Risk Prediction for Power Grid Transmission Lines Based on Sensors Fusion

In order to improve the safety and stability of the transmission line of the power grid, the sensor factors such as meteorological data, satellite remote sensing data and ground equipment status data are fused to establish a state weight matrix, and the multi-factor hidden Markov prediction and learning model is used to realize the high-precision prediction of the power outage risk of the transmission line of the power grid. Firstly, according to the multi-factor analysis of the power outage of the line, the corresponding state observation set was established by using meteorological data, satellite remote sensing data and ground equipment monitoring methods. Secondly, the graph weight analysis method was used to establish the transmission line power outage risk assessment index model and establish the possible state set. Thirdly, the multi-factor correction model was used to calculate the multi-factor combined power-off probability model through big data learning regression, and the evaluation and prediction of power grid transmission line outage under comprehensive change were realized. Finally, the simulation experiment is established, and the random sample data test proves that the consistency test results of the method in this paper are good, and the probability of normal prediction and power outage prediction is higher than 92%, which is feasible and stable. It can provide more adequate preparation time for subsequent efficient emergency response.