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IEEE International Conference on Industrial technology (ICIT)

作者： Kushan Tharuka Lulbadda Ruvini De Seram Tarlochan Sidhu Sheldon S. Williamson Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

Solid State Transformer (SST) enabled DC micro-grids are a modern solution for replacing the conventional Low-Frequency Transformer (LFT) by enhancing the system power density. Further, using SSTs in power systems has several additional advantages which are impossible with LFTs. As the SST is made out of sensitive power electronic components, safety must be guaranteed successfully to maintain a reliable operation. Also, the existing system will operate differently fundamentally with the integration of SST and the Low Voltage (LV) DC network will experience new LV fault profiles. SST-enabled power systems can be vulnerable to several kinds of faults such as overcurrent, overvoltage, and switching failures which will be discussed in this research. Therefore, it is more important to conduct a fault analysis based on different types of faults in the SST-enabled DC microgrid. The manuscript is mainly focused on three areas faults in the Medium Voltage (MV) side of the SST, faults within the SST, and faults in the Low Voltage (LV) side of the SST. The main objective of this research is to study the behavior of the SST in faulty conditions mentioned above. Different types of faults in a DC microgrid based on SST are simulated and the results are discussed and analyzed in this paper. Further, it has been found that the SST has a moderated capability for fault-tolerant operation during the faults.

关键词： Fault tolerance Low voltage Circuits Fault tolerant systems Microgrids Switches Transformers

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An In-depth Comparative Analysis and Efficiency Evaluation of Dual- and Quad-active Bridge for Solid State Transformer Applications

An In-depth Comparative Analysis and Efficiency Evaluation o...

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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Ruvini De Seram Kushan Tharuka Lulbadda Tarlochan Sidhu Sheldon S. Williamson Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

ISBN: (数字)9798350317664

ISBN: (纸本)9798350317671

Researchers have recently focused on increasing the power density of the SST by replacing the Modular Dual Active Bridge (DAB) with a Quadruple-Active-Bridge (QAB) which utilizes less number of High Frequency (HF) transformers and switches. However, to replace the DAB with QAB, it is necessary to examine the impact of efficiency based on switching frequency and transformer turns ratio. The main objective of this paper is to analyze the behavior of both DAB converter and QAB converter-based systems on the above factors to illustrate the effect of replacing the DAB in the SST. This paper contains a comparative analysis of the efficiencies of modular DAB and QAB using results obtained by PLECS simulation models of the two systems. Finally, scaled down hardware prototypes of the two systems are implemented to validate the simulation outcomes resulting higher efficiency in QAB-based system compared to the DAB-based system for particular switching frequency and transformer ratio.

关键词： Power system measurements Switching frequency Simulation Prototypes Bridge circuits Transportation Voltage

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An Analysis of Single- and Dual-Phase Shift Modulation techniques for Closed-loop Control of Dual-Active Bridge Converter

An Analysis of Single- and Dual-Phase Shift Modulation Techn...

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Annual Conference of Industrial Electronics Society

作者： Ruvini De Seram Kushan Tharuka Lulbadda Oruganti V.S.R. Varaprasad Sheldon S. Williamson Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

The Dual-Active Bridge (DAB) converter’s versatility and ability to support bidirectional power flow make it a highly suitable option for modern power systems, especially in electric vehicle (EV) charging infrastructure. This paper explores phase shift control strategies for regulating power flow in DAB converters, with a particular emphasis on Single-Phase Shift (SPS) and Dual-Phase Shift (DPS) modulation techniques. The theoretical principles of power transmission within a DAB converter are detailed, and closed-loop control under both SPS and DPS modulation is demonstrated with simulation results using PLECS software. To validate these simulations, the closed-loop controllers were implemented in a 1kVA, 200V/200V DAB converter prototype, where the average efficiencies for SPS and DPS modulation were recorded at 96.69% and 97.05%, respectively. The paper concludes with a comparative analysis of the two modulation techniques.

关键词： Industrial electronics Phase modulation Simulation Modulation Prototypes Power transmission Zero voltage switching Switched reluctance motors software Load flow

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Temporal Sensitivity Analysis for Enhanced Dynamic Equivalent Circuit Modeling of Lithium-ion Batteries in On-board Battery Management Systems

Temporal Sensitivity Analysis for Enhanced Dynamic Equivalen...

