Partial Discharge Location Research Articles

Enhanced Method for Localization of Partial Discharges in Oil-Filled Transformers Using Acoustic Emission Signals

The accurate measurement of partial discharges (PD) in power transformers is a critical component in identifying faults and planning effective maintenance strategies. Acoustic emission (AE) sensing technology is suitable for detecting partial discharges in transformers because it is less affected by external electromagnetic interference and detects PD non-invasively. This paper delves into examining the relationship between the detection intensity, the specific type of PD source, and the distance to the discharge source, using AE sensors. It was observed that AE wave intensities from corona discharges tend to be relatively strong, indicating a higher detection probability. Creepage discharges usually exhibit the next level of intensity, followed by the PD occurring in bubbles. It is considered that the intensity of the AE waves varies significantly depending on both the speed at which the discharge propagates and the medium and volume of the discharge space. Furthermore, this study has conducted experimental comparisons among three distinct methods for calculating the Time Difference of Arrival (TDOA) in localization calculations. These methods are the energy criterion, Generalized Cross-Correlation (GCC), and GCC with Phase Transformation (PHAT). The experimental results suggest that the energy criterion method is particularly effective when sensors are distributed placement around the entire tank. In contrast, the GCC-PHAT method shows greater suitability in scenarios where sensors are centralized placement in certain sections of the tank. It was clearly observed that the GCC-PHAT method, which suppresses the impact of noise and reflections, consistently achieves higher estimation accuracy in comparison to the standard GCC method. Notably, this method has shown the ability to maintain its accuracy levels even in cases of low discharge intensities. The implications of these findings are significant, as they could improve the precision and effectiveness of maintenance diagnostics in power transformers.

Localization of partial electrical discharges using compressive spherical frequency-difference beamforming.

Accurate localization of partial electrical discharges is essential for the diagnosis of high-voltage systems. The current study achieves this by employing an acoustic sensor array and a beamforming approach. The occurrence of a partial discharge is accompanied by the emission of high-frequency sounds in the ultrasonic range, making localization a challenging task requiring many sensors to avoid spatial aliasing. Compressive frequency-difference beamforming, as previously proposed, can be effective in addressing this issue. We expand the method to include near-field localization by utilizing a spherical wave and propose a two-step normalization process. This eliminates the bias associated with nonplanar waves and standardizes the field variables, thereby preserving only the phase and relative amplitude information. A distributed algorithm based on the alternating direction multiplier method is used to solve the associated convex optimization problem. The proposed method is demonstrated using simulated and experimental data.

Partial Discharge Location Accuracy in Oil-Immersed Pressboards Based on PSO

Partial Discharge Location Accuracy in Oil-Immersed Pressboards Based on PSO

Partial discharge detection, diagnosis, and location technology based on a novel transient earth voltage-ultrasonic integrated sensor used for switchgear equipment.

Medium-voltage (MV) switchgear and gas insulated switchgear (GIS) are essential components of power-grid systems. Their safe operation is crucial for the electricity consumption of users. However, insulation faults often occur during the operation of MV switchgear and GIS because of pollution, humidity, heat, mechanical shock, and other factors. Partial discharge (PD) measurements are the most effective indicator to prevent insulation failure. Transient earth voltage (TEV) and ultrasonic methods are the most popular PD measurement methods for switchgear. Currently, these two methods are used widely and independently. In this study, a novel TEV-ultrasonic integrated sensor is proposed based on the independent structure of the TEV and the ultrasonic sensor. The performance parameters of the proposed sensor were tested on test platforms. PD measurement experiments were conducted in a 10kV switchgear and 220kV GIS to analyze the performance of the integrated sensor. The results show that the sensor can simultaneously measure the TEV and ultrasonic signals in the same location. The integrated sensor can realize effective and sensitive detection, precise location, and accurate diagnosis of PD in the MV switchgear and GIS.

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.

Localization of Partial Discharges in Hydrogenerators by Ozone Emission

Localization of Partial Discharges in Hydrogenerators by Ozone Emission

Novel dual‐resonance damped alternating current testing system for offline partial discharge measurement of power cables

AbstractTo accurately detect the early deterioration of electrical insulation in power cables, an energizing system is required for offline partial discharge (PD) measurements. The damped alternating current (DAC) testing system has emerged as an effective tool for inducing PD events. However, the existing systems often suffer from limitations such as signal interference, low sensitivity, and high costs. To address these issues, this study proposes a novel dual‐resonance DAC testing system that is simple in structure and cost‐effective. The prototype was built for testing 10‐kV distribution cables, and its effectiveness was validated through experimental tests on a cable sample. The results show that this approach significantly improves PD detection sensitivity and successfully completes PD localization. This research contributes to the development of more efficient and affordable DAC generation technology for power cable testing applications.

