The behavior of fault arc in a high-speed switch （HSS） has been studied theoretically and experimentally. A simplified HSS setup is designed to support this work. A two-dimensional arc model is developed to analyze ...The behavior of fault arc in a high-speed switch （HSS） has been studied theoretically and experimentally. A simplified HSS setup is designed to support this work. A two-dimensional arc model is developed to analyze the characteristics of fault arc based on magnetic-hydrodynamic （MHD） theory. The advantage of such a model is that the thermal transfer coefficient can be determined by depending on the numerical method alone. The influence of net emission coefficients （NEC） radiation model and P1 model on fault arc is analyzed in detail. Results show that NEC model predicts more radiation energy and less pressure rise without the re-absorption effect considered. As a consequence, P1 model is more suitable to calculate the pressure rise caused by fault arc. Finally, the pressure rise during longer arcing time for different arc currents is predicted.展开更多
This paper focuses on the simulation of a fault arc in a closed tank based on the magneto-hydrodynamic （MHD） method, in which a comparative study of three radiation models, including net emission coefficients （NEC...This paper focuses on the simulation of a fault arc in a closed tank based on the magneto-hydrodynamic （MHD） method, in which a comparative study of three radiation models, including net emission coefficients （NEC）, semi-empirical model based on NEC as well as the P1 model, is developed. The pressure rise calculated by the three radiation models are compared to the measured results. Particularly when the senti-empirical model is used, the effect of different boundary temperatures of the re-absorption layer in the semi-empirical model on pressure rise is concentrated on. The results show that the re-absorption effect in the low-temperature region affects radiation transfer of fault arcs evidently, and thus the internal pressure rise. Compared with the NEC model, P1 and the semi-empirical model with 0.7 〈 α 〈 0.83 are more suitable to calculate the pressure rise of the fault arc, where is an adjusted parameter involving the boundary temperature of the re-absorption region in the semi-empirical model.展开更多
The themial transfer coefficient that represents the portion of energy heating the surrounding gas of fault arc is a key parameter in evaluating the pressure effects due to fault arcing in a closed electrical installa...The themial transfer coefficient that represents the portion of energy heating the surrounding gas of fault arc is a key parameter in evaluating the pressure effects due to fault arcing in a closed electrical installation.This paper presents experimental research on the thermal transfer coefficient in a closed air vessel for Cu,Fe and A1 electrode materials over a currcni range from 1-20 kA with an electrode gap from 10-50 mm and gas pressure from 0.05-0.4 MPa.With a simplified energy balance including Joule heating,arc radiation,ihc energies related to electrode melting,vaporization and oxidation constructed,and the influences of different factors on thermal transfer coefficient are studied and evaluated.This quantitative estimation of the energy components confirmed that the pressure rise is closely related to the change in heat transport process of fault arc.particularly in consideration of the evaluation of Joule healing and radiation.Factors such as the electrode material,arc current,filling pressure and gap length between electrodes have a considerable effect on the thermal transfer coefficient and thus,the pressure rise due to the differences in the energy balance of fault arc.展开更多
This paper proposes a fingerprint matching method integrating transfer learning and online learning to tackle the challenges of environmental adaptability and dynamic interference resistance in photovoltaic(PV)array D...This paper proposes a fingerprint matching method integrating transfer learning and online learning to tackle the challenges of environmental adaptability and dynamic interference resistance in photovoltaic(PV)array DC arc fault location methods based on electromagnetic radiation(EMR)signals.Initially,a comprehensive analysis of the time–frequency characteristics of series arc EMR signals is carried out to pinpoint effective data sources that reflect fault features.Subsequently,a multi-kernel domain-adversarial neural network(MKDANN)is introduced to extract domain-invariant features,and a feature extractor designed specifically for fingerprint matching is devised.To reduce inter-domain distribution differences,a multi-kernel maximum mean discrepancy(MK-MMD)is integrated into the adaptation layer.Moreover,to deal with dynamic environmental changes in real-world situations,the support-class passive aggressive(SPA)algorithm is utilized to adjust model parameters in real time.Finally,MKDANN and SPA technologies are smoothly combined to build a fully operational fault location model.Experimental results indicate that the proposed method attains an overall fault location accuracy of at least 95%,showing strong adaptability to environmental changes and robust interference resistance while maintaining excellent online learning capabilities during model migration.展开更多
