energies-logo

Journal Browser

Journal Browser

Special Issue "Condition Monitoring and Machine Learning Strategies for Electrical Apparatus"

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

Deadline for manuscript submissions: closed (30 November 2021).

Special Issue Editors

Dr. U. Mohan Rao
E-Mail Website
Guest Editor
Aging of Power Network Infrastructure (ViAHT), Université du Québec à Chicoutimi, Chicoutimi, QC G7H 2B1, Canada
Interests: high voltage electrical insulation; dielectric materials; condition monitoring of electrical equipment; transformer diagnostics; AIML techniques
Prof. Dr. Issouf Fofana
E-Mail Website
Co-Guest Editor

Special Issue Information

Dear Colleagues,

This special issue is intended to expand the existing knowledge on advanced condition monitoring methodologies and inclusion of computational techniques for effective monitoring. The majority of the electrical apparatus are mostly involved with high voltages, high cost, and possible risk of failures. The failure of an electrical apparatus is majorly due to vulnerable operating conditions, insulation failures, electrical and thermal stresses. Thus it is essential and customary to adopt efficient condition monitoring techniques (online and offline) for the successful operation of the electrical power network. Starting from generating stations, grid parameters, distribution aspects, and utilization, condition monitoring is of very high engineering importance. Some of them are very complex (high dimensional/ ambiguity) and challenging to handle and make decision on prognosis. Thus, adopting artificial intelligence and machine learning (AIML) techniques, sensor technologies, advanced diagnostic approaches are potential avenues of research for future grid operations. We therefore invite contributions on technical developments, regular research problems, critical reviews, and industrial case studies from the electrical engineering communities. Studies pertinent to condition monitoring, insulation failures, intelligent monitoring ideas, and AIML for precise monitoring are invited. 

Dr. U. Mohan Rao
Guest Editor


Prof. Dr. Issouf Fofana
Co-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 papers will be 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 2000 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

  • Condition Monitoring (Online/Offline) 
  • Intelligent Monitoring Techniques 
  • Diagnostic Testings 
  • Sensors and Signal processing

Published Papers (6 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

Article
Improved Monitoring and Diagnosis of Transformer Solid Insulation Using Pertinent Chemical Indicators
Energies 2021, 14(13), 3977; https://doi.org/10.3390/en14133977 - 02 Jul 2021
Viewed by 503
Abstract
Transformers are generally considered to be the costliest assets in a power network. The lifetime of a transformer is mainly attributable to the condition of its solid insulation, which in turn is measured and described according to the degree of polymerization (DP) of [...] Read more.
Transformers are generally considered to be the costliest assets in a power network. The lifetime of a transformer is mainly attributable to the condition of its solid insulation, which in turn is measured and described according to the degree of polymerization (DP) of the cellulose. Since the determination of the DP index is complex and time-consuming and requires the transformer to be taken out of service, utilities prefer indirect and non-invasive methods of determining the DP based on the byproduct of cellulose aging. This paper analyzes solid insulation degradation by measuring the furan concentration, recently introduced methanol, and dissolved gases like carbon oxides and hydrogen, in the insulating oil. A group of service-aged distribution transformers were selected for practical investigation based on oil samples and different kinds of tests. Based on the maintenance and planning strategy of the power utility and a weighted combination of measured chemical indicators, a neural network was also developed to categorize the state of the transformer in certain classes. The method proved to be able to improve the diagnostic capability of chemical indicators, thus providing power utilities with more reliable maintenance tools and avoiding catastrophic failure of transformers. Full article
Show Figures

