@article{GroteKastsianMoennigmann2013, author = {Grote, Wolfgang and Kastsian, Darya and M{\"o}nnigmann, Martin}, title = {Guaranteed stability in optimal power generation dispatching under uncertainty}, series = {IEEE Transactions on Power Systems}, volume = {28}, journal = {IEEE Transactions on Power Systems}, number = {2}, publisher = {IEEE}, issn = {1558-0679}, doi = {10.1109/TPWRS.2012.2207970}, pages = {1103 -- 1112}, year = {2013}, abstract = {Stability and cost optimality of power generation and supply systems must not be considered independently from one another. Simple examples show that optimizing cost without considering stability may result in modes of operation that, while economically optimal, are unstable. We demonstrate that stability, robustness, and optimality can be considered systematically and simultaneously by combining bifurcation theory and nonlinear optimization. Essentially, the proposed method enforces a backoff distance between the optimal point of operation and operational or stability boundaries in the space of the optimization variables, where bifurcation theory is used to describe nonlinear stability boundaries.}, language = {en} } @article{JostGroteMoellenbrucketal.2014, author = {Jost, Michael and Grote, Wolfgang and M{\"o}llenbruck, Florian and M{\"o}nnigmann, Martin}, title = {Plant-wide control of a parabolic trough power plant with thermal energy storage}, series = {IFAC Proceedings Volumes}, volume = {47}, journal = {IFAC Proceedings Volumes}, number = {3}, publisher = {Elsevier}, issn = {2589-3653}, doi = {10.3182/20140824-6-ZA-1003.00879}, pages = {419 -- 425}, year = {2014}, abstract = {We present a control scheme for a parabolic trough power plant that is equipped with a molten salt thermal energy storage system. We show that the multivariable control problem for the solar field can be decoupled by controlling the three way valve that splits the heat transfer fluid between the storage system and the steam generator. The steam generation cycle is regulated with inlet pressure control. The subsystems of the plant are modeled based on first principles, where we focus on the steam generation cycle. A multi-group extraction turbine with pre- and reheater is considered, which is suitable for solar power plants. We demonstrate the benefits of the proposed control scheme with a day-to-day simulation.}, language = {en} } @article{MierMoellenbruckJostetal.2015, author = {Mier, Dominik and M{\"o}llenbruck, Florian and Jost, Michael and Grote, Wolfgang and M{\"o}nnigmann, Martin}, title = {Model predictive control of the steam cycle in a solar power plant}, series = {IFAC-PapersOnLine}, volume = {48}, journal = {IFAC-PapersOnLine}, number = {8}, publisher = {Elsevier}, issn = {2405-8963}, doi = {10.1016/j.ifacol.2015.09.052}, pages = {710 -- 715}, year = {2015}, abstract = {We derive a nonlinear model of the steam cycle of a solar power plant, estimate its unknown parameters with measured data, and design a linear model predictive controller based on the resulting model. Simulations show that the linear model predictive controller is able to regulate the electrical power of the nonlinear steam process of the plant to a given reference trajectory very well. Moreover, it results in very good disturbance rejection.}, language = {en} } @inproceedings{SchrammKrugerNiemannetal.2019, author = {Schramm, Alexander and Kruger, Karl and Niemann, J{\"o}rg and Grote, Wolfgang}, title = {Possibilities, Limitations and Considerations for Eye Tracking in Industrial Environments: Experience from a Case Study}, series = {International Conference on Competitive Manufacturing (COMA 19) proceedings : 30 January 2019-1 February 2019, Stellenbosch, South Africa}, booktitle = {International Conference on Competitive Manufacturing (COMA 19) proceedings : 30 January 2019-1 February 2019, Stellenbosch, South Africa}, editor = {Dimitrov, Dimiter and Hagedorn-Hansen, Devon and Leipzig, Karl von}, publisher = {Department of Industrial Engineering, Stellenbosch University}, address = {Stellenbosch}, isbn = {978-0-7972-1779-9}, pages = {390 -- 396}, year = {2019}, abstract = {Eye tracking is a measurement technique for determining the orientation and viewing direction of an individual's eyes. It allows for an evaluation of the individual's gaze and other related