into prognostics and health management of wave energy converter systems.

Prognostic learning systems (PLS) primarily rely on and delve into understanding the behavior of degradation lifecycles. Linear degradation often serves as the prevailing trend for labeling performance indicators (PI) in Remaining Useful Life (RUL) or Health Index (HI) estimations. Nevertheless, some studies explore deriving HI directly from PI, capturing state of health (SoH) variations more effectively. Disregarding the impact of operating condition disturbances on PI measurements reveals that systems do not strictly follow a linear degradation path during deterioration. At certain Health Stages (HS), a system maintains a specific SoH before continuing to deteriorate, sometimes even briefly returning to a better SoH. These multivariate conditions (MVC) arise from factors such as integrated control systems and system resilience. In this less-explored perspective, following procedural data preprocessing, this study aims to enhance data quality in terms of RUL ground truth labeling by considering aspects of temporary stability and performance backup. To assess data quality improvements, a comparative study investigates various adaptive learning systems across multiple evaluation stages and metrics, focusing on generalization as the main criterion for accuracy assessment. Recognizing the inherent non-linearity in PI degradation patterns has proven to be a pivotal element in cleansing feature spaces for PLS, consequently bolstering its generalization capability. This enhancement ultimately contributes to the development of more effective maintenance schedules.

The increasing reliance on advanced power systems in critical infrastructure highlights the need to enhance cybersecurity resilience. This study tackles this crucial issue through Exploratory Data Analysis (EDA) and Representation Learning (RL). Several real-world, complex power system datasets characterized by their large size, dynamic nature, and high imbalance are examined. EDA serves as a fundamental step to provide insights into power system event scenarios, supported by a comprehensive preprocessing strategy that includes managing missing values, reducing dimensionality, denoising, removing outliers, and addressing class imbalance. Missing values are thoughtfully handled by evaluating mean values. Dimensionality reduction involves removing insignificant features that might provide misleading information, along with integrating Principal Component Analysis (PCA). Class-specific denoising and outlier removal follow, with denoising utilizing various algorithms and a diverse range of wavelet functions to improve data quality. Robust outlier removal is achieved through iterative analyses and distance-based techniques. To address class imbalance, the Synthetic Minority Over-Sampling Technique (SMOTE) is employed, ensuring a balanced and representative dataset. For RL, deep learning models such as Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Units (GRU), and their Multiverse Recurrent Expansions with Multiple Repeats (MV-REMR) are used in a comparative study. The results demonstrate promising and satisfactory outcomes for all models, with MV-REMR delivering the best performance. This study integrates diverse techniques into a unified EDA and RL approach, offering valuable insights into power system cybersecurity forensics, addressing current challenges, and promoting resilient and adaptive cybersecurity measures in power systems.

Smart grids have emerged as a complex structure integrating communication networks, distributed energy resources, and intelligent devices. However, this integration has resulted in the manifestation of various vulnerabilities, raising signifi-cant security concerns and challenges. Present-day smart grids are vulnerable to various cyberattacks, and researchers have dedicated efforts to detect, mitigate, and prevent these attacks using diverse techniques. This paper focuses specifically on replay attacks within smart grids, recognizing the critical need to address this particular threat. Through a comprehensive review of existing literature regarding the detection, mitigation, and prevention of replay attacks across smart grids, microgrids, and cyber-physical systems (CPSs), we aim to provide insights into effective defense strategies applicable to various interconnected energy infrastructures.

Biofouling poses a significant challenge to the efficiency and performance of tidal stream turbines. This study proposes a novel approach utilizing Fast R-CNN for biofouling detection and coverage estimation in tidal stream turbines. We use a real biofouling dataset collected from four turbines installed on the pier of Ikitsuki Bridge in Hirado, Nagasaki, Japan, over 8 months, containing both clean and biofouled turbine images. Employing various augmentation techniques, we enhanced the dataset’s robustness. Our method achieved a remarkable 94% accuracy in detecting and classifying biofouled turbine blades. Additionally, using the OpenCV library in Python, we estimated the extent of biofouling coverage on turbine blades, offering valuable insights for maintenance and optimization efforts and assisting informed decision-making on strategies for tidal stream turbine maintenance and performance optimization.

It has been shown that an analytical model including amplitude, phase shift, and the mean value of the phase currents in seven-phase electrical machines can be used as relevant information for fault detection, isolation, and estimation. However, this model fails to estimate the fault severity accurately under noisy conditions, especially for faults affecting the mean value. Therefore, the model is extended with the noise as the fourth parameter. Two approaches are considered before the estimation performance is evaluated: the noise level is estimated, or the currents are first denoised. The simulation results with different combinations of noise level and fault severity show that both approaches are efficient and enhance the fault severity estimation, even under high-noise conditions (SNR as low as 5dB). Besides, the analytical model, including the noise, agrees well with the numerical model.

This study addresses the biofouling challenges in Tidal Stream Turbines (TSTs) to ensure their reliable and optimal operation. In this context, it is proposed an effective methodology employing a soft voting ensemble transfer learning-based approach for the detection and extent classification of biofouling. The proposed framework incorporates essential components such as data augmentation and pre-processing, including image resizing and data segmentation, forming a comprehensive video image-based approach. To overcome the constraint of limited data, experimental investigations were conducted, resulting in the acquisition of two datasets: one from the TST platform at Shanghai Maritime University (SMU) and the other from the tidal turbulence test facility at Lehigh University (LU). The three prominent convolutional neural network models, namely Visual Geometry Group (VGG), Residual Network (ResNet) and MobileNet, trained on these datasets, demonstrate precise detection and classification of turbine conditions, achieving an accuracy of 83% for the SMU dataset and 90% for the LU dataset. The noted disparity in accuracy for the SMU dataset is attributed to its smaller size, highlighting the significant impact of dataset scale on classification performance. This study provides valuable insight into the development of effective biofouling detection and classification strategies for TST systems. • Provide a soft voting ensemble transfer learning-based tool for real-time biofouling detection and estimation. • Data augmentation using rotation, scaling, flipping, zooming, and brightening of the cropped input images. • Data pre-processing including image resizing and data segmentation using the segment anything model.

High-speed bearings play a vital role in the functionality of modern wind turbines. Accurately estimating impending failure and predicting the remaining useful life (RUL) of bearings brings significant benefits in scheduling maintenance strategies and preventing sudden turbine shutdowns. This paper discusses a reliability enhancement prognosis framework for high-speed wind turbine bearings based on a measured representative vibration signature. A health indicator is constructed using a sliding window adaptive threshold (SWAT) to map the degradation behavior of the bearings into RUL. The Gated recurrent unit (GRU)-based deep learning algorithm shows promising predictive performance in terms of mean absolute error (MAE) and relative accuracy (RA).

