Review of Methods for Reducing Circulating Currents in Parallel Connected Modular Inverters

Choi, Hye-Won; Lee, Kyo-Beum

doi:10.1007/s42835-023-01389-z

Cited by 6 publications

(1 citation statement)

References 67 publications

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“…In recent years, the demand for electric energy is increasing all over the world, and the growing of competitive markets puts forward new requirements for the safe, reliable, and economical operation of thermal power plants. [1] In thermal power plants, components such as pulverizers, fans, heating tubes, and sensors [2][3][4][5] are subjected to very high temperatures and pressures. [6] Failure of one or more components or subsystems of a power plant unit may increase the risk of shutting down, which can result in significant degradation of life and performance.…”

Section: Introductionmentioning

confidence: 99%

Data‐driven modelling for online fault pre‐warning in thermal power plant using incremental Gaussian mixture regression

Jin,

Si,

Dong

et al. 2023

Can J Chem Eng

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This study introduces a data‐driven model for online fault pre‐warning in thermal power plants using incremental Gaussian mixture regression. To tackle the issue of outdated parameters in existing fault pre‐warning models, this study puts forth an incremental Gaussian mixture regression that leverages the merging of Gaussian components to reconstruct the model and enable online modelling. Due to its criticality, a forgetting factor is introduced during the merging process to efficiently manage the weight allocation between present and historical patterns, thereby guaranteeing the model's accuracy. The results of the sine function case demonstrate that the incremental Gaussian mixture regression (IGMR) model exhibits excellent pattern control performance and modelling efficiency. Furthermore, the IGMR model is employed to forecast parameter alterations in pulverizer blockages with mode switching, and experimental validation indicates that IGMR precisely anticipates parameter changes following mode switching. Compared to on‐site solutions, the pre‐warning of coal blockage has a clear advantage in advance warning.

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