Transformer heat dissipation evaluation system and method

Abstract

The present invention discloses a transformer heat dissipation evaluation system and method, which belongs to the technical field of transformer operation and maintenance. The transformer heat dissipation evaluation system includes: a module for obtaining cooling equipment dirt data, a dirt change model training module, a transformer cleanliness early warning module, and a transformer cleanliness level judgment module. The present invention obtains cooling equipment dirt data and performs preprocessing; obtains a dirt change index based on a machine learning algorithm according to the preprocessed cooling equipment dirt data; obtains a dirt degree threshold, and performs transformer cleanliness early warning judgment according to the dirt change index; obtains a cleanliness evaluation index based on the cooling equipment dirt data, judges the cleanliness level of the transformer cooling equipment, and performs transformer cleaning judgment, thereby real-time monitoring and evaluation of the cleanliness of the transformer heat dissipation system, improving the heat dissipation performance of the transformer, achieving the improvement of the heat dissipation performance of the transformer, and solving the problem of low heat dissipation performance of the transformer in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer operation and maintenance, and in particular to a transformer heat dissipation evaluation system and method. Background Art

[0002] With the development of modern science and technology, transformers have been widely used in society, especially in industry, agriculture, transportation, urban communities and other fields. The main functions are: voltage conversion, current conversion, impedance conversion, isolation, voltage stabilization (magnetic saturation transformer), etc. Transformers are devices that use the principle of electromagnetic induction to change AC voltage. The main components are primary coils, secondary coils and iron cores (magnetic cores); in electrical equipment and wireless circuits, they are often used to increase and decrease voltage, match impedance, and isolate safely; in generators, whether the coil moves through the magnetic field or the magnetic field moves through the fixed coil, it can induce an electric potential in the coil; in both cases, the value of the magnetic flux remains unchanged, but the amount of magnetic flux intersecting with the coil changes, which is the principle of mutual induction; transformers are a type of device that uses electromagnetic mutual induction to transform voltage, current and impedance. In power systems, transformers are critical equipment that needs to be kept operating within an appropriate temperature range to ensure their performance and life, so it is necessary to evaluate the heat dissipation capacity of the transformer.

[0003] The existing transformer heat dissipation evaluation system uses temperature, humidity, wind speed and noise monitoring data as a basis to determine the overall effect of transformer ventilation, heat dissipation and noise reduction.

[0004] For example, the patent application with publication number: CN115829367A discloses a multi-physical field test system for an indoor substation and a ventilation, heat dissipation and noise reduction evaluation method, which include: a computer terminal, a monitoring host, and a physical field monitoring synchronization device; data is transmitted between the computer terminal and the monitoring host, and the monitoring host is communicatively connected with the physical field monitoring synchronization device; the physical field monitoring synchronization device includes a temperature sensor, a humidity sensor, a noise sensor, a wind speed sensor, and a wind direction sensor; the physical field monitoring synchronization device is used to collect monitoring data of the indoor substation and upload it to the monitoring host; the monitoring host is used to transmit the monitoring data to the computer terminal; the computer terminal is used to calculate the temperature field, wind field, and noise field of the environment where the indoor substation is located based on the monitoring data.

[0005] For example, the invention patent with announcement number: CN106295191B discloses a new method for calculating and evaluating the heat dissipation capacity of a large transformer, including: calculating the heat dissipation capacity of the transformer by using the change in the temperature rise of the upper oil, calculating the average temperature rise through the temperature rise of the upper oil of the transformer, thereby calculating the heat load per unit area and calculating the effective heat dissipation area of ​​the transformer together with the transformer load loss, thereby quantifying the heat dissipation capacity of the transformer, evaluating the heat dissipation capacity of the transformer through grade analysis, and taking corresponding countermeasures.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the prior art, there is no technology for evaluating the cleanliness of the transformer cooling system, and it is impossible to monitor the dirt content of the transformer cooling system in real time. If the transformer cooling equipment has a high dirt content, it may cause abnormal operation of the cooling system and the problem of low transformer cooling performance. Summary of the invention

[0008] The embodiments of the present application provide a transformer heat dissipation evaluation system and method, thereby solving the problem of low heat dissipation performance of the transformer in the prior art and improving the heat dissipation performance of the transformer.

[0009] The embodiment of the present application provides a transformer heat dissipation evaluation system, including: a module for obtaining cooling equipment dirt data, a dirt change model training module, a transformer cleanliness early warning module, and a transformer cleanliness level judgment module; wherein the module for obtaining cooling equipment dirt data is used to obtain cooling equipment dirt data, and perform preprocessing according to the cooling equipment dirt data, and the preprocessing includes removing outliers and noise; the dirt change model training module is used to analyze the preprocessed cooling equipment dirt data based on a machine learning algorithm to obtain a dirt change index; the transformer cleanliness early warning module is used to obtain a dirt degree threshold, and perform transformer cleanliness early warning judgment based on the dirt degree threshold according to the dirt change index; the transformer cleanliness level judgment module is used to obtain a cleanliness evaluation index, and judge the cleanliness level of the transformer cooling equipment based on the cooling equipment dirt data according to the cleanliness evaluation index and perform a transformer cleaning judgment. If the transformer does not meet the cleaning standard, no processing is performed; if the transformer meets the cleaning standard, it is immediately cleaned and the cleaning cycle is increased according to the cleanliness level of the transformer cooling equipment.

