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Lohar, Fateh L.
- Detection and Classification of Power Quality Disturbances Using Discrete Wavelet Transform and Rule Based Decision Tree
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Authors
Affiliations
1 Department of Electronics and Communication Engineering, Government Engineering College Jhalwar, Rajasthan, IN
2 Government Engineering College Baran, Rajasthan, IN
1 Department of Electronics and Communication Engineering, Government Engineering College Jhalwar, Rajasthan, IN
2 Government Engineering College Baran, Rajasthan, IN
Source
ICTACT Journal on Microelectronics, Vol 7, No 2 (2021), Pagination: 1141-1147Abstract
This paper presents a method for the detection and classification of power quality (PQ) disturbances using discrete wavelet transform (DWT) based 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 pure sine wave, voltage sag, voltage swell, momentary interruption, harmonics, oscillatory transient, impulsive transient and notch. These power quality signals are decomposed using discrete wavelet transform with db4 as mother wavelet up to third level of decomposition. The detail coefficients and approximation coefficients are used for the detection of PQ disturbances. The features extracted from these coefficients are fed to the rule-based decision tree for classification purpose. The effectiveness of proposed algorithm has been established by testing the 30 data sets of each PQ disturbance obtained by varying the parameters.Keywords
Discrete Wavelet Transform, Power Quality, Rule Based Decision Tree.
References
- Martin Valtierra Rodriguez, Rene de Jesus Romero Troncoso, Roque Alfredo Osornio Rios and Arturo Garcia-Perez, “Detection and Classification of Single and Combined Power Quality Disturbances using Neural Networks”, IEEE Transactions on Industrial Electronics, Vol. 61, No. 5, pp. 2473-2482, 2014.
- B. Biswal, H.S. Behera, R. Bisoi and P.K. Das, “Classification of Power Quality Data using Decision Tree and Chemotactic Differential Evolution based Fuzzy Clustering”, Swarm and Evolutionary Computation, Vol. 4, pp. 12-24, 2012.
- 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.
- Murat Uyar, Selcuk Yildirim and Mushsin Tunay Gencoglu, “An Expert System based on S-Transform and Neural Network for Automatic Classification of Power Quality Disturbances”, Expert Systems with Applications, Vol. 36, pp. 5962-5975, 2009.
- Ming Zhang, Kaicheng Li and Yisheng Hu, “A Real-Time Classification Method of Power Quality Disturbances”, Electric Power Systems Research, Vol. 81, pp. 660-666, 2011.
- Om Prakash Mahela, Abdul Gafoor Shaik and Neeraj Gupta, “A Critical Review of Detection and Classification of Power Quality Events”, Renewable and Sustainable Energy Reviews, Vol. 41, pp. 495-505, 2015.
- 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.
- 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.
- 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. 945-955, 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.
- M.A.S. Masoum, S. Jamali and N. Ghaffarzadeh, “Detection and Classification of Power Quality Disturbances using Discrete Wavelet Transform and Wavelet Networks”, IET Science, Measurement and Technology, Vol. 4, No. 4, pp. 193-205, 2010.
- Yan Cui and Wen Hui Li, “A Classification Method for Complex Power Quality Disturbances using EEMD and Rank Wavelet SVM”, IEEE Transactions on Smart Grid, Vol. 6, No. 4, pp. 1678-1685, 2015.
- Om Prakash Mahela and Abdul Gafoor Shaik, “Recognition of Power Quality Disturbances using S-Transform and Rule-Based 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 Co-Generation, Small Power Plants and District Energy, pp. 14-16, 2016.
- Hussain Shareef, Azah Mohamed and Ahmad Asrul Ibrahim, “An Image Processing based Method for Power Quality Event Identification”, Electric Power Systems Research, Vol. 46, pp. 184-197, 2013.
- 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, Proceeding, Vol. 1, pp. 77-82, 1998.
- Ying Yi Hong, and Cheng Wei Wang, “Switching Detection/ Classification Using Discrete Wavelet Transform and Self-Organizing Mapping Network”, IEEE Transactions on Power Delivery, Vol. 20, No. 2, pp. 1662-1668, 2005.
- R. H. G. Tan and V. K. Ramachandaramurthy, “Numerical Model Framework of Power Quality Events”, Numerical Journal of Scientific Research Research, Vol. 43, No. 1, pp. 30-47, 2010.
- Detection And Classification Of Complex Power Quality Disturbances Using Discrete Wavelet Transform And Rule Based Decision Tree
Abstract Views :96 |
PDF Views:0
Authors
Affiliations
1 Department of Electronics and Communication Engineering, Government Engineering College Jhalawar, IN
2 Department of Electronics and Communication Engineering, Government Engineering College Baran, IN
1 Department of Electronics and Communication Engineering, Government Engineering College Jhalawar, IN
2 Department of Electronics and Communication Engineering, Government Engineering College Baran, IN
Source
ICTACT Journal on Microelectronics, Vol 7, No 4 (2022), Pagination: 1227-1233Abstract
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 parametersKeywords
Complex Power Quality Disturbance, Discrete Wavelet Transform, Power Quality, Rule-Based Decision TreeReferences
- 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