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Article(id=1156983791110673266, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1156983783787421903, articleNumber=null, orderNo=null, doi=10.12404/j.issn.1671-1815.2309761, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1702224000000, receivedDateStr=2023-12-11, revisedDate=1731600000000, revisedDateStr=2024-11-15, acceptedDate=null, acceptedDateStr=null, onlineDate=1753776031519, onlineDateStr=2025-07-29, pubDate=1739808000000, pubDateStr=2025-02-18, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1753776031519, onlineIssueDateStr=2025-07-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1753776031519, creator=13701087609, updateTime=1753776031519, updator=13701087609, issue=Issue{id=1156983783787421903, tenantId=1146029695717560320, journalId=1146123166801305609, year='2025', volume='25', issue='5', pageStart='1753', pageEnd='2192', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1753776029774, creator=13701087609, updateTime=1769691857141, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223739602251436918, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1156983783787421903, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223739602251436919, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1156983783787421903, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1954, endPage=1962, ext={EN=ArticleExt(id=1156983792649982840, articleId=1156983791110673266, tenantId=1146029695717560320, journalId=1146123166801305609, language=EN, title=Fault Pole Selection for Flexible DC Distribution Grids Based on Res-BiLSTM, columnId=1156262733675876713, journalTitle=Science Technology and Engineering, columnName=Papers·Electrical Technology, runingTitle=null, highlight=null, articleAbstract=

Aiming at the problems of anti-noise, anti-high resistance and complex threshold setting of traditional pole selection methods, a fault selection method of flexible DC distribution line based on Res-BiLSTM network was proposed. Firstly, the original fault signal was subjected to complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and then the reconstructed signal was obtained by using the correlation coefficient and Shannon entropy for reconstruction. Secondly, the Res-BiLSTM network model was constructed for the pole selection. In order to improve the network accuracy and the convergence speed, the channel attention module was introduced into the split-attention network. The reconstructed signal features were extracted using the convolutional bidirectional long short-term memory and the improved split-attention network at the same time. The extracted features were fused using the attention feature fusion module, and the fused features are classified. Finally, PSCAD/EMTDC was employed to construct the model and to verify the proposed methodology. The simulation results show that the proposed pole selection method is highly accurate, anti-interference, and independent of fault distance.

, correspAuthors=Hao WU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Chao-wen ZHENG, Hao WU, Chuan-lan WU, Dan TANG, Chang-hua ZHONG), CN=ArticleExt(id=1156983980215062738, articleId=1156983791110673266, tenantId=1146029695717560320, journalId=1146123166801305609, language=CN, title=基于Res-BiLSTM的柔性直流配电网故障选极, columnId=1156262734506353627, journalTitle=科学技术与工程, columnName=论文·电工技术, runingTitle=null, highlight=null, articleAbstract=

针对传统的选极方法存在着抗噪、抗高阻能力弱以及阈值整定复杂等问题,提出一种基于Res-BiLSTM网络的柔性直流配电线路故障选极方法。首先,对原始故障信号进行完全自适应噪声模态分解,再采用相关系数和香农熵进行重构得到重构信号;其次,搭建Res-BiLSTM网络模型进行选极,为提高网络精度与收敛速度,在分裂注意力网络中引入通道注意力模块,并使用卷积双向长短期记忆网络与改进分裂注意力网络同时提取重构信号特征,使用注意力特征融合模块融合提取到的特征,并对融合特征进行分类;最后,利用PSCAD/EMTDC搭建模型并验证所提方法。仿真结果表明所提选极方法准确性高,抗干扰能力强,不受故障距离影响。

, correspAuthors=吴浩, authorNote=null, correspAuthorsNote=
*吴浩(1980—),男,汉族,四川南充人,博士,教授。研究方向:电力系统保护与智能控制。E-mail:。
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郑超文(2001—),女,汉族,四川绵阳人,硕士研究生。研究方向:柔性直流配电网故障诊断。E-mail:。

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郑超文(2001—),女,汉族,四川绵阳人,硕士研究生。研究方向:柔性直流配电网故障诊断。E-mail:。

