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Sensors and Fault Diagnostics in Power System

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: closed (20 January 2024) | Viewed by 9704

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Special Issue Editors

Dr. Michał Kunicki

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Guest Editor

Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Proszkowska 76, 45-758 Opole, Poland
Interests: measurements and metrology; technical diagnostics; high-voltage engineering; partial discharges
Special Issues, Collections and Topics in MDPI journals

Dr. Jan Fulneček

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Guest Editor

Department of Electrical Power Engineering, VSB Technical University of Ostrava, 17 listopadu 15/2172, Potuba, 708 00 Ostrava, Czech Republic
Interests: electric power engineering; high voltage engineering; partial discharges; fault diagnostics; overhead lines; condition-based maintenance

Prof. Dr. Pawel Rozga

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Guest Editor

Institute of Electrical Power Engineering, Lodz University of Technology, Stefanowskiego 20, 90-537 Lodz, Poland
Interests: high-voltage engineering; pre-breakdown and breakdown phenomena in dielectric liquids; alternative dielectric liquids for electrical purposes; statistics in electrical engineering; partial discharges; insulation of power transformers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The adequate technical condition assessment of key apparatus is a crucial assumption in the provision of reliable and continuous electric power delivery to customers. To meet this requirement, any fault in the power system must be detected and diagnosed as early as possible, with particular emphasis on the precision of the diagnostic process. Various on-line and off-line diagnostic methods are widely applied to the early detection of any system malfunctions. Furthermore, a number of different sensors may also be applied to capture selected physical quantities that may be used to indicate the type of potential faults. A specific fault diagnostic process is typically performed by experts in the field; however, artificial intelligence (AI)-based systems are increasingly being proposed to support the decision-making process related to this task. The essential step of the fault diagnostic process is signal analysis, supported by feautures including (but not limited to) signal processing, feature extraction, modelling and prediction methods.

With this in mind, we are launching a Special Issue entitled Sensors and Fault Diagnostics in Power System. We invite researchers to contribute high-quality original research or technical papers, reviews, and case studies to this Special Issue. Practical papers in which either examples of good present practice are described and disseminated, or new proposals of improvements and applications of innovative solutions regarding sensor design, evaluation and applications, signal processing and measurements, prediction, or modelling and classification regarding fault diagnostics in power engineering are particularly sought. The proposed papers may concern both individual elements of the power system (transformers, cables, generators, AC/DC converters, etc.), as well as comprehensive system approaches. Theoretical papers of high technical merit relying on mathematical arguments and computation are also welcomed, but authors are asked to highlight and justify their potential industrial applications.

Dr. Michał Kunicki
Dr. Jan Fulneček
Prof. Dr. Pawel Rozga
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. Sensors 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

fault diagnostics
condition-based maintenance
predictive maintenance
decision support system
high voltage
cable
transformer
overhead line
insulation
dielectrics
substation
transmission
distribution
generation
reliability

Published Papers (9 papers)

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Research

20 pages, 9808 KiB

Open AccessArticle

Measurement of Transient Overvoltages by Capacitive Electric Field Sensors

by Felipe L. Probst, Michael Beltle and Stefan Tenbohlen

Sensors 2024, 24(5), 1357; https://doi.org/10.3390/s24051357 - 20 Feb 2024

Viewed by 670

Abstract

The accurate measurement and the investigation of electromagnetic transients are becoming more important, especially with the increasing integration of renewable energy sources into the power grid. These sources introduce new transient phenomena due to the extensive use of power electronics. To achieve this, the measurement devices must have a broadband response capable of measuring fast transients. This paper presents a capacitive electric field sensor-based measurement system to measure transient overvoltages in high-voltage substations. The concept and design of the measurement system are first presented. Then, the design and concept are validated using tests performed in a high-voltage laboratory. Afterwards, two different calibration techniques are discussed: the simplified method (SM) and the coupling capacitance compensation (CCC) method. Finally, three recorded transients are evaluated using the calibration methods. The investigation revealed that the SM tends to overestimate the maximum overvoltage, highlighting the CCC method as a more suitable approach for calibrating transient overvoltage measurements. This measurement system has been validated using various measurements and can be an efficient and flexible solution for the long-term monitoring of transient overvoltages in high-voltage substations. Full article

