State Evaluation of HVDC Core Equipment Based on BP Neural Network

The damage of core power equipment will cause serious losses to the power grid, and the evaluation and diagnosis of power equipment status can effectively avoid the economic loss of the power grid. In this paper, combining the operating characteristics of the DC transmission system and the impact of related core equipment on the stability of the system, the set value iteration method and BP neural network are used to determine the time-varying weight of the evaluation index of the core equipment of the DC transmission. Ideal samples and negative ideal samples, calculate the gray correlation degree between each sample and the positive and negative ideal samples and calculate the closeness of the core equipment status to realize the real-time status evaluation of the core equipment. Finally, take the voltage source converter as an example to establish an evaluation index system for real-time evaluation of equipment status.