Self tuning fuzzy PID type load and frequency controller

Yeşil, Engin; Güzelkaya, Müjde; Eksin, İbrahim

doi:10.1016/s0196-8904(03)00149-3

Cited by 191 publications

(68 citation statements)

References 23 publications

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“…Most common method of FLC tuning is the gain tuning of input and output scaling factors as their values significantly affect the performance of FLC. Therefore, standard rule base and mfs may be utilized for dissimilar applications and input and output scaling factors can be tuned to design optimum fuzzy PI/PID controller Pal 1999, 2000;Yesil et al 2004;Sahu et al 2014Sahu et al , 2015aSekhar et al 2016). A simplified fuzzy PI controller configuration is visualized for AGC study of different systems (Sahu et al 2015a), where input scaling factors i.e., the error signal and its derivative are used as the input to the FLC.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, for higher-order systems, fuzzy PI controller during the transient phase may not exhibit good performance because of their internal integration operation. To attain an overall improved performance, generally fuzzy PID controller is recommended (Yesil et al 2004;Sahu et al 2014Sahu et al , 2015cSekhar et al 2016). A BFOAbased fuzzy integral double derivative structured controller is proposed with optimized mfs and output scaling factors for AGC of a traditional three-area hydrothermal system ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…There exist constraints on the rate of change of power generation of steam/hydro plants called GRC, and it must be incorporated in AGC study of realistic power systems (Nanda et al 2006;Kundur 2011;Kothari and Nagrath 2012;Debbarma et al 2013;Panda et al 2013;Saikia et al 2013;Padhan et al 2014;Sahu et al 2015a, b, c;Arya et al 2016). The GRC of ±0.015 pu/s is considered for non-reheat thermal GENCOs (Yesil et al 2004) and GRC of +0.045 pu/s for raising generation and −0.06 pu/s for lowering generation is considered for hydro power GENCOs (Nanda et al 2006;Saikia et al 2013;Panda et al 2013;Arya et al 2016;Sahu et al 2015a, b). Figure 16 indicates that the frequency of both areas settles to zero in steady state; however, degraded responses are observed with GRC in comparison with without considering GRC.…”

Section: Fig 13mentioning

confidence: 99%

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Design and analysis of BFOA-optimized fuzzy PI/PID controller for AGC of multi-area traditional/restructured electrical power systems

Arya

Kumar

2016

Soft Comput

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In this paper, design and performance analysis of bacterial foraging optimization algorithm (BFOA)-optimized fuzzy PI/PID (FPI/FPID) controller for automatic generation control of multi-area interconnected traditional/restructured electrical power systems is presented. Firstly a traditional two-area non-reheat thermal system is considered, and gains of the fuzzy controller are tuned employing BFOA using integral of squared error objective function. The supremacy of this controller is demonstrated by juxtaposing the results with particle swarm optimization (PSO), firefly algorithm (FA), BFOA, hybrid BFOA-PSObased PI and fuzzy PI controllers based upon pattern search (PS) and PSO algorithms for the same power system structure. The approach is then extended to a two-area reheat system, and improved results are found with the purported FPI/FPID controller in comparison with PSO and artificial bee colony optimized PI controller. Further, the approach is implemented on a traditional multi-source multi-area (MSMA) hydrothermal system and its superb performance is observed over genetic algorithm and hybrid FA-PS tuned PI controller. Additionally, to demonstrate the scalability of the designed controller to cope with restructured power system, the study is also protracted to a restructured MSMA

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Fig 13mentioning

confidence: 99%

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Design and analysis of BFOA-optimized fuzzy PI/PID controller for AGC of multi-area traditional/restructured electrical power systems

Arya

Kumar

2016

Soft Comput

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“…Zadeh [19] introduced fuzzy set theory in 1965, and the first fuzzy-logic control algorithm was implemented by Mamdani on a steam engine in 1974 [20]. In the following years, fuzzy-logic control has been widely used in many successful industrial applications and has demonstrated significant achievements [21,22]. Fuzzy logic is often used in systems where system dynamics is either very complex or exhibit a nonlinear character.…”

