Periodic Narrowband Research Articles

A Denoising Method of Partial Discharge Signal Based on Improved SVD-VMD

A denoising method combined with singular value decomposition (SVD) and Variational mode decomposition (VMD) is proposed to eliminate noise in on-site partial discharge (PD) signals from high-voltage electrical equipment. In the Fourier transform power spectrum, periodic narrowband interference was eliminated after SVD was offered to determine the number of singular values of periodic narrowband interference. Then, the PD signal was decomposed into K intrinsic mode function components by VMD. The empirical mode decomposition method was used to determine the K value of white noise’s intrinsic mode function components. An improved 3σ criterion threshold method was proposed to eliminate the residual noise in the PD signal. The denoising method was compared with the other two methods to analyze the denoising effect on the simulation and experimental PD signal. This paper’s denoising method can eliminate periodic narrowband interference and white noise in different PD signals of cavity discharge, corona discharge, and those from the motors’ coil. The denoised PD pulse waveform has a higher signal-to-noise ratio and normalized correlation coefficient and a lower mean square error, indicating that the waveform’s original characteristic remains.

Quantifying BrO and SO2 distributions in volcanic plumes—Recent advances in imaging Fabry-Pérot interferometer correlation spectroscopy

Bromine monoxide (BrO) and sulphur dioxide (SO2) are two gases frequently observed in volcanic plumes by spectroscopic techniques capable of continuous gas monitoring like, e.g., Differential Optical Absorption Spectroscopy (DOAS). The spatio-temporal resolution of DOAS measurements, however, only allows to determine average gas fluxes (minutes to hours resolution). In particular, it is insufficient to record two-dimensional images of SO2 and BrO in real-time (seconds time resolution). Thus, it is impossible to resolve details of chemical conversions of reactive plume constituents. However, these details are vital for further understanding reactive halogen chemistry in volcanic plumes. Therefore, instruments that combine high spatio-temporal resolution and high gas sensitivity and selectivity are required. In addition, these instruments must be robust and compact to be suitable for measurements in harsh and remote volcanic environments. Imaging Fabry-Pérot interferometer (FPI) correlation spectroscopy (IFPICS) is a novel technique for atmospheric trace gas imaging. It allows measuring atmospheric gas column density (CD) distributions with a high spatial and temporal resolution, while at the same time providing selectivity and sensitivity comparable to DOAS measurements. IFPICS uses the periodic transmission spectrum of an FPI, that is matched to the periodic narrowband (vibrational) absorption features of the target trace gas. Recently, IFPICS has been successfully applied to volcanic SO2. Here we demonstrate the applicability of IFPICS to much weaker (about two orders of magnitude) trace gas optical densities, such as that of BrO in volcanic plumes. Due to its high reactivity, BrO is extremely difficult to handle in the laboratory. Thus, based on the similarity of the UV absorption cross sections, we used formaldehyde (HCHO) as a spectral proxy for BrO in instrument characterization measurements. Furthermore, we present recent advances in SO2 IFPICS measurements and simultaneous measurements of SO2 and BrO from a field campaign at Mt Etna in July 2021. We find photon shot-noise limited detection limits of 4.7 × 1017 molec s0.5 cm−2 for SO2 and of 8.9 × 1014 molec s0.5 cm−2 for BrO at a spatial resolution of 512 × 512 pixels and 200 × 200 pixels, respectively. Furthermore, an estimate for the BrO to SO2 ratio (around 10–4) in the volcanic plume is given. The prototype instrument presented here provides spatially resolved measurements of the reactive volcanic plume component BrO. The temporal resolution of our approach allows studies of chemical conversions inside volcanic plumes on their intrinsic timescale.

Efficient partial discharge signal denoising method via adaptive variational modal decomposition for infrared detectors

Partial discharge detection is one of the effective means for judging cable insulation condition. However, subject to the operating environment of the cable, the measured signal exists a large amount of noise from infrared detectors. In this paper, we proposed an adaptive variational modal decomposition (AVMD)-based partial discharge signal denoising method, which can reduce the noise disturbance significantly. First, variational modal decomposition (VMD) is optimized by the quantum genetic algorithm, and weighted kurtosis is presented as an optimization index. Furthermore, the wavelet thresholding method is utilized to process the reconstructed signal by VMD. To verify the superity of AVMD, Gaussian white noise and periodic narrowband interference are considered in the simulation. When the input signal-to-noise ratio (SNR) is −6.5202 dB, the SNR of AVMD can reach 8.3716 dB. Compared with the existing methods, AVMD has the best denoising performance. Finally, AVMD is used to process the measured signals. Experimental results demonstrate that it can effectively separate the signal and noise and ultimately retain the signal characteristics for infrared detectors.

