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This paper presents the design and analysis of an efficient energy management system for a wind lens integrated with a permanent magnet synchronous generator (PMSG) and a zeta converter. The wind lens, a ring-shaped structure encircling the rotor, enhances the turbine’s capability to capture wind energy by increasing the wind influx through the turbine. In the contemporary wind energy sector, PMSGs are extensively employed due to their superior performance characteristics. This study integrates a 1 kW PMSG system with a wind lens to optimize power extraction from the wind energy conversion system (WECS) under varying wind speeds. A comparative analysis of different control strategies for maximum power point tracking (MPPT) is conducted, including the incremental conductance (INC) method and the perturb and observe (P&O) method. The performance of the MPPT controller integrated with the wind lens-based PMSG system is assessed based on output DC voltage and power delivered to the load. To evaluate the overall effectiveness of these control strategies, both steady-state voltage and dynamic response under diverse wind conditions are examined. The system is modeled and simulated using the MATLAB R2023a/Simulink 9.1 software, and the simulation results are validated to demonstrate the efficacy of the proposed energy management system.

Torque ripple in a brushless DC motor (BLDCM) seriously restricts its application in high-performance fields. This paper proposes a commutation torque ripple suppression strategy based on a Zeta converter. The expected output voltage of a Zeta converter that suppresses the commutation torque ripple is obtained, according to the effect of the duty ratio of the Zeta converter on the turn-off phase freewheeling duration and the turn-on phase rising duration, during commutation. Based on the analysis of the dynamic response of the Zeta converter, the Zeta converter is adjusted to ensure that the Zeta converter reaches stability in sufficient time. During the commutation, the output voltage of the Zeta converter is connected to the main circuit to reduce the torque ripple during commutation, and the expected regulated duty cycle of the Zeta converter during the next commutation is calculated to adjust the output voltage of the Zeta converter. Based on this analysis, the experimental results verify the effectiveness of the proposed method.

Hybrid energy storage systems significantly impact the renewable energy sector due to their role in enhancing grid stability and managing its variability. However, implementing these systems requires advanced control strategies to ensure correct operation. This paper presents an algorithm for designing the power and control stages of a hybrid energy storage system formed by a battery, a supercapacitor, and a bidirectional Zeta converter. The control stage involves an adaptive sliding-mode controller co-designed with the power circuit parameters. The design algorithm ensures battery protection against high-frequency transients that reduce lifespan, and provides compatibility with low-cost microcontrollers. Moreover, the continuous output current of the Zeta converter does not introduce current harmonics to the battery, the microgrid, or the load. The proposed solution is validated through an application example using PSIM electrical simulation software (version 2024.0), demonstrating superior performance in comparison with a classical cascade PI structure.

This paper introduces a new hybrid DC–DC converter with enhanced voltage gain and synchronized multiple output capabilities, specifically tailored for smart grid applications. The proposed converter is based on the integration of non-isolated Zeta and Mahafzah converters, comprising a single controlled switch, two diodes, three inductors, and two coupling capacitors. The primary objective of this novel hybrid converter is to improve voltage gain as compared to conventional Zeta and Mahafzah topologies. By achieving higher voltage gain at lower duty cycles, the converter effectively reduces voltage stress on semiconductor switches and output diodes, thereby enhancing overall performance and reliability. A comprehensive examination of the hybrid converter’s operating principle is presented, along with detailed calculations of duty cycle and switching losses. The paper also explores the converter’s application in smart grids, specifically in the context of renewable energy systems and electric vehicles. Two distinct scenarios are analyzed to evaluate the converter’s efficacy. Firstly, the converter is assessed as a DC–DC converter for renewable energy systems, highlighting its relevance in sustainable energy applications. Secondly, the converter is evaluated as an electric vehicle adapter, showcasing its potential in the transportation sector. To validate the converter’s performance, extensive simulations are carried out using MATLAB/SIMULINK with parameters set at 25 kW, 200 V, and 130 A. The simulation results demonstrate the converter’s ability to efficiently supply multiple loads with opposing energy flows, making it a promising technology for optimized grid management and energy distribution. Moreover, the paper investigates the total harmonic distortion (THD) of the grid current, focusing on its impact in smart grid environments. Notably, the new hybrid converter topology achieves a THD of 21.11% for the grid current, indicating its ability to effectively mitigate harmonics and improve power quality. Overall, this research introduces a cutting-edge hybrid DC–DC converter that enhances voltage gain and synchronizes multiple outputs, specifically catering to the requirements of smart grid applications. The findings underscore the converter’s potential to significantly contribute to the advancement of efficient and resilient power conversion technologies for smart grids, enabling seamless integration of renewable energy systems and electric vehicles into the grid.

Recently, the development of integrated inverters for photovoltaic systems has been widely performed to reduce overall system size, costs, and losses. Thus, integrated inverters have emerged as a prominent solution for replacing two-stage power conversion composed of a step-up converter and a voltage source inverter. Thereby, this paper proposes an integrated inverter topology for single-phase grid-tied photovoltaic systems. The proposed power converter, called a Single-Phase Integrated Zeta Inverter (SP-IZI), can boost the input voltage and inject a sinusoidal and regulated current into the mains with low harmonic distortion. The SP-IZI is based on integrating modified DC-DC Zeta converters, designed and controlled to operate in a discontinuous conduction mode, and presents similarities with the Modified Zeta Inverter (MZI). In this way, this paper compares the main parameters of both topologies and provides a complete study of the SP-IZI, involving both quantitative and qualitative studies as well as a small signals analysis. The feasibility and functionality of the proposed SP-IZI inverter are presented and evaluated through experimental results, which demonstrate that the SP-IZI presents the following advantages compared to the MZI: (i) the voltage in coupling capacitors is 13% lower; (ii) voltage stresses in switches and diodes are 40% lower; and (iii) static gain is similar to the traditional Zeta converter.

In this paper, a monitoring method for DC-DC converters in photovoltaic applications is presented. The primary goal is to prevent catastrophic failures by detecting malfunctioning conditions during the operation of the electrical system. The proposed prognostic procedure is based on machine learning techniques and focuses on the variations of passive components with respect to their nominal range. A theoretical study is proposed to choose the best measurements for the prognostic analysis and adapt the monitoring method to a photovoltaic system. In order to facilitate this study, a graphical assessment of testability is presented, and the effects of the variable solar irradiance on the selected measurements are also considered from a graphical point of view. The main technique presented in this paper to identify the malfunction conditions is based on a Multilayer neural network with Multi-Valued Neurons. The performances of this classifier applied on a Zeta converter are compared to those of a Support Vector Machine algorithm. The simulations carried out in the Simulink environment show a classification rate higher than 90%, and this means that the monitoring method allows the identification of problems in the initial phases, thus guaranteeing the possibility to change the work set-up and organize maintenance operations for DC-DC converters.