CN112949191B - Dry-type transformer state evaluation method based on cloud model - Google Patents
Dry-type transformer state evaluation method based on cloud model Download PDFInfo
- Publication number
- CN112949191B CN112949191B CN202110252868.0A CN202110252868A CN112949191B CN 112949191 B CN112949191 B CN 112949191B CN 202110252868 A CN202110252868 A CN 202110252868A CN 112949191 B CN112949191 B CN 112949191B
- Authority
- CN
- China
- Prior art keywords
- evaluation
- dry
- index
- state
- type transformer
- 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
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
-
- 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)
- Evolutionary Computation (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Medical Informatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Software Systems (AREA)
- Computer Hardware Design (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Housings And Mounting Of Transformers (AREA)
Abstract
The invention discloses a dry-type transformer state evaluation method based on a cloud model, which is used for carrying out state evaluation on a dry-type transformer through the following steps: firstly, the conditions of all parts of the dry-type transformer and grounding lightning protection means are combined, the dry-type transformer is integrally divided into different evaluation modules, and evaluation indexes of all the evaluation modules are screened out; determining the weight of the evaluation index according to the relevant guide rules and the expert opinions, and scoring the possible defect condition of the index; constructing a state comment set, and generating cloud parameters of a state judgment standard by using a cloud model algorithm; and (4) integrating the values and the weight values of all the indexes and the cloud parameters of the state judgment standard to obtain the final state of the dry-type transformer. The evaluation method provided by the invention has the advantages of clear steps, accurate result, and good popularization value and application prospect.
Description
Technical Field
The invention belongs to the technical field of power equipment state evaluation, and particularly relates to a dry-type transformer state evaluation method based on a cloud model.
Background
Nowadays, dry-type transformers are widely used in important places such as large-scale high-rise buildings, business centers, theaters, hospitals and airports, and become a part which is not opened or lacked in our lives, once the dry-type transformers break down, the normal life order of our lives can be seriously affected, and great threat is brought to the life and property safety of our lives. The state evaluation of the dry-type transformer is not only an important basis for state maintenance, but also a precondition for fault prediction and fault diagnosis of the dry-type transformer, and accurate control of the running state of the dry-type transformer is particularly necessary. At present, for the state evaluation of the dry-type transformer at home and abroad, a grading method in a guidance regulation with low flexibility or other evaluation modes with low fuzziness and randomness are mostly adopted. How to perform the evaluation of the state of the dry-type transformer with high ambiguity and randomness and more accuracy becomes a hot point of research of scholars in recent years.
Disclosure of Invention
In order to solve the technical problem, the invention provides a dry-type transformer state evaluation method based on a cloud model.
The technical scheme of the dry-type transformer state evaluation method based on the cloud model is as follows:
a dry-type transformer state evaluation method based on a cloud model comprises the following steps:
the method comprises the following steps: the method comprises the following steps of integrally dividing the dry-type transformer into different evaluation modules by combining the conditions of all parts of the dry-type transformer and grounding lightning protection means, and screening out evaluation indexes of all the evaluation modules;
step two: determining the weight of the evaluation index according to the relevant guide rules and expert opinions, and scoring the possible defect condition of the index;
step three: constructing a state comment set, and generating cloud parameters of a state comment standard by using a cloud model algorithm;
step four: and (4) integrating the values and the weight values of all the indexes and the cloud parameters of the state judgment standard to obtain the final state of the dry-type transformer.
In the first step, the dry-type transformer is integrally divided into different evaluation modules, including a body, a non-electric quantity protection and secondary circuit, lightning protection and grounding, and other accessories. The evaluation indexes of the evaluation modules are as follows, the body comprises evaluation indexes of short-circuit current recording, infrared temperature measurement, noise, winding voltage withstand test, winding direct-current resistance, winding dielectric loss factor, winding insulation resistance, absorption ratio, polarization index, appearance and the like, the non-electric quantity protection and secondary circuit comprises evaluation indexes of iron core temperature, rainproof measures, secondary circuit insulation resistance and the like, the lightning protection and grounding comprise grounding resistance and the grounding condition of main components, and other accessories comprise cooling system operation condition, sleeve external insulation and insulation resistance inspection at a connecting cable.
