风电机组关键部件故障预测技术研究
发布时间：2019-06-08 15:58
【摘要】：故障预测技术能够有效识别故障潜在信息,从而避免恶性设备损坏事故的发生,以及故障带来的高维修成本与高发电量损失,是实现故障后检修到预测性检修智能化转变的关键所在。因此,本文通过对风电机组历史运行数据、在线监测数据和试验数据的系统分析与深度挖掘,提炼出隐藏在多源数据中的故障特征参数,展开对风电机组与关键部件的故障预测技术研究,为智能化运维提供技术支撑。首先为提高数据源质量,对缺失、无效、失真的“坏数据”进行辨识与重构。然后依据SCADA数据,建立非线性状态观测(NEST)模型并加以改进,提出一种考虑样本优化的风电机组齿轮箱故障预测方法,并与不同模型进行对比,验证模型的时效性与优越性,通过对振动信号加速度值的时域、频域分析,提出一种基于Hilbert变换的齿轮箱故障预测方法,应用D-S证据理论将以上两种预测结果进行信息融合,获得更科学、更符合实际的故障预测结果。其次依据现场试验,对偏航系统进行功率特性测试、偏航精度测试,有效预测偏航系统存在偏航制导故障,并对故障加以修复、补偿,又提出优化偏航死区的新思路。最后从整机角度考虑,提出一种基于灰色熵AHP与TOPSIS法的风电机组健康状态评估方法,对同场同期风机进行综合排序,将排名靠后的风机视为性能劣化、存在潜在故障的“嫌疑风机”,从而实现故障预测的目的。通过与现有方法进行对比,结果显示文章所提方法预测精度更高,更实用、有效,可为相关研究提供参考。
[Abstract]:Fault prediction technology can effectively identify the potential fault information, so as to avoid the occurrence of malignant equipment damage accidents, as well as the high maintenance cost and high power generation loss caused by faults. It is the key to realize the intelligent transformation from post-fault maintenance to predictive maintenance. Therefore, through the systematic analysis and deep mining of the historical operation data, on-line monitoring data and test data of wind turbine, the fault characteristic parameters hidden in multi-source data are extracted in this paper. The fault prediction technology of wind turbine and key components is studied to provide technical support for intelligent operation and maintenance. First of all, in order to improve the quality of data sources, the missing, invalid and distorted "bad data" are identified and reconstructed. Then, according to SCADA data, the nonlinear state observation (NEST) model is established and improved, and a fault prediction method of wind turbine gearbox considering sample optimization is proposed and compared with different models to verify the timeliness and superiority of the model. Through the time domain and frequency domain analysis of the acceleration value of vibration signal, a fault prediction method of gearbox based on Hilbert transform is proposed. The above two prediction results are fusion by using D 鈮，
本文编号：2495412
[Abstract]:Fault prediction technology can effectively identify the potential fault information, so as to avoid the occurrence of malignant equipment damage accidents, as well as the high maintenance cost and high power generation loss caused by faults. It is the key to realize the intelligent transformation from post-fault maintenance to predictive maintenance. Therefore, through the systematic analysis and deep mining of the historical operation data, on-line monitoring data and test data of wind turbine, the fault characteristic parameters hidden in multi-source data are extracted in this paper. The fault prediction technology of wind turbine and key components is studied to provide technical support for intelligent operation and maintenance. First of all, in order to improve the quality of data sources, the missing, invalid and distorted "bad data" are identified and reconstructed. Then, according to SCADA data, the nonlinear state observation (NEST) model is established and improved, and a fault prediction method of wind turbine gearbox considering sample optimization is proposed and compared with different models to verify the timeliness and superiority of the model. Through the time domain and frequency domain analysis of the acceleration value of vibration signal, a fault prediction method of gearbox based on Hilbert transform is proposed. The above two prediction results are fusion by using D 鈮，
本文编号：2495412
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