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International Conference on Process control (PC)

作者： Simon Hellmann Arne-Jens Hempel Stefan Streif Sören Weinrich Biochemical Conversion Department DBFZ Deutsches Biomasseforschungszentrum gGmbH Leipzig Germany Saxon University of Cooperative Education Glauchau Germany Chair of Automatic Control and System Dynamics Chemnitz University of Technology Chemnitz Germany Faculty of Energy. Building Services. Environmental Engineering Munster University of Applied Sciences Münster Germany

Dynamic operation of anaerobic digestion plants requires advanced process monitoring and control. Different simplifications of the Anaerobic Digestion Model No. 1 (ADM1) have been proposed recently, which appear promising for model-based process automation and state estimation. As a fundamental requirement, observability and identifiability of these models are analyzed in this work, which was pursued through algebraic and geometric analysis. Manual algebraic assessment was successful for small models such as the ADM1-R4 and simplified versions of the ADM1-R3, which were derived in this context. However, for larger model classes the algebraic approach showed to be insufficient. By contrast, the geometric approach, implemented in the STRIKE GOLDD toolbox, allowed to show observability for more complex models (including ADM1-R4 and ADM1-R3), employing two independent algorithms. The present study lays the groundwork for state observer design, parameter estimation and advanced control resting upon ADM1-based models.

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Measurement of surface stress-strain distribution inside a grain of carbon steel via optical microscope

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Measurement 2025年 255卷

作者： I Dewa Made Oka Dharmawan Jinyi Lee Department of Control Instrumentation and Robot Engineering Graduate School of Chosun University Gwangju 61452 Republic of Korea Interdisciplinary Program in IT-BIO Convergence System Chosun University Gwangju 61452 Republic of Korea Department of Electronic Engineering Chosun University Gwangju 61452 Republic of Korea

The advancement of alloys and steel in engineering highlights the critical need for robust methods to ensure structural integrity. While traditional Non-Destructive Testing and Evaluation (NDT/E) techniques provide valuable insights, they often fail to detect pre-existing stress conditions that contribute to fatigue cracks. Recent approaches have addressed this limitation by analyzing stress and strain at the grain level using advanced measurement systems—often based on diffraction techniques—integrated with Finite Element Method (FEM) and Digital Image Correlation (DIC) platforms. However, the application of these techniques for in-service inspections remains limited by challenges in accessibility, cost, and scalability. To overcome these issues, an integrated 2D surface system combining optical microscopy with DIC and grain-level analysis within the FEM framework is proposed. This method enables efficient visualization of grain-level deformation and stress distribution, thereby providing deeper insights into the microstructural behavior of polycrystalline materials under external loads. The proposed methodology is theoretically outlined and validated through simulations and experimental analyses, demonstrating its potential as a practical solution for in-service inspections. Ultimately, this approach is expected to advance inspection techniques by offering an accessible and scalable tool for monitoring microstructural behavior and identifying potential failure mechanisms through surface analysis.

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Time Delay Model of the Turbine Pressure Water Conduit: Application to the off-the-grid regime control

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IFAC-PapersOnLine 2021年第18期54卷 41-46页

作者： Jaromír Fišer Pavel Zítek Michal Kuchař Adam Peichl Milan Kučera Pavel Kulík Tomáš Vyhlídal Department of Instrumentation and Control Engineering Faculty of Mechanical Engineering and Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Technická 4 Prague 6 16607 Czech Republic ZAT a.s. K Podlesí 541 Příbram 261 01 Czech Republic

The paper presents modeling the hydro-turbine governing system with delay in application to the turbo-machinery control. The delay originates from the wave phenomenon encountered in the pressure water conduit connected to the turbine. To facilitate the PID control tuning the turbine and conduit model is linearized and it results in linear time delay system referred to as the neutral system. Particularly, the disturbance rejection is achieved by the PID control response to varying power loads. Finally, the application example demonstrates the turbo-machinery control in the off-the-grid regime.

