Auxiliary intelligent heat dissipation control system and method for transformer

Abstract

The invention discloses a transformer auxiliary intelligent heat dissipation control system and method, and relates to the technical field of transformer heat dissipation. Transformer information, environment information and medium information are collected and comprehensively analyzed to obtain a demand judgment value, and a heat dissipation regulation and control instruction is automatically generated according to the demand judgment value; through a received heat dissipation regulation and control instruction, the oil quality coefficient is compared to judge the aging and pollution conditions of the cooling oil, a cooling oil replacement instruction is generated, and the heat dissipation effect and the insulation performance of the cooling oil are ensured; secondly, the oil pump flow is automatically calculated and adjusted through an intelligent calculus model, dynamic circulation control is conducted on the transformer according to the nearest oil pump flow, the oil pump flow is adjusted in real time, and the heat dissipation effect is optimized; and finally, after heat dissipation regulation and control are completed, automatic returning is carried out, a new round of monitoring and analysis is carried out, closed-loop control is formed, the automatic feedback mechanism ensures that the system continuously optimizes the heat dissipation effect, and heat dissipation of the transformer is kept in the optimal operation state.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer heat dissipation, and in particular to a transformer-assisted intelligent heat dissipation control system and method. Background Art

[0002] A transformer is an electrical device that uses the principle of electromagnetic induction to convert the voltage or current of one alternating current into the voltage or current of another alternating current. It is mainly composed of an iron core and windings, and the specific windings are divided into primary (input side) and secondary (output side). When alternating current is passed through the primary winding, an alternating magnetic flux is generated, which is transmitted in the iron core and induces an electromotive force (voltage) in the secondary winding. By adjusting the turns ratio of the primary and secondary windings, the output voltage can be changed. Transformers have a wide range of applications in modern society and are an indispensable core component in power and electronic equipment. However, their working efficiency and operating safety are greatly affected by temperature, so the heat dissipation of transformers is particularly important.

[0003] At present, auxiliary heat dissipation of high-power power transformers (for example, tens of kilowatts to hundreds of megawatts) usually adopts oil immersion cooling. The staff sets the oil pump flow to control the circulation speed to achieve the heat dissipation effect. However, this control method is not intelligent enough and has great limitations, resulting in energy waste or inadequate heat dissipation effect. Summary of the invention

[0004] The main purpose of the present invention is to provide a transformer-assisted intelligent heat dissipation control system and method to overcome the problems mentioned in the above background technology.

[0005] To achieve the above object, according to one aspect of the present invention, a transformer-assisted intelligent heat dissipation control system is provided, the system comprising: a server, a demand monitoring module and a heat dissipation control module;

[0006] The server communicates with the transformer and various sensors mounted on the transformer to obtain transformer information, environmental information of the transformer environment, and medium information, and saves them;

[0007] The demand monitoring module monitors and analyzes the heat dissipation needs of the transformer to generate heat dissipation control instructions and sends them to the heat dissipation control module. The specific heat dissipation control instructions include oil quality coefficient and conduction temperature;

[0008] The heat dissipation control module performs dynamic cycle control on the transformer based on the received heat dissipation control instructions. The specific control steps are as follows:

[0009] S3-1: retrieve the oil quality coefficient, compare and analyze the oil quality coefficient with the set oil quality threshold, and when the oil quality coefficient is greater than or equal to the set oil quality threshold, execute S3-2; when the oil quality coefficient is less than the set oil quality threshold, execute S3-3;

[0010] S3-2: Sending a cooling oil replacement instruction to the corresponding engineer, and returning to the heat dissipation control module if a replacement completion instruction is received from the engineer;

[0011] S3-3: retrieve the conduction temperature DTj at each acquisition time, where j = 1, 2, 3 ... J, J is a positive integer, J represents the total number of acquisition times in the monitoring window, and j represents the number of any acquisition time; set a target temperature denoted as AT; calculate the temperature deviation f(j), the specific calculation formula is: f(j) = DTj-AT;

[0012] The calculus model is used to calculate the latest oil pump flow rate;

[0013] S3-4: The flow rate of the cooling oil is adjusted according to the latest oil pump flow rate, and after the latest oil pump flow rate is kept fixed for a period of time, the cooling control module is returned until no cooling control instruction is generated.

[0014] Furthermore, the specific calculus model is:

[0015]

[0016] Among them, γ1, γ2 and γ3 are the set proportional coefficient, integral coefficient and calculus coefficient respectively.

