A method for intelligent monitoring and analysis of power transformer operation

Abstract

This invention belongs to the field of instrument transformer operation monitoring technology, specifically disclosing an intelligent monitoring and analysis method for power instrument transformer operation. This method includes: comprehensively understanding the loss status of key internal components under different operating hours by real-time monitoring of two loss parameters—eddy current loss and hysteresis loss—and thus assessing their potential thermal loss risk; determining the internal component damage coefficient of the oil-immersed power transformer at each interval within the circuit operation cycle by identifying the impact of vibration generated by the corresponding oil in the internal tank of the oil-immersed power transformer when resisting abnormal circuit fluctuations on the thermal loss of the key component structure; and predicting the health coefficient of key components of the oil-immersed power transformer by measuring the linear correlation coefficient between the internal component damage coefficient and the corresponding operating fluctuation weight at each interval within the circuit operation cycle, while simultaneously analyzing the daily environmental coefficient of the circuit in which the oil-immersed power transformer is located.

Description

Technical Field

[0001] This invention belongs to the field of instrument transformer operation monitoring technology, and relates to an intelligent monitoring and analysis method for the operation of power instrument transformers. Background Technology

[0002] With the continuous development of power systems and the ongoing expansion of power grids, power transformers, as key devices for measurement, protection, and control in power systems, are crucial for the safety and efficiency of the entire power system. However, many problems exist in the traditional operation and maintenance of power transformers that urgently need to be addressed.

[0003] In the prior art, there are also some solutions related to the monitoring of current transformer operation. For example, Chinese Patent Publication No. CN117214799A discloses a method, device, equipment, and medium for monitoring the operation of an oil-immersed capacitive current transformer. It obtains the first measured voltage value of the end screen of the current transformer and the first voltage value of the voltage transformer in the same phase, calculates the second voltage value of the end screen of the current transformer, and compares the non-parallelism or difference between the time series of the first measured voltage value and the second voltage value to obtain a first comparison result. When the first comparison result is not a predetermined comparison result, it obtains the second measured voltage value of other current transformers on the same bus and in the same phase as the current transformer; compares the non-parallelism or difference between the time series of the first measured voltage value and the second measured voltage value to obtain a second comparison result, and preliminarily judges that there is a fault defect in the current or voltage transformer based on the second comparison result.

[0004] Another Chinese patent, CN117970222A, discloses an online monitoring system and method for oil-immersed current transformers. This system measures the temperature, pressure, and height of the oil inside the transformer using sensors and transmits these parameters to a fusion system. Secondly, it designs a fusion system to analyze the data measured by the sensors. Thirdly, it compares the values ​​output by the fusion system with set values ​​to determine if there are any abnormalities in the operating status of the oil-immersed current transformer and outputs this information via a wireless transmission device, thus achieving controllable online monitoring of the oil-immersed current transformer's operation.

[0005] While the above solutions offer some methods for monitoring the operation of instrument transformers, they still have the following limitations: 1. The monitoring methods are relatively simple, mostly focusing on basic electrical parameters of the power transformer, such as voltage and current, lacking comprehensive and in-depth monitoring of key internal components. Power circuits frequently experience fluctuations in power parameters during daily operation. To prevent abnormal effects from these fluctuations, power transformers initiate self-correction control. However, this self-correction control operation generates energy losses, such as eddy current losses and hysteresis losses, resulting in heat generation within the power transformer. If these losses are excessive and the heat cannot be dissipated in time, it will lead to excessively high local temperatures. Prolonged local overheating accelerates the aging of insulation materials, degrades insulation performance, and may even cause insulation breakdown, resulting in short-circuit faults and seriously affecting the safe operation of the power transformer.

[0006] 2. Oil level monitoring data is superficial, lacking analysis of the impact of oil vibration on the thermal losses of critical component structures. When the circuit experiences abnormal fluctuations, the oil in the tank vibrates. This vibration may be transmitted to critical component structures, leading to increased friction between components or localized stress concentration, thus generating additional heat. If not identified and monitored, this additional heat may cause the temperature of critical components to rise sharply, exceeding their permissible operating temperature range, resulting in overheating damage and severely affecting the normal operation of the power transformer.

