Ensuring enhanced stability of an electrical grid
Using AI to determine if an electrical grid is stable.
By leveraging optimized AI algorithms and advanced data analytics, grid operators can gain real-time insights into grid operations, monitor critical parameters and automatically detect anomalies for early identification of potential issues enabling prompt intervention to prevent disruption and ensure a stable power supply.
Approach
The goal was to determine if an electrical grid is stable or not, only using 12 parameters such as production, consumption, time reaction, etc.
Each sample was measured every 2 seconds, 10 000 real signals were gathered.
We then used NanoEdge AI Studio to create an N-class classification project based on these inputs to try to predict whether the power grid was stable or unstable.
Sensor
Data
Signal length 12 (multi-sensors)
Data rate 0.5 Hz
Results
N-class classification:
92.35% accuracy, 0.3 Kbytes of RAM, 1.6 Kbytes of Flash memory
Green points represent well classified signals. Red points represent misclassified signals. The classes are on the abscissa and the confidence of the prediction is on the ordinate
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.
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.