Transformer Partial Discharge Research Articles

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

Retracted: Research on Pattern Recognition Method of Transformer Partial Discharge Based on Artificial Neural Network

Retracted: Research on Pattern Recognition Method of Transformer Partial Discharge Based on Artificial Neural Network

The Denoising Method for Transformer Partial Discharge Based on the Whale VMD Algorithm Combined with Adaptive Filtering and Wavelet Thresholding.

Partial discharge (PD) is the primary factor causing insulation degradation in transformers. However, the collected signals of partial discharge are often contaminated with significant noise. This makes it difficult to extract the PD signal and hinders subsequent signal analysis and processing. This paper proposes a denoising method for transformer partial discharge based on the Whale VMD algorithm combined with adaptive filtering and wavelet thresholding (WVNW). First, the WOA is used to optimize the important parameters of the VMD. The selected mode components from the VMD decomposition are then subjected to preliminary denoising based on the kurtosis criterion. The reconstructed signal is further denoised using the Adaptive Filter (NLMS) algorithm to remove narrowband interference noise. Finally, the residual white noise is eliminated using the Wavelet Thresholding algorithm. In simulation experiments and practical measurements, the proposed method is compared quantitatively with previous methods, VMD-WT, and EMD-WT, based on metrics such as SNR, RMSE, NCC, and NRR. The results indicate that the WVNW method effectively suppresses noise interference and restores the original PD signal waveform with high waveform similarity while preserving a significant amount of local discharge signal features.

An ultrasonic signal propagation model of transformer partial discharge considering nonlinear attenuation in the temperature gradient field

An ultrasonic signal propagation model of transformer partial discharge considering nonlinear attenuation in the temperature gradient field

Quantitative analysis and evaluation of the optimization method of transformer partial discharge ultrasonic sensor monitoring

Abstract Accurate partial discharge (PD) measurement is critical to ensure the stable operation of transformers. The ultrasonic method is a low-cost, safe, and reliable technology that is widely available and provides real-time monitoring capability. The PD ultrasonic signals propagation is complex and severely attenuated in the transformer, which greatly affects the measurement accuracy of the sensor. In order to improve the accurate monitoring of PD in complicated transformer environments, an optimization monitoring method based on sub-scene detection and quantitative analysis and evaluation is proposed in this paper. Firstly, to address this concern, a sub-scene monitoring method is designed and explores the optimal monitoring points separately. In addition, establish the partition model of an oil-immersed power transformer, and compare the ultrasonic wave propagation characteristics and sound pressure attenuation characteristics of different monitoring points. Then, analyzed by wavelet transform algorithm and Pearson correlation coefficient to determine the best monitoring point location for each scene. Finally, we further tested the proposed method through extensive experiments based on simulations, testbed, and trial deployment. The experimental results have demonstrated the feasibility and accuracy of the proposed method in transformer PD monitoring under complicated environments.

Research on Transformer Partial Discharge Feature Extraction Based on Empirical Wavelet Transform and Multiscale Permutation Entropy

Aiming to extract efficiently the fault features of partial discharge in the process of fault diagnosis of power transformer, a method of combining Empirical Wavelet Transform (EWT) with Multiscale Permutation Entropy (MPE) is advanced to extract fault features of transformers partial discharge. Firstly, four different partial discharge pulse signals are analyzed by EWT method, and the fault signal is decomposed according to different frequency domain characteristics of the signal to obtain the intrinsic mode function (IMF) of the signal. Then, the calculated multi-scale permutation entropy of different IMFs to complete the fault feature extraction. Finally, the multi-scale entropy of the fault semaphore is used as the eigenvector of the Support Vector Machine (SVM) for glitch diagnosis, and the accurate systematization of the partial discharge semaphore of the transformer is realized. semaphore Compared with the Continuous Wavelet Transform (CWT), Empirical Mode Decomposition (EMD), and Ensemble Empirical Mode Decomposition (EEMD) feature extraction way, it shows that the raised EWT-MPE is more valid and accurate in diagnosing and analyzing transformer partial discharge faults, and the accuracy of fault classification 96.43%.

