基于深度置信网络的双馈风机变换器开路故障诊断
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TM464

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国家电网有限公司总部科技项目(SGJSDK00JLJS1900400)


Open-switch fault diagnosis of converters of doubly-fed induction generator-based wind turbine using deep belief networks
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    摘要:

    双馈风机的定子与电网直接连接,转子通过转子侧变换器和网侧变换器与电网进行功率交换。变换器的电力电子开关容易发生开路故障,影响双馈风机的安全稳定运行。文中针对双馈风机常见的变换器开路故障,提出一种基于深度置信网络的故障诊断方法。首先分析了双馈风机在转子侧变换器和网侧变换器的单个和双个开关管故障下的输出响应。基于双馈风机的变换器开路故障数据,构造多层受限玻尔兹曼机结构,充分利用深度置信网络优异的模式识别能力,深度提取不同故障条件和运行工况下转子电流和网侧电流的信号特征,提高算法准确度。仿真结果表明,该故障诊断方法能够准确识别单开关和双开关的多类型复杂故障。

    Abstract:

    The stator of doubly-fed induction generator-based wind turbine (DFIG-WT) is directly connected to the power grid, and the rotor of DFIG-WT system exchanges power with main grid via a back-to-back converter. The power electronic switches of back-to-back converter are prone to open-switch fault, which affects the stable operation of the system. A deep belief network (DBN) based fault diagnosis method for the open-switch faults of converters of DFIG-WT system is present in this paper. Firstly, the output response of DFIG-WT system with single and double switch faults of rotor-side converter (RSC) and grid-side converter (GSC) is analyzed. Based on the open-switch fault data of DFIG-WT, multilayer restricted Boltzmann machines (RBMs) are constructed to extract the deep information of rotor currents and grid currents under various fault and operation conditions, which fully takes advantage of excellent pattern recognition ability of DBN to improve the fault diagnosis accuracy. The simulation results indicate that the proposed DBN based fault diagnosis method is able to precisely detect the single and double open-switch faults of DFIG-WT system.

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夏候凯顺,李波.基于深度置信网络的双馈风机变换器开路故障诊断[J].电力工程技术,2021,40(1):188-194

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历史
  • 收稿日期:2020-08-05
  • 最后修改日期:2020-09-12
  • 录用日期:2020-03-02
  • 在线发布日期: 2021-02-03
  • 出版日期: 2021-01-28