WO2016127598A1 - 基于油中溶解气体的变压器内部复合缺陷模糊诊断方法 - Google Patents

基于油中溶解气体的变压器内部复合缺陷模糊诊断方法 Download PDF

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WO2016127598A1
WO2016127598A1 PCT/CN2015/086109 CN2015086109W WO2016127598A1 WO 2016127598 A1 WO2016127598 A1 WO 2016127598A1 CN 2015086109 W CN2015086109 W CN 2015086109W WO 2016127598 A1 WO2016127598 A1 WO 2016127598A1
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ratio
coded
encoded
code1
transformer
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French (fr)
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高树国
范辉
陈志勇
潘瑾
刘宏亮
赵军
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HEBEI ELECTRIC POWER COMMISSIONING INSTITUDE
Electric Power Research Institute of State Grid Hebei Electric Power Co Ltd
State Grid Corp of China SGCC
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HEBEI ELECTRIC POWER COMMISSIONING INSTITUDE
Electric Power Research Institute of State Grid Hebei Electric Power Co Ltd
State Grid Corp of China SGCC
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Priority to US15/324,169 priority Critical patent/US20170336461A1/en
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/26—Oils; Viscous liquids; Paints; Inks
    • G01N33/28—Oils, i.e. hydrocarbon liquids
    • G01N33/2835—Specific substances contained in the oils or fuels
    • G01N33/2841—Gas in oils, e.g. hydrogen in insulating oils
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
    • G01F25/00—Testing or calibration of apparatus for measuring volume, volume flow or liquid level or for metering by volume
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
    • G01R31/62—Testing of transformers
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01F—MAGNETS; INDUCTANCES; TRANSFORMERS; SELECTION OF MATERIALS FOR THEIR MAGNETIC PROPERTIES
    • H01F27/00—Details of transformers or inductances, in general
    • H01F27/40—Structural association with built-in electric component, e.g. fuse
    • H01F27/402—Association of measuring or protective means

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  • the invention belongs to the technical field of power transformer fault diagnosis, and relates to a fuzzy diagnosis method for internal composite defects of a transformer based on dissolved gas in oil.
  • Power transformer is an important equipment of power system. It is of great significance for the safe and reliable operation of power grid. Its routine test is an important means to discover potential hidden dangers of transformers and avoid sudden accidents.
  • the transformer oil chromatographic test is very effective. The test, which contains a wealth of equipment insulation status information, can be found in transformer internal discharge, local overheating, insulation moisture and other defects, widely used in power systems. Accurate diagnosis of internal defects of transformers helps to judge the location and type of equipment defects. It has always been a key topic in this field. Based on this, scientific maintenance strategies can be formulated to greatly improve equipment operation and maintenance efficiency and improve grid power supply reliability.
  • the more classical methods of chromatographic analysis of transformer oil include characteristic gas method, Rogers ratio method, IEC three-ratio method, Dewey triangle method, and improved three-ratio method.
  • the characteristic gas method is a method for dividing the defect level according to each characteristic gas concentration and total hydrocarbon concentration. The method is only suitable for qualitative determination of whether there is a defect; the Rogers four-ratio method is the development of the Doerenburg five-ratio method, and the IEC Like the three-ratio method, the gas ratio is used as the basis for judging the type of transformer defect.
  • Such ratio method only has the meaning of the ratio when the transformer has defects. Under normal circumstances, it is easy to cause misjudgment, and in practice, there is no corresponding ratio.
  • the coding, criterion boundary is absolute, and it is impossible to accurately diagnose composite defects.
  • the improved three-ratio method recommended by China Standard GB/T7252-2001 is based on the IEC three-ratio method and based on the statistical analysis results of domestic transformer data.
  • the correction of the corresponding code is still based on the gas ratio as the basis for judging the type of transformer defect;
  • the Dewey triangle method is a method for distinguishing the defect types based on the triangular coordinates of the gas proportional distribution, each defect type corresponding to a certain area, This method solves the non-coding correspondence of the ratio method, but still exists. On the border of, can not accurately diagnose complex defects.
  • the diagnostic criteria criteria for various oil chromatographic defect diagnostic methods of transformers rely only on single characteristic information, such as characteristic gas species, gas concentration, gas ratio, diagnostic criteria boundaries are too absolute, and diagnostic conclusions cannot reveal defects.
  • the severity or probability of occurrence The actual transformer defects are more complicated, often combined with multiple defects, which makes the current diagnostic methods unrecognizable. Therefore, it is very necessary to improve the existing chromatographic diagnosis method of the internal transformer of the transformer.
  • the technical problem to be solved by the present invention is to provide a problem that can effectively solve the problem of absolute boundary of the dissolved gas in the conventional oil analysis and the inability to diagnose the composite defect, can comprehensively utilize various feature quantity information, and can effectively improve the defect fault. Diagnostic reliability based on the internal composite defect fuzzy diagnosis method of dissolved gas in oil.
  • a fuzzy diagnosis method for internal composite defects of a transformer based on dissolved gases in oil comprising the following steps:
  • (1) Obtaining monitoring data of the volume concentration of the five characteristic gases to be monitored, the five characteristic gases being hydrogen, methane, ethane, ethylene and acetylene; calculating methane, ethane, ethylene and acetylene from the monitoring data
  • the sum of the volume concentrations that is, the volume concentration of the total hydrocarbons; whether the monitoring data of the five characteristic gases or the volume concentration of the total hydrocarbons exceeds the attention value; the attention value is according to the provisions of the Chinese standard GB/T 7252-2001 If the monitoring data or the total hydrocarbon volume concentration exceeds the caution value, further diagnosis is required, and the step (2) is transferred; otherwise, the monitoring data and the total hydrocarbon volume concentration are normal, and the transformer is determined to have no defect;
  • c 1 (C 2 H 2 ), c 2 (C 2 H 4 ), c 3 (CH 4 ), c 4 (H 2 ), and c 5 (C 2 H 6 ) represent acetylene, ethylene, methane, respectively.