Figure 1

Article
A Wind Energy Supplier Bidding Strategy Using Combined EGA-Inspired HPSOIFA Optimizer and Deep Learning Predictor
Energies 2021, 14(11), 3059; https://doi.org/10.3390/en14113059 - 25 May 2021
Cited by 1 | Viewed by 626
Abstract
As the integration of large-scale wind energy is increasing into the electricity grids, the role of wind energy suppliers should be investigated as a price-maker as their participation would influence the locational marginal price (LMP) of electricity. The existing bidding strategies for a [...] Read more.
As the integration of large-scale wind energy is increasing into the electricity grids, the role of wind energy suppliers should be investigated as a price-maker as their participation would influence the locational marginal price (LMP) of electricity. The existing bidding strategies for a wind energy supplier faces limitations with respect to the potential cooperation, other competitors’ bidding behavior, network loss, and uncertainty of wind production (WP) and balancing market price (BMP). Hence, to solve these problems, a novel bidding strategy (BS) for a wind power supplier as a price-maker has been proposed in this paper. The new algorithm, called the evolutionary game approach (EGA) inspired hybrid particle swarm optimization and improved firefly algorithm (HPSOIFA), has been proposed to handle the bidding issue. The bidding behavior of power suppliers, including conventional power suppliers, has been encoded as one species to obtain the equilibrium where the EGA can explore dynamically reasonable behavior changes of the opponents. Each species of behavior change has been exploited by the HPSOIFA to improve the optimization solutions. Moreover, a deep learning algorithm, namely deep belief network, has been implemented for improving the accuracy of the forecasting results considering the WP and BMP, and the uncertainty revealed in the WP and BMP has been modeled by quantile regression (QR). Finally, the Shapley value (SV) has been calculated to estimate the benefits of cooperative power suppliers. The presented case studies have verified that the proposed algorithm and the established bidding strategy exhibit higher effectiveness. Full article
Show Figures

Figure 1

Article
Comparison of Machine Learning Methods in Electrical Tomography for Detecting Moisture in Building Walls
Energies 2021, 14(10), 2777; https://doi.org/10.3390/en14102777 - 12 May 2021
Cited by 3 | Viewed by 571
Abstract
This paper presents the results of research on the use of machine learning algorithms and electrical tomography in detecting humidity inside the walls of old buildings and structures. The object of research was a historical building in Wrocław, Poland, built in the first [...] Read more.
This paper presents the results of research on the use of machine learning algorithms and electrical tomography in detecting humidity inside the walls of old buildings and structures. The object of research was a historical building in Wrocław, Poland, built in the first decade of the 19th century. Using the prototype of an electric tomograph of our own design, a number of voltage measurements were made on selected parts of the building. Many algorithmic methods have been preliminarily analyzed. Ultimately, the three models based on machine learning were selected: linear regression with SVM (support vector machine) learner, linear regression with least squares learner, and a multilayer perceptron neural network. The classical Gauss–Newton model was also used in the comparison. Both the experiments based on real measurements and simulation data showed a higher efficiency of machine learning methods than the Gauss–Newton method. The tomographic methods surpassed the point methods in measuring the dampness in the walls because they show a spatial image of the interior and not separate points of the examined cross-section. Research has shown that the selection of a machine learning model has a large impact on the quality of the results. Machine learning has a greater potential to create correct tomographic reconstructions than traditional mathematical methods. In this research, linear regression models performed slightly worse than neural networks. Full article
Show Figures

Figure 1

Article
Transformer Oil Quality Assessment Using Random Forest with Feature Engineering
Energies 2021, 14(7), 1809; https://doi.org/10.3390/en14071809 - 24 Mar 2021
Cited by 2 | Viewed by 568
Abstract
Machine learning is widely used as a panacea in many engineering applications including the condition assessment of power transformers. Most statistics attribute the main cause of transformer failure to insulation degradation. Thus, a new, simple, and effective machine-learning approach was proposed to monitor [...] Read more.
Machine learning is widely used as a panacea in many engineering applications including the condition assessment of power transformers. Most statistics attribute the main cause of transformer failure to insulation degradation. Thus, a new, simple, and effective machine-learning approach was proposed to monitor the condition of transformer oils based on some aging indicators. The proposed approach was used to compare the performance of two machine-learning classifiers: J48 decision tree and random forest. The service-aged transformer oils were classified into four groups: the oils that can be maintained in service, the oils that should be reconditioned or filtered, the oils that should be reclaimed, and the oils that must be discarded. From the two algorithms, random forest exhibited a better performance and high accuracy with only a small amount of data. Good performance was achieved through not only the application of the proposed algorithm but also the approach of data preprocessing. Before feeding the classification model, the available data were transformed using the simple k-means method. Subsequently, the obtained data were filtered through correlation-based feature selection (CFsSubset). The resulting features were again retransformed by conducting the principal component analysis and were passed through the CFsSubset filter. The transformation and filtration of the data improved the classification performance of the adopted algorithms, especially random forest. Another advantage of the proposed method is the decrease in the number of the datasets required for the condition assessment of transformer oils, which is valuable for transformer condition monitoring. Full article
Show Figures

