# Detection of Wind Turbine Failures through Cross-Information between Neighbouring Turbines
###### Abstract
A small error, of the order of the error made in the training phase of the system, means that the system is working correctly. However, when systems deteriorate, measurements and calculations begin to diverge. Then, the error starts to grow and this increase provides an early signal of a possible failure. An increasingly popular and effective methodology for assessing the condition of turbine components are so-called normality models. They attempt to capture the relationship between a set of input variables and one or more target signals, which should be able to determine the condition of the analysed component. An important step in normality models is the filtering of the data to determine a subset of records that can be labelled as normal data. Event and alarm records are often used for this task. Next, a set of inputs and a target variable can be chosen, and an algorithm is fitted to the data. The goal is to infer the model that describes the normal operating conditions of the monitored component, and then use it to track deviations between the expected and observed behaviour of the tracked variable. This approach is quite common in the wide world of predictive maintenance, not only in wind turbine monitoring. One of the first, and most likely the most influential, application of normality models to wind turbine monitoring is presented in [5]. Neural networks are a popular choice due to their ability to model complex relationships in data; some examples are provided in [17; 18; 19]. Other authors have compared the performance of neural networks with standard machine learning algorithms, and regression models [9; 20] or alternative neural networks such as Extreme Learning Machines [4]. Normality models allow capturing the complex relationships that can exist between turbine operating parameters such as temperatures, pressures, etc., and external factors such as wind speed and ambient temperature. Improved modelling capability comes at the cost of reduced interpretability compared to signal trending methods. The tuning of the models, the definition of the best hyperparameters, and the architecture are not trivial and require knowledge of data modelling and machine/deep learning. Normality models in wind turbines work best when they model individual turbines [4; 21]. When a generic model is built for all the turbines in the wind farm, despite being of the same type and manufacturer, they always perform worse. The performance of the turbines depends very strongly on their location,when compared between wind farms, and significantly within the wind farm itself. In addition, when building models using SCADA data, some precautions must be taken. The main one is to train the normality model with correct data; that is, making sure that the turbine works properly in different operating regimes and preferably in all seasons of the year, being sure that no malfunctioning sections of the machine are introduced as they would bias and perturb the model. SCADA data is cheap to obtain and practically available on all farms, but has low latency (5-10 min), so it is appropriate to focus on slow time-varying targets. SCADA data are also characterised by outliers and missing data. The number of variables that the SCADA system captures is extremely high. In this paper, a way to determine the good performance of wind turbines is explored, by comparing the variables of nearby machines of the same wind-farm and at the same time instant, since it has been observed that, despite slight differences, the climatic conditions (wind, temperature, humidity, etc.) are very similar as they are geographically very close. When plotting them synchronised, the patterns are reproduced, as explained in Section 3.4. From here, a reference signal is calculated and used to compare the rest of the signals, extracting the signal difference between this reference signal and the (real) signals of each turbine.