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.

Wind energy has grown significantly over the last decade. With this, various improvements in the design of the wind turbine are geared towards increasing the reliability of several components. Wind turbulence has a huge effect on the fatigue loading of wind turbines considered in the design. Several monitoring methodologies, such as turbulence intensity analysis, are used to identify wind turbulence. In this paper, a method based on machine learning techniques and data from Supervisory Control and Data Acquisition (SCADA) systems is described. Five machine learning models are generated and compared in this study with the use of the operational data from the SCADA of wind turbines in a single wind farm. Results showed that the model based on Linear Regression in terms of a quadratic hyperparameter has lesser errors compared to the other models that were generated. Each parameter used in the creation of the model affects its performance. Observations in the nacelle system also showed higher errors due to the relationship between rotor speed and the blade angle. The rotor performance is mostly influenced by wind turbulence as the variation in wind speeds and rotational speeds have a certain correlation. Based on the results, it can be concluded that the use of SCADA data in generating turbulence models provides key insights into the relation of the turbulence intensity to the various components. It can be used as the basis for developing turbulence monitoring models that could help improve the design and operation of wind turbines.

This paper presents a performance analysis of predictive models for the generator module which can be used as a reference for improvement in the condition monitoring system using wind turbines in a wind farm in Taiwan. With the generator being a critical component prone to failures, it is important to perform data analysis on its parameters that could be used for condition monitoring. The main innovative feature in this framework is the conduct of performance analysis before the development of the condition monitoring system. Also, the consistency of the performance between the different wind turbines in the wind farm is evaluated. The predictive models are generated using the neural network algorithm with a different combination of parameters from the SCADA system. The correlation of the parameters as well as the mean square error of the predictive models were then computed for analysis. Results showed that pairing of input parameters with a higher correlation to the output parameter would give better performance for the predictive model. Furthermore, the performance of the different models was consistent throughout the different wind turbines in the wind farm which indicates that the same model can be developed and used for wind turbines belonging to the same wind farm. Employing a preliminary performance analysis of different combinations of component parameters could help in optimizing predictive models for condition monitoring.

Main bearing failures are among the most critical and costly faults in wind turbines due to prolonged downtime and complex replacement procedures, particularly in direct-drive configurations where the main bearing and the generator bearing are functionally the same component. Unlike traditional geared turbines, which use a separate, high-speed generator bearing, the direct-drive architecture relies on a single, large, integrated bearing. To reduce reliance on expensive vibration monitoring, this study introduces a SCADA-based predictive framework for early fault detection. After data cleaning, six predictors of generator bearing temperature were identified using Pearson correlation. Regression models—Linear, Stepwise Linear, Fine Tree, and Ensemble Bagged Trees—were tested in MATLAB’s Regression Learner. Healthy turbines showed stable thermal behavior within prediction bounds, while faulty ones displayed persistent deviations beyond ±2 standard deviations, signaling degradation. A condition-based maintenance plan was developed using three weekly indicators combined into a percentile-based monthly health score. This scoring method highlights turbines with sustained abnormal behavior, even when temperature fault records alone do not. The framework demonstrates that SCADA data can reliably detect main bearing faults, enabling turbine health ranking and more effective maintenance prioritization.