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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)

   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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2. Solar energy management as an Internet of Things (IoT) application

   https://doi.org/10.1109/IISA.2017.8316460  

   Spanias, Andreas S. (August 2017, 2017 IEEE 8th International Conference on Information, Intelligence, Systems & Applications (IISA))

   Photovoltaic (PV) array analytics and control have become necessary for remote solar farms and for intelligent fault detection and power optimization. The management of a PV array requires auxiliary electronics that are attached to each solar panel. A collaborative industry-university-government project was established to create a smart monitoring device (SMD) and establish associated algorithms and software for fault detection and solar array management. First generation smart monitoring devices (SMDs) were built in Japan. At the same time, Arizona State University initiated research in algorithms and software to monitor and control individual solar panels. Second generation SMDs were developed later and included sensors for monitoring voltage, current, temperature, and irradiance at each individual panel. The latest SMDs include a radio and relays which allow modifying solar array connection topologies. With each panel equipped with such a sophisticated SMD, solar panels in a PV array behave essentially as nodes in an Internet of Things (IoT) type of topology. This solar energy IoT system is currently programmable and can: a) provide mobile analytics, b) enable solar farm control, c) detect and remedy faults, d) optimize power under different shading conditions, and e) reduce inverter transients. A series of federal and industry grants sponsored research on statistical signal analysis, communications, and optimization of this system. A Cyber-Physical project, whose aim is to improve solar array efficiency and robustness using new machine learning and imaging methods, was launched recently 

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3. Estimating Snow Coverage Percentage on Solar Panels Using Drone Imagery and Machine Learning for Enhanced Energy Efficiency

   https://doi.org/10.3390/en18071729  

   Saleem, Ashraf; Awad, Ali; Mazen, Amna; Mazurkiewicz, Zoe; Dyreson, Ana (April 2025, Energies)

   Snow accumulation on solar panels presents a significant challenge to energy generation in snowy regions, reducing the efficiency of solar photovoltaic (PV) systems and impacting economic viability. While prior studies have explored snow detection using fixed-camera setups, these methods suffer from scalability limitations, stationary viewpoints, and the need for reference images. This study introduces an automated deep-learning framework that leverages drone-captured imagery to detect and quantify snow coverage on solar panels, aiming to enhance power forecasting and optimize snow removal strategies in winter conditions. We developed and evaluated two approaches using YOLO-based models: Approach 1, a high-precision method utilizing a two-class detection model, and Approach 2, a real-time single-class detection model optimized for fast inference. While Approach 1 demonstrated superior accuracy, achieving an overall precision of 89% and recall of 82%, it is computationally expensive, making it more suitable for strategic decision making. Approach 2, with a precision of 93% and a recall of 75%, provides a lightweight and efficient alternative for real-time monitoring but is sensitive to lighting variations. The proposed framework calculates snow coverage percentages (SCP) to support snow removal planning, minimize downtime, and optimize power generation. Compared to fixed-camera-based snow detection models, our approach leverages drone imagery to improve detection precision while offering greater scalability to be adopted for large solar farms. Qualitative and quantitative analysis of both approaches is presented in this paper, highlighting their strengths and weaknesses in different environmental conditions. 

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4. Partial shading by solar panels delays bloom, increases floral abundance during the late-season for pollinators in a dryland, agrivoltaic ecosystem

   https://doi.org/10.1038/s41598-021-86756-4  

   Graham, Maggie; Ates, Serkan; Melathopoulos, Andony P.; Moldenke, Andrew R.; DeBano, Sandra J.; Best, Lincoln R.; Higgins, Chad W. (April 2021, Scientific Reports)

   Abstract Habitat for pollinators is declining worldwide, threatening the health of both wild and agricultural ecosystems. Photovoltaic solar energy installation is booming, frequently near agricultural lands, where the land underneath ground-mounted photovoltaic panels is traditionally unused. Some solar developers and agriculturalists in the United States are filling the solar understory with habitat for pollinating insects in efforts to maximize land-use efficiency in agricultural lands. However, the impact of the solar panel canopy on the understory pollinator-plant community is unknown. Here we investigated the effects of solar arrays on plant composition, bloom timing and foraging behavior of pollinators from June to September (after peak bloom) in full shade plots and partial shade plots under solar panels as well as in full sun plots (controls) outside of the solar panels. We found that floral abundance increased and bloom timing was delayed in the partial shade plots, which has the potential to benefit late-season foragers in water-limited ecosystems. Pollinator abundance, diversity, and richness were similar in full sun and partial shade plots, both greater than in full shade. Pollinator-flower visitation rates did not differ among treatments at this scale. This demonstrates that pollinators will use habitat under solar arrays, despite variations in community structure across shade gradients. We anticipate that these findings will inform local farmers and solar developers who manage solar understories, as well as agriculture and pollinator health advocates as they seek land for pollinator habitat restoration in target areas. 

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5. Multifunctional Structures for Attitude Control

   https://doi.org/10.1115/SMASIS2019-5565  

   Vedant; Allison, James T. (September 2019, American Society of Mechanical Engineers)

   The Engineering Systems Design Lab (ESDL) at the University of Illinois introduced Strain-Actuated Solar Arrays (SASAs) as a solution for precise satellite Attitude Control System (ACSs). SASA is designed to provide active mechanical vibration (jitter) cancellation, as well as small slew maneuver capabilities to hold a pose for short time periods. Current SASA implementations utilize piezoelectric distributed actuators to strain deployable structures, and the resulting momentum transfer rotates the spacecraft bus. A core disadvantage, however, is small strain and slew capability. Initial SASA systems could help improve pointing accuracy, but must be coupled with another ACS technology to produce large reorientations. A novel extension of the original SASA system is presented here that overcomes the small-displacement limitation, enabling use of SASA as a sole ACS for some missions, or in conjunction with other ACSs. This extension, known as Multifunctional Structures for Attitude Control (MSAC), can produce arbitrarily-large rotations, and has the potential to scale to large spacecraft. The system utilizes existing flexible deployable structures (such as solar arrays or radiators) as multifunctional devices. This multi-role use of solar panels extends their utility at a low mass penalty, while increasing reliability of the spacecraft ACS. 

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