[0017] Furthermore, the heat dissipation of the transformer needs to be monitored and analyzed. The specific process of monitoring and analysis is as follows:

[0018] S2-1: Retrieve the medium information of the transformer, which includes the dielectric strength, viscosity, water content, thermal conductivity and oil image of the cooling oil, and quantitatively analyze the quality state of the cooling oil based on the medium information to obtain the oil quality coefficient;

[0019] S2-2: Retrieve the transformer information of the transformer at each acquisition time; the specific transformer information includes load, winding temperature and core temperature, which are recorded as Fj, RTj and XTj respectively; the load Fj, winding temperature RTj and core temperature XTj are normalized and their values ​​are taken, and the values ​​are calculated and analyzed by formula to obtain the thermal load value FRj. The specific calculation formula is:

[0020]

[0021] in are respectively set weight constants, and their values ​​are set by technicians in this field according to actual needs. The value is 0.23. The value is 0.36. The value is 0.41;

[0022] S2-3: retrieve the environmental information at each acquisition time, including the environmental temperature and air flow rate, and record them as HTj and Vj respectively; compare the winding temperature and the core temperature to obtain the conduction temperature at each acquisition time, record it as DTj, normalize the conduction temperature DTj, the environmental temperature HTj and the air flow rate Vj and take their values, perform formula calculation and analysis on the values ​​to obtain the environmental value HVj, the specific calculation formula is:

[0023]

[0024] in are respectively set weight constants, and their values ​​are set by technicians in this field according to actual needs. The value is 0.43. The value is 0.57;

[0025] S2-4: The heat load value FRj and the environment value HVj are calculated by the set formula LRj=α1×FRj+α2×HVj to obtain the heat state value LRj at each collection time, wherein α1 and α2 are respectively set weight constants; a two-dimensional rectangular coordinate system is constructed with time as the horizontal coordinate and the heat state value as the vertical coordinate, the heat state value is input into the coordinate system according to its corresponding collection time, and the position of the heat state value in the coordinate system is recorded as a hot spot, and line segments are used to connect them in sequence to obtain a line graph of the heat state value change;

[0026] S2-5: Analyze the heat state according to the heat state value change line graph to obtain the demand judgment value.

[0027] Furthermore, the specific process of quantitatively analyzing the quality status of cooling oil based on medium information is as follows:

[0028] S2-1-1: Enlarge the oil image to form a number of pixel grids, identify the brightness of each pixel grid and record it as Gi, where i = 1, 2, 3 ... I, I is a positive integer, I represents the total number of pixel grids formed after the oil image is enlarged, and i represents the number of any pixel grid; the brightness Gi of each pixel grid is calculated by the formula The clarity value Gβ is calculated, where β1 and β2 are respectively set weight constants, and the values ​​are set by those skilled in the art according to actual needs. For example, β1 is 0.43 and β2 is 0.57.

[0029] S2-1-2: Set an initial parameter corresponding to each type of cooling oil, and the specific initial parameters include initial dielectric strength, initial viscosity, and initial thermal conductivity; extract the type of cooling oil, and compare it with all the set types to match the corresponding initial parameters; the specific initial parameters include initial dielectric strength, initial viscosity, and initial thermal conductivity, and record them as Qo, Zo, and Yo;

[0030] S2-1-3: Normalize the clarity value Gβ, dielectric strength Q, viscosity Z, water content W, thermal conductivity Y, initial dielectric strength Qo, initial viscosity Zo and initial thermal conductivity Yo and take their values. Formulate and analyze the values ​​to obtain the oil quality coefficient σ. The specific calculation formula is:

[0031]

[0032] Among them, λ1, λ2, λ3, λ4, and λ5 are respectively set weight constants, and their values ​​are set by those skilled in the art according to actual conditions.

[0033] Furthermore, if the winding temperature is greater than or equal to the core temperature, the winding temperature is selected as the conduction temperature; if the winding temperature is less than the core temperature, the core temperature is selected as the conduction temperature.

