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

该项目为美国学生在可持续发展、电力系统和信号处理领域的交叉领域提供国际多学科研究机会，旨在提高光伏发电的效率。遮阳预测和故障优化算法将推动太阳能电池阵列远程管理的发展。在能源系统的机器学习、视觉和数据处理方面训练学生是独特的，需要一种综合的方法。IRES的参与者将沉浸在生产和理解太阳能分析，创建算法和软件来控制太阳能阵列。IRES项目将邀请亚利桑那州立大学senp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方面的培训，并在国际环境中展示他们的研究成果。每周在国际现场的报告和国际导师的指导将丰富队列研究经验。让学生进入KIOS中心由欧盟（EU）大笔拨款资助的研究实验室，将提供有关欧盟和国际研究实践、能源标准和政策的知识。学生们将在塞浦路斯大学KIOS中心度过六个暑期周，以提高他们的研究技能，提升他们的文化能力。这项国际研究努力将激励学生创新并在全球传播成果。太阳能或光伏（PV）阵列在遮阳、面板故障和温度变化的条件下会遇到效率损失。事实上，遮阳、天气模式、污染和温度都会大大降低功率输出。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效率，单个面板的电流-电压（I-V）测量、天气信息和成像数据是必不可少的。通过太阳能电池板矩阵切换和优化（即使用执行器将某些阵列连接从串联改为并联）可以控制功率输出。使用可编程继电器的矩阵交换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，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IRES合作项目将让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)如何使用机器学习等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测和控制概念从公用事业规模的太阳能农场扩展到家庭屋顶系统？该奖项反映了美国国家科学基金会的法定使命，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。

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.