Electrical equipment life evaluation method and system based on Weibull distribution

Abstract

The invention discloses an electrical equipment life evaluation method and system based on Weibull distribution, and the method comprises the steps: obtaining a multi-source operation data set of electrical equipment, the multi-source operation data set comprises the temperature, the vibration amplitude, the current harmonic distortion rate, the local discharge amount and the environment temperature; calculating scale parameters of Weibull distribution based on the temperature, the vibration amplitude and the current harmonic distortion rate, and calculating shape parameters of Weibull distribution based on the partial discharge amount and the environment temperature; calculating residual life and setting a failure probability threshold value based on the scale parameter and the shape parameter, calculating first residual life, and calculating second residual life according to the operating temperature of the equipment; the final residual life is calculated according to the first residual life and the second residual life, when the final residual life is lower than a preset threshold value, an equipment overhaul early warning instruction is generated, various operation parameters and service life of the equipment are comprehensively considered, the residual life of the electrical equipment can be accurately evaluated, and a scientific basis is provided for preventive maintenance of the equipment.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of electrical equipment life evaluation, and relates to an electrical equipment life evaluation method and system based on Weibull distribution. BACKGROUND

[0002] With the continuous upgrading of the power system towards high reliability and intelligence, the life evaluation and state monitoring of electrical equipment such as transformers, switch cabinets and high-voltage motors have become the core demand to ensure the safe operation of the power grid. However, there are problems as follows: Traditional methods excessively rely on a single data source, such as only relying on the running time of the equipment, a certain specific parameter or limited historical maintenance records for evaluation. However, in the actual operation process of electrical equipment, the degradation process is influenced by the interaction of multiple complex factors, including but not limited to fluctuations in operating load, changes in environmental temperature and humidity, differences in equipment manufacturing processes and small damages accumulated during long-term operation. A single data source cannot comprehensively cover these key factors affecting equipment degradation, making it difficult to accurately and completely reflect the degradation characteristics of the equipment, resulting in a large deviation between the life evaluation results and the actual condition of the equipment, and unable to provide reliable basis for the maintenance decision of the equipment; Accurate remaining life prediction is crucial for reasonable arrangement of equipment maintenance plan, avoidance of power outage accidents caused by sudden failures and optimization of equipment replacement strategy. However, at present, due to the lack of deep understanding of the degradation mechanism of the equipment, the limitations of data collection and analysis technology and the imperfection of the prediction model, the remaining life prediction result often has a large error. This not only increases the uncertainty of the power grid operation, but also may cause waste of resources due to premature replacement of the equipment, or cause serious failure due to failure to replace in time, which brings great hidden danger to the safe operation of the power grid. SUMMARY

[0003] In order to solve the above problems existing in the prior art, the application provides an electrical equipment life evaluation method and system based on Weibull distribution.

[0004] In order to achieve the above purpose, the application adopts the following technical solutions: The application provides a Weibull distribution-based electrical equipment life evaluation method, including the following steps: obtaining a multi-source operation data set of the electrical equipment, wherein the multi-source operation data set includes temperature, vibration amplitude, current harmonic distortion rate, partial discharge amount and environmental temperature; calculating a scale parameter of the Weibull distribution based on the temperature, vibration amplitude and current harmonic distortion rate, and calculating a shape parameter of the Weibull distribution based on the partial discharge amount and environmental temperature; calculating a residual life and setting a failure probability threshold based on the scale parameter and shape parameter, calculating a first residual life, and calculating a second residual life according to the operation temperature of the equipment; calculating a final residual life according to the first residual life and the second residual life, and generating an equipment maintenance warning instruction when the final residual life is lower than a preset threshold.

[0005] Further, the scale parameter of the Weibull distribution is calculated based on the data including temperature, vibration amplitude and current harmonic distortion rate, including:

[0006] wherein, is the scale parameter of the Weibull distribution, is the temperature, is the vibration amplitude, is the current harmonic distortion rate, is the weight of the temperature on the scale parameter of the Weibull distribution, is the weight of the vibration amplitude on the scale parameter of the Weibull distribution, is the weight of the current harmonic distortion rate on the scale parameter of the Weibull distribution, is a first constant term.

