Analysing the Effect of Metaheuristic Based Feature Selection Algorithms on the Classification of the Power Quality Events

Year 2024, Volume: 16 Issue: 2, 646 - 658, 30.06.2024

Birsen Gümüş Melih Çoban Suleyman Sungur Tezcan

https://doi.org/10.29137/umagd.1423997

Abstract

In this study, two different optimization algorithms have been used for the feature selection stage, which plays a crucial role in the classification of power quality disturbances. In the first part of the study, signals containing power quality events were generated to initiate the classification process. Discrete wavelet transform (DWT) has been used for feature extraction. Two different datasets were obtained by normalizing and taking logarithm of the dataset obtained after the feature extraction process. The Equilibrium Optimizer (EO) and the Salp Swarm Optimization Algorithm (SSA), which are named metaheuristic based feature selection algorithms, were used for the feature selection process. The K Nearest Neighbour Algorithm (KNN) is preferred for classification. The highest accuracy rate in classification was achieved at 96.05% when utilizing EO as the feature selection algorithm and using the logarithmic dataset. The lowest classification accuracy rate was obtained as 90.62% when the feature selection algorithm was SSA and the normalized data set was used. In the second part of the study, a histogram graph was created to identify the most frequently selected features from the first part. The highest accuracy rate obtained for the classification using the histogram graph was observed to be 95.8% and the lowest accuracy rate was 93.83%.

Keywords

Power quality events, Classification, Machine Learning

Metasezgisel Tabanlı Özellik Seçim Algoritmalarının Güç Kalitesi Olaylarının Sınıflandırılmasına Etkisinin İncelenmesi

Abstract

Bu çalışmada, güç kalitesi bozulmalarının sınıflandırmasında önemli bir role sahip olan özellik seçme aşaması için iki farklı optimizasyon algoritması kullanılmıştır. Çalışmanın birinci kısmında, sınıflandırma sürecinin başlaması için güç kalitesi olaylarını içeren sinyaller üretilmiştir. Özellik çıkarma için Ayrık Dalgacık Dönüşümü (DWT) kullanılmıştır. Özellik çıkarma işleminden sonra elde edilen veri seti, normalize edilerek ve logaritması alınarak iki farklı veri seti elde edilmiştir Özellik seçme işlemi için Denge Optimize Edici Algoritması (EO) ve Salp Sürü Optimizasyon Algoritması (SSA) olarak isimlendirilen metasezgisel tabanlı optimizasyon algoritmaları özellik seçme algoritmaları olarak kullanılmıştır. Sınıflandırma için K En Yakın Komşu Algoritması (KNN) tercih edilmiştir. En yüksek sınıflandırma doğruluk oranı, özellik seçme algoritması olarak EO ve veri seti olarak logaritmik veri setinin kullanıldığı durumda, %96.05 olarak elde edilmiştir. En düşük sınıflandırma doğruluk oranı ise özellik seçme algoritmasının SSA olduğu ve normalize veri setinin kullanıldığı durumda, % 90.62 olarak elde edilmiştir. Çalışmanın ikinci kısmında ise, birinci kısımda en çok seçilen özellikleri tespit etmek için histogram grafiği oluşturulmuştur. En çok seçilen sekiz özellik ile sınıflandırma işlemi tekrarlanmıştır. Histogram grafiği kullanılarak yapılan sınıflandırmaya ilişkin elde edilen en yüksek doğruluk oranı % 95.8 ve en düşük doğruluk oranı % 93.83 olarak gözlenmiştir.

