A new methodology for the detection and identification of insulator arc faults for the smart grid environment based on phasor angle measurements is presented in this study and the real time phase angle data are collec...A new methodology for the detection and identification of insulator arc faults for the smart grid environment based on phasor angle measurements is presented in this study and the real time phase angle data are collected using Phasor Measurement Units (PMU). Detection of insulator arcing faults is based on feature extraction and frequency component analysis. The proposed methodology pertains to the identification of various stages of insulator arcing faults in transmission lines network based on leakage current, frequency characteristics and synchronous phasor measurements of voltage. The methodology is evaluated for IEEE 14 standard bus system by modeling the PMU and insulator arc faults using MATLAB/Simulink. The classification of insulator arcs is done using Support Vector Machine (SVM) technique to avoid empirical risk. The proposed methodology using phasor angle measurements employing PMU is used for fault detection/classification of insulator arcing which further helps in efficient protection of the system and its stable operation. In addition, the methodology is suitable for wide area condition monitoring of smart grid rather than end to end transmission lines.展开更多
Remote monitoring of transmission lines of a power system is significant for improved reliability and stability during fault conditions and protection system breakdowns.This paper proposes a smart backup monitoring sy...Remote monitoring of transmission lines of a power system is significant for improved reliability and stability during fault conditions and protection system breakdowns.This paper proposes a smart backup monitoring system for detecting and classifying the type of transmission line fault occurred in a power grid.In contradiction to conventional methods,transmission line fault occurred at any locality within power grid can be identified and classified using measurements from phasor measurement unit(PMU)at one of the generator buses.This minimal requirement makes the proposed methodology ideal for providing backup protection.Spectral analysis of equivalent power factor angle(EPFA)variation has been adopted for detecting the occurrence of fault that occurred anywhere in the grid.Classification of the type of fault occurred is achieved from the spectral coefficients with the aid of artificial intelligence.The proposed system can considerably assist system protection center(SPC)in fault localization and to restore the line at the earliest.Effectiveness of proposed system has been validated using case studies conducted on standard power system networks.展开更多
提出一种基于改进最大相关最小冗余判据(maximal relevance and minimal redundancy,mRMR)的暂态稳定评估特征选择方法。首先对标准mRMR方法进行改进,在最大相关、最小冗余判据中引入一个权重因子以细化对特征相关性和冗余性的度量。然...提出一种基于改进最大相关最小冗余判据(maximal relevance and minimal redundancy,mRMR)的暂态稳定评估特征选择方法。首先对标准mRMR方法进行改进,在最大相关、最小冗余判据中引入一个权重因子以细化对特征相关性和冗余性的度量。然后,考虑相量测量单元可以提供的故障后实测信息,构造由系统特征构成的原始特征集,将改进的mRMR应用于特征选择。通过增量搜索算法得到一组嵌套的候选特征子集,并使用支持向量机分类器验证各候选特征子集的分类性能,选择得到具有最大分类正确率的特征子集。基于新英格兰39节点系统和IEEE 50机测试系统的算例结果验证了所提特征选择方法的有效性。展开更多
文摘A new methodology for the detection and identification of insulator arc faults for the smart grid environment based on phasor angle measurements is presented in this study and the real time phase angle data are collected using Phasor Measurement Units (PMU). Detection of insulator arcing faults is based on feature extraction and frequency component analysis. The proposed methodology pertains to the identification of various stages of insulator arcing faults in transmission lines network based on leakage current, frequency characteristics and synchronous phasor measurements of voltage. The methodology is evaluated for IEEE 14 standard bus system by modeling the PMU and insulator arc faults using MATLAB/Simulink. The classification of insulator arcs is done using Support Vector Machine (SVM) technique to avoid empirical risk. The proposed methodology using phasor angle measurements employing PMU is used for fault detection/classification of insulator arcing which further helps in efficient protection of the system and its stable operation. In addition, the methodology is suitable for wide area condition monitoring of smart grid rather than end to end transmission lines.
文摘Remote monitoring of transmission lines of a power system is significant for improved reliability and stability during fault conditions and protection system breakdowns.This paper proposes a smart backup monitoring system for detecting and classifying the type of transmission line fault occurred in a power grid.In contradiction to conventional methods,transmission line fault occurred at any locality within power grid can be identified and classified using measurements from phasor measurement unit(PMU)at one of the generator buses.This minimal requirement makes the proposed methodology ideal for providing backup protection.Spectral analysis of equivalent power factor angle(EPFA)variation has been adopted for detecting the occurrence of fault that occurred anywhere in the grid.Classification of the type of fault occurred is achieved from the spectral coefficients with the aid of artificial intelligence.The proposed system can considerably assist system protection center(SPC)in fault localization and to restore the line at the earliest.Effectiveness of proposed system has been validated using case studies conducted on standard power system networks.