[0007] Further, the shape parameter of the Weibull distribution is calculated based on the partial discharge amount and environmental temperature, including:

[0008] wherein, is the shape parameter of the Weibull distribution, is the partial discharge amount, is the environmental temperature, is the influence degree of the natural logarithm of the partial discharge amount on the shape parameter of the Weibull distribution, is the influence degree of the environmental temperature on the shape parameter of the Weibull distribution, is the natural logarithm of the partial discharge amount, is a second constant term.

[0009] Further, the first residual life is: =

[0010] wherein, is the first remaining life, is the scale parameter of the Weibull distribution, To set the failure probability threshold, is the shape parameter of the Weibull distribution; The second remaining life is: =

[0011] in, is the second remaining lifespan, is the material constant, is the activation energy, is the Boltzmann constant, is temperature; The final remaining life is:

[0012] in, is the first weight coefficient, is the second weight coefficient, is the first remaining life, The second remaining lifespan.

[0013] Furthermore, the second weight coefficient Dynamic adjustment based on equipment service life:

[0014] in, The service life of the equipment.

[0015] Furthermore, the first weight coefficient Dynamic adjustment based on equipment service life:

[0016] in, The service life of the equipment, =1.

[0017] Furthermore, the temperature of the multi-source operating data set is measured using a temperature sensor, and the measurement range of the temperature sensor is -50°C. 250℃, accuracy 0.2℃; The vibration amplitude of the multi-source operation data set is measured using a vibration sensor, and the measurement range of the vibration sensor is 0 100g, frequency response 5Hz 30kHz; the current harmonic distortion rate of the multi-source operation data set is measured using a current transformer, and the measurement range of the current transformer is 0 2000A, measurement accuracy is 0.1%; the partial discharge amount of the multi-source operation data set is measured by using a partial discharge detector, and the detection frequency band of the partial discharge detector is 1 MHz 100 MHz, sensitivity 0.3 pC; the ambient temperature of the multi-source operation data set is measured by using an ambient temperature sensor, and the measurement range of the ambient temperature sensor is -20 DEG C 60 DEG C, and the precision is 0.5 DEG C.

[0018] Further, the final remaining life 0.5 years, a red early warning is generated; the final remaining life is 0.5 1 year, an orange early warning is generated; the final remaining life is 1 2 years, a yellow early warning is generated.

[0019] Further, when the red early warning is generated, equipment maintenance is immediately performed; when the orange early warning is generated, equipment maintenance is performed within 72 hours; and when the yellow early warning is generated, equipment maintenance is performed within 30 days.

[0020] The application also provides an electrical equipment life evaluation system based on Weibull distribution, comprising: an acquisition module for acquiring a multi-source operation data set of electrical equipment, wherein the multi-source operation data set comprises temperature, vibration amplitude, current harmonic distortion rate, partial discharge amount and ambient temperature; a first calculation module for calculating a scale parameter of Weibull distribution based on the temperature, vibration amplitude and current harmonic distortion rate, and calculating a shape parameter of Weibull distribution based on the partial discharge amount and ambient temperature; a second calculation module for calculating a remaining life and setting a failure probability threshold based on the scale parameter and shape parameter, calculating a first remaining life, and calculating a second remaining life according to the operation temperature of the equipment; and an early warning module for calculating a final remaining life according to the first remaining life and second remaining life, and generating an equipment maintenance early warning instruction when the final remaining life is lower than a set threshold.

[0021] Compared with the prior art, the application has the following beneficial technical effects: The application discloses a Weibull distribution-based electrical equipment life evaluation method, which breaks through the limitation of traditional life evaluation relying on a single data source by acquiring multiple-source operation data sets of electrical equipment, including temperature, vibration amplitude, current harmonic distortion rate, partial discharge amount and environmental temperature, etc.

