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Optimal control strategy–based AGC of electrical power systems: A comparative performance analysis
Author(s) -
Arya Yogendra,
Kumar Narendra
Publication year - 2017
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/oca.2304
Subject(s) - control theory (sociology) , settling time , overshoot (microwave communication) , automatic generation control , robustness (evolution) , electric power system , optimal control , minification , computer science , heuristic , controller (irrigation) , pid controller , step response , power (physics) , control engineering , mathematics , mathematical optimization , engineering , control (management) , temperature control , artificial intelligence , chemistry , biology , telecommunications , biochemistry , quantum mechanics , agronomy , physics , gene
Summary This study extensively addresses the application of optimal control approach to the automatic generation control (AGC) of electrical power systems. Proportional‐integral structured optimal controllers are designed using full‐state feedback control strategy employing performance index minimization criterion. Some traditional single/multiarea and restructured multiarea power system models from the literature are explored deliberately in the present study. The dynamic performance of optimal controllers is observed superior in comparison to integral/proportional‐integral controllers tuned using some recently published modern heuristic optimization techniques. It is observed that optimal controllers show better system results in terms of minimum value of settling time, peak overshoot/undershoot, various performance indices, and oscillations corresponding to change in area frequencies and tie‐line powers along with maximum value of minimum damping ratio in comparison to other controllers. The results are displayed in the form of tables for ease of comparison. Sensitivity analysis affirms the robustness of the optimal feedback controller gains to wide variations in some system parameters from their nominal values.