Partial Discharge Defects Research Articles

Adsorption behavior of SF6 partial discharge decomposition components on metal (Pt, Pd) modified ZnO surfaces

High-voltage SF6 gas-insulated equipment generates various characteristic gas components during partial discharge faults, threatening the equipment’s normal operation. Based on density functional theory, this study investigates the adsorption behaviors of H2S, SO2, SO2F2, and SOF2 on ZnO bilayers before and after Pt/Pd modification. Analysis of adsorption energy, density of states, conductivity, and recovery time reveals that the adsorption effectiveness of ZnO bilayer on SF6 decomposition components is H2S > SO2 > SOF2 > SO2F2. Pt/Pd doping significantly reduces the adsorption energy of gas molecules on ZnO surfaces but does not alter the adsorption effectiveness. Additionally, considering the significant changes in Pt/PdZnO’s conductivity after adsorption of specific gas molecules, PtZnO is suitable as a sensor material for H2S, SO2, and SO2F2, while PdZnO is suitable for SO2 sensing. This study provides a theoretical foundation for developing novel sensor materials for SF6 decomposition components, potentially enhancing electrical equipment’s economic and operational safety.

AC breakdown and decomposition products detection characteristics of eco-friendly insulating medium in gas insulated switchgear

Gas Insulated Switchgear (GIS) was a crucial electrical equipment that provides the safety and security of power systems, and finding the eco-friendly alternatives of sulphur hexafluoride (SF6) as insulating medium in GIS has been an urgent demand in past decades. However, few studies reported the specific dielectric strength under various electrodes and partial discharge (PD) decomposition products detection of applying the eco-friendly gas in GIS. In this paper, the insulation properties among C4F7N/CO2 mixtures, dry air and CO2 were explored under the reduced-scale gas-insulated experimental device. The relationship between AC breakdown voltage and gas pressure was gained under the different electrodes and gap distances, and 0.6 MPa 7% C4F7N/93% CO2, 0.9 MPa dry air, 0.9 MPa CO2 all possess the capacity to be the insulating medium in eco-friendly gas insulated switchgear. Furthermore, the partial discharge experimental was also carried out to realize the decomposition products detection of dry air, which designs three common defect types including metal protrusion defects, air gap defects, and metal contamination defects. The decomposition products detection result shows that the contains of CO2, CO, and NO2 linearly increase with the increasing applied voltages and times, and the partial discharge defects are distinguished according to the ratios of c(CO2 + CO)/c(NO2) and c(CO2)/c(CO). The results can provide the basis for the further development of eco-friendly gas insulated switchgear.

Transformer partial discharge location technology based on gradient oil temperature

IntroductionThe traditional partial discharge localization improvement strategy mainly starts from the intelligent algorithm, but fails to consider the influence of core winding and oil temperature on partial discharge positioning.MethodsThis paper also considers the influence of the iron core winding and oil temperature. Through finite element simulation, a transformer model was established to analyze the propagation characteristics of ultrasonic signals generated by partial discharge under the interference of gradient oil temperature and winding. The chaotic firefly-particle swarm hybrid algorithm is proposed, and through the calculation of Shubert’s multi-peak function. Finally, a partial discharge defect platform based on gradient oil temperature was built to verify the chaotic firefly-particle swarm hybrid localization algorithm.ResultsThe ultrasonic velocity generated by partial discharge in transformers cannot be fixed, and it is suggested that ultrasonic sensors should be installed near the center of the top of the transformer. The proposed algorithm can be better optimized in the case of multiple local extreme points. Under gradient oil temperature experiments, the algorithm achieves positioning errors less than 100 and 55 mm for cases with and without winding obstruction, respectively, with average positioning errors of 74.2 and 35.2 mm.DiscussionThe positioning method in this paper can provide a technical reference for the partial discharge positioning of transformers in actual operation.