Suppression Method of Partial Discharge Interferences Based on Singular Value Decomposition and Improved Empirical Mode Decomposition

Partial discharge detection is an important means of insulation diagnosis of electrical equipment. To effectively suppress the periodic narrowband and white noise interferences in the process of partial discharge detection, a partial discharge interference suppression method based on singular value decomposition (SVD) and improved empirical mode decomposition (IEMD) is proposed in this paper. First, the partial discharge signal with periodic narrowband interference and white noise interference x(t) is decomposed by SVD. According to the distribution characteristics of single values of periodic narrowband interference signals, the singular value corresponding to periodic narrowband interference is set to zero, and the signal is reconstructed to eliminate the periodic narrowband interference in x(t). IEMD is then performed on x(t). Intrinsic mode function (IMF) is obtained by EMD, and based on the improved 3σ criterion, the obtained IMF components are statistically processed and reconstructed to suppress the influence of white noise interference. The methods proposed in this paper, SVD and SVD + EMD, are applied to process the partial discharge simulation signal and partial discharge measurement signal, respectively. We calculated the signal-to-noise ratio, normalized correlation coefficient, and mean square error of the three methods, respectively, and the results show that the proposed method suppresses the periodic narrowband and white noise interference signals in partial discharge more effectively than the other two methods.

Partial Discharge Signal Extraction Method Based on EDSSV and Low Rank RBF Neural Network

The detection process of partial discharge (PD) ultra-high frequency (UHF) signal is easily affected by white noise and periodic narrowband noise, which hinder the fault diagnosis of high-voltage electrical appliances. In order to extract PD UHF signal and suppress noise effectively, this paper provides a new method to detect PD UHF signal by EDSSV and low rank RBF neural network. Firstly, the singular value decomposition (SVD) is performed on the mixed noises of PD signal. Secondly, the peak index of energy difference spectrum of singular value (EDSSV) is selected as optimal singular value threshold, and then the periodic narrowband noise is eliminated by reconstructing the effective rank order. Finally, radial basis function (RBF) neural network is used to approximate the denoised PD signal, and Gaussian window filter is used to extract the PD signal. To verify the performance of the proposed method, we compared it with other three algorithms in simulation and field detection, including adaptive singular value decomposition (ASVD), singular value decomposition based on S-transform and MTFM (S-SVD) and EMD-WT algorithms. Particularly, four evaluation indices are designed for the detection data, which consider both the noise suppression and feature preservation. The results demonstrate the validity of the proposed method with higher signal-to-noise ratio and less waveform distortion.

Novel noise reduction method based on improved empirical wavelet transform and kurtosis for partial discharge signal of high‐voltage cables

Partial discharge (PD) detection is useful to the online monitoring of high-voltage cables, which are affected by periodic narrowband interference and random white noise. To suppress the influence of complex noise signals on the measurement of PD signals, a novel noise reduction method based on improved empirical wavelet transform (IEWT) and kurtosis for the PD signals of high-voltage cables is proposed. First, empirical wavelet function (EWF) signals arranged in order of frequency are obtained by decomposing the signal with IEWT. Then, the kurtosis criterion is introduced, and the reconstructed inherent modal function is adaptively screened. Finally, the improved threshold function is used in eliminating residual noise information in the reconstructed signal. Compared with the noise reduction methods based on EWT and EMD, the proposed noise reduction method can effectively suppress noise information in PD and has good practicability.

