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Analysis of the Hybrid PSO-InC MPPT for Different Partial Shading Conditions, LEOPOLDINO, A. L. M., FREITAS, C. M., MONTEIRO, L. F. C.
Issue 2/2022

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  2/2021 - 10

 HIGHLY CITED PAPER 

An Efficient and High-Speed Disturbance Detection Algorithm Design with Emphasis on Operation of Static Transfer Switch

USMAN, A. See more information about USMAN, A. on SCOPUS See more information about USMAN, A. on IEEExplore See more information about USMAN, A. on Web of Science, CHOUDHRY, M. A. See more information about CHOUDHRY, M. A. on SCOPUS See more information about CHOUDHRY, M. A. on SCOPUS See more information about CHOUDHRY, M. A. on Web of Science
 
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Download PDF pdficon (1,519 KB) | Citation | Downloads: 1,694 | Views: 1,855

Author keywords
power quality, power system, event detection, feature extraction, support vector machine

References keywords
power(47), quality(28), detection(21), classification(21), transfer(17), disturbances(17), switch(15), systems(13), static(13), voltage(11)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2021-05-31
Volume 21, Issue 2, Year 2021, On page(s): 87 - 98
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2021.02010
Web of Science Accession Number: 000657126200010
SCOPUS ID: 85107688230

Abstract
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Full text preview
Static Transfer Switch (STS) is required for high-speed transfer of essential load to the alternate power source when the main source fails due to power disturbance (PD). A fast and accurate PD detection method is required to ensure transfer time recommended by Computer Business Equipment Manufacturers Association (CBEMA) and IEEE Std. 446. This study encompasses the machine learning technique to reduce detection time for the disturbance on the preferred source. The 10 sample frames of acquired voltage signal were first differentiated and then distinctive features, i.e., Mean Absolute Deviation (MAD) and Energy (E) were extracted from the resultant frames. The features were fed to the Linear Support Vector Machine (L-SVM) classifier to detect the occurrence of PD events. The proposed approach achieved 100% accuracy for PD detection and detection time was significantly reduced. The system is robust in terms of unbalanced and marginal PDs. The system was validated using both simulated and real voltage signals. The proposed algorithm is easy to implement on an embedded system. Hence, detection time according to STS requirements can be achieved under various power system conditions.


References | Cited By  «-- Click to see who has cited this paper

[1] A. A. Abdoos, P. K. Mianaei, and M. R. Ghadikolaei, "Combined VMD-SVM based feature selection method for classification of power quality events," Applied Soft Computing, vol. 38, pp. 637-646, Oct. 2016.
[CrossRef] [Web of Science Times Cited 116]


[2] H. Mokhtari, "High speed silicon controlled rectifier static transfer switch," National Library of Canada = Bibliotheque nationale du Canada, 2000

[3] H. Mokhtari, M. Iravani, S. Dewan, P. Lehn, and J. Martinez, "Benchmark systems for digital computer simulation of a static transfer switch," IEEE Power Engineering Review, vol. 21, pp. 69-69, Jul. 2001.
[CrossRef]


[4] M. Moschakis and N. Hatziargyriou, "A detailed model for a thyristor-based static transfer switch," IEEE Transactions on Power Delivery, vol. 18, pp. 1442-1449, Oct. 2003.
[CrossRef] [Web of Science Times Cited 20]


[5] C. He, F. Li, and V. Sood, "Static transfer switch (STS) model in EMTPWorks RV," in Canadian Conference on Electrical and Computer Engineering 2004 (IEEE Cat. No. 04CH37513), 2004, Niagara Falls, pp. 111-116.
[CrossRef]


[6] S.-M. Song, J.-Y. Kim, and I.-D. Kim, "New Three-Phase Static Transfer Switch using AC SSCB," in 2018 International Power Electronics Conference (IPEC-Niigata 2018-ECCE Asia), 2018, Niigata, pp. 3229-3236.
[CrossRef]


