More Like this

1. Model-driven Per-panel Solar Anomaly Detection for Residential Arrays

   https://doi.org/10.1145/3460236  

   Feng, Menghong ; Bashir, Noman ; Shenoy, Prashant ; Irwin, David ; Kosanovic, Beka ( October 2021 , ACM Transactions on Cyber-Physical Systems)

   There has been significant growth in both utility-scale and residential-scale solar installations in recent years, driven by rapid technology improvements and falling prices. Unlike utility-scale solar farms that are professionally managed and maintained, smaller residential-scale installations often lack sensing and instrumentation for performance monitoring and fault detection. As a result, faults may go undetected for long periods of time, resulting in generation and revenue losses for the homeowner. In this article, we present SunDown, a sensorless approach designed to detect per-panel faults in residential solar arrays. SunDown does not require any new sensors for its fault detection and instead usesmore »a model-driven approach that leverages correlations between the power produced by adjacent panels to detect deviations from expected behavior. SunDown can handle concurrent faults in multiple panels and perform anomaly classification to determine probable causes. Using two years of solar generation data from a real home and a manually generated dataset of multiple solar faults, we show that SunDown has a Mean Absolute Percentage Error of 2.98% when predicting per-panel output. Our results show that SunDown is able to detect and classify faults, including from snow cover, leaves and debris, and electrical failures with 99.13% accuracy, and can detect multiple concurrent faults with 97.2% accuracy.« less
2. A cyber-physical system approach for photovoltaic array monitoring and control

   https://doi.org/10.1109/IISA.2017.8316458  

   Rao, S. ; Katoch, S. ; Turaga, P. ; Spanias, A. ; Tepedelenlioglu, C. ; Ayyanar, R. ; Braun, H. ; Lee, J. ; Shanthamallu, U. ; Banavar, M. ; et al ( August 2017 , IEEE IISA 2017)

   In this paper, we describe a Cyber-Physical system approach to Photovoltaic (PV) array control. A machine learning and computer vision framework is proposed for improving the reliability of utility scale PV arrays by leveraging video analysis of local skyline imagery, customized machine learning methods for fault detection, and monitoring devices that sense data and actuate at each individual panel. Our approach promises to improve efficiency in renewable energy systems using cyber-enabled sensory analysis and fusion.
3. An REU/RET Project: IoT Platform and Network Data Visualization

   Toutsop, O. ( November 2021 , 2021 Fall ASEE Middle Atlantic Section Meeting)

   Internet of Things (IoT) is a connected network of devices that exchange data using different protocols. The application of IoT ranges from intelligent TVs and intelligent Refrigerators to smart Transportation. This research aims to provide students with hands-on training on how to develop an IoT platform that supports device management, connectivity, and data management. People tend to build interconnected devices without having a basic understanding of how the IoT platform backend function. Studying the Arm Pelion will help to understand how IoT devices operate under the hood. This past summer, Morgan State University has hosted undergraduate engineering students and highmore »school STEM teachers to conduct IoT security research in the Cybersecurity Assurance & Policy (CAP) Center. The research project involved integrating various hardware sensor devices and real-time data monitoring using the Arm Pelion IoT development platform. Some of the student/teacher outcomes from the project include: 1) Learning about IoT Technology and security; 2) Programming an embedded system using Arm Mbed development board and IDE; 3 3) Developing a network of connected IoT devices using different protocols such as LWM2M, MQTT, CoAP; 4) Investigating the cybersecurity risks associated with the platform; and 5) Using data analysis and visualization to understand the network data and packet flow. First, the student/teacher must consider the IoT framework to understand how to address the security. The IoT framework describes the essential functions of an IoT network, breaking it down into separate layers. These layers include an application layer, middleware layer, and connectivity layer. The application layer allows the users to access the platform via a smartphone or any other dashboard. The Middleware layer represents the backend system that provides edge devices with data management, messaging, application services, and authentication. Finally, the connectivity layer includes devices that connect the user to the network, including Bluetooth or WiFi. The platform consists of several commercial IoT devices such as a smart camera, baby monitor, smart light, and other devices. We then create algorithms to classify the network data flow; to visualize the packets flow in the network and the structure of the packets data frame over time.« less
4. Design of a Laboratory Scale Solar Microgrid Cyber-Physical System for Education

   https://doi.org/10.3390/electronics10131562  

   Guo, Liping ; Kors, Jason ( July 2021 , Electronics)

   Renewable energy sources such as solar and wind provide an effective solution for reducing dependency on conventional power generation and increasing the reliability and quality of power systems. Presented in this paper are design and implementation of a laboratory scale solar microgrid cyber-physical system (CPS) with wireless data monitoring as a teaching tool in the engineering technology curriculum. In the system, the solar panel, battery, charge controller, and loads form the physical layer, while the sensors, communication networks, supervisory control and data acquisition systems (SCADA) and control systems form the cyber layer. The physical layer was seamlessly integrated with themore »cyber layer consisting of control and communication. The objective was to create a robust CPS platform and to use the system to promote interest in and knowledge of renewable energy among university students. Experimental results showed that the maximum power point tracking (MPPT) charge controller provided the loads with power from the solar panel and used additional power to charge the rechargeable battery. Through the system, students learned and mastered key concepts and knowledge of multi-disciplinary areas including data sampling and acquisition, analog to digital conversion, solar power, battery charging, control, embedded systems and software programing. It is a valuable teaching resource for students to study renewable energy in CPS.« less
5. The extent of vegetation-driven panel cooling and consequent increase in electricity generation from solar PV sites depend on climate and soil properties

   Bertel, R. ; Choi, C. S. ; Macknick, J. ; McCall, J. ; Ravi, S ( December 2021 , AGU 2021 Fall Meeting Meeting New Orleans, LA)

   Co-locating solar photovoltaics (PV) with agriculture or natural vegetation could provide a sustainable solution to meeting growing food and energy demands, particularly considering the recent concerns of solar PV encroaching on agricultural and natural areas. However, the identification and quantification of the mutual interactions between the solar panels and the underlying soil-vegetation system are scarce. This is a critical research gap, as understanding these feedbacks are important for minimizing environmental impacts and for designing resource conserving and climate-resilient food-energy production systems. We monitored the microclimate, soil moisture distribution, and soil properties at three utility-scale solar facilities (MN, USA) with differentmore »site management practices, with an emphasis on verifying previously hypothesized vegetation-driven cooling of solar panels. The microclimatic variables (air and soil temperature, relative humidity, wind speed and direction) and soil moisture were significantly different between the PV site with bare soil (bare-PV) and vegetated PV (veg.-PV) site. Compared to the bare-PV site, the veg.-PV site also had significantly higher levels of total soil carbon and total soil nitrogen, as well as higher humidity and lower air and soil temperatures. Further, soil moisture heterogeneity created by the solar panels was homogenized by vegetation at the veg.-PV sites. However, we found no significant panel cooling or increase in electricity output that could be linked to co-location of the panels with vegetation in these facilities. We link these outcomes to the background climatic conditions (not water limited system) and soil moisture conditions. In regions with persistent high soil moisture (more frequent rainfall events) soil evaporation from wet bare soil may be comparable or even higher than from a vegetated surface. Thus, the cooling effects of vegetation on solar panels are not universal but rather site-specific depending on the background climate and soil properties. Regardless, the other co-benefits of maintaining vegetation at solar PV sites including the impacts on microclimate, soil moisture distribution, and soil quality support the case for solar PV–vegetation co-located systems.« less