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A contribution to the online monitoring of partial discharges in high-voltage grid components
(2024)
Increasing utilization of HV power cable lines, and particularly the advent of long HVDC underground links, results in the need to sensitively monitor their condition, such as by tracking the emergence and development of partial discharges. This study demonstrates that existing solutions are poorly suited to the case of HVDC lines, and that a new, more sophisticated monitoring system needs to address a wide array of diverse problems. First of all, the overview of the task and main functional blocks of the monitoring system is given. Following this, selected blocks are subject to detailed examination in subsequent chapters.
The first of these blocks, a high-frequency current transformer (HFCT), is responsible for capturing the signal in the power line and transferring it to the electronics. This component receives in-depth coverage: general design considerations, computation of leakage inductance, operation in presence of a power current, resonant behavior, balanced design. Particular attention is given to the application of Finite Element Analysis (FEA) in relation to the HFCT, however, the demonstrated techniques can also be applied to other magnetic devices. Last section of the chapter presents various stages of development, experimental setups, prototypes, and the final device which has been put into series production.
The next block addresses extraction of signals from noise. A literature analysis is conducted concerning noise suppression in partial discharge recordings, and the application of the signal separation approach based on Linear Predictive Coding is shown.
In the section on localization, examples illustrating problems encountered during the development of a partial discharge localization algorithm are presented.
In the chapter devoted to laboratory tests, various auxiliary experiments conducted during the system's development are compiled, where some of them are: Time Domain Reflectometry (TDR) TDR-based line characterization, estimation of monitoring sensitivity, measurement of the cable attenuation and reflection coefficients of the accessories.
In the final chapter – field tests – the installation of the system on a 110 kV line and the measured transfer function are demonstrated.
The creation of adequate simulation models for complex assemblies is an extensive process that requires a lot of experience, and on the other hand involves a multitude of manual, tedious tasks. These are significant obstacles for improving the process performance and capabilities. The objective of this research is to develop methods which digitally imitate the way of thoughts of the engineer in the design process towards a digital system understanding and which support the automation of the involved manual workflow.
This thesis presents a strategy to translate engineering reasoning and actions to an equivalent in the computer domain. A cardinal step is to gain understanding of system arrangements, boundary conditions and its components. Based on this evaluation, the identification of assembly parts is forming the foundation for optimized process chains for the transfer to the analysis environment. Model complexity relates to computational effort, which in turn affects model capabilities and manageability. To achieve a satisfactory compromise of model quality and complexity, this transfer process is strongly dependent on the visual analysis, reasoning and manual implementation of skilled engineers.
The principle of translating engineering logics is pursued from the assembly system to its smallest parts. Component segmentation methods allow subdividing regions of interest into substructures which are assigned with a feature vector. This vector comprises metrics describing the substructures with regard to specific aspects and is the key decision point for subsequent steps as idealization, suitable Finite-Element modeling and ultimately building an analysis model. The created system database is continuously maintained and supports these process chains as well as the final setup of the assembly simulation model.
An automated workflow like this implies advantages for efficiency, but also creates opportunities for further use cases. This workflow has been exploited for generating a training data set from the different simulation variants as a basis to a knowledge representation imitating engineering experience. An algorithm from the graph neural network field is applied to this data set as a conceptual approach. The intention pursued in this concept is to model the learning progress about estimating the influence of modelling decisions on simulation results and quality.
This research proposes a holistic strategy and describes methods to achieve the objectives of decreasing manual effort, introducing an automated and geometry-based process and digitally replicating engineering experience by introducing a knowledge database.
In wire-arc additive manufacturing, a wire is molten by an electrical or laser arc and deposited droplet-by-droplet to construct the desired workpiece, given as a set of two-dimensional layers. The weld source can move freely over a substrate plate, processing each layer, but there is also the possibility of moving without welding. A primary reason for stress inside the material is the large thermal gradient caused by the weld source, resulting in lower product quality. Thus, it is desirable to control the temperature of the workpiece during the process. One way of its optimization is the trajectory of the weld source. We consider the problem of finding a trajectory of the moving weld source for a single layer of an arbitrary workpiece that maximizes the quality of the part and derive a novel mixed-integer PDE-constrained model, including the calculation of a detailed temperature distribution measuring the overall quality. The resulting optimization problem is linearized and solved using the state-of-the-art numerical solver IBM CPLEX. Its performance is examined by several computational studies.
Steel cladding structures such as sandwich panels can replace bracing systems to provide further stability to individual structural members such as beams and columns. Previous researches studied the stabilizing effects of sandwich panels on the whole structure at ambient temperatures. It was shown that considerable savings could be achieved in the case of using steel cladding systems. In the STABFI (Steel Cladding Systems for Stabilisation of Steel Buildings in Fire) project, the primary objective was to study the stabilizing behavior of cladding systems in the fire. The current thesis is a part of the STABFI project focusing on the bending and translational stiffness of sandwich panels at ambient and elevated temperatures.
