Discharge Signals Research Articles

Negative corona discharge mechanism in C4F7N–CO2 and C4F7N–N2 mixtures

Due to their good dielectric properties and low global warming potential, C4F7N–CO2 and C4F7N–N2 mixtures have shown promising potential to replace SF6 in high voltage gas insulated equipment. However, during manufacturing, installation, and transportation of power equipment, burs and metal particles can be inevitably left inside, and they can cause corona discharge. Fundamental investigation of the corona discharge mechanism is essential to monitor partial discharge signals in environmentally friendly power equipment. This paper applies the fluid model to investigate the discharge mechanism of C4F7N–CO2 and C4F7N–N2 mixtures in negative point-plane corona discharge. A 2D axisymmetric model combines the drift-diffusion equations for electrons, positive ions, and negative ions and Poisson’s equation to study the process of dynamics. The gas is a mixture of C4F7N (5%, 7%, or 13%) and CO2 or N2 (95%, 93%, or 87%). The rise time of the first discharge pulse in C4F7N–CO2 and C4F7N–N2 mixtures is about 0.1 ns. The interval time between the first and the second pulse in the 5% C4F7N–95%CO2 mixture is about 1.5 times longer than that in the 5% C4F7N–95% N2 mixture. When the C4F7N content is 7% and 13%, the interval time between the first and second pulses in C4F7N–CO2 mixtures is about 2 and 3 times longer than those in C4F7N–N2 mixtures, respectively. The suppression regions in C4F7N–CO2 mixtures are larger than those in corresponding C4F7N–N2 mixtures. The total number of electrons, positive ions, and negative ions in C4F7N–CO2 mixtures is higher than that in C4F7N–N2 mixtures, while the reduced electric field in C4F7N–CO2 mixtures is smaller than that in C4F7N–N2 mixtures.

Multiple convective storms within a single cyclone on Saturn

Here we describe a cyclone at 50∘N in the atmosphere of Saturn that was the source of eight separate convective storms. The storm clouds that appeared bright in scattered sunlight were identified using images from the Cassini Imaging Science Subsystem (ISS) and ground-based telescopes. The Cassini Radio and Plasma Wave Science (RPWS) instrument detected Saturn Electric Discharge (SED) signals from the lightning within the storms, which were correlated to the sudden appearance of bright storm clouds in and surrounding the cyclone. Using the number and flash rate of SEDs we infer the duration and intensity of the storms. Although previous storms have been observed to erupt from within cyclones, the cyclone studied here is unusual in that it survived six storms before being disrupted by the seventh and strongest storm. An eighth storm occurred in the remnant of the disrupted cyclone. We conjecture that the anticyclonic circulation generated by the seventh storm was enough to overwhelm the cyclone’s gradient wind balance.

Partial Discharge (PD) Signal Detection and Isolation on High Voltage Equipment Using Improved Complete EEMD Method

Electricity has a crucial function in contemporary civilization. The power grid must be stable to ensure the efficiency and dependability of electrical equipment. This implies that the high-voltage equipment at the substation must be reliably operated. As a result, the appropriate and dependable use of systems to monitor the operating status of high-voltage electrical equipment has recently gained attention. Partial discharge (PD) analysis is one of the most promising solutions for monitoring and diagnosing potential problems in insulation systems. Noise is a major challenge in diagnosing and detecting defects when using this measurement. This study aims to denoise PD signals using a data decomposition method, improved complete ensemble empirical mode decomposition with adaptive noise algorithm, combined with statistical significance test to increase noise reduction efficiency and to derive and visualize the Hilbert spectrum of the input signal in time-frequency domain after filtering the noise. In the PD signal analysis, both artificial and experimental signals were used as input signals in the decomposition method. For these signals, this study has yielded significant improvement in the denoising and the PD detecting process indicated by statistical measures. Thus, the signal decomposition by using the proposed method is proven to be a useful tool for diagnosing the PD on high voltage equipment.

Preparation and Performance Analysis of Transformer Aramid Nanopaper-Based Insulating Material Based on Deep Learning.

