Online identification of cascading events in power systems with renewable generation using machine learning

This PhD project deals with the Modelling of Cascading Events in Power Systems and their Online Identification with Machine Learning, considering the integration of Renewable Energy Sources. Cascading events involve highly complex dynamic phenomena and in some cases can pose significant challenges to the stability and reliability of power grids, leading even to blackouts. The intermittent nature of renewable generation introduces additional complexities, as the system dynamic behavior following a contingency becomes more unpredictable. Consequently, there is an increasing need for cascading event identification methods that can effectively handle these emerging challenges and ensure secure network operation. Machine Learning methods can extract complex relationships from power system data, by capturing the underlying dynamics, offering a promising tool for the accurate and timely identification of the online system state. In addition, due to the extensive installation of Phasor Measurement Units in modern power systems, it is possible to acquire measurement data related to electrical system variables in close-to-real time. The thesis first delves into the understanding of cascading events appearance, as defined by the discrete action of protection devices, using detailed dynamic simulations and considering uncertainties associated with network operating conditions, contingencies and renewable generation. To address the online nature of the problem, supervised machine learning methods that utilize measurement data are developed. Different contemporary machine learning approaches are investigated, to identify the most suitable techniques for the detection of the appearance of cascading events, formulated as a binary classification problem, and the prediction of the reason of the upcoming cascading event, formulated as a multi-class classification problem. Furthermore, this thesis explores the challenges associated with the application of machine learning models on power system data, such as the online inference time, class imbalance, practical considerations related to measurement data and investigates techniques for model explainability to enhance the trustworthiness of the developed models. The contributions of this thesis lie in the development of machine learning-based techniques for online identification of cascading events in power systems, enabling more proactive and efficient situational awareness. These insights have the potential to significantly enhance the resilience and stability of power grids, minimizing the risk of large-scale blackouts and improving the overall reliability of the system. Georgios Nakas is sponsored through Engineering & Physical Sciences Research Council (EPSRC) Research Excellence Award (REA) and is supervised by Dr. Panagiotis Papadopoulos and Professor Graeme Burt.This PhD project deals with the Modelling of Cascading Events in Power Systems and their Online Identification with Machine Learning, considering the integration of Renewable Energy Sources. Cascading events involve highly complex dynamic phenomena and in some cases can pose significant challenges to the stability and reliability of power grids, leading even to blackouts. The intermittent nature of renewable generation introduces additional complexities, as the system dynamic behavior following a contingency becomes more unpredictable. Consequently, there is an increasing need for cascading event identification methods that can effectively handle these emerging challenges and ensure secure network operation. Machine Learning methods can extract complex relationships from power system data, by capturing the underlying dynamics, offering a promising tool for the accurate and timely identification of the online system state. In addition, due to the extensive installation of Phasor Measurement Units in modern power systems, it is possible to acquire measurement data related to electrical system variables in close-to-real time. The thesis first delves into the understanding of cascading events appearance, as defined by the discrete action of protection devices, using detailed dynamic simulations and considering uncertainties associated with network operating conditions, contingencies and renewable generation. To address the online nature of the problem, supervised machine learning methods that utilize measurement data are developed. Different contemporary machine learning approaches are investigated, to identify the most suitable techniques for the detection of the appearance of cascading events, formulated as a binary classification problem, and the prediction of the reason of the upcoming cascading event, formulated as a multi-class classification problem. Furthermore, this thesis explores the challenges associated with the application of machine learning models on power system data, such as the online inference time, class imbalance, practical considerations related to measurement data and investigates techniques for model explainability to enhance the trustworthiness of the developed models. The contributions of this thesis lie in the development of machine learning-based techniques for online identification of cascading events in power systems, enabling more proactive and efficient situational awareness. These insights have the potential to significantly enhance the resilience and stability of power grids, minimizing the risk of large-scale blackouts and improving the overall reliability of the system. Georgios Nakas is sponsored through Engineering & Physical Sciences Research Council (EPSRC) Research Excellence Award (REA) and is supervised by Dr. Panagiotis Papadopoulos and Professor Graeme Burt

Significant Increase in Antibody Titers after the 3rd Booster Dose of the Pfizer-BioNTech mRNA COVID-19 Vaccine in Healthcare Workers in Greece.

