Full Research Paper – Page 9 – NPS Australia Submission System

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.

Analysis of Power Flow Control in Electrical Networks Considering Photovoltaic, Battery Energy Storage Systems and Electric Vehicles

Microgrids are localised power system that use local power generation to supply electricity to nearby loads. This idea has gained popularity with the development of battery energy storage system (BESS) and the emergence of renewable energy sources (RES). The integration of electric vehicles (EVs) into the grid has made it possible to integrate batteries and RES without significantly altering the system. The operation of microgrids is has discussed in this study with a special emphasis on power flow control during system disturbances and transitions. This article describes an algorithm designed to optimise the regulation of power, voltage, and frequency in a microgrid that includes EVs. The results of this study show that load control and energy distribution using simulation studies may be done effectively. This study used suggested algorithm to show stable frequency and controllable voltage dips. Additionally, this study contributes to a better knowledge of microgrid control.

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.

A conceptual model for cloud ERP adoption in SMEs in New Zealand: A case study of a retail company

The insights of this study can assist SME decision makers in adopting cloud ERP in their organisations. Further, the integration of TOE and UTAUT frameworks in a theoretical model represents an innovative approach, making a significant contribution to the existing body of knowledge.

Eve-Teasing Detection from Video Footage using Computer Vision and Artificial Intelligence

Eve teasing, a form of public harassment and assault
on women, is a significant issue that causes severe distress,
particularly among young women and girls. The rising incidence
of eve teasing in Bangladesh has led to severe crimes such as
rape and murder, with many offenders escaping due to a lack of
evidence and effective monitoring. This paper presents a novel
approach using computer vision and machine learning methods
to detect eve teasing from video material in various situations.
Our proposed solution combines gender detection, expression
analysis, and gesture recognition to identify behaviors indicative
of eve teasing. The system integrates male-female identification,
human behavior detection, and CCTV-based monitoring or video
footage analysis to identify such critical incidents. Additionally,
the approach includes determining the participants involved in
the scenario to provide comprehensive evidence of harassment.
By enabling more accurate detection and verification of eve
teasing in real time, our method offers a promising tool to
help victims prove harassment and support law enforcement in
apprehending offenders, thereby contributing to a safer public
environment.

Securing Electric Vehicle Charging Infrastructure: Attack Identification Using Machine Learning

1. Conducted multi-class classification tasks to identify distinct attack types using various machine learning algorithms.
2. Evaluated the use of Hardware Performance Counters (HPC) and kernel events as features, both individually and in combination, to compare and analyze the performance of these algorithms.
3. Extensive experiments using ten-fold cross-validation demonstrated that Random Forest based machine learning model achieved the highest overall accuracy of 93.4%.
4. Attack classes comprising more than 20% of the samples attain nearly 100% accuracy, while classes with less than 3% samples tend to underperform.

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.

Blockchain and AI-Assisted Secure Data-Exchange Framework in Smart Systems

Heat transfer enhnacment in a circular tube fitted with new twisted tape insert with rings

This study investigates the impact of using new twisted tape (TT) inserts with rings on the Nusselt number (Nu) and friction factor (f) for heat exchangers, air intercoolers and for other thermofluidics applications. The analysis compares the effects of air and water as cooling fluids. Experimental setup is used to obtain experimental data needed for a Computational Fluid Dynamics (CFD) validation. Then the CFD model is used to predict the impact of using the new TT insert with rings compared with the conventional TT without rings under various conditions which can’t be attained by the experimental facilities at the lab scale. The obtained results showed that the new proposed TT insert with rings significantly enhances heat transfer efficiency while maintaining acceptable pressure drops. The findings suggest incorporating TT with rings can optimize heat transfer performance in various heating and cooling applications. The experimental results showed that adding the rings to the TT enhances the heat transfer characteristics compared with the TT without rings, especially at larger TT pitch distances. Further, the experimental results showed that the Nu values in the case of using the TT inserts with rings significantly increased by up to 25% more than smooth pipes at low air velocities with larger pitch distance and by about 43% at higher velocities with smaller pitch distance. However, this increase in heat transfer also led to a rise in the friction factor, which went up to four times higher at low velocities and larger pitch distances and up to seven times higher at greater velocities and smaller pitch distances.

Paper Type: Full Research Paper