The load types in low-voltage distribution systems are diverse.Some loads have current signals that are similar to series fault arcs,making it difficult to effectively detect fault arcs during their occurrence and sus...The load types in low-voltage distribution systems are diverse.Some loads have current signals that are similar to series fault arcs,making it difficult to effectively detect fault arcs during their occurrence and sustained combustion,which can easily lead to serious electrical fire accidents.To address this issue,this paper establishes a fault arc prototype experimental platform,selects multiple commonly used loads for fault arc experiments,and collects data in both normal and fault states.By analyzing waveform characteristics and selecting fault discrimination feature indicators,corresponding feature values are extracted for qualitative analysis to explore changes in timefrequency characteristics of current before and after faults.Multiple features are then selected to form a multidimensional feature vector space to effectively reduce arc misjudgments and construct a fault discrimination feature database.Based on this,a fault arc hazard prediction model is built using random forests.The model’s multiple hyperparameters are simultaneously optimized through grid search,aiming tominimize node information entropy and complete model training,thereby enhancing model robustness and generalization ability.Through experimental verification,the proposed method accurately predicts and classifies fault arcs of different load types,with an average accuracy at least 1%higher than that of the commonly used fault predictionmethods compared in the paper.展开更多
Arc fault detection is desperately required in Solid State Power Controllers（SSPC） in addition to their fundamental functions because arcs will provoke growing harm and threat to aircraft safety. Experimental study ...Arc fault detection is desperately required in Solid State Power Controllers（SSPC） in addition to their fundamental functions because arcs will provoke growing harm and threat to aircraft safety. Experimental study has been done to obtain the faulted current data. In order to improve the detection speed and accuracy, two fast arc fault detection methods have been proposed in this paper with the analysis of only half cycle data. Both Fast Fourier Transform（FFT） and Wavelet Packets Decomposition（WPD） have been adopted to distinguish arc fault currents from normal operation currents. Analysis results show that Alternating Current（AC） arcs can be effectively and accurately detected with the proposed half cycle data based methods. Moreover,experimental verification results have also been provided.展开更多
This paper investigates direct current(DC) arc fault detection in photovoltaic system. In order to avoid the risk of fire ignition caused by the arc fault in the photovoltaic power supply, it is urgent to detect the D...This paper investigates direct current(DC) arc fault detection in photovoltaic system. In order to avoid the risk of fire ignition caused by the arc fault in the photovoltaic power supply, it is urgent to detect the DC arc fault in the photovoltaic system. Once an arc fault is detected, the power supply should be cut off immediately. A lot of field experiments are carried out to obtain the data of arc fault current of the photovoltaic system under different current conditions. Cable length, arc gap, and the effects of different sensors are tested.These three conditions are the most significant features of this paper. Four characteristic variables from both the time domain and the frequency domain are extracted to identify the arc fault. Then the logistic regression method in the field of artificial intelligence and machine learning is originally used to analyze the experimental results of arc fault in the photovoltaic system. The function between the probability of the arc fault and the change of the characteristic variables is obtained. After validating 80 groups of experimental data under different conditions,the accuracy rate of the arc fault detection by this algorithm is proved to reach 100%.展开更多
