METHOD FOR SENSING ANOMALIES OF HYBRID PARALLEL POWER CONVERSION SYSTEM BY USING AUTOENCODER-BASED DEEP LEARNING NEURAL NETWORK MODEL FOR INCREASING POWER GENERATION

Title:

METHOD FOR SENSING ANOMALIES OF HYBRID PARALLEL POWER CONVERSION SYSTEM BY USING AUTOENCODER-BASED DEEP LEARNING NEURAL NETWORK MODEL FOR INCREASING POWER GENERATION

Document Type and Number:

WIPO Patent Application WO/2024/072019

Kind Code:

A1

Abstract:

An embodiment relates to a system for sensing an anomalies by using an autoencoder-based deep learning neural network model for hybrid parallel power conversion, and the converter has a converter in each individual input instead of a separate battery charging circuit, and performs fault diagnosis through an autoencoder-based learning model. Therefore, in the embodiment, power generated by a piezoelectric element is efficiently stored and anomalies are sensed.

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Inventors:

KIM DONG-WAN (KR)

Application Number:

PCT/KR2023/014866

Publication Date:

April 04, 2024

Filing Date:

September 26, 2023

Export Citation:

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Assignee:

KIM DONG WAN (KR)
BUSAN PORT AUTHORITY (KR)

International Classes:

H02M1/32; G06N3/02; H02J3/32; H02J3/38; H02J7/35; H02M1/00; H02M3/158; H02N2/18

Attorney, Agent or Firm:

JUNG, Byung-Hong (KR)

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