The Power of Wind Turbine Generators Prediction Model Based on Time Series

Author constructed ARIMA model and GARCH model to predict the output power of the 4 wind turbine generators (A, B, C, D) and the total output power of the 4 and 58 wind turbine generators. Whats more, by comparing the results of two methods, it shows that the result of GARCH model is more accurate than ARIMA model in short-term prediction.

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

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2020.12.772 ◽

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

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Output power leveling of wind turbine generators using pitch angle control for all operating regions in wind farm

Electrical Engineering in Japan ◽

10.1002/eej.20448 ◽

2007 ◽

Vol 158 (4) ◽

pp. 31-41 ◽

Cited By ~ 2

Author(s):

Tomonobu Senjyu ◽

Ryosei Sakamoto ◽

Naomitsu Urasaki ◽

Toshihisa Funabashi ◽

Hideki Fujita ◽

...

Keyword(s):

Wind Turbine ◽

Output Power ◽

Pitch Angle ◽

Wind Farm ◽

Turbine Generators ◽

Wind Turbine Generators ◽

Operating Regions

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Output Power Leveling of Wind Turbine Generators using Pitch Angle Control for All Operating Regions in Wind Farm

Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems ◽

10.1109/isap.2005.1599291 ◽

2006 ◽

Cited By ~ 8

Author(s):

R. Sakamoto ◽

T. Senjyu ◽

N. Urasaki ◽

T. Funabashi ◽

H. Fujita ◽

...

Keyword(s):

Wind Turbine ◽

Output Power ◽

Pitch Angle ◽

Wind Farm ◽

Turbine Generators ◽

Wind Turbine Generators ◽

Operating Regions

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Improved Random Forest Method for Ultra-short-term Prediction of the Output Power of a Photovoltaic Cluster

Frontiers in Energy Research ◽

10.3389/fenrg.2021.749367 ◽

2021 ◽

Vol 9 ◽

Author(s):

Mao Yang ◽

Meng Zhao ◽

Dingze Liu ◽

Miaomiao Ma ◽

Xin Su

Keyword(s):

Random Forest ◽

Output Power ◽

Training Dataset ◽

Total Output ◽

Short Term ◽

Forest Model ◽

Term Prediction ◽

Two Phases ◽

Short Term Prediction ◽

Daily Time

Current models for the prediction of the output power of photovoltaic (PV) clusters suffer from low prediction accuracy and are prone to overfitting. To address these problems, we propose an improved random forest (RF)-based method for ultra-short-term prediction of PV cluster output power. The total output power data for the PV clusters are used as the training dataset and fed into the RF model to obtain preliminary predictions. The error and accuracy of the preliminary predictions for individual sampling points concerning the actual values of the PV cluster output power are assessed. Each of the daily time series of preliminary predictions is divided into two phases according to whether the output power is increasing (morning) or decreasing (afternoon). The final ultra-short-term predictions of the PV cluster output power are obtained by correcting the two phases of preliminary predictions through trend correction and peak correction, respectively. The results show that, compared with the unimproved model, the accuracy of the stochastic forest model is 1.48% higher than that of the modified random forest model., which proves the effectiveness and practicability of the proposed method.

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