Research Article
BibTex RIS Cite

Classification of Short-Term Power Quality Disturbances by Wavelet Analysis and Random Forest Method

Year 2021, Volume: 26 Issue: 3, 903 - 920, 31.12.2021

Mustafa Ercire Abdurrahman Ünsal

https://doi.org/10.17482/uumfd.976342
Cited By: 1

Abstract

The concept of quality in electrical power system has an increasing importance. Distortions in the voltage, current and frequency of a power system have adverse effects especially in economic terms. Among the power quality distortions, short-term RMS variations and transients have the highest rate. In this study, short-term RMS variations and transients which have been mathematically modeled were classified with the Random Forest (RF) classifier. The feature vector which consists of energy, skewness and kurtosis values of the DWT coefficients was applied to the RF classifier. The performance of DWT on classification performance was analyzed with different levels of decomposition. The effect of the noise on the classification performance is also analyzed. The performance of the RF classifier at different DWT levels and noise levels was evaluated. Accuracy in noise-containing disturbances was 99.8% in events with 50 dB noise, 99.4% in events with 40 dB noise, and 98.5% in events with 30 dB noise. The accuracy rate was obtained as 99.6% in the distortions where 50 dB, 40 dB and 30 dB noise levels were evaluated together. The results show that by using a RO classifier short-term RMS variations and transients are classified with high accuracy rate. 

Keywords

Power Quality Power Quality Disturbances Wavelet Analysis Random Forest Method Classification