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郑超文(2001—),女,汉族,四川绵阳人,硕士研究生。研究方向:柔性直流配电网故障诊断。E-mail:。

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articleId=1156983791110673266, language=CN, label=图14, caption=丢失250个数据时的波形图, figureFileSmall=VSDWx66qLEynetuGWwgneA==, figureFileBig=DgFOcdplTp2LB8hCjSICBg==, tableContent=null), ArticleFig(id=1225467180318372501, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156983791110673266, language=EN, label=Fig.15, caption=Confusion matrix when 300 data are lost, figureFileSmall=P4Uidk4MuSPEviWxmz41Ew==, figureFileBig=KfVfROTfT8LQBWop7wvD/Q==, tableContent=null), ArticleFig(id=1225467180490338980, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156983791110673266, language=CN, label=图15, caption=丢失300个数据时的混淆矩阵, figureFileSmall=P4Uidk4MuSPEviWxmz41Ew==, figureFileBig=KfVfROTfT8LQBWop7wvD/Q==, tableContent=null), ArticleFig(id=1225467180683276985, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156983791110673266, language=EN, label=Table 1, caption=

IMF selection of valuation values

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方法 IMF4 IMF5 IMF6 IMF7 IMF8 IMF9
相关系数值 0.027 0.017 0.007 0.006 0.075 0.110
香农熵值 7.285 7.262 7.078 6.989 6.957 7.957
), ArticleFig(id=1225467180960101068, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156983791110673266, language=CN, label=表1, caption=

IMF选取评价值

, figureFileSmall=null, figureFileBig=null, tableContent=
方法 IMF4 IMF5 IMF6 IMF7 IMF8 IMF9
相关系数值 0.027 0.017 0.007 0.006 0.075 0.110
香农熵值 7.285 7.262 7.078 6.989 6.957 7.957
), ArticleFig(id=1225467181056570072, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156983791110673266, language=EN, label=Table 2, caption=

Algorithm performance analysis under different noises

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故障极 信噪
比/dB		 分类
标签		 重构信号
选极结果		 原始信号
选极结果
输出
标签		 故障极 输出
标签		 故障极
双极
故障		 10 0 0 双极故障 0 双极故障
20 0 0 双极故障 0 双极故障
30 0 0 双极故障 0 双极故障
正极
故障		 10 1 1 正极故障 2 负极故障
20 1 1 正极故障 1 正极故障
30 1 1 正极故障 1 正极故障
负极
故障		 0 2 2 负极故障 2 负极故障
20 2 2 负极故障 2 负极故障
30 2 2 负极故障 2 负极故障
), ArticleFig(id=1225467182457467625, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156983791110673266, language=CN, label=表2, caption=

不同噪声下算法性能分析

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故障极 信噪
比/dB		 分类
标签		 重构信号
选极结果		 原始信号
选极结果
输出
标签		 故障极 输出
标签		 故障极
双极
故障		 10 0 0 双极故障 0 双极故障
20 0 0 双极故障 0 双极故障
30 0 0 双极故障 0 双极故障
正极
故障		 10 1 1 正极故障 2 负极故障
20 1 1 正极故障 1 正极故障
30 1 1 正极故障 1 正极故障
负极
故障		 0 2 2 负极故障 2 负极故障
20 2 2 负极故障 2 负极故障
30 2 2 负极故障 2 负极故障
), ArticleFig(id=1225467182595879670, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156983791110673266, language=EN, label=Table 3, caption=

Test results during data loss

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故障极 丢失数据 分类标签 选极结果
输出标签 故障极
双极
故障		 100 0 0 双极故障
200 0 0 双极故障
250 0 0 双极故障
300 0 1 正极故障
正极
故障		 100 1 1 正极故障
200 1 1 正极故障
250 1 2 正极故障
300 1 1 正极故障
负极
故障		 100 2 2 负极故障
200 2 2 负极故障
250 2 2 负极故障
300 2 2 负极故障
), ArticleFig(id=1225467182730097414, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156983791110673266, language=CN, label=表3, caption=

