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
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
Inter-turn fault is a serious stator winding short-circuit fault of permanent magnet synchronous machine(PMSM). Once it occurs, it produces a huge short-circuit current that poses a great risk to the safe operation of...Inter-turn fault is a serious stator winding short-circuit fault of permanent magnet synchronous machine(PMSM). Once it occurs, it produces a huge short-circuit current that poses a great risk to the safe operation of PMSM. Thus, an inter-turn short-circuit fault(ITSCF) diagnosis method based on high frequency(HF) voltage residual is proposed in this paper with proper HF signal injection. First, the analytical models of PMSM after the ITSCF are deduced. Based on the model, the voltage residual at low frequency(LF) and HF can be obtained. It is revealed that the HF voltage residual has a stronger ITSCF detection capability compared to the LF voltage residual. To obtain optimal fault signature, a 3-phase symmetrical HF voltage is injected into the machine drive system, and the HF voltage residuals are extracted. The fault indicator is defined as the standard deviation of the 3-phase HF voltage residuals. The effectiveness of the proposed ITSCF diagnosis method is verified by experiments on a triple 3-phase PMSM. It is worth noting that no extra hardware equipment is required to implement the proposed method.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
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
新时期,火电厂的工作内容比较复杂,需要借助分散控制系统(Distributed Control System,DCS)提供控制方面的支持,这也要求加强DCS故障处理和预防工作。首先,分析火电厂DCS常见故障;其次,分析其应急处理方法及预防策略,分别就共性处理原...新时期,火电厂的工作内容比较复杂,需要借助分散控制系统(Distributed Control System,DCS)提供控制方面的支持,这也要求加强DCS故障处理和预防工作。首先,分析火电厂DCS常见故障;其次,分析其应急处理方法及预防策略,分别就共性处理原则和硬件故障应急处理方法,以及建设故障感知模式、运用大数据组织预防等进行具体论述,呈现火电厂DCS故障特点的同时,为后续处理和预防活动提供必要参考。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.
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。
基金supported in part by the Jiangsu Carbon Peak Carbon Neutralization Science and Technology Innovation Special Fund under Grant BE2022032-1National Natural Science Foundation of China under Grant 52277035, Grant 51937006 and Grant 51907028the “SEU Zhishan Young Scholars” Program of Southeast University。
文摘Inter-turn fault is a serious stator winding short-circuit fault of permanent magnet synchronous machine(PMSM). Once it occurs, it produces a huge short-circuit current that poses a great risk to the safe operation of PMSM. Thus, an inter-turn short-circuit fault(ITSCF) diagnosis method based on high frequency(HF) voltage residual is proposed in this paper with proper HF signal injection. First, the analytical models of PMSM after the ITSCF are deduced. Based on the model, the voltage residual at low frequency(LF) and HF can be obtained. It is revealed that the HF voltage residual has a stronger ITSCF detection capability compared to the LF voltage residual. To obtain optimal fault signature, a 3-phase symmetrical HF voltage is injected into the machine drive system, and the HF voltage residuals are extracted. The fault indicator is defined as the standard deviation of the 3-phase HF voltage residuals. The effectiveness of the proposed ITSCF diagnosis method is verified by experiments on a triple 3-phase PMSM. It is worth noting that no extra hardware equipment is required to implement the proposed method.
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.
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。
文摘新时期,火电厂的工作内容比较复杂,需要借助分散控制系统(Distributed Control System,DCS)提供控制方面的支持,这也要求加强DCS故障处理和预防工作。首先,分析火电厂DCS常见故障;其次,分析其应急处理方法及预防策略,分别就共性处理原则和硬件故障应急处理方法,以及建设故障感知模式、运用大数据组织预防等进行具体论述,呈现火电厂DCS故障特点的同时,为后续处理和预防活动提供必要参考。
