Implementation of MPPT Methods for improving the Performance of Photovoltaic Systems

Abstract

Solar energy represents the main source of renewable power generation. This paper deals with the design of an efficient photovoltaic (PV) solar energy conversion system using maximum power point tracking techniques. Initially, aspects of planning and estimation of the PV systems performance were considered. The optimum operating point of a photovoltaic array is called Maximum Power Point (MPP), which varies depending on the cell temperature and the sun's insulation level. In order to achieve the goal of the Maximum Power Point Tracking (MPPT), three types of algorithms were developed in this work. The first algorithm is based on a neural network approach (ANN) that uses the backpropagation as its training algorithm. The second algorithm is based on fuzzy logic (FL), which uses the properties of the panel for its Maximum Power Point prediction, where the performance depends on the logic of the fuzzy rule. The third method is based on the Hill climbing (HC) algorithm that determines the maximum power point by correlating the changes in power with the changes in the control variable which has the advantage of network independent system and periodic tuning is not required.