Aiming at the problem of the high acquisition cost of high impedance fault (HIF) labeled data in distribution networks and the difficulty of using unlabeled data, this paper proposes a novel HIF semi-supervised detection method based on tri-training and support vector machine (SVM). Unlike supervised learning methods, this method can use labeled and unlabeled data by tri-training. Firstly, discrete wavelet transform decomposes the zero-sequence currents into different wavelet coefficients and extracts special features. Secondly, three SVM classifiers with different kernel functions are collaboratively trained to construct a semi-supervised classifier. Finally, the method is verified based on the PSCAD/EMTDC simulation software. The simulation results show that the proposed method can utilize massive unlabeled data to improve fault detection performance and reflect the differences between the classifiers through different kernel functions of SVM, which further improves the effect of tri-training.

distribution networks;high impedance fault;semi-supervised learning;support vector machine;tri-training