Search Results (10)

Wildfires pose a growing threat to Mediterranean ecosystems. This study employs advanced classification techniques for shrub fractional cover mapping from satellite imagery in a fire-prone landscape in Quinta da França (QF), Portugal. The study area is characterized by fine-grained heterogeneous land cover and a Mediterranean climate. In this type of landscape, shrub encroachment after land abandonment and wildfires constitutes a threat to ecosystem resilience—in particular, by increasing the susceptibility to more frequent and large fires. High-resolution mapping of shrub cover is, therefore, an important contribution to landscape management for fire prevention. Here, a 20 cm resolution land cover map was used to label 10 m Sentinel-2 pixels according to their shrub cover percentage (three categories: 0%, >0%–50%, and >50%) for training and testing. Three distinct algorithms, namely Support Vector Machine (SVM), Artificial Neural Networks (ANNs), and Random Forest (RF), were tested for this purpose. RF excelled, achieving the highest precision (82%–88%), recall (77%–92%), and F1 score (83%–88%) across all categories (test and validation sets) compared to SVM and ANN, demonstrating its superior ability to accurately predict shrub fractional cover. Analysis of confusion matrices revealed RF’s superior ability to accurately predict shrub fractional cover (higher true positives) with fewer misclassifications (lower false positives and false negatives). McNemar’s test indicated statistically significant differences (p value < 0.05) between all models, consolidating RF’s dominance. The development of shrub fractional cover maps and derived map products is anticipated to leverage key information to support landscape management, such as for the assessment of fire hazard and the more effective planning of preventive actions. Full article

To fully harness the potential of wind turbine systems and meet high power demands while maintaining top-notch power quality, wind farm managers run their systems 24 h a day/7 days a week. However, due to the system’s large size and the complex interactions of its many components operating at high power, frequent critical failures occur. As a result, it has become increasingly important to implement predictive maintenance to ensure the continued performance of these systems. This paper introduces an innovative approach to developing a head-mounted fault display system that integrates predictive capabilities, including deep learning long short-term memory neural networks model integration, with anomaly explanations for efficient predictive maintenance tasks. Then, a 3D virtual model, created from sampled and recorded data coupled with the deep neural diagnoser model, is designed. To generate a transparent and understandable explanation of the anomaly, we propose a novel methodology to identify a possible subset of characteristic variables for accurately describing the behavior of a group of components. Depending on the presence and risk level of an anomaly, the parameter concerned is displayed in a piece of specific information. The system then provides human operators with quick, accurate insights into anomalies and their potential causes, enabling them to take appropriate action. By applying this methodology to a wind farm dataset provided by Energias De Portugal, we aim to support maintenance managers in making informed decisions about inspection, replacement, and repair tasks. Full article

In recent times, renewable energy sources have gained considerable vitality due to their inexhaustible resources and the detrimental effects of fossil fuels, such as the impact of greenhouse gases on the planet. This article aims to be a supportive tool for the development of research in the field of artificial intelligence (AI), as it presents a solution for predicting photovoltaic energy production. The basis of the AI models is provided from two data sets, one for generated electrical power and another for meteorological data, related to the year 2017, which are freely available on the Energias de Portugal (EDP) Open Project website. The implemented AI models rely on long short-term memory (LSTM) neural networks, providing a forecast value for electrical energy with a 60-min horizon based on meteorological variables. The performance of the models is evaluated using the performance indicators MAE, RMSE, and R², for which favorable results were obtained, with particular emphasis on forecasts for the spring and summer seasons. Full article

Fault alarm time lag is one of the difficulties in fault diagnosis of wind turbine generators (WTGs), and the existing methods are insufficient to achieve accurate and rapid fault diagnosis of WTGs, and the operation and maintenance costs of WTGs are too high. To invent a new method for fast and accurate fault diagnosis of WTGs, this study constructs a stacking integration model based on the machine learning algorithms light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), and stochastic gradient descent regressor (SGDRegressor) using publicly available datasets from Energias De Portugal (EDP). This model is automatically tuned for hyperparameters during training using Bayesian tuning, and the coefficient of determination (R²) and root mean square error (RMSE) were used to evaluate the model to determine its applicability and accuracy. The fitted residuals of the test set were calculated, the Pauta criterion (3σ) and the temporal sliding window were applied, and a final adaptive threshold method for accurate fault diagnosis and alarming was created. The model validation results show that the adaptive threshold method proposed in this study is better than the fixed threshold for diagnosis, and the alarm times for the GENERATOR fault type, GENERATOR_BEARING fault type, and TRANSFORMER fault type are 1.5 h, 5.8 h, and 3 h earlier, respectively. Full article