[0010] Furthermore, the obtaining of cooling equipment dirt data is specifically analyzed as follows: obtaining cooling equipment dirt data; the cooling equipment dirt data includes environmental impact data, dirt data, and transformer heat dissipation equipment data; performing preprocessing based on the cooling equipment dirt data; the preprocessing includes removing outliers and noise.

[0011] Furthermore, the environmental impact data, dirt data, and transformer cooling equipment data are specifically analyzed as follows: the environmental impact data include air dust concentration, ambient temperature, and air humidity; the dirt data include dirt weight, dirt particle size, and dirt accumulation; the transformer cooling equipment data include oil temperature change rate, cooling equipment wind speed, and cooling equipment power consumption.

[0012] Furthermore, the dirt change model training is specifically analyzed as follows: extracting the pre-processed cooling equipment dirt data; analyzing the pre-processed cooling equipment dirt data based on a machine learning algorithm to obtain a dirt change index; the dirt change index is used to describe the changing trend of the dirt content of the transformer cooling equipment.

[0013] Furthermore, the dirt variation index includes a dirt influence index and a transformer heat dissipation equipment influence index; a preset time period is obtained; the dirt influence index is used to describe the degree of influence of dirt on the dirt variation index within the preset time period; the transformer heat dissipation equipment influence index is used to describe the degree of influence of the transformer heat dissipation equipment on the dirt variation index within the preset time period; the dirt variation index is obtained according to the analysis of the dirt influence index and the transformer heat dissipation equipment influence index.

[0014] Furthermore, the dirt change index is obtained by analyzing the dirt impact index and the transformer heat dissipation equipment impact index. Specifically, the dirt change index is analyzed as follows: according to the objective weight method, the weight ratio of the dirt impact index to the dirt change index and the weight ratio of the transformer heat dissipation equipment impact index to the dirt change index are set; the dirt change index is obtained according to the dirt impact index, the weight ratio of the dirt impact index to the dirt change index, the transformer heat dissipation equipment impact index, and the weight ratio of the transformer heat dissipation equipment impact index to the dirt change index. The dirt change index calculation formula is:

[0015] B=cosh -1 [sinh(Y*Y′+R*R′)+1]In the formula, Y represents the dirt influence index, Y′ represents the weight ratio of the dirt influence index to the dirt change index, R represents the transformer heat dissipation equipment influence index, R′ represents the weight ratio of the transformer heat dissipation equipment influence index to the dirt change index, and B represents the dirt change index.

[0016] Furthermore, the transformer cleanliness warning is specifically analyzed as follows: extracting the pre-processed dirt data; obtaining a dirt degree threshold based on the pre-processed dirt data analysis; the dirt degree threshold is used to describe the maximum dirt content that the transformer cooling system can withstand; making a transformer cleanliness warning judgment based on the dirt change index according to the dirt degree threshold; if the dirt change index exceeds the dirt degree threshold, issuing a warning to notify relevant personnel; if the dirt change index does not exceed the dirt degree threshold, no warning is issued.

[0017] Furthermore, the transformer cleanliness level judgment is specifically analyzed as follows: obtaining a cleanliness evaluation index; the cleanliness evaluation index includes a standard dirt accumulation rate and a cleanliness level; the cleanliness evaluation index is used to reflect the cleanliness of the transformer cooling equipment; the standard dirt accumulation rate is used to describe the standard value of the dirt accumulation rate, and the transformer cooling equipment needs to be cleaned if the standard value is exceeded; extracting the pre-processed dirt data; obtaining the dirt accumulation rate based on the pre-processed dirt data analysis; judging the transformer cleanliness level based on the dirt accumulation rate and the standard dirt accumulation rate.

[0018] Furthermore, the transformer cleanliness level is judged based on the dirt accumulation rate based on the standard dirt accumulation rate, and the specific analysis is as follows: if the dirt accumulation rate is greater than the standard dirt accumulation rate, the transformer reaches the cleanliness standard; the cleanliness level of the transformer cooling equipment is judged based on the cleanliness level according to the dirt change index; the cleaning cycle is increased according to the cleanliness level of the transformer cooling equipment; if the dirt accumulation rate is not greater than the standard dirt accumulation rate, no treatment is performed.

[0019] The embodiment of the present application provides a transformer heat dissipation evaluation method, comprising the following steps: obtaining cooling equipment dirt data: obtaining cooling equipment dirt data, performing preprocessing according to the cooling equipment dirt data, wherein the preprocessing includes removing outliers and noise; dirt change model training: performing analysis based on the preprocessed cooling equipment dirt data based on a machine learning algorithm to obtain a dirt change index; transformer cleanliness warning: obtaining a dirt degree threshold, and performing a transformer cleanliness warning judgment based on the dirt degree threshold according to the dirt change index; transformer cleanliness level judgment: obtaining a cleanliness evaluation index, and judging the cleanliness level of the transformer cooling equipment and performing a transformer cleaning judgment based on the cooling equipment dirt data according to the cleanliness evaluation index.

