An Efficient maximum power point tracking in solar PV systems using embedded controller

Abstract

India is the third largest producer and the third largest consumer of electricity over the globe. Generation of electricity through fossil fuel is the major source of greenhouse gas emission over the globe. Globally, India ranks third in the greenhouse emission and has taken actions to resolve the climate changes as well as guaranteeing an abrupt rise in renewable energy by 2030. Photovoltaic (PV) systems have received maximum attention in the recent years as a major renewable energy source. An important requirement of PVs is the effectiveness of its Maximum Power Point Tracking (MPPT). This characteristic has drawn huge interest among the PV researchers and industrial experts as the most economical means to improve the PV performances. MPPT is commonly employed in the PV system for the maximization of output power. It is essentially an operating point coordinating the PV module and the DC-DC converter. However, MPPT is not simple and easy to track and due to the non-linear I-V characteristics of the PV curve as well as the impact of the varying weather situations (particularly radiation and temperature); tracking the rapid Maximum Power Point (MPP) has always been a complex nature. The tracking ultimately is additionally sophisticated, if every PV module does not experience constant radiation. newlineSeveral MPPT techniques have been existed in the literature where several techniques have been focused on attaining MPP. The well-known power maximization techniques are Perturb and Observe (PandO) and/or hill climbing and Incremental Conductance (IC). Recently, few research works have focused on the design of intelligent MPPT techniques for PV module areas through the usage of metaheuristic optimization algorithms such as Particle Swarm Optimization (PSO), Fire Fly (FF) algorithm, etc. The metaheuristic optimization algorithms are highly effective in dealing with MPPT problem. With this motivation, a Modified Markovian Sequential Decision Making (MMSM) Algorithm based Modified Centralized Decision Algorithm has been designe