Load–Frequency Control Using Multi-objective Genetic Algorithm and Hybrid Sliding Mode Control-Based SMES

This paper aims to better the dynamic response of interconnected power systems following any load change using the combination of multi-objective optimization algorithm-based PID and a hybrid adaptive fuzzy sliding mode. In the proposed method, a hybrid sliding surface including two subsystems’ info...

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Published in	International journal of fuzzy systems Vol. 20; no. 1; pp. 280 - 294
Main Authors	Khosraviani, Mehrshad, Jahanshahi, Mohsen, Farahani, Mohsen, Bidaki, Amir Reza Zare
Format	Journal Article
Language	English
Published	Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2018 Springer Nature B.V
Subjects	Artificial Intelligence Computational Intelligence Control systems design Control theory Controllers Dynamic response Engineering Error compensation Feedback linearization Frequency control Frequency deviation Fuzzy control Genetic algorithms Management Science Methods Multiple objective analysis Operations Research Optimization Proportional integral derivative Sliding mode control Subsystems Load–frequency control (LFC) Multi-objective optimization algorithm Hybrid adaptive fuzzy sliding mode control Genetic algorithm Pareto-set SMES
Online Access	Get full text
ISSN	1562-2479 2199-3211
DOI	10.1007/s40815-017-0332-z