The thesis consists of two separate parts, the bending and translational performance of sandwich panels at ambient and elevated temperatures. Sandwich panels are typically composites of two thin steel sheets and a core of higher thickness and lower density. They are valued for their excellent thermal properties. This research employs two different materials, including mineral wool (MW) and Polyisocyanurate (PIR), as a core.
In the first part of the thesis, the bending tests carried out in Prague are described. The experimental results are presented in the first phase of this part. A finite element (FE) model is developed to validate simulations with experimental results, and then a comprehensive parametric study is carried out. During the parametric study, different factors such as panel thickness, width, span, the thickness of steel sheets, and the fire's influence on panels' mechanical behavior are investigated. Moreover, the analytical solutions obtained from Eurocodes (EN 14509, 2013) at ambient temperature are employed to predict the bending stiffness values. The analytical solutions are then developed to apply at elevated temperatures by incorporating the reduction factors into the equations. Eventually, the accuracy of suggested analytical equations is compared with numerical results.
In the second part of the thesis, after presenting the translational tests which also conducted in Prague and validation of FE models, an extensive parametric study on the decisive factors such as the steel sheet thicknesses, screw diameters and temperature effects on the sandwich panel connections behavior is performed. The parametric study shows how each parameter affects the shear resistance and stiffness of sandwich panel connections. Furthermore, the deterioration of shear performance at elevated temperatures is evaluated. The analytical solutions achieved from the ECCS manual are used to estimate the shear stiffness and resistance of connections at ambient temperatures. At elevated temperatures, the equations are developed to anticipate the abovementioned values in the fire case. Finally, the safety and accuracy of proposed analytical solutions are assessed.
Since the beginning of its development in the 1950s, mixed integer programming (MIP) has been used for a variety of practical application problems, such as sequence optimization. Exact solution techniques for MIPs, most prominently branch-and-cut techniques, have the advantage (compared to heuristics such as genetic algorithms) that they can generate solutions with optimality certificates. The novel process of additive manufacturing opens up a further perspective for their use. With the two common techniques, Wire Arc Additive Manufacturing (WAAM) and Laser Powder Bed Fusion (LPBD), the sequence in which a given component geometry must be manufactured can be planned. In particular, the heat transfer within the component must be taken into account here, since excessive temperature gradients can lead to internal stresses and warpage after cooling. In order to integrate the temperature, heat transfer models (heat conduction, heat radiation) are integrated into a sequencing model. This leads to the problem class of MIPDECO: MIPs with partial differential equations (PDEs) as further constraints. We present these model approaches for both manufacturing techniques and carry out test calculations for sample geometries in order to demonstrate the feasibility of the approach.
In wire-arc additive manufacturing (WAAM), the desired workpiece is built layerwise by a moving heat source depositing droplets of molten wire on a substrate plate. To reduce material accumulations, the trajectory of the weld source should be continuous, but transit moves without welding, called deadheading, are possible. The enormous heat of the weld source causes large temperature gradients, leading to a strain distribution in the welded material which can lead even to cracks. In summary, it can be concluded that the temperature gradient reduce the quality of the workpiece. We consider the problem of finding a trajectory of the weld source with minimal temperature deviation from a given target temperature for one layer of a workpiece with welding segments broader than the width of the weld pool. The temperature distribution is modeled using the finite element method. We formulate this problem as a mixed-integer linear programming model and demonstrate its solvability by a standard mixed-integer solver.
We present a general numerical solution method for control problems with PDE-defined state variables over a finite set of binary or continuous control variables. We show empirically that a naive approach that applies a numerical discretization scheme to the PDEs (and if necessary a linearization scheme) to derive constraints for a mixed-integer linear program (MILP) leads to systems that are too large to be solved with state-of-the-art solvers for MILPs, especially if we desire an accurate approximation of the state variables. Our framework comprises two techniques to mitigate the rise of computation times with increasing discretization level parameters:
First, the linear system is solved for a basis of the control space in a preprocessing step. Second, certain constraints are just imposed on demand via the IBM ILOG CPLEX feature of a lazy constraint callback. These techniques are compared with an approach where the relations obtained by the discretization of the continuous constraints are directly included in the MILP. We demonstrate our approach on two examples: modeling of the spread of wildfire and the mitigation of water contamination. In both examples the computational results demonstrate that the solution time is significantly reduced by our methods. In particular, the dependence of the computation time on the size of the spatial discretization of the PDE is significantly reduced.
