Modelling of Current Transformers Based on Measurements of Frequency Characteristics

Leszczyński, Pawel; Daszczyński, Tadeusz; Szewczyk, Marcin

doi:10.1109/paee59932.2023.10244583

Cited by 1 publication

(1 citation statement)

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“…Literature makes online prediction of errors caused by temperature, load current and interphase interference of current transformer. Literature [3] constructs the mathematical model of current transformer in the case of electromagnetic interference, and puts forward the corresponding anti-interference measures. Current transformer is widely used in power system.…”

Section: Introductionmentioning

confidence: 99%

Error state prediction method of current transformer based on improved VMD-BP neural network

Li,

Shun,

Jiao

et al. 2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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In order to improve the fault diagnosis accuracy of voltage transformer, an online diagnosis method of current transformer error state based on improved variational mode decomposition (VMD) and BP neural network is proposed. Firstly, mutual information is used as the condition of stopping VMD iteration, so that the improved VMD algorithm can self-adaptively determine the decomposition number. Then the improved VMD algorithm is used to process the secondary circuit signal of the current transformer, and the IMF components in different scales are obtained. Finally, BP neural network optimized by genetic algorithm is used to predict different IMF components respectively, and the predicted results are superimposed to obtain the final predicted results. By comparing BP and VMD-ARMA models, the results show that the improved VMD-BP neural network model has higher prediction accuracy and more stable results.

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Section: Introductionmentioning

confidence: 99%