CN117574200A - Membership-based transformer health state evaluation method and system - Google Patents
Membership-based transformer health state evaluation method and system Download PDFInfo
- Publication number
- CN117574200A CN117574200A CN202311403131.XA CN202311403131A CN117574200A CN 117574200 A CN117574200 A CN 117574200A CN 202311403131 A CN202311403131 A CN 202311403131A CN 117574200 A CN117574200 A CN 117574200A
- Authority
- CN
- China
- Prior art keywords
- transformer
- state
- membership
- evaluation
- grade
- 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.)
- Pending
Links
- 238000011156 evaluation Methods 0.000 title claims abstract description 150
- 230000036541 health Effects 0.000 title claims abstract description 41
- 230000015556 catabolic process Effects 0.000 claims abstract description 57
- 238000006731 degradation reaction Methods 0.000 claims abstract description 56
- 239000011159 matrix material Substances 0.000 claims abstract description 36
- 238000009826 distribution Methods 0.000 claims abstract description 22
- 238000007781 pre-processing Methods 0.000 claims abstract description 14
- 230000006870 function Effects 0.000 claims description 54
- 238000000034 method Methods 0.000 claims description 35
- 238000009413 insulation Methods 0.000 claims description 31
- 238000004590 computer program Methods 0.000 claims description 19
- 230000003862 health status Effects 0.000 claims description 18
- 230000002159 abnormal effect Effects 0.000 claims description 13
- 238000010521 absorption reaction Methods 0.000 claims description 12
- 238000012502 risk assessment Methods 0.000 claims description 12
- 238000012545 processing Methods 0.000 claims description 10
- 230000006866 deterioration Effects 0.000 claims description 9
- 238000003860 storage Methods 0.000 claims description 9
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 9
- 238000005259 measurement Methods 0.000 description 12
- 238000010586 diagram Methods 0.000 description 7
- 238000012360 testing method Methods 0.000 description 7
- 238000004891 communication Methods 0.000 description 5
- 230000007547 defect Effects 0.000 description 5
- 238000012986 modification Methods 0.000 description 5
- 230000004048 modification Effects 0.000 description 5
- 230000008569 process Effects 0.000 description 3
- 238000004804 winding Methods 0.000 description 3
- 230000032683 aging Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000011109 contamination Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 229910052573 porcelain Inorganic materials 0.000 description 1
- 238000000275 quality assurance Methods 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 239000010802 sludge Substances 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/10—Pre-processing; Data cleansing
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Computational Biology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Computational Mathematics (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Evolutionary Computation (AREA)
- Computing Systems (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- Housings And Mounting Of Transformers (AREA)
Abstract
The invention discloses a transformer health state evaluation method and system based on membership, comprising the steps of acquiring historical operation data of transformers in a power distribution network, preprocessing, and establishing a relation between transformer state evaluation level and relative degradation degree according to the preprocessed data; designing a membership function of a state corresponding to the transformer evaluation index, and establishing a membership matrix; and determining the state evaluation grade of the transformer according to real-time operation data of the transformer in the power distribution network and combining the membership matrix, and acquiring the relative degradation degree of the transformer according to the state evaluation grade of the transformer to complete the state evaluation of the transformer. The invention can evaluate the health state of the transformer comprehensively and accurately, and can discover and solve the problems of the transformer in time, thereby guaranteeing the stability and safety of the power system.
Description
Technical Field
The invention relates to the technical field of transformer health state evaluation, in particular to a membership-based transformer health state evaluation method and system.
Background
In a power system, a transformer is one of important devices, and normal operation of the transformer has important significance for guaranteeing stability and safety of the power system. However, due to long-term operation, equipment aging, environmental factors, etc., the health status of the transformer may be affected, resulting in performance degradation and even failure. Therefore, the health state of the transformer is evaluated and monitored, and the problems are found and solved in time, so that the method has important significance for guaranteeing the stability and safety of a power system.