关键词： penstock conduit turbine wave phenomenon neutral system PID control

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The impact of the atmospheric turbulence-development tendency on new particle formation: a common finding on three continents

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National Science Review 2021年第3期8卷 140-150页

作者： Hao Wu Zhanqing Li Hanqing Li Kun Luo Yuying Wang Peng Yan Fei Hu Fang Zhang Yele Sun Dongjie Shang Chunsheng Liang Dongmei Zhang Jing Wei Tong Wu Xiaoai Jin Xinxin Fan Maureen Cribb Marc L.Fischer Markku Kulmala Tuukka Pet?j? State Key Laboratory of Remote Sensing Science College of Global Change and Earth System ScienceBeijing Normal University ESSIC and Department of Atmospheric Science University of MarylandCollege Park State Key Laboratory of Clean Energy Utilization Zhejiang University School of Atmospheric Physics Nanjing University of Information Science and Technology Meteorological Observation Center China Meteorological Administration State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry Institute of Atmospheric Physics Chinese Academy of Sciences State Key Joint Laboratory of Environmental Simulation and Pollution Control College of Environmental Sciences and Engineering Peking University State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Tsinghua University Lawrence Berkeley National Laboratory Institute for Atmospheric and Earth System Research/Physics Faculty of ScienceUniversity of Helsinki Aerosol and Haze Laboratory Beijing Advanced Innovation Center for Soft Matter Science and Engineering Beijing University of Chemical Technology Joint International Research Laboratory of Atmospheric and Earth System Sciences School of Atmospheric Sciences Nanjing University

A new mechanism of new particle formation(NPF) is investigated using comprehensive measurements of aerosol physicochemical quantities and meteorological variables made in three continents,including Beijing,China;the Southern Great Plains site in the USA;and SMEAR Ⅱ Station in Hyytiala,Finland.D espite the considerably different emissions of chemical species among the sites,a common relationship was found between the characteristics of NPF and the stability *** stability parameter(ζ=Z/L,where Z is the height above ground and L is the Monin-Obukhov length) is found to play an important role;it drops significantly before NPF as the atmosphere becomes more unstable,which may serve as an indicator of nucleation *** the atmosphere becomes unstable,the NPF duration is closely related to the tendency for turbulence development,which influences the evolution of the condensation ***,the unstable atmosphere may dilute pre-existing particles,effectively reducing the condensation sink,especially at coarse mode to foster *** new mechanism is confirmed by model simulations using a molecular dynamic model that mimics the impact of turbulence development on nucleation by inducing and intensifying homogeneous nucleation events.

关键词： new particle formation turbulence development molecular dynamic

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State estimation of NonLinear Descriptor systems using Particle Swarm Optimization based Extended Kalman Filter

State estimation of NonLinear Descriptor Systems using Parti...

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European control Conference (ECC)

作者： Tigmanshu Patel M. S. Rao Jalesh L Purohit Vipul A Shah Department of Instrumentation & Control Engineering Faculty of Technology Dharmsinh Desai University Department of Chemical Engineering Faculty of Technology Dharmsinh Desai University

ISBN: (数字)9783907144022

ISBN: (纸本)9781728188133

Descriptor systems/Differential algebraic equation (DAE) naturally arise in dynamic systems and provides a better understanding of the system on basis of physical behaviour. Such form of system representation is different from Ordinary Differential Equations (ODEs) or conventional state space models on account of issues such as index, consistent initialization and requirement of smooth inputs. The methods for descriptor systems cannot be directly borrowed from other forms of system representation and hence be dealt with explicitly. The problem of state estimation for nonlinear descriptor systems (NLDS) has received considerable attention lately [1], [2], [3], [4], [5]. The proposed approach for state estimation of NLDS is based on a systematic study reported in [2]. The Extended Kalman Filter for descriptor systems so demonstrated in [2] is improved upon by implementation of an evolutionary algorithm. The proposed Particle Swarm Optimization based Extended Kalman Filter (PSO EKF) algorithm specifically differs from the former in terms of its approach to ensure consistent initial conditions. In order to incorporate an evolutionary algorithm within state estimation framework for descriptor systems, a novel approach for swarm initialization is put forth. The proposed algorithm is demonstrated on a NLDS of galvanostatic charge process and compared with DAE EKF of [2].

