A monitoring data analysis system and method for transformer partial discharge monitoring

Abstract

The present invention discloses a monitoring data analysis system and method for transformer partial discharge monitoring, which belongs to the technical field of power equipment status monitoring and fault diagnosis. The present invention discloses a monitoring data analysis system for transformer partial discharge monitoring, which includes: a data acquisition module, a data integration and normalization module, a data storage module, a data analysis module and a risk assessment module; the data acquisition module acquires thermal effect data and mechanical stress effect data through sensors; the data integration and normalization module is used to obtain the acquired data and perform standardized processing; the data storage module is used to store all acquired data; the data analysis module is used to assess the influence of thermal effect and mechanical stress on the degree of insulation degradation; the risk assessment module is used to perform risk assessment on the power equipment to be assessed. The present invention implements the assessment of the degree of insulation material degradation by analyzing the thermal effect and mechanical stress effect caused by partial discharge.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment status monitoring and fault diagnosis, and in particular to a monitoring data analysis system and method for transformer partial discharge monitoring. Background Art

[0002] As a key equipment in modern power systems, the operating status of transformers directly affects the safety and stability of power systems. With the increase in power system capacity and the increasing complexity of loads, partial discharge (PD) will occur in transformers during long-term operation. Partial discharge of transformers has become one of the key factors affecting the insulation status of transformers and an important manifestation of insulation material degradation. It usually occurs at insulation defects, bubbles and impurities, and will cause local high temperatures, mechanical stress and chemical effects, ultimately leading to insulation breakdown and transformer failure.

[0003] However, the existing monitoring data analysis of transformer partial discharge monitoring has the following problems: First, the transformer partial discharge signal is often relatively weak, and in a complex power environment, it is easily affected by external factors such as electromagnetic interference and power frequency noise. These noise interferences will cause the collected partial discharge signal to be distorted or mask the real discharge signal, thereby affecting the accuracy and reliability of the monitoring data. Second, partial discharge not only triggers electrical signals, but also produces thermal effects and mechanical stress effects. Existing monitoring methods usually only analyze the electrical signals of partial discharge, ignoring the thermal effect and mechanical stress coupling factors, resulting in an incomplete and inaccurate assessment of the degree of insulation degradation. Summary of the Invention

[0004] The object of the present invention is to provide a monitoring data analysis system and method for transformer partial discharge monitoring, so as to solve the problems raised in the above background technology.

[0005] To achieve the above-mentioned objectives, the present invention provides the following technical solutions: a monitoring data analysis system for transformer partial discharge monitoring, the system comprising: a data acquisition module, a data integration and normalization module, a data storage module, a data analysis module and a risk assessment module; the output end of the data acquisition module is connected to the input end of the data storage module; the output end of the data storage module is connected to the input end of the data integration and normalization module; the output end of the data integration and normalization module is connected to the input end of the data analysis module; the output end of the data analysis module is connected to the input end of the risk assessment module; the data acquisition module collects thermal effect data and mechanical stress effect data through sensors; the data integration and normalization module is used to obtain the collected data and perform standardized processing; the data storage module is used to store all collected data; the data analysis module is used to evaluate the influence of thermal effect and mechanical stress on the degree of insulation degradation, and by establishing a coupling model of thermal effect and mechanical stress, analyze the comprehensive influence of the interaction between the two on the degree of insulation degradation; the risk assessment module is used to perform risk assessment on the power equipment to be evaluated.

[0006] Furthermore, the data acquisition module includes a thermal effect data acquisition unit and a mechanical stress data acquisition unit; the thermal effect data acquisition module is used to collect thermal effect data caused by partial discharge of the transformer, including partial discharge power and ambient temperature; the mechanical stress data acquisition unit is used to collect mechanical stress data caused by partial discharge of the transformer, including mechanical stress, crack length and fatigue life, and transmit all collected data to the data storage module.

[0007] Furthermore, the data integration and normalization module includes a data integration unit and a data standardization unit. The data integration unit obtains the collected data through a multi-channel data acquisition card; the data standardization unit applies filtering and smoothing processing techniques, including bandpass filtering, wavelet denoising and low-pass filtering, to remove noise in the thermal effect and mechanical stress signals caused by partial discharge of the transformer, and then linearly scales the processed data to the range of [0,1] through the Min-Max Normalization technology.