The existing transformer health state evaluation method mainly comprises a physical model-based evaluation method, a data driving-based evaluation method, an artificial intelligence-based evaluation method and the like. The evaluation method based on data driving is used for evaluating the health state of the transformer by analyzing the historical operation data and the real-time operation data of the transformer, extracting the characteristics and analyzing the characteristics. However, the existing evaluation method based on data driving mainly focuses on evaluating by using a single feature, ignores the relevance between different features and the overall state of the transformer, and meanwhile lacks clear division and description on membership of different states, so that the evaluation result is not accurate and comprehensive enough.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description summary and in the title of the application, to avoid obscuring the purpose of this section, the description summary and the title of the invention, which should not be used to limit the scope of the invention.
The present invention has been made in view of the above-described problems occurring in the prior art.
Therefore, the invention provides a transformer health state evaluation method and system based on membership, which can solve the problems in the background technology.
In order to solve the technical problems, the invention provides a transformer health state evaluation method based on membership, which comprises the following steps:
acquiring historical operation data of a transformer in the power distribution network, preprocessing, and establishing a relation between a transformer state evaluation grade and a relative degradation degree according to the preprocessed data;
designing a membership function of a state corresponding to the transformer evaluation index, and establishing a membership matrix;
and determining a transformer state evaluation grade according to real-time operation data of the transformers in the power distribution network and combining the membership matrix, and acquiring the relative degradation degree of the transformers according to the transformer state evaluation grade to complete the transformer health state evaluation.
As a preferred embodiment of the membership-based method for evaluating the health status of a transformer according to the present invention, the method comprises: the preprocessing, establishing the relation between the transformer state evaluation grade and the relative degradation degree according to the preprocessed data, comprises the following steps:
wherein x is the relative degradation degree, and the smaller the value of x is, the better the corresponding index state is; x is the measured data of the evaluation index; x is X max And X min Is a preset maximum value and a preset minimum value;
when the relative degradation degree x of the index is less than 0, the value of x is 0;
when the index relative degradation degree x >1, the value of x takes 1.
As a preferred embodiment of the membership-based method for evaluating the health status of a transformer according to the present invention, the method comprises: the preprocessing, establishing the relation between the transformer state evaluation grade and the relative degradation degree according to the preprocessed data, comprises the following steps:
the transformer state evaluation grades comprise a normal state grade, an attention state grade, an abnormal state grade and a serious state grade;
the transformer state evaluation level and relative degradation relationship includes:
when the relative degradation degree is between 0.0 and 0.2, the normal state grade is corresponding;
when the relative degree of deterioration is between 0.2 and 0.5, the state level is noted correspondingly;
when the relative degradation degree is between 0.5 and 0.8, the abnormal state grade is corresponding;
when the relative degree of deterioration is between 0.8 and 1.0, a serious condition level is corresponding.
As a preferred embodiment of the membership-based method for evaluating the health status of a transformer according to the present invention, the method comprises: the step of designing the membership function of the state corresponding to the transformer evaluation index, and the step of establishing the membership matrix comprises the following steps:
the membership function of the state corresponding to the transformer evaluation index is as follows:
wherein mu V1 Representation transformerPiecewise function expression, mu, of triangular membership function with evaluation index corresponding to normal state V2 Piecewise function expression of triangular membership function representing corresponding attention state of transformer evaluation index, mu V3 Piecewise function expression of triangular membership function representing abnormal state corresponding to transformer evaluation index and mu V4 And a piecewise function expression of the triangle membership function representing the state of the transformer corresponding to the serious state.
As a preferred embodiment of the membership-based method for evaluating the health status of a transformer according to the present invention, the method comprises: the step of designing the membership function of the state corresponding to the transformer evaluation index, and the step of establishing the membership matrix further comprises the following steps:
the membership matrix includes:
wherein v is n1 Representing the membership value, v, of a transformer in a normal state under n indexes n2 Transformer membership value, v, representing attention state under n index n3 Transformer membership value, v, representing abnormal state under n index n4 The transformer membership value of the severe state under n indexes, wherein n represents the number of evaluation indexes including insulation resistance, absorption ratio, leakage current, dielectric loss value, micro water content in oil and oil dielectric loss value.
As a preferred embodiment of the membership-based method for evaluating the health status of a transformer according to the present invention, the method comprises: determining a transformer state evaluation grade according to real-time operation data of a transformer in a power distribution network and combining a membership matrix, acquiring the relative degradation degree of the transformer according to the transformer state evaluation grade, and completing the transformer health state evaluation comprises the following steps:
comprehensive risk assessment vector W:
W＝Q×V＝(W 1 ,W 2 ,W 3 ,W 4 )
wherein Q represents a weight value, W i Respectively corresponding to transformer state evaluationMembership of four transformer evaluation states in the relation of the grade and the relative degradation degree.