  • the ratio of r 1 is encoded as 0; when 0.1 ⁇ r 1 ⁇ 1, the ratio of r 1 is encoded as 1; when 1 ⁇ r 1 ⁇ 3, the ratio of r 1 is encoded as 1; 1 ⁇ 3, the ratio of r 1 is coded as 2;
  • the ratio of r 2 is encoded as 1; when 0.1 ⁇ r 2 ⁇ 1, the ratio of r 2 is encoded as 0; when 1 ⁇ r 2 ⁇ 3, the ratio of r 2 is encoded as 2; 2 ⁇ 3, the ratio of r 2 is coded as 2;
  • the ratio of r 3 is encoded as 0; when 0.1 ⁇ r 3 ⁇ 1, the ratio of r 3 is encoded as 0; when 1 ⁇ r 3 ⁇ 3, the ratio of r 3 is encoded as 1; 3 ⁇ 3 , the ratio of r 3 is coded as 2;
  • the fourth ratio r 4 is added ; for the defect type of the 101-coded diagnosis of the three-ratio method, if r 4 ⁇ 1.5, the transformer is determined to be a spark discharge defect; if r 4 >1.5, Then determining that the transformer is an arc discharge defect fault;
  • the method for determining the type of transformer defect fault based on the ratio encoding is as follows:
  • the transformer defect type is less than 300 ° C. ;
  • the transformer defect type is less than 300 ° C. ;
  • the transformer defect type is 300 to 700 ° C. ;
  • the transformer defect type is higher than 700 ° C. High temperature overheating
  • Type is spark discharge;
  • the transformer defect type is Arc discharge
  • ⁇ d (r) is the falling edge function
  • ⁇ a (r) is the rising edge function
  • A is the boundary parameter
  • ⁇ is the distribution parameter
  • the values of A and ⁇ are as follows:
  • the rising edge boundary parameter of r 1 is 0.08, and the corresponding distribution parameter is 0.01;
  • the falling edge boundary parameter of r 1 is 3.1, and the corresponding distribution parameter is 0.1;
  • the rising edge boundary parameter of r 2 is 0.06, and the corresponding distribution parameter is 0.02;
  • the falling edge boundary parameter of r 2 is 0.6, and the corresponding distribution parameter is 0.2;
  • the rising edge parameter of r 3 is 0.8, and the corresponding distribution parameter is 0.1;
  • the falling edge parameter of r 3 is 3.6, and the corresponding distribution parameter is 0.3;
  • the boundary parameter of r 4 is 1.43, and the corresponding distribution parameter is 0.1;
  • the ratio of r 1 is encoded as a probability of 2 f-code2(r 1 ):
  • the ratio of r 2 is encoded as a probability of 2 f-code2(r 2 ):
  • the ratio of r 3 is encoded as a probability of 2 f-code2(r 3 ):
  • the probability of the ratio coding is represented by the maximum logic and the minimum value, so as to obtain the fuzzy multi-value form of the diagnosis result of the transformer defect type, and the probability of the transformer defect type is as follows:
  • f (low temperature overheat) max ⁇ min[f-code0(r 1 ), f-code0(r 2 ), f-code1(r 3 )], min[f-code0(r 1 ), f-code2(r 2 ), f-code0(r 3 )] ⁇ ;
  • f (medium temperature overheat) min[f-code0(r 1 ), f-code2(r 2 ), f-code1(r 3 )];
  • f (high temperature overheat) max ⁇ min[f-code0(r 1 ), f-code0(r 2 ), f-code2(r 3 )], min[f-code0(r 1 ), f-code2(r 2 ), f-code2(r 3 )] ⁇ ;
  • f (spark discharge) max ⁇ f-code2(r 1 ), min[f-code1(r 1 ), f-code0(r 2 ), f-code1(r 3 ), f-code0(r 4 )] ⁇ ;
  • f (arc discharge) max ⁇ min[f-code1(r 1 ), f-code0(r 2 ), f-code1(r 3 ), f-code1(r 4 )],min[f-code1(r 1 ), f-code0(r 3 )],min[f-code1(r 1 ),f-code2(r 3 )],min[f-code1(r 1 ),f-code1(r 2 ),f -code1(r 3 )],min[f-code1(r 1 ), f-code2(r 2 ), f-code1(r 3 )] ⁇ .
  • the invention has the beneficial effects that the invention is simple and easy to implement, and is particularly suitable for the application of the transformer state online monitoring system; the invention is based on the idea of fuzzy logic, and can realize the diagnosis and severity evaluation of the composite defect in the complex state of the transformer, The mutation problem caused by the absolute boundary of the criterion is effectively avoided; the invention combines the multi-feature information such as the attention value and the ratio of the dissolved gas in the oil, thereby effectively improving the reliability of the diagnosis.
  • Figure 1 is a diagnostic flow chart of the present invention
  • Fig. 2 is a fuzzy boundary when the ratio r 3 is encoded as 2.