[0020] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0021] 1. By obtaining the dirt data of the cooling equipment, preprocessing is performed according to the dirt data of the cooling equipment; the dirt change index is obtained by analyzing the preprocessed dirt data of the cooling equipment based on the machine learning algorithm; the dirt degree threshold is obtained, and the transformer cleanliness early warning judgment is made according to the dirt change index; the cleanliness evaluation index is obtained, and the cleanliness level of the transformer cooling equipment is judged based on the dirt data of the cooling equipment according to the cleanliness evaluation index, and the transformer cleaning judgment is made, so as to monitor and evaluate the cleanliness of the transformer cooling system in real time, improve the heat dissipation performance of the transformer, and then realize the improvement of the heat dissipation performance of the transformer, and effectively solve the problem of low heat dissipation performance of the transformer in the prior art.

[0022] 2. Through the dirt level threshold, the early warning judgment of the cleanliness of the transformer is realized, so that the dirt problem can be discovered in time and the relevant personnel can be notified, and maintenance measures can be taken in time, thereby reducing equipment damage or performance degradation.

[0023] 3. The cleanliness level of the transformer cooling equipment is judged through the cleanliness assessment index, so as to adjust the cleaning cycle as needed to ensure that the equipment is kept in an appropriate clean state, extend the service life of the equipment, and thus improve the equipment reliability and operating efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of the structure of a transformer heat dissipation evaluation system provided in an embodiment of the present application;

[0025] Figure 2 A graph of the fouling impact index provided in the embodiments of the present application;

[0026] Figure 3 This is a flow chart of a transformer heat dissipation assessment method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The embodiment of the present application solves the problem of low heat dissipation performance of the transformer in the prior art by providing a transformer heat dissipation evaluation system and method, by obtaining cooling equipment dirt data, and performing preprocessing according to the cooling equipment dirt data; performing analysis based on the preprocessed cooling equipment dirt data based on a machine learning algorithm to obtain a dirt change index; obtaining a dirt degree threshold, and performing a transformer cleanliness early warning judgment according to the dirt change index; obtaining a cleanliness evaluation index, and judging the cleanliness level of the transformer cooling equipment and performing a transformer cleaning judgment based on the cleanliness evaluation index based on the cooling equipment dirt data, thereby real-time monitoring and evaluation of the cleanliness of the transformer heat dissipation system, improving the heat dissipation performance of the transformer, and achieving improved heat dissipation performance of the transformer.

[0028] The technical solution in the embodiment of the present application is to solve the problem of low heat dissipation performance of the above transformer, and the overall idea is as follows:

[0029] By obtaining the dirt data of the cooling equipment, preprocessing is performed based on the dirt data of the cooling equipment, and the preprocessing includes removing outliers and noise; the dirt data of the cooling equipment after the preprocessing is analyzed based on the machine learning algorithm to obtain the dirt change index; the dirt degree threshold is obtained, and the transformer cleanliness warning judgment is performed based on the dirt change index. If the dirt change index exceeds the dirt degree threshold, an early warning is issued to notify relevant personnel; if the dirt change index does not exceed the dirt degree threshold, no early warning is issued; the cleanliness evaluation index is obtained, and the cleanliness evaluation index includes the dirt standard accumulation rate and the cleanliness level. According to the cleanliness evaluation index and the dirt data of the cooling equipment, the cleanliness level of the transformer cooling equipment and whether the transformer meets the cleanliness standard are judged. If the transformer does not meet the cleanliness standard, no processing is performed. If the transformer meets the cleanliness standard, it is cleaned immediately and the cleaning cycle is increased according to the cleanliness level of the transformer cooling equipment, so as to monitor and evaluate the cleanliness of the transformer heat dissipation system in real time, improve the heat dissipation performance of the transformer, and achieve the improvement of the heat dissipation performance of the transformer.

[0030] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0031] like Figure 1 As shown, it is a structural schematic diagram of the transformer heat dissipation evaluation system provided by the embodiment of the present application. The transformer heat dissipation evaluation system provided by the embodiment of the present application includes: a module for obtaining cooling equipment dirt data, a dirt change model training module, a transformer cleanliness early warning module, and a transformer cleanliness level judgment module; wherein, the module for obtaining cooling equipment dirt data is used to obtain cooling equipment dirt data, and preprocess the cooling equipment dirt data, and the preprocessing includes removing outliers and noise; the dirt change model training module is used to analyze the preprocessed cooling equipment dirt data based on the machine learning algorithm to obtain the dirt change index; the transformer cleanliness early warning module is used to obtain the dirt degree threshold, and make a transformer cleanliness early warning judgment based on the dirt degree threshold according to the dirt change index; the transformer cleanliness level judgment module is used to obtain the cleanliness evaluation index, and judge the cleanliness level of the transformer cooling equipment based on the cooling equipment dirt data according to the cleanliness evaluation index and make a transformer cleaning judgment. If the transformer does not meet the cleaning standard, no treatment is performed. If the transformer meets the cleaning standard, it is immediately cleaned and the cleaning cycle is increased according to the cleanliness level of the transformer cooling equipment. Cleanliness assessment indicators are used to determine when the transformer needs to be cleaned, and a cleaner transformer means improved heat dissipation performance of the transformer.

