The electrical system's problem stabilizes the electrical system with three primary parameters: rotor angle stability, frequency stability, and voltage stability. This paper focuses on the problem of designing a low-order stable optimal controller for the generator rotor angle (load angle) stabilization system with minor disturbances. These minor disturbances are caused by lack of damping torque, change in load, or change in a generator during operation. Using the RH∞optimal robust design method for the Power System Stabilizer (PSS) to stabilize the generator’s load angle will help the PSS system work sustainably under disturbance. However, this technique's disadvantage is that the controller often has a high order, causing many difficulties in practical application. To overcome this disadvantage, we propose to reduce the order of the higher-order optimal robust controller. There are two solutions to reduce order for high-order optimal robust controller: optimal order reduction according to the given controller structure and order reduction according to model order reduction algorithms. This study selects the order reduction of the controller according to the model order reduction algorithms. In order to choose the most suitable low-order optimal robust controller that can replace the high-order optimal robust controller, we have compared and evaluated the order-reducing controllers according to many model order reduction algorithms. Using robust low-order controllers to control the generator’s rotor angle completely meets the stabilization requirements. The research results of the paper show the correctness of the controller order reduction solution according to the model order reduction algorithms and open the possibility of application in practice. Doi: 10.28991/esj-2021-01299 Full Text: PDF
Nowadays, the renewable energy sources (RES) are widely utilized in micro-grids due to technical development and emission increase, which make the planning the micro-grids integrated the RES very important. To obtain the optimal planning strategy and evaluating efficiency of the RES in micro-grids, a mixed integer programming (MIP) planning framework for a gridconnected micro-grid is presented in this study. The understudy micro-grid consists of the wind turbines and photovoltaic systems, which are connected to utility grid through the point of common coupling (PCC). The objective function is minimizing the life cycle cost of object comprising of the investment and operation cost of the RES, the energy purchased cost from the utility grid, the emission taxes cost and the replacement cost or residual value of equipment at the end of the planning period. The uncertainties of load, electrical price, wind speed and solar radiation are taken into consideration and then a combinable model with the clustering technique is utilized to integrate them. Finally, numerical simulations for a test grid-connected micro-grid are made to validate the effectiveness of the proposed model and show efficiency of the RES to can apply to practical micro-grids.
The paper proposes a theorem to assert the arbitrarily good robustness of the fully actuated mechanical system controlled by the adaptive feedback linearization controller. The fully actuated system to be controlled is considerately perturbed by input disturbances and contains constant uncertain parameters in its Euler-Lagrange forced model. It is shown in this paper that independent of input disturbances the adaptive feedback linearization controller with appropriately chosen parameters will drive the output of controlled systems to the desired trajectory for any arbitrary precision. The adaptive controller is applied to the two-link planar elbow arm robot with unknown mass of the end-effector of second link and input torque noises caused by the viscous friction forces and Coulomb friction terms. Simulation results show that the arbitrary precision of the tracking errors always are guaranteed.
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