Increase in permeability of renewable energy sources (RESs) leads to the prominent problem of voltage stability in power system, so it is urgent to have a system strength evaluation method with both accuracy and practicability to control its access scale within a reasonable range. Therefore, a hybrid intelligence enhancement method is proposed by combining the advantages of mechanism method and data driven method. First, calculation of critical short circuit ratio (CSCR) is set as the direction of intelligent enhancement by taking the multiple renewable energy station short circuit ratio as the quantitative indicator. Then, the construction process of CSCR dataset is proposed, and a batch simulation program of samples is developed accordingly, which provides a data basis for subsequent research. Finally, a multi-task learning model based on progressive layered extraction is used to simultaneously predict CSCR of each RESs connection point, which significantly reduces evaluation error caused by weak links. Predictive performance and anti-noise performance of the proposed method are verified on the CEPRI-FS-102 bus system, which provides strong technical support for real-time monitoring of system strength.

The power system is experiencing a higher penetration of renewable energy generations (REGs). The short circuit ratio (SCR) and the grid impedance ratio (GIR) are two indices to quantify the system strength of the power system with REGs. In this paper, the critical short circuit ratio (CSCR) is defined as the corresponding SCR when the system voltage is in the critical stable state. Through static voltage stability analysis, the mathematical expression of the CSCR considering the impact of GIR is derived. The maximum value of CSCR is adopted as the critical value to distinguish the weak power system. Based on the static equivalent circuit analysis, it is proved that the CSCR is still effective to evaluate critical system strength considering the interactive impact among REGs. Finally, we find that the GIR can be neglected and the SCR can be used individually to evaluate the system strength when SCR>2 or GIR>5. The correctness and rationality of the CSCR and its critical value are validated on ADPSS.

The mode-based damping torque analysis (M-DTA) method for studying the effect of an external controller on power system low-frequency oscillations is proposed in this paper. First, based on the interconnection model between the system and the controller in the frequency domain, the oscillation loop corresponding to the electromechanical oscillation mode is built, and then the mode-based damping torque of the controller can be calculated. Then, the application of the M-DTA method in the power system is illustrated. The derivation shows that in the single-machine infinite-bus power system, the M-DTA method is completely equivalent to the classical damping torque analysis (C-DTA) method. In the multi-machine power system, the mode-based damping torque directily reflects the effect of the controller on the oscillation mode, overcoming the shortcomings of the C-DTA method in which there is no direct correspondence between the damping torque and the oscillation mode. By deriving the relationship with the residue index, the M-DTA method shows higher accuracy than the residue method in applications, such as controller parameter adjustment. Finally, two example power systems are presented to demonstrate the application of the proposed M-DTA method.