On power system automation: a Digital Twin-centric framework for the next generation of energy management systems

The ubiquitous digital transformation also influences power system operation. Emerging real-time applications in information (IT) and operational technology (OT) provide new opportunities to address the increasingly demanding power system operation imposed by the progressing energy transition. This IT/OT convergence is epitomised by the novel Digital Twin (DT) concept. By integrating sensor data into analytical models and aligning the model states with the observed system, a power system DT can be created. As a result, a validated high-fidelity model is derived, which can be applied within the next generation of energy management systems (EMS) to support power system operation. By providing a consistent and maintainable data model, the modular DT-centric EMS proposed in this work addresses several key requirements of modern EMS architectures. It increases the situation awareness in the control room, enables the implementation of model maintenance routines, and facilitates automation approaches, while raising the confidence into operational decisions deduced from the validated model. This gain in trust contributes to the digital transformation and enables a higher degree of power system automation. By considering operational planning and power system operation processes, a direct link to practice is ensured. The feasibility of the concept is examined by numerical case studies.The electrical power system is in the process of an extensive transformation. Driven by the energy transition towards renewable energy resources, many conventional power plants in Germany have already been decommissioned or will be decommissioned within the next decade. Among other things, these changes lead to an increased utilisation of power transmission equipment, and an increasing number of complex dynamic phenomena. The resulting system operation closer to physical boundaries leads to an increased susceptibility to disturbances, and to a reduced time span to react to critical contingencies and perturbations. In consequence, the task to operate the power system will become increasingly demanding. As some reactions to disturbances may be required within timeframes that exceed human capabilities, these developments are intrinsic drivers to enable a higher degree of automation in power system operation. This thesis proposes a framework to create a modular Digital Twin-centric energy management system. It enables the provision of validated and trustworthy models built from knowledge about the power system derived from physical laws, and process data. As the interaction of information and operational technologies is combined in the concept of the Digital Twin, it can serve as a framework for future energy management systems including novel applications for power system monitoring and control, which consider power system dynamics. To provide a validated high-fidelity dynamic power system model, time-synchronised phasor measurements of high-resolution are applied for validation and parameter estimation. This increases the trust into the underlying power system model as well as the confidence into operational decisions derived from advanced analytic applications such as online dynamic security assessment. By providing an appropriate, consistent, and maintainable data model, the framework addresses several key requirements of modern energy management system architectures, while enabling the implementation of advanced automation routines and control approaches. Future energy management systems can provide an increased observability based on the proposed architecture, whereby the situational awareness of human operators in the control room can be improved. In further development stages, cognitive systems can be applied that are able to learn from the data provided, e.g., machine learning based analytical functions. Thus, the framework enables a higher degree of power system automation, as well as the deployment of assistance and decision support functions for power system operation pointing towards a higher degree of automation in power system operation. The framework represents a contribution to the digital transformation of power system operation and facilitates a successful energy transition. The feasibility of the concept is examined by case studies in form of numerical simulations to provide a proof of concept.Das elektrische Energiesystem befindet sich in einem umfangreichen Transformations-prozess. Durch die voranschreitende Energiewende und den zunehmenden Einsatz erneuerbarer Energieträger sind in Deutschland viele konventionelle Kraftwerke bereits stillgelegt worden oder werden in den nächsten Jahren stillgelegt. Diese Veränderungen führen unter anderem zu einer erhöhten Betriebsmittelauslastung sowie zu einer verringerten Systemträgheit und somit zu einer zunehmenden Anzahl komplexer dynamischer Phänomene im elektrischen Energiesystem. Der Betrieb des Systems näher an den physikalischen Grenzen führt des Weiteren zu einer erhöhten Störanfälligkeit und zu einer verkürzten Zeitspanne, um auf kritische Ereignisse und Störungen zu reagieren. Infolgedessen wird die Aufgabe, das Stromnetz zu betreiben anspruchsvoller. Insbesondere dort wo Reaktionszeiten erforderlich sind, welche die menschlichen Fähigkeiten übersteigen sind die zuvor genannten Veränderungen intrinsische Treiber hin zu einem höheren Automatisierungsgrad in der Netzbetriebs- und Systemführung. Aufkommende Echtzeitanwendungen in den Informations- und Betriebstechnologien und eine zunehmende Menge an hochauflösenden Sensordaten ermöglichen neue Ansätze für den Entwurf und den Betrieb von cyber-physikalischen Systemen. Ein vielversprechender Ansatz, der in jüngster Zeit in diesem Zusammenhang diskutiert wurde, ist das Konzept des so genannten Digitalen Zwillings. Da das Zusammenspiel von Informations- und Betriebstechnologien im Konzept des Digitalen Zwillings vereint wird, kann es als Grundlage für eine zukünftige Leitsystemarchitektur und neuartige Anwendungen der Leittechnik herangezogen werden. In der vorliegenden Arbeit wird ein Framework entwickelt, welches einen Digitalen Zwilling in einer neuartigen modularen Leitsystemarchitektur für die Aufgabe der Überwachung und Steuerung zukünftiger Energiesysteme zweckdienlich einsetzbar macht. In Ergänzung zu den bereits vorhandenen Funktionen moderner Netzführungssysteme unterstützt das Konzept die Abbildung der Netzdynamik auf Basis eines dynamischen Netzmodells. Um eine realitätsgetreue Abbildung der Netzdynamik zu ermöglichen, werden zeitsynchrone Raumzeigermessungen für die Modellvalidierung und Modellparameterschätzung herangezogen. Dies erhöht die Aussagekraft von Sicherheitsanalysen, sowie das Vertrauen in die Modelle mit denen operative Entscheidungen generiert werden. Durch die Bereitstellung eines validierten, konsistenten und wartbaren Datenmodells auf der Grundlage von physikalischen Gesetzmäßigkeiten und während des Betriebs gewonnener Prozessdaten, adressiert der vorgestellte Architekturentwurf mehrere Schlüsselan-forderungen an moderne Netzleitsysteme. So ermöglicht das Framework einen höheren Automatisierungsgrad des Stromnetzbetriebs sowie den Einsatz von Entscheidungs-unterstützungsfunktionen bis hin zu vertrauenswürdigen Assistenzsystemen auf Basis kognitiver Systeme. Diese Funktionen können die Betriebssicherheit erhöhen und stellen einen wichtigen Beitrag zur Umsetzung der digitalen Transformation des Stromnetzbetriebs, sowie zur erfolgreichen Umsetzung der Energiewende dar. Das vorgestellte Konzept wird auf der Grundlage numerischer Simulationen untersucht, wobei die grundsätzliche Machbarkeit anhand von Fallstudien nachgewiesen wird

