Monitoring data analysis system and method for transformer partial discharge monitoring

Abstract

The invention discloses a monitoring data analysis system and method for partial discharge monitoring of a transformer, and belongs to the technical field of state monitoring and fault diagnosis of power equipment. The system comprises a data acquisition module, a data integration and standardization module, a data storage module, a data analysis module and a risk assessment module. The data acquisition module acquires heat effect data and mechanical stress effect data through a sensor; the data integration and standardization module is used for acquiring the acquired data and performing standardization processing; the data storage module is used for storing all the collected data; the data analysis module is used for evaluating the influence of the thermal effect and the mechanical stress on the insulation degradation degree; according to the invention, the thermal effect and the mechanical stress effect caused by partial discharge are analyzed, so that the degradation degree of the insulating material is evaluated.

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 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, which will eventually lead 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. Secondly, 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 to solve the problems raised in the above background technology.

[0005] To achieve the above-mentioned purpose, 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 all collected data are transmitted 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 the noise in the thermal effect and mechanical stress signal 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 influence of thermal effects on the degree of insulation degradation;

[0013] S3: Analyze the influence 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 power equipment to be assessed and implement early warning after completing the risk level classification.

[0016] Further, 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 ={PD 1 ,PD 2 ,…,PD n}, temperature data set T = {T 0 ,T 1 ,…,T n}, mechanical stress data set σ={σ 1 ,σ 2 ,…,σ n}, crack length data set a={a 1 ,a 2 ,…,a n}, fatigue cycle number N = {N 1 ,N 2 ,…,N n}, the collected data are filtered and smoothed by applying filtering and smoothing techniques, including bandpass filtering, wavelet denoising and low-pass filtering, to remove the noise in the thermal effect and mechanical stress signal 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 technique, eliminating the influence of unit and magnitude differences and ensuring the consistency of the input data, laying the foundation for subsequent analysis and model processing, thereby improving the accuracy of the assessment of the degree of transformer insulation degradation.

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

[0018]

[0019] Among them, C 1 represents the proportionality constant associated with 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, T 0 represents the initial temperature of the transformer, ΔT represents the temperature rise, α E represents the expansion trend of transformer insulation material with temperature change, γ represents the nonlinear index, and the influence parameter TE of thermal effect on transformer insulation degradation degree is calculated by formula, which realizes the purpose of quantifying the influence of thermal effect into specific data. The formula comprehensively considers the local discharge factor, temperature rise and specific heat capacity of insulation material, and introduces nonlinear index and material expansion trend, which can capture the nonlinear behavior in thermal effect and reflect the influence of thermal effect on insulation material. In step S3: based on the collected mechanical stress data, the influence parameter ME of mechanical stress on insulation degradation degree is calculated by the following formula:

[0020]

[0021] Among them, C 2 represents the proportionality constant related to mechanical stress, σ i 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 indicates the index of the influence 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, a 0It represents the initial crack length in the transformer insulation material, n represents the index of the effect of crack on insulation degradation, N i It represents the number of fatigue cycles that the transformer insulation material is subjected to at the i-th moment, N f It represents the fatigue life of the insulating material. The influence parameter ME of mechanical stress on the insulation degradation degree is calculated through the formula, which realizes the quantification of the influence of mechanical stress. It comprehensively considers the factors of mechanical stress, yield strength, crack length and fatigue cycle number, and introduces the index of mechanical stress 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] Further, 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, the data are linearly scaled to the range of [0,1], and the normalized thermal effect influence parameter TE' and the normalized mechanical stress influence parameter ME' are obtained. Subsequently, the influence parameter D on the degree of insulation degradation under the interaction between the two is calculated by the following formula:

[0023] D=k*TE′+k*ME′+k*(TE′*ME)

[0024] Among them, k 1 and k 2 represents the coefficient for linear superposition of thermal effects and mechanical stress, k 3 The coefficient representing the nonlinear product is established by building a coupling model of thermal effect and mechanical stress, and calculating the influence parameter D of the interaction between the two on the insulation degradation degree, thus realizing a comprehensive evaluation of multiple physical effects. 1 and k 2 and the nonlinear product coefficient k 3 , which is able to model the coupling effects between thermal effects and mechanical stresses and capture their nonlinear impact on the degradation of insulation materials.

