Wave capture power forecasting based on improved particle swarm optimization neural network algorithm

被引：0

作者：

Huang B. ^{[1
]}

Yang J. ^{[1
]}

Lu S. ^{[1
]}

Chen H. ^{[1
]}

Xie D. ^{[1
]}

机构：

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

来源：

Yang, Junhua (yly93@gdut.edu.cn) | 1600年 / Science Press卷 / 42期

关键词：

Capture power; Forecasting; Neural network; Particle swarm optimization (PSO); Wave energy conversion;

D O I：

10.19912/j.0254-0096.tynxb.2018-0910

中图分类号：

学科分类号：

摘要：

The traditional BP neural network algorithm is applied to the acquisition power prediction of wave power generation system, which is easy to fall into local optimum and insufficient generalization ability. Therefore, an improved particle swarm optimization neural network algorithm is proposed to dynamically adjust the learning factor and add mutation operators. Using indirect prediction strategy to build a direct-drive wave power generation system model from wave data to wave capture power, applying an improved algorithm to predict and analyze the wave history data, input the construction model, and obtain the wave capture power prediction value. Comparing and analyzing the simulation results of different prediction steps and different algorithms, it is found that the improved algorithm can effectively overcome the shortcomings of traditional algorithms and improve the prediction accuracy. © 2021, Solar Energy Periodical Office Co., Ltd. All right reserved.

引用

页码：302 / 308

页数：6

共 19 条

[1]

LIU C Y, YU H T, HU M Q, Et al., Application of permanent magnet tubular linear generators using direct-driver wave power generation take-off systems, Proceedings of the CSEE, 33, 21, pp. 90-98, (2013)

[2]

MENG Z, LIANG H, BAI Y, Et al., Speed sliding-mode control for wave power generation system of float-type, Proceedings of the CSU-EPSA, 28, 11, pp. 130-134, (2016)

[3]

HUO Z J, WU F, TAO A F, Et al., Short-term prediction on output power of direct-drive wave power generation system, Automation of electric power system, 38, 21, pp. 13-18, (2014)

[4]

HALS J, FALNES J, MOAN T., Constrained optimal control of a heaving buoy wave-energy converter, Journal of offshore mechanics & arctic engineering, 133, 1, pp. 287-300, (2011)

[5]

GUAN C, LUH P B, MICHEL L D, Et al., Hybrid kalman filters for very short-term load forecasting and prediction interval estimation, IEEE transactions on power systems, 28, 4, pp. 3806-3817, (2013)

[6]

PAPARELLA F, MONK K, WINANDS V, Et al., Up-wave and autoregressive methods for short-term wave forecasting for an oscillating water column, IEEE transactions on sustainable energy, 6, 1, pp. 171-178, (2014)

[7]

XIAO Q, LI W H, LI Z G, Et al., Wind speed and power prediction based on improved wavelet-BP neural network, Power system protection and control, 42, 15, pp. 80-86, (2014)

[8]

MENG A B, LU H M, HU H W, Et al., Hybrid wavelef PACKET-CSO-ENN approach for short-term wind power forecasting, Acta energiae solaris sinica, 36, 7, pp. 1645-1651, (2015)

[9]

WU W B, PENG M YG., A data mining approach combining K-means clustering with bagging neural network for short-term wind power forecasting, IEEE internet of things journal, 4, 4, pp. 979-986, (2017)

[10]

ZHU H L, LI X, YAO J X, Et al., The power prediction method for photovoltaic power station based on wavelef analysis and neural networks, Acta energiae solaris sinica, 36, 11, pp. 2725-2730, (2015)

← 1 2 →