Gas In Oil Analysis Research Articles

Research on hybrid feature selection method of power transformer based on fuzzy information entropy

The data of dissolved gas in oil analysis (DGA) is uncertain affected by the influence of transformer capacity and fault location, which makes transformer fault diagnosis model based on DGA has low accuracy. Therefore, we propose a hybrid feature selection method based on fuzzy information entropy, whereby optimizing the reasonable DGA feature parameter according to the feature information between the parameter and fault type, to reduce the influence of DGA data uncertainty on the fault diagnosis accuracy. Firstly, the characteristic relevance and redundancy functions are constructed based on fuzzy information entropy theory. Secondly, these functions are taken as the optimization objectives of binary-chaotic multi-objective particle swarm optimization algorithm(B-CMOPSO), to search for the feature subsets in the feature space composed of 46 DGA feature parameters. Then, the optimal feature subset is selected based on the simulation accuracy of ELM, SVM, Adaboost.M1 and BPNN on the feature subsets. Finally, 30 simulation experiments are carried out to compare with several multi-objective optimization algorithms, common Filter methods and common DGA feature combinations, and the rationality of the proposed method is verified by the t-test method. The results show that the 4 classifiers accuracy means is maximatily improved by 18.95%, 20.77%, 19.85% and 21.27% respectively compared with common DGA feature combinations, indicating that the optimal feature subset preserves more feature information and can effectively reduce the influence of DGA data uncertainty on diagnostic accuracy.

Analisis Kegagalan Minyak Isolasi Pada Transformator Daya Berbasis Kandungan Gas Terlarut

Common problem in power transformers is the emergence of operational failure, whether the failure ofboth thermal and electrical failures. Thermal and electrical failures generally produce hazardous gases, known asfault gas. Most power transformers that use insulating oil as a coolant also serves other than to dissolve theharmful gases in order not to circulate freely. Identify the type and amount of dissolved gas concentration in theoil, to provide information to the indication of failure that occurs in the transformer. Methods for identifying andanalyzing the gases dissolved in the oil referred to as DGA (Dissolved Gas in Oil Analysis). IEEE std. C57.104 -1991 set a Total Dissolved Combustible Gas (TDCG) of ≤ 720 part per million (ppm) as a normal condition.Having analyzed both through tools and through manual calculation, Wärtsilä transformer no. 2, with thecapacity of 2000 kVA; 20 kV / 0.4 kV; Dyn5; 3 phase, 50 Hz; brand Starlit, and PLN transformer no 5 B, with thecapacity of 2000 kVA; 20 kV / 0.4 kV; Dyn5; 3 phase, 50 Hz; Unindo brand, has exceeded a predetermined limitthe IEEE std. C57.104 - 1991, with a tool TDCG 4055 ppm for Wärtsilä transformer No. 2 and 6766 ppm fortransformer PLN no 5 B. While manually TDCG 3781 ppm for Wärtsilä transformer No. 2 and 6313 ppm for PLNtransformer no 5 B. From analysis through tools and manual calculation obtained the same results by IEEE std.C57.104 - 1991 and confirmed on condition of Wärtsilä no. 3 for Transformers no.2 and PLN transformercondition no. 4 to 5B

