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Condition Monitoring of Power System Components

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

Deadline for manuscript submissions: closed (25 May 2023) | Viewed by 19331

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Special Issue Editors

Dr. Muhammad Shafiq

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Guest Editor

Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia
Interests: power systems and high voltage engineering; condition monitoring; insulation diagnosis; partial discharge; insulation breakdown; high-frequency sensor; measurement and instrumentation; data analysis; signal processing
Special Issues, Collections and Topics in MDPI journals

Dr. Ghulam Amjad Hussain

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Guest Editor

Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia
Interests: power systems; electrical machines; high voltage engineering; power system fault and transient analysis; protection and controls in modern microgrid and smart grid technologies; renewal energy systems and engineering education
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Condition monitoring is considered a proactive approach to observe the changes or degradation of the important parameters of power system components that may lead to malfunctioning, breakdown, or failure of the affected components if not attended to in a timely manner. Networks are growing, the number of components is increasing, and operation of the grid is changing. Various factors, such as rapid proliferation of renewable sources, inclusion of the hybrid network operation (AC and DC), and increased use of high voltage power electronics, are pushing network components (power transformers, switchgears, insulators, and power lines) to operate at complex power supply conditions. Above all, improved reliability is becoming an elevated concern of power grid owners especially considering the already installed aged components which are more vulnerable to additional operational changes. The changing grid demands efficient monitoring solutions, not only emphasizing on the development of improved diagnostic techniques for the assessment of the designed and operational parameters of the power components but also underlining the need for enhanced measurement and data processing capabilities.

This Special Issue invites papers presenting condition monitoring solutions for all grid voltage levels (low, medium, and high voltage) addressing issues linked to the generation, transmission, distribution, and consumer side components. Topics such as diagnosis techniques for component parameters, measurement systems, sensors and transducers, data acquisition and processing, onsite and remote sensing, offline and online monitoring, individual and integrated measurement capability, and other relevant aspects are welcomed. We look forward to receiving your valuable work.

Dr. Muhammad Shafiq
Dr. Ghulam Amjad Hussain
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

Power system
Grid operation
High voltage
Medium voltage
Low voltage
Network component
Monitoring
Proactive diagnosis
Measurement system
Sensors and transducers
Data processing
Signal analysis
Power lines (underground cables and overhead lines)
Power transformer
Switchgear
Substations
Insulators

Published Papers (10 papers)

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Research

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17 pages, 2937 KiB

Open AccessArticle

Subsea Power Cable Health Management Using Machine Learning Analysis of Low-Frequency Wide-Band Sonar Data

by Wenshuo Tang, Keith Brown, Daniel Mitchell, Jamie Blanche and David Flynn

Energies 2023, 16(17), 6172; https://0-doi-org.brum.beds.ac.uk/10.3390/en16176172 - 25 Aug 2023

Cited by 1 | Viewed by 1116

Abstract

Subsea power cables are critical assets for electrical transmission and distribution networks, and highly relevant to regional, national, and international energy security and decarbonization given the growth in offshore renewable energy generation. Existing condition monitoring techniques are restricted to highly constrained online monitoring systems that only prioritize internal failure modes, representing only 30% of cable failure mechanisms, and has limited capacity to provide precursor indicators of such failures or damages. To overcome these limitations, we propose an innovative fusion prognostics approach that can provide the in situ integrity analysis of the subsea cable. In this paper, we developed low-frequency wide-band sonar (LFWBS) technology to collect acoustic response data from different subsea power cable sample types, with different inner structure configurations, and collate signatures from induced physical failure modes as to obtain integrity data at various cable degradation levels. We demonstrate how a machine learning approach, e.g., SVM, KNN, BP, and CNN algorithms, can be used for integrity analysis under a hybrid, holistic condition monitoring framework. The results of data analysis demonstrate the ability to distinguish subsea cables by differences of 5 mm in diameter and cable types, as well as achieving an overall 95%+ accuracy rate to detect different cable degradation stages. We also present a tailored, hybrid prognostic and health management solution for subsea cables, for cable remaining useful life (RUL) prediction. Our findings addresses a clear capability and knowledge gap in evaluating and forecasting subsea cable RUL. Thus, supporting a more advanced asset management and planning capability for critical subsea power cables. Full article

(This article belongs to the Special Issue Condition Monitoring of Power System Components)

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15 pages, 2975 KiB

Open AccessArticle

Online Evaluation Method of CVT Internal Insulation Abnormality Based on Self-Supervised Learning

by Jun He, Zhihao Zhou, Chao Tong, Fan Li, Fangxi Rao and Qiu Xu

Energies 2023, 16(12), 4585; https://0-doi-org.brum.beds.ac.uk/10.3390/en16124585 - 08 Jun 2023

