S. Grzybowski

Preparation of a vegetable oil-based nanofluid and investigation of its breakdown and dielectric properties

Investigations during the last decade have shown that conductive nanoparticles can be dispersed in transformer oils to form nanofluids. Well-dispersed nanoparticles are capable of increasing the breakdown voltage of the oil under power frequency and lightning impulses. They also increase the inception voltages for partial discharge [1]. With increasing moisture content, reduction of the breakdown voltage of the nanofluid at power frequency is significantly smaller than that in the corresponding transformer oil [1]. The electrical and thermal properties of four types of nanofluid, prepared by dispersing Al2O3, Fe3O4, SiO2, and SiC nanoparticles in transformer oils, were described in [2]. It has also been reported that the thermal conductivity of such oil was enhanced by 8% when aluminum nitride nanoparticles were dispersed in it at a loading of 0.5% by weight, and its cooling capability was improved by about 20% [3]. An electrodynamic model has been developed describing streamer formation in transformer oil-based nanofluids, which presents generation, recombination, and transport equations for each charge carrier type [4]. Vegetable insulation oils are based on natural ester oils, which are environmentally friendly and fire resistant [4]���[9]. At the moment, little is known about the preparation of nanofluids using natural ester oils and their dielectric, breakdown, and aging properties. Surface modification of nanoparticles is a very effective procedure to avoid nanoparticle agglomeration in insulating nanofluids [10]���[14]. However, the surface modification procedures used for mineral oils cannot be applied to vegetable oils because of their very different molecular structures. We therefore investigated new approaches to the preparation of vegetable oil-based nanofluids. This paper presents some of the results of a study of the breakdown voltages and dielectric properties of a vegetable oil-based nanofluid. The nanofluid was prepared by dispersing Fe3O4 nanoparticles in a vegetable insulation oil obtained from a laboratory at Chongqing University. Oleic acid was used for surface modification of the nanoparticles.

An Integrated Decision-Making Model for Condition Assessment of Power Transformers Using Fuzzy Approach and Evidential Reasoning

This paper presents an integrated model based upon the fuzzy approach and evidential reasoning decision-making approach to condition assessment of power transformers. An assessing index system, including the DGA data, electrical testing, and oil testing, is established to facilitate the assessing model. The model is composed of two levels, that is, the fuzzy model and evidential reasoning model. The fuzzy model is proposed for generating the original basic probability assignments for the second-level model. Afterwards, an evidential reasoning decision-making model is utilized to combine all of the evidence and give an overall evaluation conclusion. Based upon this integrated model, a decision-making procedure is put forward to serve as an effective tool for transformer condition assessments. The results show that the assessing model is capable of offering an overall evaluation of the observed transformer condition.

Scale dependent wavelet selection for de-noising of partial discharge detection

Wavelet shrinkage methods are effective for de-noising of partial discharge (PD) detection. Base wavelets are related to distortion of PD signals de-noised by wavelet shrinkage methods. This paper presents a scale dependent wavelet selection scheme for de-noising of PD detection. The scale dependent wavelet selection scheme is called the energy based wavelet selection (EBWS) because an energy criterion is proposed for the scheme. In the proposed energy criterion, a base wavelet is selected as an optimal base wavelet if it can generate an approximation with the largest energy among all base wavelets for selection at each scale. PD high-frequency signals are simulated and PD ultra-high-frequency signals are obtained by experiments in laboratory for de-noising experiments and analysis. In comparison with the correlation-based wavelet selection (CBWS) scheme, the wavelet shrinkage, based on the EBWS, generates significantly smaller waveform distortion and magnitude errors of de-noised PD signals.

Thermal aging micro-scale analysis of power transformer pressboard

In order to analyze the thermal aging mechanism of the insulation paper inside the power transformer, a series of accelerated thermal aging tests were performed on pressboard. Subsequently, the atomic force microscope (AFM) together with scanning electron microscope (SEM) and X-ray diffraction (X-RD) were utilized to observe the micro surface of the thermal-aged pressboard. The experiments and analysis indicate that either the links among the D-glucopyranose units or the hexagonal mesh structures of the D-glucopyranose units were broken under thermal stress; the number of D-glucopyranose units after 6 weeks of aging was 0.8-1 per nm2, only about one third of un-aged value. The wall of a cellulose cell was deteriorated and thinned by thermal stress. At the same time, the cracks expanded gradually on the surface of the cellulose, which shortened the average width of cellulose fiber from about 40 mu of un-aged sample to about 25 mu after 6 weeks of aging. Meanwhile, the relative crystallinity and the size of the crystallite in the pressboard decreased nonlinearly with the thermal aging time.

