Open AccessProbabilistic approach for optimal placement and tuning of power system supplementary damping controllers
Author(s) -
Rueda José Luis,
Cepeda Jaime Cristóbal,
Erlich István
Publication year - 2014
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2013.0702
Subject(s) - probabilistic logic , electric power system , heuristic , computer science , cluster analysis , range (aeronautics) , mathematical optimization , variance (accounting) , selection (genetic algorithm) , fuzzy logic , principal component analysis , algorithm , control theory (sociology) , power (physics) , engineering , mathematics , machine learning , artificial intelligence , control (management) , physics , quantum mechanics , accounting , business , aerospace engineering
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.