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Annual Conference of Industrial Electronics Society

作者： Alvin Huynh Akash Samanta Sheldon Williamson Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

State-of-the-art lithium-ion battery state estimation techniques, including state-of-charge, state-of-health, and remaining useful life in battery management systems (BMS), are extensively dependent on equivalent electric circuit model (ECM) parameters. ECM parameters are subject to change due to battery aging, changes in operating temperature, and other operating conditions. Therefore, updating the model parameters in real-time by on-board BMS is essential to maintain the accuracy of state estimation where ECM is used as the primary building block. ECM parameters obtained in laboratory conditions cannot guarantee accuracy in onboard BMS throughout the entire life cycle of an electric vehicle battery. Currently, onboard state estimation techniques use static ECM parameters due to complexity and computational time in parameterization and online computation, especially with higher-order ECMs. Therefore, this paper presents a comparative analysis of 2-RC, 3-RC, and 4-RC ECMs concerning parameterization time, computational cost, and accuracy, providing an understanding of the possible integration of dynamic ECM models through regular parameterization in onboard BMS. The sensitivity analysis depicts that the 3-RC model would be most appropriate for considerably high accuracy for a BMS with moderate computational power, ensuring safe and reliable state estimation throughout the lifetime of the battery. Moreover, with the changes in ambient conditions, as the sensitivity of R 2 and C 2 are much lower compared to other parameters thus, these do not need update with the changes in operating temperature.

关键词： Temperature sensors Lithium-ion batteries Analytical models Accuracy Sensitivity analysis Fitting Data models Integrated circuit modeling State estimation Vehicle dynamics

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Enhancing Text Classification through LLM-Driven Active Learning and Human Annotation

arXiv

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arXiv 2024年

作者： Rouzegar, Hamidreza Makrehchi, Masoud Department of Electrical Computer and Software Engineering Ontario Tech University OshawaON Canada

In the context of text classification, the financial burden of annotation exercises for creating training data is a critical issue. Active learning techniques, particularly those rooted in uncertainty sampling, offer a cost-effective solution by pinpointing the most instructive samples for manual annotation. Similarly, Large Language Models (LLMs) such as GPT-3.5 provide an alternative for automated annotation but come with concerns regarding their reliability. This study introduces a novel methodology that integrates human annotators and LLMs within an Active Learning framework. We conducted evaluations on three public datasets. IMDB for sentiment analysis, a Fake News dataset for authenticity discernment, and a Movie Genres dataset for multi-label classification. The proposed framework integrates human annotation with the output of LLMs, depending on the model uncertainty levels. This strategy achieves an optimal balance between cost efficiency and classification performance. The empirical results show a substantial decrease in the costs associated with data annotation while either maintaining or improving model *** Codes 68T50 © 2024, CC BY.

关键词： Sentiment analysis

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Crucial Examination of Thermal Behavior of Solid-State Battery for Intelligent Gray Box Model-based Automotive Battery Management Systems

Crucial Examination of Thermal Behavior of Solid-State Batte...

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Annual Conference of Industrial Electronics Society

作者： Akash Samanta Chandan Chetri Sheldon Williamson Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Solid-state batteries (SSBs) represent one of the most promising technologies for next-generation energy storage systems, offering potential advantages in safety, energy density, and longevity compared to traditional lithium-ion batteries. However, comprehending and managing the thermal behavior of these batteries across a wide range of operating conditions is essential for ensuring their safe and reliable operation. Furthermore, gray box modeling-based state estimation and control are extremely crucial due to the higher degree of nonlinearity exhibited under dynamic operating conditions of SSBs. Gray box modeling is a fusion of equivalent circuit models and data-driven techniques, requiring battery test data and information on charging/discharging and thermal characteristics of SSBs. Therefore, this paper presents a comprehensive analysis of the thermal behavior of SSBs through laboratory experiments conducted in a controlled environment. Additionally, it investigates the thermal characteristics of SSBs under various charging and discharging conditions to assess their suitability for e-mobility applications. Moreover, it examines the thermal behavior and stability of SSBs and introduces a concept of a gray box modeling-based temperature detection scheme for an effective thermal management system. The insights gained from this study can inform the development of advanced thermal management strategies and contribute to the design of safer and more efficient solid-state battery technologies for e-mobility applications.

关键词： Predictive models Amplitude modulation Thermal management Electronic packaging thermal management Data models Stability analysis Batteries Solid state batteries Integrated circuit modeling Thermal stability

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Evaluating the Efficacy of Large Language Models in Automating Academic Peer Reviews

Evaluating the Efficacy of Large Language Models in Automati...

