Robust-momentum-learning-rate-based adaptive fractional-order least mean squares approach for power system frequency estimation using chaotic Harris hawks optimization - Pati, Subhranshu Sekhar; Subudhi, Umamani (Frontiers in Energy Research, 2024)
A novel robust adaptive technique for the estimation of instantaneous frequency in power systems is proposed, utilizing a Momentum learning control rate based Fractional Order Least Mean Squares approach optimized through an enhanced Harris Hawk Optimization algorithm. The adaptive estimation algorithm comprises two integrated modules: the first involves the design of a momentum learning control term-based Fractional order Least Mean Square Algorithm, while the second focuses on parameters tuning of the estimation algorithm using an enhanced Harris Hawk Optimization algorithm that incorporates chaotic mapping and opposition based learning.This integration yields a robust and automated adaptive algorithm for frequency estimation, exhibiting 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 multi-area power systems or microgrids.To validate the efficacy of the developed algorithm, computer-simulated data representing step changes and ramp changes in frequency has been processed. Additionally, the algorithm was tested with signals derived from a multi-control area, multi-source renewable-based power system. Detailed comparative results are presented, verified through MATLAB simulations, demonstrating the superior performance of the adaptive model.