Model predictive flexible power control for grid‐connected inverter under unbalanced network

Model predictive power control with Kalman filter for grid‐tied inverter with parameter variation

“…When λ > 0, the Lyapunov function V in (10) is positive definite and the Lyapunov derivative in (11) is negative definite. According to the Lyapunov stability theorem, the gridtied inverter is in a stable state [27].…”

“…As shown in Equations ( 4) and ( 22), the essence of the cost function is the sum of output-power deviation and the goal of MPPC is searching an optimal vector with minimizing the output-power deviation. In conventional MPPC, the virtual vector ṽ in (5) varies to ṽ΄ in (11) due to the AC-filter inductance variation. In this case, according to ( 6) and ( 22), the conventional MPPC may choose a wrong optimal vector to control the grid-tied inverter, which adds the outputpower deviation.…”

“…In conventional MPC, each vector (e.g. eight vectors in the three‐phase two‐level inverter) is substituted into the cost function by cycle calculation during a sampling interval, and an optimal vector with minimize the cost function is selected to control output power or current [10, 11]. The repeated cycle calculation increases the computational cost of MPC [12, 13].…”

“…In MPCC, an extra phase locking loop (PLL) is required to gain the grid‐current reference [10]. Thus, the control structure of MPCC is more complex than MPPC [11]. Then, this paper adopts MPPC to control output power of the grid‐tied inverter.…”

“…Commonly, the typical control algorithms of the grid-tied system are proportional-integral control [7], sliding model control [8], repetitive control and model predictive control (MPC) [9][10][11]. The MPC attracts substantial attentions due to optimize the conflicting control objectives, fast dynamic response and avoid modulation strategy [10,11].…”