Buildings are one of the prominent sectors among global primary energy consumption. A large portion of this energy consumption is influenced by occupancy interaction with the buildings. Occupancy prediction in buildings without intruding their privacy helps to enhance the building energy management. Due to the complex relations of the inputs and the temporal dependency, modeling accurate occupancy predictions is highly difficult. The use of Deep Learning (DL) algorithms is one of the best approaches for accomplishing this goal. This paper provides an analysis using horizontally cascaded Long Short-Term Memory (LSTM) model as a baseline for occupancy prediction. The proposed horizontally cascaded LSTM model focuses on learning local patterns and dependencies within their input sequences, and both short term and long terms dependencies in temporal direction along with the relation between other input features, allowing for a more comprehensive understanding of the input data. This architecture can capture a broader range of information from the collected building data and learn more heterogeneity in occupancy presence behavior. The models are also compared for different prediction window sizes. The OPTUNA optimization is utilized for hyper-parameter tuning and to determine number of LSTMs to be cascaded. The proposed models function better for smaller window size and optimization of number of cascaded LSTMs are essential for improving the accuracy of the model. The paper also shows that for window sizes, 2–4 LSTMs are optimal to cascade.

This paper investigates the use of transfer learning models for detecting and classifying biofouling extent in tidal stream turbines. It provides a comprehensive exploration of a soft voting ensemble approach to deal with a multiclass image detection problem. In this context, three well-known convolutional neural network models, namely VGG16, ResNet50, and MobileNetV2, are the primary focus of the proposed study and are trained on an experimental dataset issued from the Shanghai Maritime University tidal stream turbine platform.

This paper proposes to map a machine learning path forward for tidal stream turbines biofouling detection and estimation. The proposed review covers an overview of biofouling and its impact on tidal stream turbines, current techniques for detecting and estimating biofouling, recent developments, and challenges in the field, as well as several promising prospects for biofouling detection and estimation.

International audience

International audience

International audience

One of the main data-driven challenges when assessing bearing health is that training and test samples must be drawn from the same probability distribution. Indeed, it is difficult and almost rare to witness such a phenomenon in practical applications due to the constantly changing working conditions of rotating machines. In addition, collecting sufficient deterioration samples from the bearing life cycle is not possible due to the huge memory requirements and processing costs. As a result, accelerated life tests are believed to be the primary alternatives to such a situation. However, and unfortunately, the recorded samples always are subject to lack of real patterns. Therefore, in this paper, a transfer learning approach is performed to solve such kind of problem where PRONOSTICO dataset is used to assess the current procedures.

Condition Monitoring of photovoltaic systems plays an important role in maintenance interventions due to its ability to solve problems of loss of energy production revenue. Nowadays, machine learning-based failure diagnosis is becoming increasingly growing as an alternative to various difficult physical-based interpretations and the main pile foundation for condition monitoring. As a result, several methods with different learning paradigms (e.g. deep learning, transfer learning, reinforcement learning, ensemble learning, etc.) have been used to address different condition monitoring issues. Therefore, the aim of this paper is at least, to shed light on the most relevant work that has been done so far in the field of photovoltaic systems machine learning-based condition monitoring.

Magnetic couplings allow the design of full waterproof underwater thrusters. It is possible since magnetic couplings assure contactless mechanical power transmission, which rules out the usage of complicated and unsure mechanical seals that jeopardize underwater vessels by a hull breach. Magnetic couplings have less acoustic operation noise and inherent overload protection, which are interesting features for submarines. Moreover, it can increase the thruster performance since it eliminates the friction inherent to mechanical seals. This performance augmentation is possible when the hull material is not conductive. In this context, a way to calculate , in an unique equation, the motor power to design a new thruster is presented from the task where the drone advances in its maximum speed. Otherwise, the applied method can also be used to know the maximum waited speed from the already defined motors. It can help robots designers in sizing appropriate thruster motors for more efficient new underwater drones.

In this paper, the harmonic components of the input current of an interleaved boost converter under an open switch fault (OCF) are analyzed. A deep investigation of harmonic under different situations such as CCM, DCM, and Load’s change is conducted for a diagnosis purpose. Moreover, the study will show the effect of parameter change and switching frequency on the harmonic spectrum of the input current. Simulation results with experimental tests investigating the frequency analysis of the interleaved three boost converter under an OCF switch fault are presented.

This paper aims to highlight the methods of predicting the future behavior of a system's health and the remaining useful life to determine an appropriate maintenance schedule. Indeed, prognostic and health management techniques dedicated to renewable energy systems with special reference to wind turbine generators and papers published in the last ten years, are presented. Therefore some applications of prognostics in renewable energy systems, including power converter devices, batteries capacity degradation, and damage in wind turbines' high-speed shaft bearings, are highlighted.

Grid synchronization techniques play a significant role in integrating renewable energy sources to the electric power grid. In this context, estimating the phase and frequency of the grid voltage signal is an interesting problem. Out of various techniques available in the literature, linear Kalman filter (LKF) is one of the most popular one. In this article, we propose an alternative implementation of the LKF for grid synchronization application. The proposed implementation uses a linear parametric model of the grid voltage signal including dc offset. It does not involve any quadrature signal generation, rather it works by estimating the phase angle. This helps to estimate the unknown grid frequency directly from the phase angle. This clearly differentiates the proposed alternative implementation with respect to the existing implementations. Performance improvement by the proposed technique is verified extensively through comparative numerical simulation and experimental studies. Comparative results demonstrate the suitability of the proposed technique with respect to other state-of-the-art techniques namely second-order generalized integrator phase-locked loop and enhanced phase-locked loop.

Fast and accurate grid synchronization is very important to integrate renewable energy systems to traditional electric power grid. In this context, frequency-locked loop (FLL) became very popular in recent time as a grid synchronization technique. Numerous FLL techniques are available in the literature. Out of them, adaptive observer-based FLL technique have attracted significant attention in the last few years. In this paper, we present comparative analysis of three recently proposed adaptive observer-based FLLs. By systematically tuning the FLLs, we present a benchmark study for adaptive observer-based FLLs. Through numerical simulation, it has been demonstrated that the performance of these techniques are very similar, however, they have their own merits and demerits. Results presented in this paper can be useful as a guideline for practitioners and academic community in the grid synchronization topic.