[0034] Furthermore, the specific process of analyzing the heat state according to the heat state value change line graph is as follows:

[0035] Calculate the slope of the line segment formed by two adjacent hot spots and mark it as the thermal slope The heat state values ​​corresponding to two adjacent hot spots are averaged to obtain the heat line value, which is recorded as The heat slopes corresponding to two adjacent hot spots and heat segment value Numerical calculation and analysis to obtain the heat ratio The specific calculation formula is:

[0036]

[0037] Among them, α3, α4, α5, and α6 are the set weight constants respectively. When the heat line value is larger, the heat slope is larger, and the heat ratio is larger; when the heat slope is When the heat slope is When the heat line value is larger, the heat ratio is larger; if the heat slope is smaller (that is, the absolute value of the heat slope is larger), the heat ratio is smaller; thus, the heat ratio corresponding to any two adjacent hot spots can be obtained;

[0038] Further quantitative analysis of each heat ratio is performed to obtain the demand judgment value.

[0039] Furthermore, each heat ratio value is further quantitatively analyzed to obtain the demand judgment value. The specific process is as follows:

[0040] Compare and analyze the heat ratio with the set heat value interval. When the heat ratio is greater than the maximum value in the set heat value interval, a high ratio accumulation is accumulated; when the heat ratio is within the set heat value interval, a medium ratio accumulation is accumulated; when the heat ratio is greater than the minimum value in the set heat value interval, a low ratio accumulation is accumulated; the cumulative times of high ratio accumulation, medium ratio accumulation and low ratio accumulation are counted respectively, and they are recorded as P1, P2 and P3 respectively; the cumulative times of high ratio accumulation, medium ratio accumulation, low ratio accumulation and heat ratio are counted. The demand judgment value Pσ is obtained by numerical calculation and analysis of the oil quality coefficient σ; the specific calculation formula is:

[0041]

[0042] Among them, η1, η2, and η3 are set weight constants respectively, and η1>η2>η3>0.

[0043] Furthermore, within a monitoring window, transformer information and environmental information are collected at each collection moment within the monitoring window, but medium information is only collected once within a monitoring window. The specific monitoring window is set by the technician according to actual needs.

[0044] In order to achieve the above object, according to another aspect of the present invention, a transformer-assisted intelligent heat dissipation control method is provided, the method comprising the following steps:

[0045] Step 1: Communicate with the transformer and various sensors mounted on the transformer to obtain transformer information, environmental information of the transformer environment, and medium information, and save them;

[0046] Step 2: Generate heat dissipation control instructions by monitoring and analyzing the heat dissipation needs of the transformer, and send them to the heat dissipation control module. The specific heat dissipation control instructions include oil quality coefficient and conduction temperature;

[0047] Step 3: Dynamically cycle control the transformer based on the received heat dissipation control command. The specific control steps are as follows:

[0048] S3-1: retrieve the oil quality coefficient, compare and analyze the oil quality coefficient with the set oil quality threshold, and when the oil quality coefficient is greater than or equal to the set oil quality threshold, execute S3-2; when the oil quality coefficient is less than the set oil quality threshold, execute S3-3;

[0049] S3-2: Sending a cooling oil replacement instruction to the corresponding engineer, and returning to the heat dissipation control module if a replacement completion instruction is received from the engineer;

[0050] S3-3: retrieve the conduction temperature DTj at each acquisition time, where j = 1, 2, 3 ... J, J is a positive integer, J represents the total number of acquisition times in the monitoring window, and j represents the number of any acquisition time; set a target temperature denoted as AT; calculate the temperature deviation f(j), the specific calculation formula is: f(j) = DTj-AT;

[0051] The calculus model is used to calculate the latest oil pump flow rate. The specific calculus model is:

[0052]

[0053] Among them, γ1, γ2 and γ3 are the set proportional coefficient, integral coefficient and calculus coefficient respectively;

[0054] S3-4: The flow rate of the cooling oil is adjusted according to the latest oil pump flow rate, and after the latest oil pump flow rate is kept fixed for a period of time, the cooling control module is returned until no cooling control instruction is generated.

[0055] Beneficial effects of the present invention:

[0056] The present invention collects transformer information, environmental information and medium information, and performs comprehensive analysis on them to obtain demand judgment values, and automatically generates heat dissipation control instructions according to the demand judgment values; it realizes the fusion analysis of multi-dimensional data, can more comprehensively evaluate the heat dissipation demand of the transformer, improves the accuracy and reliability of the analysis, and reduces manual intervention;

