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Frontiers in Advanced Power Equipment and Research in Condition Diagnostic and Sensing

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

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 11747

Special Issue Editors

State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
Interests: electrical engineering; advanced power equipment; monitoring and diagnosis; optical sensing
Jiangsu Provincial Key Laboratory of Renewable Energy Generation and Power Conversion, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Interests: Online monitoring and diagnosis; optical sensing; substations; Prognostic and health management
Engineering Laboratory of Power Equipment Reliability in Complicated Coastal Environments, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
Interests: assessment of composite materials used in high-voltage engineering with LIBS; reliability of electrical equipment in extreme environments; gas discharge and its application in ceramics fabrication; advanced outdoor materials used in ultrahigh voltage transmission lines

Special Issue Information

Dear Colleagues,

The fast growth of renewable electricity has resulted in new challenges regarding electrical grids. Any failures of the power equipment may lead to a serious blackout. Therefore, there is an urgent demand to develop advanced power equipment and both convenient and low-cost condition diagnostic and sensing methods to ensure the safety of electrical grids.

In particular, the development of advanced power equipment and smart sensing technology is urgent to make breakthroughs in sensing principles, materials, devices, network transmissions, data processing, and comprehensive applications.

In this Special Issue, we aim to provide a forum for colleagues to report on the most up-to-date research results regarding the frontiers of advanced power equipment and research in condition diagnostic and sensing fields, as well as comprehensive surveys of state-of-the-art equipment in relevant specific areas. Both original contributions with theoretical novelty and practical solutions for addressing particular problems are solicited.

The topics of interest include but are not limited to:

  • Novel power equipment structure design, parameter optimization, and implementation;
  • Multi-physics coupling modeling and analysis, including electricity, temperature, and mechanical fields;
  • Multi-functional sensor design and testing;
  • Optical sensors for power equipment detection;
  • MEMS sensors for power equipment detection;
  • Distributed sensing methods;
  • Smart sensing networks;
  • Artificial intelligence-based signal processing algorithms; and
  • Field application cases.

Prof. Dr. Guoming Ma
Prof. Dr. Jun Jiang
Dr. Xilin Wang
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.

Published Papers (7 papers)