  • Step (1) Obtaining monitoring data of the volume concentration of the five characteristic gases to be monitored, wherein the five characteristic gases to be monitored include hydrogen, methane, ethane, ethylene, acetylene; methane, ethane, ethylene are calculated from the monitoring data And the volume concentration of the acetylene, that is, the volume concentration of the total hydrocarbon; determining whether the monitoring data of the five characteristic gases or the volume concentration of the total hydrocarbon exceeds the attention value; the attention value is according to the Chinese standard GB/T 7252-2001 If the monitoring data or the total hydrocarbon volume concentration exceeds the caution value, further diagnosis is required. Step (2); otherwise, the monitoring data and total The volume concentration of hydrocarbons is normal, and the transformer is determined to have no defect;
  • c 1 (C 2 H 2 ), c 2 (C 2 H 4 ), c 3 (CH 4 ), c 4 (H 2 ), and c 5 (C 2 H 6 ) represent acetylene, ethylene, methane, respectively.
  • Table 1 determines the rules for ratio coding
  • the ratio of r 1 , r 2 and r 3 is coded according to the ratio encoding rule in Chinese Standard GB/T 7252-2001 "Guidelines for Analysis and Judgment of Dissolved Gases in Transformer Oil”; in Chinese Standard GB/T 7252- The ratio coding rules of ratio r 4 and r 4 are added based on the ratio coding rules in 2001 "Guidelines for Analysis and Judgment of Dissolved Gases in Transformer Oil”;
  • the fourth ratio r 4 is added ; for the three-ratio method to diagnose the 101-type defect fault type, if r 4 ⁇ 1.5, the transformer is determined to be a spark discharge defect fault; if r 4 > 1.5 Then, it is determined that the transformer is an arc discharge defect fault.
  • the transformer defect code is increased by the ratio code 011 as a partial discharge defect fault.
  • the semi-Cauxi lifting function is used to blur the boundary range of the ratios r 1 , r 2 , r 3 and r 4 , and the rising edge and the falling edge boundary blur of the boundary are respectively represented by the semi-Cauxi lifting function.
  • the expression is
  • ⁇ d (r) is the falling edge function
  • ⁇ a (r) is the rising edge function
  • A is the boundary parameter
  • ⁇ is the distribution parameter
  • the values of A and ⁇ are verified by the actual typical fault case data of the 728 group of the grid company. The optimal value obtained is shown in Table 3.
  • rising boundary parameters r 1 A 1 (r 1) is 0.08, which corresponds to the distribution parameter A 1 (r 1) is 0.01;
  • falling edge boundary parameters r 1 A 2 (r 1) is 3.1, the corresponding distribution parameters a 2 (r 1) is 0.1;
  • the rising edge of the boundary parameters r 2 A 1 (r 2) was 0.06, which corresponds to the distribution parameters a 1 (r 2) was 0.02;
  • the rising edge of the boundary parameters r 3 A 1 (r 3) is 0.8, the corresponding distribution parameters a 1 (r 3) 0.1;
  • falling edge boundary parameters r 3 A 2 (r 3) is 3.6, the corresponding distribution parameters a 2 (r 3) 0.3;
  • the ratio of r 1 is encoded as a probability of 2 f-code2(r 1 ):
  • the ratio of r 2 is encoded as a probability of 2 f-code2(r 2 ):
  • the ratio of r 3 is encoded as a probability of 2 f-code2(r 3 ):
  • the 0 and 1 logics in the ratio coding judgment rule are respectively changed to the minimum logic and the maximum logic, and the defect diagnosis is performed according to the correspondence between the ratio coding and the transformer defect type, and the fuzzy multi-value form is adopted for the diagnosis result.
  • the result is given in the form of probability.
  • the result of the diagnosis is the probability of occurrence of the defect, that is, the severity; the sum of the probabilities of the various faults is 1; the probability of encoding the ratio is represented by the maximum logic and the minimum logic, various faults
  • the probability is:
  • f (medium temperature overheat) min[f-code0(r 1 ), f-code2(r 2 ), f-code1(r 3 )];
  • f (arc discharge) max ⁇ min[f-code1(r 1 ), f-code0(r 2 ), f-code1(r 3 ), f-code1(r 4 )],min[f-code1(r 1 ), f-code0(r 3 )],min[f-code1(r 1 ),f-code2(r 3 )],min[f-code1(r 1 ),f-code1(r 2 ),f -code1(r 3 )],min[f-code1(r 1 ), f-code2(r 2 ), f-code1(r 3 )] ⁇ .
  • the chromatographic test data of a transformer oil (volume concentration of five characteristic gases and total hydrocarbons, in units of ⁇ L/L) is shown in Table 4.