Keywords

Güç Kalitesi olayları, Sınıflandırma, Makine Öğrenmesi

References

• Abdoos, A. A., Mianaei, P. K., & Ghadikolaei, M. R. (2016). Combined VMD-SVM based feature selection method for classification of power quality events. Applied Soft Computing, 38, 637–646.
• Afroni, M. J., Sutanto, D., & Stirling, D. (2013). Analysis of nonstationary power-quality waveforms using iterative Hilbert Huang transform and SAX algorithm. IEEE Transactions on Power Delivery, 28(10), 2134–2144.
• Akmaz, D. (2022). Recognition of Power Quality Events Using Wavelet Transform, K-Nearest Neighbor Algorithm, and Gain Ratio Feature Selection Method. International Journal of Innovative Engineering Applications, 6(1).
• Balouji, E., Gu, I.Y.H., Bollen, M.H.J., Bagheri, A., & Nazari, M. (2018). A LSTM-based deep learning method with application to voltage dip classification. In: 2018 18th International Conference on Harmonics and Quality of Power (ICHQP), Ljubljana, pp. 1–5. Bih, J. (2006). Paradigm shift - an introduction to fuzzy logic. IEEE Potentials, 25(1), 6–21.
• Biswal, T., & Parida, S.K. (2022). A novel high impedance fault detection in the micro-grid system by the summation of accumulated difference of residual voltage method and fault event classification using discrete wavelet transforms and a decision tree approach. Electric Power Systems Research, 209, 108042.
• Coban, M., Sungur, S. T., & Tezcan, T. (2021). Detection and classification of short-circuit faults on a transmission line using current signal. Power Systems and Power Electronics, Bulletin of the Polish Academy of Sciences, Technical Sciences, 69(4), e137630. DOI: 10.24425/bpasts.2021.137630
• Darrow, K., Hedman, B., Bourgeois, T. & Rosenblum, D.,(2005). The Role of Distributed Generation in Power Quality and Reliability
• Dash, P. K., Panigrahi, B. K., & Panda, G. (2003). Power quality analysis using S-transform. IEEE Transactions on Power Delivery, 18(2), 406–411.
• Edomah, N. (2010). Economic Implications of Poor Power Quality. Journal of Energy and Power Engineering, 4(1), 26. ISSN 1934-8975.
• Erişti, H., Yıldırım, Ö., Eristi, B., & Demir, Y. (2013). Optimal feature selection for classification of power quality events using wavelet transform and least squares support vector machines. Electrical Power and Energy Systems, 49, 95–103.
• Faramarzi, A., Heidarinejad, M., Stephens, B., & Mirjalili, S. (2020). Equilibrium optimizer: A novel optimization algorithm. Knowledge-Based Systems, 191, 105190.
• Fikri, M., & El-Sayed, M. A. H. (1988). New algorithm for distance protection of high voltage transmission lines. IEE Proceedings C Generation Transmission Distribution, 135-C(5), 436–440.
• He, S., Li, K., & Zhang, M. (2013). A real-time power quality disturbances classification using hybrid method based on S-Transform and dynamics. IEEE Transactions on Instrumentation and Measurement, 62(9), 2465–2475.
• Hegazy, A. E., Makhlouf, M. A., & El-Tawel, Gh. S. (2020). Improved salp swarm algorithm for feature selection. Journal of King Saud University – Computer and Information Sciences, 32, 335–344.
• Hu, L.-Y., Huang, M.-W., Ke, S.-W., & Tsai, C.-F. (2016). The distance function effect on k-nearest neighbor classification for medical datasets. Springerplus, 5(1), 1304.
• Kapoor, R., Kumar, R., & Tripathi, M. M. (2018). Volterra bound interval type-2 fuzzy logic-based approach for multiple power quality events analysis. IET Electrical Systems in Transportation, 8(3), 188–196.
• Karimi, M., Mokhtari, H., & Iravani, M. R. (2000). Wavelet based on-line disturbance detection for power quality applications. IEEE Transactions on Power Delivery, 15(4), 1212–1220.
• Khetarpal, P., & Tripathi, M. M. (2020). A critical and comprehensive review on power quality disturbance detection and classification. Sustainable Computing: Informatics and Systems, 28, 100417
• Khokhar, S., Mohd Zin, A. A., Memon, A. P., & 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.
• Lee, I. W. C., & Dash, P. K. (2003). S-transform-based intelligent system for classification of power quality disturbance signals. IEEE Transactions on Industrial Electronics, 50(8), 800–805.
• Mafarja, M., & Mirjalili, S. (2018). Whale optimization approaches for wrapper feature selection. Applied Soft Computing, 62, 441–453.
• Mallat, S. G. (1989). A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(7).
• Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163–191.
• Moravej, Z., Abdoos, A. A., & Pazoki, M. (2009). Detection and classification of power quality disturbances using wavelet transform and support vector machines. Electric Power Components and Systems, 38(2), 182–196.
• Nashad, N. R., Islam, M. J., Alam, S., Rahat, R. M., Begum, M. T. A., & Alam, M. R. (2017). A Simplistic Mathematical Approach for Detection and Classification of Power Quality Events. International Conference on Electrical, Computer and Communication Engineering (ECCE), 16-18 Şubat 2017, Cox’s Bazar, Bangladesh.
• Salat, R., & Osowski, S. (2004). Accurate fault location in the power transmission line using support vector machine approach. IEEE Transactions on Power Systems, 19(2), 979–986. Saunders, C., Grobelnik, M., Gunn, S., & Shawe-Taylor, J. (Eds.). (2005). Subspace, Latent Structure and Feature Selection: Statistical and Optimization Perspectives - Workshop, SLSFS 2005, Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers.
• Sharma, A., Rajpurohit, B. S., & Singh, S. N. (2018). A review on economics of power quality: Impact, assessment and mitigation. Renewable and Sustainable Energy Reviews, 88, 363–372.
• Too, J., & Mirjalili, S. (2021). General Learning Equilibrium Optimizer: A New Feature Selection Method for Biological Data Classification. Applied Artificial Intelligence, 35(3), 247–263.
• Tse, N. C. F., Chan, J. Y. C., Lau, W., & Lai, L. L. (2012). Hybrid wavelet and Hilbert transform with frequency-shifting decomposition for power quality analysis. IEEE Transactions on Instrumentation and Measurement, 61(12), 3225–3233.
• Upadhya, M., Singh, A. K., Thakur, P., Nagata, E. A., & Ferreira, D. D. (2022). Mother wavelet selection method for voltage sag characterization and detection. Electric Power Systems Research, 211, 108246.
• Uyar, M. (2008). Güç Kalitesi Bozulma Türlerinin Akıllı Örüntü Tanıma Yaklaşımları ile Belirlenmesi (Doktora tezi). Fırat Üniversitesi, Fen Bilimleri Enstitüsü.
• Valtierra-Rodriguez, M., Romero-Troncoso, R. de J., Osornio-Rios, R. A., & Garcia-Perez, A. (2014). Detection and classification of single and combined power quality disturbances using neural networks. IEEE Transactions on Industrial Electronics, 61(5), 2473–2482.
• Valtierra-Rodriguez, M., Romero-Troncoso, R. de J., Osornio-Rios, R. A., & Garcia-Perez, A. (2014). Detection and Classification of Single and Combined Power Quality Disturbances Using Neural Networks. IEEE Transactions on Industrial Electronics, 61(5), 2473–2482.
• Zhang, S., Zong, M., Sun, K., Liu, Y., & Cheng, D. (2014). Efficient kNN Algorithm Based on Graph Sparse Reconstruction. In International Conference on Advanced Data Mining and Applications (ADMA 2014) (ss. 356–369).