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Project Details
Funding Scheme : General Research Fund
Project Number : 17204617
Project Title(English) : Fault classification of distribution power cables by detecting decaying DC components in fault currents with magnetic sensing: a robust non-invasive technique without pre-calibration  
Project Title(Chinese) : 基於配電電纜磁場測量以檢測電流中衰減直流分量的(穩固、非入侵、非預標定)故障分類法 
Principal Investigator(English) : Dr Pong, Philip Wing Tat 
Principal Investigator(Chinese) :  
Department : Department of Electrical and Electronic Engineering
Institution : The University of Hong Kong
E-mail Address : ppong@eee.hku.hk 
Tel :  
Co - Investigator(s) :
Mr Ip, Sung Tai
Panel : Engineering
Subject Area : Electrical & Electronic Engineering
Exercise Year : 2017 / 18
Fund Approved : 600,000
Project Status : Completed
Completion Date : 31-10-2020
Project Objectives :
To develop a robust algorithm for extracting three-phase DC component from the magnetic fields around the multi-core distribution power cable
To validate the feasibility of DC-component-based fault classification by simulation
To establish a measurement platform for conducting fault classification on multi-core distribution power cables
To validate the feasibility of DC-component-based fault classification by experiment
Abstract as per original application
(English/Chinese):

在故障發生後正確判斷故障的類型,是確保繼電器在適當的保護方案下進行動作的關鍵步驟。雖然一系列基於提取故障後穩態階段特征的傳統故障分類法已被採用多年,但是這類方法表現出以下缺陷:首先,對於安裝在每條電纜上的繼電器,都需要進行閾值的預標定;其次,由於故障形式複雜多樣(例如,不同故障地點、短路電阻等),預標定的閾值在某些狀態下可能無法達到而導致故障分類錯誤;再次,因為故障分類在故障后的穩態時刻中進行,繼電器的動作實際上已經發生延誤。最後,因為傳統穩態故障分類法需要同時測量電纜電壓和電流,現場佈線變得複雜。近來,雖有一系列基於暫態特征的故障分類法,但這類方法仍然需要將每條電纜的繼電器實施預標定。並且,這類方法可能受到電磁干擾產生誤動作,或者本身無法識別三相短路故障。 該項目旨在開發一種基於配電電纜磁場測量以檢測電流中的衰減直流分量的故障分類法。當電纜發生短路故障時,故障相發生較大的電流變化,而由於電力系統網絡的構成高度感性(系統存在大量發電機、變壓器等), 電流電感中的電流在短路瞬間不能突變,因而出現衰減的直流分量。基於直流分量的故障分類法將有效解決上述問題:(1)由於衰減的直流分量只在故障時出現,因此本方法可以正確的識別故障相和非故障相;(2)無需預標定;(3)電磁干擾(高頻)並不會影響直流分量的特征。與此同時,我們將發明從電纜周圍測量磁場以提取三相中可能存在的直流分量的算法。我們將在實驗室驗構建一個平台雛形以驗證該方法的可行性,並且和我們的工業搭檔合作以在現場環境下進一步驗證方法的有效性。 作為一個更有效的故障分類法,該方法的貢獻包括確保繼電器動作的有效性,提高配電系統自我修復能力以促進智能電網的實現,節省在繼電器安裝和校準的人力資源投入, 控制故障的不利影響以促進智能城市的發展等。我們團隊由學界和業界人員構成,研究經驗豐富:例如,在從磁場中提取和分析電力訊號這一領域投入超過八年。因此,我們有能力接受這項研究挑戰并將該項成果付諸現實。
Realisation of objectives: Objective 1: We have developed a robust algorithm for extracting three-phase DC component from the magnetic fields around the multi-core distribution power cable. The algorithms were studied and established for reconstructing the phase currents from the magnetic sensing, and further extracting the DC components as follows: (i) Current reconstruction: we have successfully completed work on reconstructing the phase currents of overhead transmission lines from the sensor on the ground. We continued to use this program to reconstruct the phase currents of power distribution cables. (ii) DC component extraction: the task is to further extract the potential DC components from the reconstructed currents of power distribution cables. The problem is that the traditional Fourier transform or the wavelet transform cannot be used because they would result in distortion at the front and back ends of the waveform (i.e., the fringe effects). Thus, we have developed a new method based on mathematical morphology to achieve the DC component extraction. This method was successfully validated based on different functions with decaying component. Objective 2: Regarding fault classification in simulation, we have established a virtual power distribution network in the simulation to validate the feasibility of our proposed method. Different fault currents were attained based on four different short-circuit faults (i.e., phase-to-ground short-circuit fault, phase-to-phase short-circuit fault, phase-phase-to-ground short-circuit fault, and three-phase short-circuit fault). The method was validated to extract the DC-components based on the reconstructed currents from the magnetic sensing. Objective 3: Platform has been established for conducting fault classification on multi-core distribution power cables. The magnetic field distribution around a power distribution cable is totally different from that of overhead transmission lines. Thus, we have studied the magnetic field distribution around a power distribution cable under different operating currents. After attaining these results, we have the knowledge of how to install our magnetic sensors, and what linear range should the sensors have. We have established a real-time measurement platform, and tested the function of magnetic-field measurement and current reconstruction both in the lab and power substation. Objective 4: The feasibility of DC-component-based fault classification was validated by experiment. We established a scaled power distribution network in the lab, and generated different fault currents. First, the three-phase currents are reconstructed by magnetic sensing with a stochastic optimization algorithm, which avoids the waveform distortion in the measurement by current transformers that incurred by the dc bias. Then, the dc component is extracted by mathematical morphology (MM) in phase currents to identify the fault type together with the polarity of dc components. This method was verified successfully for various fault types on a 22-kV power distribution cable in simulation and also a scaled power distribution network experimentally. The proposed method can enhance the reliability of the power distribution network and contribute to smart grid development. The fault category was successfully identified by our method.
