DOI: https://doi.org/10.20998/2074-272X.2019.6.09

UNBALANCED LOAD FLOW WITH HYBRID WAVELET TRANSFORM AND SUPPORT VECTOR MACHINE BASED ERROR-CORRECTING OUTPUT CODES FOR POWER QUALITY DISTURBANCES CLASSIFICATION INCLUDING WIND ENERGY

Ala eddine Rahmani, Linda Slimani, Tarek Bouktir

Анотація


Цель. Наиболее распространенные методы построения мультиклассовой классификации заключаются в определении набора двоичных классификаторов и их объединении. В данной статье предложена машина опорных векторов с классификатором выходных кодов исправления ошибок (ECOC-SVM) с целью классифицировать и характеризовать такие нарушения качества электроэнергии, как гармонические искажения, падение напряжения и скачок напряжения, включая генератор ветровых электростанций в системах передачи электроэнергии. Сначала выполняется анализ потока несимметричной нагрузки трех фаз для расчета разностных характеристик электрической сети, уровней напряжения, активной и реактивной мощности. После этого дискретное вейвлет-преобразование объединяется с вероятностной моделью ECOC-SVM для построения классификатора. Наконец, ECOC-SVM классифицирует и идентифицирует тип возмущения в соответствии с отклонением энергии дискретного вейвлет-преобразования. Предложенный метод дает удовлетворительную точность 99,2% по сравнению с хорошо известными методами и показывает, что каждое нарушение качества электроэнергии имеет определенные отклонения от чисто синусоидальной формы волны, что способствует распознаванию и определению типа возмущения, генерируемого ветровым генератором. 

Ключові слова


несбалансированный поток нагрузки; вейвлет-преобразование (WT); машины опорных векторов (SVM); нарушение качества электроэнергии; энергия вейвлета

Повний текст:

PDF ENG (English)

Посилання


Khokhar S., Mohd Zin A.A., Memon A.P., Mokhtar A.S. A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network. Measurement, 2017, vol.95, pp. 246-259. doi: 10.1016/j.measurement.2016.10.013.

Roosta A., Eskandari H.-R., Khooban M.-H. Optimization of radial unbalanced distribution networks in the presence of distribution generation units by network reconfiguration using harmony search algorithm. Neural Computing and Applications, 2018, vol.31, no.11, pp. 7095-7109. doi: 10.1007/s00521-018-3507-0.

Škrbić B., Mikulović J., Šekara T. Extension of the CPC power theory to four-wire power systems with non-sinusoidal and unbalanced voltages. International Journal of Electrical Power & Energy Systems, 2019, vol.105, pp. 341-350. doi: 10.1016/j.ijepes.2018.08.032.

Emiroglu S., Uyaroglu Y., Ozdemir G. Distributed reactive power control based conservation voltage reduction in active distribution systems. Advances in Electrical and Computer Engineering, 2017, vol.17, no.4, pp. 99-106. doi: 10.4316/aece.2017.04012.

Ribeiro P.F Wavelet transform: an advanced tool for analyzing non-stationary harmonic distortion in power system. Proceedings of the IEEE International Conference on Harmonics in Power Systems, Bologna, Italy, September 21-24, 1994.

Robertson D., Camps O., Mayer J. Wavelets and power system transients: feature detection and classification. Proceedings of SPIE international symposium on optical engineering in aerospace sensing, Orlando, FL, USA, April 5-8, 1994, vol.2242, pp. 474-487.

De Yong D., Bhowmik S., Magnago F. An effective power quality classifier using wavelet transform and support vector machines. Expert Systems with Applications, 2015, vol.42, no.15-16, pp. 6075-6081. doi: 10.1016/j.eswa.2015.04.002.

Abdelsalam A.A., Eldesouky A.A., Sallam A.A. Classification of power system disturbances using linear Kalman filter and fuzzy-expert system. International Journal of Electrical Power & Energy Systems, 2012, vol.43, no.1, pp. 688-695. doi: 10.1016/j.ijepes.2012.05.052.

Kanirajan P., Kumar V.S. Wavelet-based power quality disturbances detection and classification using RBFNN and fuzzy logic. International Journal of Fuzzy Systems, 2015, vol.17, iss.4, pp 623-634. doi: 10.1007/s40815-015-0045-0.

Reaz M.B.I., Choong F., Sulaiman M.S., Mohd-Yasin F., Kamada M. Expert system for power quality disturbance classifier. IEEE Transactions on Power Delivery, 2007, vol.22, no.3, pp. 1979-1988. doi: 10.1109/tpwrd.2007.899774.