Transformers are an essential part of power production. Insulating paper began to be widely used in transformers in the 1990s. The superior aramid nanofiber as the matrix gives the aramid nano-insulating paper excellent mechanical properties, insulation performance, temperature resistance, and flexibility. At first, the heat resistance and service life of insulating paper should be satisfied for use in electrical equipment. With the continuous development of power equipment, people have put forward higher requirements on the properties of insulating paper, especially heat resistance and electrical properties. Insulation paper made of aramid fibers have better thermal stability and more advantages in electrical and mechanical properties, which can significantly improve the service life and safety of electrical appliances. The purpose of this article is to study the use of aramid nanopaper-based insulating materials in transformers to explore the effect of transformer discharge mechanism on aramid nanopaper-based insulating materials. This paper proposes to design multiple deep learning models to identify the discharge mode of the voltage transformer, find the characteristic signal, and carry out related tests on the discharge signal of different modes, and find the maximum temperature value of the aramid nanopaper-based insulating material for industrial use. The experimental results in this paper show that the aramid nanopaper-based insulating material can be used in transformers discharge detection well, and the safety rate is increased by 20%.

Partial discharge ultrasonic signals pattern recognition in transformer using BSO-SVM based on microfiber coupler sensor

Detection and pattern recognition of different types of partial discharge (PD) signals in transformer are of great significance for the safe and stable operation of transformers. This paper demonstrates the application of microfiber coupler sensor (MFCS) to the pattern recognition of PD ultrasonic signals with an optimized support vector machine (BSO-SVM) algorithm. First, a PD ultrasonic detection system based on MFCS is built, compared with the traditional piezoelectric sensor, the MFCS can be directly implanted in the transformer oil to detect the PD signals, avoiding the attenuation of ultrasonic signals as they propagate to the transformer outer wall. Then, four kinds of PD ultrasonic signals are collected by the PD detection system, and feature extraction is carried out for the time domain and frequency domain characteristics of PD ultrasonic signals. Finally, a BSO-SVM classification algorithm is proposed, which uses the beetle antennae search (BAS) and the particle swarm optimization (PSO) algorithm to jointly optimize the support vector machine (SVM) algorithm. The BSO-SVM classifier is used to classify four kinds of PD signals, the classification accuracy is 93%, and the number of convergence is 5. In addition, the BSO-SVM algorithm is compared with the GA-SVM and PSO-SVM algorithms. The results show that the BSO-SVM algorithm has higher recognition accuracy and faster convergence speed. The research results show that it is feasible to use MFCS combined with BSO-SVM to identify PD patterns of transformers, and it has certain significance for transformer fault diagnosis.

A new subsequence similarity retrieval method based on inverted index in EAST

The analysis technology of massive experimental data is one of the important concerns in the field of nuclear fusion research, and the waveform similarity retrieval is the basis of realizing data mining of discharge experiment. In this work, we proposed an efficient subsequence retrieval method for matching a selected waveform segment in the discharge signal waveform. This method accurately represents the waveform characteristics based on the slope of segment and the drop distance of segment head (DDSH). In order to improve the retrieval efficiency, an inverted index method based on the slope of segment was adopted to filter the discharge signal waveforms and then perform fine retrieving. The retrieval algorithm was implemented based on Python, and was verified with the plasma density and stored energy signals of historical experimental data from EAST mdsplus database. The test results show the retrieval efficiency is improved by 43.6% on average after using slope inverted index.

Chemosensation: Corollary discharge filters out self-generated chemical cues

Corollary discharge allows organisms to discriminate external sensory inputs from self-generated cues. However, the underlying synaptic and molecular mechanisms are not well understood. A new study has identified a tyraminergic corollary discharge signal that extrasynaptically modulates chemosensory neurons in Caenorhabditis elegans.

Tyraminergic corollary discharge filters reafferent perception in a chemosensory neuron

Interpreting sensory information requires its integration with the current behavior of the animal. However, how motor-related circuits influence sensory information processing is incompletely understood. Here, we report that current locomotor state directly modulates the activity of BAG CO2 sensory neurons in Caenorhabditis elegans. By recording neuronal activity in animals freely navigating CO2 landscapes, we found that during reverse crawling states, BAG activity is suppressed by tyraminergic corollary discharge signaling. We provide genetic evidence that tyramine released from the RIM reversal interneurons extrasynaptically activates the inhibitory chloride channel LGC-55 in BAG. Disrupting this pathway genetically leads to excessive behavioral responses to CO2 stimuli. Moreover, we find that LGC-55 signaling cancels out perception of self-produced CO2 and O2 stimuli when animals reverse into their own gas plume in ethologically relevant aqueous environments. Our results show that sensorimotor integration involves corollary discharge signals directly modulating chemosensory neurons.

