Detection of high impedance faults (HIF) has been one of the biggest challenges in the power distribution network. The low current magnitude and diverse characteristics of HIFs make them difficult to be detected by over-current relays. Recently, data-driven methods based on machine learning models are gaining popularity in HIF detection due to their capability to learn complex patterns from data. Most machine learning-based detection methods adopt supervised learning techniques to distinguish HIFs from normal load conditions by performing classifications, which rely on a large amount of data collected during HIF. However, measurements of HIF are difficult to acquire in the real world. As a result, the reliability and generalization of the classification methods are limited when the load profiles and faults are not present in the training data. Consequently, this paper proposes an unsupervised HIF detection framework using the autoencoder and principal component analysis-based monitoring techniques. The proposed fault detection method detects the HIF by monitoring the changes in correlation structure within the current waveforms that are different from the normal loads. The performance of the proposed HIF detection method is tested using real data collected from a 4.16 kV distribution system and compared with results from a commercially available solution for HIF detection. The numerical results demonstrate that the proposed method outperforms the commercially available HIF detection technique while maintaining high security by not falsely detecting during load conditions.
翻译:高阻力断层(HIF)的检测是电力分配网的最大挑战之一,目前HIF的测量规模低,性质多样,难以通过超流继电器发现。最近,基于机器学习模型的数据驱动方法由于能够从数据中学习复杂模式,因此在HIF的检测中越来越受欢迎。大多数基于机械的检测方法都采用监督学习技术,通过进行分类,将HIF与正常负荷不同的正常负荷条件区分开来,从而将HIF与正常负荷不同的正常负荷条件区分开来。然而,在现实世界中很难获得HIF的测量。因此,当培训数据中未出现负载剖面和故障时,分类方法的可靠性和一般化有限。因此,本文件建议采用不受监督的HIF检测框架,使用自动编码器和主要部件分析监测技术,将HIF检测框架的检测框架与正常负荷不同的当前波状中的相关结构变化加以监测。拟议的HIF检测方法的性能测试,是使用从4.16千伏检测系统收集到的真实数据,而现有HF的检测方法则通过HIF的高级检测方法,通过模拟检测结果,通过提议的HIF的检测方法,通过可得到的测制式检测结果,在商业检测方法进行。