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In this work, samples of oil-immersed paper with different moisture levels and aging condition were studied. Frequency domain spectroscopy (FDS) of the samples was examined at 40 °C under different excitation voltages. A non-linear phenomenon in frequency domain dielectric response with different moisture contents and aging condition was observed. The non-linear phenomenon appears to be related to the hindrance effect of cellulose on the movement of ions and the Poole-Frenkel effect on the concentration of ions. With aging, the nonlinear loss peaks at different frequencies caused by the effect of nonlinear conductance and interface polarization appear in the FDS results. The non-linear phenomenon was modeled and verified experimentally. The characteristic parameters and quantitative method to estimate moisture content and the degree of aging degree of oil-immersed paper can be obtained by the proposed method.

The traditional maintenance method for power equipment is likely to cause over-maintenance or insufficient maintenance, resulting in huge waste of human and material resources. Consequently, risk based maintenance has been developed as a new maintenance method to meet the needs of the smart grid. This paper proposes a novel risk assessment method for power equipment based on health index and historical failure data, which overcomes the limitations of using single feature for risk assessment. Based on the proposed risk assessment method, an optimized maintenance strategy for power equipment is established, which greatly improves the efficiency and effectiveness of equipment maintenance.

Methods to infer the location of partial discharge (PD) in high-power transformers using acoustic emission (AE) data have been extensively studied. The inner complex structure of the transformers is one of the most critical points in localization with AE method. Windings and cores affect acoustic wave propagation by changing the arrival time because of inhomogeneous propagation. A transformer physical model has been established herein by taking these complex factors into consideration. Each node in the model is a potential PD position, and an acoustic wave route comprise a series of nodes. The velocity and propagation factors are set for each node according to its acoustic wave propagation characteristics. A propagation-time estimation algorithm is proposed to calculate the propagation-time. Based on the transformer physical model, a particle-swarm-optimization route-searching (PSORS) algorithm is employed for searching the position of the PD source. By comparing time differences of measured AE signals and the ones estimated by the PSORS algorithm, the velocities and positions of particles are continually adjusted, which can ensure their convergence to the PD source position. Localization experiments were performed in 35 and 110 kV transformers, respectively, to verify the applicability of the proposed algorithm. A protrusion defect is used to trigger PD pulses, and four AE sensors with two different arrangements are employed. The results confirm that the accuracy of proposed localization method is insensitive to the presence of metal structures blocking acoustic wave routes.

Ultra-high-frequency (UHF) sensing technique has been introduced to detect and localize partial discharge (PD) sources in air-insulated substation (AIS). This paper presents a probability-based algorithm to localize multiple PD sources which may occur simultaneously in different power equipment. Assuming that the time difference of arrival (TDOA) between all pairs of antennas in a array are normally distributed, the probability density function (PDF) of PD source coordinates can be obtained by substituting the linearized form of time difference equations into PDFs of TDOAs. When large number of PD signals are recorded, the joint PDF (JPDF) can be calculated from the product of PDF of each TDOA. Then the PD coordinates to be solved are regarded as with highest probability, and can be solved by taking the derivative of JPDF. In the case of multiple PD sources, mixed UHF signals are separated by clustering the TDOA vectors with K Means clustering method. PD experiments are performed to test the presented algorithm, and the localization accuracy of proposed algorithm is compared with other typical methods such as Newton-Raphson, Particle Swarm Optimization and plane intersection method. The results indicate that the probability-based localization algorithm reasonably integrates the TDOAs of continuous signal sequence, which can effectively reduce the influence of TDOA estimation errors and improve the localization accuracy.

Partial discharge (PD) measurement is an effective tool for insulation condition assessment of high-voltage power equipment. The occurrence of multiple PD sources causes great difficulty on pattern recognition and failure risk assessment. This paper presents a self-adaptive PD separation algorithm based on optimized feature extraction of cumulative energy (CE) function. The CE functions in time domain (TCE) and frequency domain (FCE) are calculated from PD waveforms and their FFT spectrums, respectively. By using an oblique line to cross the CE curves, width features are extracted from the intersection points between them. Through the mathematical morphology gradient (MMG) operation, sharpness features are extracted to characterize the rise steepness of CE. It is found that the separation capability of width and sharpness are dependent on the pre-selected oblique line and the structure element length (SEL) in MMG, respectively. In order to obtain satisfactory PD separation results for various experimental conditions, a density-function based parameter is proposed to evaluate the separation capability, and the oblique line and SEL are optimized with the goal of maximizing the evaluation parameter. A clustering algorithm is adopted to discover different clusters in feature space and separate PD signals. The separation algorithm is examined with mixed PD current pulses and ultra-high-frequency (UHF) signals acquired from experiments in laboratory and on-site equipment. The results indicate that the self-adaptive separation method is immune to the change of experimental conditions, and is effective for separating mixed PD signals.