Cited by 1 | Viewed by 982

Abstract

A capacitive voltage transformer (CVT) is one of the electrical quantities measurement devices, and the state of its internal insulation is the key factor for ensuring the accuracy of its measurement of electrical energy. In view of the fact that the traditional real-time evaluation method of a CVT internal insulation anomaly mainly relies on empirical rules and prior knowledge and lacks the ability to independently mine effective features, an online evaluation method of a CVT internal insulation anomaly based on self-supervised learning is proposed. Firstly, an autoencoder is constructed to extract the residual sequence of the CVT secondary voltage and eliminate the influence of primary voltage fluctuation and power system voltage regulation. Without any prior knowledge, the complex dependence of the residual sequences in time and feature dimensions is learned by using a parallel graph attention layer (GATv2). Finally, a joint optimization based on the prediction and reconstruction model is introduced to obtain the abnormal inference score at each timestamp and realize the evaluation of the CVT internal insulation status. Experimental analysis shows that this method can effectively eliminate the influence of primary voltage fluctuation and power system voltage regulation on the online evaluation of the CVT internal insulation status and independently excavate the abnormal characteristics of the CVT secondary voltage to realize real-time monitoring and early warning of the CVT internal insulation status. Full article

(This article belongs to the Special Issue Condition Monitoring of Power System Components)

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20 pages, 1549 KiB

Open AccessArticle

A Case Study on National Electricity Blackout of Turkey

by Lutfu Saribulut, Gorkem Ok and Arman Ameen

Energies 2023, 16(11), 4419; https://0-doi-org.brum.beds.ac.uk/10.3390/en16114419 - 30 May 2023

Cited by 1 | Viewed by 1760

Abstract

The necessary precautions should be taken in order to prevent service interruption during the maintenance and repairing of electricity networks. Among these measures, emergencies that may occur in the network should be foreseen, hazard scenarios should be created, and solutions should be developed. If these are not done, a blackout, which first follows the local regions and eventually results in the collapse of the national electrical network, may take place. In this study, the national blackout of Turkey that occurred on 31 March 2015 is examined. The information about Turkey’s electrical infrastructure and its energy policies was provided, as well as the reliability assessment criteria for power systems and examples of significant blackouts that occurred worldwide. The direct relation between line voltage and system frequency was provided with mathematical derivation by using real data taken from a local industrial zone. Then, a case study is presented to demonstrate this direct relation. The causes, development process, and consequences of the blackout are discussed in detail, and some recommendations are offered to increase the security of the electrical infrastructure and to prevent future occurrences while ensuring the sustainability of the system. Full article

(This article belongs to the Special Issue Condition Monitoring of Power System Components)

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16 pages, 3421 KiB

Open AccessArticle

Dynamic State Evaluation Method of Power Transformer Based on Mahalanobis–Taguchi System and Health Index

by Yunhe Luo, Xiaosong Zou, Wei Xiong, Xufeng Yuan, Kui Xu, Yu Xin and Ruoyu Zhang

Energies 2023, 16(6), 2765; https://0-doi-org.brum.beds.ac.uk/10.3390/en16062765 - 16 Mar 2023

Cited by 2 | Viewed by 1758

Abstract

Health status assessment is the key link of transformer-condition-based maintenance. The health status assessment method of power transformers mostly adopts the method based on the health index, which has the problems of multiple parameters of each component and strong subjectivity in the selection of weight value, which is easily causes misjudgment. However, the existing online monitoring system for dissolved gas in transformer oil (DGA) can judge the normal or abnormal state of the transformer according to the gas concentration in a monitoring cycle. Still, there are problems, such as fuzzy evaluation results and inaccurate judgment. This paper proposes a dynamic state evaluation method for power transformers based on the Mahalanobis–Taguchi system. First, the oil chromatography online monitoring time series is used to screen key features using the Mahalanobis–Taguchi system to reduce the problem of excessive parameters of each component. Then, a Mahalanobis distance (MD) calculation is introduced to avoid subjectivity in weight selection. The health index (HI) model of a single transformer is built using the MD calculated from all DGA data of a single transformer. Box–Cox transformation and 3 σ criteria determine the alert value and threshold value of all transformer His. Finally, taking two transformers as examples, we verify that the proposed method can reflect the dynamic changes of transformer operation status and give early warning on time, avoiding the subjectivity of parameter and weight selection in the health index, which easily causes misjudgment and other problems and can provide a decision-making basis for transformer condition-based maintenance strategies. Full article

(This article belongs to the Special Issue Condition Monitoring of Power System Components)