Improved demodulation method for fibre optic sensors applied in transformer partial discharge detection

AbstractThe 2 × 2 couplers, as a demodulation core of the fibre optic sensing system, have been extensively employed in fibre optic sensors for partial discharge (PD) detection of power equipment. However, the unstable demodulation sensitivity and low signal‐to‐noise ratio (SNR) of such a system are still unsatisfactory in engineering. This work uses a distributed feedback fibre laser as the sensor and employs a common asymmetric 2 × 3 coupler instead of a 2 × 2 coupler as the demodulation core to design a PD detection system. Then, we propose a new demodulation method for PD acoustic emission signals based on the Naval Postgraduate School method. On the PD detection platform, the performance of the conventional detection system and the proposed system is compared. The experimental results show that the proposed system has better reliability and can identify the PD signals in all instances. Furthermore, the average SNR of the new demodulation method is 6.11 dB higher than the traditional 2 × 2 coupler system and can be extended to most PD detection systems using fibre optic sensors.

Detection technology of partial discharge in transformer based on optical signal

The detection of partial discharge (PD) in transformers is of great significance for preventing insulation defects from developing into insulation failures. Transformer partial discharge detection technology includes pulse current method, radio frequency measurement method, UHF method, ultrasonic detection method, gas chromatography, and optical detection method. Among them, the optical detection method has the characteristics of strong anti-electromagnetic interference ability, and the signal can be transmitted and received without loss. The optical detection method has unique advantages. In this paper, an optical signal measuring device for partial discharge inside the transformer is designed and fabricated, the typical insulation defect inside the transformer is simulated, and the optical signal characteristics of typical partial discharge are obtained. Compared with the pulse current method, it is found that the magnitude of partial discharge can be characterized by the integral of the optical signal. The characteristic of the optical signal phase resolved partial discharge (PRPD) spectrum of typical insulation defects is obtained. The PRPD spectrum of the optical signals of different defects is obviously different. It is feasible to draw the PRPD spectrum and identify the defect types according to the optical signal, which can be further applied to the identification of internal faults in transformers.

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.

Ultra-High SNR Demodulation Method for Optical Fiber Sensors Applied in Power Transformer Partial Discharge Detection

The demodulation method of optical fiber sensors utilized in power transformer partial discharge (PD) detection is insufficient for engineering applications. We design a distributed feedback fiber laser (DFB-FL) PD detection system with an asymmetric 3 × 3 coupler and propose an ultra-high signal-to-noise ratio (SNR) demodulation scheme by eliminating the main factors that affect the traditional method using an asymmetric 3 × 3 coupler. The power transformer PD detection results reveal that the proposed scheme is free from 3 × 3 coupler asymmetry issues, with an average SNR of 38.30 dB, which is much higher than the widely used demodulation method and the piezoelectric transducer sensor. The average SNR of the system is increased by 24.2 dB with the proposed method.

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.

Research on Pattern Recognition Method of Transformer Partial Discharge Based on Artificial Neural Network

Power transformer is pivotal equipment in a power system, which is responsible for energy transmission and transformation, and its operating condition is related to the safe operation of the power system. In the 21st century, computer science has entered a stage of rapid development, advanced network structures and algorithms have been applied to the field of artificial intelligence, and pattern recognition theory and technology have also made great progress. In the past, the identification of partial discharge type mainly relied on the experience of operation and maintenance personnel, and manual analysis and judgment were made based on partial discharge mapping, which was not very accurate. The application of the computer pattern recognition method in the field of partial discharge type identification has changed the status quo of manual identification, and this method has substantially improved the accuracy and efficiency of identification. Pattern recognition using computer technology has been applied to the field of partial discharge analysis. Compared with manual recognition, its recognition results are accurate, recognition speed is fast, and it has great potential for development. This paper proposes an artificial neural network-based model for transformer partial discharge pattern recognition, which combines the advantages of artificial neural networks with accurate extraction of local spatial higher-order features and provides a new solution for transformer partial discharge pattern recognition. Extended experiments show that the method proposed in this paper achieves leading performance and has practical application value.

