An investigation of operating behavior characteristics of a wind power system using a fuzzy clustering method. (15th September 2017)

• Record Type: Journal Article
• Authors: Choi, Seongjin; Kim, Sungho
• Is Part Of: Expert systems with applications. Volume 81(2017)
• Page Start: 244
• Page End: 250
• Publication Date: 2017-09-15
• Subjects: Fault monitoring -- Fuzzy clustering -- Operating behavior -- Relative distance index -- Wind power system; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.03.046
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: The fuzzy clustering method identifies the operating point of the system. The relative distance index indicates the operating behavior of the system. The important factor in behavior analysis is not the least distance cluster center. The behavior analysis depends on the relative distance index of the cluster center. Abstract: A wind power system has diverse operating characteristics as its operations depend on many factors such as wind power, machinery ageing and breakdowns, etc. Knowledge of the operating behavior of the wind power system is helpful for monitoring its status and for isolating harmful elements when malfunctions occur. To investigate the operating status and behavior of the system, the fuzzy clustering method is introduced to classify the system's operating points. Relative distance indices of the cluster centers are defined to describe the operating behavior. With those, the location and operating behavior of the operating point are identified in relation to the cluster centers.