In step two, the method for determining the weight of the evaluation index is:
wherein, w' i Is the weight of the index i, b ij Score, x, of familiarity of j-th expert with index i ij The number of the total number of m experts is the value of the importance degree of the j-th expert to the index i, and n is the total number of the indexes in a certain evaluation module.
The scoring of the defect condition of the index is carried out according to the following mode, for the quantitative index, the attention value in the operation rule is taken as the basis for judging the quality of the index, and the score evaluation is carried out by combining the actual condition and the expert experience; for qualitative indexes, according to whether the indexes have defects or abnormalities in practice, scoring judgment is carried out by combining expert experience, and the specific expert scoring method comprises the following steps:
wherein, s' i Is the score value of index i, a ij The degree of certainty is specified for the degree of certainty (0-1, 0 means complete uncertainty, 1 means complete certainty) of the self-evaluation result of the j-th expertExpert participation scores greater than 0.9, s ij The value of j-th expert to the grade of index i is divided into [ M, N ]]And m is the total number of experts.
In step three, the set of constructed state comments is { good, general, note, abnormal, severe }, and the score interval corresponding to the set of comments is set as good: [ (M + 4N)/5,N), in general: [ (2M + 3N)/5, (M + 4N)/5), note: [ (3M + 2N)/5, (2M + 3N)/5), abnormal: [ (4M + N)/5, (3M + 2N)/5), severe: [ M, (4M + N)/5 ].
The manner of generating the cloud parameters of the state judgment criterion by using the cloud model algorithm is as follows,
wherein, c k Represents the lower limit of the score interval corresponding to the kth status comment, d k Representing the upper limit of a score interval corresponding to the kth state comment, wherein k is 1-5 and respectively represents { good, general, attention, abnormal, severe };
in the fourth step, the method for integrating the scores, the weight values and the cloud parameters of the state judgment standard of each index comprises the following steps:
wherein, Y k Representing the degree of the dry transformer belonging to the comment k, n represents the total number of indicators, NORM (En) k ,He k ) Indicates that an expected value En is generated k Standard deviation is He k The normal distribution random number of (2);
according to the principle of maximum membership degree, the final state of the dry-type transformer is max { Y k I k =1,2,3,4,5} corresponding comment k.
The invention has the following advantages:
according to the method, the dry type transformer is integrally divided into different evaluation modules through data sorting and analysis of all parts of the dry type transformer and grounding lightning protection means, evaluation indexes of all the evaluation modules are effectively screened out, so that an accurate evaluation state quantity system is constructed, and then a cloud model algorithm is applied, so that the fuzziness and randomness are considered in the evaluation process, and the state evaluation result of the dry type transformer is more accurate.
Drawings
Fig. 1 is a flow chart of a dry-type transformer state evaluation method based on a cloud model according to the present invention.
Detailed Description
The invention is further described below with reference to the figures and examples.
The invention relates to a dry-type transformer state evaluation method based on a cloud model, which comprises the following steps as shown in figure 1:
the method comprises the following steps: the method comprises the following steps of integrally dividing the dry-type transformer into different evaluation modules by combining the conditions of all parts of the dry-type transformer and grounding lightning protection means, wherein the evaluation modules comprise a body, a non-electric quantity protection and secondary circuit, lightning protection and grounding, other accessories and the like; and then screening out evaluation indexes of each evaluation module, wherein the body comprises evaluation indexes of short-circuit current recording, infrared temperature measurement, noise, winding voltage withstand test, winding direct-current resistance, winding dielectric loss factor, winding insulation resistance, absorption ratio, polarization index, appearance and the like, non-electric quantity protection and secondary circuit comprise evaluation indexes of iron core temperature, rainproof measures, secondary circuit insulation resistance and the like, lightning protection and grounding comprise grounding resistance and the grounding condition of main components, and other accessories comprise cooling system operation condition, sleeve external insulation and insulation resistance inspection at a connecting cable.
