CN107831300B - Transformer insulating oil degradation evaluation method based on three-dimensional trapezoidal probability fuzzy set - Google Patents
Transformer insulating oil degradation evaluation method based on three-dimensional trapezoidal probability fuzzy set Download PDFInfo
- Publication number
- CN107831300B CN107831300B CN201710985703.8A CN201710985703A CN107831300B CN 107831300 B CN107831300 B CN 107831300B CN 201710985703 A CN201710985703 A CN 201710985703A CN 107831300 B CN107831300 B CN 107831300B
- Authority
- CN
- China
- Prior art keywords
- oil
- characteristic
- degradation
- class
- paper
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000015556 catabolic process Effects 0.000 title claims abstract description 155
- 238000006731 degradation reaction Methods 0.000 title claims abstract description 133
- 238000011156 evaluation Methods 0.000 title claims abstract description 30
- 238000009826 distribution Methods 0.000 claims abstract description 94
- 238000012360 testing method Methods 0.000 claims description 80
- 238000000034 method Methods 0.000 claims description 45
- 238000004804 winding Methods 0.000 claims description 38
- 230000006870 function Effects 0.000 claims description 34
- 238000009413 insulation Methods 0.000 claims description 33
- HYBBIBNJHNGZAN-UHFFFAOYSA-N furfural Chemical compound O=CC1=CC=CO1 HYBBIBNJHNGZAN-UHFFFAOYSA-N 0.000 claims description 28
- 239000002253 acid Substances 0.000 claims description 27
- 238000012545 processing Methods 0.000 claims description 19
- 230000008569 process Effects 0.000 claims description 16
- 238000010276 construction Methods 0.000 claims description 15
- 230000006866 deterioration Effects 0.000 claims description 15
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 15
- 238000004519 manufacturing process Methods 0.000 claims description 14
- 238000010521 absorption reaction Methods 0.000 claims description 8
- 230000036961 partial effect Effects 0.000 claims description 8
- 229930195733 hydrocarbon Natural products 0.000 claims description 7
- 150000002430 hydrocarbons Chemical class 0.000 claims description 7
- 238000006116 polymerization reaction Methods 0.000 claims description 7
- 239000004215 Carbon black (E152) Substances 0.000 claims description 4
- 238000012423 maintenance Methods 0.000 abstract description 19
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical group [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 10
- 230000007547 defect Effects 0.000 description 9
- 238000004458 analytical method Methods 0.000 description 4
- 238000007689 inspection Methods 0.000 description 4
- 238000013528 artificial neural network Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 238000001514 detection method Methods 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 230000036541 health Effects 0.000 description 2
- 230000000670 limiting effect Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012706 support-vector machine Methods 0.000 description 2
- 238000012384 transportation and delivery Methods 0.000 description 2
- VAYOSLLFUXYJDT-RDTXWAMCSA-N Lysergic acid diethylamide Chemical compound C1=CC(C=2[C@H](N(C)C[C@@H](C=2)C(=O)N(CC)CC)C2)=C3C2=CNC3=C1 VAYOSLLFUXYJDT-RDTXWAMCSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000009529 body temperature measurement Methods 0.000 description 1
- 239000003990 capacitor Substances 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004587 chromatography analysis Methods 0.000 description 1
- 230000008094 contradictory effect Effects 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000010891 electric arc Methods 0.000 description 1
- 238000012854 evaluation process Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000007062 hydrolysis Effects 0.000 description 1
- 238000006460 hydrolysis reaction Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012067 mathematical method Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 238000013021 overheating Methods 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 230000010287 polarization Effects 0.000 description 1
- 238000001556 precipitation Methods 0.000 description 1
- 230000003449 preventive effect Effects 0.000 description 1
- 238000000197 pyrolysis Methods 0.000 description 1
- 230000002829 reductive effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000007789 sealing Methods 0.000 description 1
- 238000004904 shortening Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 230000036962 time dependent Effects 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 238000013024 troubleshooting Methods 0.000 description 1
Images
Classifications
-
- 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
-
- 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
Landscapes
- Chemical & Material Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Medicinal Chemistry (AREA)
- Food Science & Technology (AREA)
- Oil, Petroleum & Natural Gas (AREA)
- General Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Other Investigation Or Analysis Of Materials By Electrical Means (AREA)
- Housings And Mounting Of Transformers (AREA)
- Testing Relating To Insulation (AREA)
Abstract
The invention relates to the field of power systems and automation thereof, in particular to a transformer insulating oil degradation evaluation method based on a three-dimensional trapezoidal probability fuzzy set, which can evaluate the degradation state of insulating oil of a distribution transformer, reflects that a series of characteristic values of the evaluation of the degradation state of the insulating oil of the distribution transformer formed in published documents have fuzzy and random uncertainties, provides theoretical guidance for the evaluation of the degradation state of the insulating oil of the distribution transformer, and provides necessary technical support for operation and maintenance of a power distribution network.
Description
Technical Field
The invention relates to the field of power systems and automation thereof, in particular to a transformer insulating oil degradation evaluation method based on a three-dimensional trapezoidal probability fuzzy set.
Background
The traditional main equipment of the distribution network is usually required to be subjected to post maintenance and regular scheduled maintenance, a large amount of manpower and material resources are required to be invested, and the cost performance of maintenance is not high. Statistical studies with extensive data show that as the degree of automation of the equipment increases, the time-dependent failure modes of the equipment account for only 6% of all failure modes of the equipment, and therefore the time-based periodic maintenance strategy is effective for only 6% of the failure modes of the equipment. The maintenance mode of prolonging or shortening the maintenance period is determined by taking the regular maintenance as the main combination experience, and a certain effect is achieved.
With the increasing number of power equipment, the association relationship among the equipment becomes more and more complex, the social requirement on the power supply reliability becomes higher and higher, and the arrangement of power failure maintenance becomes increasingly difficult; the distribution network equipment quantity is large, the running state is complex and changeable, the state of the distribution network main equipment is difficult to detect and evaluate in time, the traditional maintenance strategy pays more attention to test data and pays less attention to running data, and the requirement of increasingly lean state maintenance management cannot be met.
The distribution transformer has a large number, can have different degrees of deterioration, deterioration and defects, has familiarity and concealment, and is difficult to detect and evaluate in time. Due to the fact that the operation years, the environment, the overhaul and the like are greatly different and are influenced by multiple factors, the difficulty and the complexity of the evaluation of the operation health state of the distribution transformer are increased, and the higher requirements of the accurate and intelligent evaluation cannot be met.