  • Table 4 A transformer oil chromatographic test data

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Abstract

一种基于油中溶解气体的变压器内部复合缺陷模糊诊断方法，包括获取被监测的5种特征气体的体积浓度的监测数据的步骤，确定比值编码的步骤，对三比值法进行修正的步骤，对边界范围模糊化的步骤，计算比值编码概率的步骤，计算每种缺陷故障发生的概率的步骤，最终得到变压器故障类型；其有益效果是：简单易于实现，特别适应于变压器状态在线监测系统的应用；基于模糊逻辑的思想，能够实现变压器复杂状态下复合缺陷的诊断以及严重程度的评估，可以有效避免判据边界绝对化带来的突变问题；将油中溶解气体的注意值、比值等多特征信息融合分析，有效提高诊断可靠性。

Description

基于油中溶解气体的变压器内部复合缺陷模糊诊断方法 技术领域
本发明属于电力变压器故障诊断技术领域，涉及一种基于油中溶解气体的变压器内部复合缺陷模糊诊断方法。
背景技术
电力变压器是电力系统的重要设备，对于电网安全可靠运行具有重要的意义，其例行试验是及时发现变压器潜在性隐患、避免突发事故的重要手段，其中变压器油色谱试验是一种十分有效的试验，其中蕴含着丰富的设备绝缘状态信息，能够发现变压器内部放电、局部过热、绝缘受潮等缺陷，在电力系统中应用广泛。准确诊断变压器内部缺陷有助于判断设备缺陷位置和类型，一直是该领域研究的重点课题，以此为基础可以制定科学的检修策略，从而大大提高设备运维检修效率，提高电网供电可靠性。目前较为经典的变压器油色谱分析方法主要有特征气体法、罗杰斯比值法、IEC三比值法、杜威三角图法、改良三比值法等。其中，特征气体法是根据各特征气体浓度以及总烃浓度进行缺陷等级划分的一种方法，该方法只适于定性判断是否存在缺陷；罗杰斯四比值法是对Doerenburg五比值法的发展，与IEC三比值法一样，均以气体比值作为判断变压器缺陷类型的依据，此类比值法仅就变压器存在缺陷时的比值具有意义，在正常情况下容易导致误判，且在实际中易出现无对应比值编码、判据边界绝对化、无法准确诊断复合缺陷等问题；中国标准GB/T7252-2001推荐使用的改良三比值法，是在IEC三比值法的基础上，根据国内变压器数据统计分析结果，进行了相应编码的修正，依然是以气体比值作为判断变压器缺陷类型的依据；杜威三角图法是基于气体比例分布的三角图坐标来区分缺陷类型的一种方法，每种缺陷类型对应一定的区域，该方法解决了比值法存在的无编码对应，但依然存在绝对化边界、无法准确诊断复合型缺陷问题。
综上所述，变压器各种油色谱缺陷诊断方法的诊断特征判据仅依赖于单一特征信息，如特征气体种类、气体浓度、气体比值，诊断判据边界过于绝对化，诊断结论无法揭示各缺陷 的严重程度或发生概率。而实际变压器缺陷较为复杂，往往是多种缺陷的复合，导致目前的诊断方法无法识别。因此，十分有必要对现有变压器内部缺陷油色谱诊断方法进行改进。
发明内容
本发明所要解决的技术问题是提供一种能有效解决传统油中溶解气体分析中判据边界绝对化和无法诊断复合缺陷的问题、可以综合利用多种特征量信息、并可有效地提高缺陷故障诊断可靠性的基于油中溶解气体的变压器内部复合缺陷模糊诊断方法。
为解决上述技术问题所采用的技术方案是：一种基于油中溶解气体的变压器内部复合缺陷模糊诊断方法，包括如下步骤：
(一)获取被监测的5种特征气体的体积浓度的监测数据，所述5种特征气体为氢气、甲烷、乙烷、乙烯和乙炔；由所述监测数据计算甲烷、乙烷、乙烯和乙炔的体积浓度之和，即总烃的体积浓度；判断所述5种特征气体的监测数据或总烃的体积浓度是否超出注意值；所述注意值根据中国标准GB/T 7252-2001中的规定选取；若所述监测数据或总烃的体积浓度超出注意值，则需进一步诊断，转步骤(二)；否则，所述监测数据及总烃的体积浓度为正常，确定变压器无缺陷故障；
(二)确定比值编码；
首先设定比值分别为：
Figure PCTCN2015086109-appb-000001
Figure PCTCN2015086109-appb-000002
Figure PCTCN2015086109-appb-000003
Figure PCTCN2015086109-appb-000004
其中，c1(C2H2)、c2(C2H4)、c3(CH4)、c4(H2)、c5(C2H6)分别表示乙炔、乙烯、甲 烷、氢气、乙烷5种特征气体的体积浓度，单位为μL/L；
然后确定比值编码，确定比值编码的规则如下：
当r1＜0.1时，r1的比值编码为0；当0.1≤r1＜1时，r1的比值编码为1；当1≤r1＜3时，r1的比值编码为1；r1≥3时，r1的比值编码为2；
当r2＜0.1时，r2的比值编码为1；当0.1≤r2＜1时，r2的比值编码为0；当1≤r2＜3时，r2的比值编码为2；r2≥3时，r2的比值编码为2；
当r3＜0.1时，r3的比值编码为0；当0.1≤r3＜1时，r3的比值编码为0；当1≤r3＜3时，r3的比值编码为1；r3≥3时，r3的比值编码为2；
当r4≤1.5时，r4的比值编码为0；r4＞1.5时，r4的比值编码为1；
(三)对中国标准GB/T 7252-2001中根据三比值确定变压器缺陷故障类型的方法进行修正：相对中国标准GB/T 7252-2001中三比值编码对应的变压器缺陷故障类型，增加比值编码011对应局部放电缺陷故障类型；
在三比值编码基础上，增加第四个比值r4；对于三比值法诊断为101编码的缺陷故障类型，若r4≤1.5时，确定变压器为火花放电缺陷故障；若r4＞1.5时，则确定变压器为电弧放电缺陷故障；
由此得到根据比值编码判断变压器缺陷故障类型的方法如下：
当r1的比值编码为0、且r2的比值编码为1、且r3的比值编码为0，1或2、且r4的比值编码为0或1时，变压器缺陷故障类型为局部放电；
当r1的比值编码为0、且r2的比值编码为0、且r3的比值编码为1、且r4的比值编码为0或1时，变压器缺陷故障类型为低于300℃低温过热；
当r1的比值编码为0、且r2的比值编码为2、且r3的比值编码为0、且r4的比值编码为0或1时，变压器缺陷故障类型为低于300℃低温过热；
