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PublicationCentralized Model Predictive Control for Transient Frequency Control in Islanded Inverter-Based Microgrids( 2023)Grid-forming inverters can establish frequency and voltage regulation in autonomous microgrids by themselves without relying on the external grid and as such are significant enablers for the integration of inverter-interfaced renewable energy sources. The most prominent grid-forming strategies, the droop-based techniques, cannot provide an error-free frequency regulation after a step-wise disturbance and thus are completed by the secondary integral action. In this paper, we propose a model predictive control for fast frequency response in an AC, purely inverter-based microgrid operating in islanded mode. The controller acts as a secondary control unit and adjusts the control actions of the underlying droop controllers leading to an error-free frequency transient control. The controller has been designed leveraging the concept of frequency divider - a simple analytical formulation that estimates the frequency at every system bus. The proposed control is successful in providing a fast frequency response, i.e., it assures a system-wide error-free frequency regulation under different operating circumstances including load changes and varying X/R-ratios. Numerical simulations are carried out in the WSCC 9-Bus and IEEE 39-Bus systems to verify the performance of the proposed method during transients and a comparison with respect to the more common secondary frequency control is given.
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PublicationTransparency in Medical Arti¯cial Intelligence Systems( 2023)Many of the artificial intelligence (AI) systems used nowadays have a very high level of accuracy but fail to explain their decisions. This is critical, especially in sensitive areas such as medicine and the health area at large but also for applications of the law, finance etc., where explanations for certain decisions are needed and are often useful and valuable as the decision itself. This paper presents a review of four different methods for creating transparency in AI systems. It also suggests a list of criteria under which circumstances one should use which methods.
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PublicationPersistent Identification for Conferences( 2022-04-05)
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PublicationA Service Oriented Architecture for the Digitalization and Automation of Distribution Grids( 2022)Modern distribution grids are complex systems that need advanced management for their secure and reliable operation. The Information and Communication Technology domain today offers unprecedented opportunities for the smart design of tools in support of grid operators. This paper presents a new philosophy for the digitalization and automation of distribution grids, based on a modular architecture of microservices implemented via container technology. This architecture enables a service-oriented deployment of the intelligence needed in the Distribution Management Systems, moving beyond the traditional view of monolithic software installations in the control rooms. The proposed architecture unlocks a broad set of possibilities, including cloud-based implementations, extension of legacy systems and fast integration of machine learning-based analytic tools. Moreover, it potentially opens a completely new market of turnkey services for distribution grid management, thus avoiding large upfront investments for grid operators. This paper presents the main concepts and benefits of the proposed philosophy, together with an example of field implementation based on open source components carried out in the context of the European project SOGNO.
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PublicationMulti-Institutional Breast Cancer Detection Using a Secure On-Boarding Service for Distributed Analytics( 2022)The constant upward movement of data-driven medicine as a valuable option to enhance daily clinical practice has brought new challenges for data analysts to get access to valuable but sensitive data due to privacy considerations. One solution for most of these challenges are Distributed Analytics (DA) infrastructures, which are technologies fostering collaborations between healthcare institutions by establishing a privacy-preserving network for data sharing. However, in order to participate in such a network, a lot of technical and administrative prerequisites have to be made, which could pose bottlenecks and new obstacles for non-technical personnel during their deployment. We have identified three major problems in the current state-of-the-art. Namely, the missing compliance with FAIR data principles, the automation of processes, and the installation. In this work, we present a seamless on-boarding workflow based on a DA reference architecture for data sharing institutions to address these problems. The on-boarding service manages all technical configurations and necessities to reduce the deployment time. Our aim is to use well-established and conventional technologies to gain acceptance through enhanced ease of use. We evaluate our development with six institutions across Germany by conducting a DA study with open-source breast cancer data, which represents the second contribution of this work. We find that our on-boarding solution lowers technical barriers and efficiently deploys all necessary components and is, therefore, indeed an enabler for collaborative data sharing.
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PublicationComparing Micromobility with Public Transportation Trips in a Data-Driven Spatio-Temporal Analysis( 2022)Micromobility service systems have recently appeared in urban areas worldwide. Although e-bike and e-scooter services have been operating for some time now, their characteristics have only recently been analyzed in more detail. In particular, the influence on the existing transportation services is not well understood. This study proposes a framework to gather data, infer micromobility trips, deduce their characteristics, and assess their relation to a public transportation network. We validate our approach by comparing it to similar approaches in the literature and applying it to data of over a year from the city of Aachen. We find hints at the recreational role of e-scooters and a larger commuting role for e-bikes. We show that micromobility services in particular are used in situations where public transportation is not a viable alternative, hence often complementing the available services, and competing with public transportation in other areas. This ambivalent relationship between micromobility and public transportation emphasizes the need for appropriate regulations and policies to ensure the sustainability of micromobility services.
