An Online Monitoring Method for Capacitor Condition of Cascaded H-Bridge STATCOM Based on Sensorless Capacitor Voltage Detection

In order to achieve the stable dc voltage support during the process of reactive power compensation, the cells within the chain of CHB STATCOM are equipped with high-capacity capacitors (along with dc voltage detection devices). However, over time, these capacitors exhibit varying degrees of aging, leading to discrepancies in capacitance reduction or variations in the equivalent series resistance (ESR) values, which not only complicates the task of balancing the capacitor voltages across the chain’s cells, but also increases the risk of device overvoltage. To address the problem, an online monitoring method for capacitor conditions based on sensorless capacitor voltage detection is proposed. Initially, the mapping relationship between the capacitance and its corresponding voltages is established on basis of the cell’s switching state and the principle of energy conservation. Subsequently, the capacitance estimation algorithm is constructed based on the data from sensorless capacitor voltage detection. In addition, the factors contributing to capacitance estimation errors are analyzed, and potential solutions are offered. This method facilitates the detection of each cell’s dc voltage and capacitance variations, while eliminating additional costs related to dc sensors and its associated communication devices. Ultimately, the effectiveness of the proposed online monitoring method is verified by MatLab/Simulink simulation and the StarSim experimental platform.

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Publication Details

Journal
IEEE Transactions on Circuits and Systems I Regular Papers
Published
2024-08-19
DOI
https://doi.org/10.1109/tcsi.2024.3407125
Citations
2
Primary Topic
Non-Destructive Testing Techniques
Type
article
Field-Weighted Citation Impact
0.43

Funders

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article

An Online Monitoring Method for Capacitor Condition of Cascaded H-Bridge STATCOM Based on Sensorless Capacitor Voltage Detection

Haoru Li, Jikai Chen, Chuang Liu, Xinhai Chang
2 citations
IEEE Transactions on Circuits and Systems I Regular Papers
Non-Destructive Testing Techniques
0.43
article

An Online Monitoring Method for Capacitor Condition of Cascaded H-Bridge STATCOM Based on Sensorless Capacitor Voltage Detection

Haoru Li, Jikai Chen, Chuang Liu, Xinhai Chang
article en
2 citations

Abstract

In order to achieve the stable dc voltage support during the process of reactive power compensation, the cells within the chain of CHB STATCOM are equipped with high-capacity capacitors (along with dc voltage detection devices). However, over time, these capacitors exhibit varying degrees of aging, leading to discrepancies in capacitance reduction or variations in the equivalent series resistance (ESR) values, which not only complicates the task of balancing the capacitor voltages across the chain’s cells, but also increases the risk of device overvoltage. To address the problem, an online monitoring method for capacitor conditions based on sensorless capacitor voltage detection is proposed. Initially, the mapping relationship between the capacitance and its corresponding voltages is established on basis of the cell’s switching state and the principle of energy conservation. Subsequently, the capacitance estimation algorithm is constructed based on the data from sensorless capacitor voltage detection. In addition, the factors contributing to capacitance estimation errors are analyzed, and potential solutions are offered. This method facilitates the detection of each cell’s dc voltage and capacitance variations, while eliminating additional costs related to dc sensors and its associated communication devices. Ultimately, the effectiveness of the proposed online monitoring method is verified by MatLab/Simulink simulation and the StarSim experimental platform.

IEEE Transactions on Circuits and Systems I Regular PapersVol. 71(12)
Northeast Electric Power University (CN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 43%
Non-Destructive Testing Techniques
0.43
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