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Energies
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Fault Detection and Diagnosis of Electrical Power System Equipments
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Fault Detection and Diagnosis of Electrical Power System Equipments

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".

Deadline for manuscript submissions: closed (24 February 2023) | Viewed by 7669

Special Issue Editors

Prof. Dr. Ibrahim B.M. Taha

E-Mail Website
Guest Editor
Department of Electrical Engineering, College of Engineering, Taif University, PO Box. 11099, Taif 21944, Saudi Arabia
Interests: steady state and transient stability of HVDC systems; FACTS; load forecasting; multi-level inverters; dissolved gas analysis; artificial intelligent technique applications; PV system fault detection; distance adaptive protective relays
Prof. Dr. Nagy Elkalashy

E-Mail Website
Guest Editor
Electrical Engineering Department, Faculty of Engineering, Menoufia University, Shebin Elkom 32511, Egypt
Interests: power systems protection; fault location determination; smart grids; power system transients; high-voltage engineering; switchgear technology; digital signal processing for power system applications
Dr. Hossam A. Abd El-Ghany

E-Mail Website
Guest Editor
Electrical Power and Machines Engineering Department, Faculty of Engineering, Tanta University, Tanta 31521, Egypt
Interests: fault detection and diagnosis in electrical power systems; FACTS; distance relays; power system protection

Special Issue Information

Dear Colleagues,

Developing fault detection and diagnosis algorithms for electrical power equipment is necessary to improve the reliability and efficiency of electrical power systems. Different converter types enable the connections between renewable energy systems (RESs) and AC or DC grids. This complexity requires the monitoring of different electrical power systems, including RESs, which are essential due to their rapid expansion for different purposes and applications. The monitoring of electrical power equipment is vital to detect and diagnose faults that might affect the operating state or performance of a power system. Methods for electrical power system equipment monitoring include HVDC and HVAC transmission systems, hybrid AC and DC distribution systems, RES fault-detection systems, classification and locations, and transformer fault detection and diagnosis based on DGA. The main drawbacks of most recent works regarding the fault detection and diagnosis of electrical power system components are that the suggested models require large case studies and long periods of training and testing. Therefore, simple detection and artificial and machine learning approaches for fault detection and diagnosis are crucial to facilitating the detection process without the requirement for complicated processes or classification methods.

The efficient monitoring of fault-detection systems reduces overall system cost, system discontinuity, and hardware-based redundancy realization. Additionally, annual maintenance plans and consequent costs can be optimized. Topics of interest for this Special Issue include (with emphasis on electrical power equipment), but are not limited to:

  • Electrical power equipment monitoring;
  • Condition monitoring;
  • Data-driven approaches, including machine learning methods;
  • Electrical power devices;
  • Fault analysis;
  • Fault detection and diagnosis;
  • Fault ride through;
  • Incipient faults;
  • Online and offline condition monitoring techniques;
  • Signal-based approaches for feature extraction.

Prof. Dr. Ibrahim B.M. Taha
Prof. Dr. Nagy Elkalashy
Dr. Hossam A. Abd El-Ghany
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

  • condition monitoring
  • cyber-attack detection
  • cyber-physical systems
  • data-driven approaches, including machine learning methods
  • electrical power devices
  • fault detection and diagnosis
  • fault ride through
  • fault-tolerant control
  • fault detection, classification, and locations
  • incipient faults
  • observer design
  • signal-based approaches for feature extraction
  • electrical power system equipment

Published Papers (4 papers)

