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.

该计划促进了以可持续性，电力系统和信号处理的重叠的美国学生的国际多学科研究机会，以提高PV发电的效率。用于阴影预测和故障优化的算法将在远程太阳能数组管理中提高最新技术。 培训学生对能源系统的机器学习，愿景和数据处理是独一无二的，需要一种综合方法。 IRES的参与者将沉浸在生产和理解太阳能分析，创建算法和软件以控制太阳能数组中。 IRES计划将与来自亚利桑那州立大学Sensip中心的教师研究人员和塞浦路斯大学套件中心的太阳能研究中心。将建立课程和讲习班，以便对IRES参与者进行能源系统的机器学习培训，并在国际环境中介绍他们的研究结果。国际网站的每周演讲和国际导师的指导将丰富队列研究经验。将学生嵌入由大型欧盟（EU）资助的Kios Center研究实验室中，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中心度过六周的夏季，以提高他们的研究技能并提高其文化能力。 这项国际研究的努力将使学生为全球创新和传播成果提供动力。 在阴影，面板断层和温度变化的条件下，太阳能或光伏阵列（PV）阵列会遇到效率的丧失。 实际上，阴影，天气模式，弄脏和温度大大降低了功率输出。例如，一个面板的故障会导致整个PV字符串失败。 为了最大程度地减少效率低下，各个面板电流电压（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开关和优化可以控制功率输出（即，使用执行器将某些数组连接从系列更改为并行）。使用可编程继电器使用矩阵切换允许不同的互连选项。研究目标是通过以下方式优化PV阵列系统：a）使用机器学习利用测得的I-V模式来检测故障，b）使用先进的成像和视觉技术来预测阴影，c）使用温度，辐照度和天气数据提高PV效率，并提高PV效率，D）包括智能网格接口。 亚利桑那州立大学和塞浦路斯大学之间的合作IRES项目将吸引学生参与以下研究问题：a）我们如何使用成像来检测云运动，预测阴影并提高效率？ 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）如何使用机器学习和其他算法实时检测和分类面板故障？ d）我们如何将这些太阳能监控和控制概念从公用事业规模的太阳能农场扩展到容纳屋顶系统？该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，认为值得通过评估来获得支持。