Abstract

Health monitoring has gained a massive interest in power systems engineering, as it has the advantage to reduce operating costs, improve reliability of power supply and provide a better service to customers. This paper presents surrogate methods to predict the electrical insulation lifetime using the neural network approach and three curve fitting models. These can be used for the health monitoring of insulating systems in electrical equipment, such as motors, generators, and transformers. The curve fit models and the supervised backpropagation neural network are employed to predict the insulation resistance trend of enameled copper wires, when stressed with a temperature of 290 °C. After selecting a suitable end of life criterion, the specimens’ mean time-to-failure is estimated, and the performance of each of the analyzed models is apprised through a comparison with the standard method for thermal life evaluation of enameled wires. Amongst all, the best prediction accuracy is achieved by a Backpropagation neural network approach, which gives an error of just 3.29% when compared with the conventional life evaluation method, whereas, the error is above 10% for all the three investigated curve fit models.

Highlights

  • Neural networks (NNs) have been applied successfully to solve difficult and diverse problems, including nonlinear system identification and control, financial market analysis, signal modelling, and power load forecasting, by training them in a supervised manner [1, 2]

  • Insulation faults have always been a major concern as the majority of stator-windings fail as a result of gradual deterioration of the electrical insulation [4], and the weakest link is often represented by the inter-turn insulation layer, that can trigger the most severe stator faults [5]

  • Surrogate approaches to estimate the lifetime of enamelled wire insulation, under thermal aging, are presented in this paper

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Summary

Introduction

Neural networks (NNs) have been applied successfully to solve difficult and diverse problems, including nonlinear system identification and control, financial market analysis, signal modelling, and power load forecasting, by training them in a supervised manner [1, 2]. Different system models can be obtained from these networks with proper arrangements of the input variables [3]. A requirement in safety critical applications is the early detection of insulation deterioration, which can be evaluated through a diagnostic property (e.g. insulation capacitance or resistance) of the insulation. Alongside the early detection of abnormal deterioration, another issue is the time required for the experimental test procedure, aiming to determine the lifetime of electrical machines, which can take thousands of testing hours lasting even months [6, 7] Insulation faults have always been a major concern as the majority of stator-windings fail as a result of gradual deterioration of the electrical insulation [4], and the weakest link is often represented by the inter-turn insulation layer, that can trigger the most severe stator faults [5].

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