Machine Learning AMR Reley Differential Curve

Implementing AMR Reley Differential Curve with Deep Neural Network.

A novel approach to the implementation differential protection scheme by using a Deep Neural Network Dataset has been obtained from Differential Characteristic plane in the Vebko AMPro software.

Features

• Using Python Tensorflow to build a Deep Neural Network model
• Converting the Tensorflow model to tflite for running on Embedded Board ARM Architecture
• Using Golang TFLite to be able to easily run tflite model
• Running on Xilinx Zynq-7020 Embedded Board
• Usable via Docker file

Installation

For Linux/MacOs amd64:

  go build main.go

For xilinx Zynq-7020 (ARM-based computers):

  sudo apt-get install gcc-arm-linux-gnueabihf
  
  CGO_ENABLED=1 GOOS=linux GOARCH=arm CC=arm-linux-gnueabihf-gcc go build -o main

Running

This running for ubuntu/MacOs amd64:

  ./main

This running for xilinx Zynq-7020 (ARM-based computers):

  export LD_LIBRARY_PATH=./arm
  
  ./main

Running with Docker

First of all, clone and the repo then run

  docker build -t dnn .

After pulling and building the image, You can get the result like this

  docker run --rm -t amr ./main

Or you can go to the container for running it manually like this

  docker run -it amr

More Info

Differential Characteristic in the AMPro software

Graph of the Deep Neural Network

Model Accuracy Plot

Model Loss Plot

Note:

If you had issue and got standard_init_linux.go:211: exec user process caused "exec format error error, try this solution.

Collaborators

• Nima Akbarzade - Dr.Mohammad Parpaei - Dr.Mohammad Haji Seyed Javadi - Sajad Ansari

License

MIT