IRES Track I: Sensors and Machine Learning for Solar Power Monitoring and Control

IRES Track I：用于太阳能监测和控制的传感器和机器学习

• 批准号: 1854273
• 负责人: Andreas Spanias
• 金额: $ 30万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1854273&HistoricalAwards=false
• 关键词: IRES; Track; Sensors; Machine; Learning

This program promotes international multidisciplinary research opportunities for U.S. students at the overlap of sustainability, power systems and signal processing with the aim of improving efficiency in PV power generation. Algorithms for shading prediction and fault optimization will advance the state of the art in remote solar array management. Training students in machine learning, vision and data processing for energy systems is unique and requires an integrative approach. IRES participants will be immersed in producing and understanding solar analytics and creating algorithms and software to control solar arrays. The IRES program will engage faculty researchers from the Arizona State University SenSIP center and from the University of Cyprus KIOS Center in solar energy research. Programs and workshops will be established so that IRES participants are trained in machine learning for energy systems and present their research results in international settings. Weekly presentations at the international site and guidance by international mentors will enrich the cohort research experience. Embedding students in the KIOS center research labs funded by large European Union (EU) grants will provide knowledge on EU and international research practices, energy standards and policies. Students will spend six summer weeks at the University of Cyprus KIOS center to improve their research skills and elevate their cultural competencies. This international research endeavor will energize students to innovate and disseminate results globally. Solar energy or photovoltaic (PV) arrays encounter loss of efficiency under conditions of shading, panel faults and temperature variations. In fact, shading, weather patterns, soiling, and temperature reduce power output considerably. For example, a malfunction of one panel will cause an entire PV string to fail. To minimize inefficiencies, individual panel current-voltage (I-V) measurements, weather information, and imaging data are essential. Controlling the power output is possible through solar panel matrix switching and optimization (i.e., changing certain array connections from series to parallel using actuators). Matrix switching using programmable relays allows for different interconnection options. The research goal is to optimize PV array systems by: a) exploiting the measured I-V patterns to detect faults using machine learning, b) employing advanced imaging and vision techniques to predict shading, c) using temperature, irradiance and weather data to elevate PV efficiency, and d) include smart grid interfaces. This collaborative IRES project between Arizona State University and University of Cyprus will engage students in the following research problems: a) How do we use imaging to detect cloud movement, predict shading and elevate efficiency? b) How can the array connections be reconfigured based on imaging, weather, and I-V data to elevate efficiency? c) How can we detect and classify panel faults in real time using machine learning and other algorithms? d) How do we extend these solar monitoring and control concepts from utility-scale solar farms to house rooftop systems?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.

该计划为美国学生在可持续性，电力系统和信号处理的重叠方面提供国际多学科研究机会，旨在提高光伏发电效率。阴影预测和故障优化算法将推动远程太阳能电池阵列管理的发展。 对学生进行能源系统的机器学习、视觉和数据处理培训是独一无二的，需要采用综合方法。 IRES参与者将沉浸在生产和理解太阳能分析和创建算法和软件来控制太阳能电池阵列。IRES项目将邀请亚利桑那州立大学SenSIP中心和塞浦路斯大学KIOS中心的研究人员参与太阳能研究。将建立项目和研讨会，以便IRES参与者接受能源系统机器学习的培训，并在国际环境中展示他们的研究成果。每周在国际网站上的演讲和国际导师的指导将丰富队列研究经验。将学生嵌入由欧盟（EU）赠款的KIOS中心研究实验室将提供有关欧盟和国际研究实践，能源标准和政策的知识。学生将在塞浦路斯大学KIOS中心度过六个夏季周，以提高他们的研究技能并提升他们的文化能力。 这一国际研究奋进将激励学生在全球范围内创新和传播成果。 太阳能或光伏（PV）阵列在遮蔽、面板故障和温度变化的条件下会遇到效率损失。 事实上，阴影，天气模式，污染和温度大大降低了功率输出。例如，一个面板的故障将导致整个PV串失效。 为了最大限度地降低低效率，单个面板的电流-电压（I-V）测量、天气信息和成像数据至关重要。通过太阳能电池板矩阵切换和优化（即，使用致动器将某些阵列连接从串联改变为并联）。使用可编程继电器的矩阵开关允许不同的互连选项。研究目标是通过以下方式优化光伏阵列系统：a）利用测量的I-V模式使用机器学习来检测故障，B）采用先进的成像和视觉技术来预测阴影，c）使用温度，辐照度和天气数据来提高光伏效率，以及d）包括智能电网接口。 亚利桑那州立大学和塞浦路斯大学之间的这个合作IRES项目将使学生参与以下研究问题：a）我们如何使用成像来检测云的运动，预测阴影和提高效率？B）如何根据成像、天气和I-V数据重新配置阵列连接以提高效率？c）我们如何使用机器学习和其他算法来真实的实时检测和分类面板故障？d）我们如何将这些太阳能监测和控制概念从公用事业规模的太阳能发电场扩展到房屋屋顶系统？该奖项反映了NSF的法定使命，并被认为是值得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的支持。