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1. 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. 

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2. 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)

   null (Ed.)

   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 uses 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. 

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3. Solar harvesting through multiple semi-transparent cadmium telluride solar panels for collective energy generation

   https://doi.org/10.1016/j.solener.2023.112047  

   Katepalli, Anudeep; Wang, Yuxin; Shi, Donglu (November 2023, Solar Energy)

   Among major energy conversion methods, photovoltaic (PV) solar cells have been the most popular and widely employed for a variety of applications. Although a PV solar panel has been shown as one of the most efficient green energy sources, its 2D surface solar light harvesting has reached great limitations as it requires large surface areas. There is, therefore, an increasing need to seek solar harvest in a three-dimensional fashion for enhanced energy density. In addition to a conventional 2D solar panel in the x-y area, we extend another dimension of solar harvesting in the z-axis through multiple CdTe solar panels arranged in parallel. The high transparency allows sunlight to partially penetrate multiple solar panels, resulting in significantly increased solar harvesting surface area in a 3D fashion. The advantages of the 3D multi-panel solar harvesting system include: i) enlarged solar light collecting surface area, therefore increased energy density, ii) the total output power from multiple panels can exceed that of the single panel, and iii) significantly reduced surface area needed for densely populated cities. With five CdTe solar panels of different transparencies in parallel, the multilayer system can produce collective output power 233% higher than that of the single solar panel under the same surface area when arranged in descending (i.e., PV panel with the highest transparency on top and lowest at bottom). The PCE of the multi-panel system has also increased 233% in descending order indicating the viability of 3D solar harvesting. The multi-panel system will dimensionally transform solar harvesting from 2D to 3D for more efficient energy generation. 

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4. Recent Advances in Cyberattack Detection and Mitigation Techniques for Renewable Photovoltaic Distributed Energy CPS

   Jessica Whitaker and Danda B. Rawat (June 2023, Computing Conference)

   Cyberattacks targeted to the energy cyber-physical system (ECPS), also known as the smart grid, could interrupt the electricity supply with major ramifications. Attackers identify and exploit any vulnerable portion of the energy power grid, including the inverters with solar-powered photovoltaic (PV) panels. PV presents unique challenges as electricity consumers have also become providers of solar energy for utilities. As mandates require increased PV penetration across the world for positive environmental impacts, increased cyberattacks targeted at PV systems impact reliability and efficiency within the ECPS. The new technologies continuously being introduced to manage the ECPS and ensure bi-directional communications and energy flow between components also lead to more attack surfaces, system vulnerabilities, and heightened malicious attacks. Data integrity attacks are increasing within PV systems. In this paper, we present a survey of different methods that are proposed and explored for identifying and preventing cyberattacks targeted at PV systems. The attack detection methods include voltage control, data diodes, and voltage measurement algorithms. Furthermore, we present blockchain, cyber switching, and other attack mitigation techniques for PV systems. 

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5. iMOST: An IoT Framework for Energy Efficient Street Lights

   Uddin, Md Milon; Stapp, Joshua; Vechione, Matthew; Sundaravadivel, Prabha (October 2022, 2022 IEEE 8th World Forum on Internet of Things (WF-IoT))

   The amount of greenhouse gas emissions from streetlights is equivalent to 2.6 million cars with as many as 26 million streetlights in the United States. The proposed IoT controller integrates sensors to make these streetlights as hubs for smart environment monitoring with effective energy usage. Conservation of energy is one of the main concerns in the modern era, and energy coming from the sun can be utilized efficiently alongside a smart streetlight management system instead of conventional streetlight management techniques. Additionally, with streetlights being present throughout a city, the opportunity to collect city-wide weather data is proposed. To this end, a solar-powered IoT-based smart street lighting and environmental monitoring system is proposed. The proposed energy-efficient IoT-based system uses a microcontroller to control light-emitting diode (LED) streetlights depending on lighting conditions and vehicle detection, ensuring that the streetlights can be turned on when needed. 

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