As a preferred embodiment of the membership-based method for evaluating the health status of a transformer according to the present invention, the method comprises: determining a transformer state evaluation grade according to real-time operation data of the transformers in the power distribution network and combining the membership matrix, acquiring the relative degradation degree of the transformers according to the transformer state evaluation grade, and completing the transformer health state evaluation further comprises:
comprehensive risk assessment factor R of transformer based on various evaluation indexes:
wherein W is i Membership degrees, E, of four transformer evaluation states respectively corresponding to the relationship between the transformer state evaluation level and the relative degradation degree Xi Is a mathematical expectation value of measured data corresponding to the evaluation index.
A membership-based transformer health state assessment system, comprising: a data acquisition and processing module, a membership acquisition module and an evaluation module,
the data acquisition and processing module is used for acquiring historical operation data of the transformers in the power distribution network, preprocessing the historical operation data, and establishing a relation between the state evaluation grade of the transformers and the relative degradation degree according to the preprocessed data;
the membership acquisition module is used for designing a membership function of a state corresponding to the transformer evaluation index and establishing a membership matrix;
the evaluation module is used for determining the state evaluation grade of the transformer according to real-time operation data of the transformer in the power distribution network and combining the membership matrix, and obtaining the relative degradation degree of the transformer according to the state evaluation grade of the transformer so as to complete the health state evaluation of the transformer.
A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method as described above when executing the computer program.
A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method as described above.
The invention has the beneficial effects that: the invention provides a transformer health state evaluation method and system based on membership, which are used for acquiring historical operation data of transformers in a power distribution network, preprocessing the historical operation data, and establishing a relation between a transformer state evaluation grade and relative degradation degree according to the preprocessed data; designing a membership function of a state corresponding to the transformer evaluation index, and establishing a membership matrix; and determining a transformer state evaluation grade according to real-time operation data of the transformers in the power distribution network and combining the membership matrix, and acquiring the relative degradation degree of the transformers according to the transformer state evaluation grade to complete the transformer health state evaluation. The invention can evaluate the health state of the transformer comprehensively and accurately, and can discover and solve the problems of the transformer in time, thereby guaranteeing the stability and safety of the power system.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is a flow chart of a method and system for evaluating the health status of a transformer based on membership according to an embodiment of the present invention;
fig. 2 is an internal structure diagram of a computer device of a method and a system for evaluating a health state of a transformer based on membership according to an embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
While the embodiments of the present invention have been illustrated and described in detail in the drawings, the cross-sectional view of the device structure is not to scale in the general sense for ease of illustration, and the drawings are merely exemplary and should not be construed as limiting the scope of the invention. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Also in the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper, lower, inner and outer", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected, and coupled" should be construed broadly in this disclosure unless otherwise specifically indicated and defined, such as: can be fixed connection, detachable connection or integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or may be indirectly connected through an intermediate medium, or may be a communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Example 1
Referring to fig. 1-2, a first embodiment of the present invention provides a method and a system for evaluating a health status of a transformer based on membership, including:
acquiring historical operation data of a transformer in the power distribution network, preprocessing, and establishing a relation between a transformer state evaluation grade and a relative degradation degree according to the preprocessed data;
the preprocessing is performed, and establishing a relation between the transformer state evaluation grade and the relative degradation degree according to the preprocessed data comprises the following steps:
wherein x is the relative degradation degree, and the smaller the value of x is, the better the corresponding index state is; x is the measured data of the evaluation index; x is X max And X min Is a preset maximum value and a preset minimum value;
it should be noted that, when the index relative degradation degree x <0, the value of x takes 0;
note that when the index relative degradation degree x >1, the value of x takes 1.
