At present, the equivalent modeling method of wind farms mostly adopts single-machine multiplication equivalence, which has the deficiencies of large model error and difficulty in accurately reflecting the fault transient characteristics of wind farms, which imposes limitations on the security and stability analysis of the power system. For this reason, this paper proposes an equivalent modeling method that can accurately reflect the fault ride-through characteristics of wind farms. Based on the control mechanism of direct-drive wind turbines, this method first analyzes the fault ride-through operating characteristics of wind turbines and establishes a single-machine fault transient model; then, taking the fault ride-through control characteristics of wind turbines as the criteria for subgroups, it calculates the relevant parameters of the group through weighted aggregation and QPSO algorithm, and constructs the fault transient equivalent model of each group; finally, combining with the principle of loss conservation, it integrates and obtains the fault transient equivalent model of the whole wind power field. Finally, the equivalent fault transient model of the whole wind farm is obtained by combining the loss conservation principle. Simulation verification shows that the established equivalent model can accurately reflect the dynamic characteristics of the wind farm, and the maximum percentage error of voltage and active power is no more than 10% in comparison with the corresponding detailed model under the same kind of fault perturbation, which not only meets the requirements of China’s wind farm modeling standards, but also shows higher adaptability and accuracy under different working conditions compared with other equivalent modeling methods. Especially under the extreme three-phase zero-passage fault condition, the maximum error of voltage and active power does not exceed 2%, which provides a reliable basic tool for the safety and stability analysis of wind farms.

The increase in installed PV capacity worldwide and the intermittent nature of solar resources highlight the importance of power prediction for grid integration of this technology. Therefore, there is an urgent need for an effective prediction model, but the choice of model hyperparameters greatly affects the prediction performance. In this paper, a multi-strategy improved snowmelt algorithm (MISAO) is proposed for optimizing intrinsic computing-expressive empirical mode decomposition with adaptive noise (ICEEMDAN) and weighted least squares support vector machine for PV power forecasting. Firstly, a cyclic chaotic mapping initialization strategy is used to generate a uniformly distributed high-quality population, which facilitates the algorithm to enter the appropriate search domain quickly. Secondly, the Gaussian diffusion strategy enhances the local exploration ability of the intelligences and extends their search in the solution space, effectively preventing them from falling into local optima. Finally, a stochastic follower search strategy is employed to reserve better candidate solutions for the next iteration, thus achieving a robust exploration–exploitation balance. With these strategies, the optimization performance of MISAO is comprehensively improved. In order to comprehensively evaluate the optimization performance of MISAO, a series of numerical optimization experiments were conducted using IEEE CEC2017 and test sets, and the effectiveness of each improvement strategy was verified. In terms of solution accuracy, convergence speed, robustness, and scalability, MISAO was compared with the basic SAO, various state-of-the-art optimizers, and some recently developed improved algorithms. The results showed that the overall optimization performance of MISAO is excellent, with Friedman average rankings of 1.80 and 1.82 in the two comparison experiments. In most of the test cases, MISAO delivered more accurate and reliable solutions than its competitors. In addition, the altered algorithm was applied to the selection of hyperparameters for the ICEEMDAN-WLSSVM PV prediction model, and seven neural network models, including WLSSVM, ICEEMDAN-WLSSVM, and MISAO-ICEEMDAN-WLSSVM, were used to predict the PV power under three different weather types. The results showed that the models have high prediction accuracy and stability. The MAPE, MAE and RMSE of the proposed model were reduced by at least 25.3%, 17.8% and 13.3%, respectively. This method is useful for predicting the output power, which is conducive to the economic dispatch of the grid and the stable operation of the power system.

The sub-synchronous oscillation accident of large-scale doubly fed wind turbines connected to a grid through series compensation has caused a serious impact on the power system. By optimizing the parameters of the doubly fed wind turbines control system, the system impedance can be effectively improved to solve the problem of sub-synchronous oscillation. However, owing to the complexity of a grid-connected system of doubly fed generators connected to wind turbines and the influence of the time-varying oscillation characteristics of the system, it is often difficult to achieve a successful suppression. To solve this problem, this paper proposes an optimized additional damping method for the rotor- and grid-side controllers, which can achieve efficient suppression of the sub-synchronous oscillation. The parameters of the proposed additional damping method are optimized for all variable operation conditions using a genetic algorithm under the established frequency–domain impedance model. The detailed time–domain simulation model was constructed with the RTLAB platform to verify the proposed method. The experimental results show that the optimized control strategy can effectively and quickly suppress the sub-synchronous oscillation under different operating conditions, and the amplitude suppression rate reached 85.99%, which effectively improved the grid-connected stability of the wind turbines.

