Fault location of distribution network with distributed generations using electrical synaptic transmission-based spiking neural P systems

This paper proposed a new electrical synaptic transmission-based Spiking Neural P system (SNP system) based on SNP systems. Some new elements are added into the original definition of SNP systems, such as new synapses, bidirectional model, two types of neurons and cancelling the delay of axon. Because SNP system is easy to express the logical relationship between graphics and has strong ability to process information in parallel, the bidirectional characteristics of electrical synaptic transmission is effectively combined with the electrical quantity (direction of current) for fault location of distribution network with distributed generations (DGs) in this paper. The fault location model and reasoning algorithm of the electrical synaptic transmission-based SNP system are studied with the advantages of high accuracy, less computation, simple and intuitive model and reasoning. Furthermore, the algorithm is applied reasonably in the bidirectional power flow characteristics of a distribution network with DGs. Finally, this paper verifies the effectiveness, accuracy and reliability of the method through two cases which involve the single fault, multiple fault and misinformation fault.With the large-scale DGs (distributed generation) access to the distribution network, which changes the power flow structure and operation mode, the traditional protection strategy of the passive distribution network is no longer applicable. The fault location problem of distribution network with DGs is carried out. Firstly, Many fault location methods in the existing literatures have been anaylsised in this paper, then the electrical synaptic transmission-based spiking neural P system (ESSNP) has been proposed to solve the fault location problem. Foremore, the fault diagnosis mode for the power outage section with ESSNP has been established, which construct with Network description matrix and Fault current matrix. In order to verify the accuracy of the method in this paper, three cases have been studied, which the single fault, multiple faults and misinformation faults in each case. Finally, conclusions have been discussed.