Localization of Dual Partial Discharge in Transformer Windings Using Fabry–Pérot Optical Fiber Sensor Array

The power transformer is one of the most critical core devices for energy exchange in power systems, and its safe and stable operation is directly related to the reliability of the power grid. Partial discharge is the main cause of insulation degradation and failure of high-voltage electrical equipment. Online monitoring and accurate localization of partial discharge can provide information on the aging of power equipment, which is of great value for improving the safe operation and maintenance of the power grid. Internal dual partial discharge in transformer windings is a more complex type of fault. Since it is located inside the windings, the signal is attenuated and distorted, making it difficult for traditional monitoring methods to capture such partial discharge signal The Fabry–Perot optical fiber sensor is an ultrasonic detection method that can be built into the transformer interior. This sensor has high sensitivity and a small size, enabling flexible placement at different locations inside the transformer for precise partial discharge detection. Especially for the narrow space inside the high and low voltage windings, F–P sensors can form an array, utilizing the array’s directivity to locate the fault points. In this study, an ultrasonic detection system based on the F–P optical fiber sensor array was developed. The system utilizes a directional cross-localization algorithm based on the multiple signal classification (MUSIC) algorithm to accurately locate dual partial discharge sources. This partial discharge detection system was applied to a 35 kV single-phase transformer, enabling the localization of dual partial discharge sources within the high and low voltage windings. Combined with experimental results, this method exhibits high localization accuracy and is particularly suitable for detecting partial discharge phenomena that occur within or between transformer windings.

Partial discharge localization in power transformer tanks using machine learning methods

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SVM) to more complex approaches like convolutional neural networks (CNN). Multiple case studies are considered, each with different attributes, including sensor position, frequency content of the PD signal, and size of the transformer tank. The paper focuses on predicting the PD location in three-dimensional space using single-sensor electric field measurements. Various aspects of each method are analyzed, such as the input signal, core methodology, correlation coefficient between the predicted location and the actual location, and root mean square error (RMSE). These features are discussed and compared across the different methods. The results indicate that the CNN model exhibits superior performance in terms of location accuracy among the methods considered.

Research on partial discharge detection based on distributed optical fiber acoustic sensor

Distributed acoustic sensors (DASs) have the advantage of long-distance and distributed monitoring of vibration signals. However, there are few studies on the application of DAS for partial discharge (PD) monitoring of power equipment. There is also a lack of related theoretical and methodological studies. In this paper, the DAS-measured phase signals are theoretically analyzed from the frequency and energy perspectives, and the method of PD broadband signal coverage using spectrum and energy accumulation is proposed. Fiber optic rings are employed for the detection of PD signals, and the precise localization of these signals is achieved by calculating the DAS energy spectrum. The fiber optic ring is wrapped around the cable joint to achieve the detection and locating of the PD signal. The results indicate that the use of a fiber optic ring can realize the sensitive detection and locating of PD. The signal strength is maximum when the length of the fiber ring is equal to the spatial resolution of DAS. The sensitivity of PD detection in cable joints can reach 40 pC, and the simultaneous detection and localization of PD at different locations can be realized. The research can provide support for theoretical and experimental for the monitoring of PD in power equipment.

Electromagnetic time reversal for online partial discharge location in power cables: Influence of interfering reflections from grid components

AbstractIn online single‐sided partial discharge (PD) location, the measured PD reflection patterns are affected by the characteristics of all the components of the associated power network. This paper analyses the performance of a PD location method based on electromagnetic time reversal (EMTR) theory, when interfering reflections contribute to the transient signals emitted by the PD event. The topology analysed is formed from a ring main unit (RMU) in a medium voltage grid with mixed cross‐linked polyethylene and paper‐insulated lead‐covered (PILC) cable sections. The PD reflection patterns, observed at the RMU, are disturbed by the reflections coming from the impedance discontinuities of the circuit and by the reflections coming from the cable ends of the PILC cables connected to the RMU. The simulated configuration is chosen such that classical location techniques tend to fail due to overlapping peaks and other signal distortion. This is because the classic techniques are based on identifying individual reflection peaks from which the PD source can be determined via differences in time of arrival. The numerical investigation shows that the accuracy of the EMTR‐based location method is robust against these effects, achieving a PD localisation with an error less than the 0.1%. The results also show that the EMTR‐based method can localise PDs using a PD monitoring point located somewhere along the network and not necessarily at the line termination.