References

  • 1. Akman, M. (2010). Veri madenciliğine genel bakış ve random forests yönteminin incelenmesi: sağlık alanında bir uygulama, Yüksek Lisans Tezi, Ankara Üniversitesi Sağlık Bilimleri Enstitüsü, Ankara.
  • 2. Borges, F.A., Fernandes, R.A., Lucas, A.M. ve Silva, I.N. (2015) Comparison between random forest algorithm and J48 decision trees applied to the classification of power quality disturbances, Proceedings of the International Conference on Data Mining (DMIN), Las Vegas, NV, 146-147.
  • 3. Breiman, L. (2001) Random forests, Machine Learning, 45(1), 5-32. doi:10.1023/A:1010933404324
  • 4. Brito, N.S.D., Souza, B.A. ve Pires, F.A.C. (1998) Daubechies wavelets in quality of electrical power, In 8th International Conference on Harmonics and Quality of Power. Proceedings (Cat. No. 98EX227), 1, 511-515. doi:10.1109/ICHQP.1998.759961
  • 5. Chun-Lin, L. (2010) A tutorial of the wavelet transform, National Taiwan University Department of Electrical Engineering (NTUEE), Taiwan, 1–72.
  • 6. Debnath, L. (2002) Wavelet Transforms & Their Applications, Birkhäuser, Boston.
  • 7. Eibe, F., Hall, M. A., ve Witten, I. H. (2016). The WEKA Workbench. Online Appendix for Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.
  • 8. Getting Started with LabVIEW. Erişim Adresi: http://www.ni.com/pdf/manuals/373427f.pdf (Erişim Tarihi: 10.10.2021)
  • 9. Goswami, J.C. ve Chan, A.K. (1999) Fundamentals of Wavelets, John Wiley&Sons, USA
  • 10. Huang, N., Lu, G., Cai, G., Xu, D., Xu, J., Li, F. ve Zhang, L. (2016) Feature selection of power quality disturbance signals with an entropy-importance-based random forest, Entropy, 18(2), 44-65. doi:10.3390/e18020044
  • 11. Ibrahim, W.A. ve Morcos, M.M. (2002) Artificial intelligence and advanced mathematical tools for power quality applications: a survey. IEEE Transactions on Power Delivery, 17(2), 668-673. doi:10.1109/61.997958
  • 12. IEEE Std 1159-2019. IEEE recommended practice for monitoring electric power quality. doi: IEEESTD.2019.8796486
  • 13. Jamali, S., Farsa, A.R. ve Ghaffarzadeh, N. (2018) Identification of optimal features for fast and accurate classification of power quality disturbances, Measurement, 116, 565-574. doi:10.1016/j.measurement.2017.10.034
  • 14. Kiranmai, S.A. ve Laxmi, A.J. (2018) Data mining for classification of power quality problems using WEKA and the effect of attributes on classification accuracy, Protection and Control of Modern Power Systems, 3(1), 1-12. doi:10.1186/s41601-018-0103-3
  • 15. Li, Y., Li, K., Liu, C., Xiao, X., Chen, X., ve Wang, M. (2021). Study on Denoising Algorithm for Power Quality Disturbances Based on Variational Mode Decomposition. Journal of Physics: Conference Series,1746(1). doi:1742-6596/1746/1/012061
  • 16. Liaw, A. ve Wiener, M. (2002) Classification and regression by randomForest, R News, 2(3), 18-22.
  • 17. Mahela, O.P., Shaik, A.G. ve Gupta, N. (2015) A critical review of detection and classification of power quality events, Renewable and Sustainable Energy Reviews, 41, 495-505. doi:10.1016/j.rser.2014.08.070
  • 18. Markovska, M. ve Taskovski, D. (2017a) On the choice of wavelet based features in power quality disturbances classification. In 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), 1-6. doi:10.1109/EEEIC.2017.7977586
  • 19. Markovska, M. ve Taskovski, D. (2017b) Optimal wavelet based feature extraction and classification of power quality disturbances using random forest, In IEEE EUROCON 2017-17th International Conference on Smart Technologies, 855-859. doi:10.1109/EUROCON.2017.8011232
  • 20. Misiti, M., Misiti, Y., Oppenheim, G., ve Poggi, J. M. (2010). Wavelet Toolbox 4 User’s Guide. The MathWorks.
  • 21. Reddy, M.V. ve Sodhi, R. (2017) A modified S-transform and random forests-based power quality assessment framework, IEEE Transactions on Instrumentation and Measurement, 67(1), 78-89. doi:10.1109/TIM.2017.2761239
  • 22. Sharma, A., Rajpurohit, B.S. ve Singh, S.N. (2018) A review on economics of power quality: Impact, assessment and mitigation, Renewable and Sustainable Energy Reviews, 88, 363-372. doi:10.1016/j.rser.2018.02.011
  • 23. Tan, R.H. ve Ramachandaramurthy, V.K. (2010) Numerical model framework of power quality events, European Journal of Scientific Research, 43(1), 30-47.
  • 24. Upadhyaya, S., Mohanty, S. ve Bhende, C.N. (2015) Hybrid methods for fast detection and characterization of power quality disturbances, Journal of Control, Automation and Electrical Systems, 26(5), 556-566. doi:10.1007/s40313-015-0204-4
  • 25. Vatansever, F. (2020) Güç bileşenlerinin dalgacık dönüşümü tabanlı hesaplanması. Uludağ University Journal of The Faculty of Engineering, 25(2), 679-692. doi:10.17482/uumfd.717451