数据丢失时的测试结果

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故障极 丢失数据 分类标签 选极结果
输出标签 故障极
双极
故障		 100 0 0 双极故障
200 0 0 双极故障
250 0 0 双极故障
300 0 1 正极故障
正极
故障		 100 1 1 正极故障
200 1 1 正极故障
250 1 2 正极故障
300 1 1 正极故障
负极
故障		 100 2 2 负极故障
200 2 2 负极故障
250 2 2 负极故障
300 2 2 负极故障
), ArticleFig(id=1225467182864315160, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156983791110673266, language=EN, label=Table 4, caption=

Comparison of different network models

, figureFileSmall=null, figureFileBig=null, tableContent=
网络模型 准确率/%
本文方案 100
ResNeSt+BiLSTM+CAM 98.82
ResNeSt+BiLSTM+AFF 96.45
ResNeSt+BiLSTM 90.53
ResNeSt+CAM 88.76
BiLSTM 84.02
ResNeSt 75.74
), ArticleFig(id=1225467182977561379, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156983791110673266, language=CN, label=表4, caption=

不同网络模型比较

, figureFileSmall=null, figureFileBig=null, tableContent=
网络模型 准确率/%
本文方案 100
ResNeSt+BiLSTM+CAM 98.82
ResNeSt+BiLSTM+AFF 96.45
ResNeSt+BiLSTM 90.53
ResNeSt+CAM 88.76
BiLSTM 84.02
ResNeSt 75.74
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基于Res-BiLSTM的柔性直流配电网故障选极
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郑超文 1 , 吴浩 1, 2, * , 吴川兰 1 , 唐丹 1 , 钟长华 1
科学技术与工程 | 论文·电工技术 2025,25(5): 1954-1962
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科学技术与工程 | 论文·电工技术 2025, 25(5): 1954-1962
基于Res-BiLSTM的柔性直流配电网故障选极
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郑超文1 , 吴浩1, 2, * , 吴川兰1, 唐丹1, 钟长华1
作者信息
  • 1 四川轻化工大学自动化与信息工程学院, 自贡 643000,
  • 2 人工智能四川省重点实验室, 自贡 643000

    • 郑超文(2001—),女,汉族,四川绵阳人,硕士研究生。研究方向:柔性直流配电网故障诊断。E-mail:。

通讯作者:

*吴浩(1980—),男,汉族,四川南充人,博士,教授。研究方向:电力系统保护与智能控制。E-mail:。
Fault Pole Selection for Flexible DC Distribution Grids Based on Res-BiLSTM
Chao-wen ZHENG1 , Hao WU1, 2, * , Chuan-lan WU1, Dan TANG1, Chang-hua ZHONG1
Affiliations
  • 1 School of Automation and Information Engineering, Sichuan University of Science & Engineering, Zigong 643000, China
  • 2 Sichuan Key Laboratory of Artificial Intelligence, Zigong 643000, China
出版时间: 2025-02-18 doi: 10.12404/j.issn.1671-1815.2309761
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摘要
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针对传统的选极方法存在着抗噪、抗高阻能力弱以及阈值整定复杂等问题,提出一种基于Res-BiLSTM网络的柔性直流配电线路故障选极方法。首先,对原始故障信号进行完全自适应噪声模态分解,再采用相关系数和香农熵进行重构得到重构信号;其次,搭建Res-BiLSTM网络模型进行选极,为提高网络精度与收敛速度,在分裂注意力网络中引入通道注意力模块,并使用卷积双向长短期记忆网络与改进分裂注意力网络同时提取重构信号特征,使用注意力特征融合模块融合提取到的特征,并对融合特征进行分类;最后,利用PSCAD/EMTDC搭建模型并验证所提方法。仿真结果表明所提选极方法准确性高,抗干扰能力强,不受故障距离影响。

柔性直流配电网  /  故障选极  /  完全自适应噪声模态分解  /  神经网络

Aiming at the problems of anti-noise, anti-high resistance and complex threshold setting of traditional pole selection methods, a fault selection method of flexible DC distribution line based on Res-BiLSTM network was proposed. Firstly, the original fault signal was subjected to complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and then the reconstructed signal was obtained by using the correlation coefficient and Shannon entropy for reconstruction. Secondly, the Res-BiLSTM network model was constructed for the pole selection. In order to improve the network accuracy and the convergence speed, the channel attention module was introduced into the split-attention network. The reconstructed signal features were extracted using the convolutional bidirectional long short-term memory and the improved split-attention network at the same time. The extracted features were fused using the attention feature fusion module, and the fused features are classified. Finally, PSCAD/EMTDC was employed to construct the model and to verify the proposed methodology. The simulation results show that the proposed pole selection method is highly accurate, anti-interference, and independent of fault distance.

