Chalmers Open Digital Repository

Välkommen till Chalmers öppna digitala arkiv!

Här hittar du:

Studentarbeten utgivna på lärosätet, såväl kandidatarbeten som examensarbeten på grund- och masternivå
Digitala specialsamlingar, som t ex Chalmers modellkammare
Utvalda projektrapporter

Forskningspublikationer, rapporter och avhandlingar hittar du i research.chalmers.se

Enheter i Chalmers ODR

Välj en enhet för att se alla samlingar.

Visar 1 - 2 av 2

Senast inlagda

Simulations of Dielectric Frequency Response of Bushings for a Non-Destructive On-site Defect Identification

(2025) Kulandaivel, Nigalyadevi

Bushings are integrated components of high voltage transformers providing connections of windings to external circuits outside the transformer shell while providing mechanical and insulation support. During normal operation, bushings are consistently influenced by operating voltage, load current, and voltage stress due to transient over-voltages during natural or switching phenomena in the power system network. These stresses gradually degrade the bushing insulation and eventually cause failure, The insulation state should be monitored and maintained periodically. Dielectric Frequency Response (DFR) measurements yielding capacitance and loss factor values in frequency domain is one of the most popular diagnostics methods, which provides fruitful information about insulation conditions and possible defects that may be present within the bushing insulation. The aim of the thesis project is to develop a transformer bushing model using COMSOL Multiphysics to perform simulations of DFR for a real scale transform bushing geometry. The model was implemented based on electrostatics physics and current continuity through the insulation structure. The loss factor and the capacitance values were computed in the frequency window typical for practical measurements. The results of the simulations conducted using the developed model are validated by comparing them with measured DFR data. Furthermore, possible defects, which may appear in practice (conductive layers on the insulation, gas bubbles in the insulation bulk) were introduced in the model and frequency dependent loss factor and capacitance values were computed for each type of defects. The results are compared with the reference (defect free) case to identify and interpret the dielectric response behavior. The sensitivity study conducted by varying the properties of the defects indicate that the developed model provides a tool for capturing presence of defects in the insulation by analyzing changes in the DF response.

Validating a Sea Ice Drift Retrieval Algorithm Through Remote Sensing and In-Situ Buoy Comparisons

(2025) Desjonqueres , Thibault

This Master’s thesis validates a sea ice drift retrieval algorithm developed at Chalmers (referred to as the Chalmers algorithm) using in situ buoy data collected during the ARTofMELT 2023 cruise in the Fram Strait from May to June 2023. The ARTofMELT 2023 expedition aimed to document melt onset in the Arctic Ocean, collecting extensive data on sea ice dynamics near the Marginal Ice Zone (MIZ) with seven OpenMETbuoys and three SIMBA buoys. These buoys were deployed on first-year ice floes, with detailed measurements of horizontal motion, rotation, deformation, wave action, and temperature. This deployment enabled analysis of ice floe movement, convergence, divergence, and the interaction between these dynamics and SAR imagery. Low-noise Radarsat Constellation Mission (RCM) and Sentinel-1A SAR images, captured in dual polarization (HH+VV or HH+HV), were acquired to match the study area both spatially and temporally. Integrating in situ weather data, wave measurements, sea ice concentration, and weather reanalysis with satellite imagery observations of ice conditions around the buoys enabled a thorough interpretation of drift patterns, floe decay, and wave signatures associated with the buoys. The Chalmers drift algorithm was successfully applied to two pairs of RCM images, each with a 10 m resolution, out of eight total image pairs. These pairs were cho sen based on criteria including a time gap of 1-24 hours between images, identical resolution, and a maximum 5-minute time gap between the buoy and the images. The remaining six pairs contained Sentinel-1A images with a resolution of 100-150 m. Although the drift estimates from the Chalmers algorithm were not statistically significant, they demonstrated the algorithm’s capability to produce coherent and realistic drift estimates, evidenced by the alignment of drift vector azimuths with ac tual buoy drift and realistic sea ice drift distances. To better assess the algorithm’s performance, future work should test it on a larger dataset, including the six re maining Sentinel-1A image pairs. Additionally, exploring the effects of increased time gaps between buoy and satellite images, and between the two satellite images, would provide further insights into the algorithm’s capabilities

Machine Learning for Wind Power Prediction. A Comparative Analysis of Traditional Machine Learning Models and Graph Neural Network for Wind Power Prediction and Forecasting in Wind Farms

