English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The larger capacity of power converters increases with the size of wind turbines over the years, which implies more failures occur in the components of power converter. Meanwhile, the strategies of fault diagnosis of power converter in wind turbine are still under discussion and research. The main purpose of this paper is proposing a strategy containing wavelet transform, feature analysis, judgment and back propagation neural network (BPNN) classification (WT-FA-JD-BP) to identify the single and double power components open-circuit faults accurately, happen in grid-side converter (GSC) of permanent magnet synchronous generator (PMSG) wind turbine systems. First, as the original signals, the three-phase bridge legs voltage of the GSC are collected under different faults. Second, wavelet transform is used to decompose and reconstruct the signals. Third, a new method includes feature analysis and judgment is conducted to amplify the divergence of data obtained by wavelet transform. Finally, the data are used as the inputs of BPNN for decision-making and classification. The simulation and experimental results show that the proposed strategy can classify the single and double open-circuit faults, and the accuracy is higher than that without data feature analysis and judgment.

Fault Diagnosis of Wind Turbine Power Converter Considering Wavelet Transform, Feature Analysis, Judgment and BP Neural Network