**Authors:** Paweł Knes, Phong B. Dao **Published:** _Energies_, 2024 **Link:** [MDPI Energies](https://doi.org/10.3390/en17205055) --- ### Summary This study presents a comparative analysis of different Machine Learning ([[ML]]) models and a Cointegration-based method for monitoring and detecting faults in wind turbines. It proposes a cascading monitoring framework that uses [[ML]] for long-term predictions and #Cointegration for short-term confirmation of anomalies, aiming to improve reliability and reduce false alarms. --- ### Key Points - **Machine Learning Models:** - Uses Artificial Neural Networks #ANN, Linear Regression #LR, Random Forest #RF, and Gradient Boosting Tree #GBT. - These models are effective for early fault detection but require large datasets and high computational costs. - RF and GBT were found to be the best performers for long-term predictions. - **Cointegration Method:** - Originating from econometrics, it identifies **long-term relationships** between turbine parameters (like wind speed, temperature, power generation). - Simpler and requires less data compared to [[ML]] but less effective for **early detection**. - Ideal for confirming **short-term anomalies** detected by [[ML]] models. - **Cascading Monitoring Framework:** - [[ML]] models provide **early fault predictions** (outer monitoring). - Cointegration **validates anomalies** flagged by [[ML]] (inner monitoring). - This combination reduces false alarms and pinpoints immediate risks. --- ### How It Works 1. **Data Collection:** SCADA data (Supervisory Control and Data Acquisition) from a 5-year wind turbine dataset. 2. **Training & Validation:** - [[ML]] models are trained on long-term data to predict faults. - Cointegration is used to detect anomalies in real-time. 3. **Monitoring:** - When an [[ML]] model flags a fault, the Cointegration model verifies the event before triggering an alarm. --- ### Performance Results - **[[ML]] Models:** Detected faults **38 days** before actual failure. - **Cointegration Model:** Detected faults **5.5 days** before failure. - **Combined Approach:** Reduced **false alarms** and improved **fault verification**. --- ### Strengths - Early detection of faults with [[ML]]. - Reliable short-term validation with #Cointegration. - Minimizes **false positives** and **unnecessary shutdowns**. - Lower **computational cost** for short-term monitoring. --- ### Limitations - [[ML]] models require **large datasets** and **high computation**. - Cointegration alone is not sufficient for very early detection. - Needs well-structured SCADA data for optimal performance.