Multi-Resolution Generalized S-Transform Denoising for Precise Localization of Partial Discharge in Substations

Denoising is a crucial step in the localization of partial discharge (PD) in a substation. In this paper, a novel multi-resolution generalized S-transform (GST) denoising algorithm for precise localization of PD in substations is proposed. The algorithm denoises the PD signal received by the ultra-high frequency (UHF) sensor in two steps. First, the GST with the high frequency resolution is used to analyze the PD signal, and a GST filter is designed to filter out periodic narrowband noises. Then, the S-transform (a particular case of GST) is applied to analyze the PD signal, and the white Gaussian noise is suppressed according to the statistical characteristic difference between the noise and effective signal. Finally, the denoised PD signal is obtained and applied to PD localization. The simulations results show that the proposed algorithm can effectively suppress noise and can extract accurate time delay data from the denoised PD signal. The experiment results show that, compared with the wavelet transform, the localization error of the proposed algorithm is the smallest, which is 1.59m. The proposed algorithm can realize the precise localization of PD.

Periodic Narrowband Radio Wave Emissions and Inward Plasma Transport at Saturn's Magnetosphere

Abstract The abrupt brightening of an Energetic Neutral Atom (ENA) blob or cloud has been interpreted as plasma injection in the Kronian magnetosphere (termed ENA injection herein). Morphologically, there appears to be two types of abrupt ENA cloud brightening: (1) the brightening of a large cloud usually seen at distances >10–12 R s (R s ∼ 60,268 km) in the midnight or postmidnight region; (2) the brightening of a smaller cloud usually seen at distances <10–12 R s around 21–03 magnetic local time. Among many radio waves observed at Saturn, type 2 ENA injections correlate best with the 5 kHz narrowband (NB) waves. Using Cassini Ion and Neutral Camera (INCA) and Radio and Plasma Wave Science (RPWS) data, we examine the periodicities of the type 2 ENA injections and the 5 kHz NB emissions as well as their cross-correlations, which have been previously used to measure the lag times or phase differences. Because correlational analysis can only establish linear relationships, we also use mutual information to establish linear and nonlinear relationships. On average, the peak of the 5 kHz NB emission lags those of the type 2 ENA injection by about a few minutes to 2 hr. The injection of hot plasma to the inner magnetosphere can lead to temperature anisotropy, which can generate electrostatic upper hybrid waves, which upon encountering the high-density gradient at the outer edge of the Enceladus density torus, can mode convert to Z mode and then to O mode. The 5 kHz NB waves commonly propagate in the O mode.

Robust Estimation of Arrival Time of Complex Noisy Partial Discharge Pulse in Power Cables Based on Adaptive Variational Mode Decomposition

Periodic narrowband signals and white noise are the main interferences in online detection and localization of cable partial discharge (PD), however, existing research has always focused on the white noise suppression only, which is not in line with the actual scene. A novel de-noising method for effectively extracting random PD pulse from complex and strong interferences is proposed in this paper and applied to PD localization. Firstly, an improved adaptive variational mode decomposition (AVMD) is used to decompose periodic narrowband interference, white noise, and PD signal into different intrinsic mode. According to the characteristic that the power of intrinsic mode component of periodic narrowband interference in the discrete Fourier transformation (DFT) power spectrum is much larger than that of PD and white noise, the periodic narrowband is removed out. In order to effectively filter out white noise, a scale adaptive wavelet packet decomposition method based on correlation coefficient is proposed, which decomposes the signal into high, middle, and low-frequency components. The components with low frequency, small amplitude are removed out as the white noise interference according to the threshold method, and the residual is the de-noising PD signal. Experimental results show that the proposed method can robustly suppress the interference of periodic narrowband signal and white noise, and effectively preserve the essential characteristics of the real PD signal. In the multi-sensor travelling wave based localization system of cable PD source using time-varying kurtosis, accurate estimation of first arrival time of PD pulse can be achieved by the de-noising results.

Extraction of Partial Discharge Pulses from the Complex Noisy Signals of Power Cables Based on CEEMDAN and Wavelet Packet

While both periodic narrowband noise and white noise are significant sources of interference in the detection and localization of partial discharge (PD) signals in power cables, existing research has focused nearly exclusively on white noise suppression. This paper addresses this issue by proposing a new signal extraction method for effectively detecting random PD signals in power cables subject to complex noise environments involving both white noise and periodic narrowband noise. Firstly, the power cable signal was decomposed using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the periodic narrowband noise and frequency aliasing in the obtained signal components were suppressed using singular value decomposition. Then, signal components contributing significantly to the PD signal were determined according to the cross-correlation coefficient between each component and the original PD signal, and the PD signal was reconstructed solely from the obtained significant components. Finally, the wavelet packet threshold method was used to filter out residual white noise in the reconstructed PD signal. The performance of the proposed algorithm was demonstrated by its application to synthesized PD signals with complex noise environments composed of both Gaussian white noise and periodic narrowband noise. In addition, the time-varying kurtosis method was demonstrated to accurately determine the PD signal arrival time when applied to PD signals extracted by the proposed method from synthesized signals in complex noise environments with signal-to-noise ratio (SNR) values as low as −6 dB. When the SNR was reduced to −23 dB, the arrival time error of the PD signal was only one sampling point.

