基于批处理结构张量收缩的高效高阶参与因子计算

Participation factors (PFs) quantify the interaction between system modes and state variables, and they play a crucial role in various applications such as modal analysis, model reduction, and control design. With increasing system complexity, especially due to power electronic devices and renewable integration, the need for scalable and high-order nonlinear PF (NPF) computation has become more critical. This paper presents an efficient tensor-based method for calculating NPFs up to an arbitrary order. Traditional computation of PFs directly from normal form theory is computationally expensive—even for second-order PFs—and becomes infeasible for higher orders due to memory constraints. To address this, a tensor contraction–based approach is introduced that enables the calculation of high-order PFs using a batching strategy. The batch sizes are dynamically determined based on the available computational resources, allowing scalable and memory-efficient computation.