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

该合作项目的目的是使用传感器、机器学习和信号处理方法来检测故障和优化功率，以提高公用事业规模太阳能电池板的效率。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阴影，被用来提高效率。一个可编程的18kW试验台由104块电池板组成，配有传感器、执行器和摄像头，用于验证所有理论结果，并测试利用太阳能分析优化发电的新方法。CPS项目的成果包括：a)基于计算机视觉算法的云层移动建模和阴影预测，b)光伏故障检测和优化方法，它将在限制光伏逆变器瞬变的同时动态切换阵列拓扑，d)所有阵列监控方法的实验(试验台)验证，以及e)安全无线传感器和数据融合。能够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光伏阵列标准和智能电网计划。该项目的任务还包括：开展教育活动，在高中开展外联活动，并与包括少数族裔和HBCU机构在内的几个组织接触，以增强多样性。

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.