[0057] The present invention first compares the oil quality coefficient to determine the aging and contamination of the cooling oil through the received heat dissipation control instruction, generates a cooling oil replacement instruction, ensures the heat dissipation effect and insulation performance of the cooling oil, and extends the service life of the transformer; secondly, through the intelligent calculus model, automatically calculates and adjusts the oil pump flow, performs dynamic cycle control on the transformer according to the latest oil pump flow, adjusts the oil pump flow in real time, and optimizes the heat dissipation effect; finally, after completing the heat dissipation control, it automatically returns and performs a new round of monitoring and analysis to form a closed-loop control. This automatic feedback mechanism ensures that the system continuously optimizes the heat dissipation effect and keeps the transformer heat dissipation in the best operating state;

[0058] In summary, the present invention realizes precise control of transformer heat dissipation through multi-level data processing, intelligent algorithm evaluation and closed-loop control. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0060] Figure 1 is a schematic diagram of system module connection of the present invention;

[0061] Figure 2 It is a schematic flow chart of the method of the present invention. DETAILED DESCRIPTION

[0062] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0063] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0064] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so as to describe the embodiments of the present invention described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0065] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0066] According to an embodiment of the present invention, Figure 1-2As shown, a transformer-assisted intelligent heat dissipation control method is provided. The method is implemented based on a transformer-assisted intelligent heat dissipation control system. A specific transformer-assisted intelligent heat dissipation control system includes: a server, a demand monitoring module and a heat dissipation control module; the specific method includes:

[0067] Step 1: The server communicates with the transformer and various sensors mounted on the transformer to obtain transformer information, environmental information and medium information of the transformer environment, and saves them; specific transformer information includes load, winding temperature and core temperature; environmental information includes ambient temperature and air flow rate; medium information includes dielectric strength, viscosity, water content, thermal conductivity and oil image of cooling oil; it should be noted that in the actual application scenario of the present invention, usually within a monitoring window, it is necessary to collect transformer information and environmental information at each collection time within the monitoring window, but within a monitoring window, usually only one medium information is collected; the specific monitoring window is set by the technician according to actual needs, usually 20 minutes, 30 minutes or 50 minutes, etc. as a monitoring window;

[0068] Step 2: The demand monitoring module monitors and analyzes the heat dissipation demand of the transformer to generate a heat dissipation control instruction and sends it to the heat dissipation control module; specifically:

[0069] S2-1: Retrieve the medium information of the transformer, which includes the dielectric strength, viscosity, water content, thermal conductivity and oil image of the cooling oil. The dielectric strength, viscosity, water content and thermal conductivity are recorded as Q, Z, W and Y respectively. It should be noted that the longer the cooling oil of the transformer is used, the more it will age and become polluted, resulting in the deterioration of its heat dissipation effect and insulation performance. The aging and pollution status of the cooling oil is analyzed based on the medium information to obtain the oil quality coefficient. The specific analysis process is as follows:

[0070] S2-1-1: The oil image is enlarged to form a number of pixel grids, and the brightness of each pixel grid is identified as Gi, where i = 1, 2, 3 ... I, I is a positive integer, I represents the total number of pixel grids formed after the oil image is enlarged, and i represents the number of any pixel grid; it should be noted that the greater the brightness and the more average the brightness of each pixel grid, the clearer the cooling oil, and vice versa, the more turbid the cooling oil; the brightness Gi of each pixel grid is set by the formula The clarity value Gβ is calculated, where β1 and β2 are respectively set weight constants, and the values ​​are set by those skilled in the art according to actual needs. For example, β1 is 0.43 and β2 is 0.57.

[0071] S2-1-2: Set an initial parameter for each type of cooling oil. The specific initial parameters include initial dielectric strength, initial viscosity, and initial thermal conductivity. It should be noted that the initial parameters are determined by the chemical properties of the cooling oil. Usually, the initial dielectric strength of the cooling oil is the largest, the initial viscosity is the smallest, and the initial thermal conductivity is the largest. As the use time increases, the dielectric strength decreases (dielectric strength refers to the ability of the cooling oil to withstand the electric field strength without electrical breakdown. Usually, the initial dielectric strength of the cooling oil is relatively large because the new oil has good chemical stability and is not easily destroyed by the electric field. As the use time increases, the oil will be affected by factors such as high temperature, oxidation, and moisture, resulting in a decrease in dielectric strength), and the viscosity increases (viscosity refers to the adhesion of the cooling oil to the surface. The cooling oil is relatively stable at the beginning due to its chemical properties. Stable, with high surface tension and low viscosity; with the passage of time, impurities, metal ions, oxides, etc. in the oil increase, the surface tension of the oil decreases, the viscosity increases, and the cooling performance decreases), the thermal conductivity decreases (thermal conductivity is the ability of the oil to transfer heat, and the thermal conductivity of the cooling oil is usually high at the beginning; as time goes by, some pollutants, bubbles or moisture will accumulate in the cooling oil, resulting in a decrease in thermal conductivity, especially after long-term use, the oil may change its properties due to oxidation, affecting its thermal conductivity), the water content increases, and the cooling efficiency of the cooling oil decreases; the type of cooling oil is extracted and compared with all the set types to match the corresponding initial parameters; the specific initial parameters include initial dielectric strength, initial viscosity, initial thermal conductivity, and are recorded as Qo, Zo and Yo;