Transformer partial discharge fault diagnosis based on improved adaptive local iterative filtering‐bidirectional long short‐term memory

AbstractInsulation deterioration, which is mainly caused by partial discharge (PD) occurring inside power transformers, is one of the prime reasons to cause transformer faults. Therefore, an effective diagnosis of PD is crucial to ensure the safe and stable operation of transformers. To extract more effective features that characterise transformers PD signals and enhance the recognition accuracy, a novel transformer PD fault diagnosis model based on improved adaptive local iterative filtering (ALIF) and bidirectional long short‐term memory (BILSTM) neural network is proposed. Addressing the issue of predetermined decomposition levels and accuracy in ALIF decomposition, the golden jackal optimisation (GJO) algorithm is introduced to optimise the parameters. The proposed fault diagnostic model extracts dominant PD features employing the improved ALIF and Refined Composite Multi‐Scale Dispersion Entropy and improves the diagnostic accuracy with the optimised BILSTM by introducing GJO. Experimental data evaluates the performance of support vector machine, long short‐term memory and BILSTM. The results verify the effectiveness and superiority of the proposed model.

DFT insights into the TM(Cu, Ni, Ag) and TMO(CuO, NiO, Ag2O) modified HfSe2 for detecting PD fault gases

Partial Discharge (PD) in oil-immersed transformers threatens the safe and stable operation of the transformer. PD causes the transformer oil to decompose and produce fault gas. Therefore, the operating status of the transformer can be evaluated through the detection of characteristic fault gas. Through simulation research, this paper proposes to use HfSe2 substrate modified with metal single atoms (Cu, Ni and Ag) and their oxides (CuO, NiO and Ag2O) to detect decomposed fault gases (CO, C2H2, CH4) in transformer oil. DFT was used to systematically study the adsorption of the target gas on the surface of the original substrate and the modified substrate. The research results show that the conductivity of the doped HfSe2 substrate increases to a certain extent, and the band gap also drops from the undoped 0.563 eV to 0.108–0.554 eV. By analyzing the adsorption parameters, it was found that the adsorption capacity of metal single atoms (Cu, Ni and Ag) is lower than metal oxides (CuO, NiO and Ag2O). Among the six substrates, the adsorption capacity of the target gases is ranked as CO > C2H2 > CH4, in which CO and C2H2 are chemically adsorbed, while CH4 is physically adsorbed. In terms of sensing performance, it was found through recovery time and sensitivity analysis that NiO-HfSe2 and Ag2O-HfSe2 can be used to selectively detect C2H2. The research results can provide a theoretical basis for the application of HfSe2 in oil-immersed transformer operating status detection and gas-sensitive sensor detection.

GIS Partial Discharge Pattern Recognition Based on IWOA-SVM Algorithm

Abstract To effectively recognize partial discharge (PD) types in gas-insulated switchgear (GIS) equipment, a novel PD pattern recognition method is proposed. This method leverages an improved whale optimization algorithm (IWOA) to optimize the support vector machine (SVM). Firstly, typical PD defects were simulated in the GIS chamber. The PRPS results demonstrated significant differences among four PD defects. Secondly, multiple feature values of PD results were calculated for feature extraction. Finally, the IWOA-SVM algorithm was employed for the recognition of PD patterns in GIS. Experimental results indicate that the IWOA-SVM algorithm can effectively recognize different PD types and the predicted accuracy rate can reach 99.1667%, which is much higher than SVM and WOA-SVM algorithms, providing information to construction units for maintenance.

Analysis and Treatment of a Partial Discharge Fault in a 220kV Transformer

Abstract Partial discharge test of transformer is one of the most important detection items in the factory test of transformer, which is related to whether the transformer can operate safely and stably. This article will analyze a case of partial discharge exceeding the standard in a 220kV transformer. Based on the joint detection technology of ultrasonic partial discharge and ultra high frequency partial discharge, the defect location will be found. And the structure of the transformer will be researched to identify the cause of the defect. The diagnosis and treatment will be successfully completed. Finally, the conclusion will be verified through finite element analysis. This provides reliable guidance for partial discharge detection and rectification measures in transformers’ production and operation.