[0007] 3. The lack of dynamic prediction of the health status of key internal components of power transformers makes it impossible to adjust maintenance plans according to the real-time health status of components. Maintenance can only be carried out according to fixed cycles or experience, which may lead to over-maintenance of healthy components, increasing maintenance costs and equipment downtime. On the other hand, components with potential problems are under-maintained, and potential faults are not detected and dealt with in a timely manner, increasing the risk of sudden equipment failure. Summary of the Invention

[0008] In view of this, in order to solve the problems mentioned in the background technology, a method for intelligent monitoring and analysis of power transformer operation is proposed.

[0009] The objective of this invention can be achieved through the following technical solution: This invention provides an intelligent monitoring and analysis method for the operation of power transformers, which includes the following steps: Step 1, detecting the daily heat generation of key components of the transformer: obtaining the power operation log of the circuit where the oil-immersed power transformer is located, setting the circuit operation cycle, dividing it into corresponding specified interval times, and numbering each interval time as follows. Identify the heat loss escalation coefficient of key internal components of oil-immersed power transformers at various intervals during circuit operation cycles. .

[0010] Step 2: Analyze the internal thermal loss of the instrument transformer: Obtain the oil state parameters of the oil-immersed power transformer, and combine them with the power operation log of the circuit in which the oil-immersed power transformer is located to analyze the thermal loss coefficient of the internal oil of the oil-immersed power transformer to the key component structure at each interval during the circuit operation cycle. .

[0011] Step 3: Detect the dynamic balance between circuit fluctuations and heat dissipation: Combining the heat loss growth coefficient of key internal components of the oil-immersed power transformer at each interval during the circuit operation cycle, and the heat loss coefficient of the internal oil on the key component structure at each interval during the circuit operation cycle, construct the damage coefficient of internal components of the oil-immersed power transformer at each interval during the circuit operation cycle. .

[0012] Step 4: Test the heat dissipation effect of the transformer: Test the internal oil temperature coefficient and the difference coefficient between the internal and external environment of the oil-immersed power transformer at various intervals during the circuit operation cycle, and analyze the daily environmental coefficient of the circuit in which the oil-immersed power transformer is located. .

[0013] Step 5: Predict the health status of key components of the current transformer: Based on the damage coefficient of internal components of the oil-immersed power transformer at each interval during the circuit operation cycle and the daily environmental coefficient of the circuit, predict the health coefficient of key components of the oil-immersed power transformer, and then use it as a fault maintenance judgment indicator for key components of the power transformer in the operating circuit.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention comprehensively understands the loss of internal key components under different working hours by real-time monitoring of the two loss parameters of eddy current loss and hysteresis loss, and then assesses their potential heat loss risk. It can detect abnormal changes in time before the heat loss problem seriously affects the normal operation of the transformer, issue fault warnings in advance, buy time for maintenance and replacement of key components, and effectively avoid power outages and equipment damage caused by sudden faults.

[0015] (2) This invention identifies the impact of vibration of the corresponding oil in the internal tank of the oil-immersed power transformer on the thermal loss of key component structures when resisting abnormal circuit fluctuations. Combined with the thermal loss growth coefficients of eddy current loss and hysteresis loss, the internal component damage coefficient of the oil-immersed power transformer at each interval during the circuit operation cycle is determined. By identifying this impact, corresponding measures can be taken in a timely manner to ensure the stability of equipment performance, thereby improving the metering and protection accuracy of the power system.

[0016] (3) This invention predicts the health coefficient of key components of oil-immersed power transformers by measuring the linear correlation coefficient between the internal component damage coefficient and the operating fluctuation weight at each interval during the circuit operation cycle. It fully considers the dynamic changes and interrelationships between the component damage coefficient and the operating fluctuation weight at different operating times, and can dynamically predict the health coefficient of key components.

[0017] (4) This invention reduces the bias of environmental conditions on the prediction of the health coefficient of key components of oil-immersed power transformers by analyzing the daily environmental coefficient of the circuit in which the oil-immersed power transformer is located. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the implementation steps of the method of the present invention;

[0020] Figure 2 This is a schematic diagram of the identification steps for the parameter set of eddy current loss growth degree in this invention;

[0021] Figure 3 This is a schematic diagram of the identification steps for the parameter set of hysteresis loss growth degree in this invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Please see Figure 1 As shown, this invention provides an intelligent monitoring and analysis method for the operation of power transformers. The method includes the following steps: Step 1: Detecting daily heat generation in key components of the transformer: Obtaining the power operation log of the circuit where the oil-immersed power transformer is located, setting the circuit operation cycle, and dividing it into corresponding specified intervals, numbering each interval as... Identify the heat loss escalation coefficient of key internal components of oil-immersed power transformers at various intervals during circuit operation cycles. .