Further, the transformer state evaluation level includes a normal state level, an attention state level, an abnormal state level, and a serious state level;
it should be noted that the relationship between the transformer state evaluation level and the relative degradation degree includes:
when the relative degradation degree is between 0.0 and 0.2, the normal state grade is corresponding;
when the relative degree of deterioration is between 0.2 and 0.5, the state level is noted correspondingly;
when the relative degradation degree is between 0.5 and 0.8, the abnormal state grade is corresponding;
when the relative degree of deterioration is between 0.8 and 1.0, a serious condition level is corresponding.
In the embodiment of the present application, the transformer state evaluation grade and the relative degradation degree are shown in table 1.
Table 1 transformer state evaluation rating and relative degradation degree
Further, a membership function of a state corresponding to the transformer evaluation index is designed, and a membership matrix is established;
furthermore, according to real-time operation data of the transformers in the power distribution network, determining transformer state evaluation grades by combining the membership matrix, and obtaining the relative degradation degree of the transformers according to the transformer state evaluation grades to complete the transformer health state evaluation.
The membership function of the state corresponding to the transformer evaluation index is as follows:
wherein mu V1 Piecewise function expression, mu, of triangular membership function representing normal state corresponding to transformer evaluation index V2 Piecewise function expression of triangular membership function representing corresponding attention state of transformer evaluation index, mu V3 Piecewise function expression of triangular membership function representing abnormal state corresponding to transformer evaluation index and mu V4 And a piecewise function expression of the triangle membership function representing the state of the transformer corresponding to the serious state.
It should be noted that, for the n selected evaluation indexes, according to the requirements in the operation regulations of the DL/T572-2010 power transformer, the evaluation indexes are normalized respectively, and the abscissa interval of the membership function is determined. And then substituting the real measured value of one transformer as input into the triangular membership function to obtain the membership of the transformer about four transformer evaluation grades.
Further, the membership matrix includes:
wherein v is n1 Representing the membership value, v, of a transformer in a normal state under n indexes n2 Transformer membership value, v, representing attention state under n index n3 Transformer membership value, v, representing abnormal state under n index n4 The transformer membership value of the severe state under n indexes, wherein n represents the number of evaluation indexes, and the evaluation indexes comprise insulation resistance, absorption ratio, leakage current, dielectric loss value, micro water content in oil and oil dielectric loss value.
Further, according to the weight Q and the membership matrix V obtained by the analytic hierarchy process, the comprehensive risk assessment vector W of the transformer can be obtained by multiplying the two matrices by each other, wherein the comprehensive risk assessment vector W is:
W＝Q×V＝(W 1 ,W 2 ,W 3 ,W 4 )
wherein Q represents a weight value, W i Respectively corresponding to the state evaluation grades of the transformersMembership to four transformer evaluation states in the relative deterioration degree relationship.
Further, the comprehensive risk assessment factor R of the transformer based on various evaluation indexes:
wherein W is i Membership degrees, E, of four transformer evaluation states respectively corresponding to the relationship between the transformer state evaluation level and the relative degradation degree Xi Is a mathematical expectation value of measured data corresponding to the evaluation index.
Further, determining the transformer health state evaluation result according to the transformer comprehensive risk evaluation factor R includes:
1. when the R value is between 0.0 and 0.2, the health state of the transformer is a normal state;
2. when the R value is between 0.2 and 0.5, the health state of the transformer is the attention state;
3. when the R value is between 0.5 and 0.8, the health state of the transformer is an abnormal state;
4. when the R value is between 0.8 and 1.0, the health state of the transformer is a serious state.
It should be noted that, when the R value is smaller than 0, the R value takes 0; when the R value is greater than 1, the R value is 1.
In an alternative embodiment, the transformer health status evaluation graph may also be drawn according to the transformer comprehensive risk evaluation factor R, including:
1. marking the transformer health state evaluation results corresponding to the R values on the abscissa axis;
2. marking the relative degradation degree corresponding to each R value on the ordinate axis;
3. and connecting the transformer health state evaluation results corresponding to the R values and the relative degradation degree into a line to obtain a transformer health state evaluation graph.
Further, generating a transformer health status assessment report according to the transformer comprehensive risk assessment factor R, including:
1. the report lists the measured data and calculated weight value Q of each evaluation index;
2. the report lists a weight Q and a membership matrix V which are obtained according to an analytic hierarchy process, and a comprehensive risk assessment vector W of the transformer is obtained by multiplying the weight Q and the membership matrix V by the membership matrix;
3. determining a health state evaluation result of the transformer according to the comprehensive risk evaluation factor R of the transformer in the report;
4. and drawing a transformer health state evaluation graph according to the comprehensive risk evaluation factor R of the transformer in the report.
