BibTeX:

@article{IJIRSTV3I2064,
     title={A DWT Approach for Detection and Classification of Transmission Line Faults},
     author={Prasad Purushottam Kawale and Prof. C. Veeresh},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={3},
     number={2},
     pages={102--114},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV3I2064.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

The rapid growth of electric power systems has resulted in a large increase of the number of lines in operation and their total length. These lines are exposed to faults because of many reasons such as a result of lightning, short circuits, faulty equipments, miss-operation, human errors, overload, and aging etc Due to these faults .long term power outages for customers and may lead to significant losses. Therefore fast detection and classification of transmission line faults is important in maintaining a reliable power system operation and to ensure quality performance of the power system. This paper aims at detecting and classifying the transmission line faults by using Discrete Wavelet Transform (DWT) and Artificial neural network (ANN). Various types of fault conditions such as Single line-to-ground faults (L-G), Line-to-Line faults (L-L) and Double Line-to-ground faults (L-L-G) are simulated in Power System Computer Added Design (PSCAD) software. An extremely large data set of current and voltage signals is generated by simulating various types of fault conditions by varying the system parameters. Then an advanced signal processing tools such as discrete wavelet transform (DWT) is used for calculating detail coefficients energy of the fault signals. Depending upon the detail coefficients energy the fault will be detected. A properly configured Artificial Neural Network (ANN) can be utilized for classification of the faults based on the DWT signal.

Power System Computer Added Design, Discrete Wavelet Transform, Artificial Neural Network, Transmission line fault detection, fault type classification