Research on awareness warning method for damage situation of key regional high voltage transmission lines based on novel power system information model
Due to the complex structure and characteristics of high-voltage transmission lines, which are affected by natural disasters and equipment failures, it is more difficult to collect data and there is a lot of noise, which leads to unsatisfactory situation awareness warning results of line damage. Therefore, a novel power system information model based on the key region of high-voltage transmission line damage situation awareness warning method is proposed. The collected high-voltage transmission line data in key areas are processed by maximum overlapping discrete wavelet transform (MODWT) algorithm. A hybrid hierarchical genetic algorithm is used to optimize the structure and parameters of radial basis function (RBF) neural network, and the filtered high-voltage transmission line data in key areas is taken as the input of RBF neural network to realize the damage situation awareness of high-voltage transmission lines in key areas. The experimental results show that the situation awareness warning results obtained by the proposed method are more consistent with the actual situation, and can provide strong support for the stable operation of HVDC transmission lines in key areas.