Figure 1

Review

Jump to: Research

Review
Perspectives of Convertors and Communication Aspects in Automated Vehicles, Part 1: Convertors and Condition Monitoring
Energies 2021, 14(7), 1795; https://doi.org/10.3390/en14071795 - 24 Mar 2021
Viewed by 648
Abstract
A critical survey has been conducted on high energy-efficient bidirectional converters, various topologies that effectively meet the automated vehicle requirements, and 24 GHz/77 GHz low-profile antennas (for automotive radar applications). The present survey has been identified into two parts on the current topic [...] Read more.
A critical survey has been conducted on high energy-efficient bidirectional converters, various topologies that effectively meet the automated vehicle requirements, and 24 GHz/77 GHz low-profile antennas (for automotive radar applications). The present survey has been identified into two parts on the current topic of study as perspectives and challenges. Part 1 of this survey covers energy-efficient power electronic convertor topologies and condition monitoring aspects of convertors to enhance the lifespan and improve performance. Condition-monitoring issues concerning the abnormalities of electrical components, high switching frequencies, electromagnetic interference, leakage currents, and unwanted joint ruptures have also been emphasized. It is observed that composite converters are proficient for automated hybrid electric vehicles due to fast dynamic response and reduced component count. Importantly, electrical component failures in power electronic converters are most common and need attention for the effective operation of the bidirectional converters. Hence, condition monitoring implementation schemes have also been summarized. Part 2 of this survey focuses on 24 and 77 GHz low-profile (microstrip-based) antennas for automotive radar applications, types of antenna structures, feed mechanisms, dielectric material requirements, design techniques, and performance parameters. The discussion in Part 2 also covers feed methodologies, beam scanning concepts, and side-lobe levels on the autonomous vehicle communication activities. Full article
Show Figures

Figure 1

Review
Perspectives of Convertors and Communication Aspects in Automated Vehicles, Part 2: Printed Antennas and Sensors for Automotive Radars
Energies 2021, 14(6), 1656; https://doi.org/10.3390/en14061656 - 17 Mar 2021
Viewed by 602
Abstract
Automated vehicles are becoming popular across the communities of e-transportation across the globe. Hybrid electric vehicles and autonomous vehicles have been subjected to critical research for decades. The research outcomes pertinent to this topic in the literature have been motivated by the industry [...] Read more.
Automated vehicles are becoming popular across the communities of e-transportation across the globe. Hybrid electric vehicles and autonomous vehicles have been subjected to critical research for decades. The research outcomes pertinent to this topic in the literature have been motivated by the industry and researchers to emphasize automated vehicles. Part 1 of this survey addressed the critical aspects that concern the bidirectional converter topologies and condition monitoring activities. In the present part, 24- and 77-GHz low-profile printed antennas are studied for automotive radar applications. These antennas are mounted on automated vehicles to avoid collision and are used for radio tracking applications. The present paper states the types of antenna structures, feed mechanisms, dielectric material requirements, design techniques, performance parameters, and challenges at 24- and 77-GHz resonating frequency applications. The recent developments in feed methodologies, beam scanning concepts, and the effect of sidelobe levels are addressed. Furthermore, the reasons behind the transition from 24 to 77 GHz are reported in detail. The recent advances in the application of various sensor schemes in an automated vehicle have also been discussed. Full article
Show Figures

Figure 1

Back to TopTop