Step two: determining the weight of the evaluation index according to the relevant guide rules and the expert opinions, wherein the determination method of the weight of the evaluation index comprises the following steps:
wherein, w' i Is the weight of the index i, b ij Score, x, of familiarity of j-th expert with index i ij The value of the importance of the j-th expert on the index i, the total of m expertsAnd n is the total number of indexes in a certain evaluation module.
The method comprises the following steps of (1) scoring possible defect conditions of indexes, wherein the scoring process is executed according to the following mode, for quantitative indexes, an attention value in an operation rule is taken as a basis for judging the quality of the indexes, and the score evaluation is carried out by combining actual conditions and expert experience; and for qualitative indexes, according to the fact whether the indexes have defects or abnormalities or not, scoring judgment is carried out by combining expert experience, and the specific expert scoring method comprises the following steps:
wherein, s' i Is the score value of index i, a ij For the degree of certainty (0-1, 0 means complete uncertainty, 1 means complete certainty) of the j-th expert on the self-evaluation result, the expert with the degree of certainty greater than 0.9 is specified to participate in the scoring, s ij The value of j-th expert to the grade of index i is divided into [ M, N ]]And m is the total number of experts.
Step three: constructing a state comment set as { good, general, note, abnormal, severe }, and setting the score interval corresponding to the comment set as good: [ (M + 4N)/5,N), in general: [ (2M + 3N)/5, (M + 4N)/5), note: [ (3M + 2N)/5, (2M + 3N)/5), abnormal: [ (4M + N)/5, (3M + 2N)/5), severe: [ M, (4M + N)/5 ].
The manner of generating the cloud parameters of the state judgment criterion using the cloud model algorithm is as follows,
wherein, c k Represents the lower limit of the score interval corresponding to the kth status comment, d k Representing the upper limit of a score interval corresponding to the kth state comment, wherein k is 1-5 and respectively represents { good, general, attention, abnormal, severe };
step four: the method comprises the following steps of synthesizing the values, the weight values and the cloud parameters of the state judgment standard of each index to obtain the final state of the dry-type transformer:
wherein, Y k Representing the degree of the dry transformer belonging to the comment k, n represents the total number of indicators, NORM (En) k ,He k ) Indicating that an expected value En is generated k Standard deviation is He k The normal distribution random number of (2);
according to the principle of maximum membership degree, the final state of the dry-type transformer is max { Y k I k =1,2,3,4,5} corresponding comment k.
In order to explain the specific implementation process of the state evaluation of the dry-type transformer, the dry-type transformer state evaluation method based on the cloud model is analyzed by selecting the test data of a certain dry-type transformer with the rated capacity of 1600KVA and the equipment model of PSCD-1600. Due to the complex environment of the actual engineering, the acquired data is difficult to be perfected, and the indexes of the data which are not acquired are ignored and are not evaluated in consideration of the possible incompleteness of data acquisition, so that the acquired data indexes are evaluated. The collected data include appearance, winding direct current resistance, cooling system running condition, winding insulation resistance, absorption ratio and insulation resistance test at a connecting cable, and the specific data are shown in table 1.
TABLE 1 test data
And according to the second step, determining the appearance, the direct-current resistance of the winding, the running condition of the cooling system, the insulation resistance of the winding, the absorption ratio and the weight of the insulation resistance test at the connecting cable, wherein the grading result of each expert on the importance degree of each index is shown in a table 2, and the familiarity degree of each expert on each index is shown in a table 3.
TABLE 2 Scoring results of each expert's importance degree to each index
TABLE 3 score of each expert's familiarity with each index
The index weights are obtained according to the following formula, as shown in table 4.
Wherein, w' i Is the weight of the index i, b ij Score, x, of familiarity of j-th expert with index i ij The value of the importance degree of the j-th expert on the index i is shown, the total number of m experts is shown, and n is the total number of indexes in a certain evaluation module.
