CPS: Synergy: Image Modeling and Machine Learning Algorithms for Utility-Scale Solar Panel Monitoring

CPS：协同：用于公用事业规模太阳能电池板监控的图像建模和机器学习算法

• 批准号: 1646542
• 负责人: Andreas Spanias
• 金额: $ 60万
• 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: 2016
• 资助国家: 美国
• 起止时间: 2016-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1646542&HistoricalAwards=false
• 关键词: CPS; Synergy; Image; Modeling; Machine

The aim of this collaborative project is to increase the efficiency of utility scale solar arrays using sensors, machine learning and signal processing methods to detect faults and optimize power. New cyber-computing strategies, that rely on sensor data and imaging methods to predict solar panel shading, are used to improve efficiency. A programmable 18kW testbed that consists of 104 panels equipped with sensors, actuators and cameras is used to validate all theoretical results and test new approaches for using solar analytics to optimize power generation. Machine learning and dynamic image modeling algorithms are used to control each individual panel and change connection topologies to optimize power for different cloud, load, and fault conditions.Outcomes of the CPS project include advances in: a) cloud movement modeling and shading prediction using computer vision algorithms, b) PV fault detection and optimization methods that will switch array topologies dynamically while limiting PV inverter transients, d) experimental (testbed) validation of all array monitoring methods, and e) secure wireless sensor and data fusion. Theoretical and experimental research which enables real-time analytics and remote connection topology control may influence PV array standards and smart grid initiatives. The project tasks also include: education activities, outreach at high schools, and engagement with several organizations including minority and HBCU institutions to enhance diversity.

该合作项目的目的是利用传感器、机器学习和信号处理方法来检测故障并优化功率，从而提高公用事业规模太阳能电池阵列的效率。新的网络计算策略依靠传感器数据和成像方法来预测太阳能电池板的遮蔽，用于提高效率。 可编程 18kW 测试台由 104 个配备传感器、执行器和摄像头的面板组成，用于验证所有理论结果并测试使用太阳能分析优化发电的新方法。 机器学习和动态图像建模算法用于控制每个单独的面板并更改连接拓扑，以优化不同云、负载和故障条件的功率。CPS 项目的成果包括以下方面的进展：a) 使用计算机进行云运动建模和阴影预测视觉算法，b) 光伏故障检测和优化方法，动态切换阵列拓扑，同时限制光伏逆变器瞬态，d) 所有阵列监控方法的实验（测试台）验证，以及 e) 安全无线传感器和数据融合。 支持实时分析和远程连接拓扑控制的理论和实验研究可能会影响光伏阵列标准和智能电网计划。该项目的任务还包括：教育活动、高中外展以及与包括少数族裔和 HBCU 机构在内的多个组织合作以增强多样性。

项目成果

A cyber-physical system approach for photovoltaic array monitoring and control

用于光伏阵列监测和控制的网络物理系统方法

• DOI: 10.1109/iisa.2017.8316458
• 发表时间: 2017-08-01
• 期刊: 2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA)
• 作者: Sunil Rao;Sameeksha Katoch;P. Turaga;A. Spanias;C. Tepedelenlioğlu;R. Ayyanar;Henry Braun;Jongmin Lee;U. Shanthamallu;M. Banavar;Devarajan Srinivasan
• 通讯作者: Devarajan Srinivasan

Solar energy management as an Internet of Things (IoT) application

作为物联网 (IoT) 应用的太阳能管理

• DOI: 10.1109/iisa.2017.8316460
• 发表时间: 2017-08
• 期刊: Systems & Applications (IISA
• 作者: Spanias; Andreas S.
• 通讯作者: Andreas S.