[0032] In this embodiment, the cooling equipment dirt data acquisition module is responsible for acquiring dirt data from the transformer cooling equipment, monitoring the dirt condition of the cooling equipment in real time, and providing a data basis for subsequent preprocessing and analysis; the preprocessing module is used to process the data acquired from the cooling equipment dirt data acquisition module, including removing outliers and noise, ensuring the quality and accuracy of the analyzed data, improving the effect of subsequent model training, and preparing clean and reliable data for use by the machine learning model training module; the dirt change model training module uses the preprocessed data to train the dirt change model based on the machine learning algorithm to analyze and predict the change trend of the cooling equipment dirt. Through model analysis, it can accurately predict the change of the transformer cooling equipment dirt and provide a dirt change index as an evaluation index; the transformer cleanliness early warning module performs transformer cleanliness early warning judgment based on the dirt change index and the preset dirt degree threshold, and promptly warns whether the transformer cooling equipment needs to be cleaned or maintained to prevent dirt from causing equipment performance degradation or failure; the transformer cleanliness level judgment module judges the cleanliness level of the transformer cooling equipment based on the cleanliness evaluation index, and judges whether it needs to be cleaned. According to the cleanliness level, a targeted cleaning plan and measures are formulated to ensure the normal operation and long-term performance of the equipment.

[0033] Furthermore, the dirt data of the cooling equipment is obtained, and the specific analysis is as follows: the dirt data of the cooling equipment is obtained; the dirt data of the cooling equipment includes environmental impact data, dirt data, and transformer heat dissipation equipment data; preprocessing is performed according to the dirt data of the cooling equipment; the preprocessing includes removing outliers and noise.

[0034] In this embodiment, by removing outliers and noise, the quality and accuracy of the cooling equipment fouling data can be improved, making subsequent analysis more reliable; the preprocessed data is more representative, which helps to accurately analyze the fouling condition of the cooling equipment, including trend changes in fouling levels and environmental impacts; removing outliers and noise can avoid these factors from interfering with model training and prediction results, and improve prediction accuracy and model credibility; through more accurate data analysis, the changing trend of equipment fouling can be discovered in a timely manner, providing more targeted suggestions and decision-making support for equipment maintenance, extending equipment life and improving operating efficiency.

[0035] Furthermore, the environmental impact data, dirt data, and transformer cooling equipment data are specifically analyzed as follows: environmental impact data include air dust concentration, ambient temperature, and air humidity; dirt data include dirt weight, dirt particle size, and dirt accumulation; transformer cooling equipment data include oil temperature change rate, cooling equipment wind speed, and cooling equipment power consumption.

[0036] In this embodiment, the environmental impact data is used to describe the factors affecting the heat dissipation system of the transformer; the dirt data is used to describe the factors affecting the heat dissipation system of the transformer; and the transformer heat dissipation equipment data is used to describe the factors affecting the heat dissipation equipment of the transformer on the heat dissipation system of the transformer. The dust concentration in the air is obtained through a sensor, and the ambient temperature and air humidity are obtained through a thermometer and a hygrometer; the weight of the dirt is obtained through a weighing instrument, and the size of the dirt particles is obtained through a particle analyzer. The size of the dirt particles is used to describe the overall length of the dirt, and the amount of dirt accumulation is used to describe the initial and final dirt weights within a preset time period; the oil temperature change rate is obtained through an oil temperature sensor, the wind speed of the cooling equipment is obtained through a wind speed sensor, and the power consumption of the cooling equipment is obtained through an electric energy detection device.

[0037] Furthermore, the dirt change model training is specifically analyzed as follows: extracting the pre-processed cooling equipment dirt data; analyzing the pre-processed cooling equipment dirt data based on the machine learning algorithm to obtain the dirt change index; the dirt change index is used to describe the changing trend of the dirt content of the transformer cooling equipment.

[0038] Furthermore, the dirt variation index includes a dirt influence index and a transformer heat dissipation equipment influence index; a preset time period is obtained; the dirt influence index is used to describe the degree of influence of dirt on the dirt variation index within the preset time period; the transformer heat dissipation equipment influence index is used to describe the degree of influence of the transformer heat dissipation equipment on the dirt variation index within the preset time period; the dirt variation index is obtained according to the analysis of the dirt influence index and the transformer heat dissipation equipment influence index.

