Partial Discharge Pulses Research Articles

A Novel Method to Enhance Partial Discharge Localization Accuracy Using Sectional Winding Analysis and a Neural Network-Based Learning Vector Quantization Model

Timely detection of insulation faults in power transformers is essential to prevent damage, outages, and financial losses. Locating partial discharge (PD) sources in transformers with complex windings poses a significant challenge, leading to insulation failure. This paper presents a novel approach combining a learning vector quantization (LVQ) network with frequency response analysis (FRA) to locate PDs along the windings, even in noisy environments, without using noise suppression techniques. The method obtains sectional winding transfer functions (SWTFs) using vector fitting (VF). A Gaussian function is injected into each SWTF, and the responses are captured as PD reference signals to train the LVQ network. In addition, a 5000 pC PD test pulse from a calibrator is injected into randomly selected laboratory winding sections, while a traveling rectangular wave is injected into the estimated SWTFs, obtained from FRA using vector fitting. The resulting responses are utilized as PD test signals to assess the proposed method across various PD pulse waveforms. The simulation and experimental results demonstrate that the proposed method achieves superior performance, particularly in high-level background noise, leading to significantly improved PD localization accuracy. Finally, the method's efficiency is compared with previous studies, particularly regarding performance in noisy conditions.

Review on peak detect and hold circuits and their applicability in partial discharge detection

Abstract This article reviews various designs of electrical peak detect and hold circuits and tests their applicability for the detection of partial discharge pulses. Since partial discharges are very short pulses in the nanosecond range, the peak detector circuit must be very fast and thus work precisely over a wide bandwidth up to the GHz range. The article begins with a comprehensive literature review and gives an overview of the state of the art of peak detector circuits. As a result of the literature research, four different peak detector designs are identified that appear suitable for measuring partial discharges. These four circuits are then simulatively tested for their performance in measuring the pulse height of various partial discharge pulses. It turns out that two of the identified circuits are able to measure nanosecond pulses of small amplitude. These two circuits must be further optimized for partial discharge detection in the future. In addition, the circuit should be tested prototypically with real partial discharge pulses.

Optimum feature selection for classification of PD signals produced by multiple insulation defects in electric motors

Partial discharges (PD) are initiated in electrical equipment during various points of the equipment’s lifecycle. The intensity of PD defects rises continuously with time, which can lead to insulation degradation and reduced operational life of the electrical equipment. The optimum feature selection of PD signals captured, from different insulation defects, can enhance the classification accuracy of PD defects and facilitate better visualization of PD parameters for electric motor (EM) insulation monitoring and diagnostics. This paper presents a hybrid approach, based on Maximize Relevancy and Minimize Redundancy (mRMR) and random forest (RF), for the optimum feature selection and classification of PD signals in EMs containing multiple defects. For this purpose, four PD defects are developed in the EMs insulation under laboratory conditions, and 800 PD signals are acquired using a conventional IEC-60,270 experimental platform. The severity of these defects is determined and investigated based on PD characteristic parameters. Several features of both PD sweep signals and conventional PD pulses are extracted. Consequently, the mRMR feature selection technique is implemented to select the significant features of the detected PD signals. To establish the plausibility of this technique, several other feature selection algorithms, including RefliefF, Gini Index (GI), and Information Gain (IG), are introduced for the same datasets. The performance of all these feature selection algorithms is validated using three commonly used classification techniques such as RF, support vector machines (SVM), and k-nearest neighbors (k-NN). In summary, the results show that the combination of mRMR and RF proves to be the most effective feature selection algorithm for the classification of insulation defects in EMs, achieving an accuracy of 99.875%. This accuracy is significantly better than other feature selection and classification techniques and indicates its potential for application to other power system components.

A Novel Method for Online Diagnostic Analysis of Partial Discharge in Instrument Transformers and Surge Arresters from the Correlation of HFCT and IEC Methods

