TY  - JOUR
T1  - Dynamic Modelling and Simulation of Grid Coupled DFIG Designed for
2 MW SHPP using ANN Scheme
AU - Kumar Gagrai, Sanjeev AU - Mishra, Sundram AU - Singh, Madhu 
JO  - Journal of Engineering and Applied Sciences
VL  - 14
IS  - 11
SP  - 3813
EP  - 3831
PY  - 2019
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2019.3813.3831
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2019.3813.3831
KW  - Artificial Neural Network (ANN)
KW  -controller
KW  -Doubly Fed Induction Generator (DFIG)
KW  -Grid Side
Converter (GSC)
KW  -Rotor Side Converter (RSC)
KW  -utility grid
AB  - Traditionally fixed speed drives are commonly used in small hydro power plants. With the
development power electronics converter an autonomous variable speed drives, i.e., Doubly Fed Induction
Generator (DFIG) came into the fashion. Small hydropower plant as an alternative source of energy plays a vital
role in altering the face of any developing country in terms of environment protection, economic growth and
energy crisis. The focal motive of this proposed research work is to implement the advanced control strategy,
i.e., Artificial Neural Network (ANN) in grid-connected Doubly Fed Induction Generator (DFIG) associated with
Small Hydro Power Plant (SHPP). The proposed control scheme regulates both sides of converter, i.e., converter
of grid side and rotor side and maintains the power factor at unity level in the grid side for diminishing the loss
as well as regulating the effectual real and reactive power flow in the rotor side. With the assistance of DFIG,
optimum real and reactive power flow from the rotor side to grid side occurs at an inconstant speed, i.e., super
synchronous, synchronous and sub-synchronous speed. Along with this, controller of the grid side also
maintains the DC bus voltage at a constant level for proper operation of rotor side as well as grid side. A 2 MW
DFIG associated with 2 MW SHPP Model is simulated in Simulink for time domain applications. The system is
stable under different load conditions and it is investigated with the help of model and simulations result. The
ANN controller improves the dynamic response of DFIG and makes the system more apposite for real-time
applications.
ER  -