A transmission line fault identification method based on long short-term memory network and random matrix principle.

In the past decade, driven by the policy of maximizing the consumption of renewable energy, renewable energy is being integrated into the power grid in the form of centralized power generation or decentralized power generation. The volatility and randomness of renewable energy generation lead to great uncertainty in the power flow of transmission lines, which leads to the increasing diversity of the types and characteristics of transmission line faults. This paper presents an intelligent fault identification method for transmission lines based on long short-term memory network and stochastic matrix principle. Firstly, a method to determine the fault time of transmission lines in stochastic matrix theory is proposed. Secondly, on this basis, a learning and training method of large sample fault random matrix is given. Furthermore, the fault types of transmission lines are further identified based on long short-term memory network. Finally, an actual transmission line is taken as an example to demonstrate the effectiveness of the proposed method.