pydynamicestimator 0.1.0

A dynamic estimator for power systems

PowerDynamicEstimator (pydynamicestimator), version 0.1.0 released on 04.12.2024 https://doi.org/10.5905/ethz-1007-842

About

PowerDynamicEstimator is a state estimation tool for power systems using a recursive dynamic state estimator based on an incomplete nonlinear DAE (Differential Algebraic Equations) model. The estimator combines dynamic evolution equations, algebraic network equations, and phasor measurements to recursively estimate dynamic and algebraic states, making it suitable for centralized power systems dynamic estimation even when some component models are missing (unknown).

Citation

If you use PowerDynamicEstimator in your academic work, please cite the following paper:

@article{powerdynamicestimator, author = {Katanic, Milos and Lygeros, John and Hug, Gabriela}, title = {Recursive dynamic state estimation for power systems with an incomplete nonlinear DAE model}, journal = {IET Generation, Transmission & Distribution}, volume = {18}, number = {22}, pages = {3657-3668}, keywords = {differential algebraic equations, Kalman filters, state estimation}, doi = {https://doi.org/10.1049/gtd2.13308}, url = {https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/gtd2.13308}, eprint = {https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/gtd2.13308}, abstract = {Power systems are highly complex, large-scale engineering systems subject to many uncertainties, which makes accurate mathematical modeling challenging. This article introduces a novel centralized dynamic state estimator designed specifically for power systems where some component models are missing. Including the available dynamic evolution equations, algebraic network equations, and phasor measurements, the least squares criterion is applied to estimate all dynamic and algebraic states recursively. The approach generalizes the iterated extended Kalman filter and does not require static network observability, relying on the network topology and parameters. Furthermore, a topological criterion is established for placing phasor measurement units (PMUs), termed topological estimability, which guarantees the uniqueness of the solution. A numerical study evaluates the performance under short circuits in the network and load changes and shows superior tracking performance compared to robust procedures from the literature with computational times in accordance with the typical PMU sampling rates.}, year = {2024} } The full version of the paper is available on ArXiv: https://arxiv.org/abs/2305.10065v2.

Installation

To get started with PowerDynamicEstimator, follow the installation instructions:

1. Clone the repository: git clone https://gitlab.nccr-automation.ch/mkatanic/powerdynamicestimator cd PowerDynamicEstimator
2. Install poetry: pip install poetry==1.8.2
3. Install dependencies: poetry install
4. Activate the virtual environment: poetry shell
5. Run the main: poetry run python main.py

Configuration:

The config.py script contains parameters that are easy to modify. Please ensure that you have the correct values for your system’s configuration.

Usage Running the Estimator Edit the config.py file to specify the parameters for your system setup (e.g., power generation, load distribution, node configurations).

The primary script to run is main.py.

Important Notes

Warning: Currently, only one injector per node is supported due to initialization ambiguity. If you need to handle more than one injector per node, you will need to create another node connected with a branch of very small impedance to simulate this.

Parameters System dynamic and static parameters including the topology are specified in data subfolder. Define the loads, generators, and their characteristics at specific nodes in the power system.Prefixes 'sim_' and 'est_' denote if the data are used for simulation or estimation. Disturbances are specified in separate files followed by '_dist'. PMU measurements used for estimation and their associated characteristics are defined in est_param.txt.

The configuration parameters that are easy to change are located in the config.py file. Kalman Filter Settings: Adjust parameters related to the recursive state estimation process (e.g., noise covariance, initial error).

License This software is licensed under the GNU General Public License v3.0 (GPL-3.0).