RdTools Overview¶

RdTools is an open-source library to support reproducible technical analysis of time series data from photovoltaic energy systems. The library aims to provide best practice analysis routines along with the building blocks for users to tailor their own analyses. Current applications include the evaluation of PV production over several years to obtain rates of performance degradation and soiling loss. They also include the capability to analyze systems for system- and subsystem-level availability. RdTools can handle both high frequency (hourly or better) or low frequency (daily, weekly, etc.) datasets. Best results are obtained with higher frequency data.

Full examples are worked out in the example notebooks in the example notebook.

To report issues, contribute code, or suggest improvements to this documentation, visit the RdTools development repository on github.

Degradation and Soiling¶

Both degradation and soiling analyses are based on normalized yield, similar to performance index. Usually, this is computed at the daily level although other aggregation periods are supported. A typical analysis of soiling and degradation contains the following:

Import and preliminary calculations
Normalize data using a performance metric
Filter data that creates bias
Aggregate data
Analyze aggregated data to estimate the degradation rate and/or soiling loss

Steps 1 and 2 may be accomplished with the clearsky workflow (see the example notebook) which can help eliminate problems from irradiance sensor drift.

Degradation¶

The preferred method for degradation rate estimation is the year-on-year (YOY) approach (Jordan 2018), available in degradation.degradation_year_on_year(). The YOY calculation yields in a distribution of degradation rates, the central tendency of which is the most representative of the true degradation. The width of the distribution provides information about the uncertainty in the estimate via a bootstrap calculation. The example notebook uses the output of degradation.degradation_year_on_year() to visualize the calculation.

Two workflows are available for system performance ratio calculation, and illustrated in an example notebook. The sensor-based approach assumes that site irradiance and temperature sensors are calibrated and in good repair. Since this is not always the case, a 'clear-sky' workflow is provided that is based on modeled temperature and irradiance. Note that site irradiance data is still required to identify clear-sky conditions to be analyzed. In many cases, the 'clear-sky' analysis can identify conditions of instrument errors or irradiance sensor drift, such as in the above analysis.

The clear-sky analysis tends to provide less stable results than sensor-based analysis when details such as filtering are changed. We generally recommend that the clear-sky analysis be used as a check on the sensor-based results, rather than as a stand-alone analysis.

Soiling¶

Soiling can be estimated with the stochastic rate and recovery (SRR) method (Deceglie 2018). This method works well when soiling patterns follow a "sawtooth" pattern, a linear decline followed by a sharp recovery associated with natural or manual cleaning. soiling.soiling_srr() performs the calculation and returns the P50 insolation-weighted soiling ratio, confidence interval, and additional information (soiling_info) which includes a summary of the soiling intervals identified, soiling_info['soiling_interval_summary']. This summary table can, for example, be used to plot a histogram of the identified soiling rates for the dataset.

Availability¶

Evaluating system availability can be confounded by data loss from interrupted datalogger or system communications. RdTools implements two methods (Anderson & Blumenthal 2020) of distinguishing nuisance communication interruptions from true production outages with the availability.AvailabilityAnalysis class. In addition to classifying data outages, it estimates lost production and calculates energy-weighted system availability.

Install RdTools using pip¶

RdTools can be installed automatically into Python from PyPI using the command line:

pip install rdtools

Alternatively it can be installed manually using the command line:

Download a release (Or to work with a development version, clone or download the rdtools repository).
Navigate to the repository: cd rdtools
Install via pip: pip install .

On some systems installation with pip can fail due to problems installing requirements. If this occurs, the requirements specified in setup.py may need to be separately installed (for example by using conda) before installing rdtools.

For more detailed instructions, see the Developer Notes page.

RdTools currently is tested on Python 3.6+.

Usage and examples¶

Full workflow examples are found in the notebooks in example notebook. The examples are designed to work with python 3.7. For a consistent experience, we recommend installing the packages and versions documented in docs/notebook_requirements.txt. This can be achieved in your environment by first installing RdTools as described above, then running pip install -r docs/notebook_requirements.txt from the base directory.