It is difficult to detect and extinguish direct current(DC)arc in power electronics systems,and the arc could easily lead to a fire and cause great damage to surrounding equipment.A DC arc generation simulation unit i...It is difficult to detect and extinguish direct current(DC)arc in power electronics systems,and the arc could easily lead to a fire and cause great damage to surrounding equipment.A DC arc generation simulation unit is established,in which DC series arcs are generated by dragging the moving electrode away from the fixed one with the help of the stepper motor.In addition,a ferrite rod antenna is used to receive the electromagnetic radiation signals induced by the arcs.Based on experiments using the unit,the general characteristics of DC arc,including the pulse characteristics of arc current and source output in corresponding time window,and the frequency-domain characteristics of arc current,are studied.With discussion on three detection methods,it is concluded that the variation of current and voltage of arc,the spectrum of the arc current during the discontinuous intervals and the radiating electromagnetic signal are all features that can be adopted for detecting DC series arc.Therefore,a synthetic judgment method is suggested for further study.展开更多
Arc grounding faults occur frequently in the power grid with small resistance grounding neutral points.The existing arc fault identification technology only uses the fault line signal characteristics to set the identi...Arc grounding faults occur frequently in the power grid with small resistance grounding neutral points.The existing arc fault identification technology only uses the fault line signal characteristics to set the identification index,which leads to detection failure when the arc zero-off characteristic is short.To solve this problem,this paper presents an arc fault identification method by utilizing integrated signal characteristics of both the fault line and sound lines.Firstly,the waveform characteristics of the fault line and sound lines under an arc grounding fault are studied.After that,the convex hull,gradient product,and correlation coefficient index are used as the basic characteristic parameters to establish fault identification criteria.Then,the logistic regression algorithm is employed to deal with the reference samples,establish the machine discrimination model,and realize the discrimination of fault types.Finally,simulation test results and experimental results verify the accuracy of the proposed method.The comparison analysis shows that the proposed method has higher recognition accuracy,especially when the arc dissipation power is smaller than 2×10^(3) W,the zero-off period is not obvious.In conclusion,the proposed method expands the arc fault identification theory.展开更多
基金supported by National Key Basic Research Program of China(973 Program)(No.2015CB251001)National Natural Science Foundation of China(Nos.51221005,51177124,51377128,51323012)+1 种基金the Science and Technology Project Funds of the Grid State Corporation SGSNKYOOKJJS1501564Shaanxi Province Natural Science Foundation of China(No.2013JM-7010)
文摘The behavior of fault arc in a high-speed switch （HSS） has been studied theoretically and experimentally. A simplified HSS setup is designed to support this work. A two-dimensional arc model is developed to analyze the characteristics of fault arc based on magnetic-hydrodynamic （MHD） theory. The advantage of such a model is that the thermal transfer coefficient can be determined by depending on the numerical method alone. The influence of net emission coefficients （NEC） radiation model and P1 model on fault arc is analyzed in detail. Results show that NEC model predicts more radiation energy and less pressure rise without the re-absorption effect considered. As a consequence, P1 model is more suitable to calculate the pressure rise caused by fault arc. Finally, the pressure rise during longer arcing time for different arc currents is predicted.
基金supported by National Key Basic Research Program of China(973 Program)(No.2015CB251002)National Natural Science Foundation of China(Nos.51221005,51177124)+2 种基金the Fundamental Research Funds for the Central Universitiesthe Program for New Century Excellent Talents in UniversityShaanxi Province Natural Science Foundation of China(No.2013JM-7010)
文摘This paper focuses on the simulation of a fault arc in a closed tank based on the magneto-hydrodynamic （MHD） method, in which a comparative study of three radiation models, including net emission coefficients （NEC）, semi-empirical model based on NEC as well as the P1 model, is developed. The pressure rise calculated by the three radiation models are compared to the measured results. Particularly when the senti-empirical model is used, the effect of different boundary temperatures of the re-absorption layer in the semi-empirical model on pressure rise is concentrated on. The results show that the re-absorption effect in the low-temperature region affects radiation transfer of fault arcs evidently, and thus the internal pressure rise. Compared with the NEC model, P1 and the semi-empirical model with 0.7 〈 α 〈 0.83 are more suitable to calculate the pressure rise of the fault arc, where is an adjusted parameter involving the boundary temperature of the re-absorption region in the semi-empirical model.
