近年來零污染的再生能源已被世界各國所重視，其中風能是一種可再生且乾淨的能源，因此得到廣泛的運用，且進而衍生出預測風力發電機的輸出功率已經變成需重視的項目。若準確的預測風能可以降低成本且提升品質，所以對風力公司或電力公司都是相當重要，然而風有隨機性與不穩定性的特性，要準確預測風能是有極大的難度。另一方面風機健康監測對於風機也是相當重要的，只要能夠監測出問題所在就能即時改善，這將對風機有很好的幫助，許多研究為了預測風能建立了許多不同的數學模型，本研究將建立出風機輸出-輸入特性的數學模型及對此模型的改善方法用來預測風能及風機健康監控，且使用的數據都是實際風機量測數據。利用風速和輸出功率間的關聯性來找出之間的延遲時間，在結合自回歸方法進一步改善輸出功率的預測準確性。預測出的模型係數運用多變量方法中的MANOVA分析，由係數間的關聯性來檢測風機狀態是否有改變，進而得知風機的健康狀態來達到監控的目的。

In the recent years, renewable energy with zero pollution has been emphasized by many countries. Wind energy is wildly used due to its clean and renewable properties. Forecasting the output power of the wind turbine generators is a highly focus topic now. It’s important to the power company and the wind power company of predicting the wind energy precisely, which they applied to reduce cost and raise the quality. However, due to the randomness and the instability characteristics, it’s a great challenge to predict wind power accurately. Moreover, monitoring wind turbine health is also important. As long as an error is detected, it can be fixed right away. There are a lots of research that built plenty of mathematical models to predict wind power. An input-output property forecasting mathematical model is established to complete the forecasting and wind turbine health monitoring by using actual data recorded from the real wind turbines. By seeking out the time delay from the coherences between wind speed and output power, the accuracy can be improved by combining with autoregressive approach. By using the MANOVA of the multivariate analysis and applications to analysis the parameters of the model. The status of the wind turbine can be detected by finding the correlations between parameters to reach the goal of monitoring the health of the wind turbine.

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