大型风电场的动态等值建模是研究风电并网问题的基础，然而基于聚类的风电场等值模型无法高精度拟合其动态特性，且应用时泛化能力差是聚类等值的固有缺陷。针对该问题，提出一种基于粒子群算法-长短期记忆神经网络-误差校正模型（particle swarm optimization-long short term memory neural network-error correction model，PSO-LSTM-ECM）的风电场等值建模方法。首先，利用K-means聚类算法和容量加权法对风电场风电机组进行分群，构建风电场聚类等值模型；然后，基于详细模型和聚类等值模型的暂态响应误差构建ECM，通过PSO优化的LSTM网络训练得到校正模型，将网络的输出值补偿给聚类等值模型；最后，在PSCAD和Matlab平台进行联合仿真，对风电场详细模型、聚类等值模型和所提出模型进行对比分析，结果验证了提出模型的有效性与优越性。

Dynamic equivalent modeling of large-scale wind farms is the foundation for studying wind power grid integration，while the clustering-based equivalent model of wind farms cannot fit the dynamic output characteristics with high accuracy，and the poor generalization ability in its application is an inherent defect of clustering based model. Aiming at this problem，this paper proposes a wind farm equivalent modeling method based on particle swarm optimization-long short term memory neural network-error correction model （PSO-LSTM-ECM）. Firstly，K-means clustering algorithm and capacity weighting method are used to cluster wind turbines in wind farms，and a clustering equivalent model of the wind farms is constructed； Then，ECM is constructed based on the transient response errors of the detailed model and the clustering equivalent model，and the correction model is obtained through the LSTM neural network training optimized by PSO，and the output value of the network is compensated to the clustering equivalent model； Finally，a joint simulation is conducted on PSCAD and Matlab platforms to compare and analyze the detailed wind farm model，clustering equivalent model，and the model proposed in this paper. The result proves the effectiveness and superiority of the proposed model.

Over the past few decades, wind energy has expanded to become a widespread, clean, and sustainable energy source. However, integrating offshore wind energy with the onshore AC grids presents many stability and control challenges that hinder the reliability and resilience of AC grids, particularly during faults. To address this issue, current grid codes require offshore wind farms (OWFs) to remain connected during and after faults. This requirement is challenging because, depending on the fault location and power flow direction, DC link over- or under-voltage can occur, potentially leading to the shutdown of converter stations. Therefore, this necessitates the proper understanding of key technical concepts associated with the integration of OWFs. To help fill the gap, this article performs an in-depth investigation of existing alternating current fault ride through (ACFRT) techniques of modular multilevel converter-based high-voltage direct current (MMC-HVDC) for OWFs. These techniques include the use of AC/DC choppers, flywheel energy storage devices (FESDs), power reduction strategies for OWFs, and energy optimization of the MMC. This article covers both scenarios of onshore and offshore AC faults. Given the importance of wind turbines (WTs) in transforming wind energy into mechanical energy, this article also presents an overview of four WT topologies. In addition, this article explores the advanced converter topologies employed in HVDC systems to transform three-phase AC voltages to DC voltages and vice versa at each terminal of the DC link. Finally, this article explores the key stability and control concepts, such as small signal stability and large disturbance stability, followed by future research trends in the development of converter topologies for HVDC transmission such as hybrid HVDC systems, which combine current source converters (CSCs) and voltage source converters (VSCs) and diode rectifier-based HVDC (DR-HVDC) systems.

针对风电场集电网络等值较为粗略、对非对称故障时等值模型精确度关注较少的情况,提出一种适用于短路故障分析的风电场动态等值建模方法。首先,依据系统潮流计算结果对集电网络进行等值解耦计算,并采用基于模块度的K均值算法对各风力机端电压进行聚类分析,实现风电机群内的多机聚合。其次,考虑到集电网络分布电容的影响,对聚合后风电场设置集中无功补偿电容。随后,为保证非对称故障过程中等值模型的有效性,提出风电场零序网络的等值方法。最后,基于时域仿真软件PSCAD/EMTDC下搭建的电磁暂态仿真模型,验证了所提动态等值方法的有效性。

