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Adaptive modelling diagnostic methods are valuable tools for gas turbine performance and condition monitoring. Component maps capable of accurate representation of engine operation are not available to the engine operators. In this context, adaptive methods can be used for tuning the component maps for simulating accurately the engine performance throughout the whole operating envelope. In most cases, the map tuning is performed at a single operating point or operating line and although scaled maps can provide accurate results close to the reference points, deviations may significantly increase away from these reference points. For industrial gas turbines, in the past, this was not a major issue, given that most engines operated at or close to baseload. Currently, due to the increased share of renewable power in the generation mix in Europe, there is a shift of industrial gas turbines operating mode from baseload to load following and part load, thus there is a need for accurately simulating engine operation in a broader operating range. This paper presents a component map adaptation methodology integrating several methods applied in the literature in six steps. The novel methodology is applied to on-engine measurements from a heavy-duty gas turbine and the betterment on simulation achieved from each step is quantified. The adapted component maps enable accurate engine condition monitoring as demonstrated by a second test case for an industrial twin shaft engine where operating data spanning over four months are assessed. INTRODUCTION One of the major challenges for gas turbine users is to ensure a high level of availability and reliability during the entire operating life of the engine while maintaining efficient operation. For tackling this challenge integration of advanced diagnostic methods can be of great assistance as discussed by Volponi (Volponi, 2014) and demonstrated through applications by (Loboda et al., 2009; Verbist et al., 2013; Roumeliotis et al., 2017). Several diagnostic methods have been proposed for assessing engine health condition (e.g. (Ganguli, 2002; Mathioudakis et al., 2002; Dewallef et al., 2004; Simon et al., 2013)). Among them, model-based methods can directly assess engine overall performance and health condition, providing information that can be used for validating maintenance procedures and for optimizing engine operation. For this purpose, a performance simulation model capable to reproduce the engine operation with acceptable accuracy must be developed. For achieving this suitable component characteristics are needed. 1 Currently Lecturer at Cranfield University

Application of an advanced adaptation methodology for gas turbine performance monitoring