[0072] S2-1-3: Normalize the clarity value Gβ, dielectric strength Q, viscosity Z, water content W, thermal conductivity Y, initial dielectric strength Qo, initial viscosity Zo and initial thermal conductivity Yo and take their values. Formulate and analyze the values ​​to obtain the oil quality coefficient σ. The specific calculation formula is:

[0073]

[0074] Wherein λ1, λ2, λ3, λ4, and λ5 are respectively set weight constants, and their values ​​are set by those skilled in the art according to actual conditions;

[0075] S2-2: retrieve the transformer information of the transformer at each acquisition time; the specific transformer information includes load, winding temperature and core temperature, and record them as Fj, RTj and XTj respectively, where j = 1, 2, 3 ... J, J is a positive integer, J represents the total number of acquisition times in the monitoring window, and j represents the number of any acquisition time; normalize the load Fj, winding temperature RTj and core temperature XTj and take their values, and perform formula calculation and analysis on the values ​​to obtain the thermal load value FRj, and the specific calculation formula is:

[0076]

[0077] in are respectively set weight constants, and their values ​​are set by technicians in this field according to actual needs. The value is 0.23. The value is 0.36. The value is 0.41;

[0078] S2-3: Retrieve environmental information at each collection time, the specific environmental information includes ambient temperature and air flow rate (the air flow rate is a value that measures the air flow state of the environment in which the transformer is located, which can be collected by a wind speed sensor), and record them as HTj and Vj respectively; it should be noted that when the ambient temperature is higher, the cooling efficiency is lower, and the heat dissipation demand increases; when the air flow rate is higher, it is helpful to dissipate heat, while the low flow rate will cause heat accumulation and increase the heat dissipation demand; for oil-immersed cooling, the influence of ambient humidity on cooling efficiency can be ignored, so the present invention does not consider the influence of ambient humidity in the actual modeling process;

[0079] Compare the winding temperature RTj and the core temperature XTj. If RTj≥XTj, the winding temperature is selected as the conduction temperature and recorded as DTj; if RTj<XTj, the core temperature is selected as the conduction temperature and recorded as DTj;

[0080] The conduction temperature DTj, ambient temperature HTj and air flow rate Vj are normalized and their values ​​are taken. The values ​​are analyzed and calculated by formula to obtain the environmental value HVj. The specific calculation formula is:

[0081]

[0082] in are respectively set weight constants, and their values ​​are set by technicians in this field according to actual needs. The value is 0.43. The value is 0.57. It should be noted that when the ambient temperature is significantly lower than the conduction temperature, it means that there is a large temperature difference between the transformer and the environment, and the transformer can dissipate heat through the environment, so the smaller the environmental value is; when the air flow rate is higher, it means that the flowing air can take away the heat and reduce the heat accumulation of the transformer, so the smaller the environmental value is;

[0083] S2-4: The heat load value FRj and the environment value HVj are calculated by the set formula LRj=α1×FRj+α2×HVj to obtain the heat state value LRj at each collection time, wherein α1 and α2 are respectively set weight constants, and the values ​​are set by those skilled in the art according to actual needs; a two-dimensional rectangular coordinate system is constructed with time as the horizontal coordinate and the heat state value as the vertical coordinate, and the heat state value is input into the coordinate system according to its corresponding collection time, and the position of the heat state value in the coordinate system is recorded as a hot spot, and a line segment is used to connect them in sequence to obtain a line graph of the heat state value change;

[0084] S2-5: Calculate the slope of the line segment formed by two adjacent hot spots and mark it as the heat slope It should be noted that when the heat slope is greater than zero, it means that the heat state value is in an increasing trend; when the heat slope is less than zero, it means that the heat state value is in a decreasing trend; the heat state values ​​corresponding to two adjacent hot spots are averaged and the heat line value is recorded as The heat slopes corresponding to two adjacent hot spots and heat segment value Numerical calculation and analysis to obtain the heat ratio The specific calculation formula is:

[0085]

[0086] Among them, α3, α4, α5, and α6 are the set weight constants respectively. When the heat line value is larger, the heat slope is larger, and the heat ratio is larger; when the heat slope is When the heat slope is When the heat line value is larger, the heat ratio is larger; if the heat slope is smaller (that is, the absolute value of the heat slope is larger), the heat ratio is smaller; it should be noted that the larger the heat ratio is, the greater the need for heat dissipation is; thus, the heat ratio corresponding to any two adjacent hot spots can be obtained;

[0087] S2-6: Compare and analyze the heat ratio with the set heat value interval. When the heat ratio is greater than the maximum value in the set heat value interval, a high ratio accumulation is accumulated; when the heat ratio is within the set heat value interval, a medium ratio accumulation is accumulated; when the heat ratio is greater than the minimum value in the set heat value interval, a low ratio accumulation is accumulated; the cumulative number of high ratio accumulation, medium ratio accumulation and low ratio accumulation are counted respectively, and they are recorded as P1, P2 and P3 respectively; the cumulative number of high ratio accumulation, the cumulative number of medium ratio accumulation, the cumulative number of low ratio accumulation, the heat ratio accumulation The demand judgment value Pσ is obtained by numerical calculation and analysis of the oil quality coefficient σ; the specific calculation formula is:

[0088]

[0089] Where η1, η2, and η3 are the set weight constants, and η1>η2>η3>0;

[0090] If the demand judgment value is greater than or equal to the set demand threshold, it means that the cooling effect of the current cooling oil does not meet the thermal state of the transformer. The greater the risk of the transformer being in an overheated or near-overheated state, the oil quality coefficient σ and the conduction temperature DTj are integrated into a heat dissipation control instruction and sent to Step 3; otherwise, no control is required;

[0091] By collecting transformer information, environmental information and medium information, and conducting a comprehensive analysis on them to obtain the demand judgment value, the heat dissipation control instructions are automatically generated based on the demand judgment value; the fusion analysis of multi-dimensional data is realized, which can more comprehensively evaluate the heat dissipation needs of the transformer, improve the accuracy and reliability of the analysis, and reduce manual intervention.

[0092] Step 3: The heat dissipation control module performs dynamic cycle control on the transformer based on the received heat dissipation control instruction to achieve optimal control of the heat dissipation of the transformer; specifically:

[0093] S3-1: retrieve the oil quality coefficient, compare and analyze the oil quality coefficient with the set oil quality threshold. When the oil quality coefficient is greater than or equal to the set oil quality threshold, it means that the cooling oil is greatly aged and contaminated and needs to be replaced, and S3-2 is executed; when the oil quality coefficient is less than the set oil quality threshold, it means that the main reason for the poor heat dissipation effect is not caused by the cooling oil, and S3-3 is executed;

[0094] S3-2: Send a cooling oil replacement instruction to the corresponding engineer. If a replacement completion instruction is received from the engineer, return to Step 2;

[0095] S3-3: retrieve the conduction temperature DTj at each acquisition moment, set a target temperature denoted as AT, and the specific value is set by the personnel in this field according to the actual needs (design specifications of the transformer, operating environment and application scenarios, etc.). It should be noted that the target temperature refers to the safe temperature of the transformer; calculate the temperature deviation f(j), and the specific calculation formula is: f(j) = DTj-AT;

[0096] The calculus model is used to calculate the latest oil pump flow U. The specific calculus model is:

[0097]

[0098] Among them, γ1, γ2 and γ3 are the set proportional coefficient, integral coefficient and calculus coefficient respectively; it should be noted that the integral term ∫f(j)dj accumulates the influence of historical deviation and is used to eliminate long-term errors; The derivative term responds to the rate of change of the deviation and reduces system oscillation;

[0099] S3-4: Adjust the flow rate of cooling oil according to the latest oil pump flow rate, keep the latest oil pump flow rate for a fixed time (the fixed time here is set by the personnel in this field, such as 10 minutes or 15 minutes, etc.), and then return to Step 2 until no heat dissipation control instruction is generated (that is, the demand judgment value is less than the set demand threshold);

[0100] Through the heat dissipation control instructions generated by the demand monitoring module, the oil quality coefficient is first compared to determine the aging and contamination of the cooling oil, and a cooling oil replacement instruction is generated to ensure the heat dissipation effect and insulation performance of the cooling oil and extend the service life of the transformer; secondly, through the intelligent calculus model, the oil pump flow is automatically calculated and adjusted, and the transformer is dynamically cycled according to the latest oil pump flow. The oil pump flow is adjusted in real time to optimize the heat dissipation effect; finally, after completing the heat dissipation control, it automatically returns and conducts a new round of monitoring and analysis to form a closed-loop control. This automatic feedback mechanism ensures that the system continuously optimizes the heat dissipation effect and keeps the transformer heat dissipation in the best operating state.

