Ultra Short Term Power Prediction of Offshore Wind Power Based on Support Vector Machine Optimized by Improved Dragonfly Algorithm

Authors

  • Yanxia Yu
  • Yingshuai Wu
  • Liang Zhao
  • Xiang Li
  • Yanan Li

DOI:

https://doi.org/10.13052/dgaej2156-3306.3734

Keywords:

Support vector machine, dragonfly algorithm, offshore power prediction.

Abstract

In order to improve the prediction effect of ultra short term power of offshore
wind power, the prediction model based on support vector machine optimized
dragonfly algorithm is constructed. Based on summary of the prediction
methods of wind power, the support vector machine optimized by dragonfly
algorithm is established. Finally, prediction simulation analysis of offshore
wind power is carried out, results show that the proposed prediction model in
this research can effectively improve the computing prediction precision.

Downloads

Download data is not yet available.

Author Biographies

Yanxia Yu

Yanxia Yu works in CRRC Dalian Locomotive & Rolling Stock Co., Ltd.,
serving as chief designer and professoral-level senior engineer. Engage in
the research and development and application of intelligent control products
and train network control system, undertake the research and development
of many major and key science and technology projects of China level and
CRRC Corporation, and win a number of science and technology awards of
CRRC Corporation and Liaoning Province.

Yingshuai Wu

Yingshuai Wu works in CRRC Dalian Locomotive & Rolling Stock Co.,
Ltd., serving as the minister of Development Department and a professoral-
level senior engineer. Engaged in technical management and electrical overall
design. Undertook a number of CRRC Group’s top technology research and
development, won a number of CRRC Group and Liaoning Province science
and technology awards.

Liang Zhao

Liang Zhao works in CRRC Dalian Locomotive & Rolling Stock Co.,
Ltd., as a project manager and senior engineer of CRRC Dalian Industrial
Company. Engaged in the application and promotion of scientific research
products, in charge of product testing and product evaluation.

Xiang Li

Xiang Li works in CRRC Dalian Locomotive & Rolling Stock Co., Ltd., as
an intelligent product test tester, responsible for product testing and reporting.

Yanan Li

Yanan Li received her PhD in Control Theory and Control Engineering from
DLUT. Currently, she is a senior engineer in LSCZ Science and Technology
Co., Ltd.. Her research interests mainly include faulty diagnosis of process
control system and intelligent detection.

References

Lin Wang, Rui Tao, Huanling Hu, Yu-Rong Zeng, Effective wind power

prediction using novel deep learning network: Stacked independently

recurrent autoencoder, Renewable Energy, 2021, 164(2):642–655.

Md Alamgir Hossain, Ripon K. Chakrabortty, Sondoss Elsawah,

Michael J. Ryan, Very short-term forecasting of wind power generation

using hybrid deep learning model, Journal of Cleaner Production, 2021,

(5):26564.

Shuang Han, Yan-hui Qiao, Jie Yan, Yong-qian Liu, Li Li, Zheng Wang,

Mid-to-long term wind and photovoltaic power generation prediction

based on copula function and long short term memory network, Applied

Energy, 2019, 239(4):181–191.

D.Y. Hong, T.Y. Ji, M.S. Li, Q.H. Wu, Ultra-short-term forecast of wind

speed and wind power based on morphological high frequency filter and

double similarity search algorithm, International Journal of Electrical

Power & Energy Systems, 2019, 104(1):868–879.

Marcelo Azevedo Costa, Ramiro Ruiz-Cárdenas, Leandro Brioschi

Mineti, Marcos Oliveira Prates, Dynamic time scan forecasting

for multi-step wind speed prediction, Renewable Energy, 2021,

(11):584–595.

Fei Li, Guorui Ren, Jay Lee, Multi-step wind speed prediction based on

turbulence intensity and hybrid deep neural networks, Energy Conver-

sion and Management, 2019, 186(4):306–322.

Bikram Kumar, Deepak Gupta, Universum based Lagrangian twin

bounded support vector machine to classify EEG signals, Computer

Methods and Programs in Biomedicine, 2021, 208(9):106244.

Ran An, Yitian Xu, Xuhua Liu, A rough margin-based multi-task ν-

twin support vector machine for pattern classification, Applied Soft

Computing, 2021, 112(11):107769.

Hao Zhang, Yuxin Shi, Xueran Yang, Ruiling Zhou, A firefly algorithm

modified support vector machine for the credit risk assessment of supply

chain finance, Research in International Business and Finance, 2021,

(12):101482.

Ricardo ManuelArias Velásquez, Support vector machine and tree mod-

els for oil and Kraft degradation in power transformers, Engineering

Failure Analysis, 2021, 127(9):105488.

Tao Sun, Renjie Wu, Yifan Cui, Yuejiu Zheng, Sequent extended

Kalman filter capacity estimation method for lithium-ion batteries based

on discrete battery aging model and support vector machine, Journal of

Energy Storage, 2021, 39(7):102594.