In this work, a new methodology is proposed for the online and non-invasive extraction of partial discharge (PD) pulses from raw measurement data obtained using a simplified setup. This method enables the creation of sub-windows with optimized size, each containing a single candidate PD pulse. The proposed approach integrates mathematical morphological filtering (MMF) with kurtosis, a first-order Savitzky-Golay smoothing filter, the Otsu method for thresholding, and a specific technique to associate each sub-window with the phase angle of the applied voltage waveform, enabling the construction of phase-resolved PD (PRPD) patterns. The methodology was validated against a commercial PD detection device adhering to the IEC (International Electrotechnical Commission) standard. Experimental results demonstrated that the proposed method, utilizing an off-the-shelf 8-bit resolution data acquisition system and a low-cost high-frequency current transformer (HFCT) sensor, effectively diagnoses and characterizes PD activity in high-voltage equipment, such as surge arresters and instrument transformers, even in noisy environments. It was able to characterize PD activity using only a few cycles of the applied voltage waveform and identify low amplitude PD pulses with low signal-to-noise ratio signals. Other contribution of this work is the diagnosis and fault signature obtained from a real surge arrester (SA) with a nominal voltage of 192 kV, corroborated by destructive disassembly and internal inspection of the tested equipment. This work provides a cost-effective and accurate tool for real-time PD monitoring, which can be embedded in hardware for continuous evaluation of electrical equipment integrity.

Locating Insulation Defects in HV Substations Using HFCT Sensors and AI Diagnostic Tools.

In general, a high voltage (HV) substation can be made up of multiple insulation subsystems: an air insulation subsystem (AIS), gas insulation subsystem (GIS), liquid insulation subsystem (power transformers), and solid insulation subsystem (power cables), all of them with their grounding structures interconnected and linked to the substation earth. Partial discharge (PD) pulses, which are generated in a HV apparatus belonging to a subsystem, travel through the grounding structures of the others. PD analyzers using high-frequency current transformer (HFCT) sensors, which are installed at the connections between the grounding structures, are sensitive to these traveling pulses. In a substation made up of an AIS, several non-critical PD sources can be detected, such as possible corona, air surface, or floating discharges. To perform the correct diagnosis, non-critical PD sources must be separated from critical PD sources related to insulation defects, such as a cavity in a solid dielectric material, mobile particles in SF6, or surface discharges in oil. Powerful diagnostic tools using PD clustering and phase-resolved PD (PRPD) pattern recognition have been developed to check the insulation condition of HV substations. However, a common issue is how to determine the subsystem in which a critical PD source is located when there are several PD sources, and a critical one is near the boundary between two HV subsystems, e.g., a cavity defect located between a cable end and a GIS. The traveling direction of the detected PD is valuable information to determine the subsystem in which the insulation defect is located. However, incorrect diagnostics are usually due to the constraints of PD measuring systems and inadequate PD diagnostic procedures. This paper presents a diagnostic procedure using an appropriate PD analyzer with multiple HFCT sensors to carry out efficient insulation condition diagnoses. This PD procedure has been developed on the basis of laboratory tests, transient signal modeling, and validation tests. The validation tests were carried out in a special test bench developed for the characterization of PD analyzers. To demonstrate the effectiveness of the procedure, a real case is also presented, where satisfactory results are shown.

Classification of Multi-Source Partial Discharge in GIS

Abstract Partial discharge (PD) analysis is an important method to identify electrical equipment faults. However, due to the influence of environmental electromagnetic interference and other equipment discharges in the power supply circuit during the on-site PD test, the collected PD signal is the combination of various PD and noise, which makes it challenging to identify the type or source of on-site partial discharge. A multi-source PD classification method based on the multiple threshold and K-means clustering algorithm is pro-posed to solve this problem. First, wavelet transform with an improved threshold is used to initially denoise the original signal. Then the multiple threshold method is used to filter the residual ripple between every two pulses. Finally, spectrum analysis is carried out for each pulse, and the maximum value of the spectrum is extracted as the feature quantity. Then, the K-means algorithm is used to classify the PD pulses, and the PRPD images of PD pulses in different frequency domains are obtained. The results show that the method adopted in this paper can denoise, extract and classify the PD signals collected on the site, and the PRPD diagram of PD pulse after classification is more obvious, which provides a basis for identifying the PD source on the site.

