Deep Learning Based TTC Predictor for Power Systems with Wind Energy Integration

Author(s):  
Gao Qiu ◽  
Youbo Liu ◽  
Junyong Liu ◽  
Lixiong Xu
2005 ◽  
Vol 1 (03) ◽  
pp. 93-98
Author(s):  
V. Rogez ◽  
◽  
H. Roisse ◽  
V. Autier ◽  
X. Guillaud

Author(s):  
Ayyarao S. L. V. Tummala

AbstractThis paper presents a novel composite wide area control of a DFIG wind energy system which combines the Robust Exact Differentiator (RED) and Discontinuous Integral (DI) control to damp out inter-area oscillations. RED generates the real-time differentiation of a relative speed signal in a noisy environment while DI control, an extension to a twisting algorithm and PID control, develops a continuous control signal and hence reduces chattering. The proposed control is robust to disturbances and can enhance the overall stability of the system. The proposed composite sliding mode control is evaluated using a modified benchmark two-area power system model with wind energy integration. Simulation results under various operating scenarios show the efficacy of the proposed approach.


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