Kognitive Funktion, gesundheitsbezogene Lebensqualität und Beschäftigungsstatus als Teil des Langzeitergebnisses bei Patienten mit ARDS als Folge einer schweren ambulant erworbenen Pneumonie

Abstrakt (dt.) Einleitung Das acute respiratory distress syndrome (ARDS) ist mit hoher Mortalität und bekannten Langzeitfolgen behaftet. Die Langzeitfolgen schwerer Verläufe ambulant erworbener Pneumonien (sCAP) mit ARDS, insbesondere Unterschiede zwischen viralen und bakteriellen Pneumonien, sind bisher unzureichend untersucht1,2. Einzelne Aspekte der Langzeitergebnisse bei Patienten mit sCAP und ARDS, insbesondere infolge einer Influenza A(H1N1)-Infektion, wurden nach der Pandemie 2009-A(H1N1)pdm09 berichtet1. Es gibt außerdem Hinweise, dass die antivirale Therapie mit Oseltamivir zu einem verstärkten Auftreten von Delir führt, wobei das Auftreten von deliranten Zuständen bei intensivmedizinischen Patienten mit Langzeitfolgen in Verbindung gebracht wird3–5. Methodik Für die retrospektive Analyse der Delir-Inzidenz und Delir-Dauer schlossen wir 42 überlebende Patienten, nach sCAP und schwerem ARDS, in die Untersuchung ein (N=42 (H1N1-Patienten=15, non-H1N1-Patienten=27)). Das Langzeitergebnis wurde prospektiv vier Jahre nach Entlassung mittels neurokognitiver Testungen, sowie Erhebungen der gesundheitsbezogenen Lebensqualität, Lungenfunktion und Beschäftigungsstatus untersucht (N=23). Ergebnisse Ein Delir, während der intensivmedizinischen Therapie, trat bei 88% der Patienten auf. Zwischen H1N1-Patienten und non-H1N1-Patienten zeigte sich kein signifikanter Unterschied der Delir-Inzidenz (80% vs. 93%, p = 1.00) sowie der Delir-Dauer (4 [1;6] vs. 7 [2;10] Tage, Median [Perzentile25/75], p = .147). Kognitive Dysfunktion zeigte sich bei den Patienten in den Dimensionen motorische Reaktionsgeschwindigkeit, visuelles Kurzzeitgedächtnis, verbal-deklaratives Kurzzeitgedächtnis sowie der Lese- bzw. Wortverarbeitungsgeschwindigkeit. H1N1- und non-H1N1-Patienten wiesen im kognitiven Langzeitergebnis keine signifikanten Unterschiede auf. Die Dauer des Delirs korrelierte negativ mit der motorischen Reaktionsgeschwindigkeit, dem räumlichen Gedächtnis sowie dem verbal-deklarativen Kurzzeitgedächtnis. Patienten nahmen Einschränkungen des körperlichen Gesundheitsstatus in der gesundheitsbezogenen Lebensqualität wahr. Kognitive Dysfunktion der Patienten spiegelte sich nicht in subjektiv wahrgenommener Einschränkung der eigenen psychischen Lebensqualität wieder. Die Lungenfunktion war lediglich leicht eingeschränkt. Vier Jahre nach Entlassung waren 26% der Patienten nicht mehr in der Lage, einen Beruf auszuüben. 30% der Patienten gingen einer Beschäftigung nach. Kognitive Dysfunktion hatte keinen Einfluss auf den Beschäftigungsstatus. Schlussfolgerung Delir stellt bei sCAP-ARDS-Patienten eine häufige neuropsychiatrische Komplikation dar. Im Langzeitergebnis zeigen sich bei sCAP-ARDS-Patienten auch vier Jahre nach Entlassung kognitive Einschränkungen, verminderte gesundheitsbezogene Lebensqualität und leicht verminderte Lungenfunktion. Es gibt bei dieser Patientengruppe Hinweise darauf, dass Komorbiditäten einen Faktor für eine geringere Wiederaufnahme einer beruflichen Tätigkeit darstellen. Sozioökonomisch scheinen diese Langzeitergebnisse als Folge einer häufigen Infektionskrankheit eine hohe gesellschaftliche Krankheitslast