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.

中文翻译：

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使用人工智能工具和预先建立的推理标准对风力涡轮机进行功率曲线建模

我们提出了一种使用人工智能工具开发功率曲线非参数模型的新方法。开发了一个参数模型和八个非参数模型来模拟风电场功率曲线所描述的行为。还提出了基于人工神经网络（ANN）的功率曲线模型与基于模糊逻辑的功率曲线模型之间的比较。使用了一些基于人工神经网络和模糊推理系统（FIS）的功率曲线模型，以及两个新的FIS，并提出了新的启发式方法。提出了一种初步的预训练，该训练是根据专家推断得出的特征进行的，然后将模糊的Mamdani系统转换为模糊的Sugeno系统。尽管错误指示符提供的值是可比较的，结果表明，与ANN和FIS模型相比，新的预训练FIS模型具有更好的精度。这项比较研究是在巴西东北部的两个风电场进行的。所提出的方法是改进基于FIS的功率曲线逼近的一种相关替代方法。