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On the Role of Data Quality and Availability in Power System Asset Management

Time: Tue 2021-06-15 13.00

Location: Eric G. Hallén, Teknikringen 31, Kungliga Tekniska Högskolan, Stockholm (English)

Subject area: Electrical Engineering

Doctoral student: Wadih Naim , Elektroteknisk teori och konstruktion, QED Asset Management

Opponent: Professor Diego Galar, Luleå University of Technology

Supervisor: Patrik Hilber, Elektroteknisk teori och konstruktion; Ebrahim Shayesteh, Elektroteknisk teori och konstruktion


In power system asset management, component data is crucial for decision making. This thesis mainly focuses on two aspects of asset data: data quality and data availability.

The quality level of data has a great impact on the optimality of asset management decisions. The goal is to quantify the impact of data errors from a maintenance optimization perspective using random population studies. In quantitative terms, the impact of data quality can be evaluated financially and technically. The financial impact is the total maintenance cost per year of a specific scenario in a population of components, whereas the technical impact is the loss of a component's useful technical lifetime due to sub-optimal replacement time. Using Monte-Carlo simulation techniques, those impacts are analyzed in a case study of a simplified random population of independent and non-repairable components. The results show that missing data has a larger impact on cost and replacement year estimation than that of under- or over-estimated data. Additionally, depending on problem parameters, after a certain threshold of missing data probability, the estimation of cost and replacement year becomes unreliable. Thus, effective decision making for a certain population of components requires ensuring a minimum level of data quality.

Data availability is another challenge that faces power system asset managers. Data can be lacking due to several factors including censoring, restricted access, or absence of data acquisition. These factors are addressed in this thesis from a decision making point of view through case studies at the operation and maintenance levels. Data censoring is handled as a data quality problem using a Monte-Carlo simulation. While the problems of restricted access and absence of data acquisition are studied using event trees and multiphysics modelling. 

While the quantitative data quality problem can be abstract, and thus applicable to different types of physical assets, the data availability problem requires a case-by-case analysis to reach an effective decision making strategy.

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Last changed: May 28, 2021