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]
Summary
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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