In a preferred embodiment, a membership-based transformer health state assessment system includes: a data acquisition and processing module, a membership acquisition module and an evaluation module,
the data acquisition and processing module is used for acquiring historical operation data of the transformers in the power distribution network, preprocessing the historical operation data, and establishing a relation between the state evaluation grade of the transformers and the relative degradation degree according to the preprocessed data;
the membership acquisition module is used for designing a membership function of a state corresponding to the transformer evaluation index and establishing a membership matrix;
the evaluation module is used for determining the state evaluation grade of the transformer according to real-time operation data of the transformer in the power distribution network and combining the membership matrix, and obtaining the relative degradation degree of the transformer according to the state evaluation grade of the transformer so as to complete the state evaluation of the transformer.
The above unit modules may be embedded in hardware or independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above units.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure of which may be as shown in fig. 2. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program, when executed by a processor, implements a membership-based method for evaluating the health status of a transformer. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring historical operation data of a transformer in the power distribution network, preprocessing, and establishing a relation between a transformer state evaluation grade and a relative degradation degree according to the preprocessed data;
designing a membership function of a state corresponding to the transformer evaluation index, and establishing a membership matrix;
and determining the state evaluation grade of the transformer according to real-time operation data of the transformer in the power distribution network and combining the membership matrix, and acquiring the relative degradation degree of the transformer according to the state evaluation grade of the transformer to complete the state evaluation of the transformer.
Example 2
Referring to fig. 1-2, for one embodiment of the present invention, a method and a system for evaluating the health status of a transformer based on membership are provided, and in order to verify the beneficial effects of the present invention, scientific demonstration is performed through a comparative experiment.
(1) Selection of evaluation index
The evaluation indexes of the transformer are selected as follows: insulation resistance, absorption ratio, leakage current, dielectric loss value, micro water content in oil and oil dielectric loss value, the first four belong to electric test items, and the second two belong to test items of insulating oil characteristics.
The insulation resistance and the absorption ratio are measured, so that the whole or partial moisture of the insulation of the transformer can be sensitively found; the measurement of insulation resistance and absorption ratio has become one of the methods commonly used in transformer insulation tests by inspecting the insulating surfaces of the components for dirt and local defects, short circuits, grounding, porcelain breakage, and the like. The larger the insulation resistance, the closer the absorption ratio is to 1, and the better the insulation performance.
The measurement of the dielectric loss tangent tan delta of the transformer can effectively find out the overall moisture, penetrability discharge channel, oil quality degradation, insulation layering, aging, adhesion of oil sludge on windings, serious local defects and the like of the transformer, and is an effective means for judging the insulation state of the transformer. The smaller the dielectric loss value, the better the insulation performance.
The principle and function of leakage current measurement and insulation resistance measurement are similar, but because of higher test voltage, the sensitivity and accuracy of the leakage current measurement are higher than those of insulation resistance measurement, the insulation defects of windings and bushings can be checked, and the local defects of the transformer which cannot be found by other test projects can be found effectively. The smaller the leakage current measurement, the better the insulation performance.
The dielectric strength of the oilpaper insulation is extremely sensitive to moisture, and in the case of multi-oil equipment such as transformers, the moisture in the oil should be measured at any time except for periodic measurement when the moisture is suspected. The smaller the micro water content in the oil, the better the insulation performance of the oil paper of the transformer is.
Dielectric loss tangent is an important parameter of transformer oil and reflects the presence of excessive moisture, polarity and ion concentration in the oil, and therefore the magnitude of tan delta value sensitively reflects the degree of oil degradation and contamination. The smaller the dielectric loss value of the oil, the better the insulation performance.
