A data-driven approach for designing STATCOM additional damping controller for wind farms

Guozhou Zhang, Weihao Hu, Di Cao, Jiabo Yi, Qi Huang, Zhou Liu, Zhe Chen, Frede Blaabjerg

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

Due to the fluctuation characteristics of the wind turbine (WT) production, the design of the control system needs to ensure the stability of the power system at different wind speeds. In this context, a WT-oriented adaptive robust control agent for a static synchronous compensator having additional damper controller (STATCOM-ADC) is proposed in this paper. An artificial neural network based estimator for system identification which can identify the equivalent transfer function of the whole system in real time is used in this paper. Then a Deep Deterministic Policy Gradient (DDPG) algorithm is adopted to train the agent on learning the adaptive robust control strategy for STATCOM-ADC. Compared with other control strategies, the proposed method has modelled self-learning abilities, and the parameter settings provided by the agent for one operation state of the system are still applicable to other states. Simulation results on the actual wind farm located in China Ningxia province show that the proposed method can enhance the stability of the system under different fault conditions and wind speeds.
OriginalsprogEngelsk
TidsskriftInternational Journal of Electrical Power & Energy Systems
Vol/bind117
Antal sider13
ISSN0142-0615
DOI
StatusUdgivet - maj 2020

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Farms
Damping
Robust control
Wind turbines
Controllers
Transfer functions
Identification (control systems)
Neural networks
Control systems
Static synchronous compensators

Citer dette

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title = "A data-driven approach for designing STATCOM additional damping controller for wind farms",
abstract = "Due to the fluctuation characteristics of the wind turbine (WT) production, the design of the control system needs to ensure the stability of the power system at different wind speeds. In this context, a WT-oriented adaptive robust control agent for a static synchronous compensator having additional damper controller (STATCOM-ADC) is proposed in this paper. An artificial neural network based estimator for system identification which can identify the equivalent transfer function of the whole system in real time is used in this paper. Then a Deep Deterministic Policy Gradient (DDPG) algorithm is adopted to train the agent on learning the adaptive robust control strategy for STATCOM-ADC. Compared with other control strategies, the proposed method has modelled self-learning abilities, and the parameter settings provided by the agent for one operation state of the system are still applicable to other states. Simulation results on the actual wind farm located in China Ningxia province show that the proposed method can enhance the stability of the system under different fault conditions and wind speeds.",
keywords = "Wind turbine, Adaptive robust control agent, STATCOM-ADC, DDPG, Estimator",
author = "Guozhou Zhang and Weihao Hu and Di Cao and Jiabo Yi and Qi Huang and Zhou Liu and Zhe Chen and Frede Blaabjerg",
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journal = "International Journal of Electrical Power & Energy Systems",
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A data-driven approach for designing STATCOM additional damping controller for wind farms. / Zhang, Guozhou; Hu, Weihao; Cao, Di; Yi, Jiabo; Huang, Qi; Liu, Zhou; Chen, Zhe; Blaabjerg, Frede.

I: International Journal of Electrical Power & Energy Systems, Bind 117, 05.2020.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - A data-driven approach for designing STATCOM additional damping controller for wind farms

AU - Zhang, Guozhou

AU - Hu, Weihao

AU - Cao, Di

AU - Yi, Jiabo

AU - Huang, Qi

AU - Liu, Zhou

AU - Chen, Zhe

AU - Blaabjerg, Frede

PY - 2020/5

Y1 - 2020/5

N2 - Due to the fluctuation characteristics of the wind turbine (WT) production, the design of the control system needs to ensure the stability of the power system at different wind speeds. In this context, a WT-oriented adaptive robust control agent for a static synchronous compensator having additional damper controller (STATCOM-ADC) is proposed in this paper. An artificial neural network based estimator for system identification which can identify the equivalent transfer function of the whole system in real time is used in this paper. Then a Deep Deterministic Policy Gradient (DDPG) algorithm is adopted to train the agent on learning the adaptive robust control strategy for STATCOM-ADC. Compared with other control strategies, the proposed method has modelled self-learning abilities, and the parameter settings provided by the agent for one operation state of the system are still applicable to other states. Simulation results on the actual wind farm located in China Ningxia province show that the proposed method can enhance the stability of the system under different fault conditions and wind speeds.

AB - Due to the fluctuation characteristics of the wind turbine (WT) production, the design of the control system needs to ensure the stability of the power system at different wind speeds. In this context, a WT-oriented adaptive robust control agent for a static synchronous compensator having additional damper controller (STATCOM-ADC) is proposed in this paper. An artificial neural network based estimator for system identification which can identify the equivalent transfer function of the whole system in real time is used in this paper. Then a Deep Deterministic Policy Gradient (DDPG) algorithm is adopted to train the agent on learning the adaptive robust control strategy for STATCOM-ADC. Compared with other control strategies, the proposed method has modelled self-learning abilities, and the parameter settings provided by the agent for one operation state of the system are still applicable to other states. Simulation results on the actual wind farm located in China Ningxia province show that the proposed method can enhance the stability of the system under different fault conditions and wind speeds.

KW - Wind turbine

KW - Adaptive robust control agent

KW - STATCOM-ADC

KW - DDPG

KW - Estimator

U2 - 10.1016/j.ijepes.2019.105620

DO - 10.1016/j.ijepes.2019.105620

M3 - Journal article

VL - 117

JO - International Journal of Electrical Power & Energy Systems

JF - International Journal of Electrical Power & Energy Systems

SN - 0142-0615

ER -