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| A new method to classify and identify composite voltage sag sources in distribution network |
| DOI：10.7667/PSPC160134 |
| Key Words:composite voltage sag sources unbalanced degree intersect unbalanced degree Mahalanobis distance probability neural network |
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| Abstract:In order to classify the kinds and fault sequence of composite voltage sag sources in distribution network with harmonic, a new method to classify these composite voltage sag sources is proposed. Firstly, according to different voltage sag waveforms triggered by different types of voltage sag sources, the composite voltage sag sources containing single-phase grounding fault are classified from the composite voltage sag source consisted of transformers and induction motor. Then the intersection unbalanced degree and second-harmonic are employed to classify the composite voltage sag sources containing single-phase grounding fault. Additionally the fault sequence is classified through Mahalanobis distance and probability neural network, and a complete classification and identification method is formed. Finally, the proposed method is verified through simulation experiment, the results show that this method can well classify the kinds and fault sequence of composite voltage sag sources, and recognition accuracy is higher than 96%. In addition, the proposed method is compared with the method of the combination of EMD energy entropy and PNN, the comparison result shows that the accuracy of the proposed method is superior to the latter’s. This work is supported by Shanghai Municipal Education Commission Key Projects of Scientific Research and Innovation (No. 11ZZ173) and Local Colleges and Universities of Shanghai Science and Technology Innovation Action Plan Ability Construction Projects (No. 10110502200 and No. 11510500900). |
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