(2) Brief description of Transformer problems
An SSZ 11-50000/110-level three-phase three-winding on-load voltage regulating transformer is positioned at an important transformer substation in a central position of a city and mainly used for supplying core electricity utilization areas of a certain commercial street nearby. The transformer was put into use in 1999 and has been running for more than 20 years today. The transformer has suffered from insulation degradation and reduced insulation quality since 2019 and the run time has exceeded the 20 year quality assurance period given by the manufacturer data. The insulating oil test in operation is recorded as: the oil property test only records a micro water content of 21.5ppm and an oil dielectric loss of 3.40%; leakage current 24pA; the insulation resistance measured at 30 ℃ was 1.35, tan δ=0.370%.
The insulation resistance value and the dielectric loss value are converted into values when the ambient temperature is 20 ℃, so that the subsequent calculation is convenient. Insulation resistance conversion value isDielectric loss transition value was tan delta=0.37% ×1.3 (30-20)/10 ＝0.481％。
(3) Construction of triangle membership functions
According to the regulations, the representation function of the influence of the insulation resistance (at 20 ℃) of a transformer of 220kV and below on the insulation state of the transformer is defined as:
where x is the actual measurement. An insulation resistance greater than 1000mΩ indicates that the transformer is in good condition, and therefore f (x) =0 if the insulation resistance is greater than 1600mΩ. From R 2 As can be seen from =1650mΩ, f (x) =0.
When the measured temperature is 10-30 ℃, the absorption ratio of the unhumked transformer is in the range of 1.3-2.0, and the absorption ratio of the transformer with local defects in the damp or insulation is close to 1. The absorption comparison transformer insulation state representation function is:
wherein x is the measured value of the absorption ratio. From the absorption ratio x=1.35, f (x) =0.325.
For a dielectric loss tan delta, the regulations prescribe that the 66-220kV transformer dielectric loss at 20 ℃ is not greater than 0.8%, so the representation function of tan delta for the transformer effect is defined as:
wherein x represents the percentage of tan delta measured. From the dielectric loss tan δ= 0.481%, f (x) =0.60.
For the state parameter leakage current of the oil immersed power transformer of 220kV and below, the transformer state is good when the leakage current is smaller than 50 mu A, and the state can be judged to be bad when the leakage current is larger than 80 mu A, so the representation function of the influence of the leakage current on the transformer state is as follows:
where x is the measured value of the leakage current of the transformer at 20 ℃. F (x) =0.3 from the measurement value x=24 pA.
According to the regulations, for transformers before being put into operation, the micro-water content in the oil is 15mg/L, while the micro-water content of the transformer oil in operation is not more than 25mg/L, then the representation function of the micro-water content on the transformer oil is set as follows:
x is the measured value of moisture in oil. From the measurement value x=21.5 ppm, f (x) =0.65.
The regulations and GB/T7595-2000 'quality standard of transformer oil in operation' also prescribe that the oil dielectric loss (330 kV transformer at 90 ℃) before the operation of the transformer is not more than 1% and the dielectric loss value of the operation oil is not more than 4%. The representation functions of the influence of the oil loss on the transformer oil quality can then be set to be respectively:
x is the measured value of oil breakdown. From the measurement value x=3.4, f (x) =0.8.
Bringing the calculated f (x) value into a membership function expression to obtain a membership degree matrix V:
(4) Comprehensive risk assessment for transformer
The comprehensive evaluation vector W of the transformer is as follows, and the comprehensive evaluation vector is obtained by a weight matrix Q and a membership matrix V:
W＝Q·V＝(0.2693,0.3234,0.3252,0.0436)
the comprehensive risk assessment factor R of the transformer is as follows:
R＝0.2693*0.1+0.3234×0.35+0.3252×0.65+0.0436×0＝0.3546
as can be seen from table 1, the subvectors 0.2693, 0.3234 and 0.3252 of W are all in the interval of 0.2-0.5, which corresponds to the "attention" of the transformer health status, and 0.0436 corresponds to the "normal" status of the transformer. The comprehensive risk assessment factor R is 0.3546, corresponding to the "attention" status. And comprehensively judging that the health state of the transformer is 'attention'. According to the estimation result, the transformer can continue to operate, but the monitoring is required to be enhanced, and meanwhile whether the insulating oil is damped is checked, so that the safe and stable operation of the transformer is maintained.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The solutions in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java, and an transliterated scripting language JavaScript, etc.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (10)
1. The transformer health state assessment method based on the membership degree is characterized by comprising the following steps of:
acquiring historical operation data of a transformer in the power distribution network, preprocessing, and establishing a relation between a transformer state evaluation grade and a relative degradation degree according to the preprocessed data;
designing a membership function of a state corresponding to the transformer evaluation index, and establishing a membership matrix;
and determining a transformer state evaluation grade according to real-time operation data of the transformers in the power distribution network and combining the membership matrix, and acquiring the relative degradation degree of the transformers according to the transformer state evaluation grade to complete the transformer health state evaluation.
