Calculating PV Degradation Rates Using Open-Source Software: Page 2 of 4

How It Works

To get started with RdTools, users first enter system configuration details such as longitude, latitude, time zone and PV system mounting configuration. Although RdTools does require some source of on-site irradiance data, on-site temperature measurements are not essential as the software can model these values. Upon start-up, RdTools automatically conducts a prescreening step to check the granularity of the collected data. At present, RdTools is set up to use high-frequency performance data such as 1-minute, 15-minute or hourly values.

Two different analysis methods are available on RdTools. The sensor-based method is best if high-quality temperature and irradiance data are available, which assumes that technicians regularly clean and calibrate the project’s sensors and reference cells. The clear-sky method, which normalizes the data based on clear-sky conditions, is best if sensors have low accuracy or in cases where low-accuracy satellite measurements are the source of the data. The clear-sky method still currently requires some source of irradiance data to identify times of sunny conditions, but it does not demand perfectly cleaned or calibrated sensors.

As detailed below, RdTools follows a four-step data analysis process: First, it normalizes the data, adjusting performance relative to irradiance and temperature; second, it filters the data; third, it aggregates the data and generates periodic totals; and lastly, it calculates the median rate of degradation.

Step 1: Data normalization. In this step, RdTools divides measured production data by modeled ideal values to calculate performance ratio (PR) values. The software derives the modeled values based on meteorological and system configuration details by passing these data into a PVLIB performance model. Currently, RdTools uses PVWatts as the default PVLIB performance model.

There are two possible workflows in the data normalization step. The sensor-based method passes site-measured irradiance and temperature data directly into the PVLIB performance model, in which case the calculations may incorrectly attribute sensor errors to system degradation. Alternatively, the clear-sky method calculates PR values by normalizing site data against modeled clear-sky irradiance and long-term monthly site temperature averages, which produces results that are relatively insensitive to drifting or erroneous ground-based sensors.

Step 2: Data filtering. This step filters data to remove problematic points, including power-curve clipping from a high dc-to-ac ratio, low and anomalous high-irradiance values, and improbable temperature measurements. For the clear-sky method, the software also filters data points based on the clear-sky index to specifically consider sunny conditions.

Step 3: Data aggregation. In this step, the analysis averages the filtered and irradiance-weighted PR data over the aggregation period. This results in a single PR value per aggregation period, which is typically daily.

Step 4: Rd calculation. RdTools utilizes a year-on-year (YOY) method of analysis to calculate degradation rates. In this step, the software calculates a series of slopes between any two daily values that are separated by 365 days. This means that if there are 3 years of production data, the software will calculate 730 annual slopes. In the event that there are no data for a particular day—due to data filtering or an outage—the software will not calculate slopes to or from that date. Once RdTools has calculated all the annual slopes, it generates a histogram based on the combined data and reports the median value as the system’s rate of degradation.

Customization. One of the best features of RdTools is that users are free to customize the software to fit their needs. Users with some knowledge of the Python programming language can customize aspects of RdTools to better match system and data characteristics. For example, while the default data aggregation period is daily, users can easily change this to a weekly period. Customizability provides users with the ability to optimize RdTools on a per-project basis. Users can adjust data filtering parameters to account for climates that are more or less cloudy than normal or to account for inverter power limiting in systems with a high dc-to-ac ratio. Users can also customize PVLIB system performance models based on specific system configuration details. Due to the open-source nature of RdTools, software developers can communicate with one another, report bugs, review code and propose new functionality via the GitHub repository.

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