Extended (Iterated) Kalman Filter for Airborne Wind Energy Systems (AWES)

This repository provides tools to process flight data and apply an Extended Kalman Filter (EKF) to estimate the dynamic state of a kite system in Airborne Wind Energy Systems (AWES). The EKF processes essential flight data inputs and generates accurate estimates of the kite's state, helping researchers and engineers model and analyze kite-based wind energy systems effectively.

Key Features

• State Estimation: Uses flight data to calculate the position, velocity, and other key states of the kite system.
• Aerodynamic and Tether Modeling: Models aerodynamic coefficients, tether shape, and forces to provide a complete system view.
• Wind Estimation: Outputs wind velocity as part of the EKF-based estimation process.

Minimum Required Inputs

To effectively estimate the kite’s state, the following inputs are required:

• Kite Position
• Kite Velocity
• Tether Force
• Tether Reel-Out Speed
• Kite Acceleration (optional, if a Kite Control Unit (KCU) is present and modeled)

Outputs

The EKF processing produces the following outputs:

• Kite Position and Velocity Estimations
• Aerodynamic Coefficients
• Tether Shape and Force
• Wind Velocity
• Additional Derived Metrics

---

Installation Instructions

1. Clone the repository:

   git clone https://github.com/ocayon/EKF-AWE

2. Navigate to the repository folder:

   cd EKF-AWE

3. Create a virtual environment:

   • Linux or Mac:

     python3 -m venv venv

   • Windows:

     python -m venv venv

4. Activate the virtual environment:

   • Linux or Mac:

     source venv/bin/activate

   • Windows:

     .\venv\Scripts\activate

5. Install the required dependencies:

   • For users:

     pip install .

   • For developers:

     pip install -e .[dev]

6. To deactivate the virtual environment:

   deactivate

---

Dependencies

• numpy
• matplotlib
• casadi>=3.6.0
• pandas
• pytest
• pyyaml
• control
• scipy
• seaborn
• dataclasses
• h5py

---

Example Usage

An example dataset is provided for the LEI V3 kite from Kitepower, flown on 2019-10-08.

• Flight Data: 2019-10-08_11.csv in data\flight_logs\v3
• Configuration File: v3_config.yaml in data\config
• Processing Script: process_v3_data.py in data\data_postprocessors

To analyze this dataset:

• Run run_analysis.py and select the file 2019-10-08_11.csv, v3_config.yaml and process_v3_data.py.

python examples\run_analysis.py

• Run plot_analysis.py to visualize the results.

python examples\plot_analysis.py

New Dataset Analysis

To analyze an AWES dataset, follow these steps:

1. Add Necessary Files

• Configuration File: Add a .yaml configuration file for the kite you want to analyze in data/config.
• Processing Script: Add a data processing script to data/data_postprocessors that processes raw flight data and saves it to postprocess_data, following the naming conventions in docs/CSV_Input_File_Specifications.md.
• Flight Data: Create a folder in data and add the raw flight data. File names should start with the format YYYY-MM-DD_HH (e.g., 2019-10-08_11.csv).

2. Run Analysis

• Run run_analysis.py from the examples folder.
• When prompted, select the configuration file, processing script, and flight data file.

3. Plot Data

• Run plot_analysis.py and select the processed flight data file for visualization.

---

Contributing Guide

Contributions are welcome! Here’s how you can contribute:

1. Create an issue on GitHub for any bugs or feature requests.
2. Create a branch from this issue:

   git checkout -b issue_number-new-feature

3. Implement your feature or fix.
4. Verify functionality using pytest:

   pytest

5. Commit your changes with a descriptive message:

   git commit -m "#<issue_number> <message>"

6. Push your changes to GitHub:

   git push origin branch-name

7. Create a pull request with base:develop to merge your feature branch.
8. Once the pull request is accepted, close the issue.

---

Citation

If you use this project in your research, please consider citing it. Citation details can be found in the CITATION.cff file.

---

License

This project is licensed under the MIT License. See the LICENSE file for details.

---

Copyright

Technische Universiteit Delft hereby disclaims all copyright interest in the code "EKF-AWE" written by the Author(s).

Prof. Dr. H.G.C. (Henri) Werij, Dean of Faculty of Aerospace Engineering

Copyright © 2024 Oriol Cayon