Adaptive neuro‐fuzzy controller for static VAR compensator to damp out wind energy conversion system oscillation

Huang, Huazhang; Chung, C.Y.

doi:10.1049/iet-gtd.2012.0609

Cited by 25 publications

(23 citation statements)

References 29 publications

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“…The generator rotor shaft is connected to the turbine shaft via gearbox and coupling; the two‐mass model is given by

\frac{d θ_{t}}{d t} = ω_{t}

\frac{d θ_{g}}{d t} = ω_{r} = ((), 1 - s_{r}) ω_{s}

2 H_{g} \frac{d s_{r}}{d t} = - T_{e m} - T_{s h}

2 H_{t} \frac{d ω_{t}}{d t} = T_{m}^{*} - T_{s h}

T_{s h} = k_{s h} θ_{t g} + D_{s h} \frac{d θ_{t g}}{d t}

where

T_{m}^{*}

is the wind torque with presence of wind shear and tower shadow effects, which is the power input of the WT; T em is the electromagnetic torque.

T_{m}^{*}

and T em are given by

T_{m}^{*} = T_{m} \underset{\underset{true⏟}{((), 1 + \frac{2}{M V_{H}} ([], V_{ω s} + V_{t s} + ((), 1 - M) V_{H}))}}{true}

T_{}

…”

Section: Proposed Model For Multi‐machine Power System With Installedmentioning

confidence: 99%

“…Referring to Figure , the equivalent wind speed due to wind shear and tower shadow can be written as follows :

V_{ω s} = V_{H} ([], \frac{α ((), α - 1)}{8} {(\frac{R}{H})}^{2} + \frac{α ((), α - 1) ((), α - 2)}{60} {(\frac{R}{H})}^{3} normalcos 3 ((), θ + ω_{t} t))

V_{t s} = \frac{M V_{H}}{3 R^{2}} true {true\sum}_{b = 1}^{3} ([], \frac{a^{2}}{\sin^{2} ((), θ_{b} + ω_{t} t)} normalln ((), \frac{R^{2} \sin^{2} ((), θ_{b} + ω_{t} t)}{x^{2}} + 1) - \frac{2 a^{2} R^{2}}{R^{2} \sin^{2} ((), θ_{b} + ω_{t} t) + x^{2}})

θ = θ 1 ,

θ_{2} = θ_{1} + \frac{2 π}{3}

, and

θ_{3} = θ_{1} + \frac{4 π}{3}

.…”

Section: Proposed Model For Multi‐machine Power System With Installedmentioning

confidence: 99%

“…where T Ã m is the wind torque with presence of wind shear and tower shadow effects, which is the power input of the WT; T em is the electromagnetic torque. T Ã m and T em are given by [16,28]…”

Section: Model Of Drive Trainmentioning

confidence: 99%

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Profit payment scheme for service created by hybrid controller of large-scale wind power generation system

Hashemi

Shayeghi

Moradzadeh

et al. 2015

Int. Trans. Electr. Energ. Syst.

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Summary The contribution of hybrid controller of large‐scale wind power generation system (LWPGS) in system stability and profit is discussed throughout the paper. To this purpose, it is necessary to formulate the impact of high infiltration of wind turbines on the dynamic stability of power networks. A mathematical model of a multi‐machine power network is developed first, based on which an extended small‐signal stability analysis procedure is projected and discussed to handle the influence of LWPGS on oscillatory modes. This model is then applied for analyzing the damping impact of the LWPGS and ancillary service (AS) associated with its controller. After the model creation, the profit created by LWPGS hybrid controller as AS is analyzed by multi‐objective optimization based on low‐frequency oscillation stability criterion and generation cost. Two scenarios for the valuation of small‐signal stability as an AS provided by LWPGS controller are considered. The study is based on a changed 16‐machine 5‐area network. The comparative analysis of the results is also presented to elucidate the suggested approach. Copyright © 2015 John Wiley & Sons, Ltd.