The safe and reliable operation of the distribution transformer firstly needs strict quality guarantee and also needs enough maintenance and overhaul guarantee. Although regular preventive maintenance can prevent the occurrence of a failure accident event due to deterioration, or defect problems to some extent, it is difficult to find defects or the like having extremely high potential and concealment. Troubleshooting is a passive mode of maintenance, has great stress and uncertainty, and is also prone to cause the problem of over-repair or overhaul. The state maintenance has pertinence and rationality, can effectively overcome the problems of overhauling and overhauling caused by regular maintenance, can prevent the expansion and the seriousness of the problems of the deterioration, the deterioration or the defect of the distribution equipment, and is the trend of the development of the equipment maintenance in future.
Traditionally, the insulation state of a distribution transformer is estimated by mostly adopting a single factor data calculation and analysis method such as dissolved gas in oil, and potential defects of the gradually developed transformer can be accurately and reliably found; the single factor data is processed, calculated and analyzed by utilizing mathematical methods such as a wavelet network method, a neural network method, a fuzzy clustering method, a gray clustering method, a support vector machine, a rough set method, an evidence reasoning method, a Bayesian network classifier and the like, and the deterioration, the deterioration and the defect states of the distribution transformer can be accurately and reliably evaluated. Although the neural network method utilizes the pre-self-training and self-learning modes to process and calculate the high-risk data, the neural network method is seriously influenced by the state value of a system or parameters, retraining and learning are needed once the state changes, the adaptability is weak and the analysis result is influenced; the fault tree method is used for carrying out detailed decomposition on faults according to a certain rule so as to analyze the fault types and reasons thereof, the integrity and the correctness of very detailed fault information are required, and potential faults are difficult to discover; the support vector machine method adopts a certain rule to carry out layered processing on data, and the problems of wrong division, wrong division and the like are easy to occur when the data amount is large; the rough set and fuzzy theory method has unique advantages in the aspect of processing randomness and fuzzy data, but the rough set can only process discrete data, and the fuzzy theory method has no self-learning and self-adaption capability; the Bayesian network classification method can well process incomplete data, but key attribute data of a system or parameters are required to be provided completely, otherwise, the calculation and evaluation accuracy is low; the evidence reasoning theory can process the redundant information or data better and accurately, but the event discrimination applied to the evidence has great limitation when the information or the data are contradictory.
The evaluation accuracy is low easily caused by using experience, single parameters or a small amount of data, and the problems of overhauling or overhauling and the like are further caused. On the basis of fusion of multi-source data of delivery, monitoring, testing, routing inspection, operation, metering, automation and the like, classification evaluation is carried out according to equipment types, operation conditions and application environments, a health state model of the distribution transformer based on data driving is established, state evaluation is carried out by means of redundancy analysis and correlation analysis of key indexes, technical support is provided for reliable operation of the distribution transformer, and risk early warning is provided for fault occurrence of the distribution transformer.
Factors causing the fault of the distribution transformer include insulation moisture, iron core fault, current loop overheating, winding fault, partial discharge, oil flow discharge, arc discharge, insulation degradation and insulation paper degradation, and parameters affecting the insulation state of the distribution transformer include insulation paper dielectric loss, water content in oil, oil breakdown voltage, insulation resistance absorption ratio, polarization index, volume resistivity, H2 content, iron core insulation resistance and the like. The differential operation and maintenance of the distribution transformer needs overall evaluation, the state evaluation relates to ledger information, routing inspection information, live detection and online monitoring data, offline test data and the like, the data volume is large, the influence mechanism is different, the conventional evaluation method focuses on research of certain layers or indexes, and the requirements of multi-dimensional and large data cannot be met. By adopting a big data technology, the state change of the main equipment can be comprehensively reflected, and the characteristics and key parameters of the main equipment can be determined. The method comprises the steps of establishing a database of distribution network main equipment such as a transformer, a breaker, a lightning arrester, a capacitor and the like by using static data such as factory test data, defect and accident records, regular and irregular test data and dynamic data such as online detection data and real-time operation information of the equipment, including real-time operation information such as voltage, current and power, fault information such as short-circuit fault, lightning trip and familial defect, inspection information such as infrared temperature measurement, sealing and pollution, state data such as power failure detection information such as direct current resistance, insulation resistance, oil chromatography and dielectric loss, researching a main equipment state feature evaluation method by adopting a big data technology, clarifying an association relation between the main equipment state and hydrolysis and pyrolysis, and extracting the main equipment state feature by adopting a fuzzy C-means clustering analysis method.
Oil dielectric loss, oil water content, oil gas content, oil breakdown voltage, oil volume resistivity, oil conductivity, oil acid value, oil breakdown voltage, total oil acid value, furfural content in oil, oil color and the like related to insulation paper, paper dielectric loss, paper water content, paper breakdown voltage, paper conductivity, paper acid value, paper polymerization degree, paper total acid value, paper furfural content, paper color and the like related to insulation paper, H2 content, C2H2 content, C2H6 content, C2H4 content, CH4 content, CO relative gas production rate, CO2 relative gas production rate, total hydrocarbon and the like related to gas, iron core related parameter data such as iron core insulation resistance, iron core grounding current and the like, winding direct current resistance, insulation resistance absorption ratio, winding resistance and unbalance rate thereof, winding short circuit resistance initial value difference, winding insulation dielectric loss, winding initial capacitance difference and the like related to winding, the fault of the distribution transformer is an accident or event of random and fuzzy uncertainty, and the factors are parameters of random and fuzzy uncertainty. These influencing factors are typically random or fuzzy uncertainty, or both random and fuzzy uncertainty, often present as random and fuzzy uncertainty events or quantities. Therefore, the uncertainty and randomness of the influence factors are not considered comprehensively in the prior art of traditional distribution transformer insulation state evaluation, and the applicability, the practicability and the applicability of the calculation method are difficult to meet.
Disclosure of Invention
The invention provides a transformer insulating oil degradation evaluation method based on a three-dimensional trapezoidal probability fuzzy set, aiming at solving the defects of the prior art. Aiming at how to process the problems of large data with multiple types, large quantity and complex interrelation related to the evaluation of the degradation state of the insulating oil of the distribution transformer, the knowledge mining and reasoning principle is adopted to process and analyze the large data on the basis of establishing a large database; and aiming at random and fuzzy uncertainty parameters related to the evaluation of the degradation state of the insulating oil of the distribution transformer, processing and analyzing by adopting a theory of a three-dimensional trapezoidal probability fuzzy set, and further accurately evaluating the degradation state of the insulating oil of the transformer.