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Research

15 pages, 1904 KiB  
Article
Early Detection of Health Condition Degradation of Circuit Breaker Based on Electrical Quantity Monitoring
by Lisheng Li, Bin Wang, Yang Liu, Haidong Yu, Shidong Zhang and Min Huang
Energies 2023, 16(14), 5581; https://doi.org/10.3390/en16145581 - 24 Jul 2023
Cited by 1 | Viewed by 1088
Abstract
Circuit breakers on the filter bank branches in converter stations are vulnerable to contact wear and mechanical deterioration caused by frequent operations, which can lead to circuit breaker breakdowns and explosions. It is imperative to conduct research on the early detection of abnormal [...] Read more.
Circuit breakers on the filter bank branches in converter stations are vulnerable to contact wear and mechanical deterioration caused by frequent operations, which can lead to circuit breaker breakdowns and explosions. It is imperative to conduct research on the early detection of abnormal states in circuit breakers. Existing electrical quantity-based detection methods are constrained by a priori assumptions, and their measurement methods are susceptible to interference, leading to misjudgments. To address this issue, this paper examines the influence of changes in critical breakdown field strength and contact spacing on circuit breaker operation states. It also proposes a technical scheme that employs breakdown current values to comprehensively characterize circuit breaker operation states, replacing the use of critical breakdown field strength and contact spacing. An early detection method for abnormal circuit breaker states based on a sequence of breakdown current ratios at different times is proposed, and its effectiveness is verified through simulation and field recording data. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis of Electrical Power System Equipments)
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23 pages, 8888 KiB  
Article
Algorithm for Fast Detection of Stator Turn Faultsin Variable-Speed Synchronous Generators
by Rodolfo V. Rocha and Renato M. Monaro
Energies 2023, 16(5), 2491; https://doi.org/10.3390/en16052491 - 06 Mar 2023
Viewed by 1133
Abstract
Faults between stator winding turns of synchronous generators have led to specific changes in the harmonic content of currents. In this paper, these changes are used to detect faults in variable-speed synchronous generators connected to three-level converters during their operation. Currents typically measured [...] Read more.
Faults between stator winding turns of synchronous generators have led to specific changes in the harmonic content of currents. In this paper, these changes are used to detect faults in variable-speed synchronous generators connected to three-level converters during their operation. Currents typically measured for control purposes are used to avoid installation of additional sensors. The neutral point current of the three-level converter is also evaluated under faults in the generator. Encoder-tuned dynamic filters based on Park transformation and Fourier coefficients together with low-pass filters are used to detect the selected harmonics under variable speeds. The geometric loci of these components are proposed as a method to distinguish between healthy and faulty conditions. Simulation and experimental data are used to test sensitivity, selectivity and detection time of the proposed technique for different fault types. Generalization for a different generator is also presented and tested. Most fault cases were detected using the harmonics. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis of Electrical Power System Equipments)
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17 pages, 2417 KiB  
Article
Application of the Analysis of Variance (ANOVA) in the Interpretation of Power Transformer Faults
by Bonginkosi A. Thango
Energies 2022, 15(19), 7224; https://doi.org/10.3390/en15197224 - 01 Oct 2022
Cited by 8 | Viewed by 2662
Abstract
Electrical power transformers are the most exorbitant and tactically prominent components of the South African electrical power grid. In contrast, they are burdened by internal winding faults predominantly on account of insulation system failure. It is essential that these faults must be swiftly [...] Read more.
Electrical power transformers are the most exorbitant and tactically prominent components of the South African electrical power grid. In contrast, they are burdened by internal winding faults predominantly on account of insulation system failure. It is essential that these faults must be swiftly and precisely uncovered and suitable measures should be adopted to separate the faulty unit from the entire system. The frequency response analysis (FRA) is a technique for tracking a transformer’s mechanical integrity. Nevertheless, classifying the category of the fault and its gravity by benchmarking measured FRA responses is still backbreaking and for the most part, anchored in personnel proficiency. This work presents a quantum leap to normalize the FRA interpretation procedure by suggesting an interpretation code criteria based on an empirical survey of transformers ranging from 315 kVA to 40 MVA. The study then proposes an analysis of variance (ANOVA) based interpretation tool for diagnosing the statistical significance of FRA fingerprint and measured profiles. The latter cannot be relied upon by an expert or by the naked eye. Additionally, descriptive FRA frequency sub-region data statistics are proposed to evaluate the shift in both the magnitude and measuring frequency characteristics to formulate the recommended interpretation code criteria. To corroborate the code criteria by incorporating ANOVA and descriptive statistics, the study presents various case studies with unknown FRA profiles for fault diagnosis. The results constitute proof of the reliability of the proposed code criteria and a proposed hybrid of ANOVA and descriptive statistics. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis of Electrical Power System Equipments)
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22 pages, 7379 KiB  
Article
Experimental Diagnosis of Broken Rotor Bar Faults in Induction Motors at Low Slip via Hilbert Envelope and Optimized Subtractive Clustering Adaptive Neuro-Fuzzy Inference System
by Seif Eddine Chehaidia, Hakima Cherif, Musfer Alraddadi, Mohamed Ibrahim Mosaad and Abdelaziz Mahmoud Bouchelaghem
Energies 2022, 15(18), 6746; https://doi.org/10.3390/en15186746 - 15 Sep 2022
Cited by 2 | Viewed by 1455
Abstract
Knowledge of the distinctive frequencies and amplitudes of broken rotor bar (BRB) faults in the induction motor (IM) is essential for most fault diagnosis methods. Fast Fourier transform (FFT) is widely applied to diagnose the faults within BRBs. However, this method does not [...] Read more.
Knowledge of the distinctive frequencies and amplitudes of broken rotor bar (BRB) faults in the induction motor (IM) is essential for most fault diagnosis methods. Fast Fourier transform (FFT) is widely applied to diagnose the faults within BRBs. However, this method does not provide satisfactory results if it is applied directly to the stator current signal at low slip because a high-resolution spectrum is required to separate the different components of the frequency. To address this problem, this paper proposes an efficient method based on a Hilbert fast Fourier transform (HFFT) approach, which is used to extract the envelope from the stator current using the Hilbert transform (HT) at low slip. Then, the stator current envelope is analyzed using the fast Fourier transform (FFT) to obtain the amplitude and frequency of the particular harmonic. These data were recently collected and selected as BRB fault features and were employed as adaptive neuro-fuzzy inference system (ANFIS) inputs for BRB fault autodiagnosis and classification. To identify the BRB defect by determining the number of broken bars in the rotor, two ANFIS models are proposed: ANFIS grid partitioning (ANFIS-GP) and ANFIS-subtractive clustering (ANFIS-SC). To validate the effectiveness of the proposed method, three different motors were used during experiments under various loads; the first was with one broken bar, the second was with two adjacent broken bars, and the third was a healthy motor. The obtained results confirmed the effectiveness and the robustness of the proposed method, which is based on the combination of HFFT-ANFIS-SC to diagnose the BRB faults and quantify the number of broken bars under different load conditions (under low and high slip) precisely with minimal errors (this method had an MSE of 10-14 and 10-7 for the RMSE) compared to the method based on the combination of HFFT-ANFIS-GP. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis of Electrical Power System Equipments)
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