Open AccessAnalysis and enhancement of small‐signal stability on DFIG‐based wind integrated power system through the optimal design of linear quadratic regulator
Author(s) -
Kumar Dipesh,
Chatterjee Kalyan
Publication year - 2020
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2018.6095
Subject(s) - control theory (sociology) , linear quadratic regulator , robustness (evolution) , electric power system , computer science , wind power , controller (irrigation) , optimal control , power (physics) , engineering , mathematics , mathematical optimization , agronomy , biochemistry , chemistry , physics , control (management) , quantum mechanics , artificial intelligence , biology , electrical engineering , gene
This study analyses the dynamic behaviour of a doubly fed induction generator (DFIG)‐based wind integrated power system (WIPS) resulting from a major disturbance. The effects of transient disturbance on WIPS are examined without and with a controller in terms of performance and stability of the system. To enhance the performance of WIPS after a sudden disturbance, an optimally designed linear quadratic regulator (LQR) controller is applied to the system. The wind energy system is described in state‐space representation, whose states and outputs are taken as feedback to the controller for improving their dynamic response. An artificial bee colony (ABC)‐based swarm optimisation technique is used to evaluate the optimal weighting matrices of the LQR controller in parallel with minimising the performance index and dynamic response characteristics of the system. The effectiveness of the proposed ABC–LQR controller in WIPS is verified by comparing their simulation and numerical results with standard proportional–integral and LQR controllers. It indicates that the proposed controller provides better enhancement of dynamic response as compared with other controllers in terms of performance index and dynamic response characteristics. Moreover, the robustness and stability of various system configurations are tested by the eigenvalue analysis.