Design of an Interval Fuzzy Type-2- PID Controller for a Gas Turbine Power Plant

In this paper, an interval fuzzy type-2 PID controllers are designed for speed and Exhaust temperature in a heavy duty Gas Turbine (HDGT) power plant and the model selected is Rowen’s model to present the mechanical behavior of the gas turbine, the work is aimed to improve the system dynamic performance of speed and Exhaust temperature for a 56.6 MW, 50 HZ, simple cycle, single shaft heavy duty gas turbine, all gains for conventional  PID and interval fuzzy type-2 PID are tuned using Social Spider Optimization(SSO) technique, we show the performance improvement for interval fuzzy type -2 PID controller in comparison with conventional PID via simulation

analysis of a simplified steam turbine governor model for power system stability studies

Abstract The present study describes an analysis performed on a simplified Steam Turbine governor model, which is useful for pre-tuning the machine regulation system. A dynamic model has been implemented in two different simulation tools, namely DigSILENT PowerFactory and Matlab/Simulink, to the aim of verifying the suitability of the latter one for power system stability studies. The proposed work paves the way to the wide range of possibilities connected to the integration of the machine governor model with other simulation blocks of a Combined Cycle Plant, by enabling the opportunity for pre-commissioning of the regulation system together with the analysis of the fulfillment of grid code regulations

Simulation Models for Analysis and Optimization of Gas Turbine Cycles

This Thesis deals with gas turbine cycle simulation models. In the firs t part it has been analysed the gas cycle theory and the models present in literature starting from the simplest ones to the most advanced ones. In the second part different models have been developed using Aspen HYSY S, a widely used process modelling tool for conceptual design, optimisat ion, business planning, asset management, and performance monitoring. Fu rthermore, parametric analysis has been undertaken for each model to fin d out which variables have the most significant effect on overall effici ency. With these variables a model optimization has been conducted using the Hyprotech SQP optimiser, a rigorous sequential quadratic programmin g (SQP) optimisation solver

Start-up vibration analysis for novelty detection on industrial gas turbines

This paper focuses on industrial application of start-up vibration signature analysis for novelty detection with experimental trials on industrial gas turbines (IGTs). Firstly, a representative vibration signature is extracted from healthy start-up vibration measurements through the use of an adaptive neuro-fuzzy inference system (ANFIS). Then, the first critical speed and the vibration level at the critical speed are located from the signature. Finally, two (s- and v-) health indices are introduced to detect and identify different novel/fault conditions from the IGT start-ups, in addition to traditional similarity measures, such as Euclidean distance and cross-correlation measures. Through a case study on IGTs, it is shown that the presented approach provides a convenient and efficient tool for IGT condition monitoring using start-up field data

Propulsion Electric Grid Simulator (PEGS) for Future Turboelectric Distributed Propulsion Aircraft

NASA Glenn Research Center, in collaboration with the aerospace industry and academia, has begun the development of technology for a future hybrid-wing body electric airplane with a turboelectric distributed propulsion (TeDP) system. It is essential to design a subscale system to emulate the TeDP power grid, which would enable rapid analysis and demonstration of the proof-of-concept of the TeDP electrical system. This paper describes how small electrical machines with their controllers can emulate all the components in a TeDP power train. The whole system model in Matlab/Simulink was first developed and tested in simulation, and the simulation results showed that system dynamic characteristics could be implemented by using the closed-loop control of the electric motor drive systems. Then we designed a subscale experimental system to emulate the entire power system from the turbine engine to the propulsive fans. Firstly, we built a system to emulate a gas turbine engine driving a generator, consisting of two permanent magnet (PM) motors with brushless motor drives, coupled by a shaft. We programmed the first motor and its drive to mimic the speed-torque characteristic of the gas turbine engine, while the second motor and drive act as a generator and produce a torque load on the first motor. Secondly, we built another system of two PM motors and drives to emulate a motor driving a propulsive fan. We programmed the first motor and drive to emulate a wound-rotor synchronous motor. The propulsive fan was emulated by implementing fan maps and flight conditions into the fourth motor and drive, which produce a torque load on the driving motor. The stator of each PM motor is designed to travel axially to change the coupling between rotor and stator. This feature allows the PM motor to more closely emulate a wound-rotor synchronous machine. These techniques can convert the plain motor system into a unique TeDP power grid emulator that enables real-time simulation performance using hardware-in-the-loop (HIL)