[0039] In this embodiment, the dirt impact index is an indicator that describes the degree of influence of dirt on the entire dirt change index within a preset time period; it can reflect factors such as the amount of dirt in the cooling device, the growth rate of dirt, and the composition of dirt; a high dirt impact index usually means that the dirt inside the cooling device increases, which may have an adverse effect on the cooling efficiency. The transformer heat dissipation device impact index describes the degree of influence of the transformer heat dissipation device on the dirt change index within a preset time period, involving the performance of the heat dissipation device, the fan speed, the efficiency of the heat sink, etc.; the aging of the equipment, the reduced efficiency, etc. will cause the transformer heat dissipation device impact index to be high, which indicates that the heat dissipation device has a greater impact on dirt. The preset time period specifies the time period for analysis, during which data will be collected, analyzed, and used to calculate the dirt impact index and the transformer heat dissipation device impact index. The dirt change index is obtained by analyzing the dirt impact index and the transformer heat dissipation device impact index, which is a process of calculating the dirt change index by combining the dirt impact index and the transformer heat dissipation device impact index. This process involves a mathematical or statistical model that combines the influence of the two indexes to obtain a comprehensive dirt change index. The dirt impact index and transformer cooling equipment impact index can be obtained by calculation, in addition to using numerical simulation methods such as heat transfer models to simulate the performance of transformer cooling equipment under different dirt accumulation conditions and predict the degree of influence of dirt on the equipment. The calculation formula of the dirt impact index is:

[0040]

[0041] In the formula, n represents the dust concentration in the air, s represents the air humidity, i = 1, 2, ..., m represents the number of each dirt particle, m represents the total number of dirt particles, g i represents the dirt weight of the i-th dirt, d i The dirt particle size of the i-th dirt itself, G1 represents the initial dirt weight within the preset time period, G2 represents the final dirt weight within the preset time period, Y represents the dirt impact index, and the dirt impact index data are shown in Table 1.

[0042] Table 1 Fouling Impact Index Data

[0043]

[0044] The higher the air dust concentration and air humidity, the easier it is for the transformer cooling equipment to generate dirt. By multiplying the dirt weight per unit time by the total dirt weight in a preset time period, it can be obtained that the higher the product, the more dirt content the transformer cooling equipment has.

[0045] like Figure 2As shown, it is a curve chart of the dirt impact index provided in an embodiment of the present application, wherein the horizontal axis represents the air dust concentration, and the vertical axis represents the dirt impact index. Because the dirt impact index is an indicator that describes the degree of influence of dirt on the entire dirt change index within a preset time period, and because the dirt change index is used to describe the changing trend of the dirt content of the transformer cooling equipment, the dirt impact index increases with the increase of the air dust concentration.

[0046] The calculation formula of transformer heat dissipation equipment impact index is:

[0047]

[0048] In the formula, w represents the ambient temperature, V represents the wind speed of the cooling equipment, represents the oil temperature change rate, T1 represents the oil temperature at the initial time within the preset time period, T2 represents the oil temperature at the final time within the preset time period, p represents the power consumption of the cooling equipment, t represents the preset time period, R represents the transformer heat dissipation equipment impact index, and the transformer heat dissipation equipment impact index data is shown in Table 2.

[0049] Table 2 Transformer cooling equipment impact index data

[0050]

[0051] The higher the ambient temperature, the slower the cooling equipment wind speed, and the transformer cooling equipment is more likely to produce dirt; the greater the oil temperature change rate, the more power is consumed in the preset time period, indicating that the cooling equipment runs slowly and produces more dirt. The dirt change index can be used to quantify the change trend of dirt in the cooling equipment and provide clear indicators for maintenance; by decomposing the dirt impact index and the transformer cooling equipment impact index, the source and impact of dirt can be understood more specifically, so as to carry out targeted maintenance; by monitoring and analyzing the dirt change index, the reasons for the decline in cooling equipment performance can be discovered in time, and corresponding measures can be taken to improve equipment performance; effective dirt management and transformer cooling equipment maintenance can reduce equipment wear, extend equipment service life, and reduce maintenance and replacement costs.

[0052] Furthermore, the dirt change index is obtained based on the dirt impact index and the transformer heat dissipation equipment impact index. The specific analysis is as follows: according to the objective weight method, the weight ratio of the dirt impact index to the dirt change index and the weight ratio of the transformer heat dissipation equipment impact index to the dirt change index are set; according to the dirt impact index, the weight ratio of the dirt impact index to the dirt change index, the transformer heat dissipation equipment impact index, and the weight ratio of the transformer heat dissipation equipment impact index to the dirt change index, the dirt change index is obtained. The calculation formula of the dirt change index is:

[0053] B=cosh-1 [sinh(Y*Y′+R*R′)+1]

[0054] In the formula, Y represents the dirt influence index, Y′ represents the weight ratio of the dirt influence index to the dirt change index, R represents the transformer heat dissipation equipment influence index, R′ represents the weight ratio of the transformer heat dissipation equipment influence index to the dirt change index, and B represents the dirt change index.

[0055] In this embodiment, in addition to being obtained by calculation, the dirt change index can also be obtained by chemical analysis or laboratory testing to analyze the dirt of the transformer cooling equipment, converting the measurement results into numerical values ​​of dirt content, and performing corrections or amendments to consider possible sources of error. Based on the results of the analysis, the dirt content and composition in the sample are interpreted, and the dirt change index data are shown in Table 3.

