Traditionally, airborne time-domain electromagnetic （ATEM） data are inverted to derive the earth model by iteration. However, the data are often highly correlated among channels and consequently cause ill-posed and ...Traditionally, airborne time-domain electromagnetic （ATEM） data are inverted to derive the earth model by iteration. However, the data are often highly correlated among channels and consequently cause ill-posed and over-determined problems in the inversion. The correlation complicates the mapping relation between the ATEM data and the earth parameters and thus increases the inversion complexity. To obviate this, we adopt principal component analysis to transform ATEM data into orthogonal principal components （PCs） to reduce the correlations and the data dimensionality and simultaneously suppress the unrelated noise. In this paper, we use an artificial neural network （ANN） to approach the PCs mapping relation with the earth model parameters, avoiding the calculation of Jacobian derivatives. The PC-based ANN algorithm is applied to synthetic data for layered models compared with data-based ANN for airborne time-domain electromagnetic inversion. The results demonstrate the PC-based ANN advantages of simpler network structure, less training steps, and better inversion results over data-based ANN, especially for contaminated data. Furthermore, the PC-based ANN algorithm effectiveness is examined by the inversion of the pseudo 2D model and comparison with data-based ANN and Zhody＇s methods. The results indicate that PC-based ANN inversion can achieve a better agreement with the true model and also proved that PC-based ANN is feasible to invert large ATEM datasets.展开更多
Integrating heterogeneous data sources is a precondition to share data for enterprises. Highly-efficient data updating can both save system expenses, and offer real-time data. It is one of the hot issues to modify dat...Integrating heterogeneous data sources is a precondition to share data for enterprises. Highly-efficient data updating can both save system expenses, and offer real-time data. It is one of the hot issues to modify data rapidly in the pre-processing area of the data warehouse. An extract transform loading design is proposed based on a new data algorithm called Diff-Match,which is developed by utilizing mode matching and data-filtering technology. It can accelerate data renewal, filter the heterogeneous data, and seek out different sets of data. Its efficiency has been proved by its successful application in an enterprise of electric apparatus groups.展开更多
This paper deals with an integration of directly measured electrical parameters with data acquired by data communication from protections and terminals into an advanced monitoring system. Based on the periodic test, t...This paper deals with an integration of directly measured electrical parameters with data acquired by data communication from protections and terminals into an advanced monitoring system. Based on the periodic test, the authors of this paper present the possibility of an extended evaluation and more accurate analysis of transient and failure events. For periodical testing, as implemented during the commissioning of power plants in the Czech Republic, a monitoring system of electrical equipment has been used, to record the courses of important electrical parameters and thus, proving the proper functioning of complex technological systems in various operation modes. Data from monitoring system were used to prove the successful results of the test or as a base data for further analysis of failures. The monitoring system has proved itself as a very useful device also when recording unexpected failure events, the cause of which was very quickly and accurately detected by the follow-up analysis. Initially, only the voltage and current data from measuring transformers, analogue transducers and contact relays were used as input data for the monitoring system. After the implementation of new digital protection technology and controlling terminals with inner data recorder, the data from digital devices could be also utilized for the monitoring system.展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 40974039)High-Tech Research and Development Program of China (Grant No.2006AA06205)Leading Strategic Project of Science and Technology, Chinese Academy of Sciences (XDA08020500)
文摘Traditionally, airborne time-domain electromagnetic （ATEM） data are inverted to derive the earth model by iteration. However, the data are often highly correlated among channels and consequently cause ill-posed and over-determined problems in the inversion. The correlation complicates the mapping relation between the ATEM data and the earth parameters and thus increases the inversion complexity. To obviate this, we adopt principal component analysis to transform ATEM data into orthogonal principal components （PCs） to reduce the correlations and the data dimensionality and simultaneously suppress the unrelated noise. In this paper, we use an artificial neural network （ANN） to approach the PCs mapping relation with the earth model parameters, avoiding the calculation of Jacobian derivatives. The PC-based ANN algorithm is applied to synthetic data for layered models compared with data-based ANN for airborne time-domain electromagnetic inversion. The results demonstrate the PC-based ANN advantages of simpler network structure, less training steps, and better inversion results over data-based ANN, especially for contaminated data. Furthermore, the PC-based ANN algorithm effectiveness is examined by the inversion of the pseudo 2D model and comparison with data-based ANN and Zhody＇s methods. The results indicate that PC-based ANN inversion can achieve a better agreement with the true model and also proved that PC-based ANN is feasible to invert large ATEM datasets.
基金Supported by National Natural Science Foundation of China (No. 50475117)Tianjin Natural Science Foundation (No.06YFJMJC03700).
文摘Integrating heterogeneous data sources is a precondition to share data for enterprises. Highly-efficient data updating can both save system expenses, and offer real-time data. It is one of the hot issues to modify data rapidly in the pre-processing area of the data warehouse. An extract transform loading design is proposed based on a new data algorithm called Diff-Match,which is developed by utilizing mode matching and data-filtering technology. It can accelerate data renewal, filter the heterogeneous data, and seek out different sets of data. Its efficiency has been proved by its successful application in an enterprise of electric apparatus groups.
文摘This paper deals with an integration of directly measured electrical parameters with data acquired by data communication from protections and terminals into an advanced monitoring system. Based on the periodic test, the authors of this paper present the possibility of an extended evaluation and more accurate analysis of transient and failure events. For periodical testing, as implemented during the commissioning of power plants in the Czech Republic, a monitoring system of electrical equipment has been used, to record the courses of important electrical parameters and thus, proving the proper functioning of complex technological systems in various operation modes. Data from monitoring system were used to prove the successful results of the test or as a base data for further analysis of failures. The monitoring system has proved itself as a very useful device also when recording unexpected failure events, the cause of which was very quickly and accurately detected by the follow-up analysis. Initially, only the voltage and current data from measuring transformers, analogue transducers and contact relays were used as input data for the monitoring system. After the implementation of new digital protection technology and controlling terminals with inner data recorder, the data from digital devices could be also utilized for the monitoring system.
