The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optim...The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization algorithm.Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing.The model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach.展开更多
ADC distribution network is an effective solution for increasing renewable energy utilization with distinct benefits,such as high efficiency and easy control.However,a sudden increase in the current after the occurren...ADC distribution network is an effective solution for increasing renewable energy utilization with distinct benefits,such as high efficiency and easy control.However,a sudden increase in the current after the occurrence of faults in the network may adversely affect network stability.This study proposes an artificial neural network(ANN)-based fault detection and protection method for DC distribution networks.The ANN is applied to a classifier for different faults ontheDC line.The backpropagationneuralnetwork is used to predict the line current,and the fault detection threshold is obtained on the basis of the difference between the predicted current and the actual current.The proposed method only uses local signals,with no requirement of a strict communication link.Simulation experiments are conducted for the proposed algorithm on a two-terminal DC distribution network modeled in the PSCAD/EMTDC and developed on the MATLAB platform.The results confirm that the proposed method can accurately detect and classify line faults within a few milliseconds and is not affected by fault locations,fault resistance,noise,and communication delay.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
直流输电线路故障行波波速不确定、波头提取困难以及噪声干扰等因素制约了直流电网中故障测距技术的应用。为了降低上述因素对定位准确性的影响,提出一种基于局部特征有理样条插值均值分解(LMD based on characteristic rational spline...直流输电线路故障行波波速不确定、波头提取困难以及噪声干扰等因素制约了直流电网中故障测距技术的应用。为了降低上述因素对定位准确性的影响,提出一种基于局部特征有理样条插值均值分解(LMD based on characteristic rational spline,CRS-LMD)和奇异值分解(singular value decomposition,SVD)的故障测距方法。首先,利用特征尺度选取最优极点系数,结合有理样条插值调节拟合曲线的松紧程度,实现对故障电压行波的局部均值分解。其次,采用奇异值分解对故障行波波头进行准确提取。最后,在PSCAD/EMTDC中搭建了张北±500 kV柔性直流电网的仿真模型,模拟各种故障情况并输出故障数据,利用Matlab对故障数据进行处理并验证定位算法。最后,仿真结果表明,所提故障测距算法在不同故障距离和故障类型下均能实现故障测距,且在叠加噪声和过渡电阻的情况下也能保障较高的精确性。展开更多
In order to guarantee quality during mass serial production of motors, a convenient approach on how to detect and diagnose the faults of a permanent-magnetic DC motor based on armature current analysis and BP neural n...In order to guarantee quality during mass serial production of motors, a convenient approach on how to detect and diagnose the faults of a permanent-magnetic DC motor based on armature current analysis and BP neural networks was presented in this paper. The fault feature vector was directly established by analyzing the armature current. Fault features were extracted from the current using various signal processing methods including Fourier analysis, wavelet analysis and statistical methods. Then an advanced BP neural network was used to finish decision-making and separate fault patterns. Finally, the accuracy of the method in this paper was verified by analyzing the mechanism of faults theoretically. The consistency between the experimental results and the theoretical analysis shows that four kinds of representative faults of low power permanent-magnetic DC motors can be diagnosed conveniently by this method. These four faults are brush fray, open circuit of components, open weld of components and short circuit between armature coils. This method needs fewer hardware instruments than the conventional method and whole procedures can be accomplished by several software packages developed in this paper.展开更多
DC motors are widely used in industry such as mechanics, robotics, and aerospace engineering. In this paper, we present a high performance control method for position control of DC motors. Fault-tolerant control model...DC motors are widely used in industry such as mechanics, robotics, and aerospace engineering. In this paper, we present a high performance control method for position control of DC motors. Fault-tolerant control model are also addressed to combine with neuro-robust control approach. It is shown that with the proposed control algorithms, external disturbances and coupled dynamics inherent in the system are effectively compensated using neural network unit in which no analytical estimation on the upper bound of the reconstruction error and uncertainties is needed. Simulations on various flight conditions also confirm the effectiveness of the proposed methods.展开更多
