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H. Li, K. L. Shi and P. G. McLaren, “Neural-Network- Based Sensorless Maximum Wind Energy Capture with Compensated Power Coefficient,” IEEE Transactions on Industry Applications, Vol. 41, No. 6, 2005, pp. 1548- 1556. doi:10.1109/TIA.2005.858282

has been cited by the following article:

• TITLE: Optimum Setting Strategy for WTGS by Using an Adaptive Neuro-Fuzzy Inference System

  AUTHORS: Yang Hu, Jizhen Liu, Zhongwei Lin

  KEYWORDS: Wind Energy; Data Processing; Adaptive; Takagi-Sugeno (T-S) Fuzzy; Neuro-Network

  JOURNAL NAME: Energy and Power Engineering, Vol.5 No.4B, October 16, 2013

  ABSTRACT: With the popularization of wind energy, the further reduction of power generation cost became the critical problem. As to improve the efficiency of control for variable speed Wind Turbine Generation System (WTGS), the data-driven Adaptive Neuro-Fuzzy Inference System (ANFIS) was used to establish a sensorless wind speed estimator. Moreover, based on the Supervisory Control and Data Acquisition (SCADA) System, the optimum setting strategy for the maximum energy capture was proposed for the practical operation process. Finally, the simulation was executed which suggested the effectiveness of the approaches.