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Jia-Ning Zhang

Bio: Jia-Ning Zhang is an academic researcher from Xi'an Jiaotong University. The author has contributed to research in topics: Partial discharge & Energy (signal processing). The author has an hindex of 6, co-authored 13 publications receiving 125 citations.

Papers
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Journal ArticleDOI
TL;DR: The results indicate that the probability-based localization algorithm reasonably integrates the TDOAs of continuous signal sequence, which can effectively reduce the influence of TDOA estimation errors and improve the localization accuracy.
Abstract: Ultra-high-frequency (UHF) sensing technique has been introduced to detect and localize partial discharge (PD) sources in air-insulated substation (AIS). This paper presents a probability-based algorithm to localize multiple PD sources which may occur simultaneously in different power equipment. Assuming that the time difference of arrival (TDOA) between all pairs of antennas in a array are normally distributed, the probability density function (PDF) of PD source coordinates can be obtained by substituting the linearized form of time difference equations into PDFs of TDOAs. When large number of PD signals are recorded, the joint PDF (JPDF) can be calculated from the product of PDF of each TDOA. Then the PD coordinates to be solved are regarded as with highest probability, and can be solved by taking the derivative of JPDF. In the case of multiple PD sources, mixed UHF signals are separated by clustering the TDOA vectors with K Means clustering method. PD experiments are performed to test the presented algorithm, and the localization accuracy of proposed algorithm is compared with other typical methods such as Newton-Raphson, Particle Swarm Optimization and plane intersection method. The results indicate that the probability-based localization algorithm reasonably integrates the TDOAs of continuous signal sequence, which can effectively reduce the influence of TDOA estimation errors and improve the localization accuracy.

45 citations

Journal ArticleDOI
TL;DR: The proposed algorithm is effective for separating mixed PD signals initiated from multiple sources and mixed current impulse signals acquired from PD experiments on artificial multi-defect models and an on-site transformer are examined.
Abstract: Partial discharge (PD) measurement and interpretation have become a powerful tool for condition monitoring and failure risk assessment of high voltage power equipment insulation. The occurrence of multiple discharge sources affects interpretation accuracy. This paper presents a PD signal separation algorithm using cumulative energy (CE) function parameters clustering technique. The waveform of PD signals are acquired by digital detection instruments with high sampling rate. Cumulative energy functions in time domain (TCE) and frequency domain (FCE) are calculated from PD waveforms and their FFT spectrums, respectively. Mathematical morphology gradient (MMG) operation is applied to the TCE and FCE to describe their variation characteristics. The feature parameters including width, sharpness and gravity are extracted from CEs and MMGs in both time and frequency domain, and compose a six-dimension feature space. The improved density-based spatial clustering of applications with noise (IDBSCAN) clustering algorithm is adopted to discover clusters in the feature space. The proposed separation algorithm is examined with mixed current impulse signals acquired from PD experiments on artificial multi-defect models and an on-site transformer. The separation results indicate that the proposed algorithm is effective for separating mixed PD signals initiated from multiple sources.

32 citations

Journal ArticleDOI
TL;DR: In this article, an UHF signal feature extraction algorithm based on cumulative energy (CE) technique is proposed for partial discharge (PD) detection in gas insulated substation (GIS).
Abstract: Ultra-high-frequency (UHF) method is regarded as an effective approach for partial discharge (PD) detection in gas insulated substation (GIS). The feature parameters representing UHF signal characteristics can be extracted from its waveform, and applied to PD source classification and mixed signals separation. In this study, an UHF signal feature extraction algorithm based on cumulative energy (CE) technique is proposed. The CE functions in time domain (TCE) and frequency domain (FCE) are calculated from UHF waveforms and their fast Fourier transform spectrums, respectively. The mathematical morphology gradient (MMG) operation is applied to TCE and FCE to characterise their rising behavior. The feature parameters including width, area and sharpness are extracted from CEs and MMGs in both time and frequency domain. Moreover, the extracted features are applied to UHF signals classification and mixed signals separation. First, experiments on four kinds of typical defects in a 252 kV GIS are performed, and the classification performance of the extracted features is examined with the acquired signals. Second, by clustering the extracted features with fuzzy maximum-likelihood algorithm, mixed UHF signals originated from multiple PD sources are successfully separated. The results indicate that the proposed features are effective for representing UHF signal characteristics.

