Robust-momentum-learning-rate-based adaptive fractional-order least mean squares approach for power system frequency estimation using chaotic Harris hawks optimization

A novel robust adaptive technique is proposed to estimate the instantaneous power system frequency using a momentum-learning-control-rate-based fractional-order least mean squares approach with enhanced Harris hawks optimization. The adaptive estimation comprises two modules, where the first part involves the design of the momentum-learning-control-term-based fractional-order least mean squares algorithm and second part focuses on parameter tuning of the algorithm through enhanced Harris hawks optimization incorporating chaotic mapping and opposition-based learning. This integration yields a robust and automated adaptive algorithm for frequency estimation with superior performance compared to traditional transform-based techniques, particularly in the presence of noise. The proposed method excels in scenarios where the estimator should manage multiple variables, including step size, fractional-order step constants, and momentum learning control terms. Moreover, it facilitates accurate power frequency estimation for real signals in multiarea power systems or microgrids. To validate the efficacy of the algorithm, computer-simulated data representing step and ramp changes in the frequency were processed. Additionally, the algorithm was tested with signals derived from a multiple-control-area, multisource renewable-based power system. Detailed comparative results were obtained and verified through MATLAB simulations and real-time experimental setup, demonstrating the superior performance of the adaptive model.