Research Article
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Classification of Single and Combined Power Quality Disturbances Using Stockwell Transform, ReliefF Feature Selection Method and Multilayer Perceptron Algorithm

Year 2022, , 13 - 23, 30.06.2022
https://doi.org/10.46572/naturengs.1033182

Abstract

: In this study, a method based on Stockwell transform (ST), ReliefF feature selection method and Multilayer Perceptron Algorithm (MPA) algorithm was developed for classification of Power Quality (PQ) disturbance signals. In the method, firstly, ST was applied to different PQ signals to obtain classification features. A total of 30 different classification features were obtained by taking different entropy values of the matrix obtained after ST and different entropy values of the PQ signals. The use of all of the classification features obtained causes the method to be complicated and the training/testing times to be prolonged. Therefore, so as to determine the effective ones among the classification features and to ensure high classification success with less classification features, ReliefF feature selection method was used in this study. PQ disturbances were classified by using 8 different classification features determined by ReliefF feature selection method and MPA. The simulation results show that the method provides a high classification success in a shorter training/testing time. At the same time, simulation results have shown that the method was successful on testing data with noise levels of 35 dB and above after only one training.

References

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  • [2]. Choong, F., Reaz, M. B. I., and Mohd-Yasin, F. (2005). Advances in signal processing and artificial intelligence technologies in the classification of power quality events: a survey. Electric Power Components and Systems, 33(12), 1333-1349.
  • [3]. Khokhar, S., Zin, A. A. B. M., Mokhtar, A. S. B., and Pesaran, M. (2015). A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances. Renewable and Sustainable Energy Reviews, 51, 1650-1663.
  • [4]. Mishra, M. (2019). Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review. International transactions on electrical energy systems, 29(8), e12008.
  • [5]. Uyar, M., Yildirim, S., and Gencoglu, M. T. (2008). An effective wavelet-based feature extraction method for classification of power quality disturbance signals. Electric power systems Research, 78(10), 1747-1755.
  • [6]. Erişti, H., and Demir, Y. (2010). A new algorithm for automatic classification of power quality events based on wavelet transform and SVM. Expert systems with applications, 37(6), 4094-4102.
  • [7]. Meher, S. K., and Pradhan, A. K. (2010). Fuzzy classifiers for power quality events analysis. Electric power systems Research, 80(1), 71-76.
  • [8]. Khokhar, S., Zin, A. A. M., Memon, A. P., and Mokhtar, A. S. (2017). A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network. Measurement, 95, 246-259.
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  • [11]. Cho, S. H., Jang, G., & Kwon, S. H. (2009). Time-frequency analysis of power-quality disturbances via the Gabor–Wigner transform. IEEE transactions on power delivery, 25(1), 494-499.
  • [12]. Kumar, R., Singh, B., and Shahani, D. T. (2015). Recognition of single-stage and multiple power quality events using Hilbert–Huang transform and probabilistic neural network. Electric Power Components and Systems, 43(6), 607-619.
