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Article(id=1251505539474206764, tenantId=1146029695717560320, journalId=1251233954884272221, issueId=1251505536634667461, articleNumber=null, orderNo=null, doi=10.13682/j.issn.2095-6533.2025.06.004, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1742832000000, receivedDateStr=2025-03-25, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1776311772459, onlineDateStr=2026-04-16, pubDate=1762704000000, pubDateStr=2025-11-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1776311772459, onlineIssueDateStr=2026-04-16, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1776311772459, creator=13701087609, updateTime=1776311772459, updator=13701087609, issue=Issue{id=1251505536634667461, tenantId=1146029695717560320, journalId=1251233954884272221, year='2025', volume='30', issue='6', pageStart='1', pageEnd='130', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1776311771782, creator=13701087609, updateTime=1776311824541, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1251505758014226723, tenantId=1146029695717560320, journalId=1251233954884272221, issueId=1251505536634667461, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1251505758014226724, tenantId=1146029695717560320, journalId=1251233954884272221, issueId=1251505536634667461, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=31, endPage=39, ext={EN=ArticleExt(id=1251505539696504880, articleId=1251505539474206764, tenantId=1146029695717560320, journalId=1251233954884272221, language=EN, title=Measurement signal enhancement method for fiber optic transformers in power systems under time-frequency plane rotation, columnId=null, journalTitle=Journal of Xi'an University of Posts and Telecommunications, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To address the issue of degraded signal quality in power system fiber optic transformers（FOTs）due to internal and external noise interferences in complex operational environments，a method for enhancing FOT measurement signals under time-frequency domain rotation is proposed.Quantitative calculations for thermal noise，shot noise，vibration noise，and attenuation noise are performed，and an improved wavelet transform is employed for multi-type noise filtering preprocessing.The short-time Fourier transform is combined with an adaptive rotation operator to decompose the signal's time-frequency domain，decoupling and separating the interference components from the effective components，while extracting amplitude-frequency parameters along the frequency axis to enhance the target features.A phase reconstruction algorithm is used to correct the propagation path delays，and the enhanced signal is output.Experimental results show that the harmonic amplitude multiples of the enhanced signal are significantly reduced to 0.06～0.18，far below the baseline value of 0.25 and other comparison methods.The signal amplitude is effectively increased to the range of[-0.5，1.5]V，with a total signal-to-noise ratio of 16.5dB.This method effectively improves the amplitude-frequency characteristic quality and noise resistance of optical fiber mutual inductor measurement signals with low distortion，meeting the precise measurement requirements of power systems，and providing an effective solution for signal processing in complex noise environments.

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针对电力系统光纤互感器计量信号在复杂运行环境中易受内外部噪声干扰而导致质量下降的问题，提出一种时频面旋转下电力系统光纤互感器计量信号增强方法。量化计算热噪声、散粒噪声、振动噪声和衰减噪声，并采用改进小波变换进行多类型噪声滤波预处理，利用短时傅里叶变换结合自适应旋转算子分解信号时频面，解耦并分离干扰分量与有效分量，同时沿频率轴扩散提取的幅频参数以增强目标特征，通过相位重构算法校正传播路径延迟，输出增强信号。实验结果表明，该方法增强后信号的谐波振幅倍数显著降低至0.06～0.18，远低于基准值0.25及其他对比方法，信号幅值有效提升至[-0.5，1.5]V范围，总信噪比为16.5dB。所提方法有效提升了光纤互感器计量信号的幅频特征质量与抗噪能力，失真度低，满足了电力系统精准计量需求，为复杂噪声环境下的信号处理提供了有效方案。

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李梅，
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张强（1986-），男，山西太原人，硕士，国网山西营销服务中心高级工程师，主要研究方向为电力系统、电力营销等。E-mail:

李洋（1982-），男，山西大同人，工程硕士，国网山西营销服务中心高级工程师，主要研究方向为电气工程、电力营销等。E-mail:

刘馨卉（1985-），女，山东烟台人，工程硕士，国网山西营销服务中心高级工程师，主要研究方向为电力营销、电能计量等。E-mail:

张俊（1982-），女，山西文水人，硕士，国网山西营销服务中心高级工程师，主要研究方向为电力系统、电力营销等。E-mail:

李梅（1976-），女，山西临汾人，国网山西营销服务中心高级工程师，主要研究方向为电力系统、电力营销等。E-mail:

