To solve the problem of sub-synchronous oscillations caused by the application of series compensation technology in long-distance wind power transmission, this paper proposes a sub-synchronous oscillation suppression strategy for the ‘black box’ Doubly-fed Induction Generator system, which makes use of a superconducting magnetic energy storage device with an additional sub-synchronous damping controller to provide positive damping to the system in order to suppress sub-synchronous oscillations of the system. First, a doubly fed induction generator system is constructed in the RTDS simulation software, and second, an impedance model of the entire system under different operating conditions is constructed using an impedance scanning technique to analyze the risk of subsynchronous oscillations occurring in the constructed wind farm model and superconducting magnetic energy storage model. Then, an additional damping controller of the energy storage device with the function of sub-synchronous oscillation suppression is designed, and the parameters are optimized using a genetic algorithm. Simulation verification shows that the designed energy storage device can effectively suppress the 8.40-11.80 Hz oscillation frequency range generated by this system in four quadrants. The results demonstrate that the proposed method provides an effective solution to the sub-synchronous oscillation problem.

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 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.