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Aravindan, P.
- Fault Current Based Fault Location in Multiterminal Transmission Line using Wavelet Transform
Authors
Source
Biometrics and Bioinformatics, Vol 9, No 2 (2017), Pagination: 35-39Abstract
The transmission line fault is a very important issue in power system area. In order to restore the power system within a minimum period of interruption should be cleared quickly. So it is needed to identify the exact fault location in a transmission line. It is more difficult to find the fault location in multiterminal transmission line manually. Locating transmission line faults quickly and exactly is very important for the economy, safety and reliability of the power system. This project deals about Travelling Wave (TW) based fault location of the multiterminal transmission line by using Wavelet Transform (WT) for identifying the accurate fault location. Wavelet transform technique can be easily synchronized with other power system protective devices. In this paper, the simulation result shows that the proposed wavelet transform technique can locate faults more accurately above 99 percentage with different fault resistance. Thus the proposed technique is well matched for implementation in digital protection schemes. The wavelet transform technique, when combined with an advanced communication technology and wide area observing system, would be an effective tool for identifying the faults in any part of the transmission line system.
Keywords
Wavelet Transform, Travelling Wave, Fault Location, Fault Resistance.
- A Design of Fault Detection for Steel Plates Using Data Mining Applications
Authors
1 Department of Computer Science, Sree Saraswathi Thyagaraja College, Pollachi – Tamil Nadu, IN
2 Department of MCA, Sri Venkadeswara College of Computer Applications and Management, Ettimadai, Coimbatore -Tamil Nadu, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 9, No 1 (2017), Pagination: 4-7Abstract
Fault Diagnosis (FD) has a major importance to enhance the quality of manufacturing and to lessen the cost of product testing. It keeps away from product quality problems and facilitates precautionary maintenance and pattern recognition problem. It has more attention to develop methods for improving the accuracy and efficiency of pattern recognition. Many computational tools and algorithms that have been recently developed could be used. This study evaluates the performances of three of the popular and effective data mining models to diagnose seven commonly occurring faults of the steel plate namely; Pastry, Z_Scratch, K_Scatch, Stains, Dirtiness, Bumps and other faults. The models include C5.0 decision tree (C5.0 DT) with boosting, Multi Perception Neural Network (MLPNN) with pruning and Logistic Regression (LR) with step forward. A training set of such patterns, the individual model learned how to differentiate a new case in the domain. The diagnosis performances of the proposed models are presented using statistical accuracy, specificity and sensitivity. The diagnostic accuracy of the C5.0 decision tree with boosting algorithm. Experimental results showed that data mining algorithms in general and decision trees in particular have the great impact of on the problem of steel plates fault diagnosis.