Section: Design Of a Fuzzy Pid Type Controllermentioning

confidence: 99%

“…Thus, in practice fuzzy PID controllers are more useful [23]. To obtain proportional, integral and derivative control actions altogether, it is intuitive and convenient to combine PI and PD actions to form a fuzzy PID controller [22,24]. A FPIDC structure that simply connects the PD type and the PI type fuzzy controllers is shown in Fig.…”

Section: Design Of a Fuzzy Pid Type Controllermentioning

confidence: 99%

Seismic-vibration mitigation of a nonlinear structural system with an ATMD through a fuzzy PID controller

Güçlü

Yazıcı

2009

Nonlinear Dyn

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This paper discusses the design of fuzzy PID type controllers (FPIDC) to improve seismic control performance of a nonlinear structural system with an active tuned mass damper (ATMD) against earthquakes. Since structural systems have nonlinearities and uncertainties, fuzzy-based controllers are adequate because of their robust character and satisfactory performance in active structural control. The main advantages of this controller are the ability to handle nonlinearities and uncertainties effectively. In the literature, various structures for fuzzy PID (including PI and PD) controllers have been proposed. In order to obtain proportional, integral and derivative control actions altogether, it is intuitive and convenient to combine PI and PD actions to form a fuzzy PID controller. The simulated system has fifteen degrees of freedom and is modeled using nonlinear behavior of the base-structure interaction. The system is then simulated against the ground motion of the Northridge earthquake (M w = 6.7) in USA on 17 January, 1994. Finally, the time history of the storey displacements, accelerations, ATMD displacements, control voltage and frequency responses of both the uncontrolled and R. Guclu ( ) · controlled cases are presented. The ground motion recorded of the El-Centro and Kocaeli earthquakes has been used to evaluate the effectiveness of the proposed control algorithm. The robustness of the controller has been checked through the uncertainty in stiffness of the structure. Simulation results exhibit that superior vibration suppression is achieved by the use of designed fuzzy PID type controllers.

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PID‐RLNN controllers for discrete mode LFC of a three‐area hydrothermal hybrid distributed generation deregulated power system

Das

Bera

Sarkar

2021

Int Trans Electr Energ Syst

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Summary The inequality between active power generation and load demand due to volatile power generation and random changes of load poses a challenge to maintain the system frequency. To address this, in the present work, reinforced learning neural network based–proportional‐integral‐derivative (PID‐RLNN) controllers are proposed in the discrete mode load frequency control (LFC) problems for a three‐area hydrothermal hybrid distributed generation (HDG) deregulated power system. The proposed power system is incorporated with super conducting magnetic energy storage system (SMES), and each control area is interconnected via AC/DC parallel links. The dynamic responses using PID‐RLNN controllers are compared with PID controllers and RLNN controllers for different loading conditions. The gains and parameters of all the controllers are optimized using grasshopper optimization algorithm (GhOA). Investigation reveals that the PID‐RLNN controllers give better dynamic performances compared to PID and RLNN controllers for different loading conditions. Sensitivity analyses are performed to investigate the robustness of the proposed controllers, and investigation reveals that their performances are quite robust for variation of inertia constant and loading conditions. The fluctuation of loads in each area is also considered to analyze the performances of proposed controllers and it is seen that the proposed PID‐RLNN controllers damp the oscillations of dynamic responses.

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Self tuning fuzzy PID type load and frequency controller

Cited by 191 publications

References 23 publications

Design and analysis of BFOA-optimized fuzzy PI/PID controller for AGC of multi-area traditional/restructured electrical power systems

Design and analysis of BFOA-optimized fuzzy PI/PID controller for AGC of multi-area traditional/restructured electrical power systems

Seismic-vibration mitigation of a nonlinear structural system with an ATMD through a fuzzy PID controller

PID‐RLNN controllers for discrete mode LFC of a three‐area hydrothermal hybrid distributed generation deregulated power system

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