关键词： State estimation Kalman filters Mathematical model Covariance matrices Differential equations Particle swarm optimization Power system dynamics

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Fault diagnosis for PV arrays considering dust impact based on transformed graphical features of characteristic curves and convolutional neural network with CBAM modules

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Applied Energy 2024年 355卷

作者： Jiaqi Qu Qiang Sun Zheng Qian Lu Wei Hamidreza Zareipour School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing China School of Electronics and Information Engineering Beihang University Beijing China The Department of Electrical and Computer Engineering University of Calgary Calgary Canada

Various faults can occur during the operation of photovoltaic (PV) arrays, and both dust-affected operating conditions and various diode configurations complicate the faults. However, current methods for fault diagnosis based on I-V characteristic curves usually do not effectively use all the distinguishable information contained in the I-V curves or often rely on calibrating the field characteristic curves to standard test conditions (STC). It is difficult to apply these methods in practice and accurately identify multiple complex faults with similarities in different blocking diode configurations of PV arrays under the influence of dust. Therefore, a novel fault-diagnosis method for PV arrays that considers the impact of dust is proposed. In the pre-processing stage, the Isc-Voc normalized Gramian angular difference field (GADF) method is presented, which normalizes and transforms the resampled PV array characteristic curves from the field, including I-V and P-V, to obtain transformed graphical feature matrices. Subsequently, in the fault diagnosis stage, the convolutional neural network (CNN) model with convolutional block attention modules (CBAM) is designed to classify faults, which identifies complex fault types from the transformed graphical matrices containing complete discriminative fault information. In addition, the performances of different graphical feature transformation and CNN-based classification methods are compared using case studies. The results indicate that the developed method for PV arrays with different blocking diode configurations under various operating conditions has high fault diagnosis accuracy and reliability.

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Unified resilience model using deep learning for assessing power system performance

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Heliyon 2025年第4期11卷 e42802页

作者： Artemchuk, Volodymyr Garbuz, Iurii Jamil Alsayaydeh, Jamil Abedalrahim Shkarupylo, Vadym Oliinyk, Andrii Bin Yusof, Mohd Faizal Herawan, Safarudin Gazali Department of Mathematical and Econometric Modelling G.E. Pukhov Institute for Modelling in Energy Engineering of the NAS of Ukraine Kyiv Ukraine Department of Environmental Protection Technologies and Radiation Safety Center for Information-analytical and Technical Support of Nuclear Power Facilities Monitoring of the NAS of Ukraine Kyiv Ukraine Department of Іnformation Systems in Economics Kyiv National Economic University Named After Vadym Hetman Kyiv Ukraine Department of Computerized Control System National Aviation University Kyiv Ukraine Department of Engineering Technology Fakulti Teknologi Dan Kejuruteraan Elektronik Dan Komputer (FTKEK) Universiti Teknikal Malaysia Melaka (UTeM) Melaka 76100 Malaysia Department of Computer Systems Networks and Cybersecurity of the National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Department of Software Tools Faculty of Computer Science and Technology Zaporizhzhia Polytechnic National University Zaporizhzhia Ukraine Research Section Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta 11480 Indonesia

Energy resilience in renewable energy sources dissemination components such as batteries and inverters is crucial for achieving high operational fidelity. Resilience factors play a vital role in determining the performance of power systems, regardless of their operating environment and interruptions. This article introduces a Unified Resilience Model (URM) using Deep Learning (DL) to enhance power system performance. The proposed model analyzes environmental factors impacting the resilience of batteries and energy storage devices. This deep learning approach trains performance-impacting factors using previously known low resilience drain data. The learning output is utilized to augment various strengthening factors, thereby improving resilience. Drain mitigation and performance improvements are combined for direct impact verification. This process validates the model's fidelity in enhancing power system performance, with a specific focus on the impact of weather factors. © 2025 The Authors

关键词： Deep learning Energy resilience Fidelity Weather impact

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SI-YOLO: A Deep Learning Algorithm Suitable for Surgical Instrument Detection

SSRN

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SSRN 2022年

作者： Liu, Zhenzhong Zhou, Yifan Sun, Qiyuan Wang, Yan Polat, Kemal Nour, Majid Alenezi, Fayadh Alhudhaif, Adi Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Tianjin300384 China Department of Electrical and Electronics Engineering Bolu Abant Izzet Baysal University Bolu14280 Turkey Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Department of Electrical Engineering College of Engineering Jouf University 72238 Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences in Al-kharj Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj 11942 Saudi Arabia

Surgical instrument detection and tracking is one of the core technologies of surgical robots, which can assist clinicians in completing operations. However, existing methods need further improvement in terms of real-time performance and accuracy. We propose a deep learning algorithm, SI-YOLO, for detection of surgical instruments. SimAM attention mechanism is used to improve the feature extraction ability of YOLOv5. Replacing PANet with BiFPN in the neck network improves the feature fusion strategy. The α-IoU loss function is used to improve the regression accuracy of detection boxes. The mAP and FPS of the SI-YOLO reach 99.2% and 120 frames/sec, respectively. Aiming at the problem of ID transformation in the disappearance and occlusion of surgical instruments, the Inception-ResNet-v2 structure is added on the basis of the appearance feature extraction network Cosine of the DeepSort tracking algorithm. Combining SI-YOLO with the improved DeepSort implements the Tracking by Detection (TBD) algorithm. In the case of device disappearance or occlusion, the frequency of ID transitions is reduced by approximately 30%. Finally, we built a simulated surgery experimental platform, established our own data set, and realized the preliminary application of the proposed algorithm. © 2022, The Authors. All rights reserved.

