Study on the estimation of harmonic impedance based on Bayesian optimized Gaussian process regression

Xia, Yankun; Tang, Wenzhang

doi:10.1016/j.ijepes.2022.108294

Cited by 8 publications

(2 citation statements)

References 25 publications

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“…Bayesian optimization is primarily based on Bayes' theorem, which fits the true objective function with a probabilistic iterative model. Finally, the most likely subsequent points are evaluated based on the results of the fit [38]. In this process, Bayesian optimization uses existing information to reduce the number of evaluations and improve the efficiency of the calculation.…”

Section: B Bayesian Optimizationmentioning

confidence: 99%

Adaptive Parameter Selection Variational Mode Decomposition Based on Bayesian Optimization and Its Application to the Detection of ITSC in PMSM

Xia,

Wang,

2024

IEEE Access

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Permanent magnet synchronous motor (PMSM) is a vital component of modern industry which is widely used in transport, aerospace and intelligent machinery. In the light of its relatively high frequency of inter-turn short circuit (ITSC) fault, it is valuable to detect the fault accurately and efficiently. Due to the convenience of motor vibration signal measurement, more attention has been paid to fault detection methods based on vibration signal. As a nonstationary, nonlinear signal, vibration signal is highly susceptible to external noise interference. Variational mode decomposition (VMD) has a wide range of applications in the field of nonlinear signal analysis, but the processing ability to the signal is affected by the parameter setting. Hence, a Bayesian optimized adaptive parameter selection variational mode decomposition (BOAPS-VMD) signal processing method is proposed and applied to detect the ITSC in PMSM. Firstly, the motor vibration signal is decomposed through BOAPS-VMD, and the intrinsic mode functions (IMFs) are obtained. Secondly, the cumulative variance contribution rate (C-VCR) is applied to identify the IMFs that contain fault signature information. Finally, using Hilbert transform (HT) to further analyze the IMFs that are identified. The results are output in 3D time-frequency diagrams to enhance the representation of fault signatures. The effectiveness and accuracy of the BOAPS-VMD were validated through the finite element simulation and experiment. The study shows that the BOAPS-VMD effectively improves the modal mixing phenomenon, has better noise robustness, improves the accuracy of fault detection, and has better engineering applicability.INDEX TERMS Permanent magnet synchronous motor (PMSM), fault detection, variational mode decomposition (VMD), Bayesian optimization, Hilbert transform (HT)

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Section: B Bayesian Optimizationmentioning

confidence: 99%

Adaptive Parameter Selection Variational Mode Decomposition Based on Bayesian Optimization and Its Application to the Detection of ITSC in PMSM

Xia,

Wang,

2024

IEEE Access

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“…(5) The machine learning method, the internal connection between the input value and the output value is established. Taking the harmonic voltage and harmonic current collected at the PCC as input samples and the reference harmonic impedance as the output value, the calculation model is obtained and the harmonic impedance is solved [21].…”

Section: Introductionmentioning

confidence: 99%

Study on Harmonic Impedance Estimation Based on Gaussian Mixture Regression Using Railway Power Supply Loads

Xia

Tang

2022

Energies

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There are a huge number of harmonics in the railway power supply system. Accurately estimating the harmonic impedance of the system is the key to evaluating the harmonic emission level of the power supply system. A harmonic impedance estimation method is proposed in this paper, which takes the Gaussian mixture regression (GMR) as the main idea, and is dedicated to calculating the harmonic impedance when the load changes or the background harmonic changes in the traction power supply system. First, the harmonic voltages and currents are measured at the point of common coupling (PCC); secondly, a Gaussian mixture model (GMM) is established and optimized parameters are obtained through the EM algorithm; finally, a Gaussian mixture regression is performed to obtain the utility side harmonic impedance. In the simulation study, different harmonic impedance estimation models with uniform distribution and Gaussian distribution are established, respectively, and the harmonic impedance changes caused by different system structures in the railway power supply system are simulated. At the same time, the error is compared with the existing method to judge the accuracy and robustness of this method. In the case analysis, the average value, average error, standard deviation and other indicators are used to evaluate this method. Among them, the average error and standard deviation of this method are about one-fifth to one-third of those of the binary linear regression (BLR) method and the independent random vector (IRV) method. At the same time, its index is slightly better than that of the support vector machine (SVM) method.

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Prediction Model-based Multi-objective Optimization for Mix-ratio Design of Recycled Aggregate Concrete

Chen,

Wu,

Yao

2024

J. Wuhan Univ. Technol.-Mat. Sci. Edit.

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Study on the estimation of harmonic impedance based on Bayesian optimized Gaussian process regression

Cited by 8 publications

References 25 publications

Adaptive Parameter Selection Variational Mode Decomposition Based on Bayesian Optimization and Its Application to the Detection of ITSC in PMSM

Adaptive Parameter Selection Variational Mode Decomposition Based on Bayesian Optimization and Its Application to the Detection of ITSC in PMSM

Study on Harmonic Impedance Estimation Based on Gaussian Mixture Regression Using Railway Power Supply Loads

Prediction Model-based Multi-objective Optimization for Mix-ratio Design of Recycled Aggregate Concrete

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