(This article belongs to the Special Issue Sensors and Fault Diagnostics in Power System)

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24 pages, 6594 KiB

Open AccessArticle

Voiceprint Fault Diagnosis of Converter Transformer under Load Influence Based on Multi-Strategy Improved Mel-Frequency Spectrum Coefficient and Temporal Convolutional Network

by Hui Li, Qi Yao and Xin Li

Sensors 2024, 24(3), 757; https://doi.org/10.3390/s24030757 - 24 Jan 2024

Viewed by 717

Abstract

In order to address the challenges of low recognition accuracy and the difficulty in effective diagnosis in traditional converter transformer voiceprint fault diagnosis, a novel method is proposed in this article. This approach takes account of the impact of load factors, utilizes a multi-strategy improved Mel-Frequency Spectrum Coefficient (MFCC) for voiceprint signal feature extraction, and combines it with a temporal convolutional network for fault diagnosis. Firstly, it improves the hunter–prey optimizer (HPO) as a parameter optimization algorithm and adopts IHPO combined with variational mode decomposition (VMD) to achieve denoising of voiceprint signals. Secondly, the preprocessed voiceprint signal is combined with Mel filters through the Stockwell transform. To adapt to the stationary characteristics of the voiceprint signal, the processed features undergo further mid-temporal processing, ultimately resulting in the implementation of a multi-strategy improved MFCC for voiceprint signal feature extraction. Simultaneously, load signal segmentation is introduced for the diagnostic intervals, forming a joint feature vector. Finally, by using the Mish activation function to improve the temporal convolutional network, the IHPO-ITCN is proposed to adaptively optimize the size of convolutional kernels and the number of hidden layers and construct a transformer fault diagnosis model. By constructing multiple sets of comparison tests through specific examples and comparing them with the traditional voiceprint diagnostic model, our results show that the model proposed in this paper has a fault recognition accuracy as high as 99%. The recognition accuracy was significantly improved and the training speed also shows superior performance, which can be effectively used in the field of multiple fault diagnosis of converter transformers. Full article

(This article belongs to the Special Issue Sensors and Fault Diagnostics in Power System)

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15 pages, 2082 KiB

Open AccessArticle

Exploitation Perspective Index as a Support of the Management of the Transformer Fleet

by Michał Kunicki, Sebastian Borucki and Jan Fulneček

Sensors 2023, 23(21), 8681; https://doi.org/10.3390/s23218681 - 24 Oct 2023

Viewed by 646

Abstract

This paper presents an alternative approach to the Transformer Assessment Index (TAI) by proposing a relatively simple rating method called the Exploitation Perspective Index (EPI). The method provides two numerical indicators: the first reflects the overall technical condition of the particular unit, and the second shows the condition of the unit in the context of the entire fleet. The objective of the EPI method is to support the decision-making process regarding the technical condition assessment of each of the transformers in the target population, considering not only technical but also economic aspects of transformer maintenance. Application of the method is described step by step, including input data, parametrization of the weights, and interpretation of the output results it provides. The proposed method is evaluated by two representative use cases and compared with two other methods. As a result, EPI confirms its applicability, and it has already been successfully implemented by the electric power industry. EPI can be potentially freely adopted for any transformer fleet, as well as for the specific situation of the utility, by adjusting the relevant parameters. Full article

(This article belongs to the Special Issue Sensors and Fault Diagnostics in Power System)

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17 pages, 708 KiB

Open AccessArticle

Smart Grid Outlier Detection Based on the Minorization–Maximization Algorithm

by Lina Qiao, Wengen Gao, Yunfei Li, Xinxin Guo, Pengfei Hu and Feng Hua

Sensors 2023, 23(19), 8053; https://doi.org/10.3390/s23198053 - 24 Sep 2023

Viewed by 877

Abstract

Outliers can be generated in the power system due to aging system equipment, faulty sensors, incorrect line connections, etc. The existence of these outliers will pose a threat to the safe operation of the power system, reduce the quality of the data, affect the completeness and accuracy of the data, and thus affect the monitoring analysis and control of the power system. Therefore, timely identification and treatment of outliers are essential to ensure stable and reliable operation of the power system. In this paper, we consider the problem of detecting and localizing outliers in power systems. The paper proposes a Minorization–Maximization (MM) algorithm for outlier detection and localization and an estimation of unknown parameters of the Gaussian mixture model (GMM). To verify the performance of the method, we conduct simulation experiments by simulating different test scenarios in the IEEE 14-bus system. Numerical examples show that in the presence of outliers, the MM algorithm can detect outliers better than the traditional algorithm and can accurately locate outliers with a probability of more than

