Summary: As the world swaps fossil-fuel power plants for solar and wind, our electrical grids are becoming “intermittent” and harder to control. Researchers developed a solution inspired by the human brain.
By using Artificial Neural Networks (ANN), Khan created “biomimetic” controllers that can predict and adapt to the unpredictable surges and dips of renewable energy in real-time. This AI-driven approach not only out-performs traditional methods but also allows grids to operate with fewer physical sensors, making the infrastructure cheaper and more reliable.
Key Facts
- The Stability Challenge: Renewable energy sources use inverters that lack the “natural inertia” of heavy spinning turbines in traditional plants, making the grid prone to crashes.
- Brain-Inspired Control: Khan’s AI controllers learn from thousands of scenarios to “predict” grid instability before it happens, adjusting voltage and current in milliseconds.
- Hardware vs. Software: The AI is so precise it can replace physical hardware. In tests, the system delivered the same results with one sensor instead of the traditional two, reducing costs and potential points of mechanical failure.
- The “Black Box” Hurdle: While the AI performed flawlessly in real-time tests, researchers acknowledge that explaining how the AI makes its decisions remains a challenge—a common hurdle for integrating AI into critical infrastructure.
- Carbon-Neutral Future: This research is a critical building block for “microgrids,” allowing local communities to safely integrate much higher percentages of wind and solar power without risking blackouts.
Source: University of Vaasa
As traditional power plants are replaced by intermittent sources like solar and wind, maintaining grid stability has become a complex engineering challenge.
Hussain Khan’s doctoral dissertation at the University of Vaasa, Finland, introduces advanced AI-based control strategies that ensure local grids remain reliable and resilient.
Power systems are undergoing a profound transformation as fossil-based generation is gradually replaced by inverter-based renewable energy. This shift introduces inherent uncertainty and low inertia, making grid operation and voltage stability significantly more complex in AC and DC microgrids.
In his dissertation in electrical engineering, Hussain Khan addresses these challenges. By utilising Artificial Neural Networks (ANN), Khan has developed controllers that can predict and compensate to grid changes in real-time, outperforming traditional control methods.
– ANNs inspired by the human brain, which processes information through interconnected neurons. This biomimetic approach allows the system to learn from diverse scenarios and adapt to the unpredictability of solar and wind power, says Khan.
Cost-effective solutions through sensor optimisation
Traditional systems rely on multiple physical sensors to monitor voltage, current, and other parameters, adding to costs and increasing the number of potential failure points. Khan’s AI-driven approach demonstrates that sophisticated software can compensate for fewer hardware components.
– By training the neural network effectively, the system can provide the same reliable results with only a single sensor instead of two. This leads to cost optimisation and improves overall reliability, as there are fewer physical parts that could fail, Khan notes.
While AI-based control can improve efficiency and reduce hardware requirements, introducing intelligent controllers into critical infrastructure also raises new considerations.
– The main concern is that AI works like a black box: we can see the inputs and outputs, but not always fully explain what is happening inside. Even so, in our tests the controller performed very well and was validated rigorously in real time, notes Khan.
Khan’s research supports the broader goal of building carbon-neutral energy systems in the coming decades. By improving stability and reducing hardware requirements, AI-based control could help electricity grids integrate larger shares of renewable energy in the future.
Key Questions Answered:
A: Traditional grids are like giant, heavy flywheels—they are hard to stop once they are spinning. Solar and wind are like “lightweight” power; they flicker on and off instantly. An AI brain acts as a super-fast stabilizer, making thousands of micro-adjustments every second to ensure your lights don’t flicker when a cloud passes over a solar farm.
A: Sensors and hardware are expensive to buy and even more expensive to fix when they break. By using “virtual sensors” (software) to do the work of physical hardware, utility companies can lower the cost of building and maintaining local microgrids, which eventually trickles down to the consumer.
A: This is the big debate in electrical engineering. Khan’s research used rigorous real-time validation to prove the AI works, but because we can’t always “see” the AI’s logic, the next step in this field is “Explainable AI” (XAI). For now, the performance gains are so high that they outweigh the transparency concerns in controlled microgrid environments.
Editorial Notes:
- This article was edited by a Neuroscience News editor.
- Journal paper reviewed in full.
- Additional context added by our staff.
About this AI and neuroscience research news
Author: Sini Heinoja
Source: University of Vaasa
Contact: Sini Heinoja – University of Vaasa
Image: The image is credited to Neuroscience News
