Simulation calculation and on-line monitoring converter transformer risk life assessment method, device and medium

Abstract

The invention relates to a converter transformer risk life assessment method and device based on simulation calculation and on-line monitoring and a medium, internal operation parameters are obtained through fusion simulation calculation, and the internal operation parameters comprise the operation life, the load rate, the hot-spot temperature rise, the insulation safety margin, the insulation failure risk level and the vibration enhancement factor. Internal operation parameters are combined with on-line monitoring parameters, obtained through temperature rise experiments, electrical tests and oil chromatography analysis, of the converter transformer to be measured, and various parameters of temperature, insulation electric field distribution and mechanical vibration are quantified in multiple aspects. A subjective weight is obtained through a hierarchy method, an objective weight is obtained through an entropy weight method, subjective and objective empowerment is integrated, a subjective and objective fusion empowerment comprehensive weight is obtained, and the influence of various characteristic parameters on converter transformer operation risks is comprehensively analyzed. Compared with the prior art, the method has the advantages of high accuracy, comprehensive evaluation, high safety and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer fault diagnosis and status assessment, and in particular to a method, device and medium for assessing the risk life of a converter transformer by simulation calculation and online monitoring. Background Art

[0002] At present, the risk life assessment of converter transformers is based on the assessment method of ordinary power transformers. Data is collected through factory test results, appearance inspection, insulation impedance test, online monitoring, etc., and the physical and electrical parameters of the converter transformer are analyzed to determine its current state, performance level and transformation trend. Since the state of the internal space of the converter transformer is not taken into account, the evaluation result is not accurate.

[0003] Chinese patent application CN118710041A obtains a qualitative assessment score of the converter transformer by qualitatively scoring the risk value of the health of the converter transformer; quantitatively evaluates multiple indicators of the converter transformer to obtain a comprehensive health score of the converter transformer; quantitatively quantifies various operating risks of the converter transformer and combines them with the comprehensive health score to obtain a quantitative assessment score of the converter transformer; and evaluates the risk of the converter transformer by weighted summation of the qualitative assessment score and the quantitative assessment score. However, its evaluation factors lack experimental test or simulation calculation results of the evaluation indicators characterizing the temperature, insulation electric field and mechanical vibration of the converter transformer. Therefore, how to quantify the impact of the internal operating parameters and online monitoring parameters of the converter transformer on risk assessment, and more accurately estimate the life of the converter transformer and evaluate it are technical problems that need to be solved. Summary of the invention

[0004] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a method, equipment and medium for assessing the risk life of a converter transformer by simulation calculation and online monitoring. By quantifying the line monitoring parameters and internal operating parameters and using the method of multi-feature parameter information fusion, the influence of various parameters on the operating risk of the converter transformer is comprehensively analyzed, thereby obtaining an accurate estimate of the life of the converter transformer.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] According to one aspect of the present invention, a method for assessing the risk life of a converter transformer by simulation calculation and online monitoring is provided, which is characterized by comprising the following steps:

[0007] S1. Obtain online monitoring parameters of the converter transformer to be tested through temperature rise experiment, electrical test and oil chromatography analysis, and obtain internal operating parameters through simulation calculation of the converter transformer to be tested;

[0008] S2. Input online monitoring parameters and internal operating parameters, score and process the input parameters to obtain a subjective and objective fusion weighted comprehensive weight, combine the comprehensive weight with the input parameters, and output a converter transformer risk life assessment matrix and an assessment score;

[0009] S3, setting the matrix elements to the levels for evaluating the risk life of the converter transformer, including normal state, caution state, abnormal state and severe state;

[0010] S4. Compare the evaluation score with the evaluation matrix, and output the risk life evaluation result of the converter transformer.

[0011] Furthermore, the internal operating parameters of S1 include operating life, load rate, hot spot temperature rise, insulation safety margin, insulation failure risk level and vibration enhancement factor.

[0012] Furthermore, the parameters obtained from the temperature rise experiment of S1 include thermal defect temperature; the parameters obtained from the electrical test include partial discharge, dielectric loss factor, power frequency capacitance change rate and winding DC resistance difference; the parameters obtained from the oil chromatography analysis include acetylene content, total hydrocarbon gas content and relative gas production rate.

[0013] Furthermore, in S2, the subjective weight is obtained by the hierarchical analysis method, the objective weight is obtained by the entropy weight method, and the subjective and objective weights are combined to obtain the subjective and objective fusion weighted comprehensive weight.

