The surface-enhanced Raman scattering(SERS)substrates enable a highly sensitive detection of furfural in the transformer oil.However,detection substrates with long-term stability are still extremely challenging.In thi...The surface-enhanced Raman scattering(SERS)substrates enable a highly sensitive detection of furfural in the transformer oil.However,detection substrates with long-term stability are still extremely challenging.In this work,we anchored the thiol-containing coupling agents 2,5-dimercapto-1,3,4-thiadiazole(DMTD)and 1,4-benzenedithiol(BDT)on the surface of bubble copper(B-Cu)and flower-like silver nanoparticles(FAg),respectively.The three-dimensional SERS detection substrates with long-term stability by using a combination of chemical reduction and self-assembly methods were constructed.The substrate has a minimum detection limit of 10^(−9) M for rhodamine B in oil with an enhancement factor of up to 2.23×10^(7).Importantly,the three-crystal BCu@F-Ag_(1)@Au_(5) substrate was used for the detection of furfural in the transformer oil with a detection limit of 2 mg/L and a relative standard deviation value of 2.46%.After 60 days of a simulated operation,the detection signal of furfural in the transformer oil samples at 75℃ and still reached the initial value of 77.53%,indicating that the substrate has a good long-term stability.This triple frame structured SERS detection platform shows great potential in tracking furfural in the aging transformer oil mixing systems.展开更多
Sulphur hexafluoride(SF6)decomposed products analysis is highly critical in the earlystage fault diagnosis of gas-insulated switchgear(GIS).Spectrum technology outperforms traditional methods on non-invasiveness,no sa...Sulphur hexafluoride(SF6)decomposed products analysis is highly critical in the earlystage fault diagnosis of gas-insulated switchgear(GIS).Spectrum technology outperforms traditional methods on non-invasiveness,no sample preparation,and no consumption.Here,the authors present an improved fibre-enhanced Raman spectroscopy(FERS)as a comprehensive analytical tool to detect a suite of SF_(6)decomposed products(SO_(2)F_(2),SOF_(2),SO_(2),H_(2)S,CF_(4),OCS,CO_(2),and CO).The FERS approach is combined with two iris diaphragms for spatial filtering and a rear-end reflector for additional Raman signal enhancement.Limits of detection down to 1×10^(−6)–8×10^(−6)are achieved for different SF6 decompositions,and quantification of an undefined multigas,sampled from an 800 kV GIS in service,is realised utilising SF6 as the internal standard gas and with a maximum error of 5.5%.The GIS is diagnosed according to the results and confirmed by an on-site check.The authors foresee that this technique will provide a route for trace gas analysis in the power industry.展开更多
Optical fibre sensing technology is a powerful method for long-term reliable sensing in harsh environments,which means it is particularly suitable for the detection of electrical equipment characteristic state paramet...Optical fibre sensing technology is a powerful method for long-term reliable sensing in harsh environments,which means it is particularly suitable for the detection of electrical equipment characteristic state parameters,including characteristic gases,abnormal vibration,ultrasonic produced by partial discharge,and abnormal temperature rise.This paper reviews several individual optical fibre sensors for different state parameters detection,discusses the advantages,limitations and possible improvement methods,and finally presents the most promising optical fibre sensor for each state parameter.展开更多
Dissolved Gas Analysis(DGA)is an important method for oil-immersed transformer fault diagnosis.However,collecting labelled DGA data is difficult because the determi-nation of the transformer fault is time-consuming an...Dissolved Gas Analysis(DGA)is an important method for oil-immersed transformer fault diagnosis.However,collecting labelled DGA data is difficult because the determi-nation of the transformer fault is time-consuming and expensive in the transformer substation,but DGA data without labels is easier to obtain.Therefore,the paper pro-posed a semi-supervised two-stage diagnostic system based DGA by using less labelled samples.The two-stage system includes a novel semi-supervised feature selection based Genetic Algorithm(GA)and Support Vector Machine(SVM)model(SSL-FS-GASVM)for selecting optimal features and a novel semi-supervised transformer fault diagnosis model based improved Artificial Fish Swarm Algorithm(AFSA)and SVM(SSL-IAFSA-SVM)for optimising the SVM parameter.Finally,the performances of SSL-FS-GASVM and SSL-IAFSA-SVM models are tested and compared with traditional supervised diagnostic models combined with other optimisation methods,respectively.The results show that the proposed two-stage system works in optimising features and parameters and has strong robustness in solving small sample classification problems.展开更多