Multi-terminal Detection and Localization of Partial Discharge in Transformer

The transformer is the key equipment in the power system, and partial discharge is the main reason for the deterioration of the insulation of the transformer. The existing partial discharge detection method cannot determine whether the discharge comes from the transformer body or the peripheral equipment of the transformer when the discharge signal is detected. In order to study the difference between the internal and external partial discharges of the transformer received signals at the detection end and form an effective positioning method, this paper injects high frequency small signals into the external leads, inside the windings and surrounding screens of the 110kV three-phase double-winding transformer to simulate the external discharge of the transformer, the discharge inside and outside of the windings. The research shows that the first wave polarity of the external discharge and the internal non-winding discharge of the transformer is the same at each detection terminal, and the first wave polarity of the winding discharge signal at each detection terminal is different. The polarity information can be used to judge whether the winding discharges; the external discharge is the same as the transformer internal discharge. Non-winding discharge has different proportions of high-frequency and low-frequency components in each detection terminal signal. The signal energy of each detection terminal of external discharge is concentrated in low frequency, and the signal energy of each detection terminal of non-winding discharge inside the transformer is concentrated in high frequency, which can be judged by frequency domain information. The external discharge and the internal non-winding discharge of the transformer provide a reference for the research on the multi-terminal detection and positioning of the transformer.

Research on the Propagation Characteristics of High Frequency Partial Discharge Signal in Transformer

In order to study the information contained in the high frequency partial discharge and the influence of transformer winding on the propagation of partial discharge signal, this paper injects high frequency small signals into the external leads, internal winding and enclosing screen of 110kV three-phase double-winding transformer to simulate the external discharge, inside and outside of the winding discharge to study the propagation characteristics of transformer partial discharge signal. The study shows that the partial discharge signal is coupled from one to others, and the signal can be detected at each detection end when partial discharge occurs; the partial discharge signal consists of high-frequency component and low-frequency component, the high-frequency component is mainly propagated through the capacitance between the windings, and the low-frequency component is mainly propagated through the windings; The polarity of the first wave of the time domain signal is different when the discharge of the internal winding of the transformer propagates to each detection point, the polarity of the first wave of the time domain signal is the same when the discharge of the outside winding propagates to each detection point. Among them, the discharge of the outside winding mainly propagates through the capacitor, and the low-frequency components of each detection terminal are seriously attenuated.

Bi-Long Short-Term Memory Networks for Radio Frequency Based Arrival Time Detection of Partial Discharge Signals

Partial discharge (PD) monitoring of electrical substations could provide early warning of insulation failures. Among the various technologies, Radio Frequency (RF) based PD monitoring system could be a promising solution. The RF-based monitoring system detects PD sources in the substation and can also localise the PD sources. The time difference of arrival (TDOA) based PD localisation system primarily require arrival time (AT) of the impulsive RF signal. Though many localisation algorithms have been proposed in the recent past to overcome the TDOA estimation errors, less attention has been given to the accurate estimation of RF PD signal arrival time. This paper presents the AT's automatic labelling in the RF PD signal using Bi-Long Short-Term Memory (Bi-LSTM) network applied on the continuous wavelet transformed (CWT) signal. Further, it also shows PD signal augmentation to overcome the problem of limited representative training data set. The behavior of the radiated RF signals is influenced by many factors and has almost stochastic characteristics. The proposed system has been validated with laboratory-based experimental signals and the data set obtained from different electrical substations. The results show that the improved performance is obtained from the combination of a multilayer Bi-LSTM model and an augmented training set.

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.

