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Energies
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Implementation of Machine Learning in Sustainable Electric Power Applications
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Implementation of Machine Learning in Sustainable Electric Power Applications

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Sustainable Energy".

Deadline for manuscript submissions: closed (25 July 2023) | Viewed by 4292

Special Issue Editors

Dr. Lauri Kütt

E-Mail Website
Guest Editor
Department of Electrical Power Engineering and Mechatronics, School of Engineering, Tallinn University of Technology, Tallinn, Estonia
Interests: fundamentals of electrical engineering; electromagnetic compatibility and electromagnetic fields; electric power quality and supply reliability
Dr. Noman Shabbir

E-Mail Website
Guest Editor
FinEst Centre for Smart Cities, Tallinn University of Technology, 19086 Tallinn, Estonia
Interests: renewable energy systems; energy management; energy forecasting energy flexibility; AI applications in energy systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the coming developments, it will be critical to utilize our resources as intelligently as possible, and to make renewable energy resources (RESs) the main providers of commercial energy. Direct implementation of this policy will see more efficient devices built, operated by and/or generating electric energy. Remaining challenges include the expected exponential increase in the penetration of renewable energy resources (RESs) in electric grids. The systems built require better state awareness more flexibility in their operation. For example, the high intermittency of RESs calls for more adaptability in the power grid, as well as more response to successful demand management. It has been shown that these complex multivariate targets require rather sophisticated controls for their successful implementation.

Machine learning (ML) methods may be a strategy for realizing this control. Tailored to process through stochastic large datasets, ML and deep learning techniques have recently received a great deal of attention. For example, ML can provide tools for better forecasting of the state to come, and thus help in the better management of grid resources and providing flexibility in the grid. These ML algorithms could be used for residential load forecasting, PV and wind energy generation forecasting, flexibility assessment, condition monitoring etc. in power systems applications such as motors and other electrical equipment.

This Special Issue covers all recent advances in machine learning and deep learning implementations for electric power applications through supervised, unsupervised, and reinforcement learning methods.

Dr. Lauri Kütt
Dr. Noman Shabbir
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

  • renewable energy systems
  • renewable energy integration 
  • renewable energy forecasting
  • load forecasting
  • flexibility
  • demand response
  • condition monitoring
  • fault detection
  • machine learning
  • deep learning

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Published Papers (2 papers)

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17 pages, 1144 KiB  
Article
Optimization with Neural Network Feasibility Surrogates: Formulations and Application to Security-Constrained Optimal Power Flow
by Zachary Kilwein, Jordan Jalving, Michael Eydenberg, Logan Blakely, Kyle Skolfield, Carl Laird and Fani Boukouvala
Energies 2023, 16(16), 5913; https://doi.org/10.3390/en16165913 - 10 Aug 2023
Cited by 1 | Viewed by 1602
Abstract
In many areas of constrained optimization, representing all possible constraints that give rise to an accurate feasible region can be difficult and computationally prohibitive for online use. Satisfying feasibility constraints becomes more challenging in high-dimensional, non-convex regimes which are common in engineering applications. [...] Read more.
In many areas of constrained optimization, representing all possible constraints that give rise to an accurate feasible region can be difficult and computationally prohibitive for online use. Satisfying feasibility constraints becomes more challenging in high-dimensional, non-convex regimes which are common in engineering applications. A prominent example that is explored in the manuscript is the security-constrained optimal power flow (SCOPF) problem, which minimizes power generation costs, while enforcing system feasibility under contingency failures in the transmission network. In its full form, this problem has been modeled as a nonlinear two-stage stochastic programming problem. In this work, we propose a hybrid structure that incorporates and takes advantage of both a high-fidelity physical model and fast machine learning surrogates. Neural network (NN) models have been shown to classify highly non-linear functions and can be trained offline but require large training sets. In this work, we present how model-guided sampling can efficiently create datasets that are highly informative to a NN classifier for non-convex functions. We show how the resultant NN surrogates can be integrated into a non-linear program as smooth, continuous functions to simultaneously optimize the objective function and enforce feasibility using existing non-linear solvers. Overall, this allows us to optimize instances of the SCOPF problem with an order of magnitude CPU improvement over existing methods. Full article
(This article belongs to the Special Issue Implementation of Machine Learning in Sustainable Electric Power Applications)
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16 pages, 4074 KiB  
Article
Signal Spectrum-Based Machine Learning Approach for Fault Prediction and Maintenance of Electrical Machines
by Hadi Ashraf Raja, Karolina Kudelina, Bilal Asad, Toomas Vaimann, Ants Kallaste, Anton Rassõlkin and Huynh Van Khang
Energies 2022, 15(24), 9507; https://doi.org/10.3390/en15249507 - 15 Dec 2022
Cited by 13 | Viewed by 2095
Abstract
Industrial revolution 4.0 has enabled the advent of new technological advancements, including the introduction of information technology with physical devices. The implementation of information technology in industrial applications has helped streamline industrial processes and make them more cost-efficient. This combination of information technology [...] Read more.
Industrial revolution 4.0 has enabled the advent of new technological advancements, including the introduction of information technology with physical devices. The implementation of information technology in industrial applications has helped streamline industrial processes and make them more cost-efficient. This combination of information technology and physical devices gave birth to smart devices, which opened up a new research area known as the Internet of Things (IoT). This has enabled researchers to help reduce downtime and maintenance costs by applying condition monitoring on electrical machines utilizing machine learning algorithms. Although the industry is trying to move from scheduled maintenance towards predictive maintenance, there is a significant lack of algorithms related to fault prediction of electrical machines. There is quite a lot of research going on in this area, but it is still underdeveloped and needs a lot more work. This paper presents a signal spectrum-based machine learning approach toward the fault prediction of electrical machines. The proposed method is a new approach to the predictive maintenance of electrical machines. This paper presents the details regarding the algorithm and then validates the accuracy against data collected from working electrical machines for both cases. A comparison is also presented at the end of multiple machine learning algorithms used for training based on this approach. Full article
(This article belongs to the Special Issue Implementation of Machine Learning in Sustainable Electric Power Applications)
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