| Author: | Lee, Chun-leung |
| Title: | An intelligent alarm system for fault diagnosis of a steam-powered generating unit |
| Degree: | M.Sc. |
| Year: | 2001 |
| Subject: | Electric power production Fault location (Engineering) Hong Kong Polytechnic University -- Dissertations |
| Department: | Multi-disciplinary Studies Department of Electrical Engineering |
| Pages: | 80 leaves : ill. ; 30 cm |
| Language: | English |
| Abstract: | Fault diagnosis plays an important part in power plant operation. Early warning of an incipient fault and recognizing the root cause of a fault event can help power plant operators to take correct remedial actions effectively & efficiently. This project has demonstrated the feasibility of incorporating the techniques in knowledge-based system within the operation, in particular, fault identification, of a power generating unit. The main objective of this project is to enhance, but not to replace, the diagnostic ability of power plant operators. In this project, an Intelligent Alarm System prototype was developed according to the basic architecture of a knowledge-based system, which has a knowledge base, an inference engine and a user interface. Rule-based approach was used in constructing both the knowledge base and the inference engine. Visual Basic was used as the programming language. The system can be executed on any personal computer run on Windows environment. To reflect the applicability of the system, about eighty alarms which are actually being used in the Lamma Unit 5, a 350MW generating unit of the Lamma Power Station of the Hong Kong Electric Co., Ltd., have been used for analysis. Although the project is not intended to be connected to the existing alarming system at the moment, it is thought to be a useful contribution to act as a training tool for the new and inexperienced operators. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b15996268.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 3.36 MB | Adobe PDF | View/Open |
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