darzustellen. Zukünftige Studiendesigns sollten daher methodisch auf die Identifizierung von Risikofaktoren für die Entwicklung eines Delirs, eine Reduktion der gesundheitsbezogenen Lebensqualität sowie eine geringe Wiederaufnahme beruflicher Tätigkeiten von Patienten mit sCAP und ARDS ausgerichtet und auf Interventionsmöglichkeiten während der intensivmedizinischen Therapie zur Reduktion der Einschränkungen im Langzeitergebnis fokussiert werden.Abstract (engl.) Introduction Acute respiratory distress syndrome (ARDS) is associated with high mortality and multiple long-term effects. Yet, for long-term outcomes of severe community acquired pneumonia (sCAP) associated with ARDS insufficient research exists, particularly concerning differences in bacterial versus viral pneumonia1,2. Only some aspects of long-term outcomes of influenza A(H1N1)-infection associated ARDS, caused by the 2009 A(H1N1)pdm09 pandemic, have been evaluated1. Several studies have suggested elevated rates of delirium in patients with severe H1N1-infections treated with Oseltamivir, but the data is inconsistent3–5. Delirium is considered a risk factor for long-time consequences. Methods We enrolled 42 patients who had survived sCAP with severe ARDS caused by either H1N1-infection or non-H1N1-infection and analyzed the incidence and duration of delirium during their hospitalization. Four years after discharge from ICU we enrolled a subgroup (N=23) to assess long-term outcomes. Therefore, we performed neurocognitive testing and collected data on health-related quality of life, lung function and employment status. Results Delirium occurred in 88% patients during ICU-treatment. There was no significant difference in the incidence of delirium in H1N1-patients and non-H1N1-patients (80% vs. 93%), nor in the duration of delirium (4 [1;6] vs. 7 [2;10] days, median [percentile25/75]). Regardless of the etiology of their ARDS, cognitive performance was reduced in the subdomains of motor responsiveness, visual memory, verbal-declarative learning and memory, as well as word processing speed. Furthermore, the duration of the delirium in ARDS patients correlated with a worse cognitive long-term outcome in the subdomains: motor response speed, spatial memory, and verbal-declarative learning and memory. Cognitive long-term outcomes did not differ between H1N1-patients and non- H1N1-patients. Cognitive dysfunction did not lead to a reduction in the mental dimension of health-related quality of life. Parameters of lung function were only reduced slightly. Four years after ICU-discharge, 26% of patients were no longer able to work. 30% of the patients were employed. Cognitive dysfunction had no impact on employment status. Conclusion The occurrence of delirium in sCAP-ARDS patients is a common neuropsychiatric complication. In survivors of sCAP with ARDS, 4 years after discharge, cognitive impairment, lowered subjective health-related quality of life, reduced lung function, and low employment rates are seen. This study also provides some evidence that the low employment rate of sCAP-ARDS patients may be caused by comorbidities. Further research, aimed at the identification of risk factors for delirium, reduced health-related quality of life and low return to work rate of patients with sCAP and ARDS, is needed to better guide methods of intervention during ICU treatment