[0024] The key internal components include windings and an iron core.

[0025] In a preferred embodiment, identifying the heat loss growth coefficient of key components inside the oil-immersed power transformer at various intervals within the circuit operating cycle includes: Step 1-1, extracting power fluctuation indicators at various intervals within the circuit operating cycle from the power operation log of the circuit containing the oil-immersed power transformer. Various power fluctuation indicators include voltage fluctuation indicators, current fluctuation indicators, and power fluctuation indicators. Indicates the number of each power fluctuation indicator. , specifically These represent the first and second cycles within the circuit's operating period. Voltage fluctuation indicators, current fluctuation indicators, and power fluctuation indicators at intervals.

[0026] It should be noted that the aforementioned power fluctuation indicators can be detected in real time using an oscilloscope and imported into the power operation log.

[0027] Steps 1-2: Identify the operational fluctuation weights of the circuit containing the oil-immersed power transformer at each time interval within the circuit's operating cycle. ,in Indicates the first Each power fluctuation indicator has a corresponding preset reference value.

[0028] Steps 1-3: Identify the parameter set of eddy current loss growth degree of key internal components of oil-immersed power transformers at various time intervals within the circuit operation cycle. Parameter set for the degree of hysteresis loss increase .

[0029] Steps 1-4: Analyze the heat loss escalation coefficient of key internal components of the oil-immersed power transformer at various intervals during the circuit operation cycle. ,in These represent the key components inside the oil-immersed power transformer during the circuit operation cycle. The parameters affected by material fluctuations and magnetic field characteristics at specific time intervals, as well as the parameters affected by the conductor dimensions and shapes of key internal components of oil-immersed power transformers. These represent the key components inside the oil-immersed power transformer during the circuit operation cycle. The domain structure drag effect parameters and temperature coefficient at intervals.

[0030] Please see Figure 2As shown, in a further preferred embodiment, the step of identifying the parameter set of eddy current loss growth degree of key components inside the oil-immersed power transformer at each interval during the circuit operation cycle includes: Step 1-3.1.1, detecting the material properties of key components inside the oil-immersed power transformer at each interval during the circuit operation cycle using a detection method based on the electromagnetic induction principle, including resistivity. magnetic permeability This is mapped to the influence parameters of material properties fluctuations in key components inside an oil-immersed power transformer at various intervals during the circuit's operating cycle. ,in These represent the corresponding preset reference values ​​for resistivity and permeability, respectively.

[0031] It should be noted that the specific content of the detection method based on the electromagnetic induction principle is as follows: a Rogowski coil is wrapped around the primary or secondary winding of the current transformer. When current flows through the winding, the Rogowski coil will induce a voltage signal proportional to the rate of change of current. By analyzing and processing this voltage signal, and combining it with information such as the winding parameters and core size of the current transformer, the permeability of the core can be indirectly calculated. At the same time, this method can also be used to estimate the resistivity of the core by measuring the resistance of the winding.

[0032] Regarding resistivity: the higher the resistivity, the greater the resistance encountered by eddy currents when flowing in the conductor, the more Joule heat is generated, and the greater the eddy current loss.

[0033] Regarding magnetic permeability: the higher the magnetic permeability, the stronger the shielding effect of the conductor on the magnetic field, the more uniform the distribution of the magnetic field inside the conductor, and the smaller the eddy current loss.

[0034] Step 1-3.1.2: Detect the magnetic field characteristics of key components inside the oil-immersed power transformer at various time intervals during the circuit operation cycle using the electromagnetic induction method, including the frequency of magnetic flux change. magnetic field strength Indicators of uniformity of magnetic field distribution This is mapped to the magnetic field characteristics of key components inside the oil-immersed power transformer at various intervals during the circuit's operating cycle. ,in These represent the preset reference values ​​for the magnetic flux change frequency, magnetic field strength, and magnetic field distribution uniformity indicators, respectively.