2. The method for evaluating the health status of a transformer based on membership according to claim 1, wherein said preprocessing, and establishing the relationship between the evaluation level of the state of the transformer and the relative degree of deterioration based on the preprocessed data, comprises:
wherein x is the relative degradation degree, and the smaller the value of x is, the better the corresponding index state is; x is the measured data of the evaluation index; x is X max And X min Is a preset maximum value and a preset minimum value;
when the relative degradation degree x of the index is less than 0, the value of x is 0;
when the index relative degradation degree x >1, the value of x takes 1.
3. The method for evaluating the health status of a transformer based on membership as set forth in claim 2, wherein said preprocessing, establishing the relationship between the evaluation level of the state of the transformer and the relative degree of deterioration based on the preprocessed data, comprises:
the transformer state evaluation grades comprise a normal state grade, an attention state grade, an abnormal state grade and a serious state grade;
the transformer state evaluation level and relative degradation relationship includes:
when the relative degradation degree is between 0.0 and 0.2, the normal state grade is corresponding;
when the relative degree of deterioration is between 0.2 and 0.5, the state level is noted correspondingly;
when the relative degradation degree is between 0.5 and 0.8, the abnormal state grade is corresponding;
when the relative degree of deterioration is between 0.8 and 1.0, a serious condition level is corresponding.
4. The method for evaluating the health status of a transformer based on membership as set forth in claim 3, wherein said designing a membership function of the state corresponding to the transformer evaluation index, and establishing a membership matrix comprises:
the membership function of the state corresponding to the transformer evaluation index is as follows:
wherein mu V1 Piecewise function expression, mu, of triangular membership function representing normal state corresponding to transformer evaluation index V2 Piecewise function expression of triangular membership function representing corresponding attention state of transformer evaluation index, mu V3 Piecewise function expression of triangular membership function representing abnormal state corresponding to transformer evaluation index and mu V4 And a piecewise function expression of the triangle membership function representing the state of the transformer corresponding to the serious state.
5. The method for evaluating the health status of a transformer based on membership as set forth in claim 4, wherein said designing a membership function of the state corresponding to the transformer evaluation index, and establishing a membership matrix further comprises:
the membership matrix includes:
wherein v is n1 Representing the membership value, v, of a transformer in a normal state under n indexes n2 Transformer membership value, v, representing attention state under n index n3 Transformer membership value, v, representing abnormal state under n index n4 The transformer membership value of the severe state under n indexes, wherein n represents the number of evaluation indexes including insulation resistance, absorption ratio, leakage current, dielectric loss value, micro water content in oil and oil dielectric loss value.
6. The method for evaluating the health state of a transformer based on the membership degree according to claim 5, wherein determining the evaluation level of the state of the transformer according to the real-time operation data of the transformer in the power distribution network in combination with the membership degree matrix, and obtaining the relative degradation degree of the transformer according to the evaluation level of the state of the transformer, and completing the evaluation of the health state of the transformer comprises:
comprehensive risk assessment vector W:
W＝Q×V＝(W 1 ,W 2 ,W 3 ,W 4 )
wherein Q represents a weight value, W i Membership degrees of four transformer evaluation states in the relation between the transformer state evaluation grade and the relative degradation degree are respectively corresponding.
7. The method for evaluating the health state of a transformer based on the membership degree according to claim 6, wherein determining the evaluation level of the state of the transformer according to the real-time operation data of the transformer in the power distribution network in combination with the membership degree matrix, and obtaining the relative degradation degree of the transformer according to the evaluation level of the state of the transformer, and completing the evaluation of the health state of the transformer further comprises:
comprehensive risk assessment factor R of transformer based on various evaluation indexes:
wherein W is i Membership degrees, E, of four transformer evaluation states respectively corresponding to the relationship between the transformer state evaluation level and the relative degradation degree Xi Is a mathematical expectation value of measured data corresponding to the evaluation index.
