Transformer partial discharge danger level assessment method and system, electronic equipment and storage medium

Abstract

The invention relates to the technical field of transformer safety monitoring, in particular to a transformer partial discharge danger level assessment method and system, electronic equipment and a storage medium, and the method comprises the following steps: S1, determining a transformer discharge defect type according to existing data, the typical discharge defect types comprise metal point discharge, internal air gap discharge and creeping discharge; s2, determining the inherent risk Rmax of the defect; s3, determining the failure probability of the transformer insulation system caused by the defect in combination with the identification confidence C of the discharge diagnosis system and the influence factor IP of the discharge risk degree of the defect; and S3, determining the risk level of the discharge defect according to the probability interval of the failure probability. According to the method, the identification confidence of the discharge diagnosis system, the defect inherent risk analysis and the influence factor analysis of the discharge risk degree are combined, a quantitative evaluation system for the partial discharge risk degree of the transformer is established, the partial discharge risk degree of the transformer can be graded, and a corresponding maintenance strategy can be given.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer safety monitoring, and specifically to a method, system, electronic equipment and storage medium for evaluating the degree of danger of partial discharge of a transformer. Background Art

[0002] Partial discharge is one of the major hidden dangers that affect the safe operation of transformers. Its complexity and diversity have brought great challenges to the safe operation and maintenance of transformers. At present, researchers have not reached a consensus on the selection of partial discharge characteristic quantities that can best distinguish discharge modes and characterize the degree of aging. Relevant scholars have selected different characteristic quantities for research, focusing on examining the changing trends of a single or several characteristic parameters during the insulation aging process.

[0003] But in fact, the evolution trends of various characteristic quantities in the aging process are different, and it is impossible to evaluate the degree of insulation degradation only by the changes in isolated characteristic parameters. In addition, if all the spectra and parameters are used for diagnosis, it will not only bring difficulties to the discharge separation and identification work, but also affect the evaluation effect due to the cross-correlation between the parameters themselves. Not only can the accuracy of diagnosis not be improved, but the real fault signal may be covered up due to mutual interference, making the task of discharge separation and identification more difficult. Summary of the invention

[0004] In view of the problem in the prior art that a large number of characteristic quantities and different evolution trends lead to poor accuracy of evaluation results, the present invention provides a method for evaluating the hazard level of partial discharge of a transformer.

[0005] The present invention is achieved through the following technical solutions:

[0006] A method for evaluating the degree of danger of partial discharge of a transformer comprises the following steps:

[0007] S1, determine the transformer discharge defect type based on the existing data. Typical discharge defect types include metal tip discharge, internal air gap discharge and surface discharge;

[0008] S2, determine the inherent risk R of the discharge defect based on the discharge defect max ;

[0009] S3, inherent risk of combined discharge defects R max , the discharge diagnosis system identification confidence C and the defect discharge risk factor IP determine the failure probability of the transformer insulation system failure caused by the discharge defect;

[0010] S4, determining the hazard level of the discharge defect according to the probability interval of the failure probability obtained in S3, and determining a corresponding maintenance strategy according to the hazard level.

[0011] Preferably, in S2, the inherent risk of the defect is determined by the maximum inherent risk R of a typical discharge type. max to characterize, among which the maximum inherent risk of metal tip discharge is 80%, the maximum inherent risk of internal air gap discharge is 70%, and the maximum inherent risk of surface discharge is 100%.

[0012] Preferably, in S3, the discharge diagnosis system identification confidence C is determined by the diagnosis system confidence C 1 and the credibility of the test data C 2 Determined jointly.

[0013] Preferably, in S3, the influencing factor IP of the discharge risk of the defect is determined by the discharge characteristic value and the discharge behavior, the discharge characteristic value includes the Weibull scale parameter α / pC, the average discharge power P / μW and the discharge interval time Δt / ms, and the discharge behavior includes the discharge trend analysis and the discharge continuity.

[0014] Preferably, in S3, the steps of obtaining the failure probability of the transformer insulation system are as follows:

[0015] First, determine the influencing parameter IP of a certain partial discharge risk characterization factor under a certain discharge type i , then determine the failure probability FP caused by the discharge influencing factor, and finally determine the total failure probability TFP of the insulation system.

[0016] Preferably, the total failure probability TFP of the insulation system is determined by the inherent risk DSR of the discharge defect and the uncertainty ΔUd of the diagnosis system.

[0017] Preferably, in S4, the hazard level is divided into three levels, specifically: when the total failure probability TFP is 0-30%, the hazard level rating is low; when the total failure probability TFP is 31%-60%, the hazard level rating is medium; when the total failure probability TFP is 61%-100%, the hazard level rating is high.

