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Research Papers

A Self-Constructing Wavelet Neural Network Controller to Mitigate the Subsynchronous Oscillations

[+] Author and Article Information
Mohsen Farahani

e-mail: m.farahani@basu.ac.ir

Soheil Ganjefar

Associate Professor
e-mail: s_ganjefar@basu.ac.ir
Department of Electrical Engineering,
Bu-Ali Sina University, Hamedan, Iran 65174-4161

Contributed by the Dynamic Systems Division of ASME for publication in the Journal of Dynamic Systems, Measurement, and Control. Manuscript received January 17, 2012; final manuscript received July 20, 2012; published online November 7, 2012. Assoc. Editor: Warren E. Dixon.

J. Dyn. Sys., Meas., Control 135(2), 021012 (Nov 07, 2012) (8 pages) Paper No: DS-12-1017; doi: 10.1115/1.4007607 History: Received January 17, 2012; Revised July 20, 2012

This study proposes a new intelligent controller based on self-constructing wavelet neural network (SCWNN) to suppress the subsynchronous resonance (SSR) in power systems compensated by series capacitors. In power systems, the use of intelligent technique is inevitable, because of the uncertainties such as operating condition variations, different kinds of disturbances, etc. Accordingly, an intelligent control system that is an on-line trained SCWNN controller with adaptive learning rates is used to mitigate the SSR. The Lyapunov stability method is used to extract the adaptive learning rates. Hence, the convergence of the proposed controller can be guaranteed. At first, there is no wavelet in the structure of controller. They are automatically generated and begin to grow during the control process. In the whole design process, the identification of the controlled plant dynamic is not necessary according to the ability of the proposed controller. The effectiveness and robustness of the proposed controller are demonstrated by using the simulation results.

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References

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Figures

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Fig. 1

IEEE second benchmark model for the SSR studies

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Fig. 2

Modeling of the turbine-generator system

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Fig. 3

The electric model of synchronous generator

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Fig. 4

Excitation system used for the generator along with the control signal

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Fig. 5

Variations of electric torque before, during and after a disturbance

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Fig. 6

Rotor speed deviation before, during and after a disturbance

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Fig. 7

Block diagram of intelligent control system

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Fig. 8

Structure of a three-layer SCWNN

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Fig. 10

The overall performance of SCWNN under OP1

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Fig. 11

The number of wavelets during the simulation under OP1

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Fig. 12

The responses of the power systems to a three-phase to ground fault under OP2

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Fig. 9

The responses of the power system to a three-phase to ground fault under OP1

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Fig. 13

The overall performance of SCWNN under OP2

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Fig. 14

The number of wavelets during the simulation under OP2

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