This paper deals with hybrid electric powered drones power management while targeting sources lifetime extension and power supply system efficiency improvement. In this context, a hybrid system topology based on fuel cell, battery, and supercapacitor, is adopted in order to combine different sources characteristics. A real power profile, extracted from experimental flight tests of a small hexacopter, is used to model the load requirement. For power management purposes, a frequency separation approach is combined with rule-based strategy to split the demand power between the three sources using DC/DC converters. The achieved simulation results clearly show that the proposed power management strategy enables extending the fuel cell and battery lifetimes inducing faster response and therefore improving the drone maneuverability.

Despite the rich literature dealing with induction motor faults detection, the automatic decision on the health operating condition has received scarce attention. This paper aims at demonstrating the usefulness of the combination of the Maximum Likelihood (ML) and the detection theory for induction motor faults detection. In particular, it proposes to use stator currents as medium for faults detection. Indeed, under faulty conditions, additional spectral components appear in the stator currents spectrum. This paper proposes to use a constant false alarm rate (CFAR) detector of these components. The objective is to decide between two hypothesis; the motor is healthy or faulty. The generalized likelihood ratio test (GLRT) is used to tackle this binary hypothesis testing problem.Monte Carlo simulations have been performed in order to evaluate the statistical performance of the proposed approach. Then, the proposed algorithm has been evaluated on experimental data for broken rotor bars fault detection.

The goal of this paper is to accurately estimate three-phase grid voltage parameters under distorted and unbalanced voltage conditions. For this purpose, a novel phase locked loop (PLL) using parallel second-order generalized integrator (Parallel-SOGIs) is developed. The proposed technique can accomplish grid synchronization under distorted and unbalanced voltage, which includes voltage drops and higher-order harmonics. It also provides fast and accurate detection of grid parameters (frequency and phase). This technique can be applied and expanded easily to any number of higher order harmonics. It has been tested and implemented in real-time under different faults and distorted conditions to ensure its effectiveness. Furthermore, a deep analysis is provided for the transformation which occurs between the three-phase grid voltages under unbalance and harmonic distortion in the stationary frame and its image in the dq rotating frame. Finally, the presented technique has the ability to detect the frequency and the phase precisely under higher order harmonic and unbalanced voltage with zero steady state error.

This paper presents the modeling steps of a shrouded tidal stream turbine. The main goal of this modeling is to allow the behavior simulation of the converter in order to anticipate its operation under specific conditions and its later development. The observation time scale of the operation system may extend from tidal period (several hours) to converter switching period (milliseconds to microseconds). An assessment of different modeling methodology, based on instantaneous and average models, is carried out. Instantaneous modeling offers the possibility to observe system electrical dynamics, which are driven by power switches, at the expense of an important computational time. In that respect, an average modeling is considered for greater period-scale analyses. So as to reduce the computational time induced by the power electronic modeling, cosimulations between Simulink and PSIM are also established. Simulation results are furthermore compared with some experimental data of an industrial shrouded tidal stream turbine prototype immersed and tested in real off-grid condition. The results and differences observed are sufficiently relevant to consider these models as references for further development.

This work presents a variational mode decomposition (VMD) based detector for bearing fault in electrical machines. Its performance is compared to that of the ensemble empirical mode decomposition (EEMD) based. A notch filter based Pearson correlation was developed and used to extract the dominant mode. Experimental results showed that the VMD outperformed in terms of statistical features. As a result, the VMD-based notch filter could be a promising methodology for bearing fault detection and degradation prediction.

This paper deals with the implementation of a Model Predictive Control (MPC) system for a classroom in a container building ventilation system and the associated indoor climate through controlling the airflow rate to the zone. A dynamic thermal model for the building system is formulated using the three resistors and two capacitors (3R2C) lumped capacitance method, and linearized using the Taylor's series expansion. This model is used for the proposed MPC implementation for thermal comfort management with energy optimization. Simulation results demonstrate the significance of the MPC controller in handling the constraints, multi-objective control, and producing optimal control strategy. The energy optimization results of the MPC have shown 31% of energy consumption reduction compared to a conventional controller.

Drones are recently receiving a growing attention in both civil and military sectors. Despite their good features such as high maneuverability, wide variety of usage, and low cost; battery-powered drones are still limited in terms of endurance. They cannot perform long flights and persistent missions. This paper proposes then a review-based discussion of the solutions addressing this issue, including swapping laser-beam inflight recharging and tethering. Hybrid power supply system is also a solution of choice. Combining battery with different sources such as fuel cell, solar cells, and supercapacitor allows the system to benefit from sources advantages and cover their limitations. In this context, this paper provides a comparative and critical study of different power supply architectures, thus facilitating the trade-off in the choice of the suitable drone power supply system. Insights and recommendations for future research are also provided.

Magnetic couplings play an essential role in designing fully waterproof mechanical transmission devices. These systems allow transmitting motions at a distance, therefore without complex and unreliable mechanical seals for rotating shafts. However, magnetic couplings naturally create less rigid joints when compared with mechanical ones. It means the driven rotor of a magnetic coupling can present a different movement of its motorised counterpart in transient conditions (while accelerating, for example). Usually, designers look for optimising the torque density. However, if we are looking for a fast response and position control accuracy (i.e. synchronisation), another approach must be carried out. The reason is that, for different magnetic couplings, the maximum transmissible torque can be achieved with widely different angles (according to device features), while we need high torques for tiny angles. Hence, our objective is to maximise the initial stiffness minimising the volume of magnets. Thus, this work aims to present a design method to find an optimal rigid axial magnetic coupling by maximising its initial stiffness density (defining the objective function) under geometric constraints. First, the stiffness analytical expression is obtained for a chosen axial magnetic coupling made with Halbach arrayed arched magnets. Secondly, an optimal magnetic coupling is sought for a given device. In conclusion, this stiffness density-based design is analysed and compared to the torque density-based one, finding the best number of poles-pairs.

Power electronic converters such as inverters and rectifiers are crucial parts of renewable energy systems. Usually, the power converters are subjected to a high failure frequency rate and lead to power system shut down. In an effort to predict power insulated gate bipolar transistor (IGBTs) device aging, this paper proposes a remaining useful life estimation algorithm for degraded power IGBTs, which are exposed to high amplitude of thermal overstress, through the Gaussian Process regression technique. The benefits of the proposed prognostic method are also illustrated with real device accelerated aging database set under thermal overstress utilizing a DC at the gate.

This paper deals with induction machines bearing failures detection and diagnosis using vibration and temperature signals. The failure detection is managed by a clustering graphical representation creating transition classes. Motivated by the computational complexity of the problem, a Variable Neighborhood Search (VNS) metaheuristic is developed including well-designed local search algorithms for data clustering to the system diagnosis. Computational experiments carried out on the PRONOSTIA experimental platform data show that the proposed algorithm seems to be efficient and effective.

