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Inventions
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Application of Machine Learning in Power Systems
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Application of Machine Learning in Power Systems

A special issue of Inventions (ISSN 2411-5134). This special issue belongs to the section "Inventions and Innovation in Electrical Engineering/Energy/Communications".

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 15452

Special Issue Editors

Prof. Dr. Tek-Tjing Lie

E-Mail Website
Guest Editor
School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand
Interests: AI applications to power systems; power system control and operation; smart grids; renewable energy resources; energy management
Special Issues, Collections and Topics in MDPI journals
Dr. Ramon Zamora

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland 1010, New Zealand
Interests: power system modelling, simulation and control; energy management and controls; power electronic applications for power systems; grid integration of renewable energy and energy storage; microgrid and smart grid; and power system applications of renewable energy
Special Issues, Collections and Topics in MDPI journals
Dr. Miftah Al-Karim

E-Mail Website
Guest Editor
Asset Information, Te Kuiti 3941, New Zealand
Interests: Machine Learning; self-healing grid; data analytics; matlab; python; Internet of Things
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The interconnection between all energy-related things within a grid, and even between the grids themselves in the energy Internet, is both technologically complex and risky. It poses significant challenges for decision makers and regulators. A power grid is needed with smart environment technologies that automates the process of monitoring the health of the grid or the usage of facilities for long-term maintenance and facility management. Tools are needed to collate such information automatically from grid in-use to aid with the maintenance or assist the facility manager in making smarter and timelier decisions. In addition, different systems can be interconnected similar to the interconnection between different web servers over the Internet. This Special Issue aims to solicit innovative research and state-of-the-art machine learning algorithms for managing the risks posed by fast-paced technology changes, the volatility of global electricity prices, system over-frequency, and cyber-physical threats, as well as for improving power system planning, operation, and control.

Prof. Dr. Tek-Tjing Lie
Dr. Ramon Zamora
Dr. Miftah Al-Karim
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. Inventions 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 1800 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
  • cyber security
  • risk
  • energy internet
  • fault detection
  • power-system health
  • monitoring
  • power-system security assessment
  • preventive and corrective control
  • demand-side management
  • outage management
  • asset management

Published Papers (4 papers)

