Jiang, H.G., Zhang, J.J., Gao, W.Z. and Wu, Z.P. (2014) Fault Detection, Identification, and Location in Smart Grid Based on Data-Driven Computational Methods. IEEE Transactions on Smart Grid, 5, 2947-2956.
http://dx.doi.org/10.1109/TSG.2014.2330624

has been cited by the following article:

• TITLE: A SVM Based Condition Monitoring of Transmission Line Insulators Using PMU for Smart Grid Environment

  AUTHORS: Kailasam Saranya, Chinnusamy Muniraj

  KEYWORDS: Phasor Measurement Units, Insulator Arc, Feature Extraction, Synchronous Phasor Measurements, Leakage Current, Support Vector Machine

  JOURNAL NAME: Journal of Power and Energy Engineering, Vol.4 No.3, March 31, 2016

  ABSTRACT: 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.