[0035] It should be noted that, regarding the frequency of magnetic flux change: the higher the frequency of magnetic flux change, the greater the induced electromotive force according to Faraday's law of electromagnetic induction, the stronger the generated eddy currents, and the greater the eddy current loss, which is proportional to the square of the frequency of magnetic flux change.

[0036] Regarding magnetic field strength: the greater the magnetic field strength, the stronger the eddy currents generated in the conductor, and the greater the eddy current losses.

[0037] Regarding the uniformity of magnetic field distribution: the more uniform the magnetic field distribution, the more uniform the eddy currents generated in the conductor, and the less energy is lost. If the magnetic field distribution is uneven, it will lead to uneven eddy current distribution, with excessively high local eddy current density, thereby increasing eddy current losses.

[0038] Step 1-3.1.3: Obtain the shape parameters of the corresponding dimensions and design structures of the key internal components of the oil-immersed power transformer, including the conductor cross-sectional area. ,length Shape complexity This is mapped to the influence parameters of the corresponding conductor size and shape of key components inside the oil-immersed power transformer. ,in These represent the preset reference values ​​for the conductor's cross-sectional area, length, and shape complexity, respectively.

[0039] It should be noted that, regarding cross-sectional area: under the same conditions, the larger the cross-sectional area of ​​the conductor, the smaller the eddy current loss. This is because with a larger cross-sectional area, the eddy current distribution is relatively dispersed, resulting in a relatively smaller resistance.

[0040] Regarding length: the longer the conductor, the greater the eddy current loss. This is because in a longer conductor, the eddy current travels a longer path, resulting in greater resistance loss.

[0041] Regarding shape complexity: the distribution and flow path of eddy currents differ for conductors of different shapes. For example, eddy current losses in a toroidal conductor are mainly internal, while those in a long, straight conductor are mainly on the surface. Conductors with complex shapes have complex eddy current paths and greater losses, while conductors with simple and regular shapes have relatively smaller eddy current losses.

[0042] Step 1-3.1.4: Construct a parameter set for the eddy current loss growth degree of key internal components of the oil-immersed power transformer at various time intervals during the circuit operation cycle. .

[0043] Please see Figure 3 As shown, in a further preferred embodiment, the identification step of the set of parameters for the degree of hysteresis loss growth includes: Step 1-3.2.1, detecting the hysteresis loop area of ​​key components inside the oil-immersed power transformer at various intervals during the circuit operation cycle using the hysteresis loop method. This is mapped to the influence parameter of magnetic domain structure resistance of key components inside an oil-immersed power transformer at various intervals during the circuit operation cycle. ,in This indicates the corresponding preset reference value for the hysteresis loop area.

[0044] It should be noted that the hysteresis loop method specifically involves using equipment such as a vibrating sample magnetometer or a superconducting quantum interference device to apply different magnetic field strengths and directions to the material, measuring and plotting the material's hysteresis loop. Analysis of the hysteresis loop yields information such as the material's hysteresis loss. The area enclosed by the hysteresis loop is directly proportional to the hysteresis loss; the larger the area, the greater the hysteresis loss.

[0045] Regarding the hysteresis loop area: Different magnetic materials have different domain structures and magnetic properties. When an external magnetic field is applied, the magnetic moments of the domains gradually tend to be uniform. During this process, the rotation of the domains and the movement of the domain walls need to overcome various resistances, consume energy, and generate hysteresis losses. For example, domain walls move relatively easily in soft magnetic materials, resulting in small hysteresis loop areas and low hysteresis losses; while domain walls move difficult in hard magnetic materials, resulting in large hysteresis loop areas and high hysteresis losses. However, the movement of domain walls is affected by factors such as lattice defects and stress within the material. These factors increase the resistance to the movement of domain walls, change the area enclosed by the hysteresis loop, and thus increase hysteresis losses.

[0046] Step 1-3.2.2: Detect the temperature of key internal components of the oil-immersed power transformer at various intervals during the circuit operation cycle using a temperature instrument. This is mapped to the temperature coefficient of key components inside the oil-immersed power transformer at various intervals during the circuit's operating cycle. ,in Preset reference temperature.

[0047] It should be noted that, regarding the temperature coefficient: as the temperature increases, the magnetization process of magnetic materials becomes easier, and the hysteresis loss decreases.

