Fuzzy multiple centrality correction algorithm and its variants for interior-point-method-based optimal power flow

In this paper, a fuzzy multiple centrality correction (FMCC) algorithm is presented to perform multiple corrections on complementarity conditions in the nonlinear predictor-corrector primal-dual interior point algorithm (PCPDIPA) for optimal power flow (OPF). This presented algorithm is different from the so-called multiple centrality correction (MCC) algorithm and is capable of incorporating the features in MCC. Three variants of FMCC are also devised in this paper. Key parameters that control the performance of these algorithms are identified by a series of tests on a real-life large-scale system. Based on the test results, it is found that conducting centrality corrections after both the predictor and the first corrector are obtained provides better performance. The total CPU time to run FMCC is significantly less than that of MCC. On average, FMCC performs better than its variants. The numerical results presented in this paper could be used as guidelines in choosing an appropriate strategy for multiple centrality corrections in the nonlinear PCPDIPA to solve other power system optimization problems.