Photonic Multiple Microwave Frequency Measurement Based on Frequency-to-Time Mapping

A photonic multiple microwave frequency measurement system is presented and demonstrated based on a swept frequency silicon microring resonator (MRR). The drop-port of a high-Q MRR is employed as a periodic narrowband scanning filter driven by a sawtooth voltage signal. The unknown frequency can be mapped to time interval between pulse appearances when scanning the modulated signal and the frequency-to-time mapping is established. In the experiments, we obtain a measurement range of 25 GHz with an error of ±510 MHz. Meanwhile, the measurement resolution for multi-frequency measurement is about 5 GHz. Our scheme offers a simple structure, low-cost solution, capability of multiple frequency measurement, and potential of chip-integration.

A Novel Partial Discharge Ultra-High Frequency Signal De-Noising Method Based on a Single-Channel Blind Source Separation Algorithm

To effectively de-noise the Gaussian white noise and periodic narrow-band interference in the background noise of partial discharge ultra-high frequency (PD UHF) signals in field tests, a novel de-noising method, based on a single-channel blind source separation algorithm, is proposed. Compared with traditional methods, the proposed method can effectively de-noise the noise interference, and the distortion of the de-noising PD signal is smaller. Firstly, the PD UHF signal is time-frequency analyzed by S-transform to obtain the number of source signals. Then, the single-channel detected PD signal is converted into multi-channel signals by singular value decomposition (SVD), and background noise is separated from multi-channel PD UHF signals by the joint approximate diagonalization of eigen-matrix method. At last, the source PD signal is estimated and recovered by the l1-norm minimization method. The proposed de-noising method was applied on the simulation test and field test detected signals, and the de-noising performance of the different methods was compared. The simulation and field test results demonstrate the effectiveness and correctness of the proposed method.

An Ultrahigh Frequency Partial Discharge Signal De-Noising Method Based on a Generalized S-Transform and Module Time-Frequency Matrix.

Due to electromagnetic interference in power substations, the partial discharge (PD) signals detected by ultrahigh frequency (UHF) antenna sensors often contain various background noises, which may hamper high voltage apparatus fault diagnosis and localization. This paper proposes a novel de-noising method based on the generalized S-transform and module time-frequency matrix to suppress noise in UHF PD signals. The sub-matrix maximum module value method is employed to calculate the frequencies and amplitudes of periodic narrowband noise, and suppress noise through the reverse phase cancellation technique. In addition, a singular value decomposition de-noising method is employed to suppress Gaussian white noise in UHF PD signals. Effective singular values are selected by employing the fuzzy c-means clustering method to recover the PD signals. De-noising results of simulated and field detected UHF PD signals prove the feasibility of the proposed method. Compared with four conventional de-noising methods, the results show that the proposed method can suppress background noise in the UHF PD signal effectively, with higher signal-to-noise ratio and less waveform distortion.

A New Method for Suppressing Periodic Narrowband Interference Based on the Chaotic van der Pol Oscillator

The chaotic van der Pol oscillator is a powerful tool for detecting defects in electric systems by using online partial discharge (PD) monitoring. This paper focuses on realizing weak PD signal detection in the strong periodic narrowband interference by using high sensitivity to the periodic narrowband interference signals and immunity to white noise and PD signals of chaotic systems. A new approach to removing the periodic narrowband interference by using a van der Pol chaotic oscillator is described by analyzing the motion characteristic of the chaotic oscillator on the basis of the van der Pol equation. Furthermore, the Floquet index for measuring the amplitude of periodic narrowband signals is redefined. The denoising signal processed by the chaotic van der Pol oscillators is further processed by wavelet analysis. Finally, the denoising results verify that the periodic narrowband and white noise interference can be removed efficiently by combining the theory of the chaotic van der Pol oscillator and wavelet analysis.

