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Tucker, David

Publications and source records attributed to Tucker, David.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Analyzing Gas Turbine-Generator Performance of the Hybrid Power System

We report kinetic energy available from the turbomachinery may boost short-term ramp rates during load shifting between generator and fuel cell that may be exploited to optimize the performance of turbine-fuel cell hybrid systems during load following. The paper starts with modeling a gas turbine, gearbox, and generator sitting on the rigid shafts as a two-mass model primarily for estimating the rotational inertia available from the turbogenerator. The gas turbine and its environment impose constraints of available space allowances, measurements from high-speed rotating parts, lack of accessibility to the shaft of measure, making replacements and/or required installation adjustments of the torque transducer difficult. It is demonstrated that in the absence of torque measurement, the data containing only a slow excursion of the rotor motion in response to a slight load change is sufficient to determine inertia and damping coefficients from the model. Furthermore, the generator’s reactive power capability was investigated because it serves as an asset in integrated hybrid systems to meet dynamic reactive power demands, reducing the burden on the fuel cell’s power electronics control in generating reactive power.

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DOE OSTI2021

Online System ID for Predicting Power Plant Performance Throughout Cycling Operations

This presentation represents a review of the background research conducted by NETL to apply artificial intelligence, i.e. auto-recursive algorithms and data analytics to detect leaks in utility scale boilers and laboratory power systems. The new project being funded by the Advanced Sensors and Controls Program is part of the Field Work Proposal funded in EY21 as Task 53 to demonstrate the application of these techniques on a utility scale power system.

Shadle, Lawrence↗
DOE OSTI2021

RIC Advanced Sensors & Controls FWP: Overview

Overview of the NETL RIC Advanced Sensors & Controls FWP. Progress from EY2020 and looking ahead to EY2021. Introduces several other presentations by project PIs at the Crosscutting Research Sensors & Controls Annual Review meeting, May 2021.

coal fired boiler↗
DOE OSTI2020

Multiobjective Optimal Controlled Variable Selection for a Gas Turbine–Solid Oxide Fuel Cell System Using a Multiagent Optimization Platform

Hybrid gas turbine–fuel cell systems have immense potential for high efficiency in electrical power generation with cleaner emissions compared with fossil-fueled power generation. We report a systematic controlled variable (CV) selection method is deployed for a hybrid gas turbine–fuel cell system in the HyPer (hybrid performance) facility at the U.S. Department of Energy’s National Energy Technology Laboratory (NETL) for maximizing its economic and control performance. A three-stage approach is used for the CV selection comprising a priori analysis, multiobjective optimization, and a posteriori analysis. The a priori analysis helps to screen off several candidate CVs, thus reducing the size of the combinatorial optimization problem for multiobjective CV selection. For optimal CV selection, a transfer function model of the HyPer facility is identified. By considering several candidate models, the final transfer function model is selected using Akaike’s Final Prediction Error criterion. Experimental data from the HyPer facility are used to estimate the noise in the measurement data. For solving the combinatorial multiobjective optimization problem for CV selection, a multiagent optimization platform comprising simulated annealing, genetic algorithm, and efficient ant colony optimization algorithms is used. Pareto-optimal CV sets exhibit a high trade-off between the economic and control objective. The a posteriori analysis is undertaken for several top Pareto-optimal CV sets. An optimal CV set is selected that shows the best compromise between process economics and controllability under both nominal and off-design conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Advanced Sensors & Controls FWP Overview

Fossil energy power generation is needed now and in the future, but its role is shifting from baseload operation to fulfilling dispatchable power needs in regions of the United States. Novel sensors and controls will help to increase efficiency, minimize emissions, and reduce operating costs of existing power generation technologies under this increased load following role; and help enable next generation power systems with high efficiency and greater operational flexibility.<br>

Buric, Michael↗
DOE OSTI2020

Development of Real-Time System Identification to Detect Abnormal Operations in a Gas Turbine Cycle