[0048] Step 1-3.2.3: Construct a parameter set for the degree of hysteresis loss growth of key internal components of an oil-immersed power transformer at various time intervals within the circuit operation cycle. .

[0049] This invention comprehensively understands the loss status of key internal components under different operating hours by real-time monitoring of two loss parameters: eddy current loss and hysteresis loss. This allows for the assessment of potential thermal loss risks and enables timely detection of abnormal changes and early warning of faults before thermal loss problems seriously affect the normal operation of the instrument transformer. This provides time for the repair and replacement of key components and effectively avoids power outages and equipment damage caused by sudden faults.

[0050] Step 2: Analyze the internal thermal loss of the instrument transformer: Obtain the oil state parameters of the oil-immersed power transformer, and combine them with the power operation log of the circuit in which the oil-immersed power transformer is located to analyze the thermal loss coefficient of the internal oil of the oil-immersed power transformer to the key component structure at each interval during the circuit operation cycle. .

[0051] In a preferred embodiment, the process of obtaining the oil state parameters of the oil-immersed power transformer includes: obtaining the oil tank level height through an oil level gauge, obtaining an internal oil tank level structure diagram of the oil-immersed power transformer through image recognition technology, and identifying the oil tank level tilt angle.

[0052] It should be noted that the tilt angle of the oil tank level refers to the angle between the plane of the oil floating layer and the direction of the vertical force of gravity.

[0053] In a further preferred embodiment, the analysis of the heat loss coefficient of the oil inside the oil-immersed power transformer at each interval during the circuit operation cycle to the key component structure includes: comparing the operating fluctuation weights of the circuit containing the oil-immersed power transformer at each interval during the circuit operation cycle with preset steady-state correction amplitudes corresponding to each operating fluctuation weight, and matching the steady-state correction amplitudes at each interval during the circuit operation cycle. This is mapped to the fluctuation impact rate of the oil inside the oil-immersed power transformer on key components at various intervals during the circuit operation cycle. ,in This indicates the preset reference steady-state correction magnitude.

[0054] The oil tank level is detected at each interval during the circuit's operating cycle. and the tilt angle of the oil tank level This is mapped to the pressure influence coefficient of the oil inside the oil-immersed power transformer on key components at various intervals during the circuit operation cycle. ,in This indicates the preset reference values ​​for the tank liquid level height and the tank liquid level tilt angle.

[0055] Analysis of the thermal loss coefficient of key components in oil-immersed power transformers at various time intervals during circuit operation. .

[0056] Step 3: Detect the dynamic balance between circuit fluctuations and heat dissipation: Combining the heat loss growth coefficient of key internal components of the oil-immersed power transformer at each interval during the circuit operation cycle, and the heat loss coefficient of the internal oil on the key component structure at each interval during the circuit operation cycle, construct the damage coefficient of internal components of the oil-immersed power transformer at each interval during the circuit operation cycle. .

[0057] In a preferred embodiment, the formula for constructing the internal component damage coefficient of the oil-immersed power transformer at various time intervals within the circuit operation cycle is as follows: ,in These represent the preset influence weights of the thermal loss growth coefficient of the internal key components of the oil-immersed power transformer and the thermal loss coefficient of the internal oil on the structure of the key components, respectively.

[0058] This invention identifies the impact of vibrations in the internal oil tank of an oil-immersed power transformer on the thermal loss of key components during abnormal circuit fluctuations. By combining the thermal loss amplification coefficients of eddy current loss and hysteresis loss, it determines the damage coefficient of internal components of the oil-immersed power transformer at each interval within the circuit operation cycle. By identifying this impact, corresponding measures can be taken in a timely manner to ensure the stability of equipment performance, thereby improving the metering and protection accuracy of the power system.

[0059] Step 4: Test the heat dissipation effect of the transformer: Test the internal oil temperature coefficient and the difference coefficient between the internal and external environment of the oil-immersed power transformer at various intervals during the circuit operation cycle, and analyze the daily environmental coefficient of the circuit in which the oil-immersed power transformer is located. .

[0060] In a preferred embodiment, the detection of the internal oil temperature coefficient of the oil-immersed power transformer at various intervals within the circuit operating cycle includes: detecting the internal oil temperature of the oil-immersed power transformer at various intervals within the circuit operating cycle using a built-in temperature sensor. Compare it with the preset oil body reference temperature. By comparison, the internal oil temperature coefficient of the oil-immersed power transformer was calculated at each interval during the circuit operation cycle. .

