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Energies
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Machine Learning Approaches to Power System Flexibility, Stability and Control...
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Machine Learning Approaches to Power System Flexibility, Stability and Control for Renewable Energy Penetration

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".

Deadline for manuscript submissions: closed (11 January 2024) | Viewed by 2085

Special Issue Editors

Dr. Mohammadreza Aghaei

E-Mail Website
Guest Editor
1. Department of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology (NTNU), 6009 Alesund, Norway
2. Solar Energy Engineering Program, Department of Sustainable Systems Engineering (INATECH), Albert Ludwigs University of Freiburg, 79110 Freiburg, Germany
Interests: energy transition; renewable energy; photovoltaics; smart grid; enabling technologies; artificial intelligence (AI); unmanned aerial vehicle (UAV)
Special Issues, Collections and Topics in MDPI journals
Dr. Aref Eskandari

E-Mail Website
Guest Editor
Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
Interests: renewable energy; smart grid; artificial intelligence (AI); big data analytics; condition monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing use of renewable energy sources, liberalized energy markets and most importantly, the integration of various monitoring, measuring and communication infrastructures into modern power system networks offer the opportunity to build a resilient and efficient grid. However, renewable generation sources, mainly wind and solar generation that are intermittent energy sources by nature, have been accompanied by their own challenges. They also bring about various threats of instability and security concerns in the form of cyberattacks, voltage instability, power quality (PQ) disturbance, etc. to the complex network.

It should be noted that along with smart grid development, modern power systems are also entering a "data-intensive" era, where a large body of data is collected through advanced sensing and communication technologies. This multi-scale dataset contains comprehensive information about the power system’s static and dynamic characteristics, renewable generation of energy, customers’ power consumption patterns, etc. If they are effectively used, enhanced situation awareness can be achieved. However, this requires the development of approaches for the optimal utilization of available data and their effective use in ensuring the timely obtainment of essential information about the system’s behaviour. Data-driven methods allow advanced data analytics to extract the system’s actual operating characteristics from the multi-scale data and turn them into practical information.

This Special Issue is devoted to the collection of state-of-the-art ideas in data analytics for power system stability analysis and control, and seeks to pave the way for smarter and more resilient power systems with a high level of renewable energy integration.

Dr. Mohammadreza Aghaei
Dr. Aref Eskandari
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • power system security
  • voltage and frequency management
  • power system stability
  • renewable resource forecasting techniques
  • energy consumption and demand forecast
  • power system flexibility
  • flexibility planning
  • protection scheme enhancement
  • power quality
  • real-time power system operation

Published Papers (2 papers)

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Research

17 pages, 5658 KiB  
Article
Remaining Useful Life Estimation Framework for the Main Bearing of Wind Turbines Operating in Real Time
by Januário Leal de Moraes Vieira, Felipe Costa Farias, Alvaro Antonio Villa Ochoa, Frederico Duarte de Menezes, Alexandre Carlos Araújo da Costa, José Ângelo Peixoto da Costa, Gustavo de Novaes Pires Leite, Olga de Castro Vilela, Marrison Gabriel Guedes de Souza and Paula Suemy Arruda Michima
Energies 2024, 17(6), 1430; https://doi.org/10.3390/en17061430 - 16 Mar 2024
Viewed by 671
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Machine Learning Approaches to Power System Flexibility, Stability and Control for Renewable Energy Penetration)
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15 pages, 1518 KiB  
Article
Wind Power Short-Term Time-Series Prediction Using an Ensemble of Neural Networks
by Tomasz Ciechulski and Stanisław Osowski
Energies 2024, 17(1), 264; https://doi.org/10.3390/en17010264 - 04 Jan 2024
Viewed by 742
Abstract
Short-term wind power forecasting has difficult problems due to the very large variety of speeds of the wind, which is a key factor in producing energy. From the point of view of the whole country, an important problem is predicting the total impact [...] Read more.
Short-term wind power forecasting has difficult problems due to the very large variety of speeds of the wind, which is a key factor in producing energy. From the point of view of the whole country, an important problem is predicting the total impact of wind power’s contribution to the country’s energy demands for succeeding days. Accordingly, efficient planning of classical power sources may be made for the next day. This paper will investigate this direction of research. Based on historical data, a few neural network predictors will be combined into an ensemble that is responsible for the next day’s wind power generation. The problem is difficult since wind farms are distributed in large regions of the country, where different wind conditions exist. Moreover, the information on wind speed is not available. This paper proposes and compares different structures of an ensemble combined from three neural networks. The best accuracy has been obtained with the application of an MLP combiner. The results of numerical experiments have shown a significant reduction in prediction errors compared to the naïve approach. The improvement in results with this naïve solution is close to two in the one-day-ahead prediction task. Full article
(This article belongs to the Special Issue Machine Learning Approaches to Power System Flexibility, Stability and Control for Renewable Energy Penetration)
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