Distribution Network Fault Location Algorithms

Based on the analysis of modern distribution network topological structure and traditional fault location algorithm, this paper propose an improved BP neural network algorithm, genetic optimization of neural network GA-BP algorithm and RBF neural network algorithm in the application of distribution network fault section location. Through a comparative analysis of three types of neural network, so as to achieve distribution network fault location, isolation and restoration of power for fault region. Distribution network fault location algorithm and its improved become the main research contents.In this paper, it studies distribution network fault location algorithm. The main research work is as follows:1. In-depth study of the topological structure for distribution network. Algorithm is based on the topological structure of distribution network. This paper adopts the modern network hand in hand ring structure, with normal closed-loop structure, open loop operation, radially to user. After Distribution network faults, the FTU which installed in the switch detects the fault information (such as overcurrent), upload to the control center which called SCADA system. Based on fault diagnosis algorithm, the system analysis fault location.Then remote order the FTU disconnection switches on both sides of the fault section, and then isolating the fault area.2. According to the BP neural network does not take into account the previous adjustment error gradient direction as well as the best learning rate problem, making the network training process occurs oscillation and converges slowly. This paper presents an improved BP neural network algorithm in fault location for distribution network, which solves the problem and through simulation.3. According to the BP neural network converges slowly and easily falling into local minimum problem, this paper puts forward the genetic neural network algorithm is applied to fault location in distribution network. Using genetic algorithm to optimize BP neural network to solve the BP network optimal initial weights and thresholds, accelerate the network convergence. Using the global search ability of genetic algorithm, further improve the BP neural network fault positioning accuracy and rapidity, and through the example simulation.4. According to Genetic algorithm training for a long time, the BP neural network fault tolerant performance, BP hidden layer neuron number is difficult to determine, converges slowly and easily to fall into local optimal problem. This paper adopts the RBF neural network algorithm for fault location. RBF networks using implicit layer for the Gauss function, which is local approximation network, effectively accelerates the convergence speed and avoid local optimum. In the practical fault diagnosis of distribution network, the RBF neural network, realizes the accurate fault location, and makes use of Visual C++development tool for master station in distribution network automation, which achieves the goal of fault diagnosis accurately and rapidly. The system fully meets the requirements of real time monitoring.