[0056] Table 3 Dirt Change Index Data

[0057]

[0058] The change in dirt content is positively correlated with both the dirt impact index and the transformer cooling equipment impact index. According to the assigned weights, the value of each factor is multiplied by its corresponding weight, and the results are added to obtain the dirt change index; this index can be used to quantify the degree of change in dirt and help decision makers better understand and deal with the dirt problem of transformers. The objective weight method is used to calculate the dirt change index based on the weight ratio value, avoiding the influence of subjective judgment and making the evaluation more objective and accurate; considering the influence of multiple factors on dirt changes, it can more comprehensively evaluate the operating status of the transformer; combining the weights and values ​​of each factor to obtain a specific index, which is easy to understand and compare, and provides practical guidance for operation and maintenance personnel.

[0059] Furthermore, the transformer cleanliness warning is specifically analyzed as follows: extract the pre-processed dirt data; obtain the dirt degree threshold based on the pre-processed dirt data analysis; the dirt degree threshold is used to describe the maximum dirt content that the transformer cooling system can withstand; make a transformer cleanliness warning judgment based on the dirt change index according to the dirt degree threshold; if the dirt change index exceeds the dirt degree threshold, issue a warning to notify relevant personnel; if the dirt change index does not exceed the dirt degree threshold, no warning is issued.

[0060] In this embodiment, since the transformer cooling equipment will be replaced when the cleanliness level is poor, the dirt level threshold is set to 30. By monitoring the dirt change index, a warning can be issued in advance before the dirt exceeds the safety threshold to avoid serious damage to the equipment; regular cleaning of the transformer can avoid performance degradation caused by dirt accumulation and reduce the maintenance costs incurred; maintaining the cleanliness of the transformer can extend the service life of the equipment, reduce the frequency of equipment replacement, and save money; ensuring the cleanliness of the transformer cooling system can reduce the risk of equipment overheating and improve the safety and stability of the system.

[0061] Furthermore, the transformer cleanliness level judgment is specifically analyzed as follows: obtaining cleanliness evaluation indicators; the cleanliness evaluation indicators include standard dirt accumulation rate and cleanliness level; the cleanliness evaluation indicators are used to reflect the cleanliness of the transformer cooling equipment; the standard dirt accumulation rate is used to describe the standard value of the dirt accumulation rate, and the transformer cooling equipment needs to be cleaned if it exceeds the standard value; extracting the pre-treated dirt data; obtaining the dirt accumulation rate based on the pre-treated dirt data analysis; judging the transformer cleanliness level based on the dirt accumulation rate and the standard dirt accumulation rate.

[0062] Furthermore, the cleanliness level of the transformer is judged based on the dirt accumulation rate and the standard dirt accumulation rate. The specific analysis is as follows: if the dirt accumulation rate is greater than the standard dirt accumulation rate, the transformer meets the cleanliness standard; the cleanliness level of the transformer cooling equipment is judged based on the cleanliness level according to the dirt change index; the cleaning cycle is increased according to the cleanliness level of the transformer cooling equipment; if the dirt accumulation rate is not greater than the standard dirt accumulation rate, no treatment is performed.

[0063] In this embodiment, the range of the standard accumulation rate of dirt is 0.01 to 0.05 mg / h.

[0064] Represents the dirt accumulation rate, G1 represents the initial dirt weight within the preset time period, G2 represents the final dirt weight within the preset time period, and t represents the preset time period. The range of cleanliness levels is: the dirt change index is less than 5 for excellent, 5-15 for good, 15-30 for general, and greater than 30 for poor. Increase the cleaning cycle according to the cleanliness level of the transformer cooling equipment, and the cleaning cycle increases by three months for each level. According to the dirt accumulation rate and the cleanliness level of the transformer cooling equipment, a reasonable cleaning plan can be formulated to ensure the long-term stable operation of the equipment; regular cleaning of the transformer cooling equipment can effectively reduce the risk of failure and improve the reliability and stability of the system; adjusting the cleaning cycle according to actual conditions can avoid excessive or insufficient maintenance, thereby saving maintenance costs and human resources; maintaining the cleanliness of the transformer cooling equipment can extend the service life of the equipment, reduce the frequency of equipment replacement, and save money.

[0065] like Figure 3 As shown, it is a structural schematic diagram of the transformer heat dissipation evaluation method provided in an embodiment of the present application. The transformer heat dissipation evaluation method provided in an embodiment of the present application includes: obtaining cooling equipment dirt data: obtaining cooling equipment dirt data, and performing preprocessing according to the cooling equipment dirt data, the preprocessing including removing outliers and noise; dirt change model training: analyzing the preprocessed cooling equipment dirt data based on a machine learning algorithm to obtain a dirt change index; transformer cleanliness warning: obtaining a dirt degree threshold, and performing a transformer cleanliness warning judgment based on the dirt degree threshold according to the dirt change index; transformer cleanliness level judgment: obtaining a cleanliness evaluation index, and judging the cleanliness level of the transformer cooling equipment and performing a transformer cleaning judgment based on the cooling equipment dirt data according to the cleanliness evaluation index.