flexible DC distribution network  /  fault pole selection  /  complete ensemble empirical mode decomposition with adaptive noise  /  neural network
郑超文, 吴浩, 吴川兰, 唐丹, 钟长华. 基于Res-BiLSTM的柔性直流配电网故障选极. 科学技术与工程, 2025 , 25 (5) : 1954 -1962 . DOI: 10.12404/j.issn.1671-1815.2309761
Chao-wen ZHENG, Hao WU, Chuan-lan WU, Dan TANG, Chang-hua ZHONG. Fault Pole Selection for Flexible DC Distribution Grids Based on Res-BiLSTM[J]. Science Technology and Engineering, 2025 , 25 (5) : 1954 -1962 . DOI: 10.12404/j.issn.1671-1815.2309761
正文
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近年来,随着电力电子技术和直流器件的发展,电力系统的供电和负荷产生了极大的变化,分布式电源在电力供能中所占比例也逐步增加[1-2],柔性直流配电网具备供电可靠性好、电能质量高、控制灵活、易于分布式电源的接入、线路损耗低等优势,逐渐成为国内外研究重点[3-5]。然而,柔性直流配电网是一个低阻尼、弱惯性系统,其故障发展快、影响范围广[6-7],但目前针对直流配电系统的特点的保护有所欠缺,没能形成完整成熟的柔性直流配电系统保护体系。因此,对柔性直流配电网故障的准确识别是当前的一个研究重点。
现有的检测方法主要分为两大类,一种是利用故障暂态量进行分析诊断的传统算法,另一种是智能算法。传统算法中常见的有电气量保护、测距式保护、纵联保护、行波保护以及边界保护这几种方法,传统算法的思路大多借鉴柔性直流输电的保护思路[8-9],但由于柔性直流配电网的拓扑结构复杂,故障特性由多种因素造成且故障特征变化迅速、参数难以识别,导致现有针对柔性直流配电系统的传统保护方法均有所欠缺:电气量幅值保护的选择性不足,微分保护耐受能力不足,距离保护的速动性不足,而基于双端信息的纵联保护存在信息时延的问题,边界保护易受配电网参数、边界设置等因素的影响,且柔性直流配电系统满足不了行波保护所需的高采样频率和行波波头识别率[10-11]。文献[12]提出一种基于线路两端故障电流拟合曲线斜率的纵联保护方案,该方案算法简单,数据窗短且对数据精度的要求不高,通信延时和同步误差的影响小。文献[13]提出一种结合过流保护、变流保护、瞬时差动电流保护的保护方案,具备较好的选择性、速动性,但没有针对方案的耐受能力提出研究。
智能算法无需阈值整定,相较于传统方法,智能算法利用算法模型离线训练的识别精度和速度都更好,具备良好的可靠性,且较于传统算法的耐受能力要更好。文献[14]提出了一种柔性直流配电网的高阻故障识别方案,高阻识别上效果好,但没有对所提方法的抗噪性能进行研究。文献[15]提出一种基于门控循环单元深度学习模型的保护方案,具有一定的抗高阻能力,但所提方法的抗噪效果仅到20 dB。文献[16]提出了一种基于颜色关系分类器的高阻抗故障识别方法,能够在较少样本下实现对各种工况的识别,但低阻状况识别率不足。智能算法作为柔性直流配电网故障诊断的一种方法,针对诊断中的过渡电阻和噪声的干扰情况,相关文献的效果不佳。
