Interval type-2 fuzzy PID load frequency controller using Big Bang–Big Crunch optimization

Yeşil, Engin

doi:10.1016/j.asoc.2013.10.031

Cited by 125 publications

(38 citation statements)

References 65 publications

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“…The model of four-area interconnected power system, used in this study, is illustrated in Figure 2. 52 In this paper as mentioned previously, BBO…”

Section: Simulation Resultsmentioning

confidence: 76%

“…In this section, FOFPID method is compared with three different control techniques in order to illustrate the efficiency of the proposed controller tuned by BBO algorithm for solving the problem of the LFC. The model of four‐area interconnected power system, used in this study, is illustrated in Figure . In this paper as mentioned previously, BBO algorithm is taken into account to optimize the parameters of PID, FOPID, FPID, and FOFPID controllers.…”

Section: Simulation Resultsmentioning

confidence: 99%

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A fractional order fuzzy PID for load frequency control of four-area interconnected power system using biogeography-based optimization

Mohammadikia

Aliasghary

2018

Int Trans Electr Energ Syst

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Summary In this study, a fractional order fuzzy proportional‐integral‐derivative (FOFPID) controller is designed for load frequency control of four‐area interconnected power systems. The model of system consists of three reheat thermal turbine units (Area1, Area2, and Area3) and one hydro power plant (Area4). Therefore, the proposed controller (with same and different parameters) is employed for the first three zones, and different controller parameters are considered for Area4 due to existence of hydro turbine. In order to minimize frequency and power tie‐line deviations in the system, biogeography‐based optimization (BBO) algorithm is utilized to tune controller parameters. Finally, the proposed technique is compared with three different controllers including PID, fuzzy PID (FPID), and fractional order PID. For a fair comparison, the parameters of aforesaid controllers are also tuned by BBO algorithm. The results of different simulation cases indicate that the FOFPID controller has a superior performance and transient response compared with the other approaches against various load disturbances.

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“…The model of four-area interconnected power system, used in this study, is illustrated in Figure 2. 52 In this paper as mentioned previously, BBO…”

Section: Simulation Resultsmentioning

confidence: 76%

Section: Simulation Resultsmentioning

confidence: 99%

A fractional order fuzzy PID for load frequency control of four-area interconnected power system using biogeography-based optimization

Mohammadikia

Aliasghary

2018

Int Trans Electr Energ Syst

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“…It has been shown that IT2-FPIDs achieve better control performances because of the additional degree of freedom provided by the Footprint of Uncertainty (FOU) in their antecedent MFs [14][15]18]. In literature, the design of the IT2-FPID controllers is usually solved by extending MFs of an existing T1-FPID controller or by employing optimization algorithms [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Peak Observer Based Self-tuning of Type-2 Fuzzy PID Controllers

Yeşil

Kumbasar

Dodurka

et al. 2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Part 11: Simulations and Fuzzy ModelingInternational audienceFuzzy PID (proportional-integral-derivative) controllers are commonly used as an alternative to the conventional PID controllers. In order to improve the control system performance of these controllers many self-tuning methods are already studied. It is mostly observed that the self-tuning mechanism should tune the scaling factors of the fuzzy controller to enhance the transient system performance. On the other hand, these studies only focus on the ordinary (Type-1) Fuzzy PID controllers. In this study, Type-2 Fuzzy PID controllers are studied and a peak observer based self-tuning method is proposed for these controllers. In order to show the benefit of the proposed approach, several Matlab simulations are performed where different type of fuzzy control structures are compared. The results obtained from the simulation studies clearly show the advantage of the proposed approach

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“…A T2-FS is characterized by a fuzzy membership function, unlike a T1-FS where the membership grade is a crisp number in [0, 1] [29]. The conventional PID controller is combined with an interval type-2 fuzzy logic system (IT2-FLS) in order to achieve better system performance over the conventional PID controller with type-1 FLS [30][31][32][33][34][35][36][37][38][39][40][41][42][43][44]. Although favorable performance can be obtained, these controllers are limited on tuning the controller parameters related to uncertain systems.…”

Section: Introductionmentioning

confidence: 99%

Embedded intelligent adaptive PI controller for an electromechanical system

El-Nagar

2016

ISA Transactions

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Interval type-2 fuzzy PID load frequency controller using Big Bang–Big Crunch optimization

Cited by 125 publications

References 65 publications

A fractional order fuzzy PID for load frequency control of four-area interconnected power system using biogeography-based optimization

A fractional order fuzzy PID for load frequency control of four-area interconnected power system using biogeography-based optimization

Peak Observer Based Self-tuning of Type-2 Fuzzy PID Controllers

Embedded intelligent adaptive PI controller for an electromechanical system

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