A genetic algorithm optimized ANN-based MPPT algorithm for a stand-alone PV system with induction motor drive

Artificial neural network (ANN) based maximum power point tracking (MPPT) algorithm makes use of the advantages of ANNs such as noise rejection capability and not requiring any prior knowledge of the physical parameters relating to PV system. This paper proposes a genetic algorithm (GA) optimized ANN-based MPPT algorithm implemented in a stand-alone PV system with direct-coupled induction motor drive. The major objective of this design is to eliminate dc–dc converter and its accompanying losses. Implementing off-line ANN in DSP needs optimization of ANN structure to obtain an ideal size. GA optimization was used in this study to determine neuron numbers in multi-layer perceptron neural network. Another objective of this work is to prevent the necessity of the trade-off between the tracking speed and the oscillations around the maximum power point. Hence, varying step size is used in MPPT algorithm and PI-controller is adopted for simple implementation. Simulation and experimental results have been used to demonstrate effectiveness of the proposed method.

► We optimized topology of multi-layer perceptron neural network using genetic algorithms.
► Optimized ANN model was used in MPPT operation.
► A direct coupled three phase induction motor was operated in constant V/f mode.
► PI control was used to improve the MPPT performance making it possible to use variable step size.