The basic principle of the transformer insulating oil degradation evaluation based on the three-dimensional trapezoidal probability fuzzy set is as follows; by utilizing multi-source data of delivery, monitoring, testing, routing inspection, operation, metering, automation and the like, establishing a large database of parameters related to insulating oil, insulating paper, an iron core and a winding, establishing a large database of parameters related to gas, capacitance, temperature and partial discharge in the oil, establishing a large database of weather such as temperature, wind power, humidity, precipitation and the like, and establishing an operation database of current, voltage, power, load rate and the like of a distribution transformer; carrying out three-dimensional trapezoidal fuzzy modeling on parameters which have random uncertainty or fuzzy uncertainty and cause the deterioration of transformer insulating oil by adopting a fuzzy set theory; the method comprises the steps of constructing a three-dimensional trapezoidal fuzzy set of transformer insulating oil degradation characteristics by utilizing mass data of results related to a transformer insulating oil degradation evaluation method in a public document; constructing a three-dimensional trapezoidal fuzzy set of the transformer insulating oil degradation test by using mass data of the transformer insulating oil degradation test; and constructing a similarity function between the three-dimensional trapezoidal fuzzy sets of the test class data and the feature class data of the transformer, and calculating a comprehensive attribute value between a random uncertainty or fuzzy uncertainty parameter and the degradation state of the insulating oil of the distribution transformer so as to determine the degradation state of the insulating oil of the distribution transformer.
The technical scheme of the invention is as follows: a transformer insulating oil degradation assessment method based on a three-dimensional trapezoidal probability fuzzy set comprises the following steps:
s1: fuzzification processing of the degradation characteristic data of the insulating oil and construction of a membership function;
s2: constructing a three-dimensional trapezoidal fuzzy set of the degradation characteristic class of the insulating oil;
s3: fuzzification processing of insulating oil test data and construction of membership functions;
s4: constructing a three-dimensional trapezoidal fuzzy set of insulating oil tests;
s5: constructing a similarity function between the test class and the feature class probability fuzzy set;
s6: and (5) evaluating the degradation state of the transformer insulating oil.
Further, the step S1 is to perform fuzzification processing on the degradation characteristic data of the insulating oil and construct a membership function:
the oil degradation characteristic class describes the combination of characteristic values of a plurality of characteristic parameters when the insulating oil of the distribution transformer enters a degradation state, the parameters and the characteristic values related to the evaluation of the degradation state of the insulating oil of the distribution transformer are collected from published documents (journal articles, academic papers and the like), and the degradation characteristic class S of the insulating oil of the distribution transformer is constructed1、S2、...、
Wherein N isSNumber of insulating oil degradation feature classes for distribution transformers, oil degradation feature class S1、S2、...、
Has different characteristic spaces, such as oil medium loss, water content in oil, gas content in oil, oil breakdown voltage, oil volume resistivity, oil conductivity, acid value in oil, oil breakdown voltage, total acid value of oil, furfural content in oil, oil color, paper medium loss, water content in paper, paper breakdown voltage, paper conductivity, acid value in paper, paper polymerization degree, total acid value of paper, furfural content in paper, paper color, H2Content, C2H2Content, C2H6Content, C2H4Content, CH4Content, relative gas production rate of CO, CO2Relative gas production rate, total hydrocarbons, core insulation resistance, core ground current, winding direct current resistance, insulation resistance absorption ratio, winding direct current resistance and unbalance rate thereof, initial value difference of winding short-circuit impedance, winding insulation dielectric loss, initial value difference of winding capacitance, high-voltage side A-phase capacitance value, high-voltage side B-phase capacitance value, high-voltage side C-phase capacitance value, low-voltage side A-phase capacitance value, low-voltage side B-phase capacitance value, low-voltage side C-phase capacitance value, hot spot temperature at typical load, hot spot temperature at high load, oil temperature, and partial discharge quantity, assuming that m (m is 1,2,3,. N, N) is a combination of 47 characteristic parametersS) Individual oil degradation characteristic class SmContaining N (1, 2, 3.., N)Sm) Individual characteristic variables, characteristic data sets x of individual characteristic variablesSmn. The mth oil degradation characteristic class S can be obtained through big data processingmData set x ofSmComprises the following steps:
mth oil degradation characteristic class SmCharacteristic data set x of the n-th characteristic quantity of (1)SmnCan be expressed as:
in the formula NSmnIs the m-th oil degradation characteristic class SmCharacteristic data set x of the n-th characteristic quantity of (1)SmnNumber of data, for different parameters NSmnThere will be a different number of values,
assuming that there are 9 fuzzy uncertainties representing the distribution transformer oil degradation characteristic levels of extremely low, very low, medium, high, very high, the mathematical expression is:
ASmn＝{ASmn1,ASmn2,ASmn3,ASmn4,ASmn5,ASmn6,ASmn7,ASmn8,ASmn9}
in the formula ASmn1、ASmn2、ASmn3、ASmn4、ASmn5、ASmn6、ASmn7、ASmn8、ASmn9Or ASmni(i 1, 2.., 9.) respectively represent the characteristic levels of extremely low, very low, medium, high, very high and extremely high degradation of distribution transformer oil, and have membership functions of fuzzy sets of three-dimensional trapezoidal distribution characteristicsComprises the following steps:
in the formula
A characteristic membership function having a three-dimensional trapezoidal distribution characteristic for a distribution transformer oil degradation level i (i 1, 2.., 9),
the characteristic coefficients of characteristic membership functions of oil degradation levels i (i ═ 1, 2.. multidot.9) with three-dimensional trapezoidal distribution characteristics are respectively, and x is the mth oil degradation characteristic class SmCharacteristic data set x of the n-th characteristic quantity of (1)SmnFor the mth oil degradation characteristic class SmCharacteristic data set x of the n-th characteristic quantity of (1)SmnLower, middle and upper bound characteristic membership functions of a three-dimensional trapezoidal fuzzy set of distribution transformer oil degradation levels i (i ═ 1, 2.., 9)Respectively as follows:
further, the step S2 is to construct a three-dimensional trapezoidal fuzzy set process of the insulating oil degradation characteristic class as follows;
construction of mth distribution transformer oil degradation characteristic class SmThe degradation level i (i ═ 1, 2.., 9) of the nth characteristic parameter of (a):
(m＝1,2,3,...,NS,n＝1,2,3,...,NSm)
in the formula
For mth distribution transformer oil degradation characteristic class SmThe degradation level i (i ═ 1, 2.., 9) of the nth characteristic parameter of (a).
