A Non-Invasive Method for Estimating Circuit and Control Parameters of Voltage Source Converters

Voltage source converters (VSCs) have been widely used in modern power systems. Consequently, reliable operations of VSCs are essential for the power system to operate properly. A parameter estimator (PE) for a VSC system acts as a useful tool for problem diagnosis and preventive maintenance. Genera...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems. I, Regular papers Regular papers, 2019-12, Vol.66 (12), p.4911-4921
Main Authors: Lin, Bing Hao, Tsai, Jin Tong, Lian, Kuo Lung
Format: Article
Language:English
Subjects:
Artificial neural networks
Circuits
Converters
Current measurement
Data models
Electric potential
Harmonic analysis
Integrated circuit modeling
Mathematical models
neural network
Parameter estimation
Parameter identification
particle swarm optimization
Power conversion
Preventive maintenance
Steady state models
Steady-state
steady-state model
Training
Voltage
voltage source converter
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Summary:Voltage source converters (VSCs) have been widely used in modern power systems. Consequently, reliable operations of VSCs are essential for the power system to operate properly. A parameter estimator (PE) for a VSC system acts as a useful tool for problem diagnosis and preventive maintenance. Generally speaking, depending on how the measurement data are attained, a parameter estimation method can be classified into invasive and non-invasive. Non-invasive methods, which do not require any external excitation, are generally preferred for parameter estimation. However, most of the PEs can only estimate some parameters of the converters while treating others constant. This paper proposes a non-invasive parameter estimation method, which is able to predict simultaneously the circuit and control parameters of a VSC. To achieve this, the accurate open-loop and closed-loop steady-state models of the VSC are first derived. These models will be used to generate the training data sets to obtain the artificial neural network models to account for various uncertainties in a practical circuit. All the training data are generated by the derived VSC models, which are constructed in MATLAB and run off-line. The proposed method has been verified via PSCAD/EMTDC and validated with the experiment.
ISSN:1549-8328
1558-0806
DOI:10.1109/TCSI.2019.2929783