[1]尹 诗,侯国莲,于晓东,等.基于 Bi-RNN的风电机组主轴承温度预警方法研究[J].郑州大学学报(工学版),2019,40(05):44-50.[doi:10.13705/j.issn.1671-6833.2019.05.008]
　Yin Shi,Hou Guolian,Yu Xiaodong,et al.Research on early warning method of wind turbine main bearing temperature based on Bi-RNN[J].Journal of Zhengzhou University (Engineering Science),2019,40(05):44-50.[doi:10.13705/j.issn.1671-6833.2019.05.008]
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基于 Bi-RNN的风电机组主轴承温度预警方法研究()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
40卷
期数:
2019年05期
页码:
44-50
栏目:
出版日期:
2019-10-23

文章信息/Info

Title:
Research on early warning method of wind turbine main bearing temperature based on Bi-RNN
作者:
尹 诗; 侯国莲; 于晓东; 李 宁; 王其乐; 弓林
华北电力大学控制与计算机工程学院，北京102206；中能电力科技开发有限公司，北京100034
Author(s):
Yin Shi 1; 2; Hou Guolian 1; Yu Xiaodong 1; Li Ning 1; Wang Qile 2; Bow Linjuan 1
1. School of Control and Computer Engineering, North China Electric Power University; 2. Zhongneng Power Technology Development Co., Ltd.
关键词:
风电机组; 主轴承; 工况辨识; Bi-RNN; 随机森林
Keywords:
battery energy storage station ;  interval control correlation ;  probabilistic load flow point estimation method
DOI:
10.13705/j.issn.1671-6833.2019.05.008
文献标志码:
A
摘要:
主轴承是风电机组能量传递的关键设备，本文以双馈风力发电机组主轴承为研究对象，首先采 用高斯混合模型(gaussian mixture model, GMM)对机组工况进行辨识；其次在各个子工况空间内建立基 于双向循环神经网络(bi-directional recurrent neural network, Bi-RNN )的风电机组主轴承温度模型；然 后，采用随机森林算法对主轴承温度模型残差进行建模与预测，从而实现机组主轴承故障预警；最后以 某大型风电场机组为对象建模并开展仿真研究.结果表明，基于工况辨识的Bi-RNN神经网络算法结合 随机森林算法对主轴承故障预警具有较强的实用性和较高的准确率.
Abstract:
With the massive integration of distributed generation and electric vehicles, the problems of power and voltage quality in active distribution network were increasingly shown. Aiming at this issue, the factors af�1Ffecting the interval control were analyzed in terms of energy storage capacity and power, daily load curve char�1Facteristics and unit time firstly. An improved interval control method for energy storage output model was then proposed to solve the problem of multiple charging/discharging operation in one cycle. Considering the correla�1Ftion of random variables, probabilistic load flow using point estimate method was analyzed to state the influence of distributed generation, electric vehicles and energy storage station on voltage level. Finally, simulation anal�1Fysis was operated on the improved IEEE-33 node active distribution network system with battery energy storage station. The results showed that the integration of energy storage station could effectively reduce the fluctuation of system power and voltage.
更新日期/Last Update: 2019-10-26