Open Science Research Excellence

G N Pillai

Publications

3

Publications

3
10590
Fault Zone Detection on Advanced Series Compensated Transmission Line using Discrete Wavelet Transform and SVM
Abstract:
In this paper a novel method for finding the fault zone on a Thyristor Controlled Series Capacitor (TCSC) incorporated transmission line is presented. The method makes use of the Support Vector Machine (SVM), used in the classification mode to distinguish between the zones, before or after the TCSC. The use of Discrete Wavelet Transform is made to prepare the features which would be given as the input to the SVM. This method was tested on a 400 kV, 50 Hz, 300 Km transmission line and the results were highly accurate.
Keywords:
Flexible ac transmission system (FACTS), thyristorcontrolled series-capacitor (TCSC), discrete wavelet transforms(DWT), support vector machine (SVM).
2
11210
High Impedance Fault Detection using LVQ Neural Networks
Abstract:
This paper presents a new method to detect high impedance faults in radial distribution systems. Magnitudes of third and fifth harmonic components of voltages and currents are used as a feature vector for fault discrimination. The proposed methodology uses a learning vector quantization (LVQ) neural network as a classifier for identifying high impedance arc-type faults. The network learns from the data obtained from simulation of a simple radial system under different fault and system conditions. Compared to a feed-forward neural network, a properly tuned LVQ network gives quicker response.
Keywords:
Fault identification, distribution networks, high impedance arc-faults, feature vector, LVQ networks.
1
12081
Accurate Fault Classification and Section Identification Scheme in TCSC Compensated Transmission Line using SVM
Abstract:
This paper presents a new approach for the protection of Thyristor-Controlled Series Compensator (TCSC) line using Support Vector Machine (SVM). One SVM is trained for fault classification and another for section identification. This method use three phase current measurement that results in better speed and accuracy than other SVM based methods which used single phase current measurement. This makes it suitable for real-time protection. The method was tested on 10,000 data instances with a very wide variation in system conditions such as compensation level, source impedance, location of fault, fault inception angle, load angle at source bus and fault resistance. The proposed method requires only local current measurement.
Keywords:
Fault Classification, Section Identification, Feature Selection, Support Vector Machine (SVM), Thyristor-Controlled Series Compensator (TCSC)