No abstract

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.

The current paper presents a method to identify and estimate gross errors for linear dynamic systems using polynomial approximation. The method presented in a previous paper which uses linear dynamic reconciliation, is extended to allow the dynamic estimation of errors. This method is applied to the doubly fed induction generator (DFIG) of a wind turbine. The technique is validated on an experimental system used to emulate the working of the wind turbine.