Abstract

Innovative test methods for circuit breakers are constantly sought after to reduce maintenance time and costs, yet still provide accurate assessment of this critical substation equipment. This paper proposes a novel method for response modelling of high voltage SF6 circuit breakers, based on artificial neural networks, to provide a means of assessing its condition. The proposed method enables a timing response model of the circuit breaker to be developed using trip command parameters. In this paper, an experimental setup is used to perform trip response testing of a three-phase 75 kV circuit breaker. The obtained data is then used to train, validate and test a Bayesian regularised artificial neural network that can predict response times of the breaker for a given set of trip command parameters.

Highlights

  • This paper presents a method for developing the response model, using an Artificial Neural Network (ANN), for a high voltage SF6 circuit breaker which can be used to assess its condition

  • Maintenance and reliability of power system equipment have become increasingly important with growing electricity demand and ageing of system components globally [1]

  • The same applies to circuit breakers where complete failure may be defined as causing the lack of one or more of its fundamental functions [6]

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Summary

Introduction

Maintenance and reliability of power system equipment have become increasingly important with growing electricity demand and ageing of system components globally [1]. Effective monitoring and assessment techniques for ensuring the reliability of circuit breakers are important factors in the maintenance of modern power systems [2]. There are different modes of failure for electrical equipment of a power system influenced by electrical, thermal, mechanical and ambient stresses [4]. These factors, through different mechanisms, produce varying intensity and progress of ageing change to the equipment [5]. The same applies to circuit breakers where complete failure may be defined as causing the lack of one or more of its fundamental functions [6]

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