KISA SÜRELİ GÜÇ KALİTESİ BOZULMALARININ DALGACIK ANALİZİ VE RASTGELE ORMAN YÖNTEMİ İLE SINIFLANDIRILMASI

Year 2021, Volume: 26 Issue: 3, 903 - 920, 31.12.2021

Mustafa Ercire Abdurrahman Ünsal

https://doi.org/10.17482/uumfd.976342
Cited By: 1

Abstract

Elektrik güç sisteminde kalite kavramı giderek artan bir öneme sahiptir. Güç kalitesi bozulmaları (GKB), bir güç sisteminin akım, gerilim ve frekansında meydana gelen bozulmaları kapsar. GKB içinde, kısa süreli RMS değişimleri ile süreksiz olaylar en yüksek orana sahiptir. Bu bozulmaların doğru tespit edilmesi önemlidir. Bu çalışmada matematiksel olarak modellenen kısa süreli RMS değişimleri ve süreksiz olaylar Rastgele Orman (RO) sınıflandırıcısı ile sınıflandırılmıştır. Öznitelik vektörü Ayrık Dalgacık Dönüşümü (ADD) ile oluşturulmuştur. ADD katsayılarının enerji, kayıklık ve basıklık değerlerinden oluşturulan öznitelik vektörü RO sınıflandırıcısına uygulanmıştır. ADD’nin sınıflandırma başarımına etkisi farklı ayrışım seviyeleri ile analiz edilmiştir. Güç sistemlerinde farklı seviyelerde var olan gürültünün sınıflandırma başarımına etkisi de analiz edilmiştir. RO sınıflandırıcısının farklı ADD seviyelerinde ve farklı gürültü düzeylerinde performansı değerlendirilmiştir. Gürültü içeren bozulmalarda doğruluk, 50 dB gürültü içeren olaylarda %99,8 oranında, 40 dB gürültü içeren olaylarda %99,4 oranında, 30 dB gürültü içeren olaylarda da %98,5 oranında elde edilmiştir. Gürültü düzeyinin 50 dB, 40 dB ve 30 dB olarak birlikte değerlendirildiği bozulmalarda doğruluk oranı %99,6 olarak elde edilmiştir. Sonuçlar kısa vadeli RMS değişimlerinin ve süreksiz olayların RO sınıflandırıcı ile yüksek doğruluk oranıyla ile sınıflandırıldığını göstermektedir.

Keywords

Güç Kalitesi Güç Kalitesi Bozulmaları Dalgacık Analizi Rastgele Orman Yöntemi Sınıflandırma

