A review of SCADA-based condition monitoring for wind turbines via artificial neural networks

In recent years, the supervisory control and data acquisition (SCADA) data has gained increasing research attention in the field of wind turbine condition monitoring. Artificial intelligence (AI) techniques have been widely applied to address condition monitoring challenges, and artificial neural networks (ANNs), recognized as a foundational component of modern AI, have proven to be particularly effective tools. Wind turbine condition monitoring focuses on analyzing the operational parameters of turbines to realize early fault detection, precise diagnostics, and accurate prognostics, thereby mitigating the risk of catastrophic faults, enhancing system reliability, and improving wind farm operational efficiency. Due to inherent issues in raw SCADA data, including missing values and abnormal data, preprocessing steps such as data cleaning are critical before feeding the data into ANN models. Additionally, the choice of ANN architecture typically depends on the specific requirements of condition monitoring tasks (e.g., fault detection, diagnosis, or prediction/prognosis) and the characteristics of SCADA datasets such as imbalance problem of fault samples. Hence, current research with respect to wind turbine condition monitoring generally follows two approaches: (1) utilizing classification models to identify fault types at specific time points, and (2) employing regression models to construct normal behavior models (NBMs) or track and predict continuous key performance indicators. This survey systematically reviews SCADA-based wind turbine condition monitoring methods within five years, emphasizing neural networks as key approaches, and structures the discussion around three core aspects: data preprocessing, classification models, and regression models. Moreover, the comparative strengths, capabilities, and limitations of various ANNs in each link are discussed. By providing an in-depth analysis, this paper aims to offer theoretical and practical insights to support the further development of condition monitoring technologies for wind turbines.