基于PSO-BP神经网络的风电功率备用容量估计模型

doi:10.16180/j.cnki.issn1007-7820.2021.12.007

Abstract

Abstract:

In view of the effect of the volatility and randomness of wind power on the dispatching of the power grid, a neural network-based wind power reserve capacity estimation model is proposed. In this model, the weights of each link layer in BP neural network are optimized by particle swarm optimization algorithm to improve the prediction of wind power value in the future. The Pearson correlation coefficient is used to extract the influence factors which are positively correlated with the prediction error, and then the multiple linear regression method is used to associate the extracted influencing factors to calculate the reserve capacity of wind power. The simulation results show that 80% of the prediction error is within the estimation range of the model, further verifying the effectiveness of the model.

Key words: wind power generation, wind power, neural network, particle swarm optimization algorithm, prediction error, Pearson correlation coefficient, multiple linear regression, reserve capacity

CLC Number:

TP393

ZHU Chengming,WEI Yunbing,JIANG Chengcheng,ZHU Jian'an. Estimation Model of Wind Power Reserve Capacity Based on PSO-BP Neural Network[J].Electronic Science and Technology, 2021, 34(12): 36-41.

Figures/Tables 9

Figure 1.

Figure 2.

Table 1

Pearson correlation coefficient value range and related intensity"

$r$	相关强度
0.8~1.0	极强相关
0.6~0.8	强相关
0.4~0.6	中等程度相关
0.2~0.4	弱相关
0.0~0.2	极弱相关或无相关

Table 1

Table 2

Figure 3.

Table 3

Figure 4.

Table 4

Figure 5.

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影响因素	相关系数
实际发电功率的波动系数	0.749 8
预测值发电功率波动系数	0.671 0
预测平均功率	0.236 8

参数	估计值	估计区间
β₀	23.758 3	[11.399 2,36.117 5]
β₁	0.013 4	[-0.002 9,0.029 7]
β₂	0.000 5	[-0.225 3,0.226 2]
β₃	0.131 1	[-0.065 2,0.327 4]

预测模型	MAPE	nRMSE
BP	10.26	3.78
PSO-BP	8.84	2.94

Estimation Model of Wind Power Reserve Capacity Based on PSO-BP Neural Network

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