Research status of partial discharge detection of power transformer based on pulse current method

There are many important equipment running in the power grid, and the power transformer is one of them. It plays an extremely important role in the stable operation of the power system. Therefore, whether the power transformer can operate safely and stably becomes particularly important. When the power transformer is running online, partial discharge will occur if there are insulation defects, which will further develop into breakdown without timely detection and treatment. The insulation inside the transformer often has certain defects and produces certain partial discharge, and the partial discharge signal is often a high-frequency pulse signal. In this paper, the definition of partial discharge, the generation process of pulse current and the detection method of transformer partial discharge based on pulse current method will be introduced in detail. Several common methods are combed and summarized, the current development status of pulse current method is introduced, and the possible research direction in the future is prospected.

Online recognition method of transformer partial discharge based on audio detection

This study aims at the problem of low accuracy and poor real-time of transformer partial discharge defection. This paper uses ultrasonic sensors for contactless detection and proposes an Ultra-Lightweight Convolutional Neural Network (UL-CNN). The UL-CNN can extract audio features during partial discharges to achieve the online detection of the transformer. Even in the case of a small number of training samples, the accuracy can reach 98.6%, the online recognition rate is nearly nine times faster than that of MobileNet, and the recognition accuracy and real-time performance are better than those of the classic lightweight network MobileNet.

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.

Application of the integrated AE and HFCT sensors for online Dry-type Transformer Partial Discharge Monitoring. Case study

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The pattern recognition of multisource partial discharge in transformers based on parallel feature domain

The traditional diagnosis methods for the multisource partial discharge in transformer have several problems, such as the poor effect of signal separation, the inaccuracy of the extracted signal features and the over fitting of the neural network algorithm used for pattern recognition. In order to solve the above problems, the improved algorithm of stacked auto-encoder is adopted to extract the adaptive features of partial discharge one dimensional defect signals in time domain and two dimensional defect signal in time frequency domain, and a parallel feature space combining the features in time domain with that the time frequency domain is constructed. For the first time, the transformed L1 norm is utilized as the regular term of the loss function which is solved by the proximally guided stochastic subgradient in the improved auto-encoder. The trained neural network model can be utilized for pattern recognition of the different multisource partial discharge signals. The results show that the proposed method has a higher recognition rate for the different multisource partial discharge defects than that in the traditional method, and alleviates the problem of over fitting to a certain extent.

Extended Use for the Frequency Response Analysis: Switching Impulse Voltage Based Preliminary Diagnosis of Potential Sources of Partial Discharges in Transformer

The Frequency Response Analysis approach (FRA) is useful in the fault diagnosis of transformers. However, its usefulness in diagnosing any potential sources of Partial Discharge (PD) in transformers has not been thoroughly investigated so far. In this work, the use of Impulse voltage-based FRA (IFRA) in diagnosing inter-turn shorts and potential sources of PD were investigated on a 315 kVA, 11 kV/433 V transformer. Inter-turn shorts and PD sources were emulated and the usefulness of IFRA in their diagnosis was investigated while using switching impulse voltage at different magnitude levels as the test signals. For emulating the inter-turn shorts and the PDs, special tappings were provided on one of the 11 kV windings through the low capacitance bushings. Low voltage impulse was successful in diagnosing the inter-turn shorts, but unsuccessful in identifying the sources of PDs. During the test condition, the test voltage was adjusted with the presence of artificially created PD sources. The frequency response of the transformer before and after the inception of PD was observed and analyzed in this article. The FRA results demonstrated that the switching impulse voltage based IFRA approach at moderate voltages could be useful in diagnosing the presence of the potential sources of PDs.

Interference analysis and suppression technology of converter transformer partial discharge test based on field measurement

The field PD test of converter transformer is more difficult than that the test of common AC transformer. The most important difference is that the interference source in converter station is more complex. This paper measured the interference signals of the converter transformer field PD test in several converter stations, analyzed the characteristics of various typical interference, formulated the targeted interference suppression measures, simulation tests are carried out to verify the effectiveness of the suppression measures. Finally, the field application shows that the interference suppression measures proposed in this paper can effectively improve the efficiency and accuracy of field PD test of converter transformer.