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  • 1.
    Vincenti, Hugo
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Koziel, Sylvie Evelyne
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering and Fusion Science.
    Hilber, Patrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering and Fusion Science.
    Profitability of Condition Monitoring in the Electric Distribution Grid2023In: 27th International Conference on Electricity Distribution, CIRED 2023:, p. 362-366Article in journal (Other academic)
    Abstract [en]

    The deployment of sensors enables the development of condition-based maintenance, as opposed to the traditional time-based and corrective maintenance. This work explores the conditions under which the use of sensors to improve maintenance scheduling on overhead lines is economically profitable. We propose a novel methodology that converts sensor measurements into an asset condition assessment, and then into a maintenance decision. The cost of predictive maintenance is then compared to the cost of corrective maintenance over several decades, ultimately allowing to evaluate the profitability of investing into sensors. This work enables to identify the parameter values that result in profitable investments in sensors. The results show that the use of sensors is particularly justified for short-lived assets, supplying many clients.

  • 2.
    Koziel, Sylvie Evelyne
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering and Fusion Science.
    Hilber, Patrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering and Fusion Science.
    Profitable sensor network design in the distribution grid2023In: 2023 IEEE Belgrade PowerTech, PowerTech 2023, Institute of Electrical and Electronics Engineers Inc. , 2023Conference paper (Refereed)
    Abstract [en]

    With an aging infrastructure and complexifying grid, sensors become essential to monitor the state of power system components. They are part of the evolution of power grids into smarter grids, as well as of the development of digital twins. Currently, sensors are largely absent from distribution grids. While much research literature exists in optimal sensor placement with cost minimization under performance constraints, few publications quantify the profitability of such sensors for distribution grid operators (DSOs). This work aims to bridge this gap and to offer a novel methodology that enables DSOs to assess if and where sensors would be profitable in the context of asset management. The methodology is based on modeling the effects of sensors on maintenance activities and then on grid reliability. We formulate a binary non linear optimization problem, and apply it to study the case of sensors that monitor the condition of power lines. Results show that sensor profitability depends on several factors including i) the component age, ii) the current replacement practice, and iii) the component importance. Thus, this work gives a tool for DSOs to decide whether and where to invest into sensors.

  • 3.
    Nalini Ramakrishna, Sindhu Kanya
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Koziel, Sylvie Evelyne
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering.
    Karlsson, David
    Stenhag, Gustav
    Hilber, Patrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering.
    Component ranking and importance indices in the distribution system2021In: 2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings, Institute of Electrical and Electronics Engineers (IEEE) , 2021, article id 9494968Conference paper (Refereed)
    Abstract [en]

    Monitoring the condition of components helps taking preventive actions to avoid failures, and increases reliability. However, performing such monitoring for all components of the distribution grid is prohibitively expensive. Instead, distribution system operators could focus efforts only on the most critical components. In particular, importance indices enable to prioritise components according to a chosen criterion, and to adapt monitoring strategies. This study presents methods to rank grid components using outage data. The importance indices are based on: 1) de-energisation time; 2) frequency of failures; 3) disconnected power; 4) energy not supplied and 5) customer outage time. The results depend largely on the time period of the outage data considered for analysis. Some components' rank varies with the chosen criterion. This indicates that they are critical with respect to a specific criterion. Other components are ranked high with all the methods, which means that they are critical, and need focused monitoring.

  • 4.
    Koziel, Sylvie Evelyne
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering.
    From data collection to electric grid performance: How can data analytics support asset management decisions for an efficient transition toward smart grids?2021Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Physical asset management in the electric power sector encompasses the scheduling of the maintenance and replacement of grid components, as well as decisions about investments in new components. Data plays a crucial role in these decisions. The importance of data is increasing with the transformation of the power system and its evolution toward smart grids. This thesis deals with questions related to data management as a way to improve the performance of asset management decisions. Data management is defined as the collection, processing, and storage of data. Here, the focus is on the collection and processing of data.

    First, the influence of data on the decisions related to assets is explored. In particular, the impacts of data quality on the replacement time of a generic component (a line for example) are quantified using a scenario approach, and failure modeling. In fact, decisions based on data of poor quality are most likely not optimal. In this case, faulty data related to the age of the component leads to a non-optimal scheduling of component replacement. The corresponding costs are calculated for different levels of data quality. A framework has been developed to evaluate the amount of investment needed into data quality improvement, and its profitability.

    Then, the ways to use available data efficiently are investigated. Especially, the possibility to use machine learning algorithms on real-world datasets is examined. New approaches are developed to use only available data for component ranking and failure prediction, which are two important concepts often used to prioritize components and schedule maintenance and replacement.

