Probabilistic approach for optimal placement and tuning of power system supplementary damping controllers

This study presents a comprehensive approach to tackle the problem of optimal placement and coordinated tuning of power system supplementary damping controllers (OPCTSDC). The approach uses a recursive framework comprising probabilistic eigenanalysis (PE), a scenario selection technique (SST) and a new variant of mean-variance mapping optimisation algorithm (MVMO-SM). Based on probabilistic models used to sample a wide range of operating conditions, PE is applied to determine the instability risk because of poorly-damped oscillatory modes. Next, the insights gathered from PE are exploited by SST, which combines principal component analysis and fuzzy c-means clustering algorithm to extract a reduced subset of representative scenarios. The multi-scenario formulation of OPCTSDC is then solved by MVMO-SM. A case study on the New England test system, which includes performance comparisons between different modern heuristic optimisation algorithms, illustrates the feasibility and effectiveness of the proposed approach.