This paper proposes an optimization algorithm based on radial basis function (RBF) neural network to deal with heavy workload and complex calculation process of wind farm reactive power capacity calculation. First, a model for power flow computation of power systems containing wind farm is established, and the actual active power of a wind farm is taken as the input of the model, to calculate the reactive compensation capacity required. Second, the actual active power of the wind farm is used as input data, and the resulting reactive power compensation capacity as the target output, to establish a RBF neural network and train it. Finally, with the trained RBF neural network replacing the power flow calculation model, the reactive power compensation capacity for the wind farm is calculated. Calculation results show that the computational complexity of RBF neural network model is lower than that of the power flow calculation model, and the workload is reduced. Thus, the RBF neural network model can be trained to replace the power flow calculation model to calculate the reactive power compensation capacity of wind farm in real time.
ZHANG Hongtao
,
ZHANG Lingyun
,
LI Xiaodan
,
QIU Daoyin
. Reactive Power Compensation Based on Radial Basis Function Neural Network for Wind Farm Connected to Power System[J]. Science & Technology Review, 2014
, 32(11)
: 49
-54
.
DOI: 10.3981/j.issn.1000-7857.2014.11.007
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