Search

The complexity and diversity of noise in corona current measurements of high-voltage direct current (HVdc) transmission lines leads to challenges in estimating corona performance. This paper proposes an offline noise suppression method for HVdc corona current measurements. Given that the corona current and background noise processes coexist within the same frequency band, we develop a novel multifaceted filtering approach for HVdc corona currents. Specifically, a cross correlation function-based center frequency recognition method is presented to provide filter design specification for a multiple notch filter to suppress narrow-band radio interference. To then suppress the residual full-band noise, wavelet denoising techniques with hard and soft thresholds are applied and compared. The proposed noise suppression method is applied to field-measured corona current data from the HVdc Experimental Base in China. We assert that the noise suppression method has versatile implementation and can be readily applied for corona loss estimation of HVdc systems. [ABSTRACT FROM AUTHOR]

In engineering and technical fields, a large number of sensors are applied to monitor a complex system. A special class of signals are often captured by those sensors. Although they often have indirect or indistinct relationships among them, they simultaneously reflect the operating states of the whole system. Using these signals, the field engineers can evaluate the operational states, even predict future behaviors of the monitored system. A novel method of future operational trend forecast of a complex system is proposed in this paper. It is based on empirical wavelet transform (EWT) and autoregressive moving average (ARMA) techniques. Firstly, empirical wavelet transform is used to extract the significant mode from each recorded signal, which reflects one aspect of the operating system. Secondly, the system states are represented by the indicator function which are obtained from those normalized and weighted significant modes. Finally, the future trend is forecast by the parametric model of ARMA. The effectiveness and practicality of the proposed method are verified by a set of numerical experiments. [ABSTRACT FROM AUTHOR]

The acoustic emission (AE) method is useful for structural health monitoring (SHM) of composite structures due to its high sensitivity and real-time capability. The main challenge, however, is how to classify the AE data into different failure mechanisms because the detected signals are affected by various factors. Empirical wavelet transform (EWT) is a solution for analyzing the multi-component signals and has been used to process the AE data. In order to solve the spectrum separation problem of the AE signals, this paper proposes a novel modified separation method based on local window maxima (LWM) algorithm. It searches the local maxima of the Fourier spectrum in a proper window, and automatically determines the boundaries of spectrum segmentations, which helps to eliminate the impact of noise interference or frequency dispersion in the detected signal and obtain the meaningful empirical modes that are more related to the damage characteristics. Additionally, both simulation signal and AE signal from the composite structures are used to verify the effectiveness of the proposed method. Finally, the experimental results indicate that the proposed method performs better than the original EWT method in identifying different damage mechanisms of composite structures. [ABSTRACT FROM AUTHOR]