Study on the partial discharge characteristics induced by the motion of cellulose particles in transformer oil

AbstractCellulose particles present a significant concern within the oil‐paper insulation of transformers, posing potential risks to insulation performance. Under the influence of the electric field, the movement of cellulose particles can compromise the transformer's insulation, leading to potential failure. An experimental platform was established to synchronously record particle motion images, partial discharge (PD) pulses, and electric voltage waveforms in oil, aiming to observe the PD characteristics resulting from particle motion under alternating current (AC) voltage and investigate the relationship between different particle motion modes, motion positions, and PD signals. The findings reveal that the phase distribution of PD signals is correlated with the particle motion mode. Specifically, the phase distribution of PD pulses during the back‐and‐forth motion mode is between 4°–94° and 182°–275°. In the suspended oscillation motion mode, the PD pulses phase is concentrated between 20°–84° and 203°–268°. The generation of PD pulses is closely linked to the particle's motion position. PD pulses occur when the particle remains on the electrode during the back‐and‐forth motion mode, generally, PD pulses rarely occur during the jumping process between the two electrodes. In the suspended oscillation motion mode, PD pulses occur when the particle moves upward, but generally do not occur during downward movement. Furthermore, the Pulse Sequence Analysis technique was used to employ the PD characteristics caused by particle motion in transformer oil. The simulation calculations of the electric field distribution for two different particle motion modes show that the particle's motion can cause distortion of the electric field distribution, leading to the generation of PD. The study of the PD characteristics at different particle motion modes and positions obtained contributes to a deeper understanding of the PD induced by cellulose particle motion under AC voltage and provides a reference for the insulation evaluation of transformers.

Magnetoelectric Sensor Operating in d15 Thickness-Shear Mode for High-Frequency Current Detection.

For the application of high-frequency current detection in power systems, such as very fast transient current, lightning current, partial discharge pulse current, etc., current sensors with a quick response are indispensable. Here, we propose a high-frequency magnetoelectric current sensor, which consists of a PZT piezoelectric ceramic and Metglas amorphous alloy. The proposed sensor is designed to work under d15 thickness-shear mode, with the resonant frequency around 1.029 MHz. Furthermore, the proposed sensor is fabricated as a high-frequency magnetoelectric current sensor. A comparative experiment is carried out between the tunnel magnetoresistance sensor and the magnetoelectric sensor, in the aspect of high-frequency current detection up to 3 MHz. Our experimental results demonstrate that the d15 thickness-shear mode magnetoelectric sensor has great potential for high-frequency current detection in smart grids.

A Study on Simultaneous Measurement of Partial Discharge in Epoxy Resin Insulator using IMC and Rogowski Coil

In practice, field measurement of partial discharge is easily suffered by interferences due to the noisy signal has been mixed with the resulting partial discharge signal. Therefore, the detection and measurement of PD are important to monitor the insulation life in high voltage (HV) power equipment. The paper presents the simultaneous measurement of partial discharge (PD) pulse parameters using the impedance matching circuit and the Rogowski coil sensor to study the features of partial discharge on the epoxy resin insulator. Two different thicknesses of samples were used to observe the PD pulse parameters with the voltage applied vary depending on the thickness of the sample to attain constant electric field intensity for both samples. The step response parameters resulted from the measurement can be used as a time domain features to discriminate various types of PD in HV power equipment. Thus, the PD can be observed clearly, maintenance can be performed, and the performance of the power system can be improved.

Deep learning and data augmentation for partial discharge detection in electrical machines

Fault testing in the production line of automotive traction machines is essential to ensure the desired lifetime. Since repetitive partial discharges (PDs) caused by anomalies in the insulation system lead to premature breakdowns of electrical machines, a reliable PD detection is of great importance. This paper proposes deep learning (DL) methods to improve the discrimination of PD from background noise in comparison with the state-of-the-art amplitude based PD detection in the production line. First, a systematic data extraction and labeling procedure is introduced to obtain correctly labeled datasets from arbitrary PD measurements. In addition, datasets are enhanced with low signal-to-noise ratio PD pulses by applying a special data augmentation approach. 13 different convolutional, recurrent and fully connected neural networks are compared for various time-frequency representations of the input signals. Hyperparameters for input transform, network topology and solver are optimized for all 13 combinations to ensure a fair case study. As a result, the two-dimensional convolutional neural network with continuous wavelet transform achieves the best accuracy of around 99.76% on a test dataset of PD signals originating from previously not utilized test objects. All DL models considered in this comparison outperform the state-of-the-art threshold-based PD classification. Even for PD events with an amplitude close to the noise level, the detection rate is still around 95% for the best network. Furthermore, without applying the proposed data augmentation procedure, the DL models investigated are not able to distinguish small PD pulses from noise.

The Design, Fabrication, and Evaluation of a Phase-Resolved Partial Discharge Sensor Embedded in a MV-Class Bushing.