[0018] A transformer partial discharge hazard assessment system includes a discharge defect determination processing module, a defect inherent risk determination module, a failure probability acquisition module, a hazard level determination module and a maintenance strategy determination module. The discharge defect determination processing module is used to determine the transformer discharge defect type according to existing data, and the defect inherent risk determination module is used to determine the inherent risk R of the discharge defect according to the discharge defect. max ; The failure probability acquisition module is used to combine the inherent risk R of discharge defects max, the discharge diagnosis system identification confidence C and the influencing factor IP of the discharge hazard of the defect determine the failure probability of the transformer insulation system caused by the defect, the hazard level determination module is used to determine the hazard level according to the probability interval of the failure probability, and the maintenance strategy determination module is used to determine the maintenance strategy according to the hazard level.

[0019] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the transformer partial discharge hazard level assessment method when executing the computer program.

[0020] A storage medium stores a computer program, which implements the steps of the transformer partial discharge hazard assessment method when executed by a processor.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] The transformer partial discharge hazard level assessment method of the present invention combines the three parts of discharge diagnosis system recognition confidence, defect inherent risk analysis and discharge hazard influencing factor analysis to establish a transformer partial discharge hazard quantitative evaluation system, which provides a comprehensive and scientific solution for the assessment and management of transformer partial discharge, and helps to improve the operating safety and reliability of the transformer.

[0023] Furthermore, the recognition confidence of the discharge diagnosis system is determined by the confidence of the diagnosis system and the credibility of the test data. The inherent risk analysis of defects takes into account the possibility of rapid insulation damage and breakdown caused by defects and the difficulty of defect detection, and proposes the maximum inherent risk R of typical defects. max To characterize the difficulty of detecting defects and the potential threat to the safe operation of equipment; the influencing factor analysis of discharge hazard takes into account two aspects: discharge characteristic value and macroscopic discharge behavior. According to the research results of discharge characteristics under various typical defects, the characteristic values ​​(Weibull scale parameter α, average discharge power P) and development stages (such as Δt spectrum characteristic values) that best characterize the aging degree and discharge hazard are selected, and different types of discharge behavior characteristics (trend and continuity) are considered. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flow chart of the method for evaluating the danger level of partial discharge of transformers of the present invention; DETAILED DESCRIPTION

[0025] The present invention is further described in detail below in conjunction with specific embodiments, which are intended to explain the present invention rather than to limit it.

[0026] The present invention discloses a method for evaluating the degree of danger of partial discharge of a transformer, referring to Figure 1, including the following steps:

[0027] S1, determine the type of transformer discharge defects based on existing data. Typical discharge defect types include metal tip discharge, internal air gap discharge and surface discharge. Among them, metal tip discharge usually occurs at the sharp edges of metal parts inside the transformer, and discharge is caused by electric field concentration. Internal air gap discharge is caused by the discharge of air gaps (such as bubbles, cracks, etc.) formed during the manufacturing or operation of the transformer under high voltage. Surface discharge occurs at the interface between the solid insulation surface and the gas or liquid, due to surface charge accumulation or electric field distortion.

[0028] S2, determine the inherent risk R of the discharge defect based on the discharge defect max .

[0029] The inherent risk of discharge defects should have two connotations: one is the possibility of rapid insulation damage and breakdown caused by discharge defects; the other is the ease of detection of discharge defects. The inherent risk of defects is measured by the maximum inherent risk R of typical discharge types. max The maximum possible risk value is derived from a comprehensive evaluation of the discharge defect type, historical data, experimental research and expert experience to characterize the difficulty of detecting the defect and the potential threat to the safe operation of the equipment. Among them, the maximum inherent risk of metal tip discharge is 80%, the maximum inherent risk of internal air gap discharge is 70%, and the maximum inherent risk of surface discharge is 100%.

[0030] S3, inherent risk of combined discharge defects R max , the discharge diagnosis system identification confidence C and the defect discharge risk influencing factor IP determine the failure probability of the transformer insulation system caused by the defect.

[0031] Among them, the discharge diagnosis system recognition confidence (Confidence) C is composed of the diagnosis system confidence C 1 and the credibility of the test data C 2 Determine together (as shown in Table 1), the expression is: C = C 1 ·C 2 .

