This Matlab Simulink module can be used to conduct time-domain simulations on the IEEE 24-bus system. (Mingjian Tuo, @July 2020)

• ‘IEEE24_bus.m’: generators and load settings of proposed model.
• ‘IEEE24_bus.slx’: simulink model: system architecture, generator models, load models, and simulation output.
• ‘IEEE24bus_simplified.slx’: simulink model of Patrice Brunelle’s work as benchmark.
• ‘IEEE24bus_simplified_benchmark.m’: generators settings of P. Brunelle’s benchmark model.

Test System Description:

• The IEEE 24-bus power system used here is one area out of the three-area system modified IEEE RTS-96 reliability test system (73-bus). The original reference is: “The IEEE Reliability Test System-1996. A report prepared by the Reliability Test System Task Force of the Application of Probability Methods Subcommittee” and link is here.

Citation:

If you use any module/data here for your work, please cite the following papers as your references:

1. Mingjian Tuo and Xingpeng Li, “Long-term Recurrent Convolutional Networks-based Inertia Estimation using Ambient Measurements”, IEEE IAS Annual Meeting, Detroit, MI, USA, Oct. 2022.
2. Mingjian Tuo and Xingpeng Li, “Machine Learning Assisted Inertia Estimation using Ambient Measurements”, IEEE Transactions on Industry Applications, Apr. 2023.

Paper1 website: https://rpglab.github.io/papers/MJ-Tuo-PGS-LRCN/

Paper2 website: https://rpglab.github.io/papers/MJ-Tuo_ML-Inertia-Est/

Acknowledgment:

This work is modified based on Patrice Brunelle’s benchmark model whose copyright notice is copied in the file “copyright_notice.txt”. The modifications we made are as follows:

• Nodes and lines are defined based on IEEE 24-bus system;
• Generators are increased from 10 to 35; basic parameters are defined in ‘IEEE24_bus.m’;
• Generator models are extended based on the benchmark case;
• Information about generator rotating speed and bus voltage level are added.

Contributions:

Mingjian Tuo developed this dataset and Simulink module. Xingpeng Li supervised this work.

Contact:

Dr. Xingpeng Li

University of Houston

Email: xli83@central.uh.edu

Website: https://rpglab.github.io/

License:

This work is licensed under the terms of the Creative Commons Attribution 4.0 (CC BY 4.0) license.

Disclaimer:

The author doesn’t make any warranty for the accuracy, completeness, or usefulness of any information disclosed; and the author assumes no liability or responsibility for any errors or omissions for the information (data/code/results etc) disclosed.