海上风电作为构建以新能源为主体的新型电力系统的重要组成部分将在沿海城市迅速发展，大规模海上风电并网将大幅削减系统惯量，降低频率抗扰能力。为此，提出基于双锁相环的海上风场综合惯量控制策略。首先，从理论上分析了锁相环对风机虚拟惯量的影响机理，探究了参数变化下风机惯量支撑能力和系统频率响应特性；其次，通过分析锁相环参数变化对风机一次调频能力的消极影响，提出基于变参数-快速双锁相环的海上风场综合惯量调频策略；最后，通过仿真验证所提调频策略的有效性。结果表明，综合惯量控制策略在频率响应快速性、抑制测量噪声等方面均具有显著优势。

To build the new power system with the main body of new energy, the offshore wind farm (OWF) is an important part and has been rapidly developed in coastal cities. However, the large-scale OWFs also reduce the inertia of power systems, which significantly decreases the anti-disturbance capability of system frequency Therefore, a synthetic inertia control strategy for OWF based on dual phase-locked loops (PLL) was proposed. First, the influence mechanism of PLL on virtual inertia of wind turbine was theoretically analyzed for variable parameter PLL. And the variation rule of wind turbine inertia support capacity and system frequency response characteristics with parameter changing PLL was explored. Then, the double PLL based OWF frequency regulation strategy was proposed through analyzing the negative effect of primary frequency regulation characteristics caused by the variable parameter of PLL. Finally, the effectiveness of the proposed frequency regulation strategy was verified though simulations. The results show that the proposed method has significant advantages in frequency response speed and frequency measurement noise suppression.

风电、光伏等新能源设备大量并网，系统逐渐呈现出低惯量的特性，将带来一系列的频率安全问题，传统的频率特性分析方法难以准确把握新能源参与惯量支撑与一次调频后的系统频率特性变化。为考虑风光储多资源参与下的系统频率特性与态势变化，首先基于传统频率响应模型构建了考虑风光储的新型频率响应模型并定义风光储调频贡献度指标，利用频率特性传递函数对系统调频单元进行参数整定；然后定量分析了电力系统的频率稳定指标，且从频域角度分析惯性时间常数、各调频单元的控制系数对频率稳定性的影响；最后通过MATLAB/Simulink平台对理论分析进行时域仿真验证。从时域与频域角度对新型电力系统频率特性进行分析的方法可准确预测考虑风光储调频贡献度下的系统频率态势，为考虑风光储参与惯量支撑与一次调频下的新型电力系统频率安全运行提供了指导依据。

A large number of wind power, photovoltaic and other new energy equipment are connected to the power grid, and the system gradually presents the characteristics of low inertia, which will bring a series of frequency safety problems.Traditional frequency characteristic analysis methods are difficult to accurately grasp the changes in system frequency characteristics after the participation of new energy in inertia support and primary frequency modulation. In order to explore the frequency characteristics and situation changes of the system considering the contribution of wind power-photovoltaic-energy storage to frequency modulation, a new frequency response model is constructed based on the traditional frequency response model and the contribution of wind power-photovoltaic-energy storage to frequency modulation is defined. The frequency characteristic transfer function is used to set the parameters of the system frequency modulation unit. Then the frequency stability index of the power system is quantitatively analyzed, and the influence of the inertia time constant and the control coefficient of each frequency modulation unit on the frequency stability is analyzed from the angle of frequency domain. Finally, the theoretical analysis is verified by MATLAB/Simulink platform. The method of analyzing the frequency characteristics of the new power system from the perspective of time domain and frequency domain can accurately predict the frequency situation of the system considering the contribution degree of wind power-photovoltaic-energy storage frequency modulation, and provide guidance for the safe operation of the new power system frequency considering the inertia support of wind power-photovoltaic-energy storage and primary frequency modulation.

超短期风速预测是保障风电机组桨距角前馈控制实施效果的关键，对提高风电机组环境适应性具有重要影响。为了提高预测精度，提出了一种改进组合神经网络的超短期风速预测方法。该方法选择适合时间序列预测且具有较强非线性学习能力的BP神经网络和长短期记忆(long short-term memory，LSTM)神经网络进行加权组合，以消除单个神经网络可能存在的较大误差；同时，为了提高组合效果，采用差分进化算法对组合权重进行优化。将该方法应用于某风场超短期风速预测中，通过与单神经网络预测、等权重组合神经网络预测的结果对比，验证了所提方法在提高预测精度上的有效性。

Ultra-short-term wind speed prediction is the key to ensure the implementation effect of wind turbine pitch angle feedforward control, and has an important impact on improving the environmental adaptability of wind turbines. In order to improve the prediction accuracy, an ultra-short-term wind speed prediction method based on an improved combined neural networks was proposed. In this method, BP neural network and long short-term memory (LSTM) neural network, which are suitable for time series prediction and have strong nonlinear learning ability, are selected for weighted combination to eliminate the large error that may exist in a single neural network. At the same time, to improve the combination effect, the differential evolution (DE) algorithm was used to optimize the combination weight. The method was applied to the ultra-short-term wind speed prediction of a wind farm. Compared with the results of single neural network prediction and equal weight combined neural networks prediction, the effectiveness of the proposed method in improving the prediction accuracy was verified.

