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fault type analysis along the San Andreas fault Zone: A Numerical Approach

Journal of Mountain Science 2010年第1期7卷 36-44页

作者： Matrika Prasad KOIRALA Daigoro HAYASHI Simulation Tectonics Laboratory Faculty of Science University of the Ryukyus

Finite Element (FE) modeling under plane stress condition is used to analyze the fault type variation with depth along and around the San Andreas fault (SAF) zone. In this simulation elastic rheology was used and was thought justifiable as the variation in depth from 0.5 km to 20 km was considered. Series of calculations were performed with the variation in domain properties. Three types of models were created based on simple geological map of California, namely, 1) single domain model considering whole California as one homogeneous domain, 2) three domains model including the North American plate, Pacific plate, and SAF zone as separate domains, and 3) Four domains model including the three above plus the Garlock fault zone. Mohr-Coulomb failure criterion and Byerlee's law were used for the calculation of failure state. All the models were driven by displacement boundary condition imposing the fixed North American plate and Pacific plate motion along N34°W vector up to the northern terminus of SAF and N50°E vector motion for the subducting the Gorda and Juan de Fuca plates. Our simulated results revealed that as the depth increased, the fault types were generally normal, and at shallow depth greater strike slip and some thrust faults were formed. It is concluded that SAF may be terminated as normal fault at depth although the surface expression is clearly strike slip.

关键词： Finite Element modeling plane stress fault type analysis San Andreas fault zone rockdomain properties failure analysis

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fault Location and Classification for Distribution Systems Based on Deep Graph Learning Methods

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Journal of Modern Power Systems and Clean Energy 2023年第1期11卷 35-51页

作者： Jiaxiang Hu Weihao Hu Jianjun Chen Di Cao Zhengyuan Zhang Zhou Liu Zhe Chen Frede Blaabjerg School of Mechanical and Electrical engineering University of Electronic Science and Technology of ChinaChengduChina Siemens Gamesa Renewable Energy A/S LyngbyDenmark Aalborg University AalborgDenmark IEEE

Accurate and timely fault diagnosis is of great significance for the safe operation and power supply reliability of distribution ***,traditional intelligent methods limit the use of the physical structures and data information of power *** this end,this study proposes a fault diagnostic model for distribution systems based on deep graph *** model considers the physical structure of the power network as a significant constraint during model training,which endows the model with stronger information perception to resist abnormal data input and unknown application *** addition,a special spatiotemporal convolutional block is utilized to enhance the waveform feature extraction *** enables the proposed fault diagnostic model to be more effective in dealing with both fault waveform changes and the spatial effects of *** addition,a multi-task learning framework is constructed for fault location and fault type analysis,which improves the performance and generalization ability of the *** IEEE 33-bus and IEEE 37-bus test systems are modeled to verify the effectiveness of the proposed fault diagnostic ***,different fault conditions,topological changes,and interference factors are considered to evaluate the anti-interference and generalization performance of the proposed *** results demonstrate that the proposed model outperforms other state-of-the-art methods.

关键词： fault diagnosis fault location fault type analysis distribution system deep graph learning multi-task learning

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