Open AccessMulti‐objective robust dynamic VAR planning in power transmission girds for improving short‐term voltage stability under uncertainties
Author(s) -
Han Tong,
Chen Yanbo,
Ma Jin
Publication year - 2018
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.2017.1521
Subject(s) - robustness (evolution) , latin hypercube sampling , electric power system , control theory (sociology) , computer science , mathematical optimization , stability (learning theory) , term (time) , voltage , reliability engineering , engineering , power (physics) , control (management) , monte carlo method , mathematics , artificial intelligence , machine learning , statistics , biochemistry , chemistry , physics , electrical engineering , quantum mechanics , gene
Modern power transmission grids are facing more and more critical short‐term voltage stability problems. This study proposes a novel robust dynamic VAR planning approach for improving the short‐term voltage stability level under uncertainties including the peak load level, the maximum proportion of dynamic load, fault clearing time and deviation of the actual capacity of dynamic VAR compensators from rated capacity when contingencies occur. The robust dynamic VAR planning problem is formulated as a multi‐objective optimisation model with objectives including the investment cost, the expectation and robustness of the short‐term voltage stability level. The complexity of the planning model is firstly reduced by selecting severe contingencies and potential buses, leading to a simplified multi‐objective optimisation model. Latin hypercube sampling is then used for the uncertainty quantification. The simplified multi‐objective optimisation model is then solved by the combination of a multi‐objective evolutionary algorithm called ϵ ‐NSGAII and extreme learning machine‐based surrogate modelling with adaptive training data sampling. This combination significantly reduces the unaffordable computing burden. Simulations are carried on the IEEE 39‐bus system, illustrating that the authors proposed methodology is reliable with high computational efficiency, and offering decision‐makers planning solutions with high mean performance and strong robustness with respect to the short‐term voltage stability level.