Operation and maintenance (O&M) activities represent a significant share of the total expenditure of a wind farm. Of these expenses, costs associated with unexpected failures account for the highest percentage. Therefore, it is clear that early detection of wind turbine (WT) failures, which can be achieved through appropriate condition monitoring (CM), is critical to reduce O&M costs. The use of Supervisory Control and Data Acquisition (SCADA) data has recently been recognized as an effective solution for CM since most modern WTs record large amounts of parameters using their SCADA systems. Artificial intelligence (AI) techniques can convert SCADA data into information that can be used for early detection of WT failures. This work presents a systematic literature review (SLR) with the aim to assess the use of SCADA data and AI for CM of WTs. To this end, we formulated four research questions as follows: (i) What are the current challenges of WT CM? (ii) What are the WT components to which CM has been applied? (iii) What are the SCADA variables used? and (iv) What AI techniques are currently under research? Further to answering the research questions, we identify the lack of accessible WT SCADA data towards research and the need for its standardization. Our SLR was developed by reviewing more than 95 scientific articles published in the last three years.

The growth of installed wind power worldwide and its significant contribution to the energy market is mainly due to the evolution of wind turbines (WTs) and their ability to withstand a wide range of dynamic loads. WT failures can be costly and lead to extended downtime. Early detection of such failures is critical in reducing the costs associated with operation and maintenance (O&M) tasks and unscheduled shutdowns of WTs. This paper applies two Deep Learning (DL) models based on the Transformer model to predict failures in the IGBT module of WTs at an onshore wind farm in Ecuador. To this end, SCADA (Supervisory Control and Data Acquisition) operational and alarm data are used, together with the maintenance record (MR). These data are analyzed and processed, applying different feature selection methods. The results show that the two proposed models perform well, with high accuracy and an approximate prediction of 4.25 months before failure occurrence. The results are promising due to the possibility of using SCADA data for early and accurate identification of faults in different components of WTs.

Accurate and reliable prediction of wind energy in the short term is of great importance for the efficient operation of wind farms. One of the procedures to search for, summarize, organize and synthesize existing information is a systematic literature review. In this article, we present a systematic literature review on the predictive models of wind energy, aiming to establish the baseline for the development of a short-term wind energy prediction model that employs artificial intelligence tools to be applied in the Villonaco Wind Power Plant. Following a systematic method of literature review, we have established 4 research questions and 37 scientific articles that answer the said questions. Consequently, we found that artificial neural networks are used more frequently for the prediction of wind energy, which highlights its use in the studies consulted for the results achieved compared with that of other methods.

In recent years, the adverse effects of climate change have increased rapidly worldwide, driving countries to transition to clean energy sources such as solar and wind. However, these energies face challenges such as cloud cover, precipitation, wind speed, and temperature, which introduce variability and intermittency in power generation, making integration into the interconnected grid difficult. To achieve this, we present a novel hybrid deep learning model, CEEMDAN-CNN-ATT-LSTM, for short- and medium-term solar irradiance prediction. The model utilizes complete empirical ensemble modal decomposition with adaptive noise (CEEMDAN) to extract intrinsic seasonal patterns in solar irradiance. In addition, it employs a hybrid encoder-decoder framework that combines convolutional neural networks (CNN) to capture spatial relationships between variables, an attention mechanism (ATT) to identify long-term patterns, and a long short-term memory (LSTM) network to capture short-term dependencies in time series data. This model has been validated using meteorological data in a more than 2400 masl region characterized by complex climatic conditions south of Ecuador. It was able to predict irradiance at 1, 6, and 12 h horizons, with a mean absolute error (MAE) of 99.89 W/m² in winter and 110.13 W/m² in summer, outperforming the reference methods of this study. These results demonstrate that our model represents progress in contributing to the scientific community in the field of solar energy in environments with high climatic variability and its applicability in real scenarios.