Switched reluctance motor based water pumping system powered by solar using hybrid approach

This paper proposes a hybrid approach for switched reluctance motor (SRM) based water pumping system. The proposed hybrid method is combination of both the Northern Goshawk Optimization (NGO) and Finite Basis Physics-Informed Neural Networks (FBPINNs). Hence, it is named as NGO-FBPINNs. The NGO method is employed to better control among the three level boost converter (TLBC) and FBPINNs is predict the optimal control of the TLBC. In this system the water is pumped using a 4-phase SRM driven by a midpoint converter. An intermediary power conversion stage called a TLBC is positioned between the motor-pump and the solar PV array. The proposed method is very efficient because of the TLBC's small inductor size, wide operating voltage range, and low voltage stress across devices. The proposed method implemented in MATLAB platform and its efficiency is compared with various existing techniques like Heap-Based Optimizer (HBO), Wild horse optimizer (WHO) and Particle Swarm Optimization (PSO). The proposed approach NGO-FBPINNs obtains high efficiency than existing approaches. The proposed system's effectiveness is 95% it is higher efficiency than other system. •This Paper Proposes A Hybrid Approach For switched Reluctance Motor (SRM).•Hybrid Approach Is Combination Of Both The Northern Goshawk Optimization (NGO).•The NGO Method Is Employed To Better Control Among The Three Level Boost Converter.•TLBC Is Used As An Intermediate Power Conversion Stage.•The Proposed Method Implemented In MATLAB Platform.