Dynamic Reconfiguration of Active Distribution Network Based on Improved Gorilla Troops Optimizer
In response to the inability of traditional algorithms to effectively address the problem of active distribution network reconstruction with distributed generation and electric vehicles,an improved gorilla troops optimizer was pro-posed.Firstly,the"no duplication"coding strategy was used to code the distribution network for reducing the generation of infeasible solutions.Secondly,the Circle chaotic map was adopted to initialize the population and increase the diversity of the population for the gorilla troop optimization algorithm.The the gorilla adaptive mutation operator was introduced to enhance the algorithm's ability to search for new locations in the search space.Finally,the lens alignment learning and adaptive β-hill climbing strategy increased the fault tolerance of the algorithm and avoid the algorithm falling into local op-timum.Through simulation analysis,it can be seen that compared with other intelligent algorithms,the proposed algo-rithm has better reconstruction effect,and has the advantages of high optimization accuracy and small convergence itera-tions.Its global optimization rate can reach 100％,which verifies the effectiveness and superiority of the proposed model.
distributed generationelectric vehicleactive distribution networkdynamic reconfigurationgorilla troops optimizer
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万方数据
维普
