In recent years, adaptive network-based fuzzy inference systems (ANFIS) applied in various engineering fields have demonstrated their ability to combine the learning capability of artificial neural networks and the representation of fuzzy inference systems, achieving promising results, and attracting the research community's interest. This article provides a valuable overview of ANFIS applications. Focusing on the field of electronic engineering, in particular for power electronics applications, the use of ANFIS has proven to be a very effective and reliable tool for applications requiring maximum power point tracking and power quality improvement. For this reason, this article presents an analysis of the review of 59 papers published between 2013 and 2023, of particular interest the applications of ANFIS for power quality improvement and harmonic content decrease in multilevel converters. This article applied a methodology for the systematic literature review that included identification, selection, eligibility, and inclusion criteria that allowed the selection of publications from the various relevant journals and databases consulted. Subsequently, the surveyed publications were analyzed and classified, highlighting the applications, characteristics of the ANFIS employed, their training algorithms, and the platforms or digital cards used. After analyzing the information from the surveyed papers, it can be concluded that using ANFIS has obtained promising results in improving inverter performance and power quality indexes in the face of variations. ANFIS offers flexibility and adaptability by performing adaptive and real-time control by continuously adjusting the inverter operation.

A fault detection and isolation (FDI) approach based on nonlinear sliding mode observers for a wind turbine model is presented. Problems surrounding pitch and drive train system FDI are addressed. This topic has generated great interest because the early detection of faults in these components allows avoiding irreparable damage in wind turbines. A fault diagnosis strategy using nonlinear sliding mode observer banks is proposed due to its ability to handle model uncertainties and external disturbances. Unlike the reported solutions, the solution approach does not need a priori knowledge of the faults and considers system uncertainty. The robustness to disturbances, uncertainties, and measurement noise is shown in the dynamic of the generated residuals, which is sensible to only one kind of fault. To show the effectiveness of the proposed FDI approach, numerical examples based on a wind turbine benchmark model, considering closed loop applications, are presented.

The main contribution of this paper is to show a new AC/DC converter based on the rearrangement of the flyback converter. The proposed circuit only manages part of the energy and the rest is delivered directly from the source to the load. Therefore, with the new topology, the efficiency is increased, and the stress of the components is reduced. The rearrangement consist of the secondary of the flyback is placed in parallel with the load, and this arrangement is connected in series with the primary side and the rectified voltage source. The re-arranged flyback is only a reductive topology and with no magnetic isolation. It was studied as a power supply for LEDs. A low frequency averaged analysis (LFAA) was used to determine the behavior of the proposed circuit and an equivalent circuit much easier to analyze was obtained. To validate the theoretical analysis, a design methodology was developed for the re-arranged flyback converter. The designed circuit was implemented in a 10 W prototype. Experimental results showed that the converter has a THDi = 21.7% and a PF = 0.9686.

This paper presents the minimization of total harmonic distortion in a seven-level cascaded H-bridge multilevel inverter with resistive load using the teaching–learning-based optimization algorithm. The minimization of Total Harmonic Distortion (THD)is a challenging optimization problem due to the fact that nonlinear equations are involved. Recently, bio-inspired algorithms have become very popular approaches to solving various optimization problems in different areas of engineering. For this reason, the results obtained with the Teaching–Learning-Based Optimization (TLBO)algorithm were compared with three other popular bio-inspired algorithms, the genetic algorithm, differential evolution, and particle swarm optimization. The comparative analysis, conducted by sweeping the modulation index, made it possible to obtain graphs and data on the behavior of the four analyzed algorithms. Finally, it was concluded that the TLBO algorithm is very effective and is able to solve the THD-minimization problem. Its main advantage over the other algorithms is the fact that it does not require control parameters for its correct operation in the solution of the problem.

This work aims to obtain the switching angles to reduce the total harmonic distortion (THD) and compare the different sets of angles obtained by modifying the modular index (Mi) in a seven-level multilevel inverter using the differential evolution (DE) algorithm. A seven-level cascaded multilevel inverter with RL load was simulated in MATLAB Simulink. The Mi was varied (0.6 to 1.27) to check which set of switching angles is optimal to reduce the THD. These sets of angles are then experimentally validated on a test bench. A brief discussion of the results is presented, comparing the minimum THD found without considering Mi, versus the minimum THD obtained if Mi is less than 1.

Uninterruptible Power Supplies (UPSs) protect electronic equipment by delivering consistent power. Among the core components of a UPS is the inverter, which converts stored DC energy from batteries into AC power. This work focuses on a cascaded multilevel inverter topology for its ability to reduce voltage Total Harmonic Distortion (THD), which is essential for maintaining UPS efficiency and power quality. Using an ANFIS (Adaptive Neuro-Fuzzy Inference System) model, enhanced with the Particle Swarm Optimization (PSO) algorithm, the switching angles were optimized to minimize THD. This work focused on an online UPS with a seven-level inverter structure powered by three LifePo4 S17 batteries, with critical load levels (100%, 95%, 50%, 15%, and 5%) represented in 35 experimental cases. The experimental design allowed the ANFIS-PSO model to adapt to varying voltages, achieving robust THD reduction. The results demonstrated that this combination of ANFIS and PSO provided effective angle optimization, with a low standard deviation of 0.06 between the training and simulated %THD, highlighting the process’s accuracy. The analysis showed that, in most cases, the simulated THD values closely aligned with, or even improved upon, the calculated values, with discrepancies not exceeding 0.2%. These findings support the ANFIS-PSO model’s potential in enhancing power electronics applications, particularly in critical sectors like renewable energy and power transmission, where THD minimization is crucial.