当r1的比值编码为0、且r2的比值编码为2、且r3的比值编码为1、且r4的比值编码为0或1 时，变压器缺陷故障类型为300～700℃中温过热；
当r1的比值编码为0、且r2的比值编码为0或2、且r3的比值编码为2、且r4的比值编码为0或1时，变压器缺陷故障类型为高于700℃高温过热；
当r1的比值编码为2、且r2的比值编码为0，1或2、且r3的比值编码为0，1或2、且r4的比值编码为0或1时，变压器缺陷故障类型为火花放电；
当r1的比值编码为1、且r2的比值编码为0、且r3的比值编码为1、且r4的比值编码为0时，变压器缺陷故障类型为火花放电；
当r1的比值编码为1、且r2的比值编码为0、且r3的比值编码为1、且r4的比值编码为1时，变压器缺陷故障类型为电弧放电；
当r1的比值编码为1、且r2的比值编码为0，1或2、且r3的比值编码为0或2、且r4的比值编码为0或1时，变压器缺陷故障类型为电弧放电；
当r1的比值编码为1、且r2的比值编码为1或2、且r3的比值编码为1、且r4的比值编码为0或1时，变压器缺陷故障类型为电弧放电；
(四)采用半柯西升降函数将比值r1、r2、r3、r4的边界范围模糊化，对边界的上升沿和下降沿分别采用半柯西升降函数表示，表达式为
Figure PCTCN2015086109-appb-000005
Figure PCTCN2015086109-appb-000006
其中，μd(r)是下降沿函数；μa(r)是上升沿函数；A是边界参数；α是分布参数；A与α的取值如下：
r1的上升沿边界参数为0.08，其对应的分布参数为0.01；
r1的下降沿边界参数为3.1，其对应的分布参数为0.1；
r2的上升沿边界参数为0.06，其对应的分布参数为0.02；
r2的下降沿边界参数为0.6，其对应的分布参数为0.2；
r3的上升沿边界参数为0.8，其对应的分布参数为0.1；
r3的下降沿边界参数为3.6，其对应的分布参数为0.3；
r4的边界参数为1.43，其对应的分布参数为0.1；
(五)通过半柯西升降函数得到每个比值r1、r2和r3的比值编码分别为0，1，2的概率，以及r4的比值编码分别为0，1的概率；表达式如下：
r1的比值编码为0的概率f-code0(r1)：
Figure PCTCN2015086109-appb-000007
r1的比值编码为1的概率f-code1(r1)：
Figure PCTCN2015086109-appb-000008
r1的比值编码为2的概率f-code2(r1)：
Figure PCTCN2015086109-appb-000009
r2的比值编码为0的概率f-code0(r2)：
Figure PCTCN2015086109-appb-000010
r2的比值编码为1的概率f-code1(r2)：
Figure PCTCN2015086109-appb-000011
r2的比值编码为2的概率f-code2(r2)：
Figure PCTCN2015086109-appb-000012
r3的比值编码为0的概率f-code0(r3)：
Figure PCTCN2015086109-appb-000013
r3的比值编码为1的概率f-code1(r3)：
Figure PCTCN2015086109-appb-000014
r3的比值编码为2的概率f-code2(r3)：
Figure PCTCN2015086109-appb-000015
r4的比值编码为0的概率f-code0(r4)：
Figure PCTCN2015086109-appb-000016
r4的比值编码为1的概率f-code1(r4)：
Figure PCTCN2015086109-appb-000017
(六)将比值编码的概率以最大值逻辑、最小值逻辑表示，从而得到变压器缺陷故障类型诊断结果的模糊多值形式，变压器缺陷故障类型的概率如下：
f(局部放电)＝min[f-code0(r1)，f-code1(r2)]；
f(低温过热)＝max{min[f-code0(r1)，f-code0(r2)，f-code1(r3)]，min[f-code0(r1)，f-code2(r2)，f-code0(r3)]}；
f(中温过热)＝min[f-code0(r1)，f-code2(r2)，f-code1(r3)]；
f(高温过热)＝max{min[f-code0(r1)，f-code0(r2)，f-code2(r3)]，min[f-code0(r1)，f-code2(r2)，f-code2(r3)]}；
f(火花放电)＝max{f-code2(r1)，min[f-code1(r1)，f-code0(r2)，f-code1(r3)，f-code0(r4)]}；
f(电弧放电)＝max{min[f-code1(r1)，f-code0(r2)，f-code1(r3)，f-code1(r4)]，min[f-code1(r1)，f-code0(r3)]，min[f-code1(r1)，f-code2(r3)]，min[f-code1(r1)，f-code1(r2)，f-code1(r3)]，min[f-code1(r1)，f-code2(r2)，f-code1(r3)]}。
本发明的有益效果是：本发明简单易于实现，特别适应于变压器状态在线监测系统的应用；本发明是基于模糊逻辑的思想，能够实现变压器复杂状态下复合缺陷的诊断以及严重程度的评估，可以有效避免判据边界绝对化带来的突变问题；本发明将油中溶解气体的注意值、比值等多特征信息融合分析，有效提高诊断可靠性。
附图说明
图1为本发明的诊断流程图；
图2为比值r3的比值编码为2时的模糊边界。
具体实施方式
下面结合附图1-2和实施例对本发明做进一步说明。
本实施例具体实现步骤如下：
(一)获取被监测的5种特征气体的体积浓度的监测数据，其中被监测的5种特征气体包括氢气、甲烷、乙烷、乙烯、乙炔；由所述监测数据计算甲烷、乙烷、乙烯和乙炔的体积浓度之和，即总烃的体积浓度；判断所述5种特征气体的监测数据或总烃的体积浓度是否超出注意值；所述注意值根据中国标准GB/T 7252-2001《变压器油中溶解气体分析和判断导则》中的规定选取；若所述监测数据或总烃的体积浓度超出注意值，则需进一步诊断，转步骤(二)；否则，所述监测数据及总烃的体积浓度为正常，确定变压器无缺陷故障；