This paper presents the solar photovoltaic energy storage as hydrogen via PEM fuel cell for later conversion back to electricity. The system contains solar photovoltaic with a water electrolysis to produce hydrogen that will be stored in a compressed storage tank at high pressure for later use. In need, the hydrogen will be re-electrified by a Proton Exchange Membrane (PEM) Fuel Cell. The output current of the solar PV system is controlled by a PI controller to stabilize the input current of the electrolyzer. The main issue with renewable energy systems is their dependence on weather conditions. For this reason, the renewable hydrogen production and storage chain is assessed through system modeling and simulation by evaluating the effect of solar irradiation on hydrogen production and re-electrification.

In order to overcome the disadvantages of conventional DPC control, this paper proposes a combination of direct power control with space vector modulation used in shunt active power filter. For this reason, from the beginning, every efforts will be directed towards developing a control strategy that achieves the best results by reducing current THD and power ripple. The approach is based on the replacement of switching table and hysteresis controllers by a vector modulator and PI controllers to ensure operation at a constant switching frequency. A series of simulations under Matlab/Simulink environment, followed by a practical implementation using a Dspace 1104 are demonstrated.

Most of signals in the electrical machines and drives are non-Gaussian and highly nonlinear in nature. A useful set of techniques for examining these kinds of signals relies on the spectral representations of higher-order statistics (HOS), well-known as polyspectra. They describe statistical dependences of frequency components that are neglected by traditional spectral measures. The bispectrum is the most used HOS, and studying higher-order correlations provides more information about the electromechanical system's behavior. It helps in building more accurate diagnostic models. Based on this proper relationship the overall aim of the current work is the interpretation of the stator current tidal turbine bispectrum under imbalanced rotor blades condition. Based on this proper relationship, the overall aim of the current work is the interpretation of the permanent magnet synchronous generator (PMSG)-based tidal current turbine (TCT) stator current bispectrum for the diagnosis of biofouling. The proposed bispectrum-based diagnosis method has been tested using experimental data issued from a TCT experiencing biofouling emulated by an attachment on the turbine rotor. The achieved results clearly indicate the feasibility and efficacy of the proposed method.

Due to the characteristics of predictability and high energy density in tidal current resources, tidal stream turbine generation systems have been developed in the last decades around the world. Speed control would be necessary to perform the maximum power point tracking (MPPT) task under varying marine current conditions. Considering that various disturbances or parameter uncertainties may deteriorate the system performance, the active disturbance rejection control (ADRC) strategy seems to be an interesting solution for controller designs. In this paper, an ADRC strategy is applied in the speed control loop for realizing MPPT under disturbances of current velocity and turbine torque. The system performance of the proposed ADRC is compared with PI and high-order sliding mode (HOSM) control strategies. The operation under swell wave disturbance is also carried out. This simulation-based comparative study shows the effectiveness and advantages of the ADRC controller over conventional PI controller in terms of quick convergence, overshoots elimination, and better performance under disturbances.

Prosumers concept has evolved with the technology advancements in renewable energy sources and intelligent responsive load devices. Digitalization paves the way for prosumers participation in energy market with the help of big data and distributed ledger technologies. Hence, power system is becoming more decentralized at distributed level, which consists of prosumers, consumers, and distributed energy resources-based microgrid systems. These microgrid participants require a transactive energy system for energy price signals-based smooth energy transactions among themselves. In this regard, this paper proposes a microgrid transactive energy system design and its functional layers. Blockchain-based transactive energy systems are also discussed. Finally, the peer-to-peer and community-based energy markets are presented.

In this paper, new frequency and phasor estimators are presented. These estimators are based on the maximum-likelihood technique and exploit the multidimensional nature of electrical signals. To minimize the likelihood function, we present an optimization algorithm based on the Newton-Raphson technique. While the performance of Fourier-based estimators significantly degrades when the window length is not equal to a multiple of the fundamental half-period, the proposed estimators perform well regardless of the window length. Simulation results show that the proposed estimators clearly outperform the discrete-time Fourier transform in terms of total vector error and frequency error whatever the signal-to-noise ratio, harmonic and interharmonic distortion, and off-nominal frequency deviation are. The benefits of the proposed estimators are also illustrated with real power system data obtained from the DOE/EPRI National Database of power system events.

Reducing greenhouse gas emissions becomes a top priority in the world with the emergence of global warming and environmental problems. Thus, a variety of renewable energy appears during the last decades. The Ocean, which covers two-thirds of the world, captures and stores huge amounts of energy which could satisfy 5 times of world energy demand. During the last 10 years, various Marine Current Energy Conversion Systems (MCECSs) have been developed around the world. In this paper, the marine current energy and its extraction forms are briefly presented. Then, various projects are classified into two categories, geared drive train system and direct drive train system, according to the different generators. The mainly characteristics of the different generators are also discussed. The relative converters are future presented. According to this paper, the researchers will easily find that the Permanent Magnet Synchrous Generator (PMSG) and Induction Generator (IG) are preferred in MCECS.

This paper deals with the design and application of an emulator to reproduce the dynamic behavior of of tidal turbines to feed a remote load. The proposed emulator provides a flexible platform to investigate different operating conditions of the tidal turbine, and supervise the system variables in real time. Simulation and experimental results are presented to validate the proposed emulator and illustrate the operation of the proposed topology. This emulator can be used for sizing analysis, site evaluation, testing power converter topologies, and evaluating control strategies.

This paper provides a comparative evaluation of phasor estimation signal processing tools, namely the least square estimator and the linear Kalman filter. Comparative investigations are carried out using simulated signals for a power grid voltage sag disturbance. For both techniques, the fundamental frequency is assumed known, hence the estimated phasor parameters are amplitude and initial phase. Simulation results are provided to highlight each technique suitability and limitations to estimate the phasor for power quality monitoring.

Spectral estimation of three-phase electrical signals is a well proven technique for the condition monitoring of electrical machines. To overcome the limitation of the Fourier Transform, recent studies have focused on the use of parametric spectral techniques such as the Maximum Likelihood or subspace approaches. In this paper, we propose to improve the estimator performance by using the phase diversity. Using a statistical analysis, we theoretically demonstrate the benefit of using the phase diversity for frequency estimation and show how to extend single-phase estimators to the three-phase case. Finally, we illustrate the advantage of our proposed methodology for the condition monitoring of electrical motors.

