Publications

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The IEC 61215 and Qualification Plus indoor aging tests are recognized as valuable assessment procedures for identifying photovoltaic (PV) modules that are prone to early-life failures or excessive degradation. However, it is unclear how well the tests match with reality, and if they can predict in-field performance. Therefore, the present work performed indoor-aging thermal cycling tests on pristine-condition modules and evaluated, using in-field current and voltage (I-V) curve scans, modules of the same make and model exposed to the actual environment within a production field. The experiment included the estimate of the overall exposure to thermal cycling in both indoor and outdoor environments, the extraction of the series resistance from the I-V curves, the development of a model based on the indoor results, and finally the testing of the model on outdoor exposure amounts to predict actual changes in resistance. Index Terms - photovoltaic, accelerated aging, series resistance.

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The integration of communication-enabled grid-support functions in distributed energy resources (DER) and other smart grid features will increase the U.S. power grid's exposure to cyber-physical attacks. Unwanted changes in DER system data and control signals can damage electrical infrastructure and lead to outages. To protect against these threats, intrusion detection systems (IDSs) can be deployed, but their implementation presents a unique set of challenges in industrial control systems (ICSs), New approaches need to be developed that not only sense cyber anomalies, but also detect undesired physical system behaviors. For DER systems, a combination of cyber security data and power system and control information should be collected by the IDS to provide insight into the nature of an anomalous event. This allows joint forensic analysis to be conducted to reveal any relationships between the observed cyber and physical events. In this paper, we propose a hybrid IDS approach that monitors and evaluates both physical and cyber network data in DER systems, and present a series of scenarios to demonstrate how our approach enables the cyber-physical IDS to achieve more robust identification and mitigation of malicious events on the DER system.

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Adoption of plug-in electric vehicles (PEVs) has expanded over the last few years, yet introduction of PEV smart charging has been stalled due to barriers in communication, controls, and an unclear method for determining the value PEVs will bring to the grid. This project will consider the grid impact of a variety of future scenarios, including adoption of different vehicle types, proliferation of extreme fast charging (xFC), expanded adoption of distributed energy resources (DER), and multiple smart charge management approaches. This project will determine how PEV charging at scale should be managed to avoid negative grid impacts, allow for critical strategies and technologies to be developed, and increase the value for PEV owners, building managers, charge network operators, grid services aggregators, and utilities.

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The U.S. DOE Regional Test Center for Solar Technologies program was established to validate photovoltaic (PV) technologies installed in a range of different climates. The program is funded by the Energy Department's SunShot Initiative. The initiative seeks to make solar energy cost competitive with other forms of electricity by the end of the decade. Sandia National Laboratory currently manages four different sites across the country. The National Renewable Energy Laboratory manages a fifth site in Colorado. The entire PV portfolio currently includes 20 industry partners and almost 500 kW of installed systems. The program follows a defined process that outlines tasks, milestones, agreements, and deliverables. The process is broken out into four main parts: 1) planning and design, 2) installation, 3) operations, and 4) decommissioning. This operations manual defines the various elements of each part.

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Monitoring of photovoltaic (PV) systems can maintain efficient operations. However, extensive monitoring of large quantities of data can be a cumbersome process. The present work introduces a simple, inexpensive, yet effective data monitoring strategy for detecting faults and determining lost revenues automatically. This was achieved through the deployment of Raspberry Pi (RPI) device at a PV system's combiner box. The RPI was programmed to collect PV data through Modbus communications, and store the data locally in a MySQL database. Then, using a Gaussian Process Regression algorithm the RPI device was able to accurately estimate string level current, voltage, and power values. The device could also detect system faults using a Support Vector Novelty Detection algorithm. Finally, the RPI was programmed to output the potential lost revenue caused by the abnormal condition. The system analytics information was then displayed on a user interface. The interface could be accessed by operations personal to direct maintenance activity so that critical issues can be solved quickly.

Current-voltage (I-V) curve traces of photovoltaic (PV) systems can provide detailed information for diagnosing fault conditions. The present work implemented an in situ, automatic I-V curve tracer system coupled with Support Vector Machine and a Gaussian Process algorithms to classify and estimate abnormal and normal PV performance. The approach successfully identified normal and fault conditions. In addition, the Gaussian Process regression algorithm was used to estimate ideal I-V curves based on a given irradiance and temperature condition. The estimation results were then used to calculate the lost power due to the fault condition.