Abstract
AbstractPresent paper proposes an FDI-system for the wind turbine benchmark designed by application of a generic automated design method, in which the number of required human decisions and assumptions are minimized. No specific adaptation of the method for the wind turbine benchmark is needed, and the number of parameter choices is small. The method contains in essence three steps: generation of potential residual generators; residual generator selection; and diagnostic test construction. The second and third step are based on novel ideas developed in this paper; a greedy selection algorithm for the second step, and a methodology based on the Kullback-Leibler divergence for the third step. The proposed FDI-system performs well in spite of no specific adaptation or tuning to the benchmark. All faults in the pre-defined test sequence can be detected and all faults, except a double fault, can also be isolated shortly thereafter. In addition, there are no false or missed detections.
Highlights
Wind turbines stand for a growing part of power production
The method contains in essence three steps: generation of candidate residual generators, residual generator selection, and diagnostic test construction
By means of Monte Carlo simulations, the fault detection and isolation (FDI) system was simulated 100 times with data from the provided wind turbine model setup according to the above-described test sequence
Summary
Wind turbines stand for a growing part of power production. The demands for reliability are high, since wind turbines are expensive and their off-time should be minimized. One potential way to meet the reliability demands is to adopt fault tolerant control (FTC), that is, prevent faults from developing into failures by taking appropriate actions. A typical action is reconfiguration of the control system. An essential part of an FTC system is the fault detection and isolation (FDI) system, see, for example, [1]. To obtain good detection and isolation of faults, model-based FDI is often necessary
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