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Energies 2017, 10(7), 878; doi:10.3390/en10070878

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

1,2
,
1,2,* and 1,2
1
Department of Electrical Engineering, Shandong University, Jinan 250061, China
2
Shandong Provincial Key Laboratory of Ultra High Voltage Transmission Technology and Equipments, #17923 Jingshi Road, Jinan 250061, China
*
Author to whom correspondence should be addressed.
Received: 4 April 2017 / Revised: 15 May 2017 / Accepted: 26 June 2017 / Published: 30 June 2017
(This article belongs to the Section Electrical Power and Energy System)

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. View Full-Text
Keywords: insulators; ESDD; NSDD; random forests; mutual information insulators; ESDD; NSDD; random forests; mutual information
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Ren, A.; Li, Q.; Xiao, H. Influence Analysis and Prediction of ESDD and NSDD Based on Random Forests. Energies 2017, 10, 878.

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