A Sensorless SynRG-Based Hybrid Solar–Wind System with ANN-Assisted Maximum Power Extraction

Abstract

This paper presents a grid-connected hybrid renewable energy system integrating a solar photovoltaic (PV) array and a wind energy conversion system (WECS) based on a position-sensorless synchronous reluctance generator (SynRG). A back-to-back voltage source converter (VSC) topology with a common DC-link is employed to achieve decoupled control of the machine-side converter (MSC) and grid-side converter (GSC). Sensorless field-oriented control (FOC) using second-order flux estimation and a frequency-locked loop ensures accurate rotor position and speed estimation without mechanical sensors. An artificial neural network (ANN)-based maximum power point tracking (MPPT) technique is implemented to extract maximum power from both wind and solar sources under varying environmental conditions. The system is modeled and simulated in MATLAB/Simulink and experimentally validated on a laboratory prototype. Results demonstrate efficient power extraction, improved dynamic response, reduced steady-state oscillations, stable DC-link voltage, and near-unity power factor operation.