[0066] The technical scheme in the above-mentioned embodiment of the present application has at least the following technical effects or advantages: relative to the indoor substation multi-physical field testing system and ventilation heat dissipation noise reduction evaluation method disclosed in the patent application with publication number: CN115829367A, the dirt level threshold of the embodiment of the present application realizes the early warning judgment of the cleanliness of the transformer, so as to timely discover the dirt problem and notify the relevant personnel, take maintenance measures in time, and thus reduce the damage to the equipment or performance degradation; relative to a new method for calculating and evaluating the heat dissipation capacity of a large transformer announced in the invention patent with announcement number: CN106295191B, the embodiment of the present application judges the cleanliness level of the transformer cooling equipment through the cleanliness evaluation index, so as to adjust the cleaning cycle as needed, ensure that the equipment is kept in an appropriate clean state, extend the service life of the equipment, and thus improve the reliability and operation efficiency of the equipment.

[0067] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0068] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0069] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0070] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0071] Although preferred embodiments of the present invention have been described, additional changes and modifications may be made to these embodiments by those skilled in the art once the basic inventive concepts are known. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention. Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A transformer heat dissipation evaluation system, characterized in that: include: Module for obtaining cooling equipment dirt data, module for training dirt change model, module for early warning of transformer cleanliness, and module for judging transformer cleanliness level; The module for obtaining cooling equipment fouling data is used to obtain cooling equipment fouling data and perform preprocessing according to the cooling equipment fouling data, wherein the preprocessing includes removing abnormal values ​​and noise; The fouling change model training module is used to analyze the pre-processed cooling equipment fouling data based on a machine learning algorithm to obtain a fouling change index; The transformer cleanliness warning module is used to obtain a dirt level threshold value, and make a transformer cleanliness warning judgment based on the dirt level threshold value according to the dirt change index; The transformer cleanliness level judgment module is used to obtain a cleanliness evaluation index, and judge the cleanliness level of the transformer cooling equipment based on the cleanliness evaluation index and the cooling equipment dirt data, and make a transformer cleaning judgment. If the transformer does not meet the cleaning standard, no treatment is performed; if the transformer meets the cleaning standard, it is cleaned immediately and the cleaning cycle is increased according to the cleanliness level of the transformer cooling equipment; The dirt change model training is specifically analyzed as follows: Extracting pre-processed cooling equipment fouling data; The fouling change index is obtained by analyzing the pre-processed cooling equipment fouling data based on the machine learning algorithm; The dirt change index is used to describe the change trend of the dirt content of the transformer cooling equipment; The dirt change index includes a dirt impact index and a transformer heat dissipation equipment impact index; Get the preset time period; The fouling impact index is used to describe the degree of influence of fouling on the fouling change index within a preset time period; The transformer heat dissipation equipment impact index is used to describe the impact of the transformer heat dissipation equipment on the dirt change index within a preset time period; The dirt change index is obtained by analyzing the dirt impact index and the transformer cooling equipment impact index: The dirt change index is obtained by analyzing the dirt impact index and the transformer heat dissipation equipment impact index. The specific analysis is as follows: According to the objective weight method, the weight ratio of the dirt impact index to the dirt change index and the weight ratio of the transformer heat dissipation equipment impact index to the dirt change index are set; The dirt change index is obtained according to the dirt influence index, the weight ratio of the dirt influence index to the dirt change index, the transformer heat dissipation equipment influence index, and the weight ratio of the transformer heat dissipation equipment influence index to the dirt change index; The calculation formula of the fouling change index is: B=cosh -1 [sinh(Y*Y′+R*R′)+1] In the formula, Y represents the dirt influence index, Y′ represents the weight ratio of the dirt influence index to the dirt change index, R represents the transformer heat dissipation equipment influence index, R′ represents the weight ratio of the transformer heat dissipation equipment influence index to the dirt change index, and B represents the dirt change index; The calculation formula of transformer heat dissipation equipment impact index is: In the formula, w represents the ambient temperature, V represents the wind speed of the cooling equipment, represents the oil temperature change rate, T1 represents the oil temperature at the initial time within the preset time period, T2 represents the oil temperature at the final time within the preset time period, p represents the power consumption of the cooling device, t represents the preset time period, and R represents the transformer heat dissipation device impact index; The calculation formula of the fouling impact index is: In the formula, n represents the dust concentration in the air, S represents the air humidity, i = 1, 2, ..., m represents the number of each dirt particle, m represents the total number of dirt particles, g i represents the dirt weight of the i-th dirt, d i The dirt particle size of the i-th dirt itself, G1 represents the initial dirt weight within the preset time period, G2 represents the final dirt weight within the preset time period, and Y represents the dirt impact index.

2. The transformer heat dissipation evaluation system according to claim 1, characterized in that: The acquisition of cooling equipment fouling data is specifically analyzed as follows: Get cooling equipment fouling data; The cooling equipment fouling data includes environmental impact data, fouling data, and transformer cooling equipment data; Pre-processing based on cooling equipment fouling data; The preprocessing includes removing outliers and noise.

3. The transformer heat dissipation evaluation system according to claim 2, characterized in that: The environmental impact data, dirt data, and transformer cooling equipment data are specifically analyzed as follows: The environmental impact data include air dust concentration, ambient temperature, and air humidity; The dirt data includes dirt weight, dirt particle size, and dirt accumulation; The transformer cooling equipment data includes oil temperature change rate, cooling equipment wind speed, and cooling equipment power consumption.