  • [13]. Achlerkar, P. D., Samantaray, S. R., and Manikandan, M. S. (2016). Variational mode decomposition and decision tree based detection and classification of power quality disturbances in grid-connected distributed generation system. IEEE Transactions on Smart Grid, 9(4), 3122-3132.
  • [14]. Lee, I. W., and Dash, P. K. (2003). S-transform-based intelligent system for classification of power quality disturbance signals. IEEE Transactions on Industrial Electronics, 50(4), 800-805.
  • [15]. Uyar, M., Yildirim, S., and Gencoglu, M. T. (2009). An expert system based on S-transform and neural network for automatic classification of power quality disturbances. Expert Systems with Applications, 36(3), 5962-5975.
  • [16]. Bhende, C. N., S. Mishra, and B. K. Panigrahi. "Detection and classification of power quality disturbances using S-transform and modular neural network." Electric power systems research 78.1 (2008): 122-128.
  • [17]. Behera, H. S., Dash, P. K., and Biswal, B. J. A. S. C. (2010). Power quality time series data mining using S-transform and fuzzy expert system. Applied Soft Computing, 10(3), 945-955.
  • [18]. Dash, P. K., Panigrahi, B. K., and Panda, G. (2003). Power quality analysis using S-transform. IEEE transactions on power delivery, 18(2), 406-411.
  • [19]. Huang, N., Xu, D., Liu, X., and Lin, L. (2012). Power quality disturbances classification based on S-transform and probabilistic neural network. Neurocomputing, 98, 12-23.
  • [20]. Hajian, M., Foroud, A. A., & Abdoos, A. A. (2014). New automated power quality recognition system for online/offline monitoring. Neurocomputing, 128, 389-406.
  • [21].Valencia-Duque, A. F., Meza, A. Á., and Orozco-Gutiérrez, A. A. (2019). Automatic identification of power quality events using a machine learning approach. Scientia Et Technica, 24(2), 183-189.
  • [22]. Karasu, S., and Saraç, Z. (2017, May). Classification of power quality disturbances with S-transform and artificial neural networks method. In 2017 25th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
  • [23]. Palma-Mendoza, R. J., Rodriguez, D., and De-Marcos, L. (2018). Distributed ReliefF-based feature selection in Spark. Knowledge and Information Systems, 57(1), 1-20.
  • [24]. Parlar, T. (2021). A heuristic approach with artificial neural network for Parkinson’s disease. International Journal of Applied Mathematics Electronics and Computers, 9(1), 1-6.
  • [25]. Morariu, D., Crețulescu, R., and Breazu, M., The weka multilayer perceptron classifier, International Journal of Advanced Statistics and IT&C for Economics and Life Sciences, 7(1). 2018.
  • [26]. Area, S., and Mesra, R., Analysis of Bayes, neural network and tree classifier of classification technique in data mining using WEKA, 2012.
  • [27]. Moravej, Z., Abdoos, A. A., and Pazoki, M. J. E. P. C. (2009). Detection and classification of power quality disturbances using wavelet transform and support vector machines. Electric Power Components and Systems, 38(2), 182-196.
  • [28]. Vinayagam, A., Veerasamy, V., Radhakrishnan, P., Sepperumal, M., and Ramaiyan, K. (2021). An ensemble approach of classification model for detection and classification of power quality disturbances in PV integrated microgrid network. Applied Soft Computing, 106, 107294.
  • [29]. Kiranmai, S. A., and 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.
Year 2022, , 13 - 23, 30.06.2022
https://doi.org/10.46572/naturengs.1033182

