The paper introduces an analytical approach to predict the no-load flux density spatial repartition of inner permanent magnet tubular-linear synchronous machines (IPM T-LSMs). It considers a trapezoidal waveform whose maximum value is predicted using a simple magnetic equivalent circuit of an elementary part of the machine. Then, the accuracy of the proposed approach is enhanced by the incorporation of a mover permeance function that accounts for the PM luxconcentrating arrangement.
A Traffic Light Controller PETRI_NET (Finite State Machine) Implementation.
An implementation of FSM approach can be followed in systems whose tasks constitute a well-structured list so all states can be easily enumerated. A Traffic light controller represents a relatively complex control function
This file would need to be unzipped for access
This dataset contains system data used in the numerical simulations in the paper Unlocking Reserves with Smart Transmission Switching by Raphael Saavedra, Alexandre Street, and José M. Arroyo.
This paper introduces a novel model for the electric current dissipation through a horizontally stratified multilayer soil, for any point source location and calculated for every coordinate in space. The soil characteristic functions, which describe the application of boundary conditions for each layer transition
The data are in simple spreadsheet files to ease the reproduction of paper results.
This data set containts the detailed parameters of the modified Kundur's two-area system that is used in the manuscript "Hybrid Symbolic-Numeric Library for Power System Modeling and Analysis"
Dataset of the paper entitled: "A stochastic distribution system market clearing and settling model with distributed renewable energy constraints"
This dataset is related to the paper entitled: "A stochastic distribution system market clearing and settling model with distributed renewable energy constraints"
The purpose of distribution network reconfiguration (DNR) is to determine the optimal topology of an electricity distribution network, which is an efficient measure to reduce network power losses. Electricity load demand and photovoltaic (PV) output are uncertain and vary with time of day, and will affect the optimal network topology. Single-hour deterministic DNR is incapable of handling this uncertainty and variability. Therefore, this paper proposes to solve a multi-hour stochastic DNR (SDNR).
The dataset contains internal faults in power transformer and phase angle regulators (PAR) in a 5-bus interconnected system. It also has 6 other power system transients which include magnetising inrush, sympathetic inrush, external faults with CT saturation, capacitor switching, non-linear load switching, and ferroresonance. There are 88128 internal fault files and 12780 files of other transient disturbances. The faults and transients are simulated in PSCAD/EMTDC and the output files are in text format.
Detailed description of all data files is provided in the *Dataset_Readme.pdf* file along with the dataset.
 Pallav K. Bera, Can Isik, Vajendra Kumar, "Transients and Faults in Power Transformers and Phase Angle Regulators”, IEEE Dataport, 2020. [Online]. Available: http://dx.doi.org/10.21227/1d1w-q940
Dataset containing normal insulator images captured by UAVs and synthetic defective insulator images.
Dataset containing UAV images divided in 2 subsets of normal and defective insulators. No intructions needed.
Grid planning benchmark dataset and scripts for the paper "A Hybrid Optimization Method Combining Network Expansion Planning and Switching State Optimization".
- Clone the repository
- Install pandapower and PowerModels.jl (see 'Installation Instructions')
- Run the optimization, e.g., with
This repository contains these subfolders with the following data:
- "power_system_data" containing the power system benchmark cases included additional line and replacement measures
- "scaled_loadcases" contains the data to reproduce the mathematical programming results
Main script to run the hybrid optimization method
The script "run_greedy.py" runs the greedy heuristic combined with the PowerModels.jl optimization framework. An example call starting a combined optimization of switching measures and line measures for the 'brigande' test case is:
python run_greedy.py -grid 'brigande' -kind 'combo' -max_iterations 3 -res_dir './results'
See more options with:
python run_greedy.py -h
If you have trouble using the PowerModels.jl interface, you can just run the script without PowerModels.jl. This gives you a greedy optimization of switching and line measures. Try:
python run_greedy.py -pm ''
Additional script to run the PowerModels.jl Optimization
The repository also contains a script "run_powermodels.py" which runs the PowerModels.jl based results with the pandapower-python to PowerModels.jl-julia interface
The script has several command line options:
- model (str) - The PowerModels.jl power model e.g. "DCPPowerModel"
- solver (str) - The solver to use, e.g. "juniper", "gurobi"
- grid (str) - optional if you only want to calculate one grid, e.g. "brigande"
- kind (str) - the optimizations to run, e.g. "tnep,ots,repl". Can only be a part of these like "tnep,repl"
Example to run the model "DCPPowerModel" with "gurobi" as a solver for the grid "brigande" with the time series loadcases "ts" for the REPl "repl" problem:
python-jl run_powermodels.py --model="DCPPowerModel" --solver="gurobi" --grid="brigande" --kind="repl"
You need the following software to run the script:
- pandapower: https://github.com/e2nIEE/pandapower
- PowerModels.jl: https://github.com/lanl-ansi/PowerModels.jl/
- tqdm: https://github.com/tqdm/tqdm
The python-julia interface might require some additional installations. See the following link for details: https://pandapower.readthedocs.io/en/latest/opf/powermodels.html