TABLE 4 weights of the indices
According to the test data in table 1, the experts are arranged to grade the actual quality degrees of various indexes, as shown in table 5, and the determination degrees of the experts on the self-evaluation results are shown in table 6.
TABLE 5 evaluation values of actual degrees of merits of various indexes
TABLE 6 determination degree of each expert's own judgment result
| Expert | Expert A | Expert B | Expert C | Expert D |
| Degree of determination | 0.98 | 0.92 | 0.97 | 0.91 |
The final score value of each index can be obtained according to the following formula, as shown in table 7.
Wherein, s' i Is the score value of index i, a ij For the degree of certainty of the j-th expert on the self-evaluation result (0-1, 0 means completely uncertain, 1 means completely definite), the expert with the degree of certainty greater than 0.9 is specified to participate in the scoring, s ij The j-th expert scores the quality of the index i in a score interval of [ M, N%]And m is the total number of experts.
TABLE 7 Final rating values of the respective indices
And according to the third step, a state comment set and a corresponding score interval are constructed, as shown in table 8, and cloud parameters of the state comment standard are generated by using a cloud model algorithm, as shown in table 9.
TABLE 8 State comment sets and corresponding score intervals
| Index item | Good effect | In general | Attention is paid to | Abnormality (S) | Severe severity of disease |
| Score value | [0.8，1) | [0.6，0.8) | [0.4，0.6) | [0.2，0.4) | [0，0.2] |
TABLE 9 cloud parameters for State evaluation criteria
According to the fourth step, the degree of the dry-type transformer belonging to each comment is obtained according to the following formula, as shown in table 10.
Wherein Y is k Representing a dry type transformerThe degree to which the depressor belongs to the comment k, n represents the total number of indicators, NORM (En) k ，He k ) Indicates that an expected value En is generated k Standard deviation is He k The normal distribution random number of (2);
TABLE 10 degree of affiliation of Dry Transformer to Each comment
| Comment (I) | Good effect | In general | Attention is paid to | Abnormality (S) | Severe severity of disease |
| Degree of membership | 0 | 0.124 | 0.602 | 0.543 | 0 |
According to the table 10, the state of the dry-type transformer is judged according to the maximum membership principle, and the state tends to be an attention state, and the degree of the abnormal state is high, so that the transformer is further inspected, after the inspection, the iron core of the transformer is found to be broken down to the ground, and the inspection result further proves the feasibility of the method.
The above embodiments are only used for illustrating the technical solutions of the present invention and are not limited, and those skilled in the art can design similar technical solutions based on the technical solutions of the present invention to achieve the above technical effects, which all fall into the protection scope of the present invention. Moreover, the protection scope of the present patent shall be subject to the appended claims.
Claims (2)
1. A dry-type transformer state evaluation method based on a cloud model is characterized by comprising the following steps:
the method comprises the following steps: the method comprises the following steps of integrally dividing the dry-type transformer into different evaluation modules by combining the conditions of all parts of the dry-type transformer and grounding lightning protection means, and screening out evaluation indexes of all the evaluation modules;
the dry-type transformer is integrally divided into different evaluation modules, wherein each evaluation module comprises a body, a non-electric quantity protection and secondary circuit, lightning protection and grounding and other accessories;
the evaluation indexes of each evaluation module are as follows, the body comprises evaluation indexes of short-circuit current recording, infrared temperature measurement, noise, winding voltage withstand test, winding direct-current resistance, winding dielectric loss factor, winding insulation resistance, absorption ratio, polarization index and appearance, the evaluation indexes of non-electric quantity protection and secondary circuit comprise iron core temperature, rainproof measures and secondary circuit insulation resistance, lightning protection and grounding comprise grounding resistance and the grounding condition of main components, and other accessories comprise cooling system operation condition, sleeve external insulation and insulation resistance inspection at a connecting cable;
