基于多特征参数耦合的风电机组性能评估

马致远; 高明辉; 林琳; 曹梦楠; 刘明达; 邓屹

doi:10.19805/j.cnki.jcspe.2026.250190

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

基于多特征参数耦合的风电机组性能评估

新能源与储能 | 更新时间：2026-06-11

- 基于多特征参数耦合的风电机组性能评估
- Performance Evaluation of Wind Turbine Based on Multi-feature Parameter Coupling
- 动力工程学报 2026年46卷第6期页码：91-97
- 作者机构：
  
  1. 上海发电设备成套设计研究院有限责任公司,上海,200240
  2. 国家电投集团风电产业创新中心,上海,200240
- 作者简介：
  
  马致远(1987—),男,江苏南京人,高级工程师,博士,研究方向为风力发电机组提质增效。高明辉(通信作者),男,助理工程师,硕士,E-mail:1203478223@qq.com。
- 基金信息：
  
  国家电投集团公司统筹科技项目(KYB12022FD06)
- DOI：10.19805/j.cnki.jcspe.2026.250190
  中图分类号： TM614
- 纸质出版：2026
- 稿件说明：
移动端阅览
马致远, 高明辉, 林琳, 曹梦楠, 刘明达, 邓屹. 基于多特征参数耦合的风电机组性能评估[J]. 动力工程学报, 2026, 46(6): 91-97.
马致远, 高明辉, 林琳, 曹梦楠, 刘明达, 邓屹. 基于多特征参数耦合的风电机组性能评估[J]. 动力工程学报, 2026, 46(6): 91-97. DOI： 10.19805/j.cnki.jcspe.2026.250190.

DOI： 10.19805/j.cnki.jcspe.2026.250190

摘要

当前风力发电机组性能评估系统的研究多是对单一设备或基于简单状态特征参数的设备进行评价

对于复杂系统的多特征参数状态综合评价系统较少。为提升风电机组性能评估效率

将新研叶片、塔筒监测数据与机组数据采集与监视控制系统(SCADA)数据进行耦合分析

对风电机组进行综合分析评估

建立风电机组性能评估模型

该模型包括机组扭矩(载荷)控制水平、安全链触发复核、最大功率控制逻辑评估、风速仪故障监测等模块。结果表明:所建模型可以有效评估风电机组的性能

具有一定的工程应用价值。

Abstract

Current research on wind turbine performance evaluation systems primarily focuses on evaluation for individual equipment or equipment based on simple state characteristic parameters. However

comprehensive evaluation systems for complex systems based on multi-feature parameters remain scarce. To enhance the efficiency of wind turbine performance evaluation

the monitoring data from newly developed blades and towers were coupled with the supervisory control and data acquisition (SCADA) system data for comprehensive analysis. A wind turbine performance evaluation model was established

which incorporated several modules: unit torque (load) control level

safety chain trigger review

maximum power control logic evaluation

and anemometer fault monitoring. The results indicate that the proposed model can effectively evaluate wind turbine performance and demonstrate definite engineering application value.

关键词

Keywords

references

刘帅, 徐文. 国内风机大型化发展的现状、趋势和挑战[J]. 电站系统工程, 2024, 40(6): 11-12, 15.LIU Shuai, XU Wen. Status, trend and challenges of domestic large scale wind turbine generator[J]. Power System Engineering, 2024, 40(6): 11-12, 15.

龙霞飞, 杨苹, 郭红霞, 等. 大型风力发电机组故障诊断方法综述[J]. 电网技术, 2017, 41(11): 3480-3491.LONG Xiafei, YANG Ping, GUO Hongxia, et al. Review of fault diagnosis methods for large wind turbines[J]. Power System Technology, 2017, 41(11): 3480-3491.

BROYNA J, STRIELKOWSKI W, FOMINA A, et al. Renewable energy and EU 2020 target for energy efficiency in the Czech Republic and Slovakia[J]. Energies, 2020, 13(4): 965.

文孝强, 杨剀勋, 许子昂. 基于改进K-means聚类算法与马尔可夫链的风机性能评估新方法[J]. 现代信息科技, 2023, 7(17): 66-70.WEN Xiaoqiang, YANG Kaixun, XU Ziang. A new method for evaluating wind turbine performance based on improved K-means clustering algorithm and Markov chain[J]. Modern Information Technology, 2023, 7(17): 66-70.

WANG Pingchuan, ZHANG Yuanpeng, YUAN Shuai, et al. Analysis and application of the relationship between wind power curve and power generation based on operating data[J]. Journal of Physics: Conference Series, 2020, 1676(1): 012205.

马东, 孔德同, 郭鹏, 等. 基于SCADA运行数据的风电机组发电性能劣化监测研究[J]. 可再生能源, 2021, 39(1): 45-49.MA Dong, KONG Detong, GUO Peng, et al. Wind turbine performance degradation monitoring with SCADA data[J]. Renewable Energy Resources, 2021, 39(1): 45-49.

范佳卿, 肖伯乐, 高升, 等. 一种实时在线诊断旋转机械振动故障的方法[J]. 动力工程学报, 2018, 38(2): 120-126.FAN Jiaqing, XIAO Bole, GAO Sheng, et al. Real-time online diagnosis of vibration faults in rotary machineries[J]. Journal of Chinese Society of Power Engineering, 2018, 38(2): 120-126.

杨明明. 基于机舱传递函数的风机性能评估[J]. 水电与新能源, 2020, 34(7): 37-40, 48.YANG Mingming. Performance evaluation of wind turbine units based on nacelle transfer function[J]. Hydropower and New Energy, 2020, 34(7): 37-40, 48.

宋品芳, 李孜军, 李蓉蓉, 等. 基于熵权模糊法的高海拔矿井风机性能影响因素分析[J]. 黄金科学技术, 2020, 28(3): 457-464.SONG Pinfang, LI Zijun, LI Rongrong, et al. Analysis of factors influencing the performance of high altitude mine fan based on entropy weight fuzzy method[J]. Gold Science and Technology, 2020, 28(3): 457-464.

郭鹏, 刘琳. 多变量风电机组功率曲线建模与监测研究[J]. 电网技术, 2018, 42(10): 3347-3354.GUO Peng, LIU Lin. Modeling and monitoring of multivariable wind turbine power curve[J]. Power System Technology, 2018, 42(10): 3347-3354.

张方红, 荆博, 钱政, 等. 基于相关向量机的风电机组功率曲线建模与监测[J]. 船舶工程, 2020, 42(增刊2): 171-174, 191.ZHANG Fanghong, JING Bo, QIAN Zheng, et al. Probabilistic wind turbine power curve modelling and monitoring based on relevance vector machine[J]. Ship Engineering, 2020, 42(Sup 2): 171-174, 191.

WEN Xiaoqiang, XU Ziang. Wind turbine fault diagnosis based on relief F-PCA and DNN[J]. Expert Systems With Applications, 2021, 178: 115016.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

固废等离子气化与小型模块化核电集成系统的性能分析

超大型风力机叶片弯弯耦合颤振特性分析

燃煤电厂膜吸收法CO2捕集系统热力性能分析

灵活调峰工况下WFGD系统出口SO2质量浓度的分段机理-数据混合建模

扇贝阻尼密封静态稳定性实验研究