[0101] The above are only embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A transformer-assisted intelligent heat dissipation control system, characterized in that: include: Demand monitoring module and heat dissipation control module; The demand monitoring module monitors and analyzes the heat dissipation needs of the transformer to generate heat dissipation control instructions and sends them to the heat dissipation control module. The specific heat dissipation control instructions include oil quality coefficient and conduction temperature; The heat dissipation control module performs dynamic cycle control on the transformer based on the received heat dissipation control instructions. The specific control steps are as follows: S3-1: retrieve the oil quality coefficient, compare and analyze the oil quality coefficient with the set oil quality threshold, and when the oil quality coefficient is greater than or equal to the set oil quality threshold, execute S3-2; when the oil quality coefficient is less than the set oil quality threshold, execute S3-3; S3-2: Sending a cooling oil replacement instruction to the corresponding engineer, and returning to the heat dissipation control module if a replacement completion instruction is received from the engineer; S3-3: retrieve the conduction temperature DTj at each acquisition time, where j=1,2,3...J, J is a positive integer, J represents the total number of acquisition times in the monitoring window, and j represents the number of any acquisition time; set a target temperature denoted as AT; calculate the temperature deviation f(j), the specific calculation formula is: f(j)=DTj-AT; The calculus model is used to calculate the latest oil pump flow rate; S3-4: The flow rate of the cooling oil is adjusted according to the latest oil pump flow rate, and after the latest oil pump flow rate is kept fixed for a period of time, the cooling control module is returned until no cooling control instruction is generated.

2. A transformer-assisted intelligent heat dissipation control system according to claim 1, characterized in that: The specific calculus model is: Among them, γ1, γ2 and γ3 are the set proportional coefficient, integral coefficient and calculus coefficient respectively.

3. A transformer-assisted intelligent heat dissipation control system according to claim 1, characterized in that: The heat dissipation of the transformer needs to be monitored and analyzed. The specific process of monitoring and analysis is as follows: S2-1: Retrieve the medium information of the transformer, which includes the dielectric strength, viscosity, water content, thermal conductivity and oil image of the cooling oil, and quantitatively analyze the quality state of the cooling oil based on the medium information to obtain the oil quality coefficient; S2-2: Retrieve transformer information of the transformer at each acquisition time; the specific transformer information includes load, winding temperature and core temperature, and normalizes them and takes their values, and performs formula calculation and analysis on the values ​​to obtain the thermal load value; S2-3: Retrieve environmental information at each acquisition time, including environmental temperature and air flow rate; compare the winding temperature and the core temperature to obtain the conduction temperature at each acquisition time, normalize the conduction temperature, the environmental temperature and the air flow rate and obtain their values, and perform formula calculation and analysis on the values ​​to obtain the environmental value; S2-4: Calculate and analyze the heat load value and the environment value to obtain the heat state value at each collection time, construct a two-dimensional rectangular coordinate system with time as the horizontal coordinate and the heat state value as the vertical coordinate, input the heat state value into the coordinate system according to its corresponding collection time, and record the position of the heat state value in the coordinate system as a hotspot, and use line segments to connect them in sequence to obtain a line graph of the heat state value change; S2-5: Analyze the heat state according to the heat state value change line graph to obtain the demand judgment value.

4. A transformer-assisted intelligent heat dissipation control system according to claim 3, characterized in that: The specific process of quantitatively analyzing the quality status of cooling oil based on medium information is as follows: S2-1-1: Enlarge the oil image to form a number of pixel grids, identify the brightness of each pixel grid; perform formulaic calculation and analysis on the brightness of each pixel grid to obtain a clarity value; S2-1-2: Set an initial parameter corresponding to each type of cooling oil, and the specific initial parameters include initial dielectric strength, initial viscosity, and initial thermal conductivity; extract the type of cooling oil, and compare it with all the set types to match the corresponding initial parameters; the specific initial parameters include initial dielectric strength, initial viscosity, and initial thermal conductivity; S2-1-3: Normalize the clarity value, dielectric strength, viscosity, water content, thermal conductivity, initial dielectric strength, initial viscosity and initial thermal conductivity and take their numerical values, and perform formula calculation and analysis on the numerical values ​​to obtain the oil quality coefficient.

