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.

The topic of microgrids (MGs) is a fast-growing and very promising field of research in terms of energy production quality, pollution reduction and sustainable development. Moreover, MGs are, above all, designed to considerably improve the autonomy, sustainability, and reliability of future electrical distribution grid. At the same time, aspects of MGs energy management, taking into consideration distribution generation systems, energy storage devices, electric vehicles, and consumption components have been widely investigated. Besides, grid architectures including DC, AC, or hybrid power generation systems, energy dispatching problems modelling, operating modes (islanded or grid connected), MGs sizing, simulations and problems solving optimization approaches, and other aspects, have been raised as topics of great interest for both electrical and computer sciences research communities. Furthermore, the United Nations Framework Convention on Climate Change and government policies and incentives have paved the way to massive electric vehicle (EV) deployment. Hence, several research studies have been conducted to investigate the integration of EVs in national power grid and future MGs. Specifically, EV charging stations’ bi-directional power flow control and energy management have been considerably explored. These issues index challenging research topics, which are in most cases still under progress. This paper gives an overview of MGs technology advancement in recent decades, taking into consideration distributed energy generation (DER), energy storage systems (ESS), EVs, and loads. It reviews the main MGs architecture, operating modes, sizing and energy management systems (EMS) and EVs integration.

This paper deals with the diagnosis of three-phase electrical machines and focuses on failures that lead to stator-current modulation. To detect a failure, we propose a new method based on stator-current demodulation. By exploiting the configuration of three-phase machines, we demonstrate that the demodulation can be efficiently performed with low-complexity multidimensional transforms such as the Concordia transform (CT) or the principal component analysis (PCA). From a practical point of view, we also prove that PCA-based demodulation is more attractive than CT. After demodulation, we propose two statistical criteria aiming at measuring the failure severity from the demodulated signals. Simulations and experimental results highlight the good performance of the proposed approach for condition monitoring.