Optimal fractional order PID controller for automatic generation control of two-area power systems

Chathoth, Ismayil; Kumar, Rahul; Sindhu, T. K.

doi:10.1002/etep.2038

Cited by 48 publications

(33 citation statements)

References 24 publications

(35 reference statements)

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“…Generally, the concept of FOPID control technique is derived from applying fractional calculus into derivative and integral parts of PID controller. As a result, FOPID control has two extra adjustable parameters which provide better outcomes compared with PID controller . Multiple definitions exist to explain both fractional integration and derivative functions.…”

Section: Structure and Components Of Controllersmentioning

confidence: 99%

“…

{{}_{T_{0}}^{italicRL}D}_{t}^{α} f ((), t) = \frac{d^{α} f ((), t)}{{dt}^{α}} = \frac{1}{Γ ((), m - α)} \frac{d^{m}}{d^{m}} \int_{T_{0}}^{t} {(t - τ)}^{m - α - 1} f ((), τ) italicdτ

{{}_{T_{0}}^{C}D}_{t}^{α} f ((), t) = ({, \begin{matrix} \frac{1}{Γ ((), m - α)} \int_{T_{0}}^{t} {(t - τ)}^{m - α - 1 f ((), m)} ((), τ) d normalτ, normalm - 1 < α < m \\ \frac{d^{m} f ((), t)}{d^{m} t} α = m \end{matrix})

where m − 1 < α < m , m ∈ N . Commonly, the FOPID controller transfer function can be presented as the following:

G_{c} ((), s) = K_{P} + \frac{K_{I}}{s^{λ}} + K_{D} s^{μ}

where K P is the proportional, K I is the integral, and K D is the derivative coefficients of FOPID and PID controllers. Also, λ and μ are the non‐integer positive order of integral and differential parts of the proposed FOPID controller.…”

Section: Structure and Components Of Controllersmentioning

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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Section: Structure and Components Of Controllersmentioning

confidence: 99%

“…

{{}_{T_{0}}^{italicRL}D}_{t}^{α} f ((), t) = \frac{d^{α} f ((), t)}{{dt}^{α}} = \frac{1}{Γ ((), m - α)} \frac{d^{m}}{d^{m}} \int_{T_{0}}^{t} {(t - τ)}^{m - α - 1} f ((), τ) italicdτ

{{}_{T_{0}}^{C}D}_{t}^{α} f ((), t) = ({, \begin{matrix} \frac{1}{Γ ((), m - α)} \int_{T_{0}}^{t} {(t - τ)}^{m - α - 1 f ((), m)} ((), τ) d normalτ, normalm - 1 < α < m \\ \frac{d^{m} f ((), t)}{d^{m} t} α = m \end{matrix})

where m − 1 < α < m , m ∈ N . Commonly, the FOPID controller transfer function can be presented as the following:

G_{c} ((), s) = K_{P} + \frac{K_{I}}{s^{λ}} + K_{D} s^{μ}

Section: Structure and Components Of Controllersmentioning

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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“…The following definition is introduced by Riemann and Liouville for a fractional‐order calculus.

{{}_{a}D}_{t}^{α} normal f ((),, t) = \frac{1}{normalΓ ((),, n - α)} \frac{d^{n}}{{italicdt}^{n}} \int_{a}^{t} \frac{f ((),, τ)}{{((),, t - τ)}^{α - n + 1}} italicdτ,

…”

Section: Fopid Controller For Chaos Controlmentioning

confidence: 99%

“…The following definition is introduced by Riemann and Liouville 15,16 for a fractional-order calculus.…”

Section: Fopid Controller For Chaos Controlmentioning

confidence: 99%

Numerical study of optimized fractional-order controller for chaos control of nonlinear dynamical power system

Nangrani

Bhat

2017

Int. Trans. Electr. Energ. Syst.

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Summary Nonlinear dynamical system exhibit bifurcation, chaos, and instability due to initial conditions and parametric variation. The power system is also nonlinear dynamical system, which exhibits these behaviors due to different initial operating conditions and uncertain variation in parameter like reactive power load demand. The fractional‐order controller is introduced as the better choice for several control systems as compared to conventional proportional‐integral‐derivative controllers owing to its merits. This paper proposes a first ever numerical study of novel use of optimized fractional‐order controller for control of chaos in power system. This paper discusses the steps to optimize the order of fractional controller. The role of fractional‐order controller is to inject or withdraw the precise amount of reactive power to control chaotic nature of voltage. The proposed controller will perturb the dynamics of the nonlinear power system and push it to nonchaotic and bounded stable state because of precise state feedback control. Precise perturbation in reactive‐power value inhibits voltage bifurcation point, chaos, and instability thereafter. Fourth order benchmark model of the power system is used as a case study in the paper.

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“…Randomization provides a good way to move from local search to global search. Multiobjective genetic algorithm (GA) has been used for AGC control . In these papers, controller of AGC have been optimized using GA. Singh et al have used particle swarm optimization (PSO) to tune the controller for AGC taking consideration of communication delay.…”

Section: Introductionmentioning

confidence: 99%

Fruit fly algorithm-based automatic generation control of multiarea interconnected power system with FACTS and AC/DC links in deregulated power environment

Shankar

Kumar

Raj

et al. 2018

Int Trans Electr Energ Syst

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Summary This paper focuses on automatic generation control (AGC) in deregulated environment for the multiarea interconnected power system. Each area consists of different kind of generating sources like thermal, hydro, and gas, each having different characteristics with physical constraint likes governor dead band and generation rate constraint. Loads are divided among different generator using the concept of economic load dispatch. The AC/DC links has been installed in all areas as well as unified power flow controller (UPFC) in tie lines to mitigate the effect of oscillation and to improve the system response. Thus, a more systematic and synchronized control of AGC with UPFC and AC/DC links has been proposed. Small signal stability of each area has been analyzed for finding out the oscillation states of the studied system. The proposed controller is optimized through Fruit Fly Algorithm, and its promising results reveal that the proposed controller is more efficient and effective in all different power transaction modes of restructured scenario.

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Optimal fractional order PID controller for automatic generation control of two-area power systems

Cited by 48 publications

References 24 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

Numerical study of optimized fractional-order controller for chaos control of nonlinear dynamical power system

Fruit fly algorithm-based automatic generation control of multiarea interconnected power system with FACTS and AC/DC links in deregulated power environment

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