As the lifeblood of the electric power system, the fault of transmission lines directly threaten the safe operation of the power system. Thus, effective and accurate fault prediction and positioning analysis of transmission lines, has important practical value and economic significance to the security of the power system. To solve the asymmetry of transmission line fault problem, the paper proposes a novel approach to utilize the fuzzy K-NN(K-Nearest Neighbor) classifier to predict the fault types and position analysis of the fault line. During the training phase, the proposed approach uses analysis of the connection between the training data to obtain relevant cluster center features to greatly reduce the amount of data, thus improve the efficiency of fuzzy K-NN algorithm in the test phase. In particular, when data classification of category boundaries is not obvious, the application of fuzzy theory can effectively solve the deviation problem produced via only K-NN algorithm. Experimental results show that the predictive results of the fuzzy K-NN has a better efficiency and accuracy than those of the ordinary K-NN algorithm.
['Yue Zhang', 'Jianxia Chen', 'Qin Fang', 'Zhiwei Ye']
Fault analysis and prediction of transmission line based on fuzzy K-Nearest Neighbor algorithm