Improving Student Learning in Power Engineering

提高学生在电力工程方面的学习

• 批准号: 2021470
• 负责人: Paras Mandal
• 金额: $ 12万
• 依托单位: University of Texas at El Paso
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2021470&HistoricalAwards=false
• 关键词: Improving; Student; Learning; Power; Engineering

This project aims to serve the national interest by improving student learning in power engineering to include the new technologies being used in the power industry. Power engineering is a field of electrical engineering that focuses on the structures and processes needed to generate, transmit, and use electric power. Today’s power engineers require multi-disciplinary engineering knowledge to manage an increasingly complex power grid. For example, the power grid includes a growing amount of renewable energy resources, as well as more digital devices, information technology tools, and sensors. To address the need to align power engineering education with the power industry’s needs, the project team will develop and assess a set of active learning modules. These modules will be integrated into existing power engineering courses and focus on Smart Energy Management Systems. The modules will include hands-on laboratory experiences, case studies, and interactive simulations. The modules will help students learn how to model power systems, solve forecasting problems for power systems, and use data analytics to characterize power systems. The electric power industry is a critical part of the nation’s infrastructure and touches the lives of everyone. Improving students’ understanding of modern electric power systems will help ensure the integrity and performance of the nation’s electric power grid.The goal of this project is to improve power engineering education by (i) developing active learning experiences that incorporate real world problems in modern power systems, and (ii) integrating issues, solutions, and emerging trends in the area of Smart Energy Management Systems, specifically targeting power distribution systems, renewable energy sources, and intelligent energy forecasting. The design of the active learning modules will be guided by a situated learning framework, using case-based evaluations of simulated data from real-world power system problems to engage students in authentic forecasting and data analytics. This project will address the following research questions: 1) Do students exposed to situated learning develop a more comprehensive understanding of energy management, integrated power system analysis, and data analytics that is relevant for emerging challenges in power systems? and 2) Do they take greater account of context and community? To answer these questions, student learning will be measured using pre- and post-tests for the modules. Formative assessment using a situated learning survey and student interviews will be used to improve the modules. A workshop will be held to disseminate project results to the power engineering education community. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目旨在通过改善学生在电力工程方面的学习来服务于国家利益，包括电力行业中使用的新技术。 电力工程是电气工程的一个领域，专注于发电，传输和使用电力所需的结构和过程。 今天的电力工程师需要多学科的工程知识来管理日益复杂的电网。 例如，电网包括越来越多的可再生能源，以及更多的数字设备，信息技术工具和传感器。为了满足电力工程教育与电力行业需求的一致性，项目团队将开发和评估一套主动学习模块。 这些模块将被整合到现有的电力工程课程，并专注于智能能源管理系统。 这些模块将包括动手实验室经验，案例研究和交互式模拟。 这些模块将帮助学生学习如何建模电力系统，解决电力系统的预测问题，并使用数据分析来表征电力系统。电力行业是国家基础设施的重要组成部分，关系到每个人的生活。提高学生对现代电力系统的理解将有助于确保国家电网的完整性和性能。该项目的目标是通过以下方式改善电力工程教育：（i）开发主动学习体验，将现代电力系统中的真实的世界问题纳入其中，以及（ii）整合智能能源管理系统领域的问题、解决方案和新兴趋势专门针对配电系统、可再生能源和智能能源预测。主动学习模块的设计将以情境学习框架为指导，使用基于案例的模拟数据评估，从现实世界的电力系统问题，让学生参与真实的预测和数据分析。该项目将解决以下研究问题：1）暴露于情境学习的学生是否对能源管理，综合电力系统分析和数据分析有更全面的了解，这与电力系统中出现的挑战有关？2）他们是否更多地考虑了背景和社区？为了回答这些问题，学生的学习将使用模块的前测试和后测试进行测量。将使用情境学习调查和学生访谈进行形成性评估，以改进这些模块。将举办一个讲习班，向电力工程教育界传播项目成果。NSF IUSE：EHR计划支持研究和开发项目，以提高所有学生STEM教育的有效性。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Leveraging Distributed EVs and PVs to Assess Networked Microgrids Resilience Against Extreme Weather Event

• DOI: 10.1109/pesgm48719.2022.9917224
• 发表时间: 2022-07
• 期刊: 2022 IEEE Power & Energy Society General Meeting (PESGM)
• 作者: Orlando Quezada Simental;P. Mandal;Eric Galvan;Zongjie Wang

Unsupervised Hybrid Deep Generative Models for Photovoltaic Synthetic Data Generation

• DOI: 10.1109/pesgm46819.2021.9637844
• 发表时间: 2021-07
• 期刊: 2021 IEEE Power & Energy Society General Meeting (PESGM)
• 作者: Dan A. Rosa de Jesús;P. Mandal;T. Senjyu;S. Kamalasadan

Efficiency Contingency Factors for Commercial EVs Optimal Centralized Charging Stations

商用电动汽车的效率应急因素 最佳集中充电站

• DOI: 10.1109/pesgm52003.2023.10252586
• 发表时间: 2023
• 期刊: IEEE
• 作者: Avila, Antonio;Mandal, Paras
• 通讯作者: Mandal, Paras

Implementation of Battery EVs and BESS into RAPSim Software to Enrich Power Engineering Education in DER-Integrated Distribution Systems

在 RAPSim 软件中实施电池电动汽车和 BESS，以丰富 DER 集成配电系统中的电力工程教育

• DOI: 10.1109/naps52732.2021.9654476
• 发表时间: 2021
• 期刊: 2021 North American Power Symposium (NAPS
• 作者: Newbolt, Travis M.;Mandal, Paras;Wang, Hongjie
• 通讯作者: Wang, Hongjie

Assessing Student Perceptions of Emerging Concepts in Power & Energy Systems via Concept Maps: Rubric Development

评估学生对新兴权力概念的看法

• DOI: 10.1109/fie49875.2021.9637297
• 发表时间: 2021
• 期刊: 2021 IEEE Frontiers in Education Conference (FIE
• 作者: Cecchi, Valentina;Smith-Orr, Courtney;Atchison, Forest;Kamalasadan, Sukumar;Mandal, Paras;Lopez, Inez
• 通讯作者: Lopez, Inez