Stability and Fault Detection Algorithm of Grid-Connected Inverter in Complex Power Grid Environment

Abstract

In order to improve the stability, this paper designs a control strategy based on adaptive grid impedance. By monitoring the grid impedance in real time and dynamically adjusting the inverter control parameters, the influence of grid impedance change on inverter stability is effectively reduced. At the same time, LCL filter is introduced, which significantly filters out the higher harmonics in the inverter output current and improves the output waveform quality. In addition, a voltage compensation device is applied to quickly generate a compensation voltage signal when the grid voltage fluctuates to ensure the stability of the inverter output voltage. In the aspect of fault detection, a data-driven fault detection algorithm is proposed, which combines machine learning and real-time monitoring technology to realize early detection and accurate diagnosis of inverter faults by collecting and analyzing inverter operation data. The experimental results show that the algorithm performs well in the detection of power switch device faults, sensor faults and other types, and has high classification accuracy. The research shows that the control strategy based on adaptive grid impedance, the addition of LCL filter and the application of voltage compensation device have significantly improved the stability of grid-connected inverter in complex grid environment. The proposed data-driven fault detection algorithm provides strong support for rapid fault location and accurate treatment of inverter.