20 citations

Journal ArticleDOI
TL;DR: In this paper, the authors explored the influence of the pulse front edge and the pulse repetitive rate on the breakdown characteristics of the pseudospark discharge driven by pulsed voltages.
Abstract: This paper explores the influence of the pulse front edge and the pulse repetitive rate on the breakdown characteristics of the pseudospark discharge driven by pulsed voltages. The breakdown voltage decreases with the increase of the pulse front voltage. The attenuation coefficients of the breakdown voltage with the increase of the pressure are 0.7, 1.17 and 1.42 in the condition of the pulse front edges of 17, 53 and 140 ns. In addition, the breakdown characteristics of the multi-gap pseudospark under repetitive nanosecond pulsed voltages has also been studied. As the increasing of the gap number, the breakdown voltage increases. The influence of the gas pressure, aperture diameter and pulse repetitive rate on the breakdown voltages shows a similar trend in different gap numbers. The breakdown voltage decreases with the increase of the pressure, gap distance and aperture diameter in the formation of power function.

11 citations

Patent
01 Apr 2015
TL;DR: In this paper, the authors proposed a method for optimizing the carrying capacity of a cable bundle, which comprises the steps of acquiring a geometric size and physical parameters of the cable bundle laying environment, and acquiring a geometry of each cable in cable bundle; establishing a temperature field limit element model of cable bundle.
Abstract: The invention provides a method for optimizing a carrying capacity of a cable bundle. The method comprises the steps of acquiring a geometric size and physical parameters of a cable bundle laying environment, and acquiring a geometric size and physical parameters of each cable in the cable bundle; establishing a temperature field limit element model of the cable bundle, leading the geometric size and physical parameter of the cable bundle laying environment and the geometric size and physical parameters of each cable into the temperature field limit element model of the cable bundle to obtain a wire core temperature of each cable; establishing a mathematical model for optimizing the carrying capacity of the cable bundle according to the wire core temperature of each cable by utilizing a preset temperature threshold value as a constraint condition, optimizing the carrying capacity of the cable bundle by adopting a preset optimization algorithm to obtain the optimized current value of each cable and the optimized total carrying capacity of the cable bundle. By implementing the method, a numerical method can be optimized, the convergence rate of the numerical method can be improved, the calculation result is more precise, the total carrying capacity of the cable bundle is increased, and a purpose of optimizing the carrying capacity of the cable bundle can be realized.

8 citations


Cited by
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Journal ArticleDOI
01 Jun 2017
TL;DR: This study reviews the research status in condition monitoring and diagnosis of power equipment, including transformer, gas insulated switchgear, cable, external insulation, generator, and power capacitor in recent years and proposes that the application of big data, internet of things and cloud computing should be expected and given special attention in the near future.
Abstract: To ensure the power system operates safely and reliably, it is essential to monitor and evaluate the health condition of power equipment on-line or off-line. This study reviews the research status in condition monitoring and diagnosis of power equipment, including transformer, gas insulated switchgear, cable, external insulation, generator, and power capacitor in recent years. Although much progress has been made in technologies of condition monitoring and fault diagnosis such as test accuracy, fast and accurate fault localisation and recognition of fault types, there are still many deficiencies which needs further research work, including the reliability of signal collection from sensors, the accuracy of data treatment and analysis, anti-interference performance of test equipment, appropriate models used for condition evaluation. The prospective of condition monitoring and diagnosis technologies of power equipment are also presented in this study. It is proposed that the application of big data, internet of things and cloud computing should be expected and given special attention in the near future.

