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IEEE14

This code base is using the Julia Language and DrWatson to make a reproducible scientific project named

IEEE14

Authors: Andreas Heuermann.

Dependencies

To (locally) reproduce this project, do the following:

  1. Make sure you have local Julia package NaiveONNX in ../NaiveONNX.jl. If not update your git submodule with

    $ git submodule update --force --init --recursive

  2. Open a Julia console and run:

    julia> using Pkg
julia> Pkg.add("DrWatson") # install globally, for using `quickactivate`
julia> Pkg.activate("examples/IEEE14/")
julia> Pkg.instantiate()

  3. Python dependencies: You'll need Python 3 and the following packages installed:

    • pandas
    • numpy
    • tensorflow
    • tf2onnx
    pip3 install -r requirements.txt

  4. OpenModelica: Tested omc version v1.21.0-dev-288-g01b6764df5-cmake with OMSimulator version OMSimulator v2.1.1.post194-g75de4c6-linux-debug.

This will install all necessary packages for you to be able to run the scripts and everything should work out of the box, including correctly finding local paths.

You may notice that most scripts start with the commands:

using DrWatson
@quickactivate "IEEE14"

which auto-activate the project and enable local path handling from DrWatson.

Run Scripts

The Modelica model Examples.IEEE14.IEEE_14_Buses from the OpenIPSL library has one large non-linear equation system that is very hard to replace with a working surrogate.

Data Generation

Run script scripts/genAllData.jl to generate training data for the IEEE_14_Buses example. The default number of data points to generate is N=1000, but can be changed in the scripts.

julia> include("scripts/genAllData.jl")

The resulting training data can be found in data/sims/IEEE_14_Buses_/data.

Train with Flux

Run script scripts/trainFlux.jl to train an ANN with Flux.jl.

julia> include("scripts/trainFlux.jl")

Train with Tensorflow

Run script scripts/trainTensorflow.jl to train an ANN with Tensorflow by calling a Python script.

You'll need to update XLA_FLAGS in scripts/trainTensorflow.jl to point to your CUDA directory, e.g. /usr/local/cuda-12.1.

julia> include("scripts/trainTensorflow.jl")