Most ground faults in distribution network are caused by insulation deterioration of power equipment.It is difficult to find the insulation deterioration of the distribution network in time,and the development trend o...Most ground faults in distribution network are caused by insulation deterioration of power equipment.It is difficult to find the insulation deterioration of the distribution network in time,and the development trend of the initial insulation fault is unknown,which brings difficulties to the distribution inspection.In order to solve the above problems,a situational awareness method of the initial insulation fault of the distribution network based on a multi-feature index comprehensive evaluation is proposed.Firstly,the insulation situation evaluation index is selected by analyzing the insulation fault mechanism of the distribution network,and the relational database of the distribution network is designed based on the data and numerical characteristics of the existing distribution management system.Secondly,considering all kinds of fault factors of the distribution network and the influence of the power supply region,the evaluation method of the initial insulation fault situation of the distribution network is proposed,and the development situation of the distribution network insulation fault is classified according to the evaluation method.Then,principal component analysis was used to reduce the dimension of the training samples and test samples of the distribution network data,and the support vector machine(SVM)was trained.The optimal parameter combination of the SVM model was found by the grid search method,and a multi-class SVM model based on 1-v-1 method was constructed.Finally,the trained multi-class SVM was used to predict 6 kinds of situation level prediction samples.The results of simulation examples show that the average prediction accuracy of 6 situation levels is above 95%,and the perception accuracy of 4 situation levels is above 96%.In addition,the insulation maintenance decision scheme under different situation levels is able to be given when no fault occurs or the insulation fault is in the early stage,which can meet the needs of power distribution and inspection for accurately sensing the insulation fault situation.The correctness and effectiveness of this method are verified.展开更多
The reliability and safety of fuel cell vehicle are crucial for the daily operation. Insulation resistance serves as a crucial index of vehicle reliability, especially when fuel cells operate at high voltages. Low ins...The reliability and safety of fuel cell vehicle are crucial for the daily operation. Insulation resistance serves as a crucial index of vehicle reliability, especially when fuel cells operate at high voltages. Low insulation resistance can lead to vehicle malfunctions, exposing the operator to the risk of electric shock. In this study, long-term insulation resistance data from thirteen vehicles equipped with three different types of fuel cell systems are analyzed to diagnose possible low insulation resistance issues. For this purpose, a robust locally weighted scatterplot smoothing method is utilized to filter the original data. In this research, an insulation variation model is developed using a data-driven long short-term memory neural network to identify insulation resistance value anomalies caused by deionizer failure. The results indicate that the coefficient of determination of the failure model is 99.78 %. Moreover, current model efficiently identifies insulation faults resulting from reliability issues, such as conductivity issues of cooling pipes and erosion of vehicle wiring harnesses.展开更多
Electrical insulation faults produce partial discharges(PD),which can be analysed to identify specific types of defects.PD clustering is a widely used method to identify PD sources,although its success depends largely...Electrical insulation faults produce partial discharges(PD),which can be analysed to identify specific types of defects.PD clustering is a widely used method to identify PD sources,although its success depends largely on the feature maps used.In this paper,three widely used feature maps,or separation maps,are compared:chromatic,energy wavelet with principal component analysis(EW-PCA),and time-frequency(TF).To compare and evaluate,five scenarios with multi-PD environments with noise were developed.The clustering ability of the maps was evaluated using two performance indicators:intercluster distance and intracluster distance.The results indicate that the EW-PCA map performed the best in all scenarios,correctly identifying the largest number of data points and producing the clearest and most distinct clusters.The TF map created distinct clusters in several scenarios,but not all.The chromatic map created distinct clusters in all scenarios but was not as well defined as the other two separation maps.Given the results,it is important in fieldwork to use a wide range of PD clustering,accompanied by performance metrics that support a less biased decision tailored to the test object.展开更多
基金funded by the Science and Technology Project of China Southern Power Grid(YNKJXM20210175)the National Natural Science Foundation of China(52177070).
文摘Most ground faults in distribution network are caused by insulation deterioration of power equipment.It is difficult to find the insulation deterioration of the distribution network in time,and the development trend of the initial insulation fault is unknown,which brings difficulties to the distribution inspection.In order to solve the above problems,a situational awareness method of the initial insulation fault of the distribution network based on a multi-feature index comprehensive evaluation is proposed.Firstly,the insulation situation evaluation index is selected by analyzing the insulation fault mechanism of the distribution network,and the relational database of the distribution network is designed based on the data and numerical characteristics of the existing distribution management system.Secondly,considering all kinds of fault factors of the distribution network and the influence of the power supply region,the evaluation method of the initial insulation fault situation of the distribution network is proposed,and the development situation of the distribution network insulation fault is classified according to the evaluation method.Then,principal component analysis was used to reduce the dimension of the training samples and test samples of the distribution network data,and the support vector machine(SVM)was trained.The optimal parameter combination of the SVM model was found by the grid search method,and a multi-class SVM model based on 1-v-1 method was constructed.Finally,the trained multi-class SVM was used to predict 6 kinds of situation level prediction samples.The results of simulation examples show that the average prediction accuracy of 6 situation levels is above 95%,and the perception accuracy of 4 situation levels is above 96%.In addition,the insulation maintenance decision scheme under different situation levels is able to be given when no fault occurs or the insulation fault is in the early stage,which can meet the needs of power distribution and inspection for accurately sensing the insulation fault situation.The correctness and effectiveness of this method are verified.
基金funded by the National Key Research and Development Program of China(No.2022YFB2502500).
文摘The reliability and safety of fuel cell vehicle are crucial for the daily operation. Insulation resistance serves as a crucial index of vehicle reliability, especially when fuel cells operate at high voltages. Low insulation resistance can lead to vehicle malfunctions, exposing the operator to the risk of electric shock. In this study, long-term insulation resistance data from thirteen vehicles equipped with three different types of fuel cell systems are analyzed to diagnose possible low insulation resistance issues. For this purpose, a robust locally weighted scatterplot smoothing method is utilized to filter the original data. In this research, an insulation variation model is developed using a data-driven long short-term memory neural network to identify insulation resistance value anomalies caused by deionizer failure. The results indicate that the coefficient of determination of the failure model is 99.78 %. Moreover, current model efficiently identifies insulation faults resulting from reliability issues, such as conductivity issues of cooling pipes and erosion of vehicle wiring harnesses.
基金support of the Secretaría Nacional de Ciencia,Tecnología e Innovación(SENACYT)under Grant IDDSE19-007the Agencia Nacional de Investigación y Desarrollo(ANID)under Grants Fondecyt 1230135 and Fondef TA24I10002the Sistema Nacional de Investigación(SNI)of Panama under Grant 16-2021.
文摘Electrical insulation faults produce partial discharges(PD),which can be analysed to identify specific types of defects.PD clustering is a widely used method to identify PD sources,although its success depends largely on the feature maps used.In this paper,three widely used feature maps,or separation maps,are compared:chromatic,energy wavelet with principal component analysis(EW-PCA),and time-frequency(TF).To compare and evaluate,five scenarios with multi-PD environments with noise were developed.The clustering ability of the maps was evaluated using two performance indicators:intercluster distance and intracluster distance.The results indicate that the EW-PCA map performed the best in all scenarios,correctly identifying the largest number of data points and producing the clearest and most distinct clusters.The TF map created distinct clusters in several scenarios,but not all.The chromatic map created distinct clusters in all scenarios but was not as well defined as the other two separation maps.Given the results,it is important in fieldwork to use a wide range of PD clustering,accompanied by performance metrics that support a less biased decision tailored to the test object.
