STUDY ON ACTIVE POWER DISTRIBUTION METHOD OF WIND FARM BASED ON UNIT CLASSIFICATION

被引：0
|
作者：
Liu J. [1 ]
Zhao H. [1 ]
Liu A. [1 ]
机构：
[1] College of Automation, Xi'an University of Technology, Xi'an
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2023年 / 44卷 / 08期
关键词：
active power; real time wind speed; wind farm; wind turbine classification;
D O I：
10.19912/j.0254-0096.tynxb.2022-0490
中图分类号：
学科分类号：
摘要：
Aiming at the problems of low prediction accuracy of single wind speed prediction method and large error of tracking power dispatching instruction when power distribution of wind farm units is carried out according to wind speed ratio, a combined wind speed prediction method is proposed. Based on the unit classification of the predicted wind speed, current wind speed and output power, a method of active power distribution for wind farms is proposed. The fuzzy C-means classification method is used to classify the units. The power regulation priority is determined according to the classification results. The power instructions are allocated to different types of units, and then allocated to each unit according to the proportion of the output power of each unit of a certain class, so as to realize the active power distribution of the whole wind farm. Wind speed prediction and active power distribution simulation research are carried out based on actual wind speed data of a wind farm. The simulation results show that the combined wind speed prediction method and active power distribution method proposed in this paper have the advantages of high wind speed prediction accuracy, high output power tracking accuracy and small number of units involved in active power regulation. © 2023 Science Press. All rights reserved.
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页码：396 / 403
页数：7
相关论文
共 13 条
  • [1] The 13th five year plan for renewable energy development（Part 2）［J］, 3, pp. 5-12, (2017)
  • [2] FLORIN I., Wind farm models and control strategies［R］, (2005)
  • [3] FAN X D, YANG X Y，, JIN X C., An overview of active power control in wind farms［J］, Power generation technology, 39, 3, pp. 268-276, (2018)
  • [4] ZHAO T., Research on wind farm active power allocation and control［D］, (2018)
  • [5] LIU J, ZHANG B B, ZHAO T., Research on wind farm active power dispatching algorithm based on fuzzy evaluation［J］, Transactions of China Electrotechnical Society, 34, 4, pp. 786-794, (2019)
  • [6] JIN X Z, WANG J J, ZHAO W L., Prediction algorithm for short- term wind speed based on gray system GM（1，1）model［J］, Control and instruments in chemical industry, 42, 11, pp. 1247-1249, (2015)
  • [7] ZHOU J Q, LI Y N, Et al., A study on BP neural network ultra-short-term wind speed forecast based on historical and spatial wind speed data［J］, Power system and clean energy, 31, 1, pp. 109-112, (2015)
  • [8] ZHANG W, QUE B., An ultra- short- term wind speed prediction model based on improved BP neural network with real- time correction［J］, Power system and clean energy, 32, 9, pp. 118-122, (2016)
  • [9] LI H, JIANG Y B，, Et al., Short-term wind speed forecasting of wind farms based on DTCWT and LSSVM ［J］, Journal of Changzhou Institute of Technology, 32, 1, pp. 14-19, (2019)
  • [10] YE L, ZHANG C H，, TANG Y，, Et al., Active power stratification predictive control approach for wind power cluster with multiple temporal and spatial scales coordination［J］, Proceedings of the CSEE, 38, 13, pp. 3767-3780, (2018)
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