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„Die Software modelliert anhand sensorbasierter Daten den Sollzustand einer Anlage und vergleicht diesen mit dem Istzustand. Gibt es Abweichungen von zuvor definierten Kennzahlen, lassen sich frühzeitig Veränderungen und Störungen erkennen“, erläutert Peter Karl Krüger, Bereichsleiter System Technologies bei SES. Aber die Technologie ist nicht nur ein Frühwarnsystem, sondern wird vor allem genutzt, um Anlagenfahrweisen zu optimieren und Instandhaltungskosten zu senken.
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In the STEAG group, decades of experience in the construction and operation of conventional power plants have led to the development of powerful IT technologies for application in the energy industry. Now renewable power plants of SNE benefit from these intelligent technologies and the huge amount of data accumulated over the years by STEAG’s control systems as well. The SES system for predictive analytics uses so-called neural networks that are modeled on the human nervous system. “On the basis of sensor-based data, the software models the reference condition of a plant and compares it with the actual condition. If there are deviations from previously defined performance indicators, changes and faults can be detected early on“, explains Peter Karl Krüger, head of the division System Technologies at SES. However, this technology is not just an early warning system, but it is mainly used to optimize plants’ modes of operation and to decrease maintenance costs.
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