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A platform for research: civil engineering, architecture and urbanism
Capacity forecasting for wind farms and connected power transformers
Transformers can be described as ’slumbering giants’ in the electric power system. This marks transformers to be big and expensive parts of equipment. Calling them slumbering refers to the unused capacity in many of them. Dynamic Transformer Rating (DTR) is a concept to utilize this potential and wind power connected transformers have been identified as a well-fitting application due to the naturally limited capacity factor and the correlation of low ambient temperature and high wind speeds. Previous scientific work and a small number of applied projects show the feasibility and benefits of combining DTR and wind power. Wind power forecasting is a standard procedure for dispatch planning and electricity trading. This thesis project aims at combining both subjects and focuses on providing and analyzing a forecasting tool. At various forecasting steps Machine-Learning (ML) approaches are tested and evaluated. The developed tool is designed for and tested on a case study comprising an existing wind farm and transformer. It is shown that in many, but not all cases an overheating (exceeding of the Hot Spot Temperature (HST) limit) can be predicted. Applying DTR adds a level of uncertainty to wind power forecasts since not only the wind power but also the transformer capacity must be predicted. In this project however the wind power forecast is identified as the main source of uncertainty. ; Transformatorer kan beskrivas som ‚sovande jättar‘ i det elektriska systemet eftersom transformatorer karakteriseras som stor och kostsam utrustning. Att kalla dem sovande hänvisar till den oanvända kapaciteten som finns i många. Dynamic Transformer Rating (DTR) är ett koncept för att använda denna potential och transformatorer kopplade till vindkraftsanläggningar blev utnämnd som en passande tillämpning på grund av deras begränsade kapacitetsfaktor och korrelationen mellan låga temperaturer och höga vindhastigheter. Tidigare vetenskapligt arbete och ett fåtal realiserade projekt visar genomförbarhet och fördelarna med ...
Capacity forecasting for wind farms and connected power transformers
Transformers can be described as ’slumbering giants’ in the electric power system. This marks transformers to be big and expensive parts of equipment. Calling them slumbering refers to the unused capacity in many of them. Dynamic Transformer Rating (DTR) is a concept to utilize this potential and wind power connected transformers have been identified as a well-fitting application due to the naturally limited capacity factor and the correlation of low ambient temperature and high wind speeds. Previous scientific work and a small number of applied projects show the feasibility and benefits of combining DTR and wind power. Wind power forecasting is a standard procedure for dispatch planning and electricity trading. This thesis project aims at combining both subjects and focuses on providing and analyzing a forecasting tool. At various forecasting steps Machine-Learning (ML) approaches are tested and evaluated. The developed tool is designed for and tested on a case study comprising an existing wind farm and transformer. It is shown that in many, but not all cases an overheating (exceeding of the Hot Spot Temperature (HST) limit) can be predicted. Applying DTR adds a level of uncertainty to wind power forecasts since not only the wind power but also the transformer capacity must be predicted. In this project however the wind power forecast is identified as the main source of uncertainty. ; Transformatorer kan beskrivas som ‚sovande jättar‘ i det elektriska systemet eftersom transformatorer karakteriseras som stor och kostsam utrustning. Att kalla dem sovande hänvisar till den oanvända kapaciteten som finns i många. Dynamic Transformer Rating (DTR) är ett koncept för att använda denna potential och transformatorer kopplade till vindkraftsanläggningar blev utnämnd som en passande tillämpning på grund av deras begränsade kapacitetsfaktor och korrelationen mellan låga temperaturer och höga vindhastigheter. Tidigare vetenskapligt arbete och ett fåtal realiserade projekt visar genomförbarhet och fördelarna med ...
Capacity forecasting for wind farms and connected power transformers
Hartmann, Maximilian (author)
2021-01-01
Theses
Electronic Resource
English
DDC:
690
Spatio-temporal Convolutional Network Based Power Forecasting of Multiple Wind Farms
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