[0022] The application discloses a Weibull distribution-based electrical equipment life evaluation method, which breaks through the limitation of traditional life evaluation relying on a single data source by acquiring multiple-source operation data sets of electrical equipment, including temperature, vibration amplitude, current harmonic distortion rate, partial discharge amount and environmental temperature, etc. 、 、 The application discloses a Weibull distribution-based electrical equipment life evaluation method, which breaks through the limitation of traditional life evaluation relying on a single data source by acquiring multiple-source operation data sets of electrical equipment, including temperature, vibration amplitude, current harmonic distortion rate, partial discharge amount and environmental temperature, etc.

[0023] The application discloses a Weibull distribution-based electrical equipment life evaluation method, which breaks through the limitation of traditional life evaluation relying on a single data source by acquiring multiple-source operation data sets of electrical equipment, including temperature, vibration amplitude, current harmonic distortion rate, partial discharge amount and environmental temperature, etc. The application discloses a Weibull distribution-based electrical equipment life evaluation method, which breaks through the limitation of traditional life evaluation relying on a single data source by acquiring multiple-source operation data sets of electrical equipment, including temperature, vibration amplitude, current harmonic distortion rate, partial discharge amount and environmental temperature, etc. The application discloses a Weibull distribution-based electrical equipment life evaluation method, which breaks through the limitation of traditional life evaluation relying on a single data source by acquiring multiple-source operation data sets of electrical equipment, including temperature, vibration amplitude, current harmonic distortion rate, partial discharge amount and environmental temperature, etc. The application discloses a Weibull distribution-based electrical equipment life evaluation method, which breaks through the limitation of traditional life evaluation relying on a single data source by acquiring multiple-source operation data sets of electrical equipment, including temperature, vibration amplitude, current harmonic distortion rate, partial discharge amount and environmental temperature, etc. The application discloses a Weibull distribution-based electrical equipment life evaluation method, which breaks through the limitation of traditional life evaluation relying on a single data source by acquiring multiple-source operation data sets of electrical equipment, including temperature, vibration amplitude, current harmonic distortion rate, partial discharge amount and environmental temperature, etc.

[0024] The application discloses a Weibull distribution-based electrical equipment life evaluation method, which breaks through the limitation of traditional life evaluation relying on a single data source by acquiring multiple-source operation data sets of electrical equipment, including temperature, vibration amplitude, current harmonic distortion rate, partial discharge amount and environmental temperature, etc. The application discloses a Weibull distribution-based electrical equipment life evaluation method, which breaks through the limitation of traditional life evaluation relying on a single data source by acquiring multiple-source operation data sets of electrical equipment, including temperature, vibration amplitude, current harmonic distortion rate, partial discharge amount and environmental temperature, etc. The application discloses a Weibull distribution-based electrical equipment life evaluation method, which breaks through the limitation of traditional life evaluation relying on a single data source by acquiring multiple-source operation data sets of electrical equipment, including temperature, vibration amplitude, current harmonic distortion rate, partial discharge amount and environmental temperature, etc.

[0025] The application discloses a Weibull distribution-based electrical equipment life evaluation method, which breaks through the limitation of traditional life evaluation relying on a single data source by acquiring multiple-source operation data sets of electrical equipment, including temperature, vibration amplitude, current harmonic distortion rate, partial discharge amount and environmental temperature, etc. The application discloses a Weibull distribution-based electrical equipment life evaluation method, which breaks through the limitation of traditional life evaluation relying on a single data source by acquiring multiple-source operation data sets of electrical equipment, including temperature, vibration amplitude, current harmonic distortion rate, partial discharge amount and environmental temperature, etc. Dynamic adjustment based on the service life of the equipment avoids the assessment deviation of the traditional static weight model throughout the life cycle of the equipment, and significantly improves the accuracy of life prediction in different service stages. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a flow chart of a method for evaluating the life of electrical equipment based on Weibull distribution according to the present invention; Figure 2 This is a structural diagram of an electrical equipment life assessment system based on Weibull distribution in the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described 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 should fall within the scope of protection of the present invention.

