This paper proposes a new algorithm for High Impedance Fault (HIF) detection using Phasor Measurement Unit (PMU). This type of faults is difficult to detect by over current protection relays because of low fault curre...This paper proposes a new algorithm for High Impedance Fault (HIF) detection using Phasor Measurement Unit (PMU). This type of faults is difficult to detect by over current protection relays because of low fault current. In this paper, an index based on phasors change is proposed for HIF detection. The phasors are measured by PMU to obtain the square summation of errors. Two types of data are used for error calculation. The first one is sampled data and the second one is estimated data. But this index is not enough to declare presence of a HIF. Therefore another index introduces in order to distinguish the load switching from HIF. Second index utilizes 3rd harmonic current angle because this number of harmonic has a special behaviour during HIF. The verification of the proposed method is done by different simulation cases in EMTP/MATLAB.展开更多
Owing to the large-scale grid connection of new energy sources, several installed power electronic devices introduce sub-/supersynchronous inter-harmonics into power signals, resulting in the frequent occurrence of su...Owing to the large-scale grid connection of new energy sources, several installed power electronic devices introduce sub-/supersynchronous inter-harmonics into power signals, resulting in the frequent occurrence of subsynchronous oscillations(SSOs). The SSOs may cause significant harm to generator sets and power systems;thus, online monitoring and accurate alarms for power systems are crucial for their safe and stable operation. Phasor measurement units(PMUs) can realize the dynamic real-time monitoring of power systems. Based on PMU phasor measurements, this study proposes a method for SSO online monitoring and alarm implementation for the main station of a PMU. First, fast Fourier transform frequency spectrum analysis is performed on PMU current phasor amplitude data to obtain subsynchronous frequency components. Second, the support vector machine learning algorithm is trained to obtain the amplitude threshold and subsequently filter out safe components and retain harmful ones. Finally, the adaptive duration threshold is determined according to frequency susceptibility, amplitude attenuation, and energy accumulation to decide whether to transmit an alarm signal. Experiments based on field data verify the effectiveness of the proposed method.展开更多
Phasor Measurement Units(PMUs)provide Global Positioning System(GPS)time-stamped synchronized measurements of voltage and current with the phase angle of the system at certain points along with the grid system.Those s...Phasor Measurement Units(PMUs)provide Global Positioning System(GPS)time-stamped synchronized measurements of voltage and current with the phase angle of the system at certain points along with the grid system.Those synchronized data measurements are extracted in the form of amplitude and phase from various locations of the power grid to monitor and control the power system condition.A PMU device is a crucial part of the power equipment in terms of the cost and operative point of view.However,such ongoing development and improvement to PMUs’principal work are essential to the network operators to enhance the grid quality and the operating expenses.This paper introduces a proposed method that led to lowcost and less complex techniques to optimize the performance of PMU using Second-Order Kalman Filter.It is based on the Asyncrhophasor technique resulting in a phase error minimization when receiving the signal from an access point or from the main access point.The MATLAB model has been created to implement the proposed method in the presence of Gaussian and non-Gaussian.The results have shown the proposed method which is Second-Order Kalman Filter outperforms the existing model.The results were tested usingMean Square Error(MSE).The proposed Second-Order Kalman Filter method has been replaced with a synchronization unit into thePMUstructure to clarify the significance of the proposed new PMU.展开更多
