基于 WaveNet+UNet 分类器的主动配电网岛屿检测
摘要
本文提出一种用于主动配电网岛屿检测的人工智能方案。通过审阅现有研究，显而易见的是该领域存在若干缺口，可能影响岛屿检测的可靠性，包括（i）模型复杂度和稳定性方面的顾虑，（ii）在噪声条件下的有限准确性，以及（iii）针对不同类型资源的有限适用性。鉴于此，本文提出一种由 WaveNet 分类器增强的去噪 U-Net 模型，以解决上述短板。该方案由于采用 1D 卷积层，结构简单，并包含残差连接显著提升模型的泛化能力。此外，采用去噪 U-Net 模型后，该方案对噪声条件具有较强鲁棒性。 Furthermore, the model is sufficiently fast using a sliding window time series of 10 milliseconds for detection. Utilizing positive/negative/zero sequence components of voltages, superimposed waveforms, and the rate of change of frequency provides the necessary features to precisely detect the islanding condition. In order to assess the effectiveness of the suggested scheme, over 3k islanding/non-islanding cases were tested, considering different load active/reactive powers values, load switching transients, capacitor bank switching, fault conditions in the main grid, different load quality factors, signal-to-noise ratio levels, and both types of conventional and inverter-based sources.
引用
@article{arxiv.2410.13926,
title = {Islanding Detection for Active Distribution Networks Using WaveNet+UNet Classifier},
author = {Amirhosein Alizadeh and Seyed Fariborz Zarei and Mohammadhadi Shateri},
journal= {arXiv preprint arXiv:2410.13926},
year = {2024}
}