Investigation for Improved Artificial Intelligence Techniques for Thyristor-controlled Series-compensated Transmission Line Fault Classification with Discrete Wavelet Packet Entropy Measures |
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Authors: | Bhargav Vyas Biswarup Das |
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Affiliation: | Department of Electrical Engineering, Indian Institute of Technology, Roorkee, Uttaranchal, India |
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Abstract: | A new transmission line fault-classification algorithm based on half-cycle post-fault current data is presented for an advanced series-compensated transmission line equipped with a thyristor-controlled series compensator. The proposed scheme was developed with the signal feature enhancement tool of discrete wavelet packet entropy measures. The Chebyshev neural network is presented as network-growing technique for protective classification, the single-layer structure of which is a more powerful classifier that eliminates the need for complicated network design. A comparative implementation study of the multi-layer perceptron and Chebyshev neural network authenticates benefits gained by the Chebyshev neural network. To demonstrate the advantage gained by Chebyshev neural networks compared to support vector machines, a comparative study is presented with a support vector machine based classification technique. The fault datawere obtained by dynamic simulation of a sample system using the real-time power system simulator PSCAD (Manitoba HVDC Research Centre, Winnipeg, Manitoba, Canada). Extensive testing reveals the effectiveness of the Chebyshev neural network for fault classification; a comparative study brings out the superiority of the Chebyshev neural network for neural network design and implementation against the multi-layer perceptron. The Chebyshev neural network proved advantageous against support vector machines as being insensitive to the classification parameter. |
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Keywords: | series-compensated transmission line fault classification discrete wavelet packet entropy support vector machine neural network Chebyshev neural network |
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