Supervised Learning based Method for Condition Monitoring of Overhead Line Insulators using Leakage Current Measurement

TL;DR

This paper introduces a supervised machine learning approach using XGBoost to predict insulator flashover risk based on leakage current and voltage, aiding proactive asset management in power grids.

Contribution

It presents a novel ML-based method for estimating flashover probability of insulators, improving risk assessment and maintenance planning.

Findings

Accurately predicts critical flashover voltage (U50%) for various insulator designs.

Effectively assesses insulator condition to guide maintenance decisions.

Demonstrates the applicability of XGBoost in power asset management.

Abstract

As a new practical and economical solution to the aging problem of overhead line (OHL) assets, the technical policies of most power grid companies in the world experienced a gradual transition from scheduled preventive maintenance to a risk-based approach in asset management. Even though the accumulation of contamination is predictable within a certain degree, there are currently no effective ways to identify the risk of the insulator flashover in order to plan its replacement. This paper presents a novel machine learning (ML) based method for estimating the flashover probability of the cup-and-pin glass insulator string. The proposed method is based on the Extreme Gradient Boosting (XGBoost) supervised ML model, in which the leakage current (LC) features and applied voltage are used as the inputs. The established model can estimate the critical flashover voltage (U50%) for various designs of OHL insulators with different voltage levels. The proposed method is also able to accurately determine the condition of the insulator strings and instruct asset management engineers to take appropriate actions.