# Application of Artificial Intelligence in Wind Power Systems
## 6 Dangers of Using Artificial Intelligence (AI) in Wind Turbines  
The biggest risk in applying artificial intelligence (AI) to wind turbines can be seen as bias and discrimination in algorithms, which can lead to unpredictable and potentially harmful consequences. Other risks include cyberattacks, ethical issues, the complexity of the system and the question of liability in the event of accidents. Although the application of artificial intelligence is not considered a risk, it should be noted that it not only leads to higher electricity production, but also consumes a lot of electricity during its operation. * Algorithmic bias and hallucinations: AI systems lack contextual understanding and can produce \"hallucinations\"--inaccurate or fabricated results. For example, in wind turbines, this can manifest itself through faulty maintenance schedules based on biased models. Fault detection models can focus on common minor problems (e.g., cracks on the blade surface) while missing rare but critical failures such as gearbox bearing wear. A study using matching contrastive learning (MCL) found that traditional methods struggle with unbalanced SCADA data, requiring specialized techniques to improve minority class fault detection [85]. A study on brake wear prediction in a slewing system using LSTM and SVM showed that models without contextual knowledge of the mechanical interactions between brake pads and rotors generated false alarms, underestimating actual gearbox problems [82]. * Data dependency: AI systems depend on large amounts of data for learning and decision making. Inconsistent or non-standardized data from turbines (e.g., different OEM status codes, sensor errors) can lead to inaccurate predictions. For example, mislabeled SCADA data can cause AI to misinterpret turbine health, delaying maintenance or causing unnecessary repairs [83]. Synthetic data generation, which is used to compensate for limited real-world data, risks increasing bias if the synthetic samples do not represent the actual operating conditions of the turbine [95]. Incorrect conclusions can also be drawn if tests are performed in laboratory conditions that differ from actual field conditions. For example, a convolutional LSTM model for diagnosing bearing failure achieved 99.5% accuracy in laboratory tests, but in field conditions due to environmental noise misclassified normal vibrations as failures [85]. * Data imbalance and data quality problems: Artificial intelligence (AI) methods for wind energy applications often struggle with data imbalances and quality issues, especially when analyzing large supervisory control and data acquisition (SCADA) datasets that contain significantly more \"healthy\" turbine measurements than true fault indicators. This discrepancy causes conventional machine learning approaches to under-detect the rare fault conditions while disproportionately focusing on the majority (normal) class [85]. To make matters worse, inconsistent labeling practices among different turbine manufacturers and irregularities in sensor reporting further complicate data quality [23]. Various solutions have been developed to mitigate these problems. Data augmentation strategies, including the synthetic minority oversampling technique (SMOTE) and other generative methods, artificially enrich the minority class (fault) and thus improve the classifier's ability to detect infrequently occurring but critical fault types [85]. Robust outlier detection and noise reduction techniques can further improve data integrity. Sensor readings that deviate from plausible physical ranges are discarded or reconciled using expert knowledge, and labeling inconsistencies are resolved when data from different turbine models are integrated [23]. Incorporating physical or technical knowledge into feature engineering helps to isolate meaningful patterns in large amounts of predominantly normal data.