| 引用本文: | 刘述喜,刘 科,王乾蕴,等.基于模态时频图与Resnet-BiGRU模型的MMC子模块开路故障诊断[J].电力系统保护与控制,2025,53(02):73-88.[点击复制] |
| LIU Shuxi,LIU Ke,WANG Qianyun,et al.Open-circuit fault diagnosis of MMC sub modules based on modal time-frequency diagrams and the Resnet-BiGRU model[J].Power System Protection and Control,2025,53(02):73-88[点击复制] |
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| 摘要: |
| 针对电力系统中模块化多电平换流器(modular multilevel converter, MMC)在故障诊断过程中存在提取特征信息易遗漏、诊断精度低和计算量大等问题，提出一种基于模态时频图与残差网络(residual network, Resnet)-双向门控循环单元(bidirectional gated recurrent unit, BiGRU)模型的分立化MMC开路故障诊断方法。根据开路故障特性，合理选择输出相电流和桥臂电压作为故障参量。使用改进灰狼优化算法搜寻自适应噪声完全经验模态分解(complete ensemble empirical mode decomposition with adaptive noise, CEEMDAN)过程中的最优参数，结合CEEMDAN最优参数将故障参量分解为敏感且优质的固有模态(intrinsic mode function, IMF)分量并进行重构。为充分挖掘重构信号中的敏感成分，利用连续小波变换将重构信号转化为模态时频图；将不同故障类别下的模态时频图输入到Resnet-BiGRU模型中进行训练、测试并输出诊断结果，完成对故障桥臂的诊断与子模块中故障绝缘栅双极型晶体管(insulated-gate bipolar transistor, IGBT)的定位。实验结果表明：其诊断故障桥臂与定位子模块中故障IGBT的准确率分别达到98.63%和99.87%，诊断精度高；诊断过程拥有秒级响应时间；与其他方法相比，所提方法在小样本、数据不平衡和噪声干扰等极端条件下具有较高准确率，为电力系统故障诊断提供了一种新思路。 |
| 关键词: 模块化多电平换流器 开路故障诊断 模态时频图 Resnet-BiGRU模型 |
| DOI：10.19783/j.cnki.pspc.240254 |
| 投稿时间：2024-03-05修订日期：2024-04-16 |
| 基金项目:国家自然科学基金项目资助(52207004)；重庆市教委科学技术研究计划项目资助(KJQN202001128) |
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| Open-circuit fault diagnosis of MMC sub modules based on modal time-frequency diagrams and the Resnet-BiGRU model |
| LIU Shuxi1.2,LIU Ke1,WANG Qianyun1,QU Yufei1,LUO Qin1 |
| (1. School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China;
2. Chongqing Energy Internet Engineering Technology Research Center, Chongqing 400054, China) |
| Abstract: |
| There are problems of easy omission of extracted feature information, low diagnostic accuracy and large computation volume in the fault diagnosis process for the modular multilevel converter (MMC) in power systems. Thus a discretized MMC open-circuit fault diagnosis method with modal time-frequency diagrams and Resnet-BiGRU model is proposed. From the open-circuit fault characteristics, the output phase currents and bridge arm voltages are selected as fault parameters. The improved gray wolf optimization algorithm is used to search the optimal parameters in the process of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Combined with the optimal parameters of CEEMDAN, the fault parameters are decomposed into sensitive and high-quality intrinsic mode function (IMF) components and reconstructed. To fully exploit the sensitive components in the reconstructed signals, those signals are transformed into modal time-frequency diagrams using continuous wavelet transforms; the modal time-frequency diagrams in different fault categories are input to the Resnet-BiGRU model for training, testing and outputting the diagnostic results, so as to complete the diagnosis of the faulty bridge arms and the localization of the faulty IGBT in the sub modules. The experimental results show that: its diagnosis of faulty bridge arms and localization of faulty IGBT in the sub modules reach an accuracy of 98.63% and 99.87%, with high diagnostic accuracy; the diagnostic process possesses a response time of seconds; compared with other methods, the proposed method has a higher accuracy in extreme conditions such as small samples, data imbalance, and noise interference. The work provides a new way for the diagnosis of power system faults. |
| Key words: modular multilevel converter open-circuit fault diagnosis modal time-frequency diagrams Resnet-BiGRU model |
