Publications

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Copper indium gallium (di)serenade (CIGS) photovoltaic cell technology has long been promoted as a cost-effective alternative to traditional PV modules based on crystalline silicon cells. However, adoption of CIGS is hindered by significant uncertainties regarding long-term reliability and performance stability, as well as a lack of accurate modeling tools to predict CIGS system performance. Sandia is conducting a multi-year study of fielded CIGS systems that range in age from 3-6 years and represent a cross-section of commercial manufacturing and packaging. Most of these arrays include modules that were thoroughly characterized prior to deployment. In this paper, we explore uncertainty in the long-term reliability and performance stability of CIGS modules by analyzing real world performance and degradation rates of these systems.

Sandia National Laboratories' continued work with bifacial PV modules has found discrepancies in the capability of bifacial PVmodules to generate energy depending on systemdesign. We have also found significant nonuniformity in rear-side irradiance across strings of bifacial PV modules, thus creating electrical mismatch between modules. Module level power electronics (MLPE) that track the maximum power point of each module alleviate some of theelectrical mismatch caused by nonuniform rear-side irradiance on bifacial PV modules. The bifacial gain of the bifacial PV modules can be increased significantly through MLPE, although the net energy gain may not be significant forunshaded bifacial PV systems. Here we present the results of a test between bifacial PV systems equipped with MLPE and the same systems without MLPE.

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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.

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.

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Accurate photovoltaic system performance monitoring is critical for profitable long-term operation. Irradiance, temperature, power, current and voltage signals contain rapid fluctuations that are not observable by typical monitoring systems. Nevertheless these fluctuations can affect the accuracy of the data that are stored. We closely examine electrical signals in one operating PV system recorded at 2000 samples per second. Rapid fluctuations are analyzed, caused by line-frequency harmonics, anti-islanding detection, MPPT and others. The operation of alternate monitoring systems is simulated using a wide range of sampling intervals, archive intervals and filtering options to assess how these factors influence final data accuracy.

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The performance of photovoltaic systems must be monitored accurately to ensure profitable long-term operation. The most important signals to be measured—irradiance and temperature, as well as power, current and voltage on both DC and AC sides of the system—contain rapid fluctuations that are not observable by typical monitoring systems. Nevertheless these fluctuations can affect the accuracy of the data that are stored. This report closely examines the main signals in one operating PV system, which were recorded at 2000 samples per second. It analyzes the characteristics and causes of the rapid fluctuations that are found, such as line-frequency harmonics, perturbations from anti-islanding detection, MPPT searching action and others. The operation of PV monitoring systems is then simulated using a wide range of sampling intervals, archive intervals and filtering options to assess how these factors influence data accuracy. Finally several potential sources of error are discussed with real-world examples.

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