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Purva Sharma

Swami Keshvanand Institute of Technology Management & Gramothan

Author-ID : 2467692

Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 3 Documents Claim Missing Document

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Articles

Fault Detection and Classification in Transmission Line Using Wavelet Transform and ANN Purva Sharma; Deepak Saini; Akash Saxena
Bulletin of Electrical Engineering and Informatics Vol 5, No 3: September 2016
Publisher : Institute of Advanced Engineering and Science

Recent years, there is an increased interest in fault classification algorithms. The reason, behind this interest is the escalating power demand and multiple interconnections of utilities in grid. This paper presents an application of wavelet transforms to detect the faults and further to perform classification by supervised learning paradigm. Different architectures of ANN aretested with the statistical attributes of a wavelet transform of a voltage signal as input features and binary digits as outputs. The proposed supervised learning module is tested on a transmission network. It is observed that ANN architecture performs satisfactorily when it is compared with the simulation results. The transmission network is simulated on Matlab. The performance indices Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Sum Square Error (SSE) are used to determine the efficacy of the neural network.

Fault Detection and Classification in Transmission Line Using Wavelet Transform and ANN Purva Sharma; Deepak Saini; Akash Saxena
Bulletin of Electrical Engineering and Informatics Vol 5, No 3: September 2016
Publisher : Institute of Advanced Engineering and Science

Recent years, there is an increased interest in fault classification algorithms. The reason, behind this interest is the escalating power demand and multiple interconnections of utilities in grid. This paper presents an application of wavelet transforms to detect the faults and further to perform classification by supervised learning paradigm. Different architectures of ANN aretested with the statistical attributes of a wavelet transform of a voltage signal as input features and binary digits as outputs. The proposed supervised learning module is tested on a transmission network. It is observed that ANN architecture performs satisfactorily when it is compared with the simulation results. The transmission network is simulated on Matlab. The performance indices Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Sum Square Error (SSE) are used to determine the efficacy of the neural network.

Fault Detection and Classification in Transmission Line Using Wavelet Transform and ANN Purva Sharma; Deepak Saini; Akash Saxena
Bulletin of Electrical Engineering and Informatics Vol 5, No 3: September 2016
Publisher : Institute of Advanced Engineering and Science

Recent years, there is an increased interest in fault classification algorithms. The reason, behind this interest is the escalating power demand and multiple interconnections of utilities in grid. This paper presents an application of wavelet transforms to detect the faults and further to perform classification by supervised learning paradigm. Different architectures of ANN aretested with the statistical attributes of a wavelet transform of a voltage signal as input features and binary digits as outputs. The proposed supervised learning module is tested on a transmission network. It is observed that ANN architecture performs satisfactorily when it is compared with the simulation results. The transmission network is simulated on Matlab. The performance indices Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Sum Square Error (SSE) are used to determine the efficacy of the neural network.

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