scholarly journals Short-term frequency scaling of clear-sky and wet amplitude scintillation

1996 ◽  
Vol 143 (6) ◽  
pp. 521 ◽  
Author(s):  
S.I.E. Touw ◽  
M.H.A.J. Herben
Keyword(s):  
Frequency Scaling ◽  
Short Term ◽  
Term Frequency ◽  
Clear Sky ◽  
Amplitude Scintillation

scholarly journals Aerodynamic-Efficiency-Constrained Short-Term Frequency Support Control of Wind Turbine Generators

2020 ◽  
Vol 53 (2) ◽  
pp. 12115-12120
Author(s):  
Zhengyang Zhang ◽  
Zaiyu Chen ◽  
Guoqiang Yu ◽  
Tianhai Zhang ◽  
Minghui Yin ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Short Term ◽  
Aerodynamic Efficiency ◽  
Term Frequency ◽  
Turbine Generators ◽  
Wind Turbine Generators

Real‐time optimisation of short‐term frequency stability controls for a power system with renewables and multi‐infeed HVDCs

2018 ◽  
Vol 12 (13) ◽  
pp. 1462-1469 ◽  
Author(s):  
Luping Wang ◽  
Xiaorong Xie ◽  
Xiaoliang Dong ◽  
Ying Liu ◽  
Hongming Shen
Keyword(s):  
Power System ◽  
Real Time ◽  
Frequency Stability ◽  
Short Term ◽  
Term Frequency

scholarly journals Prediction of Solar Power Using Near-Real Time Satellite Data

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5865
Author(s):  
Abhnil Amtesh Prasad ◽  
Merlinde Kay
Keyword(s):  
Real Time ◽  
Satellite Data ◽  
Solar Power ◽  
Short Term ◽  
Motion Vectors ◽  
Clear Sky ◽  
Cloud Motion ◽  
Visible Images ◽  
Grid Operators ◽  
Short Term Prediction

Solar energy production is affected by the attenuation of incoming irradiance from underlying clouds. Often, improvements in the short-term predictability of irradiance using satellite irradiance models can assist grid operators in managing intermittent solar-generated electricity. In this paper, we develop and test a satellite irradiance model with short-term prediction capabilities using cloud motion vectors. Near-real time visible images from Himawari-8 satellite are used to derive cloud motion vectors using optical flow estimation techniques. The cloud motion vectors are used for the advection of pixels at future time horizons for predictions of irradiance at the surface. Firstly, the pixels are converted to cloud index using the historical satellite data accounting for clear, cloudy and cloud shadow pixels. Secondly, the cloud index is mapped to the clear sky index using a historical fitting function from the respective sites. Thirdly, the predicated all-sky irradiance is derived by scaling the clear sky irradiance with a clear sky index. Finally, a power conversion model trained at each site converts irradiance to power. The prediction of solar power tested at four sites in Australia using a one-month benchmark period with 5 min ahead prediction showed that errors were less than 10% at almost 34–60% of predicted times, decreasing to 18–26% of times under live predictions, but it outperformed persistence by >50% of the days with errors <10% for all sites. Results show that increased latency in satellite images and errors resulting from the conversion of cloud index to irradiance and power can significantly affect the forecasts.

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