[0014] Furthermore, by fusing multiple characteristic parameter information, the subjective weight is obtained using the tomography method, specifically including: judging the correlation and importance between the online monitoring parameters and internal operating parameters of the converter to be tested; comparing each parameter in pairs, corresponding to the set scale, and obtaining a judgment matrix; outputting the normalized eigenvector of the judgment matrix; performing a consistency check on the matrix, if the consistency ratio of the matrix is ​​acceptable, the eigenvector is the initial subjective weight, otherwise the matrix is ​​corrected. By fusing multiple characteristic parameter information, the objective weight is obtained using the entropy weight method, specifically including: constructing a characteristic decision matrix and performing standardization processing; obtaining the information entropy and deviation degree of each online monitoring parameter and internal operating parameter of the converter to be tested; outputting the normalized deviation degree as the objective weight under the entropy weight method.

[0015] Furthermore, the comprehensive weight of S2 is obtained by subjective and objective fusion weighting, and the expression is:

[0016]

[0017] Among them, ξ j is the comprehensive weight; λ is the comprehensive weight ratio coefficient; θ j is the correction deviation; μ jis the normalized deviation, i.e., the objective weight; n is the number of evaluation parameters.

[0018] Furthermore, the correction deviation θ j , the deviation degree of the entropy weight method is corrected by the eigenvector corresponding to the maximum eigenroot of the judgment matrix of the hierarchical analysis method, and the expression is:

[0019]

[0020] Among them, μ j is the normalized deviation, i.e., the objective weight; ω j is the feature vector; n is the number of evaluation parameters.

[0021] Furthermore, the evaluation score is a sum of the multiplication of each online monitoring parameter and each internal operating parameter with the corresponding comprehensive weight.

[0022] According to a second aspect of the present invention, there is provided an electronic device, comprising a memory and a processor, wherein a computer program is stored in the memory, and the method described above is implemented when the processor executes the program.

[0023] According to a third aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, wherein the program implements the method described above when executed by a processor.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] (1) Improve the accuracy of predicted life: Through simulation calculation, various parameters such as temperature, insulation electric field distribution, and mechanical vibration are obtained, and the risk situation of various index parameters inside the converter transformer is quantitatively described. The hierarchical method and entropy weight method are used to obtain subjective and objective weights, and the comprehensive weights are obtained by fusion. This allows each parameter to have a comprehensive effect on life prediction, thereby improving the prediction accuracy.

[0026] (2) Ensure equipment safety: The elements of the converter transformer risk life assessment matrix model are divided into normal state, caution state, abnormal state and severe state. Maintenance personnel can conduct inspections based on the assessment scores and corresponding states to promptly identify risks in the operation of the converter transformer and provide early warnings for the safe operation of the equipment.

[0027] (3) Improve the comprehensiveness of risk prediction: Comprehensively consider various internal operating parameters and online monitoring parameters of the converter transformer, which are applicable to the inspection of various risk situations, making the risk prediction more comprehensive. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 The flowchart of the risk life assessment method of converter transformer based on simulation calculation and online monitoring;

[0029] Figure 2 This is the framework diagram of the hierarchical model of “target layer-indicator layer-decision-making layer”. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0031] Example 1

[0032] like Figure 1 As shown, a method for assessing the risk life of a converter transformer by simulation calculation and online monitoring is characterized in that it includes the following steps:

[0033] S1. Obtain online monitoring parameters of the converter transformer to be tested through temperature rise experiment, electrical test and oil chromatography analysis, and obtain internal operating parameters through simulation calculation of the converter transformer to be tested;

[0034] S2. Input online monitoring parameters and internal operating parameters, score and process the input parameters to obtain a subjective and objective fusion weighted comprehensive weight, combine the comprehensive weight with the input parameters, and output a converter transformer risk life assessment matrix and an assessment score;

[0035] S3, setting the matrix elements to the levels for evaluating the risk life of the converter transformer, including normal state, caution state, abnormal state and severe state;

[0036] S4. Compare the evaluation score with the evaluation matrix, and output the risk life evaluation result of the converter transformer.

