Forced oscillation management in a microgrid with distributed converter-based resources using hierarchical deep-learning neural network

In future microgrids (MGs), increasing penetration of distributed converter-based resources (DCRs) has inevitably resulted in the problem of inertia scarcity. The interaction, combination, and/or resonance among converter control loops of DCRs, forced inputs, grid parameters, parasitic elements in networks, and system dominant modes can lead to major forced oscillations (FOs). Previous research works mostly focused the problem of FOs on large-scale power systems. However, the effects of FOs in MGs may be more severe than large-scale power systems due to the lower system inertia. With different characteristics of each DCR, conventional FO management methods applied in large-scale power systems may be ineffective. In this paper, a unified AI-framework named hierarchical deep-learning neural network (HiDeNN) is proposed to effectively handle the FOs in a MG with DCRs. To properly managing the FOs, the HiDeNN is divided into three levels for FO detection, identification, and mitigation, respectively. By considering big data produced from DCRs, the HiDeNN is used to solve complicated FO management problems with a low computational demand. By comparison to conventional FO management methods, performances of the proposed HiDeNN are verified in the modified IEEE 13-node feeder with DCRs under various operating points and FO conditions.