Previous studies regards that the grid-forming (GFM) converter switches from the constant voltage control (CVC) to the current limiting control (CLC) at the point of intersection of the unsaturated and saturated power-angle curves, which might not be held in most situations. This paper firstly derives the switching rule of GFM converter between two controls from the current limiting inequality. Accordingly, the conventional algebraic-variable switched model is transferred to the proposed state-variable switched model. Based on Lyapunov Theory, the stability of equilibrium points (EPs) is studied and the stable regions under two controls are further derived. In addition, one necessary stabilizing condition for the power synchronization loop based GFM (PSL-GFM) converter being locked in CLC is derived. As a result, one global stabilization on CVC is proposed by regulating the angle between the saturated current vector and d-axis . Finally, the maximum system boundary of the first swing can be obtained by placing stable equilibrium points (SEPs) under CLC on the right switching line.

Additional active power control (AAPC) of wind turbines (WTs) is essential to improve the transient frequency stability of low-inertia power systems. Most of the existing research has focused on imitating the frequency response of the synchronous generator (SG), known as virtual inertia control (VIC), but are such control laws optimal for the power systems? Inspired by this question, this article proposes an optimal AAPC of WTs to maximize the frequency nadir post a major power deficit. By decoupling the WT response and the frequency dynamics, the optimal frequency trajectory is solved based on the trajectory model, and its universality is strictly proven. Then the optimal AAPC of WTs is constructed reversely based on the average system frequency (ASF) model with the optimal frequency trajectory as the desired control results. The proposed method can significantly improve the system frequency nadir. Meanwhile, the event insensitivity makes it can be deployed based on the on-line rolling update under a hypothetic disturbance, avoiding the heavy post-event computational burden. Finally, simulation results in a two-machine power system and the IEEE 39 bus power system verify the effectiveness of the optimal AAPC of WTs.

Existing sub-/super-synchronous (SSO) suppression methods for the direct-drive permanent magnet synchronous generators (D-PMSG) integrated power systems are mainly achieved by external devices or sub-synchronous resonance damping controller (SSRDC) at the converters, facing challenges of considerable control costs, complex parameters tuning, or inadaptability to various operating conditions. To address these problems, this paper proposes an adaptive SSRDC based on the phase-locked loop (PLL) for D-PMSG integrated power systems. Firstly, the PLL parameter is found critical to SSO suppression by a comprehensive sensitivity analysis of the dominant poles of the impedance closed-loop transfer function. Motivated by this finding, this paper then designs a PLL-based SSRDC, which features a simple structure, easy parameter tuning, and flexible adaptability to various operating modes. The simplicity in structure is guaranteed by the avoidance of phase compensation. Benefiting from the simple structure, only one key parameter needs to be tuned. Moreover, two principles of parameter tuning are proposed to enhance the efficiency, robustness, and adaptability of the proposed SSRDC. The controller-hardware-in-the-loop (CHIL) tests verify the validity of the proposed SSRDC under various operating conditions. Finally, some concerns about this method such as frequency estimation, computational efficiency, and potential impacts on PLL are thoroughly analyzed and clarified.

Current transformer (CT) saturation is one of the dominant causes of relay protection devices’ malfunctions, which pose a threat to the safe operation of the power system. To address this problem, we propose a hybrid physical model- and data-driven method. The method firstly detects the CT saturation and then compensates it to reproduce the real waveform. Considering the multi-factor and strong nonlinearity of CT saturation, a data-driven model, namely the Fully Convolutional Network (FCN), is built to detect the operation status of CT. As for the compensation, a physical model of short-circuit current is used for its conciseness and universality. Through tactfully integrating the data model and the physical model, the proposed method is endowed with two major merits: the arduous adjustment of universal thresholds and parameters in existing methods is avoided, and the deficiency in generalization and interpretability of the data-driven method is assuaged. Simulation and experimental results verify the effectiveness of the proposed method. Furthermore, its application potential to future protection is explored.

Many centralized and distributed power sharing algorithms have been proposed in the literature for de-loading operations in wind farms with variable speed wind turbines. Typically, in these strategies, two-way communications are required between the control center and the single turbines, or among the turbines. This paper solves the same problem in a truly decentralized fashion, which only requires a greatly reduced amount of one-way communications, without exchanging information among the turbines, and shows that an optimal solution can be obtained to minimize utility functions of interest of single wind turbines. In particular, we consider utility functions that take into account mechanical fluctuations and rotor over-speeds during transient, while balancing the utilization of wind turbines at steady-state operations. This is achieved by adopting the so-called Additive Increase Multiplicative Decrease (AIMD) algorithms, which are frequently used in communication applications, for solving the power sharing problem in a decentralized fashion. Extensive simulations under different working conditions, on wind farms consisting of wind turbines of different mechanical characteristics, are provided to illustrate the potential and the efficiency of the proposed methodology.

When a slightly faulty transformer closes without load, the current waveform presents the coexistence of inrush and fault current. At this time, the inrush blocking module will block the relay, which may delay the removal of the slight fault and lead to more serious faults. To address this problem, this paper proposes a data-aided power transformer differential protection without inrush blocking module. The key to eliminating the negative influence of inrush current is to extract the fundamental component from the non-inrush part of the current waveform, which corresponds to the unsaturation period of the transformer core. Firstly, a data-aided module, namely an Attention module embedded Fully Convolutional Network (A-FCN), is built to distinguish the inrush and non-inrush parts of the current waveform. Then, a physical model of the current waveform is built for the non-inrush part, and the fundamental component is extracted by the nonlinear least square (NLS) algorithm. The proposed method can avoid the block of differential protections when inrush current occurs, which improves the sensitivity and rapidity of the relay, especially in the case of a weak internal fault hidden in inrush current. Finally, simulation and experimental data verify the effectiveness and generalization of the proposed method.