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张强（1986-），男，山西太原人，硕士，国网山西营销服务中心高级工程师，主要研究方向为电力系统、电力营销等。E-mail:

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张强（1986-），男，山西太原人，硕士，国网山西营销服务中心高级工程师，主要研究方向为电力系统、电力营销等。E-mail:

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李洋（1982-），男，山西大同人，工程硕士，国网山西营销服务中心高级工程师，主要研究方向为电气工程、电力营销等。E-mail:

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李洋（1982-），男，山西大同人，工程硕士，国网山西营销服务中心高级工程师，主要研究方向为电气工程、电力营销等。E-mail:

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刘馨卉（1985-），女，山东烟台人，工程硕士，国网山西营销服务中心高级工程师，主要研究方向为电力营销、电能计量等。E-mail:

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刘馨卉（1985-），女，山东烟台人，工程硕士，国网山西营销服务中心高级工程师，主要研究方向为电力营销、电能计量等。E-mail:

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张俊（1982-），女，山西文水人，硕士，国网山西营销服务中心高级工程师，主要研究方向为电力系统、电力营销等。E-mail:

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张俊（1982-），女，山西文水人，硕士，国网山西营销服务中心高级工程师，主要研究方向为电力系统、电力营销等。E-mail:

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李梅（1976-），女，山西临汾人，国网山西营销服务中心高级工程师，主要研究方向为电力系统、电力营销等。E-mail:

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李梅（1976-），女，山西临汾人，国网山西营销服务中心高级工程师，主要研究方向为电力系统、电力营销等。E-mail:

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figureFileBig=Uzr4va2SgmtefPDtuls1/Q==, tableContent=null), ArticleFig(id=1251505548357743007, tenantId=1146029695717560320, journalId=1251233954884272221, articleId=1251505539474206764, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
输入：原始计量信号s（t）#来自光纤互感器
输出：增强信号hat_x
1：量化噪声：//式（1）～式（4）
　R1←（4κT/L）α△f#热噪声
　R2←2q（I+I0）△f#散粒噪声
　 R3←P2/（ρc）#振动噪声
　 R4←l1gδ#衰减噪声
2：小波去噪：//式（6）
　 x← √2·g·C·（H+2k）#重构信号，g为小波基，C为尺度函数
3：构建幅度矩阵：//式（7）～式（8）
　 U（x）← [a；φ]·（n/l）^{1/2}#时域幅度矩阵
　 J（x）←arctan[βA（t）]·e^φ#频域幅度矩阵
4：时频面旋转：//式（9）～式（11）
　 s（t）←∫γ（t，ω）[U；J]g^{e^{-t}}dt#稀疏表达
　 G（s）←（2πs（0））^{-1}∫s（t）^u dt#窗函数（短时傅里叶变换）
　 z←（1/σ2）G（scos（t，η）+π）#旋转算子
5：幅频参数扩散：//式（12）
　 M←B√（2σπ/（1-z（ω-φ）^2））#扩散方程，B为扩散系数
6：相位重构：//式（13）～式（14）
　△φ←cl（△I/λ）#相位变化
　 Hat_x←（π1/2E）（x+△φ）#增强信号输出
), ArticleFig(id=1251505548458406309, tenantId=1146029695717560320, journalId=1251233954884272221, articleId=1251505539474206764, language=CN, label=null, caption=

