Method for evenly distributing workloads in power distribution networks on basis of improved BP neural network

Abstract

The invention discloses a method for evenly distributing workloads in power distribution networks on the basis of an improved BP neural network. The method comprises the following steps of: determining a BP neural network structure; optimizing an initial weight value and a threshold value of the BP neural network by adoption of a genetic algorithm; training the BP neural network; carrying out permutation and combination on personnel through a queue form; and predicting the BP neural network. According to the method, intelligent distribution is carried out on power failure events by utilizing amodel, and even arrangement for workloads of team personnel is realized. The method is capable of effectively the power failure event distribution efficiency and greatly realizing the reasonable using of resources such as team personnel and vehicles.

Description

technical field [0001] The invention relates to distribution network technology, in particular to a method for dispatching orders in distribution network based on improved BP neural network. Background technique [0002] Most of the existing emergency repair mechanisms of the power grid are aimed at fault power outages, including fault location and fault repair information interaction. Most of the existing research on planned blackouts is aimed at planning, focusing on reducing the scope of influence of the plan, reducing the number of times of the plan, and how to ensure the reliability of power supply. The processing of the work order includes dispatching the work order to the nearest station of the fault point. The station can choose whether to accept the order according to its own strength. If the mobile station cannot accept the order, it will forward the work order to another mobile station. station. In addition, the work order adopts a mechanism of order grabbing. T...

AI Technical Summary

Problems solved by technology

However, the existing order dispatching for faults only considers the nearest arrangement, but does not consider the strength of the station. When the station is unable to receive orders, the efficiency of dispatching orders is low.

In the existing order grabbing system, the staff's grabbing orders are all subjective considerations, and there will be situations where capabilities do not match, which cannot form a rational use of resources

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