Research and application of intermittent partial discharge characteristics and easy-warning system for electric equipment

Intermittent partial discharge has become an essential threat to the safety and stability of high voltage electrical equipment. In order to reduce the sudden insulation breakdown failure due to the intermittent and latent partial discharge of the operating equipment, this paper analyzes the discharge characteristics of the intermittent partial discharge in the constant voltage mode. The UHF partial discharge spectrum of typical intermittent partial discharges is obtained, and the intermittent discharges are divided into low-risk and high-risk categories by adopting PCA and FCM cluster algorithms. Aiming at the characteristics of intermittent partial discharge and the on-line monitoring method of partial discharge, a partial discharge signal collection method based on dynamic frequency is proposed. An intelligent monitoring system of intermittent partial discharge is designed to acquire partial discharge signals initially locate defects. Then the BP neural network method is applied to automatically evaluate and early warn the internal insulation status. The test concluded that the designed monitoring system for intermittent partial discharge could dynamically track and warn the low-risk and high-risk, and evaluate the insulation status of the equipment.

Insulator Fouling Monitoring Based on Acoustic Signal and One-Dimensional Convolutional Neural Network

Aiming at the problem of pollution insulator discharge mode monitoring in high voltage line, a new one-dimensional convolutional neural network structure (1D-CNN) was designed, and a pollution insulator discharge mode monitoring method based on acoustic emission signal and 1D-CNN was proposed. Firstly, the data was collected in laboratory of acoustic emission signal under different discharge after sliding access way to expand the sample quantity. Thereafter, the sample time and frequency domain was used along with a third octave data as input, using convolution neural network to discharge signal samples adaptive feature extraction and feature dimension reduction. Then, appropriate stride convolution alternative pooling layer was used in order to reduce the training model parameters and the amount of calculation. Finally, Softmax function was used to classify the predicted results. The identified results show that the model can achieve a recognition rate of more than 99.84%, which effectively solves the process of manual data preprocessing in the traditional insulator pollution degree monitoring method. Moreover, at the same time it can be effectively applied to the pollution insulator discharge mode monitoring task.

Perceptual enhancement and suppression correlate with V1 neural activity during active sensing

Perception in multiple sensory modalities is an active process that involves exploratory behaviors. In humans and other primates, vision results from sensory sampling guided by saccadic eye movements. Saccades are known to modulate visual perception, and a corollary discharge signal associated with saccades appears to establish a sense of visual stability. Neural recordings have shown that saccades also modulate activity widely across the brain. To investigate the neural basis of saccadic effects on perception, simultaneous recordings from multiple neurons in area V1 were made as animals performed a contrast detection task. Perceptual and neural measures were compared when the animal made real saccades that brought a stimulus into V1 receptive fields and when simulated saccades were made (identical retinal stimulation but no eye movement). When real saccades were made and low spatial frequency stimuli were presented, we observed a reduction in both perceptual sensitivity and neural activity compared with simulated saccades; conversely, with higher spatial frequency stimuli, saccades increased visual sensitivity and neural activity. The performance of neural decoders, which used the activity of the population of simultaneously recorded neurons, showed saccade effects on sensitivity that mirrored the frequency-dependent perceptual changes, suggesting that the V1 population activity could support the perceptual effects. A minority of V1 neurons had significant choice probabilities, and the saccades decreased both average choice probability and pairwise noise correlations. Taken together, the findings suggest that a signal related to saccadic eye movements alters V1 spiking to increase the independence of spiking neurons and bias the system toward processing higher spatial frequencies, presumably to enhance object recognition. The effects of saccades on visual perception and noise correlations appear to parallel effects observed in other sensory modalities, suggesting a general principle of active sensory processing.

Research on aging state detection of reactor turn‐to‐turn insulation under high‐frequency square wave voltage excitation

In power electronic equipment, magnetic devices are important parts, and their working condition and reliability are closely related to turn-to-turn insulation. With the increasing application of high-frequency square wave voltage in the fields of frequency conversion technology and motor control, the high voltage stress on magnetic devices will accelerate insulation aging, so it is necessary to monitor the aging state of turn-to-turn insulation. At present, researchers mainly detect insulation aging by detecting partial discharge signal. However, this method cannot realize the early monitoring of aging, and the measurement cost is high and the accuracy is low. Therefore, for the dry-type core reactor with epoxy resin as the insulation medium, this paper proposes a turn-to-turn insulation aging state detection method under high-frequency square wave voltage excitation. According to the relationship between the turn-to-turn insulation aging state of the reactor and its parasitic capacitance, the turn-to-turn insulation aging degree of the reactor is predicted by measuring and analyzing the phase information of high-frequency components of the current signal. Finally, the measuring principle and method are introduced and verified by experiments.