算法：计量信号增强算法

, figureFileSmall=null, figureFileBig=null, tableContent=
输入：原始计量信号s（t）#来自光纤互感器
输出：增强信号hat_x
1：量化噪声：//式（1）～式（4）
　R1←（4κT/L）α△f#热噪声
　R2←2q（I+I0）△f#散粒噪声
　 R3←P2/（ρc）#振动噪声
　 R4←l1gδ#衰减噪声
2：小波去噪：//式（6）
　 x← √2·g·C·（H+2k）#重构信号，g为小波基，C为尺度函数
3：构建幅度矩阵：//式（7）～式（8）
　 U（x）← [a；φ]·（n/l）^{1/2}#时域幅度矩阵
　 J（x）←arctan[βA（t）]·e^φ#频域幅度矩阵
4：时频面旋转：//式（9）～式（11）
　 s（t）←∫γ（t，ω）[U；J]g^{e^{-t}}dt#稀疏表达
　 G（s）←（2πs（0））^{-1}∫s（t）^u dt#窗函数（短时傅里叶变换）
　 z←（1/σ2）G（scos（t，η）+π）#旋转算子
5：幅频参数扩散：//式（12）
　 M←B√（2σπ/（1-z（ω-φ）^2））#扩散方程，B为扩散系数
6：相位重构：//式（13）～式（14）
　△φ←cl（△I/λ）#相位变化
　 Hat_x←（π1/2E）（x+△φ）#增强信号输出
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光纤互感器元件参数/单位典型值
光源设备光源中心波长/nm1290～1320
消光比/dB1
带宽/nm30
发射功率/uW600
耦合器分光比50∶50
相位调制器输入光功率/mW15
工作波长/nm1300
回波损耗/dB50
平均插入损耗/dB3.12
半波电压/V3.33
偏振器消光比/dB32
插入损耗/dB≤0.6
光纤波片归一化温度因子≤10-4
消光比/dB≤0.5
插入损耗/dB≤0.3
光纤延时环长度/m200
最小缠绕面直径/mm50
), ArticleFig(id=1251505548621984170, tenantId=1146029695717560320, journalId=1251233954884272221, articleId=1251505539474206764, language=CN, label=表1, caption=

光纤互感器各元件主要技术参数

, figureFileSmall=null, figureFileBig=null, tableContent=
光纤互感器元件参数/单位典型值
光源设备光源中心波长/nm1290～1320
消光比/dB1
带宽/nm30
发射功率/uW600
耦合器分光比50∶50
相位调制器输入光功率/mW15
工作波长/nm1300
回波损耗/dB50
平均插入损耗/dB3.12
半波电压/V3.33
偏振器消光比/dB32
插入损耗/dB≤0.6
光纤波片归一化温度因子≤10-4
消光比/dB≤0.5
插入损耗/dB≤0.3
光纤延时环长度/m200
最小缠绕面直径/mm50
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时频面旋转下电力系统光纤互感器计量信号增强方法
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张强 , 李洋 , 刘馨卉 , 张俊 , 李梅
西安邮电大学学报 | 通信与电子 2025,30(6): 31-39
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西安邮电大学学报 | 通信与电子 2025, 30(6): 31-39
时频面旋转下电力系统光纤互感器计量信号增强方法
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张强 , 李洋 , 刘馨卉 , 张俊 , 李梅
作者信息
  • 国网山西省电力公司营销服务中心，山西太原 030032

    • 张强（1986-），男，山西太原人，硕士，国网山西营销服务中心高级工程师，主要研究方向为电力系统、电力营销等。E-mail:

    李洋（1982-），男，山西大同人，工程硕士，国网山西营销服务中心高级工程师，主要研究方向为电气工程、电力营销等。E-mail:

    刘馨卉（1985-），女，山东烟台人，工程硕士，国网山西营销服务中心高级工程师，主要研究方向为电力营销、电能计量等。E-mail:

    张俊（1982-），女，山西文水人，硕士，国网山西营销服务中心高级工程师，主要研究方向为电力系统、电力营销等。E-mail:

    李梅（1976-），女，山西临汾人，国网山西营销服务中心高级工程师，主要研究方向为电力系统、电力营销等。E-mail:

通讯作者:

Measurement signal enhancement method for fiber optic transformers in power systems under time-frequency plane rotation
Qiang ZHANG , Yang LI , Xinhui LIU , Jun ZHANG , Mei LI
Affiliations
  • State Grid Shanxi Marketing Service Center，Taiyuan 030032，China
出版时间: 2025-11-10 doi: 10.13682/j.issn.2095-6533.2025.06.004
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摘要
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针对电力系统光纤互感器计量信号在复杂运行环境中易受内外部噪声干扰而导致质量下降的问题，提出一种时频面旋转下电力系统光纤互感器计量信号增强方法。量化计算热噪声、散粒噪声、振动噪声和衰减噪声，并采用改进小波变换进行多类型噪声滤波预处理，利用短时傅里叶变换结合自适应旋转算子分解信号时频面，解耦并分离干扰分量与有效分量，同时沿频率轴扩散提取的幅频参数以增强目标特征，通过相位重构算法校正传播路径延迟，输出增强信号。实验结果表明，该方法增强后信号的谐波振幅倍数显著降低至0.06～0.18，远低于基准值0.25及其他对比方法，信号幅值有效提升至[-0.5，1.5]V范围，总信噪比为16.5dB。所提方法有效提升了光纤互感器计量信号的幅频特征质量与抗噪能力，失真度低，满足了电力系统精准计量需求，为复杂噪声环境下的信号处理提供了有效方案。

