A Novel Framework for Condition-Based Maintenance and Performance Analysis Using Data-Driven Approaches

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.