With the increasing proportion of grid-connected capacity of new energy units, such as wind power and photovoltaics, accurately constructing simulation models of these units is of great significance to the study of new power systems. However, the actual control strategies and parameters of many new energy units are often unavailable due to factors like outdated equipment or commercial confidentiality. This unavailability creates modeling challenges that compromise accuracy, ultimately affecting grid-connected power generation performance. Aiming at the problem of accurate modeling of fault ride-through control of new energy turbine “black box” controllers, this paper proposes an accurate identification method of fault ride-through control characteristics of doubly fed wind turbines based on hardware-in-the-loop testing. Firstly, according to the domestic and international new energy turbine fault ride-through standards, the fault ride-through segmentation control characteristics are summarized, and a general structured model for fault ride-through segmentation control of doubly fed wind turbines is constructed; Secondly, based on the measured hardware-in-the-loop data of the doubly fed wind turbine black box controller, the method of data segmentation preprocessing and structured model identification of the doubly fed wind turbine is proposed by utilizing statistical modal features and genetic Newton’s algorithm, and a set of generalized software simulation platforms for parameter identification is developed by combining Matlab and BPA; lastly, using the measured data of the doubly fed wind turbine in the black box and the software platform, the validity and accuracy of the proposed parameter identification method and software are tested in the simulation. Finally, the effectiveness and accuracy of the proposed parameter identification method and software are simulated and tested by using the measured data of black box doubly fed wind turbine and the software platform. The results show that the method proposed in this paper has higher recognition accuracy and stronger robustness, and the recognition error is reduced by 2.89% compared with the traditional method, which is of high value for engineering applications.

The large-scale integration of wind power, photovoltaic systems, and energy storage systems (ESSs) into power grids has increasingly influenced the transient stability of power systems due to their dynamic response characteristics. Considering the commercial confidentiality of core control parameters from equipment manufacturers, parameter identification has become a crucial approach for analyzing ESS dynamic behaviors during high-voltage ride-through (HVRT) and low-voltage ride-through (LVRT) and for optimizing control strategies. In this study, we present a multidimensional feature-integrated parameter identification framework for ESSs, combining a multi-scenario voltage disturbance testing environment built on a real-time laboratory platform with field-measured data and enhanced optimization algorithms. Focusing on the control characteristics of energy storage converters, a non-intrusive identification method for grid-connected control parameters is proposed based on dynamic trajectory feature extraction and a hybrid optimization algorithm that integrates an improved particle swarm optimization (PSO) algorithm with gradient-based coordination. The results demonstrate that the proposed approach effectively captures the dynamic coupling mechanisms of ESSs under dual-mode operation (charging and discharging) and voltage fluctuations. By relying on measured data for parameter inversion, the method circumvents the limitations posed by commercial confidentiality, providing a novel technical pathway to enhance the fault ride-through (FRT) performance of energy storage systems (ESSs). In addition, the developed simulation verification framework serves as a valuable tool for security analysis in power systems with high renewable energy penetration.

The sub-synchronous oscillation accident of large-scale doubly fed wind turbines connected to a grid through series compensation has caused a serious impact on the power system. By optimizing the parameters of the doubly fed fan control system, the system impedance can be effectively improved to solve the problem of sub-synchronous oscillation. However, owing to the complexity of a grid-connected system of doubly fed fans and the influence of the time-varying oscillation characteristics of the system, it is often difficult to achieve a successful suppression. To solve this problem, this paper proposes an optimized additional damping method for the rotor and grid-side controllers, which can achieve efficient suppression of sub-synchronous oscillation. The parameters of the proposed additional damping method are optimized for all variable operation conditions using a genetic algorithm under the established frequency-domain impedance model. The detailed time-domain simulation model was constructed with the RTLAB platform to verify the proposed method. The experimental results show that the optimized control strategy can effectively and quickly suppress the sub-synchronous oscillation under different working conditions, and the amplitude suppression rate reached 85.99%, which effectively improved the grid-connected stability of wind turbines.