Ming-Xiao Zhu

Localization of multiple partial discharge sources in air-insulated substation using probability-based algorithm

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 signals separation using cumulative energy function and mathematical morphology gradient

Partial discharge (PD) measurement and interpretation have become a powerful tool for condition monitoring and failure risk assessment of high voltage power equipment insulation. The occurrence of multiple discharge sources affects interpretation accuracy. This paper presents a PD signal separation algorithm using cumulative energy (CE) function parameters clustering technique. The waveform of PD signals are acquired by digital detection instruments with high sampling rate. Cumulative energy functions in time domain (TCE) and frequency domain (FCE) are calculated from PD waveforms and their FFT spectrums, respectively. Mathematical morphology gradient (MMG) operation is applied to the TCE and FCE to describe their variation characteristics. The feature parameters including width, sharpness and gravity are extracted from CEs and MMGs in both time and frequency domain, and compose a six-dimension feature space. The improved density-based spatial clustering of applications with noise (IDBSCAN) clustering algorithm is adopted to discover clusters in the feature space. The proposed separation algorithm is examined with mixed current impulse signals acquired from PD experiments on artificial multi-defect models and an on-site transformer. The separation results indicate that the proposed algorithm is effective for separating mixed PD signals initiated from multiple sources.

Partial discharge characteristics and trap parameters of aged oil-impregnated paper

The degradation of oil-impregnated paper induced by partial discharge (PD) seriously affects the life of power equipment. In order to investigate the relationship between partial discharge and the degradation of oil-impregnated paper, PD characteristics and trap parameters of the impregnated paper are measured. In this work, a cavity discharge model is adopted and samples with different degrees of polymerization (DP) are fabricated using thermally accelerated aging to simulate transformers with different lifetimes. For each sample, phase resolved partial discharge (PRPD) spectrograms are recorded for various partial discharge times. The characteristics of partial discharge are analyzed, and the trap parameters of specimens are obtained by the thermally stimulated depolarization current (TSDC) method. Following this, the microstructures of the samples are observed using a scanning electron microscope (SEM). It is found that the damaging breakdown process of oil-immersed paper can be divided into three stages: initial stage, developing stage and pre-breakdown stage. There also exist two levels of trap on the surface of oil-impregnated paper: shallow (0.10-0.59 eV) and relatively deep (0.60-0.90 eV) energy. Importantly, the thermal aging makes the trap energy deeper while electrical aging makes the trap energy shallower.

Influence of thermal degradation on surface discharge characteristics for oil-polypropylene insulation

Oil-polypropylene (PP) composite insulation has been widely used in power capacitors, and also being extended to other equipment such as specific transformers, etc. As discharge can readily creep along PP surface, the interface between oil and PP is a weak point in the insulation structure. The oil-PP insulation degrades under the impact of thermal and electrical stress during normal operation. In this study, the influence of thermal degradation on surface discharge characteristics for oil-PP insulation is investigated. Degraded samples are obtained by thermally accelerated aging of virgin oil-PP samples. The microscopic morphology of PP samples are observed with SEM, and the physical and electrical properties of degraded oil are measured. In addition, the isothermal surface potential decay (ISPD) measurement is employed to investigate the charge keeping capability of PP samples. After that, surface discharge detection experiments under AC voltage are performed for the virgin and degraded samples. The ISPD results indicate that charge decay rate of degraded PP sample is faster than virgin sample, which imply that space charge memory effect of virgin PP samples is more remarkable. For this reason, the PD inception phase of virgin sample is smaller than those of degraded samples. It is found that thermal degradation of oil-PP insulation enhances the surface discharge activity. It is considered that rough surface profile and higher bubble conservation capability of degraded PP sample, and impurities in degraded oil are responsible for the enhancement effect of thermal degradation on surface discharge.

UHF antenna configuration optimization for partial discharge source localization in air-insulated substation

Ultra-high-frequency (UHF) method has been applied to detect and localize partial discharge (PD) in air-insulated substation (AIS). The localization accuracy is closely dependent on the configuration of antenna array. This paper presents a PD localization error simulation method. Assuming that the estimated time difference of arrival (TDOA) error is normally distributed, the mean value and standard derivation of localization error for numerous positions in AIS are calculated with a Monte Carlo method. Then the localization error of square, Y shape, triangle and diamond arrays are compared, and the factors influence the localization error are analyzed. Since the optimal orientation is easily to be recognized and with minor distance error, the diamond array is more suitable for PD localization in AIS. Experiments are conducted for square and diamond arrays to calculate the localization error, and confirm the effectiveness of proposed simulation method.

Detection and localization of partial discharge in Air-Insulated Substations using UHF antenna array

UHF-based partial discharge (PD) early warning technique has been tried to monitor and localize PD activities in the whole Air-Insulated Substation (AIS). This paper presents a PD detection and localization system based on this method. This system is composed of four-antenna array (double-cone structure), signal conditioning unit (wide band filter, low-noise amplifier and band-stop filter) and high-speed acquisition unit The main sources of background noise are analyzed and corresponding rejection methods are developed to reduce their effect The probability-based localization algorithm which comprehensively consider the time differences of multiple signals are proposed to estimate the position of PD source. The developed system is applied to PD detection and location in substations, and the on-site results prove the effectiveness of the system.

Self-adaptive separation of multiple partial discharge sources based on optimized feature extraction of cumulative energy function

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.

Design and development of internal UHF sensor for partial discharge detection in GIS

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.

Feature optimization selection and dimension reduction for partial discharge pattern recognition

Hundreds of features have been extracted from phase resolved partial discharge (PRPD) pattern and PD waveforms to represent and recognize typical defects. Several feature selection and dimension reduction methods for pattern recognition are presented in this paper. Feature selection algorithms including forward feature selection, backward feature selection and floating forward feature selection (FFFS) are adopted to optimally select the features. |Four dimension reduction algorithms such as principal component analysis, linear discriminant analysis, kernel principal component analysis and generalized discriminant analysis (GDA) are used to further reduce the dimension of features. In order to compare the effectiveness of different selection and reduction techniques, PD tests on artificial PD defect models are performed. The results indicate that the FFFS and GDA are the optimal selection and reduction method, respectively.

Partial discharge features and parameters characterization under typical defect geometries in oil-paper insulation: Considering the aging states

The oil-paper insulation has been widely used in large power equipment, and thermal and electrical stresses are the two main origins causing the deterioration and failure of oil-paper insulation system. In this paper, accelerated aging experiments of oil impregnated paper are conducted and partial discharge (PD) measurement are performed under typical defect models, i.e. protrusion, surface discharge and cavity inside the oil-immersed paper. The PD experimental results of three typical defects indicate that discharges initiated from a protrusion in oil have the similarity with that in air but with a limited apparent charge. The surface discharge are much more disruptive for oil-paper insulation. The intervals between consecutive discharges has the good tendency of decrease in the whole discharge events.