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Real-time State Estimation for an Adaptive Vibration Energy Harvesting System

IFAC-PapersOnLine 2016年第25期49卷 127-132页

作者： Abdollahpouri, Mohammad Gulan, Martin Takács, Gergely Rohal'-Ilkiv, Boris Slovak University of Technology in Bratislava Faculty of Mechanical Engineering Institute of Automation Measurement and Applied Informatics Slovakia

Moving horizon estimation (MHE) has proven to be an efficient optimization-based technique to address the problem of joint state and parameter estimation with nonlinearities in system dynamics and constraints. This work aims to propose the application of MHE for a specific nonlinear vibration setup, often used to analyze energy harvesting concepts in the laboratory environment. Using a simulated energy harvesting scenario exhibiting a strongly nonlinear behavior, we focus on the estimation of unmeasured system states and a time-varying structural parameter that can be utilized to maximize the harvested energy, or in structural health monitoring. To exploit the recent algorithmic developments in embedded optimization, we implement the proposed scheme on a low-cost embedded computing platform using automatic code generation. A hardware-in-the-loop setup is used in order to investigate the performance of the real-time MHE scheme, and to quantify the associated computational effort. The results demonstrated in this paper, suggest the real-time feasibility of MHE, and certain practical advantages over a standard Kalman filtering in a vibration energy harvesting system. © 2016

关键词： Parameter estimation Automatic programming Cantilever beams Embedded systems Energy harvesting Structural health monitoring Vibration analysis Automatic code generations Duffing oscillator Embedded optimizations Moving horizon estimation Optimization based techniques State and parameter estimations Time varying structural parameters Vibration energy harvesting

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Real-time MHE-based nonlinear MPC of a Pendubot system

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IFAC-PapersOnLine 2015年第23期48卷 422-427页

作者： Gulan, M. Salaj, M. Abdollahpouri, M. Rohal-Ilkiv, B. Institute of Automation Measurement and Applied Informatics Faculty of Mechanical Engineering Slovak University of Technology Bratislava Slovakia

This paper addresses the real-time optimal control of a Pendubot using nonlinear model predictive control (NMPC) combined with nonlinear moving horizon estimation (MHE). This fast, under-actuated nonlinear mechatronic system apparently poses a challenging benchmark problem that may benefit from a nonlinear optimization scheme. To overcome the related computational difficulties we make use of the ACADO Code Generation tool allowing to export a highly efficient Gauss-Newton real-time iteration algorithm tailored to the nonlinear optimal control and estimation problem while respecting the imposed *** show experimental results illustrating the overall closed-loop control performance, as well as the advantages of the nonlinear MHE-based NMPC scheme. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Nonlinear analysis Computational efficiency Iterative methods Model predictive control Nonlinear programming Optimal systems Predictive control systems Real time control Robots Closed loop control performance fast NMPC Moving horizon estimation Non linear optimal control Non linear optimization Nonlinear model predictive control Pendubots Under actuated systems

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Forecasting energy time series with profile neural networks 20

Forecasting energy time series with profile neural networks

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11th ACM International Conference on Future Energy Systems, e-Energy 2020

作者： Heidrich, Benedikt Turowski, Marian Ludwig, Nicole Mikut, Ralf Hagenmeyer, Veit Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Germany

ISBN: (纸本)9781450380096

Forecasting the energy demand is essential for network operators to balance the grid, in particular with the increasing share of renewable energy sources. Neural networks, especially deep neural networks, have shown promising results in recent forecasting tasks. However, they often struggle learning periodicities in time series efficiently. In line with the finding that deep learning can be improved with statistical information, we introduce profile neural networks based on the fast and promising convolutional neural networks. The underlying idea of profile neural networks is that decomposing periodic energy time series into a standard load profile, a trend, and a colorful noise module improves the forecasting accuracy. The proposed deep neural network architecture is applied to real-world electricity data from buildings on a university campus, more specifically of one building with strong seasonal variation and one building with weak seasonal variation. The new architecture outperforms current state-of-the-art deep learning benchmark models regarding the forecasting accuracy on forecast horizons of one day and one week-ahead, improving the mean absolute scaled error by up to 25%, as well as regarding the trade-off between training time and accuracy. © 2020 Owner/Author.