[0037] The online monitoring parameters and internal operating parameters are the 14 evaluation indicators of this embodiment. The internal operating parameters include operating life, load rate, hot spot temperature rise, insulation safety margin, insulation failure risk level and vibration enhancement factor. Among them, the operating life of the converter transformer is calculated by combining the simulation results of the temperature distribution to analyze the influence of temperature change on the polymerization degree of the insulation paper, so as to accurately evaluate the aging degree of the insulation paper and the service life of the transformer. The load rate is the ratio of the actual output power of the converter transformer to its rated capacity. The hot spot temperature rise is the result of the simulation calculation of the internal temperature distribution of the converter transformer. It is used to measure the influence of temperature rise on the structure and performance of the transformer, and reflects the temperature distribution of various parts inside the transformer, especially the changes in the local maximum temperature.

[0038] Insulation safety margin (ISM) refers to the ratio of the breakdown voltage of the insulation material of the electrical equipment under normal working conditions at this temperature to the voltage it withstands during normal working, and the maximum breakdown voltage that the insulation material can withstand. The expression is:

[0039]

[0040] Among them, China E b (T) is the breakdown field strength of insulating oil or insulating paperboard at temperature T, E(x,y,z,T) is the field strength at a certain point of the oil-paper insulation structure at that temperature, and E b_max (T) is the maximum breakdown field strength of the oil-paper insulation structure at this temperature.

[0041] The insulation failure risk level assesses the possibility of failure of the converter transformer insulation system during operation, which is determined by the working environment, use conditions, equipment status and other relevant factors of the internal insulating materials such as insulating oil and insulating paper. The insulation risk level is divided into four levels. The higher the insulation failure risk level, the greater the possibility of failure of the converter transformer insulation, and more attention needs to be paid to the inspection, maintenance or replacement of the insulation structure when necessary. The vibration enhancement factor measures the impact of mechanical vibration on the structure and performance of the converter transformer, and quantitatively evaluates the contribution of harmonic currents of different frequencies to the stress of the power transformer core. This factor is the additive effect of harmonics of each frequency on the stress under working conditions.

[0042] The temperature rise test items, electrical test items and oil chromatography analysis are extracted as online monitoring parameters. The parameters obtained from the temperature rise test include thermal defect temperature; the parameters obtained from the electrical test include partial discharge, dielectric loss factor, power frequency capacitance change rate and winding DC resistance difference; the parameters obtained from the oil chromatography analysis include acetylene content, total hydrocarbon gas content and relative gas production rate. According to Table 1, a commutation transformer risk life assessment system integrating simulation calculation and online monitoring is constructed. When the health status of the converter transformer is serious, it means that some indicators exceed the warning value, and there is a risk of an immediate failure. You should immediately apply for a power outage for maintenance and find out the cause of the failure. You must not operate before finding out the cause. When the health status of the converter transformer is abnormal, that is, the values ​​of some status monitoring indicators exceed the attention value, you need to pay attention to the abnormal indicators. When the data shows a deterioration trend, you should shut down for maintenance and find out the cause. When the health status of the converter transformer is attention, it means that the status monitoring indicator data is normal, but some data is hovering on the edge of the attention value, which requires attention and the number of equipment inspections should be increased. When the health status of the converter transformer is normal, the overall condition of the equipment is relatively good, it can operate stably for a long time, and consider extending the transformer maintenance cycle. The indicator system evaluation standard table is shown in Table 1.

[0043] Table 1 Index system evaluation standard table

[0044]

[0045]

[0046] In S2, the subjective weight is obtained through the hierarchical analysis method, the objective weight is obtained through the entropy weight method, and the subjective and objective weights are combined to obtain the comprehensive weight of subjective and objective fusion weighting.

[0047] By fusing multiple characteristic parameter information, the subjective weight ω is obtained using the tomography method. The converter transformer risk life assessment model is divided into three layers, including the highest layer (target layer), the middle layer (indicator layer) and the lowest layer (decision layer), such as Figure 2 As shown in the figure, obtaining the health status assessment result of the converter transformer taking into account harmonics is the ultimate goal of the assessment model. The 14 parameters obtained through simulation calculation and online monitoring are the indicators of risk life assessment. Normal state, caution state, abnormal state and severe state are the levels for evaluating the risk life of the converter transformer.

[0048] The correlation and importance of each parameter index in the index layer are judged to construct the judgment matrix A. Based on the number of index parameters in the model, the five-scale method is used to construct the judgment matrix. Each scale is compared two by two, and the importance of each index corresponds to a scale of 1-5 from high to low. The meaning of each scale is shown in Table 2.

