Research On Protection Strategy Of Distribution Network Containing Microgrid

With the increase in the demand for electric energy from economic development,distributed power as an important means to solve the problem of energy scarcity has attracted widespread attention at home and abroad,especially wind power and photovoltaic power generation that rely solely on natural resources for power generation without causing environmental pollution.The state has vigorously promoted it When a distributed power source is connected to a distribution network,different access capacities and locations will have different impacts on the distribution network.In order to effectively use distributed power sources,it is necessary to plan the access location and capacity of the distributed power source.Scientific modeling and effective algorithms are the key to solving the distributed power source location and capacity planning problem.Based on the timing characteristics of distributed power sources,this paper improves the traditional fruit fly optimization algorithm to solve the distributed power source location.Constant volume problem.This article first analyzes the impact of distributed power sources connected to the distribution network,and mainly analyzes the impact of distributed power source access locations and capacities on the distribution network voltage and network loss to lay the foundation for subsequent research.The timing characteristics of distributed power sources and loads are analyzed,and a timing characteristic model is established.The time series characteristics of photovoltaic output are mainly analyzed.For the problem that the four-year photovoltaic time series model only considers the changes of the four seasons and ignores the impact of weather,a K-means algorithm is proposed to cluster the quarterly daily irradiance curves to describe the PV output affected by weather fluctuation influences.By analyzing the different daily irradiance curves affected by the weather in the quarter,the technical route of the clustering algorithm is determined and the 8 scenarios and corresponding weights obtained by the clustering are used to describe the annual photovoltaic output.The traditional Drosophila optimization algorithm was analyzed,and it was found that the Drosophila optimization algorithm's own search strategy could not effectively solve the distributed power source location and capacity problem.Aiming at this problem,the traditional fruit fly optimization algorithm is improved,and the one-dimensional search is used to replace the two-dimensional search mechanism of the traditional fruit fly optimization algorithm.The multi-group strategy is used to improve the diversity of fruit fly swarms.The inertia weight parameters are introduced to balance the fruit fly optimization.Algorithm global search and local search capabilities.Six non-linear test functions were used to test the performance of the improved Drosophila optimization algorithm.By comparing with the traditional Drosophila optimization algorithm,it was verified that the improved method can effectively solve the uneven generation of candidate solutions of the traditional Drosophila optimization algorithm and that the invariable variables cannot be solved.Number of questions.A distributed power source location and capacity planning model was established,with the model as the goal of maximizing the investment income of the distributed power source.To address the distributed power source location and capacity problem,an improved ftuit fly optimization algorithm coding expression and fitness function were set;based on IEEE-The 33-node distribution network system uses an improved Drosophila optimization algorithm to optimize the distributed power planning location and capacity.The analysis of the optimization results of examples shows that the improved Drosophila optimization algorithm can effectively solve the distributed power source location and capacity problem.Figur[31]table[14]reference[76]...