Localization Algorithm for the PD Source in Substation Based on L-Shaped Antenna Array Signal Processing

A novel algorithm based on L-shaped antenna array signal processing is proposed in this paper for the localization of partial-discharge (PD) sources in substations. The principle of estimation of signal parameter via rotational invariance techniques has been used for finding the direction of arrival (DOA) of signals. Third-order cumulants of signals are used in this algorithm, by which Gaussian white noises and periodic narrowband interference mixed in observed signals can be efficiently suppressed. Planar location of PD sources can be obtained by solving the intersecting point of two lines in DOAs. Therefore, solving nonlinear equations can be avoided. Besides, it is convenient to replace the observed signals with their envelopes in this algorithm. The proposed algorithm is used to process mixed signals with simulated ultra-high frequency (UHF) signals by electromagnetic-wave simulation software, Gaussian white noises of different signal-to-noise ratios, and fixed-frequency noises. The planar location of PD sources is obtained approximately. UHF signals collected in substations and their envelopes have proven to be suitable to locate PD sources effectively by the proposed algorithm as well. Therefore, the accuracy and feasibility of the proposed algorithm are proved.

Optimal Ambiguity Functions and Weil’s Exponential Sum Bound

Complex-valued periodic sequences, u, constructed by Goran Bjorck, are analyzed with regard to the behavior of their discrete periodic narrow-band ambiguity functions Ap(u). The Bjorck sequences, which are defined on ℤ/pℤ for p>2 prime, are unimodular and have zero autocorrelation on (ℤ/pℤ)∖{0}. These two properties give rise to the acronym, CAZAC, to refer to constant amplitude zero autocorrelation sequences. The bound proven is \(|A_{p}(u)| \leq2/\sqrt{p} + 4/p\) outside of (0,0), and this is of optimal magnitude given the constraint that u is a CAZAC sequence. The proof requires the full power of Weil’s exponential sum bound, which, in turn, is a consequence of his proof of the Riemann hypothesis for finite fields. Such bounds are not only of mathematical interest, but they have direct applications as sequences in communications and radar, as well as when the sequences are used as coefficients of phase-coded waveforms.

Observation of similar radio signatures at Saturn and Jupiter: Implications for the magnetospheric dynamics

We report on radio signatures observed at Saturn by the Cassini RPWS experiment which are strikingly similar to the Jovian “energetic events” observed by Galileo. They consist of sudden intensifications of the auroral radio emission (SKR) followed by the detection of a periodic narrowband radiation which most likely originates from Saturn's plasma disk. About ten “events” have been observed in 2006, showing on average temporal scales ∼3 times longer than their Jovian counterparts. We analyze the conditions of generation and the visibility of the narrowband radiation and conclude that the Kronian “events” are most likely associated with plasma evacuation from the disk. These observations provide new insights on the role of internal energy releases in Saturn's magnetosphere, known from other observations to be mainly driven by the solar wind.

Discovery of unusual large group delay in microwave millisecond oscillating events

Two events demonstrating periodic narrowband millisecond pulsations of radio emission both in in- tensity and polarization degree are analyzed. Large time delays between L -a ndR-polarization components are found. The radio emission is shown to be generated as unpolarized by a plasma mechanism at the second harmonic of the upper-hybrid frequency. Observed oscillations of the radiation polarization degree arise due to group delay between extraordinary and ordinary modes along the line of sight. The theoretically predicted dependence of the group delay on frequency (f 3 ) agrees excellently with the observed delay frequency dependence. Physical parameters of the emission source and \delay site are determined within the proposed model.

The Kondo effect in periodic narrow-band systems

The Kondo divergences owing to interaction of current carriers with local moments in highly correlated electron systems are considered within the Hubbard and s-d exchange models with infinitely strong on-site interaction, the many-electron Hubbard representation being used. The picture of density of states containing a peak at the Fermi level is obtained. Various forms of the self-consistent approximation are used. The problem of the violation of analytical properties of the Green's function is discussed. Smearing of the “Kondo” peak owing to spin dynamics and finite temperatures is investigated.

The Kondo effect in periodic narrow-band systems

The Kondo effect in periodic narrow-band systems