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 zerosequence 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.

Improved Fault Classification and Localization in Power Transmission Networks using VAE-generated Synthetic Data and Machine Learning Algorithms

“…KNN improves power transmission system fault management by detecting and categorizing defects [33]. Euclidean, Manhattan, and Mahalanobis distances are used to improve the K-Nearest Neighbors (KNN) method [34,35]. Approximate KNN approaches use indexing structures like KD-trees and Hash tables to reduce the search space and improve computing performance, especially for big, unbalanced datasets.…”

FPGA-based fault analysis for 7-level switched ladder multi-level inverter using decision tree algorithm

“…The faulty phases are extracted by the Pearson correlation coefficient-based technique for the micro-grid fed inverter based generator [14]. The classification and regression tree (CART) method can be utilized for the fault location of a single-phase grounding in real-time power converters [15]. The DC faults can be analyzed using the teager-kaiser energy operator algorithm which has low computation time and complexity [16].…”

A novel fault localization method for asymmetric phase‐to‐ground faults on onshore wind farm collector lines without phasor measurement

“…Therefore, the practicality of this above method may be affected. Zhu et al took zero-sequence current at multiple points on collector lines as the feature quantity and adopted multi-RF (Random Forest) to construct intelligent fault localization models [19]. Such artificial intelligence-based methods can avoid analysing the complex fault characteristic of WTGs, which is the greatest advantage of such methods.…”

“…Such artificial intelligence-based methods can avoid analysing the complex fault characteristic of WTGs, which is the greatest advantage of such methods. However, they need quantities of fault data for model training, and reconstruction of data sets and retraining of models may be necessary when the structure of wind farms changes; in addition, this method in [19] requires the distributed installation of current measurement points, and the number of measurement points is almost equal to that of WTGs, which means a very high cost.…”