由于风资源分布的差异性,风电场内不同机组的减载备用能力不同。在预留减载备用时,风电场应考虑不同机组的减载备用能力差异。针对该问题,构建了双馈风机的可用调频容量模型,在此基础上,提出了考虑风速差异的风电场调频备用协调控制策略。该策略根据不同风速机组的可用调频容量差异协调风电场的调频资源,在满足备用容量的同时充分利用转子动能,通常仅需控制部分风机,简化系统复杂性。在DIgSILENT/PowerFactory中搭建了系统仿真模型,仿真结果表明所提策略使风电场内不同风速的机组协调配合,改善系统的频率调节能力,缓解同步机调频压力。

Due to the difference of wind resource distribution, different units in the wind farm have different de-loading reserve capacity. When reserving de-loading reserve, the wind farm should consider the difference of de-loading reserve capacity of different units. In order to solve this problem, the available frequency-regulation capacity model of doubly-fed induction generator is constructed in this paper. According to this model, a coordinated control of frequency-regulation reserve capacity for wind farm considering wind speed difference is proposed. In this strategy, the frequency-regulation resources of wind farms are coordinated according to the differences of available frequency-regulation capacity of different wind turbines, and the kinetic energy of the rotor is fully utilized while the reserve capacity is satisfied. Usually, only part of the wind turbines are controlled, which simplifies the complexity of the control system. The system simulation model is built in DIgSILENT/PowerFactory. The simulation results show that the proposed strategy can coordinate the units with different wind speeds in the wind farm, improve the frequency-regulation capability of the system and alleviate the frequency-regulation pressure of the synchronous machine when load disturbance occurs.

风电的大规模并网给电力系统带来了惯量降低和一次调频能力不足等问题,可减载运行且具备调频功能的风电场可有效应对该问题。为此,提出了一种风电场减载方案以及相应的一次调频策略。首先,介绍了综合惯性控制和桨距角控制的原理,并分析了风电场减载运行的必要性。然后,研究了不同风速下风机减载能力的差异性并制定了风电场内减载功率分配方案,根据制定的减载方案,提出了相应的一次调频策略,以充分利用风电场的调频能力并避免频率的二次跌落。最后,基于Matlab/Simulink搭建了仿真系统模型,仿真结果表明所述减载方案和调频策略可以合理分配减载功率并提升风电场的调频效果。

The large-scale wind power integration brings some problems such as the reduction of inertia and the shortage of primary frequency-regulation capability to the power system. Wind farms with deloading operation and frequency-regulation function could effectively deal with these problems. Therefore, a deloading scheme and the corresponding primary frequency regulation strategy of wind farm are proposed. Firstly, the principle of integrated inertia control and pitch-angle control is introduced, and the necessity of deloading operation of wind farm is analyzed. Then the difference of deloading capability of wind turbines with different wind speeds is studied and the deloading power distribution scheme of wind farm is formulated. Furthermore, according to the deloading power distribution scheme, the corresponding primary frequency regulation strategy is proposed to make full use of the frequency regulation capability of wind farm and avoid the secondary frequency drop. The simulation system model is built based on Matlab/Simulink, and the simulation results show that the deloading scheme and frequency regulation strategy could reasonably distribute the deloading power and improve the frequency regulation effect of wind farm.

Ultra-short-term wind power exhibits significant minute-level fluctuation characteristics, leading to substantial instantaneous prediction errors. To mitigate the impact of instantaneous wind power prediction errors, the following steps are taken: First, the correlation between instantaneous prediction errors and meteorological factors is determined, and strongly associated variables are selected as model inputs. Next, the particle swarm optimization algorithm is employed to optimize the initial weights and threshold parameters of the spatiotemporal backpropagation neural network prediction model to enhance its performance. Subsequently, based on the nonlinear relationship between wind speed/direction data and instantaneous prediction errors, a wind speed matrix gradient correction method and a deep learning correction method with physical constraints on prediction errors are constructed to address errors caused by declining model generalization under strong disturbances. To validate the effectiveness of the proposed prediction algorithm integrating parameter optimization and the error correction method, it is compared with typical convolutional neural networks, long short-term memory networks, and backpropagation neural algorithms. The results demonstrate that compared to other wind power prediction strategies, this method reduces the mean absolute percentage error, root mean square error, and mean absolute error by 48.49%, 45.51%, and 50.8%, respectively. These results confirm that combining error correction strategies with prediction model parameter optimization effectively enhances the ability to reduce instantaneous wind power prediction errors, providing a practical technical solution for optimizing ultra-short-term wind power prediction accuracy and offering valuable insights for ensuring the stability of wind power grid integration.