Industrial

Electrical fault detection and classification

Detect and classify electrical anomalies in a power system.

Electrical fault detection and classification

Industrial

NanoEdge AI Studio

Predictive maintenance

Current sensor

In the realm of electrical systems, identifying and resolving faults swiftly is paramount to ensure safety, prevent equipment damage and maintain uninterrupted operations. From power distribution networks to complex industrial systems, accurately detecting and classifying electrical faults is critical for the smooth operation of these infrastructure. However reactive maintenance based on human expertise has its limits: lack of real-time monitoring, prone to human error, limited scalability, etc.
New technologies such as Artificial Intelligence (AI) are transforming the way we maintain electrical systems, enabling proactive and precise identification of potential faults to minimize the impact of disruptions.

Approach

This use case is based on the "Electrical Fault detection and classification" dataset from Kaggle.
The goal was to first detect an anomaly in the power system, and then classify the detected anomaly in one of the 6 classes of possible anomalies.
With a reference power system consisting of four generators with three phases (A, B and C), the data consists of 12,000 data points for the line voltages and currents for each of the three phases.
We then used NanoEdge AI Studio to create an N-Class classification model based on these inputs to detect and classify electrical anomalies.

Sensor

Current sensors and voltage sensors.

Data

2 classes for anomaly detection
  • Regular
  • Abnormal

6 classes for N-class classification
  • No fault
  • Line-to-ground (LG) fault (between Phase A and Ground)
  • Line-to-line (LL) fault (between Phase A and Phase B)
  • Line-to-line-to-ground (LLG) fault (between Phases A, B and Ground)
  • Line-to-line-to-line (LLL) fault (between all three phases)
  • Line-to-line-to-line-to-ground (LLLG) fault (three-phase symmetrical fault)

Signal length 6 (multi-sensors)
Data rate 1000 Hz

Results

Anomaly detection:
98.90% accuracy, 0.6 Kbytes of RAM, 7.1 Kbytes of Flash memory

Fault classification:
98.51% accuracy, 0.1 KB RAM, 214.9KB Flash

Model created with

NanoEdge AI Studio

Model created with

Compatible with

Any STM32 MCU

Compatible with

Resources

Model created with NanoEdge AI Studio

A free AutoML software for adding AI to embedded projects, guiding users step by step to easily find the optimal AI model for their requirements.

Model created with NanoEdge AI Studio

Compatible with Any STM32 MCU

The STM32 family of 32-bit microcontrollers based on the Arm Cortex®-M processor is designed to offer new degrees of freedom to MCU users. It offers products combining very high performance, real-time capabilities, digital signal processing, low-power / low-voltage operation, and connectivity, while maintaining full integration and ease of development.

Compatible with Any STM32 MCU

You also might be interested by

Industrial | Transportation

Smart asset tracking

Packages condition classification on sensors.

Industrial | Smart offices

Low-power anomaly detection on a fan

Low-power anomaly detection solution running on a sensor.

Industrial | Smart buildings | Smart homes | Smart offices

Face identification with ID3 Technologies

End-to-end AI solution for face identification running on STM32 microcontrollers.