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Energies
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Data-Driven Methods in Modern Power Engineering
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Data-Driven Methods in Modern Power Engineering

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

Deadline for manuscript submissions: closed (31 March 2019) | Viewed by 5978

Special Issue Editors

Dr. Aristides Kiprakis

E-Mail Website
Guest Editor
School of Engineering, University of Edinburgh, Faraday Building, Colin MacLaurin Road, Scotland EH9 3BW, UK
Interests: power systems modelling and control; distributed generation; smart grids; onshore (wind and solar) and ocean (wave and tidal) energy
Special Issues, Collections and Topics in MDPI journals
Dr. Naran Pindoriya

E-Mail Website
Guest Editor
Department of Electrical Engineering, Indian Institute of Technology Gandhinagar, Gujarat 382355, India
Interests: smart grids; microgrids; energy management; artificial intelligence applications to power systems

Special Issue Information

Dear Colleagues,

The ease of access to increased computational power and the widespread expansion of telecommunications networks in recent years, make possible the use of advanced data-driven techniques for the design, optimization, control and analysis within all areas of power engineering. This Special Issue of Energies invites submissions presenting novel, data-driven methods, such as statistical analysis, probabilistic modelling, machine intelligence, data analytics, data mining, and signal processing, applied, but not limited to the following areas of modern power engineering:

  • smart grids and microgrids;
  • smart metering and data management for power networks through ICT;
  • power system planning and operation;
  • power system resilience and reliability;
  • cyber security for power systems;
  • electricity market and economics including peer-to-peer energy transactions and blockchains;
  • renewable energy resource analysis, planning and integration;
  • electrification of transportation;
  • demand response and energy storage;
  • power apparatus condition monitoring and diagnostics.

Review papers, technical papers presenting fundamental data science research applied to power engineering as well as papers demonstrating pilot implementations and case studies are particularly welcome.

Dr. Aristides Kiprakis
Dr. Naran Pindoriya
Guest Editor

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 systems
  • smart grids
  • data science
  • artificial intelligence

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Published Papers (2 papers)

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Research

16 pages, 1931 KiB  
Article
Detection and Analysis of Multiple Events Based on High-Dimensional Factor Models in Power Grid
by Fan Yang, Robert C. Qiu, Zenan Ling, Xing He and Haosen Yang
Energies 2019, 12(7), 1360; https://doi.org/10.3390/en12071360 - 9 Apr 2019
Cited by 3 | Viewed by 2407
Abstract
Multiple event detection and analysis in real time is a challenge for a modern grid as its features are usually non-identifiable. This paper, based on high-dimensional factor models, proposes a data-driven approach to gain insight into the constituent components of a multiple event [...] Read more.
Multiple event detection and analysis in real time is a challenge for a modern grid as its features are usually non-identifiable. This paper, based on high-dimensional factor models, proposes a data-driven approach to gain insight into the constituent components of a multiple event via the high-resolution phasor measurement unit (PMU) data, such that proper actions can be taken before any sporadic fault escalates to cascading blackouts. Under the framework of random matrix theory, the proposed approach maps the raw data into a high-dimensional space with two parts: (1) factors (spikes, mapping faults); (2) residuals (a bulk, mapping white/non-Gaussian noises or normal fluctuations). As for the factors, we employ their number as a spatial indicator to estimate the number of constituent components in a multiple event. Simultaneously, the autoregressive rate of the noises is utilized to measure the variation of the temporal correlation of the residuals for tracking the system movement. Taking the spatial-temporal correlation into account, this approach allows for detection, decomposition and temporal localization of multiple events. Case studies based on simulated data and real 34-PMU data verify the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Data-Driven Methods in Modern Power Engineering)
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18 pages, 2455 KiB  
Article
Data-Driven Optimization Control for Dynamic Reconfiguration of Distribution Network
by Dechang Yang, Wenlong Liao, Yusen Wang, Keqing Zeng, Qiuyue Chen and Dingqian Li
Energies 2018, 11(10), 2628; https://doi.org/10.3390/en11102628 - 1 Oct 2018
Cited by 9 | Viewed by 2473
Abstract
To improve the reliability and reduce power loss of distribution network, the dynamic reconfiguration is widely used. It is employed to find an optimal topology for each time interval while satisfying all the physical constraints. Dynamic reconfiguration is a non-deterministic polynomial problem, which [...] Read more.
To improve the reliability and reduce power loss of distribution network, the dynamic reconfiguration is widely used. It is employed to find an optimal topology for each time interval while satisfying all the physical constraints. Dynamic reconfiguration is a non-deterministic polynomial problem, which is difficult to find the optimal control strategy in a short time. The conventional methods solved complex model of dynamic reconfiguration in different ways, but only local optimal solutions can be found. In this paper, a data-driven optimization control for dynamic reconfiguration of distribution network is proposed. Through two stages that include rough matching and fine matching, the historical cases which are similar to current case are chosen as candidate cases. The optimal control strategy suitable for the current case is selected according to dynamic time warping (DTW) distances which evaluate the similarity between the candidate cases and the current case. The advantage of the proposed approach is that it does not need to solve complex model of dynamic reconfiguration, and only uses historical data to obtain the optimal control strategy for the current case. The cases study shows that the optimization results and the computation time of the proposed approach are superior to conventional methods. Full article
(This article belongs to the Special Issue Data-Driven Methods in Modern Power Engineering)
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