This paper introduces the characteristics of VSC and MMC-MTDC and discusses the effects of different kinds of faults in HVDC systems. Special attention is given to the comparison between a pole-to-pole fault and a pol...This paper introduces the characteristics of VSC and MMC-MTDC and discusses the effects of different kinds of faults in HVDC systems. Special attention is given to the comparison between a pole-to-pole fault and a pole-to-ground fault occurring in the middle of the line or at the terminal of a VSC. Simulations using MATLAB are provided in this article which show the difference effects clearly when faults occur in a VSC-MTDC system or in a MMC-MTDC system. Understanding of such fault characteristics and the influence of the control system on them are important prerequisites on the way to MTDC systems.展开更多
Fault current suppression is the key technology to ensure the safe operation of the DC power distribution system. In order to realize the parameter collabora-tive configuration of the DC circuit breaker and the DC cur...Fault current suppression is the key technology to ensure the safe operation of the DC power distribution system. In order to realize the parameter collabora-tive configuration of the DC circuit breaker and the DC current limiter and improve the fault current suppression capability, the fault current suppression mechanism of the DC power distribution system is revealed based on the circuit model. Then, based on the mathematical model of the DC breaker, the characteristic parameters of DC breaking are extracted, and then the influence of different characteristic parameters on the breaking characteristics of fault current is studied. Finally, the mathematical model of the collaborative process between DC circuit breaker and DC current limiter is established. The charac-teristic parameters of fault current collaborative suppression are extracted. The coupling effects of different characteristic parameters on the fault current col-laborative suppression are studied. The principle of collaborative configuration of DC circuit breaker and DC current limiter is proposed, and the collaborative suppression ability of DC circuit breaker and DC current limiter to fault current is fully exploited to ensure the safe and reliable operation of the DC power distribution system.展开更多
柔性直流输电系统中,直流故障的分析与判别是柔直系统中亟待解决的重要问题。为了更好地分析故障特征,推导出一种多端柔性直流输电网(modular multilevel converter-high voltage direct current,MMC-MTDC)的母线侧简化电路故障计算方法...柔性直流输电系统中,直流故障的分析与判别是柔直系统中亟待解决的重要问题。为了更好地分析故障特征,推导出一种多端柔性直流输电网(modular multilevel converter-high voltage direct current,MMC-MTDC)的母线侧简化电路故障计算方法,并分析了母线侧单极接地故障和极间短路故障的故障特征,在此基础上设计了一种适用于多端柔性直流输电网的故障判别方案和保护措施。该判别方案通过故障启动判据、故障区域判别和故障类型判别3个模块进行故障判定,采用了故障电压瞬时跌落值与系统电压之差及故障线路电流值作故障特征量,整套判别方案不受其他元件参数、故障位置等因素的影响,将Spearman相关性分析与传统电气故障区域判别相结合,准确判定系统区内外故障的同时,解决了故障区域判别中受线路各参数干扰导致误判的问题,仿真测试验证了本保护方案的可行性。展开更多
基金the National Natural Science Foundation of China(52177074).
文摘The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization algorithm.Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing.The model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach.
基金supported by Key Natural Science Research Projects of Colleges and Universities in Anhui Province(No.2022AH051831).
文摘ADC distribution network is an effective solution for increasing renewable energy utilization with distinct benefits,such as high efficiency and easy control.However,a sudden increase in the current after the occurrence of faults in the network may adversely affect network stability.This study proposes an artificial neural network(ANN)-based fault detection and protection method for DC distribution networks.The ANN is applied to a classifier for different faults ontheDC line.The backpropagationneuralnetwork is used to predict the line current,and the fault detection threshold is obtained on the basis of the difference between the predicted current and the actual current.The proposed method only uses local signals,with no requirement of a strict communication link.Simulation experiments are conducted for the proposed algorithm on a two-terminal DC distribution network modeled in the PSCAD/EMTDC and developed on the MATLAB platform.The results confirm that the proposed method can accurately detect and classify line faults within a few milliseconds and is not affected by fault locations,fault resistance,noise,and communication delay.
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.