[0008] Furthermore, the data analysis module includes a thermal effect analysis unit, a mechanical stress analysis unit and a coupling effect analysis unit. The thermal effect analysis unit is used to analyze the influence of thermal effect on the degree of insulation degradation; the mechanical stress analysis unit is used to analyze the influence of mechanical stress on the degree of insulation degradation; the coupling effect analysis unit is used to establish a coupling model of thermal effect and mechanical stress, and analyze the comprehensive influence of the interaction between the two on the degree of insulation degradation.

[0009] Furthermore, the risk assessment module includes a risk level classification unit and a risk warning unit. The risk level classification unit is used to analyze the comprehensive impact of the interaction between thermal effect and mechanical stress on the degree of insulation degradation based on the coupling model of the two, and divide the risks into low, medium and high levels; the risk warning unit is used to monitor risks, and when medium and high level risks are detected, it prompts operation and maintenance personnel to pay attention and take measures.

[0010] A monitoring data analysis method for transformer partial discharge monitoring comprises the following steps:

[0011] S1: Collect thermal effect data and mechanical stress data caused by partial discharge of transformer;

[0012] S2: Analyze the impact of thermal effects on insulation degradation;

[0013] S3: Analyze the impact of mechanical stress on insulation degradation;

[0014] S4: Establish a coupling model of thermal effect and mechanical stress to analyze the comprehensive impact of the interaction between the two on the degree of insulation degradation;

[0015] S5: Conduct risk assessment on the electrical equipment to be assessed and implement early warning after completing the risk level classification.

[0016] Furthermore, in step S1: collecting thermal effect and mechanical stress data caused by partial discharge of the transformer, including partial discharge power data set PD total ={PD1,PD2,…,PD n}, temperature data set T={T0,T1,…,T n}, mechanical stress data set σ={σ1,σ2,…,σ n}, crack length data set a={a1,a2,…,a n}, number of fatigue cycles N={N1,N2,…,N n}, the collected data are filtered and smoothed by applying band-pass filtering, wavelet denoising and low-pass filtering to remove noise from the thermal effect and mechanical stress signals caused by partial discharge of the transformer. Then, the processed data are linearly scaled to the range of [0,1] through the Min-Max Normalization technology, eliminating the influence of unit and magnitude differences, ensuring the consistency of the input data, laying the foundation for subsequent analysis and model processing, and thus improving the accuracy of the assessment of the degree of transformer insulation degradation.

[0017] Furthermore, in step S2: based on the collected thermal effect data, the thermal effect influence parameter TE on the insulation degradation degree is calculated by the following formula:

[0018]

[0019] Where C1 is the proportional constant related to the thermal effect, PD i represents the partial discharge power of the transformer at the i-th moment, t represents the duration of partial discharge, m represents the quality of the transformer insulation material, c p Indicates the specific heat capacity of transformer insulation material, E a represents activation energy, R represents gas constant, T0 represents initial temperature of transformer, ΔT represents temperature rise, α E represents the expansion trend of the transformer insulation material with temperature changes, γ represents the nonlinear index, and the thermal effect on the transformer insulation degradation degree parameter TE is calculated by the formula, achieving the purpose of quantifying the thermal effect into specific data. The formula comprehensively considers the partial discharge factor, temperature rise and specific heat capacity of the insulation material, and introduces the nonlinear index and material expansion trend. It can capture the nonlinear behavior in the thermal effect and reflect the impact of the thermal effect on the insulation material. In step S3: based on the collected mechanical stress data, the mechanical stress on the insulation degradation degree parameter ME is calculated by the following formula:

[0020]

[0021] Where C2 represents the proportionality constant related to mechanical stress, σ i It represents the mechanical stress borne by the transformer insulation material during operation at the i-th moment, σ y It indicates the yield strength at which the transformer insulation material begins to undergo plastic deformation under the action of external force, m is the index of the effect of mechanical stress on the deterioration of the insulation material, a i represents the crack length of the transformer insulation material at the i-th moment, a0 represents the initial crack length in the transformer insulation material, n represents the index of the impact of the crack on insulation degradation, N i Indicates the number of fatigue cycles that the transformer insulation material endures at the i-th moment, N f The fatigue life of the insulating material is represented by the formula to calculate the parameter ME of the influence of mechanical stress on the degree of insulation degradation, which realizes the quantification of the influence of mechanical stress. It comprehensively considers the factors of mechanical stress, yield strength, crack length and number of fatigue cycles, and introduces the index of mechanical stress influence on degradation and the crack influence index. It can capture the nonlinear effect of mechanical stress on the degradation process of insulating material and reflect the effect of mechanical stress on the degradation of insulating material.