8. A membership-based transformer health state assessment system, comprising: a data acquisition and processing module, a membership acquisition module and an evaluation module,
the data acquisition and processing module is used for acquiring historical operation data of the transformers in the power distribution network, preprocessing the historical operation data, and establishing a relation between the state evaluation grade of the transformers and the relative degradation degree according to the preprocessed data;
the membership acquisition module is used for designing a membership function of a state corresponding to the transformer evaluation index and establishing a membership matrix;
the evaluation module is used for determining the state evaluation grade of the transformer according to real-time operation data of the transformer in the power distribution network and combining the membership matrix, and obtaining the relative degradation degree of the transformer according to the state evaluation grade of the transformer so as to complete the health state evaluation of the transformer.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311403131.XA CN117574200A (en) | 2023-10-26 | 2023-10-26 | Membership-based transformer health state evaluation method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311403131.XA CN117574200A (en) | 2023-10-26 | 2023-10-26 | Membership-based transformer health state evaluation method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117574200A true CN117574200A (en) | 2024-02-20 |
Family
ID=89892569
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311403131.XA Pending CN117574200A (en) | 2023-10-26 | 2023-10-26 | Membership-based transformer health state evaluation method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117574200A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117809729A (en) * | 2024-02-29 | 2024-04-02 | 山东云海国创云计算装备产业创新中心有限公司 | Storage device life prediction method, device, equipment and storage medium |
-
2023
- 2023-10-26 CN CN202311403131.XA patent/CN117574200A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117809729A (en) * | 2024-02-29 | 2024-04-02 | 山东云海国创云计算装备产业创新中心有限公司 | Storage device life prediction method, device, equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Azmi et al. | Evolution of transformer health index in the form of mathematical equation | |
| CN117574200A (en) | Membership-based transformer health state evaluation method and system | |
| KR100945142B1 (en) | Pad mounted transformer | |
| Cong et al. | Reviews on sulphur corrosion phenomenon of the oil–paper insulating system in mineral oil transformer | |
| CN108089038B (en) | Test device and method for analyzing influence of winding defect heating on oil paper insulation performance | |
| CN106597196A (en) | State monitoring and intelligent diagnosis and analysis system and method for power transmission and transformation equipment | |
| CN203534622U (en) | Parameter acquisition system of state overhauling platform for distribution transformer | |
| Sousa et al. | Decision making in emergency operation for power transformers with regard to risks and interruptible load contracts | |
| Müllerová et al. | Life cycle management of power transformers: results and discussion of case studies | |
| CN109615093A (en) | Repair of Transformer mode determines method and device | |
| CN113884839A (en) | Multi-parameter insulation state evaluation method and system for capacitor voltage transformer | |
| Medina et al. | Health index assessment for power transformers with thermal upgraded paper up to 230kV using fuzzy inference systems | |
| US20220291296A1 (en) | Method and system for monitoring at least one inductive operating means | |
| CN112881877A (en) | Method and system for evaluating high-voltage insulation aging state | |
| CN109615557B (en) | Substation equipment maintenance management system | |
| CN116840614A (en) | Cable line defect sensing and early warning method based on harmonic fluctuation characteristics | |
| CN115526351A (en) | Equipment inspection method and device applied to transformer substation and electronic equipment | |
| Miyagi et al. | Thermal aging characteristics of insulation paper in mineral oil in overloaded operation of transformers | |
| Arabul et al. | Determination of transformer board creep parameters under high‐mechanical and thermal stresses | |
| Bunn et al. | A smart supervisory system for distribution transformers | |
| Bojie et al. | Technical life assessment of main equipment in power grid considering the operating state and aging mechanism | |
| CN213957491U (en) | Transformer bushing frequency domain dielectric spectrum testing arrangement | |
| Velasco et al. | An approach to improve power supply continuity throughout the estimation of insulated power cable life expectance indexes | |
| Sefidgaran et al. | Reliability model of power transformer with ONAN cooling | |
| Glass et al. | Assessment of EPRI’s Tan Delta Approach to Manage Cables in Submerged Environments: Statistical Review of EPRI Data |
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 |