A Machine-learning based Probabilistic Perspective on Dynamic Security Assessment

Probabilistic security assessment and real-time dynamic security assessments (DSA) are promising to better handle the risks of system operations. The current methodologies of security assessments may require many time-domain simulations for some stability phenomena that are unpractical in real-time. Supervised machine learning is promising to predict DSA as their predictions are immediately available. Classifiers are offline trained on operating conditions and then used in real-time to identify operating conditions that are insecure. However, the predictions of classifiers can be sometimes wrong and hazardous if an alarm is missed for instance. A probabilistic output of the classifier is explored in more detail and proposed for probabilistic security assessment. An ensemble classifier is trained and calibrated offline by using Platt scaling to provide accurate probability estimates of the output. Imbalances in the training database and a cost-skewness addressing strategy are proposed for considering that missed alarms are significantly worse than false alarms. Subsequently, risk-minimised predictions can be made in real-time operation by applying cost-sensitive learning. Through case studies on a real data-set of the French transmission grid and on the IEEE 6 bus system using static security metrics, it is showcased how the proposed approach reduces inaccurate predictions and risks. The sensitivity on the likelihood of contingency is studied as well as on expected outage costs. Finally, the scalability to several contingencies and operating conditions are showcased.Comment: 42 page

MARL-iDR: Multi-Agent Reinforcement Learning for Incentive-based Residential Demand Response

This paper presents a decentralized Multi-Agent Reinforcement Learning (MARL) approach to an incentive-based Demand Response (DR) program, which aims to maintain the capacity limits of the electricity grid and prevent grid congestion by financially incentivizing residential consumers to reduce their energy consumption. The proposed approach addresses the key challenge of coordinating heterogeneous preferences and requirements from multiple participants while preserving their privacy and minimizing financial costs for the aggregator. The participant agents use a novel Disjunctively Constrained Knapsack Problem optimization to curtail or shift the requested household appliances based on the selected demand reduction. Through case studies with electricity data from $25$ households, the proposed approach effectively reduced energy consumption's Peak-to-Average ratio (PAR) by $14.48$% compared to the original PAR while fully preserving participant privacy. This approach has the potential to significantly improve the efficiency and reliability of the electricity grid, making it an important contribution to the management of renewable energy resources and the growing electricity demand.Comment: 8 pages, IEEE Belgrade PowerTech, 202

Regularised Learning with Selected Physics for Power System Dynamics

Due to the increasing system stability issues caused by the technological revolutions of power system equipment, the assessment of the dynamic security of the systems for changing operating conditions (OCs) is nowadays crucial. To address the computational time problem of conventional dynamic security assessment tools, many machine learning (ML) approaches have been proposed and well-studied in this context. However, these learned models only rely on data, and thus miss resourceful information offered by the physical system. To this end, this paper focuses on combining the power system dynamical model together with the conventional ML. Going beyond the classic Physics Informed Neural Networks (PINNs), this paper proposes Selected Physics Informed Neural Networks (SPINNs) to predict the system dynamics for varying OCs. A two-level structure of feed-forward NNs is proposed, where the first NN predicts the generator bus rotor angles (system states) and the second NN learns to adapt to varying OCs. We show a case study on an IEEE-9 bus system that considering selected physics in model training reduces the amount of needed training data. Moreover, the trained model effectively predicted long-term dynamics that were beyond the time scale of the collected training dataset (extrapolation)

Exploring Operational Flexibility of Active Distribution Networks with Low Observability

Power electronic interfaced devices progressively enable the increasing provision of flexible operational actions in distribution networks. The feasible flexibility these devices can effectively provide requires estimation and quantification so the network operators can plan operations close to real-time. Existing approaches estimating the distribution network flexibility require the full observability of the system, meaning topological and state knowledge. However, the assumption of full observability is unrealistic and represents a barrier to system operators' adaptation. This paper proposes a definition of the distribution network flexibility problem that considers the limited observability in real-time operation. A critical review and assessment of the most prominent approaches are done based on the proposed definition. This assessment showcases the limitations and benefits of existing approaches for estimating flexibility with low observability. A case study on the CIGRE MV distribution system highlights the drawbacks brought by low observability.Comment: This paper has been accepted to the IEEE Belgrade Powertech 2023. It has 6 pages, 4 figures, and 2 table

