The uncertainties associated with technology‐specific and geography‐specific degradation rates make it difficult to calculate the levelized cost of energy, and thus the economic viability of solar energy. In this regard, millions of fielded photovoltaic modules may serve as a global testbed, where we can interpret the routinely collected time series maximum power point (MPP) data to assess the time‐dependent “health” of solar modules. The existing characterization methods, however, cannot effectively mine/decode these datasets to identify various degradation pathways. In this paper, we propose a new methodology called the Suns‐Vmp method, which offers a simple yet powerful approach to monitoring and diagnosing time‐dependent degradation of solar modules by using the MPP data. The algorithm reconstructs “IV” curves by using the natural illumination‐dependent and temperature‐dependent daily MPP characteristics as constraints to fit physics‐based circuit models. These synthetic IV characteristics are then used to determine the time‐dependent evolution of circuit parameters (e.g. series resistance), which in turn allows one to deduce the dominant degradation modes (e.g. solder bond failure) of solar modules. The proposed method has been applied to a test facility at the National Renewable Energy Laboratory. Our analysis indicates that the solar modules degraded at a rate of ~0.7%/year because of discoloration and weakened solder bonds. These conclusions are validated by independent outdoor IV measurements and on‐site imaging characterization. Integrated with physics‐based degradation models or machine learning algorithms, the method can also serve to predict the lifetime of photovoltaic systems.

Publication
X. Sun, R. Vamsi K. Chavali, and M. A. Alam, “Real ‐ time monitoring and diagnosis of photovoltaic system degradation only using maximum power point — the Suns ‐ Vmp method,” Prog. Ph, no. March, pp. 1–12, 2018. (https://doi.org/10.1002/pip.3043 )

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Figure 1. A schematic illustration to explain the working principles of the Suns‐Vmp methods



Figure 2. The flow chart of the Suns‐Vmp method. Degradation mechanisms that affect the performance
of solar modules are reflected in the time‐dependent changes of these circuit parameters.



Figure 3. (a) Three‐day maximum power point (MPP) and environmental data (circles) from November 09, 2002 to November 11, 2002 of the test facility. The fitting results of the MPP data (solid lines) using the Suns‐Vmp method is also present. (b) An illustration of reconstructing “IV” from the MPP data in (a)



Figure 4. Temporal degradation deconvolution with respect to circuit parameters for the negative monopole.

Key findings from the Suns‐Vmp method that analyzes the PV degradation

1. The Suns‐Vmp method enables monitoring and diagnosis of PV reliability in real time by systemically and physically mining the time series MPP data. The method can extract physically defined circuit parameters by fitting IVs consisting of the varying MPP data over a measurement window. The extracted circuit parameters can be used to estimate the STC efficiency, quantitatively deconvolve PV degradation pathways, and identify the dominant degradation pathways.
   
   
2. We have demonstrated the Suns‐Vmp method by analyzing MPP data from an NREL test facility, where physics‐based circuit parameters and efficiencies of the solar modules have been extracted as a function of time. Independent outdoor IV measurements have systemically validated our results. Our analysis suggests that the PV system degrades at a rate of 0.7%/year, primarily because of reduced short‐circuit current and increased series resistance most likely caused by discoloration and weakened solder bond, respectively. The on‐site optical photograph and IR image indeed substantiate our interpretation of the physical degradation pathways, ie, discoloration and solder bond failure.
   
   
3. The analysis of deconvolving the underlying degradation pathways by the Suns‐Vmp method can deepen the current understanding of technology‐dependent and geographic‐dependent degradation, and inspire more robust environment‐specific designs for the next‐generation reliability‐aware solar modules. The Suns‐Vmp method can be used to calibrate physics‐based degradation models as well as train machine learning algorithms, both of which can then predict power degradation of PV and improve the evaluation of “bankability.”
   
   

The Solar PV Diagnosis dataset stores all the files (input, output and simulation code) and provides an easy access to users for their perusal. A pre-processing tool has been added to System 50 files to show how the raw field data and weather data are converted into appropriately formatted input files.

The source of the data is NREL (Golden, Colorado, USA) – System 50 and System 51 are included as two example cases.

Alam CEED Group website
Funding - National Science Foundation under Grant #1724728

[1] X. Sun, R. Vamsi K. Chavali, and M. A. Alam, “Real ‐ time monitoring and diagnosis of photovoltaic system degradation only using maximum power point — the Suns ‐ Vmp method,” Prog. Ph, no. March, pp. 1–12, 2018.

• Muhammed Tahir Patel, Reza Asadpour, Xingshu sun, Muhammad A. Alam (2018), "Solar PV Diagnosis," https://datacenterhub.org/deedsdv/publications/view/442.