Distribution network reconfiguration method containing a distributed power source based on improved species birth and death algorithm

Abstract

The invention provides a distribution network reconfiguration method containing a distributed power source based on an improved species birth and death algorithm, which relates to the technical fieldof distribution network reconfiguration. Firstly, the parameters of distribution network and improved birth and death algorithm are initialized, and all the surviving species are taken as the primaryand secondary species. Then, the surviving species are exploded, new species are derived, and the new species which do not satisfy the radiation condition are treated by the radiation judgment of distribution network. Perform a mass extinction operation on the original surviving species and all new species, completing a birth and death cycle; Finally, several birth and death cycles are carried outuntil the maximum life cycle is reached, and the optimal solution is obtained. The invention provides a distribution network reconfiguration method containing a distributed power source based on an improved species birth and death algorithm, which applies the improved biological birth and death algorithm to the distribution network reconfiguration containing the distributed power source, has goodoptimization performance, and further can obtain a better reconfiguration result.

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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