Further, the fuzzification processing of the insulating oil test data and the construction process of the membership function in the step S3 are as follows;
the oil test data is data obtained from the test, and the insulating oil test class T of the distribution transformer is constructed according to the oil test data1、T2、...、
Wherein N isTThe number of insulating oil degradation test classes for the distribution transformer. Oil deterioration test type T1、T2、...、
Has different characteristic spaces, such as oil medium loss, water content in oil, gas content in oil, oil breakdown voltage, oil volume resistivity, oil conductivity, acid value in oil, oil breakdown voltage, total acid value of oil, furfural content in oil, oil color, paper medium loss, water content in paper, paper breakdown voltage, paper conductivity, acid value in paper, paper polymerization degree, total acid value of paper, furfural content in paper, paper color, H2Content, C2H2Content, C2H6Content, C2H4Content, CH4Content, relative gas production rate of CO, CO2Relative gas production rate, total hydrocarbons, core insulation resistance, core ground current, winding direct current resistance, insulation resistance absorption ratio, winding direct current resistance and unbalance rate thereof, initial value difference of winding short-circuit impedance, winding insulation dielectric loss, initial value difference of winding capacitance, high-voltage side A-phase capacitance value, high-voltage side B-phase capacitance value, high-voltage side C-phase capacitance value, low-voltage side A-phase capacitance value, low-voltage side B-phase capacitance value, low-voltage side C-phase capacitance value, hot spot temperature at typical load, hot spot temperature at high load, oil temperature, and partial discharge quantity, assuming that m (m is 1,2,3,. N, N) is a combination of 47 characteristic parametersT) AnOil test class TmContaining N (1, 2, 3.., N)Tm) Individual characteristic variables, characteristic data sets x of individual characteristic variablesTmnThe mth oil test class T can be obtained through big data processingmData set x ofTmComprises the following steps:
mth oil test class TmCharacteristic data set x of the n-th characteristic quantity of (1)TmnCan be expressed as:
in the formula NTmnFor the m oil test class TmCharacteristic data set x of the n-th characteristic quantity of (1)SmnNumber of data, for different parameters NTmnThere will be different values for the m-th oil test class TmCharacteristic data set x of the n-th characteristic quantity of (1)TmnConstructing membership function k of lower bound, middle bound and upper bound of three-dimensional trapezoidal fuzzy set for distribution transformer oil testSLmnk(x)、kSMmnk(x)、kSUmnk(x) Respectively as follows:
further, the step S4 is to construct a three-dimensional trapezoidal fuzzy set of the insulating oil test class as follows;
construction of mth distribution transformer oil test type TmThe three-dimensional trapezoidal fuzzy set of the nth characteristic parameter:
Tmn＝{TSLmn,TSMmn,TSUmn}
＝{(aTLmn,bTLmn,cTLmn,dTLmn；kTLmn),(aTMmn,bTMmn,cTMmn,dTMmn；kTMmn),
(aTUmn,bTUmn,cTUmn,dTUmn；kTUmn)}
(m＝1,2,3,...,NT,n＝1,2,3,...,NTm)
in the formula TmnIs a three-dimensional trapezoidal fuzzy set of the nth characteristic parameter of the mth oil test class.
Further, the process of constructing the similarity function between the test class and the feature class probability fuzzy set in step S5 is as follows;
using oil degradation characteristics class S1、S2、...、
And insulating oil test type T1、T2、...、
The probability fuzzy set of the distribution transformer is constructed, and the three-dimensional trapezoidal fuzzy set of the kth characteristic parameter and the mth characteristic class S of the jth oil test class of the distribution transformer are constructedmThe degradation level i (i ═ 1, 2.., 9) of the nth characteristic parameter of (a) is determined as follows:
(m＝1,2,3,...,NS,n＝1,2,3,...,NSm,j＝1,2,3,...,NT,k＝1,2,3,...,NTm) Wherein the kth characteristic parameter of the jth oil test class has a lower bound, a middle bound and an upper bound three-dimensional trapezoidal fuzzy set and the mth characteristic class SmThe similarity functions of the lower-bound, middle-bound, and upper-bound three-dimensional trapezoidal fuzzy sets of the n-th characteristic parameter i (i ═ 1, 2.·,9) are respectively:
further, step S6 performs a transformer insulating oil degradation state evaluation process as follows;
total similarity between degradation levels i of distribution transformer oil test class and oil degradation feature class
Average similarity between degradation levels i of distribution transformer oil test class and oil degradation feature class
When in use
Higher than
(e.g., 0.95, etc.), it is determined that the transformer insulating oil has been in a state of a degradation level i (i ═ 1, 2.., 9), i.e., nine degradation states: extremely low, very low, medium, high, very high.
The invention has the beneficial effects that: the transformer insulating oil degradation evaluation method based on the three-dimensional trapezoidal probability fuzzy set can evaluate the degradation state of the insulating oil of the distribution transformer, reflects that a series of characteristic values of the distribution transformer insulating oil degradation state evaluation formed in the open literature have fuzzy and random uncertainties, provides theoretical guidance for the evaluation of the degradation state of the insulating oil of the distribution transformer, and provides necessary technical support for operation and maintenance of a power distribution network.
Drawings
Fig. 1 is a flow chart of a transformer insulating oil degradation evaluation method based on a three-dimensional trapezoidal probability fuzzy set, which is provided by the invention.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent; for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted. The positional relationships depicted in the drawings are for illustrative purposes only and are not to be construed as limiting the present patent.
Example 1:
step 1 in fig. 1 describes a process and a method for fuzzification processing of transformer insulating oil degradation characteristic class data and construction of a membership function. The transformer insulating oil degradation characteristic class describes the combination of a plurality of characteristic parameter characteristic values when the insulating oil of the distribution transformer enters a degradation state. Collecting parameters and characteristic values thereof related to the evaluation of the degradation state of the insulating oil of the distribution transformer from published documents (journal articles, academic papers and the like), and constructing a degradation characteristic class S of the insulating oil of the distribution transformer1、S2、...、
Wherein N isSNumber of insulating oil degradation feature classes for distribution transformers. Oil degradation characteristic class S1、S2、...、
Has different characteristic spaces, such as oil dielectric loss, water content in oil, gas content in oil, oil breakdown voltage and oil volumeResistivity, oil conductivity, acid value in oil, oil breakdown voltage, total acid value of oil, amount of furfural in oil, oil color, paper dielectric loss, water content in paper, paper breakdown voltage, paper conductivity, acid value in paper, degree of polymerization of paper, total acid value of paper, amount of furfural in paper, paper color, H2Content, C2H2Content, C2H6Content, C2H4Content, CH4Content, relative gas production rate of CO, CO2The combination of 47 characteristic parameters such as relative gas production rate, total hydrocarbon, iron core insulation resistance, iron core grounding current, winding direct current resistance, insulation resistance absorption ratio, winding direct current resistance and unbalance rate thereof, winding short-circuit impedance initial value difference, winding insulation dielectric loss, winding capacitance initial value difference, high-voltage side A phase capacitance value, high-voltage side B phase capacitance value, high-voltage side C phase capacitance value, low-voltage side A phase capacitance value, low-voltage side B phase capacitance value, low-voltage side C phase capacitance value, typical load hot spot temperature, high load hot spot temperature, oil temperature, partial discharge quantity and the like. Let m (m ═ 1,2, 3.., N)S) Individual oil degradation characteristic class SmContaining N (1, 2, 3.., N)Sm) Individual characteristic variables, characteristic data sets x of individual characteristic variablesSmn. The mth oil degradation characteristic class S can be obtained through big data processingmData set x ofSmComprises the following steps:
mth oil degradation characteristic class SmCharacteristic data set x of the n-th characteristic quantity of (1)SmnCan be expressed as:
in the formula NSmnIs the m-th oil degradation characteristic class SmCharacteristic data set x of the n-th characteristic quantity of (1)SmnNumber of data, for different parameters NSmnThere will be different values.
