Partial Discharge Severity Research Articles

Using SF6 Decomposed Component Analysis for the Diagnosis of Partial Discharge Severity Initiated by Free Metal Particle Defect

The decomposition characteristics of a SF6 gas-insulated medium were used to diagnose the partial discharge (PD) severity in DC gas-insulated equipment (DC-GIE). First, the PD characteristics of the whole process were studied from the initial PD to the breakdown initiated by a free metal particle defect. The average discharge magnitude in a second was used to characterize the PD severity and the PD was divided into three levels: mild PD, medium PD, and dangerous PD. Second, two kinds of voltage in each of the above PD levels were selected for the decomposition experiments of SF6. Results show that the negative DC-PD in these six experiments decomposes the SF6 gas and generates five stable decomposed components, namely, CF4, CO2, SO2F2, SOF2, and SO2. The concentrations and concentration ratios of the SF6 decomposed components can be associated with the PD severity. A minimum-redundancy-maximum-relevance (mRMR)-based feature selection algorithm was used to sort the concentrations and concentration ratios of the SF6 decomposed components. Back propagation neural network (BPNN) and support vector machine (SVM) algorithms were used to diagnose the PD severity. The use of C(CO2)/CT1, C(CF4)/C(SO2), C(CO2)/C(SOF2), and C(CF4)/C(CO2) shows good performance in diagnosing PD severity. This finding serves as a foundation in using the SF6 decomposed component analysis (DCA) method to diagnose the insulation faults in DC-GIE and assess its insulation status.

Assessment of PD severity in gas‐insulated switchgear with an SSAE

Scientific partial discharge (PD) severity evaluation is highly important to the safe operation of gas-insulated switchgear. However, describing PD severity with only a few statistical features such as discharge time and discharge amplitude is unreliable. Hence, a deep-learning neural network model called stacked sparse auto-encoder (SSAE) is proposed to realise feature extraction from the middle layer with a small number of nodes. The output feature that is almost similar to the input PD information is produced in the model. The features extracted from PD data are then fed into a soft-max classifier to be classified into one of four defined PD severity states. In addition, unsupervised greedy layer-wise pre-training and supervised fine-tuning are utilised to train the SSAE network during evaluation. Results of testing and simulation analysis show that the features extracted by the SSAE model effectively characterise PD severity. The performance of the SSAE model, which possesses an average assessment accuracy of up to 92.2%, is better than that of the support vector machine algorithm based on statistical features. According to the tested number of SSAE layers and features and the training sample size, the SSAE model possesses good expansibility and can be useful in practical applications.

Study on the Characteristic Decomposition Components of DC SF6-Insulated Equipment under Positive DC Partial Discharge

Pulses with different amplitudes occur when an inner partial discharge fault exists in SF6-gas-insulated DC high-voltage electrical equipment. SF6 decomposes following complex physical and chemical processes. Discussing and quantifying the mathematical relationship of pulse discharge quantities to SF6 decomposition component characteristics is helpful to evaluate and predict the insulation fault severity and development trends in SF6-insulated DC equipment. Numerous simulation experiments on SF6 decomposition under positive DC partial discharge were performed on a needle-plane model. The DC partial discharge quantities of pulses are obtained, and decomposition characteristics based on the mechanism of SF6 decomposition are analyzed. Results show that generation, effective generation rate and concentration ratio c(SO2F2 + SOF4)/c(SOF2 + SO2) of components increase with partial discharge severity, and the first two act in the following order: SOF4 > SOF2 > SO2F2 > SO2 > CF4 when the discharge quantity level is higher than 238,957 pC. Finally, a coefficient matrix illustrates the mathematical effects of pulses with different discharge quantities on different SF6 decomposition components. The pulses whose discharge quantity is higher than 50 pC have obvious promotion effects on the formation of decomposition components, whereas the pulses whose discharge quantity lower than 50 pC decrease the positive promotion effects that large discharge pulses have on decomposition components. The higher the effective generation rates are, the greater the partial discharge severity is, and their change laws provide a new method to evaluate and predict insulation fault severity and development trends in DC SF6-insulated equipment with high reliability and detection stability.