Electricity price forecasting has been a booming field over the years, with many methods and techniques being applied with different degrees of success. It is of great interest to the industry sector, becoming a must-have tool for risk management. Most methods forecast the electricity price itself; this paper gives a new perspective to the field by trying to forecast the dynamics behind the electricity price: the supply and demand curves originating from the auction. Given the complexity of the data involved which include many block bids/offers per hour, we propose a technique for market curve modeling and forecasting that incorporates multiple seasonal effects and known market variables, such as wind generation or load. It is shown that this model outperforms the benchmarked ones and increases the performance of ensemble models, highlighting the importance of the use of market bids in electricity price forecasting. Full article

Nowadays, the energy sector is experiencing a profound transition. Among all renewable energy sources, wind energy is the most developed technology across the world. To ensure the profitability of wind turbines, it is essential to develop predictive maintenance strategies that will optimize energy production while preventing unexpected downtimes. With the huge amount of data collected every day, machine learning is seen as a key enabling approach for predictive maintenance of wind turbines. However, most of the effort is put into the optimization of the model architectures and its parameters, whereas data-related aspects are often neglected. The goal of this paper is to contribute to a better understanding of wind turbines through a data-centric machine learning methodology. In particular, we focus on the optimization of data preprocessing and feature selection steps of the machine learning pipeline. The proposed methodology is used to detect failures affecting five components on a wind farm composed of five turbines. Despite the simplicity of the used machine learning model (a decision tree), the methodology outperformed model-centric approach by improving the prediction of the remaining useful life of the wind farm, making it more reliable and contributing to the global efforts towards tackling climate change. Full article

The objective of this work is to propose and apply a methodology to obtain the cost of industrial process shutdowns due to voltage sag and short interruption. A field survey, aided by a specific questionnaire, was carried out in several industries connected to medium voltage networks, in the states of Espírito Santo and São Paulo in Brazil. The results obtained were the costs per event and the costs per demand in a total of 33 companies in 12 different types of activities. It is noteworthy that this survey brings a relevant technical contribution to the electricity sector, helping to fill, even partially, an existing gap in both national and international literature. Full article

Increasingly demanding environmental regulations are forcing companies to reduce their impacts caused by their activity while defending the economic viability of their manufacturing processes, especially energy and carbon-intensive ones. Therefore, these challenges must be addressed by posing optimization problems that involve several objectives simultaneously, corresponding to different conditions, and often conflicting between. In this study, the residual gases of an integral steel factory were evaluated and modeled with the goal of developing an optimization problem considering two opposing objectives: CO₂ emissions and profit. The problem was first approached in a mono-objective manner, optimizing profit through Mixed Integer Linear Programming (MILP), and then was extended to a bi-objective problem solved by means of the ε-constraint method, to find the Pareto front relating profit and CO₂ emissions. The results show that multiobjective optimization is a very valuable resource for plant managers’ decision-making processes. The model makes it possible to identify inflection points from which the level of emissions would increase disproportionately. It gives priority to the consumption of less polluting fuels. The model also makes it possible to make the most of temporary buffers such as the gas holders, adapting to the hourly price of the electricity market. By applying this method, CO₂ emissions decrease by more than 3%, and profit amounts up to 14.8% compared to a regular case under normal operating conditions. The sensitivity analysis of the CO₂ price and CO₂ constraints is also performed. Full article

This paper proposes a methodology for establishing base values for short-term voltage variation indices. The work is focused on determining which variables best describe the disturbance and based on that, establish clusters that allow a more adequate definition of base values for the indices. To test the proposed methodology, real data from 19 distribution systems belonging to a Brazilian electricity utility were used and consequently the index presented in the country standard was considered. This study presents a general methodology that can be applied to all distribution systems in Brazil and could serve as a guide for the regulatory agencies in other countries, to establish base values for their indices. Furthermore, the objective is to show through the results that, with the database used is possible to establish clusters of distribution systems related to the voltage sag and with these establish a base impact factor, distinct for each distribution system. Full article

Off-gas is one of the by-products of the steelmaking process. Its potential energy can be transformed into heat and electricity by means of cogeneration. A case study using a coke oven and Linz–Donawitz converter gas is presented. This work addresses the gas allocation problem for a cogeneration system producing steam and electricity. In the studied facility, located in northern Spain, the annual production of the plant requires 95,000 MWh of electrical energy and 525,000 MWh of thermal energy. The installed electrical and thermal power is 20.4 MW and 81 MW, respectively. A mixed integer linear programming model is built to optimize gas allocation, thus maximizing its benefits. This model is applied to a 24-h scenario with real data from the plant, where gas allocation decision-making was performed by the plant operators. Application of the model generated profit in a scenario where there were losses, increasing benefits by 16.9%. A sensitivity analysis is also performed. The proposed model is useful not only from the perspective of daily plant operation but also as a tool to simulate different design scenarios, such as the capacity of gasholders. Full article