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An Artificial intelligence approach to detection of high impedance fault
| Content Provider | Semantic Scholar |
|---|---|
| Author | Kumar, Rakesh Saini, N. Saini, Ankita |
| Copyright Year | 2014 |
| 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 back propagation neural network as a classifier for identifying high impedance arctype 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 back propagation network gives quicker response. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.ijarcce.com/upload/2014/october/IJARCCE1B%20a%20ankita%20An%20Artificial%20intelligence%20approach%20to%20detection%20of%20high%20impedance%20fault.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Artificial intelligence Artificial neural network Backpropagation Biological Neural Networks CNS disorder Characteristic impedance Emoticon Fault model Feature vector Feedforward neural network High impedance Hypoxia Inducible Factor Family Memory-level parallelism Multilayer perceptron Neural Network Simulation Quantitative impedance Radial (radio) Radio frequency Sensor Simulation Software propagation Synaptic Transmission Transmission line |
| Content Type | Text |
| Resource Type | Article |
