Composite denoising-based LSTM prediction method of supercapacitor performance degradation law and remaining useful life

Purpose This paper aims to reduce the impact of noise on the prediction accuracy of remaining useful life (RUL) for supercapacitor. First, Savitzky–Golay (SG) smoothing filter method (Savitzky and Golay, 1964) is used to eliminate the local small fluctuation and high-frequency noises that are generated by the capacity drop and rebound during the charging and discharging process of supercapacitor. Then, the variational mode decomposition (VMD) method is used to eliminate large fluctuation noises that are caused by internal temperature change of supercapacitor and chemical reaction of the supercapacitor. Its parameters are optimized by using marine predators algorithm (MPA), and the capacity sequence after denoising is reconstructed. Finally, long short term memory neural networks (LSTM) is used to predict the performance degradation law (PDL) and remaining useful life (RUL) of supercapacitor for the reconstructed sequence, then the comparative analysis is conducted with other methods, which results show this method improves the prediction accuracy effectively, and provides theoretical support for timely and accurately understanding the PDL and RUL of supercapacitor backup power supply. Design/methodology/approach First, SG smoothing filter method is used to eliminate the local small fluctuation and high-frequency noises that are generated by the capacity drop and rebound during the charging and discharging process of supercapacitor. Then, the VMD method is used to eliminate large fluctuation noises that are caused by internal temperature change of supercapacitor and chemical reaction of the supercapacitor. Its parameters are optimized by using MPA, and the capacity sequence after denoising is reconstructed. Finally, LSTM is used to predict the PDL and RUL of supercapacitor for the reconstructed sequence, then the comparative analysis is conducted with other methods, the results show that this method improves the prediction accuracy effectively, and provides theoretical support for timely and accurate understanding the PDL and RUL of supercapacitor backup power supply. Findings These factors will bring different types of noise during the service process of supercapacitor backup power supply, such as capacity regeneration, differences of charging and discharging rate, internal temperature change of supercapacitor, chemical reaction and external electromagnetic interference. Therefore, the paper proposes an LSTM prediction method of supercapacitor’s PDL and RUL based on composite denoising, which is divided into three stages: smoothing, noise reduction and prediction. First, SG smoothing filter method is used to eliminate the local small fluctuation and high-frequency noises, and MPA-VMD method is used to eliminate the nonlinear and nonstationary noises. Then, the capacity sequence after denoising is reconstructed, LSTM is used to predict PDL and RUL of supercapacitor. Finally, the comparative analysis with other methods is carried out. The results show that SG-VMD-LSTM method has higher prediction accuracy, which can accurately predict PDL and RUL of supercapacitor backup power supply, and improve the safety and reliability of wind turbine operation under the severe wind conditions. Originality/value The comparative analysis with other methods is carried out. The results show that SG-VMD-LSTM method has higher prediction accuracy, which can accurately predict PDL and RUL of supercapacitor backup power supply, and improve the safety and reliability of wind turbine operation under the severe wind conditions.