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An Efficient Method to Estimate Admittance of Black-boxed Inverter-based Resources for Varying Operating Points

CSEE Journal of Power and Energy systems 2024年第1期10卷 421-426页

作者： Weihua Zhou Bin Liu Nabil Mohammed Behrooz Bahrani Department of Electrical and Computer Systems Engineering Monash UniversityMelbourne 3800Australia Energy Centre Commonwealth Scientific and Industrial Research Organisation(CSIRO)Newcastle 2300Australia.He is now with the Network Planning DivisionTransgridSydney 2000Australia

Traditional analytical approaches for stability assessment of inverter-based resources(IBRs),often requiring detailed knowledge of IBR internals,become impractical due to IBRs’proprietary *** measurements,relying on electromagnetic transient simulation or laboratory settings,are not only time-intensive but also operationally inflexible,since various non-linear control loops make IBRs’admittance models operating-point ***,such admittance measurements must be performed repeatedly when operating point *** avoid time-consuming and cumbersome measurements,admittance estimation for arbitrary operating points is highly ***,existing admittance estimation algorithms usually face challenges in versatility,data demands,and *** this challenge,this letter presents a simple and efficient admittance estimation method for blackboxed IBRs,by utilizing a minimal set of seven operating points to solve a homogeneous linear equation *** studies demonstrate this proposed method ensures high accuracy across various types of *** accuracy is satisfying even when non-negligible measurement errors exist.

关键词： Admittance estimation black box inverterbased resource measurement error operating point

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A hybrid machine-learning model for solar irradiance forecasting

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Clean Energy 2024年第1期8卷 100-110页

作者： Ameera M.Almarzooqi Maher Maalouf Tarek H.M.El-Fouly Vasileios E.Katzourakis Mohamed S.El Moursi Constantinos V.Chrysikopoulos Department of Industrial and Systems Engineering Khalifa UniversityAbu DhabiPO Box 127788UAE Electrical Engineering and Computer Science Department Khalifa University Abu DhabiUAE Department of Civil Infrastructure and Environmental Engineering Khalifa UniversityAbu DhabiPO Box 127788UAE School of Chemical and Environmental Engineering Technical University of CreteChaniaGreece

Nowcasting and forecasting solar irradiance are vital for the optimal prediction of grid-connected solar photovoltaic(PV)power *** plants face operational challenges and scheduling dispatch difficulties due to the fluctuating nature of their power *** the generation capacity within the electric grid increases,accurately predicting this output becomes increasingly essential,especially given the random and non-linear characteristics of solar irradiance under variable weather *** study presents a novel prediction method for solar irradiance,which is directly in correlation with PV power output,targeting both short-term and medium-term forecast *** proposed hybrid framework employs a fast trainable statistical learning technique based on the truncated-regularized kernel ridge regression *** proposed method excels in forecasting solar irradiance,especially during highly intermittent weather periods.A key strength of our model is the incorporation of multiple historical weather parameters as inputs to generate accurate predictions of future solar irradiance values in its scalable *** evaluated the performance of our model using data sets from both cloudy and sunny days in Seattle and Medford,USA and compared it against three forecasting models:persistence,modified 24-hour persistence and least *** on three widely accepted statistical performance metrics(root mean squared error,mean absolute error and coefficient of determination),our hybrid model demonstrated superior predictive accuracy in varying weather conditions and forecast horizons.

关键词： solar power generation kernel ridge regression hybrid model forecasting

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The Lynchpin of In-Memory Computing: A Benchmarking Framework for Vector-Matrix Multiplication in RRAMs

The Lynchpin of In-Memory Computing: A Benchmarking Framewor...

