Publication Type:

Conference Paper

Source:

TENCON 2017 - 2017 IEEE Region 10 Conference, IEEE, Penang, Malaysia (2017)

ISBN:

9781509011346

URL:

https://ieeexplore.ieee.org/document/8228133

Keywords:

adaptive neuro controller, Capacitors, Damping, damping SSR oscillations, Dynamic Neural Network, electrical system, global energy consumption, grid impedance conditions, Grid integration, high transmission line capability, Induction generators, learning (artificial intelligence), linear controllers, neurocontrollers, oscillations, Oscillators, power carrying capability, Power grids, Power transmission lines, reactive power control, Real Time Recurrent Learning Algorithm (RTRL), recurrent neural nets, Recurrent neural network (RNN), renewable energy sources, RTRL based adaptive neuro-controller, SCIG based windfarms, Series compensation, Shafts, Sub-synchronous Resonance, Sub-Synchronous Resonance oscillations, Subsynchronous resonance, Wind energy, wind farms, Wind power, wind power plants, Wind speed, wind speeds, Wind turbines

Abstract:

As the global energy consumption is rising dramatically, wind energy is a prominent one among the renewable energy sources. The penetration of wind energy into grid is increasing day by day. In order to carry huge amount of wind power during the grid integration of large scale wind farms, high transmission line capability is demanded. In order to improve the power carrying capability of the transmission line and to improve the stability of the system, series compensation is the best practical solution. Series compensation can result in Sub-Synchronous Resonance (SSR) oscillations in the electrical system which will lead to damages in the system such as shaft failure. In this paper, a novel idea of using the Real Time Recurrent Learning (RTRL) based adaptive neuro controller is proposed for damping SSR oscillations in grid connected windfarms. The controller is trained in real time without a reference model. The effectiveness of the proposed controller is tested under varying series compensation, wind speeds and grid impedance conditions and it has been proved that the proposed controller performs far better than any other linear controllers.

Cite this Research Publication

Dr. Sindhu Thampatty K.C. and Raj, P. C. R., “RTRL based adaptive neuro-controller for damping SSR oscillations in SCIG based windfarms”, in TENCON 2017 - 2017 IEEE Region 10 Conference, Penang, Malaysia, 2017.