Facing constraints imposed by storage and bandwidth limitations,the vast volume of phasor meas-urement unit(PMU)data collected by the wide-area measurement system(WAMS)for power systems cannot be fully utilized.This l...Facing constraints imposed by storage and bandwidth limitations,the vast volume of phasor meas-urement unit(PMU)data collected by the wide-area measurement system(WAMS)for power systems cannot be fully utilized.This limitation significantly hinders the effective deployment of situational awareness technologies for systematic applications.In this work,an effective curvature quantified Douglas-Peucker(CQDP)-based PMU data compression method is proposed for situational awareness of power systems.First,a curvature integrated distance(CID)for measuring the local flection and fluc-tuation of PMU signals is developed.The Doug-las-Peucker(DP)algorithm integrated with a quan-tile-based parameter adaptation scheme is then proposed to extract feature points for profiling the trends within the PMU signals.This allows adaptive adjustment of the al-gorithm parameters,so as to maintain the desired com-pression ratio and reconstruction accuracy as much as possible,irrespective of the power system dynamics.Fi-nally,case studies on the Western Electricity Coordinat-ing Council(WECC)179-bus system and the actual Guangdong power system are performed to verify the effectiveness of the proposed method.The simulation results show that the proposed method achieves stably higher compression ratio and reconstruction accuracy in both steady state and in transients of the power system,and alleviates the compression performance degradation problem faced by existing compression methods.Index Terms—Curvature quantified Douglas-Peucker,data compression,phasor measurement unit,power sys-tem situational awareness.展开更多
集中送出的新能源场站大多位于电网末端,随着其有功出力的增加,易出现静态电压失稳。该文将传统的阻抗模指标的应用对象由负荷推广至新能源场站,丰富了该指标在静态电压稳定评估方面的适用性场景,包括含无功补偿的新能源系统、新能源集...集中送出的新能源场站大多位于电网末端,随着其有功出力的增加,易出现静态电压失稳。该文将传统的阻抗模指标的应用对象由负荷推广至新能源场站,丰富了该指标在静态电压稳定评估方面的适用性场景,包括含无功补偿的新能源系统、新能源集群馈入系统、以及新能源多机多馈入系统。具体地,首先,分析新能源单馈入系统的临界静态电压稳定条件,并据此给出用于评估新能源场站静态电压稳定性的阻抗模裕度指标及稳定判据;其次,通过将无功补偿设备并入系统阻抗,分析无功补偿对于指标的影响;再次,证明在新能源的集群馈入系统中,公共耦合点(point of common coupling,PCC)的电压失稳将发生在单个新能源场站之前,并据此确定PCC点作为指标的计算节点;之后,为考虑多机多馈入系统中不同新能源场站间的影响,在指标的计算过程中,保留待评估的关键新能源场站,将其他新能源场站等值为阻抗,并入节点阻抗矩阵中,实现方法在多机多馈入系统中的扩展应用。最后,基于PSD-BPA中建立的单机单馈入系统、多机多馈入系统、以及某省实际大电网算例验证指标的有效性。展开更多
高渗透率新能源波动下系统动态频率预测是实现受端网络频率安全态势感知的基础。该文提出一种基于混合量测和物理状态方程联合驱动的新能源电力系统双向树状长短期记忆网络(combined equation-of-state-driven and data-driven bi-direc...高渗透率新能源波动下系统动态频率预测是实现受端网络频率安全态势感知的基础。该文提出一种基于混合量测和物理状态方程联合驱动的新能源电力系统双向树状长短期记忆网络(combined equation-of-state-driven and data-driven bi-directional tree-struct long short term memory,CEOSD-BITREE-LSTM)动态频率预测方法。首先,引入双层多头注意力图神经网络,提出考虑同步相量测量单元(synchronous phasor measurement unit,PMU)和数据采集与监视控制系统装置(supervisory control and data acquisition,SCADA)量测差异性和时序同步性的混合量测融合策略;其次,依据PMU密集采样特性,建立计及源网荷物理联系的线性时变状态方程,刻画物理-数据空间的频率特征交互关系;然后,考虑新能源出力、负荷波动等不确定因素,结合以PMU并行搜索调频资源形成的拓扑结构,构建CEOSD-BITREE-LSTM动态频率预测模型,实现系统频率态势的高精度预测。最后,以改进新英格兰10机39节点、三区互联系统为算例,验证该文所提方法的可行性和有效性。展开更多
With the advent of phasor measurement unit (PMU) technology, the grid observability has got a new dimension. This facet of technology helps in getting the real-time and dynamic scenario of the grid operations which wa...With the advent of phasor measurement unit (PMU) technology, the grid observability has got a new dimension. This facet of technology helps in getting the real-time and dynamic scenario of the grid operations which was a remote possibility some decades before. Achieving this level of observability puts us at an advantage of responding to the system faults with reduced response time, and helps in restoring the grid stability within fraction of second. This paper demonstrates the detailed fault characterization from the PMU inputs, after illustrations from various real-time examples and different faults occurred in India. This paper tries to shed some light on areas where the accurate fault characterization can help the operator in taking the right decision for reliable grid operations.展开更多