Summary of objectives addressed:
Objectives Addressed Percentage achieved
1.To develop a robust algorithm for extracting three-phase DC component from the magnetic fields around the multi-core distribution power cableYes100%
2.To validate the feasibility of DC-component-based fault classification by simulationYes100%
3.To establish a measurement platform for conducting fault classification on multi-core distribution power cablesYes100%
4.To validate the feasibility of DC-component-based fault classification by experimentYes100%
Research Outcome
Major findings and research outcome: There are two major findings and research outcomes. A new fault-classification technique by detecting decaying dc components in fault currents with magnetic sensing was proposed and verified. The dc component was extracted by the MM method from the current reconstructed by magnetic sensing. The feasibility of the method was validated both by simulation and experiment. This method does not require precalibration in terms of network, does not suffer the dc bias issue as in CTs [47], is invulnerable to EMI, and can reliably identify three-phase short-circuit faults. The impacts of this method are summarized as follows. First, the reliability of fault classification in distribution networks can be enhanced for ensuring the proper function of relays and facilitating the repair work. Second, the workforce of the precalibration process for installing relays on each underground power cable can be eliminated, and thus the power system can be more cost-effective. Last but not the least, this work can facilitate the smart grid construction by improving the self-healing ability in distribution systems and boost the smart city development by safeguarding the continuity of power supply. This finding and outcome have been published in K. Zhu, Philip W. T. Pong, “Fault Classification of Power Distribution Cables by Detecting Decaying DC Components with Magnetic Sensing”, IEEE Transactions on Instrumentation and Measurement, vol. 69, 2016 (2020) which is also reported in Part C of this project completion report. A current-measurement technique for power distribution cables with magnetic sensing and computational intelligence is proposed in this paper. This technique can successfully measure the DC offset in fault currents, which can then be used to identify the phase-to-ground fault and faulty phase. The deployment of DC component for fault identification is advantageous: firstly, the serviceman does not need to set a threshold for each individual power distribution cable. Secondly, the DC component is free from the effect of electromagnetic coupling or EMI. The current-measurement technique based on magnetic-field sensing also enables the double-ended current method to locate the fault since it can be implemented with magnetic sensors at a low cost to both ends of a cable. The implementing procedure of this technique to identify and locate faults is presented for guiding its usage in practical scenarios. This finding and outcome have been published in K. Zhu, K. H. Lam, Philip W. T. Pong, “Identification and Location for Phase-to-Ground Fault with Magnetic Sensing in Power Distribution Network: Principle and Practical Implementation”, IEEE Power and Energy Society Asia-Pacific Power and Energy Engineering Conference (APPEEC), Macau, 2019, Paper No. 1570574301 and K. Zhu, Philip W. T. Pong, “Curved Trapezoidal Magnetic Flux Concentrator Design for Current Measurement of Multi-Core Power Cable with Magnetic Sensing”, IEEE Transactions on Magnetics, vol. 55, 4001809 (2019) which are also reported in Part C of this project completion report.
Potential for further development of the research
and the proposed course of action:
1. Future work will focus on validating the developed sensing technique on-site with the real corona effect under a bipolar HVDC transmission line in the field. This will help putting this technique into real application in industry. 2. The technique will be tested under different conditions (e.g., different fault times, fault distances, and locations, and network voltage) in a real on-site environment of a power system to validate its practical effectiveness in the future work.
Layman's Summary of
Completion Report:
This research project researched a more reliable fault classification to ensure the proper function of relays, boosting self-healing ability in distribution systems to realize smart grid, saving the manpower of pre-calibration and controlling the adverse effects of faults to facilitate the development of smart city.
Research Output
Peer-reviewed journal publication(s)
arising directly from this research project :
(* denotes the corresponding author)
Year of
Publication
Author(s) Title and Journal/Book Accessible from Institution Repository
2019 K. Zhu, W. K. Lee, Philip W. T. Pong*  Non-Contact Voltage Monitoring of HVDC Transmission Lines Based on Electromagnetic Fields; IEEE Sensors  No 
2019 K. Zhu, Philip W. T. Pong*  Curved Trapezoidal Magnetic Flux Concentrator Design for Current Measurement of Multi-Core Power Cable With Magnetic Sensing; IEEE Transactions on Magnetics  No 
2020 K. Zhu, Philip W. T. Pong*  Fault Classification of Power Distribution Cables by Detecting Decaying DC Components with Magnetic Sensing, IEEE Transactions on Instrumentation and Measurement, vol. 69, 2016 (2020)  No 
Recognized international conference(s)
in which paper(s) related to this research
project was/were delivered :
Month/Year/City Title Conference Name
Houston Fault Classification of Power Distribution Cables by Detecting Decaying DC Components in Fault Currents with Magnetic Sensing  IEEE International Instrumentation & Measurement Technology Conference 
New Delhi On-Site Real-Time Current Monitoring of Three-Phase Three-Core Power Distribution Cables with Magnetic Sensing  IEEE Sensors 2018 
Macau Identification and Location for Phase-to-Ground Fault with Magnetic Sensing in Power Distribution Network: Principle and Practical Implementation, , Macau, 2019, Paper No. 1570574301  IEEE Power and Energy Society Asia-Pacific Power and Energy Engineering Conference (APPEEC) 
Other impact
(e.g. award of patents or prizes,
collaboration with other research institutions,
technology transfer, etc.):

  SCREEN ID: SCRRM00542