Research on Early Warning of Abnormal Working Conditions of Wind Turbine Based on QM-DBSCAN and BiLSTM
MA Liangyu1,2,LIANG Shuyuan1,CHENG Dongyan1,GENG Yanzhu1,DUAN Xinhui1,3
1.Department of Automation, North China Electric Power University, Baoding, Hebei 071003, China
2.Baoding Key Laboratory of State Detection and Optimization for Integrated Energy System, Baoding, Hebei 071003, China
3.Baoding SinoSimu Technology Co.Ltd, Baoding, Hebei 071000, China
Abstract：A wind turbine fault warning method based on quartile method(QM)-density-based spatial clustering of applications with noise(DBSCAN) and Bi-directional long and short-term memory network (BiLSTM) is proposed.Firstly, in view of the difficulty of cleaning the power limit point in the wind speed-power diagram, the combination of QM and DBSCAN is proposed to preprocess the modeling operation data. secondly, by analyzing the operation principle of wind turbine and determining the input and output parameters of the normal working condition prediction model of wind turbine combined with LightGBM feature selection method, a high-precision normal performance prediction model of wind turbine is established based on BiLSTM.Then, the state performance index of the fan is determined by the sliding window algorithm, and the index threshold is determined by statistical interval estimation method.Finally, the real fault data of the fan is used to carry out the early warning experiment of the abnormal working condition of the whole wind turbine, which verifies the effectiveness of the method.
马良玉,梁书源,程东炎,耿妍竹,段新会. 基于QM-DBSCAN与BiLSTM的风电机组异常工况预警研究[J]. 计量学报, 2024, 45(9): 1384-1393.
MA Liangyu,LIANG Shuyuan,CHENG Dongyan,GENG Yanzhu,DUAN Xinhui. Research on Early Warning of Abnormal Working Conditions of Wind Turbine Based on QM-DBSCAN and BiLSTM. Acta Metrologica Sinica, 2024, 45(9): 1384-1393.
MA L Y, CHENG S Z. Abnormal state early warning of wind turbine generator based on support vector data description and XGboost. ［J］. Transactions of China Electrotechnical Society, 2022, 37(13): 3241-3249.
MA R, LI W Y, QI Y S. Online cleaning of abnormal data for the prediction of wind turbine health condition ［J］. Transactions of China Electrotechnical Society, 2021, 36(10): 2127-2139.
HU Y, QIAO Y L. Wind power data cleaning method based on confidence equivalent boundary model ［J］. Automation of Electric Power Systems, 2018, 42(15): 18-23.
SHEN X J, FU X J, ZHOU C C, et al. Characteristics of outliers in wind speed-power operation data of wind turbines and its cleaning method ［J］. Transactions of China Electrotechnical Society, 2018, 33(14): 3353-3361.
HE Q, YIN F F, WU X, et al. Fault prediction of wind turbine gearbox based on long short-term memory network. ［J］. Acta Metrologica Sinica, 2020, 41(10): 1284-1290.
HE Q, WANG H, JIANG G Q, et al. Research of wind turbine main bearing condition monitoring based on correlation PCA and ELM. ［J］. Acta Metrologica Sinica, 2018, 39(1): 89-93.
MEI Y, LI X, HU Z C, et al. Identification and cleaning of wind power data methods based on control principle of wind turbine generator system ［J］. Journal of Chinese Society of Power Engineering, 2021, 41(4): 316-322.
LI Z S, YAO X, LIU Z G, et al. Feature selection algorithm based on LightGBM ［J］. Journal of Northeastern University(Natural Science), 2021, 42(12): 1688-1695.
LV W C, MA J L, CHEN J X, et al. Current situation and restriction bottleneck of development of wind power industry ［J］. Renewable Energy Resources, 2018, 36(8): 1214-1218.
ZHU Q W, YE L, ZHAO Y N, et al. Methods for elimination and reconstruction of abnormal power data in wind farms ［J］. Power System Protection and Control, 2015, 43(3): 38-45.
YANG S M, YUAN A J, YU Z Q. A novel model rased on CEEMDAN, IWOA, and BiLSTM for ultra-short-term wind power forecasting ［J］. Environmental Science and Pollution Research, 2023,30(5): 11689-11705.
MA L Y, SUN J M, YU S L, et al. DBSCAN and SDAE-based abnormal condition early warning for a wind turbine unit ［J］. Journal of Chinese Society of Power Engineering, 2021, 41(9): 786-793.
［8］
WANG Z, WANG L, HUANG C. A Fast Abnormal Data Cleaning Algorithm for Performance Evaluation of Wind Turbine ［J］. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 5006512.
LOU J L, XU J, LU H, et al. Wind turbine data-cleaning algorithm based on power curve ［J］. Automation of Electric Power Systems, 2016, 40(10): 116-121.
OHADI N, KAMANDI A, SHABANKHAH M, et al. Sw-dbscan: A grid-based dbscan algorithm for large datasets［C］// IEEE. 2020 6th International Conference on Web Research (ICWR). 2020: 139-145.
CUI K, XU Y F, LI X S, et al. Wind turbine performance prediction model and early warning of abnormal condition based on GRNN ［J］. Science Technology and Engineering, 2020, 20(32): 13220-13228.
［18］
AHMAD M A, HAO M R, ISMAIL R M T R, et al. Model-free wind farm controlbased on random search［C］// IEEE. 2016 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS). 2016: 131-134.
2024, Vol. 45 
