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Energies
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Machine Learning and Artificial Intelligence for Power and Energy Networks
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Machine Learning and Artificial Intelligence for Power and Energy Networks

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

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 14085

Special Issue Editors

Prof. Dr. M. Hadi Amini

E-Mail Website
Guest Editor
School of Computing and Information Sciences, College of Engineering and Computing, Florida International University, Miami, FL 33199, USA
Interests: distributed algorithms; machine learning; artificial intelligence; energy systems
Dr. Shahab Bahrami

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Interests: demand response; game theory; optimization; renewable energy; network economics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Miadreza Shafiekhah

E-Mail Website
Guest Editor
School of Technology and Innovations, University of Vaasa, 65200 Vaasa, Finland
Interests: smart grid; demand response; electric vehicle; power system; electricity market
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

You are cordially invited to submit a contribution to this special issue, of either original research or survey papers.  This special issue calls for contributions from researchers in the fields of computer science, power and energy systems, system engineering, and other related areas on the applications of artificial intelligence and machine learning in power and energy networks. The emerging issue of large-scale learning and optimization problems in power and energy networks poses an imperative to deploy novel algorithms to deal with complex operational decision making. This special issue aims at tackling this challenge by covering a wide range of efficient algorithms and their applications to real-world power and energy system-related problems.

The topic covered in this special issue includes but not limited to the following:

  • Regression methods for load demand prediction
  • Clustering methods for load disaggregation
  • Neural networks for customer behaviour modelling
  • Stochastic gradient algorithms to tackle renewable generation uncertainties.
  • Application of reinforcement learning in demand response programs
  • Time-series analysis in data centre demand response and power market
  • Online algorithms for real-time decision making in energy networks.
  • Anomaly detection techniques for cybersecurity in power and energy networks.
  • Classification techniques for power systems protection and fault detection.
  • Deep learning methods for power systems state estimation

Prof. Dr. M. Hadi Amini
Dr. Shahab Bahrami
Prof. Dr. Miadreza Shafiekhah
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

  • Machine Learning
  • Artificial Intelligence
  • Power and Energy Networks
  • Resilient Operation
  • Power Market
  • Demand Response
  • Microgrid
  • Energy Hub
  • Energy Demand
  • Intelligent Decision Making

Published Papers (4 papers)

