Arc Fault Circuit Interrupter (AFCI) solution

Did you know that one out of four fires is triggered by an electrical issue? Most of them are caused by electrical arcs. That is why it is critical to detect them and react as quickly as possible.

As the market evolves, we see new and different types of applications emerging that request more & more innovations to protect against electrical arcs: solar panels, batteries, power tools, e-bikes and so on.

Rule-based algorithms exist today to improve the safety of electrical installations, but their adaptability is limited, and they generate a large number of false positives, thus reducing machine yield. There are also cloud-based AI solutions, which offer greater accuracy but come with concerns about latency and privacy.

This is where edge AI solutions offer the ideal compromise. Requiring no connectivity or external processing, these solutions enable instant detection and response, while eliminating privacy and security concerns since data is processed locally on devices. Their ability to continuously learn and adapt to different environments also reduces false positive rate and improves efficiency.

In this use case, we will show you how you can simply build your own arc detection mechanism with edge AI and STM32!

Approach

Here, we opted for the NanoEdge AI Studio tool due to its user-friendly interface and ease of use. NanoEdge automatically selects and generates the most appropriate model based on the user’s data. If we had a pre-trained neural network model, we could have used STM32Cube.AI to compress and optimize it for use in an embedded environment.

Our approach was as follows:

We used a custom arc fault circuit interrupter (AFCI) board with an STM32G4 at the heart of it for this demonstration.

We first collected the normal operating data from the setup, totaling around 1000 signals.

Then, we gathered arc fault data, also around 1000 signals.

Both sets of signals were imported into a classification project within NanoEdge AI Studio.

The tool then generated the best AI library for this project and we integrated it into the code to monitor the current and trigger an alert if an arc is detected.

Sensor

Current Sensor.

Data

2 classes of data: Arc fault, No arc
Length data: 2048 * 1 axis
Data rate: 150 kHz

Results

2 classes (no arc & arc fault):
100% accuracy, 16.7 Kbytes RAM, 0.5 Kbytes Flash

A green point means we are able to correctly predict if the finish will pass a visual inspection or not.
A red point means we were incorrect.