Hongfei Zhu, Lianhe Yang, Jianwu Fei, Longgang Zhao, Zhongzhi

Han, Recognition of carrot appearance quality based on deep feature

and support vector machine, Computers and Electronics in Agriculture,

, 186(7):106185.

Xiaoyu Liu, Nan Li, Hailin Mu, Miao Li, Xinxin Liu, Techno-energy-

economic assessment of a high capacity offshore wind-pumped-storage

hybrid power system for regional power system, Journal of Energy

Storage, 2021, 41(9):102892.

Behzad Golparvar, Petros Papadopoulos, Ahmed Aziz Ezzat, Ruo-Qian

Wang, A surrogate-model-based approach for estimating the first and

second-order moments of offshore wind power, Applied Energy, 2021,

(10):117286.

Jovana Dakic, Marc Cheah-Mane, Oriol Gomis-Bellmunt, Eduardo

Prieto-Araujo, Low frequency AC transmission systems for offshore

wind power plants: Design, optimization and comparison to high voltage

AC and high voltage DC, International Journal of Electrical Power &

Energy Systems, 2021, 133(12):107273.

Qian Liu, Yan Sun, Mengcheng Wu, Decision-making methodologies

in offshore wind power investments: A review, Journal of Cleaner

Production, 2021, 295(5):126459.

Lin Zhou, Pengxiang Huang, Shukai Chi, Ming Li, Hu Zhou, Hong-

bin Yu, Hongda Cao, Kai Chen, Structural health monitoring of off-

shore wind power structures based on genetic algorithm optimization

and uncertain analytic hierarchy process, Ocean Engineering, 2020,

(12):108201.

Julian David Hunt, Behnam Zakeri, Alexandre Giulietti de Barros,

Walter Leal Filho, Augusto Delavald Marques, Paulo Sérgio Franco

Barbosa, Paulo Smith Schneider, Marcelo Farenzena, Buoyancy Energy

Storage Technology: An energy storage solution for islands, coastal

regions, offshore wind power and hydrogen compression, Journal of

Energy Storage, 2021, 40(8):102746.

Bagesh Kumar, Ayush Sinha, Sourin Chakrabarti, O.P. Vyas, A fast

learning algorithm for One-Class Slab Support Vector Machines,

Knowledge-Based Systems, 2021, 228(9):107267.

Hongzheng Shen, Kongtao Jiang, Weiqian Sun, Yue Xu, Xiaoyi Ma,

Irrigation decision method for winter wheat growth period in a supple-

mentary irrigation area based on a support vector machine algorithm,

Computers and Electronics in Agriculture, 182(3):106032.

Jing Zheng, Junliang Fan, Fucang Zhang, Lifeng Wu, Yufeng Zou,

Qianlai Zhuang, Estimation of rainfed maize transpiration under vari-

ous mulching methods using modified Jarvis-Stewart model and hybrid

support vector machine model with whale optimization algorithm,

Agricultural Water Management, 2021, 249(4):106799

Wen-Chieh Cheng, Xue-Dong Bai, Brian B. Sheil, Ge Li, Fei Wang,

Identifying characteristics of pipejacking parameters to assess geo-

logical conditions using optimisation algorithm-based support vec-

tor machines, Tunnelling and Underground Space Technology, 2020,

(12):103592.

Dong Liu, Maoxun Li, Yi Ji, Qiang Fu, Mo Li, Muhammad Abrar Faiz,

Shoaib Ali, Tianxiao Li, Song Cui, Muhammad Imran Khan, Spatial-

temporal characteristics analysis of water resource system resilience in

irrigation areas based on a support vector machine model optimized

by the modified gray wolf algorithm, Journal of Hydrology, 2021,

(6):125758.

Beikun Zhang, Liyun Xu, Jian Zhang, Balancing and sequencing prob-

lem of mixed-model U-shaped robotic assembly line: Mathematical

model and dragonfly algorithm based approach, Applied Soft Comput-

ing, 2021, 98(1):106739.

Shilaja C., Arunprasath T., Internet of medical things-load optimiza-

tion of power flow based on hybrid enhanced grey wolf optimization

and dragonfly algorithm, Future Generation Computer Systems, 2019,

(9):319–330.

Dipayan Guha, Provas Kumar Roy, Subrate Banerjee, Optimal tuning

of 3 degree-of-freedom proportional-integral-derivative controller for

hybrid distributed power system using dragonfly algorithm, Computers

& Electrical Engineering, 2018, 72(11):137–153.

Maya Rocha-Ortega, Pilar Rodríguez, Alex Córdoba-Aguilar, Can drag-

onfly and damselfly communities be used as bioindicators of land use

intensification? Ecological Indicators, 2019, 107(12):105553.

Downloads

Published

2021-11-30

How to Cite

Yu, Y. ., Wu, Y. ., Zhao, L. ., Li, X. ., & Li, Y. (2021). Ultra Short Term Power Prediction of Offshore Wind Power Based on Support Vector Machine Optimized by Improved Dragonfly Algorithm. Distributed Generation &Amp; Alternative Energy Journal, 37(3), 465–484. https://doi.org/10.13052/dgaej2156-3306.3734

Issue

Section

Articles