Whale Optimization Algorithm for PV Based Water Pumping System Driven by BLDC Motor Using Sliding Mode Controller

Abstract

Water pumping system (WPS) plays a key role in human civilization, including agriculture, drinking, and industrial usage. To decrease the load demand on grid and provide electrical supply to WPS in rural areas, a locally placed standalone photovoltaic (PV)-based WPS is one of the best feasible solutions. Among many motors, brushless direct current motors (BLDCs) are attractive for WPS due to many advantages. However, integrating batteries to WPS will increase the cost and required maintenance, which will not be affordable for farmers. Hence, PV-fed BLDC motor-based WPS without battery is considered and implemented sensorless speed controller with sliding mode controller (SMC) in this article. Generally, power generated by PV systems will be affected by partial shading condition (PSC) and unable to produce maximum availability power due to failure of the conventional perturbed and observe (P&O) algorithm. In order to overcome this condition, a whale optimization algorithm (WOA) has been integrated to the conventional P&O algorithm in this article. A hybrid whale optimization–P&O (WOPO) algorithm can effectively work to extract the maximum possible power from PV during both PSC and normal condition. The results are compared with genetic algorithm (GA), particle swarm optimization (PSO), and gray wolf optimization (GWO) for maximum power point tracking (MPPT) under PSC. In order to reduce the cost of system, the inverter is also working as an MPPT device for the PV system by integrating the WOPO algorithm with SMC of inverter. Hence, an extra dc–dc converter will not be required for MPPT. Extensive results are presented with OPAL-RT to validate the proposed system under both steady-state and transient conditions. In addition to this, dSPACE-based hardware results are also presented in this article to validate the proposed system.