基于分布式深度神经网络的双馈风机低压故障穿越研究

张哲源; 顾幸生

doi:10.14135/j.cnki.1006-3080.20220105005

基于分布式深度神经网络的双馈风机低压故障穿越研究

doi: 10.14135/j.cnki.1006-3080.20220105005

张哲源,
顾幸生^,

华东理工大学能源化工过程智能制造教育部重点实验室, 上海200237

详细信息

作者简介:
张哲源（1997—），男，山西忻州人，硕士生，主要研究方向为电力系统仿真。E-mail：867878095@qq.com

通讯作者:
顾幸生，E-mail：xsgu@ecust.edu.cn

中图分类号: TP183
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- 文章访问数: 55
- HTML全文浏览量: 21
- PDF下载量: 1
- 被引次数: 0
出版历程
- 收稿日期: 2022-01-05
- 网络出版日期: 2022-05-14

Low Voltage Ride Through Research on Distributed DNN-Based DFIG

ZHANG Zheyuan,
GU Xingsheng^,

Key Laboratory of Smart Manufacturing in Energy chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

摘要

摘要: 双馈风机的低电压穿越性能不仅依靠着控制策略，还取决于对控制参数选择。由于控制参数优化算法耗时太久，在实时控制中达不到相应的效果，所以本文提出了一种基于离线参数优化和模型训练、在线故障识别的方法。首先通过已经建立的双馈风机(Doubly Fed Induction Generator，DFIG)并网模型获取大量不同类型的故障数据并根据故障类型进行控制参数的离线优化，形成相应的低压穿越方式，然后将不同的故障数据进行分类，构成神经网络的训练样本。电网故障瞬间可以将故障数据直接通过训练好的分布式深度神经网络(Deep Neural Networks, DNN)迅速判断故障种类，并根据故障类型选择合适的控制策略。通过双馈风机模型的故障识别和参数优化方法验证了该方法的可行性以及在控制效果和速度方面的优势。
- 双馈风机 /
- 深度神经网络 /
- 低压穿越 /
- 故障识别 /
- 参数优化
Abstract: The low voltage ride through performance of doubly fed fan depends not only on the control strategy, but also on the selection of control parameters.Because the control parameter optimization algorithm takes too long to achieve the corresponding effect in real-time control, a method based on off-line parameter optimization, model training and on-line fault identification is proposed in this paper.Firstly, a large number of different types of fault data are obtained through the established DFIG grid connection model, and the control parameters are optimized offline according to the fault type to form the corresponding low-voltage ride through mode, and then the different fault data are classified to form the training samples of neural network.At the moment of power grid fault, the fault data can be directly used to quickly judge the fault type through the trained distributed deep neural network, and select the appropriate control strategy according to the fault type.The feasibility of this method and its advantages in control effect and speed are verified by the fault identification and parameter optimization method of doubly fed fan model.
- doubly fed fan /
- deep neural network /
- low pressure crossing /
- fault identification /
- parameter optimization

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图 1 转子侧变流器控制框图

Figure 1. Control block diagram of rotor side converter

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图 2 撬棒保护电路

Figure 2. Crowbar protection circuit

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图 3 电压跌落30%以内低压穿越特性对比

Figure 3. Comparison of low voltage ride through characteristics within 30% voltage drop

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图 4 电压跌落至32%时低压穿越特性对比

Figure 4. Comparison of low voltage ride through characteristics when the voltage drops to 32%

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图 5 电压跌落至45%时低压穿越特性对比

Figure 5. Comparison of low voltage ride through characteristics when voltage drops to 45%

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图 6 DNN结构图

Figure 6. DNN structure diagram

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图 7 分布式DNN训练流程图

Figure 7. Distributed DNN training flow chart

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表 1 控制器参数对比

Table 1. Controller parameter comparison

Parameter item	Initial value	Optimization
k_pd	0.8	0.98
k_id	0.2	0.15
k_pq	972.222	1138
k_iq	8.0e-05	0.01
hy	0.1	1.08

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表 2 优化前后Crowbar电阻

Table 2. Crowbar resistance before and after optimization

Crowbar resister/Ω
Before optimization	After optimization/Ω
30	24.58

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表 3 优化前后控制参数对比

Table 3. Comparison of control parameters before and after optimization

Parameter value	Before optimization	Optimization (<1.25I_r)	Optimization (>=1.25I_r)
k_pd	0.8	0.51
k_id	0.2	0.13
k_pq	972.222	1003.25
k_iq	8.0e-05	8.0e-05
hy	0.1	0.15
R_c/Ω	30		13.38

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表 4 故障仿真训练集参数表

Table 4. Parameter table of fault simulation training set

Fault divider resistor/Ω	Fault fall degree	Fault type
5	94.22%	Minor
2	87.23%	Minor
1	77.35%	Minor
0.5	63.58%	General
0.35	52.44%	General
0.25	45.57%	General
0.15	32.69%	Critical
0.1	24.88%	Critical

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表 5 故障仿真测试集参数表

Table 5. Parameter table of fault simulation test set

故障分压电阻/Ω	Fault degree	Fault type
1.5	83.58%	Minor
0.3	49.88%	General
0.13	30.32%	Critical

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表 6 不同策略下DNN故障识别仿真结果

Table 6. Simulation results of DNN fault identification under different strategies

Strategy	Network structure	Maximum number of iterations	Training rate	Average error	Test accuracy/%	Average training time/s
1	30-20-10	5000	0.01	0.00156	98.10	2241
2	30-10	5000	0.01	0.01488	95.79	1318
3	30-20-10	5000	0.01	0.00541	96.93	1577
4	30-20-10	5000	0.01	0.00459	97.64	1511

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