Abstract
Accurate vibrational tendency forecasting of hydropower generator unit (HGU) is of great significance to guarantee the safe and economic operation of hydropower station. For this purpose, a novel hybrid approach combined with multiscale dominant ingredient chaotic analysis, kernel extreme learning machine (KELM), and adaptive mutation grey wolf optimizer (AMGWO) is proposed. Among the methods, variational mode decomposition (VMD), phase space reconstruction (PSR), and singular spectrum analysis (SSA) are suitably integrated into the proposed analysis strategy. First of all, VMD is employed to decompose the monitored vibrational signal into several subseries with various frequency scales. Then, SSA is applied to divide each decomposed subseries into dominant and residuary ingredients, after which an additional forecasting component is calculated by integrating the residual of VMD with all the residuary ingredients orderly. Subsequently, the proposed AMGWO is implemented to simultaneously adapt the intrinsic parameters in PSR and KELM for all the forecasting components. Ultimately, the prediction results of the raw vibration signal are obtained by assembling the results of all the predicted prediction components. Furthermore, six relevant contrastive models are adopted to verify the feasibility and availability of the modified strategies employed in the proposed model. The experimental results illustrate that (1) VMD plays a positive role for the prediction accuracy promotion; (2) the proposed dominant ingredient chaotic analysis based on the realization of time-frequency decomposition can further enhance the capability of the forecasting model; and (3) the appropriate parameters for each forecasting component can be optimized by the proposed AMGWO effectively, which can contribute to elevating the forecasting performance distinctly.
Highlights
Hydropower generator unit (HGU) is the key equipment of hydropower stations, which plays an important role in emergency reserve as well as regulation of peak load and frequency
Based on the above discussion, to achieve accurate vibration tendency forecasting for HGU, multiscale dominant ingredient chaotic analysis based on variational mode decomposition (VMD), singular spectrum analysis (SSA), and phase space reconstruction (PSR) is organically integrated with kernel extreme learning machine (KELM) and adaptive mutation grey wolf optimizer (AMGWO) in this paper
To achieve accuracy forecasting for the vibration tendency, a novel hybrid approach combined with VMD, SSA, PSR, KELM, and AMGWO-based parameters optimization strategy is proposed in this paper
Summary
Hydropower generator unit (HGU) is the key equipment of hydropower stations, which plays an important role in emergency reserve as well as regulation of peak load and frequency. The potential fault information can be effectively excavated by accurate prediction of vibration trends, while the forecasting of vibration tendency can be equivalent to the problem of time series prediction that achieves prediction by adequately utilizing historical status For this purpose, various state-of-the-art prediction techniques have been developed in practice engineering, which can be classified into statistical models and artificial intelligence (AI) models [8]. Due to the fact that the operation of HGU is usually accompanied by background noise and electromagnetic interference, the corresponding vibration signal usually possess strong nonlinearity and nonstationarity, which can greatly affect the predictive performance [6, 20] To this end, various time-frequency decomposition approaches have been rapidly developed to efficaciously weaken the nonstationary and nonlinear data. Based on the above discussion, to achieve accurate vibration tendency forecasting for HGU, multiscale dominant ingredient chaotic analysis based on VMD, SSA, and PSR is organically integrated with KELM and AMGWO in this paper. The abbreviations of technical terms appeared in this paper are listed in the Abbreviations section
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