Partial Discharge Fault Diagnosis Research Articles

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.

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.

Improved High-Order Cumulant TDE Parameter Accumulation Algorithm for Locating UHF Signals of Pulsed Electromagnetic Source

Ultra-high frequency (UHF) signal detection is one of the most effective methods for spatial orientation and partial discharge fault diagnosis. However, when the background noise, especially Gaussian white noise and narrow-band interference, is very high, or the detection region is remote, location accuracy may decrease. To improve this, a location system based on the improved higher-order cumulant time delay estimation (TDE) method combined with energy accumulation is proposed. In the system, the UHF waves are received by a four-antenna array. Improved fourth-order cumulants with a smoothed coherence transform (SCOT) window are used in the TDE, by which Gaussian white noise and narrow-band interference can be efficiently suppressed. The energy accumulation algorithm is applied to the cross-correlation results, by which the accuracy of TDE can be enhanced. The applicability of the proposed localization algorithm is evaluated by simulation and experiment. The simulation results show that the improved fourth-order cumulant TDE-parameter accumulation algorithm is superior in accuracy to traditional location methods. In the experimental measurements, two partial discharge measurement points located in a complex electromagnetic environment are tested. The results illustrate that the proposed method can effectively suppress Gaussian white and narrow-band noise, and the location results can satisfy accuracy requirements when the measurement point is within 70 m.

Partial discharge fault diagnosis based on zernike moment and improved bacterial foraging optimization algorithm

Feature extraction and classifier optimization are important parts in the diagnosis of partial discharge (PD). Traditional feature extraction methods generally target not the images but structured data. Traditional classifier optimization methods have many disadvantages such as the many parameters that need to be adjusted, difficulty in determining the best parameters, and the ease in falling into local optimal solutions. To eliminate these defects, a PD diagnosis method of gas-insulated switchgear (GIS) based on the Zernike moment and improved support vector machine (SVM) is proposed. Firstly, five typical PD models of corona discharge, metal particle discharge, floating electrode discharge, void discharge, and surface discharge were designed. Then, the UHF sensor was used to collect discharge signals of different defects and different types of interference signals to build a PD phase-resolved pulse sequence (PRPS) patterns library. After that, the Zernike moment of the PRPS images were extracted as the PD features. Finally, the SVM model based on the differential evolution adaptive bacterial foraging optimization (DEABFO) algorithm was used to fulfill the GIS PD diagnosis. The results show that the Zernike moment feature extraction method is suitable for PD PRPS images, and DEABFO improves the convergence speed of the SVM parameter optimization process and can effectively avoid the probability of falling into the local optimum. Compared with other image feature extraction methods and other SVM optimization algorithms, this method has high accuracy in GIS PD fault diagnosis, with the recognition rate reaching 91.23%.

A Comprehensive Review of Signal Processing and Machine Learning Technologies for UHF PD Detection and Diagnosis (I): Preprocessing and Localization Approaches

Partial discharge (PD) detection and diagnosis based on the ultra-high frequency (UHF) signals is one of the most widely adopted methods to evaluate the internal insulation status of high voltage equipment. Benefit from the rapid development of computing hardware and data processing algorithms, the intelligent PD fault diagnosis method based on the UHF data has made considerable progress in the past two decades. This two-part paper aims to give a comprehensive review about the application of signal processing and machine learning technologies in UHF PD detection and diagnosis. These technologies are divided into three categories according to their respective purpose, which are the preprocessing technology, source localization technology and pattern recognition technology. As the first one of the two-part review, we focus on the preprocessing and localization approaches in this paper. Specifically, for the preprocessing topic, the methods for signal denoising, multi-source separation, and pulse segmentation are included. While for the localization topic, the time difference of arrival (TDOA) method, direction of arrival (DOA) method, received signal strength indicator (RSSI) method, and other latest methods are reviewed. For each topic, the basic ideas, recent research progresses, advantages and limitations are discussed in detail. Before the conclusion, we also make a discussion about the application effects of the above technologies and prospect some future directions accordingly. In the second paper, the pattern recognition problems based on the UHF PD data will be concentrated, especially the application of deep learning algorithms.

