Research On Fault Location Of Distribution Network With DG Based On Improved Particle Swarm Optimization Algorithm

With the stepwise clean-up of the energy framework,the electricity market is constantly reformed,gradually breaking up monopolies and increasing efficiency.Cleaner energy generation has received more attention in an environment where traditional resources are increasingly stretched.Distributed Generation(DG)is used in distribution grids in large numbers for its cleanliness,independence,ease of dispatch and economic advantages.The large-scale grid connection of DG can change the network topology and lead to a more complex distribution network.In the event of a power system failure,failure to remove the fault in a timely and accurate manner will threaten the stability of the distribution network and may lead to the collapse of the power system in serious cases,so it is crucial to study the fault location of distribution networks containing DG.In this paper,a fault location method using an improved BPSO algorithm is proposed.The improved BPSO algorithm can shorten the fault location time and improve the fault location accuracy,and the operation and maintenance level of the power grid is significantly improved to guarantee the safe and reliable transportation of electrical energy and promote the power system to move towards intelligence.It introduces the principle and classification of distributed power sources as well as the grid connection mode and impact,analyses the types of faults in distribution networks,illustrates the principle of distribution network fault location,constructs the radiating and DG-containing distribution network fault location models,and designs the corresponding coding methods,switching functions and adaptation functions,and finally analyses the impact of different locations of DG in feeders on the transmission of fault information in distribution networks.In response to the poor accuracy and premature convergence of the BPSO swarm algorithm,this paper introduces shrinkage factors,adaptive weights and adaptive locations for solving these defects.The shrinkage factor avoids the problems of reduced optimisation and falling into local optima when the learning factor is large;the adaptive weights can change with the degree of adaptation;and the adaptive position can overcome the problem of premature convergence.The global merit-seeking ability and convergence performance of the binary particle swarm algorithm and the improved binary particle swarm algorithm are tested by three classical test functions,namely Ackley,Griewank and Sphere.Based on their test results and iterative convergence curves as well as the analysis of the fault tolerance of the algorithm,it is verified that the proposed improved binary particle swarm algorithm has good convergence performance and merit-seeking ability compared with the binary particle swarm The proposed improved binary particle swarm algorithm is verified to have better convergence performance and superiority finding capability compared with binary particle swarm.The IEEE-33 node model is used as an example in the MATLAB platform environment to classify the network into a radiating distribution network and a distribution network with DG,and the simulation related to fault location is carried out using the PSO algorithm,GA and improved BPSO algorithm considering whether information distortion occurs in fault location.It is demonstrated that the proposed improved BPSO algorithm is feasible and effective in distribution network fault location due to its fast convergence speed,high accuracy in locating faults and good fault tolerance of the algorithm.