Considering the Parameters of Pulse Width Modulation Voltage to Improve the Signal-to-Noise Ratio of Partial Discharge Tests for Inverter-Fed Motors

The improvements in signal-to-noise ratio (SNR) are indispensable to obtain the desired output signal of partial discharge (PD) under square wave pulsewidth modulation (PWM) voltage. In this article, the effect of complex PWM voltage parameters (rise time, fall time, frequency, duty cycle) on the SNR of PD tests is investigated. The results revealed that the sensor's high-frequency gain should be increased to improve the SNR under faster switching. PD impulses are more likely to be submerged in the residual interference under high-frequency voltage. The SNR can be improved by only detecting the PD at the rising front because the PD signal overlaps with commutation noise at the falling front of short duty cycle voltage, such as 0.03% duty cycle. The above results can reference the PWM parameters and improve the SNR in PD tests for rotating machines controlled by power electronics.

Multiscale Fusion Simulation of the Influence of Temperature on the Partial Discharge Signal of GIS Insulation Void Defects

Gas insulated switchgear (GIS) in service may tolerate large temperature changes, which leads to uncertainty in the evaluation of the partial discharge (PD) signal. To clarify the influence of temperature on the discharge signals of GIS insulation void defects, this paper proposes a multiscale fusion simulation method and used experiments to verify it. This method combines the streamer simulation at the micro level and the circuit simulation model at the macro level, and a more accurate simulation signal of GIS insulation void PDs under the influence of temperature can be obtained. Experiments have also been carried out, and a PDs detection experiment platform that can be set in different temperature environments was built. The discharge signals at different temperatures were obtained follow IEC60270. Finally, by combining analyses of simulation and experimental data, the law of the influence of temperature on the PD signal of GIS insulation voids was summarized, and the mechanism of the influence was analyzed. The results showed that the multiscale fusion simulation method of GIS insulation void PD signals proposed in this paper is consistent with the experimental results, which can provide a reference for the digital twin model of power equipment status.

Research on excavation and identification method of typical interference signal during transformer partial discharge test

—In order to remove interference signal and external discharge signal during partial discharge test and get the effective signal of partial discharge, this paper carries out the research of typical interference signal mining and identification method for transformer partial discharge test. Firstly, based on the experience of previous researchers in partial discharge research, 47 feature parameters including statistical, texture and shape features are extracted, and the original feature space is constructed based on them, which provides the data basis for the optimization of the feature space and signal classification identification later. Next, the random forest algorithm is used to measure and rank the importance of the feature parameters, and the original feature space is optimized according to the ranking results to obtain the optimized feature space. Finally, the optimized feature space is used as the input of linear discriminant analysis algorithm and K-nearest neighbor algorithm to classify and identify the local discharge signal and typical interference signal, and the recognition accuracy of these two classification and identification algorithms is compared and analyzed, and the K-nearest neighbor algorithm is selected as the final classification and identification algorithm.

Investigation of the corona discharge activity in liquid nitrogen under transient voltage conditions using fluorescent fiber sensor

In the present study, corona inception voltage in liquid nitrogen under standard lightning impulse voltage (SLIV) and oscillatory impulse voltages were studied using fluorescent fiber sensor. A single discharge signal is observed near the peak of the applied SLIV. The amplitude of the fluorescent signal is high with positive polarity than under negative polarity. At higher voltages, the magnitude of the fluorescence signal increases and it shifts towards the rising edge of the applied voltage. Under unidirectional oscillatory impulse voltage (UOIV) and sinusoidal bidirectional oscillatory impulse voltage (SBOIV) conditions, Corona discharge occurs around the peak of the first cycle. When the applied voltage is increased, the discharges occur in the subsequent cycles. Under switching impulse voltage (SIV), corona discharge is noticed at the rising edge in positive polarity, whereas in negative polarity, it is observed at the falling edge. The corona discharge inception voltage (CDIV) is determined to be highest under Unidirectional Oscillating Lightning Impulse (UOLI) and lowest under SI. The negative polarity exhibits more temporal spread than the positive polarity under oscillating impulse voltages.