Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK

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Process data based Anomaly detection in distributed energy generation using Neural Networks

2020 , Klein, Max , Thiele, Gregor , Fono, Adalbert , Khorsandi, Niloufar , Schade, David , Krüger, Jörg

The increasing share of renewable energies in the total energy supply includes a growing number of small, decentralized energy generation which also provides control energy. These decentralized stations are usually combined to a virtual power plant which takes over the monitoring and control of the individual participants via an Internet connection. This high degree of automation and the large number of frequently changing subscribers creates new challenges in terms of detecting anomalies. Quickly adaptable, variable and reliable methods of anomaly detection are required. This paper compares two approaches using Neural Networks (NN) with respect to their ability to detect anomalous behavior in real process data of a combined heat and power plant. In order to include process dynamics, one approach includes specifically engineered features, while the other approach uses Long-Short-Term-Memory (LSTM). Both approaches are able to detect rudimentary anomalies. For more demanding anomalies, the respective strengths and weaknesses of the two approaches become apparent.

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Energy Efficiency Optimization using AutomationML modeling and an EnPI methodology

2019 , Thiele, Gregor , Khorsandi, Niloufar , Krüger, Jörg

Industrial facilities are complex and heterogeneous systems in permanent technological change. The ambitions towards smart factories heighten the requirements for the flexible interconnection of various devices. These industrial entities are controlled, observed and optimized by many services. The tuning of process parameters of several linked components in order to boost the overall energy efficiency is one example of such services. AutomationML (AML) provides a hierarchical description language for industrial systems considering both structure and properties. An extension of the established standard allows for intuitive modeling of energy optimization problems. An approved energy performance indicator (EnPI) methodology was integrated in the libraries of AML in order to simplify and shorten the modeling procedure for the optimization task. The procedure is demonstrated using the example of an industrial cooling system.

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