Artificial Intelligence Automatically Detects Disturbances In Power Supply Grids

The grid is changing as the big, centralized providers of the past are replaced by smaller, distributed suppliers. Keeping such complex networks running stable requires high-resolution sensor technology – AI provides a way to make accurate predictions and automatically detect any disturbances or anomalies in real time. Here is how Fraunhofer researchers developed the compression techniques, algorithms and neural networks to make a power supply fit for the future.

The way power is generated is in transition: Whereas, before, all our power came from big power plants, these days it comes from a range of distributed sources as well, including wind turbines, photovoltaic systems and other similar facilities. This shift has a big impact on our grid – with particular challenges for operators of transmission grids. How to monitor the proper functioning of grid parameters such as phase angle and frequencies? Might there be discrepancies or anomalies in the proper functioning of the grid? Or are there lines or power plants down? Today's standard measurement technology is no longer able to reliably furnish answers to these sorts of questions. More and more operators are, therefore, turning to additional phasor measurement units (PMUs) and other digital solutions. These systems measure the amplitude and phase of current and voltage up to 50 times a second. This process generates huge volumes of data, easily several gigabytes a day. READ MORE ON: PHYS.ORG

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