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1.华中科技大学电气与电子工程学院,湖北省武汉市 430074
2.强电磁工程与新技术国家重点实验室(华中科技大学),湖北省武汉市 430074
吴志鹏(1997—),男,硕士研究生,主要研究方向:新能源系统短路等效建模和影响分析。E-mail:wuzhipeng@hust.edu.cn
裴建华(1998—),男,博士研究生,主要研究方向:人工智能在电力系统中的应用。E-mail:jianhuapei@hust.edu.cn
李银红(1976—),女,通信作者,博士,研究员,硕士生导师,主要研究方向:电力系统继电保护整定计算理论、电力系统直流及可再生能源保护整定计算。E-mail:liyinhong@hust.edu.cn
收稿日期:2021-09-09,
修回日期:2022-03-01,
网络出版日期:2022-06-06,
纸质出版日期:2022-10-10
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吴志鹏,裴建华,李银红.基于低电压穿越功率特性的双馈风电场多机等值方法[J].电力系统自动化,2022,46(19):95-103.
WU Zhipeng,PEI Jianhua,LI Yinhong.Multi-machine Equivalent Method for DFIG-based Wind Farm Based on Power Characteristic of Low Voltage Ride-through[J].Automation of Electric Power Systems,2022,46(19):95-103.
吴志鹏,裴建华,李银红.基于低电压穿越功率特性的双馈风电场多机等值方法[J].电力系统自动化,2022,46(19):95-103. DOI: 10.7500/AEPS20210909004.
WU Zhipeng,PEI Jianhua,LI Yinhong.Multi-machine Equivalent Method for DFIG-based Wind Farm Based on Power Characteristic of Low Voltage Ride-through[J].Automation of Electric Power Systems,2022,46(19):95-103. DOI: 10.7500/AEPS20210909004.
为实现双馈风电场的多机等值,需要准确地将动态特性不同的机组区分开,但现有研究并未针对机组低电压穿越功率特性的不同建立定量机群划分指标。为此,提出了一种基于低电压穿越功率特性的双馈风电场多机等值方法。首先,基于典型低电压穿越控制策略,建立了表征机组输出功率特性的数学描述。然后,通过分析机组间功率动态特性的差异性,提出了一种两步分群方法:先以故障稳态有功功率能否恢复至故障前的值为分群指标,将所有机组分为两群;再利用聚类算法进一步划分机群
以降低等值误差。为提高等值方法对不同风速场景的适应性,引入近邻传播算法实现自适应分群。最后,基于10组实测风速场景,对比了所提方法和其他方法的功率曲线及其误差水平,仿真及计算结果验证了所提方法的有效性。
In order to realize the multi-machine equivalence of doubly-fed induction generator (DFIG)-based wind farms
it is necessary to accurately distinguish the units with different dynamic characteristics. However
the existing research has not established the quantitative clustering index based on the different power characteristics of low voltage ride-through for units. Therefore
a multi-machine equivalent method for DFIG-based wind farm based on power characteristics of low voltage ride-through is proposed. First
based on the typical low voltage ride-through control strategy
the mathematical description of output power characteristics for units is established. Then
a two-step clustering method is proposed by analyzing the difference of power dynamic characteristics among units: in the first step
all units are divided into two groups based on whether the steady-state active power during the fault can be restored to the value before the fault; in the second step
the clustering algorithm is used to further divide the cluster to reduce the equivalent error. In order to improve the adaptability of the equivalent method to different wind speed scenarios
the affinity propagation (AP) algorithm is introduced to implement adaptive clustering. Finally
based on 10 sets of measured wind speed scenarios
the power curves and their error levels of the proposed method and other methods are compared. Simulation and calculation results verify the effectiveness of the proposed method.
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