information.There is a wide variety of application areas for eye tracking, however the usage in engineeringindustry is still limited. This paper presents a case study for the use of eye tracking in a testingstation for electrical circuit breakers. The case study aims to evaluate the measurement and analysispossible with the Tobii wearable eye tracking system. From the case study, possibilities, limitationsand considerations for eye tracking in industrial settings are identified and discussed. The paperconcludes that while eye tracking can be used to effectively capture and visualize insightfulinformation, it faces major challenges related to subjectivity in analysis and labour intensiveness.}, language = {en} } @inproceedings{BeckersPawlikKieletal.2019, author = {Beckers, Sebastian and Pawlik, Julian and Kiel, J{\"u}rgen and Grote, Wolfgang}, title = {Simulation von Partikelflugbahnen zur Auslegungshilfe von elektrostatischen Luftfiltern}, publisher = {ARGESIM, ASIM}, address = {Wien}, isbn = {978-3-901608-06-3}, doi = {10.11128/arep.57}, pages = {147 -- 152}, year = {2019}, language = {de} } @inproceedings{GroteRammSchoenig2022, author = {Grote-Ramm, Wolfgang and Sch{\"o}nig, Felix}, title = {Model Predictive Control of Solar Receivers}, series = {25th Cologne Solar Colloquium (DLR)}, booktitle = {25th Cologne Solar Colloquium (DLR)}, pages = {17}, year = {2022}, language = {en} } @article{GroteRammLanuschnyLorenzenetal.2023, author = {Grote-Ramm, Wolfgang and Lanuschny, David and Lorenzen, Finn and Oliveira Brito, Marcel and Sch{\"o}nig, Felix}, title = {Continual learning for neural regression networks to cope with concept drift in industrial processes using convex optimisation}, series = {Engineering Applications of Artificial Intelligence}, volume = {120}, journal = {Engineering Applications of Artificial Intelligence}, publisher = {Elsevier}, address = {New York}, issn = {1873-6769}, doi = {10.1016/j.engappai.2023.105927}, pages = {10}, year = {2023}, abstract = {Process models in industrial applications, e.g. predictive maintenance or automation, are subject to both divergence from the underlying system due to their time-variant nature and to high complexity resulting from a wide operational range being covered. Hence, regression models require high accuracy for the present system state and at the same time need to be valid across the whole system operating space. While accuracy for the current system state can be gained by updating the model on the current data, the overall validity must often be retrieved from historical or design data. We propose a method to find an appropriate compromise for these two demands. A pre-trained artificial neural network (ANN) is continually updated on the current sensor data stream using convex optimisation. Thus, a unique and optimal solution is generated in each update step, while robust regression accuracy on the domain that is not covered by the arriving data subset is maintained. This is achieved by introducing a data management system to provide some historical data, constraining the optimisation problem and manipulating the architecture of the ANN. Models updated with this method show reasonable stability but display plastic behaviour at the current operating point.}, language = {en} } @inproceedings{LorenzenSchoenigGroteRammetal.2023, author = {Lorenzen, Finn and Sch{\"o}nig, Felix and Grote-Ramm, Wolfgang and Oliveira Brito, Marcel and Neuberger, Heiko and Sobolyev, Alexander}, title = {Continual Adaptation of Compressor Maps During Operation on Measurement Data For Increased Performance of Model-Based Applications in Turbomachinery}, series = {ASME Turbo Expo 2023: Turbomachinery Technical Conference and Exposition, June 26-30, 2023 Boston, Massachusetts, USA}, volume = {Volume 4}, booktitle = {ASME Turbo Expo 2023: Turbomachinery Technical Conference and Exposition, June 26-30, 2023 Boston, Massachusetts, USA}, publisher = {ASME}, address = {Boston}, isbn = {978-0-7918-8697-7}, doi = {10.1115/GT2023-103458}, pages = {1 -- 7}, year = {2023}, abstract = {Higher control engineering concepts, including artificial intelligence and model-based control, are recently becoming more popular in complex industrial applications because they enable a significant increase in efficiency. E.g., a model-based approach can be used to enhance