Wind energy is becoming a common source of renewable energy in the world. Wind turbines are increasing in number, both for onshore and offshore applications. One challenge with wind turbines is in detecting anomalies that cause their breakdown. Due to the complex nature of the wind turbine assembly, it is quite an extensive process to detect causes of malfunctions in the system. This study uses the Mahalanobis distance (MD) to detect anomalies in wind turbine operation, using SCADA alarm data as a comparison. Different predictive models were generated as the bases for analyses in MD computations. Using the SCADA alarm data as a reference, trend patterns that deviated from the threshold value were compared. Results showed that the MD could be used to detect anomalies within a group of data sets, with behaviors learned based on the model used. A large portion of those data sets deviated from the threshold level, corresponding to serious alarms in the SCADA data. We concluded that the MD can detect anomalies in different wind turbine components, based on this study. MD analysis of models can be used in conditions monitoring systems of wind turbines.

As wind energy assumes greater importance in remote and offshore locations, an effective and reliable condition monitoring system (CMS) has become necessary for wind turbines (WTs). Conventional CMSs used in the power generation industry have been applied to WTs commercially. However, the operating environment of a WT is much different from that of a power plant. Moreover, current CMSs require the deployment of various sensors and computationally intensive analysis techniques. An empirical and low-cost CMS against shaft imbalance faults and generator circuit faults is proposed in this work. The diagnostic process of the CMS is merely based on generator outputs. Since the air gap between the rotor and the stator of a generator is limited by rigid bearings, diagnosis rules for shaft imbalance faults are formulated on the basis of experimental results with the aid of a specially designed WT simulation platform. Once a test at a specified speed has been performed, the error limits of the diagnosis for generator outputs can be determined. The proposed fault recognition procedure is practical and easy to conduct. The efficacy of the proposed CMS against four types of fault, namely, low-speed shaft imbalance, high-speed shaft imbalance, and short circuit and open circuit of a generator, has been validated practically on the WT simulation platform.