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Individual Failure Rate Modelling and Exploratory Failure Data Analysis for Power System Components
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering. (QED Asset Management Group)ORCID iD: 0000-0002-3543-9326
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

A set of vital societal functions such as health and safety are necessary for today's society to function and to secure the life of its individuals. Infrastructure is required to provide and maintain these functions. This for society critical infrastructure includes electronic communication technology, transport systems, oil \& gas supply, water supply, and the supply of electric power. The electric power system plays a central role in the critical infrastructure since it is required to operate all others. Therefore, outages in the power system can have severe consequences not solely for the supply of electricity but also for the supply of water, gas, and food. To provide a reliable and safe power supply, power system operators are applying asset management strategies to investigate, plan, maintain, and utilize the system and its components while improving the performance under its own financial constraints.

One approach to increase the reliability of the power grid while decreasing costs is maintenance planning, scheduling, and optimization. To optimize maintenance, a reliability measure for power system components is required. The failure rate, which is the probability of failure in a predefined interval, is utilized in maintenance optimization. Thus far, an average failure rate has been assigned to all components of the same type due to a shortage of component failure data. However, this limits the accuracy of maintenance techniques since the component heterogeneity is neglected. Moreover, the actual failure rate is being underrated or overrated and it is a challenge to identify the impact of conducted maintenance tasks.

This thesis presents how the failure rate accuracy can be improved despite limited failure data available. Firstly, an introduction to failure rate modelling theory, concepts, and definitions is given to provide a common understanding for the later chapters and papers. Secondly, regression models are presented which can be used to model, predict, and characterise the failure rate and failure intensity for power system components. The Cox regression and regression models for count data are applied to two case studies of disconnector and circuit breaker failure data. The results contribute to an improved modelling of the failure rate on individual level but also improve the understanding of risk factor's impact on component failures. However, the aforementioned regression models have rarely been applied in the power system domain due to the limited failure data. Thirdly, the necessity to distinguish between population and individual failure rates is illustrated and risk factors and methods are presented, which are frequently used in failure rate modelling. Moreover, the thesis presents a method to calculate and predict individual failure rates despite the occurrence of actual failures which is of particular advantage for new components.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2018. , p. 79
Series
TRITA-EECS-AVL ; 2018:67
Keywords [en]
Asset management, condition monitoring, failure rate, failure rate modeling
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-235519ISBN: 978-91-7729-950-9 (print)OAI: oai:DiVA.org:kth-235519DiVA, id: diva2:1251662
Public defence
2018-10-19, E3, Osquars backe 14, Kungl. Tekniska högskolan, Stockholm, 10:00 (English)
Opponent
Supervisors
Projects
SweGRIDS, the Swedish Centre for Smart Grids and Energy Storage
Funder
SweGRIDS - Swedish Centre for Smart Grids and Energy Storage
Note

QC 20180928

Available from: 2018-09-28 Created: 2018-09-27 Last updated: 2022-06-26Bibliographically approved
List of papers
1. Impact Assessment of Remote Control and Preventive Maintenance on the Failure Rate of a Disconnector Population
Open this publication in new window or tab >>Impact Assessment of Remote Control and Preventive Maintenance on the Failure Rate of a Disconnector Population
2018 (English)In: IEEE Transactions on Power Delivery, ISSN 0885-8977, E-ISSN 1937-4208, Vol. 33, no 4, p. 1501-1509Article in journal (Refereed) Published
Abstract [en]

This paper presents the impact of different explanatory variables such as remote control availability and conducted preventive maintenance, among others, on failure statistics of a disconnector population in Sweden using the proportional hazard model. To do so, 2191 work orders were analysed which included 1626 disconnectors and 278 major failures. Here, the results show that the remote control availability for disconnectors - an example of such Smart Grid technology - has a negative effect on the failure rate, whereas preventive maintenance has a positive impact. It is also shown that the disconnector age is not significant and that certain disconnector types have a significant and positive correlation towards failures when compared to other disconnector types. The results increase the understanding of disconnector failures to improve asset management.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Asset management, control equipment reliability, monitoring, diagnostic measures, failure rate, failure rate estimation, preventive maintenance
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-216785 (URN)10.1109/TPWRD.2017.2710482 (DOI)000431959600001 ()2-s2.0-85046947535 (Scopus ID)
Funder
SweGRIDS - Swedish Centre for Smart Grids and Energy Storage
Note

QC 20171102

Available from: 2017-10-24 Created: 2017-10-24 Last updated: 2024-03-15Bibliographically approved
2. Assessment of Explanatory Variables on the Failure Rate of Circuit Breakers Using the Proportional Hazard Model
Open this publication in new window or tab >>Assessment of Explanatory Variables on the Failure Rate of Circuit Breakers Using the Proportional Hazard Model
Show others...
2018 (English)In: 2018 POWER SYSTEMS COMPUTATION CONFERENCE (PSCC), Dublin, Ireland: IEEE conference proceedings, 2018, article id 8442567Conference paper, Published paper (Refereed)
Abstract [en]

This paper utilises the proportional hazard model to understand and quantify the impact of explanatory variables on the failure rate of circuit breakers (CB). Particularly, 4496 work orders with 2622 high voltage CBs are investigated with an occurrence of 281 major failures. Different explanatory variables such as CB type, manufacturer, preventive maintenance (PM), and others are gathered to quantify their significance and magnitude of their effect. The results present that PM has a positive impact, the number of operations within the last year a negative impact, and age has a small but negative impact on the failure rate. The CB type is not significant in all analyses which can be explained by examining the PM and age of these CB types. This paper contributes to the understanding of how explantatory variables impact the failure rate which is essential for power system asset management.

