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Applied Sciences
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Automated Diagnostics and Analytics for Smart Energy and Power Networks
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Automated Diagnostics and Analytics for Smart Energy and Power Networks

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (15 March 2021) | Viewed by 11356

Special Issue Editors

Dr. Hamid Reza Shaker

E-Mail Website
Guest Editor
Center for Energy Informatics, University of Southern Denmark, 5230 Odense, Denmark
Interests: fault detection and diagnosis; fault and critical event prediction; proactive and predictive maintenance; digital energy solutions
Special Issues, Collections and Topics in MDPI journals
Dr. Rahman Dashti

E-Mail Website
Guest Editor
Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University, Bushehr I.R., Iran
Interests: power system protection; fault location; distribution networks; transient analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The market for digitalization in the energy sector is growing fast and is expected to reach $64 billion by 2025. Energy and power networks are extremely important for the energy sector. Several forecasts confirm that distribution automation will see the digital revenues rise from $4 billion in 2017 to $10 billion in 2025. Digitalization can yield 10%–20% in energy savings in district heating systems, cut peak loads by 20%, and save up to 30% in maintenance costs in district heating systems. Digital transformation is inevitable. It is a crucial driver of revenue and economic growth. It is important to invest in automated diagnostics and analytics to enjoy the full potential and benefits of the digitalization of power and energy networks. This Special Issue aims to serve as a platform to report research results and findings on diagnostics, prognostics, and analytics with applications in energy networks such as power distribution grids, district heating networks, gas networks, etc. We would therefore welcome your submissions within the aforementioned areas. 

Assoc. Prof. Hamid Reza Shaker
Dr. Rahman Dashti
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. Applied Sciences 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 2400 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

  • Fault detection and diagnosis in power and energy networks
  • Fault location and prediction in power and energy networks
  • Critical event detection and prediction in power and energy networks
  • Data analytics for power and energy networks
  • Predictive maintenance for in power and energy distribution networks
  • Forecasting for power and energy networks

Published Papers (4 papers)

