Gas Turbine Speed Monitoring Using a Generalized Predictive Adaptive Control Algorithm

Abstract

The outstanding performance of modern gas turbine power plants requires an efficient and robust control strategy in order to recover the losses caused by the undesirable effects and the instabilities related to their operation. Also, these losses can be the cause of their unstable dynamic behaviour during full-load and partial load operation in the unplanned shutdown state. Hence, choosing the right control strategy can improve the operation of this type of machine by increasing its efficiency by up to 60%. This paper proposes the implementation of an innovative control strategy based on a generalized predictive adaptive control algorithm for monitoring the rotation speed of a Solar Titan 130 gas turbine, with the purpose of considering the constraints related to the nonlinear behavior of a turbine during large variations in machine load. The aim is to automatically adjust the parameters of the regulator of the turbine's control loop in real time, with a recursive estimation of these parameters. Using the experimental input/output measurements in order to study its behavior, by integrating the predictive control estimators, ensures the superior performance of the analyzed gas turbine in terms of energy, efficiency and robustness regarding the parametric uncertainties and the variation of the turbine load.

A Comparative Modeling Study of Gas Turbine Using Adaptive Neural Network, Nonlinear Autoregressive Exogenous, and Fuzzy Logic Approaches for Modeling and Control

“…Fuzzy logic has been extensively applied in industrial applications, including system control, modeling and diagnosis, process monitoring, speech and image processing, data classification, decisionmaking, and more. Notable research studies in these areas include the works of Hafaifa et al [4], Ahmed Zohair Djeddi et al [5], Bezdek et al [11], Boon Chiang et al [12], Choayb Djeddi et al [13], Gustafson et al [17], Hakim Bagua et al [18], Jang et al [21], Merouane Alaoui et al [27][28], Nadji Hadroug et al [35][36] and Sidali Aissat et al [41][42]. The advantage of using fuzzy logic lies in its ability to incorporate human reasoning into analytical models.…”

ARMAX-based identification and diagnosis of vibration behavior of gas turbine bearings

“…Sidali Aissat et al [27] employed a multi-model approach for the identification of turbine variables, using decoupled states to detect defects through residue generation based on parity space. Merouane Alaoui et al [14] proposed a turbine monitoring strategy using a generalized predictive adaptive control structure for turbine speed monitoring, which demonstrated a good response time and efficiency in regulating turbine parameters.…”