The following functions are used for degradation and soiling analysis:

import rdtools

The most frequently used functions are:

normalization.normalize_with_expected_power(pv, power_expected, poa_global,
                                            pv_input='power')
  '''
  Inputs: Pandas time series of raw power or energy, expected power, and
     plane of array irradiance.
  Outputs: Pandas time series of normalized energy and POA insolation
  '''

filtering.poa_filter(poa_global); filtering.tcell_filter(temperature_cell);
filtering.clip_filter(power_ac); filtering.normalized_filter(energy_normalized);
filtering.csi_filter(poa_global_measured, poa_global_clearsky);
  '''
  Inputs: Pandas time series of raw data to be filtered.
  Output: Boolean mask where `True` indicates acceptable data
  '''

aggregation.aggregation_insol(energy_normalized, insolation, frequency='D')
  '''
  Inputs: Normalized energy and insolation
  Output: Aggregated data, weighted by the insolation.
  '''

degradation.degradation_year_on_year(energy_normalized)
  '''
  Inputs: Aggregated, normalized, filtered time series data
  Outputs: Tuple: `yoy_rd`: Degradation rate
    `yoy_ci`: Confidence interval `yoy_info`: associated analysis data
  '''

soiling.soiling_srr(energy_normalized_daily, insolation_daily)
  '''
  Inputs: Daily aggregated, normalized, filtered time series data for normalized performance and insolation
  Outputs: Tuple: `sr`: Insolation-weighted soiling ratio
    `sr_ci`: Confidence interval `soiling_info`: associated analysis data
  '''

availability.AvailabilityAnalysis(power_system, power_subsystem,
                                  energy_cumulative, power_expected)
  '''
  Inputs: Pandas time series system and subsystem power and energy data
  Outputs: DataFrame of production loss and availability metrics
  '''

Citing RdTools¶

The underlying workflow of RdTools has been published in several places. If you use RdTools in a published work, please cite the following as appropriate:

D. Jordan, C. Deline, S. Kurtz, G. Kimball, M. Anderson, "Robust PV Degradation Methodology and Application", IEEE Journal of Photovoltaics, 8(2) pp. 525-531, 2018 ‌‌
M. G. Deceglie, L. Micheli and M. Muller, "Quantifying Soiling Loss Directly From PV Yield," in IEEE Journal of Photovoltaics, 8(2), pp. 547-551, 2018
K. Anderson and R. Blumenthal, "Overcoming Communications Outages in Inverter Downtime Analysis", 2020 IEEE 47th Photovoltaic Specialists Conference (PVSC)." ‌‌
RdTools, version x.x.x, https://github.com/NREL/rdtools, https://doi.org/10.5281/zenodo.1210316
- Be sure to include the version number used in your analysis!

References¶

The clear sky temperature calculation, clearsky_temperature.get_clearsky_tamb(), uses data from images created by Jesse Allen, NASA’s Earth Observatory using data courtesy of the MODIS Land Group.
- https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MOD_LSTD_CLIM_M
- https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MOD_LSTN_CLIM_M

Other useful references which may also be consulted for degradation rate methodology include:

D. C. Jordan, M. G. Deceglie, S. R. Kurtz, "PV degradation methodology comparison — A basis for a standard", in 43rd IEEE Photovoltaic Specialists Conference, Portland, OR, USA, 2016, DOI: 10.1109/PVSC.2016.7749593.
Jordan DC, Kurtz SR, VanSant KT, Newmiller J, Compendium of Photovoltaic Degradation Rates, Progress in Photovoltaics: Research and Application, 2016, 24(7), 978 - 989.
D. Jordan, S. Kurtz, PV Degradation Rates – an Analytical Review, Progress in Photovoltaics: Research and Application, 2013, 21(1), 12 - 29.
E. Hasselbrink, M. Anderson, Z. Defreitas, M. Mikofski, Y.-C.Shen, S. Caldwell, A. Terao, D. Kavulak, Z. Campeau, D. DeGraaff, "Validation of the PVLife model using 3 million module-years of live site data", 39th IEEE Photovoltaic Specialists Conference, Tampa, FL, USA, 2013, p. 7 – 13, DOI: 10.1109/PVSC.2013.6744087.