Data-Based Line Trip Fault Prediction in Power Systems Using LSTM Networks and SVM
Data-Based Line Trip Fault Prediction in Power Systems Using LSTM Networks and SVM
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DOI：
10.1109/access.2017.2785763
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发表时间：
2018-01-01
期刊：
影响因子：
3.9
通讯作者：
Bao, Zhejing
中科院分区：
文献类型：
--
作者：
Zhang, Senlin;Wang, Yixing;Bao, Zhejing
电力系统故障是电力系统输配电中的重要问题。近年来提出了基于继电保护动作和电气元件动作的方法。然而，它们在处理电力系统故障时存在不足。提出了一种基于数据的长短期记忆（LSTM）网络和支持向量机（SVM）的电力系统线路跳闸故障预测方法。多源数据的时间特征是用LSTM网络捕获的，它在提取长时间跨度的时间序列特征方面表现良好。LSTM网络强大的学习和挖掘能力适用于输配电中大量的时间序列。利用支持向量机具有较强的泛化能力和鲁棒性，将其用于分类，得到最终的预测结果。考虑到故障预测中的过拟合问题，在网络中加入了丢包层和批量归一化。本文详细介绍了完整的网络体系结构。并根据实际电力系统的具体情况对参数进行了调整。实验数据取自南方电网万江变电站。真实的实验证明了该方法相对于现有的数据挖掘方法的改进。文中对结果进行了具体分析。讨论了实际应用，以证明在真实的场景的可行性。
Power system faults are significant problems in power transmission and distribution. Methods based on relay protection actions and electrical component actions have been put forward in recent years. However, they have deficiencies dealing with power system fault. In this paper, a method for data-based line trip fault prediction in power systems using long short-term memory (LSTM) networks and support vector machine (SVM) is proposed. The temporal features of multisourced data are captured with LSTM networks, which perform well in extracting the features of time series for a long-time span. The strong learning and mining ability of LSTM networks is suitable for a large quantity of time series in power transmission and distribution. SVM, with a strong generalization ability and robustness, is introduced for classification to get the final prediction results. Considering the overfitting problem in fault prediction, layer of dropout and batch normalization are added into the network. The complete network architecture is shown in this paper in detail. The parameters are adjusted to fit the specific situation of the actual power system. The data for experiments are obtained from the Wanjiang substation in the China Southern Power Grid. The real experiments prove the proposed method's improvements compared with current data mining methods. Concrete analyses of results are elaborated in this paper. A discussion of practical applications is presented to demonstrate the feasibility in real scenarios.