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Reconstruction of CT Secondary Waveform Using ANN and Exponential Smoothing
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.
Keywords- Current Transformer Saturation, Artificial Neural Network, Exponential Smoothing, Genetic Algorithm
Unique Identification Number - IJEDR1404028Page Number(s) - 3559-3564Pubished in - Volume 2 | Issue 4 | Dec 2014DOI (Digital Object Identifier) -    Publisher - IJEDR (ISSN - 2321-9939)
Cite this Article
Salil Bhat,   "Reconstruction of CT Secondary Waveform Using ANN and Exponential Smoothing"
, International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.2, Issue 4, pp.3559-3564, Dec 2014, Available at :http://www.ijedr.org/papers/IJEDR1404028.pdf