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Research

11 pages, 2517 KiB  
Article
Early Warning of High-Voltage Reactor Defects Based on Acoustic–Electric Correlation
by Shuguo Gao, Chao Xing, Zhigang Zhang, Chenmeng Xiang, Haoyu Liu, Hongliang Liu, Rongbin Shi, Sihan Wang and Guoming Ma
Energies 2022, 15(19), 7196; https://0-doi-org.brum.beds.ac.uk/10.3390/en15197196 - 30 Sep 2022
Cited by 1 | Viewed by 1001
Abstract
Traditional high-voltage reactor monitoring and diagnosis research has problems such as high sampling demand, difficulty in noise reduction on site, many false alarms, and lack of on-site data. In order to solve the above problems, this paper proposes an acoustic–electric fusion high-voltage reactor [...] Read more.
Traditional high-voltage reactor monitoring and diagnosis research has problems such as high sampling demand, difficulty in noise reduction on site, many false alarms, and lack of on-site data. In order to solve the above problems, this paper proposes an acoustic–electric fusion high-voltage reactor acquisition system and defect diagnosis method based on reactor pulse current and ultrasonic detection signal. Using the envelope peak signal as the basic detection data, the sampling requirement of the system is reduced. To fill the missing data with partial discharge (PD) information, a method based on k-nearest neighbor (KNN) is proposed. An adaptive noise reduction method is carried out, and a noise threshold calculation method is given for the field sensors. A joint analysis method of acoustic and electrical signals based on correlation significance is established to determine whether a discharge event has occurred based on correlation significance. Finally, the method is applied to a UHV reactor on the spot, which proves the effectiveness of the method proposed in this paper. Full article
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14 pages, 2497 KiB  
Article
Power Transformer Diagnosis Based on Dissolved Gases Analysis and Copula Function
by Xiaoqin Zhang, Hongbin Zhu, Bo Li, Ruihan Wu and Jun Jiang
Energies 2022, 15(12), 4192; https://0-doi-org.brum.beds.ac.uk/10.3390/en15124192 - 07 Jun 2022
Cited by 4 | Viewed by 1257
Abstract
The traditional DGA (Dissolved Gas Analysis) diagnosis method does not consider the dependence between fault characteristic gases and uses the relationship between gas ratio coding and fault type to make the decision. As a tool of the dependence mechanism between variables, a copula [...] Read more.
The traditional DGA (Dissolved Gas Analysis) diagnosis method does not consider the dependence between fault characteristic gases and uses the relationship between gas ratio coding and fault type to make the decision. As a tool of the dependence mechanism between variables, a copula function can effectively analyze the correlation between variables when it cannot determine whether the linear correlation coefficient can correctly measure the correlation between variable relationships. In this paper, the edge variable of a copula function is selected from the fault characteristic gas of a transformer, and the distribution type of the edge variable is fitted at the same time. Then, Bayesian estimation with the Gaussian residual likelihood function is used to fit the parameters of a copula function and a copula function is selected to describe the optimal dependence of the fault characteristic gas of transformer. The relationship between a copula function and the state of transformer is studied. The results show that the copula function boundary with hydrocarbon gas as edge variable can divide the transformer as healthy or defective state. When the cumulative distribution probability (CDF) value of the dissolved gas in the oil in the copula function is close to 0.8, the fluctuation of its gas concentration leads to a sharp change in the probability. Therefore, the analysis of dissolved gas in oil based on a copula function can be used as a powerful technical solution for oil-immersed power transformer fault diagnosis. Full article
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19 pages, 729 KiB  
Article
Recovery Algorithm of Power Metering Data Based on Collaborative Fitting
by Yukun Xu, Xiangyong Kong, Zheng Zhu, Chao Jiang and Shuang Xiao
Energies 2022, 15(4), 1570; https://0-doi-org.brum.beds.ac.uk/10.3390/en15041570 - 21 Feb 2022
Viewed by 1317
Abstract
Electric energy metering plays a crucial role in ensuring fair and equitable transactions between grid companies and power users. With the implementation of the State Grid Corporation’s energy Internet strategy, higher requirements have been put forward for power grid companies to reduce costs [...] Read more.
Electric energy metering plays a crucial role in ensuring fair and equitable transactions between grid companies and power users. With the implementation of the State Grid Corporation’s energy Internet strategy, higher requirements have been put forward for power grid companies to reduce costs and increase efficiency and user service capabilities. Meanwhile, the accuracy and real-time requirements for electric energy measurements have also increased. Electricity information collection systems are mainly used to collect the user-side energy metering data for the power users. Attributed to communication errors, communication delays, equipment failures and other reasons, some of the collected data is missed or confused, which seriously affects the refined management and service quality of power grid companies. How to deal with such data has been one of the important issues in the fields of machine learning and data mining. This paper proposes a collaborative fitting algorithm to solve the problem of missing collected data based on latent semantics. Firstly, a tree structure of user history data is established, and then the user groups adjacent to the user with missing data are obtained from this. Finally, the missing data are recovered using the alternating least-squares matrix factorization algorithm. Through numerical verification, this method has high reliability and accuracy in recoverying the missing data. Full article
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14 pages, 5072 KiB  
Article
Development of Broadband Resistive–Capacitive Parallel–Connection Voltage Divider for Transient Voltage Monitoring
by Shijun Xie, Zhou Mu, Weidong Ding, Zhenbo Wan, Shaochun Su, Chenmeng Zhang, Yu Zhang, Yalong Xia and Donghui Luo
Energies 2022, 15(2), 451; https://0-doi-org.brum.beds.ac.uk/10.3390/en15020451 - 10 Jan 2022
Cited by 6 | Viewed by 1416
Abstract
The on-site measurement of transient voltages is of great significance in analyzing the fault cause of power systems and optimizing the insulation coordination of power equipment. Conventional voltage transformers normally have a narrow bandwidth and are unable to accurately measure various transient voltages [...] Read more.
