Identification and Fault Diagnosis of an Industrial Gas Turbine Using State-Space Methods

2011 ◽  
Vol 383-390 ◽  
pp. 1000-1006
Author(s):  
Iman Yousefi ◽  
Hamid Khaloozadeh ◽  
Ali Ashraf-Modarres
Keyword(s):  
State Space ◽  
Gas Turbine ◽  
State Space Model ◽  
Fault Detection And Isolation ◽  
Neural Network Classifier ◽  
Detection Scheme ◽  
State Space Methods ◽  
Space Model ◽  
Industrial Gas Turbine ◽  
Residual Generation

The objective of this paper is to identify, detect and isolate faults to an industrial gas turbine. The detection scheme is based on the generation of so-called "residuals" that are errors between estimated and measured variables of the process. A State-Space model is used for identification and some observer-based methods are used for residual generation, while for residual evaluation a neural network classifier for MLP is used. The proposed fault detection and isolation tool has been tested on a single-shaft industrial gas turbine simulator.

Development of L1-norm sliding mode observer for sensor fault diagnosis of an industrial gas turbine

2021 ◽  
pp. 095965182199617
Author(s):  
Mahyar Akbari ◽  
Abdol Majid Khoshnood ◽  
Saied Irani
Keyword(s):  
Fault Detection ◽  
State Space ◽  
Gas Turbine ◽  
Sliding Mode ◽  
State Space Model ◽  
Sensor Fault ◽  
Space Model ◽  
Decision Logic ◽  
Sensor Fault Detection ◽  
Industrial Gas Turbine

In this article, a novel approach for model-based sensor fault detection and estimation of gas turbine is presented. The proposed method includes driving a state-space model of gas turbine, designing a novel L1-norm Lyapunov-based observer, and a decision logic which is based on bank of observers. The novel observer is designed using multiple Lyapunov functions based on L1-norm, reducing the estimation noise while increasing the accuracy. The L1-norm observer is similar to sliding mode observer in switching time. The proposed observer also acts as a low-pass filter, subsequently reducing estimation chattering. Since a bank of observers is required in model-based sensor fault detection, a bank of L1-norm observers is designed in this article. Corresponding to the use of the bank of observers, a two-step fault detection decision logic is developed. Furthermore, the proposed state-space model is a hybrid data-driven model which is divided into two models for steady-state and transient conditions, according to the nature of the gas turbine. The model is developed by applying a subspace algorithm to the real field data of SGT-600 (an industrial gas turbine). The proposed model was validated by applying to two other similar gas turbines with different ambient and operational conditions. The results of the proposed approach implementation demonstrate precise gas turbine sensor fault detection and estimation.

Health Estimation of Gas Turbine: A Symbolic Linearization Model Approach

2017 ◽  
Author(s):  
Qingcai Yang ◽  
Yunpeng Cao ◽  
Fang Yu ◽  
Jianwei Du ◽  
Shuying Li
Keyword(s):  
Steady State ◽  
State Space ◽  
Gas Turbine ◽  
Linear Model ◽  
Piecewise Linear ◽  
State Space Model ◽  
Space Model ◽  
Linear State ◽  
Model Approach ◽  
Health Parameters

This paper is mainly concerned with the health estimation of a gas turbine using a symbolic linearization model approach. Health parameters will change with the degradation of gas turbine performance. Monitoring and evaluating these health parameters can assist in the development of predictive control techniques and maintenance schedules. Currently, various health parameter estimation methods have been studied extensively, but there have been less related studies on how to obtain statespace models. In this paper, a symbolic linearization model method is presented to overcome the shortcoming of high time consumption suffered by existing methods. In this method, each component model of the dynamic nonlinear gas turbine model is decomposed into several sub-modules, each of which contains a simple nonlinear equation. By means of symbolic computation, a linear model of the components is derived by linearizing these sub-modules, and then the generalized linear state-space model of the gas turbine is derived from the relationship among the components. In the generalized linear state-space model, the Jacobian matrices are functions of the parameters under a steady-state operating condition. Therefore, it is easy to obtain a linear model that represents the dynamics of the gas turbine under a given operating condition. To estimate the health parameters of a gas turbine, a piecewise linear model is developed using the proposed approach, and this model is verified in a simulation environment. The results show that the developed piecewise linear model can capture the behavior of a gas turbine quite closely. Then, a linearized Kalman filter is designed for estimating the health parameters under steady-state and transient conditions. The results show that the generalized linear model established using the presented method can be used to accurately estimate the health parameters of a gas turbine.

Simulation of the Dynamic Braking Process of Gas Turbine Propulsion System for a Ship

1997 ◽  
Author(s):  
Zang Shusheng ◽  
Zheng Hongtao ◽  
Dong Keyong
Keyword(s):  
Mathematical Model ◽  
State Space ◽  
Gas Turbine ◽  
State Space Model ◽  
Propulsion System ◽  
Discrete Simulation ◽  
Space Model ◽  
Braking Process ◽  
Multivariate Systems ◽  
Dynamic Braking

Based on a ship driven by two controllable pitch propellers (CPP), one single CPP driven by one single gas turbine (GT), a mathematical model for a propulsion system is deduced, and its state-space model is established. In this paper, it is verified that the use of Phase-Linearized Discrete Simulation (PLDS) to estimate large perturbations is practicable. Especially for multivariate systems, this method appears to be rather convenient. We also present the results of simulation to the dynamic braking process of gas turbine propulsion system for a ship at the combined speed/power governing mode and the power governing mode of gas turbine.

Modeling of CCLP in koryo extract production

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Ji Chol ◽  
Ri Jun Il
Keyword(s):  
Process Control ◽  
Medical Care ◽  
State Space ◽  
State Space Model ◽  
The State ◽  
Space Model ◽  
Effective Components ◽  
Counter Current

Abstract The modeling of counter-current leaching plant (CCLP) in Koryo Extract Production is presented in this paper. Koryo medicine is a natural physic to be used for a diet and the medical care. The counter-current leaching method is mainly used for producing Koryo medicine. The purpose of the modeling in the previous works is to indicate the concentration distributions, and not to describe the model for the process control. In literature, there are no nearly the papers for modeling CCLP and especially not the presence of papers that have described the issue for extracting the effective components from the Koryo medicinal materials. First, this paper presents that CCLP can be shown like the equivalent process consisting of two tanks, where there is a shaking apparatus, respectively. It allows leachate to flow between two tanks. Then, this paper presents the principle model for CCLP and the state space model on based it. The accuracy of the model has been verified from experiments made at CCLP in the Koryo Extract Production at the Gang Gyi Koryo Manufacture Factory.

Reduced-Order State Space Model for Dynamic Phasors in Active Distribution Networks

2020 ◽  
Vol 11 (3) ◽  
pp. 1928-1941
Author(s):  
Huifang Wang ◽  
Kuan Jiang ◽  
Mohammad Shahidehpour ◽  
Benteng He
Keyword(s):  
State Space ◽  
State Space Model ◽  
Distribution Networks ◽  
Reduced Order ◽  
Space Model ◽  
Dynamic Phasors ◽  
Active Distribution Networks ◽  
Order State
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