[0049] Table 2 The meaning of each scale in the judgment matrix A

[0050]

[0051] n×n order judgment matrix A=(a ij ) n×n , where i = 1, 2, ..., n; j = 1, 2, ..., n; a ij It represents the relationship between the importance of the i-th indicator and the j-th indicator, and n is the order of the matrix. The expression of the judgment matrix A is:

[0052]

[0053] Calculate the maximum eigenvalue λ of the judgment matrix A max And the corresponding eigenvector ω, and the eigenvector ω is normalized, the expression is:

[0054] Aω=λ max ω.

[0055] The judgment matrix A is subjected to consistency test to obtain the consistency index CI and consistency ratio CR of the judgment matrix A. The smaller the consistency ratio CR, the higher the reliability of the judgment matrix A. When the consistency ratio CR<0.1, the logical relationship of the judgment matrix A meets the requirements, that is, the consistency ratio of the matrix is ​​acceptable, and the normalized eigenvector ω is the initial subjective weight; when the consistency ratio CR>0.1, it is considered that the logical relationship of the judgment matrix A does not meet the requirements and the matrix needs to be corrected.

[0056] By fusing multiple feature parameter information, the objective weight μ is obtained using the entropy weight method. j , construct a characteristic decision matrix and perform standardization processing; obtain the information entropy and deviation degree of each online monitoring parameter and internal operating parameter of the converter transformer to be tested; output the normalized deviation degree as the objective weight under the entropy weight method.

[0057] Specifically, construct a decision matrix X. If there are m evaluation objects and n evaluation indicators, then the decision matrix X is a matrix with m rows and n columns X = (x ij ) m×n , where the element x ij is the evaluation index corresponding to the i-th row and j-th column in the decision matrix. The expression of constructing the decision matrix X is:

[0058]

[0059] Find the optimal value of each indicator and establish the characteristic decision matrix X′. First, make the indicator data in the decision matrix X dimensionless. ij When the larger the value, the better (such as insulation safety margin), When x ij When the indicator is smaller, the better (such as insulation failure risk level), record The feature decision matrix is ​​X′=(x′ ij ) m×n The value range of each indicator in the feature decision matrix X′ is [0,1], that is, the better the indicator performance, the larger the value, and its optimal value is 1.

[0060] The feature decision matrix X' is standardized, and the data of each indicator is converted into a real number b between [0,1] without changing the correlation of each indicator ij , where b ij It only represents the relative importance of each indicator and has no dimension. The expression of the feature decision matrix B after standardization is:

[0061]

[0062] If the index values ​​of different evaluation objects differ greatly for the same evaluation index, it means that the index contains rich effective information and has a significant impact on the evaluation object. j The expression is:

[0063]

[0064] Deviation of each evaluation d j The expression is:

[0065] d j =1-E j .

[0066] Normalize the deviation degree to obtain the normalized deviation degree μ j As the objective weight under the entropy weight method, the expression is:

[0067]

[0068] The normalized deviation μ j is the objective weight of each indicator.

[0069] The comprehensive weight is obtained by subjective and objective fusion weighting, and the expression is:

[0070]

[0071] Among them, ξ j is the comprehensive weight; λ is the comprehensive weight ratio coefficient; θ j is the correction deviation; μ j is the normalized deviation, i.e., the objective weight; n is the number of evaluation parameters. Corrected deviation θ j , the deviation degree of the entropy weight method is corrected by the eigenvector corresponding to the maximum eigenroot of the judgment matrix of the hierarchical analysis method, and the expression is:

[0072]

[0073] Among them, μ j is the normalized deviation, i.e., the objective weight; ω j is the feature vector; n is the number of evaluation parameters. The evaluation score is the sum of the multiplication of each online monitoring parameter and each internal operation parameter with the corresponding comprehensive weight.

[0074] Based on the four operating status levels of 14 indicators in the characteristic decision matrix X', and the comprehensive weight ξ j The corresponding 14 index weights are used to obtain the insulation health status matrix model Q of the transformer after integrating all characteristic indicators. The expression is:

[0075]

[0076] The four elements in the matrix Q are normal state, caution state, abnormal state, and severe state. The value range of each element is [0,1], where 1 means that all characteristic indicators of the converter transformer are better than the normal value in the standard, that is, the transformer is in a completely normal state; 0 means that all characteristic indicators of the transformer are worse than the severe value in the standard, that is, the transformer is in a completely failed state. The evaluation index data obtained in the case are weighted and summed to obtain the evaluation score, which is compared with the insulation health status model matrix Q to obtain the insulation health status evaluation result.

