Prediction Algorithm of Wind Waterlogging Disaster in Distribution Network Based on Multi-Source Data Fusion

Abstract

It is very important for power grid development research and related technical improvement to obtain the disaster situation of fine-scale distribution network, such as the transportation condition evaluation of distribution network and the wind waterlogging disaster prediction of distribution network. Among them, the wind waterlogging disaster prediction of distribution network is the main one, and the prediction of its disaster degree often determines whether the distribution network can be prevented before and rescued after the disaster. Therefore, in view of the above problems, combined with the actual transmission situation of the distribution network, after collecting the measured disaster data of the distribution network in relevant areas, combined with the multi-source data fusion technology and neural network modeling technology, this paper analyzes the disaster degree indicators of different distribution networks and constructs the relevant fuzzy matrix through the fuzzy theory to evaluate the disaster degree, which is verified by the measured data. This distribution network disaster loss prediction model can effectively implement the disaster loss prediction of distribution network and compare its prediction results with the other two different common models. The comparison results show that the prediction accuracy of the multi-source data fusion prediction model constructed in this paper is more than 0.95 compared with the other two models, while the prediction accuracy of the other two models is not more than 0.9, which proves that the model constructed in this paper has smaller errors. It has the advantages of higher accuracy and faster convergence speed.

Strategies for Improving the Resiliency of Distribution Networks in Electric Power Systems during Typhoon and Water-Logging Disasters

“…Some studies modeled the fault situation of the distribution network in typhoons and extreme temperature weather [20][21][22][23][24], which can be roughly divided into two categories. Some use event probability models and vulnerability curves to simulate power system faults.…”

“…The other category does not require detailed models of extreme events but evaluates based on neural networks and other technologies. For example, study [23] uses multi-source data fusion technology and neural network modeling technology and constructs fuzzy evaluation functions to analyze the fault degree of different distribution networks under typhoon weather. Study [24] estimates the total power demand of the distribution network under extreme temperature conditions by simulating and modeling the distribution network.…”

Multi-Source Data Fusion and Hydrodynamics for Urban Waterlogging Risk Identification

“…Li et al combined multi-source data fusion and neural network modeling to build a prediction model. By constructing a fuzzy matrix, they achieved the goal of a low error rate and faster computing speed [21].…”