Assuming that there are 9 fuzzy uncertainties representing the distribution transformer oil degradation characteristic levels of extremely low, very low, medium, high, very high, the mathematical expression is:
ASmn＝{ASmn1,ASmn2,ASmn3,ASmn4,ASmn5,ASmn6,ASmn7,ASmn8,ASmn9}
in the formula ASmn1、ASmn2、ASmn3、ASmn4、ASmn5、ASmn6、ASmn7、ASmn8、ASmn9Or ASmni(i 1, 2.., 9.) respectively represent the characteristic levels of extremely low, very low, medium, high, very high and extremely high degradation of distribution transformer oil, and have membership functions of fuzzy sets of three-dimensional trapezoidal distribution characteristics
Comprises the following steps:
in the formula
A characteristic membership function having a three-dimensional trapezoidal distribution characteristic for a distribution transformer oil degradation level i (i 1, 2.., 9),
the characteristic coefficients of characteristic membership functions of oil degradation levels i (i ═ 1, 2.. multidot.9) with three-dimensional trapezoidal distribution characteristics are respectively, and x is the mth oil degradation characteristic class SmCharacteristic data set x of the n-th characteristic quantity of (1)SmnThe data of (1).
For the mth oil degradation characteristic class SmCharacteristic data set x of the n-th characteristic quantity of (1)SmnLower, middle and upper bound characteristic membership functions of a three-dimensional trapezoidal fuzzy set of distribution transformer oil degradation levels i (i ═ 1, 2.., 9)
Respectively as follows:
step 2 in fig. 1 describes a process and method of constructing a three-dimensional trapezoidal fuzzy set of transformer insulating oil degradation feature classes. Construction of mth distribution transformer oil degradation characteristic class SmThe degradation level i (i ═ 1, 2.., 9) of the nth characteristic parameter of (a):
(m＝1,2,3,...,NS,n＝1,2,3,...,NSm)
in the formula
For mth distribution transformer oil degradation characteristic class SmThe degradation level i (i ═ 1, 2.., 9) of the nth characteristic parameter of (a).
Step 3 in fig. 1 describes the process and method of fuzzification processing of transformer insulating oil test data and the construction of membership function. Oil test data is the data obtained from the test. Aiming at oil test data, a distribution transformer insulating oil test type T is constructed1、T2、...、
Wherein N isTThe number of insulating oil degradation test classes for the distribution transformer. Oil deterioration test type T1、T2、...、
Has different characteristic spaces, such as oil medium loss, water content in oil, gas content in oil, oil breakdown voltage, oil volume resistivity, oil conductivity, acid value in oil, oil breakdown voltage, total acid value of oil, furfural content in oil, oil color, paper medium loss, water content in paper, paper breakdown voltage, paper conductivity, acid value in paper, paper polymerization degree, total acid value of paper, furfural content in paper, paper color, H2Content, C2H2Content, C2H6Content, C2H4Content, CH4Content, relative gas production rate of CO, CO2The combination of 47 characteristic parameters such as relative gas production rate, total hydrocarbon, iron core insulation resistance, iron core grounding current, winding direct current resistance, insulation resistance absorption ratio, winding direct current resistance and unbalance rate thereof, winding short-circuit impedance initial value difference, winding insulation dielectric loss, winding capacitance initial value difference, high-voltage side A phase capacitance value, high-voltage side B phase capacitance value, high-voltage side C phase capacitance value, low-voltage side A phase capacitance value, low-voltage side B phase capacitance value, low-voltage side C phase capacitance value, typical load hot spot temperature, high load hot spot temperature, oil temperature, partial discharge quantity and the like. Let m (m ═ 1,2, 3.., N)T) Individual oil test class TmContaining N (1, 2, 3.., N)Tm) Individual characteristic variables, characteristic data sets x of individual characteristic variablesTmn. The mth oil test class T can be obtained through big data processingmData set x ofTmComprises the following steps:
xTm＝[xTm1,xTm2,...,xTmNTm]
mth oil test class TmCharacteristic data set x of the n-th characteristic quantity of (1)TmnCan be expressed as:
in the formula NTmnFor the m oil test class TmCharacteristic data set x of the n-th characteristic quantity of (1)SmnNumber of data, for different parameters NTmnThere will be different values.
For the m oil test class TmCharacteristic data set x of the n-th characteristic quantity of (1)TmnConstructing membership function k of lower bound, middle bound and upper bound of three-dimensional trapezoidal fuzzy set for distribution transformer oil testSLmnk(x)、kSMmnk(x)、kSUmnk(x) Respectively as follows:
step 4 in fig. 1 describes the process and method of constructing a three-dimensional trapezoidal fuzzy set of transformer insulating oil test classes. Construction of mth distribution transformer oil test type TmThe three-dimensional trapezoidal fuzzy set of the nth characteristic parameter:
Tmn＝{TSLmn,TSMmn,TSUmn}
＝{(aTLmn,bTLmn,cTLmn,dTLmn；kTLmn),(aTMmn,bTMmn,cTMmn,dTMmn；kTMmn),
(aTUmn,bTUmn,cTUmn,dTUmn；kTUmn)}
(m＝1,2,3,...,NT,n＝1,2,3,...,NTm)
in the formula TmnIs a three-dimensional trapezoidal fuzzy set of the nth characteristic parameter of the mth oil test class.
