Low Voltage Ride Through Research on Distributed DNN-Based DFIG
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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。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.
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表 1 控制器参数对比
Table 1. Controller parameter comparison
Parameter item Initial value Optimization kpd 0.8 0.98 kid 0.2 0.15 kpq 972.222 1138 kiq 8.0e-05 0.01 hy 0.1 1.08 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 表 3 优化前后控制参数对比
Table 3. Comparison of control parameters before and after optimization
Parameter
valueBefore
optimizationOptimization
(<1.25Ir)Optimization
(>=1.25Ir)kpd 0.8 0.51 kid 0.2 0.13 kpq 972.222 1003.25 kiq 8.0e-05 8.0e-05 hy 0.1 0.15 Rc/Ω 30 13.38 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 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 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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