Soft Computation of Turbine Inlet Temperature of Gas Turbine Power Plant Using Type-2 Fuzzy Logic Systems

Abstract

This paper aims to demonstrate application of Type-2 Fuzzy Logic Systems (FLS) to predict a critical parameter of Gas Turbine in a power plant viz., the Turbine Inlet Temperature (TIT). Maintaining higher TIT than allowed severely affects the life of the components whereas operating at lower TIT may cause low efficiency and low load. Nonavailability of TIT, which cannot be measured directly, puts great limitations on efficient gas turbine operation. Accurate estimation of this parameter requires significant computing power and the time required places limitations on the conventional modeling methods for use in real time applications. It is also demonstrated here by way of comparison, that a type-2 FLS is more robust in the presence of noise uncertainties than a type-1 conventional FLS for this application. Results are verified through the practical plant data obtained from an 88 MW gas turbine power plant.

Industrial applications of type-2 fuzzy sets and systems: A concise review

“…Gupta et al [40] demonstrated an application of type-2 fuzzy logic systems to predict turbine inlet temperature (TIT) of gas turbines in a power plant viz. They also demonstrated that type-2 fuzzy logic systems are more robust in the presence of noise uncertainties than type-1 conventional fuzzy logic systems.…”

Generalized Type-2 Fuzzy Systems for controlling a mobile robot and a performance comparison with Interval Type-2 and Type-1 Fuzzy Systems

“…design method for single input IT2 fuzzy PID controllers(Kumbasar, 2013).In recent research of FCs, especially in relation to IT2FCs, resilience to external perturbations has been an important topic, as well as the focus of this paper, e.g. study on the robustness of T1 and IT2 FLS in control when dealing with small deviations of the sampling points(Biglarbegian, Melek, & Mendel, 2011), GA optimization of an IT2 FC for a perturbed mobile robot(Martínez, Castillo, & Aguilar, 2009), Type-2 fuzzy sliding controller for a wing rock system(Tao et al, 2012), motion planning in dynamic and unknown environments using an IT2 FC(Baklouti & Alimi, 2007), IT2 FLS to predict a critical parameter of gas in a power plant in the presence of noise uncertainties(Gupta, Pareek, & Kar, 2007), IT2 FL congestion control for video streaming with the presence on uncertainties associated of a dynamic network environment (Jammeh, Fleury, Wagner, Hagras, & Ghanbari, 2009), online rule generation and Q-Value-Aided ACO for IT2 FC with a focus on noise robustness (Chia-Feng Juang & Chia-Hung Hsu, 2009), Type-2 sliding mode control for nonlinear systems with uncertainty and external perturbations (Al-khazraji, Essounbouli, Hamzaoui, Nollet, & Zaytoon, 2011), observer-based adaptive IT2 FC for nonlinear MIMO systems involving external disturbances (Lin, 2010), IT2fuzzy sliding mode control of a z-axis MEMS gyroscope which is robust to external disturbances and measurement noise(Fazlyab, Pedram, Salarieh, & Alasty, 2013). As for GT2FC, with the consideration that research in GT2FS is fairly recent and still very limited in quantity; no other publications exist as of the writing of this paper where a GT2FC is implemented.…”