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2024 International Conference on Neuromorphic systems, ICONS 2024

作者： Rahman Chowdhury, Md Tawsif Nguyen Vo, Huynh Quang Ramanan, Paritosh Yildirim, Murat Tutuncuoglu, Gozde Wayne State University Electrical and Computer Engineering Detroit United States Oklahoma State University Industrial Engineering and Management Stillwater United States Wayne State University Industrial and Systems Engineering Detroit United States

ISBN: (纸本)9798350368659

The Von Neumann bottleneck, a fundamental challenge in conventional computer architecture, arises from the inability to execute fetch and data operations simultaneously due to a shared bus linking processing and memory units. This bottleneck significantly limits system performance, increases energy consumption, and exacerbates computational complexity. Emerging technologies such as Resistive Random Access Memories (RRAMs), leveraging crossbar arrays, offer promising alternatives for addressing the demands of data-intensive computational tasks through in-memory computing of analog vector-matrix multiplication (VMM) operations. However, the propagation of errors due to device and circuit-level imperfections remains a significant challenge. In this study, we introduce MELISO (In-Memory Linear Solver), a comprehensive end-to-end VMM benchmarking framework tailored for RRAM-based systems. MELISO evaluates the error propagation in VMM operations, analyzing the impact of RRAM device metrics on error magnitude and distribution. This paper introduces the MELISO framework and demonstrates its utility in characterizing and mitigating VMM error propagation using state-of-the-art RRAM device metrics. © 2024 IEEE.

关键词： RRAM

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Wheat Field Fire Smoke Detection from UAV Images using CNN-CBAM 2

Wheat Field Fire Smoke Detection from UAV Images using CNN-C...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Alsalem, Ahmed Zohdy, Mohamed Oakland University Rochester Department of Industrial and Systems Engineering MI United States Oakland University Rochester Department of Electrical and Computer Engineering MI United States

ISBN: (纸本)9798331529529

Wheat field fires pose a significant threat to wildlife habitats, agricultural resources, and especially farmer incomes. The frequency of these fires has increased noticeably in recent years, likely due to human activity in agricultural areas and the exacerbating effects of climate change. Rapid action is crucial once smoke is detected, as it marks the first step in early fire detection. To address this challenge, we propose an enhanced CNN model specifically designed to identify smoke in wheat field fires. As part of this approach, a dataset of 3900 UAV images was compiled to depict smoke from wheat field fires. The proposed work also integrates the CBAM into the CNN to improve the model's ability to extract features for smoke patch characteristics of wheat field fires. Furthermore, the CNN architecture includes decoupled heads to extract relevant data from various sources. Testing on our wheat field fire smoke dataset has demonstrated the effectiveness of this proposed method, achieving an accuracy of up to 0.91 and outperforming the previous model. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A General Framework for Human-Drone Interaction under Limited On-board Sensing

A General Framework for Human-Drone Interaction under Limite...

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2023 systems and Information engineering Design Symposium, SIEDS 2023

作者： Nayhouse, S. Chadha, S. Hourican, P. Moore, C. Bezzo, N. University of Virginia Departments of Systems and Information Engineering and Electrical and Computer Engineering CharlottesvilleVA22903 United States University of Virginia Department of Electrical and Computer Engineering CharlottesvilleVA22903 United States

ISBN: (纸本)9798350300642

Recent advancements in unmanned aerial vehicles (UAVs), has allowed their deployment for numerous applications like aerial photography, infrastructure inspection, search and rescue, and surveillance. Despite the potential for full autonomy, many applications still necessitate human operators for navigating complex environments and decision-making. Existing solutions often employ high-precision and simple sensors like 2-D or 3-D LiDAR, which may provide more data than necessary and contribute to increased system complexity and cost. To address these challenges and bridge the gap between full autonomy and human-controlled UAVs, this work develops a shared-autonomy framework for UAVs, leveraging lightweight, low-cost 1-D LiDAR sensors combined with mobility behaviors to obtain performance comparable to more advanced 2-D/3-D LiDAR sensors while minimizing energy, computation overhead, and weight. Our framework includes a novel state machine method that exploits the UAV mobility to compensate for the limitations of 1-D LiDAR sensors, ensuring safety and obstacle avoidance through a physics-based algorithm that transitions between teleoperation and autonomous mode as needed based on environmental conditions and safety-critical issues. Experimental validations on real UAVs demonstrates the effectiveness of this shared autonomy scheme in complex environments, and the system is further generalized to larger UAVs and prototyped with a custom sensor configuration and onboard obstacle avoidance. © 2023 IEEE.

