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A machine-learning approach to estimating the performance and stability of the electric frequency containment reserves
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

For a number of years, the frequency quality has been decreasing in the Nordic synchronous area. The Revision of the Nordic Frequency Containment Process project has introduced a proposed set of pre-qualification requirements to ensure the stability and performance of frequency containment reserves. The purpose of this thesis has been to examine the potential of complementing the evaluation of the requirements through the use of machine learning methods applied to signals sampled during normal operation of a power plant providing frequency containment. Several simulation models have been developed to generate such signals with the results fed into five machine learning algorithms for classification: decision tree, adaboost of decision tree, random forest, support vector machine, and a deep neural network. The results show that on all of the simulation models it is possible to extract information regarding the stability and performance while with high accuracy preserving the distribution of physical parameters of the approved samples. The conclusion is that machine learning methods can be used to extract information from operation signals and that further research is recommended to determine how this could be put to practice and what precision are needed.

Abstract [sv]

Stabilitet och pålitlighet hos kraftsystemet är av yttersta vikt, med frekvenskvaliteten som en indikator. Under ett antal år har frekvenskvaliteten sjunkit inom det nordiska synkronområdet. Projektet The Revision of the Nordic Frequency Containment Process har föreslagit nya pre-kvalificeringskrav syftandes till att säkerställa stabilitet och prestanda hos frequency containment reserves. Syftet med detta examensarbete har varit att utforska möjligheterna att komplettera utvärderingen av dessa krav genom att använda masininlärningsmetoder applicerade på signaler hämtade från normal drift av ett kraftverk som levererar frequency containment. Flera simuleringsmodeller har utvecklats för att generera sådana signaler som sedan har analyserats av fem olika maskininlärningsmetoder för klassificering: beslutsträd, adaboost av beslutsträd, random forest, stödvektormaskin samt ett djup neuralt nätverk. Resultaten visar att det för samtliga simuleringsmodeller har varit möjligt att extrahera information kring stabilitet och prestanda och samtidigt med hög noggrannhet bevara fördelningen av fysikaliska parametrar hos godkända prover. Slutsatsen är att maskininlärningsmetoder kan användas för att extrahera information från driftsignaler samt att fortsatta undersökningar rekommenderas för att avgöra hur denna information kan användas praktiskt, och vilken precision i bedömningarna som då skulle krävas.

Place, publisher, year, edition, pages
2018.
Series
TRITA-SCI-GRU ; 2018:283
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-231005OAI: oai:DiVA.org:kth-231005DiVA, id: diva2:1221304
External cooperation
Svenska Kraftnät
Subject / course
Optimization and Systems Theory
Educational program
Master of Science - Applied and Computational Mathematics
Supervisors
Examiners
Available from: 2018-06-19 Created: 2018-06-19 Last updated: 2018-08-11Bibliographically approved

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CiteExportLink to record
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