hh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Data analytics for weak spot detection in power distribution grids
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-5863-0748
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This research aims to develop data-driven methods that extract information from the available data in distribution grids for detecting weak spots, including the components with degraded reliability and areas with power quality problems. The results enable power distribution companies to change from reactive maintenance to predictive maintenance by deriving benefits from available data. In particular, the data is exploited for three purposes: (a) failure pattern discovery, (b) reliability evaluation of power cables, and (c) analyzing and modeling propagation of power quality disturbances (PQDs) in low-voltage grids.

To analyze failure characteristics it is important to discover which failures share common features, e.g., if there are any types of failures that happen mostly in certain parts of the grid or at certain times. This analysis provides information about correlation between different features and identifying the most vulnerable components. In this case, we applied statistical analysis and association rules to discover failure patterns. Furthermore, we propose a visualization of the correlations between different factors representing failures by using an approximated Bayesian network. We show that the Bayesian Network constructed based on the interesting rules of two items is a good approximation of the real dataset.

The main focus of reliability evaluation is on failure rate estimation and reliability ranking. In case of power cables, the limited amount of recorded events makes it difficult to perform failure rate modeling. Therefore, we propose a method for interpreting the results of goodness-of-fit measures with confidence intervals, estimated using synthetic data.

To perform reliability ranking of power cables, in addition to the age of cables, we consider other factors. Then, we use the proportional hazard model (PHM) to assess the impact of the factors and calculate the failure rate of each individual cable. In reliability evaluation, it is important to consider the fact that power cables are repairable components. We discuss that the conclusions about different factors in PHM and cables ranking will be misleading if one considers the cables as non-repairable components.

In low-voltage distribution grids, analyzing PQDs is important as we are moving towards smart grids with the next generation of producers and consumers. Installing Power Quality and Monitoring Systems (PQMS) at all the nodes in the network, for monitoring the impacts of the new consumer/producer, is prohibitively expensive. Instead, we demonstrate that power companies can utilize the available smart meters, which are widely deployed in the low-voltage grids, for monitoring power quality events and identifying areas with power quality problems. In particular, several models for propagation of PQDs, within neighbor customers in different levels of the grid topology, are investigated. The results show that meters data can be used to detect and describe propagation in low-voltage grids.

The developed methods of (a) failure pattern discovery are applied on data from Halmstad Energi och Miljö (HEM Nät), Öresundskraft, Göteborg Energy, and Växjö Energy, four different distribution system operators in Sweden. The developed methods of (b) reliability evaluation of power cables and (c) analyzing and modeling propagation of PQDs are applied on data from HEM Nät.

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2019. , p. 117
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:hh:diva-39067ISBN: 978-91-88749-18-5 (print)ISBN: 978-91-88749-19-2 (electronic)OAI: oai:DiVA.org:hh-39067DiVA, id: diva2:1296816
Public defence
2019-04-24, Haldasalen, Kristian IV:s väg 3, Halmstad, 13:00 (English)
Opponent
Supervisors
Available from: 2019-03-19 Created: 2019-03-18 Last updated: 2019-04-03Bibliographically approved
List of papers
1. Reliability Evaluation of Underground Power Cables with Probabilistic Models
Open this publication in new window or tab >>Reliability Evaluation of Underground Power Cables with Probabilistic Models
2015 (English)In: DMIN'15: The 2015 International Conference on Data Mining, 2015, p. 37-43Conference paper, Published paper (Refereed)
Abstract [en]

Underground power cables are one of the fundamental elements in power grids, but also one of the more difficult ones to monitor. Those cables are heavily affected by ionization, as well as thermal and mechanical stresses. At the same time, both pinpointing and repairing faults is very costly and time consuming. This has caused many power distribution companies to search for ways of predicting cable failures based on available historical data.

In this paper, we investigate five different models estimating the probability of failures for in-service underground cables. In particular, we focus on a methodology for evaluating how well different models fit the historical data. In many practical cases, the amount of data available is very limited, and it is difficult to know how much confidence should one have in the goodness-of-fit results.

We use two goodness-of-fit measures, a commonly used one based on mean square error and a new one based on calculating the probability of generating the data from a given model. The corresponding results for a real data set can then be interpreted by comparing against confidence intervals obtained from synthetic data generated according to different models.

Our results show that the goodness-of-fit of several commonly used failure rate models, such as linear, piecewise linear and exponential, are virtually identical. In addition, they do not explain the data as well as a new model we introduce: piecewise constant.