This paper investigates the use of instantaneous symmetrical components (ISCs) for mechanical faults detection in inverter-fed induction motors under closed-loop control. The proposed fault detection approach is based on the computation of the ISCs of the stator currents. The positive sequence power spectral density (PSD) is estimated using ESPRIT and least squares (LS). Then, mechanical fault detection is considered as a binary hypothesis test and solved using the generalized likelihood ratio test (GLRT). Both stator currents and the modulating signals issued from the control-loops are demonstrated to be efficient for fault detection. Simulation results on an analytical model of an inverter-fed induction motor illustrate the effectiveness of the proposed approach, leading to an effective fault detection procedure for load torque oscillation in inverter-fed induction motor under closed-loop operation.

This paper deals with the improvement of autonomous underwater robot propulsion systems. These need upgrades to enhance robots capabilities demanded by complex tasks, such as marine current turbine maintenance. We propose a Radial design for the concept of Reconfigurable Magnetic Coupling Thruster (R-RMCT). This design is based on the usage of a radial magnetic coupling as the core of a magnetic joint. Design parameters of a feasible R-RMCT are explored to meet project requirements with minimizing the magnets volume. In this context, the magnetic coupling is modeled by a magnetostatic approach, using a finite volume integral method. The numerical model is implemented using the free 3D RADIA tool together with the Wolfram Mathematica™ language. Following this, a feasible design is therefore achieved, evaluated, and analyzed.

This paper deals with induction machines bearing failures detection and diagnosis using vibration and temperature signals. It proposes the use of a new Classical Mechanics-inspired Optimization (CMO) metaheuristic for data clustering. To ensure failure detection, transitions from a state to another is analyzed in order to form a transitional model between system states generated by the clustering. The performances of the proposed new metaheuristic are evaluated on the PRONOSTIA experimental platform data.

This paper deals with a control strategy of a grid-connected marine current energy conversion system driven by a 5-phase permanent magnet synchronous generator. A supercapacitor is used as an energy storage device in order to smooth the output power and improve the low-voltage ride-through capability of the energy conversion system. The proposed approach is based on a model predictive approach with finite control set, where the future system states are predicted and evaluated to choose the optimal control action. Simulation results are carried out to validate the efficiency of the proposed control strategy. The system is supposed to be located in the Raz-de-Seine site in Bretagne, France. The output power fluctuation caused by the swell effect is efficiently smoothed by the supercapacitor energy storage system. The low-voltage ride-through performances are tested in two different fault scenarios when the grid voltage reduces to 50 and 100% of the rated value.

Track degradation of wind turbine high-speed shaft bearing can reduce unscheduled maintenance events, and safe power generation system. This paper proposes a particle filter-based prognostic approach for high-speed shaft bearing track degradation; this approach is validated by inspecting a real data from a wind turbine drivetrain. The particle filter-based prognostic results are compared with the standard support vector regression and Kalman smoother results. The particle filter method shows better results. For longer prediction times, the error of the proposed method is equal to or smaller than that of the regression method. The main improvement of the particle filter-based prognostic approach is its ability to produce a probabilistic result based on input parameters with uncertainties. The distributions of the input parameters propagate through the filter, and the remaining useful life is presented using a particle distribution.

This work proposes a hybrid of GA and immune algorithm for permutation flowshop scheduling problems to overcome the problem of GAs early convergence during the evolutionary processes. The proposed algorithm, called VacGA, introduces vaccination into the field of GAs based on the theory of immunity in biology. VacGA employs a GA to perform global search and an artificial immune system to perform local search. VacGA has been tested on Taillard’s benchmarks, and compared with standard GA and the best existing hybrid GAs. The obtained results shed light on the efficiency of our new hybrid method. Furthermore, the effects of some parameters are discussed.

This paper deals with a fault detection method based on an empirically data-driven approach combined to a statistical tool. This approach is an enhanced version of the empirical mode decomposition. The proposed fault detector application to bearing defects in wind turbine based on induction generator clearly shows that it is well suited for stationary and non-stationary behavior regardless the rank of the intrinsic mode function introduced by the fault.

This paper focalizes on sizing of hybrids sources composed with Fuel cells FCs and battery pack. Also an experimental validation of energy management of FC/Battery electric vehicle is tested. The system is composed of a fuel cell system as the main source and batteries as an assisting one. This last one is connected to a bidirectional DC/DC converter, and a DC/DC boost converter is associated to the fuel cell stack. To increase the power efficiency and to achieve the best performances of the hybrid source, an online EM strategy is used to share the power between the main and the auxiliary source by determining the power profile of each one. This strategy is based on frequency separation; it takes into account the slow dynamics of FC, fuel consumption and the batteries limits. Also, it is needed to make these results as a reference to be compared with other strategies which are currently under development in our laboratory. In the objective to verify the efficiency of the proposed approach, both simulation and experimental results leads to confirm its efficiency, the robustness and stability regrading dynamic performances during power demand, and regenerative braking, fuel consumption.

This paper deals with a hybrid control scheme for two-phase interleaved boost converter devoted to renewable energy and automotive applications. The control requirements, resumed in low input current ripples, are formulated as an optimal limit cycle stabilization problem, and are solved using hybrid systems formalism. The closed-loop consists in associating each discrete mode to a clearly defined region of the state space, in such a way that the optimal limit cycle is stabilized asymptotically. Mathematical proofs are provided alongside simulations to illustrate the effectiveness and the robustness of the proposed controller, especially with the use of a fuel cell as an input source.

In this paper, an accurate method is proposed to detect static eccentricity (SE) in induction motors under unbalanced supply voltages. To accomplish this task, one important problem is dealt with: in stator current spectrum of induction machines, the frequency components characteristic of unbalanced supply voltages are similar to some of the frequency components caused by SE. In this direction, the present paper attempts to give an analytical explanation of the generation mechanism of all frequencies related to the SE in order to distinguish them from frequencies related to unbalanced voltages. The theoretical study and the simulation results have been verified by experimental tests conducted on a 1.1 -kW squirrel cage induction motor.

This paper deals with the optimal design and energy management of a hybrid wind/tidal/PV power generation system. The optimal combination of these renewable power sources is achieved using linear programming with high reliability as main constraints under various scenarios/conditions such as the location, solar radiation, and temperature. The proposed optimization approach is assessed for the off-grid site of the Ouessant French Island located in Bretagne (France), where the load demand is about 16GWh/year. The achieved results highlight the efficiency and reliability of the proposed algorithm for sizing a hybrid power generation system to match the site load demand.

This paper deals with a new classification techniques for power quality analysis. Specifically, the proposed technique aims at discriminating between four classes, where each class depends on the number of non-zero symmetrical components. By reformulating the classification problem as a pure model order selection one, we propose a classifier based on Information Theoretical Criteria. The performances of this proposed classifier are evaluated using Monte Carlo simulations with synthetic three-phase signals. Simulation results illustrate the effectiveness of the proposed classifier for power quality disturbances classification.