A novel meta‐learning network for partial discharge source localization in gas‐insulated switchgear via digital twin

AbstractDue to the requirement for highly precise synchronous sampling and the substantial reliance on time difference calculations, the current partial discharge (PD) localization based on the time difference of arrival is only applicable in certain situations. As digital twin technology has advanced, it is possible to employ virtual models to support gas‐insulated switchgear (GIS) PD localization. To do this, we propose a meta‐learning (ML) network with the aid of digital twin for actual GIS PD localization. Firstly, a GIS digital twin model was established to acquire an auxiliary simulated sample library. Then, a temporal convolutional network is established to extract the discriminable features, effectively obtain the time dependence between features, and improve the accuracy of localization. Next, ML is adopted to quickly learn meta‐knowledge that can be applied across tasks, and the model's sensitivity to task changes is improved. Finally, the model is fine‐tuned through a limited number of samples from the target task, and high precise PD localization is achieved. The experimental results demonstrate that the ML has an average localization error of only 9.25 cm and a probability density rose to 93% within 20 cm, which is clearly superior to previous methods.

Multiple ultrasonic partial discharge DOA estimation performance of KPCA Pseudo-Whitening mnc-FastICA

Empirical studies into partial discharge (PD) localization face the difficulty of signal contamination from external noise and interference. To extract relevant information from PD signals collected by acoustic sensor array, Kernel Principal Component Analysis (KPCA) pseudo-whitening with modified noncircular Fast Independent Component Analysis (mnc-FastICA) were used. By this approach, separated matrix and observed signals can be achieved. The reconstructed array manifold matrix created from the separated matrix generated by KPCA-mnc-FastICA may be used in the Direction of Arrival (DOA) estimation approach. An angle error correction matrix is designed to compensate for the sensor's phase inaccuracy. The simulation results show that angle identification accuracy is outstanding, with mistakes limited to a small margin of 2°. Furthermore, the results of trials show that this technique can efficiently isolate individual target signals even when they are polluted with significant noise and interference. The average errors in azimuth and pitch angle are less than 2° and 1.1°, respectively. These data support the effectiveness of the approach for effectively separating signals and estimating DOA of numerous signal sources.

Domain-alignment multitask learning network for partial discharge condition assessment with digital twin in gas-insulated switchgear

Deep-learning-driven methods have made great progress in the condition assessment of partial discharge (PD) which including diagnosis and location in gas-insulated switchgear (GIS). However, these methods perform diagnosis and location as two separate tasks and ignore the coupling relationship. In addition, these methods all require obtaining sufficient samples to develop models, and the model becomes ineffective when there is a significant difference in sample distribution. Therefore, we propose a novel domain-alignment multitask learning network (DAMTLN) for condition assessment including diagnosis and location assisted by digital twin. Firstly, a digital virtual model is established to assist the actual condition assessment of GIS PD. Then, a novel multitask network is constructed to mine the coupling relationship between the two tasks. Finally PD condition assessment guided by a digital twin model are achieved via a combination of local-maximum-mean-discrepancy-based and adversarial -based domain adaptation, in which fine-grained information on each category is captured. Experimental results show that the proposed DAMTLN achieved a diagnostic accuracy of 98.73%, and the mean absolute error of location was 9.06 cm, which were significantly better than the results of other methods. The DAMTLN thus provides a new avenue for PD diagnosis and location driven by ‘data–physics’ coupling.

Application of Pathfinding Algorithms in Partial Discharge Localization in Power Transformers.

