An Interval NLPV Parity Equations Approach for Fault Detection and Isolation of a Wind Farm

Abstract

Abstract-In this paper, the problem of fault diagnosis of a wind farm is addressed using interval nonlinear parameter varying (NLPV) parity equations. Fault detection is based on the use of parity equations assuming unknown but bounded description of the noise and modeling errors. The fault detection test is based on checking the consistency between the measurements and the model by finding if the formers are inside the interval prediction bounds. The fault isolation algorithm is based on analyzing the observed fault signatures on-line, and matching them with the theoretical ones obtained using structural analysis. Finally, the proposed approach is tested using the wind farm benchmark proposed in the context of the wind farm FDI/FTC competition.

Fault detection and isolation in flat navigation canals

“…On the other hand, in order to cope with errors due to uncertainty in transport delays that are present in open-flow canal systems [18], following the ideas in [19], [20], the discrete residual has been computed as…”

Real-time fault diagnosis and fault-tolerant control

“…There are three papers that deal with model-based fault diagnosis methods [12][13][14], all with applications to wind turbine energy systems. In the paper [12] contributed by Simani et al, Takagi-Sugeno fuzzy model based fault detection and isolation methods are proposed, where the fuzzy models are derived using fuzzy clustering and dynamic system identification techniques.…”

“…The fault isolation techniques based on column and row reasoning applied to the signature matrix obtained from the simulation tests, have shown that only some of the faults are completely isolable, which deserves a further research in the future. In the paper [14] by Belsa et al, fault diagnosis for a wind farm is investigated by using interval parity equations and nonlinear parameter varying models. The noises and modelling errors are assumed to unknown but bounded.…”

“…Twelve selected papers pay attention on fault diagnosis methods and applications [10][11][12][13][14][15][16][17][18][19][20][21], and the other eleven papers are concentrated on real-time fault tolerant control and applications [22][23][24][25][26][27][28][29][30][31][32]. We are going to overview the selected papers following fault diagnosis techniques and fault-tolerant control techniques sequentially.…”

“…Therefore, knowledge-based diagnosis method is also called data-driven fault diagnosis methods (e.g., see the survey paper [10,11]). The selected research papers [12][13][14][15][16][17][18][19][20][21] fall into the respective categories readily (e.g., see Table 1). …”