[0028] Example 1 The present invention provides an electrical equipment life assessment method based on Weibull distribution, such as Figure 1 As shown, the method includes the following steps: obtaining a multi-source operating data set of electrical equipment, wherein the multi-source operating data set includes temperature, vibration amplitude, current harmonic distortion rate, partial discharge amount and ambient temperature; calculating the scale parameter of the Weibull distribution based on the temperature, vibration amplitude and current harmonic distortion rate, and calculating the shape parameter of the Weibull distribution based on the partial discharge amount and ambient temperature; calculating the remaining life and setting the failure probability threshold based on the scale parameter and the shape parameter, calculating the first remaining life, and calculating the second remaining life according to the operating temperature of the equipment; calculating the final remaining life according to the first remaining life and the second remaining life, and generating an equipment maintenance warning instruction when the final remaining life is lower than the preset threshold.

[0029] The measuring range of the temperature sensor is -50℃ 250℃, accuracy 0.2℃, installed in the key heating parts of the equipment, such as transformer windings, switch cabinet contacts, etc. These parts will generate a lot of heat during the operation of the equipment. The temperature change directly reflects the thermal state of the equipment and has a significant impact on the life of the equipment. The measurement range of the vibration sensor is 0 100g, frequency response 5Hz 30 kHz, installed in sensitive points of the motor bearing seat, gear box shell, etc. Vibration data can reflect the mechanical operation state of the equipment, such as whether there is mechanical failure, imbalance, etc. These problems will accelerate the wear and tear of the equipment and affect the service life of the equipment. The measurement range of the current transformer is 0 2000 A, the measurement accuracy is 0.1%, access to the main circuit of the device to monitor the current harmonic distortion rate, which reflects the influence of harmonics in the power grid on the equipment. Harmonics can cause additional losses in the equipment to increase, accelerate insulation aging, and thus affect the service life of the equipment. The detection frequency band of the partial discharge detector is 1 MHz 100 MHz, the sensitivity is 0.3 pC, installed in weak insulation parts such as cable terminals and GIS tank bodies. Partial discharge is an early sign of insulation deterioration. By detecting the amount of partial discharge, insulation problems can be detected in time and the service life of the equipment can be assessed. The measurement range of the environmental temperature sensor is -20°C 60°C, the accuracy is 0.5°C, installed in the operating environment of the equipment. The ambient temperature will affect the heat dissipation and aging speed of the equipment.

[0030] Real-time data collection through wireless sensor networks, sampling frequency set to: temperature / environmental temperature: 1 time / minute, temperature changes relatively slowly, 1 time / minute sampling frequency can meet the monitoring needs of temperature changes. Vibration amplitude: 1000 Hz (100 sampling points per cycle), vibration signal changes quickly, higher sampling frequency can accurately capture the details of the vibration signal and reflect the mechanical operation state of the equipment. Current harmonic distortion rate 1 time / second, the change of current harmonic distortion rate is relatively frequent, 1 time / second sampling frequency can reflect the influence of harmonics on the equipment in time. Partial discharge amount: 10 MHz (100 sampling points per cycle), partial discharge signal has transient and high frequency characteristics, higher sampling frequency can accurately detect partial discharge signal and assess the insulation state of the equipment.

[0031] Based on the data covering temperature, vibration amplitude, current harmonic distortion rate, the scale parameter of Weibull distribution is calculated

[0032] wherein, is the scale parameter of the Weibull distribution, is the temperature, is the vibration amplitude, is the current harmonic distortion rate, is the weight of the temperature on the scale parameter of the Weibull distribution, is the weight of the vibration amplitude on the scale parameter of the Weibull distribution, is the weight of the current harmonic distortion rate on the scale parameter of the Weibull distribution, is the first constant term.