电力系统  /  信号增强  /  光纤互感器  /  计量信号  /  互感器信号  /  时频面旋转

To address the issue of degraded signal quality in power system fiber optic transformers（FOTs）due to internal and external noise interferences in complex operational environments，a method for enhancing FOT measurement signals under time-frequency domain rotation is proposed.Quantitative calculations for thermal noise，shot noise，vibration noise，and attenuation noise are performed，and an improved wavelet transform is employed for multi-type noise filtering preprocessing.The short-time Fourier transform is combined with an adaptive rotation operator to decompose the signal's time-frequency domain，decoupling and separating the interference components from the effective components，while extracting amplitude-frequency parameters along the frequency axis to enhance the target features.A phase reconstruction algorithm is used to correct the propagation path delays，and the enhanced signal is output.Experimental results show that the harmonic amplitude multiples of the enhanced signal are significantly reduced to 0.06～0.18，far below the baseline value of 0.25 and other comparison methods.The signal amplitude is effectively increased to the range of[-0.5，1.5]V，with a total signal-to-noise ratio of 16.5dB.This method effectively improves the amplitude-frequency characteristic quality and noise resistance of optical fiber mutual inductor measurement signals with low distortion，meeting the precise measurement requirements of power systems，and providing an effective solution for signal processing in complex noise environments.

