Open AccessModified hybrid model of boost converters for parameter identification of passive components
Author(s) -
Ren Lei,
Gong Chunying
Publication year - 2018
Publication title -
iet power electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.637
H-Index - 77
eISSN - 1755-4543
pISSN - 1755-4535
DOI - 10.1049/iet-pel.2017.0528
Subject(s) - converters , control theory (sociology) , inductor , electronic engineering , computer science , identification (biology) , noise (video) , voltage , engineering , botany , control (management) , artificial intelligence , electrical engineering , image (mathematics) , biology
Condition monitoring has been recognised as an effective and low‐cost method to enhance the reliability and improve the maintainability of power electronic converters. Hybrid model‐based monitoring method is an advanced implementation means due to its non‐invasive ways. However, the existing hybrid model is not fit for boost‐derived converters for its ignorance of the suddenly change in the diode current caused by switching actions. To solve this problem, a correction term is introduced to modify the primary model. The inductor parasitic resistance is considered for higher accuracy and the gate signal sensor is cancelled for lower complexity. In order to verify the modified model, a parameter identification method based on wavelet denoising (WTD) and recursive least squares arithmetic is proposed. Non‐ideal factors (noise and voltage spikes) are analysed and the superiority of the proposed parameter identification scheme is verified. Compared to the primary model, the modified model can achieve higher identification accuracy with fewer sensors, which is more suitable for condition monitoring of Boost converters. The proposed model has been successfully substantiated by the simulation and experimental results of a Boost converter.