Ogar Vincent Nsed, Hussain Sajjad, Gamage Kelum A A
Department of Electrical and Electronic Engineering, James Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom.
Heliyon. 2023 Feb 2;9(2):e13376. doi: 10.1016/j.heliyon.2023.e13376. eCollection 2023 Feb.
One of the most critical concerns in power system reliability is the timely and accurate detection of transmission line faults. Therefore, accurate detection and localisation of these faults are necessary to avert system collapse. This paper focuses on using Artificial Neural Networks in faults detection and localisation to attain accuracy, precision and speed of execution. A 330 kV, 500 km three-phase transmission line was modelled to extract faulty current and voltage data from the line. The Artificial Neural Network technique was used to train this data, and an accuracy of 100% was attained for fault detection and about 99.5% for fault localisation at different distances with 0.0017 μs of detection and an average error of 0%-0.5%. This model performs better than Support Vector Machine and Principal Component Analysis with a higher fault detection time. This proposed model serves as the basis for transmission line fault protection and management system.
电力系统可靠性中最关键的问题之一是及时、准确地检测输电线路故障。因此，准确检测和定位这些故障对于避免系统崩溃是必要的。本文着重于使用人工神经网络进行故障检测和定位，以实现执行的准确性、精确性和速度。对一条330 kV、500 km的三相输电线路进行建模，以从线路中提取故障电流和电压数据。使用人工神经网络技术对这些数据进行训练，在不同距离下故障检测的准确率达到了100%，故障定位的准确率约为99.5%，检测时间为0.0017 μs，平均误差为0%-0.5%。该模型在故障检测时间更长的情况下，比支持向量机和主成分分析表现更好。该模型为输电线路故障保护和管理系统奠定了基础。