(二)确定比值编码；
首先设定比值分别为：
Figure PCTCN2015086109-appb-000018
Figure PCTCN2015086109-appb-000019
Figure PCTCN2015086109-appb-000020
Figure PCTCN2015086109-appb-000021
其中，c1(C2H2)、c2(C2H4)、c3(CH4)、c4(H2)、c5(C2H6)分别表示乙炔、乙烯、甲烷、氢气、乙烷5种特征气体的体积浓度，单位为μL/L；
然后确定比值编码，确定比值编码的规则见表1。
表1确定比值编码的规则
Figure PCTCN2015086109-appb-000022
其中，r1、r2和r3的比值编码为根据中国标准GB/T 7252-2001《变压器油中溶解气体分析和判断导则》中比值编码规则得到的；在中国标准GB/T 7252-2001《变压器油中溶解气体分析和判断导则》中比值编码规则基础上增加了比值r4及r4的比值编码规则；
(三)通过对国家电网公司728组实际典型故障案例的分析，对中国标准GB/T 7252-2001《变压器油中溶解气体分析和判断导则》中的根据三比值确定变压器缺陷故障类型的方法进行修正，得到变压器缺陷故障类型比值编码判断规则见表2。
在三比值编码基础上，增加了第四个比值r4；对于三比值法诊断为101编码的缺陷故障类型，若r4≤1.5时，确定变压器为火花放电缺陷故障；若r4＞1.5时，则确定变压器为电弧放电缺陷故障。
相对中国标准GB/T7252-2001《变压器油中溶解气体分析和判断导则》中的变压器缺陷故障编码增加比值编码011为局部放电缺陷故障。
表2根据比值编码判断变压器缺陷故障类型的方法
Figure PCTCN2015086109-appb-000023
(四)为了改变非此即彼的绝对化边界判断，采用半柯西升降函数将表1中的编码边界模糊化，对边界的上升沿和下降沿分别采用半柯西升降函数表示。然后，通过半柯西升降函数得到每个比值r1、r2、r3编码分别为0，1，2的概率(分别用f-code0(ri)，f-code1(ri)，f-code2(ri)表示)，以及r4编码为0，1的概率。例如，比值r3编码为2时的概率用f-code2(r3)表示，模糊边界采用半柯西上升沿函数表示如图2所示。
采用半柯西升降函数将比值r1、r2、r3、r4的边界范围模糊化，对边界的上升沿和下降沿边界模糊分别采用半柯西升降函数表示，表达式为
Figure PCTCN2015086109-appb-000024
Figure PCTCN2015086109-appb-000025
其中，μd(r)是下降沿函数；μa(r)是上升沿函数；A是边界参数；α是分布参数；A与α的取值是利用电网公司728组实际典型故障案例数据验证得到的最优值，取值见表3。
表3边界参数A及分布参数α
A1(r1) A2(r1) A1(r2) A2(r2) A1(r3) A2(r3) A(r4)
0.08 3.1 0.06 0.6 0.8 3.6 1.43
a1(r1) a2(r1) a1(r2) a2(r2) a1(r3) a2(r3) a(r4)
0.01 0.1 0.02 0.2 0.1 0.3 0.1
在表3中：
r1的上升沿边界参数A1(r1)为0.08，其对应的分布参数a1(r1)为0.01；
r1的下降沿边界参数A2(r1)为3.1，其对应的分布参数a2(r1)为0.1；
r2的上升沿边界参数A1(r2)为0.06，其对应的分布参数a1(r2)为0.02；
r2的下降沿边界参数A2(r2)为0.6，其对应的分布参数a2(r2)为0.2；
r3的上升沿边界参数A1(r3)为0.8，其对应的分布参数a1(r3)为0.1；
r3的下降沿边界参数A2(r3)为3.6，其对应的分布参数a2(r3)为0.3；
r4的边界参数A(r4)为1.43，其对应的分布参数a(r4)为0.1；
(五)通过半柯西升降函数得到每个比值r1、r2和r3的比值编码为0，1，2的概率，以及r4的比值编码为0，1的概率；表达式如下：
r1的比值编码为0的概率f-code0(r1)：
Figure PCTCN2015086109-appb-000026
   (式1)
r1的比值编码为1的概率f-code1(r1)：
Figure PCTCN2015086109-appb-000027
   (式2)
r1的比值编码为2的概率f-code2(r1)：
Figure PCTCN2015086109-appb-000028
   (式3)
r2的比值编码为0的概率f-code0(r2)：
Figure PCTCN2015086109-appb-000029
   (式4)
r2的比值编码为1的概率f-code1(r2)：
Figure PCTCN2015086109-appb-000030
   (式5)
r2的比值编码为2的概率f-code2(r2)：
Figure PCTCN2015086109-appb-000031
   (式6)
r3的比值编码为0的概率f-code0(r3)：
   (式7)
r3的比值编码为1的概率f-code1(r3)：
Figure PCTCN2015086109-appb-000033
   (式8)
r3的比值编码为2的概率f-code2(r3)：
Figure PCTCN2015086109-appb-000034
   (式9)
r4的比值编码为0的概率f-code0(r4)：
Figure PCTCN2015086109-appb-000035
   (式10)
r4的比值编码为1的概率f-code1(r4)：
Figure PCTCN2015086109-appb-000036
   (式11)
(六)将比值编码判断规则中的0、1逻辑分别改为最小值逻辑、最大值逻辑，按照比值编码与变压器缺陷故障类型的对应关系进行缺陷故障诊断，对诊断的结果采用模糊多值形式表示，结果以概率形式给出，诊断的结果为缺陷发生的概率，即严重程度；各种故障的概率其总和为1；将比值编码的概率以最大值逻辑、最小值逻辑表示，各种故障的概率分别为：