References

  • 1. Akman, M. (2010). Veri madenciliğine genel bakış ve random forests yönteminin incelenmesi: sağlık alanında bir uygulama, Yüksek Lisans Tezi, Ankara Üniversitesi Sağlık Bilimleri Enstitüsü, Ankara.
  • 2. Borges, F.A., Fernandes, R.A., Lucas, A.M. ve Silva, I.N. (2015) Comparison between random forest algorithm and J48 decision trees applied to the classification of power quality disturbances, Proceedings of the International Conference on Data Mining (DMIN), Las Vegas, NV, 146-147.
  • 3. Breiman, L. (2001) Random forests, Machine Learning, 45(1), 5-32. doi:10.1023/A:1010933404324
  • 4. Brito, N.S.D., Souza, B.A. ve Pires, F.A.C. (1998) Daubechies wavelets in quality of electrical power, In 8th International Conference on Harmonics and Quality of Power. Proceedings (Cat. No. 98EX227), 1, 511-515. doi:10.1109/ICHQP.1998.759961
  • 5. Chun-Lin, L. (2010) A tutorial of the wavelet transform, National Taiwan University Department of Electrical Engineering (NTUEE), Taiwan, 1–72.
  • 6. Debnath, L. (2002) Wavelet Transforms & Their Applications, Birkhäuser, Boston.
  • 7. Eibe, F., Hall, M. A., ve Witten, I. H. (2016). The WEKA Workbench. Online Appendix for Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.
  • 8. Getting Started with LabVIEW. Erişim Adresi: http://www.ni.com/pdf/manuals/373427f.pdf (Erişim Tarihi: 10.10.2021)
  • 9. Goswami, J.C. ve Chan, A.K. (1999) Fundamentals of Wavelets, John Wiley&Sons, USA
  • 10. Huang, N., Lu, G., Cai, G., Xu, D., Xu, J., Li, F. ve Zhang, L. (2016) Feature selection of power quality disturbance signals with an entropy-importance-based random forest, Entropy, 18(2), 44-65. doi:10.3390/e18020044
  • 11. Ibrahim, W.A. ve Morcos, M.M. (2002) Artificial intelligence and advanced mathematical tools for power quality applications: a survey. IEEE Transactions on Power Delivery, 17(2), 668-673. doi:10.1109/61.997958
  • 12. IEEE Std 1159-2019. IEEE recommended practice for monitoring electric power quality. doi: IEEESTD.2019.8796486
  • 13. Jamali, S., Farsa, A.R. ve Ghaffarzadeh, N. (2018) Identification of optimal features for fast and accurate classification of power quality disturbances, Measurement, 116, 565-574. doi:10.1016/j.measurement.2017.10.034
  • 14. Kiranmai, S.A. ve Laxmi, A.J. (2018) Data mining for classification of power quality problems using WEKA and the effect of attributes on classification accuracy, Protection and Control of Modern Power Systems, 3(1), 1-12. doi:10.1186/s41601-018-0103-3
  • 15. Li, Y., Li, K., Liu, C., Xiao, X., Chen, X., ve Wang, M. (2021). Study on Denoising Algorithm for Power Quality Disturbances Based on Variational Mode Decomposition. Journal of Physics: Conference Series,1746(1). doi:1742-6596/1746/1/012061
  • 16. Liaw, A. ve Wiener, M. (2002) Classification and regression by randomForest, R News, 2(3), 18-22.
  • 17. Mahela, O.P., Shaik, A.G. ve Gupta, N. (2015) A critical review of detection and classification of power quality events, Renewable and Sustainable Energy Reviews, 41, 495-505. doi:10.1016/j.rser.2014.08.070
  • 18. Markovska, M. ve Taskovski, D. (2017a) On the choice of wavelet based features in power quality disturbances classification. In 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), 1-6. doi:10.1109/EEEIC.2017.7977586
  • 19. Markovska, M. ve Taskovski, D. (2017b) Optimal wavelet based feature extraction and classification of power quality disturbances using random forest, In IEEE EUROCON 2017-17th International Conference on Smart Technologies, 855-859. doi:10.1109/EUROCON.2017.8011232
  • 20. Misiti, M., Misiti, Y., Oppenheim, G., ve Poggi, J. M. (2010). Wavelet Toolbox 4 User’s Guide. The MathWorks.
  • 21. Reddy, M.V. ve Sodhi, R. (2017) A modified S-transform and random forests-based power quality assessment framework, IEEE Transactions on Instrumentation and Measurement, 67(1), 78-89. doi:10.1109/TIM.2017.2761239
  • 22. Sharma, A., Rajpurohit, B.S. ve Singh, S.N. (2018) A review on economics of power quality: Impact, assessment and mitigation, Renewable and Sustainable Energy Reviews, 88, 363-372. doi:10.1016/j.rser.2018.02.011
  • 23. Tan, R.H. ve Ramachandaramurthy, V.K. (2010) Numerical model framework of power quality events, European Journal of Scientific Research, 43(1), 30-47.
  • 24. Upadhyaya, S., Mohanty, S. ve Bhende, C.N. (2015) Hybrid methods for fast detection and characterization of power quality disturbances, Journal of Control, Automation and Electrical Systems, 26(5), 556-566. doi:10.1007/s40313-015-0204-4
  • 25. Vatansever, F. (2020) Güç bileşenlerinin dalgacık dönüşümü tabanlı hesaplanması. Uludağ University Journal of The Faculty of Engineering, 25(2), 679-692. doi:10.17482/uumfd.717451
There are 25 citations in total.