[0022] Furthermore, in step S4: based on the interaction between thermal effect and mechanical stress, a coupling model is established, the calculated thermal effect influence parameter TE and mechanical stress influence parameter ME are normalized, and the data are linearly scaled to the range of [0,1] to obtain the normalized thermal effect influence parameter TE' and the normalized mechanical stress influence parameter ME'. Subsequently, the influence parameter D on the degree of insulation degradation under the interaction between the two is calculated using the following formula:

[0023] D=k*TE′+k*ME′+k*(TE′*ME)

[0024] Among them, k1 and k2 represent the coefficients used for linear superposition of thermal effects and mechanical stresses, and k3 represents the coefficient of the nonlinear product. By establishing a coupling model of thermal effects and mechanical stresses, the influence parameter D of their interaction on the degree of insulation degradation is calculated, thereby realizing a comprehensive evaluation of multiple physical effects. By introducing the linear superposition coefficients k1 and k2 and the nonlinear product coefficient k3, the coupling effect between thermal effects and mechanical stresses can be modeled, and their nonlinear influence on the degradation of insulating materials can be captured.

[0025] Furthermore, in step S5: a risk assessment is performed on the power equipment to be assessed, and the risk level is divided into three categories: low, medium and high according to the influencing parameter D under the thermal effect and mechanical stress coupling model. The specific risk level classification standard is: when D is less than or equal to 0.5, it is low risk; when it is between 0.5 and 0.7, it is medium risk; when D is greater than or equal to 0.7, it is high risk. According to the risk level, corresponding measures are taken: when it is at low risk, monitor the equipment status and conduct regular inspections; when it is at medium risk, strengthen monitoring and prepare preventive maintenance; when it is at high risk, immediately take emergency measures such as load reduction and shutdown for maintenance, so as to effectively prevent failures and improve the safety and reliability of equipment operation.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] 1. Analyze the electrical signals caused by partial discharge and establish a coupling model of thermal effects and mechanical stress. Comprehensively consider the impact of these physical effects on insulation material degradation. Through multi-physics field coupling analysis, a more comprehensive and accurate insulation degradation assessment is provided, overcoming the limitations of existing methods that rely solely on single signal analysis.

[0028] 2. By calculating the influencing parameters of thermal effect and mechanical stress on the degree of insulation degradation, the specific impact of these factors on insulation performance is quantified. The comprehensive influencing parameter D is further calculated through the coupling model, realizing the quantitative analysis of these factors.

[0029] 3. Through monitoring data analysis and degradation assessment, the present invention can promptly identify potential problems with transformer insulation materials and take preventive measures such as enhanced monitoring, preventive maintenance, and emergency shutdown and overhaul according to the risk level, thereby effectively preventing the occurrence of serious insulation breakdown failures and ensuring the safe and stable operation of the transformer. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a structural diagram of a monitoring data analysis system for transformer partial discharge monitoring according to the present invention;

[0031] Figure 2 The present invention is a flow chart of a monitoring data analysis method for transformer partial discharge monitoring. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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 making creative efforts are within the scope of protection of the present invention.

[0033] See also Figure 1-Figure 2 As shown, the present invention provides a technical solution: a monitoring data analysis system for transformer partial discharge monitoring, the system comprising: a data acquisition module, a data integration and normalization module, a data storage module, a data analysis module and a risk assessment module; the output end of the data acquisition module is connected to the input end of the data storage module; the output end of the data storage module is connected to the input end of the data integration and normalization module; the output end of the data integration and normalization module is connected to the input end of the data analysis module; the output end of the data analysis module is connected to the input end of the risk assessment module; the data acquisition module collects thermal effect data and mechanical stress effect data through sensors; the data integration and normalization module is used to obtain the collected data and perform standardized processing; the data storage module is used to store all collected data; the data analysis module is used to evaluate the influence of thermal effect and mechanical stress on the degree of insulation degradation, and by establishing a coupling model of thermal effect and mechanical stress, analyze the comprehensive influence of the interaction between the two on the degree of insulation degradation; the risk assessment module is used to perform risk assessment on the power equipment to be evaluated.