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Abstract	This paper aims to better the dynamic response of interconnected power systems following any load change using the combination of multi-objective optimization algorithm-based PID and a hybrid adaptive fuzzy sliding mode. In the proposed method, a hybrid sliding surface including two subsystems’ information is introduced to produce a control effort to move both subsystems toward their related sliding surface. A feedback linearization control law is mimicked by an adaptive fuzzy controller. To compensate the error between the feedback linearization and adaptive fuzzy controller, a hitting controller is developed. The design of PID controller is formulated into a multi-objective optimization problem. The performance of suggested method is assessed on two interconnected power systems. These results validate that the suggested method confirms better disturbance rejection, keeps the control quality in different situations, reduces the frequency deviations preventing the overshoot and has more robustness to uncertainties and change in parameters in the power system.
AbstractList	This paper aims to better the dynamic response of interconnected power systems following any load change using the combination of multi-objective optimization algorithm-based PID and a hybrid adaptive fuzzy sliding mode. In the proposed method, a hybrid sliding surface including two subsystems’ information is introduced to produce a control effort to move both subsystems toward their related sliding surface. A feedback linearization control law is mimicked by an adaptive fuzzy controller. To compensate the error between the feedback linearization and adaptive fuzzy controller, a hitting controller is developed. The design of PID controller is formulated into a multi-objective optimization problem. The performance of suggested method is assessed on two interconnected power systems. These results validate that the suggested method confirms better disturbance rejection, keeps the control quality in different situations, reduces the frequency deviations preventing the overshoot and has more robustness to uncertainties and change in parameters in the power system.
Author	Khosraviani, Mehrshad Bidaki, Amir Reza Zare Jahanshahi, Mohsen Farahani, Mohsen
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Keywords	Load–frequency control (LFC) Multi-objective optimization algorithm Hybrid adaptive fuzzy sliding mode control Genetic algorithm Pareto-set SMES
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References_xml	– reference: KhoobanMHNiknamTA new intelligent online fuzzy tuning approach for multi-area load frequency control: self adaptive modified bat algorithmInt. J. Electr. Power Energy Syst.20157125426110.1016/j.ijepes.2015.03.017 – reference: AnwarMNPanSA new PID load frequency controller design method in frequency domain through direct synthesis approachInt. J. Electr. Power Energy Syst.20156756056910.1016/j.ijepes.2014.12.024 – reference: SudhaKRSanthiRVLoad frequency control of an interconnected reheat thermal system using type-2 fuzzy system including SMES unitsInt. J. Electr. Power Energy Syst.20124311383139210.1016/j.ijepes.2012.06.065 – reference: BhattPRoyRGhoshalSPComparative performance evaluation of SMES–SMES, TCPS–SMES and SSSC–SMES controllers in automatic generation control for a two-area hydro–hydro systemInt. J. Electr. Power Energy Syst.201133101585159710.1016/j.ijepes.2010.12.015 – reference: PoulinEPomerleauAPID tuning for integrating and unstable processesIEE Proc. Control Theory Appl.1996143542943410.1049/ip-cta:199604420875.93132 – reference: ParmarKSMajhiSKothariDPLoad frequency control of a realistic power system with multi-source power generationInt. J. Electr. Power Energy Syst.201242142643310.1016/j.ijepes.2012.04.040 – reference: PandaSidharthaMulti-objective evolutionary algorithm for SSSC-based controller designElectr. Power Syst. Res.200979693794410.1016/j.epsr.2008.12.004 – reference: PoulinEPomerleauAUnified PID design method based on a maximum peak resonance specificationIEE Proc. Control Theory Appl.1997146656657410.1049/ip-cta:199714930936.93036 – reference: ShayeghiHJaliliAShayanfarHAA robust mixed H2/H∞ based LFC of a deregulated power system including SMESEnergy Convers. Manag.200849102656266810.1016/j.enconman.2008.04.006 – reference: KundurPPower System Stability and Control1994NYMcGraw-Hill – reference: TripathySCAdaptive automatic generation control with superconducting magnetic energy storage in power systemsIEEE Prana Energy Convers19927343444110.1109/60.148563 – reference: BhattPRoyRGhoshalSPGA/particle swarm intelligence based optimization of two specific varieties of controller devices applied to two-area multi-units automatic generation controlElectr. Power Energy Syst.20103229931010.1016/j.ijepes.2009.09.004 – reference: SaadatHPower System Analysis1999NYMcGraw-Hill – reference: PadhanSSahuRKPandaSApplication of firefly algorithm for load frequency control of multi-area interconnected power systemElectr. Power Compon. Syst.201442131419143010.1080/15325008.2014.933372 – reference: AbrahamRJDasDPatraAAutomatic generation control of an interconnected hydrothermal power system considering superconducting magnetic energy storageInt. J. Electr. Power Energy Syst.200729857157910.1016/j.ijepes.2007.01.004 – reference: Moon, Y.H., Ryu, H.S., Lee, J.G., Kim, S.: Power system load frequency control using noise-tolerable PID feedback. In: Proceedings IEEE International Symposium on Industrial Electronics (ISIE), vol. 3, pp. 1714–1718 (2001) – reference: Al-HamouzZAl-DuwaishHAl-MusabiNOptimal design of a sliding mode AGC controller: application to a nonlinear interconnected modelElectr. Power Syst. Res.20118171403140910.1016/j.epsr.2011.02.004 – reference: JaleeliNEwartDNFinkLHUnderstanding automatic generation controlIEEE Trans. Power Syst.1992731106112210.1109/59.207324 – reference: SahuRKPandaSYegireddyNKA novel hybrid DEPS optimized fuzzy PI/PID controller for load frequency control of multi-area interconnected power systemsJ. Process Control201424101596160810.1016/j.jprocont.2014.08.006 – reference: Khodabakhshian, A., Golbon, N.: Unified PID design for load frequency control. 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Snippet	This paper aims to better the dynamic response of interconnected power systems following any load change using the combination of multi-objective optimization...
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SubjectTerms	Artificial Intelligence Computational Intelligence Control systems design Control theory Controllers Dynamic response Engineering Error compensation Feedback linearization Frequency control Frequency deviation Fuzzy control Genetic algorithms Management Science Methods Multiple objective analysis Operations Research Optimization Proportional integral derivative Sliding mode control Subsystems
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Title	Load–Frequency Control Using Multi-objective Genetic Algorithm and Hybrid Sliding Mode Control-Based SMES
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