[0061] In a further preferred embodiment, the analysis of the daily environmental coefficient of the circuit in which the oil-immersed power transformer is located includes: detecting the ambient temperature of the oil-immersed power transformer at various intervals during the circuit's operating cycle using a thermometer. By comparing the temperature with the internal oil temperature, the difference coefficient between the internal and external environments of the oil-immersed power transformer at different intervals during the circuit operation cycle is analyzed. In the formula This indicates the corresponding preset reference deviation temperature for the internal and external environments.

[0062] Analyze the daily environmental factor of the circuit in which the oil-immersed power transformer is located. , It indicates the number of time intervals within the circuit's operating cycle.

[0063] This invention reduces the impact of environmental conditions on the prediction of the health coefficient of key components of oil-immersed power transformers by analyzing the daily environmental coefficient of the circuit in which the oil-immersed power transformer is located.

[0064] Step 5: Predict the health status of key components of the current transformer: Based on the damage coefficient of internal components of the oil-immersed power transformer at each interval during the circuit operation cycle and the daily environmental coefficient of the circuit, predict the health coefficient of key components of the oil-immersed power transformer, and then use it as a fault maintenance judgment indicator for key components of the power transformer in the operating circuit.

[0065] In a preferred embodiment, the prediction of the health coefficient of key components of the oil-immersed power transformer includes: obtaining the standard deviation of the damage coefficient of internal components and the corresponding operating fluctuation weight through a standardization method. Furthermore, the covariance matrix is ​​used to measure the linear correlation coefficient between the internal component damage coefficient and the operating fluctuation weight of the oil-immersed power transformer at each interval within the circuit operating cycle. Its value ranges from 1 to -1. This represents the function for calculating the covariance matrix, specifically... The degree of linear correlation between the damage coefficient of internal components and the operational fluctuation weight is judged based on the magnitude and sign of the correlation coefficient. A value close to 1 indicates a strong positive correlation, a value close to -1 indicates a strong negative correlation, and a value close to 0 indicates a weak or almost non-existent linear correlation.

[0066] Based on the operational fluctuation weights of the circuit containing the oil-immersed power transformer at various time intervals within the circuit's operating cycle, the empirical weights for daily operational fluctuations of the circuit containing the oil-immersed power transformer are identified. .

[0067] Predicting the health coefficient of key components in oil-immersed power transformers In the formula This indicates the maximum value of the internal component damage coefficient of an oil-immersed power transformer at each time interval within the circuit operating cycle. These represent the preset range values ​​for strong negative correlation and strong positive correlation, respectively, of the linear correlation coefficient.

[0068] This invention predicts the health coefficient of key components in oil-immersed power transformers by measuring the linear correlation coefficient between the internal component damage coefficient and the operating fluctuation weight at various intervals within the circuit's operating cycle. It fully considers the dynamic changes and interrelationships between component damage coefficients and operating fluctuation weights over different operating times, enabling dynamic prediction of key component health coefficients. Compared to traditional prediction methods based on a fixed single indicator, this method more accurately reflects the changing trends of component health status during actual operation, providing maintenance personnel with more forward-looking equipment status information.

[0069] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A method for intelligent monitoring and analysis of the operation of power transformers, characterized in that, The method includes the following steps: Step 1: Detect daily heat generation of key components of the instrument transformer: Obtain the power operation log of the circuit where the oil-immersed power transformer is located, set the circuit operation cycle, divide it into corresponding specified intervals, and number each interval. Identify the heat loss escalation coefficient of key internal components of oil-immersed power transformers at various intervals during circuit operation cycles. ; Step 2: Analyze the internal thermal loss of the instrument transformer: Obtain the oil state parameters of the oil-immersed power transformer, and combine them with the power operation log of the circuit in which the oil-immersed power transformer is located to analyze the thermal loss coefficient of the internal oil of the oil-immersed power transformer to the key component structure at each interval during the circuit operation cycle. ; Step 3: Detect the dynamic balance between circuit fluctuations and heat dissipation: Combining the heat loss growth coefficient of key internal components of the oil-immersed power transformer at each interval during the circuit operation cycle, and the heat loss coefficient of the internal oil on the key component structure at each interval during the circuit operation cycle, construct the damage coefficient of internal components of the oil-immersed power transformer at each interval during the circuit operation cycle. ; Step 4: Test the heat dissipation effect of the transformer: Test the internal oil temperature coefficient and the difference coefficient between the internal and external environment of the oil-immersed power transformer at various intervals during the circuit operation cycle, and analyze the daily environmental coefficient of the circuit in which the oil-immersed power transformer is located. ; Step 5: Predict the health status of key components of the current transformer: Based on the damage coefficient of internal components of the oil-immersed power transformer at each interval during the circuit operation cycle and the daily environmental coefficient of the circuit, predict the health coefficient of key components of the oil-immersed power transformer.