power system  /  signal enhancement  /  fiber optic transformer  /  metering signal  /  transformer signal  /  time-frequency plane rotation
张强, 李洋, 刘馨卉, 张俊, 李梅. 时频面旋转下电力系统光纤互感器计量信号增强方法. 西安邮电大学学报, 2025 , 30 (6) : 31 -39 . DOI: 10.13682/j.issn.2095-6533.2025.06.004
Qiang ZHANG, Yang LI, Xinhui LIU, Jun ZHANG, Mei LI. Measurement signal enhancement method for fiber optic transformers in power systems under time-frequency plane rotation[J]. Journal of Xi'an University of Posts and Telecommunications, 2025 , 30 (6) : 31 -39 . DOI: 10.13682/j.issn.2095-6533.2025.06.004
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光纤互感器凭借其不易受到电磁干扰的优势，逐渐成为电力系统中计量电力参数的设备工具之一。通过对光纤互感器计量信号的分析，能够准确获悉电力系统的运行情况，从而对发生的故障进行及时的检测与维护[1]。在这一过程中，光纤互感器计量信号的质量十分关键。为了避免信号受到噪声的干扰，或者在传输过程中发生衰减等因素导致信号质量的下降，如何对信号进行有效的增强处理成为领域内共同关注的热点。
文献[2]设计了非正交多址接入下的信号增强方法，利用黎曼共轭梯度算法实现非正交多址接入信号的增强处理，但该方法难以有效估计信号衰减下的功率幅值，导致增强后的信号质量不高。文献[3]设计了结合小波变换和稀疏主成分分析的信号增强方法，但小波变换对于突变非平稳信号无法有效去噪，增强后的信号质量有待提升。文献[4]设计了基于RSETransformer的信号增强方法，建立基于RSETransformer的信号增强模型，实现区块信号的增强，但RSETransformer模型在训练数据有限时，可能无法充分学习信号区块特征，影响了信号增强的最终质量。文献[5]设计了基于滤波器组的信号增强方法，利用伽马通滤波器组进行信号的分频带增强处理，但滤波器组在信号增强的过程中需要分辨信号的频率与时间特征，如果信号的时间特性较弱，可能会影响信号增强效果。文献[6]设计了基于卷积递归神经网络的信号增强方法，利用网络卷积核为信号递归分配增强权值，输出增强后的信号，但该神经网络在实际应用中存在梯度爆炸的问题，可能无法有效增强信号质量。
鉴于上述文献所提出的信号增强方法存在不同程度的弊端，提出一种时频面旋转下电力系统光纤互感器计量信号增强方法。分别量化计算光纤互感器计量信号的内外部噪声，采用小波变换方法，滤波去除信号的多类型噪声值，解决突变非平稳信号难以有效去噪的问题。利用短时傅里叶变换，进行时频面旋转，提取并扩散其幅频参数，在有效估计信号衰减下功率幅值的同时，解决训练数据有限难以学习信号局部区块特征，影响分辨精度的问题。最后，通过计量信号相位重构，有效识别完整的信号参数，得到增强后的计量信号。
1　光纤互感器计量信号增强方法
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1.1　预处理光纤互感器计量信号噪声
电力系统中，光纤互感器在运行的过程中受到内外部环境的双重影响，存在着多种噪声的干扰。为了避免在信号增强处理中信号噪声被一并增强，本研究利用小波变换的方法对光纤互感器计量信号的噪声进行预处理，量化计算光纤互感器计量信号中的多种噪声。计量信号在光纤互感器内部元件的干扰下，主要包含热噪声和散粒噪声，在外部环境振动干扰下，主要包含振动噪声和衰减噪声。
计量信号的热噪声由光纤互感器元件内部载流子的热运动所产生，故以白噪声形式表示，通过对原始信号进行幅频参数扩散处理，结合改进型小波包变换对信号进行多尺度分解，提取各频带能量分布特征[7]，即
式中：R1表示热噪声；α表示热噪声常系数；κ表示玻尔兹曼常数；L表示光纤互感器元件的实时负载；T表示元件内部的绝对温度；Δf表示光纤互感器的光源带宽。
由于光纤互感器元件内部载流子的运行呈现不规则的运动状态，计量信号产生了一定的散粒噪声，通过调节扩散系数实现信号高频分量与低频分量的自适应加权融合[8]，即
式中：R2表示散粒噪声；q表示光生载流子；I表示计量信号的电流值；I0表示光生载流子的暗电流。
振动主导频段通常为100～2000Hz，12层小波分解可产生4096个子频带，每个子带宽度约为1.06GHz。该分辨率确保对振动噪声频段实现至少2个以上子带覆盖，满足动态阈值调整的粒度需求。对原始光信号进行12层小波包分解时，结合振动特征频带分析结果，动态调整各子带阈值。在重构阶段，对包含50Hz工频及其谐波的关键频带保留原始系数，振动噪声[9]为
式中：P表示元件振动声压；ρ表示声能密度；c表示光速。
光纤互感器的计量信号在光纤信道中受到介质的影响，存在衰减噪声[10]
