Moth‐flame optimization algorithm optimized dual‐mode controller for multiarea hybrid sources AGC system

Moth‐flame optimization algorithm optimized dual‐mode controller for multiarea hybrid sources... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Optimal Control Applications and Methods Wiley

Moth‐flame optimization algorithm optimized dual‐mode controller for multiarea hybrid sources AGC system

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Publisher
Wiley
Copyright
Copyright © 2018 John Wiley & Sons, Ltd.
ISSN
0143-2087
eISSN
1099-1514
D.O.I.
10.1002/oca.2373
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Optimal Control Applications and MethodsWiley

Published: Jan 1, 2018

Keywords: ; ; ;

References

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