A combined preprocessing method for UHF PD detection based on kurtosis features

Ultra-high-frequency (UHF) partial discharge (PD) detection has been proved as a powerful tool to evaluate the status of electrical equipment. Unfortunately, on-site UHF detection suffers from intensive noise interference and great computational burden, which have long been the key bottleneck for its wide utilization in the field environments. In order to provide more valuable information for subsequent processing like PD location, pattern recognition, or severity assessment, a combined preprocessing method for UHF PD detection is proposed in this paper. This scheme integrates a novel Ensemble Empirical Mode Decomposition-based signal reconstruction method and a kurtosis-based pulse window extraction method. To demonstrate the effectiveness of the presented technique, both simulated and laboratory measured PD data are obtained and further tested. Results reveal that this preprocessing method can accurately detect and extract the PD pulse from raw data, which may lessen the computational burden for any further processing. In addition, more accurate and reliable feature information can be retained.

Feature extraction and severity assessment of partial discharge under protrusion defect based on fuzzy comprehensive evaluation

Partial discharge (PD) is an early manifestation of insulation fault that occurs in electrical equipment, which holds rich feature information used to insulation assessment. In this study, step voltage method was applied to investigate the PD development process of typical protrusion defects in gas-insulated switchgear. Nine features were extracted to represent the deterioration degree of internal insulation. Fuzzy C means clustering was applied to solve the state centres. PD severity was divided into four states based on the PD developing process, and the harmfulness of each state was illustrated. Finally, adaptive objective weight technology based on the theory of maximising deviation was adopted, and two-level fuzzy comprehensive evaluation (TL-FCE) model was established to address the problem of the weight allocation of different features on the evaluation result. The experimental test has been conducted to test the validity and effectiveness of the model. It is proved that TL-FCE owns good performance to assess PD severity, and it is more suitable to handle fuzzy boundary problems than support vector machine.

Features Extraction and Mechanism Analysis of Partial Discharge Development under Protrusion Defect

In order to study the development of partial discharge (PD) under typical protrusion defects in gas-insulated switchgear, we applied step voltages on the defect and obtained the φ-u and φ-n spectrograms of ultra-high frequency (UHF) PD signals in various PD stages. Furthermore, we extracted seven kinds of features to characterize the degree of deterioration of insulation and analyzed their values, variation trends, and change rates. These characteristics were inconsistent with the development of PD. Hence, the differences of these features could describe the severity of PD. In addition, these characteristics could provide integrated characteristics regarding PD development and improve the reliability of PD severity assessment because these characteristics were extracted from different angles. To explain the variation laws of these seven kinds of parameters, we analyzed the relevant physical mechanism by considering the microphysical process of PD formation and development as well as the distortion effect generated by the space charges on the initial field. The relevant physical mechanism effectively allocated PD severity among these features for assessment, and the effectiveness and reliability of using these features to assess PD severity were proved by testing a large number of PD samples.

Formation Characteristics of SF6 Decomposition under Partial Discharge Induced by Metal Protrusions with Varying Degrees of Severity

Due to its excellent insulating and arc-suppression properties, sulfur hexafluoride (SF6) is widely used in gas-insulated switchgear. In the presence of partial discharge, SF6 decomposes into various by-products according to discharge scenarios. These by-products are often used to detect and identify partial discharge. In this article, formation characteristics of SF6 decomposition under partial discharge induced by metal protrusions with varying degrees of severity are investigated. The varying degrees of severity of partial discharge were assessed by partial discharge applied voltage and partial discharge inception voltage. Partial discharge applied voltage and partial discharge inception voltage are related to magnitude of partial discharge and varying degrees of the non-uniform electric field distribution distortion, respectively. The results show that the production rates of SO2F2 and SOF2+SO2 decrease with increasing partial discharge inception voltage, while increasing proportionally to partial discharge applied voltage. The ratio of SO2F2 to (SOF2+SO2) exhibits a similar trend. As partial discharge applied voltage increases, the production rates of CO2 and CF4 increase, but the ratio of CO2 to CF4 decreases. Based on the experiments, the formation mechanisms of SF6 decomposition by-products under varying degrees of severity of partial discharge are analyzed.

Investigation of the Evaluation of the PD Severity and Verification of the Sensitivity of Partial-Discharge Detection Using the UHF Method in GIS

The measurement of partial discharge (PD) using ultra-high frequency (UHF) method is considered an effective approach to monitor the online condition of the equipment, which is used to facilitate the detection of incipient defects in gas-insulated switchgear (GIS). In this paper, in order to assess the severity of PD defects using UHF method, the actual 220-kV GIS were subjected to experimentation. The PD was measured simultaneously by using the UHF method and the IEC60270 measurement technique. The combined measurement has been carried out on different defects in PD test cells, including the floating metal, metallic protrusion, and void in epoxy resin. The inner coupler installed in the GIS tank, and the outer coupler placed outside the spacer, are used to measure the UHF signal. The relationship of the apparent charge and the amplitude of the time-domain signal, and the integral energy from 300 MHz to 1.6 GHz of the signal induced by PD in GIS are analyzed thoroughly. In this way, the severity of PD can be quantified using the UHF signal. In addition, to verify the sensitivity of the UHF detection method, a pulse generator with a rise time of about 500 ps is designed. The GIGRE method for the verification of sensitivity, which is used worldwide, is applied in the experiment. The results of the pulse generator experiment are compared to the results produced by the actual discharge sources, and several possible approaches of improving this method are proposed.

