Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

Power Curve Modelling for Wind Turbine Using Artificial Intelligence Tools and Pre-established Inference Criteria
Author:
Affiliation:

1.Department of Electrical Engineering, Federal University of Pernambuco, Recife, Brazil;2.DASE, Federal Institute of Pernambuco, Recife, Brazil

Fund Project:

This work was supported by Coordination for the Improvement of Higher Education Personnel (CAPES) - Research Financers in Brazil.

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    Abstract:

    We propose a new way to develop non-parametric models of power curves using artificial intelligence tools. One parametric model and eight non-parametric models are developed to emulate the behavior described by the power curve of the wind farms. A comparison between the power curve models based on artificial neural networks (ANNs) and those based on fuzzy logic are also proposed. Some of the power curve models based on ANNs and fuzzy inference systems (FISs) are used as well as two new FISs with the proposed new heuristic. An initial pre-training is proposed, resulting from the characteristics derived from the expert inference followed by a transformation of a fuzzy Mamdani system into a fuzzy Sugeno system. Although the presented values by the error indicators are comparable, the results show that the new pre-trained FIS models have better precision compared with the ANN and FIS models. The comparative study is conducted in two wind farms located in northeastern Brazil. The proposed method is a relevant alternative to improve power curve approximation based on an FIS.

    表 5 Table 5
    表 1 Table 1
    表 2 Table 2
    表 4 Table 4
    图1 微型靴压对辊装配三维图Fig.1 3D assembly diagram of micro shoe press matching roller
    图3 线压力与最大挠度的关系Fig.3 Relationship between line pressure and maximum deflection
    图5 微型靴压对辊温度分布云图Fig.5 Temperature distribution cloud diagram
    图7 靴压对辊热应力云图Fig.7 Thermal stress cloud diagram of micro shoe press matching roller
    图6 辊体表面结点温度随时间变化曲线Fig.6 Curve of temperature change of surface node of roll body with time
    图8 微型靴压对辊1~10阶的模态振型图Fig.8 Vibration diagram of micro shoe press matching roller
    图1 Average speed versus average power for a wind farm.Fig.1
    图2 Polynomial approximations relative to average power curves in wind farms 1 and 2.Fig.2
    图3 PC1-fuzzy resulting from learning process related to wind farms 1 and 2.Fig.3
    图4 PC2-fuzzy surface resulting from learning process related to wind farms 1 and 2.Fig.4
    图5 PC1-fuzzy (Sugeno-ANFIS) resulting from learning process related to wind farms 1 and 2.Fig.5
    图6 Trend curve resulting from learning process related to wind farms 1 and 2.Fig.6
    图7 PC2-fuzzy (Sugeno-ANFIS) resulting from learning process related to wind farms 1 and 2.Fig.7
    图8 Trend surfaces resulting from learning process related to wind farms1 and 2.Fig.8
    图9 MAE of simulation set for models of active power curves in wind farms 1 and 2.Fig.9
    图10 NMAE of simulation set for models of active power curves in wind farms 1 and 2.Fig.10
    图11 RMSE of simulation set for models of active power curves in wind farms 1 and 2.Fig.11
    图12 Responses of power curves for entries of simulation dataset.Fig.12
    表 3 Table 3
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History
  • Received:April 04,2019
  • Online: May 19,2021