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 problem’s 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.