式中：l表示光纤信道长度；δ表示介质特性。
选取噪声滤波处理的最优小波基，根据计量信号中各类型噪声的量化值，分析相应的信息熵。信号噪声信息熵[11]为
式中：pR表示不同噪声类型的小波系数分布概率；W表示噪声在计量信号中的频带。对振动主导频段（通常为100～2000Hz）进行收缩处理，同时通过相位补偿算法校正振动引起的偏振态随机波动，且不影响信号上升沿等关键暂态特征的保真度。
小波去噪重构的计量信号[12]为
式中：g表示选定的小波基；C表示尺度函数；k表示小波基的共轭转置。
在分解过程中，针对光纤信号特有的1/f噪声和周期性干扰，采用改进的阈值函数（如SURE阈值）对小波系数进行非线性处理，对高频细节系数进行自适应收缩。通过重构算法将处理后的近似系数和细节系数进行合成，在保留信号中有效谐波成分（如50Hz工频及其谐波）的同时，显著抑制了宽频带噪声和随机脉冲干扰。
1.2　计量信号幅频参数扩散
计量信号幅频参数扩散是指信号在传输或处理过程中，其幅度和频率特征因噪声、非线性器件或信道干扰而发生的失真或展宽现象。典型表现为幅度扩散、频率扩散和时频关联性。
解析去噪处理后的光纤互感器计量信号的信号特征。为了深度解构计量信号的特征参数，本研究采用时频面旋转的方法将信号分解为不同频率分量的时间序列，以此解析信号特征。在此基础上，根据信号特征的幅频参数将其扩散处理。
构建计量信号的时域幅度矩阵U（x）和频域幅度矩阵J（x）[13-14]分别为
式中：a表示泰勒常数；β表示信号频程；φ表示信号相位；n表示信号时域的一阶分量；t表示时间点；A（t）表示相应时间点下，计量信号的瞬时幅值；e表示自然常数。
结合构建的计量信号时频域幅度矩阵，本研究对其扩充运算。在矩阵中引入Dirac delta函数，将信号矩阵转换为稀疏表达的形式。转换后的信号时频为[15]
式中：s（t）表示信号时频的稀疏表达；γ表示Dirac delta函数；ω表示信号的一阶时变。
在此基础上，在原始信号中添加汉宁窗口进行帧分割，将窗口长度设置为工频周期的整数倍（20ms），以5ms的步长滑动并计算局部频谱，并在时频域中引入旋转算子。电力系统标准工频为50Hz，整数倍窗口20ms可完整包含1个基波周期，避免频谱泄漏；5ms步长对应时域分辨率5ms，采样频率为200Hz，大于信号最高频率（暂态信号频率一般低于100Hz）的两倍，满足Nyquist准则。通过自适应调整时频平面的旋转角度，使信号能量在倾斜的时频平面上重新分布。旋转角度通过分数阶次的迭代优化自适应生成，其数值由信号能量聚集程度动态决定。一方面，通过旋转操作解耦原始耦合的时频特性，可以在旋转的时频平面上对信号中的瞬态脉冲分量（如振动干扰）和时不变分量（如基波）进行空间分离；另一方面，通过利用旋转时频平面的方向选择性，有效地抑制了与信号主能量方向正交的宽带噪声。短时傅里叶变换的窗函数表达为[16-17]
式中：G（s）表示短时傅里叶变换的窗函数；s（0）表示信号在0时的时频稀疏值；u表示信号时域在频域方向上的积分。
通过调节分数阶次使信号时频能量在旋转下实现最大聚集[18-19]。基于窗函数旋转计量信号的时频面，则旋转算子定义为
式中：σ表示信号二阶时变阶次；η表示信号时频离散系数。
提取包含幅值、频率的深度特征参数，在保持信号相位连续性的同时，将深度特征参数沿频率轴自适应扩散[20]，扩散方程为
式中：B表示扩散系数。
基于幅频参数的扩散处理，增强计量信号的幅值特征，对目标频段进行选择性增强，同时提取瞬时幅值包络，最终通过逆变换重构信号，完成计量信号的时频面旋转，以及相应信号特征的幅频参数扩散处理[21]。
1.3　计量信号增强
计量信号增强是提升有效信号信噪比的过程。在电力系统中，针对光纤互感器等设备存在的噪声干扰，如电磁干扰、热噪声等，本研究以信号幅值特征计量信号的相位为基准进行重构，得到增强后的光纤互感器计量信号[22-23]。
解析增强前后，计量信号的相位变化为
式中：λ表示光纤互感器光源的波长。
依据求解的相位变化值，根据计量信号的轴向传播特性重构信号相位，建立信号传播的轴向位置-相位变化映射关系，针对不同介质界面的相位突变，对轴向传播路径上的相位延迟进行逐段校正。重构后的增强计量信号[24-25]为
式中：E表示光纤折射率；ε表示弹光系数。
经过信号的相位重构，得到增强处理后的计量信号。计量信号增强算法的伪代码如下。
2　实例应用
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根据本次实例应用的实际需求，选用由上海电气互感器有限公司生产的EPI-HV型号光纤互感器。该型号光纤互感器的主要元件包含光源设备、耦合器、相位调制器、偏振器、光纤波片以及光纤延时环等元件。各元件在光纤互感器实际运行过程中的主要技术参数如表1所示。
选用符合表1中参数的光纤互感器作为本次应用测试的实验对象。将该设备连接至上位机，获取相应的计量信号。
基于电力系统的实际运行环境，搭建本次测试中光纤互感器的计量环境。由于条件有限，本次测试仅模拟搭建了IEC61850-9-1一种电力系统的组网方式，其组网架构的拓扑示意图如图1所示。