Erfolgskontrolle von Hartholzauenwald-Aufforstungen in der Kliekener Aue

Hartholz-Auenwälder (Querco-Ulmetum minoris und weitere Vegetationseinheiten des Ulmenion) sind charakteristische Vegetationsgesellschaften entlang der großen Flussauen und wichtige Retentionsräume. Bedingt durch den Wechsel von Überflutung und Trockenheit sowie eine hohe standörtliche Dynamik und Heterogenität sind Hartholz-Auenwälder die struktur- und artenreichsten Lebensräume in Mitteleuropa. In früheren Jahrhunderten wurden viele Auenwälder zu Gunsten von Siedlungen und landwirtschaftlichen Nutzflächen gerodet, was eine erhebliche Verringerung des Flächenanteils der Auenwälder zur Folge hatte. Die verbliebenen Hartholz-Auenwälder wurden im 19. Jahrhundert durch zahlreiche wasserbaulichen Maßnahmen beeinträchtigt. Angesichts der hohen naturschutzfachlichen Bedeutung regelmäßig überfluteter Hartholz-Auenwälder und ihres heute geringen Flächenanteiles ist deren Erhaltung, Entwicklung und Erweiterung ein wesentliches Ziel des Naturschutzes in Flusslandschaften. Ziel des von der Biosphärenreservatsverwaltung „Mittlere Elbe“ 2000/2001 durchgeführten EU-LIFE-Projektes „Renaturierung von Fluss, Altwasser und Auenwald an der Mittleren Elbe“ war u. a. die Entwicklung von ca. 60 ha Auenwald auf ehemals beweideten Alteichenbeständen und Grünland. Da bisher Erfolgskontrollen von Hartholz-Auenwaldanpflanzungen fast vollständig fehlen, erfolgte 2007 eine flächendeckende Erhebung des aktuellen Zustandes aller gepflanzten Gehölzbestände in der Kliekener Aue

Using source-specific models to test the impact of sediment source classification on sediment fingerprinting