关键词： Time series

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Towards a Real-World Dispatchable Feeder 8

Towards a Real-World Dispatchable Feeder

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8th IEEE Workshop on the Electronic Grid, eGRID 2023

作者： Beichter, Sebastian Beichter, Maximilian Werling, Dorina Galenzowski, Johannes Weise, Victor Hildenbrand, Christoph Wiegel, Friedrich Mikut, Ralf Waczowicz, Simon Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany

ISBN: (纸本)9798350327007

With the integration of renewable energy sources, electric vehicles, and batteries at the low-voltage distribution grid level, residential buildings are increasingly evolving from consumers to prosumers. As the prosumers burden the distribution grid with their uncertain interactions, the need for solutions to counteract this uncertainty becomes more important. One such solution is schedule-based control strategies, such as the dispatchable feeder. Thereby, the dispatchable feeder uses intelligent managed flexible components such as battery energy storage system (BESS) and controllable loads to follow a committed schedule. While the concept of the dispatchable feeder is widely discussed in the literature, the practical challenges associated with its real-world implementation remain under-explored. Therefore, this paper identifies challenges associated with the real-world implementation of the dispatchable feeder in residential buildings. Further, it discusses important future research questions regarding the concept and its valuable integration into the real world. It also proposes an experimental research infrastructure for systematically investigating the raised questions using the real-world laboratory of the Energy Lab 2.0. © 2023 IEEE.

关键词： Renewable energy

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A fuzzy logic approach for economic energy-efficient heat pump operation in thermal building control

A fuzzy logic approach for economic energy-efficient heat pu...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Kovačević, Jovana Süß, Anne-Christin Çakmak, Hüseyin K. Hagenmeyer, Veit Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Germany

ISBN: (纸本)9789531842976

Advanced thermal control has great potential to decrease energy consumption in buildings. Thereby, encountering the balance between thermal comfort, and electricity load reduction is a challenge. For this, advanced control systems need to be developed that can respond to external signals such as electricity prices and are robust to different types of buildings to be widely utilized. In this paper, we propose and investigate two fuzzy control systems, an economic-energy-efficient controller and a comfort controller. The comfort controller focuses on thermal comfort, whereas the economic-energy-efficient controller is attentive to electricity prices and the energy efficiency of the heat pump. For the simulation, we use a reduced-order building model identified by measurements of a real-world building. Furthermore, we compare the proposed controllers with a hysteresis controller as a lower benchmark control strategy. Simulation results reveal that an economic-energy-efficient controller can reduce electricity consumption by 14.92% and costs by 18.41 % when compared to a benchmark controller and with mean thermal discomfort of 0.216 °C over an evaluation period of two months. The comfort controller has slightly smaller costs and electricity consumption than the hysteresis one, but less zone temperature fluctuations, enhancing the quality of controlled zone temperature and occupants' comfort. © 2024 IEEE.

关键词： Fuzzy control

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Transformer training strategies for forecasting multiple load time series

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Energy informatics 2023年第1期6卷 20页

作者： Hertel, Matthias Beichter, Maximilian Heidrich, Benedikt Neumann, Oliver Schäfer, Benjamin Mikut, Ralf Hagenmeyer, Veit Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany

In the smart grid of the future, accurate load forecasts on the level of individual clients can help to balance supply and demand locally and to prevent grid outages. While the number of monitored clients will increase with the ongoing smart meter rollout, the amount of data per client will always be limited. We evaluate whether a Transformer load forecasting model benefits from a transfer learning strategy, where a global univariate model is trained on the load time series from multiple clients. In experiments with two datasets containing load time series from several hundred clients, we find that the global training strategy is superior to the multivariate and local training strategies used in related work. On average, the global training strategy results in 21.8% and 12.8% lower forecasting errors than the two other strategies, measured across forecasting horizons from one day to one month into the future. A comparison to linear models, multi-layer perceptrons and LSTMs shows that Transformers are effective for load forecasting when they are trained with the global training strategy. © 2023, The Author(s).

关键词： Time series

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Boost short-term load forecasts with synthetic data from transferred latent space information

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Energy informatics 2022年第1期5卷 1-20页

作者： Heidrich, Benedikt Mannsperger, Lisa Turowski, Marian Phipps, Kaleb Schäfer, Benjamin Mikut, Ralf Hagenmeyer, Veit Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany

Sustainable energy systems are characterised by an increased integration of renewable energy sources, which magnifies the fluctuations in energy supply. Methods to to cope with these magnified fluctuations, such as load shifting, typically require accurate short-term load forecasts. Although numerous machine learning models have been developed to improve short-term load forecasting (STLF), these models often require large amounts of training data. Unfortunately, such data is usually not available, for example, due to new users or privacy concerns. Therefore, obtaining accurate short-term load forecasts with little data is a major challenge. The present paper thus proposes the latent space-based forecast enhancer (LSFE), a method which combines transfer learning and data augmentation to enhance STLF when training data is limited. The LSFE first trains a generative model on source data similar to the target data before using the latent space data representation of the target data to generate seed noise. Finally, we use this seed noise to generate synthetic data, which we combine with real data to enhance STLF. We evaluate the LSFE on real-world electricity data by examining the influence of its components, analysing its influence on obtained forecasts, and comparing its performance to benchmark models. We show that the Latent Space-based Forecast Enhancer is generally capable of improving the forecast accuracy and thus helps to successfully meet the challenge of limited available training data. © 2022, The Author(s).