f(局部放电)＝min[f-code0(r1)，f-code1(r2)]；  (式12)
f(低温过热)＝max{min[f-code0(r1)，f-code0(r2)，f-code1(r3)]，min[f-code0(r1)，f-code2(r2)，f-code0(r3)]}；  (式13)
f(中温过热)＝min[f-code0(r1)，f-code2(r2)，f-code1(r3)]；
f(高温过热)＝max{min[f-code0(r1)，f-code0(r2)，f-code2(r3)]，min[f-code0(r1)，f-code2(r2)，f-code2(r3)]}；  (式14)
f(火花放电)＝max{f-code2(r1)，min[f-code1(r1)，f-code0(r2)，f-code1(r3)，f-code0(r4)]}；  (式15)
f(电弧放电)＝max{min[f-code1(r1)，f-code0(r2)，f-code1(r3)，f-code1(r4)]，min[f-code1(r1)，f-code0(r3)]，min[f-code1(r1)，f-code2(r3)]，min[f-code1(r1)，f-code1(r2)，f-code1(r3)]，min[f-code1(r1)，f-code2(r2)，f-code1(r3)]}。  (式16)
实施例1：
某变压器油色谱试验数据(5种特征气体及总烃的体积浓度，单位为μL/L)见表4。
表4某变压器油色谱试验数据
试验日期 c4(H2) c3(CH4) c5(C2H6) c2(C2H4) c1(C2H2) cz(总烃)
2012.4.26 31.33 10.52 1.98 4.01 6.09 22.60
由表4可知，乙炔体积浓度超过注意值，该变压器不正常。
1.分别计算四个比值：
Figure PCTCN2015086109-appb-000037
Figure PCTCN2015086109-appb-000038
Figure PCTCN2015086109-appb-000039
Figure PCTCN2015086109-appb-000040
2.根据(式1)至(式11)，通过计算，得到四个比值各个比值编码的概率：
f-code0(r1)＝0；f-code1(r1)＝1；f-code2(r1)＝0.004；
f-code0(r2)＝1；f-code1(r2)＝0.0051；f-code2(r2)＝0.37；
f-code0(r3)＝0.00657；f-code1(r3)＝1；f-code2(r3)＝0.035；
f-code0(r4)＝1；f-code2(r4)＝0.5；
3.根据(式12)至(式16)，通过计算，得到各种故障的概率：
f(局部放电)＝0％；
f(低温过热)＝0％；
f(中温过热)＝0％；
f(高温过热)＝0％；
f(火花放电)＝66.7％；
f(电弧放电)＝33.3％；
4.对变压器故障进行诊断
由上述故障的概率可以判断出该变压器存在火花放电兼电弧放电故障。
以上所述实施方式仅为本发明的优选实施例，而并非本发明可行实施的穷举。对于本领域一般技术人员而言，在不背离本发明原理和精神的前提下对其所作出的任何显而易见的改动，都应当被认为包含在本发明的权利要求保护范围之内。

Claims (1)

  1. 一种基于油中溶解气体的变压器内部复合缺陷模糊诊断方法，其特征在于包括如下步骤：
    (一)获取被监测的5种特征气体的体积浓度的监测数据，所述5种特征气体为氢气、甲烷、乙烷、乙烯和乙炔；由所述监测数据计算甲烷、乙烷、乙烯和乙炔的体积浓度之和，即总烃的体积浓度；判断所述5种特征气体的监测数据或总烃的体积浓度是否超出注意值；所述注意值根据中国标准GB/T 7252-2001中的规定选取；若所述监测数据或总烃的体积浓度超出注意值，则需进一步诊断，转步骤(二)；否则，所述监测数据及总烃的体积浓度为正常，确定变压器无缺陷故障；
    (二)确定比值编码；
    首先设定比值分别为：
    Figure PCTCN2015086109-appb-100001
    Figure PCTCN2015086109-appb-100002
    Figure PCTCN2015086109-appb-100003
    Figure PCTCN2015086109-appb-100004
    其中，c1(C2H2)、c2(C2H4)、c3(CH4)、c4(H2)、c5(C2H6)分别表示乙炔、乙烯、甲烷、氢气、乙烷5种特征气体的体积浓度，单位为μL/L；
    然后确定比值编码，确定比值编码的规则如下：
    当r1＜0.1时，r1的比值编码为0；当0.1≤r1＜1时，r1的比值编码为1；当1≤r1＜3时，r1的比值编码为1；r1≥3时，r1的比值编码为2；
    当r2＜0.1时，r2的比值编码为1；当0.1≤r2＜1时，r2的比值编码为0；当1≤r2＜3时，r2的比值编码为2；r2≥3时，r2的比值编码为2；
    当r3＜0.1时，r3的比值编码为0；当0.1≤r3＜1时，r3的比值编码为0；当1≤r3＜3时， r3的比值编码为1；r3≥3时，r3的比值编码为2；
    当r4≤1.5时，r4的比值编码为0；r4＞1.5时，r4的比值编码为1；
    (三)对中国标准GB/T 7252-2001中根据三比值确定变压器缺陷故障类型的方法进行修正：相对中国标准GB/T 7252-2001中三比值编码对应的变压器缺陷故障类型，增加比值编码011对应局部放电缺陷故障类型；
    在三比值编码基础上，增加第四个比值r4；对于三比值法诊断为101编码的缺陷故障类型，若r4≤1.5时，确定变压器为火花放电缺陷故障；若r4＞1.5时，则确定变压器为电弧放电缺陷故障；
    由此得到根据比值编码判断变压器缺陷故障类型的方法如下：
    当r1的比值编码为0、且r2的比值编码为1、且r3的比值编码为0，1或2、且r4的比值编码为0或1时，变压器缺陷故障类型为局部放电；
