METAHEURISTIC PI CONTROLLER TUNING FOR PMSG WIND ENERGY SYSTEMS: GRAYLAG GOOSE OPTIMISATION WITH INTEGRATED TIP SPEED RATIO MPPT AND IEEE 519 COMPLIANCE
DOI:
https://doi.org/10.66811/eijrihs.vol1.no4.56Keywords:
Graylag Goose Optimisation, PI controller tuning, PMSG, Wind Energy conversion System, MetaheuristicAbstract
Permanent magnet synchronous generators (PMSG) wind turbines require precise multi-loop converter control to guarantee stable grid interaction and thus high-power quality over a wide range of wind conditions. Tuning methods for proportional-integral (PI) controllers that are based on a linearised version of the plant often fail to meet demands in wind generation environments where the plant is inherently nonlinear and stochastic. This paper presents a novel application of a recently proposed nature-inspired metaheuristic called the Graylag Goose Optimisation (GGO) algorithm to the simultaneous calibration of eight PI gain parameters for the rotor side converter (RSC) and grid side converter (GSC) of the 2MW direct-drive PMSG system. The maximum power point tracking (MPPT) scheme is based on a tip speed ratio (TSR) strategy, which provides the rotor speed reference without requiring direct anemometric measurement. The integral time absolute error (ITAE) is used here as the overall fitness criterion, which takes care of overshoot of the DC-link and settling time errors. The comparison of the proposed algorithm is made with Particle Swarm Optimisation (PSO), Grey Wolf Optimiser (GWO), Whale Optimisation Algorithm (WOA) and classical Ziegler-Nichols (ZN) tuning in MATLAB/Simulink using a 600-second stochastic Kaimal wind profile with voltage sag superimposed on it. GGO-PI achieves an ITAE of 0.0317, a 31.4% reduction in the overshoot of the DC-link compared to PSO-PI and grid current total harmonic distortion (THD) of 2.31%, that is 0.0231 of the IEEE 519-2022 limits of 0.05. The efficiency of energy capture by MPPT is 97.2%. Wilcoxon signed-rank testing for 30 stochastic trials reaches statistical superiority of GGO at p < 0.01. These outcomes suggest that the proposed controller synthesis approach is an effective, grid code-compliant and practically deployable controller synthesis approach for utility-scale PMSG wind installations.
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