The on-site measurement of transient voltages is of great significance in analyzing the fault cause of power systems and optimizing the insulation coordination of power equipment. Conventional voltage transformers normally have a narrow bandwidth and are unable to accurately measure various transient voltages in power systems. In this paper, a wideband parallel resistive–capacitive voltage divider is developed, which can be used for online monitoring of transient voltages in a 220 kV power grid. The structures of the high-voltage and low-voltage arms were designed. The internal electric field distribution of the high-voltage arm was analyzed. The influence factors and improvement techniques of the upper frequency limit were studied. The parameters of the elements of the divider were determined. The voltage withstand performances and scale factors under lightning impulses and AC and DC voltages, the temperature stabilities of scale factors and the step response and bandwidth of the developed voltage divider were tested. The results show that the deviations of the scale factors under various voltage waveforms and different temperatures ranging from −20 to 40 °C are within 3%. The withstand voltage meets the relevant requirements specified in IEC60071-1-2011. The step response 10~90% rise time is approximately 29 ns, and the 3 dB bandwidth covers the range of DC to 10 MHz. Full article
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13 pages, 2889 KiB  
Article
Partial Discharge Pulse Segmentation Approach of Converter Transformers Based on Higher Order Cumulant
by Dingqian Yang, Weining Zhang, Guanghu Xu, Tiangeng Li, Jiexin Shen, Yunkai Yue and Shuaibing Li
Energies 2022, 15(2), 415; https://0-doi-org.brum.beds.ac.uk/10.3390/en15020415 - 06 Jan 2022
Cited by 2 | Viewed by 1125
Abstract
As one of the most effective methods to detect the partial discharge (PD) of transformers, high frequency PD detection has been widely used. However, this method also has a bottleneck problem; the biggest problem is the mixed pulse interference under the fixed length [...] Read more.
As one of the most effective methods to detect the partial discharge (PD) of transformers, high frequency PD detection has been widely used. However, this method also has a bottleneck problem; the biggest problem is the mixed pulse interference under the fixed length sampling. Therefore, this paper focuses on the study of a new pulse segmentation technology, which can separate the partial discharge pulse from the sampling signal containing impulse noise so as to suppress the interference of pulse noise. Based on the characteristics of the high-order-cumulant variation at the rising edge of the pulse signal, a method for judging the starting and ending time of the pulse based on the high-order-cumulant is designed, which can accurately extract the partial discharge pulse from the original data. Simulation results show that the location accuracy of the proposed method can reach 94.67% without stationary noise. The field test shows that the extraction rate of the PD analog signal can reach 79% after applying the segmentation method, which has a great improvement compared with a very low location accuracy rate of 1.65% before using the proposed method. Full article
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22 pages, 5058 KiB  
Article
Sinusoidal Noise Removal in PD Measurement Based on Synchrosqueezing Transform and Singular Spectrum Analysis
by Shaorui Qin, Siyuan Zhou, Taiyun Zhu, Shenglong Zhu, Jianlin Li, Zheran Zheng, Shuo Qin, Cheng Pan and Ju Tang
Energies 2021, 14(23), 7967; https://0-doi-org.brum.beds.ac.uk/10.3390/en14237967 - 29 Nov 2021
Cited by 3 | Viewed by 1487
Abstract
In electrical engineering, partial discharge (PD) measurement has been widely used for inspecting and judging insulation conditions of high voltage (HV) apparatus. However, on-site PD measurement easily becomes contaminated by noises. Particularly, sinusoidal noise makes it difficult to recognize real PD signal, thus [...] Read more.
In electrical engineering, partial discharge (PD) measurement has been widely used for inspecting and judging insulation conditions of high voltage (HV) apparatus. However, on-site PD measurement easily becomes contaminated by noises. Particularly, sinusoidal noise makes it difficult to recognize real PD signal, thus leading to the misjudgment of insulation conditions. Therefore, sinusoidal noise removal is necessary. In this paper, instantaneous frequency (IF) is introduced, and the synchrosqueezing transform (SST) as well as singular spectrum analysis (SSA) is proposed for sinusoidal noise removal. A continuous analytic wavelet transform is firstly applied to the noisy PD signal and then the time frequency representation (TFR) is reassigned by SST. Narrow-band sinusoidal noise has fixed IF, while PD signal has much larger frequency range and time-varying IF. Due to the difference, the reassigned TFR enables the sinusoidal noise to be distinguished from PD signal. After synthesizing the signal with the recognized IF, SSA is further applied to signal refinement. At last, a numerical simulation is carried out to verify the effectiveness of the proposed method, and its robustness to white noise is also validated. After the implementation of the proposed method, wavelet thresholding can be further applied for white noise reduction. Full article
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17 pages, 5123 KiB  
Article
Oil Pressure Monitoring for Sealing Failure Detection and Diagnosis of Power Transformer Bushing
by Jiansheng Li, Zhi Li, Judong Chen, Yifan Bie, Jun Jiang and Xiaoping Yang
Energies 2021, 14(23), 7908; https://0-doi-org.brum.beds.ac.uk/10.3390/en14237908 - 25 Nov 2021
Cited by 2 | Viewed by 2918
Abstract
Power transformer bushings withstand great electrical and mechanical stress during high voltage and high current working conditions. Sealing failure poses a great threat to the long-term and reliable operation of the bushing and power transformer; however, the criterion to evaluate the sealing status [...] Read more.
Power transformer bushings withstand great electrical and mechanical stress during high voltage and high current working conditions. Sealing failure poses a great threat to the long-term and reliable operation of the bushing and power transformer; however, the criterion to evaluate the sealing status of a bushing caused by mechanical problems is still lacking. In this paper, a transformer bushing model is established to gain theoretical insight into the relationship between temperature and pressure of a compact multilayer bushing. To evaluate the bushing mechanical status, different sealing conditions are tested based on the temperature and pressure monitoring within the physical 110 kV bushing. The results show that mechanical sealing failure can be diagnosed when the fluctuation of the oil pressure value exceeds the theoretical curve in steady state by 3 kPa. With different reliability coefficients, gas leakage and oil leakage are available to be further determined. The primary and auxiliary criteria based on oil pressure and its gradient are proposed to evaluate comprehensively the actual sealing condition of the bushing, and a wireless oil pressure module is developed at the bottom valve, which is quite beneficial to field online application. It is promising to extend the online mechanical monitoring and diagnosis to oil-immersed power equipment. Full article
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