I-COSTE 2024 – Page 5 – NPS Australia Submission System

Compact Voltage Doubler Rectifier for RF Energy Harvesting in Wearable Biomedical Devices

The compact voltage doubler rectifier incorporates a rectangular square wave-shaped DC filter to smooth the rectified signal. With dimensions of 33 × 13 mm, it is well-suited for wearable biomedical devices. The rectifier demonstrates high sensitivity at low input power levels, effectively balancing size and power conversion efficiency (PCE), making it ideal for WBDs where both factors are crucial. This work contributes to the advancement of efficient RF energy harvesting (RFEH) solutions for wearable biomedical applications.

Maximum Power Penetration of Distributed Energy Resources with Optimal Sizing and Location

The motivations for incorporating renewable energy sources into power distribution networks are the diminishing availability of non-renewable energy resources, increasing demand for electricity, and the imperative for clean energy generation. It is important to improve the total capacity of distributed energy resources (DERs) that can be smoothly integrated into a specific feeder without adversely affecting voltage levels, protection mechanisms, power quality, and without requiring feeder upgrades or modifications. However, the escalating injection of DERs into the network may lead to operational challenges, including voltage fluctuations, reverse power flow, power quality issues, and thermal overloading of distribution lines, among others. This study presents an optimization technique for efficient incorporation of DERs into a distribution system. Here, a particle swarm optimization (PSO)-based algorithm is developed for the maximum penetration of DERs not for the only optimal size but also their location in the power system. We employ the Newton-Raphson load flow method to analyze power flow, considering major constraints such as overvoltage, undervoltage, and ampacity. The bus voltages were significantly improved after the penetration of three DER units in the system. The analysis is validated through MATLAB/Simulink simulation using the IEEE-33 bus distribution system as a testbed.

Integrating Decision Matrix and Mind Mapping for Optimal Residential Battery Storage Solutions

This paper is important because it simplifies how consumers choose residential battery energy storage systems. By introducing a practical framework that combines decision tools like decision matrices and mind mapping, it helps individuals evaluate key factors such as costs, payback periods, tariffs, and energy use patterns. Applied to the Australian market, it shows how consumers can balance financial returns with operational efficiency while considering uncertainties like battery degradation and policy changes. This research enhances decision-making and promotes energy sustainability by encouraging informed adoption of residential battery systems.

Purification of Exhaust Gas from the Marine Fuels Applied to Next-generation Ships

Generation Expansion Planning Model Towards Decarbonization: Assessing the Dunkelflaute

This paper explores the required capacity of renewable and storage resources for an isolated grid in long-term planning. The study also examines the impact of Dunkelflaute events on capacity planning and demonstrates how a balanced mix of variable renewable energy can mitigate network challenges.

Unsupervised Symbolization with Adaptive Features for LoRa-based Localization and Tracking

A novel adaptive feature extraction technique is proposed in partitioning
to overcome the problems of over-tracking and under-tracking. Mean spectral kurtosis analysis is performed across several partitioning techniques to assess their symbolization effectiveness. This enables the selection of the most appropriate partitioning technique. This enhances the localization and tracking of target objects by focusing on robustness to noise and multipath effects. The proposed method learns and estimates the distance range simultaneously, thereby eliminating the need for a separate offline training phase and the storage of reference coordinates. Experimental results using LoRa highlight the proposed method’s efficacy in real-time localization, tracking, and superiority over the state-of-the-art method.

Impact of Microstrain and Dislocation Density on the Quality and Properties of MAPbI3 Perovskite Films

The study aims to improve the quality of MAPbI3-based perovskite films by varying the MAI precursor concentration ratios using a sequential deposition method. The effect of microstrain and dislocation density on the film quality of MAPbI3 perovskite is investigated for various MAI precursor concentrations. However, the perovskite layer was prepared using the spin coating technique to achieve better structural properties. The main challenge is determining the optimal MAI precursor concentration ratio, which influences the final quality of the perovskite films. XRD measurements show that the crystal quality of the perovskite is improved by achieving the lowest microstrain and dislocation density. SEM results show that the perovskite material has relatively larger crystal grains, uniform surface coverage, and fewer pinholes.

Determining the Colliding Vehicle in Traffic Accidents Using Hybrid Machine Learning Models

In a world rife with vehicular accidents and traffic incidents, it is known that drivers are more likely than not to shift the blame in an accident rather than admit it. Other than that, there is a noticeable lack of models in the academic sector that allow neural networks to differentiate colliding vehicles from one another and are instead fixated on tracking and detecting traffic accidents as a whole. As such, the researchers propose a way of detecting colliding vehicles and classifying both vehicles as either the ‘colliding’ vehicle or the ‘collided’ vehicle. The processes in this machine learning pipeline are split into three main parts: crash detection—to which the model would use a crash detection algorithm; footage tracking—of which the model would utilise DeepSORT; and lastly a colliding vehicle classification algorithm that uses Gated Recurrent Units (GRUs), all of which will be combined to form a novel machine learning pipeline. The model exhibits very mixed performances when detecting both Vehicle 1 and Vehicle 2 in our testing phase. When detecting Vehicle 1, the model provides a very poor recall and F1-score, meanwhile the detection of Vehicle 2 exhibits a decent amount of precision, recall, and F1-score. Overall, the model provides an accuracy of 42% with a macro average precision of 0.45, a macro average recall of 0.29, and a macro F1-score of about 0.30.

Utilizing Caesium-based Vacancy-formed Materials For All-Perovskite Tandem Solar Cells: Photovoltaic Evaluation Using SCAPS 1-D

In this study, we reported the simulation study of lead-free all-perovskite tandem solar cell comprised of varied vacancy formation perovskite structure as wide bandgap absorber layer for FAMASnGeI3. The observations revealed that all-perovskite tandem solar cell of FTO/ZnO; 400 nm/Cs2AgBiBr6; 600 nm/FAMASnGeI3; 200 nm/Cu2O; 100 nm/Au achieved a notable PCE of 22.63 % at an operating temperature of 300 K with Jsc; 27.09 mA/cm2 Voc; 1.10 V and FF; 75.68 %. These findings suggests that this cell has strong potential in converting sunlight into electrical energy and indirectly will contribute to the advancement of environmentally friendly and high-performance solar cells, promoting the broader adoption of renewable energy technologies.

Impacts of Uncoordinated Electric Ferry Charging on Distribution Network

This study examines the potential effects of uncoordinated EF (electric ferry) charging on local distribution networks, focusing on Gladstone Marina in Queensland, Australia. Using OpenDSS software, power flow analysis assesses the simulated network with BESSs (Battery Energy Storage Systems) which represent proposed charging stations.

Conference Site: I-COSTE 2024