针对上述智能故障诊断方法中存在的问题,现提出一种基于完全自适应噪声模态分解(complete ensemble empirical mode decomposition with adaptive noise, CEEMDAN)重构信号和Res-BiLSTM网络的柔性直流配电系统故障选极方法。该方案首先对采集到的故障电流进行CEEMDAN分解,利用相关系数和香农熵分别选取波形最相近和幅值最接近的本征模态分量来进行信号重构,以此降低模态混叠的影响;然后将归一化后的重构信号输入Res-BiLSTM网络中进行测试与训练。网络改进如下:在分裂注意力网络(split-attention network, ResNeSt)增加了通道注意力模块(channel attention module,CAM),加强了ResNeSt网络的全局特征提取,得到的故障信号空间特征的效果更好;其次,为更好地提取故障信号的时序特征,引入卷积双向长短期记忆网络(bidirectional long short-term memory, BiLSTM)网络,并引入注意力特征融合模块(attentional feature fusion,AFF)对故障信号的空间特征与时序特征进行融合,提高网络的精确度;最后,利用Adam优化器代替随机梯度下降方式来更新网络权重,提高网络的鲁棒性。通过仿真实验验证本文算法的抗噪能力和耐过渡电阻能力,且在一定的数据丢失情况下仍能较好地实现选极。
1 柔性直流配电网的拓扑结构分析
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将MMC-VSC四端混合中压柔性直流配电系统作为研究对象,主要由电源、换流站、直流断路器及直流线路等构成,其拓扑结构如图1所示。S1和S2是交流电源,经MMC换流站与直流线路相连,风力发电和直流负荷经VSC换流站与直流线路相连;L1~L4是直流线路,均为电缆线路;F1~F4是不同线路故障;变压器T1、T2的绕组接地方式为Y/Δ接地;Ip1为故障原始信号,是由换流站MMC1流向直流线路的正极电流信号,是直流线路L1上的电流信号。
柔性直流配电网的保护包括换流站及换流阀保护、交流侧保护以及直流侧保护,主要针对直流线路故障进行分析。直流侧故障主要有单极接地故障、双极短路故障以及断线故障3种故障类型,其中,单极接地故障的发生频率最高,但由于系统在单极接地故障发生后的短时间内仍可正常运行,其故障特性与采用的换流器拓扑结构有关,而双极短路故障时不同拓扑结构的换流器下故障特性相似,因此在双极短路故障发生后,故障电流极短时间内就迅速上升,器件极易损坏,危害更大。
2 基于Res-BiLSTM网络的选极方法
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2.1 CEEMDAN信号重构方法
CEEMDAN是在集合经验模态分解(ensemble empirical mode decomposition, EEMD)基础上进行了一定改进的一种自适应数据分解方法,分解得到一系列反映非平稳信号局部特性的本征模态分量(intrinsic mode function,IMF),有效解决了EMD和EEMD存在的模态混叠和残余噪声过高等问题[17-18]。
CEEMDAN的具体步骤如下将构建的自适应高斯白噪声序列εkωi(t)添加到原始信号h(t)中,形成多个试验信号;然后对每个试验信号进行EMD分解并取均值,得到一组平均IMF及其残余分量r(t);最后对平均IMF进行再次EMD分解并进行平均,得到最终的分解结果,如图2所示(以F4处负极接地故障为例)。
CEEMDAN的k阶分量为
$\operatorname{IMF}_{k}(t)=\frac{1}{N} \sum_{i=1}^{N} E_{k-1}\left\{r_{k-1}(t)+\varepsilon_{k} E_{k}\left[\omega^{i}(t)\right]\right\}$
$r_{k}(t)=r_{k-1}(t)-\operatorname{IMF}_{k}(t)$
式中:εk为k阶噪声系数;Ek为EMD分解后的第k个固有模态;N为产生的固有模态总数。
由图2可知,前3个分量的噪声含量大,不宜选用其进行数据重构,因此在除去前3个的IMF分量中选取信号进行重构。常用相关系数法对IMF分量进行筛选,筛选出的IMF分量与原信号波形相似但幅值可能相差过大,因此引入香农熵与相关系数一起来进行筛选。
相关系数R计算公式为
$R=\frac{\sum_{i=1}^{n}\left(X_{i}-\bar{X}\right)\left(Y_{i}-\bar{Y}\right)}{\sqrt{\sum_{i=1}^{n}\left(X_{i}-\bar{X}\right)^{2}} \sqrt{\sum_{i=1}^{n}\left(Y_{i}-\bar{Y}\right)^{2}}}$