Details

Primary Language Turkish
Subjects Electrical Engineering
Journal Section Research Articles
Authors

Mustafa Ercire 0000-0003-4157-4951

Abdurrahman Ünsal 0000-0002-7053-517X

Publication Date December 31, 2021
Submission Date July 30, 2021
Acceptance Date November 13, 2021
Published in Issue Year 2021 Volume: 26 Issue: 3

Cite

APA Ercire, M., & Ünsal, A. (2021). KISA SÜRELİ GÜÇ KALİTESİ BOZULMALARININ DALGACIK ANALİZİ VE RASTGELE ORMAN YÖNTEMİ İLE SINIFLANDIRILMASI. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 26(3), 903-920. https://doi.org/10.17482/uumfd.976342
AMA Ercire M, Ünsal A. KISA SÜRELİ GÜÇ KALİTESİ BOZULMALARININ DALGACIK ANALİZİ VE RASTGELE ORMAN YÖNTEMİ İLE SINIFLANDIRILMASI. UUJFE. December 2021;26(3):903-920. doi:10.17482/uumfd.976342
Chicago Ercire, Mustafa, and Abdurrahman Ünsal. “KISA SÜRELİ GÜÇ KALİTESİ BOZULMALARININ DALGACIK ANALİZİ VE RASTGELE ORMAN YÖNTEMİ İLE SINIFLANDIRILMASI”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 26, no. 3 (December 2021): 903-20. https://doi.org/10.17482/uumfd.976342.
EndNote Ercire M, Ünsal A (December 1, 2021) KISA SÜRELİ GÜÇ KALİTESİ BOZULMALARININ DALGACIK ANALİZİ VE RASTGELE ORMAN YÖNTEMİ İLE SINIFLANDIRILMASI. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 26 3 903–920.
IEEE M. Ercire and A. Ünsal, “KISA SÜRELİ GÜÇ KALİTESİ BOZULMALARININ DALGACIK ANALİZİ VE RASTGELE ORMAN YÖNTEMİ İLE SINIFLANDIRILMASI”, UUJFE, vol. 26, no. 3, pp. 903–920, 2021, doi: 10.17482/uumfd.976342.
ISNAD Ercire, Mustafa - Ünsal, Abdurrahman. “KISA SÜRELİ GÜÇ KALİTESİ BOZULMALARININ DALGACIK ANALİZİ VE RASTGELE ORMAN YÖNTEMİ İLE SINIFLANDIRILMASI”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 26/3 (December 2021), 903-920. https://doi.org/10.17482/uumfd.976342.
JAMA Ercire M, Ünsal A. KISA SÜRELİ GÜÇ KALİTESİ BOZULMALARININ DALGACIK ANALİZİ VE RASTGELE ORMAN YÖNTEMİ İLE SINIFLANDIRILMASI. UUJFE. 2021;26:903–920.
MLA Ercire, Mustafa and Abdurrahman Ünsal. “KISA SÜRELİ GÜÇ KALİTESİ BOZULMALARININ DALGACIK ANALİZİ VE RASTGELE ORMAN YÖNTEMİ İLE SINIFLANDIRILMASI”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, vol. 26, no. 3, 2021, pp. 903-20, doi:10.17482/uumfd.976342.
Vancouver Ercire M, Ünsal A. KISA SÜRELİ GÜÇ KALİTESİ BOZULMALARININ DALGACIK ANALİZİ VE RASTGELE ORMAN YÖNTEMİ İLE SINIFLANDIRILMASI. UUJFE. 2021;26(3):903-20.

Cited By

Batarya sağlık durumunun makine öğrenmesi ile kestirimi
Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi
https://doi.org/10.28948/ngumuh.1112985

Announcements:

30.03.2021-Beginning with our April 2021 (26/1) issue, in accordance with the new criteria of TR-Dizin, the Declaration of Conflict of Interest and the Declaration of Author Contribution forms fulfilled and signed by all authors are required as well as the Copyright form during the initial submission of the manuscript. Furthermore two new sections, i.e. ‘Conflict of Interest’ and ‘Author Contribution’, should be added to the manuscript. Links of those forms that should be submitted with the initial manuscript can be found in our 'Author Guidelines' and 'Submission Procedure' pages. The manuscript template is also updated. For articles reviewed and accepted for publication in our 2021 and ongoing issues and for articles currently under review process, those forms should also be fulfilled, signed and uploaded to the system by authors.