Research and Simulation of Partial Discharge Phenomenon Sensing Technology for Substation Inspection Robots

Abstract This paper thoroughly investigates the phenomenon of partial discharge in substations and proposes a set of simulation experimental apparatus to replicate various types of partial discharge defects in switchgear. By modifying the switchgear, incorporating models for different types of partial discharge, and equipping various standard partial discharge sensors, accurate simulations of phenomena such as point discharge, suspended discharge, gap discharge, and particle discharge are achieved. Through this system, the effectiveness of different detection methods, such as ultrasonic, high-frequency, and transient voltage methods, is validated for various defect models. Furthermore, utilizing an indoor inspection robot guide rail, the paper simulates the environment of partial discharge in substations, enabling the validation of the robot’s partial discharge detection performance and typical patterns under different conditions. This work is dedicated to providing situational awareness technology for partial discharge phenomenon detection for substation inspection robots, so that the inspection robot can detect various abnormal partial discharge phenomena in transformers or other substation equipment through micro sensors to promote the stable operation of the substation.

Partial Discharge Fault Diagnosis in Power Transformers Based on SGMD Approximate Entropy and Optimized BILSTM.

Partial discharge (PD) fault diagnosis is of great importance for ensuring the safe and stable operation of power transformers. To address the issues of low accuracy in traditional PD fault diagnostic methods, this paper proposes a novel method for the power transformer PD fault diagnosis. It incorporates the approximate entropy (ApEn) of symplectic geometry mode decomposition (SGMD) into the optimized bidirectional long short-term memory (BILSTM) neural network. This method extracts dominant PD features employing SGMD and ApEn. Meanwhile, it improves the diagnostic accuracy with the optimized BILSTM by introducing the golden jackal optimization (GJO). Simulation studies evaluate the performance of FFT, EMD, VMD, and SGMD. The results show that SGMD-ApEn outperforms other methods in extracting dominant PD features. Experimental results verify the effectiveness and superiority of the proposed method by comparing different traditional methods. The proposed method improves PD fault recognition accuracy and provides a diagnostic rate of 98.6%, with lower noise sensitivity.

Pattern Recognition of Partial Discharge Faults in Switchgear Using a Back Propagation Neural Network Optimized by an Improved Mantis Search Algorithm.

The dependable functioning of switchgear is essential to maintain the stability of power supply systems. Partial discharge (PD) is a critical phenomenon affecting the insulation of switchgear, potentially leading to equipment failure and accidents. PDs are generally grouped into metal particle discharge, suspended discharge, and creeping discharge. Different types of PDs are closely related to the severity of a PD. Partial discharge pattern recognition (PDPR) plays a vital role in the early detection of insulation defects. In this regard, a Back Propagation Neural Network (BPNN) for PDPR in switchgear is proposed in this paper. To eliminate the sensitivity to initial values of BPNN parameters and to enhance the generalized ability of the proposed BPRN, an improved Mantis Search Algorithm (MSA) is proposed to optimize the BPNN. The improved MSA employs some boundary handling strategies and adaptive parameters to enhance the algorithm's efficiency in optimizing the network parameters of BPNN. Principal Component Analysis (PCA) is introduced to reduce the dimensionality of the feature space to achieve significant time saving in comparable recognition accuracy. The initially extracted 14 feature values are reduced to 7, reducing the BPNN parameter count from 183 with 14 features to 113 with 7 features. Finally, numerical results are presented and compared with Decision Tree (DT), k-Nearest Neighbor classifiers (KNN), and Support Vector Machine (SVM). The proposed method in this paper exhibits the highest recognition accuracy in metal particle discharge and suspended discharge.

