Napovedno vzdrževanje električnih transformatorjev s strojnim učenjem
delo diplomskega seminarja
Abstract
Oskrba Slovenije z električno energijo je tesno povezana z vzdrževanjem vseh obstoječih prenosnih objektov. V delu analiziramo objekt RTP Divača, čigar zelo pomembna gradnika sta prečna transformatorja s stikaloma, ki kontrolirata regulacijo napetosti in moči. Pregledi teh dveh transformatorjev se izvajajo glede na čas ali pa glede na stanje naprave. Pregledi so povezani s številom opravljenih preklopov stikal. Cilj diplomskega dela je boljše načrtovanje pregledov in vzdrževanje naprav, za kar uporabimo strojno učenje za gradnjo modelov, ki napovedujejo število dnevnih, tedenskih ter meseščnih preklopov. Modeli so se učili na podlagi podatkov izmerjenih v časovnem obdobju od leta 2017 do leta 2019, njihove napake pa so bile vrednotene na podatkih pridobljenih v časovnem obdobju od leta 2020 do leta 2021. Rezultati vrednotenja kažejo, da je za dnevno napovedovanje števila preklopov najuspešnejši model LSTM, za tedensko in mesečno napovedovanje števila preklopov pa je najuspešnejši model linearne regresije.
Keywords
električni transformatorji;strojno učenje;linearna regresija;odločitvena drevesa;naključni gozd;rangiranje napovednih spremenljivk;modeliranje;
Data
| Language: | Slovenian |
|---|---|
| Year of publishing: | 2022 |
| Typology: | 2.11 - Undergraduate Thesis |
| Organization: | UL FMF - Faculty of Mathematics and Physics |
| Publisher: | [M. Rojec] |
| UDC: | 004 |
| COBISS: |
120340995
|
| Views: | 701 |
| Downloads: | 121 |
| Average score: | 0 (0 votes) |
| Metadata: |
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Other data
| Secondary language: | English |
|---|---|
| Secondary title: | Predictive maintenance of electric transformers with machine learning |
| Secondary abstract: | The supply of electricity to Slovenia is closely connected with the maintenance of all existing portable facilities in Slovenia. In this B.A. thesis, we analyse the RTP Divača facility, whose very important building blocks are two phase-shifting transformer with taps, which control voltage and power regulation. The checks of these two transformers are carried out in regard to time or the state of the transformer. In the thesis, the number of switches per day, per week and per month is modelled with the help of machine learning. The models were trained based on the data measured in the time period from 2017 to 2019, and their errors were evaluated on the basis of the data measured in the period from 2020 to 2021. The results of the test data show that for daily forecasting the number of switches LSTM is the most successful model, while for weekly and monthly forecasting the number of switches a linear regression model is best. |
| Secondary keywords: | electric transformers;machine learning;linear regression;decision trees;random forest;feature selection;modeling; |
| Type (COBISS): | Final seminar paper |
| Study programme: | 0 |
| Embargo end date (OpenAIRE): | 1970-01-01 |
| Thesis comment: | Univ. v Ljubljani, Fak. za matematiko in fiziko, Oddelek za matematiko, Finančna matematika - 1. stopnja |
| Pages: | 35 str. |
| ID: | 16363424 |
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