Incorporating approximate dynamic programming-based parameter tuning into PD-type virtual inertia control of DFIGs

Wentao Guo, Feng Liu, Jennie Si, Shengwei Mei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)

Abstract

Doubly fed induction generators (DFIGs) are widely used in wind power generation. For controlling DFIGs to maintain network frequency within a safety range, the proportional-derivative (PD) type virtual inertia controllers (VIC) are used in the active power control of DFIGs. However, as is well known, wind power generation conditions change directly with wind conditions in nature. Such changes create great challenge for the VIC design and actually force the control designs to go beyond the traditional problem formulation of using explicit objective functions associated with specific optimality. Controller parameter tuning thus necessarily becomes a part of the controller design. In this paper, we propose an approximate dynamic programming (ADP) structure for online tuning of the PD type virtual inertia controller parameters. The proposed ADP structure naturally takes into account the PD control into design objective and provides the PD controller with online parameter tuning capability through learning. Design and implementation details of the proposed methodology, including neural network weight initialization, design of the reinforcement signal, data preprocessing, and a bound on the online tuned parameters are discussed in this paper. Simulation studies carried out on the Power System Computer Aided Design/ Electro Magnetic Transient in DC System (PSCAD/EMTDC) software are used to demonstrate the effectiveness and efficiency of the proposed ADP-based online VIC parameter tuning methodology.

Original languageEnglish (US)
Title of host publicationProceedings of the International Joint Conference on Neural Networks
DOIs
StatePublished - 2013
Event2013 International Joint Conference on Neural Networks, IJCNN 2013 - Dallas, TX, United States
Duration: Aug 4 2013Aug 9 2013

Other

Other2013 International Joint Conference on Neural Networks, IJCNN 2013
CountryUnited States
CityDallas, TX
Period8/4/138/9/13

Fingerprint

Asynchronous generators
Dynamic programming
Tuning
Derivatives
Controllers
Wind power
Power generation
Power control
Computer aided design
Reinforcement
Neural networks

Keywords

  • Approximate Dynamic Programming (ADP)
  • Direct Heuristic Dynamic Programming (direct HDP)
  • Doubly Fed Induction Generator (DFIG)
  • Parameter Tuning
  • Virtual Inertia Control (VIC)

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Guo, W., Liu, F., Si, J., & Mei, S. (2013). Incorporating approximate dynamic programming-based parameter tuning into PD-type virtual inertia control of DFIGs. In Proceedings of the International Joint Conference on Neural Networks [6707069] https://doi.org/10.1109/IJCNN.2013.6707069

Incorporating approximate dynamic programming-based parameter tuning into PD-type virtual inertia control of DFIGs. / Guo, Wentao; Liu, Feng; Si, Jennie; Mei, Shengwei.

Proceedings of the International Joint Conference on Neural Networks. 2013. 6707069.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Guo, W, Liu, F, Si, J & Mei, S 2013, Incorporating approximate dynamic programming-based parameter tuning into PD-type virtual inertia control of DFIGs. in Proceedings of the International Joint Conference on Neural Networks., 6707069, 2013 International Joint Conference on Neural Networks, IJCNN 2013, Dallas, TX, United States, 8/4/13. https://doi.org/10.1109/IJCNN.2013.6707069
Guo W, Liu F, Si J, Mei S. Incorporating approximate dynamic programming-based parameter tuning into PD-type virtual inertia control of DFIGs. In Proceedings of the International Joint Conference on Neural Networks. 2013. 6707069 https://doi.org/10.1109/IJCNN.2013.6707069
Guo, Wentao ; Liu, Feng ; Si, Jennie ; Mei, Shengwei. / Incorporating approximate dynamic programming-based parameter tuning into PD-type virtual inertia control of DFIGs. Proceedings of the International Joint Conference on Neural Networks. 2013.
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