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Energies
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Artificial Intelligence and Machine Learning in Smart Grids
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Artificial Intelligence and Machine Learning in Smart Grids

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (10 April 2025) | Viewed by 3730

Special Issue Editors

Dr. Donghe Li

E-Mail Website
Guest Editor
Faculty of Electronics and Information, Xi’an Jiaotong University, Xi’an 710049, China
Interests: smart grid; artificial intelligence
Dr. Yu Xiao

E-Mail Website
Guest Editor
Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
Interests: DC power system; power electronics; power-to-heat
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As societal demand for clean and efficient energy continues to escalate, smart grids are gradually emerging as a crucial means to achieve this objective. The application of emerging technologies such as artificial intelligence and machine learning in smart grids has become a pivotal driver for enhancing the efficiency and reliability of power systems, as well as catalysing the transition to sustainable energy sources. This Special Issue explores the application of artificial intelligence and machine learning in the field of smart grids, delving into the potential impact of these advanced technologies within the domain of power systems. The primary objective is to provide a comprehensive resource for researchers, practitioners, and decision-makers in the power sector, assisting them in better understanding and applying these technologies to propel the development of smart grids. Special emphasis is placed on their pivotal roles in data processing, predictive performance optimization, and fault detection, as well as the transformative effects they bring to the production, transmission, and distribution of electrical energy. We invite original and unpublished contributions for this Special Issue, focusing on innovative approaches to enhance artificial intelligence and machine learning technologies across all relevant applications in smart grids. The ultimate goal is to foster discussions and contributions that will advance the state of the art in these technologies, further driving innovation in the field of smart grids.

Additionally, please ensure that the summary aligns with the aims and scope of Energies: https://www.mdpi.com/journal/energies/about.

Dr. Donghe Li
Dr. Yu Xiao
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

  • artificial intelligence
  • deep learning
  • machine learning
  • smart grid
  • artificial intelligence and machine learning techniques in smart grids
  • power quality measurement and assessment in smart grids using artificial intelligence and machine learning techniques

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Published Papers (3 papers)

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Research

18 pages, 2795 KiB  
Article
Convolutional Neural Network for the Classification of the Control Mode of Grid-Connected Power Converters
by Rabah Ouali, Martin Legry, Jean-Yves Dieulot, Pascal Yim, Xavier Guillaud and Frédéric Colas
Energies 2024, 17(24), 6458; https://doi.org/10.3390/en17246458 - 22 Dec 2024
Viewed by 807
Abstract
With the integration of power converters into the power grid, it becomes crucial for the Transmission System Operator (TSO) to ascertain whether they are operating in Grid Forming or Grid Following modes. Due to intellectual properties, classification can only be performed based on [...] Read more.
With the integration of power converters into the power grid, it becomes crucial for the Transmission System Operator (TSO) to ascertain whether they are operating in Grid Forming or Grid Following modes. Due to intellectual properties, classification can only be performed based on non-intrusive measurements and models, such as admittance at the PCC. This classification poses a challenge as the TSO lacks precise knowledge of the actual control structures and algorithms. This paper introduces a novel classification algorithm based on Convolutional Neural Networks (CNN), capable of detecting patterns in sequential data. The proposed CNN utilizes a new architecture to separate admittances along the d and q axes, and a decision layer allows to determine the correct converter mode. The performance of the proposed CNN model was assessed through two tests and compared to the traditional feedforward model. The proposed CNN architecture demonstrates significant classification capabilities, as it is able to identify the control mode of the converter even when its control structure is not part of the training dataset. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Smart Grids)
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23 pages, 6217 KiB  
Article
An Approach for Detecting Faulty Lines in a Small-Current, Grounded System Using Learning Spiking Neural P Systems with NLMS
by Yangheng Hu, Yijin Wu, Qiang Yang, Yang Liu, Shunli Wang, Jianping Dong, Xiaohua Zeng and Dapeng Zhang
Energies 2024, 17(22), 5742; https://doi.org/10.3390/en17225742 - 16 Nov 2024
Viewed by 798
Abstract
Detecting faulty lines in small-current, grounded systems is a crucial yet challenging task in power system protection. Existing methods often struggle with the accurate identification of faults due to the complex and dynamic nature of current and voltage signals in these systems. This [...] Read more.
Detecting faulty lines in small-current, grounded systems is a crucial yet challenging task in power system protection. Existing methods often struggle with the accurate identification of faults due to the complex and dynamic nature of current and voltage signals in these systems. This gap in reliable fault detection necessitates more advanced methodologies to improve system stability and safety. Here, a novel approach, using learning spiking neural P systems combined with a normalized least mean squares (NLMS) algorithm to enhance faulty line detection in small-current, grounded systems, is proposed. The proposed method analyzes the features of current and voltage signals, as well as active and reactive power, by separately considering their transient and steady-state components. To improve fault detection accuracy, we quantified the likelihood of a fault occurrence based on feature changes and expanded the feature space to higher dimensions using an ascending dimension structure. An adaptive learning mechanism was introduced to optimize the convergence and precision of the detection model. Simulation scheduling datasets and real-world data were used to validate the effectiveness of the proposed approach, demonstrating significant improvements over traditional methods. These findings provide a robust framework for faulty-line detection in small-current, grounded systems, contributing to enhanced reliability and safety in power system operations. This approach has the potential to be widely applied in power system protection and maintenance, advancing the broader field of intelligent fault diagnosis. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Smart Grids)
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13 pages, 4021 KiB  
Article
A Deep Reinforcement Learning Optimization Method Considering Network Node Failures
by Xueying Ding, Xiao Liao, Wei Cui, Xiangliang Meng, Ruosong Liu, Qingshan Ye and Donghe Li
Energies 2024, 17(17), 4471; https://doi.org/10.3390/en17174471 - 6 Sep 2024
Cited by 3 | Viewed by 1278
Abstract
Nowadays, the microgrid system is characterized by a diversification of power factors and a complex network structure. Existing studies on microgrid fault diagnosis and troubleshooting mostly focus on the fault detection and operation optimization of a single power device. However, for increasingly complex [...] Read more.
Nowadays, the microgrid system is characterized by a diversification of power factors and a complex network structure. Existing studies on microgrid fault diagnosis and troubleshooting mostly focus on the fault detection and operation optimization of a single power device. However, for increasingly complex microgrid systems, it becomes increasingly challenging to effectively contain faults within a specific spatiotemporal range. This can lead to the spread of power faults, posing great harm to the safety of the microgrid. The topology optimization of the microgrid based on deep reinforcement learning proposed in this paper starts from the overall power grid and aims to minimize the overall failure rate of the microgrid by optimizing the topology of the power grid. This approach can limit internal faults within a small range, greatly improving the safety and reliability of microgrid operation. The method proposed in this paper can optimize the network topology for the single node fault and multi-node fault, reducing the influence range of the node fault by 21% and 58%, respectively. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Smart Grids)
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