Automatic location method for high-risk area of ice dance disaster on 220kV high voltage transmission line

To locate high-risk areas of ice-dancing disasters on transmission lines and maintain the normal operation of the power grid, an automated positioning method for high-risk areas of ice-dancing disasters on 220 kV high-voltage transmission lines was studied. The three-axis acceleration and angular velocity signals of high-voltage transmission lines during icing and dancing were collected, and a wavelet packet transform method was used to preprocess the signals for noise removal. A time-varying measurement matrix was established to extract high-voltage transmission lines' amplitude and frequency characteristics during icing and galloping disasters. A wavelet neural network was constructed to determine the positional relationship between the transmission line's characteristics and the high-risk area affected by icing and galloping disasters. The motion characteristics of the high-voltage transmission line were determined using this network. The automatic location of high-risk areas was accomplished by applying a particle swarm optimization wavelet neural network. The experimental results show that the denoising effect of this method is good. When the number of nodes in the hidden layer of the wavelet neural network is 150, the positioning error is the smallest, and it can accurately and accurately locate high-risk areas where ice-dancing disasters occur.