The aim of our study was to assess the immunogenicity of the third dose of the BNT162b2 mRNA COVID-19 vaccine (Comirnaty) in a cohort of 129 health-care workers in Greece whose anti-S1 RBD IgG titers were monitored over the course of nine months. Titers were measured for each participant just before the third dose (nine months after the second dose) and also one month after the third dose. Of the 129 participants, 19 had been previously infected before starting the vaccination scheme. The SARS-CoV-2 IgG II Quant assay on the Architect System was employed to longitudinally assess the titers of IgG against the receptor-binding domain of the S1 subunit of the spike protein (anti-S1 RBD). Boosters raised Geometric Mean Concentrations (GMCs) by a factor of approximately 47 relative to levels at 9 months and by a factor of approximately 23 relative to levels at 6 months. The immune response one month after the third dose was significantly higher than the response achieved one month after the second dose (p = 0.008). In conclusion, our findings verify the potent immunogenicity elicited by the third dose in all age and prior COVID-19 status groups, suggesting that the timely administration of the third (booster) dose maximizes the immunogenic potential of the vaccine

Investigation of the impact of load tap changers and automatic generation control on cascading events

This paper presents an assessment of the impact of control mechanisms, specifically load tap changers (LTCs) and automatic generation control (AGC), on cascading events in power systems with renewable generation. In order to identify the impact of these voltage and frequency related mechanisms, a large number of dynamic RMS simulations for various operating conditions is performed taking into consideration renewable generation, system loading and the action of protection devices. The sequences in which the cascading events appear are analysed, and each cascading event is described by the component that trips, the time and the reason for tripping. The number and reason of cascading events, the average load loss and the time between consecutive events are used as metrics to quantify the impact of LTCs and AGC. The study is demonstrated on a modified version of the IEEE-39 bus model with renewable generation and protection devices

Moderation is best: Effects of grazing intensity on plant-flower visitor networks in Mediterranean communities

The structure of pollination networks is an important indicator of ecosystem stability and functioning. Livestock grazing is a frequent land use practice that directly affects the abundance and diversity of flowers and pollinators and, therefore, may indirectly affect the structure of pollination networks. We studied how grazing intensity affected the structure of plant-flower visitor networks along a wide range of grazing intensities by sheep and goats, using data from 11 Mediterranean plant-flower visitor communities from Lesvos Island, Greece. We hypothesized that intermediate grazing might result in higher diversity as predicted by the Intermediate Disturbance Hypothesis, which could in turn confer more stability to the networks. Indeed, we found that networks at intermediate grazing intensities were larger, more generalized, more modular, and contained more diverse and even interactions. Despite general responses at the network level, the number of interactions and selectiveness of particular flower visitor and plant taxa in the networks responded differently to grazing intensity, presumably as a consequence of variation in the abundance of different taxa with grazing. Our results highlight the benefit of maintaining moderate levels of livestock grazing by sheep and goats to preserve the complexity and biodiversity of the rich Mediterranean communities, which have a long history of grazing by these domestic animals.The research has been co-financed by the European Union (European Social Fund—ESF) and Greek National funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF)—Research Funding Program: THALES: Investing in knowledge society through the European Social FundPeer Reviewe

Analysis of the volatile organic compound fingerprint of Greek grape marc spirits of various origins and traditional production styles

