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• W2213967046 endingPage "125008" @default.
• W2213967046 startingPage "125008" @default.
• W2213967046 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." @default.
• W2213967046 created "2016-06-24" @default.
• W2213967046 creator A5004947965 @default.
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• W2213967046 date "2015-10-29" @default.
• W2213967046 modified "2023-10-18" @default.
• W2213967046 title "A state tendency measurement for a hydro-turbine generating unit based on aggregated EEMD and SVR" @default.
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• W2213967046 doi "https://doi.org/10.1088/0957-0233/26/12/125008" @default.
• W2213967046 hasPublicationYear "2015" @default.
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