Guo-Sheng Hu, Jing Xie, Feng-Feng Zhu. Classification of power quality disturbances using wavelet and fuzzy support vector machines. 2005 International Conference on Machine Learning and Cybernetics, 2005. doi: 10.1109/icmlc.2005.1527633.

Upadhyaya S., Mohanty S. Localization and classification of power quality disturbances using maximal overlap discrete wavelet transform and data mining based classifiers. IFAC-PapersOnLine, 2016, vol.49, iss.1, pp 437-442. doi: 10.1016/j.ifacol.2016.03.093.

Manimala K., Selvi K. Power disturbances classification using S-transform based GA–PNN. Journal of The Institution of Engineers (India): Series B, 2015, vol.96, iss.3, pp 283-295. doi: 10.1007/s40031-014-0144-6.

Li J., Teng Z., Tang Q., Song J. Detection and classification of power quality disturbances using double resolution S-transform and DAG-SVMs. IEEE Transactions on Instrumentation and Measurement, 2016, vol.65, no.10, pp. 2302-2312. doi: 10.1109/tim.2016.2578518.

Ahila R., Sadasivam V., Manimala K. An integrated PSO for parameter determination and feature selection of ELM and its application in classification of power system disturbances. Applied Soft Computing, 2015, vol.32, pp 23-37. doi: 10.1016/j.asoc.2015.03.036.

Bosnic J.A., Petrovic G., Putnik A., Mostarac P. Power quality disturbance classification based on wavelet transform and support vector machine. 2017 11th International Conference on Measurement, May 2017. doi: 10.23919/measurement.2017.7983524.

Bhavani R., Prabha N.R. A hybrid classifier for power quality (PQ) problems using wavelets packet transform (WPT) and artificial neural networks (ANN). 2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), Mar. 2017. doi: 10.1109/itcosp.2017.8303073.

Available at: https://www.mathworks.com/matlabcentral/fileexchange/45936-ieee-9-bus?s_tid=srchtitle (accessed 13 May 2018).

Muhammad Zaid M., Malik M.U., Bhatti M.S., Razzaq H., Aslam M.U. Detection and classification of short and long duration disturbances in power system. Journal of Electrical Systems, 2017, vol.13, iss.4, pp 779-789.

Ekici S. Classification of power system disturbances using support vector machines. Expert Systems with Applications, 2009, vol.36, iss.6, pp 9859-9868. doi: 10.1016/j.eswa.2009.02.002.

Escalera S., Pujol O., Radeva P. On the decoding process in ternary error-correcting output codes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, vol.32, no.1, pp. 120-134. doi: 10.1109/tpami.2008.266.

Biswal M., Dash P.K. Measurement and classification of simultaneous power signal patterns with an S-transform variant and fuzzy decision tree. IEEE Transactions on Industrial Informatics, 2013, vol.9, no.4, pp. 1819-1827. doi: 10.1109/tii.2012.2210230.


