Modified Grey Wolf Optimization Algorithm for PV Fed Water Pumping System Driven by Switched Reluctance Motor under Partial Shading Condition

Abstract

Water Pumping Systems (WPSs) are playing a major role in society for fulfilling human's daily needs. Generally WPSs are consuming much power and burden on main electric grid. The best idea to decrease load on utility grid is to establish a standalone Photovoltaic (PV) fed WPS in both rural and urban areas. Usually WPSs are necessary for agriculture, drinking and industrial purposes. However, an electrical motor is required to drive the mechanical pump for adding the underground water to the utility point. Among many motors, switched reluctance motor (SRM) is attracting many scholars to use in WPS due to many advantageous. To obtain maximum torque which is required to lift the water from high depth from ground an 8/6 pole SRM is considered in this paper. However, using batteries in WPS will lead system more expensive as well as required frequent maintenance including replacements due to less life span of batteries as compared with PV modules. Hence, PV powered SRM driven battery less WPS is implemented with sliding mode controller (SMC) based speed sensorless direct torque controller (DTC) in this paper. Moreover, Modified Grey Wolf Optimization (MGWO) algorithm is integrated with Perturbed and Observed (P&O) algorithm to harvest maximum energy from PV modules during normal as well as partial shading conditions. Hybrid MGWO-P&O mechanism is integrated with converter controller to avoid an extra DC to DC device for using maximum power point tracker of PV modules. Extensive results by using MATLAB/Simulink platform are examined in this article under many situations to validate the proposed method.