2022 IEEE Green Energy and Smart System Systems(IGESSC)最新文献

A Comparison of Numerical Techniques used for PV Module Model Parameter Extraction 光伏组件模型参数提取的数值技术比较

2022 IEEE Green Energy and Smart System Systems(IGESSC) Pub Date : 2022-11-07 DOI: 10.1109/IGESSC55810.2022.9955323

A. Leedy, Muhammad Abdelraziq, Kristen Booth

{"title":"A Comparison of Numerical Techniques used for PV Module Model Parameter Extraction","authors":"A. Leedy, Muhammad Abdelraziq, Kristen Booth","doi":"10.1109/IGESSC55810.2022.9955323","DOIUrl":"https://doi.org/10.1109/IGESSC55810.2022.9955323","url":null,"abstract":"In this paper, a step-by-step solution procedure used to estimate the single-diode model parameters is proposed. The procedure is a combination of least-squares, Newtonian, and quasi- Newtonian numerical methods. Current and voltage measurements are acquired from a conventional 36-cell photovoltaic (PV) module manufactured by AMERESCO Solar. The single-diode equation was then fit to the acquired data in the least-squares sense. The developed least-squares equation is solved by two different numerical methods, the Newton-Raphson (NR) method, and Broyden’s method. Since there are five different parameters to be determined, a system of five nonlinear equations was developed and solved. The main distinction between the NR and Broyden’s algorithms is the way they handle the Jacobian matrix. The NR algorithm requires the computation of a new Jacobian matrix at every iteration. Broyden’s algorithm only requires an initial Jacobian, and then the Jacobian is updated iteratively by means of a correction formula. The functionality of the two algorithms is compared, and the five parameters extracted from each algorithm are used to simulate a 36-cell PV module. The simulation results are compared to experimental data to provide validation and to determine how accurate each procedure was in estimating the model parameters.","PeriodicalId":166147,"journal":{"name":"2022 IEEE Green Energy and Smart System Systems(IGESSC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126329578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reducing the Number of Central Inverters of a Photovoltaic Plant Using Medium-Voltage Capacitor Banks 利用中压电容器组减少光伏电站中央逆变器的数量

2022 IEEE Green Energy and Smart System Systems(IGESSC) Pub Date : 2022-11-07 DOI: 10.1109/IGESSC55810.2022.9955335

Loai Al-Adim, Mehrdad Aliasgari, M. Mozumdar, Saleh Al Jufout

引用次数: 1

A Reinforcement Learning Approach to the Dynamic Job Scheduling Problem 动态作业调度问题的强化学习方法

2022 IEEE Green Energy and Smart System Systems(IGESSC) Pub Date : 2022-11-07 DOI: 10.1109/IGESSC55810.2022.9955328

Farshina Nazrul Shimim, Bradley M. Whitaker

引用次数: 0

Improvement of F-1 Score in Classifying Shark Data into Shark Behaviors F-1分在鲨鱼数据行为分类中的改进

2022 IEEE Green Energy and Smart System Systems(IGESSC) Pub Date : 2022-11-07 DOI: 10.1109/IGESSC55810.2022.9955331

Ibrahim M Ali, H. Yeh, Yu Yang

引用次数: 0

Real-time Vehicle Detection System for Intelligent Transportation using Machine Learning 基于机器学习的智能交通实时车辆检测系统

2022 IEEE Green Energy and Smart System Systems(IGESSC) Pub Date : 2022-11-07 DOI: 10.1109/IGESSC55810.2022.9955329

Ruihan Wu, Ziaur Chowdhury, Gustavo Velasquez Sanchez, Xin Gao, Cesar Villa, Xunfei Jiang

{"title":"Real-time Vehicle Detection System for Intelligent Transportation using Machine Learning","authors":"Ruihan Wu, Ziaur Chowdhury, Gustavo Velasquez Sanchez, Xin Gao, Cesar Villa, Xunfei Jiang","doi":"10.1109/IGESSC55810.2022.9955329","DOIUrl":"https://doi.org/10.1109/IGESSC55810.2022.9955329","url":null,"abstract":"Vehicle detection plays an important role in analyzing traffic flow data for efficient planning in intelligent transportation. Machine Learning technology has been increasingly used for vehicle detection in traffic flows. However, adverse weather conditions bring challenges for 2D vehicle detection. There is a lack of research on real-time vehicle detection using 3D LiDAR point clouds, which are more resistant to adverse weather conditions. In this paper, we proposed a system for collecting real-time traffic data using both 2D and 3D LiDAR cameras, processing the collected data for vehicle detection, and providing a web-based service with statistical traffic flow data visualization and 2D real-time vehicle detection stream display. We generated 1980 images from the 2D traffic flow videos that were collected in California Highway, and trained a 2D machine learning model on Darknet using YOLO algorithm. Approximately, 7000 frames of LiDAR point cloud data were labeled and pre-processed, and a new deep learning model for 3D vehicle detection was proposed. Compared with YOLO’s original pre-trained mode, our 2D machine learning model improved the vehicle detection that 6 different types of vehicles could be classified with an average precision of 89.25%.","PeriodicalId":166147,"journal":{"name":"2022 IEEE Green Energy and Smart System Systems(IGESSC)","volume":"921 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131993741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Challenges of Vehicle-Grid Integration as Modern Distributed Energy Implementation 车网一体化作为现代分布式能源实现的挑战

2022 IEEE Green Energy and Smart System Systems(IGESSC) Pub Date : 2022-11-07 DOI: 10.1109/IGESSC55810.2022.9955326

Ching-Yen Chung, Yingqi Xiong, E. Kim, Charlie Qiu, C. Chu, R. Gadh

引用次数: 1

Fundamental Studies of Signal Detection Based on Dynamic Power Management for Wireless Systems 基于动态电源管理的无线系统信号检测基础研究

