Finding Faults in 3-phase electrical lines using lossless reconstruction
The problem of finding faults in oscillating data in the form of 3-phase current and voltage values is considered. While these kind of data have high resolutions, we give a solution to multivariate changing point detection, framed as an anomaly detection problem from predetermined cycles in the data.
![Sample output of service](/sites/default/files/styles/16_9_100/public/2022-09/anomaly2.png?itok=3JsPYnsM)
This solution utilizes signal reconstruction loss with autoencoders and 1D convolutional layers for fault detection in electrical line data. The service is packaged as a docker container and implemented in python. Tensorflow 2.10 is utilized for the machine learning components implementation.
This solution was developed under the I-NERGY project.
We provide a docker container to easily build and run an autoencoder model to discover faults in 3-phase electrical lines (comprising the current values and voltage values). The model utilizes a 6-column csv file with of phase 1 to phase 3 current and voltage values , respectively.. To build the Docker container we provide the following files:
- Dockerfile
- inference.py
- requirements.txt
The inference file takes a csv file and makes sequences of 100 cycles - padding the remainder with previous cycle values.
The scripts were implemented in tensorflow.