This paper provides a comprehensive review of the research work related to Reliability Assessment Methodologies for grid‐connected photovoltaic (PV) systems performed in recent literature. Solar power is emerging as the fast growing source of energy in the world as a result of rising environmental concerns regarding the hazards of climatic change linked with the production of electricity using fossil fuels. Although PV systems can support small businesses and households on their own, many people prefer a grid‐connected PV system (PVS) because of the net profit it provides. Grid‐integrated PV system, however, comes with many reliability issues. Evaluating the reliability of grid‐integrated photovoltaic system is thus an important area of research. The article presents a critical survey of the state‐of‐art technologies for assessing the reliability of a PV system. Issues related to the reliability of the grid‐integrated PVS are spotted along with the solution techniques. Reliability indices for analyzing the PVS performance are also discussed.

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In this article, the selection of weight factor (WF) is improved using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) based cost function (CF) optimization for predictive control in surface mounted permanent magnet synchronous motor (PMSM) drive under different conditions. Basically, the empirical approach used for WF is instinctive and consumes excess time. TOPSIS approach chooses a control action that is closest to the ideal control action's positive solution and farthest from its ideal control action's negative solution. Prioritizing control variables in the CF assures the choice of the best control action throughout each sampling duration. Additionally, predefined switching instants are employed for the CF optimization depending on past optimal control action, reducing the computing duration of the suggested technique. dSPACE controller is used for real-time execution of traditional and proposed methods under steady and dynamic states. In order to verify the worth of predictive control based on TOP-SIS, the hardware responses were compared with traditional DTC and predictive control strategies.

With the maturation of nonlinear systems, considerable endeavors have been made to provide valid and high-speed controllers to supervise superior and more complex systems. Artificial intelligence has been remembered as the head topic among designers in the last decade. One of the popular control techniques is fuzzy logic, which is known to provide a controller that simulates the behavior of an expert operator. On the other hand, due to the necessity of change in human energy sources and the popularity of solar energy, attention to the greatest utilization of this category of green resources has significantly increased. Maximum power point tracking (MPPT) in solar systems is a headed topic, with innovative methods being presented every day despite numerous articles. However, the less discussed topic is the choice of a fuzzy inference system. In this article, the two classes of Mamdani and Sugeno are discussed to introduce the best controller for extracting more power from a solar system by implementing both types and gaining an understanding of their differences. In addition, the influence of the number of input membership functions on the controller performance is investigated. Therefore, two different input membership functions are given to each fuzzy system model. It should be noted that fuzzy system setup has been done by genetic algorithm to respond to the mortal desire to automate various processes, which is a subset of artificial intelligence. Accordingly, four different fuzzy systems have been designed and implemented on a solar system. The results were tested and summarized in various radiations in MATLAB Simulink.