This paper proposes a novel phase-resolved partial discharge (PRPD) sensor embedded in a MV-class bushing for high-accuracy insulation analysis. The design, fabrication, and evaluation of a PRPD sensor embedded in a MV-class bushing aimed to achieve the detection of partial discharge (PD) pulses that are phase-synchronized with the applied primary HV signal. A prototype PRPD sensor was composed of a flexible printed circuit board (PCB) with dual-sensing electrodes, utilizing a capacitive voltage divider (CVD) for voltage measurement, the D-dot principle for PD detection, and a signal transducer with passive elements. A PD simulator was prepared to emulate typical PD defects, i.e., a metal protrusion. The voltage measurement precision of the prototype PRPD sensor was satisfied with the accuracy class of 0.2 specified in IEC 61869-11, as the maximum corrected voltage error ratios and corrected phase errors in 80%, 100%, and 120% of the rated voltage (13.2 kilovolts (kV)) were less than 0.2% and 10 min, respectively. In addition, the prototype PRPD sensor had good linearity and high sensitivity for PD detection compared with a conventional electrical detection method. According to performance evaluation tests, the prototype PRPD sensor embedded in the MV-class bushing can measure PRPD patterns phase-synchronized with the primary voltage without any additional synchronization equipment or system. Therefore, the prototype PRPD sensor holds potential as a substitute for conventional commercial PD sensors. Consequently, this advancement could lead to the enhancement of power system monitoring and maintenance, contributing to the digitalization and minimization of power apparatus.

Diagnostics analysis of partial discharge events of the power cables at various voltage levels using ramping behavior analysis method

Partial discharge events can occur in high-voltage cables. It can be caused by defects in the cable insulation, contamination, or a combination of both. Partial discharge in cables can lead to insulation failure and cable failure. This investigation aims to identify the trends and patterns in the internal partial discharge (PD) occurrences in the power cables when exposed to different voltage levels - 6.4, 7.4, 9.4, and 11.3 kV. For pattern extraction, a well-established method, ramping behavior analysis, is implemented to extract and classify PD occurrences into sets of significant and stationary events. In this investigation, significant events correspond to an absolute peak (the discharge pulse) and subsequent oscillations from the measurement sensor. The stationary events represent a collection of noise that is recorded during the measurement. These noise signals are essentially small variations within a pre-determined threshold range. Furthermore, a comparative analysis is performed for each voltage level and for the voltage levels.This investigation brings new knowledge on how internal partial discharge pulses occur at various voltage stress levels. Specifically, the emerging patterns and trends of internal partial discharge events. The results indicate that there is a positive correlation between the number of PD events and the increase in stress levels. Furthermore, negative PD peaks are more frequent at lower stress levels.

An efficient methodology of simultaneous three-phase withstand voltage and sensitive partial discharge testing of medium voltage power cables

Withstand voltage tests and partial discharge (PD) measurement are widely accepted as indispensable techniques for assessing the insulation condition of power equipment. To reduce testing time, a favorable tool that can perform insulation tests simultaneously for three-core medium voltage (MV) power cables is urgently needed. This study aims to develop a high-efficiency solution for this purpose. The proposed methodology’s topology and operation principle are presented and illustrated, which can be used to conduct three-phase withstand voltage tests and sensitive PD measurement of MV cables simultaneously under different excitation voltages. Circuit analysis and parameter optimization are carried out to adapt the system to the application. Thus, the proposed methodology is implemented, and a prototype is built. A new method for achieving high sensitivity in screening PD pulses is proposed, and some experimental tests on a three-core MV power cable with artificial defects are performed to validate the effectiveness and practicality of the proposed methodology.

A Denoising Method of Partial Discharge Signal Based on Improved SVD-VMD

A denoising method combined with singular value decomposition (SVD) and Variational mode decomposition (VMD) is proposed to eliminate noise in on-site partial discharge (PD) signals from high-voltage electrical equipment. In the Fourier transform power spectrum, periodic narrowband interference was eliminated after SVD was offered to determine the number of singular values of periodic narrowband interference. Then, the PD signal was decomposed into K intrinsic mode function components by VMD. The empirical mode decomposition method was used to determine the K value of white noise’s intrinsic mode function components. An improved 3σ criterion threshold method was proposed to eliminate the residual noise in the PD signal. The denoising method was compared with the other two methods to analyze the denoising effect on the simulation and experimental PD signal. This paper’s denoising method can eliminate periodic narrowband interference and white noise in different PD signals of cavity discharge, corona discharge, and those from the motors’ coil. The denoised PD pulse waveform has a higher signal-to-noise ratio and normalized correlation coefficient and a lower mean square error, indicating that the waveform’s original characteristic remains.

