A Grid-Forming Control Strategy Based on a Hybrid Approach Combining a Physical Model and LSTM for Photovoltaic and Energy Storage Systems

Traditional grid-forming converter (GFC) control faces fundamental challenges in maintaining DC bus stability during rapid power transients, primarily due to the limited dynamic response capability of source-side energy storage devices. To address this, this paper proposes a hybrid control strategy integrating long short-term memory (LSTM) networks with a joint GFC and storage converter (SC) control scheme. The LSTM detects short-term voltage trends from historical DC bus data to generate a feedforward compensation signal, while the joint SC-GFC control dynamically incorporates the GFC’s inertial power demand into the SC’s power reference. Hardware-in-the-loop experiments show that, compared to traditional independent control under the same step transient conditions, the proposed method can reduce power overshoot by approximately 79.2%. The LSTM-enhanced joint control maintains stable power flow and significantly suppresses low-frequency oscillations, validating the necessity of data-driven trend prediction for achieving superior inertial support in practical constrained environments. This work provides a communication-free, practical solution for enhancing GFC performance.