In recent times, there has been considerable interest in fault detection within electrical power systems, garnering attention from both academic researchers and industry professionals. Despite the development of numerous fault detection methods and their adaptations over the past decade, their practical application remains highly challenging. Given the probabilistic nature of fault occurrences and parameters, certain decision-making tasks could be approached from a probabilistic standpoint. Protective systems are tasked with the detection, classification, and localization of faulty voltage and current line magnitudes, culminating in the activation of circuit breakers to isolate the faulty line. An essential aspect of designing effective fault detection systems lies in obtaining reliable data for training and testing, which is often scarce. Leveraging deep learning techniques, particularly the powerful capabilities of pattern classifiers in learning, generalizing, and parallel processing, offers promising avenues for intelligent fault detection. To address this, our paper proposes an anomaly-based approach for fault detection in electrical power systems, employing deep autoencoders. Additionally, we utilize Convolutional Autoencoders (CAE) for dimensionality reduction, which, due to its fewer parameters, requires less training time compared to conventional autoencoders. The proposed method demonstrates superior performance and accuracy compared to alternative detection approaches by achieving an accuracy of 97.62% and 99.92% on simulated and publicly available datasets.

翻译：近年来，电力系统中的故障检测引起了学术界研究人员和行业专业人士的广泛关注。尽管在过去十年中开发了众多故障检测方法及其改进方案，但其实际应用仍极具挑战性。鉴于故障发生及其参数的概率特性，某些决策任务可以从概率角度进行处理。保护系统的任务是检测、分类和定位故障电压与电流线路幅值，最终触发断路器以隔离故障线路。设计有效故障检测系统的一个关键方面在于获取用于训练和测试的可靠数据，而此类数据往往稀缺。利用深度学习技术，特别是模式分类器在学习、泛化和并行处理方面的强大能力，为智能故障检测提供了有前景的途径。为此，本文提出了一种基于异常的电力系统故障检测方法，采用深度自编码器。此外，我们利用卷积自编码器进行降维，由于其参数较少，相比传统自编码器需要更短的训练时间。所提方法在模拟和公开数据集上分别达到了97.62%和99.92%的准确率，相较于其他检测方法展现出更优的性能和精度。