Harmonic Detection for Active Power Filter Based on Two-Step Improved EEMD

Wang, Rongkun; Huang, Wenjie; Hu, Bingtao; Du, Quankai; Guo, Xinhua

doi:10.1109/tim.2022.3146913

Cited by 25 publications

(10 citation statements)

References 29 publications

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“…Ensemble empirical mode decomposition (EEMD) [32] is an improvement on the empirical mode decomposition (EMD) by addition of white noise components to original signal, and then decomposition and averaging. Its advantage is to overcome the problem of mode aliasing in EMD.…”

Section: Eemd Analysis Of Vibration Signal and Of Main Pumpmentioning

confidence: 99%

Health State Identification Method of Nuclear Power Main Circulating Pump Based on EEMD and OQGA-SVM

Liu

Zhu

et al. 2023

Electronics

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Main circulation pump is the only high-speed rotating equipment in primary loop of nuclear power plant. Its function is to ensure the normal operation of primary loop system by controlling the circulating flow of reactor coolant. In order to ensure long-term healthy operation of nuclear power main circulating pump, a method for identifying the health states of nuclear power main circulating pump based on ensemble empirical mode decomposition (EEMD) and support vector machine optimized by optimized quantum genetic algorithm (OQGA-SVM) is proposed. Vibration signal of main circulating pump is decomposed by EEMD. Vibration signal characteristics of nuclear power main circulating pump in healthy state and different fault states are analyzed and target characteristic indexes are put forward. Then, health state identification model of main circulation pump of OQGA-SVM is established, and target characteristic indexes are used as input parameter of the model. Finally, combined with experimental data, the model analysis and validation show that the health state identification method of nuclear power main circulating pump based on EEMD-OQGA-SVM can accurately and effectively identify the states of main circulation pump, has a higher identification accuracy than EEMD-SVM method and is more efficient and accurate than EEMD-QGA-SVM method.

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Section: Eemd Analysis Of Vibration Signal and Of Main Pumpmentioning

confidence: 99%

Health State Identification Method of Nuclear Power Main Circulating Pump Based on EEMD and OQGA-SVM

Liu

Zhu

et al. 2023

Electronics

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“…To solve the problem, people has taken a series of measures to control the electronic power quality, among which, to carry out high-precision detection of electronic power quality is the precondition. And high-precision detection and analysis of the harmonics is significant [1][2][3][4][5]. Currently, the measures to detect the harmonics mainly including instantaneous reactive power theory [6], wavelet analysis [7], S-Transform [8] and Fourier analysis [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of high-precision power harmonics detection system based FFT-FT

Tao,

Zhu,

Chen

2024

Second International Conference on Electrical, Electronics, and Information Engineering (EEIE 2023)

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This paper proposed a high-precision and high-order power voltage harmonics detection method based on STM32H7. The system which uses STM32H7 as the core utilizes FFT-FT algorithm to find the peak of panoramic spectrum to further refine the spectrum and compensate amplitude error, so as to achieve high precision real-time power voltage harmonics measurement. The input voltage is attenuated, amplified whose gain is programmed, and input into STM32 MCU after digital-to-analog. Then, the harmonic amplitude is measured through searching the fundamental frequency after the FFT of voltage signal. This system adopts one-dimensional linear search algorithm to improve its real-time performance. The actual test results show the absolute error is less than or equal to 0.01Hz and the relative error is less than 0.03% for the frequency measurement in the range of 40~70Hz AC voltage. And the harmonic amplitudes' absolute error of rectangular wave is less than or equal to 4mV. In particular, the relative error of the fundamental wave and the third harmonic is less than 0.5%, and the absolute error of the 63rd harmonic is only 1mV. Therefore, the system has the characteristics of high-orders of harmonic measurement, high precision of frequency and amplitude measurement, which can meet the application requirements of high precision and high order harmonics measurement in power system.

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“…Vashishtha et al [25] proposed a deep learning-based scheme using time-varying filter based EMD for bearing fault identification. Wang et al [26] proposed the FI-EEMD to suppress mode mixing and fundamental attenuation by injecting noise and analytical signal. Variational mode decomposition (VMD) [27] transforms signal decomposition into a variational constraint problem, suppressing modal aliasing.…”

Section: Introductionmentioning

confidence: 99%

Bearing fault diagnosis under variable speed conditions on adaptive time frequency extraction mode decomposition

Huo,

Yang,

Yao

et al. 2024

Meas. Sci. Technol.

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Fault diagnosis of rolling bearings is crucial for their operation in variable working conditions in mechanical systems. Time-frequency distribution (TFD) methods are widely used in variable-speed rolling bearing fault diagnosis due to their ability to capture the time-frequency characteristics of signals. However, they suffer from weak robustness, poor resolution, and lack of feature information concentration. To address these issues, we construct a method: adaptive time frequency extraction mode decomposition (ATFEMD) by utilizing the characteristic of instantaneous frequency (IF) energy concentration in TFD. The method first enhances the adaptive time-frequency distribution (ADTFD) via an iterative algorithm, applying random sample consensus (RANSAC) algorithm enables ridges extraction from the TFD. The harmonics of the extracted ridges are then verified by passing the proxy data method, followed by a noise test to determine if the harmonics are true. From the above steps, ATFEMD decomposing the signal into physically meaningful adaptive mode (AM). This article analyzes the simulated bearing vibration and test bench bearing vibration signals to demonstrate the performance of ATFEMD. Results indicated that the proposed method is characterized by strong robustness, and good feature extraction results compared to other methods.

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Harmonic Detection for Active Power Filter Based on Two-Step Improved EEMD

Cited by 25 publications

References 29 publications

Health State Identification Method of Nuclear Power Main Circulating Pump Based on EEMD and OQGA-SVM

Health State Identification Method of Nuclear Power Main Circulating Pump Based on EEMD and OQGA-SVM

Design and implementation of high-precision power harmonics detection system based FFT-FT

Bearing fault diagnosis under variable speed conditions on adaptive time frequency extraction mode decomposition

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