Deep learning‑based estimation technique for capacitance and ESR of input capacitors in single‑phase DC/AC converters

Hye-Jin Park; Jae-Chang Kim; Sangshin Kwak

Deep learning‑based estimation technique for capacitance and ESR of input capacitors in single‑phase DC/AC converters

Hye-Jin Park

Jae-Chang Kim

Sangshin Kwak

Vol. 22, No. 3, pp. 513-521, Mar. 2022

10.1007/s43236-021-00366-x

Capacitance estimation

ESR

Deep Learning

DC/AC converter

Abstract

This study proposes an algorithm to estimate the state of an input capacitor based on a deep neural network (DNN). This algorithm runs in a DC/AC single-phase converter. According to the analysis result of the data from the capacitor, the component with twice the fundamental and switching frequencies demonstrated dominant characteristics. The most dominant low-frequency and mid-frequency components are extracted from the collected experimental voltage and current through a fast Fourier transform. With these four components, 11 combinations of input variables were created and used as inputs for the DNN. After training and testing, we determined which combination had the best performance. Therefore, in the case of capacitance, the use of a mid-frequency component together shows better performance than a low-frequency component alone. In the case of an equivalent series resistor, using both the mid-frequency capacitor voltage and the capacitor current component shows better performance than otherwise.

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Cite this article

[IEEE Style]

H. Park, J. Kim, S. Kwak, "Deep learning‑based estimation technique for capacitance and ESR of input capacitors in single‑phase DC/AC converters," Journal of Power Electronics, vol. 22, no. 3, pp. 513-521, 2022. DOI: 10.1007/s43236-021-00366-x.

[ACM Style]

Hye-Jin Park, Jae-Chang Kim, and Sangshin Kwak. 2022. Deep learning‑based estimation technique for capacitance and ESR of input capacitors in single‑phase DC/AC converters. Journal of Power Electronics, 22, 3, (2022), 513-521. DOI: 10.1007/s43236-021-00366-x.