Partial discharge fault identification method for GIS equipment based on improved deep learning

AbstractAiming at the problems of large consumption of computational resources and insufficient data feature extraction in the current partial discharge fault identification process of GIS equipment, a partial discharge fault identification method of GIS equipment based on improved deep learning is proposed. Firstly, the audio information of GIS equipment is filtered by a simple power normalised cepstral coefficient (SPNCC). Secondly, the spatial correlation between audio data streams is obtained by a convolutional neural network, the temporal correlation of audio is obtained and the next time slice data stream is predicted by using bi‐directional long short‐term memory (BiLSTM) network, and the attention mechanism is designed to extract deeper data features. Finally, the partial discharge fault identification model of GIS equipment based on improved SPNCC‐CNN‐BiLSTM‐Multi‐att is established, which improves the accuracy of the partial discharge identification method of GIS equipment. Experiments show that when the number of iterations is 100, the accuracy, recall, and F1 value of the proposed GIS equipment partial discharge fault recognition method on the dataset are 0.876, 0.812, and 0.843, respectively.

Fine-grained partial discharge pattern recognition method based on substation equipment knowledge graph

Discharge-type faults are one of the main types of faults in substation equipment, and partial discharge (PD) faults are an important cause of equipment discharge faults. When different types of PD faults occur in equipment, their fault handling plans also vary accordingly. Therefore, the identification of PD types in equipment is very important. Aiming at the problem that the recognition accuracy of the existing methods is not high enough due to the low quality of PD data and unbalanced samples in the process of equipment PD pattern recognition, we propose a fine-grained method to recognize PD patterns based on substation equipment knowledge graph. This model combines a visual encoder with a knowledge graph and uses the fine-grained attribute features and the association feature information of the PD entity provided by the knowledge graph for the research of equipment PD pattern recognition. After experimental verification, the accuracy of PD pattern recognition in our proposed method is significantly higher than that of previous methods.

Pt decorated Janus WSSe monolayer: A gas-sensitive material candidate for SF6 decomposition gases based on the first-principles

In gas insulated switchgear, unavoidable partial discharge faults cause SF6 to decompose various low-fluorine compounds. These compounds react with micro-water to form acids, leading to gas insulated switchgear corrosion as well as reduced insulation properties. For this reason, on-line monitoring of SF6 decomposition gases is essential. In this work, based on density functional theory, the structures of pristine WSSe and platinum doped WSSe monolayer are established, and eight adsorption systems of SO2, SO2F2, SOF2 and HF gas molecules on pristine WSSe and Pt-WSSe monolayer are constructed by geometrical optimization. By analyzing parameters such as adsorption energy, charge density, density of states, orbital theory, conductivity, sensing response and recovery time, the adsorption performance and gas-sensing mechanism of each adsorption system were investigated. The analytical results showed that both intrinsic WSSe and Pt-WSSe were unsuitable for HF gas detection and exhibited physical adsorption. The adsorption energies of Pt-WSSe for SO2, SO2F2, and SOF2 were −1.596 eV, −1.702 eV, and −2.288 eV, respectively, which showed strong chemisorption. At the optimum operating temperature, the Pt-WSSe/SO2F2 system has a fast recovery time of 1.928 s, a high response of 28.436 and a strong resistivity change, and this gas-sensitive material is well suited for the detection of this gas molecule. The Pt-WSSe/SO2 system has a small response value of 1.928 and the Pt-WSSe/SOF2 system has a strong adsorption energy of −2.288 eV. Therefore, the Pt-WSSe monolayer is not suitable as a gas-sensitive material for SO2 and SOF2 detection. This work provides theoretical guidance for the future development of low power, highly sensitive, fast response gas sensors for gas insulated switchgear fault detection.

Partial discharge fault detection method of switchgear based on signal aliasing spectrum separation model

At present, the detection nodes of partial discharge fault in switchgear are generally set in one direction, and the fault detection range is small, which leads to an increase in the false recognition rate of partial discharge fault detection. Therefore, the design and verification analysis of the detection method of partial discharge fault in switchgear is proposed. According to the current measurement requirements and standards, firstly, the characteristics of switchgear discharge faults are extracted, and the fault detection range is expanded by the multi-step method. Multi-step partial discharge fault detection nodes are deployed, and the partial discharge fault detection model with signal aliasing and spectrum separation is constructed. The fault detection is completed by hierarchical fuzzy automatic identification. The test results show that the final detection error rate of partial discharge fault is well controlled below 20% by comparing the seven selected test cycles and combining the signal aliasing spectrum separation model, which shows that the designed detection method of partial discharge fault is more flexible and changeable, and has strong pertinence and reliability. Facing the complex switchgear operating environment, it can also mark the fault position in the shortest time, strengthen the detection results and ensure the detection accuracy.

