Influence Analysis and Prediction of ESDD and NSDD Based on Random Forests

Ang Ren,Qingquan Li,Huaishuo Xiao

doi:10.3390/en10070878

Abstract

Equivalent salt deposit density (ESDD) and non-soluble deposit density (NSDD) measurements are a basic requirement of power systems. In order to predict the site pollution severity (SPS) of insulators, a new method based on random forests (RFs) is proposed. Using mutual information (MI) theory and RFs, the weights of factors related to the SPS of insulators are analyzed. The samples of contaminated insulators are extracted from the transmission lines of high voltage alternating current (HVAC) and high voltage direct current transmission (HVDC). The regression models of RFs and support vector machines (SVM) are constructed and compared, which helps to support the lack of information in predicting NSDD in previous works. The results are as follows: according to the mean decrease accuracy (MDA), mean decrease Gini, (MDG), and MI, the types of the insulators (including surface area, surface orientation, and total length) as well as the hydrophobicity are the main factors affecting both ESDD and NSDD. Compared with NSDD, the electrical parameters have a significant effect on ESDD. For the influence factors of ESDD, the weights of the insulator type, hydrophobicity, and meteorological factors are 52.94%, 6.35%, and 21.88%, respectively. For the influence factors of NSDD, the weights of the insulator type, hydrophobicity, and meteorological factors are 55.37%, 11.04%, and 14.26%, respectively. The influence voltage level (vl), voltage type (vt), polarity/phases (pp) exerted on ESDD are 1.5 times, 3 times, and 4.5 times of NSDD, respectively. The influence that distance from the coastline (d), wind velocity (wv), and rainfall (rf) exert on NSDD are 1.5 times, 2 times, and 2.5 times that of ESDD, respectively. Compared with the natural contamination test and the SVM regression model, the RFs regression model can effectively predict the contamination degree of insulators, and the relative error of the predicted ESDD and NSDD is 8.31% and 9.62%, respectively.

Highlights

Research on insulator natural contamination is a basic requirement of external insulation.The contamination degree of insulators is a result of the actual operating environment, which can reflect the pollution resistance characteristics of insulators under natural conditions [1]
Machine learning algorithms are widely used in the study of the natural contamination characteristics of insulators: Jiao et al combined particle swarm optimization (PSO) and support vector machine (SVM) to build an insulator contamination on-line monitoring system to predict the contamination degree, and the relative error was less than 10% [2]
Ahmad et al modeled the relationship between equivalent salt deposit density (ESDD) with temperature, humidity, pressure, Energies 2017, 10, 878; doi:10.3390/en10070878

Summary

Introduction

Research on insulator natural contamination is a basic requirement of external insulation. The contamination degree of insulators is a result of the actual operating environment, which can reflect the pollution resistance characteristics of insulators under natural conditions [1]. Due to the complex working environment of insulators, the natural contamination characteristics of the insulator are difficult to depict with mathematical expressions. Machine learning algorithms are widely used in the study of the natural contamination characteristics of insulators: Jiao et al combined particle swarm optimization (PSO) and support vector machine (SVM) to build an insulator contamination on-line monitoring system to predict the contamination degree, and the relative error was less than 10% [2]. Karamousantas et al provides the foundation for routine maintenance of insulators according to the same algorithm, and the relative error was less than

Results

Discussion

Conclusion