A Wind Farm Equivalent Method Based on Multi-View Transfer Clustering and Stack Sparse Auto Encoder

被引:6
作者
Han, Ji [1 ,2 ]
Miao, Shihong [1 ,2 ]
Li, Yaowang [1 ,2 ]
Yang, Weichen [1 ,2 ]
Yin, Haoran [1 ,2 ]
机构
[1] Huazhong Univ Sci & Technol, State Key Lab Adv Electromagnet Engn & Technol, Wuhan 430074, Peoples R China
[2] Huazhong Univ Sci & Technol, Sch Elect & Elect Engn, Hubei Elect Power Secur & High Efficiency Key Lab, Wuhan 430074, Peoples R China
基金
中国国家自然科学基金;
关键词
Feature extraction; Reactive power; Time series analysis; Voltage control; Clustering algorithms; Clustering methods; Wind farms; Wind farm equivalence; multi-view; transfer learning; deep learning; MVT-FCM; SSAE; MODELING METHOD;
D O I
10.1109/ACCESS.2020.2993808
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Large-scale wind farm (WF) constitutes dozens or even hundreds of wind turbines (WTs), making it complex and even impractical to develop each individual WT in detail when building WF model. Thus, the equivalent model of WF, with a reasonable reduction of the detailed model, is essential to be developed. In this paper, we propose a multi-view transfer clustering and stack sparse auto encoder (SSAE) based WF equivalent method, which can be used in the low voltage ride through (LVRT) analysis of WF. First, to obtain distinguishable deep-level and multi-view representation of wind turbine (WT), stack sparse auto encoder (SSAE) is used to extract features from the time series of several WT physical quantities, and these features are used as the clustering indicator (CI). Then, a multi-view transfer FCM (MVT-FCM) clustering algorithm, which combines transfer learning with multi-view FCM (MV-FCM), is put forward for WTs clustering. Two transfer rules are designed in this algorithm, and the clustering center and membership degree in the source domain are transferred to guide the clustering process of target domain samples. Finally, the calculation method of equivalent parameters is presented. To verify the effectiveness of the proposed method, a modified actual system in East Inner Mongolia of China is utilized for case study, and the performance of the proposed model is compared with several state-of-the-art models. Simulation results show that the equivalent errors of the proposed model decrease at least 3% when comparing with other models. Also, the error fluctuations are within 6% under different simulation conditions, which illustrates the well-performed robustness of the proposed model.
引用
收藏
页码:92827 / 92841
页数:15
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