Neural network controller for microturbine power plants

Sisworahardjo, N.S.; El-Sharkh, M.Y.; Alam, Mohammad Saad

doi:10.1016/j.epsr.2007.12.004

Cited by 40 publications

(21 citation statements)

References 8 publications

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“…The closed-loop system containing system (5), the disturbance observer system (8), and the proposed controller (38) can be rendered globally ISS provided that 1 ≥ 5, 2 ≥ 4, 3 ≥ 3, and 4 ≥ 2.…”

Section: Theorem 6 Assume Assumption 4 Is Satisfied In System (5)mentioning

confidence: 99%

“…In the work [8], the speed control loop adopts an artificial neural network, which refers a better load following performance, but the mechanical power output experiences a momentary delay with the rotor speed decreasing/increasing. On the other hand, most of the existing results can be regarded as an "inactive" antidisturbance approach.…”

Section: Introductionmentioning

confidence: 99%

“…Noticing that the nonlinearities and disturbances are inevitable in the MT system, several nonlinear controllers [7][8][9][10] and so forth are designed to control the output speed of the MT system. In the work [8], the speed control loop adopts an artificial neural network, which refers a better load following performance, but the mechanical power output experiences a momentary delay with the rotor speed decreasing/increasing.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust Speed Tracking Control for a Micro Turbine as a Distributed Energy Resource via Feedback Domination and Disturbance Observer

Chen

2017

Mathematical Problems in Engineering

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Micro turbine (MT) is characterized with complex dynamics, parameter uncertainties, and variable working conditions. In this paper, a novel robust controller is investigated for a single-shaft micro turbine as a distributed energy resource by integrating a feedback domination control technique and a feedforward disturbance compensation. An active estimation process of the mismatched disturbances is firstly enabled by constructing a disturbance observer. Secondly, we adopt a feedback domination technique, rather than popularly used feedback linearization methods, to handle the system nonlinearities. In an explicit way, the composite controllers are then derived by recursive design based on Lyapunov theory while a global input-to-state stability can be guaranteed. Abundant comparison simulation results are provided to demonstrate the effectiveness of the proposed scheme, which not only perform an improved closed-loop control performance comparing to all existing results, but also render a simple control law which will ease its practical implementation.

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Section: Theorem 6 Assume Assumption 4 Is Satisfied In System (5)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust Speed Tracking Control for a Micro Turbine as a Distributed Energy Resource via Feedback Domination and Disturbance Observer

Chen

2017

Mathematical Problems in Engineering

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“…Besides, AMPC could be gained analytically, required less computational time and avoided local minima. Sisworahardjo et al (2008) presented a neural network controller for power plant micro gas turbines (MGT). They applied both proportional-integrator (PI) and ANN controllers to control voltage, speed, temperature and power.…”

Section: Black-box Modelsmentioning

confidence: 99%

Design of conventional and neural network based controllers for a single-shaft gas turbine

Asgari

Jegarkandi

Chen

et al. 2017

AEAT

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Purpose -The purpose of this paper is to develop and compare conventional and neural network based controllers for gas turbines.Design/methodology/approach -Design of two different controllers is considered. These controllers consist of a NARMA-L2 which is an ANN-based nonlinear autoregressive moving average (NARMA) controller with feedback linearization, and a conventional proportional-integrator-derivative (PID) controller for a low-power aero gas turbine.They are briefly described and their parameters are adjusted and tuned in Simulink-MATLAB environment according to the requirement of the gas turbine system and the control objectives. For this purpose, Simulink and neural network based modelling is employed. Performances of the controllers are explored and compared on the base of design criteria and performance indices.Findings -It is shown that NARMA-L2, as a neural network based controller, has a superior performance to the PID controller.Practical implications -Using artificial intelligence in gas turbine control systems.Originality/value -Providing a novel methodology for control of gas turbines.

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“…The Many different excitation methods have been put forward recently. IEEE has decided to prepare a standard about the excitation buildings of synchronous generators, and these have been classified as three DC-based excitations, seven AC-based excitations and three static-based excitations [2,3].…”

Section: Introductionmentioning

confidence: 99%

Development of a Renewable Energy Based DC Excitation System in a Micro Hydro Power Plant

Özdemir¹,

Bayrak²,

Gençoğlu³

et al. 2016

JEPE

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MHPPs (micro hydro power plants) have become prominent in hydropower plants as a solution to provide the energy demands of the grid. In this study, a new hybrid renewable energy based DC excitation system for synchronous generator in the developed MHPP system is introduced. Proposed hybrid DC excitation system consists of solar & hydrogen energy based power generating systems. Hybrid renewable energy based system is used for the excitation of the synchronous generator in the MHPP test system. The renewables are used as a secondary energy source to provide the excitation current to a synchronous generator that generates energy in MHPP. A PV (photovoltaic) array is used as the main source of excitation, and a FC (fuel cell) stack is used for DC excitation in the lack of sunshine. In the experimental setup, an electrical control card is developed, and a microcontroller is used to perform the proposed excitation system. All experimental results obtained from 5 kW rated power MHHP test system. Experimental results show that, the proposed method provides the continuous excitation current, and the operation of the synchronous generator is uninterrupted. The proposed method is also practical and easily implemented for MHPP systems.

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Neural network controller for microturbine power plants

Cited by 40 publications

References 8 publications

Robust Speed Tracking Control for a Micro Turbine as a Distributed Energy Resource via Feedback Domination and Disturbance Observer

Robust Speed Tracking Control for a Micro Turbine as a Distributed Energy Resource via Feedback Domination and Disturbance Observer

Design of conventional and neural network based controllers for a single-shaft gas turbine

Development of a Renewable Energy Based DC Excitation System in a Micro Hydro Power Plant

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