[0032] Table 1 Confidence determination method of partial discharge diagnosis system

[0033]

[0034] Table 2 Risk factors affecting tip defects

[0035]

[0036] Table 3 Hazard influencing factors of internal air gap defects

[0037]

[0038] Table 4 Danger influencing factors of surface discharge defects

[0039]

[0040] The influencing factor IP of the discharge hazard of the defect is determined by the discharge characteristic value and discharge behavior. The discharge characteristic value includes the Weibull scale parameter α / pC, the average discharge power P / μW and the discharge interval time Δt / ms. The discharge behavior includes the discharge trend analysis and the discharge continuity. Specifically, based on the research results of the discharge characteristics of various typical defects in the existing literature, the characteristic parameters that best characterize the degree and development stage of the discharge hazard are selected, and the discharge characteristic value and discharge behavior are integrated to comprehensively establish the quantitative evaluation index of the local discharge hazard. The methods for determining the influencing factors of the discharge hazard of three typical defects are listed in Table 2, Table 3 and Table 4 respectively.

[0041] The steps to obtain the failure probability of the transformer insulation system are as follows:

[0042] First, determine the influencing parameter IP of a certain partial discharge risk characterization factor under a certain discharge type i , and then determine the failure probability FP caused by the discharge influencing factor, and finally determine the total failure probability TFP of the insulation system, where the total failure probability TFP of the insulation system is determined by the inherent risk DSR of the discharge defect and the uncertainty ΔUd of the diagnostic system. The details are as follows:

[0043] (1) Determine the influencing parameter IP of a certain partial discharge risk characterization factor under the defect i

[0044]

[0045] Where N I Indicates that the partial discharge risk assessment is divided into N I aspects (taking Table 2 as an example, N I =2), N A Indicates the number of parameters contained in each aspect, A j Indicates the risk value corresponding to each parameter (between 0 and 1).

[0046] (2) Determine the failure probability FP caused by the discharge influence factor

[0047] The failure probability FP (Failure Probability) caused by discharge is the sum of the three influencing parameters that characterize the partial discharge risk factor, namely:

[0048]

[0049] (3) Total insulation system failure probability TFP

[0050] The total failure probability TFP (Total Failure Probability) of the system is determined by the inherent risk (Defect Specific Risk, DSR) of the discharge defect and the uncertainty (Uncertainty of Diagnosis, ΔUd) of the diagnosis system, that is,

[0051] TFP=DSR+ΔUd (3)

[0052] The uncertainty ΔUd of the diagnostic system is mainly determined by the confidence C of the diagnostic system. If the confidence C of the diagnostic system is very low, that is, the C value is very small, the uncertainty of the diagnostic system will be very high; if the C value is very high, the uncertainty of the system is close to zero. The calculation of ΔUd is given by the following formula:

[0053] ΔUd=(1-C)·(1-DSR) (4)

[0054] In the formula, the inherent risk DSR of the defect is determined by the failure probability FP caused by the discharge hazard factor and the maximum inherent risk Rmax of the defect, that is,

[0055] DSR=FP·R max (5)

[0056] The combined equations (2) to (5) can be solved to obtain the total failure probability TFP of the insulation system caused by partial discharge, as shown in the following equation:

[0057]

[0058] S4, based on S3, the failure probability (danger level) of the transformer insulation system caused by partial discharge is obtained, and the corresponding danger level is determined according to the probability interval. The partial discharge danger level of the power transformer is divided into three levels: low, medium and high, and the corresponding transformer maintenance strategy is formulated accordingly, as shown in Table 5. The determination and optimization of the threshold intervals of many parameters in this evaluation system depend on effective field data and evaluation applications. Only by continuously adjusting the optimization parameters in practice and improving the accuracy of diagnosis and evaluation can it play a greater role in transformer asset management and operation and maintenance.

[0059] Table 5 Partial discharge hazard rating and maintenance strategy for power transformers

[0060]

[0061] The present invention provides a method for assessing the hazard level of partial discharge of transformers by quantifying the factors that affect the results of partial discharge assessment, such as the diagnostic algorithm, the professionalism of test personnel, the inherent risk of defects, the discharge characteristic value, and the discharge behavior, and comprehensively considers the influence of various factors on the results, thereby establishing a set of quantitative evaluation system for the hazard level of partial discharge of transformers, which can rate the hazard level of partial discharge of transformers and provide corresponding maintenance strategies.

[0062] The present invention also discloses a transformer partial discharge hazard assessment system, including a discharge defect determination processing module, a defect inherent risk determination module, a failure probability acquisition module, a hazard level determination module and a maintenance strategy determination module. The discharge defect determination processing module is used to determine the transformer discharge defect type according to existing data, and the defect inherent risk determination module is used to determine the inherent risk R of the discharge defect according to the discharge defect. max ; The failure probability acquisition module is used to combine the inherent risk R of discharge defects max , the discharge diagnosis system identification confidence C and the defect discharge risk factor IP determine the failure probability of the transformer insulation system caused by the defect, the risk level determination module is used to determine the risk level according to the probability interval of the failure probability, and the maintenance strategy determination module is used to determine the maintenance strategy according to the risk level. The system can improve the efficiency and accuracy of partial discharge risk assessment and greatly reduce the labor cost and time cost in the traditional assessment process.