This paper deals with a control strategy for marine current energy conversion systems based on 5-phase permanent magnet synchronous generator (PMSG) in both normal and faulty operation modes. The PMSG currents are controlled by the finite control set model predictive control method. Based on future behaviors of the system, which are predicted from a finite set of possible switching states of a 3-level 5-phase inverter, the optimal voltage vector, which minimizes the cost-function, is selected. When an open-circuit fault occurs, the reference currents are reconfigured to ensure continuous and safe operation of the system and to avoid high torque ripple. Several simulations are carried-out for validation purposes of the proposed fault-tolerant control strategy.

This paper presents a novel approach for induction machine condition monitoring using stator current measurements. The proposed method, based on hypothesis testing, specifically investigates a binary detection problem: the machine is healthy or faulty. The Generalized Likelihood Ratio Test (GLRT) is used to address this statistical detection problem with unknown signal and noise parameters. It is indeed a Constant False Alarm Rate (CFAR) detector. Decision is obtained according to a threshold, which is set to reach a desired false alarm probability. The proposed detector implementation needs estimations that are based on the Maximum Likelihood Estimator (MLE). In particular, Total Least Squares-Estimation of Signal Parameters via Rotational Invariance Techniques (TLS-ESPRIT) estimates frequencies. The proposed CFAR detector is tested on experimental data of bearings faults and broken rotor bars that clearly show it effectiveness.

Marine current turbine (MCT) have been widely used nowadays, it is important to monitor their health state. Unnecessary marine biological growth or marine pollutants attached to the moving parts will affect the operation of the system by introducing imbalance. The imbalance, regarded as faults, would change the performance of turbine and lead to progressively increasing damages. In this paper, a marine current turbine prototype with permanent magnet synchronous generator (PMSG) has been studied. An innovative imbalance fault detection method is proposed for marine current turbines under the condition of wave and turbulence. In the proposed method, the average frequency of current is calculated through synchronous sampling. Meanwhile, current fluctuation influence is reduced during one revolution. The empirical mode decomposition (EMD) and spectrum analysis are used to achieve fault characteristics. Theoretical analysis, simulation and experimental results under different conditions validate the proposed method. Moreover the proposed method could be used for long-term marine current turbine monitoring in respect to its simplicity and low time cost.

This paper focuses on the estimation of voltage sags in three-phase power systems. Specifically, it proposes a new approach for estimating the amplitude and phase angle of the sag based on the Unconditional Maximum Likelihood technique. As opposed to other techniques, this approach is well suited for signals with amplitude and/or phase modulation such as those encountered in smart grid applications. Simulation and experimental results illustrate the effectiveness of the proposed approach.

This paper presents a hybrid approach for online fault detection in nonlinear processes. To solve the possible monitoring difficulties caused by nonlinear characteristics of industrial process data, two applications of the Kernel Method: Hypersphere Support Vector Machine (HSSVM) and Kernel Principal Component Analysis (KPCA) are used as fault detection methods. On top of that, to obtain the adaptive models for online monitoring and fault detection in unsteady-stage conditions, instead of the static ones established by traditional HSSVM and KPCA, multiple methods are adopted, including Recursive KPCA, Adaptive Control Limit (ACL) and Online Sequential Extreme Learning Machine (OS-ELM), all of which update the detection model in real time with dynamically adjusting. The T2 control limit of Recursive KPCA, the classification hyperspheres of HSSVM and the single hidden layer feedforward network (SLFN) trained with OS-ELM work collaboratively in monitoring the real time process data to detect the possible faults. The proposed approach was tested and validated via a set of experimental data collected from a bearing test rig. Experimental results show that this approach is adequate for fault detection while meets the needs of real time performance.

Smart grids have urged a radical reappraisal of distribution networks and power quality requirements, which demand to continuously monitor grid conditions. This allows for early classification of power quality degeneration. In this context, this paper proposes an improved algorithm for phasor estimation and fault type classification in smart grid applications. The proposed algorithm is specifically composed of two steps: first, the phasor estimation from the input signals, which can be executed on the Phasor Measurement Unit (PMU). While step two concerns the fault type classification using the pha-sor estimation. Specifically, we propose to estimate the phasor parameters, (frequency, amplitude and initial phase) using least square method. Then, fault type classification is obtained using symmetrical component algorithm. The proposed algorithm can be used for the identification of power quality disturbances. It has been evaluated, compared, and studied with simulated data. Simulation results show accurate phasor estimations and accurate fault type classification in presence of noise.

Sensor fault is one of the most frequent causes leading to the failure of renewable energy conversion system. Several fault-tolerant control strategies have been proposed to maintain the operation of these systems in the case of sensor unavailability. This paper compares their fault-tolerance efficiency and their ability to be applied in real-world applications. This comparison is based on their performances on a direct-drive permanent magnet synchronous generator (PMSG)-based variable speed marine current energy converter under the swell effect.

This paper aims to develop a condition monitoring architecture for induction machines, with focus on bearing faults. The main objective of this paper is to identify fault signatures at an early stage by using high-resolution frequency estimation techniques. In particular, we present two subspace methods, which are Root-MUSIC and ESPRIT. Once the frequencies are determined, the amplitude estimation is obtained by using the Least Squares Estimator (LSE). Finally, the amplitude estimation is used to derive a fault severity criterion. The experimental results show that the proposed architecture has the ability to measure the faults severity.

This paper presents an evaluation method for assessing different Autonomous Underwater Vehicle (AUV) propulsion systems (or topologies). This evaluation is based on solid/fluid dynamics simulation and uses a model-based control (feedback linearisation) to achieve the robotic mission assigned to the AUV. The objective of this work is to provide an evaluation relevant enough to be used for optimisation purposes. A model of an existing AUV (RSM) is proposed and detailed as well as two studied propulsion systems in competition: Fixed and vectorial thrusters. The main contribution of this work is to bring a consistent evaluation including models, tasks and a generic high-level control adapted to each system. Simulations of our AUV achieving a diving task are carried out for both propulsion systems. The simulated task analysis allows to evaluate the influence of the propulsive strategy over AUVs operability. Subsequently, numerical results are discussed in terms of energy efficiency and control.

The sustainable energy based generation systems, such as photovoltaic and wind turbine generation systems, normally adopt inverters to connect to the grid. These power electronics interfaces possess characteristics of small inertia and small output impedance, which create difficulties to stabilize the voltage and frequency of a distributed micro power source. To deal with this problem, this paper is focus on the research of a virtual synchronous generator based control method by introducing virtual inertia into the control loop and emulating the control scheme of a traditional synchronous generator. After discussing the design process of the virtual speed and excitation regulators, system stability and parameter sensibility have been analyzed. At last, some simulations have been carried out and the effectiveness of proposed method is demonstrated by simulation results comparison and analysis.