Step 5 in fig. 1 describes the process and method of constructing the similarity function between the trial class and the feature class probabilistic fuzzy set. Using oil degradation characteristics class S1、S2、...、
And insulating oil test type T1、T2、...、
The probability fuzzy set of the distribution transformer is constructed, and the three-dimensional trapezoidal fuzzy set of the kth characteristic parameter and the mth characteristic class S of the jth oil test class of the distribution transformer are constructedmThe degradation level i (i ═ 1, 2.., 9) of the nth characteristic parameter of (a) is determined as follows:
(m＝1,2,3,...,NS,n＝1,2,3,...,NSm,j＝1,2,3,...,NT,
k＝1,2,3,...,NTm)
wherein the kth characteristic parameter of the jth oil test class has a lower bound, a middle bound and an upper bound three-dimensional trapezoidal fuzzy set and the mth characteristic class SmThe similarity functions of the lower-bound, middle-bound, and upper-bound three-dimensional trapezoidal fuzzy sets of the n-th characteristic parameter i (i ═ 1, 2.·,9) are respectively:
step 6 in fig. 1 describes the process and method of transformer insulating oil degradation state evaluation. Total similarity between degradation levels i of distribution transformer oil test class and oil degradation feature class
Average similarity between degradation levels i of distribution transformer oil test class and oil degradation feature class
When in use
Higher than
(e.g., 0.95, etc.), it is determined that the transformer insulating oil has been in a state of a degradation level i (i ═ 1, 2.., 9), i.e., nine degradation states: extremely low, very low, medium, high, very high.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (7)
1. A transformer insulating oil degradation assessment method based on a three-dimensional trapezoidal probability fuzzy set is characterized by comprising the following steps:
s1: fuzzification processing of the degradation characteristic data of the insulating oil and construction of a membership function;
s2: constructing a three-dimensional trapezoidal fuzzy set of the degradation characteristic class of the insulating oil;
s3: fuzzification processing of insulating oil test data and construction of membership functions;
s4: constructing a three-dimensional trapezoidal fuzzy set of insulating oil tests;
s5: constructing a similarity function between the test class and the feature class probability fuzzy set;
s6: and (5) evaluating the degradation state of the transformer insulating oil.
2. The method for evaluating the degradation of the insulating oil of the transformer based on the three-dimensional trapezoidal probability fuzzy set as claimed in claim 1, wherein the step S1 comprises the steps of fuzzification processing of the degradation characteristic class data of the insulating oil and construction of the membership function:
the oil degradation characteristic class describes the combination of a plurality of characteristic parameter characteristic values when the insulating oil of the distribution transformer enters a degradation state, the parameters and the characteristic values related to the evaluation of the degradation state of the insulating oil of the distribution transformer are collected from published documents, and the degradation characteristic class of the insulating oil of the distribution transformer is constructedWherein N isSNumber of insulating oil degradation feature classes for distribution transformers, oil degradation feature classes
Has different characteristic spaces, such as oil medium loss, water content in oil, gas content in oil, oil breakdown voltage, oil volume resistivity, oil conductivity, acid value in oil, oil breakdown voltage, total acid value of oil, furfural content in oil, oil color, paper medium loss, water content in paper, paper breakdown voltage, paper conductivity, acid value in paper, paper polymerization degree, total acid value of paper, furfural content in paper, paper color, H2Content, C2H2Content, C2H6Content, C2H4Content, CH4Content, relative gas production rate of CO, CO2Relative gas production rate, total hydrocarbon, core insulation resistance, core ground current, winding direct current resistance, insulation resistance absorption ratio, winding direct current resistance and unbalance rate, initial value difference of winding short-circuit impedance, winding insulation dielectric loss, initial value difference of winding capacitance, high-voltage side A phase capacitance value, high-voltage side B phase capacitance value, high-voltage side C phase capacitance value, low-voltage side C phase capacitance valueAssuming a combination of 47 characteristic parameters in total, i.e., an a-phase capacitance value, a low-side B-phase capacitance value, a low-side C-phase capacitance value, a typical load hot spot temperature, a high load hot spot temperature, an oil temperature, and the number of partial discharges, an m-th (m ═ 1,2,3S) Individual oil degradation characteristic class SmContaining N (1, 2, 3.., N)Sm) Individual characteristic variables, characteristic data sets x of individual characteristic variablesSmnThe mth oil degradation characteristic class S can be obtained through big data processingmData set x ofSmComprises the following steps:
mth oil degradation characteristic class SmCharacteristic data set x of the n-th characteristic quantity of (1)SmnCan be expressed as:
in the formula NSmnIs the m-th oil degradation characteristic class SmCharacteristic data set x of the n-th characteristic quantity of (1)SmnNumber of data, for different parameters NSmnThere will be a different number of values,
assuming that there are 9 fuzzy uncertainties representing the distribution transformer oil degradation characteristic levels of extremely low, very low, medium, high, very high, the mathematical expression is:
ASmn＝{ASmn1,ASmn2,ASmn3,ASmn4,ASmn5,ASmn6,ASmn7,ASmn8,ASmn9}
in the formula ASmn1、ASmn2、ASmn3、ASmn4、ASmn5、ASmn6、ASmn7、ASmn8、ASmn9Or ASmni(i 1, 2.., 9.) respectively represent the characteristic levels of extremely low, very low, medium, high, very high and extremely high degradation of distribution transformer oil, and have membership functions of fuzzy sets of three-dimensional trapezoidal distribution characteristics
Comprises the following steps:
in the formulaA characteristic membership function having a three-dimensional trapezoidal distribution characteristic for a distribution transformer oil degradation level i (i 1, 2.., 9),
the characteristic coefficients of characteristic membership functions of oil degradation levels i (i ═ 1, 2.. multidot.9) with three-dimensional trapezoidal distribution characteristics are respectively, and x is the mth oil degradation characteristic class SmCharacteristic data set x of the n-th characteristic quantity of (1)SmnFor the mth oil degradation characteristic class SmCharacteristic data set x of the n-th characteristic quantity of (1)SmnLower, middle and upper bound characteristic membership functions of a three-dimensional trapezoidal fuzzy set of distribution transformer oil degradation levels i (i ═ 1, 2.., 9)
Respectively as follows:
3. the method for evaluating the deterioration of the transformer insulating oil based on the three-dimensional trapezoidal probability fuzzy set of claim 2, wherein the step S2 is to construct the three-dimensional trapezoidal fuzzy set of the insulating oil deterioration characteristic class as follows;
construction of mth distribution transformer oil degradation characteristic class SmThe degradation level i (i ═ 1, 2.., 9) of the nth characteristic parameter of (a):
in the formula
For mth distribution transformer oil degradation characteristic class SmThe degradation level i (i ═ 1, 2.., 9) of the nth characteristic parameter of (a).
