ICTACT Journal on Microelectronics

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Detection And Classification Of Complex Power Quality Disturbances Using Discrete Wavelet Transform And Rule Based Decision Tree


Affiliations
1 Department of Electronics and Communication Engineering, Government Engineering College Jhalawar, India
2 Department of Electronics and Communication Engineering, Government Engineering College Baran, India
     

   Subscribe/Renew Journal


This paper presents a method for the detection and classification of complex power quality (PQ) disturbances using discrete wavelet transform (DWT) based ruled decision tree. The power quality disturbances are generated with the help of MATLAB using the mathematical relations as per IEEE Standard-1159. The investigated PQ disturbances include various combinations of voltage sag, voltage swell, momentary interruption, harmonics, oscillatory transient and impulsive transient. These power quality signals are decomposed using discrete wavelet transform with db4 as mother wavelet up to level 4 of decomposition. The detail coefficients and approximation coefficients are used for recognition of complex PQ disturbances. The features extracted from plots of these coefficients are given as input to the rulebased decision tree for classification of complex PQ disturbances. The effectiveness of proposed algorithm has been established by testing 30 data sets of each complex PQ disturbance obtained by varying the parameters

Keywords

Complex Power Quality Disturbance, Discrete Wavelet Transform, Power Quality, Rule-Based Decision Tree
Subscription Login to verify subscription
User
Notifications
Font Size

  • Harish K. Sahoo and P.K. Dash, “Robust Estimation of Power Quality Disturbances using Unscented H Filter”, International Electrical Power and Energy Systems, Vol. 73, pp. 438-447,2015.

  • P. Kanirajan and V. Suresh Kumar, “Power Quality Disturbance Detection and Classification using Wavelet and RBFNN”, Applied Soft Computing, Vol. 35, pp. 470-481, 2015.

  • Abdelazeem A. Abdelsalam, Azza A. Eldesouky and Abdelhay A. Sallam, “Classification of Power System Disturbances using Linear Kalman Filter and Fuzzy-Expert System”, International Journal of Electrical Power and Energy Systems, Vol. 43, pp. 688-695, 2015.

  • A. Rodriguez, J.A. Aguado, F. Martin, J.J. Lopez, F. Munoz and J.E. Ruiz, “Rule-Based Classification of Power Quality Disturbances using S-Transform”, Electric Power Systems Research, Vol. 86, pp. 113-121, 2012.

  • R. Hooshmand, and A. Enshaee, “Detection and Classification of Single and Combined Power Quality Disturbances using Fuzzy Systems Oriented by Particle Swarm Optimization Algorithm”, Electric Power Systems Research, Vol. 80, pp. 1552-1561, 2010.

  • N.K. Suyan, M. Kumar and F.L. Lohar, “Detection and Classification of Power Quality Disturbances Using Discrete Wavelet Transform and Rule Based Decision Tree”, ICTACT Journal on Microelectronics, Vol. 7, No. 2, pp.1141-1147, 2021.

  • Suhail Khokhar, Abdullah Asuhaimi B. Mohd Zin, Ahmad Safawi B. Mokhtar and Mahmoud Pesaran, “A Comprehensive Overview on Signal Processing and Artificial Intelligence Techniques Applications in Classification of Power Quality Disturbances”, Renewable and Sustainable Energy Reviews, Vol. 51, pp. 1650-1663, 2015.

  • H.S. Behera, P.K. Dash and B. Biswal, “Power Quality Time Series Data Mining using S-Transform and Fuzzy Expert System”, Applied Soft Computing, Vol. 10, pp. 945955,2010.

  • Huseyin Eristi, Ozal Yildirim, Belkis Eristi and Yakup Demir, “Automatic Recognition System of Underlying causes of Power Quality Disturbances based on S-Transform and Extreme Learning Machine”, Electric Power Systems Research, Vol. 61, pp. 553-562, 2014.

  • Om Prakash Mahela and Abdul Gafoor Shaik, “Recognition of Power Quality Disturbances using S-transform and RuleBased Decision Tree”, Proceedings of IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems, pp. 1-6, 2016.

  • Om Prakash Mahela and Abdul Gafoor Shaik, “Recognition of Power Quality Disturbances using S-Transform and Fuzzy C-Means Clustering”, Proceedings of IEEE International Conference and Utility Exhibition on CoGeneration, Small Power Plants and District Energy, pp. 14-16, 2016.

  • B. Perunicic, M. Mallini and Z. Wang, “Power Quality Disturbance Detection and Classification using Wavelets and Artificial Neural Networks”, Proceedings of IEEE International Conference on Harmonics and Quality of Power, pp. 77-82, 1998.

  • Ying Yi Hong, and Cheng Wei Wang, “Switching Detection Classification using Discrete Wavelet Transform and SelfOrganizing Mapping Network”, IEEE Transactions on Power Delivery, Vol. 20, No. 2, pp. 1662-1668, 2005.

  • Om Prakash Mahela and Abdul Gafoor Shaik, “Power Quality Recognition in Distribution System with Solar Energy Penetration using S- Transform and Fuzzy C-Means Clustering”, Renewable Energy, Vol. 106, pp. 37-51,2017.

  • Wang Zhan, Zeng Xiangjun, Hu Xiaoxi and Hu Jingying, “The Multi-Disturbance Complex Power Quality Signal HHT Detection Technique”, Proceedings of IEEE International Conference on Innovative Smart Grid Technologies, pp. 1-5,2012.

  • R.H.G. Tan and V.K. Ramachandra Murthy, “Numerical Model Framework of Power Quality Events”, Numerical Journal of Scientific Research Research, Vol. 43, No. 1, pp. 30-47, 2010


Abstract Views: 152

PDF Views: 0




  • Detection And Classification Of Complex Power Quality Disturbances Using Discrete Wavelet Transform And Rule Based Decision Tree

Abstract Views: 152  |  PDF Views: 0

Authors

Nitin Kumar Suyan
Department of Electronics and Communication Engineering, Government Engineering College Jhalawar, India
Mahendra Kumar
Department of Electronics and Communication Engineering, Government Engineering College Baran, India
Fateh L. Lohar
Department of Electronics and Communication Engineering, Government Engineering College Jhalawar, India
Deepak Agrawal
Department of Electronics and Communication Engineering, Government Engineering College Jhalawar, India

Abstract


This paper presents a method for the detection and classification of complex power quality (PQ) disturbances using discrete wavelet transform (DWT) based ruled decision tree. The power quality disturbances are generated with the help of MATLAB using the mathematical relations as per IEEE Standard-1159. The investigated PQ disturbances include various combinations of voltage sag, voltage swell, momentary interruption, harmonics, oscillatory transient and impulsive transient. These power quality signals are decomposed using discrete wavelet transform with db4 as mother wavelet up to level 4 of decomposition. The detail coefficients and approximation coefficients are used for recognition of complex PQ disturbances. The features extracted from plots of these coefficients are given as input to the rulebased decision tree for classification of complex PQ disturbances. The effectiveness of proposed algorithm has been established by testing 30 data sets of each complex PQ disturbance obtained by varying the parameters

Keywords


Complex Power Quality Disturbance, Discrete Wavelet Transform, Power Quality, Rule-Based Decision Tree

References