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Machine Learning Applications in Power System
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Machine Learning Applications in Power System

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 1338

Special Issue Editors

Prof. Dr. Vinod Kumar DM

E-Mail Website
Guest Editor
Department of Electrical Engineering, National Institute of Technology, Warangal 506 004, India
Interests: artificial intelligence (AI) applications to power systems; machine learning applications to power systems; swarm intelligence applications to power systems; smart grid technology and applications; evolutionary multi-objective applications to micro grids; power system deregulation and restructuring; operation and control of power systems; stability and security distribution system state estimation; meter placement for distribution; system state estimation
Dr. Chintham Venkaiah

E-Mail Website
Guest Editor
Department of Electrical Engineering, National Institute of Technology, Warangal 506 004, India
Interests: AI applications to power system

Special Issue Information

Dear Colleagues,

Machine learning (ML) has augmented change in the field of artificial intelligence, which espouses the power of human discernment. As electrical engineering systems generate large amounts of data, one can apply data mining to discover new relationships in these systems. With the advent of deep neural networks, one can learn new mappings between the inputs and outputs of these systems. Compared to traditional computational approaches, machine learning algorithms display advantages due to their intrinsic generalization capability, and they also provide accurate results with greater computational efficiency and scalability. Several previous studies have investigated the use of suitable machine learning models to address different issues in the field of power grid operation and management. Furthermore, the ongoing transition towards smart grids is generating new research opportunities for the real-time application of machine learning algorithms in power systems. Researchers and utilities are exploring the latest findings that concern the application of machine learning to electrical engineering systems. Novel applications of machine learning and data mining exist in areas of electrical engineering, such as antennas, communications, controls, devices, hardware design, power and energy, sensor systems, and signal processing.

Prof. Dr. Vinod Kumar DM
Dr. Chintham Venkaiah
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
  • Deep Learning
  • Neural Networks
  • Power System
  • Evolutionary Algorithms

Published Papers (1 paper)

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Research

21 pages, 6833 KiB  
Article
A Deep Neural Network as a Strategy for Optimal Sizing and Location of Reactive Compensation Considering Power Consumption Uncertainties
by Manuel Jaramillo, Diego Carrión and Jorge Muñoz
Energies 2022, 15(24), 9367; https://0-doi-org.brum.beds.ac.uk/10.3390/en15249367 - 10 Dec 2022
Cited by 4 | Viewed by 914
Abstract
This research proposes a methodology for the optimal location and sizing of reactive compensation in an electrical transmission system through a deep neural network (DNN) by considering the smallest cost for compensation. An electrical power system (EPS) is subjected to unexpected increases in [...] Read more.
This research proposes a methodology for the optimal location and sizing of reactive compensation in an electrical transmission system through a deep neural network (DNN) by considering the smallest cost for compensation. An electrical power system (EPS) is subjected to unexpected increases in loads which are physically translated as an increment of users in the EPS. This phenomenon decreases voltage profiles in the whole system which also decreases the EPS’s reliability. One strategy to face this problem is reactive compensation; however, finding the optimal location and sizing of this compensation is not an easy task. Different algorithms and techniques such as genetic algorithms and non-linear programming have been used to find an optimal solution for this problem; however, these techniques generally need big processing power and the processing time is usually considerable. That being stated, this paper’s methodology aims to improve the voltage profile in the whole transmission system under scenarios in which a PQ load is randomly connected to any busbar of the system. The optimal location of sizing of reactive compensation will be found through a DNN which is capable of a relatively small processing time. The methodology is tested in three case studies, IEEE 14, 30 and 118 busbar transmission systems. In each of these systems, a brute force algorithm (BFA) is implemented by connecting a PQ load composed of 80% active power and 20% reactive power (which varies from 1 MW to 100 MW) to every busbar, for each scenario, reactive compensation (which varies from 10 Mvar to 300 Mvar) is connected to every busbar. Then power flows are generated for each case and by selecting the scenario which is closest to 90% of the original voltage profiles, the optimal scenario is selected and overcompensation (which would increase cost) is avoided. Through the BFA, the DNN is trained by selecting 70% of the generated data as training data and the other 30% is used as test data. Finally, the DNN is capable of achieving a 100% accuracy for location (in all three case studies when compared with BFA) and objective deviation has a difference of 3.18%, 7.43% and 0% for the IEEE 14, 30 and 118 busbar systems, respectively (when compared with the BFA). With this methodology, it is possible to find the optimal location and sizing of reactive compensation for any transmission system under any PQ load increment, with almost no processing time (with the DNN trained, the algorithm takes seconds to find the optimal solution). Full article
(This article belongs to the Special Issue Machine Learning Applications in Power System)
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