This paper investigates the online monitoring of electrical machine winding insulation systems based on the parametric modeling and identification. The proposed method consists in monitoring the drift of diagnostic indicators built from in-situ estimation of high-frequency electrical model parameters. The involved model structures are derived from the RLC network modeling of the winding insulation. Because they often present an important modeling noise, we propose to use the output error method not only to estimate the model parameter values but also to evaluate their uncertainty. This approach is based on the numerical integration of the model sensitivity functions. The so-called global identification scheme is coupled with an optimization algorithm that brings the best combination of any diagnostic model structure and its excitation protocol usable in operating conditions. Experimental data recorded from an industrial wound machines are used to illustrate the methodology.

The failure rate of non-steady conditions is much higher than the failure rate of steady conditions. So, it is important to monitor non-steady conditions of system. The systems' monitoring results indicate that there are large false alarms or missing alarms based on traditional process control methods. The primary problems are higher data dimension, more complex correlation among variables, non-Gaussian distribution, the signal mutations and so on. Hence, this paper proposed a novel Longitudinal-standardization multi-period PCA fault detection strategy based on adaptive confidence limit (LMPCA-ACL) for periodic non-steady conditions. This LMPCA-ACL strategy comprises three helpful parts as follows. First one is to transform the non-Gaussian normal data into Gaussian data through a novel longitudinal-standardization (LS). The second part is to utilize the proposed multi-period PCA algorithm to reduce dimensions, remove correlation and improve the monitoring accuracy. The third part is to build the adaptive confidence limit to resolve the problems of signal mutations and real-time monitoring by the dynamic data window method. In this paper, the LMPCA-ACL strategy is applied to real-time monitor the motor cyclical process of loading and unloading. The examination results indicate that the LMPCA-ACL strategy is superior to other methods in fault detection if the system is under periodic non-steady conditions.

The multi-level inverter system is becoming a very promising candidate to replace the conventional two-level inverter, but system reliability remains an open issue. The most common reliability problem is that power switch transistors have open-circuit or short-circuit faults during operation. In order to improve the accuracy of the fault diagnosis and accelerate the operation speed in a cascaded H-bridge multilevel inverter system, a novel fault diagnosis strategy based on principle component analysis and multiclass relevance vector machine (PCA-mRVM) is presented in this paper. In this strategy, the output voltage of CHMLIS, which is preprocessed through the fast Fourier transform, is used to identify the type and location of occurring fault through the mRVM model. Then PCA is utilized to reduce input sample's dimension, which decrease the training time of diagnostic model. The PCA-mRVM strategy could not only achieve higher model sparsity and shorter diagnosis time, but also provide probabilistic outputs for every class membership. Hardware experimental results of simple-fault have shown that the PCA-mRVM could achieve the best diagnosis performance than traditional fault diagnosis methods. In addition, simulation results of complicated-fault have further validated that the PCA-mRVM strategy is useful in multi-fault diagnosis, which is better than other methods.

Smart grids have initiated a radical reappraisal of distribution networks function where the integration of renewable energy sources, load demand control, and effective use of the network are indexed as the most important keys for smart grid expansion and deployment regardless each country policies. One of the most efficient ways of effective use of these grids would be to continuously monitor their conditions. This allows for early detection of power quality degeneration facilitating therefore a proactive response, prevent a fault ride-through the renewable power sources, minimizing downtime, and maximizing productivity. In this smart grid context, this paper proposes the evaluation and comparison of advanced signal processing tools, namely the Hilbert transform and the ensemble empirical mode decomposition method for the detection of voltage sags as they are the most commonly encountered power quality disturbances.

This paper deals with the Fault Ride-Through (FRT) capability assessment of a Doubly-Fed Induction Generator (DFIG)-based Wind Turbine (WT) using High-Order Sliding Mode (HOSM) control. Indeed, it has been recently suggested that sliding mode control is a solution of choice to the FRT problem. In this context, this paper proposes HOSM as an improved solution that handle the classical sliding mode chattering problem. Indeed, the main and attractive features of HOSMs are robustness against external disturbances (e.g. grid) and chattering-free behavior (no extra mechanical stress on the drive train). Simulations using the NREL FAST code on a 1.5-MW wind turbine are carried-out to evaluate ride-through performance of the proposed HOSM control strategy in case of grid frequency variations and unbalanced voltage sags.

Current spectrum analysis is a proven technique for fault diagnosis in electrical machines. Current spectral estimation is usually performed using classical techniques such as periodogram (FFT) or its extensions. However, these techniques have several drawbacks since their frequency resolution is limited and additional post-processing algorithms are required to extract a relevant fault detection criterion. Therefore, this paper proposes a new parametric spectral estimator that fully exploits the faults sensitive frequencies. The proposed technique is based on the maximum likelihood estimator and offers high-resolution capabilities. Based on this approach, a fault criterion is derived for detecting several fault types. The proposed faults detection technique is assessed using simulations, issued from a coupled electromagnetic circuits approach-based simulation tool. It is afterwards validated using experiments on a 0.75-kW induction machine test bed for the particular case of bearing faults.

Development of large scale offshore wind and marine current turbine farms implies to minimize and predict maintenance operations. In direct- or indirect-drive, fixed- or variable-speed turbine generators, advanced signal processing tools are required to detect and diagnose the generator faults from vibration, acoustic, or generator current signals. The induction generator is traditionally used for wind turbines power generation. Even if induction machines are highly reliable, they are subjected to many types of faults. The aim then, is to detect them at an early stage in order to prevent breakdowns and consequently ensure the continuity of power production. In this context, this paper deals with wind turbines condition monitoring using a fault detection technique based on the generator stator current. The detection algorithm uses a recursive maximum likelihood estimator to track the time-varying fault characteristic frequency and the related energy. Furthermore, a decision-making scheme and a related criterion are proposed. The feasibility of the proposed approach has been demonstrated using simulation data issued from coupled magnetic circuits induction generator model driven by a wind turbine for both electrical asymmetry and mechanical imbalance.