Пристатейна бібліографія ГОСТ


  1. Khokhar S., Mohd Zin A.A., Memon A.P., Mokhtar A.S. A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network. Measurement, 2017, vol.95, pp. 246-259. doi: 10.1016/j.measurement.2016.10.013.
  2. Roosta A., Eskandari H.-R., Khooban M.-H. Optimization of radial unbalanced distribution networks in the presence of distribution generation units by network reconfiguration using harmony search algorithm. Neural Computing and Applications, 2018, vol.31, no.11, pp. 7095-7109. doi: 10.1007/s00521-018-3507-0.
  3. Škrbić B., Mikulović J., Šekara T. Extension of the CPC power theory to four-wire power systems with non-sinusoidal and unbalanced voltages. International Journal of Electrical Power & Energy Systems, 2019, vol.105, pp. 341-350. doi: 10.1016/j.ijepes.2018.08.032.
  4. Emiroglu S., Uyaroglu Y., Ozdemir G. Distributed reactive power control based conservation voltage reduction in active distribution systems. Advances in Electrical and Computer Engineering, 2017, vol.17, no.4, pp. 99-106. doi: 10.4316/aece.2017.04012.
  5. Ribeiro P.F Wavelet transform: an advanced tool for analyzing non-stationary harmonic distortion in power system. Proceedings of the IEEE International Conference on Harmonics in Power Systems, Bologna, Italy, September 21-24, 1994.
  6. Robertson D., Camps O., Mayer J. Wavelets and power system transients: feature detection and classification. Proceedings of SPIE international symposium on optical engineering in aerospace sensing, Orlando, FL, USA, April 5-8, 1994, vol.2242, pp. 474-487.
  7. De Yong D., Bhowmik S., Magnago F. An effective power quality classifier using wavelet transform and support vector machines. Expert Systems with Applications, 2015, vol.42, no.15-16, pp. 6075-6081. doi: 10.1016/j.eswa.2015.04.002.
  8. Abdelsalam A.A., Eldesouky A.A., Sallam A.A. Classification of power system disturbances using linear Kalman filter and fuzzy-expert system. International Journal of Electrical Power & Energy Systems, 2012, vol.43, no.1, pp. 688-695. doi: 10.1016/j.ijepes.2012.05.052.
  9. Kanirajan P., Kumar V.S. Wavelet-based power quality disturbances detection and classification using RBFNN and fuzzy logic. International Journal of Fuzzy Systems, 2015, vol.17, iss.4, pp 623-634. doi: 10.1007/s40815-015-0045-0.
  10. Reaz M.B.I., Choong F., Sulaiman M.S., Mohd-Yasin F., Kamada M. Expert system for power quality disturbance classifier. IEEE Transactions on Power Delivery, 2007, vol.22, no.3, pp. 1979-1988. doi: 10.1109/tpwrd.2007.899774.
  11. Guo-Sheng Hu, Jing Xie, Feng-Feng Zhu. Classification of power quality disturbances using wavelet and fuzzy support vector machines. 2005 International Conference on Machine Learning and Cybernetics, 2005. doi: 10.1109/icmlc.2005.1527633.
  12. Upadhyaya S., Mohanty S. Localization and classification of power quality disturbances using maximal overlap discrete wavelet transform and data mining based classifiers. IFAC-PapersOnLine, 2016, vol.49, iss.1, pp 437-442. doi: 10.1016/j.ifacol.2016.03.093.
  13. Manimala K., Selvi K. Power disturbances classification using S-transform based GA–PNN. Journal of The Institution of Engineers (India): Series B, 2015, vol.96, iss.3, pp 283-295. doi: 10.1007/s40031-014-0144-6.
  14. Li J., Teng Z., Tang Q., Song J. Detection and classification of power quality disturbances using double resolution S-transform and DAG-SVMs. IEEE Transactions on Instrumentation and Measurement, 2016, vol.65, no.10, pp. 2302-2312. doi: 10.1109/tim.2016.2578518.
  15. Ahila R., Sadasivam V., Manimala K. An integrated PSO for parameter determination and feature selection of ELM and its application in classification of power system disturbances. Applied Soft Computing, 2015, vol.32, pp 23-37. doi: 10.1016/j.asoc.2015.03.036.
  16. Bosnic J.A., Petrovic G., Putnik A., Mostarac P. Power quality disturbance classification based on wavelet transform and support vector machine. 2017 11th International Conference on Measurement, May 2017. doi: 10.23919/measurement.2017.7983524.
  17. Bhavani R., Prabha N.R. A hybrid classifier for power quality (PQ) problems using wavelets packet transform (WPT) and artificial neural networks (ANN). 2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), Mar. 2017. doi: 10.1109/itcosp.2017.8303073.
  18. Available at: https://www.mathworks.com/matlabcentral/fileexchange/45936-ieee-9-bus?s_tid=srchtitle (accessed 13 May 2018).
  19. Muhammad Zaid M., Malik M.U., Bhatti M.S., Razzaq H., Aslam M.U. Detection and classification of short and long duration disturbances in power system. Journal of Electrical Systems, 2017, vol.13, iss.4, pp 779-789.
  20. Ekici S. Classification of power system disturbances using support vector machines. Expert Systems with Applications, 2009, vol.36, iss.6, pp 9859-9868. doi: 10.1016/j.eswa.2009.02.002.
  21. Escalera S., Pujol O., Radeva P. On the decoding process in ternary error-correcting output codes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, vol.32, no.1, pp. 120-134. doi: 10.1109/tpami.2008.266.
  22. Biswal M., Dash P.K. Measurement and classification of simultaneous power signal patterns with an S-transform variant and fuzzy decision tree. IEEE Transactions on Industrial Informatics, 2013, vol.9, no.4, pp. 1819-1827. doi: 10.1109/tii.2012.2210230.

 

 

 

 





Copyright (c) 2019 Ala eddine Rahmani, Linda Slimani, Tarek Bouktir


This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

ISSN 2074–272X (Print)
ІSSN 2309–3404 (Online)