4. The method for evaluating the degradation of the transformer insulating oil based on the three-dimensional trapezoidal probability fuzzy set as claimed in claim 3, wherein the fuzzification processing of the insulating oil test data and the construction process of the membership function in step S3 are as follows;
the oil test data is data obtained from the test, and the insulating oil test class of the distribution transformer is constructed according to the oil test data
Wherein N isTNumber of insulating oil degradation test classes for distribution transformers, oil degradation test classes
Has different characteristic spaces, such as oil dielectric loss, water content in oil, gas content in oil, oil breakdown voltage, oil volume resistivity, oil conductivity, acid value in oil, oil breakdown voltage, total acid value of oil, furfural content in oil, oil color, paper dielectric loss, water content in paper, paper breakdown voltage, paper dielectric loss, paper,Paper conductivity, acid value in paper, paper polymerization degree, total acid value of paper, amount of furfural in paper, paper color and H2Content, C2H2Content, C2H6Content, C2H4Content, CH4Content, relative gas production rate of CO, CO2Relative gas production rate, total hydrocarbons, core insulation resistance, core ground current, winding direct current resistance, insulation resistance absorption ratio, winding direct current resistance and unbalance rate thereof, initial value difference of winding short-circuit impedance, winding insulation dielectric loss, initial value difference of winding capacitance, high-voltage side A-phase capacitance value, high-voltage side B-phase capacitance value, high-voltage side C-phase capacitance value, low-voltage side A-phase capacitance value, low-voltage side B-phase capacitance value, low-voltage side C-phase capacitance value, hot spot temperature at typical load, hot spot temperature at high load, oil temperature, and partial discharge quantity, assuming that m (m is 1,2,3,. N, N) is a combination of 47 characteristic parametersT) Individual oil test class TmContaining N (1, 2, 3.., N)Tm) Individual characteristic variables, characteristic data sets x of individual characteristic variablesTmnThe mth oil test class T can be obtained through big data processingmData set x ofTmComprises the following steps:
mth oil test class TmCharacteristic data set x of the n-th characteristic quantity of (1)TmnCan be expressed as:
in the formula NTmnFor the m oil test class TmCharacteristic data set x of the n-th characteristic quantity of (1)SmnNumber of data, for different parameters NTmnThere will be different values for the m-th oil test class TmCharacteristic data set x of the n-th characteristic quantity of (1)TmnConstructing membership function k of lower bound, middle bound and upper bound of three-dimensional trapezoidal fuzzy set for distribution transformer oil testSLmnk(x)、kSMmnk(x)、kSUmnk(x) Respectively as follows:
5. the method for evaluating the deterioration of the transformer insulating oil based on the three-dimensional trapezoidal probability fuzzy set of claim 4, wherein the step S4 is to construct the three-dimensional trapezoidal fuzzy set of the insulating oil test class as follows;
construction of mth distribution transformer oil test type TmThe three-dimensional trapezoidal fuzzy set of the nth characteristic parameter:
Tmn＝{TSLmn,TSMmn,TSUmn}
＝{(aTLmn,bTLmn,cTLmn,dTLmn；kTLmn),(aTMmn,bTMmn,cTMmn,dTMmn；kTMmn),(aTUmn,bTUmn,cTUmn,dTUmn；kTUmn)}
(m＝1,2,3,...,NT,n＝1,2,3,...,NTm)
in the formula TmnIs a three-dimensional trapezoidal fuzzy set of the nth characteristic parameter of the mth oil test class.
6. The method for evaluating the deterioration of the transformer insulating oil based on the three-dimensional trapezoidal probability fuzzy set of claim 5, wherein the step S5 is to construct a similarity function between the test class probability fuzzy set and the feature class probability fuzzy set as follows;
using oil degradation characteristics
And insulating oil test class
The probability fuzzy set of the distribution transformer is constructed, and the three-dimensional trapezoidal fuzzy set of the kth characteristic parameter and the mth characteristic class S of the jth oil test class of the distribution transformer are constructedmThe degradation level i (i ═ 1, 2.., 9) of the nth characteristic parameter of (a) is determined as follows:
wherein the kth characteristic parameter of the jth oil test class has a lower bound, a middle bound and an upper bound three-dimensional trapezoidal fuzzy set and the mth characteristic class SmThe similarity functions of the lower-bound, middle-bound, and upper-bound three-dimensional trapezoidal fuzzy sets of the n-th characteristic parameter i (i ═ 1, 2.·,9) are respectively:
7. the method for evaluating the deterioration of the transformer insulating oil based on the three-dimensional trapezoidal probability fuzzy set as claimed in claim 6, wherein the step S6 is performed as follows;
total similarity between degradation levels i of distribution transformer oil test class and oil degradation feature class
Average similarity between degradation levels i of distribution transformer oil test class and oil degradation feature class
When in use
Higher than
When the transformer insulating oil is judged to be in a state of a degradation level i (i ═ 1, 2.., 9), namely nine degradation states: extremely low, very low, medium, high, very high.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710985703.8A CN107831300B (en) | 2017-10-20 | 2017-10-20 | Transformer insulating oil degradation evaluation method based on three-dimensional trapezoidal probability fuzzy set |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710985703.8A CN107831300B (en) | 2017-10-20 | 2017-10-20 | Transformer insulating oil degradation evaluation method based on three-dimensional trapezoidal probability fuzzy set |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107831300A CN107831300A (en) | 2018-03-23 |
| CN107831300B true CN107831300B (en) | 2020-02-04 |
Family
ID=61648648
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710985703.8A Active CN107831300B (en) | 2017-10-20 | 2017-10-20 | Transformer insulating oil degradation evaluation method based on three-dimensional trapezoidal probability fuzzy set |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107831300B (en) |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108897717B (en) * | 2018-05-09 | 2021-09-10 | 广东电网有限责任公司 | Method for calculating degradation fault rate of transformer insulating oil |
| CN108959769A (en) * | 2018-06-29 | 2018-12-07 | 国网北京市电力公司 | A kind of state evaluating method and device of insulating oil |
| CN108931270B (en) * | 2018-09-05 | 2020-12-01 | 河北大学 | Two-phase flow parameter detection method based on porous throttling and acoustic emission technology |
| CN110110784B (en) * | 2019-04-30 | 2020-03-24 | 贵州电网有限责任公司 | Transformer fault identification method based on transformer related operation data |
| WO2020232716A1 (en) * | 2019-05-23 | 2020-11-26 | 西门子股份公司 | Method and device for assessing state of health of transformer, and storage medium |
| CN112924651B (en) * | 2020-12-30 | 2022-07-29 | 广东电网有限责任公司电力科学研究院 | Method, device and equipment for detecting aging degree of transformer oil |
