Automated Classification and Detection of Power Quality Disturbances using RBF Fault Classifier
Swapnil B. Mohod1, Vilas N. Ghate2
1Swapnil B. Mohod, Department of Electrical Engineering, Prof. Ram Meghe College of Engineering and Management, Badnera-Amravati, (Maharashtra), India.
2Dr. Vilas N. Ghate, Department of Electrical Engineering, Government College of Engineering, Chandrapur, (Maharashtra), India.
Manuscript received on 20 September 2015 | Revised Manuscript received on 30 September 2015 | Manuscript published on 30 September 2015 | PP: 17-22 | Volume-4 Issue-4, September 2015 | Retrieval Number: D1485094415©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: The proliferation of power electronic devices in a modern industrial control pronounced more power quality disturbances. There is an urgent need of technique which automatically classifies and detects power quality disturbances. In this paper authors developed an online radial-basis-function NN-based detection technique. In proposed scheme simple statistical parameters described which are used as input noise signals to classify vital conditions of power system like sag, swell of Induction motor, arc load, short circuit of welding machine, phase to earth fault and healthy condition. Detailed design procedure for RBF based classifier is presented for which experimental data of one HP, single phase, 50 Hz squirrel cage Induction motor, Welding machine to generate actual arcing load, Advantech data acquisition system is used. A Wavelet Transform Technique is applied to extract features from monitored data. By principle component analysis and sensitivity analysis dimension reduction is also achieved which classify the six types of PQ disturbances.
Keyword: Power Quality, Wavelet transform, RBF, PCA
Scope of the Article: Classification