This paper deals with low-voltage ride-through (LVRT) capability of wind turbines (WTs) and in particular those driven by a doubly-fed induction generator (DFIG). This is one of the biggest challenges facing massive deployment of wind farms. With increasing penetration of WTs in the grid, grid connection codes in most countries require that WTs should remain connected to the grid to maintain the reliability during and after a short-term fault. This results in LVRT with only 15% remaining voltage at the point of common coupling (PCC), possibly even less. In addition, it is required for WTs to contribute to system stability during and after fault clearance. To fulfill the LVRT requirement for DFIG-based WTs, there are two problems to be addressed, namely, rotor inrush current that may exceed the converter limit and the dc-link overvoltage. Further, it is required to limit the DFIG transient response oscillations during the voltage sag to increase the gear lifetime and generator reliability. There is a rich literature addressing countermeasures for LVRT capability enhancement in DFIGs; this paper is therefore intended as a comprehensive state-of-the-art review of solutions to the LVRT issue. Moreover, attempts are made to highlight future issues so as to index some emerging solutions.

In recent years the electric power generation has entered into a new development era, which can be described mainly by increasing concerns about climate change, through the energy transition from hydrocarbon to clean energy resources. In order to power system enhance reliability, efficiency and safety, renewable and nonrenewable resources are integrated together to configure so-called hybrid systems. Despite the experience accumulated in the power networks, designing hybrid system is a complex task. It has become more challenging as far as most renewable energy resources are random and weather/climatic conditions-dependant. In this challenging context, this paper proposes a critical state-of-the-art review of hybrid generation systems planning expansion and indexes multi-objective methods as strategies for hybrid energy systems optimal design to satisfy technical and economical constraints.

This paper proposes a fault-tolerant control scheme for PWM inverter-fed induction motor-based electric vehicles. The proposed strategy deals with power switch (IGBTs) failures mitigation within a reconfigurable induction motor control. In a vehicle context, 4-wire and 4-leg PWM inverter topologies are investigated and their performances discussed. Two topologies exploit the induction motor neutral accessibility for fault-tolerant purposes. The 4-wire topology uses then classical hysteresis controllers to account for the IGBT failures. The 4-leg topology, meanwhile, uses a specific 3D space vector PWM to handle vehicle requirements in terms of size (DC bus capacitors) and cost (IGBTs number). Experiments on an induction motor drive and simulations on an electric vehicle are carried-out using a European urban driving cycle to assess the FTC scheme performance and effectiveness.

This paper focuses on the microgrid control method in different operating modes. The conventional droop control scheme is typically used to achieve autonomous voltage and frequency regulation, which considers only local information, and the global optimal performance can't be guaranteed. In island mode, when the load or generation inside the MG changes, circulations will be generated between the inverters in case the line impedances are mismatch. Furthermore, the MG active power output does not follow the reference in grid connected mode. Dealing with the above problems, a central controller is designed to maintain the stable operation of the microgrid in different modes in this paper. Some simulations are carried out and the results validate the efficiency of the proposed method.

Failure detection has always been a demanding task in the electrical machines community; it has become more challenging in wind energy conversion systems because sustainability and viability of wind farms are highly dependent on the reduction of the operational and maintenance costs. Indeed the most efficient way of reducing these costs would be to continuously monitor the condition of these systems. This allows for early detection of the generator health degeneration, facilitating a proactive response, minimizing downtime, and maximizing productivity. This paper provides then an assessment of a failure detection techniques based on the homopolar component of the generator stator current and attempts to highlight the use of the Ensemble Empirical Mode Decomposition (EEMD) as a tool for failure detection in wind turbine generators for stationary and non stationary cases.

Wind turbines proliferation in industrial and residential applications is facing the problem of maintenance and fault diagnosis. Periodic maintenances are necessary to ensure an acceptable life span. The aim of this work is to develop theoretical tools to diagnose the failure or the malfunction of the doubly-fed induction generator (DFIG). The diagnosis method is based on the impedance spectroscopy which is used for the diagnosis of batteries, fuel cells, and electrochemical systems. This first attempt for using this method to the diagnosis of the DFIG provides good results for the detection of the grounding connection, short circuit and stator resistance variation.

This paper deals with the sensorless control of a doubly-fed induction generator (DFIG) based wind turbine. The sensorless control scheme is based on a high-order sliding mode (HOSM) observer to estimate the DFIG rotational speed. Indeed, high-order sliding mode observers provide theoretically finite time exact state observation and estimation of absolutely continuous unknown inputs. The proposed global control strategy combines an MPPT using a high-order sliding mode speed observer and a high-order sliding mode for the DFIG control. This strategy presents attractive features such as chattering-free behavior, finite reaching time, robustness and unmodeled dynamics (generator and turbine). Simulations using the wind turbine simulator FAST on a 1.5-MW three-blade wind turbine are carried out for the validation of the proposed sensorless control strategy.

Fault detection in squirrel cage induction machines based on stator current spectrum has been widely investigated. Several high resolution spectral estimation techniques have been developed and used to detect induction machine abnormal operating conditions. In this paper, a modified version of MUSIC algorithm has been developed based on the faults characteristic frequencies. This method has been used to estimate the stator current spectrum. Then, an amplitude estimator has been proposed and a fault indicator has been derived for fault severity measurement. Simulated stator current data issued from a coupled electromagnetic circuits approach has been used to prove the appropriateness of the method for air gap eccentricity and broken rotor bars faults detection.

Swell is the main disturbance for marine tidal speed. The power harnessed by a marine current turbine (MCT) can be highly fluctuant due to swell effect. Conventional Maximum Power Point Tracking (MPPT) algorithm will require the generator to accelerate/decelerate frequently under swell effect and therefore cause severe fluctuations in the generator power. This paper deals with power smoothing control of grid-connected MCT system. In the first step, a modified MPPT algorithm with filter strategy is proposed in the generator side control to reduce the fluctuation of generator power. In the second step, Supercapacitor (SC) Energy Storage System (ESS) is added to compensate the residual power fluctuations. As the average tidal speed is predictable, it is possible to control the SC ESS to compensate the swell effect and realize a smoothed power injected to the grid. Simulations carried-out on a 1.5 MW direct-driven grid-connected MCT generation system demonstrate that the association of the generator side filter strategy with the SC ESS system achieves a smoothed grid-injected power in case of swell disturbances.

Flexibility has long been recognized as a manufacturing capability that has the potential to impact mainly the competitive position of an organization. The entropy approach, which was extended from information theory, fell in handling problems with incomplete and uncertain data, because it depicts only the stochastic aspects included with measured observations. In order to get a global view, this work proposes a new approach based on fuzzy entropy concept. The development of the fuzzy model results in a set of nonlinear constrained problems to be solved using a metaheuristics method. The applicability of our approach is illustrated through a flexible manufacturing cell. By adopting such framework, both dimensions of uncertainty in system modeling, expressed by stochastic variability and imprecision, can be taken into consideration.