Python implementation of the Dynamic Extended Equal Area Criterion (DEEAC) to analyze the transient stability in transmission networks.
- About DEEAC
- Contributors
- License
- Reference paper
- Requirements
- Installation
- Usage
- Code Architecture
- Getting started
For transient stability analysis (TSA) of a multi-machine power system, the extended equal area criterion (EEAC) method applies the classic equal area criterion (EAC) concept to an approximate one-machine infinite bus (OMIB) equivalent of the system to find the critical clearing angle. The system critical clearing time (CCT) can then be obtained by numerical integration of OMIB equations. The EEAC method was proposed in the 1980s and 1990s as a substitute for time-domain simulation for transmission system operator (TSO) to provide fast TSA with the limited computational power of those days. To ensure the secure operation of the power system, TSOs have to identify and prevent potential critical scenarios by offline analyses of a few dangerous ones. These days, due to increased uncertainties in electrical power systems, the number of these critical scenarios is increasing tremendously, calling for fast TSA techniques once more. Among them, the EEAC is a unique approach that provides not only valuable information, but also a graphical representation of system dynamics.
The EEAC method consists of four main stages:
- Critical clusters identification (CCI) to identify the critical machines (CMs) and to form a number of possible critical clusters (CCs) of synchronous machines. It can be based on acceleration, composite, during-fault trajectory, or (post-fault) trajectory criteria.
- OMIB model formation for each CC, it can be of type zero offset OMIB (ZOOMIB), constant offset OMIB (COOMIB), or dynamic OMIB (DOMIB).
- EAC for calculation of the critical clearing angle (CCA) of each OMIB equivalent model
- Numerical integration of each OMIB equation to find the CCT corresponding to the OMIB CCA that can be based on Taylor series or numerical integration.
Finally, EEAC selects the CC with the smallest CCT as the true CC.
These functions can be combined in different ways to form a branch of functions to make an estimation of the CCT and the CC.
This code is the fruit of a joint project between Haulogy Intelligent Systems Solutions and RTE. The main contributors are listed in AUTHORS.md.
This project and is licensed under the terms of the Mozilla Public License V2.0. See LICENSE for more information.
If you use this EEAC implementation in your work or research, it would be appreciated if you could quote the following paper in your publications or presentations:
A. Bahmanyar, D. Ernst, Y. Vanaubel, Q. Gemine, C. Pache and P. Panciatici,
“Extended Equal Area Criterion Revisited: a direct method for fast transient stability analysis”,
published in Energies 2021, 14.
- Python >= 3.6.4
The package and its dependencies should be installed in a python virtual environment. First, create a virtual environment called venv:
python -m venv venvActivate the virtual environment:
source venv/bin/activateMake sure that pip is up-to-date:
(venv) pip install --upgrade pipPull the git repository and install DEEAC and its dependencies by running the following command from the cloned repository:
(venv) pip install -e .[tests]If you don't plan to run the tests, you can omit installing the test dependencies by running instead (venv) pip install -e .
The regular way to run EEAC is as follows:
python -m deeac \
# Eurostag static network file
-e /PATH/TO/STATIC/NETWORK/DATA/fech.ech \
# Eurostag dynamic network file
-d /PATH/TO/DYNAMIC/NETWORK/DATA/fdta.dta \
# Load flow data
-l /PATH/TO/LOADFLOW/fech.lf \
# Eurostag sequence file describing the fault
-s /PATH/TO/SEQFILE.seq \
# Tree branch describing the execution path of DEEAC
-t /PATH/TO/TREE/BRANCH.json \
# Optional output folder saving the intermediary results and plots
-o /PATH/TO/OUTPUT/FOLDER \
# Optional output file saving the critical cluster and the CCT
-j /PATH/TO/OUTPUT/FILE.json \
# Optional list of never critical generators
-n GENERATOR \
# Verbose
-v \
# Rewrite the output folder
-rA more compact call is possible provided that the arguments listed above are embedded within the json configuration file. Then, the command line becomes:
python -m deeac -g branch.json
DEEAC prints out report presenting the main results which can be saved as json, and it can also produce plots of the graph and area plots of the EEAC itself.
Configuration:
Type of selector: MinCriticalClusterSelector
Inputs:
Cluster N:
...
Outputs:
Critical generators: [GENERATOR NAMES]
Critical angle: X.XXX rad [X.XXX deg]
Critical time: XXX.XXX ms
Maximum angle: X.XXX rad [XXX.XXX deg]
Maximum time: XXX.XXX ms
OMIB stability state: [STABILITY STATE]
OMIB swing state: [FORWARD OR BACKWARD]
* Generators disconnected from the main network component due to the mitigations: [GENERATOR NAMES]
Execution times:
> Input execution tree file reading: X.XXX seconds
> Input data files reading: X.XXX seconds
> Event processing: X.X seconds
> EEAC tree execution: X.XXX seconds
> Total time: XX.XXX seconds
A plots of the generator angle trajectory can be generated, as well as a plot of the EAC area. See the section Nodes configuration, section GTC and EAC.
Output file that can be set as input in the command line, contains only the critical results of the critical_cluster_selector_node or omib_trajectory_calculator: node id, CCT, OMIB stability state, critical cluster
A graphical representation of the branch, chaining the nodes to one another.
A txt file is generated per node, containing its report, detailing its inputs, outputs, and execution status. Added together they represent the report mentioned above.
DEEAC follows the hexagonal design pattern. It defines a perimeter, called the "domain", representing the inner part of the software. It contains a data model describing the network, load flows, event, etc..., and methods, called "services", communicating with each other and working with data only from the data model. There is then a third type of component, called the ports, which are abstract classes templating the communication method between the domain and the outside world. Services using data from outside model can use implementations of these abstract classes, called "adapters", specifically designed for their specific tasks. Namely, each data parsing services are using adapter code design to parse Eurostag input data, and convert them into a network as defined by the data model.
The folder examples/eurostag_cases contains a bunch of very simple cases that can be run with EEAC. Each case contains:
- a .ech file, which contains the topology data,
- a .lf file, which contains the power flow output data,
- a .dta file, which contains the dynamic data, namely the inertias and transient reactances,
- a .seq file, which list the sequence of events for the fault (location of the fault and action of breakers),
- a .json file, which describes the DEEAC configuration,
- a cmd_eeac.txt file, which contain the command line to run in the terminal (remember to source the virtual environment first!).
Besides, the folder examples/configuration_files contains several DEEAC configuration files that could be used in order to run EEAC. branch_1.json or branch_1_global.json are recommended.