The most well-known traditional Greek grape marc distillate made from winemaking pomace is called “Tsipouro”. Its production involves various grape pomace cultivars, preparation protocols, and anise-flavoring or not, and it should be a colorless liquid with intense organoleptic properties due to the raw materials used in its production and have a minimum alcoholic strength of 37.5% by volume. This study aimed to characterize the volatilome of tsipouro products by covering as many geographical areas and production styles as possible, as there is a lack of characterization of the aromatic composition of this Greek traditional alcoholic beverage. A Headspace Solid Phase Microextraction Gas Chromatography–Mass Spectrometry (HS-SPME-GC-MS) method was applied in 60 samples, resulting in the identification and semi-quantification of over 90 volatile compounds. The statistical analysis pointed out the metabolites that characterized each traditional product group and underlined the influence of the geographical origin and the production protocol. Aniseed spirits from Northern Greece, Macedonia, Limnos Island, and Thessaly, produced from Muscat pomaces, were found to be richer in terpenes, terpenoids, and flavored compounds, attributing to product aroma and quality; different terpenoids were found to be dominant in Muscat distillates from different regions, showing the importance of geographical origin and production process. In conclusion, the results demonstrated the high aroma variability of the Greek Tsipouro, explained that this diversity is caused mainly by the raw material, and could be helpful in the better protection of the origin of this traditional product and the improvement of its qualit

Tuber pulchrosporum sp. nov., a black truffle of the Aestivum clade (Tuberaceae, Pezizales) from the Balkan peninsula

Knowledge on the diversity of hypogeous sequestrate ascomycetes is still limited in the Balkan Peninsula. A new species of truffle, Tuber pulchrosporum, is described from Greece and Bulgaria. Specimens were collected from habitats dominated by various oak species (i.e. Quercus ilex, Q. coccifera, Q. robur) and other angiosperms. They are morphologically characterised by subglobose, ovoid to irregularly lobed, yellowish-brown to dark brown ascomata, usually with a shallow basal cavity and surface with fissures and small, dense, almost flat, trihedral to polyhedral warts. Ascospores are ellipsoid to subfusiform, uniquely ornamented, crested to incompletely reticulate and are produced in (1–)2–8-spored asci. Hair-like, hyaline to light yellow hyphae protrude from the peridium surface. According to the outcome of ITS rDNA sequence analysis, this species forms a distinct well-supported group in the Aestivum clade, with T. panniferum being the closest phylogenetic taxon

Online identification of cascading events in power systems with renewable generation using measurement data and machine learning

This paper introduces a framework for online identification of cascading events in power systems with renewable generation, based on supervised machine learning techniques and measurement data. Cascading events are low-probability, high-impact events, the propagation of which can lead even to large-scale blackouts, with severe consequences to society. The proposed methodology is based on Long-short term memory networks, considering uncertainties associated with renewable generation, system loading and initial contingencies. By utilizing time-series measurement data, the proposed method can predict the appearance of cascading events, as defined by the discrete action of protection devices which can capture voltage, frequency or transient instability related dynamic phenomena. The proposed framework is applied on a modified version of the IEEE-39 bus model incorporating detailed dynamic renewable generation and protection devices implementations. Results highlight that the suggested method can successfully identify cases with cascading events with up to 95.6% accuracy and with an average inference time of 0.042s, taking into account practical considerations related to phasor measurement units, such as availability and noise in measurement data

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Μεταπυρική διαδοχή στο σύστημα φυτών–επικονιαστών σε μεσογειακά οικοσυστήματα

