Discharge Signals Research Articles

Corollary discharge promotes a sustained motor state in a neural circuit for navigation.

Animals exhibit behavioral and neural responses that persist on longer timescales than transient or fluctuating stimulus inputs. Here, we report that Caenorhabditis elegans uses feedback from the motor circuit to a sensory processing interneuron to sustain its motor state during thermotactic navigation. By imaging circuit activity in behaving animals, we show that a principal postsynaptic partner of the AFD thermosensory neuron, the AIY interneuron, encodes both temperature and motor state information. By optogenetic and genetic manipulation of this circuit, we demonstrate that the motor state representation in AIY is a corollary discharge signal. RIM, an interneuron that is connected with premotor interneurons, is required for this corollary discharge. Ablation of RIM eliminates the motor representation in AIY, allows thermosensory representations to reach downstream premotor interneurons, and reduces the animal's ability to sustain forward movements during thermotaxis. We propose that feedback from the motor circuit to the sensory processing circuit underlies a positive feedback mechanism to generate persistent neural activity and sustained behavioral patterns in a sensorimotor transformation.

GIS fast-acting live detection of power equipment in complex area based on linear electron accelerator

GIS is widely used in the fields of high voltage, ultra-high voltage and ultra-high voltage. The structure of GIS is compact, the floor area is small, the configuration is flexible and the reliability is high. Moreover, it has strong security and strong environmental adaptability. However, the electric field strength in GIS is very strong, once there are some defects in the equipment, partial discharge will occur, resulting in insulation deterioration of GIS equipment. In this paper, positioning detection technology is used, A fast live detection method of power equipment in complex area based on lilac is proposed. The position of the discharge source is roughly located, and then the linear electron accelerator is used to accurately locate the partial discharge source, which improves the efficiency of defect location. Based on the linear electron accelerator, GIS signal detection device is studied. In the detection, it is necessary to obtain the data generated by partial discharge signal, high-frequency current data and data for comparative analysis, so as to effectively eliminate the interference signal in GIS field, and improve the accuracy and defect type identification.

Completion detection and efficiency improvement for breakout stage of fast EDM drilling

Electrical discharge machining (EDM) is a non-conventional machining process that removes material by consecutive electrical sparks. Fast EDM drilling is one of such processes, which employs rotary tubular electrodes and inner flushing to enhance the machining efficiency and stability. It is widely used to produce small holes on various kinds of products. The breakout event occurs when the electrode just penetrates the workpiece during the drilling process, which marks the start of the breakout stage of the process. At this moment, the outlet of the hole is smaller than the desired due to severe radial tool wear, and the hole is not completed. As a result, further feeding is needed before the electrode fully penetrates the workpiece. To avoid back-strikes, the hole completion detection is crucial. However, the uncertainty of longitudinal and radial tool wear makes it difficult. This paper proposes a novel method to detect the hole completion by analyzing the discharge signals in the breakout stage. Besides, for the machining efficiency to become much lower in the breakout stage, the control strategy is also proposed to improve it by adjusting the reference voltage and gain factor of the servo controller. The optimal parameters were found by a full-factorial experiment. Experiments were conducted to validate the proposed methods by drilling holes at various inclinations. The results show that the proposed hole completion detection method could successfully sense the hole completion without misjudgments. And the electrode was fed at most 1.81 mm out of the hole outlet when the detection was made. Besides, the proposed control strategy improved the machining efficiency by up to 56.2% in the breakout stage in the experiments.

부분방전 데이터 처리 방법에 따른 CNN 기반 패턴 분류기의 비교 연구

This study is focused on solution to the problem that may occur due to noise signals when using the image obtained through the conventional partial discharge preprocessing methods as inputs to the CNN-based partial discharge pattern classifier. To solve such problem, a new data preprocessing method is proposed by considering a projection technique. In the data information obtained through the conventional partial discharge preprocessing methods, data information of useless noise signal leads to the problem of performance degradation when constructing highly qualified pattern classifier based on the data information of partial discharge signal. The proposed preprocessing method called as ‘Projection’ in this study is designed to solve this problem and improve a classification performance of CNN-based partial discharge pattern classifier. First of all, through GIS simulation, one-dimensional partial discharge data is obtained as five cases such as corona discharge, floating discharge, insulator discharge, free particle discharge, and steady state (noise) in an environment with a noise signal by using a UHF sensor. After that, by applying the two conventional partial discharge preprocessing methods such as PRPS(Phase Resolved Pulse Sequence), PRPD(Phase Resolved Partial Discharge) and the proposed partial discharge preprocessing method, one-dimensional partial discharge data is transformed into 3 data types such as image set of PRPS, image set of PRPD, and image set of Projection. Finally each image set is used as inputs to the designed CNN-based partial discharge pattern classifier. Through the comparative analysis of the feature maps of each layer in CNN as well as the performance accuracy of partial discharge pattern classification for each image set, the superiority of the proposed preprocessing method is demonstrated.

