Grid synchronization control method, device and equipment of photovoltaic inverter and medium

By predicting the phase deviation of photovoltaic inverters through recurrent neural networks and combining it with the weighted fusion of PI controllers, the synchronization problem of traditional phase-locked loops in complex power grid environments is solved, achieving high-precision and stable grid-connected synchronization of photovoltaic inverters.

CN122267879APending Publication Date: 2026-06-23CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
Filing Date
2026-05-27
Publication Date
2026-06-23

AI Technical Summary

Technical Problem

In the existing grid-connected synchronous control of photovoltaic inverters, traditional phase-locked loops (PLLs) have a contradiction between response speed and stability in terms of dynamic performance. In particular, they are difficult to achieve real-time synchronous tracking of the grid phase in complex grid environments, leading to safety hazards and power quality problems.

Method used

A recurrent neural network is used for phase deviation prediction. By constructing a short-time phase deviation time series and a lightweight recurrent neural network model, and combining it with the angular frequency output by the PI controller for weighted fusion, dynamic prediction and compensation of phase changes within future control cycles can be achieved, thereby improving synchronization accuracy and stability.

Benefits of technology

It significantly improves the dynamic adaptability of photovoltaic inverters under complex grid conditions, shortens the synchronization establishment time, avoids the risk of phase overshoot or loss of lock-in, and enhances the system's response sensitivity and steady-state control capability.

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Abstract

The application provides a grid synchronization control method, device, equipment and medium of a photovoltaic inverter, comprising: collecting three-phase voltage signals at a grid point of common connection in real time, and preprocessing the three-phase voltage signals to obtain q-axis voltage residuals; serializing sampling the q-axis voltage residuals to construct a short-time phase deviation time sequence; inputting the short-time phase deviation time sequence into a pre-trained recurrent neural network model to obtain a predicted phase angle increment; based on the predicted phase angle increment, combining a photovoltaic grid-connected working condition, and calculating a synchronization phase angle; and based on the synchronization phase angle, controlling the grid synchronization phase of the photovoltaic inverter. The phase deviation is predicted through the recurrent neural network, the phase error problem caused by the lag of the traditional PI control is overcome, and the accuracy and stability of the grid synchronization of the photovoltaic inverter are effectively improved.
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