基金National Natural Science Foundation of China,Grant/Award Number:51977017Chongqing Natural Science Foundation,Grant/Award Number:cstc2021jcyj-msxmX0617。
文摘The surface-enhanced Raman scattering(SERS)substrates enable a highly sensitive detection of furfural in the transformer oil.However,detection substrates with long-term stability are still extremely challenging.In this work,we anchored the thiol-containing coupling agents 2,5-dimercapto-1,3,4-thiadiazole(DMTD)and 1,4-benzenedithiol(BDT)on the surface of bubble copper(B-Cu)and flower-like silver nanoparticles(FAg),respectively.The three-dimensional SERS detection substrates with long-term stability by using a combination of chemical reduction and self-assembly methods were constructed.The substrate has a minimum detection limit of 10^(−9) M for rhodamine B in oil with an enhancement factor of up to 2.23×10^(7).Importantly,the three-crystal BCu@F-Ag_(1)@Au_(5) substrate was used for the detection of furfural in the transformer oil with a detection limit of 2 mg/L and a relative standard deviation value of 2.46%.After 60 days of a simulated operation,the detection signal of furfural in the transformer oil samples at 75℃ and still reached the initial value of 77.53%,indicating that the substrate has a good long-term stability.This triple frame structured SERS detection platform shows great potential in tracking furfural in the aging transformer oil mixing systems.
基金National Natural Science Foundation of China,Grant/Award Number:U1766217Fundamental Research Funds for the Central Universities,Grant/Award Number:2022CDJKYJH027CSC Scholarship。
文摘Sulphur hexafluoride(SF6)decomposed products analysis is highly critical in the earlystage fault diagnosis of gas-insulated switchgear(GIS).Spectrum technology outperforms traditional methods on non-invasiveness,no sample preparation,and no consumption.Here,the authors present an improved fibre-enhanced Raman spectroscopy(FERS)as a comprehensive analytical tool to detect a suite of SF_(6)decomposed products(SO_(2)F_(2),SOF_(2),SO_(2),H_(2)S,CF_(4),OCS,CO_(2),and CO).The FERS approach is combined with two iris diaphragms for spatial filtering and a rear-end reflector for additional Raman signal enhancement.Limits of detection down to 1×10^(−6)–8×10^(−6)are achieved for different SF6 decompositions,and quantification of an undefined multigas,sampled from an 800 kV GIS in service,is realised utilising SF6 as the internal standard gas and with a maximum error of 5.5%.The GIS is diagnosed according to the results and confirmed by an on-site check.The authors foresee that this technique will provide a route for trace gas analysis in the power industry.
基金supported by the National Natural Science Foundation of China(U1766217)the State Grid Corporation of China Science and Technology Project(52110418000Q).
文摘Optical fibre sensing technology is a powerful method for long-term reliable sensing in harsh environments,which means it is particularly suitable for the detection of electrical equipment characteristic state parameters,including characteristic gases,abnormal vibration,ultrasonic produced by partial discharge,and abnormal temperature rise.This paper reviews several individual optical fibre sensors for different state parameters detection,discusses the advantages,limitations and possible improvement methods,and finally presents the most promising optical fibre sensor for each state parameter.
基金Research Initiation Project of Introducing Talents of Chengdu University of Information Technology,Grant/Award Number:KYTZ201902National Natural Science Foundation of China,Grant/Award Number:51977017。
文摘Dissolved Gas Analysis(DGA)is an important method for oil-immersed transformer fault diagnosis.However,collecting labelled DGA data is difficult because the determi-nation of the transformer fault is time-consuming and expensive in the transformer substation,but DGA data without labels is easier to obtain.Therefore,the paper pro-posed a semi-supervised two-stage diagnostic system based DGA by using less labelled samples.The two-stage system includes a novel semi-supervised feature selection based Genetic Algorithm(GA)and Support Vector Machine(SVM)model(SSL-FS-GASVM)for selecting optimal features and a novel semi-supervised transformer fault diagnosis model based improved Artificial Fish Swarm Algorithm(AFSA)and SVM(SSL-IAFSA-SVM)for optimising the SVM parameter.Finally,the performances of SSL-FS-GASVM and SSL-IAFSA-SVM models are tested and compared with traditional supervised diagnostic models combined with other optimisation methods,respectively.The results show that the proposed two-stage system works in optimising features and parameters and has strong robustness in solving small sample classification problems.
