A Substation Fire Early Warning Scheme Based on Multi-Information Fusion

In view of the substation fire early warning using a single information sensor monitoring, it is easy to make mistakes and omissions. Taking the cable in substation as the research object, a multi-information fusion fire prediction model based on back propagation neural network (BPNN) and fuzzy set theory is proposed. Firstly, the BPNN model is trained by using the existing data. Secondly, the artificial fish swarm algorithm (AFSA) is used to optimize the BPNN, which speeds up convergence speed of the model and improves the accuracy of prediction. The fuzzy set theory is applied to fuse the predicted fire probability to obtain the optimal fire prevention and control decision. Finally, the fire protection measures are taken according to the fire decision. The experimental show that the average absolute errors of no fire, smoldering and open fire decreased by 26.06%, 38.5% and 43.13% respectively. The model has higher prediction accuracy, can reasonably output different levels of fire alarm signals, establish substation fire warning and prevention and control system, and provide reference for future substation fire and other disasters warning and prevention and control.

Automated summary

A multi-information fusion fire prediction model based on back propagation neural network (BPNN) and fuzzy set theory is proposed in this study. The BPNN model is trained using existing data, and the artificial fish swarm algorithm (AFSA) is used for optimization, which improves the prediction accuracy. The fuzzy set theory is applied to fuse the predicted fire probability for optimal fire prevention and control decision-making.