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16 pages, 4165 KiB

Open AccessArticle

Precision and Accuracy of Pulse Propagation Velocity Measurement in Power Cables

by Ivar Kiitam, Muhammad Shafiq, Maninder Choudhary, Martin Parker, Ivo Palu and Paul Taklaja

Energies 2023, 16(6), 2702; https://0-doi-org.brum.beds.ac.uk/10.3390/en16062702 - 14 Mar 2023

Viewed by 1186

Abstract

The partial discharge (PD) measurement is an important method used in determining the condition of medium- and high-voltage cable insulation. Considering the propagation velocity of PD signals in power cables is necessary for determining the location of PD defects. However, the determination of velocity is not straightforward due to propagation-related attenuation and dispersion, which distorts the PD pulse waveform. This introduces a degree of uncertainty into the pulse velocity as well as the PD source locations determined based on that velocity, which is usually considered to be of constant value in PD analysis. This paper investigates the accuracy of the pulse propagation velocity measurement in power cables. Tests were performed on a medium voltage power cable in a laboratory setting using two sets of PD-specific measurement equipment: a high-frequency current transformer (HFCT) and an IEC 60270-compliant conventional measurement system. The propagation velocities and their statistical variability were determined using both devices to assess the uncertainty of the propagation velocity measurement. The results indicate that the measured velocity is slightly higher in the case of the HFCT and that the 50% peak threshold value should be used rather than the peak value of PD sensor response waveforms to increase the precision of velocity measurements. Full article

(This article belongs to the Special Issue Condition Monitoring of Power System Components)

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20 pages, 8076 KiB

Open AccessArticle

Condition Assessment of Gas Insulated Switchgear Using Health Index and Conditional Factor Method

by Nattapon Panmala, Thanapong Suwanasri and Cattareeya Suwanasri

Energies 2022, 15(24), 9393; https://0-doi-org.brum.beds.ac.uk/10.3390/en15249393 - 12 Dec 2022

Cited by 3 | Viewed by 2083

Abstract

This paper proposes a comprehensive procedure to assess the condition of gas insulated switchgear (GIS) equipment by using the conventional weight and score method and introducing a conditional factor to improve the accuracy of the health index evaluation. Generally, the inspection and testing of GIS components are conducted according to manufacturer recommendations and guidelines in the international standards. However, this raw data has not been simplified and systematically processed for condition assessment. The score and weight technique are applied to transform the physical condition according to visible and measurable aging to numerical values in terms of component and bay health index values. The accuracy of the obtained health index has been improved by a conditional factor, which considers invisible aging factors, such as age, number of switching operations, degree of satisfactory operation, obsolescence, and adequacy of the interrupting rating. Here, a condition evaluation procedure has been developed and compared with the fuzzy logic method and the health index dominant score technique with satisfactory results. Subsequently, the proposed procedure has been developed as web application software to evaluate 175 bays of GIS in both 115 and 230 kV networks of an independent power producer supplying electricity of 3094 MW to a large industrial estate in Thailand. Eight GIS bays showed moderate or poor condition and the proper actions were assigned to prevent their failure. The software is in use in practice as a decision support tool to effectively manage the maintenance tasks and to improve supply reliability. Full article

(This article belongs to the Special Issue Condition Monitoring of Power System Components)

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17 pages, 1559 KiB

Open AccessArticle

Comparison of Traditional Image Segmentation Methods Applied to Thermograms of Power Substation Equipment

by Renan de Oliveira Alves Takeuchi, Leandra Ulbricht, Fabiano Gustavo Silveira Magrin, Francisco Itamarati Secolo Ganacim, Leonardo Göbel Fernandes, Eduardo Félix Ribeiro Romaneli and Jair Urbanetz Junior

Energies 2022, 15(20), 7477; https://0-doi-org.brum.beds.ac.uk/10.3390/en15207477 - 11 Oct 2022

Cited by 2 | Viewed by 1228

Abstract

The variation in the thermal state of electrical energy substation equipment is normally associated with natural wear or equipment failure. This can be detected by infrared thermography, but technically it demands a long time to analyze these images. Computational analysis can allow an automated, more agile, and more efficient analysis to detect overheated regions in thermographic images. Therefore, it is necessary to segment the region of interest in the images; however, the results may diverge depending on the technique used. Thus, this article presents the improvement of four different techniques implemented in Python and applied in a substation under real operating conditions for a period of eleven months. The performance of the four methods was compared using eight statistical performance measures, and the efficiency was measured by the runtime. The segmentation results showed that the methods based on a threshold (Otsu and Histogram-Based Threshold) were fast, with processing times of 0.11 to 0.24 s, but caused excessive segmentation, presenting the lowest accuracy (0.160 and 0.444) and precision (0.004 and 0.049, respectively). The clustering-based methods (Cluster K-means and Fuzzy C-means) showed similar results to each other but were more accurate (0.936 to 1.000), more precise (0.965 to 1.000), and slower, with 2.55 and 38.8 s, respectively, compared to the threshold methods. The Fuzzy C-means method obtained the highest values of specificity, accuracy, and precision among the methods under analysis, followed by the Cluster K-means method. Full article