2022 IEEE Green Energy and Smart System Systems(IGESSC) Pub Date : 2022-11-07 DOI: 10.1109/IGESSC55810.2022.9955324

Masato Yokoyama, S. Narieda, H. Naruse

引用次数: 0

Optimal sizing of microgrid DERs for specialized critical load resilience 面向特殊临界负荷弹性的微电网der优化尺寸

2022 IEEE Green Energy and Smart System Systems(IGESSC) Pub Date : 2022-11-07 DOI: 10.1109/IGESSC55810.2022.9955343

Shreya Agarwal, D. Black

{"title":"Optimal sizing of microgrid DERs for specialized critical load resilience","authors":"Shreya Agarwal, D. Black","doi":"10.1109/IGESSC55810.2022.9955343","DOIUrl":"https://doi.org/10.1109/IGESSC55810.2022.9955343","url":null,"abstract":"Distributed energy resources (DER) microgrids, especially photovoltaic (PV) and battery energy storage systems (BESS) are being more widely deployed behind the meter for decarbonizing the grid. This paper studies their impact on providing resilience to critical infrastructure, particularly which have a flat daily load profile such as hospitals and data centers. The study models load data from Lawrence Berkeley National Lab (LBNL) which has a flat critical load profile. The authors model a single and multi-day outage using the Distributed Energy Resources Customer Adoption Model (DERCAM) to optimize the configuration of DER based microgrids to support these outages. The authors expand these microgrid configurations to determine the microgrid DER sizes for other critical load levels which have a similar flat profile. The economic analysis presented here includes savings from cost of lost load due to outages, utility bill savings and carbon emission savings to compute a more complete accounting of costs and benefits. These results are then compared to the cost of resilience support traditionally provided by diesel generators. Finally, the net economic benefits are summarized suggesting that including resilience costs from lost load and other economic factors supports investments in DER microgrids for resilience support.","PeriodicalId":166147,"journal":{"name":"2022 IEEE Green Energy and Smart System Systems(IGESSC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121878013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classification of High Frequency NILM Transients Based on Convolutional Neural Networks 基于卷积神经网络的高频NILM瞬态分类

2022 IEEE Green Energy and Smart System Systems(IGESSC) Pub Date : 2022-11-07 DOI: 10.1109/IGESSC55810.2022.9955332

Ian Guzmán, Keith Goossen, K. Barner

{"title":"Classification of High Frequency NILM Transients Based on Convolutional Neural Networks","authors":"Ian Guzmán, Keith Goossen, K. Barner","doi":"10.1109/IGESSC55810.2022.9955332","DOIUrl":"https://doi.org/10.1109/IGESSC55810.2022.9955332","url":null,"abstract":"Smart electric meters require efficient signal processing algorithms for load identification and energy disaggregation. Non-intrusive load monitoring (NILM) systems are able to extract features from the fundamental power signal in order to collect information about the end use of electric loads. Switching transients induced by turning on or off a certain appliance can be used to identify which appliance is connected or disconnected at a given time in the electrical network. The dataset used in this work is the most recent version of the Plug-Load Appliance Identification Dataset (PLAID) which contains records of voltages and currents of different electrical appliances captured at a high sampling frequency (30 kHz). This paper presents a new approach for appliance classification with deep learning techniques by using a finite impulse response (FIR) high pass filter to remove the fundamental signal, then the short time Fourier transform (STFT) is computed for the feature extraction of high frequency start-up transients induced in the fundamental signal. The proposed convolutional neural network architecture yields a classification accuracy of 95.22% and 88.20% for twelve and sixteen different appliances, respectively.","PeriodicalId":166147,"journal":{"name":"2022 IEEE Green Energy and Smart System Systems(IGESSC)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130679777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal Size of Energy Storage Systems in Microgrids Under Rapid Growth of EV Charging Demands 电动汽车充电需求快速增长下微电网储能系统的最优尺寸

2022 IEEE Green Energy and Smart System Systems(IGESSC) Pub Date : 2022-11-07 DOI: 10.1109/IGESSC55810.2022.9955336

A. Jamehbozorg, Masood Shahverdi, Christopher Serrato, Nelson Flores

{"title":"Optimal Size of Energy Storage Systems in Microgrids Under Rapid Growth of EV Charging Demands","authors":"A. Jamehbozorg, Masood Shahverdi, Christopher Serrato, Nelson Flores","doi":"10.1109/IGESSC55810.2022.9955336","DOIUrl":"https://doi.org/10.1109/IGESSC55810.2022.9955336","url":null,"abstract":"Utilizing an energy storage system (ESS) is an effective solution for both solving the uncertainty problem of renewable energy sources and optimizing the cost of operation of the microgrid (MG). When planning for the sizing of an ESS in a longer span (e.g., a decade ahead), precise formulation of the optimization objective function relies on ESS degradation and O&M costs, and the predicted trends of variables like hourly electricity rates, load, and generation. In addition, the characteristics of the to-be-deployed control strategy significantly affect the optimal size. Thus, this paper proposes a modular solution to the sizing problem of ESS under the rapid growth of Electric vehicle charging demand while all the mentioned concerning factors are considered. The same to-be-deployed top layer of operation hierarchical control is used at the time of sizing and an innovative cost function is developed to model the complexity of the time of use plan. The results of the optimization determine the optimal size of the battery storages in each stage and the yearly savings in operation cost considering the battery cost.","PeriodicalId":166147,"journal":{"name":"2022 IEEE Green Energy and Smart System Systems(IGESSC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129286945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0