The introduction of artificial intelligence (AI) to ultra-high-frequency (UHF) partial discharge (PD) monitoring systems in power transformers for the localization of PD sources can help create a robust and reliable system with high usability and precision. However, training the AI with experimental data or data from electromagnetic simulation is costly and time-consuming. Furthermore, electromagnetic simulations often calculate more data than needed, whereas, for localization, the signal time-of-flight information is the most important. A tailored pathfinding algorithm can bypass the time-consuming and computationally expensive process of simulating or collecting data from experiments and be used to create the necessary training data for an AI-based monitoring system of partial discharges in power transformers. In this contribution, Dijkstra's algorithm is used with additional line-of-sight propagation algorithms to determine the paths of the electromagnetic waves generated by PD sources in a three-dimensional (3D) computer-aided design (CAD) model of a 300 MVA power transformer. The time-of-flight information is compared with results from experiments and electromagnetic simulations, and it is found that the algorithm maintains accuracy similar to that of the electromagnetic simulation software, with some under/overestimations in specific scenarios, while being much faster at calculations.

Ultra‐high frequency sensors positioning on the power transformer to mitigate the negative effects on the partial discharge localization accuracy

AbstractThis paper proposes a new method for improving the partial discharge (PD) localization accuracy by detecting the best places of ultra‐high frequency (UHF) sensors on the power transformer tank. In this way, firstly, the critical sites inside the power transformer that can produce PD are determined. Then the sensors’ positioning is done in such a way that if the PD happens at each point of these critical sites, the distance between the estimated PD location obtained by solving the time difference of arrival (TDOA) equations and the actual PD location is laid in an acceptable range. For this purpose, the TDOA equations are linearized around the critical PD coordinates as a matrix form A.X = B. The configuration of the sensor positions that minimizes the condition number of the matrix A is the best configuration. To evaluate the effectiveness of the proposed method, several simulations and evaluations are performed on a power transformer in the CST software. The obtained results and comparing them with the previous method and also conventional arbitrary sensors positioning confirm the effectiveness of this method.

A Machine Learning Based Hybrid Algorithm for Partial Discharge Localization in Power Transformers

A Machine Learning Based Hybrid Algorithm for Partial Discharge Localization in Power Transformers

Novel Approach for Partial Discharge Localization Based on Fiber-Optic F–P Sensing Array and Modified TDOA in a 110-kV Transformer

Novel Approach for Partial Discharge Localization Based on Fiber-Optic F–P Sensing Array and Modified TDOA in a 110-kV Transformer

Fault localization on power cables using time delay estimation of partial discharge signals

Precise localization of partial discharge (PD) sources on power cables is vital to prevent power line failures that can lead to significant economic losses for electrical suppliers. This study proposes four methods to estimate the time delay of PD signals under electromagnetic interference, including white Gaussian noise (WGN) and discrete sinusoidal interference (DSI), using denoised PD signals with signal-to-noise ratios ranging from 10.6 to -7.02 dB. The maximum peak detection (MPD) and cross-correlation (CC) approaches, as well as two new techniques, interpolation cross-correlation (ICC) and envelope cross-correlation (ECC), are evaluated for their effectiveness in PD source localization. The researchers employ the time difference of arrival (TDoA) algorithm to compute PD location using the double-end PD location algorithm, where the PD location precision serves as an indicator of the accuracy of the time delay estimation methods. The study concludes that CC and ICC are the most suitable methods for estimating the time delay of PD signals in the PD location algorithm, as they exhibit the lowest error rates. These results suggest that CC and ICC can be used effectively for precise PD source localization under electromagnetic interference on power cables.

Partial Discharge Localization through k-NN and SVM

Power transformers are essential for the distribution and transmission of electricity, but they are prone to degradation due to faults early on. Partial Discharge (PD) is the most significant pointer of insulation breakdown in high-voltage apparatus. Dissolved Gas Analysis (DGA) is a commonly used technique for detecting and diagnosing PD. However, DGA data often contain missing values, which can significantly affect the accuracy of PD diagnosis. To mitigate the issues of missing values, this paper proposes using the k-Nearest Neighbors (kNN) technique to impute the missing values in the dataset. Further, it combines kNN with a Support Vector Machine (SVM) to detect the possibility of a PD source in the high-voltage apparatus. The approach was evaluated on a real-world DGA dataset and achieved high classification performance and discriminatory power for distinguishing between PD and non-PD instances. The effectiveness of the missing value imputation technique was evaluated, and the proposed approach demonstrated improved accuracy and precision compared to methods without imputation. The proposed approach offers a current solution for PD analysis in power transformers using DGA data with missing values.