Research On Fault Location And Recovery Algorithm For Distribution Network

With the development of social economy, the scale of power system has expanded gradually and the requirement of power supply reliability has become higher and higher, which lead to the development of Distribution Automation System. Distribution network fault location and fault recovery are core content of Distribution Automation, which have become research hotspots both in domestic and overseas. The main content of this paper includes two parts:Fault location and Fault recovery ant colony algorithm. They are both based on topology analysis.This paper describes the structural characteristics of the distribution network and the conventional electric network model, pointing out the limitation of conventional electric network model and the necessity of distribution network topology analysis. After comparing two methods, the broad first search method and the deep first search method, this paper chooses the broad first search method as topology analysis method, which can be well prepared for fault location and fault recovery.Ant colony algorithm is widely used in distribution network fault location for its better astringency and operating rate. However, traditional ant colony algorithm is easy to fall into local minimum. Based on the previous literature, in this paper, traditional ant colony algorithm is modified in the aspects of initial pheromone settings, pheromone updating mechanism and disturbance principle. Also, a new method used to construct the switch function is proposed, which can avoid multiple location. As the simulation examples show, the improved ant colony algorithm has feasibility and fault tolerance.This paper studies and compares heuristic rule, genetic algorithm and improved ant colony algorithm which is finally used to fault recovery. The concept of operational switch set and a new method used to construct merit function based on fuzzy criterion are introduced. In addition, combined with critical path method which can reduce the size of operational switch set, the search scope is narrowed and the search speed is improved a lot. The simulation examples demonstrate the feasibility, high convergence and strong global stability of the improved ant colony algorithm.