Power Grid Fault Positioning Technology Based on Particle Swarm Optimization

Using the transient fault signal to realize the power grid fault location is a hotspot, which plays an important role in rapid recovery of power grid. This paper studies the principle of the wavelet neural network. Because wavelet neural network is easy to fall into local optimum and has the disadvantage of slow convergence rate, it is improved by immune particle swarm algorithm. The improved algorithm is applied to grid fault positioning, and the results show that the accuracy of improved algorithm is obviously superior to accuracy of wavelet neural network. It can provide important reference for the actual power grid fault positioning system.