    A large part of the scientific literature assumes that the future of smart grids lies in big data collection, and in developing algorithms to process huge amounts of data. On the contrary, this work contributes to show how automatization and machine learning techniques can actually be used to reduce the need to collect huge amount of data, by using the available data more efficiently. One major challenge is the trade-offs needed between precision of modeling results, and costs of data management.

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  • 5.
    Koziel, Sylvie Evelyne
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering.
    Hilber, Patrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering.
    Westerlund, Per
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering.
    Shayesteh, Ebrahim
    Vattenfall Serv Nordic AB, Evenemangsgatan 13C, S-16956 Solna, Sweden..
    Investments in data quality: Evaluating impacts of faulty data on asset management in power systems2021In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 281, article id 116057Article in journal (Refereed)
    Abstract [en]

    Data play an essential role in asset management decisions. The amount of data is increasing through accumu-lating historical data records, new measuring devices, and communication technology, notably with the evolution toward smart grids. Consequently, the management of data quantity and quality is becoming even more relevant for asset managers to meet efficiency and reliability requirements for power grids. In this work, we propose an innovative data quality management framework enabling asset managers (i) to quantify the impact of poor data quality, and (ii) to determine the conditions under which an investment in data quality improvement is required. To this end, an algorithm is used to determine the optimal year for component replacement based on three scenarios, a Reference scenario, an Imperfect information scenario, and an Investment in higher data quality scenario. Our results indicate that (i) the impact on the optimal year of replacement is the highest for middleaged components; (ii) the profitability of investments in data quality improvement depends on various factors, including data quality, and the cost of investment in data quality improvement. Finally, we discuss the implementation of the proposed models to control data quality in practice, while taking into account real-world technological and economic limitations.

  • 6.
    Koziel, Sylvie Evelyne
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering.
    Hilber, Patrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering.
    Ichise, R.
    A review of data-driven and probabilistic algorithms for detection purposes in local power systems2020In: 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Institute of Electrical and Electronics Engineers (IEEE) , 2020, p. 1-6Conference paper (Refereed)
    Abstract [en]

    Power grid operators use data to guide their asset management decisions. However, as the complexity of collected data increases with time and amount of sensors, it becomes more difficult to extract relevant information. Therefore, methods that perform detection tasks need to be developed, especially in distribution systems, which are impacted by distributed generation and smart appliances. Until now, methods employed in local power systems for detection purposes using data with low sampling rate, have not been reviewed. This paper provides a literature review focused on anomaly detection, fault location, and load disaggregation. We analyze the methods in terms of their type, data requirements and ways they are implemented. Many belong to the machine learning field. We find that some methods are typically combined with others and perform specific tasks, while other methods are more ubiquitous and often used alone. Continued research is needed to identify how to guide the choice of methods, and to investigate combinations of methods that have not been studied yet.

  • 7.
    Koziel, Sylvie Evelyne
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering.
    Hilber, Patrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering.
    Ichise, R.
    Application of big data analytics to support power networks and their transition towards smart grids2019In: Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 6104-6106Conference paper (Refereed)
    Abstract [en]

    Power systems are becoming more complex, which increases instability issues and outage risks. The development of smart grids could help manage such complex systems. One important pillar in smart grids is big data analytics. In this poster paper, we discuss where and how machine learning could contribute to more efficient asset management. We also identify challenges that stand in the way of the widespread use of big data analytics in smart grids. While the nature of data, as well as data and asset management systems themselves make the use of big data challenging, data analytics could improve the reliability of power supply by providing the functions of detection, prediction, and selection. 

  • 8.
    Koziel, Sylvie Evelyne
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering.
    Hilber, Patrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering.
    Westerlund, Per
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering.
    Shayesteh, E.
    Forecasting cross-border power exchanges through an HVDC line using dynamic modelling2019In: Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 4390-4394Conference paper (Refereed)
    Abstract [en]

    As smart grids develop, power systems become more complex, and the role of data gain considerable importance for the reliability of power supply. Thus, data processing techniques have to be investigated and compared to increase the efficiency of asset management decisions. In this paper, we explore several black-box models in order to predict power exchanges through a high-voltage direct-current line between Sweden and Denmark, using publicly available data on loads and power prices. An auto-regressive moving average with external input model based on load data provides the most accurate forecasts according to mean square error and other selected criteria. This is the first step to build a more comprehensive model that will also include other technical data such as maintenance and unplanned outages, but also macroeconomic factors. The final goal is to provide network operators with a parsimonious sequential model composed of several modules giving accurate predictions that support efficient asset management decisions. 

  • 9.
    Koziel, Sylvie Evelyne
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering.
    Hilber, Patrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering.
    Detecting rare events for low frequency, sequential, and unspecific datasets: application to failure prediction of an HVDC lineIn: Article in journal (Refereed)
1 - 9 of 9
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  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
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