(This article belongs to the Special Issue Condition Monitoring of Power System Components)

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12 pages, 2511 KiB

Open AccessArticle

Hierarchical Clustering-Based Framework for Interconnected Power System Contingency Analysis

by Bassam A. Hemad, Nader M. A. Ibrahim, Shereen A. Fayad and Hossam E. A. Talaat

Energies 2022, 15(15), 5631; https://0-doi-org.brum.beds.ac.uk/10.3390/en15155631 - 03 Aug 2022

Cited by 3 | Viewed by 1321

Abstract

This paper investigates a conceptual, theoretical framework for power system contingency analysis based on agglomerative hierarchical clustering. The security and integrity of modern power system networks have received considerable critical attention, and contingency analysis plays a vital role in assessing the adverse effects of losing a single element or more on the integrity of the power system network. However, the number of possible scenarios that should be investigated would be enormous, even for a small network. On the other hand, artificial intelligence (AI) techniques are well known for their remarkable ability to deal with massive data. Rapid developments in AI have led to a renewed interest in its applications in many power system studies over the last decades. Hence, this paper addresses the application of the hierarchical clustering algorithm supported by principal component analysis (PCA) for power system contingency screening and ranking. The study investigates the hierarchy clustering under different clustering numbers and similarity measures. The performance of the developed framework has been evaluated using the IEEE 24-bus test system. The simulation results show the effectiveness of the proposed framework for contingency analysis. Full article

(This article belongs to the Special Issue Condition Monitoring of Power System Components)

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23 pages, 6897 KiB

Open AccessArticle

Analysis of the Suitability of Signal Features for Individual Sensor Types in the Diagnosis of Gradual Tool Wear in Turning

by Joanna Kossakowska, Sebastian Bombiński and Krzysztof Ejsmont

Energies 2021, 14(20), 6489; https://0-doi-org.brum.beds.ac.uk/10.3390/en14206489 - 10 Oct 2021

Cited by 5 | Viewed by 1408

Abstract

There are many items in the literature indicating that certain signal features (SFs) of cutting forces, vibrations or acoustic emission are useful for the diagnosis of tool wear in certain single experiments. There is no answer to whether these SFs are universal. The novelty of this article is an attempt to answer these questions and propose a large set of SFs related to tool wear, but without including superfluous SFs. The analysis of the usefulness of the signal properties for the state of the cutting tool in turning was carried out on a large experiment. A number of various SFs obtained for various signal analysis methods were selected for the study. It is found that no SF is always related to the tool wear, so we define many different signal characteristics that can be related to the tool wear (basic set) and automatically select those associated with it in a given machining case. To this end, the relationship between the measures and the wear of the tool was analyzed. Interrelated measures were excluded from it. The obtained results can be used to build a new generation of more effective tool wear diagnostics systems. One of the goals of the tool wear diagnosis system is to save the energy used. The results can also enable the refinement of existing algorithms that predict the energy consumption of a machine. Full article

(This article belongs to the Special Issue Condition Monitoring of Power System Components)

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Review

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20 pages, 3474 KiB

Open AccessReview

A Review of Aging Models for Electrical Insulation in Power Cables

by Maninder Choudhary, Muhammad Shafiq, Ivar Kiitam, Amjad Hussain, Ivo Palu and Paul Taklaja

Energies 2022, 15(9), 3408; https://0-doi-org.brum.beds.ac.uk/10.3390/en15093408 - 06 May 2022

Cited by 16 | Viewed by 4531

Abstract

Electrical insulation is an integral part of power components. The aging of electrical insulation is an undeniable fact that limits the operational lifetime of power components. Apart from regular aging, abnormal stresses and the development of defects are real threats because of their contribution in accelerating the aging rate and thereby leading to a premature failure of the power components. Over the decades, various studies have been carried out to understand the aging behavior of electrical insulation mainly considering electrical and thermal stresses. Similarly, a number of mathematical (aging) models have been developed based on the theoretical and experimental investigations and evidences. However, a dependable formulation of the models that can provide more practical estimation of the insulation degradation profile has not been achieved yet. This paper presents a comprehensive review of the aging models considering single and multistress conditions. Further, the paper discusses possible challenges and barricades averting the conventional models to achieve a suitable accuracy. Finally, suggestions are provided that can be considered to improve the modeling approaches and their performance. Full article

(This article belongs to the Special Issue Condition Monitoring of Power System Components)

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