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

Abstract

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.