Impact of Load Ramping on Power Transformer Dissolved Gas Analysis

Dissolved gas in oil analysis (DGA) is one of the most reliable condition monitoring techniques, which is currently used by the industry to detect incipient faults within the power transformers. While the technique is well matured since the development of various offline and online measurement techniques along with various interpretation methods, no much attention was given so far to the oil sampling time and its correlation with the transformer loading. A power transformer loading is subject to continuous daily and seasonal variations, which is expected to increase with the increased penetration level of renewable energy sources of intermittent characteristics, such as photovoltaic (PV) and wind energy into the current electricity grids. Generating unit transformers also undergoes similar loading variations to follow the demand, particularly in the new electricity market. As such, the insulation system within the power transformers is expected to exhibit operating temperature variations due to the continuous ramping up and down of the generation and load. If the oil is sampled for the DGA measurement during such ramping cycles, results will not be accurate, and a fault may be reported due to a gas evolution resulting from such temporarily loading variation. This paper is aimed at correlating the generation and load ramping with the DGA measurements through extensive experimental analyses. The results reveal a strong correlation between the sampling time and the generation/load ramping. The experimental results show the effect of load variations on the gas generation and demonstrate the vulnerabilities of misinterpretation of transformer faults resulting from temporary gas evolution. To achieve accurate DGA, transformer loading profile during oil sampling for the DGA measurement should be available. Based on the initial investigation in this paper, the more accurate DGA results can be achieved after a ramping down cycle of the load. This sampling time could be defined as an optimum oil sampling time for transformer DGA.

Multi‐DGAS: A pattern based educational framework design for power transformers faults interpretation and comparative performance analysis

AbstractTaking necessary measures in advance for early prediction of the transformer failures are very crucial. Dissolved gas in oil analysis (DGA) techniques that used in electrical power systems has become an internationally accepted standard in condition based maintenance (CBM) studies have high complexity and increasing demands of the quick‐service‐ready paradigm in power transformers. This standard includes the implementation steps of various complex DGA methods named basic gas, Rogers, Doernenburg, Duval triangle, and pentagon. Mentioned methods are quite complicated and their application steps are strict rules defined in detailed procedures. The adaptation of these methods to transformer maintenance procedures that prepared according to standards and tested for correctness creates a valuable tool. In this study, a pattern based educational framework has been designed to integrate state of arts DGA techniques help for trainers, graduate researchers working on DGA techniques, and also for maintenance teams while taking critical decisions on power transformers. Developed Multi‐DGAS educational framework is designed for modular construction that can be integrated with online condition monitoring and diagnostic systems for electric power systems. Success of the proposed framework revealed with newly proposed pentagon method in regard with comparison of classical methods by using oil samples which have been taken under the energized condition of different 135 transformers within multi‐phase which failure histories already known. Analysis of dissolved gases in transformer oil is performed according to ASTM‐D 3612 procedure using 3800 GC Varian gas chromatograph apparatus.

Dissolved gas analysis (DGA) of natural ester insulating fluids with different chemical compositions

This study presents experimental results of the application of the dissolved gas-inoil analysis (DGA) technique to natural ester insulating fluids. The investigations covered two common types of transformer faults, discharge and overheating, and four different commercial insulating oils, one mineral and three natural esters: Biovolt A (from corn oil), Biovolt HW (from sunflower oil with high oleic acid) and Envirotemp FR3 (from soya oil). The natural esters insulating fluids (NEIFs) analyzed had different chemical compositions. Experiments were performed in the laboratory with the test oils at three different moisture levels (low, medium and high). The purpose was to see the effect of moisture content and oil composition on the quantities of fault gases dissolved in the different oil types. The overheating tests were performed by heating the oils in an oven at 150 °C. Discharges were generated using a flat plate-plate electrode arrangement immersed in oil. Five key dissolved hydrocarbon gases - hydrogen (H2), methane (CH4), ethane (C2H6), ethylene (C2H4) and acetylene (C2H2) - generated by the two fault types in NEIFs were analyzed and compared with mineral oil (MIO) fault gases. The classic Duval triangle 1, modified Duval triangle 3, Roger's ratio, Dornenburg and IEC 60599 methods were applied to predict the fault type. The findings confirmed that the direct application of classic DGA fault interpretation methods, developed for mineral oils, to non-mineral oils needs some modifications. IR and RMN spectroscopy analyses were performed to observe differences in triacylglycerol group compositions before and after heating tests.

