# OpenOA Documentation

## Software Overview

OpenOA [^1] is a software framework written in Python for assessing wind plant performance using
operational assessment (OA) methodologies that consume time series data from wind plants. The goal
of the project is to provide an open source implementation of common data structures, analysis
methods, and utility functions relevant to wind plant OA, while providing a platform to collaborate
on new functionality.

Development of OpenOA was motivated by the Wind Plant Performance Prediction (WP3) Benchmark project
[^2], led by the National Renewable Energy Laboratory (NREL), which focuses on quantifying and
understanding differences between the expected and actual energy production of wind plants. To
support the WP3 Benchmark project, OpenOA was initially developed to provide a baseline
implementation of a long-term operational annual energy production (AEP) estimation method. It has
since grown to incorporate several more OA analysis methods, lower-level utility functions, and a
schema for time-series data from wind power plants.

```{warning}
OpenOA is a research software library and is released under a BSD-3 license. Please refer
to the accompanying [license file](https://github.com/NREL/OpenOA/blob/docs/main/LICENSE.txt) for
the full terms. We encourage caution, use of best practices, and engagement with subject matter
experts when performing any data analysis.
```

### Analysis Methods

| Name | Description | Citations |
| --- | --- | --- |
| `MonteCarloAEP` | This routine estimates the long-term annual energy production (AEP) of a wind power plant (typically over 10-20 years) based on operational data from a shorter period of record (e.g., 1-3 years), along with the uncertainty. | [^3], [^4] |
| `TurbineLongTermGrossEnergy`| This routine estimates the long-term turbine ideal energy (TIE) of a wind plant, defined as the long-term AEP that would be generated by the wind plant if all turbines operated normally (i.e., no downtime, derating, or severe underperformance, but still subject to wake losses and moderate performance losses), along with the uncertainty. | [^5] |
| `ElectricalLosses`| The ElectricalLosses routine estimates the average electrical losses at a wind plant, along with the uncertainty, by comparing the energy produced at the wind turbines to the energy delivered to the grid. | [^5] |
| `EYAGapAnalysis`| This class is used to perform a gap analysis between the estimated AEP from a pre-construction energy yield estimate (EYA) and the actual AEP. The gap analysis compares different wind plant performance categories to help understand the sources of differences between EYA AEP estimates and actual AEP, specifically availability losses, electrical losses, and TIE. | [^5] |
| `WakeLosses`| This routine estimates long-term internal wake losses experienced by a wind plant and for each individual turbine, along with the uncertainty. | [^6]. Based in part on approaches in [^7], [^8], [^9] |
| `StaticYawMisalignment`| The StaticYawMisalignment routine estimates the static yaw misalignment for individual wind turbines as a function of wind speed by comparing the estimated wind vane angle at which power is maximized to the mean wind vane angle at which the turbines operate. The routine includes uncertainty quantification. **Warning: This method has not been validated using data from wind turbines with known static yaw misalignments and the results should be treated with caution.** | Based in part on approaches in [^10], [^11], [^12], [^13], [^14] |

### PlantData Schema

OpenOA contains a `PlantData` class, which is based on Pandas data frames and provides a
standardized base schema to combine raw data from wind turbines, meteorological (met) towers,
revenue meters, and reanalysis products, such as MERRA-2 or ERA5. Additionally, the `PlantData`
class can perform some basic validation for the data required to perform the operational analyses.

### Utility Functions

Lower-level utility modules are provided in the utils subpackage.
They can also be used individually to support general wind plant data analysis activities.
Some examples of utils modules include:

- **Quality Assurance**: This module provides quality assurance methods for identifying potential
  quality issues with SCADA data prior to importing it into a `PlantData` object.
- **Filters**: This module provides functions for flagging operational data based on a range of
  criteria (e.g., outlier detection).
- **Power Curve**: The power curve module contains methods for fitting power curve models to SCADA data.
- **Imputing**: This module provides methods for filling in missing data with imputed values.
- **Met Data Processing**: This module contains methods for processing meteorological data, such as
  computing air density and wind shear coefficients.
- **Plotting**: This module contains convenient functions for creating plots, such as power curve
  plots and maps showing the wind plant layout.

For further information about the features and citations, please see the
[OpenOA documentation website](https://openoa.readthedocs.io/en/latest/).

## Installation

Compatible with Python 3.8 through 3.11 with pip.

We strongly recommend using the Anaconda Python distribution and creating a new conda environment
for OpenOA. You can download Anaconda through
[their website.](https://www.anaconda.com/products/individual)

After installing Anaconda, create and activate a new conda environment with the name "openoa-env":

```bash
conda create --name openoa-env python=3.10
conda activate openoa-env
```

### Source Code

Clone the repository and install the library and its dependencies using pip:

```bash
git clone https://github.com/NREL/OpenOA.git
cd OpenOA
pip install .
```

### Pip

```bash
pip install openoa
```

## Citing OpenOA

**To cite analysis methods or individual features:** Please cite the original authors of these
methods, as noted in the [documentation](#analysis-methods) and inline comments.