Decomposing Mechanism of SF6 under Positive DC Partial Discharge in the Presence of Trace H2O.

The influence of H2O on SF6 decomposition characteristics under positive DC partial discharge (PD) is significant. To evaluate PD fault severity in DC SF6-insulated equipment using the production characteristics of SF6 decomposition components, the corresponding relationship and mathematical expression between the production of SF6 decomposition components and the H2O content should be identified and achieved. Thus, SF6 decomposition experiments under positive DC PD are performed to reflect the influence of H2O on SF6 decomposition components. Results show that the total discharge quantity and the discharge repetition rate averaged for 1 s decrease slightly when the H2O content increases from 0 to 970 ppmv and then increase when the H2O content increases from 970 to 5120 ppmv. The effective production rates of SO2F2, SOF2, and SO2 increase with the H2O content, whereas that of SOF4 decreases. Finally, the corresponding relationship and mathematical expression between the characteristic ratio (c(SO2F2) + c(SOF4))/(c(SOF2) + c(SO2)) of components and the H2O content have been achieved, which can afford references for PD fault diagnosis in DC SF6 gas-insulated equipment.

Partial Discharge Fault Diagnosis Based on Multi-Scale Dispersion Entropy and a Hypersphere Multiclass Support Vector Machine.

Partial discharge (PD) fault analysis is an important tool for insulation condition diagnosis of electrical equipment. In order to conquer the limitations of traditional PD fault diagnosis, a novel feature extraction approach based on variational mode decomposition (VMD) and multi-scale dispersion entropy (MDE) is proposed. Besides, a hypersphere multiclass support vector machine (HMSVM) is used for PD pattern recognition with extracted PD features. Firstly, the original PD signal is decomposed with VMD to obtain intrinsic mode functions (IMFs). Secondly proper IMFs are selected according to central frequency observation and MDE values in each IMF are calculated. And then principal component analysis (PCA) is introduced to extract effective principle components in MDE. Finally, the extracted principle factors are used as PD features and sent to HMSVM classifier. Experiment results demonstrate that, PD feature extraction method based on VMD-MDE can extract effective characteristic parameters that representing dominant PD features. Recognition results verify the effectiveness and superiority of the proposed PD fault diagnosis method.

Fault Diagnosis and Condition Division Criterion of DC Gas Insulating Equipment Based on SF6 Partial Discharge Decomposition Characteristics

SF6 decomposition characteristics directly reflect the generation, development, and evolution of internal faults in gas-insulated equipment (GIE). However, establishing the DC-GIE internal insulation condition comprehensive division criterion and fault diagnosis method based on SF6 decomposition characteristics remains unsolved. Therefore, partial discharge (PD) and SF6 decomposition characteristics of typical insulation defects from initial discharge to the breakdown process are discussed in this paper. The average discharge magnitude in a second $Q_{\text {sec}}$ is selected as an electrical feature to characterize the PD condition, and the PD state is divided into three levels, namely, mild PD, medium PD, and severe PD. SF6 decomposition experiments are conducted under different PD conditions of each defect, and the SF6 decomposition characteristics under different failure conditions are obtained. On this basis, we construct five feature sets of 36 characteristic quantities for GIE PD fault diagnosis and analyze the validity. A minimum-redundancy-maximum-relevance-based feature selection algorithm is used to optimize the characteristic quantities, and an optimal feature set is selected, i.e., {C(SO2)/C(T), C(SOF2)/C(CT), C(SO2F2)/C(ST)}. The characteristic quantities of SF6 decomposition component, which characterizes PD condition, are reconstructed, and the component characteristic criterion of PD condition division is proposed. Thus, a decision tree evaluation model for the PD condition of the typical insulation defect inside DC-GIE is constructed. This paper lays a foundation for the insulation condition evaluation and fault diagnosis of DC-GIE.