关键词： Benchmarking

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Gossen’s first law in the modeling for demand side management: a thorough heat pump case study with deep learning based partial time series data generation

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Energy informatics 2024年第1期7卷 47页

作者： Li, Chang Brecher, Gina Kovačević, Jovana Çakmak, Hüseyin K. Förderer, Kevin Matthes, Jörg Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany

Gossen’s First Law describes the law of diminishing marginal utility. This paper aims to further verify the proposed hypothesis that Gossen’s First Law also holds in the modeling for Demand Side Management (DSM) with a thorough heat pump case study. The proposed hypothesis states that in general the complexity-utility relationship in the field of DSM modeling could be represented by a diminishing marginal utility curve. On the other hand, in data based modeling, when utilizing a large dataset for validation, the data integrity is critical to the reliability of the results. However, the absence of partial time series data may occur during the measurement due to missing sensors or IT related issues. In this work, an extensive real-world open dataset of a ground source heat pump is utilized for the case study. In the raw data, one key variable namely the flow rate is missing. Thus, three different algorithms based on machine learning and deep learning architectures namely Random Forest (RF), Long Short-Term Memory (LSTM) and Transformer are applied to predict the flow rate by utilizing an open loop forecasting. The raw data are first pre-processed with a time interval of one hour and then used for training, validation and forecast. Furthermore, a modified persistence model as the baseline is also defined. The predicted flow rate using LSTM yields the lowest error of 7.47% nMAE and 10.56% nRMSE respectively. The forecast results are then utilized in the following step of modeling of a heat pump use case. With the introduced quantification method for complexity and a modified version for utility, we further verify the proposed hypothesis with a longer time horizon of 7 days. © The Author(s) 2024.

关键词： Forecasting

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Energy pulsation reduction in modular multilevel converters using optimized current trajectories

IEEE Open Journal of Power Electronics

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IEEE Open Journal of Power Electronics 2021年 2卷 171-186页

作者： Braeckle, Dennis Himmelmann, Patrick Groll, Lutz Hagenmeyer, Veit Hiller, Marc Institute of Electrical Engineering Karlsruhe Institute of Technology Karslruhe76131 Germany Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karslruhe76131 Germany

In power electronics, the modular multilevel converter (MMC) is an easily scalable topology with an high output voltage quality. It is suitable for the transmission of large amounts of electrical power over long distances, which supports the realization of the ongoing energy transition. State-of-the-art methods require a comparatively large total cell capacitance in the system for energy pulsations during operation. In the present paper, in order to minimize this total capacitance, first a new method is developed to model the system, and second, by help of this model, optimal current trajectories are calculated. These currents are used for control to reduce the energy pulsation over the complete operating range, and thus, to better utilize the hardware. The new method independent on the Clarke transformations is implemented on a laboratory scale setup, and measurement results are presented which validate the new method. Furthermore, the new method is compared to the state-of-the-art method of the compensation of the 2nd harmonic and outperforms the latter significantly. This applies to the entire operating range for different power factors. A total reduction of up to 44% of the energy pulsations is achieved. © 2020 IEEE.

关键词： Trajectories

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Optimised Inkjet-Printing Parameters and Processing Techniques for an Exemplary Structure of an Oxygen Sensor on a Polyimide Substrate 5

Optimised Inkjet-Printing Parameters and Processing Techniqu...

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5th IEEE International Conference on Flexible and Printable Sensors and Systems, FLEPS 2023

作者： Petanil, Lisa Schweizer, Anja Pylatiuk, Christian Stiller, Peter Reichert, Klaus-Martin Gengenbach, Ulrich Ungerer, Martin Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9781665457330

Inkjet-printing of electrochemical sensors is a rapidly developing fabrication method. Careful selection of pre-processing methods, inkjet-printing parameters, and post-processing methods is crucial for the features of the final sensor. In particular the interaction of inks and substrate is essential for setting the pre- and post-processing conditions as well as the inkjet-printing parameters. Here, commercial gold, silver, and passivation inks are printed onto a Polyimide substrate and investigated with different processing parameters. A literature search has shown that there is a lack of systematic investigation of the processing conditions for this material combination. Optimal processing parameters are presented here for this combination of materials. With the identified processing parameters, we reproducibly achieve low resistivities for gold and silver nanoparticle inks. © 2023 IEEE.

关键词： Silver nanoparticles

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