[7] S. C. Yanez-Campos, G. Cerda-Villafana, and J. M. Lozano-Garcia, "Two-Feeder Dynamic Voltage Restorer for Application in Custom Power Parks," Energies, vol. 12, p. 3248, Aug. 2019.
[CrossRef] [Web of Science Times Cited 5]


[8] J. Schwartzenberg and R. De Doncker, "15 kV medium voltage static transfer switch," in IAS'95. Conference Record of the 1995 IEEE Industry Applications Conference Thirtieth IAS Annual Meeting, 1995, Orlando, pp. 2515-2520.
[CrossRef]


[9] M. Faisal, M. A. Hannan, P. J. Ker, M. S. B. A. Rahman, M. S. Mollik, and M. B. Mansur, "Review of Solid State Transfer Switch on Requirements, Standards, Topologies, Control, and Switching Mechanisms: Issues and Challenges," Electronics, vol. 9, p. 1396, Aug. 2020.
[CrossRef] [Web of Science Times Cited 9]


[10] H. Mokhtari, S. B. Dewan, and M. Travani, "Performance evaluation of thyristor based static transfer switch," IEEE Transactions on Power Delivery, vol. 15, pp. 960-966, Jul. 2000.
[CrossRef] [Web of Science Times Cited 40]


[11] H. Mokhtari, S. B. Dewan, and M. R. Iravani, "Effect of regenerative load on a static transfer switch performance," IEEE transactions on power delivery, vol. 16, pp. 619-624, Oct. 2001.
[CrossRef] [Web of Science Times Cited 25]


[12] H. Mokhtari, "Performance evaluation of thyristor-based static transfer switch with respect to cross current," in IEEE/PES Transmission and Distribution Conference and Exhibition, 2002, Yokohama, pp. 1326-1331.
[CrossRef]


[13] H. Mokhtari, S. B. Dewan, and M. R. Iravani, "Analysis of a static transfer switch with respect to transfer time," IEEE Transactions on Power Delivery, vol. 17, pp. 190-199, Nov. 2001.
[CrossRef]


[14] A. Sannino, "Static transfer switch: analysis of switching conditions and actual transfer time," in 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No. 01CH37194), 2001, Columbus, pp. 120-125.
[CrossRef]


[15] H. Mokhtari and M. R. Iravani, "Effect of source phase difference on static transfer switch performance," IEEE transactions on power delivery, vol. 22, pp. 1125-1131, Apr. 2007.
[CrossRef] [Web of Science Times Cited 15]


[16] H. Mokhtari and M. R. Iravani, "Impact of difference of feeder impedances on the performance of a static transfer switch," IEEE transactions on power delivery, vol. 19, pp. 679-685, Mar. 2004.
[CrossRef] [Web of Science Times Cited 9]


[17] R. K. Pachar, H. Tiwari, and R. C. Bansal, "A decisive evaluation of parks transformation based commonly used voltage detection method," 2014

[18] B. Tian, C. Mao, J. Lu, D. Wang, Y. He, Y. Duan, et al., "400 V/1000 kVA hybrid automatic transfer switch," IEEE Transactions on Industrial Electronics, vol. 60, pp. 5422-5435, Jan. 2013.
[CrossRef] [Web of Science Times Cited 29]


[19] F. R. Zaro, S. O. Al-Takrouri, and M. A. Abido, "Efficient on-line detection scheme of voltage events using quadrature method," Sep. 2018.
[CrossRef]


[20] F. Dekhandji, "Signal processing deployment in power quality disturbance detection and classification," Acta Phys Pol, A, vol. 132, pp. 415-419, 2017.
[CrossRef] [Web of Science Times Cited 6]


[21] M. B. Latran and A. Teke, "A novel wavelet transform based voltage sag/swell detection algorithm," International Journal of Electrical Power & Energy Systems, vol. 71, pp. 131-139, Oct. 2015.
[CrossRef] [Web of Science Times Cited 64]


[22] F. Z. Dekhandji, "Detection of power quality disturbances using discrete wavelet transform," in 2017 5th International Conference on Electrical Engineering-Boumerdes (ICEE-B), 2017, Boumerdes, pp. 1-5.
[CrossRef]


