Radial Basis Function trained with Dynamic Differential Annealed Optimization algorithm based Maximum Power Point Tracking Control of PV system under Uniform and Non-Uniform irradiance

Excessive use of fossil fuel power plants has destroyed the environment beyond repair. Solar energy used in the form of PV systems can help to meet the energy demand. One drawback faced by PV systems is their non-linear output as a result of non-uniform irradiance levels on it. This paper presents a Maximum Power Point Tracking control technique, that is, radial basis function network trained with differential annealed optimization algorithm. High optimization of DDAO combined with high precision of RBFN makes it an effective MPPT technique. Comparison is made with RBFN-PSO and RBFN-INC to check the performance of the proposed technique. Two cases are presented to validate the superior performance of RBFN-DDAO. Comparison showed that RBFN-DDAO tracks the global maxima with greater than 99.93% efficiency and 11\pmbms faster tracking time under fast varying irradiance and partial shading condition. The analysis of statistical data has also been exhibited to examine the robustness and responsiveness of the technique presented.