Does Change Incite Abusive Supervision? The Role of Transformational Change and Hindrance Stress

To remain competitive, organizations tend to change their established ways of working, their strategy, the core values, and the organizational structure. Such thorough changes are referred to as transformational change. Unfortunately, transformational change is often unsuccessful because organizational members do not always welcome the change. Although organizations often expect their supervisors to be successful role-models and change-agents during the transformational change process, we argue that initiating transformational change could increase supervisors\u27 hindrance stress levels, which may result in abusive behaviors towards employees. More specifically, in a multi-source survey and an experimental study, we find evidence that transformational change is associated with supervisors\u27 experienced hindrance stress, which subsequently led to more abusive behaviors towards employees

Deep Statistical Solver for Distribution System State Estimation

Implementing accurate Distribution System State Estimation (DSSE) faces several challenges, among which the lack of observability and the high density of the distribution system. While data-driven alternatives based on Machine Learning models could be a choice, they suffer in DSSE because of the lack of labeled data. In fact, measurements in the distribution system are often noisy, corrupted, and unavailable. To address these issues, we propose the Deep Statistical Solver for Distribution System State Estimation (DSS$^2$), a deep learning model based on graph neural networks (GNNs) that accounts for the network structure of the distribution system and for the physical governing power flow equations. DSS$^2$ leverages hypergraphs to represent the heterogeneous components of the distribution systems and updates their latent representations via a node-centric message-passing scheme. A weakly supervised learning approach is put forth to train the DSS$^2$ in a learning-to-optimize fashion w.r.t. the Weighted Least Squares loss with noisy measurements and pseudomeasurements. By enforcing the GNN output into the power flow equations and the latter into the loss function, we force the DSS$^2$ to respect the physics of the distribution system. This strategy enables learning from noisy measurements, acting as an implicit denoiser, and alleviating the need for ideal labeled data. Extensive experiments with case studies on the IEEE 14-bus, 70-bus, and 179-bus networks showed the DSS$^2$ outperforms by a margin the conventional Weighted Least Squares algorithm in accuracy, convergence, and computational time, while being more robust to noisy, erroneous, and missing measurements. The DSS$^2$ achieves a competing, yet lower, performance compared with the supervised models that rely on the unrealistic assumption of having all the true labels.Comment: 10 pages, manuscript is under revie

Repetitive application of remote ischemic conditioning (RIC) in patients with peripheral arterial occlusive disease (PAOD) as a non-invasive treatment option: study protocol for a randomised controlled clinical trial

Background The best medical treatment (BMT) for most patients with early stage of peripheral arterial occlusive disease (PAOD) is often limited to gait training and pharmacological therapy besides endovascular surgery. The application of remote ischemic conditioning (RIC) has been described as a promising experimental strategy for the improvement of therapeutic outcome in cardiovascular disease but has not proven beneficial effects in clinical practice and treatment of PAOD yet. Methods Here we describe a prospective, randomized trial for the evaluation of possible effects of repeated application of RIC in patients with PAOD. This monocentric study will enrol 200 participants distributed to an intervention group receiving RIC + BMT and a control group only receiving BMT for four weeks. Patients are at least 18 years of age and have diagnosed PAOD Fontaine stage II b. Pain-free and total walking distance will be measured via treadmill test (primary endpoints). In addition, ankle-brachial index (ABI) and quality of life (QoL) will be assessed using the SF-36 and VascuQoL-6 questionnaire. Moreover, evaluation of markers for atherosclerosis, angiogenic profiling and mononuclear cell characterization will be performed using biochemical assays, proteome profiling arrays and flow cytometry (secondary endpoints). Discussion Our prospective, randomized monocentric trial is the first of its kind to analyse the effects of chronic and repetitive treatment with RIC in patients with PAOD and might provide important novel information on the molecular mechanisms associated with RIC in PAOD patients. Trial registration Prospectively registered in the German Clinical Trials Register (Deutsche Register Klinischer Studien) Registration number: DRKS00025735; Date of registration: 01.07.2021