[23] F. Ucar, O. F. Alcin, B. Dandil, and F. Ata, "Power quality event detection using a fast extreme learning machine," Energies, vol. 11, p. 145, Jan. 2018.
[CrossRef] [Web of Science Times Cited 32]


[24] M. Lopez-Ramirez, E. Cabal-Yepez, L. M. Ledesma-Carrillo, H. Miranda-Vidales, C. Rodriguez-Donate, and R. A. Lizarraga-Morales, "FPGA-based online PQD detection and classification through DWT, mathematical morphology and SVD," Energies, vol. 11, p. 769, Mar. 2018.
[CrossRef] [Web of Science Times Cited 15]


[25] J. Li, Z. Teng, Q. Tang, and J. Song, "Detection and classification of power quality disturbances using double resolution S-transform and DAG-SVMs," IEEE Transactions on Instrumentation and Measurement, vol. 65, pp. 2302-2312, Jun. 2016.
[CrossRef] [Web of Science Times Cited 155]


[26] R. Kapoor, R. Gupta, S. Jha, and R. Kumar, "Boosting performance of power quality event identification with KL divergence measure and standard deviation," Measurement, vol. 126, pp. 134-142, Oct. 2018.
[CrossRef] [Web of Science Times Cited 38]


[27] A. Shaik and A. S. Reddy, "Flexible entropy based feature selection and multi class SVM for detection and classification of power quality disturbances," International Journal of Intelligent Engineering and Systems, vol. 11, Apr. 2018.
[CrossRef]


[28] T. Zhong, S. Zhang, G. Cai, Y. Li, B. Yang, and Y. Chen, "Power quality disturbance recognition based on multiresolution s-transform and decision tree," IEEE Access, vol. 7, pp. 88380-88392, Jun. 2019.
[CrossRef] [Web of Science Times Cited 47]


[29] K. Thirumala, S. Pal, T. Jain, and A. C. Umarikar, "A classification method for multiple power quality disturbances using EWT based adaptive filtering and multiclass SVM," Neurocomputing, vol. 334, pp. 265-274, Mar. 2019.
[CrossRef] [Web of Science Times Cited 70]


[30] O. Jeba Singh, D. Prince Winston, B. Chitti Babu, S. Kalyani, B. Praveen Kumar, M. Saravanan, et al., "Robust detection of real-time power quality disturbances under noisy condition using FTDD features," Automatika, vol. 60, pp. 11-18, Jan. 2019.
[CrossRef] [Web of Science Times Cited 15]


[31] D. De Yong, S. Bhowmik, and F. Magnago, "An effective power quality classifier using wavelet transform and support vector machines," Expert Systems with Applications, vol. 42, pp. 6075-6081, Sep. 2015.
[CrossRef] [Web of Science Times Cited 117]


[32] M. Lopez-Ramirez, L. Ledesma-Carrillo, E. Cabal-Yepez, C. Rodriguez-Donate, H. Miranda-Vidales, and A. Garcia-Perez, "EMD-based feature extraction for power quality disturbance classification using moments," Energies, vol. 9, p. 565, Jul. 2016.
[CrossRef] [Web of Science Times Cited 26]


[33] R. Kumar, B. Singh, and D. T. Shahani, "Recognition of single-stage and multiple power quality events using Hilbert-Huang transform and probabilistic neural network," Electric Power Components and Systems, vol. 43, pp. 607-619, Mar. 2015.
[CrossRef] [Web of Science Times Cited 38]


[34] M. Gok and I. Sefa, "Research and implementation of a USB interfaced real-time power quality disturbance classification system," Advances in Electrical and Computer Engineering, vol. 17, pp. 61-71, Aug. 2017.
[CrossRef] [Full Text] [Web of Science Times Cited 10]


[35] M. I. Gursoy, A. S. Yilmaz, and S. V. Ustun, "A practical real-time power quality event monitoring applications using discrete wavelet transform and artificial neural network," Journal of Engineering Science and Technology (JESTEC), vol. 13, pp. 1764-1781, 2018

