Ensuring enhanced stability of an electrical grid
Using AI to determine if an electrical grid is stable.
The electrical grid is the backbone of our modern life, delivering power to homes, business and industries. However, with the increasing demand for electricity, renewable energy integration, and the rise of decentralized energy sources, maintaining grid stability has become a complex challenge. This is where Artificial Intelligence (AI) comes into play, offering innovative solutions to optimize grid performance and resilience.
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.
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
This use case is based on the "Electrical Grid Stability" dataset from the UCI Machine Learning Repository.
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.
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
Generic sensors.
Data
2 classes of classification Stable, Unstable
Signal length 12 (multi-sensors)
Data rate 0.5 Hz
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
92.35% accuracy, 0.3 Kbytes of RAM, 1.6 Kbytes of Flash memory
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.
Compatible with Any STM32 MCU
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