Impact of an Interleukin-1 Receptor Antagonist and Erythropoietin on Experimental Myocardial Ischemia/Reperfusion Injury

Background. Revascularization of infarcted myocardium results in release of inflammatory cytokines mediating myocardial reperfusion injury and heart failure. Blockage of inflammatory pathways dampens myocardial injury and reduces infarct size. We compared the impact of the interleukin-1 receptor antagonist Anakinra and erythropoietin on myocardial ischemia/reperfusion injury. In contrast to others, we hypothesized that drug administration prior to reperfusion reduces myocardial damage. Methods and Results. 12–15 week-old Lewis rats were subjected to myocardial ischemia by a 1 hr occlusion of the left anterior descending coronary artery. After 15 min of ischemia, a single shot of Anakinra (2 mg/kg body weight (bw)) or erythropoietin (5000 IE/kg bw) was administered intravenously. In contrast to erythropoietin, Anakinra decreased infarct size (P < 0.05, N = 4/group) and troponin T levels (P < 0.05, N = 4/group). Conclusion. One-time intravenous administration of Anakinra prior to myocardial reperfusion reduces infarct size in experimental ischemia/reperfusion injury. Thus, Anakinra may represent a treatment option in myocardial infarction prior to revascularization

Sex-specific risk factors for early mortality and survival after surgery of acute aortic dissection type a: a retrospective observational study

Results Women were older (70.7 years vs. 60.6 years; p <  0.001) and showed a higher logistic EuroSCORE I (31.0% vs. 19.7%, p <  0.001). In the male group, a higher portion of smokers (27.6% vs. 16.0%, p = 0.015) and intraoperatively, more complex procedures and longer cardiopulmonary bypass (CPB) (171 min vs. 149 min, p = 0.001) and cross-clamping times (94 min vs. 85 min, p = 0.018) occurred. 30-day mortality was 19.0% in the female and 16.5% in the male group (p = 0.545). Predictive for 30-day mortality in both genders was intraoperative blood transfusion, while in the female group chronic obstructive pulmonary disease (COPD), peripheral arterial disease and preoperative intubation were predictive. Preoperative cardiopulmonary resuscitation and duration of CPB time were predictors only in males. Averaged follow-up time was 5.2 years and survival did not differ between genders, even if it was stratified by age over 70 years. Conclusions This analysis demonstrated a similar and satisfactory survival in both genders after surgical treatment of AADA. Women and men differed significantly in age, unadjusted and adjusted risk factors and complexity of surgical treatment, but gender itself was no risk factor for mortality. These results suggest that the decision-making for surgical treatment should not depend on gender, but that accounting for sex-specific risk factors rather than common risk factors may help to improve the outcome in both genders

Case report—CARMAT: the first experience with the Aeson bioprosthetic total artificial heart as a bridge to transplantation in a case of post-infarction ventricular septal rupture

BackgroundPost-infarction ventricular septal defects remain one of the most feared complications after myocardial infarction with high mortality rates. In special cases, surgical or interventional treatment strategies are technically not feasible and do not always lead to a good outcome.Case presentationA 58-year-old male patient in cardiogenic shock with a very large ventricular septal (VSD) defect (4.9 cm × 5 cm) due to myocardial infarction was presented in our department. Acute stabilization was achieved using peripheral venoarterial extracorporeal membrane oxygenation (VA-ECMO) support. Neither surgical nor interventional therapy was considered as a sufficient option due to the unsuitable anatomy of the VSD and the patient was listed for heart transplantation. After 2 weeks on ECMO, bleeding and infectious complications occurred. Due to organ shortage, urgent implantation of the bioprosthetic total artificial heart (TAH) Aeson device (CARMAT) remained the only useful strategy to achieve a mid- or long-term bridge to transplantation. After successful implantation and good recovery with the Aeson device, the patient was transplanted 4 weeks after implantation.ConclusionPost-infarction ventricular septal defects are highly challenging and are commonly associated with a poor prognosis. The implantation of the new Aeson TAH device is a promising therapeutic option, allowing a safe and long-term bridging to heart transplantation