The diagnosis of dynamic linear systems has been studied in a previous paper by using the polynomial representation of each variable. Data validation and gross error detection were investigated. This paper uses fault signature analysis to detect errors in the doubly fed induction generator (DFIG) of a wind turbine. The major contribution of this paper is that the wind turbine is operating at variable speed.
This paper presents a method of identifying and estimating gross errors for linear dynamic systems. The method is applied to wind power, in particular the doubly-fed induction generator. Measurements have errors, but it is possible to reduce the effect of such errors on control by exploiting relationships between the different variables of the system. Such analysis is called ‘data validation’. Data validation uses a mathematical model, based on equations, to simulate the real dynamic system. An analysis of systematic errors is made using a measurement test. The method has the potential to support the on-line multiparameter data analysis, and hence maintenance, of complex systems, such as wind turbines.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.