基金National Natural Science Foundation of China(Grant Nos.51707145,51807162.51577144)Shaanxi province key R&D program 2019ZDLGY18-05+1 种基金the China Postdoctoral Science Foundation(Grant Nos.2016M600792,2018M641007)was selected from the 1st International Symposium on Insulation and Discharge Computation for Power Equipment.
文摘The themial transfer coefficient that represents the portion of energy heating the surrounding gas of fault arc is a key parameter in evaluating the pressure effects due to fault arcing in a closed electrical installation.This paper presents experimental research on the thermal transfer coefficient in a closed air vessel for Cu,Fe and A1 electrode materials over a currcni range from 1-20 kA with an electrode gap from 10-50 mm and gas pressure from 0.05-0.4 MPa.With a simplified energy balance including Joule heating,arc radiation,ihc energies related to electrode melting,vaporization and oxidation constructed,and the influences of different factors on thermal transfer coefficient are studied and evaluated.This quantitative estimation of the energy components confirmed that the pressure rise is closely related to the change in heat transport process of fault arc.particularly in consideration of the evaluation of Joule healing and radiation.Factors such as the electrode material,arc current,filling pressure and gap length between electrodes have a considerable effect on the thermal transfer coefficient and thus,the pressure rise due to the differences in the energy balance of fault arc.
基金financially supported in part by the Natural Science Foundation of Fujian,China,under Grant 2021J01633.
文摘This paper proposes a fingerprint matching method integrating transfer learning and online learning to tackle the challenges of environmental adaptability and dynamic interference resistance in photovoltaic(PV)array DC arc fault location methods based on electromagnetic radiation(EMR)signals.Initially,a comprehensive analysis of the time–frequency characteristics of series arc EMR signals is carried out to pinpoint effective data sources that reflect fault features.Subsequently,a multi-kernel domain-adversarial neural network(MKDANN)is introduced to extract domain-invariant features,and a feature extractor designed specifically for fingerprint matching is devised.To reduce inter-domain distribution differences,a multi-kernel maximum mean discrepancy(MK-MMD)is integrated into the adaptation layer.Moreover,to deal with dynamic environmental changes in real-world situations,the support-class passive aggressive(SPA)algorithm is utilized to adjust model parameters in real time.Finally,MKDANN and SPA technologies are smoothly combined to build a fully operational fault location model.Experimental results indicate that the proposed method attains an overall fault location accuracy of at least 95%,showing strong adaptability to environmental changes and robust interference resistance while maintaining excellent online learning capabilities during model migration.
基金This work was funded by Beijing Key Laboratory of Distribution Transformer Energy-Saving Technology(China Electric Power Research Institute).
文摘The load types in low-voltage distribution systems are diverse.Some loads have current signals that are similar to series fault arcs,making it difficult to effectively detect fault arcs during their occurrence and sustained combustion,which can easily lead to serious electrical fire accidents.To address this issue,this paper establishes a fault arc prototype experimental platform,selects multiple commonly used loads for fault arc experiments,and collects data in both normal and fault states.By analyzing waveform characteristics and selecting fault discrimination feature indicators,corresponding feature values are extracted for qualitative analysis to explore changes in timefrequency characteristics of current before and after faults.Multiple features are then selected to form a multidimensional feature vector space to effectively reduce arc misjudgments and construct a fault discrimination feature database.Based on this,a fault arc hazard prediction model is built using random forests.The model’s multiple hyperparameters are simultaneously optimized through grid search,aiming tominimize node information entropy and complete model training,thereby enhancing model robustness and generalization ability.Through experimental verification,the proposed method accurately predicts and classifies fault arcs of different load types,with an average accuracy at least 1%higher than that of the commonly used fault predictionmethods compared in the paper.