[36] O. I. Abiodun, A. Jantan, A. E. Omolara, K. V. Dada, A. M. Umar, O. U. Linus, et al., "Comprehensive review of artificial neural network applications to pattern recognition," IEEE Access, vol. 7, pp. 158820-158846, Oct. 2019.
[CrossRef] [Web of Science Times Cited 201]


[37] A. K. Sadigh and K. Smedley, "Fast and precise voltage sag detection method for dynamic voltage restorer (DVR) application," Electric Power Systems Research, vol. 130, pp. 192-207, Jan. 2016.
[CrossRef] [Web of Science Times Cited 51]


[38] M. S. Manikandan, S. Samantaray, and I. Kamwa, "Detection and classification of power quality disturbances using sparse signal decomposition on hybrid dictionaries," IEEE Transactions on Instrumentation and Measurement, vol. 64, pp. 27-38, Jun. 2014.
[CrossRef] [Web of Science Times Cited 143]


[39] R. Kumar, B. Singh, and D. Shahani, "Symmetrical components-based modified technique for power-quality disturbances detection and classification," IEEE Transactions on Industry Applications, vol. 52, pp. 3443-3450, Mar. 2016.
[CrossRef] [Web of Science Times Cited 51]


[40] J. Ma, J. Zhang, L. Xiao, K. Chen, and J. Wu, "Classification of power quality disturbances via deep learning," IETE Technical Review, vol. 34, pp. 408-415, Jul. 2016.
[CrossRef] [Web of Science Times Cited 43]


[41] E. A. Nagata, D. D. Ferreira, C. A. Duque, and A. S. Cequeira, "Voltage sag and swell detection and segmentation based on independent component analysis," Electric Power Systems Research, vol. 155, pp. 274-280, Feb. 2018.
[CrossRef] [Web of Science Times Cited 34]


[42] V. A. Katic and A. M. Stanisavljevic, "Smart detection of voltage dips using voltage harmonics footprint," IEEE Transactions on Industry Applications, vol. 54, pp. 5331-5342, Mar. 2018.
[CrossRef] [Web of Science Times Cited 27]


[43] V. A. Katic, A. M. Stanisavljevic, R. L. Turovic, B. P. Dumnic, and B. P. Popadic, "Extended Kalman filter for voltage dips detection in grid with distributed energy resources," in 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), 2018, Sarajevo, pp. 1-6.
[CrossRef]


[44] P. N. Kumawat, D. K. Verma, and N. Zaveri, "Comparison between wavelet packet transform and M-band wavelet packet transform for identification of power quality disturbances," Power Research, vol. 14, pp. 37-45, Jun. 2018.
[CrossRef]


[45] W. Qiu, Q. Tang, J. Liu, Z. Teng, and W. Yao, "Power quality disturbances recognition using modified s transform and parallel stack sparse auto-encoder," Electric Power Systems Research, vol. 174, p. 105876, Sep. 2019.
[CrossRef] [Web of Science Times Cited 43]


[46] H. Wang, J. Liu, S. Luo, and X. Xu, "Research on power quality disturbance detection method based on improved ensemble empirical mode decomposition," Electronics, vol. 9, p. 585, Mar. 2020.
[CrossRef] [Web of Science Times Cited 2]


[47] E. G. Ribeiro, T. M. Mendes, G. L. Dias, E. R. Faria, F. M. Viana, B. H. Barbosa, et al., "Real-time system for automatic detection and classification of single and multiple power quality disturbances," Measurement, vol. 128, pp. 276-283, Nov. 2018.
[CrossRef] [Web of Science Times Cited 51]


[48] S. Jamali, A. R. Farsa, and N. Ghaffarzadeh, "Identification of optimal features for fast and accurate classification of power quality disturbances," Measurement, vol. 116, pp. 565-574, Feb. 2018.
[CrossRef] [Web of Science Times Cited 50]