Partial Discharge Detection of Multiple‐Narrow Band Antenna under the Radio Communication Noise

The ultra‐high frequency (UHF) method is attempted to diagnose the insulation condition of high voltage equipment as detecting partial discharge (PD) – emitted electromagnetic (EM) wave. Although the EM wave caused by PD has broadband, the UHF band includes many communication and broadcasting line spectral frequencies, which can affect PD detection as a noise. The authors have developed the multiple‐narrow band (MNB) antenna to detect discharge signals with high resistance against noisy line spectral frequencies. In this paper, the MNB antenna was applied to measure PD in SF6 gas and compared with a standard wide‐band Horn antenna to identify the PD detection ability. Furthermore, the MNB antenna was applied to measure PD signal in SF6 gas under the artificial and the real communication noise to estimate the noise elimination ability of MNB antenna. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Fault diagnosis of power capacitors using a convolutional neural network combined with the chaotic synchronisation method and the empirical mode decomposition method

This study combined a Convolutional Neural Network (CNN) with the chaos theory and the Empirical Mode Decomposition (EMD) method for the attenuation fault recognition of power capacitors. First, it built six capacitor analysis models, including normal capacitors, failed capacitors, and normal capacitors attenuated by 20–80%. Then a power testing machine was used for an applied voltage test of the capacitor. The EMD method was combined with the chaos synchronisation detection method to chart the discharge signals of the voltage and current that was captured by a high frequency oscilloscope into a 3D chaotic error scatter plot, as the fault diagnosis feature image. Finally, the CNN algorithm was used for the capacitor fault detection. The advantages of the proposed method are that big data are compressed to extract meaningful feature images, the operating state of the power capacitor can be detected effectively, and faults can be diagnosed according to the electrical signal change of the power capacitor. The actual measurement results showed that the accuracy of the proposed method was as high as 97% and has a high efficiency of noise rejection ability, which indicates that the method could be applied to other power-related fields in the future.

Fault Diagnosis for Power Cables Based on Convolutional Neural Network With Chaotic System and Discrete Wavelet Transform

In this paper, the discrete wavelet transform (DWT) and a chaotic system were combined with a convolutional neural network (CNN) and applied to the diagnosis of insulation faults in XLPE (cross-linked polyacetylene) power cables. First, four different types of insulation faults in power cables were constructed, including the normal state of the cable, the short outer semi-conducting layer, impurities in the insulation layer, and insulation layer damage, and a high-speed capture card (NI PXI-5105) was adopted to measure the partial discharge (PD) signal, which was then filtered through discrete wavelet transform. Then, based on the Lorenz chaotic system, a dynamic error scatter diagram was established as the feature of each fault state. Finally, the dynamic error scatter diagram was processed by CNN to recognize four different types of faults in the power cable. The test results show that the method proposed in this paper can quickly recognize the fault state of power cables and has excellent performance in terms of recognition accuracy, which reaches 97.5%. Therefore, the proposed method can effectively detect the fault signal changes of power cables and identify the fault state of power cables in real time.

A Current Sensor Based on Capillary Microresonator Filled With Terfenol-D Nanoparticles

Electric current sensors are widely utilized in high-frequency power and electronic circuits which spread over numerous industries. Here, a current sensor based on the high-Q whispering gallery mode optical capillary microresonator filled with Terfenol-D nanoparticles is experimentally demonstrated. The device shows a peak AC current sensitivity of 5.285 μA Hz <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1/2</sup> with a corresponding high accuracy of 0.0009%. It possesses a wide detection frequency bandwidth of approximately 12 MHz and exhibits good linearity, enabling for partial discharge signals detection.

Application of optical sensor array in partial discharge signal location of GIS/GIL

The optical sensor array inside the high-voltage equipment can effectively realize the on-line measurement of the high-voltage switch operation state, significantly improve the efficiency and accuracy of SF6 Electrical equipment fault diagnosis, and effectively guarantee the safety and reliability of equipment operation. Firstly, the structure and performance of the micro optical sensor are analyzed, and the system model and data processing unit of the optical sensor array are given. Furthermore, the influence characteristics of single sensor and array sensor Gaussian noise on signal are given, and the noise elimination strategy of observation signal based on Kalman filter is given. The results show that: the optical sensor has a good focusing ability for the optical signal, the light of the sensor array is reflected and propagated many times in the GIS/GIL of the high voltage switch-gear, and the internal partial discharge signal is located more accurately. The research results of this paper can provide important technical support for the GIS/GIL fault monitoring and early warning of the gas insulated electrical equipment on the transmission line.

