基于窥孔结构LSTM的电力系统跳闸故障诊断

跳闸是输电和配电电力系统中普遍存在的故障.目前一般采用基于继电保护动作和电气元件动作的保护方法应对跳闸故障.然而，这些面向电气保护的方法在处理跳闸故障时存在滞后性.因此，提前预测跳闸故障对处理隐藏问题和电力恢复起着至关重要的作用.本文提出一种基于多源时序数据的电力系统跳闸故障预测方法，使用窥孔长短时记忆网络（LSTM）提取多源数据的时间特征，缓解了循环神经网络（RNN）在长时间序列上的梯度消失问题.模型在三层栅极上添加窥孔连接结构使得单个单元能够查看上一阶段的LSTM单元状态以此强化网络时序记忆能力；使用参数归一化等L2正则措施缓解故障预测中的过拟合问题对结果的影响；引入支持向量机分类器提高总体模型的泛化能力和鲁棒性.实验结果表明，与现有的数据挖掘方法相比，所提方法具有分类准确性高的优点.最后，对实际应用进行讨论，证明了所提方法在实际场景中的可行性.

Power system tripping fault diagnosis based on peephole structure LSTM

Tripping is a common fault in power transmission and distribution systems.Protection measures against tripping used to be relaying operation and electrical component action, which have hysteresis in handling tripping faults.Therefore, the prediction of tripping faults plays a vital role in dealing with hidden problems and power recovery.Here, a method of power system tripping fault prediction based on multisource time series data is proposed.LSTM is used to extract the time characteristics of multisource data, which alleviates the problem of RNN gradient disappearance on long time series.A peephole connection structure is added to the three-layer grid to enable single units to check the LSTM unit status in the previous stage, thereby strengthening the network timing memory capability.Then L2 regularization measures such as parameter normalization are used to mitigate the impact of over fitting in fault prediction.Finally, support vector machine classifier is introduced to improve the generalization ability and robustness of the overall model.The experimental data were obtained from relevant institutions of the State Grid of China.Experiment results show that the proposed method has higher classification accuracy compared with existing data mining methods.The practical application is discussed for its feasibility in actual scenarios.