A GA trained ANN model for maximum power point tracking in solar photo voltaic systems - Rajaji, V. Dega; Sekhar, K. Chandra (International Journal of Engineering and Technology, 2018)
Sunlight based vitality is one of the imperative segment of sustainable power source assets and reaping of sun oriented vitality is an actually difficult assignment. Sunlight based PV cells display non liner conduct and their execution is affected by an assortment of components. Fluctuating insolation and temperature assumes a critical part in characterizing the working purpose of the PV cell. Most extreme Power Point Tracking (MPPT) calculations are important to amplify the yield control. The target of getting MPP in PV frameworks is to manage the real working voltage of PV boards. The fundamental reason for acquiring MPP in PV frameworks is to direct the real working voltage of PV boards. In this paper a Genetic Algorithm (GA) –Artificial Neural Network (ANN) MPPT approach is presented. GA is employed to adaptively change the weights during the course of ANN training. The proposed approach is validated by comparing its performance against a data set which contains voltages forecast by ANN approach, Adaptive Neuro Fuzzy (ANFIS) approach and reference voltage obtained through mathematical modeling. The results demonstrate the suitability of the proposed approach in maximum power point tracking through its better performance when compared to the other two approaches. Pearson product-moment correlation has also been calculated to compare the correlation between the predicted voltage and the reference voltage.