WPD for Detecting Disturbances in Presence of Noise in Smart Grid for PQ Monitoring

Bhuiyan, Sharif; Khan, Jesmin; Murphy, Gregory

doi:10.1109/tia.2017.2753720

Cited by 28 publications

(20 citation statements)

References 34 publications

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“…Considering the advantages of Tsallies entropy compared with Shannon entropy, WPT compared with WT, and singular value decomposition, Liu et al suggested a hybrid approach combing all these three to detect the PQ disturbances. 52 The automatic PQ monitoring system based on wavelet packet decomposition (WPD) was presented by Bhuiyan et al 53 for the detection of five different types of disturbances in noisy environment. The suggested technique uses the interscale and intrascale relationship of the WPD coefficients for the recognition of the PQ disturbances upon noise elimination.…”

Section: Variational Mode Decompositionmentioning

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

138

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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.

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Section: Variational Mode Decompositionmentioning

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

138

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“…The multiple types consist of sag with harmonics, swell with harmonics, and interrupt with harmonics. The definition of the PQD signals with the parameter variations is based on the IEEE-1159 standard [29], as presented in Table 1. Waveform signals with 10 cycles are generated for 2000 points at a 10 kHz sampling frequency.…”

Section: Computer Simulation Of Pqdsmentioning

confidence: 99%

Power Quality Disturbance Recognition Using VMD-Based Feature Extraction and Heuristic Feature Selection

Zhu

Pan

et al. 2019

Applied Sciences

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Power quality disturbances (PQDs) have a large negative impact on electric power systems with the increasing use of sensitive electrical loads. This paper presents a novel hybrid algorithm for PQD detection and classification. The proposed method is constructed while using the following main steps: computer simulation of PQD signals, signal decomposition, feature extraction, heuristic selection of feature selection, and classification. First, different types of PQD signals are generated by computer simulation. Second, variational mode decomposition (VMD) is used to decompose the signals into several instinct mode functions (IMFs). Third, the statistical features are calculated in the time series for each IMF. Next, a two-stage feature selection method is imported to eliminate the redundant features by utilizing permutation entropy and the Fisher score algorithm. Finally, the selected feature vectors are fed into a multiclass support vector machine (SVM) model to classify the PQDs. Several experimental investigations are performed to verify the performance and effectiveness of the proposed method in a noisy environment. Moreover, the results demonstrate that the start and end points of the PQD can be efficiently detected.

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“…Traditionally, root-mean-square (RMS) or half-cycle RMS values are used to classify sag, swell, interruption events; however, other PQDEs, such as notch, flicker, oscillatory transients could not be classified by RMS approach only. Hence, over the last few decades, various signal processing techniques, such as Fourier Transform (FT), Discrete Cosine Transform (DCT), Short-Time Fourier Transform (STFT), Hilbert-Huang Transform (HHT), Discrete Wavelet Transform (DWT), S-Transform (ST), etc., have been investigated to classify and characterize PQDEs [2], [4][5][6][7][8][9][10][11][12][13][14]. Fourier Transform (FT) is simple but fails to classify and characterize non-stationary PQ events [4].…”

Section: Introductionmentioning

confidence: 99%

“…With the introduction of Short-Time Fourier Transform (STFT), the problem is reported to be resolved; however, STFT fails to achieve standard resolution in time and frequency domain, consequently, performance is degraded [6]. Recently, the Discrete Wavelet Transform (DWT) has widely been adopted by many researchers across the world to detect and classify PQDEs [6][7][8][9][10][11]. However, noise in signals may deteriorate the performance of DWT [14].…”

Section: Introductionmentioning

confidence: 99%

Classification and Visualization of Power Quality Disturbance-Events Using Space Vector Ellipse in Complex Plane

Alam

Bai

Yan

2021

IEEE Trans. Power Delivery

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This article proposes a novel algorithm employing space vector ellipse (SVE) in a complex plane to classify and visualize power quality disturbance-events (PQDEs). In the proposed method, at first, the time-domain signal and a reference signal, which are separated by 90°, are mapped in a complex 2D coordinates. Thus, the tip of resultant rotating vector traces an ellipse, from which three parameters, namely, semi-major axis, semi-minor axis and inclination angle, are obtained. Then, the ellipse parameters are exploited to classify and visualize nine types of PQDEs, namely, voltage sag, swell, interruption, harmonic, sagharmonic, swell-harmonic, notch, flicker and transient. To validate the practicability of the proposed approach, an extensive realtime simulation study is carried out on RTDS platform using a test microgrid network to generate a large number of PQDEs. The test events were successfully classified and visualized in complex plane. Moreover, the noisy and practical signals, recorded by IEEE 1159.2 Working Group, were successfully classified to demonstrate the effectiveness of the proposed method.

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WPD for Detecting Disturbances in Presence of Noise in Smart Grid for PQ Monitoring

Cited by 28 publications

References 34 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

Power Quality Disturbance Recognition Using VMD-Based Feature Extraction and Heuristic Feature Selection

Classification and Visualization of Power Quality Disturbance-Events Using Space Vector Ellipse in Complex Plane

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