Abstract
There is limited research focused on achieving optimal torque control performance of Switched Reluctance Generators (SRGs). The majority of existing studies tend to favor voltage or power control strategies. However, a significant drawback of SRGs is their susceptibility to high torque ripple. In power generation systems, torque ripple implicates fluctuations in the generated power of the generator. Moreover, high torque ripple can lead to mechanical vibrations and noise in the powertrain, impacting the overall system performance. In this paper, a Torque Sharing Function (TSF) with Indirect Instantaneous Torque Control (IITC) for SRG applied to Wind Energy Conversion Systems (WECS) is proposed to minimize torque ripple. The proposed method adjusts the shared reference torque function between the phases based on instantaneous torque, rather than the existing TSF methods formulated with a mathematical expression. Additionally, this paper introduces an innovative speed control scheme for SRG drive using a Fuzzy Super-Twisting Sliding Mode Command (FSTSMC) method. Notably robust against parameter uncertainties and payload disturbances, the proposed scheme ensures finite-time convergence even in the presence of external disturbances, while effectively reducing chattering. To assess the effectiveness of the proposed methods, comprehensive comparisons are made with traditional control techniques, including Proportional-Integral (PI), Integral Sliding Mode Control (ISMC), and Super-Twisting Sliding Mode Control (STSMC). The simulation results, obtained using MATLAB (R)/SIMULINK (R) under various speeds and mechanical torque conditions, demonstrate the superior performance and robustness of the proposed approaches. This study presents a thorough experimental analysis of a 250 W four-phase 8/6 SRG. The generator was connected to a DC resistive load, and the analysis focuses on assessing its performance and operational characteristics across different rotational speeds. The primary objective is to validate and confirm the efficacy of the SRG under varying conditions.

Abstract
In this paper, the performance analysis of Machine Learning (ML) algorithms for fault analysis in photovoltaic (PV) plants, is given for different algorithms. To make the comparison more relevant, this study is made based on a real dataset. The goal was to use electric and environmental data from a PV system to provide a framework for analysing, comparing, and discussing five ML algorithms, such as: Multilayer Perceptron (MLP), Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Light Gradient Boosting Machine (LightGBM). The research findings suggest that an algorithm from the Gradient Boosting family called LightGBM can offer comparable or better performance in fault diagnosis for PV system.

Abstract
Switched reluctance machines are gaining importance due to their low cost, simple construction, and non-use of rare earth magnets. However, for the development of advanced torque controllers, accurate torque estimation is crucial, especially under varying load conditions. There are different torque estimation methods, which fall into different well-established classes, however, the characterization of their performance and operating conditions are not well known. This paper provides a comparative study of the most significant estimation algorithms: average torque, analytical and area approximation estimators. To assess the performance of these algorithms, a set of numerical simulations is presented and their results are compared based on signal similarity criteria. Results show a better performance when using the area approximation algorithm in comparison with the other two.

Abstract
This paper aims at researching the design of a current controller for an interleaved Buck converter used to feed a high current 5 kW Polymer electrolyte membrane (PEM) electrolyzer representing a module stack level. The main challenge is to design a robust controller that ensures operation over a wide range of electrolyzer operating points while guaranteeing control requirements and current sharing between the converters. The developed control scheme ensures responsiveness to the requirements of the grid's ancillary services and control over the dynamics of the electrolyzer. MATLAB/Simulink simulation results with dSPACE compatible models are presented to validate the lead-lag controller, designed using root locus, achieving a ripple current of 0.1 A, a 0.3% steady-state error, and a settling time of 50 ms for a step response.

Abstract
This paper presents a new strategy for recharging electric vehicles in residential buildings. The proposed approach minimizes the difference between desired and final state of charge (SOC) by the end of the charging period, by adjusting the charging power for each vehicle in real-time. A non-linear optimization problem is formulated, considering the initial and final SOC, as well as available charging time, and total available power. Results were compared to a baseline and show that the proposed solution outperforms the currently most used nonoptimized method, particularly in high demand scenarios, where we achieve values of 9.3% of curtailed range when compared with the non-optimized methodology.

Abstract