[0025] Further, 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 for 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 beneficial effects achieved by the present invention are:

[0027] 1. Analyze the electrical signals caused by partial discharge, establish a coupling model of thermal effect and mechanical stress, comprehensively consider the role of these physical effects on the degradation of insulation materials, and provide a more comprehensive and accurate insulation degradation assessment through the coupling analysis of multiple physical fields, overcoming the limitation of existing methods that only rely 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, and the comprehensive influencing parameter D is further calculated through the coupling model to achieve quantitative analysis of these factors.

[0029] 3. Through monitoring data analysis and degradation assessment, the present invention can timely identify potential problems of 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 It 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 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 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 creative work are within the scope of protection of the present invention.

[0033] See also Figure 1-Figure 2As 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 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 the noise in the thermal effect and mechanical stress signal caused by partial discharge of the transformer, and then linearly scales the processed data 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, and when medium and high level risks are detected, it prompts the 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 influence of thermal effects on the degree of insulation degradation;

[0041] S3: Analyze the influence 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 power 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 ={PD 1 ,PD 2 ,…,PD n}, temperature data set T = {T 0 ,T 1 ,…,T n}, mechanical stress data set σ={σ 1 ,σ 2 ,…,σ n}, crack length data set a={a 1 ,a 2 ,…,a n}, fatigue cycle number data set N = {N 1 ,N 2 ,…,N n}, the collected data are filtered and smoothed by applying filtering and smoothing techniques, including bandpass filtering, wavelet denoising and low-pass filtering, to remove the noise in the thermal effect and mechanical stress signal 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 technique, eliminating the influence of unit and magnitude differences and ensuring the consistency of the input data, laying the foundation for subsequent analysis and model processing, thereby 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 influence parameter TE of the thermal effect on the insulation degradation degree is calculated by the following formula:

[0046]

[0047] Among them, C 1 represents the proportionality constant associated with 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, T 0 represents the initial temperature of the transformer, ΔT represents the temperature rise, α E represents the expansion trend of transformer insulation material with temperature change, γ represents the nonlinear index, and the influence parameter TE of thermal effect on transformer insulation degradation degree is calculated by formula, which realizes the purpose of quantifying the influence of thermal effect into specific data. The formula comprehensively considers the local discharge factor, temperature rise and specific heat capacity of insulation material, and introduces nonlinear index and material expansion trend, which can capture the nonlinear behavior in thermal effect and reflect the influence of thermal effect on insulation material. In step S3: based on the collected mechanical stress data, the influence parameter ME of mechanical stress on insulation degradation degree is calculated by the following formula:

[0048]

[0049] Among them, C 2 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, a 0 It represents the initial crack length in the transformer insulation material, n represents the index of the effect of crack on insulation degradation, N iIt represents the number of fatigue cycles that the transformer insulation material is subjected to at the i-th moment, N f It represents the fatigue life of the insulating material. The influence parameter ME of mechanical stress on the insulation degradation degree is calculated through the formula, which realizes the quantification of the influence of mechanical stress. It comprehensively considers the factors of mechanical stress, yield strength, crack length and fatigue cycle number, and introduces the index of mechanical stress 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, the data are linearly scaled to the range of [0,1], and the normalized thermal effect influence parameter TE' and the normalized mechanical stress influence parameter ME' are obtained. Subsequently, the influence parameter D on the degree of insulation degradation under the interaction between the two is calculated by the following formula:

[0051] D=k*TE′+k*ME′+k3*(TE′*ME)

[0052] Among them, k 1 and k 2 represents the coefficient for linear superposition of thermal effects and mechanical stress, k 3 The coefficient representing the nonlinear product is established by building a coupling model of thermal effect and mechanical stress, and calculating the influence parameter D of the interaction between the two on the insulation degradation degree, thus realizing a comprehensive evaluation of multiple physical effects. 1 and k 2 and the nonlinear product coefficient k 3 , which is able to model the coupling effects between thermal effects and mechanical stresses and capture their nonlinear impact on the degradation of insulation materials.

