The windings of a traction transformer are always be threatened by the fluctuation of the load. Frequency response analysis (FRA) is regarded as the most effective method to diagnose the winding deformation. Appropriate FRA modelling method may help to predict typical winding faults for large transformers. The existing model usually simplify dozens of winding disks into several disks, it is applicable when analysing small displacement. However, when the disks between windings are seriously misplaced, the conventional model may not be suitable. This study presents an improved lumped parameter circuit model considering intersection capacitances and mutual inductance between windings. The feasibility of the proposed model is verified based on a 10 kV test transformer. Finally, the model is applied for a 220 kV traction transformer, the variation of parameters and features are analysed for axial displacement of different windings. The results show that the axial displacement may lead to increase of resonance frequency and decrease of amplitude. The low-frequency band (1-30 kHz) may be regarded as visible features for diagnosing axial displacement of H winding and the middle frequency band (50-200 kHz) may be a characteristic band for diagnosing axial displacement of T winding.

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Dam is an important part of the national infrastructure, and its safety has been widely concerned. Risk identification of dams plays a significant role in risk assessment and control. Finding out some critical failure paths through adopting timely measures can help reduce the risk occurrence probability effectively. This paper develops an identification method based on the credibility and the interval analytic hierarchy process (IAHP) methods, namely, consistency and difference-based interval analytic hierarchy process (CDB-IAHP) method, to identify the critical failure paths of dams exactly considering the dynamic cognition degree of decision-makers. Based on the fault tree analysis (FTA) method, the framework and analysis for critical failure paths identification of a gravity dam and an Earth-rockfill dam are conducted and made. The results show that the critical failure paths obtained by the proposed method are in line with the statistical data, and the importance of disaster causing factors has some difference with the traditional method. Additionally, some engineering and nonengineering measures are suggested to reduce the impact of potential failure paths. The applications demonstrate that the proposed method shows good applicability for risk analysis and critical failure path mining of dams.

Ground-based synthetic aperture radar (GB-SAR) is a relatively new technique that can be used to monitor the deformation of large-volume targets, such as dams, slopes, and bridges. In this study, the permanent scatterer (PS) technique is used to address the issues encountered in the continuous monitoring of the external deformation of an arch-gravity dam in a hydraulic and hydropower engineering structure in Hubei, China; the technique includes large image data sizes, high accuracy requirements, a susceptibility of the monitoring data to atmospheric disturbances, complex phase unwrapping, and pronounced decoherence. Through an in-depth investigation of PS extraction methods, a combined PS selection (CPSS) method is proposed by fully taking advantage of the signal amplitude and phase information in the monitored scene. The principle and implementation of CPSS are primarily studied. In addition, preliminarily selected PS candidates are directly used to construct and update a triangular irregular network (TIN) to maintain the stability of the subsequent Delaunay TIN. To implement this method, a differential-phase standard-deviation threshold method is proposed to extract PSs that are highly spatially coherent and consistent. Finally, the proposed CPSS was applied to the safety monitoring of the dam. The monitoring results are compared with conventional inverted plumb line monitoring results, and the proposed CPSS is found to be effective and reliable.

Frequency response analysis (FRA) is a widely used approach for detecting winding faults in a transformer. Appropriate and quantitative FRA features will help to improve the accuracy of fault diagnosis. In this study, a novel FRA interpretation including new image features is proposed based on the image processing technique. First, winding faults of different windings are simulated in a test autotransformer and the FRA curves are measured under various faults. Then frequency region division method and image processing technique are first applied to the measured FRA curves. The area ratio and centroid deviation in different frequency regions are calculated through a novel algorithm. Finally, the image features are used as the inputs to support vector machine model. Additionally, three different parametric optimisation algorithm are compared during the training process. The results show that the particle swarm optimisation and image feature exhibit best performance for identifying winding faults.

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The temperature field and stress field of RCC dam during construction time were calculated in complete simulation by using the calculation means of "negative heat of hydration" and "volumetric force method" and so on as well as adapting the e-variable temperature stress calculation finite-element method which considered concrete creep influence in connection with the construction temperature control requirement of RCC dam in cold spell region. Yunnan lead factory hydropower station RCC dam was used for the instance, fluctuating main temperature control parameters to proceed simulation calculation and sensitivity analysis about the outcome, which drew the influence that main temperature control parameters had on dam crack, meanwhile, sensitivity curves were fitted via adapting Lagrangian method, cutoff value and surplus cutoff value of temperature control parameters were worked out while the best program results were found out. It has been applied to the actual projects and has reference value for similar programs of other projects to choose.

Accelerated stress-migration testing under 200℃ of Cu (M1/M2) interconnects has been done for 700h. Finite element analysis and focused-ion beam techniques have been used to study the stress-induced voiding in the Cu interconnects with vias of 500 and 350nm in diameter. The voiding mechanism and the effect of via size on the stress migration have been studied. The results show that peak values of stress and stress gradient in M1 lines are reached underneath the edge of vias. The stress gradient shows crucial effect on the voiding process. The vacancies introduced by thermo-mechanical stress diffuse along Cu M1/SiN interfaces under the force of stress gradient and nucleate at the peak values of the stress gradient. The void grows faster along the length direction because the stress in M1 lines changes faster horizontally. The stress and stress gradient increase with increasing via diameter, leading to a faster voiding rate.