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All Journal Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Akash Saxena
Swami Keshvanand Institute of Technology Management & Gramothan
Computer Science & IT Electrical & Electronics Engineering Engineering

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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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1014.239 KB) | DOI: 10.11591/eei.v5i3.537

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1014.239 KB) | DOI: 10.11591/eei.v5i3.537

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1014.239 KB) | DOI: 10.11591/eei.v5i3.537

Abstract

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.
Electric Price Forecast using Interbreed Approach of Linear Regression and SVM Deepak Saini; Akash Saxena
Indonesian Journal of Electrical Engineering and Computer Science Vol 2, No 3: June 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v2.i3.pp537-544

Abstract

Electricity price forecasting is a hypercritical issue due to the involvement of consumers and producers in electricity markets. Price forecasting plays an important role in planning and managing economic operations related with the electrical power (bidding, trading) and other decisions related with load shedding and generation rescheduling. It is also useful for optimization in electrical energy trade. This paper explores an interbreed technique based on Support Vector Machine (SVM) and linear regression to predict the day ahead electricity price using historical data as a raw insert. Different 27 linear regression models are formed to create initial framework for forecasting engine. Comparison of the performance of different forecasting engines is carried out on the basis of error indices namely Mean Square Error (MSE), Sum Square Error (SSE) and other conventional error indices. A detailed explanation of linear regression system based model is presented and simulation results exhibit that the proposed learning method is able to forecast electricity price in an effective manner.
Co-Authors Deepak Saini Purva Sharma