Design and experimental validation of a rule-based real-time MPPT for wind-powered water pumping

Standalone wind-powered water pumping systems exhibit significant power fluctuations under rapidly varying wind conditions, resulting in reduced energy extraction and degraded tracking performance when conventional fixed-step Maximum Power Point Tracking (MPPT) methods are employed. This paper proposes a novel rule-based adaptive MPPT strategy that integrates a supervisory rule layer with the conventional Perturb and Observe (P&O) algorithm to dynamically regulate the perturbation step size and direction according to real-time power variation, rotor-speed deviation, and wind transient conditions. A second-order Extended State Observer (ESO) is incorporated to enhance disturbance rejection and dynamic stability. The originality of the proposed approach lies in achieving adaptive MPPT operation without training datasets, artificial-intelligence models, or population-based optimization, while retaining low computational complexity and suitability for real-time implementation. The proposed controller is implemented on a LabVIEW–SPEEDGOAT Test Bench Rapid Control (TRC) platform and experimentally validated using a 500-W permanent magnet synchronous generator (PMSG)-based standalone wind-powered water pumping system under emulated wind speeds of 5–8 m/s. Experimental results demonstrate faster convergence, reduced steady-state oscillations, and improved tracking stability compared with conventional fixed-step P&O MPPT under identical operating conditions. The proposed controller achieves an MPPT tracking efficiency of 83–87%, compared with 72–75% for conventional P&O, corresponding to an improvement of approximately 15% under dynamic wind conditions. The results demonstrate that the proposed low-complexity rule-based MPPT provides an effective and practical solution for reliable wind-energy extraction in standalone water pumping applications.