Abstract
In Chaps. 4 and 5, all the theories needed for developing fault detection relevant models in either static or dynamic conditions, and a method for estimating their uncertainties, respectively, have been thoroughly discussed. The good side about the presented approaches is that they are all nonlinear in parameter models. Even more interesting is the dynamic models in the framework of OBFs. In this chapter, the application of the proposed theories to a specific Cogeneration and Cooling Plant (CCP) is presented. First, detail description of the CCP is provided in Sect. 6.2. Secondly, the use of the static and dynamic models developed for the critical sub-systems GTG, HRSG and SAC are investigated in Sect. 6.3. In the same section are contained analyses of the effect of model structure, error assumption and optimization algorithms on model uncertainty. Validation of the semi-empirical models for the GTG and HRSG are also included. Finally, Sect. 6.4 provides a summary of the contribution from this chapter.
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Lemma, T.A. (2018). Application Studies, Part-I: Model Identification and Validation. In: A Hybrid Approach for Power Plant Fault Diagnostics. Studies in Computational Intelligence, vol 743. Springer, Cham. https://doi.org/10.1007/978-3-319-71871-2_6
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DOI: https://doi.org/10.1007/978-3-319-71871-2_6
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