关键词： Drones

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EV Adoption Impacts on Residential, Commercial and Urban-Commercial Feeders

EV Adoption Impacts on Residential, Commercial and Urban-Com...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Ravi, Abhijith Bai, Linquan Wollam, Grant Enslin, Johan Unc Charlotte Industrial and Systems Engineering Charlotte United States Clemson University Electrical and Computer Engineering Clemson United States

ISBN: (纸本)9798350316445

This paper analyzes the impacts of electric vehicle (EV) adoption on real distribution networks and compares mitigation options at the distribution level. The impact of EV adoption on residential, commercial, and urban-commercial feeders was analyzed. EV load growth rates and feeder load profiles were extracted as inputs to scale up the effects of adoption. Various mitigation options, such as conventional system upgrades, battery energy storage systems, and time of use for all feeders, were compared. The results show that managed charging is likely to be adopted by commercial fleets, as they can be controlled by charging management systems to reduce peak demand. Commercial fleets can play a vital role in peak load shifting and overload mitigation if appropriately incentivized. © 2023 IEEE.

关键词： distribution grid electric vehicles fleets grid vulnerabilities mitigation time of use

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Assessing the Performance of AI-Generated Code: A Case Study on GitHub Copilot 35

Assessing the Performance of AI-Generated Code: A Case Study...

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35th IEEE International Symposium on Software Reliability engineering, ISSRE 2024

作者： Li, Shuang Cheng, Yuntao Chen, Jinfu Xuan, Jifeng He, Sen Shang, Weiyi Wuhan University School of Computer Science Wuhan China University of Arizona Department of Systems and Industrial Engineering Tucson United States University of Waterloo Department of Electrical and Computer Engineering Waterloo Canada

ISBN: (纸本)9798350353884

The integration of Large Language Models (LLMs) into software development tools like GitHub Copilot holds the promise of transforming code generation processes. While AI-driven code generation presents numerous advantages for software development, code generated by large language models may introduce challenges related to security, privacy, and copyright issues. However, the performance implications of AI-generated code remain insufficiently explored. This study conducts an empirical analysis focusing on the performance regressions of code generated by GitHub Copilot across three distinct datasets: HumanEval, AixBench, and MBPP. We adopt a comprehensive methodology encompassing static and dynamic performance analyses to assess the effectiveness of the generated code. Our findings reveal that although the generated code is functionally correct, it frequently exhibits performance regressions compared to code solutions crafted by humans. We further investigate the code-level root causes responsible for these performance regressions. We identify four major root causes, i.e., inefficient function calls, inefficient looping, inefficient algorithm, and inefficient use of language features. We further identify a total of ten sub-categories of root causes attributed to the performance regressions of generated code. Additionally, we explore prompt engineering as a potential strategy for optimizing performance. The outcomes suggest that meticulous prompt designs can enhance the performance of AI-generated code. This research offers valuable insights contributing to a more comprehensive understanding of AI-assisted code generation. © 2024 IEEE.

