基于多时间尺度故障过程分区的DFIG参数分层递进式辨识策略

doi:10.11930/j.issn.1004-9649.202507006

摘要/Abstract

摘要：

针对多工况下双馈风电机组黑盒模型参数辨识问题，提出基于多时间尺度故障过程分区的分层递进式参数辨识策略。首先，基于黑盒模型动态响应特性确定了模型结构及待辨识参数。其次，通过摄动理论量化分析了不同时间尺度参数灵敏度，依据不同阶段主导参数响应特性建立了层次化递进式辨识方法。然后，通过不同层级参数响应差异化特性，利用差分进化方法实现了多参数自适应辨识。最后，建立了适用不同厂家不同型号的白盒模型参数辨识方法，结果表明：所提出的分层递进式辨识策略对不同工况和型号的具有适用性和鲁棒性，与传统参数辨识方法相比，所提辨识方法具有更好的快速性和准确性。

关键词: 双馈风力发电机, 分层递进式辨识策略, 电磁暂态模型, 差分进化法, 轨迹灵敏度

Abstract:

To address the parameter identification problem of black-box models for doubly-fed induction generator (DFIG) wind turbines under multiple operating conditions, this paper proposes a hierarchical progressive parameter identification strategy based on the partitioning of multi-timescale fault process. Firstly, the model structure and parameters to be identified are determined according to the dynamic response characteristics of the black-box model. Subsequently, the sensitivity of parameters across different time scales is quantitatively analyzed using perturbation theory, and a hierarchical progressive identification method is established according to the dominant parameter response characteristics in different operational stages. Furthermore, by leveraging the differential response characteristics of parameters across hierarchical levels, the differential evolution method is adopted to realize adaptive identification of multiple parameters. Finally, a white-box model parameter identification method applicable to various manufacturers and models is developed. Comparative results show that the proposed hierarchical progressive identification strategy has good applicability and robustness under different operating conditions and for different models. Additionally, comparisons results with traditional parameter identification methods also demonstrate that the proposed approach exhibits superior rapidity and accuracy.

Key words: doubly-fed induction generator, hierarchical progressive identification strategy, electromagnetic transient model, differential evolution algorithm, trajectory sensitivity

王宇, 王彤, 王潇桐. 基于多时间尺度故障过程分区的DFIG参数分层递进式辨识策略[J]. 中国电力, 2026, 59(3): 142-155.

WANG Yu, WANG Tong, WANG Xiaotong. Hierarchical progressive identification strategy for DFIG parameters based on multi-timescale fault process partitioning[J]. Electric Power, 2026, 59(3): 142-155.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202507006

https://www.electricpower.com.cn/CN/Y2026/V59/I3/142

图/表 18

图 1 不同工况下RSC与GSC的功率电流响应特性

Fig.1 Response characteristics of power and current of RSC and GSC under varying operating conditions

表 1 待辨识参数表

Table 1 Model parameters to be identified

类别	名称	待辨识参数
故障穿越主导参数	故障穿越电压投入、切除阈值	V_in、V_out
	电流、无功电流限幅值	I_M、I_qM
	低穿有功功率系数、设定值	K_{p_lvrt}、P_{set_lvrt}
	低穿有功电流计算系数1、2和设定值	K_{1_Ip_lvrt}、K_{2_Ip_lvrt}、 I_{p_set_lvrt}
	有功恢复斜率	K₂
	无功支撑系数	K₁
PI控制器参数	RSC有功功率外环控制PI参数	k_p1、k_i1
	RSC有功电流内环控制PI参数	k_p2、k_i2
	RSC无功功率外环控制PI参数	k_p3、k_i3
	RSC无功电流内环控制PI参数	k_p4、k_i4
	GSC直流电压外环控制PI参数	k_p5、k_i5
	GSC有功电流内环控制PI参数	k_p6、k_i6
	GSC无功电流内环控制PI参数	k_p7、k_i7

图 2 故障穿越主导参数摄动分析结果

Fig.2 Perturbation analysis of dominant parameters in fault ride-through performance

图 3 RSC控制环中PI控制器参数摄动分析结果

Fig.3 PI controller parameter perturbation analysis of RSC control loop

图 4 GSC控制环中PI控制器参数摄动分析结果

Fig.4 PI controller parameter perturbation analysis of GSC control loop

图 5 DFIG故障穿越中多时间尺度动态过程

Fig.5 Multi-timescale dynamic process during DFIG fault ride-through

图 6 双馈风机典型控制结构及参数分层递进式辨识策略

Fig.6 Typical control structure of doubly-fed induction generator and hierarchical progressive identification strategy of parameters

