Cross-Spectrum Analysis-Based Scheme for Multiple Power Quality Disturbance Sensing Device

Dalai, Sovan; Dey, Debangshu; Chatterjee, Biswendu; Chakravorti, S.; Bhattacharya, Kesab

doi:10.1109/jsen.2014.2377775

Cited by 34 publications

(19 citation statements)

References 30 publications

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“…The MPDs, which are contaminated with Gaussian white noise, consist of the most popular SPDs in [2,5,7,8,10,11,17,19] in recent years. These studied SPDs include sag, swell, interruption, harmonic, flicker, oscillation, impulse and notch.…”

Section: Simulation Testmentioning

confidence: 99%

“…These studied SPDs include sag, swell, interruption, harmonic, flicker, oscillation, impulse and notch. The PDs are the sample rate of all test signals of ten cycles, which is 6.4 KHz, and the models of those SPDs are based on the IEEE PQ standard [27] and previous works [10,17] as shown in Table 2. Table 2.…”

Section: Simulation Testmentioning

confidence: 99%

“…In terms of SPD assessment, many algorithms have been proposed, for example, the discrete wavelet transform (DWT) [2], empirical mode decomposition [3,4], the S-transform (ST) [5][6][7][8], the Kalman filter (KF) [9], etc. However, the actual PQ events generally consist of two or more SPDs which occur simultaneously, i.e., MPDs [10,11]. These MPDs, including sag, swell, interruption, harmonic, flicker, oscillation, impulse, notch and other SPDs proposed in [12][13][14][15], carry a high risk of causing economic losses and serious damage to electrical elements.…”

Section: Introductionmentioning

confidence: 99%

“…et al [18] used wavelet transform and the 4 class support vector machine (SVM) technique for the identification and the location of a MPD, and it demonstrated a classification outperformance. Sovan D. et al [10] efficiently employed cross-wavelet transform aided Fischer linear discriminant analysis, cooperating with linear SVM to conduct MPD recognition. Nevertheless, the SVM-based approach may lose efficacy when taking the unpredictability of real MPDs into consideration because its classification scope is limited by training sample models.…”

Section: Introductionmentioning

confidence: 99%

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On the Application of Joint-Domain Dictionary Mapping for Multiple Power Disturbance Assessment

Cai

et al. 2018

Energies

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This paper proposes a joint-domain dictionary mapping method to obtain high assessment accuracy of multiple power disturbances. Firstly, in order to achieve resolutions in both the time and frequency domains, a joint-domain dictionary is proposed which consists of a discrete Hartley base and an identity matrix. Due to the low correlation between the discrete Hartley base and the identity matrix, the joint-domain dictionary mapping can separately capture the approximations of the sinusoidal components and transients. Since the mapping coefficients contain the physical quantities, the eigenvalues of each component can be effectively estimated. A quantified eigenvalue classifier was designed for identifying power disturbances using the estimated eigenvalues. The proposed method was compared with several advanced methods through simulated power disturbances under different noise conditions, and actual data from the Institute of Electrical and Electronics Engineers Power and Energy Society database. The results reveal that the joint-domain dictionary mapping technique shows good performance on parameter estimation and recognition precision, even dealing with complicated multiple power disturbances.

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Section: Simulation Testmentioning

confidence: 99%

Section: Simulation Testmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the Application of Joint-Domain Dictionary Mapping for Multiple Power Disturbance Assessment

Cai

et al. 2018

Energies

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“…An algorithm, based on Stockwell's transform, artificial neural network-based classifier and rule-based decision tree, is proposed. Some of the latest research on combined disturbance recognition can be found in [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Combined Power Quality Disturbances Recognition Using Wavelet Packet Entropies and S-Transform

Liu

Cui

2015

Entropy

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Aiming at the combined power quality +disturbance recognition, an automated recognition method based on wavelet packet entropy (WPE) and modified incomplete S-transform (MIST) is proposed in this paper. By combining wavelet packet Tsallis singular entropy, energy entropy and MIST, a 13-dimension vector of different power quality (PQ) disturbances including single disturbances and combined disturbances is extracted. Then, a ruled decision tree is designed to recognize the combined disturbances. The proposed method is tested and evaluated using a large number of simulated PQ disturbances and some real-life signals, which include voltage sag, swell, interruption, oscillation transient, impulsive transient, harmonics, voltage fluctuation and their combinations. In addition, the comparison of the proposed recognition approach with some existing techniques is made. The experimental results show that the proposed method can effectively recognize the single and combined PQ disturbances.

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An Efficient and Accurate Method for Detection and Classification of Power Quality Disturbances

Gong

Liao

2020

IEEJ Transactions Elec Engng

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Aiming to the problem of large computation amount and high redundancy of traditional S‐transform methods available in the detection and classification of transient power quality disturbances, an efficient and accurate method for the detection and classification is proposed. First, the maximum power spectrum dynamics method is used to extract the main frequency points. Then, a called modified incomplete S‐transform proposed in the paper is performed on the main frequency points so as to obtain a modulus time‐frequency matrix involving the characteristic information of disturbances. The modified incomplete S‐transform is implemented by introducing a bi‐Gaussian window function with two parameters to replace the Gaussian window function of the traditional S‐transform. And then, the characteristic quantities for detecting power quality disturbance and identifying the classification of the disturbances are extracted based on the matrix. Finally, the disturbance parameters such as the amplitude, start time, and stop time of the disturbances are calculated based on the relevant theories and rules, and also the classification of the disturbances is identified by extracting some important characteristic quantities. A great number of simulation experiments are conducted. The results obtained show that by the method, the start time, stop time, and amplitude changes of power quality disturbance signals can be fast and accurately detected, and the classification of the disturbances can be fast and accurately identified, and that the method has the characteristics of less operation time and strong anti‐interference ability. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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Cross-Spectrum Analysis-Based Scheme for Multiple Power Quality Disturbance Sensing Device

Cited by 34 publications

References 30 publications

On the Application of Joint-Domain Dictionary Mapping for Multiple Power Disturbance Assessment

On the Application of Joint-Domain Dictionary Mapping for Multiple Power Disturbance Assessment

Combined Power Quality Disturbances Recognition Using Wavelet Packet Entropies and S-Transform

An Efficient and Accurate Method for Detection and Classification of Power Quality Disturbances

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