关键词： Software design

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Public Healthcare Informatics for COVID-19 from Social Media Data 46

Public Healthcare Informatics for COVID-19 from Social Media...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Kumar, Vishwa V. Sahoo, Avimanyu Kumar, Ritika Loyd, Nicholas Industrial & Systems Engineering and Engineering Management United States The University of Alabama in Huntsville Department of Electrical and Computer Engineering United States North Alabama Medical Center FlorenceAL United States

ISBN: (纸本)9798350371499

The COVID-19 pandemic has profoundly affected various facets of society. This research explores the discourse on social media, specifically "X"(Twitter), to understand public sentiment and needs regarding healthcare supply chain challenges, such as the availability of testing kits, oxygen supplies, and hospital beds, during the pandemic. By identifying pertinent opinionated keyphrases and employing advanced natural language processing (NLP) techniques, we sifted through over 3.9 million tweets to collect health informatics. The content of these tweets is analyzed to investigate the word trends and sentiments. The extracted sentiments were then organized into ten distinct categories using the K-means clustering method that pointed out top-ten healthcare supply needs. The findings underscore the value of social media as a tool for gathering timely and actionable insights on healthcare supply chain logistics, offering critical data for effective response strategies during health crises. © 2024 IEEE.

关键词： COVID-19

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Just wing it: near-optimal estimation of missing mass in a Markovian sequence

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 15100-15142页

作者： Ashwin Pananjady Vidya Muthukumar Andrew Thangaraj Schools of industrial and systems engineering and electrical and computer engineering georgia institute of technology atlanta Schools of Electrical and Computer Engineering and Industrial and Systems Engineering Georgia Institute of Technology Atlanta Department of Electrical Engineering Indian Institute of Technology Madras Chennai India

We study the problem of estimating the stationary mass--also called the unigram mass-- that is missing from a single trajectory of a discrete-time, ergodic Markov chain. This problem has several applications--for example, estimating the stationary missing mass is critical for accurately smoothing probability estimates in sequence models. While the classical Good-Turing estimator from the 1950s has appealing properties for i.i.d. data, it is known to be biased in the Markovian setting, and other heuristic estimators do not come equipped with guarantees. Operating in the general setting in which the size of the state space may be much larger than the length n of the trajectory, we develop a linear-runtime estimator called Windowed Good-Turing (WINGIT) and show that its risk decays as Õ(Tmix/n), where Tmix denotes the mixing time of the chain in total variation distance. Notably, this rate is independent of the size of the state space and minimax-optimal up to a logarithmic factor in n/Tmix. We also present an upper bound on the variance of the missing mass random variable, which may be of independent interest. We extend our estimator to approximate the stationary mass placed on elements occurring with small frequency in the trajectory. Finally, we demonstrate the efficacy of our estimators both in simulations on canonical chains and on sequences constructed from natural language text.

关键词： missing mass good-turing Markov chains minimax optimal

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Intent-Guided Trajectory Prediction for E-Scooter Riders and Bicyclists 27

Intent-Guided Trajectory Prediction for E-Scooter Riders and...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Zhang, Zhengming Ding, Zhengming Chen, Yaobin Chien, Stanley Li, Lingxi Sheroy, Rini Domeyer, Joshua Tian, Renran School of Industrial Engineering Purdue University United States Tulane University Department of Computer Science United States School of Electrical and Computer Engineering Purdue University United States United States North Carolina State University Department of Industrial and Systems Engineering United States

ISBN: (纸本)9798331505929

The rapidly increasing popularity of micromobility devices, such as e-scooters and bicycles, in urban environments underscores the critical need for advanced trajectory prediction models to enhance road safety and facilitate seamless integration into intelligent transportation systems. This study introduces a novel approach to predicting the movements of bicyclists and e-scooter riders by leveraging a unique dataset that combines bird's-eye and egocentric views, collected from diverse urban settings across the United States. Employing state-of-the-art deep learning techniques, our model significantly outperforms traditional linear and polynomial regression models in terms of Average Displacement Error (ADE) and Final Displacement Error (FDE), demonstrating superior accuracy in forecasting the trajectories of vulnerable road users. Furthermore, our approach incorporates cognitive annotations to predict crossing intentions, enriching the model's predictive capabilities. The findings highlight the potential of our method to contribute to the development of proactive safety measures and collision avoidance systems, ultimately fostering safer urban mobility landscapes for all road users. © 2024 IEEE.

关键词： Prediction models

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