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.

该协作项目的目的是使用传感器，机器学习和信号处理方法提高实用程序尺度太阳能数组的效率，以检测故障并优化功率。依靠传感器数据和成像方法预测太阳能电池板阴影的新的网络计算策略用于提高效率。 一个可编程的18kW测试床，由配备传感器，执行器和相机配备的104个面板组成，用于验证所有理论结果，并测试使用太阳能分析来优化发电的新方法。 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，以优化不同的云，负载和故障条件的功率。CPS项目的外观包括以下方面的进步：所有数组监视方法以及E）安全无线传感器和数据融合。 可以实现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PV阵列标准和智能电网计划。项目任务还包括：教育活动，高中的宣传以及与包括少数派和HBCU机构在内的几个组织的互动，以增强多样性。

项目成果

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

• DOI: 10.1109/iisa.2017.8316458
• 发表时间: 2017-08
• 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

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
• 期刊: Systems & Applications (IISA
• 作者: Spanias, Andreas S.