Power system stabilizers have been widely used and successfully implemented for the improvement of power system damping. However, a fixed parameter power system stabilizer tends to be sensitive to variations in generator dynamics so that, for operating conditions away from those used for design, the effectiveness of the stabilizer can be greatly impaired. With the advent of microprocessor techn...

This paper has been designed optimize feed-back controller for dynamic response of the power systems. The power system consists of the infinite bus through a transmission line supplied by a synchronous machine and also multi machine power system. The effect of two control signals fed to the voltage regulator and the mechanical system is investigated. Robust Linear quadratic Gawssian (LQG) contr...

In this paper, a fractional order universal high gain adaptive stabilizer is proposed which guarantees the Lp stability of fractional order multiple–input and multiple–output (MIMO) systems with finite control effort. The boundedness of the control gain for the fractional order universal adaptive strategy is discussed for fractional order MIMO systems, and an upper bound of the control gain is ...

Synergism of two intelligent control techniques namely Artificial Neural Network and Genetic Algorithm has been presented in this paper. The technique has been implemented to design power system stabilizer. The power system stabilizer designed with help of neural network while the network is optimized by the genetics algorithm. The power system stabilizer has been used to generate the appropria...

This paper proposes a reinforcement learning based SVC controller to improve the damping of power systems in the presence of load model parameters uncertainty. The proposed method is trained over a wide range of typical load parameters in order to adapt the gains of the SVC stabilizer. The simulation results show that the tuned gains of the SVC stabilizer using Reinforcement Learning can provid...

There is a vivid trend in engineering optimization problems towards the adoption of heuristic optimization algorithms to arrive at optimal solutions. This is mainly due to the simplicity of these algorithms and the great cut down of complicated mathematical manipulations that are required in other optimization theory methods. This paper demonstrates the application of an iterative heuristic opt...