Power Automation Equipment•2026•DOI: 10.16081/j.epae.202606020
The escalating 'double-high' penetration of power electronics in new-type power systems has rendered conventional electromechanical transient small-signal stability analysis inadequate, necessitating electromagnetic transient (EMT) small-signal stability assessment. Eigenvalue analysis, grounded in rigorous theoretical foundations, is widely applied but faces two critical bottlenecks when scaled to large systems: the construction of EMT linearized state-space equations and the solution of high-order state-matrix eigenvalues. This review examines the urgent demand for EMT model eigenvalue analysis in large-scale new-type power systems. It systematically surveys research status and challenges across three domains: equilibrium-point modeling of EMT models, linearized state-space modeling, and efficient computation of critical eigenvalues. For equilibrium-point modeling, the paper evaluates Park transformation, double Park transformation, shifted frequency analysis (SFA), time-scale transformation, and Floquet-theory-based trajectory linearization. For linearized state-space modeling, it assesses component-level and network-level linearization strategies. For eigenvalue computation, it reviews partial eigenvalue algorithms, sparse matrix techniques, and model-order reduction methods. Key challenges include the inability of Park transformation to handle asymmetric or single-phase systems, the computational burden of Floquet transition matrix eigendecomposition, and the poor scalability of dense eigenvalue solvers for systems exceeding thousands of states. The paper concludes by identifying future research directions, including structure-preserving linearization, GPU-accelerated sparse eigenvalue algorithms, and data-driven model reduction, to enable practical EMT small-signal stability analysis for systems with 18.4 billion kW of installed renewable capacity by 2030.
Power Automation Equipment•2026•DOI: 10.16081/j.epae.20251130012
Parameter identification for electromagnetic transient (EMT) models of grid-connected photovoltaic (PV) systems suffers from weak identifiability of controller parameters when environmental, electrical, and controller parameters are optimized simultaneously. This paper proposes a two-stage identification framework that partitions parameters by physical meaning into an environmental/electrical set and a controller set. For the environmental/electrical set, a Sobol global sensitivity analysis based on variance decomposition screens key parameters. For the controller set, a dynamic response feature clustering method combined with an unsupervised screening strategy using an inter-cluster mean difference index reduces the parameter space. Differentiated fitness functions are constructed for each stage, and an improved quantum dung beetle optimization (IQDBO) algorithm incorporating quantum angle encoding and a stagnation perturbation mechanism performs the identification sequentially. Case studies demonstrate that the proposed method compresses the search space and improves controller parameter identifiability. Compared with particle swarm optimization (PSO) and grey wolf optimizer (GWO), the IQDBO-based method achieves superior identification accuracy and convergence stability. Environmental and electrical parameter identification errors remain below 1%, while controller parameter errors remain below 3%. The framework addresses the weak identifiability bottleneck in unified optimization and provides a practical pathway for EMT model calibration in PV grid-connected systems. Future work will extend the method to multiple operating conditions and noisy field data, and develop accelerated computation strategies.
Power Automation Equipment•2026•DOI: 10.16081/j.epae.202606002
This study addresses the electromagnetic transient (EMT) modeling and operational stability of high-head hydropower units connected to a modular multilevel converter-based high-voltage direct current (MMC-HVDC) islanded transmission system, a configuration critical for developing hydropower resources in Tibet. A refined model of the high-head unit incorporating dynamic penstock characteristics and a detailed MMC-HVDC system model are established. An initialization method tailored for hydro-DFACTS EMT simulation is proposed, and a complete model is implemented on the CloudPSS platform. Simulation results demonstrate that the dynamic characteristics of high-head units degrade short-term stability compared to conventional units. A pronounced hydraulic-electrical coupling between the unit and the MMC-HVDC system can induce ultra-low-frequency oscillations in the sending-end system. Furthermore, the system exhibits elevated subsynchronous oscillation risk in the [30, 50] Hz band, with phase differences exceeding 180°. The study concludes that high-head characteristics are a key factor influencing small-signal stability, necessitating refined modeling of the penstock and water turbine for accurate stability assessment.