Back propagation neural network clustering architecture for stability enhancement and harmonic suppression in wind turbines for smart cities.

Abstract With increasing demand for a renewable source of energy and the rapid development of smart cities and cluster computing, wind energy is gaining widespread significance on a global basis. There has been the unprecedented increase in annual installation of wind turbines reaching 60 GW for the first time in history from 51.7 GW of wind energy installation in 2014. Most of the high-power installations utilized double-fed induction generators (DFIG) due to low power losses, which reduced the cost of power converters required in the circuit. Nevertheless, natural factors, such as varying wind speed, influence the output of generators which results in the unsteady generation of power thus affecting the stability of the system. A neural network-based DFIG integrated with super capacitor energy storage system (SCESS) is proposed in this paper. Modeling and simulation have been carried out in MATLAB/Simulink and the observations indicate the significant improvement in the stability of the system. [ABSTRACT FROM AUTHOR]