[49] U. Singh and S. N. Singh, "A new optimal feature selection scheme for classification of power quality disturbances based on ant colony framework," Applied Soft Computing, vol. 74, pp. 216-225, Jan. 2019.
[CrossRef] [Web of Science Times Cited 45]


[50] H. Eristi, A. Ucar, and Y. Demir, "Wavelet-based feature extraction and selection for classification of power system disturbances using support vector machines," Electric power systems research, vol. 80, pp. 743-752, Jul. 2010.
[CrossRef] [Web of Science Times Cited 109]


[51] F. A. Borges, R. A. Fernandes, I. N. Silva, and C. B. Silva, "Feature extraction and power quality disturbances classification using smart meters signals," IEEE Transactions on Industrial Informatics, vol. 12, pp. 824-833, Oct. 2015.
[CrossRef] [Web of Science Times Cited 159]


[52] H. Eristi and Y. Demir, "An efficient feature extraction method for classification of power quality disturbances," in Int. Conf. on Power Systems Transients (IPST2009), in Kyoto, Japan, 2009

[53] S. Aziz, M. Awais, T. Akram, U. Khan, M. Alhussein, and K. Aurangzeb, "Automatic scene recognition through acoustic classification for behavioral robotics," Electronics, vol. 8, p. 483, Apr. 2019.
[CrossRef] [Web of Science Times Cited 24]


[54] S. Aziz, M. U. Khan, Z. A. Choudhry, A. Aymin, and A. Usman, "ECG-based biometric authentication using empirical mode decomposition and support vector machines," in 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2019, Vancouver, pp. 0906-0912.
[CrossRef] [Web of Science Times Cited 19]


[55] M. U. Khan, S. Aziz, S. Ibraheem, A. Butt, and H. Shahid, "Characterization of term and preterm deliveries using electrohysterograms signatures," in 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2019, Vancouver, pp. 0899-0905.
[CrossRef] [Web of Science Times Cited 11]


[56] M. U. Khan, S. Aziz, K. Iqtidar, A. Zainab, and A. Saud, "Prediction of acute coronary syndrome using pulse plethysmograph," in 2019 4th International Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST), 2019, Karachi, pp. 1-6.
[CrossRef]


[57] M. U. Khan, S. Aziz, A. Malik, and M. A. Imtiaz, "Detection of myocardial infarction using pulse plethysmograph signals," in 2019 International Conference on Frontiers of Information Technology (FIT), 2019, Islamabad, pp. 95-955.
[CrossRef]


[58] T. Andrews, "Computation time comparison between Matlab and C++ using launch windows," 2012.

[59] H. Yu and B. M. Wilamowski, "C++ implementation of neural networks trainer," in 2009 International Conference on Intelligent Engineering Systems, 2009, Barbados, pp. 257-262.
[CrossRef]


[60] W. L. Rodrigues Junior, F. A. Borges, R. d. A. Rabelo, J. J. Rodrigues, R. A. Fernandes, and I. N. da Silva, "A methodology for detection and classification of power quality disturbances using a real‐time operating system in the context of home energy management systems," International Journal of Energy Research, 2020

[61] M. R. Al Koutayni, V. Rybalkin, J. Malik, A. Elhayek, C. Weis, G. Reis, et al., "Real-time energy efficient hand pose estimation: A Case Study," Sensors, vol. 20, p. 2828, May 2020.
[CrossRef] [Web of Science Times Cited 8]


[62] M. Mishra, "Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review," International Transactions on Electrical Energy Systems, vol. 29, p. e12008, Mar. 2019.
[CrossRef] [Web of Science Times Cited 87]


[63] M. U. Khan, S. Aziz, M. Bilal, and M. B. Aamir, "Classification of EMG signals for assessment of neuromuscular disorder using empirical mode decomposition and logistic regression," in 2019 International Conference on Applied and Engineering Mathematics (ICAEM), 2019, Islamabad, pp. 237-243.
[CrossRef] [Web of Science Times Cited 1]




References Weight

Web of Science® Citations for all references: 2,060 TCR
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Web of Science® Average Citations per reference: 32 ACR
SCOPUS® Average Citations per reference: 0

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