[0033] In this embodiment, the weight of temperature on the scale parameter of Weibull distribution is 0.15, and the life is shortened by 15% for every 10℃ rise in temperature. The weight of vibration amplitude on the scale parameter of Weibull distribution is 0.25, and the mechanical fatigue is accelerated when the vibration exceeds the standard. The weight of current harmonic distortion rate on the scale parameter of Weibull distribution is 0.6, and the additional loss caused by harmonic distortion dominates the degradation. The first constant term is 30, which is the basic life constant of the equipment.

[0034] The shape parameter of Weibull distribution is calculated based on the partial discharge quantity and the environmental temperature.

[0035] wherein, is the shape parameter of Weibull distribution, is the partial discharge quantity, is the environmental temperature, is the influence degree of the natural logarithm of the partial discharge quantity on the shape parameter of Weibull distribution, is the influence degree of the environmental temperature on the shape parameter of Weibull distribution, is the natural logarithm of the partial discharge quantity, is the second constant term.

[0036] In this embodiment, the influence degree of the natural logarithm of the partial discharge quantity on the shape parameter of Weibull distribution is 0.4. The failure dispersion degree is expanded by 40% for every 10 times increase in the partial discharge quantity, which means that the larger the partial discharge quantity is, the more dispersed the time distribution of the equipment failure is, and the uncertainty of the equipment life increases. The influence degree of the environmental temperature on the shape parameter of Weibull distribution is 0.6, and the high-temperature environment accelerates the insulation aging, which will cause the performance of the insulation material to decline and accelerate the insulation aging process, thereby affecting the life distribution of the equipment. The second constant term is 10.

[0037] The remaining life and the failure probability threshold are calculated based on the scale parameter and the shape parameter, and the first remaining life is calculated. =

[0038] wherein, is the first remaining life, is the scale parameter of Weibull distribution, is the failure probability threshold, is the shape parameter of Weibull distribution.

[0039] ​​The second residual life is calculated according to the operating temperature of the equipment, and the second residual life is: =

[0040] wherein, is the second residual life, is a material constant, is an activation energy, is a Boltzmann constant, is a temperature. The material constant and the activation energy are determined by an accelerated aging test, which is an aging test on the equipment under conditions higher than the normal operating temperature of the equipment, and the material constant and the activation energy are determined by measuring the aging rate of the equipment at different temperatures.

[0041] The first weight coefficient is dynamically adjusted according to the service life of the equipment:

[0042] The second weight coefficient is dynamically adjusted according to the service life of the equipment:

[0043] wherein, is the service life of the equipment, =1, and the second weight coefficient is gradually increased as the service life Y increases, the second weight coefficient is 0.3 when the service life Y is less than 3, the second weight coefficient is 0.4 when the service life Y is equal to 6, and 0.4 is the upper limit value.

[0044] According to the size of the final residual life , the warning level is divided into red, orange and yellow three levels, when 0.5 years, a red warning is generated, indicating that the residual life of the equipment is extremely short, and there is a high risk of failure, which needs to be immediately repaired. When 0.5 1, an orange warning is generated, indicating that the residual life of the equipment is relatively short, and the risk of failure is high, which needs to be repaired within 72 hours. When 1 2, a yellow warning is generated, indicating that the residual life of the equipment is relatively short, and there is a certain risk of failure, which needs to be repaired within 30 days.

[0045] When the different levels of early warning instructions are generated, the early warning information is sent to the relevant maintenance personnel and managers in time. The maintenance personnel make corresponding maintenance plans according to the early warning level and the actual situation of the equipment, and carry out equipment maintenance work according to the plan. At the same time, the maintenance process is recorded and tracked to ensure the quality of equipment maintenance and improve the reliability and service life of the equipment.

