The recent increase in renewable energy penetration at the distribution level
introduces a multi-directional power flow that outdated traditional fault
location techniques. To this extent, the development of new methods is needed
to ensure fast and accurate fault localization and, hence, strengthen power
system reliability. This paper proposes a data-driven ground fault location
method for the power distribution system. An 11-bus 20 kV power system is
modeled in Matlab/Simulink to simulate ground faults. The faults are generated
at different locations and under various system operational states. Time-domain
faulted three-phase voltages at the system substation are then analyzed with
discrete wavelet transform. Statistical quantities of the processed data are
eventually used to train an Artificial Neural Network (ANN) to find a mapping
between computed voltage features and faults. Specifically, three ANNs allow
the prediction of faulted phase, faulted branch, and fault distance from the
system substation separately. According to the results, the method shows good
potential, with a total relative error of 0,4% for fault distance prediction.
The method is applied to datasets with unknown system states to test
robustness.

该研究提出了一种数据驱动的接地故障定位方法，通过分析处理后的数据的统计量，利用人工神经网络找到计算的电压特征与故障之间的映射关系，实现了接地故障的定位。研究结果表明该方法具有良好的潜力，并为测试鲁棒性而应用于未知系统状态的数据集。

分布式发电下配电系统基于数据驱动的接地故障定位方法

Data-Driven Ground-Fault Location Method in Distribution Power System  With Distributed Generation

Transformer fault diagnosis (TFD) is a critical aspect of power system
maintenance and management. This review paper provides a comprehensive overview
of the current state of the art in TFD using artificial intelligence (AI) and
dissolved gas analysis (DGA). The paper presents an analysis of recent
advancements in this field, including the use of deep learning algorithms and
advanced data analytics techniques, and their potential impact on TFD and the
power industry as a whole. The review also highlights the benefits and
limitations of different approaches to transformer fault diagnosis, including
rule-based systems, expert systems, neural networks, and machine learning
algorithms. Overall, this review aims to provide valuable insights into the
importance of TFD and the role of AI in ensuring the reliable operation of
power systems.

本文探讨了利用人工智能和溶解气体分析的变压器故障诊断的最新技术，并分析了不同方法的优缺点。研究旨在提供对变压器故障诊断和人工智能在保证电力系统可靠运行中的作用的有价值的见解。

基于人工智能和溶解气体分析的变压器故障诊断研究现状：文献综述

The State of the Art in transformer fault diagnosis with artificial  intelligence and Dissolved Gas Analysis: A Review of the Literature

Conventional power system reliability suffers from the long run time of Monte
Carlo simulation and the dimension-curse of analytic enumeration methods. This
paper proposes a preliminary investigation on end-to-end machine learning for
directly predicting the reliability index, e.g., the Loss of Load Probability
(LOLP). By encoding the system admittance matrix into the input feature, the
proposed machine learning pipeline can consider the impact of specific topology
changes due to regular maintenances of transmission lines. Two models (Support
Vector Machine and Boosting Trees) are trained and compared. Details regarding
the training data creation and preprocessing are also discussed. Finally,
experiments are conducted on the IEEE RTS-79 system. Results demonstrate the
applicability of the proposed end-to-end machine learning pipeline in
reliability assessment.

本研究探讨使用端到端机器学习直接预测可靠性指标，提出了基于输入特征的系统阻抗矩阵编码的机器学习流程，通过对输电线路的定期维护对特定拓扑变化的影响进行考虑，使用支持向量机和 Boosting Trees 进行训练和比较，实验证明了该方法在可靠性评估中的适用性。