2. The intelligent monitoring and analysis method for the operation of power transformers according to claim 1, characterized in that, The identification of the heat loss growth coefficient of key internal components of an oil-immersed power transformer at various time intervals during the circuit operation cycle includes: Step 1-1: Extract power fluctuation indicators at each interval within the circuit's operating cycle from the power operation log of the circuit containing the oil-immersed power transformer. Various power fluctuation indicators include voltage fluctuation indicators, current fluctuation indicators, and power fluctuation indicators. Indicates the number of each power fluctuation indicator. ; Steps 1-2: Identify the operational fluctuation weights of the circuit containing the oil-immersed power transformer at each time interval within the circuit's operating cycle. ,in Indicates the first Each power fluctuation indicator has a corresponding preset reference value; Steps 1-3: Identify the parameter set of eddy current loss growth degree of key internal components of oil-immersed power transformers at various time intervals within the circuit operation cycle. Parameter set for the degree of hysteresis loss increase ; Steps 1-4: Analyze the heat loss escalation coefficient of key internal components of the oil-immersed power transformer at various intervals during the circuit operation cycle. ,in These represent the key components inside the oil-immersed power transformer during the circuit operation cycle. The parameters affected by material fluctuations and magnetic field characteristics at specific time intervals, as well as the parameters affected by the conductor dimensions and shapes of key internal components of oil-immersed power transformers. These represent the key components inside the oil-immersed power transformer during the circuit operation cycle. The domain structure drag effect parameters and temperature coefficient at intervals.

3. The intelligent monitoring and analysis method for the operation of power transformers according to claim 2, characterized in that, The parameter set for identifying the degree of eddy current loss growth of key internal components of an oil-immersed power transformer at various time intervals during the circuit operation cycle includes the following identification steps: Step 1-3.1.1: Detect the material properties of key components inside the oil-immersed power transformer at various time intervals within the circuit operating cycle using the electromagnetic induction principle, including resistivity and permeability. Map these properties to parameters affecting the material fluctuations of key components inside the oil-immersed power transformer at various time intervals within the circuit operating cycle. ; Step 1-3.1.2: Detect the magnetic field characteristics of key internal components of the oil-immersed power transformer at various time intervals within the circuit operating cycle using the electromagnetic induction method. This includes indicators such as magnetic flux change frequency, magnetic field strength, and magnetic field distribution uniformity. Map these characteristics to parameters influencing the magnetic field characteristics of key internal components of the oil-immersed power transformer at various time intervals within the circuit operating cycle. ; Step 1-3.1.3: Obtain the shape parameters of the corresponding dimensions and design structures of the key internal components of the oil-immersed power transformer, including conductor cross-sectional area, length, and shape complexity, and map them to the influence parameters of the corresponding conductor dimensions and shapes of the key internal components of the oil-immersed power transformer. ; Step 1-3.1.4: Construct a parameter set for the eddy current loss growth degree of key internal components of the oil-immersed power transformer at various time intervals during the circuit operation cycle. .

4. The intelligent monitoring and analysis method for the operation of power transformers according to claim 2, characterized in that, The identification steps for the set of parameters representing the degree of hysteresis loss increase include: Step 1-3.2.1: Detect the hysteresis loop area of ​​key internal components of the oil-immersed power transformer at various time intervals during the circuit operation cycle using the hysteresis loop method. This is mapped to the influence parameter of magnetic domain structure resistance of key components inside an oil-immersed power transformer at various intervals during the circuit operation cycle. ; Step 1-3.2.2: Measure the temperature of key components inside the oil-immersed power transformer at various intervals during the circuit operation cycle using a temperature instrument, and map this temperature to the temperature coefficient of the key components inside the oil-immersed power transformer at various intervals during the circuit operation cycle. ; Step 1-3.2.3: Construct a parameter set for the degree of hysteresis loss growth of key internal components of an oil-immersed power transformer at various time intervals within the circuit operation cycle. .

