Combined Probability Prediction of Wind Power Considering the Conflict of Evaluation Indicators

Wind power combination probability prediction can effectively describe the uncertainty of wind farm output power and reduce the negative impact of this uncertainty on grid dispatching and operation. However, there is a conflict between coverage and interval width in probability prediction that affec...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.174709-174724
Main Authors: Huang, Nantian, Wu, Yinyin, Lu, Guobo, Wang, Wenting, Cao, Xu
Format: Article
Language:English
Subjects:
area gray correlation decision
combined probability prediction
Comprehensive evaluation
Correlation
Covariance
Electric power grids
Estimation
Forecasting
Gaussian process
Gaussian process regression
Ground penetrating radar
Indicators
Mathematical analysis
Prediction models
Reliability
Signal processing
Statistical analysis
Support vector machines
Uncertainty
variational mode decomposition
Wind power
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Summary:Wind power combination probability prediction can effectively describe the uncertainty of wind farm output power and reduce the negative impact of this uncertainty on grid dispatching and operation. However, there is a conflict between coverage and interval width in probability prediction that affects the construction of the optimal prediction model. To resolve the conflicts between different probabilistic evaluation indicators, this paper proposes a new method for wind power combination probability prediction based on area gray correlation decision. First, the original wind power output data is reconstructed using energy-optimized variational mode decomposition to reduce the randomness of the original wind power signal. Processed wind power output data is used to establish an input feature set containing 96-dimensional historical wind power output data. Then, different Gaussian process regression prediction models are established based on 10 covariance functions. The area gray correlation method is used to calculate the area gray correlation degree of the five evaluation indicators, and a comprehensive evaluation of multiple indicators is carried out to eliminate the conflict between indicators. Finally, the weights of the prediction results of the 10 GPR models are determined according to the area gray correlation closeness, and the prediction interval and mean are reconstructed by combining the calculation results of each model. Simulation results show that the model determined by the new method has a more reliable prediction interval and faster prediction speed and can therefore provide decision support for wind power probability prediction and for the safe and stable operation of wind power grid connections.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2954699