Application of a Rogowski Coil Sensor for Separating Internal and External Partial Discharge Pulses in Power Transformers

Partial discharge (PD) measurement is an essential tool for evaluating the health condition of a transformer insulation system. The Conventional PD measurement systems have a major drawback since they cannot discriminate the internal PD pulses and external pulse-shaped interferences, especially in online and on-site conditions. Various de-noising techniques (e.g. wavelets) have been also used in the literature, which is effective for extracting the PD signal from the noisy signals or classifying the PD types. However, the PD pulses and some of the randomly external pulse-shaped interferences have similar time-frequency characteristics, which makes them difficult to separate. In this paper, a new cost-effective and simple logic method is proposed for the separation of these interferences. This uses the polarity comparison of the output voltages of the Rogowski Coil (RC) sensor, mounted on the bushing, and measurement impedance, connected to the test tap. The same polarity represents the internal pulse, while the opposite polarity indicates the external pulses. A distribution transformer ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$20/0.4\ {\bm{kV}},\ 500\ {\bm{kVA}})$</tex-math></inline-formula> and a condenser bushing ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$72.5\ {\bm{kV}})$</tex-math></inline-formula> were considered to evaluate the performance of the proposed system. The results confirm the effectiveness of the proposed PD measurement system for separating the pulses.

Validation of a Qualification Procedure Applied to the Verification of Partial Discharge Analysers Used for HVDC or HVAC Networks

The insulation condition of HVDC grids consisting of cable systems, GIS, and converters should be monitored by partial discharge (PD) analysers using artificial intelligence (AI) tools for efficient insulation diagnosis. Although there are many experiences of PD monitoring solutions developed for the supervision of the insulation condition of HVAC grids using PD analysers, there are no standardised requirements for their qualification available yet. The international technical specification TS IEC 62478 provides general rules for PD measurements using electromagnetic methods but does not define performance requirements for qualification tests. HVDC and HVAC PD analysers must be tested by unambiguous test procedures. This paper compiles experiences of using PD analysers with HFCT sensors in HVAC grids (cable systems, GIS, and AIS) to define a qualification procedure for HVAC systems. This procedure is applicable to HVDC grids (cable systems, GIS, AIS, and converters) because the particularities related to the insulation behaviour under HVDC voltage are also considered. Representative PD sources are discussed in HVAC and HVDC positive and negative polarity. The PD pulse trend of representative insulation defects in HVDC cable systems is quite different from that of HVAC grids. Special attention should be paid to the acquisition of PD signals in HVDC grids since few pulses appear in solid insulations, mainly during voltage changes (polarity reversals or surges), but rarely in continuous operation with constant direct voltage. A synthetic PD simulator has been developed to reproduce trains of PD pulses or noise signals, similar to those that can appear in the power network. A set of three functionality tests has been developed for qualification of the diagnostic capabilities of PD analysers working up to 30 MHz addressed to HVDC or HVAC grids: (1) PD recognition test, (2) PD clustering test, and (3) PD location test. This qualification procedure has been validated by means of a round-robin test performed by five research institutes (RISE, FFII, TUDelft, TAU, and UPM) using commercial and in-development AI PD recognition and clustering tools to demonstrate its robustness and applicability. Applying this qualification procedure, two PD methods for electrical detection and prevention of insulation defects have been approved, one for HVAC and the other for HVDC grids.

New Synthetic Partial Discharge Calibrator for Qualification of Partial Discharge Analyzers for Insulation Diagnosis of HVDC and HVAC Grids.

