A Hybrid Artificial Intelligence Method for Estimating Flicker in Power Systems

2025-06-16

AI Summary

To address the low accuracy and poor adaptability of real-time voltage flicker estimation in distribution networks under strong noise and dynamic disturbances, this paper proposes a hybrid intelligent method synergizing H∞ filtering and an adaptive linear neuron (ADALINE). Without requiring prior knowledge of noise statistics or large-scale training data, the method employs H∞ robust filtering to suppress uncertain disturbances and integrates ADALINE for online tracking of flicker envelope and frequency, enabling end-to-end dynamic identification. Evaluated via IEC 61000-4-15 modeling and Monte Carlo simulation, it achieves a 32% improvement in flicker amplitude estimation accuracy over FFT and DWT approaches, significantly enhanced noise immunity, faster convergence, and 45% lower computational overhead. The novel tightly coupled filter–learning architecture overcomes the adaptability limitations of conventional frequency-domain methods under time-varying operating conditions.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Adversarial Learning & Robustness
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

This paper introduces a novel hybrid AI method combining H filtering and an adaptive linear neuron network for flicker component estimation in power distribution systems.The proposed method leverages the robustness of the H filter to extract the voltage envelope under uncertain and noisy conditions followed by the use of ADALINE to accurately identify flicker 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 AI model handles complex power disturbances without prior knowledge of noise characteristics or extensive training.To validate the method 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 and Discrete Wavelet Transform based estimators.

Problem

• Estimating flicker components in power distribution systems
• Combining H filtering and adaptive neuron network
• Improving accuracy and robustness in noisy conditions

Innovation

• Hybrid AI combining H filtering and ADALINE
• Robust voltage envelope extraction under noise
• Efficient time domain flicker frequency identification