Here, we present a novel online system identification methodology for monitoring the performance of power systems. This methodology was demonstrated in a gas turbine recuperated power plant designed for a hybrid configuration. A 120-kW Garrett microturbine modified to test dynamic control strategies for hybrid power systems designed at the National Energy Technology Laboratory (NETL) was used to implement and validate this online system identification methodology. The main component of this methodology consists of an empirical transfer function model implemented in parallel to the turbine speed operation and the fuel control valve, which can monitor the process response of the gas turbine system while it is operating. During fully closed-loop operations or automated control, the output of the controller, fuel valve position, and the turbine speed measurements were fed for a given period of time to a recursive algorithm that determined the transfer function parameters during the nominal condition. After the new parameters were calculated, they were fed into the transfer function model for online prediction. The turbine speed measurement was compared against the transfer function prediction, and a control logic was implemented to capture when the system operated at nominal or abnormal conditions. To validate the ability to detect abnormal conditions during dynamic operations, drifting in the performance of the gas turbine system was evaluated. A leak in the turbomachinery working fluid was emulated by bleeding 10% of the airflow from the compressor discharge to the atmosphere, and electrical load steps were performed before and after the leak. This tool could detect the leak 7 s after it had occurred, which accounted for a fuel flow increase of approximately 15.8% to maintain the same load and constant turbine speed operations.

algorithms↗
DOE OSTI2018

Fuel utilization effects on system efficiency in solid oxide fuel cell gas turbine hybrid systems

Here, a computational analysis was conducted to optimize the design of a solid oxide fuel cell - gas turbine hybrid power generator, focusing on the impact that fuel utilization within the fuel cell has on system efficiency and installed costs. This is the first ever design-study considering the effect of fuel utilization on performance, as well as on the optimum power split. This hybrid system attained high electric generation efficiencies (>70%) over a wide range of operating conditions (60% < fuel utilization < 90%) while the fuel cell stack size decreased in proportion to decreasing the fuel utilization. A one-dimensional fuel cell model was used to simulate the fuel cell while GateCycle® was used to simulate the performance of the associated recuperated turbine and various subsystems necessary for thermal management. For each test case, the size of the solid oxide fuel cell, gas turbine, and recuperator, as well as the fuel and air flow rates, hot-air bypass set point, and heat exchange effectiveness in the solid oxide fuel cell manifold were varied to obtain 550 MWe output. In addition, anode recycle, turbomachinery efficiency, and various thermal management options were tested. The maximum system efficiency (75.6%) was attained for the single-pass solid oxide fuel cell with highly efficient turbomachinery when the solid oxide fuel cell used 80% of the incoming fuel. Efficiency was essentially flat from 75% fuel utilization through 85% fuel utilization. Employing anode recycle starting at 65% resulted in roughly 1 percentage point efficiency decrease for each percent increase in fuel utilization. For minimized solid oxide fuel cell degradation, a near 50:50 power split case was studied resulting in 68.6% efficiency and the solid oxide fuel cell using 55% of the incoming fuel. Because of shifting half of the power generation to the gas turbine, the size of the fuel cell stack was reduced by 25% as compared to that at maximum efficiency (80% fuel utilization).

42 ENGINEERING↗
DOE OSTI2018

Multicoordination Control Strategy Performance in Hybrid Power Systems

This paper evaluates a state-space methodology of a multi-input multi-output (MIMO) control strategy using a 2 × 2 tightly coupled scenario applied to a physical gas turbine fuel cell hybrid power system. A centralized MIMO controller was preferred compared to a decentralized control approach because previous simulation studies showed that the coupling effect identified during the simultaneous control of the turbine speed and cathode airflow was better minimized. The MIMO controller was developed using a state-space dynamic model of the system that was derived using first-order transfer functions empirically obtained through experimental tests. The controller performance was evaluated in terms of disturbance rejection through perturbations in the gas turbine operation, and setpoint tracking maneuver through turbine speed and cathode airflow steps. The experimental results illustrate that a multicoordination control strategy was able to mitigate the coupling of each actuator to each output during the simultaneous control of the system, and improved the overall system performance during transient conditions. On the other hand, the controller showed different performance during validation in simulation environment compared to validation in the physical facility, which will require a better dynamic modeling of the system for the implementation of future multivariable control strategies.

24 POWER TRANSMISSION AND DISTRIBUTION↗