Regionalized Fault Location In Distribution Networks Based On Multi-Strategy Improved Intelligence Algorithm

The distribution network fault location problem has always been one of the concerns of power researchers.In recent years,Distributed Generation has been widely used because of its excellent characteristics such as clean and efficient.With the continuous access of Distributed Generation to the distribution network,the traditional single power network structure will be changed.The input and output of distributed generation,the distortion of switch node information and the increasing scale of distribution network nodes have put forward new requirements and challenges for distribution network fault location.Therefore,this paper takes distribution network fault location containing DG as the research object.Improving the fault location accuracy and shortening the location time in complex multi-power distribution networks are considered as the main research objectives.In view of the development and application of distribution automation systems and a variety of excellent intelligent algorithms are continuously proposed.The improvement analysis is carried out from intelligent algorithms and location models.The research is carried out for the distribution network fault zone location method based on whale optimization algorithm.Firstly,considering the influence of distributed power supply to distribution network on fault location,this paper adopts a mathematical model applicable to distribution network fault location with DG.It mainly includes the coding methods of switching nodes and feeder segments,the construction of switching functions and the establishment of adaptation functions,and the effectiveness of the fault location model is analyzed with examples.Secondly,to address the problem that the distribution network cannot be accurately located due to the distortion or missing information of switch nodes in the distribution network,this paper proposes a distribution network fault location method based on the whale optimization algorithm.Based on the strong fault tolerance capability of the whale optimization algorithm,different types of fault cases are set and compared with particle swarm optimization algorithm and genetic algorithm.The effectiveness and superiority of the whale optimization algorithm in distribution network fault location are verified.And the deficiency of the whale optimization algorithm in achieving multiple fault location which tends to fall into local optimum is also analyzed.Thirdly,a chaotic whale optimization algorithm incorporating differential evolutionary algorithm is proposed to address the shortcomings of the whale optimization algorithm in fault location.Firstly,Sine chaotic mapping is introduced at the beginning of the algorithm to generate the initial population as a way to increase the population diversity.Second,new adaptive inertia weights are introduced into the individual whale position update formula to lay the foundation for the global search of the algorithm.Finally,the differential evolution algorithm is integrated to improve the global search speed and accuracy of the whale optimization algorithm.The performance of the improved algorithm is tested using the CEC2014 test function set and different fault location cases,and it is verified that the improved strategy is effective in improving the algorithm’s search performance and increasing the fault location accuracy of the distribution network.Finally,to address the problem that the fault location accuracy decreases with the increase of the number of nodes in the distribution network,this paper adopts a regionalized hierarchical fault location model for the distribution network.The first layer uses a chaotic whale optimization algorithm incorporating differential evolutionary algorithm to complete fault area localization,and the second layer uses a manta ray foraging optimization algorithm based on opposition-based learning to complete specific fault zone localization.Simulation experiments using MATLAB in a 118-node distribution network topology diagram show that this method effectively reduces the computational dimensionality and improves the average fault location accuracy by 15.12% compared to the original fault location algorithm,and also reduces the fault location time by 86.23%,which effectively improves the power supply reliability of the distribution network.