A Simple Decision Tree Based Event Classification Technique for Multiple Power Quality Disturbance Signals
Rajesh Kumar Patnaik1, Kanche Anjaiah2, Rakesh Kumar Pattanaik3
1Rajesh Kumar Patnaik, Department of EEE, GMRIT, Rajam (Andhra Pradesh), India.
2Kanche Anjaiah, Department of EEE, GMRIT, Rajam (Andhra Pradesh), India.
3Rakesh Kumar Patnaik, Department of ECE, GMRIT, Rajam (Andhra Pradesh), India.
Manuscript received on 07 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript published on 30 April 2019 | PP: 1315-1321 | Volume-8 Issue-6, April 2019 | Retrieval Number: F3849048619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper presents an efficient event detection and classification technique for multiple power quality (PQ) disturbances. Initially synthetic power quality disturbances are simulated and then are directly processed to generate the target feature sets which comprises of energy, entropy, root mean square (RMS), mean, standard deviation, kurtosis, variance and maximum peak respectively. After the overall data analysis, it was found that total five events out of the overall generated PQ disturbances were distinctively classified. Eventually these target features are passed through simple decision tree based event classifier for PQ events classification. The proposed technique has been scrutinized for number of disturbances presented in the PQ events where it is has been verified as a superior technique as compared with the some of the existing event classification techniques such as wavelet transform (WT). The entire process has been verified in the in the MATLAB /Editor. The proposed technique evades the need of further signal processing techniques for detection and classification PQ events, thus ensconced less computational complexity and faster execution. Hence it is an efficient algorithm for real time applications.
Keyword: Multiple PQ Events, Simple Decision Tree, Signal Processing.
Scope of the Article: Classification