A voted based random forests algorithm for smart grid distribution network faults prediction. Issue 4 (20th April 2020)

• Record Type: Journal Article
• Authors: Lin, Rongheng; Pei, Zixiang; Ye, Zezhou; Wu, Budan; Yang, Geng
• Is Part Of: Enterprise information systems. Volume 14:Issue 4(2020)
• Page Start: 496
• Page End: 514
• Publication Date: 2020-04-20
• Subjects: Random forests (RF) -- voting algorithm -- fault prediction -- smart distribution network; Information storage and retrieval systems -- Periodicals; Management information systems -- Periodicals; Electronic commerce -- Periodicals; 658.4038011
• DOI: 10.1080/17517575.2019.1600724
• Languages: English
• ISSNs: 1751-7575

Abstract

ABSTRACT: In this paper, we focus on fault prediction in the smart distribution network. modified version of voted random forest algorithm (VRF) is proposed for enhancing the predicting accuracy of the faults. We change the decision process by redesigning the voting algorithm by introducing multiple SVM models for voting model training. Based on the trained models, a simple NSGA algorithm is applied to find the best voting model. Results showed that the new algorithm could improve the accuracy and recall rate of the fault prediction, especially for the recall rate of the negative samples.