Abstract

References

  • [1]. Ahsan, M. K., Pan, T., and Li, Z. (2018). A three decades of marvellous significant review of power quality events regarding detection & classification. Journal of Power and Energy Engineering, 6(8), 1-37.
  • [2]. Choong, F., Reaz, M. B. I., and Mohd-Yasin, F. (2005). Advances in signal processing and artificial intelligence technologies in the classification of power quality events: a survey. Electric Power Components and Systems, 33(12), 1333-1349.
  • [3]. Khokhar, S., Zin, A. A. B. M., Mokhtar, A. S. B., and Pesaran, M. (2015). A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances. Renewable and Sustainable Energy Reviews, 51, 1650-1663.
  • [4]. Mishra, M. (2019). Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review. International transactions on electrical energy systems, 29(8), e12008.
  • [5]. Uyar, M., Yildirim, S., and Gencoglu, M. T. (2008). An effective wavelet-based feature extraction method for classification of power quality disturbance signals. Electric power systems Research, 78(10), 1747-1755.
  • [6]. Erişti, H., and Demir, Y. (2010). A new algorithm for automatic classification of power quality events based on wavelet transform and SVM. Expert systems with applications, 37(6), 4094-4102.
  • [7]. Meher, S. K., and Pradhan, A. K. (2010). Fuzzy classifiers for power quality events analysis. Electric power systems Research, 80(1), 71-76.
  • [8]. Khokhar, S., Zin, A. A. M., Memon, A. P., and Mokhtar, A. S. (2017). A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network. Measurement, 95, 246-259.
  • [9]. Panigrahi, B. K., and Pandi, V. R. (2009). Optimal feature selection for classification of power quality disturbances using wavelet packet-based fuzzy k-nearest neighbour algorithm. IET generation, transmission & distribution, 3(3), 296-306.
  • [10]. Manimala, K., Selvi, K., and Ahila, R. (2012). Optimization techniques for improving power quality data mining using wavelet packet based support vector machine. Neurocomputing, 77(1), 36-47.
  • [11]. Cho, S. H., Jang, G., & Kwon, S. H. (2009). Time-frequency analysis of power-quality disturbances via the Gabor–Wigner transform. IEEE transactions on power delivery, 25(1), 494-499.
  • [12]. Kumar, R., Singh, B., and Shahani, D. T. (2015). Recognition of single-stage and multiple power quality events using Hilbert–Huang transform and probabilistic neural network. Electric Power Components and Systems, 43(6), 607-619.
  • [13]. Achlerkar, P. D., Samantaray, S. R., and Manikandan, M. S. (2016). Variational mode decomposition and decision tree based detection and classification of power quality disturbances in grid-connected distributed generation system. IEEE Transactions on Smart Grid, 9(4), 3122-3132.
  • [14]. Lee, I. W., and Dash, P. K. (2003). S-transform-based intelligent system for classification of power quality disturbance signals. IEEE Transactions on Industrial Electronics, 50(4), 800-805.
  • [15]. Uyar, M., Yildirim, S., and Gencoglu, M. T. (2009). An expert system based on S-transform and neural network for automatic classification of power quality disturbances. Expert Systems with Applications, 36(3), 5962-5975.
  • [16]. Bhende, C. N., S. Mishra, and B. K. Panigrahi. "Detection and classification of power quality disturbances using S-transform and modular neural network." Electric power systems research 78.1 (2008): 122-128.
  • [17]. Behera, H. S., Dash, P. K., and Biswal, B. J. A. S. C. (2010). Power quality time series data mining using S-transform and fuzzy expert system. Applied Soft Computing, 10(3), 945-955.
  • [18]. Dash, P. K., Panigrahi, B. K., and Panda, G. (2003). Power quality analysis using S-transform. IEEE transactions on power delivery, 18(2), 406-411.
  • [19]. Huang, N., Xu, D., Liu, X., and Lin, L. (2012). Power quality disturbances classification based on S-transform and probabilistic neural network. Neurocomputing, 98, 12-23.
  • [20]. Hajian, M., Foroud, A. A., & Abdoos, A. A. (2014). New automated power quality recognition system for online/offline monitoring. Neurocomputing, 128, 389-406.
  • [21].Valencia-Duque, A. F., Meza, A. Á., and Orozco-Gutiérrez, A. A. (2019). Automatic identification of power quality events using a machine learning approach. Scientia Et Technica, 24(2), 183-189.
  • [22]. Karasu, S., and Saraç, Z. (2017, May). Classification of power quality disturbances with S-transform and artificial neural networks method. In 2017 25th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
  • [23]. Palma-Mendoza, R. J., Rodriguez, D., and De-Marcos, L. (2018). Distributed ReliefF-based feature selection in Spark. Knowledge and Information Systems, 57(1), 1-20.
  • [24]. Parlar, T. (2021). A heuristic approach with artificial neural network for Parkinson’s disease. International Journal of Applied Mathematics Electronics and Computers, 9(1), 1-6.
  • [25]. Morariu, D., Crețulescu, R., and Breazu, M., The weka multilayer perceptron classifier, International Journal of Advanced Statistics and IT&C for Economics and Life Sciences, 7(1). 2018.
  • [26]. Area, S., and Mesra, R., Analysis of Bayes, neural network and tree classifier of classification technique in data mining using WEKA, 2012.
  • [27]. Moravej, Z., Abdoos, A. A., and Pazoki, M. J. E. P. C. (2009). Detection and classification of power quality disturbances using wavelet transform and support vector machines. Electric Power Components and Systems, 38(2), 182-196.
  • [28]. Vinayagam, A., Veerasamy, V., Radhakrishnan, P., Sepperumal, M., and Ramaiyan, K. (2021). An ensemble approach of classification model for detection and classification of power quality disturbances in PV integrated microgrid network. Applied Soft Computing, 106, 107294.
  • [29]. Kiranmai, S. A., and 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.
There are 29 citations in total.

Details

Primary Language English
Journal Section Research Articles
Authors

Düzgün Akmaz 0000-0002-4183-6424

Publication Date June 30, 2022
Submission Date December 6, 2021
Acceptance Date May 18, 2022
Published in Issue Year 2022

Cite

APA Akmaz, D. (2022). Classification of Single and Combined Power Quality Disturbances Using Stockwell Transform, ReliefF Feature Selection Method and Multilayer Perceptron Algorithm. NATURENGS, 3(1), 13-23. https://doi.org/10.46572/naturengs.1033182