    当r1的比值编码为0、且r2的比值编码为0、且r3的比值编码为1、且r4的比值编码为0或1时，变压器缺陷故障类型为低于300℃低温过热；
    当r1的比值编码为0、且r2的比值编码为2、且r3的比值编码为0、且r4的比值编码为0或1时，变压器缺陷故障类型为低于300℃低温过热；
    当r1的比值编码为0、且r2的比值编码为2、且r3的比值编码为1、且r4的比值编码为0或1时，变压器缺陷故障类型为300～700℃中温过热；
    当r1的比值编码为0、且r2的比值编码为0或2、且r3的比值编码为2、且r4的比值编码为0或1时，变压器缺陷故障类型为高于700℃高温过热；
    当r1的比值编码为2、且r2的比值编码为0，1或2、且r3的比值编码为0，1或2、且r4的比值编码为0或1时，变压器缺陷故障类型为火花放电；
    当r1的比值编码为1、且r2的比值编码为0、且r3的比值编码为1、且r4的比值编码为0时，变压器缺陷故障类型为火花放电；
    当r1的比值编码为1、且r2的比值编码为0、且r3的比值编码为1、且r4的比值编码为1时，变压器缺陷故障类型为电弧放电；
    当r1的比值编码为1、且r2的比值编码为0，1或2、且r3的比值编码为0或2、且r4的比值编码为0或1时，变压器缺陷故障类型为电弧放电；
    当r1的比值编码为1、且r2的比值编码为1或2、且r3的比值编码为1、且r4的比值编码为0或1时，变压器缺陷故障类型为电弧放电；
    (四)采用半柯西升降函数将比值r1、r2、r3、r4的边界范围模糊化，对边界的上升沿和下降沿分别采用半柯西升降函数表示，表达式为
    Figure PCTCN2015086109-appb-100005
    Figure PCTCN2015086109-appb-100006
    其中，μd(r)是下降沿函数；μd(r)是上升沿函数；A是边界参数；a是分布参数；A与a的取值如下：
    r1的上升沿边界参数为0.08，其对应的分布参数为0.01；
    r1的下降沿边界参数为3.1，其对应的分布参数为0.1；
    r2的上升沿边界参数为0.06，其对应的分布参数为0.02；
    r2的下降沿边界参数为0.6，其对应的分布参数为0.2；
    r3的上升沿边界参数为0.8，其对应的分布参数为0.1；
    r3的下降沿边界参数为3.6，其对应的分布参数为0.3；
    r4的边界参数为1.43，其对应的分布参数为0.1；
    (五)通过半柯西升降函数得到每个比值r1、r2和r3的比值编码分别为0，1，2的概率，以及r4 的比值编码分别为0，1的概率；表达式如下：
    r1的比值编码为0的概率f-code0(r1)：
    Figure PCTCN2015086109-appb-100007
    r1的比值编码为1的概率f-code1(r1)：
    Figure PCTCN2015086109-appb-100008
    r1的比值编码为2的概率f-code2(r1)：
    Figure PCTCN2015086109-appb-100009
    r2的比值编码为0的概率f-code0(r2)：
    Figure PCTCN2015086109-appb-100010
    r2的比值编码为1的概率f-code1(r2)：
    Figure PCTCN2015086109-appb-100011
    r2的比值编码为2的概率f-code2(r2)：
    Figure PCTCN2015086109-appb-100012
    r3的比值编码为0的概率f-code0(r3)：
    Figure PCTCN2015086109-appb-100013
    r3的比值编码为1的概率f-code1(r3)：
    Figure PCTCN2015086109-appb-100014
    r3的比值编码为2的概率f-code2(r3)：
    Figure PCTCN2015086109-appb-100015
    r4的比值编码为0的概率f-code0(r4)：
    Figure PCTCN2015086109-appb-100016
    r4的比值编码为1的概率f-code1(r4)：
    Figure PCTCN2015086109-appb-100017
    (六)将比值编码的概率以最大值逻辑、最小值逻辑表示，从而得到变压器缺陷故障类型诊断结果的模糊多值形式，变压器缺陷故障类型的概率如下：
    f(局部放电)＝min[f-code0(r1),f-code1(r2)]；
    f(低温过热)＝max{min[f-code0(r1),f-code0(r2),f-code1(r3)],min[f-code0(r1),f-code2(r2),f-code0(r3)]}；
    f(中温过热)＝min[f-code0(r1),f-code2(r2),f-code1(r3)]；
    f(高温过热)＝max{min[f-code0(r1),f-code0(r2),f-code2(r3)],min[f-code0(r1),f-code2(r2),f-code2(r3)]}；
    f(火花放电)＝max{f-code2(r1),min[f-code1(r1),f-code0(r2),f-code1(r3),f-code0(r4)]}；
    f(电弧放电)＝max{min[f-code1(r1),f-code0(r2),f-code1(r3),f-code1(r4)],min[f-code1(r1),f-code0(r3)],min[f-code1(r1),f-code2(r3)],min[f-code1(r1),f-code1(r2),f-code1(r3)],min[f-code1(r1),f-code2(r2),f-code1(r3)]}。
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