| CN112784480B (en) * | 2021-01-13 | 2023-08-08 | 西安交通大学 | Oil liquid state self-learning quantitative characterization method, storage medium and equipment |
| CN113514739B (en) * | 2021-06-16 | 2022-09-06 | 国网吉林省电力有限公司电力科学研究院 | IWOA-BP algorithm-based oil paper insulation aging evaluation method |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH11231949A (en) * | 1998-02-18 | 1999-08-27 | Daihen Corp | Voltage adjusting device |
| CN102663500A (en) * | 2012-03-27 | 2012-09-12 | 许继集团有限公司 | Fuzzy Petri net transformer station fault diagnosis method based on time membership analysis |
| CN104091238A (en) * | 2014-07-11 | 2014-10-08 | 国家电网公司 | Method for analyzing and evaluating electricity utilization safety risk evolution of user in severe weather |
| CN105262113A (en) * | 2015-11-26 | 2016-01-20 | 国网河南省电力公司平顶山供电公司 | Photovoltaic power generation system reactive power control method based on probabilistic fuzzy neural network |
| CN105405066A (en) * | 2015-11-18 | 2016-03-16 | 中国电力科学研究院 | Distribution transformer health index determination method |
| WO2016100934A1 (en) * | 2014-12-18 | 2016-06-23 | Ali Mohd Hasan | Apparatus for mitigation of adverse effects of geomagnetically induced currents on transformers |
| CN105719664A (en) * | 2016-01-14 | 2016-06-29 | 盐城工学院 | Likelihood probability fuzzy entropy based voice emotion automatic identification method at tension state |
| CN106251027A (en) * | 2016-08-17 | 2016-12-21 | 合肥工业大学 | Electric load probability density Forecasting Methodology based on fuzzy support vector quantile estimate |
| CN106651189A (en) * | 2016-12-27 | 2017-05-10 | 广东电网有限责任公司惠州供电局 | Transformer state evaluation method based on multilayer compound rule |
| CN107145475A (en) * | 2017-05-16 | 2017-09-08 | 国网四川省电力公司电力科学研究院 | A kind of bushing shell for transformer probability of malfunction computational methods based on Fuzzy-valued |
-
2017
- 2017-10-20 CN CN201710985703.8A patent/CN107831300B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH11231949A (en) * | 1998-02-18 | 1999-08-27 | Daihen Corp | Voltage adjusting device |
| CN102663500A (en) * | 2012-03-27 | 2012-09-12 | 许继集团有限公司 | Fuzzy Petri net transformer station fault diagnosis method based on time membership analysis |
| CN104091238A (en) * | 2014-07-11 | 2014-10-08 | 国家电网公司 | Method for analyzing and evaluating electricity utilization safety risk evolution of user in severe weather |
| WO2016100934A1 (en) * | 2014-12-18 | 2016-06-23 | Ali Mohd Hasan | Apparatus for mitigation of adverse effects of geomagnetically induced currents on transformers |
| CN105405066A (en) * | 2015-11-18 | 2016-03-16 | 中国电力科学研究院 | Distribution transformer health index determination method |
| CN105262113A (en) * | 2015-11-26 | 2016-01-20 | 国网河南省电力公司平顶山供电公司 | Photovoltaic power generation system reactive power control method based on probabilistic fuzzy neural network |
| CN105719664A (en) * | 2016-01-14 | 2016-06-29 | 盐城工学院 | Likelihood probability fuzzy entropy based voice emotion automatic identification method at tension state |
| CN106251027A (en) * | 2016-08-17 | 2016-12-21 | 合肥工业大学 | Electric load probability density Forecasting Methodology based on fuzzy support vector quantile estimate |
| CN106651189A (en) * | 2016-12-27 | 2017-05-10 | 广东电网有限责任公司惠州供电局 | Transformer state evaluation method based on multilayer compound rule |
| CN107145475A (en) * | 2017-05-16 | 2017-09-08 | 国网四川省电力公司电力科学研究院 | A kind of bushing shell for transformer probability of malfunction computational methods based on Fuzzy-valued |
Non-Patent Citations (2)
| Title |
|---|
| Fault Diagnosis of Power Transformer Based on Fuzzy Logic, Rough Set theory and Inclusion Degree Theory;Hossam A. Nabwey，et al.;《The Online Journal on Power and Energy Engineering (OJPEE)》;20101231;第1卷(第2期);第45-49页 * |
| 基于随机模糊理论的电力变压器剩余寿命评估;石光 等;《电气应用》;20141130;第33卷(第21期);第59-64页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107831300A (en) | 2018-03-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107843718B (en) | Method for evaluating aging state of transformer insulating oil | |
| CN107831415B (en) | Interval value fuzzy set method for transformer insulation paper aging state evaluation | |
| CN107831300B (en) | Transformer insulating oil degradation evaluation method based on three-dimensional trapezoidal probability fuzzy set | |
| CN107843816B (en) | Transformer insulation defect state evaluation method considering load rate influence | |
| CN108680811B (en) | Transformer fault state evaluation method | |
| CN108802584B (en) | Method for evaluating aging state of transformer insulation paper | |
| CN109102171A (en) | A kind of substation equipment condition intelligent evaluation system and method based on big data | |
| CN107808044A (en) | A kind of transformer insulating paper deterioration appraisal procedure for considering running temperature and influenceing | |
| Li et al. | An integrated method of set pair analysis and association rule for fault diagnosis of power transformers | |
| Li et al. | Condition assessment of power transformers using a synthetic analysis method based on association rule and variable weight coefficients | |
| CN106447205A (en) | Method for evaluating state of distribution automation terminal based on analytic hierarchy process | |
| CN113466597A (en) | Intelligent detection method for equipment state of rail transit power supply system | |
| Wang et al. | Review on evolution of intelligent algorithms for transformer condition assessment | |
| CN103699668A (en) | Power distribution network electric equipment combination state evaluation method based on data section consistency | |
| CN114254818A (en) | Low-voltage distribution network cascading failure early warning method based on risk assessment model | |
| McGrail et al. | Data mining techniques to assess the condition of high voltage electrical plant | |
| CN112836436A (en) | Power distribution network line risk quantitative prediction method based on probability graph model | |
| CN108681835B (en) | Method for evaluating degradation state of transformer insulating oil | |
| CN109086483A (en) | A kind of evidence fusion and Method of Set Pair Analysis of the assessment of transformer ageing state | |
| CN105741184A (en) | Transformer state evaluation method and apparatus | |
| CN109086484A (en) | A kind of evidence fusion and Method of Set Pair Analysis of transformer health state evaluation | |
| CN109033198A (en) | A kind of more element Fuzzy Analysis that transformer insulating paper recognizes extremely | |
| Yong et al. | Research on condition evaluation algorithm of oil-immersed transformer based on Naive Bayes | |
| CN108805467A (en) | A kind of Probabilistic Fuzzy set method of transformer ageing state assessment | |
| Yang | Comprehensive method detecting the status of the transformer based on the artificial intelligence |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20210315 Address after: No. 757, Dongfeng East Road, Yuexiu District, Guangzhou, Guangdong 517000 Patentee after: GUANGDONG POWER GRID Co. Patentee after: HEYUAN POWER SUPPLY BUREAU, GUANGDONG POWER GRID Co.,Ltd. Address before: No.19 Heyuan Avenue North, Heyuan City, Guangdong Province 517001 Patentee before: HEYUAN POWER SUPPLY BUREAU, GUANGDONG POWER GRID Co.,Ltd. Patentee before: GUANGDONG University OF TECHNOLOGY |