Multiple wind farms (WFs) should be spatially and temporally coordinated during inertial control to avoid the under- or over-power supply. For this purpose, we propose a second-order cone predictive model to exploit the optimal inertial control scheme (ICS) of multiple WFs for under-frequency and over-frequency events. Specifically, we firstly build the nonlinear predictive model including the predictive models of synchronous generators and wind turbines (WTs), power flow model and the stable operating constraints. In this nonlinear model, multiple WFs can be properly coordinated to achieve the optimal ICS. Then, the approximation methods are proposed to recast the above nonlinear model as a second-order cone programming (SOCP), and a sequential SOCP algorithm (SSA) is presented for the optimal solution. The SOCP model is offline executed to generate the optimal power references of WFs, and power references are further fitted with 5-parameter piecewise curves to reduce the communication burden. Lastly, the fitted curves can be distributed to WF controllers to be online tracked after detecting the disturbance. Numerical tests are conducted based on the 5 WFs integrated IEEE 39-bus system to validate the effectiveness and superiority of the proposed method.

中文翻译：

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使用二阶锥预测模型优化惯性控制的风电场级协调

多个风电场（WF）在惯性控制期间应在空间和时间上进行协调，以避免供电不足或供电过剩。为此，我们提出了一个二阶锥预测模型，以针对欠频和过频事件利用多个 WF 的最佳惯性控制方案 (ICS)。具体来说，我们首先建立了非线性预测模型，包括同步发电机和风力涡轮机（WT）的预测模型、潮流模型和稳定运行约束。在这个非线性模型中，多个 WF 可以适当协调以实现最佳 ICS。然后，提出近似方法将上述非线性模型重铸为二阶锥规划（SOCP），并提出序列SOCP算法（SSA）以获得最优解。SOCP模型离线执行以生成WF的最佳功率参考，功率参考进一步拟合5参数分段曲线以减少通信负担。最后，拟合曲线可以分配给 WF 控制器，以便在检测到扰动后进行在线跟踪。基于 5 个 WF 集成的 IEEE 39 总线系统进行了数值测试，以验证所提出方法的有效性和优越性。