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Levenberg-Marquardt (LM) algorithm, a powerful off-line batch training method for neural networks, is adapted here for online estimation of power system dynamic behavior. A special form of neural network compatible with the feedback linearization framework is used to enable non-linear self-tuning control. Use of LM is shown to yield better closed-loop performance compared to conventional recursive...
An adaptive critic design (ACD)-based damping controller is developed for a thyristor-controlled series capacitor (TCSC) installed in a power system with multiple poorly damped interarea modes. The performance of this ACD computational intelligence-based method is compared with two classical techniques, which are observer-based state-feedback (SF) control and linear matrix inequality LMI-Hinfin robust...
This paper presents the design of a new control strategy for gate-controlled series compensators (GCSCs). GCSCs are new FACTS devices which can provide active power flow control on a transmission line. Proper placement of GCSCs in proximity to generators can also provide damping to system oscillations. This paper has investigated the effectiveness of multiple multi-module gate controlled series compensators...
This paper provides some detailed analysis of the stability of the proposed direct adaptive controller for a special type of nonlinear system based on the previously published papers. The special learning algorithm similar to back propagation provides better stability and wide domain of attraction for the controller provided that the neural network parameters are chosen carefully. The controller acts...
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