95 %

. Therefore, the algorithm provides an effective method for the handling of outliers in the power system, which helps to improve the monitoring analyzing and controlling ability of the power system and to ensure the stable and reliable operation of the power system. Full article

(This article belongs to the Special Issue Sensors and Fault Diagnostics in Power System)

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12 pages, 1877 KiB

Open AccessArticle

Simplifying Rogowski Coil Modeling: Simulation and Experimental Verification

by Alessandro Mingotti, Christian Betti, Roberto Tinarelli and Lorenzo Peretto

Sensors 2023, 23(19), 8032; https://doi.org/10.3390/s23198032 - 22 Sep 2023

Viewed by 1007

Abstract

The integration of renewable energy sources, electric vehicles, and other electrical assets has introduced complexities in monitoring and controlling power networks. Consequently, numerous grid nodes have been equipped with sensors and complex measurement systems to enhance network observability. Additionally, real-time power network simulators have become crucial tools for predicting and estimating the behavior of electrical quantities at different network components, such as nodes, branches, and assets. In this paper, a new user-friendly model for Rogowski coils is presented and validated. The model’s simplicity stems from utilizing information solely from the Rogowski coil datasheet. By establishing the input/output relationship, the output of the Rogowski coil is obtained. The effectiveness and accuracy of the proposed model are tested using both simulations and commercially available Rogowski coils. The results confirm that the model is simple, accurate, and easily implementable in various simulation environments for a wide range of applications and purposes. Full article

(This article belongs to the Special Issue Sensors and Fault Diagnostics in Power System)

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17 pages, 2673 KiB

Open AccessArticle

Decentralized Sensor Fault-Tolerant Control of DC Microgrids Using the Attracting Ellipsoid Method

by Hisham M. Soliman, Ehab H. E. Bayoumi, Farag A. El-Sheikhi and Michele De Santis

Sensors 2023, 23(16), 7160; https://doi.org/10.3390/s23167160 - 14 Aug 2023

Cited by 1 | Viewed by 739

Abstract

System stability deterioration in microgrids commonly occurs due to unpredictable faults and equipment malfunctions. Recently, robust control techniques have been used in microgrid systems to address these difficulties. In this paper, for DC-islanded microgrids that have sensors faults, a new passive fault-tolerant control strategy is developed. The suggested approach can be used to maintain system stability in the presence of flaws, such as faulty actuators and sensors, as well as component failures. The suggested control is effective when the fault is never recognized (or when the fault is not being precisely known, and some ambiguity in the fault may be interpreted as uncertainty in the system’s dynamics following the fault). The design is built around a derived sufficient condition in the context of linear matrix inequalities (LMIs) and the attractive ellipsoid technique. The ellipsoidal stabilization idea is to bring the state trajectories into a small region including the origin (an ellipsoid with minimum volume) and the trajectories will not leave the ellipsoid for the future time. Finally, computational studies on a DC microgrid system are carried out to assess the effectiveness of the proposed fault-tolerant control approach. When compared with previous studies, the simulation results demonstrate that the proposed control technique can significantly enhance the reliability and efficiency of DC microgrid systems. Full article

(This article belongs to the Special Issue Sensors and Fault Diagnostics in Power System)

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20 pages, 6159 KiB

Open AccessArticle

Condition Assessment of Natural Ester–Mineral Oil Mixture Due to Transformer Retrofilling via Sensing Dielectric Properties

by Hesham S. Karaman, Diaa-Eldin A. Mansour, Matti Lehtonen and Mohamed M. F. Darwish