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Research

17 pages, 3117 KiB  
Article
Advanced Edge-Cloud Computing Framework for Automated PMU-Based Fault Localization in Distribution Networks
by Denis Sodin, Urban Rudež, Marko Mihelin, Miha Smolnikar and Andrej Čampa
Appl. Sci. 2021, 11(7), 3100; https://doi.org/10.3390/app11073100 - 31 Mar 2021
Cited by 10 | Viewed by 2438
Abstract
The detection and localization of faults plays a huge role in every electric power system, be it a transmission network (TN) or a distribution network (DN), as it ensures quick power restoration and thus enhances the system’s reliability and availability. In this paper, [...] Read more.
The detection and localization of faults plays a huge role in every electric power system, be it a transmission network (TN) or a distribution network (DN), as it ensures quick power restoration and thus enhances the system’s reliability and availability. In this paper, a framework that supports phasor measurement unit (PMU)-based fault detection and localization is presented. Besides making the process of fault detecting, localizing and reporting to the control center fully automated, the aim was to make the framework viable also for DNs, which normally do not have dedicated fiber-optic connectivity at their disposal. The quality of service (QoS) for PMU data transmission, using the widespread long-term evolution (LTE) technology, was evaluated and the conclusions of the evaluation were used in the development of the proposed edge-cloud framework. The main advantages of the proposed framework can be summarized as: (a) fault detection is performed at the edge nodes, thus bypassing communication delay and availability issues, (b) potential packet losses are eliminated by temporally storing data at the edge nodes, (c) since the detection of faults is no longer centralized, but rather takes place locally at the edge, the amount of data transferred to the control center during the steady-state conditions of the network can be significantly reduced. Full article
(This article belongs to the Special Issue Automated Diagnostics and Analytics for Smart Energy and Power Networks)
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16 pages, 17170 KiB  
Article
Extended Use for the Frequency Response Analysis: Switching Impulse Voltage Based Preliminary Diagnosis of Potential Sources of Partial Discharges in Transformer
by Kamalaselvan Arunachalam, Balasubramanian Madanmohan, Rajesh Rajamani, Natarajan Prabaharan, Hassan Haes Alhelou and Pierluigi Siano
Appl. Sci. 2020, 10(22), 8283; https://doi.org/10.3390/app10228283 - 22 Nov 2020
Cited by 1 | Viewed by 2341
Abstract
The Frequency Response Analysis approach (FRA) is useful in the fault diagnosis of transformers. However, its usefulness in diagnosing any potential sources of Partial Discharge (PD) in transformers has not been thoroughly investigated so far. In this work, the use of Impulse voltage-based [...] Read more.
The Frequency Response Analysis approach (FRA) is useful in the fault diagnosis of transformers. However, its usefulness in diagnosing any potential sources of Partial Discharge (PD) in transformers has not been thoroughly investigated so far. In this work, the use of Impulse voltage-based FRA (IFRA) in diagnosing inter-turn shorts and potential sources of PD were investigated on a 315 kVA, 11 kV/433 V transformer. Inter-turn shorts and PD sources were emulated and the usefulness of IFRA in their diagnosis was investigated while using switching impulse voltage at different magnitude levels as the test signals. For emulating the inter-turn shorts and the PDs, special tappings were provided on one of the 11 kV windings through the low capacitance bushings. Low voltage impulse was successful in diagnosing the inter-turn shorts, but unsuccessful in identifying the sources of PDs. During the test condition, the test voltage was adjusted with the presence of artificially created PD sources. The frequency response of the transformer before and after the inception of PD was observed and analyzed in this article. The FRA results demonstrated that the switching impulse voltage based IFRA approach at moderate voltages could be useful in diagnosing the presence of the potential sources of PDs. Full article
(This article belongs to the Special Issue Automated Diagnostics and Analytics for Smart Energy and Power Networks)
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19 pages, 3601 KiB  
Article
Optimal Outage Management Model Considering Emergency Demand Response Programs for a Smart Distribution System
by Sobhan Dorahaki, Rahman Dashti and Hamid Reza Shaker
Appl. Sci. 2020, 10(21), 7406; https://doi.org/10.3390/app10217406 - 22 Oct 2020
Cited by 13 | Viewed by 2142
Abstract
In this paper, a novel smart outage management system considering Emergency Demand Response Programs (EDRPs) and Distributed Generations (DGs) denoted as SOMSDGsEDRPs is proposed. The EDRPs are provided to decrease the cost [...] Read more.
In this paper, a novel smart outage management system considering Emergency Demand Response Programs (EDRPs) and Distributed Generations (DGs) denoted as SOMSDGsEDRPs is proposed. The EDRPs are provided to decrease the cost of load shading in a time of emergency. The objective function of the problem is proposed to minimize the load shading cost, the DG dispatch cost, the demand response cost and the repair dispatch time for crews. The SOMSDGsDERPs solves an optimization problem that is formulated as Mixed Integer Linear Programming (MILP) taking into account the grid topology constraints, EDRP constraints, DG constraints and crew constraints. The MILP formulation was demonstrated in the GAMS software and solved with the CPLEX solver. The proposed method was tested on the IEEE 34 bus test system as well as an actual Iranian 66 bus power distribution feeder. The results show that the EDRPs and DGs can be effective in decreasing the outage cost and increasing the served load of the distribution power system in a crisis time. Full article
(This article belongs to the Special Issue Automated Diagnostics and Analytics for Smart Energy and Power Networks)
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14 pages, 1480 KiB  
Article
Application of Machine Learning for Fault Classification and Location in a Radial Distribution Grid
by Yordanos Dametw Mamuya, Yih-Der Lee, Jing-Wen Shen, Md Shafiullah and Cheng-Chien Kuo
Appl. Sci. 2020, 10(14), 4965; https://doi.org/10.3390/app10144965 - 19 Jul 2020
Cited by 35 | Viewed by 3851
Abstract
Fault location with the highest possible accuracy has a significant role in expediting the restoration process, after being exposed to any kind of fault in power distribution grids. This paper provides fault detection, classification, and location methods using machine learning tools and advanced [...] Read more.
Fault location with the highest possible accuracy has a significant role in expediting the restoration process, after being exposed to any kind of fault in power distribution grids. This paper provides fault detection, classification, and location methods using machine learning tools and advanced signal processing for a radial distribution grid. The three-phase current signals, one cycle before and one cycle after the inception of the fault are measured at the sending end of the grid. A discrete wavelet transform (DWT) is employed to extract useful features from the three-phase current signal. Standard statistical techniques are then applied onto DWT coefficients to extract the useful features. Among many features, mean, standard deviation (SD), energy, skewness, kurtosis, and entropy are evaluated and fed into the artificial neural network (ANN), Multilayer perceptron (MLP), and extreme learning machine (ELM), to identify the fault type and its location. During the training process, all types of faults with variations in the loading and fault resistance are considered. The performance of the proposed fault locating methods is evaluated in terms of root mean absolute percentage error (MAPE), root mean squared error (RMSE), Willmott’s index of agreement (WIA), coefficient of determination ( R 2 ), and Nash-Sutcliffe model efficiency coefficient (NSEC). The time it takes for training and testing are also considered. The proposed method that discrete wavelet transforms with machine learning is a very accurate and reliable method for fault classifying and locating in both a balanced and unbalanced radial system. 100% fault detection accuracy is achieved for all types of faults. Except for the slight confusion of three line to ground (3LG) and three line (3L) faults, 100% classification accuracy is also achieved. The performance measures show that both MLP and ELM are very accurate and comparative in locating faults. The method can be further applied for meshed networks with multiple distributed generators. Renewable generations in the form of distributed generation units can also be studied. Full article
(This article belongs to the Special Issue Automated Diagnostics and Analytics for Smart Energy and Power Networks)
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