用混合AI方法精准估算电网闪烁，抗噪强且无需预训练。
A Hybrid Artificial Intelligence Method for Estimating Flicker in Power Systems
- 结合H滤波与自适应线性神经网络，先提取电压包络再识别闪烁频率。
- 相比FFT和离散小波变换，精度更高、计算量更小，且收敛快。
- 适合复杂电网环境，无需噪声特征先验，实测数据验证有效。
本文提出一种新型混合AI方法，结合H滤波与自适应线性神经元（ADALINE）网络，用于电力配电系统中闪烁分量的估计。该方法利用H滤波在不确定和噪声环境下稳健提取电压包络，随后通过ADALINE精确识别包络中的闪烁频率。二者协同实现高效时域估计，具备快速收敛与强抗噪能力，克服了传统频域方法的局限。与常规技术不同，该混合模型无需预先知道噪声特性或大量训练数据，即可处理复杂电力扰动。为验证性能，基于IEC标准61000-4-15开展仿真研究，结合统计分析、蒙特卡洛模拟及真实数据。结果表明，该方法在准确性、鲁棒性和计算负载方面均优于基于快速傅里叶变换（FFT）和离散小波变换（DWT）的估计算法。
原文摘要 · Abstract (English)
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.
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