학술논문

Modeling and identification of an industrial gas turbine using classical and non-classical approaches
Document Type
Conference
Source
2017 Iranian Conference on Electrical Engineering (ICEE) Electrical Engineering (ICEE), 2017 Iranian Conference on. :667-672 May, 2017
Subject
Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Load modeling
Turbines
Lead
Software packages
System identification
Heavy Duty Gas Turbine
Neural Networks
GGOV1 model
Language
Abstract
In this paper, data-driven modeling and identification of an industrial, simple cycle, heavy duty Gas Turbine is taken into consideration. The GGOV1 model that was introduced by Western Electricity Coordinating Council (WECC), is suggested here for describing dynamics and behaviour of Gas Turbines. It is shown that GGOV1 model that is expressed as the classical approach, can fulfill our needs in academic studies and engineering purposes. Non-classical identification of Gas turbine is also done via Artificial Neural Networks. Comparison between the two methods and real data shows reliability and acceptable precision of both models.