Rare Events Early Detection in Wind Turbines

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.advisorAhmed Attia, Sid
dc.contributor.authorBayoumy, Amro
dc.contributor.schoolSähkötekniikan korkeakoulufi
dc.contributor.supervisorRaisch, Jorg
dc.date.accessioned2022-12-18T18:04:45Z
dc.date.available2022-12-18T18:04:45Z
dc.date.issued2022-12-12
dc.description.abstractThis thesis approaches the challenge of detecting rare events, specifically wind gusts, in the context of wind turbines operations. The goal by predicting wind speeds in the short horizon is to foresee gusts and enable enhanced control responses. While wind turbulence remains elusive, wind gusts are characterized as events where wind speed reaches at least 8 m/s and the variation between the peak and lull of the speed is beyond 4.5 m/s. Wind gusts impact turbines integrity by causing motor fatigue and tower/blade extreme loads. Such impacts can cause electric grid instability. Therefore, by being able to predict wind gusts occurrence and intensity, it is possible to reduce extreme loads, resulting in reduced component sizing, and naturally wind turbine costs while increasing product competitiveness. The pipeline developed here is the first combined pipeline for combined wind speed prediction and wind gusts detection. It consists of two consecutive stages: prediction and detection. In the prediction stage, a Long Short Term Memory network is used to predict wind speeds over a horizon range of 0.05 seconds to 0.5 seconds. In the detection stage, wavelet transformation is applied to the predicted wind speed to detect gust-like profiles by using generic wavelets (Morlet & DOG2) that can be adapted according to installation site characteristics. Each pipeline stage is benchmarked against multiple methods, representing the industry standard. The data set used is from a mast-measurement at the Hamburg Weather Mast in Germany, containing wind speed measurements sampled at 0.05 s for the duration of about 8.3 s. The method has shown that LSTM is the best performing prediction algorithm, when evaluated according to the Mean Squared Error and Mean Absolute Error metrics compared to other DL algorithms as well as ARIMA, the standard statistical technique for time series forecasting. Additionally, wavelet transformation has shown robust performance with minimal false positive detections compared to CUSUM, an industry standard for wind gusts detection. The two wavelets are combined with a 2-point derivative test that evaluates the wind acceleration to minimize error. The combination of the individual thresholds for each method result in a different control strategy recommendation.en
dc.format.extent60+17
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/118329
dc.identifier.urnURN:NBN:fi:aalto-202212187071
dc.language.isoenen
dc.locationP1fi
dc.programmeMaster's Programme in ICT Innovationfi
dc.programme.majorAutonomous Systemsfi
dc.programme.mcodeELEC3055fi
dc.subject.keywordwind gusten
dc.subject.keywordwaveleten
dc.subject.keywordturbineen
dc.subject.keywordLSTMen
dc.subject.keywordforecastingen
dc.subject.keyworddetectionen
dc.titleRare Events Early Detection in Wind Turbinesen
dc.typeG2 Pro gradu, diplomityöfi
dc.type.ontasotMaster's thesisen
dc.type.ontasotDiplomityöfi
local.aalto.electroniconlyyes
local.aalto.openaccessyes
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
master_Bayoumy_Amro_2022.pdf
Size:
9.39 MB
Format:
Adobe Portable Document Format