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International Conference on Machine Learning and Applications (ICMLA)

作者： WeiminZhao Qusay H. Mahmoud Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

This paper explores the application of large language models (LLMs) in automating the peer review process for academic papers, a critical area for enhancing the efficiency and consistency of scholarly publication. We utilized GPT-4-0125 to automatically generate reviews for 20 papers sourced from *** and analyzed the AI-generated peer reviews for quality and effectiveness. The analysis includes a detailed assessment of the text properties of the reviews, such as sentiment, revealing that LLM-generated reviews tend to be more uniformly positive than their human-written counterparts. In addition, we conducted a user survey in which participants attempted to distinguish between AI-generated and human-written reviews. The survey results indicated a low correct identification rate, suggesting that participants often could not discern the origin of the review, thereby highlighting the potential of LLMs to mimic human-like review qualities. However, the study also identifies limitations in LLM's performance, particularly concerning the variability in review quality, which appears to correlate with the model's vocabulary usage of the generated content.

关键词： Surveys Vocabulary Reviews Large language models Machine learning Transformers

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A Comprehensive Analysis for Efficient Single Input Multiple Output Inductive Wireless Power Transfer Systems

A Comprehensive Analysis for Efficient Single Input Multiple...

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Annual Conference of Industrial Electronics Society

作者： Ummemisbah Bhisti Sheldon Williamson Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

The area of wireless power transfer (WPT) technology is developing rapidly and has a wide range of possible uses. In-depth reviews, comparisons, and analysis of power converters and compensation topologies designed especially for Single Input Multiple Output (SIMO) WPT systems are provided in this study. A particular focus is on evaluating compensation topologies in terms of their efficiency and output load independence. To achieve both independence and higher efficiency in SIMO WPT systems, a hybrid compensation strategy with different converters topology is suggested based on a thorough examination and analysis of the literature. This review article opens opportunities for future developments and applications in this emerging sector by offering insightful information about the state of SIMO WPT systems today.

关键词： Industrial electronics Fluctuations Reviews Wireless power transfer Robustness Hybrid power systems Topology Power conversion SIMO Faces

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Advanced Battery Aging Prediction through Impedance Spectroscopy Including Thermal Behavior and Degradation Analysis

Advanced Battery Aging Prediction through Impedance Spectros...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Latha Anekal Sheldon Williamson Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

Understanding the thermal behaviour of lithiumion batteries (LIBs) with aging and degradation is crucial for developing an effective thermal management system and state-of-temperature estimation technique ensuring higher levels of accuracy over the entire battery cycle. The aging and degradation of LIBs are greatly affected by operating temperatures and battery degradation in turn increases the battery reversible and irreversible heat generation. This study deduces the relationship between aging parameters and temperature rise through cycling under normal operating temperatures using the electrochemical impedance spectroscopy (EIS) method. The critical analysis of the temperature and EIS data shows that the equilibrium temperature has increased by 6°C with cycling due to the associated impedance rise.

关键词： Degradation Temperature distribution Estimation Aging Thermal management Batteries Thermal analysis Steady-state Impedance Thermal degradation

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A Prediction Model for Short Price Jump in Cryptocurrency Market

A Prediction Model for Short Price Jump in Cryptocurrency Ma...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Mohammadjavad Rajaei Qusay H. Mahmoud Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

In recent years, the global cryptocurrency market has attracted a diverse range of traders, from seasoned professionals to newcomers, resulting in a highly volatile environment. This volatility presents numerous opportunities for traders to capitalize on rapid price fluctuations. In this context, we introduce a Random Forest model designed to predict whether a coin will experience growth in the next trading candle, using several input features. We used Binance historical daily data from 1 Jan 2018 to 31 Dec 2021 to train our models and evaluated them using different time spans (varied between Jan 2022 to Oct 2023) as testing datasets. Moreover, we also used an over sampled training dataset to enhance the training process. Demonstrating notable precision, especially with a growth rate of 1%, the model has proven effective across various scenarios, consistently yielding profits. To be more specific, regarding the testing datasets of 1 to 31 Oct 2023, 1 Jul 2023 to 30 Sep 2023, and LSK/USDT from 1 Jan 2022 to 31 OCT 2023, using a growth rate of 1%, we achieved 18%, 30%, and 68% profits, respectively. This study underscores the potential for leveraging well-designed machine learning models to achieve significant profits, even in bearish market conditions.

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