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

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阴影，用于提高效率。 一个可编程的18kW测试平台由104块配备传感器、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，以针对不同的云、负载和故障条件优化功率。CPS项目的成果包括以下方面的进步：a）使用计算机视觉算法的云移动建模和阴影预测，B）PV故障检测和优化方法，其将动态地切换阵列拓扑同时限制PV逆变器瞬变，d）所有阵列监测方法的实验（试验台）验证，以及e）安全的无线传感器和数据融合。 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：教育活动，在高中的推广，以及与包括少数民族和HBCU机构在内的几个组织的接触，以加强多样性。

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.