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ANFIS modeling and validation of a variable speed wind turbine based on actual data

机译：基于实际数据的变速风力涡轮机的ANFIS建模和验证

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In this research paper, ANFIS modeling and validation of Vestas 660 kW wind turbine based on actual data obtained from Eoun-Ebn-Ali wind farm in Tabriz, Iran, and FAST is performed. The turbine modeling is performed by deriving the non-linear dynamic equations of different subsystems. Then, the model parameters are identified to match the actual response. ANFIS is an artificial intelligent technique which creates a fuzzy inference system based on input and output information of the model. In this research, the ANFIS algorithm combines neural network and fuzzy logic with 5 layers which utilize different node functions for learning and setting fuzzy inference system parameters. After learning, by assuming constant parameters, a hybrid method is used to update the results. Employing the proposed method, computation time and complexity are remarkably reduced. Results of the proposed method are then compared and validated with the actual data of Eoun-Ebn-Ali wind farm in Tabriz. It is shown and concluded that the proposed model matches favorably well with the actual data and FAST model.
机译：在这篇研究论文中，根据从伊朗大不里士Eoun-Ebn-Ali风电场获得的实际数据和FAST，对Vestas 660 kW风力发电机组进行了ANFIS建模和验证。通过推导不同子系统的非线性动力学方程来执行涡轮机建模。然后，确定模型参数以匹配实际响应。 ANFIS是一种人工智能技术，可基于模型的输入和输出信息创建模糊推理系统。在这项研究中，ANFIS算法将神经网络和模糊逻辑与5层相结合，这些层利用不同的节点功能来学习和设置模糊推理系统参数。学习后，通过假设常数参数，使用混合方法来更新结果。使用所提出的方法，显着减少了计算时间和复杂度。然后，将该方法的结果与大不里士Eoun-Ebn-Ali风电场的实际数据进行比较和验证。结果表明，所提出的模型与实际数据和FAST模型吻合良好。

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