High order contingency selection based on particle swarm optimization and branch reordering

This paper first presents a heuristic searching method based on a modified particle swarm optimization (PSO) to select severe high order contingencies. The original PSO algorithm gives an intelligent strategy to search the feasible solution space, but tends to find the best solution only. The proposed method combines the original PSO with tabu search such that a number of top candidates will be identified. This fits the need of high order contingency screening, which can be eventually the input to many other more complicate security analyses. Next, reordering of branches based on the severity of N-1 contingencies is applied as a pre-processing to increase the convergence properties and efficiency of the algorithm. With this reordering approach, many critical high order contingencies are located in a small area in the whole searching space. Therefore, the proposed algorithm tends to concentrate in searching this area such that the number of critical branch combinations searched will increase. The proposed algorithm is tested and validated with N-2 contingency selection using a sample system modified from the original IEEE 118-bus system. Satisfying results are observed.