CGAN-Based Load Scenario Generation under Typhoon Weather

doi:10.11930/j.issn.1004-9649.202405076

Abstract

Abstract:

The violent fluctuation of power load level under typhoon weather threatens the power balance of power grid. Therefore, load scenario generation under typhoon weather conditions has attracted increasing attention from power supply companies. A load scenario generation algorithm based on conditional generative adversarial network (CGAN) model for typhoon weather is proposed. Firstly, considering the fact that the typhoon samples have the characteristics of scattered landing locations, different duration periods and different grades, a load sample classification and label setting method for typhoon weather is proposed. Then, a sample expansion strategy based on conditional probability is proposed to expand the sample set to solve the problem of scarce load samples under typhoon weather. Finally, in order to further improve the actual effectiveness of the sample set, based on the idea of migration training, the load samples under normal weather are firstly used to train the CGAN, and then the typhoon sample sets are applied to train CGAN. After the model training is completed, the corresponding load scenarios can be quickly generated by inputting random noise and typhoon labels. The effectiveness and advancement of the proposed model and algorithm are verified by data set from a practical power system.

Key words: typhoon weather, load scenario generation, data expansion, label setting, artificial intelligence

Pingping LUO, Ao SHENG, Jikeng LIN, Zhongyue WANG, Qiben LI, Ping ZHOU. CGAN-Based Load Scenario Generation under Typhoon Weather[J]. Electric Power, 2025, 58(2): 176-185.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202405076

https://www.electricpower.com.cn/EN/Y2025/V58/I2/176

Figures/Tables 10

Fig.1 Load label setting under a typhoon of type $ {T^{\text{y}}} $ for $ {C^{\text{y}}} $ days

Fig.2 CGAN structure

Table 1 Structure of the generator

结构	神经元个数	动态均值动量	激活函数
输入层	89	/	0.2
隐藏层1	64	0.8	0.2
隐藏层2	128	0.8	0.2
输出层	96	/	—

Table 2 Structure of the discriminator

结构	神经元个数	动态均值动量	激活函数
输入层	123	/	0.2
隐藏层1	128	0.8	0.2
隐藏层2	64	0.8	0.2
输出层	1	/	—

Fig.3 Flow chart of CGAN-based load scenario generation under typhoon weather

Fig.4 Variation curve of the discriminator loss function

Fig.5 Sample quality generated simulation by different training methods

Fig.6 Box diagram of autocorrelation coefficient and partial autocorrelation coefficient of scenario generation

Fig.7 Confidence comparison of generation results by different training methods

Table 3 Calculation results of load generation scenario indicators

置信度/%	台风类型			对比方法		未扩充样本训练		扩充样本训练
置信度/%	登录位置	等级	持续天数	覆盖率/%	负荷区间	覆盖率/%	负荷区间	覆盖率/%	负荷区间
90	3	3	4	87.5	0.63	91.1	0.51	92.9	0.49
80				66.1	0.46	69.8	0.36	71.8	0.34
50				42.5	0.30	50.2	0.25	51.8	0.21
90	1	2	6	88.2	0.65	89.3	0.57	90.1	0.56
80				65.1	0.47	68.6	0.44	70.4	0.42
50				41.6	0.32	43.2	0.29	44.7	0.26

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