In this study, a case-based diagnostic model of a power transformer for oil chromatography is proposed. A nonlinear normalised mapping method is proposed for achieving ideal distribution characteristics of oil chromatogram gas in the case base. A similarity degree algorithm is proposed to calculate the similarity degree in the target spatial domain between the case to be diagnosed and the cases in the case base. A threshold method is proposed to determine the number of similar cases. Also, a method for calculating the weight of the diagnosis result according to the case distribution in the case base is proposed. The diagnostic method proposed is used to deploy the diagnostic system on the Internet. Defeat diagnosis conclusions of two transformers by the method and system in this paper are consistent with the disintegration results. It can be concluded that the proposed method has important practical application value in engineering.

This article introduced a case of on-site detection, return test and diagnostic analysis of a multiple voids discharge in 550 kV GIS insulating spacer. First, the abnormal signal of the on-site 550 kV GIS insulating spacer is detected by employing the UHF method, whose UHF signal pattern presents as void discharge. After the replacement of the insulating spacer, the withstand voltage, partial discharge (PD) test, X-ray inspection, lightning impact test are applied to this insulating spacer. The pulse current and UHF method are combined together to investigate the discharge characteristics of multiple voids in this insulating spacer. The discharge characteristics including initial discharge voltage and the extinction voltage, are compared before and after the lightning impact voltage test. Finally, the UHF signals of this insulating spacer are clustered by multisource discharge separation method. It is found that, there are eight voids linearly distributed inside the insulating spacer. The amplitudes of UHF and pulse current signals are positively correlated, and the discharge spectrum characteristic changes from single-void discharge to multiple-void discharge with the increase of voltage. The PD initial voltage of the insulating spacer decreases while the PD extinction voltage keeps same after the lightning impact voltage test, and the signal amplitudes of the UHF and pulse current methods increase rapidly. The discharge multiple source separation shows that the UHF signal of the insulating spacer can be divided into two clusters, which is consistent with the two distribution areas of voids. This article provides on-site experience for insulation detection and diagnosis of GIS.

The insulation diagnosis method based on frequency domain dielectric spectrum is widely used in field test because of its nondestructive testing, rich insulation information and so on. FDS measurement results are easy to be affected by many factors. However, interpretation on the dielectric properties of oil paper insulation with moisture content, impurity ion concentration influence mechanism of the current research is not very ideal, so it is necessary to determine more appropriate and effective dielectric response simulation and experimental analysis of characteristics. In addition, moisture content (m.c.) is an extremely important factor which can determine the insulation state of Oil-immersed paper. Therefore, the evaluation of moisture content of oil-immersed paper has obvious significance. In this paper, based on the experimental results, the extended Debye model parameters of the field transformer are obtained. Then, the influence of the insulation resistance value in the Debye model on the FDS curve is simulated by MATLAB modeling. The simulation and experimental verification show that the regularity of the change of the FDS conductance process is obviously different from the moisture content influenced by the impurity ions. The impurity ions mainly affect the low frequency section of the imaginary part of the complex capacitor. A non-linear phenomenon in dielectric response with different moisture contents under different is observed. We found that the non-linear phenomenon has relationship with the hindrance effect of cellulose (m.c.