Evaluation Research on “Anti-Electromagnetic Interference Performance” of Partial Discharge Detection Device in Complicated Strong Electromagnetic Interference Environment

In a strong electromagnetic environment, most of the current anti-interference methods for partial discharge signals of electronic transformers use digital notch filter, which has poor adaptability and stability. This article studies the source of interference and its impact on partial discharge detection, and has accumulated a relatively complete partial discharge sample database. On this basis, a high-performance arbitrary waveform generator is proposed to restore typical partial discharges, and a platform for quantitatively evaluating the anti-electromagnetic interference capability of partial discharge UHF detection devices through interference simulation is established. Experimental results show that the proposed method has high anti-interference performance.

Experimental investigations of energy channelization behavior in ultrasonic assisted electrochemical discharge machining

The present work analyzes the energy channelization behavior during the triplex hybrid process of ultrasonic assisted electrochemical discharge machining (UA-ECDM). In this work, a high frequency (35 kHz) low amplitude configuration of ultrasonic vibrations has been employed to investigate the ECDM process. The underlying energy channelization mechanism during the UA-ECDM process has been demonstrated while considering the evidence from high-speed imaging of gas film, discharge signals, optical images, simulation results, structural and compositional analysis of machined work material. The controlled assistance of ultrasonic in the ECDM process shifts the energy paradigm from the edges to the center of the tool electrode. Moreover, in the UA-ECDM process, the material is removed by additional cavitation and high-pressure evacuation actions along with melting, vaporization, and chemical etching phenomenon. An improved frequency of spark discharges indicates the initiation of breakdown phenomena in upward and downward strokes of ultrasonic vibrations. Energy channelization index and specific energy were taken as the performance indicators. The incorporation of ultrasonic vibrations consumed 2.23 times less energy than the ECDM process to remove a unit material per unit time. The energy channelization index with ultrasonic assistance is also improved by five times. The incorporation of ultrasonic vibrations in the ECDM process reduced the heat-affected zone and produced the machined surfaces without the alkali deficient layer. The article reports the parametric effect and their contribution to the response characteristics. The involvement of high electrochemical discharge energy with ultrasonic assistance exhibits better results in higher material removal rate and depth/diameter ratio due to the parallel provision for the evacuation of sludge/debris and electrolyte replenishment in the machining zone.

A novel de‐noising method based on coherence average for ultrasonic signal of partial discharge in transformer

Ultrasonic method is widely used for the detection and location of partial discharge (PD) in transformer, however, the measured ultrasonic signal is usually corrupted by noise, and sometimes is even buried by noise entirely. Therefore, the de-noising of the measured signal is essential. Conventional de-nosing methods, such as wavelet method and singular value decomposition (SVD) method, generally require empirical parameter selection or estimation, and the best parameters for de-noising vary with the PD source and noise condition, which will bring some limitation to their applicability. More importantly, conventional de-noising methods usually have poor performance for the low signal-to-noise-ratio (SNR) signals. To improve this problem, the paper proposes a de-noising method based on coherence average for the ultrasonic signal. The de-noising performance of the proposed method is evaluated based on a comparison with conventional methods, the results indicate that the proposed method has great de-noising effect for the ultrasonic signals, even for the low-SNR signals. Besides, the proposed method is free from empirical parameter selection or estimation, which can make its applicability more extensive. The proposed method can offer a practical and effective solution to the de-noising of ultrasonic signal of PD in transformer.

Influence of stream characteristics and population size on downstream transport of freshwater mollusk environmental DNA

Many freshwater mussel species are highly endangered. Knowledge of their occurrence and distribution is a crucial prerequisite for their conservation. The use of environmental DNA (eDNA) is a relatively new approach and an effective tool to detect the presence of target species in freshwater environments. This study aimed to establish a molecular identification tool for eDNA for the endangered thick-shelled river mussel Unio crassus Philipsson in Retzius, 1788 and to analyze the relationships between mussel abundance, stream discharge, sampling distance, and freshwater mollusk DNA signal (detectability and DNA quantity) by using binary logistic and linear regression modeling. We additionally correlated turbidity and mollusk DNA detectability. We developed a species-specific eDNA detection system in silico and tested the efficiency and species specificity on water samples collected downstream of extant populations of different sizes (100–42,000 individuals) in Bavaria, Germany. This system allowed detection of very small U. crassus populations over long distances (>3 km) and excluded amplification of DNA from other potentially co-occurring species. Signal strength was most strongly associated with population size and distance of sampling point from mussel population. Additionally, a negative correlation with stream turbidity was evident. This species-specific tool may help in screening of streams for target species that are otherwise difficult to monitor.