**To cite the open-source software framework as a whole, or the OpenOA open source development
effort more broadly,** please use citation [^1], which is provided below in BibTeX:

```bibtex
   @article{Perr-Sauer2021,
      doi = {10.21105/joss.02171},
      url = {https://doi.org/10.21105/joss.02171},
      year = {2021},
      publisher = {The Open Journal},
      volume = {6},
      number = {58},
      pages = {2171},
      author = {Jordan Perr-Sauer and Mike Optis and Jason M. Fields and Nicola Bodini and Joseph C.Y. Lee and Austin Todd and Eric Simley and Robert Hammond and Caleb Phillips and Monte Lunacek and Travis Kemper and Lindy Williams and Anna Craig and Nathan Agarwal and Shawn Sheng and John Meissner},
      title = {OpenOA: An Open-Source Codebase For Operational Analysis of Wind Farms},
      journal = {Journal of Open Source Software}
   }
```

## Documentation Table of Contents

```{eval-rst}
.. toctree::
   :maxdepth: 2

   getting_started/index
   examples/index
   api/index
   credit
```

## References

[^1]: Perr-Sauer, J., and Optis, M., Fields, J.M., Bodini, N., Lee, J.C.Y., Todd, A., Simley, E., Hammond, R., Phillips, C., Lunacek, M., Kemper, T., Williams, L., Craig, A., Agarwal, N., Sheng, S., and Meissner, J. OpenOA: An Open-Source Codebase For Operational Analysis of Wind Farms. *Journal of Open Source Software*, 6(58):2171 (2022). https://doi.org/10.21105/joss.02171.

[^2]: Fields, M. J., Optis, M., Perr-Sauer, J., Todd, A., Lee, J. C. Y., Meissner, J., Simley, E., Bodini, N., Williams, L., Sheng, S., and Hammond, R.. Wind plant performance prediction benchmark phase 1 technical report, NREL/TP-5000-78715. Technical Report, National Renewable Energy Laboratory, Golden, CO (2021). https://doi.org/10.2172/1826665.

[^3]: Bodini, N. & Optis, M. Operational-based annual energy production uncertainty: are its components actually uncorrelated? *Wind Energy Science* 5(4):1435–1448 (2020). https://doi.org/10.5194/wes-5-1435-2020.

[^4]: Bodini, N., Optis, M., Perr-Sauer, J., Simley, E., and Fields, M. J. Lowering post-construction yield assessment uncertainty through better wind plant power curves. *Wind Energy*, 25(1):5–22 (2022). https://doi.org/10.1002/we.2645.

[^5]: Todd, A. C., Optis, M., Bodini, N., Fields, M. J., Lee, J. C. Y., Simley, E., and Hammond, R. An independent analysis of bias sources and variability in wind plant pre‐construction energy yield estimation methods. *Wind Energy*, 25(10):1775-1790 (2022). https://doi.org/10.1002/we.2768.

[^6]: Simley, E., Fields, M. J., Perr-Sauer, J., Hammond, R., and Bodini, N. A Comparison of Preconstruction and Operational Wake Loss Estimates for Land-Based Wind Plants. Presented at the NAWEA/WindTech 2022 Conference, Newark, DE, September 20-22 (2022). https://www.nrel.gov/docs/fy23osti/83874.pdf.

[^7]: Barthelmie, R. J. and Jensen, L. E. Evaluation of wind farm efficiency and wind turbine wakes at the Nysted offshore wind farm, *Wind Energy* 13(6):573–586 (2010). https://doi.org/10.1002/we.408.

[^8]: Nygaard, N. G. Systematic quantification of wake model uncertainty. Proc. EWEA Offshore, Copenhagen, Denmark, March 10-12 (2015).

[^9]: Walker, K., Adams, N., Gribben, B., Gellatly, B., Nygaard, N. G., Henderson, A., Marchante Jimémez, M., Schmidt, S. R., Rodriguez Ruiz, J., Paredes, D., Harrington, G., Connell, N., Peronne, O., Cordoba, M., Housley, P., Cussons, R., Håkansson, M., Knauer, A., and Maguire, E.: An evaluation of the predictive accuracy of wake effects models for offshore wind farms. *Wind Energy* 19(5):979–996 (2016). https://doi.org/10.1002/we.1871.

[^10]: Bao, Y., Yang, Q., Fu, L., Chen, Q., Cheng, C., and Sun, Y. Identification of Yaw Error Inherent Misalignment for Wind Turbine Based on SCADA Data: A Data Mining Approach. Proc. 12th Asian Control Conference (ASCC), Kitakyushu, Japan, June 9-12 (2019). 1095-1100.

[^11]: Xue, J. and Wang, L. Online data-driven approach of yaw error estimation and correction of horizontal axis wind turbine. *IET J. Eng.* 2019(18):4937–4940 (2019). https://doi.org/10.1049/joe.2018.9293.

[^12]: Astolfi, D., Castellani, F., and Terzi, L. An Operation Data-Based Method for the Diagnosis of Zero-Point Shift of Wind Turbines Yaw Angle. *J. Solar Energy Engineering* 142(2):024501 (2020). https://doi.org/10.1115/1.4045081.

[^13]: Jing, B., Qian, Z., Pei, Y., Zhang, L., and Yang, T. Improving wind turbine efficiency through detection and calibration of yaw misalignment. *Renewable Energy* 160:1217-1227 (2020). https://doi.org/10.1016/j.renene.2020.07.063.

[^14]: Gao, L. and Hong, J. Data-driven yaw misalignment correction for utility-scale wind turbines. *J. Renewable Sustainable Energy* 13(6):063302 (2021). https://doi.org/10.1063/5.0056671.