Xiaotian Bi

Shanghai Electric (China)

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Chuan Long

Peking University

Yang Liu

Chongqing University

Weihuang Huang

China Southern Power Grid (China)

Jiakun An

State Grid Corporation of China (China)

Shiwu Xiao

North China Electric Power University

Jun Zhao

Guangxi Medical University

Peng Pei

Zhejiang Chinese Medical University

Rong Ye

Yunnan Agricultural University

Haiyu Huang

Sun Yat-sen University

Benqian Qin

Yunnan Normal University

Cooperative Institutions

Chinese Academy of Sciences

State Grid Corporation of China (China)

University of Chinese Academy of Sciences

North China Electric Power University

China Southern Power Grid (China)

Tsinghua University

Central South University

Sun Yat-sen University

Shandong University

Shanghai Jiao Tong University

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An Advanced Partial Discharge Recognition Strategy of Power Cable

Journal of Electrical and Computer Engineering (2015)

Xiaotian Bi Ang Ren Simeng Li Mingming Han Qingquan Li

Detection and localization of partial discharge are very important in condition monitoring of power cables, so it is necessary to build an accurate recognizer to recognize the discharge types. In this paper, firstly, a power cable model based on FDTD simulation is built to get the typical discharge signals as training samples. Secondly, because the extraction of discharge signal features is crucial, fractal characteristics of the training samples are extracted and inputted into the recognizer. To make the results more accurate, multi-SVM recognizer made up of six Support Vector Machines (SVM) is proposed in this paper. The result of the multi-SVM recognizer is determined by the vote of the six SVM. Finally, the BP neural networks and ELM are compared with multi-SVM. The accuracy comparison shows that the multi-SVM recognizer has the best accuracy and stability, and it can recognize the discharge type efficiently.

SIGNAL (programming language)

10.1155/2015/174538

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Citations (7)

Abnormal Action Recognition with Lightweight Pose Estimation Network in Electric Power Training Scene

Computers, materials & continua/Computers, materials & continua (Print) (2024)

Yunfeng Cai Qin Ran Jin Tang Long Zhang Xiaotian Bi

Electric power training is essential for ensuring the safety and reliability of the system.In this study, we introduce a novel Abnormal Action Recognition (AAR) system that utilizes a Lightweight Pose Estimation Network (LPEN) to efficiently and effectively detect abnormal fall-down and trespass incidents in electric power training scenarios.The LPEN network, comprising three stages-MobileNet, Initial Stage, and Refinement Stage-is employed to swiftly extract image features, detect human key points, and refine them for accurate analysis.Subsequently, a Poseaware Action Analysis Module (PAAM) captures the positional coordinates of human skeletal points in each frame.Finally, an Abnormal Action Inference Module (AAIM) evaluates whether abnormal fall-down or unauthorized trespass behavior is occurring.For fall-down recognition, three criteria-falling speed, main angles of skeletal points, and the person's bounding box-are considered.To identify unauthorized trespass, emphasis is placed on the position of the ankles.Extensive experiments validate the effectiveness and efficiency of the proposed system in ensuring the safety and reliability of electric power training.

Action Recognition

10.32604/cmc.2024.050435

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Prediction method of leakage current of insulators on the transmission line based on BP neural network

2018 IEEE 2nd International Electrical and Energy Conference (CIEEC) (2018)

Song Gao Yongyong Jia Xiaotian Bi Bin Cao Xu Li

Pollution flashover is a serious threat to the safe and stable operation of power system, and the pollution flashover voltages of insulators on the transmission lines are related to the leakage currents. In this paper, a neural network model was proposed to predict the leakage currents on the insulators, which could provide references for preventing pollution flashover. By the analysis of a large number of leakage current data obtained by the monitoring device on the insulators of operating lines, the characteristics of the leakage current is extracted, and then combined with BP neural network, the prediction model of leakage current based on the actual operation data is established. By adjusting the parameters of the BP neural network, the prediction results can be accords with the actual operation situation. The reliability of the predicted results was verified by the leakage current on the insulator surface.

Arc flash

Leakage (economics)

10.1109/cieec.2018.8745839

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Citations (7)

CIEEC 2019 Table of Contents

2019 IEEE 3rd International Electrical and Energy Conference (CIEEC) (2019)

Xiaotian Bi Dabing Chen Song Gao Jie Chen Lei Sun

Table (database)

10.1109/cieec47146.2019.9077270

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Citations (0)

Analysis of ground wire fault in the section of incoming lines of electrified railway traction station

Xiaotian Bi Jian‐Jun Liu Yang Liu Yongyong Jia Liheng Yang

The paper analyzes reasons for the failure of strain clamp hardware in the power supply line of an electrified railway traction substation, carries out strain clamp material test, grounding current measurement, arc simulation and other relevant tests, and the reasons for the fault are analyzed as follows: the contact surface is narrow between the strain clamp steel anchor and the U-shaped hanging ring because of the connection structure between them, which is likely to result in gap under the influence of vibration and other environment factors, thus giving birth to suspended potential and bringing about arc ablation under the joint effects of voltage, current and environment and melting and fracturing them gradually. The paper also proposes control measures and recommendations against the fracture fault of strain clamp hardware involved in the power supply line at the side of traction substation system. side power supply line of traction substation.

Clamp

Traction substation

Collar

10.1109/ei2.2018.8582026

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Application of X-RAY Digital Imaging Technology in Hardware Quality Test of Transmission Line

2019 IEEE 3rd International Electrical and Energy Conference (CIEEC) (2019)

Xiaotian Bi Dabing Chen Song Gao Jie Chen Lei Sun

X-ray spectrum nondestructive testing of electric power fittings is able to provide convenient, quick and accurate detection. The principles of X-ray and detection methods are introduced in this paper. By changing the steel anchor and external aluminum sleeve crimping parts, core wire and anchor pipe or core wire connecting pipe crimping size, external tube and wire or intermediate casing crimping size, X-ray detection technology is used to detect the actual crimping size, and the influence of typical defects on crimping quality of transmission line fittings are analyzed. It is proved that X-ray digital imaging technology is effective in nondestructive testing of the quality of transmission line fittings, and X-ray digital imaging technology can be widely applied in the quality inspection of wire bonding.

Line (geometry)

10.1109/cieec47146.2019.cieec-20197

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Citations (3)

Study on the Surface Contamination of Insulators with Laser-Induced Breakdown Spectroscopy Technique

2021 International Conference on Power System Technology (POWERCON) (2018)

Song Gao Yang Liu Yuansheng Li Yujun Guo Xueqin Zhang

In this article, using Laser Induced Breakdown Spectroscopy(LIBS)on insulation surface contamination noncontact measurement and analysis of artificial and natural blur insulation piece, base on National Institute of Standards and Technology(NIST) to make qualitative analysis, and according to Lomakin-Schiebe formula model, take Na and Al two elements to expression ESDD and NSDD, and make half quantitative analysis. The experimental results show that LIBS can make accurate elements qualitative analysis on artificial and natural blur insulation piece. For the artificial blur insulator piece, when the ESDD increased 2.5 times and the NSDD is constant, the intensity of spectral lines of Na and Al increase by 327.57% and 16.0% respectively. For the two kinds of natural blur insulation piece, compared with light pollution insulation piece, the spectral line intensities of Na and Al in the serious polluted insulation piece were increased by 541.5% and 97.8% respectively. LIBS has the characteristics of convenient operation, no need to process the sample, and it will become one of the important method to detect the contamination degree of insulators in the future.

Laser-induced breakdown spectroscopy

NIST

10.1109/powercon.2018.8601766

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Citations (1)