Simultaneous and Dynamic Detection of SF6 Decomposition Products Under Partial Discharge Defect of Gas-Insulated Power Equipment by Fiber-Enhanced Raman Spectroscopy

Simultaneous and Dynamic Detection of SF₆ Decomposition Products Under Partial Discharge Defect of Gas-Insulated Power Equipment by Fiber-Enhanced Raman Spectroscopy

Recognition of partial discharge in GIS based on image feature fusion

<p>Partial discharge (PD) is a significant electrical fault in gas-insulated switchgear (GIS), with various types posing different risks to insulation. Accurate identification of PD types is essential for enhancing GIS management and ensuring the reliability of electrical grids. This study proposes a novel approach for PD identification in GIS integrating completed local binary pattern (CLBP) feature extraction, feature engineering, and an optimized support vector machine (SVM). PD faults were simulated in GIS and phase-resolved pulse sequence (PRPS) data for four different forms of PD were gathered. CLBP was used to extract image features, and then the support vector machine recursive feature elimination (SVM-RFE) algorithm was used to evaluate feature importance. Then, linear discriminant analysis (LDA) was used to fuse the selected features and reduce redundancy. The fused features were classified using a bald eagle search algorithm combined with differential evolution (IBES)-optimized SVM, achieving a recognition accuracy of 99.38%. The results indicate that the proposed method effectively distinguishes between different PD PRPS patterns in GIS.</p>

Relevance Analysis between Decomposition Product Characteristics of C6F12O Gas Mixture under Partial Discharge and Insulation Faults

Relevance Analysis between Decomposition Product Characteristics of C₆F₁₂O Gas Mixture under Partial Discharge and Insulation Faults

Comparison of Different Coupling Types of Fiber-Optic Fabry–Perot Ultrasonic Sensing for Detecting Partial Discharge Faults in Oil-Paper Insulated Equipment

Comparison of Different Coupling Types of Fiber-Optic Fabry–Perot Ultrasonic Sensing for Detecting Partial Discharge Faults in Oil-Paper Insulated Equipment

Research on Transformer Partial Discharge Fault Location Based on Improved UCA-RB-MUSIC Algorithm

Research on Transformer Partial Discharge Fault Location Based on Improved UCA-RB-MUSIC Algorithm

BiLSTM-FCN based vibration signal diagnosis of smart grid cables

Abstract Cable faults threaten the safe and stable operation of smart grids, and vibration signal diagnosis research on cables based on artificial intelligence technology can effectively enhance the reliability of smart grids. In order to improve the speed and accuracy of cable defect identification, this paper proposes a partial discharge identification method for cables based on a fully convolutional bidirectional long short-term memory neural network (BiLSTM-FCN). The time-domain characteristics of different working conditions under industrial frequency AC voltage are collected by building a vibration signal-based partial discharge test platform for cables. The Overlap strategy enhances the data set. The BiLSTM layer is introduced to process the one-dimensional time domain waveform signal, and combined with the local detail features extracted by the FCN layer, the data features are more abundant. Thus, a more accurate diagnosis of localized discharge in high voltage cables under different operating conditions. The results show that the diagnostic accuracy based on the BiLSTM-FCN model reaches 92.2%, which is 1.2% and 1.4% higher compared to the FCN model and the BiLSTM model. BiLSTM-FCN model possesses a better recognition effect and faster recognition speed in the identification of partial discharge defects type, which can effectively achieve the automatic detection of abnormal fault nodes of smart grid cables. It is significant for realizing online dynamic evaluation of small portable online monitoring equipment and provides a reference for future related research.