[0046] A 220kV oil-immersed power transformer is taken as an example, the service life is 4 years, the temperature is 85℃, the vibration amplitude is 0.8g, the current harmonic distortion rate is 5%, the partial discharge quantity is 50pC, and the environmental temperature is 25℃.

[0047] The scale parameter of the Weibull distribution .

[0048] The shape parameter of the Weibull distribution .

[0049] The first residual life =

[0050] =

[0051]

[0052]

[0053]

[0054] is In the final residual life 1 2 years, a yellow early warning is generated, and equipment maintenance is carried out within 30 days.

[0055] With the increase of the service life , the second weight coefficient gradually increases, when the service life is less than 3, the second weight coefficient is 0.3, when the service life is equal to 6, the second weight coefficient is 0.4, and 0.4 is the upper limit value.

[0056] In summary, the application provides a Weibull distribution-based electrical equipment life evaluation method, which obtains a multi-source operation data set of the electrical equipment, calculates the scale parameter and shape parameter of the Weibull distribution, further calculates the remaining life of the equipment, and generates a warning instruction according to the remaining life. The remaining life of the electrical equipment is accurately evaluated by comprehensively considering various operating parameters and service life of the equipment, and a scientific basis is provided for preventive maintenance of the equipment. The feasibility and effectiveness of the method are verified through actual case analysis. In practical application, the sensor installation position, sampling frequency and parameter weight can be adjusted according to the characteristics and operating environment of different equipment, so as to further improve the accuracy of life evaluation.

[0057] Embodiment 2 The application provides a Weibull distribution-based electrical equipment life evaluation system, which comprises an acquisition module, a first calculation module, a second calculation module and a warning module, as shown in Figure 2 .

[0058] The acquisition module is used for acquiring a multi-source operation data set of the electrical equipment, and the multi-source operation data set comprises temperature, vibration amplitude, current harmonic distortion rate, partial discharge amount and environmental temperature. The first calculation module is used for calculating the scale parameter of the Weibull distribution based on the temperature, vibration amplitude and current harmonic distortion rate, and calculating the shape parameter of the Weibull distribution based on the partial discharge amount and environmental temperature. The second calculation module is used for calculating the remaining life based on the scale parameter and shape parameter, setting a failure probability threshold, calculating a first remaining life, and calculating a second remaining life according to the operating temperature of the equipment. The warning module is used for calculating a final remaining life according to the first remaining life and the second remaining life, and generating a device maintenance warning instruction when the final remaining life is lower than a set threshold.

[0059] The water turbine maintenance effect evaluation system based on the multi-source data dynamic benchmark provided by the application can realize the method steps consistent with the above method, and therefore will not be described again.

[0060] It should be noted that the terms "first", "second" and the like in the specification and claims of the application and the above drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product or device.

Claims

1. A method for evaluating the life of electrical equipment based on Weibull distribution, characterized in that: The following steps are involved: Acquire a multi-source operating data set of the electrical equipment, wherein the multi-source operating data set includes temperature, vibration amplitude, current harmonic distortion rate, partial discharge amount, and ambient temperature; The scale parameter of the Weibull distribution is calculated based on the temperature, vibration amplitude, and current harmonic distortion rate, and the shape parameter of the Weibull distribution is calculated based on the partial discharge amount and ambient temperature; Calculate the remaining life based on the scale parameter and shape parameter and set the failure probability threshold, calculate the first remaining life, and calculate the second remaining life based on the operating temperature of the equipment; The final remaining life is calculated based on the first remaining life and the second remaining life. When the final remaining life is lower than a preset threshold, an equipment maintenance warning instruction is generated.

2. The electrical equipment life assessment method based on Weibull distribution according to claim 1, characterized in that: The scale parameters of the Weibull distribution are calculated based on the data covering temperature, vibration amplitude, and current harmonic distortion rate, including: in, is the scale parameter of the Weibull distribution, For temperature, is the vibration amplitude, is the current harmonic distortion rate, is the weight of temperature on the scale parameter of the Weibull distribution, is the weight of the vibration amplitude on the scale parameter of the Weibull distribution, is the weight of the current harmonic distortion rate on the scale parameter of the Weibull distribution, is the first constant term.