Sensors 2023, 23(14), 6440; https://doi.org/10.3390/s23146440 - 16 Jul 2023

Cited by 8 | Viewed by 1737

Abstract

Mineral oil (MO) is the most popular insulating liquid that is used as an insulating and cooling medium in electrical power transformers. Indeed, for green energy and environmental protection requirements, many researchers introduced other oil types to study the various characteristics of alternative insulating oils using advanced diagnostic tools. In this regard, natural ester oil (NEO) can be considered an attractive substitute for MO. Although NEO has a high viscosity and high dielectric loss, it presents fire safety and environmental advantages over mineral oil. Therefore, the retrofilling of aged MO with fresh NEO is highly recommended for power transformers from an environmental viewpoint. In this study, two accelerated aging processes were applied to MO for 6 and 12 days to simulate MO in service for 6 and 12 years. Moreover, these aged oils were mixed with 80% and 90% fresh NEO. The dielectric strength, relative permittivity, and dissipation factor were sensed using a LCR meter and oil tester devices for all prepared samples to support the condition assessment performance of the oil mixtures. In addition, the electric field distribution was analyzed for a power transformer using the oil mixtures. Furthermore, the dynamic viscosity was measured for all insulating oil samples at different temperatures. From the obtained results, the sample obtained by mixing 90% natural ester oil with 10% mineral oil aged for 6 days is considered superior and achieves an improvement in dielectric strength and relative permittivity by approximately 43% and 48%, respectively, compared to fresh mineral oil. However, the dissipation factor was increased by approximately 20% but was at an acceptable limit. On the other hand, for the same oil sample, due to the higher molecular weight of the NEO, the viscosities of all mixtures were at a higher level than the mineral oil. Full article

(This article belongs to the Special Issue Sensors and Fault Diagnostics in Power System)

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24 pages, 9756 KiB

Open AccessArticle

Realization of a Test Tool for Diagnosis of Contact Resistance and Measurement of Selected Types of Conductive Materials

by Petr Kacor, Petr Bernat, Tomas Mlcak and Leopold Hrabovsky

Sensors 2023, 23(13), 5867; https://doi.org/10.3390/s23135867 - 24 Jun 2023

Viewed by 1223

Abstract

Contact connections in electrical machines and apparatus are important elements in the whole power supply network and a high level of reliability is expected there. Contact resistance is a fundamental criterion in the design of an electrical contact or contact system. The contact resistance should be as low as possible to minimize losses due to the current passage and the related heating of the contact connection. The value of the contact resistance depends on the material used, the value of the applied force, the type of contact, and, last but not least, the quality of the surface and chemical layers. In this paper, an initial diagnosis of the contact material is performed based on the determination of the sample’s specific resistivity by the four-wire method and the evaluation of the measurement uncertainty. The work is followed by the design of a testing device that uses crossed bars to measure the change in contact resistance as a function of the magnitude of the applied force. An analysis of the sample mounting method is performed here using FEM simulations of the current field and shows the interaction between the holder and the sample in terms of current line transfer. The proposed system is then used for experimental measurements of the material-dependent coefficient K_C for verification of existing or newly developed materials in electrical engineering, where the values of the K_C coefficient are not known. Finally, the paper also deals with the measurement of fritting voltage for individual contact pairs having surface quality corresponding to brushing. Full article

(This article belongs to the Special Issue Sensors and Fault Diagnostics in Power System)

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18 pages, 7306 KiB

Open AccessArticle

An Improved Magnetic Field Method to Locate the Grounding Conductor

by Fan Yang, Songlin Liu, Yijun Lai, Jiayuan Hu and Shaohua Wang

Sensors 2023, 23(8), 3879; https://doi.org/10.3390/s23083879 - 11 Apr 2023

Cited by 1 | Viewed by 1188

Abstract

The location of the grounding grid conductors is critical for performing corrosion diagnosis and maintenance work. An improved magnetic field differential method to locate the unknown grounding grid based on truncation errors and the round-off errors analysis is presented in this paper. It was proven that a different order of the magnetic field derivative can be used to determine the position of the grounding conductor according to the peak value of the derivative. Due to the accumulative error of higher differentiation, the truncation error and rounding error were used to analyze to accumulative error and to determine the optimal step size to measure and calculate the higher differentiation. The possible range and probability distribution of the two kinds of errors at each order are described, and the index of peak position error was derived, which can be used to locate the grounding conductor in the power substation. Full article

(This article belongs to the Special Issue Sensors and Fault Diagnostics in Power System)

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