基金co-supported by the National Natural Science Foundation of China(Nos.51407144 and 51777169)the Aviation Research Funds(No.20164053029)+1 种基金the Fundamental Research Funds for the Central Universities(Nos.3102017ZY027 and 3102017GX08001)the Young Elite Scientist Sponsorship Program by CAST
文摘Arc fault detection is desperately required in Solid State Power Controllers（SSPC） in addition to their fundamental functions because arcs will provoke growing harm and threat to aircraft safety. Experimental study has been done to obtain the faulted current data. In order to improve the detection speed and accuracy, two fast arc fault detection methods have been proposed in this paper with the analysis of only half cycle data. Both Fast Fourier Transform（FFT） and Wavelet Packets Decomposition（WPD） have been adopted to distinguish arc fault currents from normal operation currents. Analysis results show that Alternating Current（AC） arcs can be effectively and accurately detected with the proposed half cycle data based methods. Moreover,experimental verification results have also been provided.
文摘This paper investigates direct current(DC) arc fault detection in photovoltaic system. In order to avoid the risk of fire ignition caused by the arc fault in the photovoltaic power supply, it is urgent to detect the DC arc fault in the photovoltaic system. Once an arc fault is detected, the power supply should be cut off immediately. A lot of field experiments are carried out to obtain the data of arc fault current of the photovoltaic system under different current conditions. Cable length, arc gap, and the effects of different sensors are tested.These three conditions are the most significant features of this paper. Four characteristic variables from both the time domain and the frequency domain are extracted to identify the arc fault. Then the logistic regression method in the field of artificial intelligence and machine learning is originally used to analyze the experimental results of arc fault in the photovoltaic system. The function between the probability of the arc fault and the change of the characteristic variables is obtained. After validating 80 groups of experimental data under different conditions,the accuracy rate of the arc fault detection by this algorithm is proved to reach 100%.
基金Project supported by International Cooperation Project in Shaanxi Province of China(2012KW-01)
文摘It is difficult to detect and extinguish direct current(DC)arc in power electronics systems,and the arc could easily lead to a fire and cause great damage to surrounding equipment.A DC arc generation simulation unit is established,in which DC series arcs are generated by dragging the moving electrode away from the fixed one with the help of the stepper motor.In addition,a ferrite rod antenna is used to receive the electromagnetic radiation signals induced by the arcs.Based on experiments using the unit,the general characteristics of DC arc,including the pulse characteristics of arc current and source output in corresponding time window,and the frequency-domain characteristics of arc current,are studied.With discussion on three detection methods,it is concluded that the variation of current and voltage of arc,the spectrum of the arc current during the discontinuous intervals and the radiating electromagnetic signal are all features that can be adopted for detecting DC series arc.Therefore,a synthetic judgment method is suggested for further study.
基金This work was supported in part by the Natural Science Foundation of Henan Province,and the specific grant number is 232300420301。
文摘Arc grounding faults occur frequently in the power grid with small resistance grounding neutral points.The existing arc fault identification technology only uses the fault line signal characteristics to set the identification index,which leads to detection failure when the arc zero-off characteristic is short.To solve this problem,this paper presents an arc fault identification method by utilizing integrated signal characteristics of both the fault line and sound lines.Firstly,the waveform characteristics of the fault line and sound lines under an arc grounding fault are studied.After that,the convex hull,gradient product,and correlation coefficient index are used as the basic characteristic parameters to establish fault identification criteria.Then,the logistic regression algorithm is employed to deal with the reference samples,establish the machine discrimination model,and realize the discrimination of fault types.Finally,simulation test results and experimental results verify the accuracy of the proposed method.The comparison analysis shows that the proposed method has higher recognition accuracy,especially when the arc dissipation power is smaller than 2×10^(3) W,the zero-off period is not obvious.In conclusion,the proposed method expands the arc fault identification theory.
