Voltage stability evaluation of power system with FACTS devices using fuzzy neural network

2007 ◽  
Vol 20 (4) ◽  
pp. 481-491 ◽  
Author(s):  
P.K. Modi ◽  
S.P. Singh ◽  
J.D. Sharma
Keyword(s):  
Neural Network ◽  
Power System ◽  
Voltage Stability ◽  
Fuzzy Neural Network ◽  
Stability Evaluation ◽  
Facts Devices ◽  
Fuzzy Neural

scholarly journals Command Filtered Adaptive Fuzzy Neural Network Backstepping Control for Marine Power System

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Xin Zhang ◽  
Longhua Mu
Keyword(s):  
Neural Network ◽  
Power System ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Chaotic Oscillation ◽  
Lyapunov Stability Theory ◽  
Backstepping Control ◽  
Adaptive Fuzzy Neural Network ◽  
Adaptive Fuzzy ◽  
Fuzzy Neural

In order to retrain chaotic oscillation of marine power system which is excited by periodic electromagnetism perturbation, a novel command-filtered adaptive fuzzy neural network backstepping control method is designed. First, the mathematical model of marine power system is established based on the two parallel nonlinear model. Then, main results of command-filtered adaptive fuzzy neural network backstepping control law are given. And the Lyapunov stability theory is applied to prove that the system can remain closed-loop asymptotically stable with this controller. Finally, simulation results indicate that the designed controller can suppress chaotic oscillation with fast convergence speed that makes the system return to the equilibrium point quickly; meanwhile, the parameter which induces chaotic oscillation can also be discriminated.

Wavelet Energy Fuzzy Neural Network-Based Fault Protection System for Microgrid

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 1007 ◽  
Author(s):  
Cheng-I Chen ◽  
Chien-Kai Lan ◽  
Yeong-Chin Chen ◽  
Chung-Hsien Chen ◽  
Yung-Ruei Chang
Keyword(s):  
Neural Network ◽  
Power System ◽  
Fuzzy Neural Network ◽  
Discrete Wavelet ◽  
Protective Relay ◽  
Fault Protection ◽  
Wavelet Energy ◽  
Fuzzy Neural ◽  
Point Of Common Coupling ◽  
Feature Information

To perform the fault protection for the microgrid in grid-connected mode, the wavelet energy fuzzy neural network-based technique (WEFNNBT) is proposed in this paper. Through the accurate activation of protective relay, the microgrid can be effectively isolated from the utility power system to prevent serious voltage fluctuation when the power quality of power system is disturbed. The proposed WEFNNBT can be divided into three stages—feature extraction (FE), feature condensation (FC), and disturbance identification (DI). In the FE stage, the feature of power signal at the point of common coupling (PCC) between microgrid and utility power system would be extracted with discrete wavelet transform (DWT). Then, the wavelet energy and variation of singular power signal can be obtained according to Parseval Theorem. To determine the dominant wavelet energy and enhance the robustness to the noise, the feature information is integrated in the FC stage. The feature information then would be processed in the DI stage to perform the fault identification and activate the protective relay if necessary. From the experimental results, it is realized that the proposed WEFNNBT can effectively perform the fault protection of microgrid.

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