Using a particle swarm method to optimize the weighting in extension theory for the detection of islanding in photovoltaic systems
This paper proposes a two-dimensional particle swarm optimization (2D-PSO) method for optimizing the weighting in extension theory for the detection of islanding in photovoltaic (PV) power generation systems. Generally, using extension theory to implement and analyze a system with a correlation function would involve constructing a weighting determined by trial and error to help judge the problems performance. However, the judgment accuracy can be degraded if one uses an inappropriate weighting set. Hence, this paper proposes a weighting determination method for optimizing the performance of the extension method using the 2D-PSO algorithm. Some simulation results are obtained to verify the effectiveness of the proposed islanding detection method. In addition, the simulated results obtained using the proposed 2D-PSO algorithm are also compared with those obtained using genetic algorithm (GA) and evolutionary programming (EP) algorithms in order to reveal the search performance of the proposed method.
extension theory two-dimensional particle swarm optimization (2d-pso) photovoltaic (pv) power generation system islanding detection