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Wind power forecasting for the Villonaco wind farm
Fecha de publicación: Nov. 4, 2020

Autores:
Maldonado-Correa, J., Valdiviezo-Condolo, M., Viñan-Ludeña, M. S., Samaniego-Ojeda, C., & Rojas-Moncayo, M.
Resumen:

Wind energy is a non-programmable form of generation, hence, accurate and reliable wind energy prediction is of great importance for the efficient operation of wind farms. This article presents a study for the prediction of active power for the Villonaco Wind Farm (VWF), located in southern Ecuador at approximately 2700 m above sea level. Through the use of artificial neural networks, experimental tests are developed based on the models of Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) to obtain a hybrid model that fits the best characteristics of the individual models. Data from the active power SCADA (Supervisory Control and Data Acquisition) system for the years 2014 to 2018 are used to train and validate the models. Hybrid model is presented as the most appropriate option by the values obtained, viz., the mean absolute error (MAE), the mean squared error (MSE), and mean absolute percentage error (MAPE) that were 0.1365, 0.0974, and 144.26, respectively, outperforming to the others wind power forecast models.

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Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review
Fecha de publicación: June 17, 2020

Autores:
Maldonado-Correa, J., Martín-Martínez, S., Artigao, E., & Gómez-Lázaro, E.
Resumen:

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.

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Wind Energy Forecasting with Artificial Intelligence Techniques: A Review
Fecha de publicación: March 3, 2020

Autores:
Maldonado-Correa, J., Valdiviezo, M., Solano, J., Rojas, M., & Samaniego-Ojeda, C.
Resumen:

The World Wind Energy Association (WWEA) forecasts that installed wind capacity worldwide will reach 800 GW by the end of 2021. Because wind is a random resource, both in speed and direction, the short-term forecasting of wind energy has become an important issue to be investigated. In this paper, a Systematic Literature Review (SLR) on non-parametric models and techniques for predicting short-term wind energy is presented based on four research questions related to both already applied methodologies and wind physical variables in order to determine the state of the art for the development of the research project “Artificial intelligence system for the short-term prediction of the energy production of the Villonaco wind farm”. The results indicate that artificial neural networks (ANN) and support-vector machines (SVMs) were mainly used in related studies. In addition, ANNs are highlighted in comparison with other techniques of Wind Energy Forecasting.

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USO EFICIENTE DE ENERGÍA ELÉCTRICA EN ILUMINACIÓN PÚBLICA EN EL ECUADOR MEDIANTE LED.
Fecha de publicación: Dec. 14, 2019

Autores:
Muñoz, J., Rojas. M., & Barreto, R.
Resumen:

El estudio da guias para el cambio de luminarias de vapor de sodio por vapor de mercurio y en el futuro mediato luminarias LED por luminarias de vapor de sodio

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Wind power forecasting: A systematic literature review
Fecha de publicación: Dec. 6, 2019

Autores:
Maldonado-Correa, J., Solano, J. C., & Rojas-Moncayo, M.
Resumen:

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.

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