Multiple Random Forests Based Intelligent Location of Single-phase Grounding Fault in Power Lines of DFIG-based Wind Farm

Abstract

To address the problems of wind power abandonment and the stoppage of electricity transmission caused by a short circuit in a power line of a doubly-fed induction generator (DFIG) based wind farm, this paper proposes an intelligent location method for a single-phase grounding fault based on a multiple random forests (multi-RF) algorithm. First, the simulation model is built, and the fundamental amplitudes of the zero-sequence currents are extracted by a fast Fourier transform (FFT) to construct the feature set. Then, the random forest classification algorithm is applied to establish the fault section locator. The model is resampled on the basis of the bootstrap method to generate multiple sample subsets, which are used to establish multiple classification and regression tree (CART) classifiers. The CART classifiers use the mean decrease in the node impurity as the feature importance, which is used to mine the relationship between features and fault sections. Subsequently, a fault section is identified by voting on the test results for each classifier. Finally, a multi-RF regression fault locator is built to output the predicted fault distance. Experimental results with PSCAD/EMTDC software show that the proposed method can overcome the shortcomings of a single RF and has the advantage of locating a short hybrid overhead/cable line with multiple branches. Compared with support vector machines (SVMs) and previously reported methods, the proposed method can meet the location accuracy and efficiency requirements of a DFIG-based wind farm better.