Reconfiguration Method of Distribution Network Containing Distributed Power Based on Improved Species Birth and Death Algorithm

Abstract

The invention provides a distribution network reconfiguration method with distributed power sources based on an improved species birth and death algorithm, and relates to the technical field of distribution network reconfiguration. This method first initializes the network parameters of the distribution network and the parameters of the improved species birth and death algorithm, and uses all surviving species as the initial main branch species; The network radiation judgment deals with new species that do not meet the radiation conditions; a mass extinction operation is performed on the original surviving species and all new species to complete a birth-death cycle; finally, multiple birth-death cycles are performed until the maximum life cycle is reached, and the final Excellent solution. The distribution network reconstruction method with distributed power sources based on the improved species birth and death algorithm provided by the present invention applies the improved biological birth and death algorithm to the distribution network reconstruction with distributed power sources, and has good optimization performance, and further Better reconstruction results can be obtained.

Description

technical field [0001] The invention relates to the technical field of distribution network reconfiguration, in particular to a method for reconfiguring a distribution network containing distributed power sources based on an improved species birth and death algorithm. Background technique [0002] From a mathematical point of view, distribution network reconfiguration refers to obtaining the optimal network topology of the distribution network through the combination of switches in the network under the constraints of radial distribution network topology, so that the power of the entire system One or more objectives such as loss, node voltage deviation, and load balancing rate are optimized. Scholars at home and abroad have tried a variety of algorithms for decades, hoping to solve this optimization problem more quickly and accurately. Among them, artificial intelligence algorithms are developing rapidly and are widely used. However, almost all swarm intelligence optimizat...

AI Technical Summary

Problems solved by technology

However, almost all swarm intelligence optimization algorithms inevitably have problems such as premature convergence and easy to fall into local optimal solutions.

From the perspective of introducing new species, the existing species birth and death algorithm is optimized through the operation of species explosion and mass extinction, which overcomes the existing swarm intelligence algorithm that is easy to fall into local extremum in path search, and the optimization accuracy is not high. shortcoming

However, there are some problems with this species birth and death algorithm, including that the original surviving species is not considered in the mass extinction operation; the shrinkage coefficient is a constant value and cannot be changed accordingly with the iterative process

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