Contrast study on power quality detection using EMD and EEMD

Bao, Yu-Qing; Xie, Jihua

doi:10.1109/cecnet.2011.5768979

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…The same rule can be applied to the remainder signal under EEMD. White noise in minimal amplitude has a uniform distribution [28]. Therefore, the target signal can be automatically decomposed into an optimized scale with increasing addition of random white noise.…”

Section: Emd and Eemdmentioning

confidence: 99%

Combined Line Fault Location Method for MMC–HVDC Transmission Systems

et al. 2020

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A combined algorithm based on traveling wave (TW) theory and time-frequency characteristics is proposed to estimate the DC line fault location in modular multilevel converter-high voltage direct current (MMC-HVDC) systems. The relationship between instantaneous frequency and TW propagation velocity is analysed, and the local voltage signal is utilized to extract fault features. A TW distortion-based scheme is improved to obtain the approximate fault location and accurately detect TW arrival times. The arrival times of the initial TW from the fault point and opposite terminal are determined using intrinsic mode functions under variational mode decomposition. Given the attenuation property of the TW-based method, the Teager energy operator is used to overcome the weakness in fault feature extraction. The instantaneous frequency of the TW front is obtained via ensemble empirical mode decomposition and Hilbert-Huang transform. The fault location is predicted with the detected TW arrival times and corresponding TW propagation velocity. The effectiveness and superiority of this method are verified through simulations in PSCAD/EMTDC and computation in MATLAB.

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Section: Emd and Eemdmentioning

confidence: 99%

Combined Line Fault Location Method for MMC–HVDC Transmission Systems

et al. 2020

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“…Hilbert Haung Transform based on empirical mode decomposition is proposed in [16,17], to analyze the power quality disturbances of the electrical signals by decomposing them into intrinsic mode functions (IMFs). Here the signal is decomposed into IMFs initially and then they are processed for analysis through Hilbert transform.…”

Section: Feature Extraction Approachesmentioning

confidence: 99%

Flexible Entropy Based Feature Selection and Multi Class SVM for Detection and Classification of Power Quality Disturbances

Shaik¹,

Reddy²

2018

IJIES

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Power quality disturbances (PQD) are becoming more and more complicated due to the vast growth of power quality oriented applications particularly the power electronic equipment's. Since the power with less power quality consequences to a huge loss in the electronic devices, there is a need to detect the power quality disturbances more effectively. This paper proposes a new method for PQD detection and classification based on the entropy characteristics of PQD signals and a novel support vector machine algorithm (SVM). The proposed approach develops a flexible entropy based feature selection (FEFS) mechanism to extract the unique characteristics of all types of PQDs such that the detection system can detect more effectively with in less time. Further to increase the accuracy of individual PQDs, this paper proposed Multi Class SVM (MC-SVM). Various experiments are conducted over the proposed work and at every experiment; the performance is measured through Classification accuracy, False Alarm Rate and computation time. Further a comparative analysis reveals the outstanding performance of proposed approach when compared with conventional approaches.

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“…The white noise with minimal amplitude has the characteristics of uniform distribution [34]. With the increase of noise, the signal will be continuous in different scales, so the signal region will automatically correspond to the suitable scale.…”

Section: ) Obtain the Mean Value Of Corresponding Decomposed Imfs Asmentioning

confidence: 99%

A Classification Method for Complex Power Quality Disturbances Using EEMD and Rank Wavelet SVM

Liu

Cui

2015

IEEE Trans. Smart Grid

165

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This paper aims to develop a combination method for the classification of power quality complex disturbances based on ensemble empirical mode decomposition (EEMD) and multilabel learning. EEMD is adopted to extract the features of complex disturbances, which is more suitable to the nonstationary signal processing. Rank wavelet support vector machine (rank-WSVM) is proposed to apply in the classification of complex disturbances. First, the characteristic quantities of complex disturbances are obtained with EEMD through defining standard energy differences of each intrinsic mode function. Second, after the optimization of rank-SVM, based on wavelet kernel function, the ranking function, and multilabel function are, respectively, constructed. Lastly, rank-WSVM is applied to classify the complex disturbances. Simulation results and real-time digital simulator tests show that for different signal to noise ratio, the rank-WSVM classification performance of complex disturbances including hamming loss, ranking loss, one-error, coverage, and average precision, is generally better than the other three methods, namely rank-SVM, multilabel naive Bayes, and multilabel learning with backpropagation.Index Terms-Classification, complex disturbances, ensemble empirical mode decomposition (EEMD), rank wavelet support vector machine (rank-WSVM).

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Contrast study on power quality detection using EMD and EEMD

Cited by 6 publications

References 10 publications

Combined Line Fault Location Method for MMC–HVDC Transmission Systems

Combined Line Fault Location Method for MMC–HVDC Transmission Systems

Flexible Entropy Based Feature Selection and Multi Class SVM for Detection and Classification of Power Quality Disturbances

A Classification Method for Complex Power Quality Disturbances Using EEMD and Rank Wavelet SVM

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