A Framework for In-Service Life Extension of Hydroelectric Generation Assets

Author:

Melani Arthur Henrique de Andrade1,Michalski Miguel Angelo de Carvalho1,da Silva Renan Favarão1,de Souza Gilberto Francisco Martha1

Affiliation:

1. Department of Mechatronics and Mechanical Systems Engineering, Polytechnic School of the University of São Paulo , Av. Prof. Mello Moraes 2231 – Cidade Universitária, São Paulo, SP 05508-030, Brazil

Abstract

Abstract The integrity assessment of aged or worn out large electromechanical equipment units, such as in hydroelectric generators, for possible life extension has been identified as a growing challenge in the electrical power generation industry worldwide. Although the available recommended practices provide a general assessment process, it is necessary to have more detailed guidelines. This can be achieved by adding relevant theories and models which can capture time-dependent equipment unit degradation more precisely. Seeking to fulfill this gap, this work aims to present a framework that combines several techniques of data analysis, reliability, and decision-making to support engineers, operators, and managers in the often-complex decision process, regarding whether or not to extend the time in service of an equipment or system, thus postponing the moment of a scheduled maintenance shutdown. To demonstrate the application of the proposed framework, a case study is presented considering simulated scenarios based on data and information from a real Hydroelectric Power Plant. The results show how the reliability of the components and the remaining useful life of those in fault can impact the decision-making regarding the in-service life extension of a system.

Publisher

ASME International

Subject

Mechanical Engineering,Safety Research,Safety, Risk, Reliability and Quality

Reference39 articles.

1. A Combined Goal Programming – AHP Approach Supported With TOPSIS for Maintenance Strategy Selection in Hydroelectric Power Plants;Renew. Sustain. Energy Rev.,2017

2. Fuzzy-FMSA: Evaluating Fault Monitoring and Detection Strategies Based on Failure Mode and Symptom Analysis and Fuzzy Logic;ASCE-ASME J. Risk Uncert. Eng. Sys. Part B Mech. Eng.,2020

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