[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: collecting thermal effect and mechanical stress data caused by partial discharge of transformer, including 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 stressσ i =220, crack length a i = 0.0065 and fatigue cycle number N i = 2500000, 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 fatigue cycle number is 0.2391, and the proportional constant C related to the thermal effect is set. 1 = 0.5 and nonlinear index γ = 2, by the initial transformer temperature T 0 =30℃, material mass m=10kg, duration t=3600s, material specific heat 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 into the calculation, and the influence parameter of thermal effect on insulation degradation degree TE≈68.32 is calculated, and the proportional constant C related to mechanical stress is set. 2 = 0.4, stress influence index m = 3 and crack influence index n = 2, by calculating the initial crack length a in the transformer insulation material 0 =0.002m, data material yield strength σ y =250MPa and fatigue life N f = 2000000 times, the influence parameter of mechanical stress on insulation degradation degree ME≈0.000051 is calculated, TE and ME at other times are calculated, and normalized, and the normalized TE'=0.827 and ME'=0.0202 are obtained, and the linear superposition coefficient k is set 1 =0.5, k 2 =0.7, nonlinear product coefficient k 3 =0.3, based on the interaction between thermal effect and mechanical stress, a coupling model is established, and the influencing parameter D on the degree of insulation degradation under the interaction between the two is calculated by substituting TE' and ME'. At this time, D falls into the low risk interval D≤0.5. The operating status of the transformer continues to be monitored, and regular inspections are arranged to ensure that the equipment remains in normal operation.

[0055] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A monitoring data analysis system for transformer partial discharge monitoring, 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 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.

2. A monitoring data analysis system for transformer partial discharge monitoring according to claim 1, 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.

3. The monitoring data analysis system for transformer partial discharge monitoring according to claim 1, characterized in that: 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 the noise in the thermal effect and mechanical stress signal 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.

4. A monitoring data analysis system for transformer partial discharge monitoring according to claim 1, 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; 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.

5. The monitoring data analysis system for transformer partial discharge monitoring according to claim 1, characterized in that: The risk assessment module includes a risk level division unit and a risk warning unit. The risk level division unit is used to analyze the comprehensive impact of the interaction between thermal effect and mechanical stress on the degree of insulation degradation according to the coupling model of the thermal effect and mechanical stress, and divide 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, it prompts operation and maintenance personnel to pay attention and take measures.

6. 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 influence of thermal effects on the degree of insulation degradation; S3: Analyze the influence of mechanical stress on insulation degradation; 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 power equipment to be assessed and implement early warning after completing the risk level classification.

7. The monitoring data analysis method for transformer partial discharge monitoring according to claim 6 is 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 }, 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 bandpass filtering, wavelet denoising and low-pass filtering, are applied to the collected data to remove the noise in 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] by the Min-MaxNormalization technique.

8. The monitoring data analysis method for transformer partial discharge monitoring according to claim 6, characterized in that: In step S2: based on the collected thermal effect data, the influence parameter TE of the thermal effect 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 represents the expansion trend of transformer insulation material with temperature change, γ represents the nonlinear index, and in step S3: based on the collected mechanical stress data, the influence parameter ME of mechanical stress on insulation degradation degree is calculated by the following formula: Among them, 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 crack's influence on insulation degradation, N i It represents the number of fatigue cycles that the transformer insulation material is subjected to at the i-th moment, N f Indicates the fatigue life of the insulating material.

9. The monitoring data analysis method for transformer partial discharge monitoring according to claim 8, 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, the data are linearly scaled to the range of [0,1], and the normalized thermal effect influence parameter TE' and the normalized mechanical stress influence parameter ME' are obtained. Subsequently, the influence parameter D on the degree of insulation degradation under the interaction between the two is calculated by the following formula: D=k1*TE′+k2*ME′+k3*(TE′*ME) Among them, k1 and k2 represent the coefficients for linear superposition of thermal effect and mechanical stress, and k3 represents the coefficient of nonlinear product.

10. The monitoring data analysis method for transformer partial discharge monitoring according to claim 6, 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.

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