Transformer Fault Dignosis Based on Feature Selection and Parameter Optimization

Failure of transformer is very complex, dissolved Gas in Oil Analysis (DGA) is presently the easier and simpler way for fault diagnosis of oil-immersed transformers.The correct selection of features of dissolved gas data can improve efficiency of transformer fault diagnosis. SVM is more effective than traditional methematic model to discribe the type of fault of transformer.As for the problem of difficulty of determining parameters in SVM applications, genetic algorithm (GA) was used to select SVM parameters. The test results show that this GA-SVM model is effective to detect failure of transformer.

유침 절연에서 전기적 및 열적 열화에 따른 유중가스분포특성

본 논문에서는 유침절연에 대한 열열화와 전기적열화 실험을 실시하여 유중가스분석법을 이용한 유침절연의 고장원인 진단에 대한 신뢰성을 향상시키는 방안에 대하여 연구하였다. 유침절연의 고장원인에 대하여 주도가스법과 탄화수소가스의 구성률, CO/CO₂비와 같은 진단요소를 단독으로 사용하는 것보다는 진단요소들의 상호관계를 이용하는 것이 유침절연의 고장원인을 신뢰성 있게 진단하는 것으로 나타났다.

A novel fuzzy logic approach to transformer fault diagnosis

Dissolved gas in oil analysis is an well established in-service technique for incipient fault detection in oil-insulated power transformers. A great deal of experience and data in dissolved gas in oil analysis (DGA) is now available within the utilities. Actually, diagnostic interpretations were solely done by human experts using past knowledge and standard techniques such as the ratio method. In this paper, a novel fuzzy logic approach is adopted to develop a computer based intelligent interpretation of transformer faults using Visual basic and C/sup ++/ programming. The proposed fuzzy logic based software as been tested and tuned using over 800 dissolved gas in oil analysis (DGA) case histories. This highly reliable tool has then been utilized in detection and verification of 20 transformer faults. The proposed diagnostic tool is very useful to both expert and novice engineers in DGA result interpretation.

Life assessment of cable paper using slow thermal ramp methods

Rapid thermal ramp techniques are widely used to assess the properties of polymers (e.g. DTA, TGA, etc.). For certain types of processes, slow thermal ramp methods can assist the process of assessing the longer term ageing and life prediction of a polymeric material in complex environments. We will illustrate this in relation to recent work on the ageing of Kraft electrical insulation paper in insulating oil in relation to the longer term ageing of oil-filled power cables. In this case the key indicators of degradation were the degree of polymerisation (DP) and the production of gases capable of dissolving in the oil and measured by dissolved gas in oil analysis (DGA). Kinetic modelling of the change in the DP value was used to generate Arrhenius parameters and cable life prediction calculations were made for a range of potential cable operating temperatures. The DP decreases with age and increasing temperature in a complex chain scission reaction. In parallel, above 150°C there is a rapid exponential increase in the evolution of CO, CO 2, CH 4, C 2H 4 and C 2H 6 with increasing temperature. We found that the rate of degradation of the paper/oil system is dependent on the degree of containment of the ageing experiments. These results indicate that containment effects and resulting pressure effects exist in this type of accelerated non-isothermal ageing experiment and should be accounted for in practical ageing environments. The estimated life of the paper can be calculated if we assume that the Arrhenius parameters are representative of the entire degradation process and if suitable end-of-life criteria can be defined. It is also possible to calculate the average operating temperature of an electrical cable over a fixed time period, knowing the starting and finishing DP. Some of the attractions, potential weaknesses and use of the slow thermal ramp method in complex environments are also discussed.

IEEE and IEC Codes to Interpret Incipient Faults in Transformers, Using Gas in Oil Analysis

The detection of incipient faults in oil immersed transformers by examination of the gases dissolved in the oil developed from the original Buchholz relay application. The gases produced during the deterioration of mineral oil and cellulose were examined and techniques were established to assist in the interpretation of the type of fault, incipient or growing, occurring within the transformer. The major technique now employed is the study of ratios of pairs of certain deterioration gases. The concepts used in the development of this technique and the modifications made to enable the technique to be established as the present method of fault interpretation, recommended by the IEC are discussed.