Elhoussin Elbouchikhi

Bio: Elhoussin Elbouchikhi is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: Fault (power engineering) & Fault detection and isolation. The author has an hindex of 13, co-authored 52 publications receiving 1050 citations. Previous affiliations of Elhoussin Elbouchikhi include Institut supérieur d'électronique et du numérique.

Microgrid Transactive Energy: Review, Architectures, Distributed Ledger Technologies, and Market Analysis

Abstract: Prosumer concept and digitilization offer the exciting potential of microgrid transactive energy systems at distribution level for reducing transmission losses, decreasing electric infrastructure expenditure, improving reliability, enhancing local energy use, and minimizing customers' electricity bills. Distributed energy resources, demand response, distributed ledger technologies, and local energy markets are integral parts of transaction energy system for emergence of decentralized smart grid system. Hence, this paper discusses transactive energy concept and proposes seven functional layers architecture for designing transactive energy system. The proposed architecture is compared with practical case study of Brooklyn microgrid. Moreover, this paper reviews the existing architectures and explains the widely known distributed ledger technologies (blockchain, directed acyclic graph, hashgraph, holochain, and tempo) alongwith their advantages and challenges. The local energy market concept is presented and critically analyzed for energy trade within a transactive energy system. This paper also reviews the potential and challenges of peer-to-peer and community-based energy markets. Proposed architecture and analytical review of distributed ledger technologies and local energy markets pave the way for advanced research and industrialization of transactive energy systems.

Cascaded H-Bridge Multilevel Inverter System Fault Diagnosis Using a PCA and Multiclass Relevance Vector Machine Approach

Abstract: Multilevel inverters, for their distinctive performance, have been widely used in high voltage and high-power applications in recent years. As power electronics equipment reliability is very important and to ensure multilevel inverter systems stable operation, it is important to detect and locate faults as quickly as possible. In this context and to improve fault diagnosis accuracy and efficiency of a cascaded H-bridge multilevel inverter system (CHMLIS), a fault diagnosis strategy based on the principle component analysis and the multiclass relevance vector machine (PCA-mRVM), is elaborated and proposed in this paper. First, CHMLIS output voltage signals are selected as input fault classification characteristic signals. Then, a fast Fourier transform is used to preprocess these signals. PCA is used to extract fault signals features and to reduce samples dimensions. Finally, an mRVM model is used to classify faulty samples. Compared to traditional approaches, the proposed PCA-mRVM strategy not only achieves higher model sparsity and shorter diagnosis time, but also provides probabilistic outputs for every class membership. Experimental tests are carried out to highlight the proposed PCA-mRVM diagnosis performances.

An Efficient Hilbert–Huang Transform-Based Bearing Faults Detection in Induction Machines

Abstract: This paper focuses on rolling elements bearing fault detection in induction machines based on stator currents analysis. Specifically, it proposes to process the stator currents using the Hilbert–Huang transform. This approach relies on two steps: empirical mode decomposition and Hilbert transform. The empirical mode decomposition is used in order to estimate the intrinsic mode functions (IMFs). These IMFs are assumed to be mono-component signals and can be processed using demodulation technique. Afterward, the Hilbert transform is used to compute the instantaneous amplitude (IA) and instantaneous frequency (IF) of these IMFs. The analysis of the IA and IF allows identifying fault signature that can be used for more accurate diagnosis. The proposed approach is used for bearing fault detection in induction machines at several fault degrees. The effectiveness of the proposed approach is verified by a series of simulation and experimental tests corresponding to different bearing fault conditions. The fault severity is assessed based on the IMFs energy and the variance of the IA and IF of each IMF.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

A Data-Driven Fault Diagnosis Methodology in Three-Phase Inverters for PMSM Drive Systems

Abstract: Permanent magnet synchronous motor and power electronics-based three-phase inverter are the major components in the modern industrial electric drive system, such as electrical actuators in an all-electric subsea Christmas tree. Inverters are the weakest components in the drive system, and power switches are the most vulnerable components in inverters. Fault detection and diagnosis of inverters are extremely necessary for improving drive system reliability. Motivated by solving the uncertainty problem in fault diagnosis of inverters, which is caused by various reasons, such as bias and noise of sensors, this paper proposes a Bayesian network-based data-driven fault diagnosis methodology of three-phase inverters. Two output line-to-line voltages for different fault modes are measured, the signal features are extracted using fast Fourier transform, the dimensions of samples are reduced using principal component analysis, and the faults are detected and diagnosed using Bayesian networks. Simulated and experimental data are used to train the fault diagnosis model, as well as validate the proposed fault diagnosis methodology.

An Overview of Artificial Intelligence Applications for Power Electronics

Abstract: This article gives an overview of the artificial intelligence (AI) applications for power electronic systems. The three distinctive life-cycle phases, design, control, and maintenance are correlated with one or more tasks to be addressed by AI, including optimization, classification, regression, and data structure exploration. The applications of four categories of AI are discussed, which are expert system, fuzzy logic, metaheuristic method, and machine learning. More than 500 publications have been reviewed to identify the common understandings, practical implementation challenges, and research opportunities in the application of AI for power electronics. This article is accompanied by an Excel file listing the relevant publications for statistical analytics.