Automatic generation control of a multi-area ST – Thermal power system using Grey Wolf Optimizer algorithm based classical controllers

Sharma, Yashna; Saikia, Lalit Chandra

doi:10.1016/j.ijepes.2015.06.005

Cited by 225 publications

(131 citation statements)

References 23 publications

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“…Dash et al and Sahu et al performed AGC study on multi‐unit conventional 2‐, 3‐, and 4‐area systems. AGC systems with number of sources such as hydro, thermal, and diesel plants are studied by Sahu et al Among the conventional units, combined cycle gas turbine (CCGT) is unique in nature . The unique modeling system of CCGT includes temperature and air flow controller along with secondary controller; to control the frequency is one of its kind in conventional system.…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, almost all research on AGC is centered on the design of supplementary controllers . Conventional integral (I) and proportional‐integral (PI) controllers are used in load frequency control (LFC) .…”

Section: Introductionmentioning

confidence: 99%

“…To overcome such difficulties, evolutionary algorithms (EAs) are used for searching near‐optimal solutions to problems . Grey wolf optimizer (GWO) developed by Sharma and Saikia is available, which is somewhat dependent or limited on some of the mechanisms in the balance between exploration and exploitation. Debbarma et al presented the superiority of firefly algorithm (FA) over BFO technique.…”

Section: Introductionmentioning

confidence: 99%

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Automatic generation control of an interconnected CCGT-thermal system using stochastic fractal search optimized classical controllers

Saha

Saikia

2018

Int Trans Electr Energ Syst

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Summary During the past few decades, frequency regulation of combined cycle gas turbine (CCGT) has become an interesting issue of research. A larger power system model experiences many challenges when base‐loaded CCGT unit is incorporated in it. This article presents the load frequency regulation of CCGT model in conjunction with thermal units in an unequal 3‐area interconnected system. The control strategy of CCGT plant includes temperature and air flow controllers that are designed on the basis of minimization of area control error. The main drive of proposed control strategy is to successfully tackle the system dynamics during step and realistic load disturbances. The eigenvalues of proposed system commensurate with dynamic stability. A new metaheuristic algorithm known as stochastic fractal search has been applied for simultaneous optimization of classical controller (proportional [PI], proportional‐integral [PI] and proportional‐integral‐derivative [PID] gains, and other parameters such as governor speed regulation parameter [Ri], frequency bias [Bi]). Comprehensive simulations are performed to ensure stable system dynamics with easier and cheaper realization of governor, and higher values of Bi in respective areas and robustness of stochastic fractal search optimized PID controller.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic generation control of an interconnected CCGT-thermal system using stochastic fractal search optimized classical controllers

Saha

Saikia

2018

Int Trans Electr Energ Syst

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“…GWO is proven to be superior or competitive to other classical metaheuristics such as differential evolutionary, genetic algorithm and particle swarm optimization algorithm. GWO has been successfully applied to many engineering fields, such as parameter estimation in surface waves [24] , optimization of controller's gains [25] , the optimal power flow problem [26] , hyperspectral image classification [27] and designing photonic crystal waveguides [28] . Based on the effectiveness of GWO and the nature of the multiobjective (MOP), a new multi-objective discrete grey wolf optimizer (MODGWO) is proposed to solve this multi-objective WSP.…”

Section: Introductionmentioning

confidence: 99%

An effective multi-objective discrete grey wolf optimizer for a real-world scheduling problem in welding production

Xiao

et al. 2016

Advances in Engineering Software

108

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a b s t r a c tThis paper aims to provide a solution method for a real-world scheduling case from a welding process, which is one of the important processes in modern industry. The unique characteristic of the welding scheduling problem (WSP) is that multiple machines can process one operation at a time. Thus, WSP is a new scheduling problem. We first formulate a new multi-objective mixed integer programming model for this WSP based on a comprehensive investigation. This model involves some realistic constraints, controllable processing times (CPT), sequence dependent setup times (SDST) and job dependent transportation times (JDTT). Then we propose a multi-objective discrete grey wolf optimizer (MODGWO) considering not only production efficiency but also machine load on this real-world scheduling case. The solution is encoded as a two-part representation including a permutation vector and a machine assignment matrix. A reduction machine load strategy is used to adjust the number of machines aiming to minimize the machine load. To evaluate the effectiveness of the proposed MODGWO, we compare it with other well-known multi-objective evolutionary algorithms including NSGA-II and SPEA2 on a set of instances. Experimental results demonstrate that the proposed MODGWO is superior to the compared algorithms in terms of convergence, spread and coverage on most instances. Finally, MODGWO is successfully applied to this real-world WSP. This implies that the proposed model is feasible and the proposed algorithm can solve this real-world scheduling problem very well.

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“…In AGC, the basic problem is highlighted while designing the controller parameters. In literature it has been used ISE [7] Bacterial Foraging optimization technique [8] has been applied and results shows application of artificial intelligence technique provides better results than conventional technique [9]. However, the results obtained in [10,11] has much peak deviation and taking significant time to settle down to steady state.…”

Section: Introductionmentioning

confidence: 99%

Application of modular multilevel converter for AGC in an interconnected power system

Madichetty¹,

Dasgupta²,

Kumar³

2016

International Journal of Electrical Power & Energy Systems

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Automatic generation control of a multi-area ST – Thermal power system using Grey Wolf Optimizer algorithm based classical controllers

Cited by 225 publications

References 23 publications

Automatic generation control of an interconnected CCGT-thermal system using stochastic fractal search optimized classical controllers

Automatic generation control of an interconnected CCGT-thermal system using stochastic fractal search optimized classical controllers

An effective multi-objective discrete grey wolf optimizer for a real-world scheduling problem in welding production

Application of modular multilevel converter for AGC in an interconnected power system

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