图1所示的IEC61850-9-1拓扑结构采用点对点直接通信模式，其网络结构相对简化，存在未覆盖星型、环网等复杂拓扑的局限性。但本文研究的是信号质量增强，影响信号质量的光纤互感器的关键噪声源（热噪声、衰减噪声等）及其量化模型与组网方式无关，而是由设备内部元件和外部环境决定。而IEC61850-9-1是电力系统数字化变电站的基准协议，能够直接反映计量信号的核心需求，具有组网典型性。此外，为了准确模拟计量环境实际运行条件，采用广泛的光源中心波长作为实验对象，光源带宽扩展至30nm，确保噪声干扰与信号衰减符合工程实际。因此可以按照如图1所示的组网方式构建光纤互感器运行的电力系统环境。通过在电力系统的测控单元中连接选定的光纤互感器，得到光纤互感器对电力系统运行状态的实时计量信号。信号采样率为4.8kHz；噪声源设置如下：内部噪声为热噪声；外部噪声为机械振动噪声（10～200Hz）和信道衰减噪声。信号AD采集卡分辨率为16位。总信噪比为16.5dB。在该环境下，测试对光纤互感器计量信号的增强处理效果。
采集一组光纤互感器的原始计量信号，并采用所提方法对其进行增强处理。所采集时段的噪声干扰下光纤互感器计量信号原始幅值如图2所示。分析图2可知，所采集的原始光纤互感器信号的幅值较弱，这表明光纤互感器计量信号在传输的过程中，受到环境噪声干扰等因素的影响出现了较大幅度的衰减。基于此，采用所提方法增强该时段的信号。经过预处理后，转换该信号的时频面，计量信号时频面转换图像如图3所示。
根据图3所示的时频面转换图像，本研究根据光纤互感器信号的主要频率区域，着重增强了[-1，1.5]Hz频段的计量信号幅值，并将相应的信号幅值区间扩大到了[-0.5，1.5]V的范围内，大幅度增强了信号的幅值。
采用对比分析的方法检验所提方法增强处理下，光纤互感器计量信号的质量水平。分别应用所提方法、文献[4]、文献[5]以及文献[6]所提出的信号增强方法对同一时段内部噪声和外部噪声干扰下衰减的计量信号进行增强处理。噪声干扰下增强后计量信号所表现出的幅值图像如图4所示。
对比图4和图2的信号幅值图像可知，经过所提方法的增强处理后，缓解了信号内外部噪声的干扰问题，不仅大幅度增强了干扰衰减信号的整体幅值，还有效增强了信号的频率，增强效果较为明显。而文献[6]方法增强了信号的频率，但幅值增强效果不佳。而文献[4]方法、文献[5]方法虽然提升了信号幅值，但仍低于所提方法。
信号谐波振幅倍数越大，说明谐波能量越强，信号的基波失真程度越大，信号质量也就越差。根据这一原理利用谐波振幅倍数分析不同方法增强信号的质量水平。谐波振幅倍数的基准为0.25，计算谐波振幅与信号基波振幅之间的倍数。得到不同方法下的增强信号质量状况，如图5所示。
在本次对比测试中，采用不同频率的原始光纤互感器计量信号。从图5可以明显看出，与原始频率的信号幅值相比，所提方法增强处理下的谐波振幅倍数更小，保持在0.06～0.18，低于基准要求，谐波能量较低，信号的失真程度较低。而文献[4]方法增强后信号所表现出的谐波振幅倍数为0.17～0.29，文献[5]方法增强后信号所表现出的谐波振幅倍数为0.13～0.41，文献[6]方法增强后信号所表现出的谐波振幅倍数为0.28～0.42。这些文献方法增强后的信号谐波振幅倍数虽然与原始信号比有所降低，但均较所提方法有着较大的差距，谐波能量仍会导致信号失真程度较高。
基于这一对比测试结果，分析可知所提方法在电力系统光纤互感器计量信号的增强处理实践中能够得到较高质量的信号数据。这表明了所提方法对信号的增强处理不会导致计量信号出现较严重的失真问题，能够在很大程度上保留原始信号所携带的信息，满足了计量信号的应用分析需求。
3　结语
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为缓解信号噪声干扰，增强信号质量，提出时频面旋转下电力系统光纤互感器计量信号增强方法。通过对电力系统光纤互感器计量信号内外部噪声进行量化计算，解析信号特征并根据信号特征的幅频参数将其扩散处理，利用短时傅里叶变换，完成计量信号的时频面旋转。通过计量信号相位重构，得到增强后的计量信号。使用所提方法的谐波振幅倍数范围为0.06～0.18，明显低于其它方法的谐波振幅倍数，信号失真度更低。因此，所提方法能够有效增强信号的幅值及频率参数，保证信号的整体质量，可广泛应用于电力系统光纤互感器计量信号的应用与分析。但所提方法只是降低了谐波振幅倍数，对信号的增强处理仍存在计量信号失真问题，未来仍需进一步研究。
文献
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2025年第30卷第6期
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doi: 10.13682/j.issn.2095-6533.2025.06.004
  • 接收时间：2025-03-25
  • 首发时间：2026-04-16
  • 出版时间：2025-11-10
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    国网山西省电力公司营销服务中心，山西太原 030032

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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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