Neural Direct Torque Control of Permanent Magnet Synchronous Motor Based on Fuzzy Logic Stator Resistance Estimator Fed by a Photovoltaic Generator

Abstract

This paper investigates the neural control of a permanent magnet synchronous Motor (PMSM) utilizing direct torque control (DTC) techniques, powered by a photovoltaic generator. The study emphasizes the effectiveness of DTC in regulating motor torque and flux while integrating Perturb and Observe (P&O) control for optimizing energy extraction from the photovoltaic generator, The proposed DTCANN framework incorporates two Artificial Neural Network (ANN) controllers. The first controller replaces the traditional switch table, providing the voltage vector. The second ANN controller replaces the conventional PI controller and operates on a feedforward back mechanism. Additionally, a Fuzzy Logic Stator Resistance Estimator is employed to accurately estimate stator resistance, compensating for variations caused by temperature changes and load fluctuations. Simulation results illustrate the performance improvements achieved through this novel approach, underscoring its applicability in renewable energy systems and advanced motor control applications