NEW POWER SYSTEM LOAD DATA FEATURE ENHANCEMENT METHOD WITH DISTRIBUTED PHOTOVOLTAICS

Hui Qian; Liu Junyu; Liu Xin; Li Changyu; Song Xiaowen

doi:10.19912/j.0254-0096.tynxb.2024-0692

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (4) : 240-247. DOI: 10.19912/j.0254-0096.tynxb.2024-0692

NEW POWER SYSTEM LOAD DATA FEATURE ENHANCEMENT METHOD WITH DISTRIBUTED PHOTOVOLTAICS

Hui Qian, Liu Junyu, Liu Xin, Li Changyu, Song Xiaowen

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Abstract

It is difficult to detect abnormal power consumption events in the new power system with distributed photovoltaic, and the characteristics of abnormal power consumption behavior are complex, which is difficult to meet the requirements of the new power system for sample data quality. A new data feature enhancement method for load stealing behavior of power system based on deep recurrent neural network-improved generative adversarial network （DRNN-WGAN） model is proposed. Firstly, aiming at the problem of abnormal data caused by power stealing by power users, the abnormal data caused by power stealing by distributed photovoltaic users and power stealing by pure load users are analyzed. Secondly, the generative adversarial network is improved by Wasserstein distance. The generative adversarial network model is used to generate and discriminate the abnormal power consumption data of the new power system load, and the generative adversarial network model of the new power system load abnormal power consumption sample is established. Then, a new power system abnormal electricity data enhancement method based on DRNN-WGAN network is proposed, and the LightGBM algorithm is used to classify and detect the power grid load data. Finally, the simulation is carried out with the data of a distribution network with high proportion of new energy in Liaoning Province. The simulation results show that compared with other data enhancement methods, the new power system abnormal electricity data enhancement method based on DRNN-WGAN network proposed in this paper can accelerate the convergence speed of the model and improve the data quality and detection accuracy of the power grid.

Key words

distributed PV / generative adversarial network / abnormal electricity / new power system / Wasserstein distance / data enhancement

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Hui Qian, Liu Junyu, Liu Xin, Li Changyu, Song Xiaowen. NEW POWER SYSTEM LOAD DATA FEATURE ENHANCEMENT METHOD WITH DISTRIBUTED PHOTOVOLTAICS[J]. Acta Energiae Solaris Sinica. 2025, 46(4): 240-247 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0692

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