Pure intelligent monitoring system for steam economizer trips

Firas Basim Ismail, Khalid Hamzah Abed, Deshvin Singh, Mohammad Shakir Nasif

Research output: Contribution to journalConference article

Abstract

Steam economizer represents one of the main equipment in the power plant. Some steam economizer's behavior lead to failure and shutdown in the entire power plant. This will lead to increase in operating and maintenance cost. By detecting the cause in the early stages maintain normal and safe operational conditions of power plant. However, these methodologies are hard to be achieved due to certain boundaries such as system learning ability and the weakness of the system beyond its domain of expertise. The best solution for these problems, an intelligent modeling system specialized in steam economizer trips have been proposed and coded within MATLAB environment to be as a potential solution to insure a fault detection and diagnosis system (FDD). An integrated plant data preparation framework for 10 trips was studied as framework variables. The most influential operational variables have been trained and validated by adopting Artificial Neural Network (ANN). The Extreme Learning Machine (ELM) neural network methodology has been proposed as a major computational intelligent tool in the system. It is shown that ANN can be implemented for monitoring any process faults in thermal power plants. Better speed of learning algorithms by using the Extreme Learning Machine has been approved as well.

Original languageEnglish
Article number04008
JournalMATEC Web of Conferences
Volume131
DOIs
Publication statusPublished - 25 Oct 2017
Event2017 UTP-UMP Symposium on Energy Systems, SES 2017 - Perak, Malaysia
Duration: 26 Sep 201727 Sep 2017

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Materials Science(all)
  • Engineering(all)

Cite this