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Research

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21 pages, 5791 KiB  
Article
Real-Time LFO Damping Enhancement in Electric Networks Employing PSO Optimized ANFIS
by Md Ilius Hasan Pathan, Md Juel Rana, Mohammad Shoaib Shahriar, Md Shafiullah, Md. Hasan Zahir and Amjad Ali
Inventions 2020, 5(4), 61; https://doi.org/10.3390/inventions5040061 - 14 Dec 2020
Cited by 10 | Viewed by 2506
Abstract
In recent years, machine learning (ML) tools have gained tremendous momentum and received wide-spread attention in different segments of modern-day life. As part of digital transformation, the power system industry is one of the pioneers in adopting such attractive and efficient tools for [...] Read more.
In recent years, machine learning (ML) tools have gained tremendous momentum and received wide-spread attention in different segments of modern-day life. As part of digital transformation, the power system industry is one of the pioneers in adopting such attractive and efficient tools for various applications. Apparently, a nonthreatening, but slow-burning issue of the electric power systems is the low-frequency oscillations (LFO), which, if not dealt with appropriately and on time, could result in complete network failure. This paper addresses the role of a prominent ML family member, particle swarm optimization (PSO) tuned adaptive neuro-fuzzy inference system (ANFIS) for real-time enhancement of LFO damping in electric power system networks. It adopts and models two power system networks where in the first network, the synchronous machine is equipped with only a power system stabilizer (PSS), and in the other, the PSS of the synchronous machine is coordinated with the unified power flow controller (UPFC), a second-generation flexible alternating current transmission system (FACTS) device. Then, it develops the proposed ML approach to enhance LFO damping for both adopted networks based on the customary practices of statistical judgment. The performance measuring metrics of power system stability, including the minimum damping ratio (MDR), eigenvalue, and time-domain simulation, were used to analyze the developed approach. Moreover, the paper presents a comparative analysis and discussion with the referenced works’ achieved results to conclude the proposed PSO-ANFIS technique’s ability to enhance power system stability in real-time by damping out the unwanted LFO. Full article
(This article belongs to the Special Issue Application of Machine Learning in Power Systems)
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20 pages, 4239 KiB  
Article
Non-Intrusive Load Monitoring of Residential Water-Heating Circuit Using Ensemble Machine Learning Techniques
by Attique Ur Rehman, Tek Tjing Lie, Brice Vallès and Shafiqur Rahman Tito
Inventions 2020, 5(4), 57; https://doi.org/10.3390/inventions5040057 - 23 Nov 2020
Cited by 7 | Viewed by 2709
Abstract
The recent advancement in computational capabilities and deployment of smart meters have caused non-intrusive load monitoring to revive itself as one of the promising techniques of energy monitoring. Toward effective energy monitoring, this paper presents a non-invasive load inference approach assisted by feature [...] Read more.
The recent advancement in computational capabilities and deployment of smart meters have caused non-intrusive load monitoring to revive itself as one of the promising techniques of energy monitoring. Toward effective energy monitoring, this paper presents a non-invasive load inference approach assisted by feature selection and ensemble machine learning techniques. For evaluation and validation purposes of the proposed approach, one of the major residential load elements having solid potential toward energy efficiency applications, i.e., water heating, is considered. Moreover, to realize the real-life deployment, digital simulations are carried out on low-sampling real-world load measurements: New Zealand GREEN Grid Database. For said purposes, MATLAB and Python (Scikit-Learn) are used as simulation tools. The employed learning models, i.e., standalone and ensemble, are trained on a single household’s load data and later tested rigorously on a set of diverse households’ load data, to validate the generalization capability of the employed models. This paper presents a comprehensive performance evaluation of the presented approach in the context of event detection, feature selection, and learning models. Based on the presented study and corresponding analysis of the results, it is concluded that the proposed approach generalizes well to the unseen testing data and yields promising results in terms of non-invasive load inference. Full article
(This article belongs to the Special Issue Application of Machine Learning in Power Systems)
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13 pages, 3397 KiB  
Article
Power Optimization Control Scheme for Doubly Fed Induction Generator Used in Wind Turbine Generators
by Darya Khan, Jamshed Ahmed Ansari, Shahid Aziz Khan and Usama Abrar
Inventions 2020, 5(3), 40; https://doi.org/10.3390/inventions5030040 - 17 Aug 2020
Cited by 9 | Viewed by 3331
Abstract
Scientists and researchers are exploring different methods of generating and delivering electrical energy in an economical and reliable way, enabling them to generate electricity focusing on renewable energy resources. All of these possess the natural property of self-changing behavior, so the connection of [...] Read more.
Scientists and researchers are exploring different methods of generating and delivering electrical energy in an economical and reliable way, enabling them to generate electricity focusing on renewable energy resources. All of these possess the natural property of self-changing behavior, so the connection of these separate independent controllable units to the grid leads to uncertainties. This creates an imbalance in active power and reactive power. In order to control the active and reactive power in wind turbine generators with adjustable speed, various control strategies are used to allay voltage and current variations. This research work is focused on the design and implementation of effective control strategies for doubly fed induction generator (DFIG) to control its active and reactive power. A DFIG system with its control strategies is simulated on MATLAB software. To augment the transient stability of DFIG, the simulation results for the active and reactive power of conventional controllers are compared with three types of feed forward neural network controllers, i.e., probabilistic feedforward neural network (PFFNN), multi-layer perceptron feedforward neural network (MLPFFN) and radial basic function feedforward neural network (RBFFN) for optimum performance. Conclusive outcomes clearly manifest the superior robustness of the RBFNN controller over other controllers in terms of rise time, settling time and overshoot value. Full article
(This article belongs to the Special Issue Application of Machine Learning in Power Systems)
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14 pages, 3806 KiB  
Technical Note
An Ethereum Blockchain-Based Prototype for Data Security of Regulated Electricity Market
by Aasim Ullah, S M Shahnewaz Siddiquee, Md Akbar Hossain and Sayan Kumar Ray
Inventions 2020, 5(4), 58; https://doi.org/10.3390/inventions5040058 - 27 Nov 2020
Cited by 9 | Viewed by 6230
Abstract
Data security of present-day power systems, such as the electricity market, has spurred global interest in both industry and academia. The electricity market can either be regulated (state-controlled entrance, policies, and pricing) or deregulated (open for competitors). While the security threats in a [...] Read more.
Data security of present-day power systems, such as the electricity market, has spurred global interest in both industry and academia. The electricity market can either be regulated (state-controlled entrance, policies, and pricing) or deregulated (open for competitors). While the security threats in a deregulated electricity market are commonly known and have been investigated for years, those in a regulated market still have scope for extensive research. Our current work focuses on exploring the data security of the regulated electricity market, and the regulated New Zealand Electricity Market (NZEM) has been considered for this research. Although the chances of cyberattacks on state-controlled regulated electricity market are relatively less, different layers of the current SCADA systems do pose some threats. In this context, we propose a decentralized Ethereum Blockchain-based end-to-end security prototype for a regulated electricity market such as the NZEM. This prototype aims to enhance data security between the different layers of the current SCADA systems. The detailed operation process and features of this prototype are presented in this work. The proposed prototype has prospects of offering improved data security solutions for the regulated electricity market. Full article
(This article belongs to the Special Issue Application of Machine Learning in Power Systems)
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