Multi-level model predictive controller with satisfactory optimization for multi-level converters

Dynamic modelling and a dual vector modulated improved model predictive control with auto tuning feature of active front‐end converters for distributed energy resources

“…The existence of non-linear loads may result in unregulated harmonic currents, which possess the capacity to induce voltage distortion at the point of common coupling (PCC). In conjunction with power quality considerations, the presence of harmonic currents generates discrepancies in power distribution and has the potential to give rise to instability issues, as shown by the performance waveforms seen in the implementation of strategies [19,20] depicted in Figure 26. The control algorithm that was developed successfully demonstrated a reduced recovery time, minimum overshoot, and lower impedance in comparison to conventional controllers by the permanent cancellation of low-frequency poles.…”

“…This contributes to improved robustness and performance of the control system, as it can adapt to different operating conditions and disturbances. This contrasts with other control algorithms [18, 19, 30] that require a detailed mathematical model of the system. The RLS algorithm can handle non‐linearities in the system, making it a suitable method for estimating the model parameters of AFE converters with non‐linear characteristics. This is a significant advantage over linear control algorithms that cannot accurately model non‐linear systems [23, 24].…”

“…Finally, the latter is used to develop a perfect converter controller (RLSbased MPC) by considering the non-linearities of the PLL and the dynamic interactions between the AFE and the grid. In MPC, a system-level model is used to make predictions about future behaviour, and these predictions are used to fine-tune the control inputs [19,20]. The proposed method provides a systematic tuning strategy that guarantees a stable controller with the required performance, which is an improvement over earlier work [21].…”

A Novel Control Approach to Hybrid Multilevel Inverter for High-Power Applications

“…This control technique is easily realized with less tuning complexity and can also include nonlinear systems and constraints easily. Moreover, the MPC directly achieves its objectives without angular calculation and reference voltage approximation methods, which reduces its implementation complexity in power electronic systems [11]. Finite set model predictive control (FS-MPC) has been achieved popularity in power electronic systems due to its simple and flexible implementation [9].…”

“…The FS-MPC is discussed widely in the literature for controlling DC-DC and AC-DC converters [12][13][14][15], multilevel converters such as packed U-cell (PUC) [9], and flying capacitor-based ANPC hybrid converters in [16]. The FS-MPC comprises finite control actions, among which the optimal control action is attained by solving optimization problem [11]. Moreover, nonlinear control schemes, such as sliding mode control (SML) is used to control the switches of the multilevel converter and reduce the THD levels [17][18][19][20].…”