the control and monitoring of several coupled components of a larger turbomachinery train. This requires dynamic models of the components, including all machine characteristics, which may experience a shift in their behavior over the course of their lifetime. Such changes occur due to natural fouling, wear of sub-components or external effects induced by maintenance measures. To overcome this problem, we model machine-characteristic maps with artificial neural networks, which may be used as part of prediction models in a model predictive control unit. If an appropriate data set is available, this allows us to adapt the model to the current behavior of the machine without requiring in-depth knowledge about the underlying physics of this "concept drift", as it is known in literature. However, the training of neural networks is usually connected with high computational effort while the task needs to be executed in real-time during operation. Furthermore, in real operating conditions, machine sensors can only provide labeled data from the current operating point of the machine, which can be stationary for long operation periods of up to several months. In general, this yields only an unrepresentative data subset of the machine-characteristics, which is not sufficient to retrain the whole model to a new state. To solve these problems, we firstly reduce the model adaptation to a convex optimization problem, which can be efficiently solved in real-time conditions. Secondly, we use a specialized data management system with which we can integrate historical data to supplement the unrepresentative data subset. In this paper we show the application of the outlined method on a compressor map and discuss the advantages and requirements of the method in the context of modeling applications for industrial turbomachinery.}, language = {en} } @misc{Musall2024, author = {Musall, Eike}, title = {Suffizienz als Ausweg?!}, series = {Klimafreundlich bauen, 2024: Magazin f{\"u}r Passivhaus \& Co}, volume = {19}, journal = {Klimafreundlich bauen, 2024: Magazin f{\"u}r Passivhaus \& Co}, number = {2024}, editor = {Laible, Johannes}, publisher = {Laible Verlagsprojekte}, isbn = {978-3-944549-51-4}, pages = {30 -- 33}, year = {2024}, language = {de} } @article{MolloyMuellerDeeganetal.2024, author = {Molloy, Dara and M{\"u}ller, Patrick and Deegan, Brian and Mullins, Darragh and Horgan, Jonathan and Ward, Enda and Jones, Edward and Braun, Alexander and Glavin, Martin}, title = {Analysis of the Impact of Lens Blur on Safety-Critical Automotive Object Detection}, series = {IEEE Access}, volume = {12}, journal = {IEEE Access}, publisher = {IEEE}, issn = {2169-3536}, doi = {10.1109/ACCESS.2023.3348663}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-42999}, pages = {3554 -- 3569}, year = {2024}, abstract = {Camera-based object detection is widely used in safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous vehicle research. Road infrastructure has been designed for human vision, so computer vision, with RGB cameras, is a vital source of semantic information from the environment. Sensors, such as LIDAR and RADAR, are also often utilized for these applications; however, cameras provide a higher spatial resolution and color information. The spatial frequency response (SFR), or sharpness of a camera, utilized in object detection systems must be sufficient to allow a detection algorithm to localize objects in the environment over its lifetime reliably. This study explores the relationship between object detection performance and SFR. Six state-of-the-art object detection models are evaluated with varying levels of lens defocus. A novel raw image dataset is created and utilized, containing pedestrians and cars over a range of distances up to 100-m from the sensor. Object detection performance for each defocused dataset is analyzed over a range of distances to determine the minimum SFR necessary in each case. Results show that the relationship between object detection performance and lens blur is much more complex than previous studies have found due to lens field curvature, chromatic aberration, and astigmatisms. We have found that smaller objects are disproportionately impacted by lens blur, and different object detection models have differing levels of robustness to lens blur}, language = {en} }