[0063] The present invention also discloses an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the transformer partial discharge hazard assessment method when executing the computer program. The memory in the electronic device stores multiple instructions to implement a multimodal named entity recognition method for power equipment, and the processor can execute the multiple instructions to achieve the method.

[0064] The present invention also discloses a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method for evaluating the degree of danger of partial discharge of transformers are implemented. If the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0065] The above description is only a preferred embodiment of the present invention and is not intended to impose any limitation on the technical solution of the present invention. Those skilled in the art should understand that, without departing from the spirit and principles of the present invention, the technical solution can also be subjected to several simple modifications and substitutions, and these modifications and substitutions are also within the scope of protection covered by the claims.

Claims

1. A method for assessing the risk level of partial discharge of a transformer, characterized in that: The following steps are involved: S1, determine the transformer discharge defect type based on the existing data. Typical discharge defect types include metal tip discharge, internal air gap discharge and surface discharge; S2, determine the inherent risk R of the discharge defect based on the discharge defect max ; S3, inherent risk of combined discharge defects R max , the discharge diagnosis system identification confidence C and the defect discharge risk factor IP determine the failure probability of the transformer insulation system failure caused by the discharge defect; S4, determining the hazard level of the discharge defect according to the probability interval of the failure probability obtained in S3, and determining a corresponding maintenance strategy according to the hazard level.

2. The transformer partial discharge risk assessment method according to claim 1, characterized in that: In S2, the inherent risk of the defect is determined by the maximum inherent risk R of the typical discharge type. max to characterize, among which the maximum inherent risk of metal tip discharge is 80%, the maximum inherent risk of internal air gap discharge is 70%, and the maximum inherent risk of surface discharge is 100%.

3. The transformer partial discharge risk assessment method according to claim 1, characterized in that: In S3, the discharge diagnosis system identification confidence C is determined by the diagnosis system confidence C1 and the credibility C2 of the test data.

4. The transformer partial discharge risk assessment method according to claim 3 is characterized in that: In S3, the influencing factor IP of the discharge hazard of the defect is determined by the discharge characteristic value and discharge behavior. The discharge characteristic value includes the Weibull scale parameter α / pC, the average discharge power P / μW and the discharge interval time Δt / ms. The discharge behavior includes discharge trend analysis and discharge continuity.

5. The transformer partial discharge risk assessment method according to claim 4, characterized in that: In S3, the steps for obtaining the failure probability of the transformer insulation system are as follows: First, determine the influencing parameter IP of a certain partial discharge risk characterization factor under a certain discharge type i , then determine the failure probability FP caused by the discharge influencing factor, and finally determine the total failure probability TFP of the insulation system.

6. The transformer partial discharge risk assessment method according to claim 5, characterized in that: The total failure probability TFP of the insulation system is determined by the inherent risk DSR of the discharge defect and the uncertainty ΔUd of the diagnostic system.

7. The transformer partial discharge risk assessment method according to claim 5, characterized in that: In S4, the risk level is divided into three levels, specifically: when the total failure probability TFP is 0-30%, the risk level rating is low; when the total failure probability TFP is 31%-60%, the risk level rating is medium; When the total failure probability TFP is between 61% and 100%, the hazard rating is high.

8. A transformer partial discharge hazard assessment system, characterized in that: It includes a discharge defect determination processing module, a defect inherent risk determination module, a failure probability acquisition module, a hazard level determination module and a maintenance strategy determination module. The discharge defect determination processing module is used to determine the transformer discharge defect type according to the existing data, and the defect inherent risk determination module is used to determine the inherent risk R of the discharge defect according to the discharge defect. max ; The failure probability acquisition module is used to combine the inherent risk R of discharge defects max , the discharge diagnosis system identification confidence C and the influencing factor IP of the discharge hazard of the defect determine the failure probability of the transformer insulation system caused by the defect, the hazard level determination module is used to determine the hazard level according to the probability interval of the failure probability, and the maintenance strategy determination module is used to determine the maintenance strategy according to the hazard level.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the transformer partial discharge hazard level assessment method according to any one of claims 1 to 7 are implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the transformer partial discharge hazard level assessment method according to any one of claims 1 to 7 are implemented.

Images