[0077] This embodiment takes the actual operation data of a ±800kV UHV converter transformer as an example to perform risk life assessment. The judgment matrix is:

[0078]

[0079] Get the maximum characteristic root λ max =14.8178, and its corresponding eigenvector ω=(0.0675, 0.1409, 0.1275, 0.0611, 0.1557, 0.4465, 0.4465, 0.3588, 0.2602, 0.2602, 0.4465, 0.0746, 0.1782, 0.1782) T , normalize the eigenvector ω and get the normalized eigenvector ω=(0.0210779, 0.0439983, 0.0398139, 0.0190794, 0.0486198, 0.1394267, 0.1394267, 0.1120410, 0.0812516, 0.0812516, 0.1394267, 0.0232950, ​​0.0556458, 0.0556458) T .

[0080] The consistency index CI of judgment matrix A is 0.0629, the average random consistency index RI is 1.58, and the consistency test calculation of judgment matrix A is performed. The consistency ratio CR is 0.0398<0.1. The consistency ratio of judgment matrix A meets the requirements, and the eigenvector ω is the subjective weight.

[0081] Construct a decision matrix X, where each element A in the decision matrix X is the classification value of different risk levels of the converter transformer in Table 1. Perform dimensionless processing on the indicator data in the decision matrix X, find the optimal value of each indicator, and obtain the characteristic decision matrix X′ and the standardized characteristic decision matrix B, which are expressed as follows:

[0082]

[0083] The information entropy matrix E of each indicator is obtained from each element in the B matrix j and the deviation matrix d j , the deviation matrix μ after normalization j The final corrected deviation θ j and comprehensive weight ξ j The expression is:

[0084]

[0085] According to the correction deviation θ j and comprehensive weight ξ j The obtained converter transformer risk life assessment matrix model Q = (1, 0.5104, 0.3105, 0.2294) T , where the value 1 is classified as a normal state, the value 0.5104 is classified as a caution state, the value 0.3105 is classified as an abnormal state, and the value 0.2294 is classified as a severe state.

[0086] The transformer failed in 2021. Before the failure, its C2 H2 content was in the range of 0-0.7μL / L, the total hydrocarbon gas content was in the range of 0-140μL / L, the relative gas production rate was in the range of 0-7% / month, and the partial discharge was within 0-79pC. After the failure, the C2 H2 content reached 12.2351μL / L, the total hydrocarbon gas content reached 287.9234L / L, the relative gas production rate reached 17% / month, and the partial discharge reached 196.5682pC. The integrated data of 14 evaluation indicators are shown in Table 3.

[0087] Table 3 Example of converter performance data

[0088] Evaluation indicators Numeric Evaluation indicators Numeric Operating life 357 Acetylene content (μL / L) 12.2351 Load factor 1.2 Total hydrocarbon gas content (μL / L) 287.9234 Hot spot temperature rise (K) 73 Absolute gas production rate (mL / d) 17.0 Vibration Enhancement Factor 1.4623 Partial discharge (pC) 196.5682 Insulation safety margin 2.0 Dielectric loss factor tanδ(%) 0.0067 Insulation failure risk level 3 Power frequency capacitance change rate (%) 2.0 Thermal defect temperature(℃) 182 Winding DC resistance difference (%) 3.0

[0089] The evaluation score of the characteristic parameter value of the converter transformer is 0.4835, and the evaluation result obtained by comparing with the model matrix Q is attention. In actual operation, the maintenance personnel found that the lower shielded aluminum tube equipotential wire terminal of the lower parallel lead had ablation marks when dissecting the upper and lower parallel leads of the valve side winding, and there were ablation marks at the overlap between the terminal and the parallel lead. After disassembly inspection, it was found that the insulation at the terminal lead of the equipotential wire of the converter transformer was partially damaged due to long-term overheating, resulting in a metallic short circuit. The model evaluation results are consistent with the actual risk life state of the UHV converter transformer, and the evaluation results are accurate.

