A General Noise-Resilient Technique Based on the Matrix Pencil Method for the Assessment of Harmonics and Interharmonics in Power Systems

Sheshyekani, Keyhan; Fallahi, Ghasem; Hamzeh, Mohsen; Kheradmandi, Morteza

doi:10.1109/tpwrd.2016.2625329

Cited by 49 publications

(43 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Matz et al 136 presented a digital filter and MM technique to detect and classify the transient and waveform distortion. 154 Teager energy operator, 155 principle curves (PCs), 155 spectral kurtosis (SK), 156 amplitude and frequency demodulation (AFD) with FPARR classifier, 157 adaptive local iterative filter decomposition (ALIFD), 158 delay small angle dq transform, 159 generalized morphological filter, smart meter, 160 PLL, 161 ensemble EMD (EEMD), 162 morphological lifting wavelet (AMLW), 163 pencil matrix method, 164 and information theory. Based on MM and grille fractal, a new method on PQD detection and location was presented in Li et al 139 In Huang et al, 140 the authors presented a technique using MM and HHT, which was operated for the detection and analysis of PQDs.…”

Section: Mathematical Morphology-based Methodsmentioning

confidence: 99%

Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review

Mishra

2019

Int Trans Electr Energ Syst

130

View full text Add to dashboard Cite

Summary Power quality (PQ) studies have gained huge attention from the academics and the industry over the past three decades. The main objective of this article is to provide a comprehensive review on the state‐of‐the‐art techniques based on digital signal processing (DSP) and machine learning for automatic recognition of PQ events. It is aimed to present extensive information on the status of detection and classification of PQ events to the academics following a line of investigation on the similar domain. On the other hand, microgrid is one of the emerging architecture under the umbrella of smart grid infrastructure. In microgrid environment, the integration of renewable energy sources and distributed generators boosts the application of power electronic technology, which is the main cause of various PQ issues. Therefore, various PQ detection and classification (PQD&C) schemes for microgrid application using DSP and machine learning techniques have been reviewed in this article. Moreover, a comparative assessment on various PQD&C techniques has been carried out in details considering several criteria, such as type of data used (synthetic or real world), type of PQ disturbance studied (single or multiple), and performance in terms of accuracy (both noiseless and noisy environment). Hence, several types of research work in PQD&C area, such as the detection principles, benefits, and weaknesses of former works related to each PQD&C technique, are tinted in this manuscript. Therefore, from this review, the opportunities in PQD&C research domain in the power system can be explored further.

show abstract

Section: Mathematical Morphology-based Methodsmentioning

confidence: 99%

Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review

Mishra

2019

Int Trans Electr Energ Syst

130

View full text Add to dashboard Cite

show abstract

“…The total least squares-estimation of signal parameters via rotational invariance techniques (TLS-ESPRIT) method needs to operate the singular value decomposition (SVD) twice in the calculation which increases computational complexity [16]. Matrix Pencil (MP) is a representative and powerful one in these modern mode identification algorithms [17], [18]. Nonetheless, How to determine the order of SVD is the critical problem of the MP algorithm.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Matrix Pencil Algorithm Based-Wavelet Soft-Threshold Denoising for Analysis of Low Frequency Oscillation in Power Systems

Chen

Mohamed

et al. 2020

IEEE Access

View full text Add to dashboard Cite

One of the main reasons that affecting the stability of power systems is low frequency oscillation (LFO). The existence of noise influences the accuracy of LFO mode identification extracted from wide-area measurement system (WAMS). The wavelet threshold de-noising is widely used in signal processing. In this paper, wavelet soft threshold is illustrated to attenuate the noise of LFO signal, the optimal wavelet basis and decomposition level for de-noising LFO signal with noise are obtained and verified by experiments. Following the signal de-noising, an improved Matrix Pencil (MP) algorithm is used for mode identification of LFO. This improvement particularly lies in the ratio of adjacent singular entropy increment difference (RASEID) designed as an adaptive order determination method in the MP algorithm proposed in the paper. RASEID not only makes the MP algorithm adaptive, but also enhances the stability of the order determination in the mode identification process. The proposed method ensures the accuracy of mode identification with lower sensitivity to noise interference. Finally, the validity of the proposed method is verified by three cases studies. The first study is on the analysis of synthetic signal typically performed in many literatures. The second study is to identify the mode of active power LFO signal which is generated by IEEE fourgenerator and two-area system given with disturbance on RT-LAB experimental platform. The third study is the oscillation analysis of the actual LFO data in the North American power grid. The results validate the feasibility of the proposed method for mode identification of noisy LFO. INDEX TERMS Low-frequency oscillation (LFO), wavelet soft-threshold de-noising, ratio of adjacent singular entropy increment difference (RASEID), adaptive, Matrix Pencil (MP), RT-LAB platform.

show abstract

“…In this letter, we adopted the concept of deconvolution, which is commonly used in areas such as optical imaging and spectroscopy to break the Rayleigh criterion for point spread functions [22,23], for scanning receiver systems for RF frequency measurement. Specifically, the matrix pencil method, which enables a precise recovery of frequency components via solving a generalized eigenvalue problem, was adopted for deconvolution due to its high robustness to noise [24]. The information of the input microwave signals is directly obtained from its reconstructed spectrum by means of deconvolving the system's output using matrix pencil method, thus improving the resolution and accuracy of the scanning system.…”

Section: Introductionmentioning

confidence: 99%

Photonic-Assisted Scanning Receivers for Microwave Frequency Measurement

Song

Liu

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

We present a novel technique based on matrix pencil assisted deconvolution to improve the measurement resolution in scanning receiver systems for microwave frequency measurements. By modeling the scanning receiver output as the cross-correlation of the input modulated signal with the filter's spectral response and applying the matrix pencil algorithm to convolve the detected optical signal at the receiver output, our technique offers precise estimations of both the frequency and power information of microwave signals with an improved measurement resolution. A multi-tone microwave signal measurement based on an optical filter is experimentally demonstrated, showing a significant measurement resolution reduction from 1 GHz to 0.4 GHz for two radio frequency (RF) tones, which is only about 30.2% of the optical filter bandwidth.

show abstract

A General Noise-Resilient Technique Based on the Matrix Pencil Method for the Assessment of Harmonics and Interharmonics in Power Systems

Cited by 49 publications

References 29 publications

Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review

Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review

An Adaptive Matrix Pencil Algorithm Based-Wavelet Soft-Threshold Denoising for Analysis of Low Frequency Oscillation in Power Systems

Photonic-Assisted Scanning Receivers for Microwave Frequency Measurement

Contact Info

Product

Resources

About