Fire is one of the most common disturbances worldwide influencing the evolution of organisms for millions of years and shaping plant and animal diversity. The Mediterranean region is one of the most fire-prone regions in the world, and fire is considered a key factor responsible for many of the present plant and animal adaptations and functional traits. This thesis focuses on the study of the responses of species diversity, abundance, and functional traits, as well as plant–pollinator interactions to fire. To this end, we used data collected in the ten first post-fire years (2013-2022) at four unburnt and nine burnt sites following a large-scale wildfire on Chios Island, Greece. Our results show that insect-pollinator and flowering plant communities were affected by the fire. In particular, beta diversity of the entomophilous plant communities increased at the burnt sites compared to the unburnt sites. Beta diversity peaked in the first year and slowly decreased after the fire. The fire favored the populations and the communities of the two most important guilds of pollinators, i.e. bees and hoverflies, with few exceptions. The abundance of both bee and hoverfly populations peaked in the first post-fire year but soon declined to numbers similar to those at the unburnt sites. Certain species were found to be favored under the post-fire conditions, as the fire favored the populations of species with specific functional traits in both bees and hoverflies. For hoverflies, the migratory multivoltine species were favored the most, while for bees, the below-ground nesters and polylectic species were favored the most. On the contrary, oligolectic and above-ground nesting bees were constrained by fire. Moreover, fire also had a positive effect on intra-specific traits. We found that the body size of the most common Lasioglossum bee after the fire was larger than that of individuals that had been fed with floral rewards just before the fire. Fire favored the populations of polylectic bees and made the hoverfly-plant networks less specialized. However, at the species level, we found that the populations of the two most common bee species were more consistent in their interactions with their plant partners in the burnt sites and showed a high dependence on specific plant species. Finally, the hoverfly–plant pollination networks were rather positively affected by fire as the networks at the burnt sites were larger, more nested, and less specialized compared with the unburnt ones. The results of the thesis indicate that the pollinator communities, the flowering plant communities, and the interactions between them are rather resilient to fire. Nevertheless, caution is needed as fires, are expected to become more severe, more frequent, with longer seasons, and be more challenging to control under the influence of climate change.Η φωτιά είναι μία από τις πιο κοινές οικοσυστημικές διαταραχές σε παγκόσμιο επίπεδο, η οποία έχει επηρεάσει την εξέλιξη των ειδών και έχει συμβάλλει στην διαμόρφωση της χλωρίδας και της πανίδας. Οι περιοχές με Μεσογειακό κλίμα, και ιδιαίτερα η Μεσογειακή Λεκάνη, είναι μεταξύ των περιοχών που μπορούν να χαρακτηριστούν ως «επιρρεπείς στην φωτιά». Οι περιοχές αυτές έχουν διαμορφωθεί από τη φωτιά εδώ και χιλιετίες, γεγονός που έχει οδηγήσει σε πυροπροσαρμογή των ειδών. Η παρούσα διδακτορική διατριβή επικεντρώνεται στη μελέτη των επιπτώσεων της φωτιάς στους πληθυσμούς, την ποικιλότητα, τα λειτουργικά χαρακτηριστικά των εντόμων-επικονιαστών, των ανθοφόρων φυτικών ειδών και των μεταξύ τους αλληλεπιδράσεων. Η έρευνα εστιάζει στα πρώτα, και πιο κρίσιμα για την αποκατάσταση των κοινοτήτων, μεταπυρικά έτη έπειτα από περιστατικό φωτιάς