uu.seUppsala University Publications
Change search
ReferencesLink to record
Permanent link

Direct link
Data Mining via Association Rules for Power Ramps Detected by Clustering or Optimization
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity. Florida State Univ, Dept Math, Tallahassee, FL 32310 USA..
2016 (English)In: Transactions on Computational Science XXVIII: Special Issue on Cyberworlds and Cybersecurity / [ed] Gavrilova, ML; Tan, CJK; Sourin, A, Springer Berlin/Heidelberg, 2016, 163-176 p.Conference paper (Refereed)
Abstract [en]

Power ramp estimation has wide ranging implications for wind power plants and power systems which will be the focus of this paper. Power ramps are large swings in power generation within a short time window. This is an important problem in the power system that needs to maintain the load and generation at balance at all times. Any unbalance in the power system leads to price volatility, grid security issues that can create power stability problems that leads to financial losses. In addition, power ramps decrease the lifetime of turbine and increase the operation and maintenance expenses. In this study, power ramps are detected by data mining and optimization. For detection and prediction of power ramps, data mining K means clustering approach and optimisation scoring function approach are implemented [1]. Finally association rules of data mining algorithm is employed to analyze temporal ramp occurrences between wind turbines for both clustering and optimization approaches. Each turbine impact on the other turbines are analyzed as different transactions at each time step. Operational rules based on these transactions are discovered by an Apriori association rule algorithm for operation room decision making. Discovery of association rules from an Apriori algorithm will serve the power system operator for decision making.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2016. 163-176 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9590
Keyword [en]
Data mining, Big data, Power ramp, Clustering, Optimization, Association rules, Apriori algorithm
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:uu:diva-307326DOI: 10.1007/978-3-662-53090-0_9ISI: 000389498600009ISBN: 9783662530900 (print)ISBN: 9783662530894 (print)OAI: oai:DiVA.org:uu-307326DiVA: diva2:1046169
Conference
15th International Conference on Cyberworlds, Uppsala Univ, Gotland, SWEDEN, OCT 07-09, 2015
Available from: 2016-11-12 Created: 2016-11-12 Last updated: 2017-01-23Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Uzunoglu, Bahri
By organisation
Electricity
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 79 hits
ReferencesLink to record
Permanent link

Direct link