Performance Evaluation of ST-Based Methods for Simulating and Analyzing Power Quality Disturbances

[1] P. K. Dash, B. K. Panigrahi and G. Panda, “Power quality analysis using S-transform,” in IEEE Transactions on Power Delivery, vol. 18, no. 2, pp. 406–411, April 2003, doi: 10.1109/TPWRD.2003.809616. DashP. K. PanigrahiB. K. PandaG. “Power quality analysis using S-transform,” in IEEE Transactions on Power Delivery 18 2 406 411 April 2003 10.1109/TPWRD.2003.809616 Open DOISearch in Google Scholar

[2] M. Gouda, M. M. A. Salama, M. R. Sultan, and A. Y. Chikhani, “Power quality detection and classification using wavelet multi-resolution signal decomposition,” IEEE Trans. Power Delivery, vol. 14, pp. 1469–1476, Oct. 1999. GoudaM. SalamaM. M. A. SultanM. R. ChikhaniA. Y. “Power quality detection and classification using wavelet multi-resolution signal decomposition,” IEEE Trans. Power Delivery 14 1469 1476 Oct. 1999 Search in Google Scholar

[3] P. Pillay and A. Bhattacharjee, “Application of wavelets to model short–term power system disturbances,” IEEE Trans. Power Delivery, vol. 11, pp. 2031–2037, Oct. 1996. PillayP. BhattacharjeeA. “Application of wavelets to model short–term power system disturbances,” IEEE Trans. Power Delivery 11 2031 2037 Oct. 1996 Search in Google Scholar

[4] Rahim Abdullah, Abdul, et al. “Performance Evaluation of Real Power Quality Disturbances Analysis Using S-Transform.” Applied Mechanics and Materials, vol. 752–753, Trans Tech Publications, Ltd., Apr. 2015, pp. 1343–1348. doi:10.4028/www.scientific.net/amm.752-753.1343. Rahim AbdullahAbdul “Performance Evaluation of Real Power Quality Disturbances Analysis Using S-Transform.” Applied Mechanics and Materials 752–753 Trans Tech Publications, Ltd. Apr. 2015 1343 1348 doi:10.4028/www.scientific.net/amm.752-753.1343. Search in Google Scholar

[5] Beuter, C., Oleskovicz, M.: S-transform: from main concepts to some power quality applications. IET Signal Process. 14(3), 115–123 (2019). BeuterC. OleskoviczM. S-transform: from main concepts to some power quality applications IET Signal Process 14 3 115 123 2019 Search in Google Scholar

[6] J. Ladan and Edward R. Vrscay, “The Discrete Orthonormal Stockwell Transform and Variations, with Applications to Image Compression”, ICIAR 2013, LNCS 7950, pp. 235–244, 2013. LadanJ. VrscayEdward R. “The Discrete Orthonormal Stockwell Transform and Variations, with Applications to Image Compression” ICIAR 2013 LNCS 7950 235 244 2013 Search in Google Scholar

[7] Drabycz, S., Stockwell, R.G., Mitchell, J.R.: Image texture characterization using the discrete orthonormal s-transform. Journal of Digital Imaging 22(6), 696–708 (2009). DrabyczS. StockwellR.G. MitchellJ.R. Image texture characterization using the discrete orthonormal s-transform Journal of Digital Imaging 22 6 696 708 2009 Search in Google Scholar

[8] Jaya Bharata Reddy, M., Raghupathy, R. K., Venkatesh, K. P., & Mohanta, D. K. (2013). Power quality analysis using discrete orthogonal S-transform (DOST). Digital Signal Processing, 23(2), 616–626. http://dx.doi.org/10.1016/j.dsp.2012.09.013 Jaya Bharata ReddyM. RaghupathyR. K. VenkateshK. P. MohantaD. K. 2013 Power quality analysis using discrete orthogonal S-transform (DOST) Digital Signal Processing 23 2 616 626 http://dx.doi.org/10.1016/j.dsp.2012.09.013 Search in Google Scholar

[9] M. Jaya Bharata Reddy, B. Karthik Chandra, D.K. Mohanta, A DOST based approach for the condition monitoring of 11 kV distribution line insulators, IEEE Trans. Dielectr. Electr. Insul. 18 (2) (April 2011) 588–595. Jaya Bharata ReddyM. Karthik ChandraB. MohantaD.K. A DOST based approach for the condition monitoring of 11 kV distribution line insulators IEEE Trans. Dielectr. Electr. Insul. 18 2 April 2011 588 595 Search in Google Scholar

[10] Seckin Karasu, Zehra Sarac, “Investigation of power quality disturbances by using 2d discrete orthonormal s-transform, machine learning and multi-objective evolutionary algorithms Swarm Evol. Comput., 44 (2019), pp. 1060–1072. https://doi.org/10.1016/j.swevo.2018.11.002 KarasuSeckin SaracZehra “Investigation of power quality disturbances by using 2d discrete orthonormal s-transform, machine learning and multi-objective evolutionary algorithms Swarm Evol. Comput. 44 2019 1060 1072 https://doi.org/10.1016/j.swevo.2018.11.002 Search in Google Scholar

[11] Xi Zhong Cui, Han Ping Hong; Use of Discrete Orthonormal S Transform to Simulate Earthquake Ground Motions. Bulletin of the Seismological Society of America 2020; 110 (2): 565–575. doi: https://doi.org/10.1785/0120190212 CuiXi Zhong HongHan Ping Use of Discrete Orthonormal S Transform to Simulate Earthquake Ground Motions Bulletin of the Seismological Society of America 2020 110 2 565 575 doi: https://doi.org/10.1785/0120190212 Search in Google Scholar

[12] Cohen, L. (1995). Time-Frequency Analysis, Vol. 778, Prentice Hall, Upper Saddle River, New Jersey. CohenL. 1995 Time-Frequency Analysis 778 Prentice Hall Upper Saddle River, New Jersey Search in Google Scholar

[13] A. A. Ahmad, A. Ehimen Airoboman, A. Abdulaziz and H. Hussaini, “Power Quality Disturbances Analysis using Two Forms of Wigner-Ville Distribution,” 2019 IEEE PES/IAS Power Africa, Abuja, Nigeria, 2019, pp. 424–428, doi: 10.1109/PowerAfrica.2019.8928911. AhmadA. A. Ehimen AirobomanA. AbdulazizA. HussainiH. “Power Quality Disturbances Analysis using Two Forms of Wigner-Ville Distribution,” 2019 IEEE PES/IAS Power Africa Abuja, Nigeria 2019 424 428 10.1109/PowerAfrica.2019.8928911 Open DOISearch in Google Scholar

[14] Wang, J.; Ye, N.; Ge, L. Steady-State Power Quality Synthetic Evaluation Based on the Triangular Fuzzy BW Method and Interval VIKOR Method. Appl. Sci. 2020, 10, 2839. https://doi.org/10.3390/app10082839 WangJ. YeN. GeL. Steady-State Power Quality Synthetic Evaluation Based on the Triangular Fuzzy BW Method and Interval VIKOR Method Appl. Sci. 2020 10 2839 https://doi.org/10.3390/app10082839 Search in Google Scholar

[15] Martinez, R.; Castro, P.; Arroyo, A.; Manana, M.; Galan, N.; Moreno, F.S.; Bustamante, S.; Laso, A. Techniques to Locate the Origin of Power Quality Disturbances in a Power System: A Review. Sustainability 2022, 14, 7428. https://doi.org/10.3390/su14127428 MartinezR. CastroP. ArroyoA. MananaM. GalanN. MorenoF.S. BustamanteS. LasoA. Techniques to Locate the Origin of Power Quality Disturbances in a Power System: A Review Sustainability 2022 14 7428 https://doi.org/10.3390/su14127428 Search in Google Scholar

[16] Gopal lal Rajora, Miguel A. Sanz-Bobi, Carlos Mateo Domingo, ‘Application of Machine Learning Methods for Asset Management on Power Distribution Networks’, Emerging Science Journal, Vol. 6, No. 4, p. 905–920, 2022. DOI: http://dx.doi.org/10.28991/ESJ-2022-06-04-017] RajoraGopal lal Sanz-BobiMiguel A. DomingoCarlos Mateo ‘Application of Machine Learning Methods for Asset Management on Power Distribution Networks’ Emerging Science Journal 6 4 905 920 2022 http://dx.doi.org/10.28991/ESJ-2022-06-04-017] Search in Google Scholar