式(3)中:$\stackrel{-}{X}$、$\stackrel{-}{Y}$分别为X、Y的平均值;n为序列X的元素总数。
若离散变量X={x1,x2,…,xn}的概率分布为P={p1,p2,…,pn},则X的香农熵为
$H(X)=-\sum_{i=1}^{n} p_{i} \ln p_{i}$
R越大,则IMF与原信号波形越相似;H越小,信号的无序性越小,时频分布越集中;选取相关系数值最大的IMF与香农熵值越小的IMF作为重构信号的分量,将选取的两个IMF与分解得到的残余分量相加得到最终的重构信号。以F4处负极接地故障为例,图3(a)所示原始信号经CEEMDAN分解为10个IMF,在去除前3个含噪高的分量后,计算除残余分量外的剩下6个IMF的相关系数值与香农熵值,计算结果如表1所示,其中IMF9的相关系数值最大,IMF8的香农熵值最小,因此选取IMF8和IMF9与残余分量IMF10相加得到图3(b)所示的重构信号。
2.2 改进ResNeSt网络
ResNeSt[19]在ResNet的基础上进行了改进,引入了多通道和拆分注意力(split-attention,SA),解决了ResNet缺少跨通道交互的问题,在不显著增加网络参数量的同时实现特征提取能力的增强。
ResNeSt借鉴了ResNeXt[20]的思想,将输入(长h、宽w、通道数c)分为K组,再将每组分成R个,共分为K×R组;将K组中的R个输入进行1×1Conv和3×3Conv后,在分组输入到K个SA模块中;在SA模块中,通过将R个输入融合后进行全局平均池化得到集合具有通道统计数据的全局上下文信息,特征向量为c'(即c/K)维,池化结果经过全连接层以及BN+ReLU后分组进行Softmax计算权重,输入分别与其权重相乘后,将R个输入相加得到SA模块的输出;将K组输出进行拼接操作,再通过1×1Conv保证通道数与输入相同。 ResNeSt结构如图4所示[21]。
ResNeSt网络更关注局部特征的提取,为此引入CAM[22]模块,提高模型的准确度。CAM的结构如图5所示,改进ResNeSt网络结构如图6所示。
注意力机制的实质就是分配输入的权重,CAM通过整合所有通道映射间的相关特征来选择性地强调存在相互依赖的通道映射,以此模拟通道间的相互依赖性。CAM通过将输入矩阵I分别进行重组得到特征矩阵X、Y和Z,将X进行转置得到的特征矩阵和X相乘后,再与Y相乘后进行Softmax、与矩阵Y相乘、重组、与Z相加一系列操作得到输出矩阵B。令aij为A矩阵中的每一个元素,则输出矩阵B中的元素表示为
$B_{j}=\beta \sum_{i=1}^{C}\left(\frac{\exp I_{i} I_{j}}{\sum_{n=1}^{C} \exp I_{n} I_{j}} I_{i}\right)+I_{j}$
式(5)中:β为尺度系数,初始化为0;C为通道数;Ii、Ij分别为I中第i列和第j列的元素。
2.3 卷积BiLSTM网络
BiLSTM[23]是基于长短期记忆神经网络(long short term memory, LSTM)的改进网络。LSTM通过遗忘门、输入门与输出门3个门控制函数来调节网络单元,它能将前一时刻的输出作为后一时刻的输入,在分析时间序列时有很好的优势。BiLSTM由两个分别进行前向传播和反向传播的LSTM构成,能够更好地处理数据的时序特征。而ResNeSt网络针对重构后的故障数据的空间特征进行了提取,但未对数据中的时序特征进行分析,因此,引入卷积BiLSTM网络对故障数据的时序特征进行提取分析。卷积BiLSTM网络先通过3个Conv+BN+Rule层对输入信号进行简单的特征选取,且为了防止网络过拟合,在卷积层引入了L2正则化,再将卷积结果输入BiLSTM网络进行时序特征的提取,其网络结构如图7中的卷积BiLSTM模型所示。
2.4 基于Res-BiLSTM网络的选极方法