Place, publisher, year, edition, pages
Dublin, Ireland: IEEE conference proceedings, 2018
Keywords
Asset management, circuit breaker reliability, failure rate, preventive maintenance, proportional hazard model
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-235079 (URN)10.23919/PSCC.2018.8442567 (DOI)000447282400052 ()2-s2.0-85053160170 (Scopus ID)9781910963104 (ISBN)
Conference
20th Power Systems Computation Conference, PSCC 2018; University College Dublin Dublin; Ireland; 11 June 2018 through 15 June 2018
Note

QC 20180921

Available from: 2018-09-14 Created: 2018-09-14 Last updated: 2024-03-15Bibliographically approved
3. Modelling of Recurrent Circuit Breaker Failures with Regression Models for Count Data
Open this publication in new window or tab >>Modelling of Recurrent Circuit Breaker Failures with Regression Models for Count Data
Show others...
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

High voltage circuit breaker (CB) are of fundamental importance to protect and operate the power system. To improve their performance and to better predict failures, it is necessary to understand the effect of covariates such as preventive maintenance, age, voltage level, and the CB type. A straightforward approach is to investigate recurrent failures with regression models for count data. In this paper, several regression models are developed to estimate the impact of the aforementioned covariates to predict the recurrence of failures. The results show that age has a significant and negative impact, preventive maintenance before the first failure has a positive impact, and that the voltage level has a negative impact. Moreover, the Poisson, Negative Binomial, and zero-inflated models are compared. The comparison shows that the Negative Binomial model has the best fit to the studied recurrent failure data.

Keywords
Asset management, circuit breaker reliability, rate of occurrence of failures, Poisson regression, preventive maintenance
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-235206 (URN)10.1109/PMAPS.2018.8440209 (DOI)000451295600008 ()2-s2.0-85053136177 (Scopus ID)
Conference
2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)
Note

QC 20180921

Available from: 2018-09-18 Created: 2018-09-18 Last updated: 2024-03-15Bibliographically approved
4. A Review and Discussion of Failure Rate Heterogeneity in Power System Reliability Assessment
Open this publication in new window or tab >>A Review and Discussion of Failure Rate Heterogeneity in Power System Reliability Assessment
2016 (English)Manuscript (preprint) (Other academic)
Abstract [en]

The failure rate is a reliability measure which isused for planning and operation of the power system. Thus far, average or experience based failure rates were applied to power system equipment due to their straightforward implementation. However, this approach limits the accuracy of the gained resultsand neglects the important differentiation between populationand individual failure rates. Hence, this paper discusses and demonstrates the necessity to distinguish between populationand individual failure rates and reviews the existing literature offailure rate estimation within the power system domain. The literature is categorized into statistical data driven approaches and failure rate modelling with focus on different criteria whichcan be used to describe the heterogeneity within populations. The review reveals that the environmental impact was modelled predominantly.

Keywords
failure rate modelling; heterogeneity; individual failure rate; relative risk model
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-194408 (URN)978-1-5090-1970-0 (ISBN)
Conference
Probabilistic Methods Applied to Power Systems (PMAPS), 2016 International Conference on, Beijing
Note

QC 20161026

Available from: 2016-10-26 Created: 2016-10-26 Last updated: 2024-03-15Bibliographically approved
5. Individual failure rates for transformers within a population based on diagnostic measures
Open this publication in new window or tab >>Individual failure rates for transformers within a population based on diagnostic measures
2016 (English)In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 141, p. 354-362Article in journal (Refereed) Published
Abstract [en]

The high monetary value of a transformer has placed the transformer life-time optimization into the focus of asset management. The average failure rate has created reasonable results within reliability modeling, however, it cannot reflect the probability of failure for an individual transformer. In this paper, a method is introduced to calculate individual failure rates for a transformer population based on failure statistics and diagnostic measurements such as dissolved gas, and 2-furfuraldehyde analysis. The method is applicable to all types of components and the comprehensibility makes it effective for practical implementation. The results are evaluated against two health indices based on a weight factor and fuzzy logic. It can be observed that the presented individual failure rates are plausible representatives of the transformer's probability of failure. Therefore, the results can also be utilized for asset management decision-making.

Place, publisher, year, edition, pages
Elsevier, 2016
Keywords
Asset management; Condition monitoring; Diagnostic measures; Failure rate; Failure rate modeling; Health index
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-192290 (URN)10.1016/j.epsr.2016.08.015 (DOI)000385598200034 ()2-s2.0-84984984238 (Scopus ID)
Note

QC 20160912

Available from: 2016-09-08 Created: 2016-09-08 Last updated: 2024-03-15Bibliographically approved
6. Estimation of Individual Failure Rates for Power System Components based on Risk Functions
Open this publication in new window or tab >>Estimation of Individual Failure Rates for Power System Components based on Risk Functions
2018 (English)Manuscript (preprint) (Other academic)
Abstract [en]

The failure rate is essential in power system reliability assessment and thus far it has been commonly assumed as constant. This is a basic approach that delivers reasonable results. However, this approach neglects the heterogeneity in component populations which has a negative impact on the accuracy of the failure rate. This paper proposes a method based on risk functions, which describes the risk behaviour of condition measurements over time, to compute individual failure rates within populations. The method is applied to a population of 12 power transformers on transmission level. The computed individual failure rates depict the impact of maintenance and that power transformers with long operation times have a higher failure rate. Moreover, the paper presents a procedure based on the proposed approach to forecast failure rates. Finally, the individual failure rates are calculated over a specified prediction horizon and depicted with a 95\% confidence interval.

Keywords
Asset management, condition monitoring, failure rate, failure rate modeling, power transformer diagnostics
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-235518 (URN)
Note

QC 20180928

Available from: 2018-09-27 Created: 2018-09-27 Last updated: 2024-03-15Bibliographically approved

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