[0034] The data acquisition module includes a thermal effect data acquisition unit and a mechanical stress data acquisition unit; the thermal effect data acquisition module is used to collect thermal effect data caused by partial discharge of the transformer, including partial discharge power and ambient temperature; the mechanical stress data acquisition unit is used to collect mechanical stress data caused by partial discharge of the transformer, including mechanical stress, crack length and fatigue life, and transmit all collected data to the data storage module.

[0035] The data integration and normalization module includes a data integration unit and a data normalization unit. The data integration unit obtains collected data through a multi-channel data acquisition card; the data normalization unit applies filtering and smoothing processing techniques, including bandpass filtering, wavelet denoising, and low-pass filtering, to remove noise from the thermal effect and mechanical stress signals caused by partial discharge of the transformer. Then, the processed data is linearly scaled to the range of [0,1] through the Min-MaxNormalization technology.

[0036] The data analysis module includes a thermal effect analysis unit, a mechanical stress analysis unit and a coupling effect analysis unit. The thermal effect analysis unit is used to analyze the influence of thermal effect on the degree of insulation degradation; the mechanical stress analysis unit is used to analyze the influence of mechanical stress on the degree of insulation degradation; the coupling effect analysis unit is used to establish a coupling model of thermal effect and mechanical stress, and analyze the comprehensive influence of the interaction between the two on the degree of insulation degradation.

[0037] The risk assessment module includes a risk level classification unit and a risk warning unit. The risk level classification unit is used to analyze the comprehensive impact of the interaction between thermal effect and mechanical stress on the degree of insulation degradation based on the coupling model of the two, and divide the risks into low, medium and high levels; the risk warning unit is used to monitor risks. When medium and high level risks are detected, it prompts operation and maintenance personnel to pay attention and take measures.

[0038] A monitoring data analysis method for transformer partial discharge monitoring comprises the following steps:

[0039] S1: Collect thermal effect data and mechanical stress data caused by partial discharge of transformer;

[0040] S2: Analyze the impact of thermal effects on insulation degradation;

[0041] S3: Analyze the impact of mechanical stress on insulation degradation;

[0042] S4: Establish a coupling model of thermal effect and mechanical stress to analyze the comprehensive impact of the interaction between the two on the degree of insulation degradation;

[0043] S5: Conduct risk assessment on the electrical equipment to be assessed and implement early warning after completing the risk level classification.

[0044] In step S1: collecting thermal effect and mechanical stress data caused by partial discharge of transformer, including partial discharge power data set PD total ={PD1,PD2,…,PD n}, temperature data set T={T0,T1,…,T n}, mechanical stress data set σ={σ1,σ2,…,σ n}, crack length data set a={a1,a2,…,a n}, fatigue cycle number data set N={N1,N2,…,N n}, the collected data are filtered and smoothed by applying band-pass filtering, wavelet denoising and low-pass filtering to remove noise from the thermal effect and mechanical stress signals caused by partial discharge of the transformer. Then, the processed data are linearly scaled to the range of [0,1] through the Min-Max Normalization technology, eliminating the influence of unit and magnitude differences, ensuring the consistency of the input data, laying the foundation for subsequent analysis and model processing, and thus improving the accuracy of the assessment of the degree of transformer insulation degradation.

[0045] In step S2: Based on the collected thermal effect data, the thermal effect influence parameter TE on the insulation degradation degree is calculated by the following formula:

[0046]

[0047] Where C1 is the proportional constant related to the thermal effect, PD i represents the partial discharge power of the transformer at the i-th moment, t represents the duration of partial discharge, m represents the quality of the transformer insulation material, c p Indicates the specific heat capacity of transformer insulation material, E a represents activation energy, R represents gas constant, T0 represents initial temperature of transformer, ΔT represents temperature rise, α E represents the expansion trend of the transformer insulation material with temperature changes, γ represents the nonlinear index, and the thermal effect on the transformer insulation degradation degree parameter TE is calculated by the formula, achieving the purpose of quantifying the thermal effect into specific data. The formula comprehensively considers the partial discharge factor, temperature rise and specific heat capacity of the insulation material, and introduces the nonlinear index and material expansion trend. It can capture the nonlinear behavior in the thermal effect and reflect the impact of the thermal effect on the insulation material. In step S3: based on the collected mechanical stress data, the mechanical stress on the insulation degradation degree parameter ME is calculated by the following formula:

[0048]

[0049] Where C2 represents the proportional constant related to mechanical stress, σi represents the mechanical stress borne by the transformer insulation material during operation at the i-th moment, σy represents the yield strength at which the transformer insulation material begins to undergo plastic deformation under the action of external force, m represents the index of the influence of mechanical stress on the deterioration of the insulation material, and a i represents the crack length of the transformer insulation material at the i-th moment, a0 represents the initial crack length in the transformer insulation material, n represents the index of the impact of the crack on insulation degradation, N i Indicates the number of fatigue cycles that the transformer insulation material endures at the i-th moment, N f The fatigue life of the insulating material is represented by the formula to calculate the parameter ME of the influence of mechanical stress on the degree of insulation degradation, which realizes the quantification of the influence of mechanical stress. It comprehensively considers the factors of mechanical stress, yield strength, crack length and number of fatigue cycles, and introduces the index of mechanical stress influence on degradation and the crack influence index. It can capture the nonlinear effect of mechanical stress on the degradation process of insulating material and reflect the effect of mechanical stress on the degradation of insulating material.

[0050] In step S4: Based on the interaction between thermal effect and mechanical stress, a coupling model is established. The calculated thermal effect influence parameter TE and mechanical stress influence parameter ME are normalized, and the data are linearly scaled to the range of [0, 1] to obtain the normalized thermal effect influence parameter TE' and the normalized mechanical stress influence parameter ME'. Subsequently, the influence parameter D on the degree of insulation degradation under the interaction between the two is calculated using the following formula:

[0051] D=k*TE′+k*ME′+k3*(TE′*ME)

[0052] Among them, k1 and k2 represent the coefficients used for linear superposition of thermal effects and mechanical stresses, and k3 represents the coefficient of the nonlinear product. By establishing a coupling model of thermal effects and mechanical stresses, the influence parameter D of their interaction on the degree of insulation degradation is calculated, thereby realizing a comprehensive evaluation of multiple physical effects. By introducing the linear superposition coefficients k1 and k2 and the nonlinear product coefficient k3, the coupling effect between thermal effects and mechanical stresses can be modeled, and their nonlinear influence on the degradation of insulating materials can be captured.

[0053] In step S5: perform risk assessment on the power equipment to be assessed, and divide the risk level into three categories: low, medium and high according to the influencing parameter D under the thermal effect and mechanical stress coupling model. The specific risk level classification standard is: when D is less than or equal to 0.5, it is low risk; when it is between 0.5 and 0.7, it is medium risk; when D is greater than or equal to 0.7, it is high risk. According to the risk level, take corresponding measures: when it is at low risk, monitor the equipment status and conduct regular inspections; when it is at medium risk, strengthen monitoring and prepare preventive maintenance; when it is at high risk, immediately take emergency measures such as load reduction and shutdown for maintenance, so as to effectively prevent failures and improve the safety and reliability of equipment operation.

