Table4_A method of single-phase grounding fault line selection based on optimization spiking neural P systems.docx

It is important to select the fault line rapidly when single-phase grounding fault occurs in the small current grounding system. The fault information acquisition of existing methods generally need hardware modification, so the high cost makes it difficult to apply to the power grid in underdeveloped areas. Taking that into consideration, this paper proposed a method of steady-state information small current grounding fault line selection based on Optimization Spiking Neural P Systems (OSNPS). The method only needs the steady-state voltage and current data of the dispatch side to effectively identify the fault line, which greatly improves the range of application. According to the characteristics of power dispatching big data, the objective function is established and the normalized model parameters are optimized by OSNPS to improve the accuracy of fault line selection stably. Furthermore, PSCAD/EMTDC is used to simulate the small current grounding system, the main factors affecting the accuracy of fault line selection are analyzed and the relationship between fault information features and fault identification accuracy is revealed. What’s more, It is pointed out that the model parameters without optimization may have line selection failure. Finally, specific examples are given to verify that the model parameters optimized by OSNPS can effectively improve the accuracy of fault line selection.

在小型接地系统中发生单相接地故障时，迅速选择故障线路至关重要。现有方法的故障信息获取通常需要硬件修改，高昂的成本使得其在欠发达地区的电网应用变得困难。鉴于此，本文提出了一种基于优化脉冲神经网络系统（Optimization Spiking Neural P Systems，简称OSNPS）的稳态信息小电流接地故障线路选择方法。该方法仅需调度端的稳态电压和电流数据，即可有效识别故障线路，极大地拓宽了应用范围。根据电力调度大数据的特点，建立了目标函数，并通过OSNPS优化归一化模型参数，以稳定提高故障线路选择的准确性。此外，利用PSCAD/EMTDC对小型接地系统进行仿真，分析了影响故障线路选择准确性的主要因素，并揭示了故障信息特征与故障识别准确性之间的关系。此外，指出未经优化的模型参数可能导致线路选择失败。最后，通过具体实例验证了由OSNPS优化后的模型参数可以有效提高故障线路选择的准确性。