Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Call For Paper : Vol 11, Issue 08, August 2024

Call for Paper

Vol 11, Issue 08, August 2024

Indexing

Power Quality Event Classification using Machine Learning Techniques

Author : Challa Sairam Reddy 1 Divya R 2 Dr. Manjula G Nair 3

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Date of Publication :24th February 2021

Abstract: The rapid transformation of the conventional electric power grid to the sophisticated smart grid involves many challenging factors like power quality and reliability issues. Smart grid is nothing but introduction of information and communication technology to the present existing grid, so the process of realizing smart grids involves various kinds of devices which results in degradation of power quality. As power quality is the promising factor for utilities as well as the end users for proper functioning of equipment, it should be maintained high and with in specified limits. Thus detection and classification of different power quality events and their causes must be known prior to take appropriate mitigating actions. The task of classification involves storing of huge amount of data for analysis which became feasible with increased databases. The power quality events like sag, swell, interruption and unbalance are classified with data mining algorithms like SVM, KNN, Random forest with supervised machine learning techniques using python software.

Reference :

    1. Power Quality Standards IEC 61000-4-30ANN Based Solar Power Forecasting in a Smart Microgrid System for Power Flow Management; 2019 Innovations in Power and Advanced Computing Technologies (i-PACT).
    2. D. R. Nair, Devi S, M. G. Nair and K. Ilango, ”Tariff based fuzzy logic controller for active power sharing between microgrid to grid with improved power quality,” 2016 International Conference on Energy Efficient Technologies for Sustainability (ICEETS), Nagercoil, pp. 406- 409, 2016.
    3. Hardware implementation of power sharing and power quality improvement for grid integration of microgrid; 2017 International Conference on Technological Advancements in Power and Energy ( TAP Energy).
    4. Lekshmi R. Chandran, Karuppasamy, D. Ilango, and Dr. Manjula G. Nair, “Voltage quality enhancement in distribution systems using dynamic voltage restorer with adaptive fuzzy pi controller”, vol. 8, pp. 3036-3041, 2019.
    5. Power quality improvement using STATCOM with renewable energy sources, K Ilango, A Bhargav, A Trivikram, PS Kavya, G Mounika, MG Nair 2012 IEEE 5th India International Conference on Power Electronics (IICPE).
    6. Fault Classification and Diagnosis of Industrial Application Motor Drives using Soft Computing Techniques, GS Ayyappan, K Venugopal, RM Raja, R Poonthalir, K Ilango; 2019 4th International Conference on Recent Trends on Electronics.
    7. Vinit Kumar, Manbir Kaur,“Power Quality Event Generation in MATLAB/Simulink Environment,”International Journal of Advanced Research in Engineering and Applied Sciences.
    8. MATLAB/Simulink Based Modeling and Simulation of Power Quality Disturbances,”978- 1-4799-4848-2/14/$31.00 ©2014 IEEE”
    9. Dr.A jaya Laxmi,S Asha Kiranmai “DATA EXTRACTION FOR CLASSIFICATION AND CHARACTERIZATION OF POWER QUALITY PROBLEMS,” International Journal of Advanced Research in Engineering and Applied Sciences .
    10. Abdullah Asuhaimi Mohd zin,”MATLAB/Simulink Based Modeling and Simulation of Power Quality Disturbances”, IEEE Conference paper.
    11. Pandit, N., & Chakrasali, R. L. (2017). Power quality issues in Indian power distribution utilities and feasible solutions. International Journal of Scientific & Technology Research, 6(5), 23–25.
    12. Sharmila, M., Sundarabalan, C. K., & Selvi, K. (2017). Power quality disturbances classification using data mining technique. International Journal of Research in Science & Engineering, 3(3), 77–90.
    13. Akinola, S., & Oyabugbe, O. (2015). Accuracies and training times of data mining classification algorithms: An empirical comparative study. Journal of Software Engineering and Applications, 8, 470–477
    14. Upadhyaya, S., & Mohanty, S. (2016). Localization and classification of power quality disturbances using maximal overlap discrete wavelet transform and data mining based classifiers. IFAC-Papers OnLine, 49(1), 437– 442.
    15. Jeya Sheela, Y., & Krishnaveni, S. H. (2017). A comparative analysis of various classification trees (pp. 1346–1352). Kollam: IEEE International Conference on Circuit, Power and Computing Technologies (ICCPCT).
    16. Pandey, P., & Prabhakar, R. (2016). An analysis of machine learning techniques (J48 & AdaBoost)-for classification (pp. 1–6). Delhi: IEEE 1st India International Conference on Information Processing (IICIP).
    17. Hamsagayathri, P., & Sampath, P. (2017). Performance analysis of breast cancer classification using decision tree classifiers. Int J Curr Pharm Res, 9(2), 19–25.
    18. Suresh, K., & Chandrashekhar, T. (2012). Characteristics analysis of voltage sag in distribution system using RMS voltage method. ACEEE Int J on Electrical and Power Engineering, 3(1), 55–61.
    19. Soman, K. P., Diwakar, S., & Ajay, V. (2006). Data mining: Theory and practice. Delhi: PHI Learning Pvt. Ltd..
    20. Dinakaran, S., & Thangaiah, D. R. J. (2013). Role of attribute selection in classification algorithms. International Journal of Scientific & Engineering Research, 4(6), 67–71.
    21. Udemy : Data Analytics using Python

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