Statistical size effect in steel structure and corresponding influence on structural reliability
(2018)
This thesis aims to investigate the statistical size effect in the elasto-plastic material and the corresponding reliability of steel structures. The core idea is that the stochastic material properties are directly embedded in mechanical calculations to develop a more accurate and economical design method for steel structure. Moreover, the results of the experimental investigation with different specimen sizes, whose diameter is limit up to 32 mm, show that the statistical size effect exists in steel structures. This thesis demonstrates finally that the structural reliability is affected by the statistical size effect and the structural safety can be optimized by considering this effect.
Because of the uncertainty and non-uniformity of the microscopic imperfection distribution, the material strength in macroscale presents complex randomness. This study described the randomness of material properties through two different ways: developing a stochastic material model for elasto-plastic material and establishing a discrete random field with a general mathematical program. The proposed stochastic material model is extended to analyze the steel structure with multiaxial stress and is integrated into the commercial FEM software for analysis of the complex structures with stress gradient. The stochastic finite element method is implemented to analyze the response of the 3D structures by a general-purpose FEM program when the random field file is imported into the finite element model.
The uniaxial tensile tests with different specimen sizes and different material are carried out to demonstrate the statistical size effect in steel structures. The results show that the variations of the yield and tensile strength increase with the decreasing specimen volume. Moreover, according to the bending tests, it is obvious that the structural component strength is not only related to the specimen volume, but also the stress distribution. These two proposed simulation methods, which are an extension and supplement to traditional simulation methods, can effectively simulate the statistical size effect for the tensile and flexural components in steel structures.
Finally, it is found by studying the influence of statistical size effect on structural reliability that the strength, which is obtained by small specimens through statistical analysis in the laboratory, is no more accurately applicable to large construction. The reliability theory for the structural safety which exists over the decades can be compared and validated or improved through the embedding the stochastic material properties in the numerical simulation.
Safety evaluation of truss structures depends upon the determination of the axial forces and corresponding stresses in axially loaded members. Due to presence of damages, change in intended use, increase in service loads or accidental actions, structural assessment of existing truss structures is necessary. Precise identification of the stresses plays a crucial role for the preservation of historic truss structures. The assessment measures require non-destructiveness, minimum intervention and practical applicability.
Motivated by the preservation of existing truss−type constructions composed of axially loaded slender members, the present work aims to develop a non-destructive methodology to identify the axial forces or corresponding stress states in iron and steel truss structures. The approach is based on vibration measurements and the finite element method combined with optimization techniques.
After a state of the art review, numerical and experimental studies were carried out in the research work on different partial systems of truss-type structures. The examined aspects included the effects of structural loading on the dynamic performance of truss structures, modelling of joint connections, mode pairing criteria, selection of updating parameters and definition of an objective function, as well as the use of different optimization techniques.
A methodology consisted of a two-stage model updating procedure using optimization techniques was proposed for the determination of multiple member axial forces and estimation of the joint flexibility of truss-type constructions. In the first stage optimization, the validation criterion is based on the experimentally identified global natural frequencies and mode shapes of the truss. Additionally, the axial forces in selected individual members of the truss are used. They are estimated from the natural frequencies and five amplitudes of the corresponding local mode shapes of the members using an analytically−based algorithm. Based on the results of the identified axial forces in the first stage, a second optimization procedure for the joint stiffness is performed. In this stage, the modal parameters of the global natural frequencies and mode shapes are used as validation criterion.
From the results of the investigated systems, the identified axial forces by the proposed methodology agree well with the experimentally measured axial forces. Furthermore, recommendations are given in the work for a guideline of measuring concepts and assessment strategies applied to existing iron and steel truss-type structures.
This paper is concerned with the inverse identification of the stress state in axially loaded slender members of iron and steel truss structures using measured dynamic data. A methodology is proposed based on the finite element model updating coupled with nature-inspired optimization techniques, in particular the particle swarm optimization. The numerical model of truss structures is calibrated using natural frequencies and mode shapes from vibration tests, as well as additional information of the axial forces in selected truss members based on the experimentally identified modal parameters. The results of the identification are the axial forces or corresponding stresses in truss structures and the joint rigidity in relation to pinned and rigid conditions. Attention is given to several examined aspects, including the effects of the axial tensile and compressive forces on the dynamic responses of trusses, mode pairing criteria, as well as modeling assumptions of joints and the use of a joint rigidity parameter. Considering the pairing of modes, it is performed by adapting an enhanced modal assurance criterion that allows the selection of desired clusters of degrees-of-freedom. Thus, information extracted from the measurements related to specific modes is utilized in a more beneficial way. For modeling of joints, the numerical model of a truss structure includes rotational springs of variable stiffness to represent semi-rigid connections. Moreover, a fixity factor is introduced for practical estimation of the joint flexibility. The effectiveness of the proposed methodology is demonstrated by case studies involving simulated and laboratory experimental data.