[17] Muhammad Bilal Akhtar, ‘The Use of a Convolutional Neural Network in Detecting Soldering Faults from a Printed Circuit Board Assembly’, HighTech and Innovation Journal, Vol. 3, No. 1, 2022, p:1–14. http://dx.doi.org/10.28991/HIJ-2022-03-01-01 AkhtarMuhammad Bilal ‘The Use of a Convolutional Neural Network in Detecting Soldering Faults from a Printed Circuit Board Assembly’ HighTech and Innovation Journal 3 1 2022 1 14 http://dx.doi.org/10.28991/HIJ-2022-03-01-01 Search in Google Scholar

[18] R.G. Stockwell, A basis for efficient representation of the S-transform, Digital Signal Process. 17 (1) (2007) 371–393. StockwellR.G. A basis for efficient representation of the S-transform Digital Signal Process 17 1 2007 371 393 Search in Google Scholar

[19] S. Drabycz, R.G. Stockwell, J.R. Mitchell, Image texture characterisation using the discrete orthogonal S-transform, J. Digit. Imaging 22(6) (2009) 696–708. DrabyczS. StockwellR.G. MitchellJ.R. Image texture characterisation using the discrete orthogonal S-transform J. Digit. Imaging 22 6 2009 696 708 Search in Google Scholar

[20] Xiao, Y.; Yang, H.; Liu, A. Short time disturbance detection using DCT analysis in distribution system. In Proceedings of the CIRED 2005-18th International Conference and Exhibition on Electricity Distribution, Turin, Italy, 6–9 June 2005. XiaoY. YangH. LiuA. Short time disturbance detection using DCT analysis in distribution system In Proceedings of the CIRED 2005-18th International Conference and Exhibition on Electricity Distribution Turin, Italy 6–9 June 2005 Search in Google Scholar

[21] Imtiaz, H.; Sanam, T.F. Frequency domain feature extraction for power quality disturbance classification. In Proceedings of the 2013 International Conference on Informatics, Electronics and Vision, ICIEV 2013, Dhaka, Bangladesh, 17–18 May 2013. ImtiazH. SanamT.F. Frequency domain feature extraction for power quality disturbance classification In Proceedings of the 2013 International Conference on Informatics, Electronics and Vision, ICIEV 2013 Dhaka, Bangladesh 17–18 May 2013 Search in Google Scholar

[22] Hüseyin Erişti, Özal Yıldırım, Belkıs Erişti, Yakup Demir, ‘Automatic recognition system of underlying causes of power quality disturbances based on S-Transform and Extreme Learning Machine, International Journal of Electrical Power & Energy Systems, Volume 61, 2014, pp. 553–562, https://doi.org/10.1016/j.ijepes.2014.04.010 EriştiHüseyin YıldırımÖzal EriştiBelkıs DemirYakup ‘Automatic recognition system of underlying causes of power quality disturbances based on S-Transform and Extreme Learning Machine International Journal of Electrical Power & Energy Systems 61 2014 553 562 https://doi.org/10.1016/j.ijepes.2014.04.010 Search in Google Scholar

[23] K. Thirumala, M. S. Prasad, T. Jain and A. C. Umarikar, “Tunable-Q Wavelet Transform and Dual Multiclass SVM for Online Automatic Detection of Power Quality Disturbances,” in IEEE Transactions on Smart Grid, vol. 9, no. 4, pp. 3018–3028, July 2018, doi: 10.1109/TSG.2016.2624313. ThirumalaK. PrasadM. S. JainT. UmarikarA. C. “Tunable-Q Wavelet Transform and Dual Multiclass SVM for Online Automatic Detection of Power Quality Disturbances,” in IEEE Transactions on Smart Grid 9 4 3018 3028 July 2018 10.1109/TSG.2016.2624313 Open DOISearch in Google Scholar

[24] H. I. Hussein, G, A. Salman and A. M. Ghadban “Employment of PSO Algorithm to Improve the Neural Network Technique for Radial Distribution System State Estimation”, INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS, Vol. 12, Issue 1 (2019) pp. 1–10. DOI: 10.21307/ijssis-2019-005 HusseinH. I. SalmanG, A. GhadbanA. M. “Employment of PSO Algorithm to Improve the Neural Network Technique for Radial Distribution System State Estimation” INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS 12 1 2019 1 10 10.21307/ijssis-2019-005 Open DOISearch in Google Scholar