Name
Title | ||
|---|---|---|
A Wind Turbine Fault Detection Approach Based on Cluster Analysis and Frequent Pattern Mining. |
Abstract | ||
|---|---|---|
Wind energy has proven its viability by the emergence of countless wind turbines around the world which greatly contribute to the increased electrical generating capacity of wind farm operators. These infrastructures are usually deployed in not easily accessible areas; therefore, maintenance routines should be based on a well-guided decision so as to minimize cost. To aid operators prior to the maintenance process, a condition monitoring system should be able to accurately reflect the actual state of the wind turbine and its major components in order to execute specific preventive measures using as little resources as possible. In this paper, we propose a fault detection approach which combines cluster analysis and frequent pattern mining to accurately reflect the deteriorating condition of a wind turbine and to indicate the components that need attention. Using SCADA data, we extracted operational status patterns and developed a rule repository for monitoring wind turbine systems. Results show that the proposed scheme is able to detect the deteriorating condition of a wind turbine as well as to explicitly identify faulty components. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.3837/tiis.2014.02.0020 | KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS |
Keywords | DocType | Volume |
Condition Monitoring System,Wind Turbine,SCADA,cluster analysis,frequent pattern mining | Journal | 8 |
Issue | ISSN | Citations |
2 | 1976-7277 | 4 |
PageRank | References | Authors |
0.93 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Frank Elijorde | 1 | 5 | 1.61 |
| Sungho Kim | 2 | 219 | 24.80 |
| Jaewan Lee | 3 | 62 | 14.66 |