表 2 待辨识控制模式与参数

Table 2 Control modes and parameters to be identified

控制模式	参数
无附加控制
指定功率控制	K_{p_lvrt}、P_{set_lvrt}
指定电流控制	K_{1_Ip_lvrt}、K_{2_Ip_lvrt}、I_{p_set_lvrt}
按穿越前电流控制

图 7 变流器控制参数轨迹灵敏度曲线

Fig.7 Trajectory sensitivity plot of converter control parameters

表 3 变流器控制参数轨迹灵敏度

Table 3 Converter control parameter trajectory sensitivity

RSC控制参数		GSC控制参数
参数	轨迹灵敏度	参数	轨迹灵敏度
k_p1	84.0481	k_p5	1363.5959
k_i1	16.0609	k_i5	33.4606
k_p2	1333.5661	k_p6	204.9335
k_i2	701.3466	k_i6	147.8609
k_p3	122.2233	k_p7	1746.6619
k_i3	247.4973	k_i7	147.8609
k_p4	595.0996
k_i4	370.0723

图 8 变流器控制参数综合判定指标热力图

Fig.8 Heat map of comprehensive evaluation metrics for converter control parameters

图 9 厂家黑盒封装模型与白盒模型故障响应特性全过程对比

Fig.9 Comparison of full-process fault response characteristics between manufacturer-encapsulated black-box model and white-box model

表 4 某厂家2.5 MW黑盒封装模型与白盒模型故障响应对比平均绝对误差

Table 4 Mean absolute errors for comparison of fault response between a manufacturer's 2.5 MW encapsulated black-box model and white-box model

时段	区间	正序电压	有功功率	无功功率	有功电流	无功电流
故障前	稳态区间	0.0010	0.0028	0.0013	0.0011	0.0016
故障期间	暂态区间	0.0043	0.0177	0.0181	0.0187	0.0163
故障期间	稳态区间	0.0010	0.0058	0.0109	0.0087	0.0160
故障后	暂态区间	0.0049	0.0191	0.0188	0.0195	0.0176
	恢复区间	0.0015	0.0043	0.0066	0.0053	0.0061
	稳态区间	0.0010	0.0026	0.0012	0.0011	0.0015

表 5 某厂家2.2 MW黑盒封装模型与白盒模型故障响应对比平均绝对误差

Table 5 Mean absolute errors for comparison of fault response between a manufacturer's 2.2 MW encapsulated black-box model and white-box model

时段	区间	正序电压	有功功率	无功功率	有功电流	无功电流
故障前	稳态区间	0.0009	0.0020	0.0011	0.0011	0.0016
故障期间	暂态区间	0.0031	0.0151	0.0186	0.0170	0.0166
故障期间	稳态区间	0.0010	0.0049	0.0108	0.0090	0.0155
故障后	暂态区间	0.0049	0.0188	0.0172	0.0187	0.0164
	恢复区间	0.0017	0.0040	0.0065	0.0051	0.0066
	稳态区间	0.0010	0.0023	0.0011	0.0012	0.0015

表 6 某厂家3.6 MW黑盒封装模型与白盒模型故障响应对比平均绝对误差

Table 6 Mean absolute errors for comparison of fault response between a manufacturer's 3.6 MW encapsulated black-box model and white-box model

时段	区间	正序电压	有功功率	无功功率	有功电流	无功电流
故障前	稳态区间	0.0015	0.0032	0.0016	0.0011	0.0021
故障期间	暂态区间	0.0045	0.0180	0.0176	0.0193	0.0172
故障期间	稳态区间	0.0013	0.0065	0.0110	0.0087	0.0164
故障后	暂态区间	0.0055	0.0193	0.0179	0.0191	0.0176
	恢复区间	0.0015	0.0055	0.0057	0.0067	0.0062
	稳态区间	0.0012	0.0037	0.0010	0.0015	0.0020

图 10 本文方法与传统方法辨识结果在小功率电压跌落至0.2 p.u.时与黑盒模型响应对比

Fig.10 Comparison of responses between identification results of the proposed method and traditional methods and the black-box model under the condition of low-power voltage sag down to 0.2 p.u.

图 11 本文方法与传统方法辨识结果在大功率电压跌落至0.2 p.u.时与黑盒模型响应对比

Fig.11 Comparison of responses between identification results of the proposed method and traditional methods and the black-box model under the condition of large-power voltage sag down to 0.2 p.u.

表 7 本文方法与传统方法误差和耗时对比

Table 7 Comparison of error and time consumption between the proposed method and the traditional method

辨识方法	测试故障	误差/%	时间/s
本文方法	大功率下电压跌落至0.2 p.u.	1.33	186
本文方法	小功率下电压跌落至0.2 p.u.	1.45	178
传统方法	大功率下电压跌落至0.2 p.u.	3.17	255
传统方法	小功率下电压跌落至0.2 p.u.	4.98	246

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