Propulsion Powertrain Real-Time Simulation Using Hardware-in-the-Loop (HIL) for Aircraft Electric Propulsion System

It is essential to design a propulsion powertrain real-time simulator using the hardware-in-the-loop (HIL) system that emulates an electrified aircraft propulsion (EAP) systems power grid. This simulator would enable us to facilitate in-depth understanding of the system principles, to validate system model analysis and performance prediction, and to demonstrate the proof-of-concept of the EAP electrical system. This paper describes how subscale electrical machines with their controllers can mimic the power components in an EAP powertrain. In particular, three powertrain emulations are presented to mimic 1) a gas turbo-=shaft engine driving a generator, consisting of two permanent magnet (PM) motors with brushless motor drives, coupled by a shaft, 2) a motor driving a propulsive fan, and 3) a turbo-shaft engine driven fan (turbofan engine) operation. As a first step towards the demonstration, experimental dynamic characterization of the two motor drive systems, coupled by a mechanical shaft, were performed. The previously developed analytical motor models1 were then replaced with the experimental motor models to perform the real-time demonstration in the predefined flight path profiles. This technique can convert the plain motor system into a unique EAP power grid emulator that enables rapid analysis and real-time simulation performance using hardware-in-the-loop (HIL)

Performance assessment of classical and fractional controllers for transient operation of gas turbine engines

The nonlinear behaviour of gas turbine engines has motivated the development of advanced controllers for ensuring their safe and reliable operation. In this paper, the problem of controller design for a two-shaft industrial gas turbine is addressed. Specifically, a transient dynamic engine model has been developed in MATLAB/Simulink for assessing the performance behavior of the engine. Observed engine behavior during transient manoeuvres has enabled the development of a PI controller capable of ensuring a smooth gas turbine operation. The performance of the gas turbine engine implementing the developed PI controller has been also compared to a fractional PI controller. Results demonstrate and illustrate the remarkable impact that transient engine simulation has in the development of robust controllers.Scopu

Modelling Axial Turbomachinery for Compressed Air Energy Storage

Climate change and negative environmental effects from a human’s life style have ignited the search for grid-scale electricity production from renewable energy sources. Significant advances with respect to renewable energy conversion into electricity has been achieved in recent years. However, their integration with the electrical grid is still an unsolved problem because the natural intermittency associated with them. Grid-scale energy storage can be the solution to the intermittency problem; of all the types or storage, compressed air energy storage (CAES) is one of the most promising potential solutions to effectively integrate renewable energies to the grid. CAES consists in compressing air during times when the electricity production is larger than the demand. This compressed air is stored in a huge reservoir; it is then used during peak demand times to run gas turbines which are connected to power generators. Literature about steady state CAES is widely available; topics such as system efficiency, fuel free CAES, and reservoir sizing being arguably the most commonly published. However, there are very few works about dynamic state of a CAES system; and there are next to no publications regarding the transient modelling of the turbomachinery for CAES. This thesis develops two independent dynamic models of axial turbomachines for CAES systems; one for a compressor and one for a turbine. These models should be able to represent the behavior of the machines, as well as easily connect with the rest of the components of the system. For these reasons, the models are developed in the SIMULINK® environment, which is a tool commonly used for electrical modelling. The models use the time domain, and are based on compressor and turbine performance maps, which make them representative, and let them require a short simulation time. The objective of the simulations is to see how the models behave with regards to power inefficiency, part-load operation; and the effects on the complete operation caused by the system inertias. The models can replicate the operation of any axial turbomachine provided the respective map, and can be also used with centrifugal compressors. Upon running the simulations, a round trip efficiency of 55% was obtained. Furthermore, it takes 296 seconds to increase the pressure of a cavern of 3000 m3 from 422.18 Kpa to 747.4 Kpa. The simulated turbine of 167 MW and design speed of 17000 rpm, with a nominal mass flow rate of 400 kg/s, will take 778 seconds to reach its nominal speed