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PublicationOn Modeling Depths of Power Electronic Circuits for Real-Time Simulation( 2022)Investigations of the dynamic behaviour of power electronic components integrated into electric networks require suitable and established simulation methodologies. Real-time simulation represents a frequently applied methodology for analyzing the steady-state and transient behavior of electric power systems. This work introduces a guideline on how to model power electronics converters in digital real time simulators, taking into account the trade-off between model accuracy and the required computation time. Based on this concept, possible execution approaches with respect to the usage of central processing unit and field-programmable gate array components are highlighted. Simulation test scenario, such as primary frequency regulation and low voltage ride through, have been performed and accuracy indices are discussed for each implemented real-time model and each test scenario, respectively. Finally, a run-time analysis of presented real-time setups is given and real-time simulation results are compared. This manuscript demonstrates important differences in real-time simulation modelling, providing useful guidelines for the decision making of power engineers.
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PublicationOn using contextual correlation to detect multi-stage cyber attacks in smart grids( 2022)While the digitization of the distribution grids brings numerous benefits to grid operations, it also increases the risks imposed by serious cyber security threats such as coordinated, timed attacks. Addressing this new threat landscape requires an advanced security approach beyond established preventive IT security measures such as encryption, network segmentation, or access control. Here, detective capabilities and reactive countermeasures as part of incident response strategies promise to complement nicely the security-by-design approach by providing cyber security situational awareness. However, manually evaluating extensive cyber intelligence within a reasonable timeframe requires an unmanageable effort to process a large amount of cross-domain information. An automated procedure is needed to systematically process and correlate the various cyber intelligence to correctly assess the situation to reduce the manuel effort and support security operations. In this paper, we present an approach that leverages cyber intelligence from multiple sources to detect multi-stage cyber attacks that threaten the smart grid. We investigate the detection quality of the presented correlation approach and discuss the results to highlight the challenges in automated methods for contextual assessment and understanding of the cyber security situation.
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PublicationHow Can Interactive Process Discovery Address Data Quality Issues in Real Business Settings? Evidence from a Case Study in Healthcare( 2022)The focus of this paper is on how data quality can affect business process discovery in real complex environments, which is a major factor determining the success in any data-driven Business Process Management project. Many real-life event logs, especially healthcare ones, can suffer from several data quality issues, some of which cannot be solved by pre-processing or data cleaning techniques, leading to inaccurate results. We take an innovative Process Mining (PM) approach, termed Interactive Process Discovery (IPD), which combines domain knowledge with available data. This approach can overcome the limitations of noisy and incomplete event logs by putting “humans in the loop”, leading to improved business process modelling. This is particularly valuable in healthcare, where physicians have a tacit domain knowledge not available in the event log, and, thus, difficult to elicit. We conducted a two-step approach based on a controlled experiment and a case study in an Italian hospital. At each step, we compared IPD with traditional PM techniques to assess the extent to which domain knowledge helps to improve the accuracy of process models. The case study tests the effectiveness of IPD to uncover knowledge-intensive processes extracted from noisy real-life event logs. The evaluation has been carried out by exploiting a real dataset of an Italian hospital, involving the medical staff. IPD can produce an accurate process model that is fully compliant with the clinical guidelines by addressing data quality issues. Accurate and reliable process models can support healthcare organizations in detecting process-related issues and in taking decisions related to capacity planning and process re-design.
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PublicationAnalyzing Intersectoral Benefits of District Heating in an Integrated Generation and Transmission Expansion Planning Model( 2022)In the field of sector integration, the expansion of district heating (DH) is traditionally discussed with regard to the efficient integration of renewable energy sources (RES) and excess heat. But does DH exclusively benefit from other sectors or does it offer advantages in return? So far, studies have investigated DH only as a closed system or determined intersectoral benefits in a highly aggregated approach. We use and expand an integrated generation and transmission expansion planning model to analyze how the flexibility of DH benefits the energy system and the power transmission grid in particular. First of all, the results confirm former investigations that show DH can be used for efficient RES integration. Total annual system cost can be decreased by expanding DH, due to low investment cost and added flexibility, especially from large-scale heat storage. The high short-term efficiency of heat storage - in combination with electric heating technologies—can be exploited to shift heat demand temporally and, using multiple distributed units, locally to solve electric grid congestion. Although it is unclear whether these results can be replicated in the real world, due to the aggregation and detail of the model, further research in this direction is justified.