150 citations

Journal ArticleDOI
TL;DR: This paper presents a state-of-the-art review on machine learning (ML) based intelligent diagnostics that have been applied for partial discharge (PD) detection, localization, and pattern recognition.
Abstract: This paper presents a state-of-the-art review on machine learning (ML) based intelligent diagnostics that have been applied for partial discharge (PD) detection, localization, and pattern recognition. ML techniques, particularly those developed in the last five years, are examined and classified as conventional ML or deep learning (DL). Important features of each method, such as types of input signal, sampling rate, core methodology, and accuracy, are summarized and compared in detail. Advantages and disadvantages of different ML algorithms are discussed. Moreover, technical roadblocks preventing intelligent PD diagnostics from being applied to industry are identified, such as insufficient/imbalanced dataset, data inconsistency, and difficulties in cost-effective real-time deployment. Finally, potential solutions are proposed, and future research directions are suggested.

95 citations

Journal ArticleDOI
TL;DR: In this article, a transformer physical model is established by taking these complex factors into consideration, and the velocity and propagation factors are set for each node according to its acoustic wave propagation characteristics.
Abstract: Methods to infer the location of partial discharge (PD) in high-power transformers using acoustic emission (AE) data have been extensively studied. The inner complex structure of the transformers is one of the most critical points in localization with AE method. Windings and cores affect acoustic wave propagation by changing the arrival time because of inhomogeneous propagation. A transformer physical model has been established herein by taking these complex factors into consideration. Each node in the model is a potential PD position, and an acoustic wave route comprise a series of nodes. The velocity and propagation factors are set for each node according to its acoustic wave propagation characteristics. A propagation-time estimation algorithm is proposed to calculate the propagation-time. Based on the transformer physical model, a particle-swarm-optimization route-searching (PSORS) algorithm is employed for searching the position of the PD source. By comparing time differences of measured AE signals and the ones estimated by the PSORS algorithm, the velocities and positions of particles are continually adjusted, which can ensure their convergence to the PD source position. Localization experiments were performed in 35 and 110 kV transformers, respectively, to verify the applicability of the proposed algorithm. A protrusion defect is used to trigger PD pulses, and four AE sensors with two different arrangements are employed. The results confirm that the accuracy of proposed localization method is insensitive to the presence of metal structures blocking acoustic wave routes.

73 citations

Journal ArticleDOI
09 May 2018-Energies
TL;DR: This study proposes an approach to detecting PD patterns in gas-insulated switchgear using a long short-term memory (LSTM) recurrent neural network (RNN) using phase-resolved PD signals as input, extracts low-level features, and finally, classifies faults in GIS.
Abstract: The analysis of partial discharge (PD) signals has been identified as a standard diagnostic tool for monitoring the condition of different electrical apparatuses. This study proposes an approach to detecting PD patterns in gas-insulated switchgear (GIS) using a long short-term memory (LSTM) recurrent neural network (RNN). The proposed method uses phase-resolved PD (PRPD) signals as input, extracts low-level features, and finally, classifies faults in GIS. In the proposed method, LSTM networks can learn temporal dependencies directly from PRPD signals. Most existing models use support vector machines (SVMs) and mainly focus on improving feature representation and extraction manually to analyze PRPD signals. However, the proposed model captures important temporal features with the help of its low-level feature extraction capability from raw inputs. It outperforms conventional SVMs and achieves 96.74% classification accuracy for PRPDs in GIS.

65 citations

Journal ArticleDOI
25 Oct 2017-Energies
TL;DR: A comprehensive survey of the state-of-the-art knowledge on PD mechanism, PD pulse propagation in stator windings, PD signal detection methods and signal processing techniques is presented in this article.
Abstract: Online partial discharge (PD) measurements have long been used as an effective means to assess the condition of the stator windings of large generators. An increase in the use of PD online measurement systems during the last decade is evident. Improvements in the detection capabilities are partly the reason for the increased popularity. Another reason has been the development of digital signal processing techniques. In addition, rapid progress is being made in automated single PD source classification. However, there are still some factors hindering wider application of the system, such as the complex PD mechanism and PD pulse propagation in stator windings, the presence of detrimental noise and disturbances on-site, and multiple PD sources occurring simultaneously. To avoid repetition of past work and to provide an overview for fresh researchers in this area, this paper presents a comprehensive survey of the state-of-the-art knowledge on PD mechanism, PD pulse propagation in stator windings, PD signal detection methods and signal processing techniques. Areas for further research are also presented.

62 citations