Transient Stability Improvement with Neuro-Fuzzy Control of FACTS Devices

The designing and training circumstance of artificial neural networks (ANN) to applying for efficient controllers of flexible alternating current transmission system (FACTS) devices controller configurations based the fuzzy logic is presented in this paper. The arrangement of neurons and layers and the neuron activity functions of ANN have vital roles in the power system stability. Training data and method of decent error minimizing of training process are very important to design of neuro-fuzzy controller. The proposed neuro-fuzzy controllers combine the robustness and simplicity designing of fuzzy controller and quick response and adaptability nature of ANN. The results of this research prove that the suitable designing of neural configuration based fuzzy logics to control of FACTS devices can improve the tie line load ability and transient stability of the power system more efficient than single fuzzy controller of FACTS devices.