Reconstruction of CT Secondary Waveform Using ANN and Exponential Smoothing
Current Transformer Saturation, Artificial Neural Network, Exponential Smoothing, Genetic Algorithm
Instrumentation transformers act as eyes and ears of a power system. Many measurement and protection related activities depend on current transformers (CTs) as primary sensing unit. Hence, it is of utmost important that the output of a CT should be absolutely trust-worthy. However, CTs show a tendency of getting saturated. This leads to an erroneous secondary waveform, which can lead to malfunctioning of systems which are dependent on CT. This paper proposes a technique to enhance ANN based reconstruction of erroneous secondary current waveform. The proposed technique uses artificial neural network to forecast ideal waveform. The network uses two inputs: 1. Erroneous secondary waveform. 2. Exponentially smoothed secondary waveform, which acts as an assisting input. The smoothing factor is determined using genetic algorithm. Extensive simulations indicate that the proposed technique efficiently generates reconstructed CT secondary waveform.
Salil Bhat. "Reconstruction of CT Secondary Waveform Using ANN and Exponential Smoothing".INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH ISSN:2321-9939, Vol.2, Issue 4, pp.3559-3564, URL :https://rjwave.org/ijedr/papers/IJEDR1404028.pdf
Volume 2 Issue 4
Pages. 3559-3564
