Grey Wolf Optimization Inspired Maximum Power Extraction from SPV System for Water Pumping Application

The use of renewable energy resources is increasing to provide energy efficient and uninterrupted supply of power. The use of solar photovoltaics (SPV) in the agriculture sector is gaining importance due to its availability in abundance and other advantages. This paper presents SPV based water pumping system (WPS) for rural agricultural practice in Indian scenario. The proposed water pumping system comprises of SPV system, maximum power point controller, boost converter, inverter, and 3-Φ induction motor driving a water pump. The stochastic nature of SPV power is a concern for electrical engineers. Thus, the use of maximum power point tracking (MPPT) algorithms is of key focus to improve the efficiency and ensure the robust performance of SPV system. The proposed SPV system incorporates an artificial intelligence method for designing MPPT algorithm. The paper introduces a grey wolf optimization inspired algorithm to extract the maximum power under varying irradiance conditions from SPV system. The performance of the proposed algorithm is compared with other algorithms for partially shaded conditions on the basis of computational burden, maximum power tracked, and fluctuations. The proposed controller is better with least rise time of 0.11 s, and 0% error in tracking the power.