| 1. |
- Naim, Wadih, 1991-, et al.
(författare)
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A framework for component ranking based on a data quality importance index : applications in power system asset management
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Data-driven decision making is essential for efficient power system asset management. Faulty data causes distortions in component failure model estimations. This leads to inaccurate maintenance cost minimization and sub-optimal component replacements. In this paper, we review existing standards and frameworks related to data quality and asset management. We then study the sensitivity of a maintenance optimization model to the Weibull distribution shaping parameter and observe a non-linear relation. Based on this study, we propose using the Weibull shaping parameter as a data quality importance index to rank power system components in terms of data requirements.
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| 2. |
- Naim, Wadih, 1991-
(författare)
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Data Importance in Power System Asset Management
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The current shift towards a higher degree of data-driven decision making in power system asset management highlights the importance of asset data. This thesis identifies, investigates, and proposes methods for data-related research gaps that are encountered by asset managers. These research gaps are in data availability and data quality. It is challenging to generalize the data availability problem on an abstract level. Thus, data availability is studied through three different case studies. Each case study addresses a factor that contributes to data availability problems. Data censoring is modeled as a data quality problem using a Monte-Carlo simulation. Lack of access to and acquisition of data are studied through event tree analysis and multiphysics modelling. These case studies reveal that even in a low data availability environment, informed decision making is feasible. Monte-Carlo simulation techniques are powerful when analyzing the data quality problem. Asset data quality is studied based on two perspectives; namely, maintenance optimization and reliability evaluation. First, using random population studies shows that data quality can have a notable financial and technical impact on maintenance optimization. A critical finding is that missing data can lead to distortions in estimates of the optimal replacement time of a component. It is shown that there exists a certain threshold of missing data proportion beyond which maintenance optimization becomes unreliable. The specific percentage value of this threshold depends on the failure model parameters. Second, incorporating the data quality model in a reliability test system simulation shows that the impact on the annual estimation of system- and energy-oriented reliability indices is nearly non-existent.Finally, this thesis introduces a method to rank component types based on data quality importance. The data quality importance (DQI) ranking is derived from the Weibull function’s sensitivity to data errors. This method indicates that distortions in Weibull parameters have a non-linear impact on maintenance optimization. This leads to a conclusion that investments in data quality must be allocated based on the DQI ranking of a certain component. Reaching the right level of data quality for a component leads to efficient decision making.
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| 3. |
- Naim, Wadih, 1991-, et al.
(författare)
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Impact of Asset Data Quality on Power System Reliability Performance Estimation
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Data-driven planning and decision making are significant for effective asset management. In power systems, several data-related challenges are present. This paper investigates the effect of two main data quality issues, missing and inaccurate data, on reliability evaluation of a small power distribution system. We use the IEEE RBTS-Bus2 system as a model of a small distribution grid and to assess the asset age data quality impact on its reliability indices. Results show that, on average, faulty component age data does not have an impact on annual reliability evaluation.
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| 4. |
- Naim, Wadih, 1991-, et al.
(författare)
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Impact of Data Quality on Power System AssetManagement - A Monte-Carlo Based Approach
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- In power system asset management, component datais crucial for decision making. Consequently, the quality level ofdata has a great impact on the optimality of asset managementdecisions. The goal of the paper is to quantify the impact ofdata errors from a maintenance optimization perspective usingrandom population studies. In quantitative terms, the impact ofdata quality can be evaluated financially and technically. In thispaper, the financial impact is the total maintenance cost per yearof a specific scenario in a population of components, whereasthe technical impact is the loss of a component’s useful technicallifetime due to sub-optimal replacement time. Using Monte-Carlosimulation techniques, those impacts are analyzed in a case studyof a simplified random population of independent and nonrepairablecomponents. The results show that missing data hasa larger impact on cost and replacement year estimation thanthat of under- or over-estimated data. Additionally, dependingon problem parameters, after a certain threshold of missingdata probability, the estimation of cost and replacement yearbecomes unreliable. Thus, effective decision making for a certainpopulation of components requires ensuring a minimum level ofdata quality.
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| 5. |
- Naim, Wadih, 1991-, et al.
(författare)
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Impact of Geomagnetic Disturbances on Power Transformers:Risk Assessment of Extreme Events and Data Availability
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Certain rare events can have a drastic impact on power systems. Such events are generally known as low-probability high-consequence (LPHC) or high-impact low-probability (HILP) events. It is challenging to predict the occurrence of a LPHC event mainly due to lack of data or sparsity and scarcity of data points. Yet, it is essential to implement an evidence-driven asset management strategy. In this paper, event tree analysis is used to assess the risk of power transformer failure due to a geomagnetically induced currents (GIC). Those currents are caused by geomagnetic disturbances in Earth's magnetic field due to solar activity. In order to assess the impact on power transformers, an understanding of the mechanism and sequence of sub-events that lead to failure is required to be able to construct an event tree. Based on the constructed event tree, mitigation actions can be derived. GIC blockers or reducers can be used. However, that would require extensive installation and maintenance efforts, and the impact on system reliability has to be studied. Also, such technology is still in its infancy and needs extensive validation. A suggested alternative is to combine early warning data from solar observatories with a load management plan to keep transformers below their rated operation point such that a DC offset due to GIC would not cause magnetic core saturation and overheating. Load management and the risk of early warning false positives can incur a negative effect on reliability. Nevertheless, the risk assessment performed in this paper show that incorporating load management in asset planning is a viable measure that would offset the probability of catastrophic failure.
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