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“…The generator rotor shaft is connected to the turbine shaft via gearbox and coupling; the two‐mass model is given by

\frac{d θ_{t}}{d t} = ω_{t}

\frac{d θ_{g}}{d t} = ω_{r} = ((), 1 - s_{r}) ω_{s}

2 H_{g} \frac{d s_{r}}{d t} = - T_{e m} - T_{s h}

2 H_{t} \frac{d ω_{t}}{d t} = T_{m}^{*} - T_{s h}

T_{s h} = k_{s h} θ_{t g} + D_{s h} \frac{d θ_{t g}}{d t}

where

T_{m}^{*}

is the wind torque with presence of wind shear and tower shadow effects, which is the power input of the WT; T em is the electromagnetic torque.

T_{m}^{*}

and T em are given by

T_{m}^{*} = T_{m} \underset{\underset{true⏟}{((), 1 + \frac{2}{M V_{H}} ([], V_{ω s} + V_{t s} + ((), 1 - M) V_{H}))}}{true}

T_{}

…”

Section: Proposed Model For Multi‐machine Power System With Installedmentioning

confidence: 99%

“…Referring to Figure , the equivalent wind speed due to wind shear and tower shadow can be written as follows :

V_{ω s} = V_{H} ([], \frac{α ((), α - 1)}{8} {(\frac{R}{H})}^{2} + \frac{α ((), α - 1) ((), α - 2)}{60} {(\frac{R}{H})}^{3} normalcos 3 ((), θ + ω_{t} t))

V_{t s} = \frac{M V_{H}}{3 R^{2}} true {true\sum}_{b = 1}^{3} ([], \frac{a^{2}}{\sin^{2} ((), θ_{b} + ω_{t} t)} normalln ((), \frac{R^{2} \sin^{2} ((), θ_{b} + ω_{t} t)}{x^{2}} + 1) - \frac{2 a^{2} R^{2}}{R^{2} \sin^{2} ((), θ_{b} + ω_{t} t) + x^{2}})

θ = θ 1 ,

θ_{2} = θ_{1} + \frac{2 π}{3}

, and

θ_{3} = θ_{1} + \frac{4 π}{3}

.…”

Section: Proposed Model For Multi‐machine Power System With Installedmentioning

confidence: 99%

See 1 more Smart Citation

Profit payment scheme for service created by hybrid controller of large-scale wind power generation system

Hashemi

Shayeghi

Moradzadeh

et al. 2015

Int. Trans. Electr. Energ. Syst.

View full text Add to dashboard Cite

show abstract

“…S. Kamalasadan and A.A Ghandakly [9] proposed Neural Network based parallel adaptive controller to track the pitch rate of a fighter aircraft. Huang Huazhang and Chung Chi-Yung [10] implemented an adaptive neuro fuzzy Controller for static VAR compensator to damp out the oscillations of wind energy. Guo Lusu and L Parsa [11] designed a Model reference adaptive controller for a five phase IP motor.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Pi Adaptive Learning Controller for Controlling the Angle of Attack of an Aircraft

Swain¹,

Khuntia²

2017

ijacsa

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“…SVC feeds appropriate inductive or capacitive susceptance to the power system by controls of the thyristors. Various control techniques have been employed to develop SVC controllers such as adaptive backstepping sliding mode [6], adaptive fuzzy logic [5], and adaptive neural network [7].…”

Section: Introductionmentioning

confidence: 99%

A hybrid system approach to static var compensator modeling and control

Zhu

Han

Zhang

2015

2015 International Conference on Control, Automation and Robotics

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This paper proposes a hybrid model of Static var compensator (SVC) capturing the continuous and discrete behaviors of SVC in term of hybrid system and an approach to design a controller of SVC to maintain voltage of power system in normal range based on maximal controlled invariant set. The proposed SVC's model is representable complex interactions between continuous dynamics and discrete events of SVC. The proposed controller is able to ensure the power system working in safe set despite the change of the grid parameters.

show abstract

Adaptive neuro‐fuzzy controller for static VAR compensator to damp out wind energy conversion system oscillation

Cited by 25 publications

References 29 publications

Profit payment scheme for service created by hybrid controller of large-scale wind power generation system

Profit payment scheme for service created by hybrid controller of large-scale wind power generation system

Fuzzy Pi Adaptive Learning Controller for Controlling the Angle of Attack of an Aircraft

A hybrid system approach to static var compensator modeling and control

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