Dynamic Pollution Prediction Model of Insulators Based on Atmospheric Environmental Parameters

Author:

Chen Siyi,Zhang Zhijin

Abstract

Pollution-induced flashover is one of the most serious power accidents, and the pollution degree of insulators depends on atmospheric environmental parameters. The pollution models used in the power system research are usually static, but the environmental parameters are dynamic. Therefore, the study on the dynamic pollution prediction model is of great importance. In this paper, the dynamic pollution prediction model of insulators based on atmospheric environmental parameters was built, and insulators’ structure coefficients were proposed based on the model. Firstly, the insulator dynamic pollution model based on meteorological data (PM2.5, PM10, TSP (total suspended particulate), and wind speed) was proposed, and natural pollution tests were also conducted as verification tests. Furthermore, insulator structure coefficients c1, c2 (c1: pollution ratio of U210BP/170 to XP-160; c2: calculated pollution ratio of U210BP/170T to XP-160) were then obtained, and their influence factors were discussed. At last, insulator structure coefficients were calculated, and it can be seen that the calculated error of insulator structure coefficients was acceptable, with the average re (relative errors) at 9.0% (c1) and 13.5% (c2), which verifies the feasibility of the model. Based on the results in this paper, the NSDD (non-soluble deposit density) of insulators with different structures can be obtained using the insulators’ structure coefficient and the reference XP-160 insulator’s NSDD.

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous)

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1. Refinement of an Environmental Pollution Model for the Needs of the Electric Power Industry by Addition of Precipitation Attributes;2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI);2023-05-23

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