https://towardsdatascience.com/thunderstruck-disaster-cnn-visualization-of-ac-power-lines-2a57fab30f09

NET Centre at VŠB is trying to detect partial discharge patterns from overhead power lines by analyzing power signals. This Kaggle challenge was a fun one for any electrical power enthusiasts. Ideally, we would be able to detect the slowly increasing damage to the power lines before it suffers a power outage or starts an electrical fire. However, there are many miles of powerlines. Also, damage to powerline isn’t immediately apparent, small damage from about anything (trees, high wind, manufacturing flaws, etc.) can be the start of cascading damages from discharges which increase the likely hood of failure in the future. It is a great goal. If we can successfully estimate the lines that need repairs, we can reduce costs while maintaining the flow of electricity. I mean money talks.

The tabular data set is very massive with 800,000 points for each signal and in total comes to about 10 GB. The bloated set wasn’t what I was looking for just coming off the Microsoft Malware. I had spent so much time just trying to get that dataset into my computer and was taken back at the possibility of doing so much data management again.