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Moth‐flame optimization algorithm optimized dual‐mode controller for multiarea hybrid sources AGC system
Author(s) -
Mohanty Banaja,
Acharyulu B.V.S.,
Hota P.K.
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.2373
Subject(s) - particle swarm optimization , control theory (sociology) , differential evolution , robustness (evolution) , automatic generation control , bat algorithm , meta optimization , controller (irrigation) , electric power system , algorithm , mode (computer interface) , engineering , power (physics) , mathematical optimization , computer science , mathematics , artificial intelligence , biochemistry , chemistry , physics , control (management) , quantum mechanics , biology , agronomy , gene , operating system
Summary A new algorithm called moth‐flame optimization (MFO) algorithm is proposed to optimize a dual‐mode controller (DMC) for a multiarea hybrid interconnected power system. Initially, a 2‐area nonreheat system is considered. The optimum gains of DMC and proportional‐integral controller are optimized using the MFO algorithm. The superiority of the proposed approach is established while comparing the results with genetic algorithm, bacterial forging optimization algorithm, differential evolution, and hybrid bacterial forging optimization algorithm particle swarm optimization for the same system. The proposed approach is further extended to 2 unequal areas of a 6‐unit hybrid‐sources interconnected power system. The optimum gain of DMC and sliding mode controller (SMC) is optimized with MFO algorithm. The performance of an MFO tuned DMC is compared with particle swarm optimization and genetic algorithm tuned DMC, MFO tuned SMC, and teaching‐learning–based optimization optimized SMC for the same system. Furthermore, robustness analysis is performed by varying the system parameters from their nominal values. It is observed that the optimum gains obtained for nominal condition need not be reset for a wide variation in system parameters.