National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-29331 (URN)
Conference
The 11th International Conference on Data Mining (DMIN'15), Las Vegas, Nevada, USA, July 27-30, 2015
Funder
Knowledge Foundation
Available from: 2015-08-31 Created: 2015-08-31 Last updated: 2019-03-19Bibliographically approved
2. Bayesian Network Representation of Meaningful Patterns in Electricity Distribution Grids
Open this publication in new window or tab >>Bayesian Network Representation of Meaningful Patterns in Electricity Distribution Grids
2016 (English)In: 2016 IEEE International Energy Conference (ENERGYCON), 2016Conference paper, Published paper (Refereed)
Abstract [en]

The diversity of components in electricity distribution grids makes it impossible, or at least very expensive, to deploy monitoring and fault diagnostics to every individual element. Therefore, power distribution companies are looking for cheap and reliable approaches that can help them to estimate the condition of their assets and to predict the when and where the faults may occur. In this paper we propose a simplified representation of failure patterns within historical faults database, which facilitates visualization of association rules using Bayesian Networks. Our approach is based on exploring the failure history and detecting correlations between different features available in those records. We show that a small subset of the most interesting rules is enough to obtain a good and sufficiently accurate approximation of the original dataset. A Bayesian Network created from those rules can serve as an easy to understand visualization of the most relevant failure patterns. In addition, by varying the threshold values of support and confidence that we consider interesting, we are able to control the tradeoff between accuracy of the model and its complexity in an intuitive way. © 2016 IEEE

Keywords
Smart Grids, Condition Monitoring, Data Mining, Failure Statistics, Association Rules, Bayesian Networks
National Category
Computer Sciences Probability Theory and Statistics Bioinformatics (Computational Biology) Other Computer and Information Science
Identifiers
urn:nbn:se:hh:diva-31710 (URN)10.1109/ENERGYCON.2016.7513929 (DOI)000390822900059 ()2-s2.0-84982836497 (Scopus ID)978-1-4673-8463-6 (ISBN)
Conference
2016 IEEE International Energy Conference (ENERGYCON), 4-8 April, Leuven, Belgium, 4-8 april, 2016
Available from: 2016-08-04 Created: 2016-08-04 Last updated: 2019-03-19Bibliographically approved
3. Reliability Evaluation of Power Cables Considering the Restoration Characteristic
Open this publication in new window or tab >>Reliability Evaluation of Power Cables Considering the Restoration Characteristic
Show others...
2019 (English)In: International Journal of Electrical Power & Energy Systems, ISSN 0142-0615, E-ISSN 1879-3517, Vol. 105, p. 622-631Article in journal (Refereed) Published
Abstract [en]

In this paper Weibull parametric proportional hazard model (PHM) is used to estimate the failure rate of every individual cable based on its age and a set of explanatory factors. The required information for the proposed method is obtained by exploiting available historical cable inventory and failure data. This data-driven method does not require any additional measurements on the cables, and allows the cables to be ranked for maintenance prioritization and repair actions.

Furthermore, the results of reliability analysis of power cables are compared when the cables are considered as repairable or non-repairable components. The paper demonstrates that the methods which estimate the time-to-the-first failure (for non-repairable components) lead to incorrect conclusions about reliability of repairable power cables.

The proposed method is used to evaluate the failure rate of each individual Paper Insulated Lead Cover (PILC) underground cables in a distribution grid in the south of Sweden. © 2018 Elsevier Ltd

Place, publisher, year, edition, pages
London: Elsevier, 2019
Keywords
Power cable, historical data, reliability, proportional hazard model, preventive maintenance.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-35470 (URN)10.1016/j.ijepes.2018.08.047 (DOI)000449447200055 ()2-s2.0-85053080255 (Scopus ID)
Available from: 2017-11-24 Created: 2017-11-24 Last updated: 2019-03-19Bibliographically approved
4. Analyzing and Modeling Propagation of SmartMeters' Alarms in Low-Voltage Grids
Open this publication in new window or tab >>Analyzing and Modeling Propagation of SmartMeters' Alarms in Low-Voltage Grids
(English)In: Data mining and knowledge discovery, ISSN 1384-5810, E-ISSN 1573-756XArticle in journal (Refereed) Submitted
Abstract [en]

In low-voltage energy distribution networks, analyzing and modelingpropagation of disturbances is important as we are moving towards smartgrids. Today, smart meters are generally deployed at all customers and continuouslymeasure several features related to power consumption and quality.However, the data collected from such low-cost devices has been, until now,considered unsuitable for analysis of disturbance propagation, mainly due to itsvery low time resolution. This paper demonstrates that the existence of propagationin the low-voltage grids can be detected using smart meters alarm data.In particular, several models for propagation of disturbances, within neighborcustomers in different levels of the grid topology, are investigated. A methodfor measuring how the reality corresponds to each of the models, by measuringthe similarity between real data and synthetic data, is proposed. Theresults show that the models which include propagation within both deliverypointsand branches are better representation of the disturbances in the realdata, compared to other models. Furthermore, the paper presents smart metersalarm dataset (SMAData), an open-source dataset containing power qualitydisturbances of over 1000 customers.

Keywords
Smart meter, power quality disturbance, alarm, propagation modeling, low-voltage distribution grids
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-39041 (URN)
Available from: 2019-03-12 Created: 2019-03-12 Last updated: 2019-03-19

Open Access in DiVA

fulltext(975 kB)90 downloads
File information
File name FULLTEXT01.pdfFile size 975 kBChecksum SHA-512
a425395b66d077c230c494315326b42fedea6d04d694f54a255fb9fafc19a9580a5a616db398224a2fe27e2ea933348a70884f0a6a5c63db615b0dece8db946f
Type fulltextMimetype application/pdf

Authority records BETA

Mashad Nemati, Hassan

Search in DiVA

By author/editor
Mashad Nemati, Hassan
By organisation
CAISR - Center for Applied Intelligent Systems Research
Other Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 90 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 457 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf