MRI: Development of a Sensors and Machine Learning Instrument Suite for Solar Array Monitoring

MRI：开发用于太阳能阵列监测的传感器和机器学习仪器套件

• 批准号: 2019068
• 负责人: Andreas Spanias
• 金额: $ 34.99万
• 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: 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=2019068&HistoricalAwards=false
• 关键词: MRI; Development; Sensors; Machine; Learning

This project develops a testbed for solar energy grids that brings together several labs, centers, and researchers to address • Elevating power efficiency in solar farms, • Automatic fault detection, • Maximizing solar power output, • Optimizing inverter performance, • Providing secure communication for analytics, and m• Making provisions for Smart Grid. Solar energy research strives to solve challenging problems in increased photovoltaic (PV) efficiency, grid management, power storage, intelligent inverters, and various problems of economic nature, such as financing and manufacturing. The instrument is intended to enable several innovations and elevate the state of the art of solar technologies. It enables research on integrating sensors and machine learning to elevate considerably the output power and robustness of solar arrays. The seamless integration of several software and hardware components is designed to enhance all aspects of solar array monitoring and control. 'A key component of the instrument, an Intelligent Monitoring and Control Device (IMCD) consists of sensors, actuators, a processor/controller chip, a secure radio, embedded machine learning software, and signal processing and authentication algorithms. The overarching long term goal is to Miniaturize IMCDs with the goal to embed it in photovoltaic (PV) modules, to enable the building of a new generation of smart programmable PV modules. This platform integrates a new intelligent monitoring and control instrument suite and enabling researchers to obtain, process, and utilize real-time PV and environmental data in order to: • Develop, integrate and test detection and classification algorithms for PV faults;• Track cloud movement and predict panel shading, and hence optimize further the output of PV arrays; • Use secure networked connectivity and protocols to protect data and avoid MRI instrument hacking;• Integrate all the acquired data using fusion algorithms and enable appropriate control of the panels; • Predict and eliminate inverter transients caused by faults and dynamic shading conditions; • Provide continuous analytics and create a mobile dashboard to monitor and control the array; • Use data and design ground breaking optimization algorithms to improve the PV array power output;• Elevate overall efficiency of solar farms in terms of power output by more than 16%; • Create the foundations for designing a new generation of solar panel technology.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.

该项目为太阳能电网开发了一个测试平台，汇集了多个实验室，中心和研究人员，以解决·提高太阳能发电场的功率效率，·自动故障检测，·最大限度地提高太阳能发电量，·优化逆变器性能，·为分析提供安全通信，以及·为智能电网做准备。太阳能研究致力于解决提高光伏（PV）效率，电网管理，电力存储，智能逆变器以及各种经济性问题（如融资和制造）方面的挑战性问题。该仪器旨在实现几项创新并提升太阳能技术的最新水平。它使集成传感器和机器学习的研究能够大大提高太阳能电池阵列的输出功率和鲁棒性。多个软件和硬件组件的无缝集成旨在增强太阳能电池阵列监测和控制的各个方面。作为仪器的关键组件，智能监控设备（IMCD）由传感器，执行器，处理器/控制器芯片，安全无线电，嵌入式机器学习软件以及信号处理和认证算法组成。首要的长期目标是将IMCD小型化，目标是将其嵌入光伏（PV）模块中，以实现新一代智能可编程光伏模块的构建。该平台集成了一个新的智能监测和控制仪器套件，使研究人员能够获取、处理和利用实时光伏和环境数据，以便：·开发、集成和测试光伏故障的检测和分类算法;·跟踪云层移动并预测面板阴影，从而进一步优化光伏阵列的输出;·使用安全的网络连接和协议来保护数据并避免MRI仪器黑客攻击;·使用融合算法整合所有采集的数据并实现对面板的适当控制; ·预测并消除由故障和动态阴影条件引起的逆变器瞬变;·提供持续分析并创建移动的仪表板来监控和控制阵列; ·使用数据和设计突破性的优化算法来提高光伏阵列的功率输出;·将太阳能发电场的整体效率提高16%以上;·为设计新一代太阳能电池板技术奠定基础。该奖项反映了NSF的法定使命，并通过使用基金会的学术价值和更广泛的影响审查标准。