A synthetic partial discharge (PD) calibrator has been developed to qualify PD analyzers used for insulation diagnosis of HVAC and HVDC grids including cable systems, AIS, GIS, GIL, power transformers, and HVDC converters. PD analyzers that use high-frequency current transformers (HFCT) can be qualified by means of the metrological and diagnosis tests arranged in this calibrator. This synthetic PD calibrator can reproduce PD pulse trains of the same sequence as actual representative defects (cavity, surface, floating potential, corona, SF6 protrusion, SF6 jumping particles, bubbles in oil, etc.) acquired in HV equipment in service or by means of measurements made in HV laboratory test cells. The diagnostic capabilities and PD measurement errors of the PD analyzers using HFCT sensors can be determined. A new time parameter, "PD Time", associated with any arbitrary PD current pulse i(t) is introduced for calibration purposes. It is defined as the equivalent width of a rectangular PD pulse with the same charge value and amplitude as the actual PD current pulse. The synthetic PD calibrator consists of a pulse generator that operates on a current loop matched to 50 Ω impedance to avoid unwanted reflections. The injected current is measured by a reference measurement system built into the PD calibrator that uses two HFCT sensors to ensure that the current signal is the same at the input and output of the calibration cage where the HFCT of the PD analyzer is being calibrated. Signal reconstruction of the HFCT output signal to achieve the input signal is achieved by applying state variable theory using the transfer impedance of the HFCT sensor in the frequency domain.

Defects Detection of Onboard Cable Termination in EMUs Using Partial Discharge Measurement and SDP-DL Framework

Termination is the most important part of on-board cable, which plays a vital role in ensuring the continuous and reliable power supply to EMUs (electrical multiple units). The maintenance time of EMUs is limited, so the time left for PD (partial discharge) measurement of on-board cable termination is very short, leading to the obvious decreasing of detection accuracy. For addressing this issue, this paper proposed an SDP (symmetrized dot pattern)–DL (deep learning) framework to detect the insulation defects using time series PD pulse signal. First, a PD measurement platform and experimental samples were prepared in laboratory to obtain the time series PD pulse signals of four typical insulation defects. Then, the time series PD signals were converted into SDP images using the proposed parameter optimization method. Finally, the SDP-DL framework was proposed, and three specific and typical methods were utilized, i.e., CNN (convolutional neural network), SAE (stacked auto encoder) and DBN (deep belief network). The results show that the performance of SDP-DBN method is the best, and the insulation defects can be detected with accuracy of 96.1%. In addition, the visualization ability of data increases after SDP transformation of the original PD time series signal.

Partial discharge detection on power equipment using a magneto-resistive sensor

Partial discharges (PD) detection is an effective diagnostic method to assess the insulation condition of electrical power equipment in the high-voltage laboratory or field tests. This paper presents a non-contacting PD detection method for power equipment. The method is based on an extra high-sensitivity adapted giant magneto-resistive (xMR) sensor that measures the magnetic field produced by the PD currents. Firstly, this paper describes the sensor’s relevant principle and signal conditioning circuit. Next, the sensor’s typical performance, including the frequency response and time-domain response to calibrator PD pulses, is measured and compared with our previous work. The results indicate that the xMR system’s bandwidth is improved to the MHz range. Finally, PD experiments are carried out and compared with measurements using a commercially available high-frequency current transformer (HFCT), which allows for verification of the coherence of the results concerning the PD pulses and phase-resolved PD (PRPD) patterns. The results show that PD in a cross-linked polyethylene (XLPE) cable or a gas-insulated system (GIS) with artificial discharging defects is successfully measured, demonstrating the sensitivity and performance of the xMR system for PD detection.

Identification of Electrical Tree Aging State in Epoxy Resin Using Partial Discharge Waveforms Compared to Traditional Analysis.

Electrical treeing is one of the main degradation mechanisms in high-voltage polymeric insulation. Epoxy resin is used as insulating material in power equipment such as rotating machines, power transformers, gas-insulated switchgears, and insulators, among others. Electrical trees grow under the effect of partial discharges (PDs) that progressively degrade the polymer until the tree crosses the bulk insulation, then causing the failure of power equipment and the outage of the energy supply. This work studies electrical trees in epoxy resin through different PD analysis techniques, evaluating and comparing their ability to identify tree bulk-insulation crossing, the precursor of failure. Two PD measurement systems were used simultaneously-one to capture the sequence of PD pulses and another to acquire PD pulse waveforms-and four PD analysis techniques were deployed. Phase-resolved PD (PRPD) and pulse sequence analysis (PSA) identified tree crossing; however, they were more sensible to the AC excitation voltage amplitude and frequency. Nonlinear time series analysis (NLTSA) characteristics were evaluated through the correlation dimension, showing a reduction from pre- to post-crossing, and thus representing a change to a less complex dynamical system. The PD pulse waveform parameters had the best performance; they could identify tree crossing in epoxy resin material independently of the applied AC voltage amplitude and frequency, making them more robust for a broader range of situations, and thus, they can be exploited as a diagnostic tool for the asset management of high-voltage polymeric insulation.