为了解决改进ResNeSt网络和卷积BiLSTM网络提取出的特征如何融合的问题,引入针对不同网络结构的不同尺度特征融合的注意力问题而提出的AFF[24]模块来代替Add层,AFF结构如图8所示。其中,输入的长为H、宽为W、通道数为C。多尺度通道注意模块(MS-CAM)通过改变空间池的大小可以在多个尺度上实现通道关注,通过逐点卷积将输入的特征的通道数减少为原先的1/r,为了尽可能保持轻量级,MS-CAM将局部上下文添加到注意力模块中的全局上下文中。AFF是在MS-CAM模块的基础熵改进的一种注意力机制,基于多尺度通道注意模块M,AFF可以表示为
$\boldsymbol{Z}=M(\boldsymbol{X} \oplus \boldsymbol{Y}) \otimes \boldsymbol{X}+[1-M(\boldsymbol{X} \oplus \boldsymbol{Y})] \otimes \boldsymbol{Y}$
式(6)中:Z∈RC×H×W为融合特征;$\oplus$和$\otimes$分别表示逐点相加和逐点相乘;M(X$\oplus$Y)和[1-M(X$\oplus$Y)]分别为X、Y的融合权重。
基于Res-BiLSTM网络的选极模型如图9所示,选极方法详细步骤如下。
步骤1 获取柔性直流配电网故障时的电流数据。
步骤2 将原始故障数据进行CEEMDAN分解得到IMF分量,并通过相关系数和香农熵对IMF进行筛选,实现数据重构,得到重构信号。
步骤3 将重构信号按一定比例划分为测试样本和训练样本。
步骤4 将训练样本输入到Res-BiLSTM网络模型进行训练。输入信号分别通过改进ResNeSt网络和卷积BiLSTM网络对信号的空间特征和时序特征进行提取,提取得到的数据特征通过AFF模块进行特征融合,再将融合特征通过两个FC层进行分类。
步骤5 将测试样本放入Res-BiLSTM模型进行故障选极分类。Res-BiLSTM模型采用Sigmoid激活函数和Softmax函数进行分类,得到分类结果,并采用Adam优化器[25]来优化Res-BiLSTM网络的参数,同时选用交叉熵损失函数对网络进行评价,交叉熵是用来评估当前训练得到的概率分布与真实分布的差异情况,交叉熵的值越小,两个分布就越接近。将离线训练好的模型进行保存,发生故障后,对样本数据进行采集和处理,得到网络输入样本向量并将其输入保存的网络模型中,并通过网络模型对故障信号进行分类,实现故障选极。
3 仿真分析
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基于PSCAD/EMTDC平台上建立如图1所示的±20 kV四端柔性直流配电网仿真模型,L1、L2、L3、L4线路长度分别为15、10、20、10 km,线路采用直流频变电缆并设置线路参数为标准参数。选取数据采样频率为10 kHz,并设置故障发生在0.5 s时,持续0.1 s。在4个位置F1~F4对双极故障、正极故障和负极故障3种类型的故障分别进行采样,共采取1 683组故障数据,按9∶1的比例生成训练集和测试集。设置网络的batch为16,epoch为80,Adam优化器的学习率取2×10-5。
3.1 选极方法验证
为验证所提算法在不同过渡电阻与不同故障距离下的性能,采集不同线路上过渡电阻为10~100 Ω(步长为10)和距离线路首端10%、50%、100%的不同故障位置的故障信号来制作数据集。将数据集输入模型中进行训练,经过80次迭代后,得到如图10所示的准确率和损失率变化曲线。训练集和测试集的混淆矩阵如图11所示。
从图10和图11可以看出,损失函数逐渐下降并接近为0.1,模型基本收敛,训练集和测试集准确率均能达到100%,所提算法能够准确实现在不同过渡电阻和不同故障距离下故障的选极,能够完成过渡电阻为100 Ω的故障工况下的故障选极,效果不错。
3.2 模型抗噪性能分析
实际工况中,设备会产生噪声干扰的现象,为测试噪声干扰对所提算法的影响程度,在不同线路上不同故障距离处采集到的故障原始信号中分别添加10、20、30 dB的高斯白噪声,接地电阻设置为0.01 Ω,将含噪数据集输入选极模型中,为比较CEEMDAN信号重构在去噪方面的作用,将加噪原始信号直接输入网络模型中,对比重构信号和原始信号的选极结果如表2所示,混淆矩阵如图12所示。CEEMDAN针对10 dB加噪信号的分解重构结果如图13所示(以F1处的双极故障为例),可以直观看出本文方法有利于含噪信号的去噪。
