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Artificial Intelligence in Power Systems Operation and Control
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Artificial Intelligence in Power Systems Operation and Control

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

Deadline for manuscript submissions: closed (20 July 2021) | Viewed by 6839

Special Issue Editors

Prof. Dr. Pedro Rodriguez

E-Mail Website
Guest Editor
Luxembourg Institute of Science and Technology (LIST), 4362 Belval, Luxembourg
Interests: inverters; power converters; renewable energy applications; energy storage applications; distributed generation; power systems analysis; smart grids; microgrids; power electronics; power generation; electrical power engineering; electrical & electronics engineering; engineering, applied and, computational mathematics; algorithms
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tomislav Dragicevic

E-Mail Website
Guest Editor
Department of Electrical Engineering, Center for Electric Power and Energy, Technical University of Denmark - DTU, 2800 Kgs, Lyngby, Denmark
Interests: renewable energy; power electronics; energy storage; e-mobility; artificial intelligence; IoT-driven digitalization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, power system operation and control are challenged by the higher penetration of power-electronic-interfaced energy sources at both the transmission and distribution sides. Furthermore, the complexity and uncertainty of supply and demand, in conjunction with the forthcoming electrification of the transportation and heating sectors, necessitates the active participation and coordination of distribution system operators with transmission system operators to maximize flexibility. However, as the different time scales of operation and control become less distinct and start to overlap, traditional methods struggle to maintain the same level of performance.

The realization of smart grids permits the adaptation of artificial intelligence (AI), which as in many other scientific fields, appear as an ideal candidate for modernizing monitoring, optimization and control, due to their ability for modelling complex relationships, detecting anomalies, and revealing the fundamental structure of a given system, to name a few things. As the number of controllable elements (with different characteristics) increases in smart grids, fast and accurate predictions/estimations can contribute to improving the situational awareness, exploitation, operation and protection of the system. However, recent advances have also demonstrated the ability of AI, and specifically neural networks, to integrate the physical model of a system to exploit knowledge already acquired. This opens a new avenue of possibilities for AI, such as the embedment of the knowledge synthesized by neural networks into power-system optimization processes.

This Special Issue is dedicated to promoting high-quality research addressing the aforementioned concerns by means of AI. Specifically, the topics that this Special Issue intends to cover, but is not limited to, are:

  • Hybrid AI-physics models.
  • AI applications in power-system optimization.
  • AI-driven smart grids.
  • Renewables and demand integration through AI-based methodologies.
  • AI-based solutions for system protection.
  • Interpretable AI models.
  • Novel AI-based monitoring/situational awareness algorithms.
  • Risk and uncertainty modelling with AI.
Prof. Dr. Pedro Rodriguez
Prof. Dr. Tomislav Dragicevic
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
  • Intelligent control
  • Smart grids
  • Distributed energy resources
  • Power electronics
  • Renewable energy

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Published Papers (2 papers)

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Research

15 pages, 1884 KiB  
Article
Complementary Feature Extractions for Event Identification in Power Systems Using Multi-Channel Convolutional Neural Network
by Do-In Kim
Energies 2021, 14(15), 4446; https://doi.org/10.3390/en14154446 - 23 Jul 2021
Cited by 4 | Viewed by 2202
Abstract
This paper presents an event identification process in complementary feature extractions via convolutional neural network (CNN)-based event classification. The CNN is a suitable deep learning technique for addressing the two-dimensional power system data as it directly derives information from a measurement signal database [...] Read more.
This paper presents an event identification process in complementary feature extractions via convolutional neural network (CNN)-based event classification. The CNN is a suitable deep learning technique for addressing the two-dimensional power system data as it directly derives information from a measurement signal database instead of modeling transient phenomena, where the measured synchrophasor data in the power systems are allocated by time and space domains. The dynamic signatures in phasor measurement unit (PMU) signals are analyzed based on the starting point of the subtransient signals, as well as the fluctuation signature in the transient signal. For fast decision and protective operations, the use of narrow band time window is recommended to reduce the acquisition delay, where a wide time window provides high accuracy due to the use of large amounts of data. In this study, two separate data preprocessing methods and multichannel CNN structures are constructed to provide validation, as well as the fast decision in successive event conditions. The decision result includes information pertaining to various event types and locations based on various time delays for the protective operation. Finally, this work verifies the event identification method through a case study and analyzes the effects of successive events in addition to classification accuracy. Full article
(This article belongs to the Special Issue Artificial Intelligence in Power Systems Operation and Control)
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19 pages, 3059 KiB  
Article
Optimization for Data-Driven Preventive Control Using Model Interpretation and Augmented Dataset
by Junyu Ren, Benyu Li, Ming Zhao, Hengchu Shi, Hao You and Jinfu Chen
Energies 2021, 14(12), 3430; https://doi.org/10.3390/en14123430 - 10 Jun 2021
Cited by 7 | Viewed by 2258
Abstract
Transient stability preventive control (TSPC) ensures that power systems have a sufficient stability margin by adjusting power flow before faults occur. The generation of TSPC measures requires accuracy and efficiency. In this paper, a novel model interpretation-based multi-fault coordinated data-driven preventive control optimization [...] Read more.
Transient stability preventive control (TSPC) ensures that power systems have a sufficient stability margin by adjusting power flow before faults occur. The generation of TSPC measures requires accuracy and efficiency. In this paper, a novel model interpretation-based multi-fault coordinated data-driven preventive control optimization strategy is proposed. First, an augmented dataset covering the fault information is constructed, enabling the transient stability assessment (TSA) model to discriminate the system stability under different fault scenarios. Then, the adaptive synthetic sampling (ADASYN) method is implemented to deal with the imbalanced instances of power systems. Next, an instance-based machine model interpretation tool, Shapley additive explanations (SHAP), is embedded to explain the TSA model’s predictions and to find out the most effective control objects, thus narrowing the number of control objects. Finally, differential evolution is deployed to optimize the generation of TSPC measures, taking into account the security and economy of TSPC. The proposed method’s efficiency and robustness are verified on the New England 39-bus system and the IEEE 54-machine 118-bus system. Full article
(This article belongs to the Special Issue Artificial Intelligence in Power Systems Operation and Control)
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