step two: determining the weight of the evaluation index according to the relevant guide rules and the expert opinions, and scoring the possible defect condition of the index;
the method for determining the weight of the evaluation index comprises the following steps:
wherein, w' i Is the weight of the index i, b ij Score, x, of familiarity of j-th expert with index i ij The value of the importance of j-th expert to index iThe total number of m experts, n is the total number of indexes in a certain evaluation module;
the scoring of the defect condition possibly occurring in the index is executed according to the following mode, for the quantitative index, the attention value in the operation procedure is taken as the basis for judging the quality of the index, and the score evaluation is carried out by combining the actual condition and the expert experience; and for qualitative indexes, according to the fact whether the indexes have defects or abnormalities or not, scoring judgment is carried out by combining expert experience, and the specific expert scoring method comprises the following steps:
wherein, s' i Is the score value of index i, a ij Taking 0-1,0 to represent complete uncertainty, 1 to represent complete determination, and specifying that experts with the determination degree larger than 0.9 participate in scoring s ij The value of j-th expert to the grade of index i is divided into [ M, N ]]M is the total number of experts;
step three: constructing a state comment set, and generating cloud parameters of a state judgment standard by using a cloud model algorithm;
the manner of generating the cloud parameters of the state judgment criterion using the cloud model algorithm is as follows,
wherein, c k Represents the lower limit of the score interval corresponding to the kth status comment, d k Representing the upper limit of a score interval corresponding to the kth state comment, wherein k is 1-5 and respectively represents { good, general, attention, abnormal, severe };
step four: integrating the values and the weight values of all the indexes and the cloud parameters of the state judgment standard to obtain the final state of the dry-type transformer;
the method for integrating the cloud parameters of the scores, the weight values and the state judgment standards of all the indexes comprises the following steps:
wherein, Y k Representing the degree of the dry transformer belonging to the comment k, n represents the total number of indicators, NORM (En) k ,He k ) Indicates that an expected value En is generated k Standard deviation is He k The normal distribution random number of (2);
according to the principle of maximum membership, the final state of the dry-type transformer is max { Y } k I k =1,2,3,4,5} corresponding comment k.
2. The method for evaluating the state of a dry-type transformer based on a cloud model according to claim 1, wherein in step three, the set of build state comments is { good, general, note, abnormal, severe }, and the score interval corresponding to the set of comments is set as good: [ (M + 4N)/5,N), in general: [ (2M + 3N)/5, (M + 4N)/5), note: [ (3M + 2N)/5, (2M + 3N)/5), abnormal: [ (4M + N)/5, (3M + 2N)/5), severe: [ M, (4M + N)/5 ].
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110252868.0A CN112949191B (en) | 2021-03-03 | 2021-03-03 | Dry-type transformer state evaluation method based on cloud model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110252868.0A CN112949191B (en) | 2021-03-03 | 2021-03-03 | Dry-type transformer state evaluation method based on cloud model |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112949191A CN112949191A (en) | 2021-06-11 |
| CN112949191B true CN112949191B (en) | 2023-02-24 |
Family
ID=76228820
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110252868.0A Active CN112949191B (en) | 2021-03-03 | 2021-03-03 | Dry-type transformer state evaluation method based on cloud model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112949191B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108491358A (en) * | 2018-03-21 | 2018-09-04 | 广东电网有限责任公司电力科学研究院 | A kind of bushing shell for transformer state evaluating method and device |
| CN109490726A (en) * | 2018-11-23 | 2019-03-19 | 广西电网有限责任公司南宁供电局 | Electric power transformer insulated state evaluating method based on Clouds theory |
| CN110988745A (en) * | 2019-11-12 | 2020-04-10 | 中国海洋石油集团有限公司 | Method and system for evaluating operation state of dry-type transformer of offshore platform |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105894096B (en) * | 2016-04-22 | 2019-06-07 | 国家电网公司 | A kind of grounding net of transformer substation state evaluating method based on cloud model |