Predictive Maintenance of Critical Equipment for Floating Liquefied Natural Gas Liquefaction Process

Predictive Maintenance of Critical Equipment for Liquefied Natural Gas Liquefaction Process Meeting global energy demand is a massive challenge, especially with the quest of more affinity towards sustainable and cleaner energy. Natural gas is viewed as a bridge fuel to a renewable energy. LNG as a processed form of natural gas is the fastest growing and cleanest form of fossil fuel. Recently, the unprecedented increased in LNG demand, pushes its exploration and processing into offshore as Floating LNG (FLNG). The offshore topsides gas processes and liquefaction has been identified as one of the great challenges of FLNG. Maintaining topside liquefaction process asset such as gas turbine is critical to profitability and reliability, availability of the process facilities. With the setbacks of widely used reactive and preventive time-based maintenances approaches, to meet the optimal reliability and availability requirements of oil and gas operators, this thesis presents a framework driven by AI-based learning approaches for predictive maintenance. The framework is aimed at leveraging the value of condition-based maintenance to minimises the failures and downtimes of critical FLNG equipment (Aeroderivative gas turbine). In this study, gas turbine thermodynamics were introduced, as well as some factors affecting gas turbine modelling. Some important considerations whilst modelling gas turbine system such as modelling objectives, modelling methods, as well as approaches in modelling gas turbines were investigated. These give basis and mathematical background to develop a gas turbine simulated model. The behaviour of simple cycle HDGT was simulated using thermodynamic laws and operational data based on Rowen model. Simulink model is created using experimental data based on Rowen’s model, which is aimed at exploring transient behaviour of an industrial gas turbine. The results show the capability of Simulink model in capture nonlinear dynamics of the gas turbine system, although constraint to be applied for further condition monitoring studies, due to lack of some suitable relevant correlated features required by the model. AI-based models were found to perform well in predicting gas turbines failures. These capabilities were investigated by this thesis and validated using an experimental data obtained from gas turbine engine facility. The dynamic behaviours gas turbines changes when exposed to different varieties of fuel. A diagnostics-based AI models were developed to diagnose different gas turbine engine’s failures associated with exposure to various types of fuels. The capabilities of Principal Component Analysis (PCA) technique have been harnessed to reduce the dimensionality of the dataset and extract good features for the diagnostics model development. Signal processing-based (time-domain, frequency domain, time-frequency domain) techniques have also been used as feature extraction tools, and significantly added more correlations to the dataset and influences the prediction results obtained. Signal processing played a vital role in extracting good features for the diagnostic models when compared PCA. The overall results obtained from both PCA, and signal processing-based models demonstrated the capabilities of neural network-based models in predicting gas turbine’s failures. Further, deep learning-based LSTM model have been developed, which extract features from the time series dataset directly, and hence does not require any feature extraction tool. The LSTM model achieved the highest performance and prediction accuracy, compared to both PCA-based and signal processing-based the models. In summary, it is concluded from this thesis that despite some challenges related to gas turbines Simulink Model for not being integrated fully for gas turbine condition monitoring studies, yet data-driven models have proven strong potentials and excellent performances on gas turbine’s CBM diagnostics. The models developed in this thesis can be used for design and manufacturing purposes on gas turbines applied to FLNG, especially on condition monitoring and fault detection of gas turbines. The result obtained would provide valuable understanding and helpful guidance for researchers and practitioners to implement robust predictive maintenance models that will enhance the reliability and availability of FLNG critical equipment.Petroleum Technology Development Funds (PTDF) Nigeri