Wind turbine generators (WTGs) are one of the most widely used sources of renewable energy currently available. To accurately predict their production and quickly notice any anomalies, it is important to analyze the data produced by these turbines to understand their behavior and patterns.
The purpose of this thesis is to create a data-driven digital twin of a wind turbine generator capable of simulating its ideal behavior. To carry out this task, the model receives input data of environmental variables, including wind speed and ambient temperature, and produces output values of parameters that a turbine should have under ideal conditions, including produced power, rotor speed, and more. The digital twin serves as a reference model that can be used as a comparison metric for the real turbines to evaluate their real-time performance and verify that the turbine is working properly by comparing the parameters of the internal components.
The work was carried out in collaboration with the Turin-based company Sirius s.r.l. and exploits data provided by the company itself and collected at some wind farms in southern Italy. The data is acquired through Supervisory Control And Data Acquisition (SCADA) systems installed on the turbines with the aim of monitoring and collecting data both on the environment and on the internal components of the turbine.
The work is structured in several parts: the initial part is characterized by data extraction and dataset creation. The data is in the form of a ten-minute average and is taken from turbines of the same model belonging to the same wind farm. Subsequently, a significant work was done on filtering the data with the goal of keeping only the data related to moments in which the behavior of the turbine can be defined as ideal. In this way, the algorithms can be trained only with ideal data and can adequately learn its trends without being misled by other non-ideal data. To obtain this result, multiple filters have been used considering both environmental and turbine variables, also using algorithms such as the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for outlier removal. Finally, several models have been created with the filtered data using different algorithms from both machine learning and deep learning, trying many combinations of inputs and outputs. Specifically, the Feedforward Neural Network (FNN), Support Vector Regression (SVR), and Gated Recurrent Unit (GRU) were tested.
The tests can be divided into two parts: the first part is characterized by the fact that the algorithms are trained on multiple turbines and tested on others never previously seen by the algorithm. Instead, the second part concerns some variables representing the temperature of the internal components of the turbine, whose behavior is highly variable from turbine to turbine, even if they belong to the same model. In these cases, further studies were needed, leading to alternative solutions. In both cases, satisfactory results were achieved.

Over the past decade, wind energy has become increasingly significant in the global energy sector. Nonetheless, operation and maintenance (O&M) account for at least one-third of the overall energy generation cost. Condition-based maintenance (CBM) provides a remedy for this issue: instead of scheduling maintenance, it monitors turbine components and performs maintenance only when warnings of possible malfunctions are provided.
All strategies related to fault detection and diagnosis of wind turbine generators (WTGs) can be categorized as model-based approaches and data-driven approaches. Model-based techniques rely mostly on a precise mathematical model of the WTG and its subsystems. In contrast, data-driven systems do not require physical or exact mathematical models but infer the defect detection system from observed sensor data. The latter techniques have shown to be particularly successful in recent years for modeling complex interactions associated with wind turbines. However, the existence of various nonlinearities in the examined issues and measurement noise requires the adoption of complex and robust algorithms.
This thesis proposes a framework for data-driven condition-based maintenance: the objective of this work is to develop anomaly and failure detection algorithms, that can be later used to provide maintenance on condition. To this end, an unsupervised learning method is provided, involving several feedforward (FNN) or recurrent (RNN) type auto-encoder (AE) neural network models.
The dataset of this work was provided by Sirius s.r.l., a partner of important companies in the world of renewable energies. The SCADA data, belonging to various wind farms located in southern Italy, is collected every 10 minutes from mechanical and thermic sensors measurements. The considered problem also includes different turbine designs, with distinct geometrical and mechanical features.
In the process, data from SCADA systems is acquired and clustered based on WTGs performances, relying on key performance indicators, states of the turbines, and alarms. The best-performing time sequences are then selected as inputs for the subsequent training phase. In an unsupervised learning manner, several AE models are trained in a multivariate time series reconstruction task. During this phase, the models learn a robust latent representation of the time series key features. When used on unseen data, the algorithm will reconstruct the provided input sequences and the reconstruction error is then analyzed for anomaly detection.
In this study, the different autoencoder models will then be exposed to different regularization approaches, such as dropout and de-noise autoencoder (DAE), to evaluate the different robustness of the models produced. The various AE architectures are then tested in a simulated benchmark environment, in which anomalies, noises, and faulty behaviors are injected in order to be detected. The most promising models are then employed in a real-world test case, where previously labeled WTG critical events must be detected. The objective of the analysis will not only be to detect adverse events, but also to correctly identify the measures and subsystems implicated in the anomalies.
With this study, the efficacy of data-driven AI-powered ways to acquire evaluations on the existence and nature of anomalies has been demonstrated. Also, the efficacy of the method permits an evaluation of the performance of wind farms and their subsystems.