A nonlinear model for the analysis of the turbine-generator vibrations including the design of a flywheel damper

Tsai, Wen-Chang; Tsao, Ta‐Peng; Chyn, C.

doi:10.1016/s0142-0615(97)00017-3

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…The steam turbine has been largely applied to power plant because of the costs efficiencies with respect to the capacity, application, and desired performance; a different level of complexity is offered for the structure of steam turbines, to increase the thermal efficiency so that the steam turbine consists of high pressure, intermediate pressure, and low pressure stages due to the complexity of turbine structure using artificial neural network to study the performance of steam turbine and more difficultly to predict the effects of proposed control system on the steam turbine in power plant, therefore, developing nonlinear analytical models. Design, synthesis, and performing real-time simulations and monitoring the desired state can be used, these models for control system in power plant [11][12][13][14][15][16][17]. A steam turbine of a 160MW power plant consists of steam extractions, feed water heaters, moisture separators, and the related motives.…”

Section: Simulation Of Steam Turbine Control Systemmentioning

confidence: 99%

Enhancement of Performance for Steam Turbine in Thermal Power Plants Using Artificial Neural Network and Electric Circuit Design

Salim

Faisal

Jawad

2018

Applied Computational Intelligence and Soft Computing

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Design and implantation of electric circuit for enhanced performance of steam power plant and artificial neural networks technique are used to control turbine. Artificial neural networks technique is used to control a lot of industrial models practically. Artificial neural network has been applied to control the important variables of turbine in AL–Dura power plant in Baghdad such as pressure, temperature, speed, and humidity. In this study Simulink model was applied in MATLAB program (v 2014 a) by using artificial neural network (ANN). The method of controlling model is by using NARMA to generate data and train network. ANN is offline. ANN requires data to obtain results and for comparison with actual power plant. The values of the input variables have a large effect on the number of nodes and epochs and in hidden layer of the artificial neural network they also affect performance of ANN. The electric circuit of sensors consists of transformer, DC bridge, and voltage regulator. Comparing the results from modeling by ANN and electric circuit with experimental data reveals a good agreement and the maximum deviation between the experimental data and predicted results from ANN and circuit design is less than 1%. The novelty in this paper is applying NARMA controller for the purpose of enhancement of turbine performance.

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Section: Simulation Of Steam Turbine Control Systemmentioning

confidence: 99%

Enhancement of Performance for Steam Turbine in Thermal Power Plants Using Artificial Neural Network and Electric Circuit Design

Salim

Faisal

Jawad

2018

Applied Computational Intelligence and Soft Computing

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“…Therefore, developing non-linear analytical models is necessary in order to examine the turbine transient dynamics. These models can be used to be utilized for control system design synthesis, performing real-time simulations and monitoring the desired states [42].…”

Section: An Industrial Steam Turbinementioning

confidence: 99%

Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers

Salahshoor

Kordestani

Khoshro

2010

Energy

207

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“…Therefore, developing nonlinear analytical models is necessary in order to study the turbine transient dynamics. These models can be used for control system design synthesis, performing real-time simulations and monitoring the desired states [1]. System identification during normal operation without any external excitation or disruption would be an ideal target, but in many cases, using operating data for identification faces limitations and external excitation is required [2].…”

Section: Introductionmentioning

confidence: 99%

Simulation Model of Al-Dura Electro-station Plant of 160 MW with Genetic Algorithm Method

H.Aljanabi¹,

Hamid²

2016

ETJ

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In the present paper, a thermodynamic analysis of Al-Dura, Baghdad station type (K-160-13.34-0.0068), power plant has been carried out. The power plant system was simulated and a detailed parametric study undertaken. This study can be helpful to identify the plant site conditions that cause losses of useful energy taken place and also helpful to resolve some problems encountered in steam turbine, capacity unit. Developing nonlinear mathematical models based on system identification approaches during normal operation without any external excitation or disruption is always a hard effort, assuming that parametric models are available. This study included on using soft computing methods that would be helpful in order to adjust model parameters over full range of input-output operational data. In this case, the model parameters are adjusted by applying genetic algorithms as optimization methods. Comparison between the responses of the turbine -generator model with the responses of real system validates the accuracy of the proposed model in steady state and transient conditions. Simulation results shows that the efficiencies and feasibility of the developed model in term of more accurate and less deviation with the responses of read system in the steady and transient conditions, and the error of proposed function is less than 0.37%. This study presents the usage of the Cycle -tempo and Matlab/Simulink package to implement the model of the power plant. Finally, many recommendations have been suggested for improved plant performance.

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A nonlinear model for the analysis of the turbine-generator vibrations including the design of a flywheel damper

Cited by 7 publications

References 11 publications

Enhancement of Performance for Steam Turbine in Thermal Power Plants Using Artificial Neural Network and Electric Circuit Design

Enhancement of Performance for Steam Turbine in Thermal Power Plants Using Artificial Neural Network and Electric Circuit Design

Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers

Simulation Model of Al-Dura Electro-station Plant of 160 MW with Genetic Algorithm Method

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