[0054] In an embodiment, the thermal effect and mechanical stress data caused by partial discharge of the transformer are collected, including the partial discharge power data set PD total ={2.3, 5.6, 7.8, 10.4, 14.9}, temperature data set T = {30, 50, 65, 75, 82}, mechanical stress data set σ = {80, 150, 220, 290, 370}, crack length data set a = {0.002, 0.004, 0.0065, 0.011, 0.017}, fatigue cycle number N = {300000, 1000000, 2500000, 6000000, 95000000}, partial discharge power PD at time i i =7.8, temperature T i =65℃, mechanical stress σ i =220, crack length a i = 0.0065 and fatigue cycle number N i =2500000, and the normalized partial discharge power is 0.4365, the temperature rise is 0.6731, the mechanical stress is 0.4828, the crack length is 0.3, and the number of fatigue cycles is 0.2391. The proportional constant C1 related to the thermal effect is set to 0.5 and the nonlinear index γ is set to 2. The initial temperature of the transformer is T0 = 30℃, the material mass is m = 10kg, the duration is t = 3600 seconds, and the material specific heat capacity is cp = 2. 3J / g℃, activation energy Ea=0.8eV, gas constant R=8.314J / (mol.k) and thermal expansion coefficient αE=0.0004 / ℃ are substituted, and the influence parameter of thermal effect on insulation degradation degree TE≈68.32 is calculated. The proportional constant C2=0.4, stress influence index m=3 and crack influence index n=2 related to mechanical stress are set. By calculating the initial crack length a0=0.002m and the yield strength σ y =250MPa and fatigue life N f= 2,000,000 times, and the influence parameter of mechanical stress on the degree of insulation degradation is calculated to be ME≈0.000051. TE and ME at other times are calculated and normalized, and the normalized TE'=0.827 and ME'=0.0202 are obtained. The linear superposition coefficients k1=0.5 and k2=0.7, and the nonlinear product coefficient k3=0.3 are set. Based on the interaction between thermal effect and mechanical stress, a coupling model is established. By substituting TE' and ME', the influence parameter D=0.4229 on the degree of insulation degradation under the interaction between the two is calculated. At this time, D falls into the low risk range D≤0.5. The operating status of the transformer will continue to be monitored, and regular inspections will be arranged to ensure the normal operation of the equipment.

[0055] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. A monitoring data analysis method for transformer partial discharge monitoring, comprising the following steps: S1: Collect thermal effect data and mechanical stress data caused by partial discharge of transformer; S2: Analyze the impact of thermal effects on insulation degradation; In step S2: Based on the collected thermal effect data, the thermal effect influence parameter TE on the insulation degradation degree is calculated by the following formula: ； Where C1 is the proportional constant related to the thermal effect, PD i represents the partial discharge power of the transformer at the i-th moment, t represents the duration of partial discharge, m represents the quality of the transformer insulation material, c p represents the specific heat capacity of the transformer insulation material, Ea represents the activation energy, R represents the gas constant, T0 represents the initial temperature of the transformer, ΔT represents the temperature rise, α E It represents the expansion trend of transformer insulation material with temperature change, and γ represents the nonlinear index; S3: Analyze the impact of mechanical stress on insulation degradation; In step S3: Based on the collected mechanical stress data, the influence parameter ME of the mechanical stress on the insulation degradation degree is calculated by the following formula: ； Where C2 represents the proportionality constant related to mechanical stress, σ i It represents the mechanical stress borne by the transformer insulation material during operation at the i-th moment, σ y It indicates the yield strength at which the transformer insulation material begins to undergo plastic deformation under the action of external force, m is the index of the effect of mechanical stress on the deterioration of the insulation material, a i represents the crack length of the transformer insulation material at the i-th moment, a0 represents the initial crack length in the transformer insulation material, n represents the index of the impact of the crack on insulation degradation, N i Indicates the number of fatigue cycles that the transformer insulation material endures at the i-th moment, N f Indicates the fatigue life of the insulation material; S4: Establish a coupling model of thermal effect and mechanical stress to analyze the comprehensive impact of the interaction between the two on the degree of insulation degradation; S5: Conduct risk assessment on the electrical equipment to be assessed and implement early warning after completing the risk level classification.

2. The monitoring data analysis method for transformer partial discharge monitoring according to claim 1, characterized in that: In step S1: collecting thermal effect and mechanical stress data caused by partial discharge of transformer, including partial discharge power data set PD total ={PD1,PD2,…,PD n }, temperature data set T={T0,T1,…,T n }, the mechanical stress data set σ={σ1,σ2,…,σ n }, crack length data set a={a1,a2,…,a n }, fatigue cycle number data set N={N1,N2,…,N n }, filtering and smoothing techniques, including band-pass filtering, wavelet denoising and low-pass filtering, are applied to the collected data to remove noise from the thermal effect and mechanical stress signals caused by partial discharge of the transformer. Then, the processed data are linearly scaled to the range of [0,1] using the Min-Max Normalization technique.