Sediment fingerprinting estimates sediment source contributions directly from river sediment. Despite being fundamental to the interpretation of sediment fingerprinting results, the classification of sediment sources and its impact on the accuracy of source apportionment remain underinvestigated. This study assessed the impact of source classification on sediment fingerprinting based on diffuse reflectance infrared Fourier transform spectrometry (DRIFTS), using individual, source-specific partial least-squares regression (PLSR) models. The objectives were to (a) perform a model sensitivity analysis through systematically omitting sediment sources and (b) investigate how sediment source-group discrimination and the importance of the groups as actual sources relate to variations in results. Within the Aire catchment (United Kingdom), five sediment sources were classified and sampled (n = 117): grassland topsoil in three lithological areas (limestone, millstone grit, and coal measures); riverbanks; and street dust. Experimental mixtures (n = 54) of the sources were used to develop PLSR models between known quantities of a single source and DRIFTS spectra of the mixtures, which were applied to estimate source contributions from DRIFTS spectra of suspended (n = 200) and bed (n = 5) sediment samples. Dominant sediment sources were limestone topsoil (45 ± 12%) and street dust (43 ± 10%). Millstone and coals topsoil contributed on average 19 ± 13% and 14 ± 10%, and riverbanks 16 ± 18%. Due to the use of individual PLSR models, the sum of all contributions can deviate from 100%; thus, a model sensitivity analysis assessed the impact and accuracy of source classification. Omitting less important sources (e.g., coals topsoil) did not change the contributions of other sources, whereas omitting important, poorly-discriminated sources (e.g., riverbank) increased the contributions of all sources. In other words, variation in source classification substantially alters source apportionment depending on source discrimination and source importance. These results will guide development of procedures for evaluating the appropriate type and number of sediment sources in DRIFTS-PLSR sediment fingerprinting

Sediment source fingerprinting: benchmarking recent outputs, remaining challenges and emerging themes

Abstract: Purpose: This review of sediment source fingerprinting assesses the current state-of-the-art, remaining challenges and emerging themes. It combines inputs from international scientists either with track records in the approach or with expertise relevant to progressing the science. Methods: Web of Science and Google Scholar were used to review published papers spanning the period 2013–2019, inclusive, to confirm publication trends in quantities of papers by study area country and the types of tracers used. The most recent (2018–2019, inclusive) papers were also benchmarked using a methodological decision-tree published in 2017. Scope: Areas requiring further research and international consensus on methodological detail are reviewed, and these comprise spatial variability in tracers and corresponding sampling implications for end-members, temporal variability in tracers and sampling implications for end-members and target sediment, tracer conservation and knowledge-based pre-selection, the physico-chemical basis for source discrimination and dissemination of fingerprinting results to stakeholders. Emerging themes are also discussed: novel tracers, concentration-dependence for biomarkers, combining sediment fingerprinting and age-dating, applications to sediment-bound pollutants, incorporation of supportive spatial information to augment discrimination and modelling, aeolian sediment source fingerprinting, integration with process-based models and development of open-access software tools for data processing. Conclusions: The popularity of sediment source fingerprinting continues on an upward trend globally, but with this growth comes issues surrounding lack of standardisation and procedural diversity. Nonetheless, the last 2 years have also evidenced growing uptake of critical requirements for robust applications and this review is intended to signpost investigators, both old and new, towards these benchmarks and remaining research challenges for, and emerging options for different applications of, the fingerprinting approach

A Range of Earth Observation Techniques for Assessing Plant Diversity

AbstractVegetation diversity and health is multidimensional and only partially understood due to its complexity. So far there is no single monitoring approach that can sufficiently assess and predict vegetation health and resilience. To gain a better understanding of the different remote sensing (RS) approaches that are available, this chapter reviews the range of Earth observation (EO) platforms, sensors, and techniques for assessing vegetation diversity. Platforms include close-range EO platforms, spectral laboratories, plant phenomics facilities, ecotrons, wireless sensor networks (WSNs), towers, air- and spaceborne EO platforms, and unmanned aerial systems (UAS). Sensors include spectrometers, optical imaging systems, Light Detection and Ranging (LiDAR), and radar. Applications and approaches to vegetation diversity modeling and mapping with air- and spaceborne EO data are also presented. The chapter concludes with recommendations for the future direction of monitoring vegetation diversity using RS

