Power converters systems aimed at renewable energy applications have become a common option for sustainable electricity and distributed generation, since their performance has improved, and prices have steadily been reduced in the last years. However, there are still several drawbacks that hinder their widespread installation, such as the simultaneous minimization of cost and volume, efficiency maximization, size reduction, etc. Quite often, accomplishing these goals requires dealing with complicated optimization problems, which are difficult to solve by classical methods. Metaheuristic techniques provide a viable alternative to solve complex intricate optimization problems, such as those encountered in the development of power electronics converters. This paper presents a comprehensive coverage of metaheuristic methodologies applied in the area of power converters. The review includes a classification of the methodologies and main objective functions in each paper surveyed. An aim for this paper is to highlight the importance of the optimization tools, and the many benefits they provide to tackle the challenges encountered in the design, operation, and control of power converters.

This paper aims to perform a literature review and statistical analysis based on data extracted from 38 articles published between 2018 and 2023 that address hybrid renewable energy systems. The main objective of this review has been to create a bibliographic database that organizes the content of the articles in different categories, such as system architecture, energy storage systems, auxiliary generation components used, and software employed, in addition to showing the algorithms and economic and reliability criteria for the optimization of these systems. In total, 38 articles have been analyzed, compared, and classified to provide an overview of the current status of simulation and optimization projects for hybrid renewable energy systems, highlighting clearly and appropriately the relevant trends and conclusions. A list of review articles has also been provided, which cover the aspects required for understanding HRESs.

This paper presents the reliability analysis of a push-pull converter intended for connection to a 125-W photovoltaic (PV) panel. Four prototypes of the converter were built and tested, using transistors with different ratings. Failure rates were calculated using the MIL HDBK 217 and the IEC TR 62380 procedures. In the latter case, the prediction was performed taking into account an annual mission profile obtained from the intended installation site, in an area with desert climate temperatures. Failure rate results obtained with MIL HDBK 217 show small differences among the converters, the best performance obtained from the prototype with the lowest on-resistance. Results obtained with IEC TR 62380 indicate that thermal cycles have a significant effect in reliability performance, and should be considered carefully, because PV systems often see large temperature variations. With both procedures, the failure rate contributions from magnetic devices were higher than expected.

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.

Current methods for reliability prediction are based on physics-of-failure approaches. Such is the case of the FIDES methodology that takes into account the times sequences of operation of an electronic assembly. In this paper, the reliability of a dc/dc converter aimed at photovoltaic applications is explored, considering the mission profiles for three different installations sites, at latitudes ranging from N 18.65 to N 32.67. Meteorological data collected in a ten-year time span were available for these sites. The mission profiles were obtained by first dividing the output power provided by a photovoltaic panel into ten levels, and then retrieving the temperature and humidity conditions that concur at each level. The components with the highest contribution to the overall failure rate λ were the diodes, followed by the capacitor at the output. The results indicate that the thermal factor has the largest impact on the failure rate λ, highlighting the importance of efficient heat-management techniques. When the effect of the individual operational phases was assessed, it was also found that those linked to power levels above 70% are responsible of the largest contribution to λ. In the case of the dormant condition, the highest humidity produces the highest failure rate.

Microgrids and hybrid renewable energy systems play a crucial role in today’s energy transition. They enable local power generation and distribution, reducing dependence on large centralized infrastructures, can operate independently or connected to a grid, and can provide backup power, thus increasing system resilience. In addition, they combine multiple renewable energy sources, such as solar, wind, hydro, and biomass, to maximize the efficiency and reliability of the supply, and are also adaptable to location-specific conditions, taking advantage of locally available energy resources and reducing the need for energy imports. Moreover, they contribute to decarbonization goals by offering a cleaner and more sustainable alternative. In this article, a documentary review is presented on the interaction of Homer Pro software 3.16.2 (July 2023), used for the design of hybrid renewable energy systems (HRES), with other methods of optimization or sizing. Allusion is made to the type of architecture in the most prominent clean and fossil source configurations, the levelized cost, net annual cost, and maintenance and capital investment cost. A comparison is made among the works reported in the last five years regarding the use of this software tool, based on load demand, geographical area, renewable energy sources, fossil sources, and objective functions, applied to the educational, rural, and industrial sectors. It is shown that India is one of the countries that has reported the most number of HRES techno-economic environmental analysis works, and that the case studies have focused approximately 47% on rural areas, 20% on educational agencies, 14% on commerce and industry, and 29% on urban buildings.