| CN105956779A (en) * | 2016-05-09 | 2016-09-21 | 山东大学 | Power transformer running state evaluation method and power transformer running state evaluation device |
| CN108399340A (en) * | 2018-03-06 | 2018-08-14 | 中国民航大学 | Based on the onboard networks safety risk estimating method for improving FAHP and cloud model |
-
2021
- 2021-03-03 CN CN202110252868.0A patent/CN112949191B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108491358A (en) * | 2018-03-21 | 2018-09-04 | 广东电网有限责任公司电力科学研究院 | A kind of bushing shell for transformer state evaluating method and device |
| CN109490726A (en) * | 2018-11-23 | 2019-03-19 | 广西电网有限责任公司南宁供电局 | Electric power transformer insulated state evaluating method based on Clouds theory |
| CN110988745A (en) * | 2019-11-12 | 2020-04-10 | 中国海洋石油集团有限公司 | Method and system for evaluating operation state of dry-type transformer of offshore platform |
Non-Patent Citations (3)
| Title |
|---|
| Cloud model-based failure mode and effects analysis for prioritization of failures of power transformer in risk assessment;Ruijin Liao 等;《EUROPEAN TRANSACTIONS ON ELECTRICAL POWER》;20121231;第1-19页 * |
| 基于云模型的变压器运行状态综合评估方法研究;韩丽维;《中国优秀博硕士学位论文全文数据库(硕士) 工程科技Ⅱ辑》;20160215(第02期);第C042-305页 * |
| 基于云理论的电力变压器绝缘状态评估方法;张镱议 等;《电工技术学报》;20120526;第27卷(第05期);第13-20页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112949191A (en) | 2021-06-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110689234B (en) | Power transformer state evaluation method based on multi-source data fusion | |
| CN111784175A (en) | Distribution transformer risk assessment method and system based on multi-source information | |
| Liu et al. | Health assessment and prediction of overhead line based on health index | |
| Li et al. | An integrated method of set pair analysis and association rule for fault diagnosis of power transformers | |
| CN108414898A (en) | A kind of condition test method and system of wind farm device live detection | |
| CN108649892B (en) | Defect diagnosis method and device for photovoltaic power station | |
| CN103513125A (en) | Integrated intelligent diagnosis system and method of above-220KV transformers | |
| CN110940374A (en) | Transformer health grade evaluation system and method based on big data fusion | |
| CN107491783B (en) | Transformer fault type classification method based on improved density peak value clustering algorithm | |
| CN110705887A (en) | Low-voltage transformer area operation state comprehensive evaluation method based on neural network model | |
| CN112288293A (en) | Comprehensive evaluation method for electric energy quality of large charging station | |
| CN109142991A (en) | A kind of infrared survey zero-temperature coefficient threshold determination method of porcelain insulator based on Burr distribution | |
| CN110930057A (en) | Quantitative evaluation method for reliability of distribution transformer test result based on LOF algorithm | |
| CN115096366A (en) | SF6 ring main unit running state monitoring system and state evaluation method thereof | |
| CN112116276A (en) | Transformer substation operation risk assessment method considering time-varying state of electrical main equipment | |
| CN113313403B (en) | Power distribution network comprehensive evaluation method, device and system based on large-scale high-power electric vehicle charging and discharging and storage medium | |
| CN106651206A (en) | Method for evaluating testability evaluation index system of relay protection | |
| CN111999695B (en) | State evaluation and abnormity diagnosis method for metering device of transformer substation | |
| CN112949191B (en) | Dry-type transformer state evaluation method based on cloud model | |
| CN109784777B (en) | Power grid equipment state evaluation method based on time sequence information fragment cloud similarity measurement | |
| Zhang et al. | Fuzzy failure rate model of power transformer based on condition monitoring | |
| CN112561231A (en) | Switch cabinet quality judgment method and system | |
| Wu et al. | CNN-MLP-based transformer digital twin model construction and fault diagnosis and condition evaluation analysis | |
| Lingang et al. | Research on integrated calculation method of theoretical line loss of MV and LV distribution Network based on Adaboost integrated learning | |
| Li et al. | A practical evaluation method for the operation quality of smart substation secondary equipment |
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 |