6 November 2006 Power distribution system diagnosis with uncertainty information based on rough sets and clouds model
Author Affiliations +
Abstract
During the distribution system fault period, usually the explosive growth signals including fuzziness and randomness are too redundant to make right decision for the dispatcher. The volume of data with a few uncertainties overwhelms classic information systems in the distribution control center and exacerbates the existing knowledge acquisition process of expert systems. So intelligent methods must be developed to aid users in maintaining and using this abundance of information effectively. An important issue in distribution fault diagnosis system (DFDS) is to allow the discovered knowledge to be as close as possible to natural languages to satisfy user needs with tractability, and to offer DFDS robustness. At this junction, the paper describes a systematic approach for detecting superfluous data. The approach therefore could offer user both the opportunity to learn about the data and to validate the extracted knowledge. It is considered as a "white box" rather than a "black box" like in the case of neural network. The cloud theory is introduced and the mathematical description of cloud has effectively integrated the fuzziness and randomness of linguistic terms in a unified way. Based on it, a method of knowledge representation in DFDS is developed which bridges the gap between quantitative knowledge and qualitative knowledge. In relation to classical rough set, the cloud-rough method can deal with the uncertainty of the attribute and make a soft discretization for continuous ones (such as the current and the voltage). A novel approach, including discretization, attribute reduction, rule reliability computation and equipment reliability computation, is presented. The data redundancy is greatly reduced based on an integrated use of cloud theory and rough set theory. Illustrated with a power distribution DFDS shows the effectiveness and practicality of the proposed approach.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Qiuye Sun, Huaguang Zhang, "Power distribution system diagnosis with uncertainty information based on rough sets and clouds model", Proc. SPIE 6357, Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic Technology, and Artificial Intelligence, 63575G (6 November 2006); doi: 10.1117/12.717601; https://doi.org/10.1117/12.717601
PROCEEDINGS
6 PAGES


SHARE
RELATED CONTENT

A dynamic access control method based on QoS requirement
Proceedings of SPIE (March 14 2013)
A service-oriented data access control model
Proceedings of SPIE (January 23 2017)
Embedded diagnostics in combat systems
Proceedings of SPIE (July 29 2004)
Discovery of diagnostic knowledge from multisensor data
Proceedings of SPIE (March 27 2001)
The impact of AIRS data on weather prediction
Proceedings of SPIE (June 01 2005)

Back to Top