Partial discharge (PD) in power transformer degrades the dielectric insulation and results in insulation failure and breakdown after a long period. In fact, a partial discharge detector can gather signals from two or more PD sources which increase the difficulty on pattern recognition and insulation state assessment. How to separate multiple PD signals is meaningful to subsequent data processing. In this paper, a PD separation algorithm based on cumulative energy (CE) function is introduced. The CE functions in time domain (TCE) and frequency domain (FCE) are calculated from PD waveforms and their FFT spectra, respectively. By using an oblique line to cross the CE curves, width features are extracted from the intersection points between them. Through the mathematical morphology gradient (MMG) operation, sharpness features are extracted to characterize the rise steepness of CE. A clustering algorithm is adopted to discover different clusters in feature space and separate PD signals. After that, a transformer experiment platform is introduced and some different defects are placed at different places in this transformer to produce PD signals. The pulse current method is applied to gather their PD waveforms and to record phase resolved partial discharge (PRPD) which can be used to analyse discharge characteristics of different defects. The separation algorithm is examined with mixed PD current pulses acquired from experiments. The results prove the feasibility of separation algorithm with different PD defects.

So as to adapt to the condition-based maintenance of power equipment and to overcome the disadvantages and deficiencies of traditional condition evaluation model in data acquisition approaches, component classifications, performance indicators and scoring model, a comprehensive condition evaluation model (CCEM) was proposed in this paper based on multi-source information. Combined with the offline test, online detection, online monitoring, inspection and operating environment, equipment criteria, undesirable service condition (USC) and family defects were reclassified in this model. Practice example verified that the proposed model can describe the power transformer state more comprehensively and immediately. The evaluation results provide scientific guidance for front-line operation and maintenance staffs to develop equipment maintenance strategy, and also practical basis for the condition evaluation of other power equipment.

Partial discharge (PD) ultra-high frequency (UHF) sensors are widely used for condition monitoring and defect location in gas insulated substation (GIS). There are many designs of UHF sensors which can detect electromagnetic waves that radiate from PD sources. The general types of UHF sensors are disc, monopole, probe, spiral, and conical types with each type of sensor having different characteristics and applications. Sensitivity is an important index for evaluating the performance of UHF sensors. In order to test the electrical sensitivity, a simulation model to calculate the effective height is created by using FDTD Microwave software, and the size of UHF sensor are optimally designed. After that, an internal disk sensor is machined and tested with the GTEM cell. The results indicate that the effective height of UHF sensor is basically above 10 mm in the frequency region of 300MHz to 1500MHz.

The ultra-high frequency (UHF: 300MHz–3GHz) detection has been presented as a promising and effect way to diagnose the partial discharge (PD) activities in power equipments in recent years. The characteristics of UHF detection in the near field when the receiving antennas arranged and worked in the near electromagnetic (EM) field radiated from PD source are different from the far field detection. In this paper, the receiving characteristics of UHF dipole antennas both in the near and far field detection are investigated by using finite difference time domain (FDTD) method. The near and far field antenna factor (AF) of three UHF dipole antennas are computed and compared by coupling the FDTD method and total-field/scattered field (TF/SF) technology together. The spherical and plane waves are employed as near and far field incident EM waves, respectively. It is revealed that, the PD detection can be considered as the far field detection when the received antenna arranged about 0.1-1m from PD source corresponding to the UHF band. The simulation results show that, the multiple odd resonances of dipole antennas can be observed in both near and far field AFs. The lowest AF values in the far field appear near their designed frequencies and the AF values at the resonant frequency are in good agreement with the factory designed data. However, the frequency with the lowest AF values in the near field and their AF values are higher and lower than that of designed data, respectively, indicating the sensitivity of dipole antennas working in the near field is lower than that working in the far field PD detection. The AF values difference of dipole antennas between the near field and designed data decreases gradually with the increment of resonant frequency. This research is helpful for people to select and design the antennas and analyze detection data in UHF PD diagnosis.

In this paper, the influence of metal particle position at insulating spacer on partial discharge (PD) characteristics is studied by using the ultra high frequency (UHF) detection method in a 252kV GIS PD experimental platform. The PD inception voltage (PDIV), phase resolved partial discharge (PRPD), phase resolved pulse sequence (PRPS) and UHF signal waveform and its frequency spectrum are investigated with different defect position and under stepped applied voltage amplitude. By combining the electric field analysis of insulating spacer surface and PD investigation results, the influence of metal particle position on PD characteristics is clarified. It is shown that, the PDIV of metal particle defect increases with electric field strength on spacer surface due to the distortion of electric field on surface. The PRPD and PRPS images behave as the floating discharge pattern. The high order modes of EM waves are easier excited when the metal particle placed near the flange of space. The detection sensitivity of extractor-type sensor is much higher than that of external sensor, however it is a little lower than that of interior sensor.