TY - JOUR
T1 - Robust AI-Based Flicker Estimation for Power Quality Applications
AU - Daniel Kim
AU - Jihoon Park
JF - Journal of Smart Grids, Energy Analytics and Optimization
JO - J Smart Grids Energy Anal Optim
Y1 - 2022
VL - 1
IS - 1
SP - 45
N2 - The present study proposes an original hybrid technique that combines H-∞ filtering with an adaptive linear neuron (ADALINE) architecture to estimate flicker components in power distribution networks. By taking advantage of the H-∞ filter’s strong immunity to uncertainty and noise, the approach first recovers the voltage envelope; subsequently, the ADALINE identifies with high precision the relative magnitudes (ΔVi/Vt) of the flicker constituents located at the discrete frequencies specified by IEC standards within that envelope. The resulting cooperation yields fast, reliable time-domain estimation that converges quickly and maintains performance even under heavy noise, thereby resolving the principal weaknesses of most frequency-domain solutions. In contrast to established procedures, this combined framework effectively processes complex power-system disturbances without requiring prior knowledge of noise statistics or prolonged learning phases. Performance evaluation relies on simulations aligned with IEC Standard 61000-4-15, reinforced by statistical metrics, Monte Carlo runs, and actual field recordings. Comparative outcomes reveal markedly better precision, greater tolerance to disturbances, and substantially lower computational requirements than estimators based on the Fast Fourier Transform (FFT) or the Discrete Wavelet Transform (DWT).
UR - https://isespub.com/m460916916
ER -