The expansion of renewable power plants has transformed the role and operation of gas turbines to a great extent. From the base load operation era we are moving into a flexible and dynamic engine operation of gas turbines. Advances in computational intelligence have amplified the importance of condition monitoring, diagnostics and prognostics capabilities in the face of gas turbine operation. Performing diagnostics in transient conditions is beneficial since the gas turbines are now acting as partners of renewables. This paper presents a novel diagnostic approach for determining the health of a gas turbine when it works in conjunction with a wind farm in a hybrid power plant. In this paper we propose a model-based diagnostic method without reconstructing component maps according to their degradation. Once the engine model is adapted to its clean condition, it is tuned in real time to reflect the changes in both the operation and degradation with respect to a benchmark engine model. Time evolving multiple component degradation scenarios are simulated to test the accuracy and efficiency of the proposed method. From a bank of simulated measurements, data trending is performed which facilitates the detection of degradation and provides useful conclusions about the health state of the engine. This diagnostic method is suitable for gas turbines that spend most of their life time in part-load and transient operation and it can be a useful tool for gas turbine operators in planning their assets maintenance in a computational efficient and accurate manner.