Recently, fault detection has played an essential role in ensuring the safety and reliability of inverter operation. This paper illustrates the efficiency of employing the random forest decision tree approach for fault detection of single open circuit switch fault in a Packed E-Cell inverter. First, Discrete Wavelet Analysis is utilized to analyze the output voltage, using the first level of detailed coefficients. Then, three statistical features are extracted from this analysis to serve as input to the classifier during training. The output of the classifier, which indicates the fault status, is then trained accordingly. Finally, a fault detection strategy is implemented, and the results obtained using MATLAB/Simulink affirm the effectiveness of the proposed algorithm highlighting its high accuracy and robustness.

Multilevel Inverters (MLI) have become important for electrical energy supplement to grids due to their modularity, lower Total Harmonic Distortion (THD), and decreased filter needs. Recently, a new compact MLI has been introduced named Packed E Cell (PEC) inverter, where an auxiliary dc-link cell is made along with the shunted capacitors. This paper proposes a new switching controller using redundant states in signal builder blocks to balance voltage capacitors. Simulation results have shown the optimum operation of PEC topology and validated the proposed technique.

Multi-Level Inverters (MLI) have become of great importance for electrical energy supplement to grids due to its modularity, lower Total Harmonic Distortion (THD) and decreased filter needs. However, although multilevel inverters achieve high voltage levels, increased number of power switches are required which make them more prone to breakdowns and faults. Among active devices failures, Open-Circuit (OC) faults are extensively explored in research studies. Hence, this paper presents a deep overview concerning methods based on Artificial Intelligence (AI) algorithms involved in diagnosis and localization of OC failure in different multilevel inverter architectures. Initially, two major classifications of fault diagnosis methods for OC switch failure are listed and clarified briefly. Then, AI algorithms are discussed thoroughly. Further, certain criteria with several standards are formulated to differentiate between strategies investigated in publications. Then, various implemented techniques in literature are widely overviewed and compared in an original table.

The paper presents a fault-tolerant multi-sensor fusion approach with Fault Detection and Exclusion (FDE) based on information theory. The Maximum Correntropy Criterion (MCC) in Unscented Information Filter (UIF) form, called (MCCUIF), is used as estimator. The Unscented Transformation (UT) provides an efficient tool to restrict the non-linear state estimation problem. However, the UIF works well with Gaussian noises, where its performance may decrease when dealing with non-Gaussian noises. The MCC is used to deal with non-Gaussian noises (for instance shot noises or Gaussian mixture noises). For detection and exclusion of erroneous measurements, a residual is designed using a- Rényi Divergence (a-RD) between a priori and a posteriori probabilities distributions. Then α-Rényi criterion (a-Rc) is used in the decision part of the proposed approach in order to calculate an adaptive threshold for FDE. In order to target both high integrity and accuracy of the navigation function of an autonomous vehicle in stringent environments (urban canyon, forests ...), this paper presents a tightly coupled architecture by merging raw data of a Global Navigation Satellite System (GNSS) with odometer (odo) measurements through the proposed approach. The main contributions of this paper are: - the proposition of a multisensor fusion approach using MCCUIF, - the development of an FDE method using a residual based on a-RD with an adequate choice of a value and adaptive thresholding, - the validation of the proposed approach with real experimental data.

Pulse width modulation converters are increasingly used to connecting to the power grid. This paper presents an enhanced systematic comparative analytic approach for the indirect matrix converters that are actively modulated by two frames: DSVPWM & Hybrid SPWM. Details of proposed mathematical models, simulations & graphical qualifications are presented & discussed.

The paper proposes a novel integrity of both a topological aspect characterized by the indirect matrix converter, and a modulation aspect characterized by the pulse width technique. This integrity brings to completion an entire akin grid-tie topology implemented using digital signal processing operation. The latter gives birth to a solid competent solution in terms of system performance, quality of signals, computation time reduction, & unity power factor feature.

This paper presents a concise review of the novel matrix converter technology widely recognized in the last two decades attracting all scientific institutions, industrial markets, and governmental foundations towards developing it. The appealing features of the topology along with its structural characteristics are clearly highlighted. A state-of-art demonstration of the highly adopted modulation techniques is presented. Brief clarifications covering converters' efficiency and losses, protection issues including input filters are shown.

