A state tendency measurement for a hydro-turbine generating unit based on aggregated EEMD and SVR. (29th October 2015)

• Record Type: Journal Article
• Authors: Fu, Wenlong; Zhou, Jianzhong; Zhang, Yongchuan; Zhu, Wenlong; Xue, Xiaoming; Xu, Yanhe
• Is Part Of: Measurement science & technology. Volume 26:Number 12(2015:Dec.)
• Publication Date: 2015-10-29
• Subjects: hydro-turbine generating unit (HGU) -- state tendency measurement -- aggregated ensemble empirical mode decomposition (AEEMD) -- support vector regression (SVR) -- frequency and energy conditions; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/0957-0233/26/12/125008
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: The reliable measurement of state tendency for a hydro-turbine generating unit (HGU) is significant in guaranteeing the security of the unit and promoting stability of the power system. For this purpose, an aggregated ensemble empirical mode decomposition (AEEMD) and optimized support vector regression (SVR)-based hybrid model is developed in this paper in order to enhance the measuring accuracy of state tendency for a HGU. First of all, the non-stationary time series of the state signal are decomposed into a collection of intrinsic mode functions (IMFs) by EEMD. Subsequently, to obtain the refactored intrinsic mode functions (RIMFs), the IMFs with different scales are aggregated with the proposed reconstruction strategy in consideration of the frequency and energy conditions. Later, the phase–space matrix in accordance with each RIMF is deduced by phase–space reconstruction and all the RIMFs are predicted through establishing homologous optimal SVR forecasting models with a grid search. Finally, the ultimate measuring values of state tendency can be determined through the accumulation of all the RIMF forecasting values. Furthermore, the effectiveness of the proposed method is validated in engineering experiments and comparative analyses.