Abstract
Bayesian Deep Neural Network to Compensate for Current Transformer Saturation
Highlights
Current transformers (CTs) are used to convert a highvoltage primary current to a relatively lower-voltage secondary current that can be read and isolate the circuit from fault conditions
This paper presents a current transformer saturation compensation method which is of Bayesian optimization (BO) and Deep Neural Network (DNN)
The performance of Bayesian Deep Neural Network (BDNN) is evaluated on simulated data from PSCAD/EMTDC on the variation of saturation such as different fault angles, remnant flux, and power system level
Summary
Current transformers (CTs) are used to convert a highvoltage primary current to a relatively lower-voltage secondary current that can be read and isolate the circuit from fault conditions. The integration CT saturation detection and compensation was proposed in [8] based on sample-based extraction from the identified unsaturated samples using Kalman filter and reconstructed with wave shape properties and fault current characteristics. Another compensation approach utilized a least-error square (LES) filter to estimate the phasor parameters of the CT secondary current, and CT burden [9]. Propose a BDNN framework for CT saturation compensation for the first time using SDAE to extract feature that can accurately correct the distorted waveform and handle noise problem without using low-pass filter.
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