The paper proposes a hybrid modulation algorithm that combines the space vector modulation technique (SVPWM) to control the rectifier stage and a new digital scalar modulation method (DSPWM) applied to the inverter stage. Compared to the conventional SVPWM method, the proposed DSPWM technique of the inverter stage is easier to understand and to be implemented. DSPWM allows reducing real-time computation difficulties under high quality preservation of grid and load currents' performance.

In this paper we develop a fault detection and isolation method based on data-driven approach. Data-driven methods are effective for feature extraction and feature analysis using statistical techniques. In the proposal, the Cumulated Sum (CUSUM) efficiency is explored for incipient fault detection. The fault is assumed to be a Gain variation, an Offset evolution, a Phase shifting or one of the multiple possible combination of such faults. A first preprocessing stage is proposed for this study and using some statistical other features we proceed to the operations of a Fault Detection and Diagnosis process: Detection, and Isolation. For the detection, the CUSUM efficiency is proved. For the isolation, we propose a specific algorithm based on the combination of several multivariate statistical techniques such as Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA). The classification of each fault or one of their combination is accurately obtained. The results show that for incipient faults (<;10%), the fault detection and isolation is accurate with a relative classification error lower than 3%.

Due to the increasing requirements of safety and reliability in more electrified applications (transportation for example), fault detection and diagnosis (FDD) of electrical systems has become a hot research topic. In the process of FDD, there are three steps: fault detection, fault isolation and fault estimation. The first two items are the commonly addressed while the last one is less tackled as it requires the development of a fault model which parameters are relevant of the fault. In this paper, we propose an analytical model of multiple fault effects on a three phases electrical system. The model is based on the expressions of the currents in the (d,q) or Park synchronous rotating reference frame. We prove through simulation results the efficiency of the model for fault combination of gain, offset and phase shift. The results show also the accuracy of the model that could be used for fault estimation purpose.

It is well known that the Conventional Droop method is commonly used to share real and reactive power between distributed Generation interfaced converters (DICs) based Islanded microgrid. The share of power is done autonomously without communication between Converters. Despite the lot of desirable features the Droop method had, there exist some limitations concerning the accuracy of the reactive power shared between converters when line impedance mismatches exist. This paper presents the droop control law for islanded microgrid, and proposes an online state estimation of the point of common coupling (PCC) voltage, which is then used as a feedback signal for the Droop control to accurately share the power between parallel converters. Thus the proposed method keeps the advantages of the Droop control method to manage the power between distributed generations in autonomous fashion without additional communication links.

This paper introduces a new control scheme based on a Finite Set-Model Predictive Control (FS-MPC) and aims of a fixed switching frequency operation. The proposed scheme allows of obtaining a voltage vector that is responsible of nullifying the cost function's derivative for its optimization. Moreover, a closed-loop space vector modulation (SVM) based on the proposed predictive control scheme is introduced for a three-level grid connected inverter. The proposed algorithm that injects a desired current to the grid is investigated through external perturbations such as change in the line current amplitude as well as DC voltage variation. Discrete-time system model was developed and simulated. Simulation results on MATLAB/Simulink are provided to verify the fast dynamic performance, and good voltage balancing of DC bus capacitors of the NPC inverter.

Increasing worldwide demand for energy has made wind energy a forerunner as a renewable energy resource. Due to this, and due to the intermittent nature of wind, Wind Turbines (WT) reliability should be considered. Improving the reliability of a WT translates into reduced operation and maintenance cost. This highlights the aim of this paper which is a technical review on WTs critical failures as well as an update on recent progress in fault diagnosis and condition monitoring methods used.

The fluctuations in the power delivered to the grid remain a problem in photovoltaic grid-connected systems. Energy storage systems are proposed as a solution in order to comply with these standard power quality requirements. Due to their distinctive properties, supercapacitors can be considered as the most efficient energy storage systems available in the market today. Thus, their capability of fast charging and discharging and their high capacitance (hundred of farads) compared to the traditional ones have been the main reason of their massive integration in PV systems. This paper presents a comparison between a conventional capacitor and a supercapacitor used as DC-link in a PV grid connected system. The superiority of the supercapacitor is demonstrated through a performance evaluation based on simulation results.