A hybrid Artificial Intelligence method for estimating flicker in power systems

This paper introduces a novel hybrid method combining H-$\infty$ filtering and an adaptive linear neuron (ADALINE) network for flicker component estimation in power distribution systems. The proposed method leverages the robustness of the H-$\infty$ filter to extract the voltage envelope under uncertain and noisy conditions, followed by the use of ADALINE to accurately identify the relative amplitudes of flicker components ($\Delta V_i/V_t$) at standard IEC-defined frequencies embedded in the envelope. This synergy enables efficient time-domain estimation with rapid convergence and noise resilience, addressing key limitations of existing frequency-domain approaches. Unlike conventional techniques, this hybrid model handles complex power disturbances without prior knowledge of noise characteristics or extensive training. To validate the method’s performance, we conduct simulation studies based on IEC Standard 61000-4-15, supported by statistical analysis, Monte Carlo simulations, and real-world data. Results demonstrate superior accuracy, robustness, and reduced computational load compared to Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT)-based estimators.