关键词： Object detection

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Towards Phytoplankton Parasite Detection Using Autoencoders

arXiv

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arXiv 2023年

作者： Bilik, Simon Batrakhanov, Daniel Eerola, Tuomas Haraguchi, Lumi Kraft, Kaisa Van den Wyngaert, Silke Kangas, Jonna Sjöqvist, Conny Madsen, Karin Lensu, Lasse Kälviäinen, Heikki Horak, Karel Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Yliopistonkatu 34 Lappeenranta53850 Finland Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Technická 3058/10 Brno61600 Czech Republic Marine Ecology Measurements Finnish Environment Institute Agnes Sjöbergin Katu 2 Helsinki00790 Finland Department of Biology University of Turku Vesilinnantie 5 Turku20014 Finland Environmental and Marine Biology Åbo Akademi University Henrikinkatu 2 Turku20014 Finland

Phytoplankton parasites are largely understudied microbial components with a potentially significant ecological impact on phytoplankton bloom dynamics. To better understand their impact, we need improved detection methods to integrate phytoplankton parasite interactions in monitoring aquatic ecosystems. Automated imaging devices usually produce high amount of phytoplankton image data, while the occurrence of anomalous phytoplankton data is rare. Thus, we propose an unsupervised anomaly detection system based on the similarity of the original and autoencoder-reconstructed samples. With this approach, we were able to reach an overall F1 score of 0.75 in nine phytoplankton species, which could be further improved by species-specific fine-tuning. The proposed unsupervised approach was further compared with the supervised Faster R-CNN based object detector. With this supervised approach and the model trained on plankton species and anomalies, we were able to reach the highest F1 score of 0.86. However, the unsupervised approach is expected to be more universal as it can detect also unknown anomalies and it does not require any annotated anomalous data that may not be always available in sufficient quantities. Although other studies have dealt with plankton anomaly detection in terms of non-plankton particles, or air bubble detection, our paper is according to our best knowledge the first one which focuses on automated anomaly detection considering putative phytoplankton parasites or infections. © 2023, CC BY.

关键词： Object detection

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Anomaly Detection, Discrimination and Identification Using Real-Time State Estimation for Distribution Grids

SSRN

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SSRN 2022年

作者： Veerakumar, Nidarshan Kongurai, Krit Ćetenović, Dragan Popov, Marjan Jongepier, Arjen Terzija, Vladimir Delft University of Technology Faculty of EEMCS Mekelweg 4 Delft2628CD Netherlands The Department of Electrical & Electronic Engineering The University of Manchester Manchester United Kingdom Stedin B.V. Netherlands Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education School of Electrical Engineering Shandong University Jinan250061 China

In this paper, a PMU-based real-time Distribution system State Estimation (DSSE) platform is developed. The platform applies Forecasting-Aided State Estimator (FASE) to utilize the benefits of the process model. Two of the pre-estimation methods, namely the conventional-, and the improvedinnovation analysis, are implemented and compared to each other in the applied Anomaly Detection, Discrimination and Identification (ADDI) module. The module is used to classify the anomalies into either bad data due to erroneous measurements, or sudden variation of the system state due to sudden load or renewable generation change. Once an anomaly is classified, the ADDI attempts to identify either the PMU corrupted with bad data or most likely the location that drives sudden change of the state. The platform is tested using an actual 50 kV ring network of Enduris medium voltage grid located in the Netherlands, which is simulated using a Real Time Digital Simulator (RTDS). Finally, the necessity of using the ADDI module against possible disturbances in real-life networks has been pointed out. The results showed that the average execution time for FASE coupled with ADDI can be adequately fast for the rapid refresh rate of PMUs under normal and abnormal conditions, particularly the bad data. © 2022, The Authors. All rights reserved.

关键词： State estimation

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