UNCONSTRAINED CONTINUOUS CONTROL SET MODEL PREDICTIVE CONTROL BASED ON KALMAN FILTER FOR ACTIVE POWER FILTER

摘要

In the life and industrial power, due to the nonlinear load and new energy grid, many harmonics will be generated, which will reduce the power quality. Active power filter (APF) is the most used device in harmonic suppression, so it is very important to improve the rapidity and steady-state performance of APF. Model predictive control (MPC) appears with industry. It has been successfully applied in motor, power electronic converter and so on. Continuous control set model predictive control (CCS-MPC) is widely used in power industry because of its good control effect, CCS-MPC uses the predictive model, and the minimization cost function is calculated at each moment to obtain the output sequence in N time horizon in the future. In this paper, a CCS-MPC with dynamic feedback based on Kalman observer is proposed to overcome the influence of unmeasurable disturbance on the output of the system in industrial environment. Compared with the traditional model with disturbance term, it has the characteristics of small amount of calculation and fast response. The simulation results show that it has a good ability to deal with interference, and also has a good steady-state performance.