5. The intelligent monitoring and analysis method for the operation of power transformers according to claim 1, characterized in that, The process of obtaining the oil state parameters of the oil-immersed power transformer includes: obtaining the oil tank level height through an oil level gauge, obtaining the internal oil tank level structure diagram of the oil-immersed power transformer through image recognition technology, and identifying the oil tank level tilt angle.

6. The intelligent monitoring and analysis method for the operation of a power transformer according to claim 5, characterized in that, The analysis of the thermal loss coefficient of the oil inside the oil-immersed power transformer on key components at various intervals within the circuit operating cycle includes: comparing the operating fluctuation weights of the circuit containing the oil-immersed power transformer at various intervals within the circuit operating cycle with preset steady-state correction amplitudes corresponding to each operating fluctuation weight, matching the steady-state correction amplitudes at various intervals within the circuit operating cycle, and mapping them to the fluctuation impact rate of the oil inside the oil-immersed power transformer on key components at various intervals within the circuit operating cycle. ; The oil tank level height and tilt angle at each interval within the circuit's operating cycle are detected and mapped to the pressure influence coefficient of the oil inside the oil-immersed power transformer on key components at each interval within the circuit's operating cycle. ; Analysis of the thermal loss coefficient of key components in oil-immersed power transformers at various time intervals during circuit operation. .

7. The intelligent monitoring and analysis method for the operation of power transformers according to claim 1, characterized in that, The formula for constructing the internal component damage coefficient of the oil-immersed power transformer at various time intervals within the circuit operation cycle is as follows: ,in These represent the preset influence weights of the thermal loss growth coefficient of the internal key components of the oil-immersed power transformer and the thermal loss coefficient of the internal oil on the structure of the key components, respectively.

8. The intelligent monitoring and analysis method for the operation of power transformers according to claim 1, characterized in that, The method for detecting the internal oil temperature coefficient of the oil-immersed power transformer at various intervals within the circuit operating cycle includes: detecting the internal oil temperature of the oil-immersed power transformer at various intervals within the circuit operating cycle using a built-in temperature sensor. Compare it with the preset oil body reference temperature. By comparison, the internal oil temperature coefficient of the oil-immersed power transformer was calculated at each interval during the circuit operation cycle. .

9. The intelligent monitoring and analysis method for the operation of a power transformer according to claim 8, characterized in that, The analysis of the daily environmental coefficient of the circuit in which the oil-immersed power transformer is located includes: measuring the ambient temperature of the oil-immersed power transformer at various intervals during the circuit's operating cycle using a thermometer. By comparing the temperature with the internal oil temperature, the difference coefficient between the internal and external environments of the oil-immersed power transformer at different intervals during the circuit operation cycle is analyzed. In the formula This indicates the corresponding preset reference deviation temperature for the internal and external environments; Analyze the daily environmental factor of the circuit in which the oil-immersed power transformer is located. , It indicates the number of time intervals within the circuit's operating cycle.

10. The intelligent monitoring and analysis method for the operation of a power transformer according to claim 2, characterized in that, The prediction of the health coefficient of key components of oil-immersed power transformers includes: obtaining the standard deviation of the damage coefficient of internal components and the corresponding standard deviation of the operational fluctuation weight through a standardized method. Furthermore, the covariance matrix is ​​used to measure the linear correlation coefficient between the internal component damage coefficient and the operating fluctuation weight of the oil-immersed power transformer at each interval within the circuit operating cycle. , This represents a function for calculating the covariance matrix; Experience weights for identifying daily operating fluctuations in circuits containing oil-immersed power transformers ; Predicting the health coefficient of key components in oil-immersed power transformers In the formula This indicates the maximum value of the internal component damage coefficient of an oil-immersed power transformer at each time interval within the circuit operating cycle. These represent the preset range values ​​for strong negative correlation and strong positive correlation, respectively, of the linear correlation coefficient.

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