Wavelet de-noising of partial discharge signals based on genetic adaptive threshold estimation

Wavelet shrinkage is efficient for de-noising the partial discharge (PD) detection. An improved wavelet de-noising approach for PD online measurement is presented. The wavelet de-noising approach is based on a genetic adaptive threshold estimation (GATE) scheme. The thresholding functions with continuous derivatives are used for the GATE scheme. A genetic algorithm is used to obtain global optimum thresholds of the GATE, and to improve the robustness and computation speed of the adaptive threshold estimation. De-noising experiments of simulative high-frequency PD signals, actual PD ultra-high-frequency (UHF) signals, and a field detected PD signal are presented. The GATE generates significantly smaller waveform distortion and magnitude errors than the Donohos soft threshold estimation.

Aging condition assessment of transformer oil-paper insulation model based on partial discharge analysis

This paper presents aging condition assessment of oil-paper transformer insulation based on partial discharge analysis in order to realize statistical parameters reduction. The extracted feature factors of this proposed model were used to identify oil-paper samples with different aging degrees. An accelerated aging test was implemented using artificial oil-paper samples with an internal flat air gap. During the aging test, partial discharge signal acquisition was conducted periodically. In the new model, conventional statistical parameters of phase resolved partial discharge (PRPD) patterns were analyzed using principal component and factor analysis (PCFA), and a group of new features constituted by the extracted factors was obtained. These factors were not only independent of one another, they had their own specific properties. To a great extent, these factors represent information on PRPD patterns through a limited number of variables. Through the use of the new features extracted from PCFA method, the clustering and discriminating results of the samples with different aging stages provided significantly referenced information on the condition assessment of oil-paper insulation.

Partial Discharge Image Recognition Influenced by Fractal Image Compression

This paper presents a quadtree partitioning fractal image compression (QPFIC) method used for the partial discharge (PD) image remote recognition system. Self-similarity in PD images is the premise of fractal image compression and is described for the typical PD images acquired from defect model experiments in laboratory. Influences of fractal image compression on a group of PD image features are discussed. Fifty PD data samples are used to qualify the QPFIC to be used in remote PD pattern recognition. Analysis results show that the QPFIC method produces errors of the computational features. Such errors could not influence the PD image recognition results under the control of the PD image compression errors.

An estimation of lightning insulation level of overhead distribution lines

An attempt is made to develop a practical procedure for determining the total insulation strength of multiple or composite insulators most commonly used in the design of overhead distribution lines. Laboratory test data were obtained on a wide range of combinations and configurations utilizing porcelain, polymer, and fiberglass insulators as well as wood poles and wool crossarms. Data were obtained for both dry and wet conditions with positive and negative polarities of the impulses. >

Partial discharge image recognition using a new group of features

This paper presents a new group of features used for partial discharge (PD) pattern recognition, based on the description of detail and statistical characteristics of PD images by using fractal features and statistical parameters, respectively. An improved differential box-counting method is proposed for fractal dimension estimation of PD images. The new group of features is used as the input parameters of a back-propagation neural network (BPNN) for PD image recognition. During defect model experiments in the laboratory, five types of artificial defect models are used to acquire the data samples, which are used to qualify the proposed PD recognition method. Analysis results show that the proposed features are effective for PD images recognition

Dielectric properties of rapeseed oil paper insulation

This paper presents the basic physical, chemical and electrical properties of a type of rapeseed oil based dielectric liquid. Dielectric properties including relative dielectric permittivity, dissipation factor, and breakdown voltage of the rapeseed oil paper insulation are presented and also compared with those parameters of mineral transformer oil paper insulation. Moreover, four types of small oil gaps are designed for experiments. These four types of oil gaps include a simple oil gap, an oil gap with a paper-covered high voltage electrode, and two oil gaps with one and two layers of papers, respectively, between the high voltage and grounding electrodes. Both the refined rapeseed oil and the mineral transformer oil are used for the small oil gap experiments. Breakdown properties of the small oil gaps are measured. The analysis results of test data show the differences in dielectric properties between the rapeseed oil paper insulation and the mineral transformer oil paper insulation.