A fixed-frequency model predictive current control method for single-phase grid-connected inverters

Abstract

The present invention provides a single-phase grid-connected inverter fixed-frequency model predictive current control method: Step 1: at k sampling time, by obtaining the current i o (k) and u g (k), through the output voltage u out calculate t NZ ;Step 2: If 0≤t NZ ≤T s , go to step 3, otherwise go to step 4; step 3: get t NZ After that, calculate t Z ; through the value function g 1 Evaluate and output the optimal switch state; step 4: use the traditional model to predict the current control calculation method, and use the value function g 2 Evaluate and output the optimal switching state. Step 5: Select the optimal switching state in step 3 or 4, and act on the inverter to output the optimal selected output voltage, and then output the grid current to realize the tracking of the given current. The invention realizes the fixed-frequency model predictive current control in the single-phase grid-connected inverter through the collective control set of the single-cycle non-zero output voltage switch state and the zero-output voltage switch state, and improves the single-phase grid-connected inverter Waveform quality of the output grid-connected current.

Description

technical field [0001] The invention belongs to the technical field of current control of a grid-connected inverter power supply, and relates to a fixed-frequency model predictive current control method for a single-phase grid-connected inverter. Background technique [0002] For digital discrete control systems, finite state set model predictive control (finite control set model predictive control, FCS-MPC) is a nonlinear control method with fast dynamic response and strong robustness. It has broad application prospects. With the rapid development of digital processing chips, FCS-MPC is a hot method that is widely researched and applied today. By virtue of its discrete control characteristics and the mutual adaptation of the inverter's finite switching state, a good nonlinear control effect is achieved without excessive parameter design. However, model predictive control also has problems such as dependence on high sampling frequency, unfixed switching frequency, dispersi...

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Problems solved by technology

However, model predictive control also has problems such as dependence on high sampling frequency, unfixed switching frequency, dispersion of harmonic spectrum, and heavy computational burden caused by ergodic optimization of multiple switching variables.

[0003] In the application of the single-phase grid-connected inverter model predictive current control algorithm, for the typical inverter circuit topology, its three limited effective switching states greatly limit the control performance and algorithm superiority of FCS-MPC

At the same time, for the grid-connected inverter, compared with the load-type inverter, the grid itself has the characteristics of small impedance and high harmonic pass rate, and combined with the increase of the DC side voltage, the fluctuation error of its output signal is further increased. Big

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