In order to study the dynamic characteristics of interactions between the large-scale wind farms and grid, dynamic equivalent modeling methods for wind farms has attracted extensive attention. Aiming at the existing problems that the equivalence of the wind farm collection network is relatively rough and the accuracy of equivalent models is often ignored during asymmetric faults, a dynamic equivalent modeling method, which is suitable for short-circuit fault analysis for wind farm, is proposed in the paper. Firstly, the decoupling of the collector network is carried out according to the power flow calculation results. Secondly, the modularity based <em>K</em>-means algorithm is adopted for clustering the wind turbine system output ac voltages to realize the optimal multi-machine aggregation. Considering the influence of distributed capacitance of the collector network, the centralized reactive compensation capacitor is set for the wind farm after aggregation. Then, in order to ensure the validity of the equivalent model during asymmetric faults, the equivalent method of zero-sequence network is proposed. Finally, the electromagnetic transient model built in the time-domain simulation software PSCAD/EMTDC verifies the effectiveness of the proposed method.

基于现有针对单个风电场的等值建模方法，提出一种适用于风电集群的等值建模方法，利用CloudPSS-XStudio套件完成了风电集群等值建模系统开发，该系统将预想故障选择、等值参数计算和结果分析进行了集成，为含大规模风电集群的电力系统的动态安全分析提供了支撑。以各风电场平均风速以及预想故障作为输入，基于等值模型的迭代仿真得到各个风电机组的分群指标，并完成对风电集群的分群等值建模。以某实际风电集群拓扑接入IEEE 39节点系统作为测试算例，验证了所提方法的正确性。

Based on an existing equivalent modeling method for individual wind farm, an iterative simulation-based equivalent modeling method for wind power clusters is proposed and a software development for equivalent modeling of wind power clusters is completed by using CloudPSS-XStudio suite. The system integrates expected fault selection, equivalent parameter calculation and result analysis, which provides support for dynamic security assessment of power systems with large-scale wind power clusters. The equivalent method takes the average wind speed of each wind farm and the expected faults as input, and obtains the cluster index of each wind turbine based on the iterative simulation of the equivalent model. Moreover, the clustering equivalent model is formed based on the cluster index. The correctness of the method is verified based on the example of IEEE 39-node system integrated with a wind power cluster.

The correlated renewable energy farms are usually aggregated as a cluster in economic dispatch to relieve computational burden. This strategy can also achieve better performance since the precision of predicting the power generation of a cluster can be higher than those of individual farms. This paper proposes an optimal decomposition method to allocatedispatch schedules among renewable energy farms (REFs) in the cluster under existing stochastic optimization framework. The proposed model takes advantage of probabilistic characteristics of renewable generation to minimize the curtailment and ensure the feasibility of dispatch schedule of the clusters. Approximated tractable formulation and efficient solution method are the proposed to solve the proposed model. Numerical tests show that the proposed method achieves the optimal decomposition of dispatch schedule among REFs and facilitates the utilization of renewable generation.

针对风电场动态运行条件下等值建模精度偏低、聚类依据不足的难题,提出一种基于高斯混合模型聚类思想的风电场等值建模方法。首先,分析单台双馈感应式风力发电机在低电压穿越期间的动态响应特性,根据响应特性的集群特征构建聚类指标。然后,提出基于高斯混合模型动态初步聚类、优化聚类数目的两阶段等值建模方法,推导出赤池信息和贝叶斯信息准则下聚类数目的寻优算法。以典型中等规模风电场为例,在Matlab/Simulink平台进行不同故障穿越条件的仿真测试,结果表明所提风电场等值建模方法聚类有效、精度高。

In response to the difficulties of low accuracy of equivalence modeling and insufficient clustering basis under dynamic operating conditions of wind farms, a wind farm equivalence modeling method based on the idea of Gaussian mixture model clustering is proposed. First, the dynamic response characteristics of a single doubly-fed induction wind turbine during LVRT are analyzed, and the clustering indexes are constructed based on the clustering characteristics of the response characteristics. Then, a two-stage equivalence modeling method based on Gaussian mixture model with dynamic preliminary clustering and optimized number of clusters is proposed, and an optimization search algorithm for the number of clusters under the criteria of red pool information and Bayesian information is derived. Simulation tests with different fault ride-through conditions are carried out in Matlab/Simulink platform for a typical medium-scale wind farm, and the results show that the proposed equivalence modeling method for wind farms is effective in clustering with high accuracy.