[0090] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0091] Example 2

[0092] The electronic device of the present invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The CPU, ROM and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0093] A plurality of components in the device are connected to the I / O interface, including: an input unit, such as a keyboard, a mouse, etc.; an output unit, such as various types of displays, speakers, etc.; a storage unit, such as a disk, an optical disk, etc.; and a communication unit, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit allows the device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks. The processing unit performs the various methods and processes described above, such as the method of the present invention. For example, in some embodiments, the method of the present invention may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via a ROM and / or a communication unit. When the computer program is loaded into the RAM and executed by the CPU, one or more steps of the method of the present invention described above may be performed. Alternatively, in other embodiments, the CPU may be configured to perform the method of the present invention in any other appropriate manner (e.g., by means of firmware).

[0094] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0095] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0096] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0097] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for assessing the risk life of a converter transformer by simulation calculation and online monitoring, characterized in that: The following steps are involved: S1. Obtain online monitoring parameters of the converter transformer to be tested through temperature rise experiment, electrical test and oil chromatography analysis, and obtain internal operating parameters through simulation calculation of the converter transformer to be tested; S2. Input online monitoring parameters and internal operating parameters, score and process the input parameters to obtain a subjective and objective fusion weighted comprehensive weight, combine the comprehensive weight with the input parameters, and output a converter transformer risk life assessment matrix and an assessment score; S3, setting the matrix elements to the levels for evaluating the risk life of the converter transformer, including normal state, caution state, abnormal state and severe state; S4. Compare the evaluation score with the evaluation matrix, and output the risk life evaluation result of the converter transformer.

2. The method for assessing the risk life of a converter transformer by simulation calculation and online monitoring according to claim 1 is characterized in that: The internal operating parameters of S1 include operating life, load rate, hot spot temperature rise, insulation safety margin, insulation failure risk level and vibration enhancement factor.

3. The method for assessing the risk life of a converter transformer by simulation calculation and online monitoring according to claim 1 is characterized in that: The parameters obtained from the temperature rise experiment of S1 include thermal defect temperature; the parameters obtained from the electrical test include partial discharge, dielectric loss factor, power frequency capacitance change rate and winding DC resistance difference; the parameters obtained from the oil chromatography analysis include acetylene content, total hydrocarbon gas content and relative gas production rate.

4. The method for assessing the risk life of a converter transformer by simulation calculation and online monitoring according to claim 1 is characterized in that: In S2, the subjective weight is obtained by the hierarchical analysis method, the objective weight is obtained by the entropy weight method, and the subjective and objective weights are combined to obtain the subjective and objective fusion weighted comprehensive weight.

5. The method for assessing the risk life of a converter transformer by simulation calculation and online monitoring according to claim 4 is characterized in that: By fusing multiple feature parameter information, the subjective weight is obtained using tomography, specifically including: To judge the correlation and importance between the online monitoring parameters and internal operating parameters of the converter transformer to be tested; Compare each parameter pairwise, correspond to the set scale, and obtain a judgment matrix; output the normalized eigenvector of the judgment matrix; Perform a consistency check on the matrix. If the consistency ratio of the matrix is ​​acceptable, the eigenvector is the initial subjective weight. Otherwise, the matrix is ​​modified. By fusing multiple feature parameter information, the objective weight is obtained using the entropy weight method, specifically including: Construct a feature decision matrix and perform standardization; Obtaining the information entropy and deviation degree of each online monitoring parameter and internal operating parameter of the converter transformer to be tested; The normalized deviation degree is output as the objective weight under the entropy weight method.

6. The method for assessing the risk life of a converter transformer by simulation calculation and online monitoring according to claim 1 is characterized in that: The comprehensive weight of S2 is obtained by subjective and objective fusion weighting, and the expression is: Among them, ξ j is the comprehensive weight; λ is the comprehensive weight ratio coefficient; θ j is the correction deviation; μ j is the normalized deviation, i.e., the objective weight; n is the number of evaluation parameters.

7. The method for assessing the risk life of a converter transformer by simulation calculation and online monitoring according to claim 6 is characterized in that: The correction deviation θ j , the deviation degree of the entropy weight method is corrected by the eigenvector corresponding to the maximum eigenroot of the judgment matrix of the hierarchical analysis method, and the expression is: Among them, μ j is the normalized deviation, i.e., the objective weight; ω j is the feature vector; n is the number of evaluation parameters.

8. The method for assessing the risk life of a converter transformer by simulation calculation and online monitoring according to claim 1 is characterized in that: The evaluation score is the sum of each online monitoring parameter and each internal operating parameter multiplied by the corresponding comprehensive weight.

9. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the program, the method according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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