A Novel Distribution-Level Phasor Estimation Algorithm Using Empirical Wavelet Transform

Chauhan, Kapil; Reddy, Motakatla Venkateswara; Sodhi, Ranjana

doi:10.1109/tie.2018.2801837

Cited by 52 publications

(11 citation statements)

References 31 publications

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“…Previous methodologies that fulfill the requirements stated in the IEEE Std. C37.118.1 use techniques such as Taylor series [23], empirical wavelet transform [24], interpolated discrete Fourier Transform (DFT) and Taylor-Fourier transform [25], Taylor weighted least square [26], frequency tracking and fixed-filters [27]; however, they only cover a limited frequency range stablished by the IEEE Std. C37.118.1.…”

Section: Introductionmentioning

confidence: 99%

“…C37.118.1. On the other hand, some works that improve the performance of PMUs have been presented in the literature, where new phasor estimation algorithms, new adaptive filtering-based methodologies and several compensation strategies have been proposed [1,6,8,[21][22][23][24]. For instance, some remarks about the performance of PMUs under dynamic conditions for electric signals are presented in References [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

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Improved Performance of M-Class PMUs Based on a Magnitude Compensation Model for Wide Frequency Deviations

et al. 2020

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Phasor measurement units (PMUs) are important elements in power systems to monitor and know the real network condition. In order to regulate the performance of PMUs, the IEEE Std. C37.118.1 stablishes two classes—P and M, where the phasor estimation is carried out using a quadrature oscillator and a low-pass (LP) filter for modulation and demodulation, respectively. The LP filter plays the most important role since it determines the accuracy, response time and rejection capability of both harmonics and aliased signals. In this regard and by considering that the M-class filters are used for more accurate measurements, the IEEE Std. presents different M-class filters for different reporting rates (when a result is given). However, they can degrade their performance under frequency deviations if the LP frequency response is not properly considered. In this work, a unified model for magnitude compensation under frequency deviations for all the M-class filters is proposed, providing the necessary values of compensation to improve their performance. The model considers the magnitude response of the M-class filters for different reporting rates, a normalized frequency range based on frequency dilation and a fitted two-variable function. The effectiveness of the proposal is verified using both static and dynamic conditions for frequency deviations. Besides that, a real-time simulator to generate test signals is also used to validate the proposed methodology.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved Performance of M-Class PMUs Based on a Magnitude Compensation Model for Wide Frequency Deviations

et al. 2020

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“…For instance, the WVD has a higher time–frequency resolution, but it is restricted by cross term interference [6,7]. The EWT is an excellent signal decomposition method, but it suffers from the binary band allocation [8,9]. The WPT has good de-noising performance, but the basic functions need to be provided in advance [10,11].…”

Section: Introductionmentioning

confidence: 99%

A Novel Fault Detection Method for Rolling Bearings Based on Non-Stationary Vibration Signature Analysis

Zhen

Guo

et al. 2019

Sensors

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To realize the accurate fault detection of rolling element bearings, a novel fault detection method based on non-stationary vibration signal analysis using weighted average ensemble empirical mode decomposition (WAEEMD) and modulation signal bispectrum (MSB) is proposed in this paper. Bispectrum is a third-order statistic, which can not only effectively suppress Gaussian noise, but also help identify phase coupling. However, it cannot effectively decompose the modulation components which are inherent in vibration signals. To alleviate this issue, MSB based on the modulation characteristics of the signals is developed for demodulation and noise reduction. Still, the direct application of MSB has some interfering frequency components when extracting fault features from non-stationary signals. Ensemble empirical mode decomposition (EEMD) is an advanced nonlinear and non-stationary signal processing approach that can decompose the signal into a list of stationary intrinsic mode functions (IMFs). The proposed method takes advantage of WAEEMD and MSB for bearing fault diagnosis based on vibration signature analysis. Firstly, the vibration signal is decomposed into IMFs with a different frequency band using EEMD. Then, the IMFs are reconstructed into a new signal by the weighted average method, called WAEEMD, based on Teager energy kurtosis (TEK). Finally, MSB is applied to decompose the modulated components in the reconstructed signal and extract the fault characteristic frequencies for fault detection. Furthermore, the efficiency and performance of the proposed WAEEMD-MSB approach is demonstrated on the fault diagnosis for a motor bearing outer race fault and a gearbox bearing inner race fault. The experimental results verify that the WAEEMD-MSB has superior performance over conventional MSB and EEMD-MSB in extracting fault features and has precise and effective advantages for rolling element bearing fault detection.

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“…Many non-DFT-based PSMAs, such as level crossing [18], Kalman filtering [19,20], Newton algorithm [21], adaptive neural network [22], least squares [23][24][25], wavelet [26,27], Taylor 2 of 14 algorithm [28,29], and subspace algorithm [30], have been proposed in the literature. However, these methods usually have some disadvantages that limit their applications in synchrophasor measurement situations.…”

Section: Introductionmentioning

confidence: 99%

A Fixed Length Adaptive Moving Average Filter-Based Synchrophasor Measurement Algorithm for P Class PMUs

2019

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Accurate and fast synchrophasor measurement is the key to the wide applications of PMUs in the system-wide monitoring and reliable operation of smart grid. To address this issue, a fixed length moving average filter-based synchrophasor measurement algorithm for P class phasor measurement units (PMUs) (FA-PSMA) is proposed in this paper. Firstly, a novel fixed length adaptive moving average filter (FAMAF) is proposed. The FAMAF has an adaptive filter capability with a fixed data window length. Then, the FAMAF is applied after a phase-locked loop (PLL) for enhanced disturbance rejection capability under frequency drifts. Finally, a detailed performance assessment is presented to validate the performance of the proposed FA-PSMA. Theoretical analysis and simulation results validate that the proposed FA-PSMA can track the grid frequency and phasor accurately under distorted grid conditions. The response time and measurement accuracy satisfy the requirements specified in IEC/IEEE 60255-118-1.

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A Novel Distribution-Level Phasor Estimation Algorithm Using Empirical Wavelet Transform

Cited by 52 publications

References 31 publications

Improved Performance of M-Class PMUs Based on a Magnitude Compensation Model for Wide Frequency Deviations

Improved Performance of M-Class PMUs Based on a Magnitude Compensation Model for Wide Frequency Deviations

A Novel Fault Detection Method for Rolling Bearings Based on Non-Stationary Vibration Signature Analysis

A Fixed Length Adaptive Moving Average Filter-Based Synchrophasor Measurement Algorithm for P Class PMUs

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