List of Articles Multi-model controller

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  1 - Improving the Stability of a Power System Including SVC Based on Energy Function Minimization in a Multi-Model Optimal Coordinated Control Structure
  Elaheh Pagard Shahrokh Shojaeian Mohammad Mahdi Rezaei
  10.30486/teeges.2023.1990843.1083
  In this paper, the improvement of low frequency oscillation (LFO) damping in a power system including SVC is investigated. To achieve this goal, a new control strategy has been presented in which the multi-model controller is optimized using the linear optimal controlle More

  In this paper, the improvement of low frequency oscillation (LFO) damping in a power system including SVC is investigated. To achieve this goal, a new control strategy has been presented in which the multi-model controller is optimized using the linear optimal controller (LOC) and the particle swarm algorithm (PSO). The control bank in the multi-model controller includes three LOC controllers that generate optimal signals through the linearization of the nonlinear equations of the system and the minimization of an energy function to be combined by the Bayes recursive algorithm simultaneously to the generator excitation system and SVC. In order to generate an optimal linear signal, Riccati's equation must be solved; Riccati's equation includes two weight matrices Rric and Qric. These matrices elements are optimized by PSO algorithm. The PSO algorithm has calculated the optimal Rric and Qric with two different objective functions of maximizing the eigenvalues and minimizing the area under the speed curve. To evaluate the MMC-LOC-PSO control strategy, the symmetrical three-phase error is applied to the worst bus and the results of these two objective functions are compared. The simulation of the single machine power system has been done by MATLAB. The proposed control strategy, while maintaining stability, also effectively damps the LFOs, in addition, the permanent rotor speed and rotor angle error have also been favorably pushed to zero. Manuscript profile