Research on Power System Fault Diagnosis Technology

2014 ◽  
Vol 898 ◽  
pp. 634-637
Author(s):  
Wei Gao ◽  
Zhong Tang
Keyword(s):  
Fault Diagnosis ◽  
Power System ◽  
Set Theory ◽  
Rough Set ◽  
Network Optimization ◽  
Rough Set Theory ◽  
Optimization Methods ◽  
Neural Network Optimization ◽  
Key Technology ◽  
Main Development

The current proposed technologies for fault diagnosis of power system were investigated, including expert system, artificial neural network, optimization methods, and rough set theory. The applicability and characteristics of these technologies for power system fault diagnosis were briefly analyzed and also. The existing flaws and improvements are given. Finally, the recently research status were reviewed and discussed, the key technology issues on and main development trends of the issues are pointed out to motivate the developments.

Research on Fault Diagnosis of Forklift’s Hydraulic System Based on Rough Set Theory

2012 ◽  
Vol 524-527 ◽  
pp. 819-823
Author(s):  
Xin Ping Su ◽  
Guang Kun Nie ◽  
Wei Xin Fan
Keyword(s):  
Fault Tolerance ◽  
Fault Diagnosis ◽  
Knowledge Acquisition ◽  
Set Theory ◽  
Rough Set ◽  
Hydraulic System ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Train Of Thought ◽  
Fault Information

An approach of forklift’s fault diagnostic knowledge acquisition and discrete date based on rough set theory was put forward, according to the rough set theory in fault diagnosis of fault tolerance, the use of rough set theory in fault knowledge attribute reduction and value reduction, as in incomplete fault information of forklift hydraulic system fault diagnosis provides a train of thought. The inferential strategy and process of fault diagnosis of hydraulic system for forklift were described. Examples show that the proposed approach is very effective.

Rules Mining of Power System Transient Stability Assessment Based on Clustering Analysis and Rough Set Theory

2014 ◽  
Vol 521 ◽  
pp. 418-422 ◽  
Author(s):  
Yan Xu ◽  
Xin Chen
Keyword(s):  
Power System ◽  
Set Theory ◽  
Rough Set ◽  
Clustering Analysis ◽  
Transient Stability ◽  
Rough Set Theory ◽  
Decision Table ◽  
Final Decision ◽  
Stability Assessment ◽  
Decision Attributes

Transient Stability Assessment (TSA) aims at assessing stability of power system operation state quickly. This paper introduces rough set theory and clustering analysis to assess power system transient stability. At first, the stability operation parameters and fault places are taken as feature attributes based on the trait of power system transient ability. K-means algorithm is used to make continuous attributes among feature attributes discrete. Then feature attributes and stability types are taken as conditional attributes and decision attributes respectively. Initial decision table is established. Finally, rough set theory is used to form final decision table and rules of TSA are obtained. The IEEE 9-Bus system is employed to demonstrate the validity of the proposed approach.

A rough set approach to the treatment of continuous-valued attributes in multi-concept classification for mechanical diagnosis

2001 ◽  
Vol 15 (3) ◽  
pp. 211-221 ◽  
Author(s):  
LI-PHENG KHOO ◽  
LIAN-YIN ZHAI
Keyword(s):  
Fault Diagnosis ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Operating Conditions ◽  
Prototype System ◽  
Novel Approach ◽  
Discrete Manner ◽  
Critical Machines ◽  
Concept Classification

The efficient use of critical machines or equipment in a manufacturing system requires reliable information about their current operating conditions. This information is often used as a basis for machine condition monitoring and fault diagnosis—which essentially is an endeavor of knowledge extraction. Rough set theory provides a novel way to knowledge acquisition, especially when dealing with vagueness and uncertainty. It focuses on the discovery of patterns in incomplete and/or inconsistent data. However, rough set theory requires the data analyzed to be in discrete manner. This paper proposes a novel approach to the treatment of continuous-valued attributes in multi-concept classification for mechanical diagnosis using rough set theory. Based on the proposed approach, a prototype system called RClass-Plus has been developed. RClass-Plus is validated using a case study on mechanical fault diagnosis. Details of the validation are described.

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