This paper presents the design and simulation of a multimodular power conversion system based on voltage source inverters (VSI), composed of two parallel-connected three-phase modules driving an induction motor. A self-error-compensated predictive current control strategy is proposed to coordinate the modules, enabling dynamic current sharing and fault-tolerant operation. In the event of a failure, the control scheme incorporates the tracking error of the faulty module into the cost function of the operational one, improving performance and continuity. Simulation results demonstrate the effectiveness of the proposed method, with improved dynamic response, reduced root mean square error (RMSE), and lower total harmonic distortion (THD) under both normal and fault conditions.

This paper presents the implementation of LoRaWAN-based non-invasive temperature sensor nodes for detecting loss points in distribution networks. The primary challenges involve integrating and calibrating the electronic components, including temperature sensors, LoRaWAN modules, and digital processors, to develop a robust system for analyzing loss points using thermal measurement techniques. The system employs activation by personalization for deploying sensor nodes, which simplifies the activation process and enhances network efficiency. The data collected is securely transmitted to a network server, then analyzed on an application server to identify loss points. This methodology aims to automate the analysis and detection of losses in medium and high voltage distribution networks, offering a technologically advanced, rapidly deployable, and cost-effective solution. Initial results demonstrate the system’s effectiveness in providing accurate and timely detection of loss points, contributing to improved network efficiency and sustainability.

Multiphase electric drive systems, which consist of power electronic converters and multiphase machines (with n greater than 3), have become a reliable solution for various high-power industrial needs, including propulsion systems for electric ships, vehicles and renewable energy applications, due to their phase redundancy. However, fully exploiting their inherent benefits requires more sophisticated control techniques. The finite control set model predictive control has emerged as a suitable strategy due to its ease of implementation and applicability across different systems. Nevertheless, tuning the weighting factor in the cost function significantly impacts control outcomes, making it an important aspect to consider. In this context, novel control techniques named Sequential Model Predictive Control (SMPTC) and Sequential Model Predictive Control based on Virtual Vectors (SMPTC-VV) have emerged as viable alternatives. Both methods eliminate weighting factors by using simple cost functions applied sequentially. SMPTC-VV integrates virtual vectors to reduce the secondary-plane currents, decreasing vibrations in induction machines. Despite the recent introduction of SMPTC and SMPTC-VV strategies, studies comparing the performance of these controllers have not yet been conducted. Therefore, this article presents a comparative study of these control techniques.

This paper investigates the problem of stator current control in presence of uncertainties and unmeasurable rotor current for a six-phase induction motor drive. An inner control loop based on a robust discrete-time sliding mode with time delay estimation method is proposed to ensure the finite-time convergence of the stator currents to their desired references while the proportional-integral controller is used for the outer speed control. Sufficient conditions are established to ensure the stability of the closed-loop system. Simulation results were carried out to verify the performance of the proposed robust control strategy for a six-phase induction motor drive.