4. The transformer heat dissipation evaluation system according to claim 1, characterized in that: The transformer cleanliness warning is specifically analyzed as follows: Extract pre-processed dirt data; Obtaining a dirt level threshold value based on pre-processed dirt data analysis; The dirt level threshold is used to describe the maximum dirt content that the transformer cooling system can withstand; Perform transformer cleanliness early warning based on dirt change index according to dirt level threshold; If the dirt change index exceeds the dirt level threshold, an early warning will be issued to notify relevant personnel; If the dirt change index does not exceed the dirt level threshold, no warning will be issued.

5. The transformer heat dissipation evaluation system according to claim 1, characterized in that: The transformer cleanliness level judgment is specifically analyzed as follows: Obtain cleanliness assessment indicators; The cleanliness evaluation index includes a standard dirt accumulation rate and a cleanliness grade; The cleanliness evaluation index is used to reflect the cleanliness of the transformer cooling equipment; The standard dirt accumulation rate is used to describe the standard value of the dirt accumulation rate. If the standard value is exceeded, the transformer cooling equipment needs to be cleaned; Extract pre-processed dirt data; The fouling accumulation rate is obtained based on the fouling data analysis after pretreatment; The transformer cleanliness level is determined based on the dirt accumulation rate and the standard dirt accumulation rate.

6. The transformer heat dissipation evaluation system according to claim 5, characterized in that: The transformer cleanliness level is judged based on the dirt accumulation rate and the dirt standard accumulation rate, and the specific analysis is as follows: If the dirt accumulation rate is greater than the dirt standard accumulation rate, the transformer reaches the cleanliness standard; Determine the cleanliness level of the transformer cooling equipment based on the cleanliness level according to the dirt change index; Increase the cleaning cycle according to the cleanliness level of the transformer cooling equipment; If the dirt accumulation rate is not greater than the standard dirt accumulation rate, no treatment will be performed.

7. A transformer heat dissipation evaluation method, characterized in that: The following steps are involved: Acquiring cooling equipment fouling data: acquiring cooling equipment fouling data, and performing preprocessing according to the cooling equipment fouling data, wherein the preprocessing includes removing outliers and noise; Fouling change model training: The fouling data of the pre-processed cooling equipment is analyzed based on the machine learning algorithm to obtain the fouling change index; Transformer cleanliness warning: Obtain the dirt level threshold, and make transformer cleanliness warning judgment based on the dirt level threshold according to the dirt change index; Transformer cleanliness level judgment: obtain cleanliness evaluation indicators, judge the cleanliness level of the transformer cooling equipment based on the cleanliness evaluation indicators and the cooling equipment dirt data, and make a transformer cleaning judgment; The dirt change model training is specifically analyzed as follows: Extracting pre-processed cooling equipment fouling data; The fouling change index is obtained by analyzing the pre-processed cooling equipment fouling data based on the machine learning algorithm; The dirt change index is used to describe the change trend of the dirt content of the transformer cooling equipment; The dirt change index includes a dirt impact index and a transformer heat dissipation equipment impact index; Get the preset time period; The fouling impact index is used to describe the degree of influence of fouling on the fouling change index within a preset time period; The transformer heat dissipation equipment impact index is used to describe the impact of the transformer heat dissipation equipment on the dirt change index within a preset time period; The dirt change index is obtained by analyzing the dirt impact index and the transformer heat dissipation equipment impact index; The dirt change index is obtained by analyzing the dirt impact index and the transformer heat dissipation equipment impact index. The specific analysis is as follows: According to the objective weight method, the weight ratio of the dirt impact index to the dirt change index and the weight ratio of the transformer heat dissipation equipment impact index to the dirt change index are set; The dirt change index is obtained according to the dirt influence index, the weight ratio of the dirt influence index to the dirt change index, the transformer heat dissipation equipment influence index, and the weight ratio of the transformer heat dissipation equipment influence index to the dirt change index; The calculation formula of the fouling change index is: B=cosh -1 [sinh(Y*Y′+R*R′)+1] In the formula, Y represents the dirt influence index, Y′ represents the weight ratio of the dirt influence index to the dirt change index, R represents the transformer heat dissipation equipment influence index, R′ represents the weight ratio of the transformer heat dissipation equipment influence index to the dirt change index, and B represents the dirt change index; The calculation formula of transformer heat dissipation equipment impact index is: In the formula, w represents the ambient temperature, V represents the wind speed of the cooling equipment, represents the oil temperature change rate, T1 represents the oil temperature at the initial time within the preset time period, T2 represents the oil temperature at the final time within the preset time period, p represents the power consumption of the cooling device, t represents the preset time period, and R represents the transformer heat dissipation device impact index; The calculation formula of the fouling impact index is: In the formula, n represents the dust concentration in the air, S represents the air humidity, i = 1, 2, ..., m represents the number of each dirt particle, m represents the total number of dirt particles, g i represents the dirt weight of the i-th dirt, d i The dirt particle size of the i-th dirt itself, G1 represents the initial dirt weight within the preset time period, G2 represents the final dirt weight within the preset time period, and Y represents the dirt impact index.

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