Reconstruction Method for Missing Data in Photovoltaic Based on Generative Adversarial Network and Crisscross Particle Swarm Optimization Algorithm

被引：0

作者：

Yin H. ^{[1
]}

Ding W. ^{[1
]}

Chen S. ^{[1
]}

Wang C. ^{[1
]}

Chen J. ^{[1
]}

Meng A. ^{[1
]}

机构：

[1] School of Automation, Guangdong University of Technology, Guangzhou

来源：

Dianwang Jishu/Power System Technology | 2022年 / 46卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Crisscross particle swarm optimization algorithm; Generative adversarial network; Loss of reconstruction; Reconstruction for missing data in photovoltaic;

D O I：

10.13335/j.1000-3673.pst.2021.0694

中图分类号：

学科分类号：

摘要：

Aiming at the problem of photovoltaic data missing caused by the equipment failure or the human interference, a reconstruction method for the missing data in photovoltaics is proposed based on the generative adversarial network and the crisscross particle swarm optimization algorithm. Firstly, the Wasserstein divergence generation antagonism network (WGAN-div) is used to learn the timing law and the coupling relationship of the photovoltaic data. Secondly, the reconstruction constraints are designed to optimize the noise input of the generator, so that the reconstructed samples are close to the actual samples to the maximum extent. As for the problem of optimizing the high-dimensional variables, the vertical and horizontal crossover algorithm is used to catalyze the optimization process of particle swarm optimization to prevent the premature problems during the optimization. Experimental results show that the proposed method has high reconstruction accuracy when the photovoltaic data contains a large number of missing values. This method is also suitable for the missing value reconstruction of similar data in the power system, which has a good application prospect. © 2022, Power System Technology Press. All right reserved.

引用

页码：1372 / 1381

页数：9

共 25 条

[21] ARJOVSKY M, CHINTALA S, BOTTOU L., Wasserstein GAN, Proceedings of the 34th International Conference on Machine Learning (ICML), (2017)
[22] GULRAJANI I, AHMED F, ARJOVSKY M, Et al., Improved training of wasserstein GANs, Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 5767-5777, (2017)
[23] WU Jiqing, HUANG Zhiwu, THOMA J, Et al., Wasserstein divergence for GANs, Proceedings of the 15th European Conference on Computer Vision, (2018)
[24] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, Et al., Deep residual learning for image recognition, Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, (2016)
[25] MIRJALILI S, MIRJALILI S M, LEWIS A., Grey wolf optimizer, Advances in Engineering Software, 69, pp. 46-61, (2014)

← 1 2 3 →