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An integrated control method for a wind farm to reduce frequency deviations in a small power system

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  • Kaneko, Toshiaki
  • Uehara, Akie
  • Senjyu, Tomonobu
  • Yona, Atsushi
  • Urasaki, Naomitsu

Abstract

Output power of wind turbine generator (WTG) is not constant and fluctuates due to wind speed changes. To reduce the adverse effects of the power system introducing WTGs, there are several published reports on output power control of WTGs detailing various researches based on pitch angle control, variable speed wind turbines, energy storage systems, and so on. In this context, this paper presents an integrated control method for a WF to reduce frequency deviations in a small power system. In this study, the WF achieves the frequency control with two control schemes: load estimation and short-term ahead wind speed prediction. For load estimation in the small power system, a minimal-order observer is used as disturbance observer. The estimated load is utilized to determine the output power command of the WF. To regulate the output power command of the WF according to wind speed changing, short-term ahead wind speed is predicted by using least-squares method. The predicted wind speed adjusts the output power command of the WF as a multiplying factor with fuzzy reasoning. By means of the proposed method, the WF can operate according to the wind and load conditions. In the WF system, each output power of the WTGs is controlled by regulating each pitch angle. For increasing acquisition power of the WF, a dispatch control method also is proposed. In the pitch angle control system of each WTG, generalized predictive control (GPC) is applied to enhance the control performance. Effectiveness of the proposed method is verified by the numerical simulations.

Suggested Citation

  • Kaneko, Toshiaki & Uehara, Akie & Senjyu, Tomonobu & Yona, Atsushi & Urasaki, Naomitsu, 2011. "An integrated control method for a wind farm to reduce frequency deviations in a small power system," Applied Energy, Elsevier, vol. 88(4), pages 1049-1058, April.
    • Handle: RePEc:eee:appene:v:88:y:2011:i:4:p:1049-1058
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    References listed on IDEAS

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    1. Nagai, Baku M. & Ameku, Kazumasa & Roy, Jitendro Nath, 2009. "Performance of a 3Â kW wind turbine generator with variable pitch control system," Applied Energy, Elsevier, vol. 86(9), pages 1774-1782, September.
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    3. Hansen, Anca D. & Sørensen, Poul & Iov, Florin & Blaabjerg, Frede, 2006. "Centralised power control of wind farm with doubly fed induction generators," Renewable Energy, Elsevier, vol. 31(7), pages 935-951.
    4. Senjyu, Tomonobu & Kaneko, Toshiaki & Uehara, Akie & Yona, Atsushi & Sekine, Hideomi & Kim, Chul-Hwan, 2009. "Output power control for large wind power penetration in small power system," Renewable Energy, Elsevier, vol. 34(11), pages 2334-2343.
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    2. Latif, Abdul & Hussain, S.M. Suhail & Das, Dulal Chandra & Ustun, Taha Selim, 2020. "State-of-the-art of controllers and soft computing techniques for regulated load frequency management of single/multi-area traditional and renewable energy based power systems," Applied Energy, Elsevier, vol. 266(C).
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    5. Ochoa, Danny & Martinez, Sergio, 2018. "Frequency dependent strategy for mitigating wind power fluctuations of a doubly-fed induction generator wind turbine based on virtual inertia control and blade pitch angle regulation," Renewable Energy, Elsevier, vol. 128(PA), pages 108-124.
    6. Liu, Hui & Tian, Hong-qi & Li, Yan-fei, 2012. "Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction," Applied Energy, Elsevier, vol. 98(C), pages 415-424.
    7. Yan, Ruifeng & Saha, Tapan Kumar & Modi, Nilesh & Masood, Nahid-Al & Mosadeghy, Mehdi, 2015. "The combined effects of high penetration of wind and PV on power system frequency response," Applied Energy, Elsevier, vol. 145(C), pages 320-330.
    8. Désiré D. Rasolomampionona & Michał Połecki & Krzysztof Zagrajek & Wiktor Wróblewski & Marcin Januszewski, 2024. "A Comprehensive Review of Load Frequency Control Technologies," Energies, MDPI, vol. 17(12), pages 1-77, June.
    9. Xi, Lei & Yu, Tao & Yang, Bo & Zhang, Xiaoshun & Qiu, Xuanyu, 2016. "A wolf pack hunting strategy based virtual tribes control for automatic generation control of smart grid," Applied Energy, Elsevier, vol. 178(C), pages 198-211.
    10. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
    11. Nahid-Al-Masood, & Yan, Ruifeng & Saha, Tapan Kumar, 2015. "A new tool to estimate maximum wind power penetration level: In perspective of frequency response adequacy," Applied Energy, Elsevier, vol. 154(C), pages 209-220.
    12. Liu, Hui & Tian, Hong-qi & Pan, Di-fu & Li, Yan-fei, 2013. "Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks," Applied Energy, Elsevier, vol. 107(C), pages 191-208.
    13. Abdul Motin Howlader & Naomitsu Urasaki & Atsushi Yona & Tomonobu Senjyu & Ahmed Yousuf Saber, 2013. "Design and Implement a Digital H∞ Robust Controller for a MW-Class PMSG-Based Grid-Interactive Wind Energy Conversion System," Energies, MDPI, vol. 6(4), pages 1-26, April.
    14. Sudhanshu Ranjan & Smriti Jaiswal & Abdul Latif & Dulal Chandra Das & Nidul Sinha & S. M. Suhail Hussain & Taha Selim Ustun, 2021. "Isolated and Interconnected Multi-Area Hybrid Power Systems: A Review on Control Strategies," Energies, MDPI, vol. 14(24), pages 1-20, December.
    15. Carranza, O. & Figueres, E. & Garcerá, G. & Gonzalez-Medina, R., 2013. "Analysis of the control structure of wind energy generation systems based on a permanent magnet synchronous generator," Applied Energy, Elsevier, vol. 103(C), pages 522-538.
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