3. The electrical equipment life assessment method based on Weibull distribution according to claim 2, characterized in that: The calculation of the shape parameters of the Weibull distribution based on the partial discharge amount and the ambient temperature includes: in, is the shape parameter of the Weibull distribution, is the amount of partial discharge, is the ambient temperature, is the influence of the natural logarithm of the partial discharge on the shape parameter of the Weibull distribution, is the influence of ambient temperature on the shape parameter of Weibull distribution, is the natural logarithm of the partial discharge, is the second constant term.

4. The electrical equipment life assessment method based on Weibull distribution according to claim 3, characterized in that: The first remaining life is: = in, is the first remaining life, is the scale parameter of the Weibull distribution, To set the failure probability threshold, is the shape parameter of the Weibull distribution; The second remaining life is: = in, is the second remaining lifespan, is the material constant, is the activation energy, is the Boltzmann constant, is temperature; The final remaining life is: in, is the first weight coefficient, is the second weight coefficient, is the first remaining life, The second remaining lifespan.

5. The electrical equipment life assessment method based on Weibull distribution according to claim 4, characterized in that: The second weight coefficient Dynamic adjustment based on equipment service life: in, The service life of the equipment.

6. The electrical equipment life assessment method based on Weibull distribution according to claim 5, characterized in that: The first weight coefficient Dynamic adjustment based on equipment service life: in, The service life of the equipment, =1.

7. The electrical equipment life assessment method based on Weibull distribution according to claim 4, characterized in that: The temperature of the multi-source running data set is measured using a temperature sensor, and the measurement range of the temperature sensor is -50°C. 250℃, accuracy 0.2℃; The vibration amplitude of the multi-source operation data set is measured using a vibration sensor, and the measurement range of the vibration sensor is 0 100g, frequency response 5Hz 30kHz; The current harmonic distortion rate of the multi-source operation data set is measured using a current transformer, and the measurement range of the current transformer is 0 2000A, measurement accuracy is 0.1%; The partial discharge amount of the multi-source operation data set is measured using a partial discharge detector, and the detection frequency band of the partial discharge detector is 1 MHz. 100MHz, sensitivity 0.3pC; The ambient temperature of the multi-source running data set is measured using an ambient temperature sensor, and the measurement range of the ambient temperature sensor is -20°C. 60℃, accuracy is 0.5℃.

8. The electrical equipment life assessment method based on Weibull distribution according to claim 4, characterized in that: The final remaining life 0.5 years, a red alert is generated; The final remaining life is 0.5 1 year, an orange alert is generated; The final remaining life 1 2 years, a yellow warning is generated.

9. The electrical equipment life assessment method based on Weibull distribution according to claim 8, characterized in that: When a red alert is generated, perform equipment maintenance immediately; When an orange alert is generated, equipment maintenance must be carried out within 72 hours; When a yellow alert is generated, equipment maintenance must be performed within 30 days.

10. An electrical equipment life assessment system based on Weibull distribution, characterized in that: include: Acquisition module: used to acquire multi-source operation data sets of electrical equipment, wherein the multi-source operation data sets include temperature, vibration amplitude, current harmonic distortion rate, partial discharge amount and ambient temperature; The first calculation module is used to calculate the scale parameter of the Weibull distribution based on the temperature, vibration amplitude, and current harmonic distortion rate, and calculate the shape parameter of the Weibull distribution based on the partial discharge amount and ambient temperature; The second calculation module is used to calculate the remaining life based on the scale parameter and the shape parameter and set the failure probability threshold, calculate the first remaining life, and calculate the second remaining life according to the operating temperature of the equipment; Early warning module: used to calculate the final remaining life based on the first remaining life and the second remaining life, and generate an equipment maintenance early warning instruction when the final remaining life is lower than the preset threshold.

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