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Research

16 pages, 1243 KiB  
Article
Self-Attention Network for Partial-Discharge Diagnosis in Gas-Insulated Switchgear
by Vo-Nguyen Tuyet-Doan, Tien-Tung Nguyen, Minh-Tuan Nguyen, Jong-Ho Lee and Yong-Hwa Kim
Energies 2020, 13(8), 2102; https://doi.org/10.3390/en13082102 - 23 Apr 2020
Cited by 20 | Viewed by 3320
Abstract
Detecting, measuring, and classifying partial discharges (PDs) are important tasks for assessing the condition of insulation systems used in different electrical equipment. Owing to the implementation of the phase-resolved PD (PRPD) as a sequence input, an existing method that processes sequential data, e.g., [...] Read more.
Detecting, measuring, and classifying partial discharges (PDs) are important tasks for assessing the condition of insulation systems used in different electrical equipment. Owing to the implementation of the phase-resolved PD (PRPD) as a sequence input, an existing method that processes sequential data, e.g., the recurrent neural network, using a long short-term memory (LSTM) has been applied for fault classification. However, the model performance is not further improved because of the lack of supporting parallel computation and the inability to recognize the relevance of all inputs. To overcome these two drawbacks, we propose a novel deep-learning model in this study based on a self-attention mechanism to classify the PD patterns in a gas-insulated switchgear (GIS). The proposed model uses a self-attention block that offers the advantages of simultaneous computation and selective focusing on parts of the PRPD signals and a classification block to finally classify faults in the GIS. Moreover, the combination of LSTM and self-attention is considered for comparison purposes. The experimental results show that the proposed method achieves performance superiority compared with the previous neural networks, whereas the model complexity is significantly reduced. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence for Power and Energy Networks)
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20 pages, 10472 KiB  
Article
Neutral Current Reduction in Three-Phase Four-Wire Distribution Feeders by Optimal Phase Arrangement Based on a Full-Scale Net Load Model Derived from the FTU Data
by Yih-Der Lee, Jheng-Lun Jiang, Yuan-Hsiang Ho, Wei-Chen Lin, Hsin-Ching Chih and Wei-Tzer Huang
Energies 2020, 13(7), 1844; https://doi.org/10.3390/en13071844 - 10 Apr 2020
Cited by 3 | Viewed by 4207
Abstract
An increase in the neutral current results in a malfunction of the low energy over current (LCO) protective relay and raises the neutral-to-ground voltage in three-phase, four-wire radial distribution feeders. Thus, the key point for mitigating its effect is to keep the current [...] Read more.
An increase in the neutral current results in a malfunction of the low energy over current (LCO) protective relay and raises the neutral-to-ground voltage in three-phase, four-wire radial distribution feeders. Thus, the key point for mitigating its effect is to keep the current under a specific level. The most common approach for reducing the neutral current caused by the inherent imbalance of distribution feeders is to rearrange the phase connection between the distribution transformers and the load tapped-off points by using the metaheuristics algorithms. However, the primary task is to obtain the effective load data for phase rearrangement; otherwise, the outcomes would not be worthy of practical application. In this paper, the effective load data can be received from the feeder terminal unit (FTU) installed along the feeder of Taipower. The net load data consisting of customers’ power consumption and the power generation of distributed energy resources (DERs) were measured and transmitted to the feeder dispatch control center (FDCC). This paper proposes a method of establishing the equivalent full-scale net load model based on FTU data format, and the long short-term memory (LSTM) was adopted for monthly load forecasting. Furthermore, the full-scale net load model was built by the monthly per hour load data. Next, the particle swarm optimization (PSO) algorithm was applied to rearrange the phase connection of the distribution transformers with the aim of minimizing the neutral current. The outcomes of this paper are helpful for the optimal setting of the limit current of the LCO relay and to avoid its malfunction. Furthermore, the proposed method can also improve the three-phase imbalance of distribution feeders, thus reducing extra power loss and increasing the operating efficiency of three-phase induction motors. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence for Power and Energy Networks)
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28 pages, 5464 KiB  
Article
Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants
by Jesus L. Lobo, Igor Ballesteros, Izaskun Oregi, Javier Del Ser and Sancho Salcedo-Sanz
Energies 2020, 13(3), 740; https://doi.org/10.3390/en13030740 - 08 Feb 2020
Cited by 9 | Viewed by 3018
Abstract
The prediction of electrical power produced in combined cycle power plants is a key challenge in the electrical power and energy systems field. This power production can vary depending on environmental variables, such as temperature, pressure, and humidity. Thus, the business problem is [...] Read more.
The prediction of electrical power produced in combined cycle power plants is a key challenge in the electrical power and energy systems field. This power production can vary depending on environmental variables, such as temperature, pressure, and humidity. Thus, the business problem is how to predict the power production as a function of these environmental conditions, in order to maximize the profit. The research community has solved this problem by applying Machine Learning techniques, and has managed to reduce the computational and time costs in comparison with the traditional thermodynamical analysis. Until now, this challenge has been tackled from a batch learning perspective, in which data is assumed to be at rest, and where models do not continuously integrate new information into already constructed models. We present an approach closer to the Big Data and Internet of Things paradigms, in which data are continuously arriving and where models learn incrementally, achieving significant enhancements in terms of data processing (time, memory and computational costs), and obtaining competitive performances. This work compares and examines the hourly electrical power prediction of several streaming regressors, and discusses about the best technique in terms of time processing and predictive performance to be applied on this streaming scenario. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence for Power and Energy Networks)
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12 pages, 2215 KiB  
Article
Power Quality Disturbances Classification via Fully-Convolutional Siamese Network and k-Nearest Neighbor
by Ruijin Zhu, Xuejiao Gong, Shifeng Hu and Yusen Wang
Energies 2019, 12(24), 4732; https://doi.org/10.3390/en12244732 - 11 Dec 2019
Cited by 15 | Viewed by 2663
Abstract
The classification of disturbance signals is of great significance for improving power quality. The existing methods for power quality disturbance classification require a large number of samples to train the model. For small sample learning, their accuracy is relatively limited. In this paper, [...] Read more.
The classification of disturbance signals is of great significance for improving power quality. The existing methods for power quality disturbance classification require a large number of samples to train the model. For small sample learning, their accuracy is relatively limited. In this paper, a hybrid algorithm of k-nearest neighbor and fully-convolutional Siamese network is proposed to classify power quality disturbances by learning small samples. Multiple convolutional layers and full connection layers are used to construct the Siamese network, and the output result of the Siamese network is used to judges the category of the signal. The simulation results show that: For small sample sizes, the accuracy of the proposed approach is significantly higher than that of the existing methods. In addition, it has a strong anti-noise ability. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence for Power and Energy Networks)
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