3. The monitoring data analysis method for transformer partial discharge monitoring according to claim 2, characterized in that: In step S4: Based on the interaction between thermal effect and mechanical stress, a coupling model is established. The calculated thermal effect influence parameter TE and mechanical stress influence parameter ME are normalized, and the data are linearly scaled to the range of [0, 1] to obtain the normalized thermal effect influence parameter TE' and the normalized mechanical stress influence parameter ME'. Subsequently, the influence parameter D on the degree of insulation degradation under the interaction between the two is calculated using the following formula: ； Among them, k1 and k2 are coefficients for linear superposition of thermal effect and mechanical stress, and k3 is the coefficient for nonlinear product.

4. The monitoring data analysis method for transformer partial discharge monitoring according to claim 3, characterized in that: In step S5: perform risk assessment on the power equipment to be assessed, and divide the risk level into three categories: low, medium and high according to the influencing parameter D under the thermal effect and mechanical stress coupling model. The specific risk level classification standard is: when D is less than or equal to 0.5, it is low risk; when it is between 0.5 and 0.7, it is medium risk; when D is greater than or equal to 0.7, it is high risk. According to the risk level, take corresponding measures: when it is at low risk, monitor the equipment status and conduct regular inspections; when it is at medium risk, strengthen monitoring and prepare for preventive maintenance; when it is at high risk, immediately take emergency measures such as load reduction and shutdown for maintenance.

5. A monitoring data analysis system for transformer partial discharge monitoring, applying a monitoring data analysis method for transformer partial discharge monitoring according to any one of claims 1 to 4, characterized in that: The system includes: a data acquisition module, a data integration and normalization module, a data storage module, a data analysis module and a risk assessment module; the output end of the data acquisition module is connected to the input end of the data storage module; the output end of the data storage module is connected to the input end of the data integration and normalization module; the output end of the data integration and normalization module is connected to the input end of the data analysis module; the output end of the data analysis module is connected to the input end of the risk assessment module; the data acquisition module collects thermal effect data and mechanical stress effect data through sensors; the data integration and normalization module is used to obtain the collected data and perform standardization processing; the data storage module is used to store all collected data; the data analysis module is used to evaluate the influence of thermal effect and mechanical stress on the degree of insulation degradation, and by establishing a coupling model of thermal effect and mechanical stress, analyze the comprehensive influence of the interaction between the two on the degree of insulation degradation; the risk assessment module is used to perform risk assessment on the power equipment to be evaluated.

6. A monitoring data analysis system for transformer partial discharge monitoring according to claim 5, characterized in that: The data acquisition module includes a thermal effect data acquisition unit and a mechanical stress data acquisition unit; the thermal effect data acquisition module is used to collect thermal effect data caused by partial discharge of the transformer, including partial discharge power and ambient temperature; the mechanical stress data acquisition unit is used to collect mechanical stress data caused by partial discharge of the transformer, including mechanical stress, crack length and fatigue life, and transmit all collected data to the data storage module.

7. A monitoring data analysis system for transformer partial discharge monitoring according to claim 6, characterized in that: The data integration and normalization module includes a data integration unit and a data normalization unit. The data integration unit obtains collected data through a multi-channel data acquisition card. The data normalization unit applies filtering and smoothing processing techniques, including bandpass filtering, wavelet denoising, and low-pass filtering, to remove noise from the thermal effect and mechanical stress signals caused by partial discharge of the transformer. Then, the processed data is linearly scaled to the range of [0, 1] through the Min-Max Normalization technique.

8. The monitoring data analysis system for transformer partial discharge monitoring according to claim 7, characterized in that: The data analysis module includes a thermal effect analysis unit, a mechanical stress analysis unit and a coupling effect analysis unit. The thermal effect analysis unit is used to analyze the influence of thermal effect on the degree of insulation degradation; the mechanical stress analysis unit is used to analyze the influence of mechanical stress on the degree of insulation degradation; and the coupling effect analysis unit is used to establish a coupling model of thermal effect and mechanical stress to analyze the comprehensive influence of the interaction between the two on the degree of insulation degradation.

9. A monitoring data analysis system for transformer partial discharge monitoring according to claim 8, characterized in that: The risk assessment module includes a risk level classification unit and a risk warning unit. The risk level classification unit is used to analyze the comprehensive impact of the interaction between thermal effect and mechanical stress on the degree of insulation degradation based on the coupling model of the two, and classify the risk into low, medium and high levels; The risk warning unit is used to monitor risks and, when medium or high-level risks are detected, prompts operation and maintenance personnel to pay attention and take measures.

Images