Impact of high voltage harmonics on interpretation of partial discharge patterns

Typically, in laboratory and diagnostics tests focused on measuring partial discharges, a pure sinusoidal voltage waveform is assumed. However, in practice the spectral content of the applied voltage is rarely so ideal and additional spectral components have a significant impact on the discharge behavior in electrical insulation systems. In this paper the influence of voltage harmonics on partial discharge behavior and pattern evolution is analyzed. The presented experiments were conducted on a transformer installed in a municipal network and on specimens representing defects in a transformer insulation system, in the form of a micro blade protrusion on a winding conductor and an embedded gas inclusion. The experimental results showed that various harmonic compositions superimposed on the fundamental sinusoidal waveform have a significant impact on PD intensity and maximum charge. In consequence, PD pattern phase and amplitude distributions are distorted and calculated statistical parameters are changed. The rough parameters reflecting the waveform spectral quality, such as THD, are rather superficial and do not provide a unique and reliable signature of the applied voltage in terms of phase angle variations. In certain environments, neglecting harmonic content in the voltage may lead to a misleading interpretation and assessment of partial discharge severity. A complete knowledge of harmonic content is essential and critical in conducting a proper assessment of the impact of PD, including acceptability criteria and compatibility with the standards.

Fault Pattern Recognition for Partial Discharge of Electrical Power Equipment Based on Properties of Electrical Materials

Partial discharge causes mainly the insulation deterioration. It is the significant symptom and manifestation, and is an important factor of the insulation failure for the electrical power equipment. On the basis of analyzing the physical model of partial discharge, this paper used the online monitoring technology of partial discharge that combines the ultra high frequency (UHF) method and the acoustic emission (AE) method, studied the fault pattern recognition method of partial discharge based on the case-based reasoning algorithm, and established the intelligent fault identification system of partial discharge based on the case-based reasoning. The system can accurately and reliably identify the fault mode type, the specific fault location and severity of partial discharge for the electrical power equipment to make the health evaluation and improve the reliability. Through the application of the new materials and new technology, the load loss of the transformer can drop by 15%, the no-load loss can decline by 50% and the fee of electricity loss can down by 32.5%.

Simultaneous measurement of partial discharges using IEC60270 and radio-frequency techniques

Partial discharge (PD) measurement is an established condition monitoring technique used to facilitate the detection of incipient faults in high voltage electrical insulation systems such as gas-insulated switchgear and power transformers. By simultaneously recording partial discharge using both the IEC60270 'apparent charge' measurement technique and the more recent Radio Frequency method, which measures the energy radiated from the discharge, it is thought that more information can be gained about the nature of the PD source. Combined measurement has been carried out on various common PD source topologies recreated under laboratory conditions using PD test cells in a range of insulating media including SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> , oil and epoxy resin. Test cell geometries include floating electrodes, free particles and protrusions. By plotting apparent charge against the energy of the RF signal for a large number of PD pulses, the relationship between the two techniques can be quantified for a given test configuration. It has been found that the correlation between the two techniques produces characteristic patterns specific to each defect type. It is envisioned that combined RF/IEC measurements will contribute to a more widespread acceptance of the RF technique in terms of its ability to quantify PD severity by relating the RF energy to the more widely accepted IEC60270 pC level.

Practical Aspects of Apparent Charge Partial Discharge Measurements

While other industries have adopted charge measurement methods to monitor partial dis'charge severity, the power transformer industry has used RIV methods as the standard test. Very little practical comparative information has been published which relates these two methods. RIV meters can be used as charge indicators by making appropriate charge calibrations on the transformer under test. These procedures have been used to make parallel charge and RIV measurements on selected large power transformers. The results of these measurements show that there is no simple relationship between these two units of measurement. The actual relationship is a function of several transformer design parameters including physical size, type, and ratings. The results suggest that, for a given design type, RIV techniques may become less sensitive to partial discharge severity as transformer size increases. The results also show that RIV instruments can be used with charge calibration procedures to provide a more uniform measure of transformer quality.