Power transformer is a core equipment of power system, which undertakes the important functions of power transmission and transformation, and its safe and stable operation has great significance to the normal operatio...Power transformer is a core equipment of power system, which undertakes the important functions of power transmission and transformation, and its safe and stable operation has great significance to the normal operation of the whole power system. Due to the complex structure of the transformer, the use of single information for condition-based maintenance (CBM) has certain limitations, with the help of advanced sensor monitoring and information fusion technology, multi-source information is applied to the prognostic and health management (PHM) of power transformer, which is an important way to realize the CBM of power transformer. This paper presents a method which combine deep belief network classifier (DBNC) and D-S evidence theory, and it is applied to the PHM of the large power transformer. The experimental results show that the proposed method has a high correct rate of fault diagnosis for the power transformer with a large number of multi-source data.展开更多
Aiming at the problem of abnormal data generated by a power transformer on-line monitoring system due to the influences of transformer operation state change,external environmental interference,communication interrupt...Aiming at the problem of abnormal data generated by a power transformer on-line monitoring system due to the influences of transformer operation state change,external environmental interference,communication interruption,and other factors,a method of anomaly recognition and differentiation for monitoring data was proposed.Firstly,the empirical wavelet transform(EWT)and the autoregressive integrated moving average(ARIMA)model were used for time series modelling of monitoring data to obtain the residual sequence reflecting the anomaly monitoring data value,and then the isolation forest algorithm was used to identify the abnormal information,and the monitoring sequence was segmented according to the recognition results.Secondly,the segmented sequence was symbolised by the improved multi-dimensional SAX vector representation method,and the assessment of the anomaly pattern was made by calculating the similarity score of the adjacent symbol vectors,and the monitoring sequence correlation was further used to verify the assessment.Finally,the case study result shows that the proposed method can reliably recognise abnormal data and accurately distinguish between invalid and valid anomaly patterns.展开更多
Transformers may suffer multiple short-circuit impacts during long-term operation,and axial instability is one of the typical types of serious accidents caused by short-circuit faults.The axial instability form of the...Transformers may suffer multiple short-circuit impacts during long-term operation,and axial instability is one of the typical types of serious accidents caused by short-circuit faults.The axial instability form of the winding-block structure is analysed,and the dynamic solution of the winding short-circuit electromagnetic force is obtained by estab-lishing the three-dimensional magnetic-circuit-force multi-physical field coupling simulation model.The influence of strain rate on the cushion block constitutive equation is corrected,and the modified model is verified by short-circuit impact test and quasi-static test.The research results show that for 110 kV 31.5 MVA transformers,the maximum electromagnetic axial resultant force of winding is 363.16 kN,and the ultimate tilt force is 1214.2 kN.The pre-tightening force configuration is accordingly recom-mended to range from 363.16 to 608.28 kN,which is narrowed by 18.49%compared with the static calculation method;Meanwhile,adding a logarithmic strain rate correction term to the classical constitutive equation of the cushion block can achieve a good correction of the stress-strain relationship with the coefficient of determination above 0.99,and the cushion block has a larger elastic modulus under high strain rate load.The research results provide an important theoretical reference for the axial stability structure of transformers.展开更多
文摘Power transformer is a core equipment of power system, which undertakes the important functions of power transmission and transformation, and its safe and stable operation has great significance to the normal operation of the whole power system. Due to the complex structure of the transformer, the use of single information for condition-based maintenance (CBM) has certain limitations, with the help of advanced sensor monitoring and information fusion technology, multi-source information is applied to the prognostic and health management (PHM) of power transformer, which is an important way to realize the CBM of power transformer. This paper presents a method which combine deep belief network classifier (DBNC) and D-S evidence theory, and it is applied to the PHM of the large power transformer. The experimental results show that the proposed method has a high correct rate of fault diagnosis for the power transformer with a large number of multi-source data.
基金supported by State Grid Hebei Electric Power Co.,Ltd.(kj2020-040).
文摘Aiming at the problem of abnormal data generated by a power transformer on-line monitoring system due to the influences of transformer operation state change,external environmental interference,communication interruption,and other factors,a method of anomaly recognition and differentiation for monitoring data was proposed.Firstly,the empirical wavelet transform(EWT)and the autoregressive integrated moving average(ARIMA)model were used for time series modelling of monitoring data to obtain the residual sequence reflecting the anomaly monitoring data value,and then the isolation forest algorithm was used to identify the abnormal information,and the monitoring sequence was segmented according to the recognition results.Secondly,the segmented sequence was symbolised by the improved multi-dimensional SAX vector representation method,and the assessment of the anomaly pattern was made by calculating the similarity score of the adjacent symbol vectors,and the monitoring sequence correlation was further used to verify the assessment.Finally,the case study result shows that the proposed method can reliably recognise abnormal data and accurately distinguish between invalid and valid anomaly patterns.
基金Natural Science Foundation of Hebei Province,Grant/Award Number:E2021521004。
文摘Transformers may suffer multiple short-circuit impacts during long-term operation,and axial instability is one of the typical types of serious accidents caused by short-circuit faults.The axial instability form of the winding-block structure is analysed,and the dynamic solution of the winding short-circuit electromagnetic force is obtained by estab-lishing the three-dimensional magnetic-circuit-force multi-physical field coupling simulation model.The influence of strain rate on the cushion block constitutive equation is corrected,and the modified model is verified by short-circuit impact test and quasi-static test.The research results show that for 110 kV 31.5 MVA transformers,the maximum electromagnetic axial resultant force of winding is 363.16 kN,and the ultimate tilt force is 1214.2 kN.The pre-tightening force configuration is accordingly recom-mended to range from 363.16 to 608.28 kN,which is narrowed by 18.49%compared with the static calculation method;Meanwhile,adding a logarithmic strain rate correction term to the classical constitutive equation of the cushion block can achieve a good correction of the stress-strain relationship with the coefficient of determination above 0.99,and the cushion block has a larger elastic modulus under high strain rate load.The research results provide an important theoretical reference for the axial stability structure of transformers.