Device for online monitoring of insulation faults in high-voltage switchgears

Partial discharges are the major cause of deterioration in the insulation characteristics of switchgears. Therefore, timely detection of partial discharge in switchgear and potential insulation faults is an urgent problem that needs to be addressed in the power supervision industry. In this study, a device was proposed for online monitoring of high-voltage switchgears based on pulse current method and ozone (O3) detection. The pulse current method obtains the PD signal by monitoring the phase holes on the switch indicator. Occurrence of a partial discharge in a certain phase leads to the production of a discharge pulse, which can be coupled out by a capacitive sensor. The current spectrum and the O3 produced by partial discharge were processed via fast Fourier transform for accurate diagnosis of the occurrence of partial discharge and its severity in switchgears. The proposed method allows for convenient acquisition of the partial discharge signal, simple installation of the device, and realization with inexpensive sensors.

Location Technique based on Multiple Partial Discharge Signal in 11kV Underground Power Cable using EMTP-ATP Software

Power cables are very critical in electrical power systems as power cables failure can interrupt the electrical flow due to unexpected power failure. There are a few sorts of partial discharge (PD) estimations gadgets in the market. For instance, PD can be distinguished by utilizing Rogowski coil (RC) sensors in the disconnected procedure. The current issue PD signal does not usually occur as a single source. Thus, the analysis of multiple PD sources is required to ensure that the cable insulation is in a healthy condition. PD location technique based on multiple signals in 11kV underground power cable was conducted in this research to estimate the accurate location of the PD signal. Modelling of single power cable in a distance of 10km with the RC sensor is installed at several distances to capture the PD signal that travels along the power cable. By selecting the distance between six RC sensors and synchronous multiple PD signal, the design of the power system has been constructed by using EMTP-ATP software. Multi-point technique based on time difference of arrival (TDOA) was performed in the single line power cable to obtain the PD location. The measurement using multi-point of RC sensor technique is preferred based on the conditions due to the value of velocity elimination. Based on the results, the accurate location of PD Source 1 is detected 501 m along RC sensor A1 to RC sensor A3. In contrast, PD source 2 has been detected 2800.15 m along RC sensor A4 to RC sensor A6 with the percentage error of 0.2% and 0.0053%, respectively. The findings show that the location of multiple PD signal that occurred along the line cable can be detected accurately by using the multi-point technique and TDOA. Hence, the performance of the power system has been improved.

Pattern Recognition of Development Stage of Creepage Discharge of Oil–Paper Insulation under AC–DC Combined Voltage Based on OS-ELM

The recognition of the creepage discharge development process of oil–paper insulation under AC–DC combined voltage is the basis for fault monitoring and diagnosis of converter transformers; however, only a few related studies are available. In this study, the AC–DC combined voltage with a ratio of 1:1 was used to develop a recognition method for the creepage discharge development process of an oil–paper insulation under a cylinder–plate electrode structure. First, the pulse current method was used to collect the discharge signals in the creepage discharge development process. Then, 24 characteristic parameters were extracted from four types of creepage discharge characteristic spectra after dimensionality reduction. Finally, based on the online sequential extreme learning machine (OS-ELM) algorithm, these characteristic parameters were used to recognize the development stage of the creepage discharge of the oil–paper insulation. The results showed that when the size of the sample training set used in the OS-ELM algorithm is close to the number of hidden layer neurons, a high recognition accuracy can be obtained, and the type of activation function has little influence on the recognition accuracy. Four stages of the creepage discharge development process were recognized using the OS-ELM algorithm; the trend was the same as that of the characteristic parameters of the entire creepage discharge development process. The recognition accuracy was 91.4%. The algorithm has a high computing speed and high accuracy and can train data in batches. Therefore, it can be widely used in the field of online monitoring and evaluation of electrical equipment status.