PI3K as Mediator of Apoptosis and Contractile Dysfunction in TGFβ1-Stimulated Cardiomyocytes

Background: TGFβ1 is a growth factor that plays a major role in the remodeling process of the heart by inducing cardiomyocyte dysfunction and apoptosis, as well as fibrosis thereby restricting heart function. TGFβ1 mediates its effect via the TGFβ receptor I (ALK5) and the activation of SMAD transcription factors, but TGFβ1 is also known as activator of phosphoinositide-3-kinase (PI3K) via the non-SMAD signaling pathway. The aim of this study was to investigate whether PI3K is also involved in TGFβ1–induced cardiomyocytes apoptosis and contractile dysfunction. Methods and Results: Incubation of isolated ventricular cardiomyocytes with TGFβ1 resulted in impaired contractile function. Pre-incubation of cells with the PI3K inhibitor Ly294002 or the ALK5 inhibitor SB431542 attenuated the decreased cell shortening in TGFβ1–stimulated cells. Additionally, TGFβ-induced apoptosis was significantly reduced by the PI3K inhibitor Ly294002. Administration of a PI3Kγ-specific inhibitor AS605240 abolished the TGFβ effect on apoptosis and cell shortening. This was also confirmed in cardiomyocytes from PI3Kγ KO mice. Induction of SMAD binding activity and the TGFβ target gene collagen 1 could be blocked by the PI3K inhibitor Ly294002, but not by the specific PI3Kγ inhibitor AS605240. Conclusions: TGFβ1-induced SMAD activation, cardiomyocyte apoptosis, and impaired cell shortening are mediated via both, the ALK5 receptor and PI3K, in adult cardiomyocytes. PI3Kγ specifically contributes to apoptosis induction and impairment of contractile function independent of SMAD signaling

A novel framework for synchrophasor based online recognition and efficient post-mortem analysis of disturbances in power systems

Synchrophasor based applications become more and more popular in today’s control centers to monitor and control transient system events. This can ensure secure system operation when dealing with bidirectional power flows, diminishing reserves and an increased number of active grid components. Today’s synchrophasor applications provide a lot of additional information about the dynamic system behavior but without significant improvement of the system operation due to the lack of interpretable and condensed results as well as missing integration into existing decision-making processes. This study presents a holistic framework for novel machine learning based applications analyzing both historical as well as online synchrophasor data streams. Different methods from dimension reduction, anomaly detection as well as time series classification are used to automatically detect disturbances combined with a web-based online visualization tool. This enables automated decision-making processes in control centers to mitigate critical system states and to ensure secure system operations (e.g., by activating curate actions). Measurement and simulation-based results are presented to evaluate the proposed synchrophasor application modules for different use cases at the transmission and distribution level

Machine learning and digital twins: monitoring and control for dynamic security in power systems

The reader of the chapter will be able to connect techniques from machine learning (ML) and digital twins (DTs) to gain insights for monitoring and control of (dynamic) security for electrical power systems. DTs are validated and verified high-fidelity (hf) models providing high simulation accuracy. DTs can be used for simulation of the supervised process of system operation and are therefore able to provide synthetic studied data, where measurement data are scarce. However, for some real-time applications in monitoring and control, such high-fidelity simulation models are not appropriate due to the corresponding computational barrier. There, ML aims to create an application-specific, low-fidelity (lf) approximation of the digital twin. Such trained lf models are used in real-time applications where computational time is scarce and lf information is sufficient. The conceptual intersection of hf and lf models has been little explored and becomes increasingly complex. This chapter aims to provide a conceptual overview of how such hf and lf models can be combined. This chapter is split into two parts where the first part is to introduce ML, lf models, and digital twins, hf models, for power systems analysis, and the second chapter is to use these two types of models to form purpose-driven surrogate lf models, illustrated on the example of dynamic security assessment (DSA). In the first part, the concepts for using DTs as hf models for online power system studies and their corresponding tuning of model parameters are introduced. Subsequently, ML i.e., lf models, are introduced and their corresponding training frameworks. Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Intelligent Electrical Power Grid