Driven by the development of machine learning (ML) and deep learning techniques, prognostics and health management (PHM) has become a key aspect of reliability engineering research. With the recent rise in popularity of quantum computing algorithms and public availability of first-generation quantum hardware, it is of interest to assess their potential for efficiently handling large quantities of operational data for PHM purposes. This paper addresses the application of quantum kernel classification models for fault detection in wind turbine systems (WTSs). The analyzed data correspond to low-frequency SCADA sensor measurements and recorded SCADA alarm logs, focused on the early detection of pitch fault failures. This work aims to explore potential advantages of quantum kernel methods, such as quantum support vector machines (Q-SVMs), over traditional ML approaches and compare principal component analysis (PCA) and autoencoders (AE) as feature reduction tools. Results show that the proposed quantum approach is comparable to conventional ML models in terms of performance and can outperform traditional models (random forest, k-nearest neighbors) for the selected reduced dimensionality of 19 features for both PCA and AE. The overall highest mean accuracies obtained are 0.945 for Gaussian SVM and 0.925 for Q-SVM models.

The prognosis of wind turbine failures in real operating conditions is a significant gap in the academic literature and is essential for achieving viable performance parameters for the operation and maintenance of these machines, especially those located offshore. This paper presents a framework for estimating the remaining useful life (RUL) of the main bearing using regression models fed operational data (temperature, wind speed, and the active power of the network) collected by a supervisory control and data acquisition (SCADA) system. The framework begins with a careful data filtering process, followed by creating a degradation profile based on identifying the behavior of temperature time series. It also uses a cross-validation strategy to mitigate data scarcity and increase model robustness by combining subsets of data from different available turbines. Support vector, gradient boosting, random forest, and extra trees models were created, which, in the tests, showed an average of 20 days in estimating the remaining useful life and presented mean absolute error (MAE) values of 0.047 and mean squared errors (MSE) of 0.012. As its main contributions, this work proposes (i) a robust and effective regression modeling method for estimating RUL based on temperature and (ii) an approach for dealing with a lack of data, a common problem in wind turbine operation. The results demonstrate the potential of using these forecasts to support the decision making of the teams responsible for operating and maintaining wind farms.

Accurate wind power forecasting is essential to optimize wind farm operations and ensure the stable integration of renewable energy into the grid. This study explores Transformer-based architectures to address the challenges of wind variability and temporal dependencies in short-term forecasting. A sensitivity analysis on model architecture is conducted, incorporating Time2Vec—a temporal encoding technique that captures complex temporal patterns. In addition, we replace the standard FullAttention mechanism with ProbSparse Attention, FlowAttention and FlashAttention, resulting in the Informer, Flowformer and Flashformer models, to improve computational efficiency while maintaining predictive accuracy. The novelty of this work lies in applying FlashAttention within the context of wind power forecasting and integrating Time2Vec into the Informer, Flowformer and Flashformer models. We propose four architectures—T2V-Transformer, T2V-Informer, T2V-Flowformer, and T2V-Flashformer—and compare them against benchmark models: Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and DLinear. Real-world data from a wind farm in the Northeast of Brazil is used under two forecasting scenarios. In Scenario A, T2V-Transformer, T2V-Informer and T2V-Flashformer achieved Improvement over Reference RMSE (IoR-RMSE) scores of 17.73%, 17.59% and 16.67%, respectively. In Scenario B, T2V-Flowformer and T2V-Flashformer reached 27.84% and 27.45%, respectively. These results confirm the effectiveness of the proposed models in advancing short-term wind power forecasting.

Northeastern Brazil (NEB) has a high potential for wind energy generation, making it a strategic area for the development of this renewable source. However, the region’s complex wind regime, driven by interactions between large-scale atmospheric systems, local circulations, and coastal topography, presents significant challenges for weather forecasting and wind energy applications. Despite this, detailed assessments of forecast performance using mesoscale models remain limited. The main objective was to develop an efficient strategy that enables satisfactory results by optimizing data assimilation, land use and topography information as well as improvements in physical parameterizations and post-processing, optimizing computational effort. Forecasting conducted during the year 2020 were validated with data from 20 anemometric measurement towers (AMTs), located at strategic points across various wind power complexes. The model’s performance was evaluated using statistical metrics such as MBE, MAE, nRMSE, standard deviation ratio, and correlation. Additionally, the impact of bias removal was assessed using two approaches: one that eliminates the mean error per forecasted time step and another employing artificial intelligence for bias removal training. The results revealed distinct characteristics for each analyzed location, with errors of diverse nature due to the local nuances of the measurements. However, both bias removal approaches showed significant improvements in wind characterization across all complexes.

In the framework of the wind energy network for distributed generation in urban environments for most South American countries, wind resource assessment studies have been carried out in activities to establish a suitable methodology to assess the wind potential in urban environments. Some methodologies are already published in research studies, and the wind tunnel is the most accurate solution to obtain insight into the wind resource when measurements are unavailable, which is the most frequent case. Nevertheless, besides its validity, one cannot disregard the high cost of producing a scaled urban model and access to a wind tunnel. In this sense, this paper compares results from a wind tunnel experiment and different numerical modeling approaches. Two commercial models (WindSim and Wasp Engineering) and one open-source CFD code (OpenFOAM) are used. The results from the modeling simulation concluded that CFD models could achieve lower deviations for the mean wind speed and turbulence intensity when compared with non-CFD models. With such results, CFD modeling is a promising tool for reliably evaluating wind potential in urban environments.

A maioria dos métodos de previsão de potência de centrais fotovoltaicas (FV) utiliza como entrada previsões de temperatura ambiente ou de temperatura da célula. Este trabalho apresenta uma metodologia para previsão de temperatura ambiente como time step horário e um horizonte máximo de previsão (H) de 24h . Trata-se de uma metodologia simples e univariada que utiliza modelos autorregressivos(AR). Foi realizado um estudo para definição da ordem dos modelos AR utilizando dentre outras coisas, função de autocorrelação parcial e o RMSE (Root Mean Square Error) dos modelos de diversas ordens. Duas bases de dados foram utilizadas para esse fim, a de Petrolina-PE (1 ano de calibração e 1 de validação) e a de Remanso-BA (3 anos de calibração e 2 anos de validação). Os erros médios provocados pelos modelos AR obtiveram uma diminuição sutil na base de dados de Remanso em relação à de Petrolina. Em Petrolina, as previsões horárias para horizontes de até 24h à frente atingiram erros médios entre 0,6°C e 1,59°C, o que é considerado satisfatório quando comparado com modelos de outros autores. Os modelos AR também superaram o desempenho da Persistência em todos os horizontes testados. Após uma análise de sensibilidade, estimou-se que os erros médios da temperatura prevista pelos modelos AR provocariam um desvio médio nas previsões da saída da central FV de 1,17%, sendo por isso considerados adequados para este fim.