# Detection of Wind Turbine Failures through Cross-Information between Neighbouring Turbines
###### Abstract  
In this paper, the time variation of signals from several SCADA systems of geographically closed turbines are analysed and compared. When operating correctly, they show a clear pattern of joint variation. However, the presence of a failure in one of the turbines causes the signals from the faulty turbine to decouple from the pattern. From this information, SCADA data is used to determine, firstly, how to derive reference signals describing this pattern and, secondly, to compare the evolution of different turbines with respect to this joint variation. This makes it possible to determine whether the behaviour of the assembly is correct, because they maintain the well-functioning patterns, or whether they are decoupled. The presented strategy is very effective and can provide important support for decision making in turbine maintenance and, in the near future, to improve the classification of signals for training supervised normality models. In addition to being a very effective system, it is a low computational cost strategy, which can add great value to the SCADA data systems present in wind farms. wind turbine; fault diagnosis; renewable energy; feature engineering; normal behaviour models +
Footnote †: journal: Applied  
Typically, turbines have a Supervisory Control and Data Acquisition (SCADA) system that provides a range of data to monitor the status of subsystems. More specifically, a SCADA system gathers data from the wind turbines' systems and subsystems, and analyses and visualises data. Operators can view important measurements such as temperature, rotation speed, power consumption, etc., without having to visit each wind turbine on site. A typical SCADA architecture has the following five main elements: (i) Human Machine Interface (HMI) software for visualisation, which is a dashboard through which operators can interact with a WT, system, or device, and track real-time. These user interfaces allow full remote control of the equipment. (ii) Historian software, which are responsible for logging and storing asset data allowing operators to reference historical trends for analysis and reporting by providing a complete picture of their operations. (iii) Supervisory system, the core of SCADA systems, are servers and software that gather sensor data and send control commands to connected field devices. The supervisory computer also communicates with the human-machine interfaces to visualize real-time data for users. (iv) Communication infrastructure, which enables data transmission between the programmable logic controllers (PLC), remote terminal units (RTU), and the master SCADA system. (v) Finally, the remote terminal units and programmable logic controllers, which are the physical devices that both gather telemetry data from the wind turbines and execute control programs. Sensors placed in different parts of the WT are responsible for collecting this data, which is obtained through statistics such as minimum value, maximum value, average value, and standard deviation of various physical measurements, such as temperature, pressure, voltage, current, etc., in different parts of the subsystems. Over the past decade, the importance of SCADA data for predictive maintenance and monitoring has increased significantly. In [5; 6; 7; 8] some of the first attempts to use SCADA data for turbine condition monitoring can be seen. The way to analyse and extract information has improved significantly since the early days. Several algorithms are available in the literature to assess the states of all principal components using different approaches based on statistical analysis, machine learning, and deep learning [9; 10; 11]. SCADA benefits from the highly desirable characteristics of wide availability, highly standardized formats, and low cost. Supervised learning and classification problems require complex pre-processing pipelines for data preparation. First, reliable data labels must be assigned.