为保证同步相量测量装置(phasor measurement unit,PMU)采集数据的准确应用,须排除其量测值中的异常数据。现有PMU异常数据辨识算法存在算法复杂度高、难以在线更新、多源数据难以校准、依赖多源数据应用难度大等不足。为此,文中从PMU...为保证同步相量测量装置(phasor measurement unit,PMU)采集数据的准确应用,须排除其量测值中的异常数据。现有PMU异常数据辨识算法存在算法复杂度高、难以在线更新、多源数据难以校准、依赖多源数据应用难度大等不足。为此,文中从PMU事件数据和异常数据模型及PMU异常数据判别信息熵定义出发,提出基于该信息熵的异常数据辨识框架。在此框架基础上,基于利用层次方法的平衡迭代规约和聚类(balanced iterative reducing and clustering using hierarchies,BIRCH)算法提出PMU异常数据辨识算法;然后,对所提出的算法进行原型实现,并针对某变电站的PMU采集数据集进行算法实验验证。实验结果表明,与一类支持向量机(one-class support vector machine,OCSVM)算法与间隙统计算法相比,文中算法的准确度及实时性均具有较强的优势。展开更多
文摘This paper proposes a new algorithm for High Impedance Fault (HIF) detection using Phasor Measurement Unit (PMU). This type of faults is difficult to detect by over current protection relays because of low fault current. In this paper, an index based on phasors change is proposed for HIF detection. The phasors are measured by PMU to obtain the square summation of errors. Two types of data are used for error calculation. The first one is sampled data and the second one is estimated data. But this index is not enough to declare presence of a HIF. Therefore another index introduces in order to distinguish the load switching from HIF. Second index utilizes 3rd harmonic current angle because this number of harmonic has a special behaviour during HIF. The verification of the proposed method is done by different simulation cases in EMTP/MATLAB.
基金supported by the National Key R&D Pro gram (2017YFB0902901)National Nature Science Founda tion of China (51725702, 51627811, 51707064)。
文摘Owing to the large-scale grid connection of new energy sources, several installed power electronic devices introduce sub-/supersynchronous inter-harmonics into power signals, resulting in the frequent occurrence of subsynchronous oscillations(SSOs). The SSOs may cause significant harm to generator sets and power systems;thus, online monitoring and accurate alarms for power systems are crucial for their safe and stable operation. Phasor measurement units(PMUs) can realize the dynamic real-time monitoring of power systems. Based on PMU phasor measurements, this study proposes a method for SSO online monitoring and alarm implementation for the main station of a PMU. First, fast Fourier transform frequency spectrum analysis is performed on PMU current phasor amplitude data to obtain subsynchronous frequency components. Second, the support vector machine learning algorithm is trained to obtain the amplitude threshold and subsequently filter out safe components and retain harmful ones. Finally, the adaptive duration threshold is determined according to frequency susceptibility, amplitude attenuation, and energy accumulation to decide whether to transmit an alarm signal. Experiments based on field data verify the effectiveness of the proposed method.
文摘Phasor Measurement Units(PMUs)provide Global Positioning System(GPS)time-stamped synchronized measurements of voltage and current with the phase angle of the system at certain points along with the grid system.Those synchronized data measurements are extracted in the form of amplitude and phase from various locations of the power grid to monitor and control the power system condition.A PMU device is a crucial part of the power equipment in terms of the cost and operative point of view.However,such ongoing development and improvement to PMUs’principal work are essential to the network operators to enhance the grid quality and the operating expenses.This paper introduces a proposed method that led to lowcost and less complex techniques to optimize the performance of PMU using Second-Order Kalman Filter.It is based on the Asyncrhophasor technique resulting in a phase error minimization when receiving the signal from an access point or from the main access point.The MATLAB model has been created to implement the proposed method in the presence of Gaussian and non-Gaussian.The results have shown the proposed method which is Second-Order Kalman Filter outperforms the existing model.The results were tested usingMean Square Error(MSE).The proposed Second-Order Kalman Filter method has been replaced with a synchronization unit into thePMUstructure to clarify the significance of the proposed new PMU.
基金supported by the National Natural Sci-ence Foundation of China(No.52077195).
