Application of artificial intelligence methods for supporting the control monitoring of thermal power plants

Abstract

The present thesis introduces a methodology for the design and development of a supporting system for control monitoring of Thermal Power Plants (TPP), having as final objective the improvement of its performance. The thesis proposes the combined application of three research areas: Knowledge Engineering, Knowledge Discovery in Databases (KDD) and Intelligent Agents. The basic principles that were adopted and their adaptation to the needs of industrial systems are analyzed thoroughly. For the methodical capture of the domain knowledge (exploitation of the personnel’s implicit knowledge) and the proper specifications writing, a KE approach, CommonKADS, was applied. It resulted, among others, to a rule base for identifying false sensor measurements. The rules were applied both for cleaning the historical data that was used for the Data Mining (DM) and for the on-line validation of the sensor measurements. For deriving models, the KDD procedure was applied to historical TPP operation data ...
show more

All items in National Archive of Phd theses are protected by copyright.

DOI
10.12681/eadd/23461
Handle URL
http://hdl.handle.net/10442/hedi/23461
ND
23461
Alternative title
Υποστήριξη ελέγχου λειτουργίας σταθμών παραγωγής ηλεκτρικής ενέργειας με χρήση μεθόδων τεχνητής νοημοσύνης
Author
Athanasopoulou, Christina (Father's name: Athanasios)
Date
2009
Degree Grantor
Aristotle University Of Thessaloniki (AUTH)
Committee members
Χατζηαθανασίου Βασίλειος
Ντοκόπουλος Πέτρος
Λαμπρίδης Δημήτριος
Αντωνόπουλος-Ντόμης Μιχαήλ
Μήτκας Περικλής
Συμεωνίδης Ανδρέας
Μαγγίνα Ελένη
Discipline
Engineering and TechnologyElectrical Engineering, Electronic Engineering, Information Engineering
Keywords
Power plants; Control monitoring; Knowledge engineering; Data mining; Intelligent agents; Decision support systems
Country
Greece
Language
Greek
Description
xvii, 183 σ., im.
Usage statistics
VIEWS
Concern the unique Ph.D. Thesis' views for the period 07/2018 - 07/2023.
Source: Google Analytics.
ONLINE READER
Concern the online reader's opening for the period 07/2018 - 07/2023.
Source: Google Analytics.
DOWNLOADS
Concern all downloads of this Ph.D. Thesis' digital file.
Source: National Archive of Ph.D. Theses.
USERS
Concern all registered users of National Archive of Ph.D. Theses who have interacted with this Ph.D. Thesis. Mostly, it concerns downloads.
Source: National Archive of Ph.D. Theses.
Related items (based on users' visits)