An Educational Guide to Extract the Parameters of Heavy Duty Gas Turbines Model in Dynamic Studies Based on Operational Data

Abstract

In this paper, the parameters of Rowen's model for heavy duty gas turbines in dynamic studies are estimated by use of available operational and performance data. The work is aimed to create insight into various parts of the model and to introduce a simple and comprehensive procedure to derive the parameters out of simple physical laws, focusing especially on trainers and students who are interested in dynamic models and simulations. A 172-MW simple cycle, single shaft heavy duty gas turbine and its available operational data are also presented and studied for deriving the parameters of the model. Gas turbine parameters are approximated by using simple thermodynamic assumptions, resulting in good correspondence with typical values. The step response of the model is also simulated for few scenarios and is presented.

Load Inertia Estimation Using White and Grey-Box Estimators for Power Systems with High Wind Penetration

“…In particular, the load power sensitivity to frequency, which links to load inertia, is important for system overall frequency response simulation. The white-box approach has been applied in different areas of power system modelling as well as load modelling (Bank Tavakoli, et al, 2009;Inoue, et al, 1997). The grey-box approach is widely used in power system load modelling as well (Welfonder, et al, 1989;O'Sullivan and O'Malley, 1996).…”

Heavy duty gas turbine monitoring based on adaptive neuro-fuzzy inference system: speed and exhaust temperature control

“…In this context an adaptive neuro-fuzzy inference system (ANFIS)-based hybrid control approach is proposed in this paper. The main objective of the ANFIS control is to ensure the proper control of the speed and the exhaust temperature of a gas turbine based on the Rowen model [18]. It is important to note that the control system proposed in this paper is able to cope with changes in environmental parameters such as ambient temperature, not only by reducing the controlled parameter errors, but also by improving the quality of the response time and reducing the maximum overshoot of the proposed control system.…”

“…This paper proposes the use of the adaptive neuro-fuzzy inference system (ANFIS) approach to ensure the control of the speed and the exhaust temperature of a gas turbine based on Rowen model [18], in order to maintain its optimum performance.…”

“…The representation of the HDGT studied here is based on the Rowen model [6]. In the Rowen model the control system is based on three typical control loops: a speed control loop, a temperature control loop, and an acceleration control loop [9,[11][12][13]18]. The main data of a representative HDGT turbine studied here are assembled in Table 1.…”

“…Furthermore, these linguistic variables are processed using rules that refer to some knowledge of the system behavior [18].…”