Optimization of Information Management for Dynamic Risk Analysis of Large-scale Power Grids
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Modern societies strongly rely on the reliable functioning of power grids. However, the management of such infrastructures is a challenging task. It requires the existence of efficient solutions enabling adequate decision making, both during normal working conditions and in emergency situations. Unfortunately, blackouts and other large-scale outages continue to be regularly observed over the globe, impacting millions of people with sometimes fatal consequences. One reason for the occurrence of such events is the inadequate capture and processing of data, which has hindered the development of performant risk-focused tools to be used in practice by utilities. This can be observed in the management of vegetation along power lines, a common source of disturbances that has contributed to the occurrence of multiple well-known high-impact outages. The digitalization of our society represents an opportunity for more accurate, data-informed risk analysis, as it supports facilitated access to more and better data. However, there is still a lack of standards, guidelines, and recommendations indicating how the data can be processed in the field of power grid management to reduce the probabilities and consequences of undesired, critical events. The present Ph.D. addresses this gap by investigating both the fields of risk analysis and power grid management. It starts by taking advantage of the strong industrial environment in which this Ph.D. has been executed to adequately identify the relevant stakeholders and understand their needs and constraints. This is particularly important, as it directly impacts the development and thus the performance of tools to be used in the future by power grid managers. The thesis also determines, after an intensive literature review, which gaps need to be addressed in the field of risk analysis to enable efficient, large-scale heterogeneous data processing. The main contributions of this thesis can be summarized as follows: (1) We diversify the panel of exploitable data sources for risk analysis and fully explore the analysis level scale. (2) We augment conventional risk assessment frameworks to enable efficient, large-scale heterogeneous data processing. (3) We provide multiple solution development propositions enabling power grid operators to make better risk-based decisions. The propositions are based on various perspectives and enable finding an adequate trade-off between global and local analyses of the grid, by always keeping the user-needs at the center of the solution definition. (4) We make multiple recommendations usable by power grid operators to optimize the exploitation of historical data and the planning of future data capture. (5) We use the four previously reported contributions to indicate how vegetation management along power lines may be improved From a risk perspective, this Ph.D. first contributes to the understanding and clarification of basic risk-related concepts. The findings of this work then enable risk analysis processes to better leverage accessible data sources. Those especially enable more robust decision-making by reducing uncertainties relative to data integration, therefore “better knowing how well we know”. In addition, the results strongly contribute to a better quantification of problems at scope in risk analysis, de facto enabling more objective decision-making. The thesis is also particularly valuable from a power grid management perspective. It first provides a familiarization opportunity with the notion of risk for the stakeholders requiring further insights in that field. It then shows how this knowledge can be used in combination with news data capture and processing solutions to enable the emergence of innovative tools supporting power grid operators in their daily operations. The final results are discussed, and different evolution opportunities are reported along with the provided contributions, such as executing risk quantification or analyzing other hazards. The provided suggestions represent as many possibilities to reinforce and further extend the results of this doctoral project. They are also an indication that further development is required to facilitate more robust decision-making when practical implications and currently existing technical limitations are faced. This thesis is a good illustration of the benefits of braking silos and encouraging cross-disciplinary cooperation. It stimulates power grid operators to further investigate the advances made in the academic world. At the same time, it also favors the communication of constraints faced in real-world situations but maybe too often excluded from the research scope in fundamental research
Has partsArticle 1: Pacevicius, Michael Felix; Roverso, Davide; Salvo Rossi, Pierluigi; Paltrinieri, Nicola. Risk of crack formation in power grid wooden poles and relationship with meteorological conditions: A Norwegian case study. I: Safety and Reliability – Safe Societies in a Changing World. Proceedings of ESREL 2018 10.1201/9781351174664-193 This is an open access article distributed under the Creative Commons, CC BY-NC-ND License
Article 2: Pacevicius, Michael Felix; Roverso, Davide; Salvo Rossi, Pierluigi; Paltrinieri, Nicola. Smart Grids: Challenges of Processing Heterogeneous Data for Risk Assessment. 14th International Conference on Probabilistic Safety Assessment and Management (PSAM 14)
Article 3: Pacevicius, Michael Felix; Haskins, Cecilia; Paltrinieri, Nicola. Supporting the Application of Dynamic Risk Analysis to Real-World Situations using Systems Engineering: A focus on the Norwegian Power Grid Management. 18th Annual Conference on Systems Engineering Research (CSER 2020) https://doi.org/10.1007/978-3-030-82083-1_57
Article 3: Pacevicius, Michael Felix; Ramos, Marilia Abilio; Paltrinieri, Nicola. Optimizing Technology-Based Decision-Support for Management of Infrastructures Under Risk: The Case of Power Grids. I: e-proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference (ESREL2020 PSAM15)
Article 5: Paltrinieri, Nicola; Patriarca, Riccardo; Pacevicius, Michael Felix; Salvo Rossi, Pierluigi. Lessons from past hazardous events: data analytics for severity prediction. I: e-proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference (ESREL2020 PSAM15).
Article 6: Gazzea, Michele; Pacevicius, Michael Felix; Dammann, Dyre Oliver; Sapronova, Alla; Lunde, Torleif Markussen; Arghandeh, Reza. Automated Power Lines Vegetation Monitoring using High-Resolution Satellite Imagery. IEEE Transactions on Power Delivery 2021 ;Volum 37.(1) s. 308-316 https://doi.org/10.1109/TPWRD.2021.3059307
Article 7: Pacevicius, Michael Felix; Ramos, Marilia; Roverso, Davide; Eriksen, Christian Thun; Paltrinieri, Nicola. Managing Heterogeneous Datasets for Dynamic Risk Analysis of Large-Scale Infrastructures Energies 2022, 15(9), 3161; https://doi.org/10.3390/en15093161 This is an open access article distributed under the Creative Commons Attribution License (CC BY 4.0)
Article 8: Pacevicius, Michael Felix; Ramos, Marilia; Roverso, Davide; Eriksen, Christian Thun; Paltrinieri, Nicola. Data-informed risk analysis of power grids: application of method for managing heterogeneous datasets ASME J: Risk Uncertainty Part B.
Article 9: Pacevicius, Michael Felix; Dammann, Dyre Oliver; Gazzea, Michele; Sapronova, A.. Heterogeneous Data-merging Platform for Improved Risk Management in Power Grids. The 67th Annual Reliability and Maintainability Symposium © 2021 IEEE. If you are reusing a substantial portion of your article and you are not the senior author, obtain the senior author’s approval before reusing the text
Article 10: Pacevicius, Michael Felix; Paltrinieri, Nicola; Thieme, Christoph Alexander; Salvo Rossi, Pierluigi. Addressing the Importance of Data Veracity during Data Acquisition for Risk Assessment Processes. The 67th Annual Reliability and Maintainability Symposium; The 67th Annual Reliability and Maintainability Symposium (RAMS)https://doi.org/10.1109/RAMS48097.2021.9605737 © 2021 IEEE. If you are reusing a substantial portion of your article and you are not the senior author, obtain the senior author’s approval before reusing the text