Solar energy is a potential energy source in Myanmar and its application is ever increasing. In solar PV application, the photovoltaic module is needed to harvest this kind of energy. The PV module exhibit nonlinear I–V and P– V characteristics. The maximum power produced varies with both irradiance and temperature. The maximum efficiency is achieved when PV works at its maximum power point which can be obtained by using suitable MPPT algorithm. Most of PV systems use conventional MPPT methods such as incremental conductance (IC) and perturb and observe (P and O). With the advanced in control technology, the intelligent control techniques are commonly used in all areas. A conventional MPPT controller is used to maximise the conversion efficiency under normal conditions but fails in abnormal conditions. This paper proposes an intelligent ANN-P&O MPPT controller for the Boost converter that utilises the effective regions of both ANN and P&O methods to identify the global maximum point in order to improve the conversion efficiency of a PV system and a comparative simulation study of three MPPT algorithms specifically (i) perturb and observe, (ii) artificial neural network (ANN), and (iii) NN – P&O. MATLAB/SIMULINK software is used to test how well the controller works in unusual situations and compare it to its individual counterparts.
Maximum Power Point Tracking, Perturb and Observe Method, ANN Method, Boost Converter, Hybrid NN – P&O
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