文摘Facing constraints imposed by storage and bandwidth limitations,the vast volume of phasor meas-urement unit(PMU)data collected by the wide-area measurement system(WAMS)for power systems cannot be fully utilized.This limitation significantly hinders the effective deployment of situational awareness technologies for systematic applications.In this work,an effective curvature quantified Douglas-Peucker(CQDP)-based PMU data compression method is proposed for situational awareness of power systems.First,a curvature integrated distance(CID)for measuring the local flection and fluc-tuation of PMU signals is developed.The Doug-las-Peucker(DP)algorithm integrated with a quan-tile-based parameter adaptation scheme is then proposed to extract feature points for profiling the trends within the PMU signals.This allows adaptive adjustment of the al-gorithm parameters,so as to maintain the desired com-pression ratio and reconstruction accuracy as much as possible,irrespective of the power system dynamics.Fi-nally,case studies on the Western Electricity Coordinat-ing Council(WECC)179-bus system and the actual Guangdong power system are performed to verify the effectiveness of the proposed method.The simulation results show that the proposed method achieves stably higher compression ratio and reconstruction accuracy in both steady state and in transients of the power system,and alleviates the compression performance degradation problem faced by existing compression methods.Index Terms—Curvature quantified Douglas-Peucker,data compression,phasor measurement unit,power sys-tem situational awareness.
文摘集中送出的新能源场站大多位于电网末端,随着其有功出力的增加,易出现静态电压失稳。该文将传统的阻抗模指标的应用对象由负荷推广至新能源场站,丰富了该指标在静态电压稳定评估方面的适用性场景,包括含无功补偿的新能源系统、新能源集群馈入系统、以及新能源多机多馈入系统。具体地,首先,分析新能源单馈入系统的临界静态电压稳定条件,并据此给出用于评估新能源场站静态电压稳定性的阻抗模裕度指标及稳定判据;其次,通过将无功补偿设备并入系统阻抗,分析无功补偿对于指标的影响;再次,证明在新能源的集群馈入系统中,公共耦合点(point of common coupling,PCC)的电压失稳将发生在单个新能源场站之前,并据此确定PCC点作为指标的计算节点;之后,为考虑多机多馈入系统中不同新能源场站间的影响,在指标的计算过程中,保留待评估的关键新能源场站,将其他新能源场站等值为阻抗,并入节点阻抗矩阵中,实现方法在多机多馈入系统中的扩展应用。最后,基于PSD-BPA中建立的单机单馈入系统、多机多馈入系统、以及某省实际大电网算例验证指标的有效性。
文摘高渗透率新能源波动下系统动态频率预测是实现受端网络频率安全态势感知的基础。该文提出一种基于混合量测和物理状态方程联合驱动的新能源电力系统双向树状长短期记忆网络(combined equation-of-state-driven and data-driven bi-directional tree-struct long short term memory,CEOSD-BITREE-LSTM)动态频率预测方法。首先,引入双层多头注意力图神经网络,提出考虑同步相量测量单元(synchronous phasor measurement unit,PMU)和数据采集与监视控制系统装置(supervisory control and data acquisition,SCADA)量测差异性和时序同步性的混合量测融合策略;其次,依据PMU密集采样特性,建立计及源网荷物理联系的线性时变状态方程,刻画物理-数据空间的频率特征交互关系;然后,考虑新能源出力、负荷波动等不确定因素,结合以PMU并行搜索调频资源形成的拓扑结构,构建CEOSD-BITREE-LSTM动态频率预测模型,实现系统频率态势的高精度预测。最后,以改进新英格兰10机39节点、三区互联系统为算例,验证该文所提方法的可行性和有效性。
文摘With the advent of phasor measurement unit (PMU) technology, the grid observability has got a new dimension. This facet of technology helps in getting the real-time and dynamic scenario of the grid operations which was a remote possibility some decades before. Achieving this level of observability puts us at an advantage of responding to the system faults with reduced response time, and helps in restoring the grid stability within fraction of second. This paper demonstrates the detailed fault characterization from the PMU inputs, after illustrations from various real-time examples and different faults occurred in India. This paper tries to shed some light on areas where the accurate fault characterization can help the operator in taking the right decision for reliable grid operations.
文摘为保证同步相量测量装置(phasor measurement unit,PMU)采集数据的准确应用,须排除其量测值中的异常数据。现有PMU异常数据辨识算法存在算法复杂度高、难以在线更新、多源数据难以校准、依赖多源数据应用难度大等不足。为此,文中从PMU事件数据和异常数据模型及PMU异常数据判别信息熵定义出发,提出基于该信息熵的异常数据辨识框架。在此框架基础上,基于利用层次方法的平衡迭代规约和聚类(balanced iterative reducing and clustering using hierarchies,BIRCH)算法提出PMU异常数据辨识算法;然后,对所提出的算法进行原型实现,并针对某变电站的PMU采集数据集进行算法实验验证。实验结果表明,与一类支持向量机(one-class support vector machine,OCSVM)算法与间隙统计算法相比,文中算法的准确度及实时性均具有较强的优势。