An optical fiber sensing method for partial discharge in the HVDC cable system

To improve the safety and efficiency performance of partial discharge detection in a high-voltage direct current (HVDC) cable system, an optical fiber ultrasonic detection system for partial discharge is carried out in the long-distance HVDC operating environment. The correlation model between partial discharge ultrasonic wave and discharge quantity is established by analyzing the partial discharge mechanism and ultrasonic characteristics of DC cable. The partial discharge repetition rate and discharge magnitude of several common insulation defects are obtained experimentally, and the ultrasonic characteristic Sagnac fiber detection system is accomplished based on the data above. Then, the standard ultrasonic time-domain signal detection and fiber optic sensing probe directional experiment are fulfilled, and the ultrasonic wave is measured based on fiber-optic probe and can perform multi-point detection. The influence of different length fiber sensing probes on the sensitivity of partial discharge signals is investigated based on the frequency distribution characteristics of partial discharge. The effectiveness of the optical fiber sensing system is verified based on the zero-frequency point localization method with ultrasonic location detection of partial discharge in a 15 km HVDC cable system. The measurement results obtained show the deviations of determining the location of the partial discharge point is less than ±80 m. The reliability of the proposed PD optical fiber detection system for intrinsic safety and detection efficiency of partial discharge position in the HVDC cable system is verified.

Detection of Partial Discharge in GIS and Transformer Under Impulse Voltage by Fluorescent Optical Fiber Sensor

Partial discharge (PD) is a common phenomenon in high voltage (HV) equipment, which will radiate light signals outward in the process of discharge, by detecting this optical signal, we can judge whether the HV equipment have PDs or not, the conventional detection methods under impulse voltage are very complex due to the interference of electromagnetic (EM) wave, but optical method does not have this problem. Fluorescent optical fiber is not affected by numerical aperture problem, so the sensitivity of fluorescent optical fiber sensor is much higher than that of traditional quartz optical fiber sensor. In this work, the fluorescent optical fiber sensor was manufactured and applied to the PD detection in gas insulated switch (GIS) and transformer, fluorescent optical fiber was used as a probe, which was connected with the transmission optical fiber by coupler, and the photomultiplier tube was used to convert the optical signal into electrical signal for displaying by oscilloscope. The discharge signal can be detected by the sensor when the impulse voltage was applied to the GIS and transformer, and it was less affected by the interference of EM wave signals. As the PD source at different positions in GIS, we found that the optical signal attenuates greatly when the optical was blocked by some internal structures, and the attenuation was increase with the increase of distance between PD source and the sensor. The optical signals of PDs can also be detected by the sensor in the transformer, but the attenuation of the optical signal in the transformer was greater than that in GIS, it was greatly affected by the blocking of windings. The two experiments were verified in the TracePro simulation software, and the average deviations between results of experiments and simulations were less than 10%.

Recognition of pollution layer location in 11 kV polymer insulators used in smart power grid using dual-input VGG Convolutional Neural Network

This paper portrays the application of a Partial Discharge (PD) signal combined with the dual-input VGG Convolution Neural Network (CNN) to predict the location of the pollution layer on 11 kV polymer insulators subjected to alternating current for smart grid applications. First, a non-uniform pollution layer artificially created for HV insulator with three straight shed ball end fitting in a laboratory setup and corresponding PD readings are measured. The wavelet transform is employed to represent the measured PD signal as scalogram patterns. In general CNN uses a single input pattern for feature extraction. If the pattern quality is low, it is easy to cause misclassification. Hence in this proposed work, the feature fusion of a dual-input Visual Geometry Group (VGG) based CNN is used for the classification of contamination layer. VGG 19 is a pretrained deep learning network used for extracting the rich features from the patterns. In continuation to that, hyperparameter (HP) play a vital role in deep learning algorithms because they directly manage the behaviours of training algorithms and have a significant effect on the performance of deep learning models. Hence, Bayesian Optimization (BO) is used for tuning the HP. At last, to check the practicality of the proposed algorithm, a new dataset is created for 11 kV polymer insulator with three alternate shed clevis end fitting and different pollution levels—acceptable results obtained by using dual-input CNN with the minimum quantity of data.

A new radial basis function neural network-based method for denoising of partial discharge signals

Partial discharge (PD) assessment is one of the most prevalent techniques in the condition monitoring of high voltage (HV) equipment. In this regard, one of the main stages in the procedure of PD assessment is noise reduction of PD signals. Hence in this paper, a novel artificial neural network (ANN) based denoising method is introduced. Unlike the existing denoising algorithms, the proposed technique treats the noisy PD signal, in a data window, as a set of points that can be estimated by a curve. Hence, radial basis function (RBF)-ANN is utilized being a self-structured algorithm compared to multi-layer perceptron (MLP)-ANN that is influenced by the number of neurons. The performance of the proposed method has been compared with MLP-ANN, wavelet transform (WT) and empirical mode decomposition (EMD) based algorithms for different laboratory generated PD signals suppressed with white noise. The obtained results have proved the superiority of the proposed method.