文摘直流输电线路故障行波波速不确定、波头提取困难以及噪声干扰等因素制约了直流电网中故障测距技术的应用。为了降低上述因素对定位准确性的影响,提出一种基于局部特征有理样条插值均值分解(LMD based on characteristic rational spline,CRS-LMD)和奇异值分解(singular value decomposition,SVD)的故障测距方法。首先,利用特征尺度选取最优极点系数,结合有理样条插值调节拟合曲线的松紧程度,实现对故障电压行波的局部均值分解。其次,采用奇异值分解对故障行波波头进行准确提取。最后,在PSCAD/EMTDC中搭建了张北±500 kV柔性直流电网的仿真模型,模拟各种故障情况并输出故障数据,利用Matlab对故障数据进行处理并验证定位算法。最后,仿真结果表明,所提故障测距算法在不同故障距离和故障类型下均能实现故障测距,且在叠加噪声和过渡电阻的情况下也能保障较高的精确性。
文摘In order to guarantee quality during mass serial production of motors, a convenient approach on how to detect and diagnose the faults of a permanent-magnetic DC motor based on armature current analysis and BP neural networks was presented in this paper. The fault feature vector was directly established by analyzing the armature current. Fault features were extracted from the current using various signal processing methods including Fourier analysis, wavelet analysis and statistical methods. Then an advanced BP neural network was used to finish decision-making and separate fault patterns. Finally, the accuracy of the method in this paper was verified by analyzing the mechanism of faults theoretically. The consistency between the experimental results and the theoretical analysis shows that four kinds of representative faults of low power permanent-magnetic DC motors can be diagnosed conveniently by this method. These four faults are brush fray, open circuit of components, open weld of components and short circuit between armature coils. This method needs fewer hardware instruments than the conventional method and whole procedures can be accomplished by several software packages developed in this paper.
文摘DC motors are widely used in industry such as mechanics, robotics, and aerospace engineering. In this paper, we present a high performance control method for position control of DC motors. Fault-tolerant control model are also addressed to combine with neuro-robust control approach. It is shown that with the proposed control algorithms, external disturbances and coupled dynamics inherent in the system are effectively compensated using neural network unit in which no analytical estimation on the upper bound of the reconstruction error and uncertainties is needed. Simulations on various flight conditions also confirm the effectiveness of the proposed methods.
文摘This paper introduces the characteristics of VSC and MMC-MTDC and discusses the effects of different kinds of faults in HVDC systems. Special attention is given to the comparison between a pole-to-pole fault and a pole-to-ground fault occurring in the middle of the line or at the terminal of a VSC. Simulations using MATLAB are provided in this article which show the difference effects clearly when faults occur in a VSC-MTDC system or in a MMC-MTDC system. Understanding of such fault characteristics and the influence of the control system on them are important prerequisites on the way to MTDC systems.
文摘Fault current suppression is the key technology to ensure the safe operation of the DC power distribution system. In order to realize the parameter collabora-tive configuration of the DC circuit breaker and the DC current limiter and improve the fault current suppression capability, the fault current suppression mechanism of the DC power distribution system is revealed based on the circuit model. Then, based on the mathematical model of the DC breaker, the characteristic parameters of DC breaking are extracted, and then the influence of different characteristic parameters on the breaking characteristics of fault current is studied. Finally, the mathematical model of the collaborative process between DC circuit breaker and DC current limiter is established. The charac-teristic parameters of fault current collaborative suppression are extracted. The coupling effects of different characteristic parameters on the fault current col-laborative suppression are studied. The principle of collaborative configuration of DC circuit breaker and DC current limiter is proposed, and the collaborative suppression ability of DC circuit breaker and DC current limiter to fault current is fully exploited to ensure the safe and reliable operation of the DC power distribution system.
文摘柔性直流输电系统中,直流故障的分析与判别是柔直系统中亟待解决的重要问题。为了更好地分析故障特征,推导出一种多端柔性直流输电网(modular multilevel converter-high voltage direct current,MMC-MTDC)的母线侧简化电路故障计算方法,并分析了母线侧单极接地故障和极间短路故障的故障特征,在此基础上设计了一种适用于多端柔性直流输电网的故障判别方案和保护措施。该判别方案通过故障启动判据、故障区域判别和故障类型判别3个模块进行故障判定,采用了故障电压瞬时跌落值与系统电压之差及故障线路电流值作故障特征量,整套判别方案不受其他元件参数、故障位置等因素的影响,将Spearman相关性分析与传统电气故障区域判别相结合,准确判定系统区内外故障的同时,解决了故障区域判别中受线路各参数干扰导致误判的问题,仿真测试验证了本保护方案的可行性。