μεγάλης κλίμακας και έχει πραγματοποιηθεί στο νησί της Χίου. Οι δειγματοληψίες έγιναν κατά τα πρώτα δέκα μεταπυρικά έτη (2013-2022) σε 13 περιοχές δειγματοληψίας εκ των οποίων οι τέσσερις ήταν άκαυτες και οι εννιά καμένες. Σύμφωνα με τα αποτελέσματα της μελέτης, οι κοινότητες των εντόμων-επικονιαστών αλλά και των ανθοφόρων φυτών επηρεάστηκαν από την φωτιά. Συγκεκριμένα, οι καμένες εντομόφιλες φυτοκοινότητες είχαν υψηλότερη β-ποικιλότητα σε σύγκριση με τις άκαυτες. Η μέγιστη τιμή β-ποικιλότητας καταγράφηκε κατά την πρώτη μεταπυρική χρονιά, ενώ σταδιακά μειώθηκε μετά τη φωτιά. Οι πληθυσμοί και οι κοινότητες των δύο κυριότερων ομάδων επικονιαστών, των μελισσών και των συρφίδων, ευνοήθηκαν από την φωτιά, με λίγες εξαιρέσεις. Οι πληθυσμοί τους ήταν μεγαλύτεροι στις καμένες περιοχές κατά την πρώτη μεταπυρική χρονιά, αλλά σύντομα μειώθηκαν και ήταν ανάλογοι των άκαυτων περιοχών. Η επίδραση ήταν εντονότερη σε συγκεκριμένα είδη και σχετίστηκε με λειτουργικά χαρακτηριστικά τους. Όσον αφορά τις συρφίδες, ευνοήθηκαν τα μεταναστευτικά είδη, όπως και αυτά που έχουν πολλές γενιές ανά έτος. Όσον αφορά τις μέλισσες, τα είδη που ευνοήθηκαν ήταν αυτά που κάνουν τις φωλιές τους υπογείως και τα πολύ-συλλεκτικά είδη. Αντίθετα τα ολιγο-συλλεκτικά και τα είδη που φωλιάζουν υπεργείως ήταν αυτά που επηρεάστηκαν αρνητικά από την φωτιά. Η φωτιά επηρέασε τα είδη που μελετήθηκαν και σε ενδο-ειδικό επίπεδο και η επίδραση ήταν θετική. Συγκεκριμένα, το μέγεθος σώματος των ατόμων του πιο κοινού είδους μέλισσας του γένους Lasioglossum ήταν μεγαλύτερο στις μεταπυρικές περιοχές, σε σχέση με τα άτομα που είχαν ανατραφεί με ανθικές παροχές που είχαν συλλεχθεί κατά την χρονιά αμέσως πριν τη φωτιά. Όσον αφορά την τροφική ειδίκευση, η φωτιά είχε θετική επίδραση στους πληθυσμούς των πολύ-συλλεκτικών μελισσών, με τις καμένες περιοχές να φιλοξενούν λιγότερο εξειδικευμένα δίκτυα σε σχέση με τις άκαυτες. Ωστόσο, σε ενδο-ειδικό επίπεδο βρέθηκε πως τα άτομα των δύο ποιο κοινών μελισσών ήταν πιο σταθερά στις σχέσεις τους με τα φυτά-εταίρους στις καμένες περιοχές στις οποίες εμφάνισαν και μεγαλύτερη εξάρτηση σε συγκεκριμένα φυτικά είδη. Τέλος, η φωτιά είχε μάλλον θετική επίδραση στα δίκτυα συρφίδων–φυτών καθώς οι καμένες περιοχές είχαν μεγαλύτερα και λιγότερο εξειδικευμένα δίκτυα, με αυξημένο εγκιβωτισμό σε σχέση με τις άκαυτες. Τα αποτελέσματα της διατριβής δείχνουν πως οι κοινότητες των επικονιαστών, των ανθοφόρων φυτών αλλά και οι μεταξύ τους αλληλεπιδράσεις τείνουν να είναι ανθεκτικές στην φωτιά. Ωστόσο, χρειάζεται προσοχή καθώς, εξαιτίας της κλιματικής αλλαγής, οι φωτιές τα επόμενα χρόνια αναμένεται να γίνουν μεγαλύτερες, σφοδρότερες, συχνότερες, ακόμη και σε περιόδους εκτός καλοκαιριού, και συνεπώς πιο δύσκολο να τεθούν υπό έλεγχο

Investigation of cascading events in power systems with renewable generation

The paper introduces a framework for characterisation and investigation of cascading events in power systems with renewable generation using time domain dynamic simulations. The paper aims at identifying the cascading event patterns by including protection device operation in RMS simulations and analyzing them. The cascading events are characterised by the power system components involved, the sequence of trippings and the reason for failure (e.g. voltage/frequency), while considering a wide range of possible operating conditions defined by economic dispatch. Changes in observed cascading failure patterns for different operating conditions are identified and investigated, taking also into consideration the impact of renewable generation. The framework is demonstrated on a modified version of the Anderson-Fouad 9 bus model incorporating renewable generation and protection devices