5. A transformer-assisted intelligent heat dissipation control system according to claim 4, characterized in that: If the winding temperature is greater than or equal to the core temperature, the winding temperature is selected as the conduction temperature; if the winding temperature is less than the core temperature, the core temperature is selected as the conduction temperature.

6. A transformer-assisted intelligent heat dissipation control system according to claim 5, characterized in that: The specific process of analyzing the heat status based on the heat status value change line chart is as follows: The slope of the line segment composed of two adjacent hot spots is calculated and marked as the heat slope. The heat state values ​​corresponding to the two adjacent hot spots are averaged to obtain the heat line segment value. The heat slope and heat line segment value corresponding to the two adjacent hot spots are numerically calculated and analyzed to obtain the heat ratio value, thereby obtaining the heat ratio value corresponding to any two adjacent hot spots. Further quantitative analysis of each heat ratio is performed to obtain the demand judgment value.

7. A transformer-assisted intelligent heat dissipation control system according to claim 6, characterized in that: Each heat ratio is further quantitatively analyzed to obtain the demand judgment value. The specific process is as follows: Compare and analyze the heat ratio with the set heat value interval. When the heat ratio is greater than the maximum value in the set heat value interval, a high ratio accumulation is accumulated; when the heat ratio is within the set heat value interval, a medium ratio accumulation is accumulated; when the heat ratio is greater than the minimum value in the set heat value interval, a low ratio accumulation is accumulated; the cumulative number of high ratio accumulation, the cumulative number of medium ratio accumulation, and the cumulative number of low ratio accumulation are counted respectively; The accumulated times of high ratio accumulation, medium ratio accumulation and low ratio accumulation, thermal ratio and oil quality coefficient are numerically calculated and analyzed to obtain the demand judgment value.

8. The transformer-assisted intelligent heat dissipation control system according to claim 1, characterized in that: Also includes servers; The server communicates with the transformer and various sensors mounted on the transformer to obtain transformer information, environmental information of the transformer's environment, and medium information, and saves the information.

9. A transformer-assisted intelligent heat dissipation control system according to claim 8, characterized in that: In one monitoring window, transformer information and environmental information are collected at each collection moment in the monitoring window. However, medium information is only collected once in one monitoring window. The specific monitoring window is set by the technicians according to actual needs.

10. A transformer-assisted intelligent heat dissipation control method, characterized in that Applied to a transformer-assisted intelligent heat dissipation control system as described in any one of claims 1 to 9, the method comprises the following steps: Step 1: Communicate with the transformer and various sensors mounted on the transformer to obtain transformer information, environmental information of the transformer environment, and medium information, and save them; Step 2: Generate heat dissipation control instructions by monitoring and analyzing the heat dissipation needs of the transformer, and send them to the heat dissipation control module. The specific heat dissipation control instructions include oil quality coefficient and conduction temperature; Step 3: Dynamically cycle control the transformer based on the received heat dissipation control command. The specific control steps are as follows: S3-1: retrieve the oil quality coefficient, compare and analyze the oil quality coefficient with the set oil quality threshold, and when the oil quality coefficient is greater than or equal to the set oil quality threshold, execute S3-2; when the oil quality coefficient is less than the set oil quality threshold, execute S3-3; S3-2: Sending a cooling oil replacement instruction to the corresponding engineer, and returning to the heat dissipation control module if a replacement completion instruction is received from the engineer; S3-3: retrieve the conduction temperature DTj at each acquisition time, where j=1,2,3...J, J is a positive integer, J represents the total number of acquisition times in the monitoring window, and j represents the number of any acquisition time; set a target temperature denoted as AT; calculate the temperature deviation f(j), the specific calculation formula is: f(j)=DTj-AT; The calculus model is used to calculate the latest oil pump flow rate; S3-4: The flow rate of the cooling oil is adjusted according to the latest oil pump flow rate, and after the latest oil pump flow rate is kept fixed for a period of time, the cooling control module is returned until no cooling control instruction is generated.

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