(2025) Antonsson, Emma; Vind, Lisa

This thesis presents a comparative study of machine learning (ML) models and the deep learning (DL) model graph neural network (GNN) for wind power prediction and short- to medium-term forecasting in wind farms. Using high-resolution SCADA data from a 16-turbine onshore wind farm in Sweden, along with re-analysis and forecast weather datasets, various models including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), k-nearest neighbours (kNN), Multi-Layer Perceptron (MLP), and GNN were trained and evaluated. Two baseline approaches, the farm’s theoretical power curve and FLORIS wake model, were used as references. Results show that ML models outperform baseline models in predicting wind power output, with GNN achieving the best overall performance, although all ML models perform similarly. The ability of the models to generalize from wind power prediction to forecasting is however limited. The findings indicate that re-analysis data with low spatial resolution fails to adequately capture local weather conditions necessary for accurate power prediction. The study also investigates the effects of input feature selection, temporal resolution, and multi-task learning on model performance. Furthermore, it identifies challenges related to input data quality, particularly in the estimation of global wind conditions from SCADA-based measurements. These results underscore the potential of ML methods for wind power applications and highlight the critical importance of accurately representing global weather data, as well as accounting for discrepancies between training data and forecast data.

Steering behavior-based fatigue detection: Evaluation and implementation for drowsy driver warning system

(2025) Hassan Ananda Kumar, Sanjana

This research was conducted to analyze steering behavior as an alternative approach to detecting driver drowsiness. The thesis examined standard metrics such as steering wheel angle, steering wheel angle rate, yaw rate, and lane position—variables directly and indirectly related to steering behavior—to assess how much information they bear about drowsiness. A derived metric, the steering reversal rate, was also analyzed to further explain the effects of drowsiness. These metrics demonstrated a strong correlation with driver drowsiness, which was subjectively measured using the Karolinska Sleepiness Scale (KSS). Based on this analysis, two parameters derived from the steering reversal rate—micro and macro corrections—were used to develop two methods for detecting drowsiness. These parameters were significant because, as a driver becomes drowsier, the frequency of micro-corrections tends to decrease, while macro-corrections increase. Two methods have been developed to detect a drowsy driver based on the above analysis. The first method employed a logistic regression model, using the absolute values of micro and macro corrections to directly correlate with the KSS ratings. This approach did not account for temporal patterns and treated the data independently of its time-series nature. In contrast, the second method incorporated a time-series perspective by evaluating changes in micro and macro correction rates over time rather than relying on their absolute values. During development, it was observed that vehicle speed significantly influenced steering behavior. At lower speeds, even non-drowsy drivers exhibited more macro corrections and fewer microcorrections. However, at speeds above 65 km/h, non-drowsy drivers typically made more micro-corrections and fewer macro-corrections. This insight enhanced the robustness of the second method, wherein vehicle speed was considered one of the contributing factors in analyzing driver steering behavior. Additionally, the second method involved a learning phase for each individual driver, allowing for personalized threshold values. This driver-specific calibration improved adaptability. Overall, the second method of real-time analysis of changes in micro and macro correction rates proved to be a more effective and reliable approach, yielding better results than the current system(based on lane distance) for detecting driver drowsiness.

Enhancing Gas Adsorption In Sensors：Au–Pt Nanoparticles and Methylated Coordination Cages

(2025) Advand, Marzyeh

This thesis investigates two nanostructured materials, bimetallic gold platinum (AuPt) nanoparticles and a methylated cobalt(II)/iron(II) coordination cage, for their potential use in gas sensors designed to detect acetone, a key biomarker for non-invasive glucose monitoring. The AuPt nanoparticles were synthesized using a modified co-reduction method based on Britto’s procedure. The synthesis was optimized to achieve uniform and well-dispersed nanoparticles through precise control of reaction time and washing steps. The nanoparticles were characterized using SEM and, EDX, to confirm their morphology, composition, and crystal structure. They were immobilized on quartz substrates through silane functionalization, and UV–Vis spectroscopy was used to verify both immobilization and acetone vapor adsorption. A clear spectral shift observed after acetone exposure confirmed successful adsorption on the nanoparticle surface, indicating their suitability for gas sensing applications. In parallel, methylated Co(II) and Fe(II) coordination cages were synthesized through subcomponent self-assembly and characterized using NMR and UV–Vis spectroscopy. The immobilization of Co(II) cages on glass and silicon substrates was verified using UV–Vis and TOF-SIMS. The data is consistent with successful immobilization of the cages on both glass and silicon surfaces. Notably, this study demonstrates for the first time that this particular cage has been successfully adsorbed onto glass and silicon substrates. Gas exposure experiments were performed on the Fe(II) cage solutions and analyzed by NMR; however, no significant spectral changes were detected, likely due to gas dissipation or weak interactions at ambient conditions. Overall, the study presents promising results for the AuPt nanoparticles, while preliminary findings for the coordination cages indicate the need for further optimization. These results contribute to the development of nanostructured mate- rials for more sensitive and selective gas-sensing technologies aimed at advancing non-invasive diagnostic methods.