A Normal Behavior Model Based on Power Curve and Stacked Regressions for Condition Monitoring of Wind Turbines

Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review

“…It should be mentioned that the "ideal power curve", given by the wind turbine builder and curated through the standard procedure set by the IEC, doesn't consider the "wear and tear" of the wind turbine. Hence, it may result in a curve deviation when analyzed in comparison with the "actual power curve" attained through data from the SCADA system [20,21]. The main factor for this deviation may be attributed to the variation in power curve values for equal wind speeds.…”

“…In fact, anomalies and fault signatures detectable via the power curve, can play a significant role in early detection as the "first indication" that something is wrong [44,51,52]. When applied in the context of NBMs as describe above, such a model is capable of detecting any unusual operations by performing calculations with regards to the difference between the actual and the predicted values, also known as residuals [18,21,47,53], which can be described as: R e = y i − ŷi (5) where y i and ŷi denote the actual and the predicted values, respectively. Subsequently, a "control chart" is employed for detection of abnormal behavior.…”

“…The procedure is shown in Algorithm 1. For instance, this approach (DBSCAN) has been used in [21,74,75] for data correction purpose. The so-called SOM is generally an "unsupervised technique" which is usually employed to produce a low-dimensional representation of a higher dimensional dataset while preserving the topological structure of the data.…”

“…Therefore, in order to enhance the data integrity, it is often required to employ a combination of different algorithms to tackle different problems. An example of a procedure that combines different methods to attain an optimal result is found in [21], where a four-step method as illustrated in Figure 12 is introduced as follows: (i) Anomaly Detection: Firstly, the clustering algorithm DBSCAN is used in order to mitigate anomalies in the dataset; (ii) Robust Power Curve: Secondly, the LOESS is used to attain a robust power curve; (iii) Upper-Lower Limits: Thirdly, the robust power curve is used to derive an "upper-lower bound" envelop; (iv) Optimal Power Curve Data: Lastly, the dataset within the "upper-lower bound" limits is extracted as the optimal power curve data required for accurate power curve modeling. Several research articles have proposed different signal processing schemes for data correction, by using the aforementioned methods, a combination, or a totally different approach.…”

“…The main advantage of such an ensemble model is that different mechanisms can be employed simultaneously, in order to solve the same problem; hence, optimal solution can be achieved. Examples of such an approach can be found in [21,66], where typical ensemble methods were used for modeling and condition monitoring of wind turbines.…”