e-POSTER: Reducing Annual Energy Production Sensitivity to Precipitation with Remote Sensors
21 May 19
2:30 PM
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2:55 PM
Tracks:
Poster Presentations, Resource Assessment
Anemometers, SoDARs, and LiDARs all have different responses to rain and snow events. This research examines the sensitivity of sensor accuracy to rain and snow and its effect on annual energy production (AEP) estimates. Experimental data shows that, compared to SoDARs and LiDARs, anemometers report wind speeds -3% to -0.5% lower during rain events, in agreement with the literature. Ice accumulation leads to lower wind speeds as well, up to -10%, even after filtering for icing. Relative changes to the clear-weather baseline relationship between anemometers and RSDs are between -10% to -0.5%, dependent on anemometer type, wind speed, and precipitation intensity. Precipitation is often accompanied by high winds, enhancing the effect of the low bias on project AEP. Turbine icing can lead to turbine underperformance, but research shows that rainfall has little effect on turbine performance. RSDs, equipped with heating systems, window wipers, and algorithmic precipitation filtering do not exhibit these low biases during precipitation events In regions with significant periods of precipitation, anemometer low biases will lead to underestimates of AEP, up to -2%. When SoDARs, LiDARs, and precipitation sensors are included in wind resource assessment (WRA) campaigns in regions that experience significant periods of rainfall and snowfall, the influence of precipitation events on energy estimates can be greatly reduced, increasing project value and WRA reliability.