由图12和表2可知,所提算法基本不受噪声影响,能准确识别含10 dB噪声干扰的故障信号,而原始信号加噪后直接进行选极时,将含10 dB噪声干扰的正极故障信号误分为负极故障信号,其准确率为99.07%,识别率较本文方法低。本文方法实现了对选极模型的抗干扰能力的增强。
3.3 数据丢失实验
在采集数据时可能造成数据的丢失,为验证所提算法是否能在数据丢失时完成故障选极,分别设置丢失100、200、250、300个采样点的情况,丢失250个数据时,数据波形图如图14示。数据丢失时网络测试结果如表3所示,混淆矩阵如图15所示。
实验结果表明,所提算法能在丢失200个数据的情况下完成故障选极,但在丢失250个数据时出现错误,准确率为99.07%,丢失300个数据时准确率为96.30%。本文方法对数据丢失100、200个采样点时仍能准确完成故障选极。
3.4 不同方法对比实验
将本文方法与ResNeSt、BiLSTM等方法进行比较,将准确率作为评价指标,进一步验证本文方案的有效性和先进性,比较结果如表4所示。可以看出,本文方法效果最佳。本文方法的准确率比ResNeSt网络的准确率高出24.26%,比BiLSTM网络的准确率高出15.98%。相较于其他方案而言,本文方法对故障信号的特征的学习能力更佳,网络泛化能力更好,准确率最高且收敛速度更快,在柔性直流配电网故障选极上具有显著优势。
4 结论
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针对多段柔性直流配电系统直流侧故障,提出基于Res-BiLSTM网络的故障选极方案,并基于PSCAD进行仿真验证,得到以下结论。
(1)该方案采用CEEMDAN对故障电流进行分解,并利用分解所得信号对原始信号进行重构,降低了故障信号的模态混叠。信号重构提高了方案的准确率和抗噪性能。
(2)Res-BiLSTM网络提取并融合了故障信号的空间特征和时序特征,提高了网络的准确率和鲁棒性。该方案克服了传统方法阈值整定困难问题,且收敛速度较快。
(3)仿真验证了本文方法且不受过渡电阻、故障距离、噪声以及部分数据丢失的影响,本文方法在信号含噪10 dB时仍能取得不错的效果,且能完成过渡电阻为100 Ω的故障工况下的故障选极,抗干扰能力较强。
基金
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  • 四川省科技厅项目(2022YFS0518)
  • 四川省科技厅项目(2022ZHCG0035)
  • 人工智能四川省重点实验室项目(2023RYY06)
  • 人工智能四川省重点实验室项目(2022RZY01)
  • 企业信息化与物联网测控技术四川省高校重点实验室项目(2022WYY04)
文献
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文章信息
doi: 10.12404/j.issn.1671-1815.2309761
  • 接收时间：2023-12-11
  • 首发时间：2025-07-29
  • 出版时间：2025-02-18
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出版历史
  • 收稿日期：2023-12-11
  • 修回日期：2024-11-15
基金
四川省科技厅项目(2022YFS0518)
四川省科技厅项目(2022ZHCG0035)
人工智能四川省重点实验室项目(2023RYY06)
人工智能四川省重点实验室项目(2022RZY01)
企业信息化与物联网测控技术四川省高校重点实验室项目(2022WYY04)
作者信息
    1 四川轻化工大学自动化与信息工程学院, 自贡 643000,
    2 人工智能四川省重点实验室, 自贡 643000

通讯作者:

*吴浩(1980—),男,汉族,四川南充人,博士,教授。研究方向:电力系统保护与智能控制。E-mail:。
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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