Control of Heavy-duty Gas Turbine Plants for Parallel Operation Using Soft Computing Techniques

Balamurugan, S.; Xavier, R. Joseph; Jeyakumar, A. Ebenezer

doi:10.1080/15325000902994371

Cited by 25 publications

(32 citation statements)

References 12 publications

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“…Model parameters of various blocks in the simplified model are described in [15,17]. Because of the drooping nature of the speed governor, the steady-state responses of HDGT models are poor [17]. In order to improve the dynamic as well as steady-state responses, the controller has to be developed for maintaining stable operation by controlling the fuel flow.…”

Section: Compressor Gas Turbine Combustor Fuelmentioning

confidence: 99%

“…A simplified model of the HDGT for simple cycle operation is shown in Figure 2. Model parameters of various blocks in the simplified model are described in [15,17]. Because of the drooping nature of the speed governor, the steady-state responses of HDGT models are poor [17].…”

Section: Compressor Gas Turbine Combustor Fuelmentioning

confidence: 99%

“…A triangular membership function and a constant membership function were chosen for the input signals and the output control signal, respectively. Seven fuzzy sets, namely, negative big (NB), negative medium (NM), negative small (NS), zero (Z), positive small (PS), positive medium (PM), and positive big (PB), are used for both the input and output membership functions [17]. From expert knowledge about HDGT performance, the range of membership functions for e , ce, and C were selected as [0, 1], [-1, 0], and [-1, 1] respectively.…”

Section: Fuzzy Logic Controllermentioning

confidence: 99%

“…Since controllers based on soft computing techniques can be easily embedded in a microprocessor, it becomes a viable option for HDGT plants controlled by a Speedtronic governor. Balamurugan et al developed an artificial neural network (ANN) controller and a fuzzy logic controller (FLC) for the 7001E a model of HDGT [17]. However, these controllers cannot be optimal until the responses of all HDGT plants are analyzed.…”

Section: Introductionmentioning

confidence: 99%

“…Because of the reduced influence of these limiters during normal operation, they have been eliminated. A speed control loop with speed governor and fuel system dynamics has been identified as the predominant control loop [17]. The speed governor, as expressed in Eq.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A neuro-fuzzy controller for grid-connected heavy-duty gas turbine power plants

Iqbal¹,

Xavier²,

Kanakaraj³

2017

Turk J Elec Eng & Comp Sci

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Abstract:Frequent load fluctuation and set-point variation may affect the stability of grid-connected heavy-duty gas turbine power plants. To overcome such problems, a novel neuro-fuzzy controller is proposed in this paper for singleshaft heavy-duty gas turbines ranging from 18.2 MW to 106.7 MW. A neuro-fuzzy controller was developed using a hybrid learning algorithm and the effectiveness of the controller for all heavy-duty gas turbine plants (5, 6, 7, and 9 series) is demonstrated against load disturbance and set-point variation in a grid-connected environment. Various time domain specifications and performance index criteria of the neuro-fuzzy controller are compared with that of a fuzzy logic controller and an artificial neural network controller. The simulation results indicate that the neuro-fuzzy controller yields optimal transient and steady-state responses and tracks set-point variation faster than a fuzzy logic or artificial neural network controller. Hence, the neuro-fuzzy controller is identified as an optimal controller for heavy-duty gas turbine plants. The neuro-fuzzy controller proposed in this paper is also applicable to the latest derivative Speedtronic controller.

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Section: Compressor Gas Turbine Combustor Fuelmentioning

confidence: 99%

Section: Compressor Gas Turbine Combustor Fuelmentioning

confidence: 99%

Section: Fuzzy Logic Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A neuro-fuzzy controller for grid-connected heavy-duty gas turbine power plants

Iqbal¹,

Xavier²,

Kanakaraj³

2017

Turk J Elec Eng & Comp Sci

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Development of optimal reduced‐order model for gas turbine power plants using particle swarm optimization technique

Iqbal

Xavier²

2020

Int Trans Electr Energ Syst

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Summary Analysis of higher‐order gas turbine plant in real time would be tedious and expensive. In order to overcome this complexity, reduced‐order model for 5001M heavy‐duty gas turbine rated 18.2 MW has been obtained by Routh approximation, clustering technique, modified pole clustering, eigen permutation, Mihailov criterion, and Padé approximation algorithms. The step responses are obtained using MATLAB/Simulink and compared based on time domain specifications and performance index criteria. It indicates that the mixed method, namely, Routh approximation–Padé approximation algorithm–based reduced‐order model, retains the original characteristics. Further, particle swarm optimization (PSO) algorithm has also been applied to develop an optimal reduced‐order model. Based on the dynamic response against the load disturbance and set point variations, PSO‐based reduced‐order model has been identified as an optimal reduced‐order model for heavy‐duty gas turbine. The reduced‐order model proposed in this paper will be suitable for analyzing the dynamic behavior of heavy‐duty gas turbine plants in real‐time environment.

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Model order reduction of heavy duty gas turbine power plants with field test parameters

Iqbal

Sarumathi²,

Jothi

et al. 2018

Int Trans Electr Energ Syst

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Summary Frequent load fluctuation and set point variations are the major issues in grid connected heavy duty gas turbine power plants. It may affect the stability of the plant and may lead to inevitable shutdown. Dynamic stability analysis of higher order heavy duty gas turbine in real‐time environment would be tedious and expensive. Various model order reduction techniques namely clustering technique‐Pade approximation (CT‐PA), modified clustering technique‐Pade approximation (MCT‐PA), and Mihailov criterion‐Pade approximation (MC‐PA) are proposed for identifying the reduced order model of 18.2 MW industrial heavy duty gas turbine with field test parameter. The step response of gas turbine plant against the load disturbance and set point variations is obtained using the reduced order models and compared with that of higher order system. The time domain specifications and error criteria have witnessed that the MC‐PA‐based reduced order model retains the original characteristics and yields an improved transient and steady‐state responses. Therefore, MC‐PA‐based reduced order model is identified as an effective reduced order model for the higher order gas turbine plant. The proposed reduced order model would be useful for analysing the behaviour of latest derivative speedtronic controlled gas turbine plant.

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Control of Heavy-duty Gas Turbine Plants for Parallel Operation Using Soft Computing Techniques

Cited by 25 publications

References 12 publications

A neuro-fuzzy controller for grid-connected heavy-duty gas turbine power plants

A neuro-fuzzy controller for grid-connected heavy-duty gas turbine power plants

Development of optimal reduced‐order model for gas turbine power plants using particle swarm optimization technique

Model order reduction of heavy duty gas turbine power plants with field test parameters

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