Transformer drying treatment process control system and method based on artificial intelligence technology

Abstract

The invention discloses a transformer drying treatment process control system and method based on an artificial intelligence technology, and relates to the technical field of electrical equipment manufacturing. The method comprises the following steps: acquiring adjustable parameters and real-time data of a drying process, extracting index parameters for evaluating drying efficiency and quality, performing weighting processing, determining a comprehensive evaluation factor of the drying process, performing correlation analysis on the adjustable parameters and the comprehensive evaluation factor, calculating the correlation degree between the adjustable parameters and the comprehensive evaluation factor, and calculating the drying quality of the drying process. The method comprises the following steps: screening out adjustable parameters within a set correlation degree threshold range as strong correlation parameters of a comprehensive evaluation factor, constructing an adjustable parameter optimization model by using the obtained historical data of the comprehensive evaluation factor and the strong correlation parameters, and obtaining optimal values of the strong correlation parameters under different initial conditions by using the optimization model. Adjustable parameters of all stages in the drying process are set according to the obtained optimal values, and self-adaptive control is conducted on the drying process according to the set adjustable parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power equipment manufacturing, and in particular to a transformer drying process control system and method based on artificial intelligence technology. Background Art

[0002] Transformer drying is a vital part of the manufacturing process of power equipment, and has a direct impact on the insulation performance, service life and operational safety of the transformer. The quality of the transformer's internal insulation system is crucial and is a key factor in ensuring the safe and reliable operation of the transformer under high voltage and high charge. Drying is one of the basic links in ensuring the quality of the insulation system during manufacturing and maintenance.

[0003] Traditional transformer drying processes include hot air circulation, vacuum drying and other methods. Although they can effectively remove the trace water content in the insulating material, they have significant disadvantages such as low efficiency, long drying time and high energy consumption. The VD-109 closed-cycle hot air vacuum drying equipment currently used in actual production has improved the drying efficiency of transformers with voltage levels of 35kV and below by combining closed-cycle hot air and transformer vacuum drying technology. In addition, there is no need to discharge high-temperature and humid gases into the workshop during the drying process, which improves the production environment. The transformer drying process of this set of equipment is divided into 6 stages: closed-cycle hot air, preheating, step-by-step vacuum, intermittent vacuum, high vacuum, and endpoint judgment.

[0004] However, the existing process relies on the fixed parameter settings of the equipment manufacturer to determine the drying endpoint, and lacks a scientific and intelligent model to support real-time data analysis and dynamic judgment of the transformer. This leads to the following technical problems in actual drying operations: 1) Low drying efficiency: The drying endpoint judgment process is cumbersome. The complete drying process of a transformer usually requires repeated vacuum operations and temperature cycles, and long-term stage monitoring. The static operation mode judgment and repeated operation of the existing equipment make the drying process time longer, resulting in low production efficiency; 2) Energy waste: During the drying process, long-term high-temperature heating and vacuum exhaust operations require a lot of energy, resulting in high power consumption. The static preset mode of the existing equipment cannot accurately control the parameter settings and endpoint time according to specific transformers and environmental conditions, which may cause unnecessary extension of the drying time, resulting in energy waste and increased production costs for enterprises; 3) Unstable drying quality: Since the parameters of each stage are set based on past experience and lack a real-time feedback control mechanism, the drying effect is easily affected by environmental conditions and the initial state of the transformer core, resulting in fluctuations in drying quality.

[0005] In summary, in order to achieve dynamic modeling and optimal regulation of key parameters in the drying process, thereby improving the efficiency and accuracy of the transformer in the closed-cycle hot air-transformer vacuum drying process and reducing unnecessary energy consumption in the production process, we propose a transformer drying process control system and method based on artificial intelligence technology. Summary of the invention

[0006] The main purpose of the present invention is to provide a transformer drying process control system and method based on artificial intelligence technology, which can effectively solve the problems in the background technology.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is:

[0008] The transformer drying process control method based on artificial intelligence technology includes:

[0009] The drying process of the transformer equipment to be controlled is divided into n stages, and the jth adjustable parameter x of the i-th stage is obtained. ij , collect real-time data during the drying process, extract index parameters used to evaluate drying efficiency and quality, perform weighted processing, and determine the comprehensive evaluation factor Ce of the hth drying process h ;

[0010] For the adjustable parameter x ij The comprehensive evaluation factor Ce h Perform correlation analysis to calculate the correlation between the adjustable parameters and the comprehensive evaluation factors. ij , set the correlation threshold r min , filter r ij ≥r min The adjustable parameter of is used as a strong correlation parameter of the comprehensive evaluation factor;

[0011] The obtained historical data of comprehensive evaluation factors and strongly correlated parameters are used to construct an adjustable parameter optimization model, wherein the expression of the optimization model is:

[0012]

[0013]

[0014] In the formula, A represents the adjustable space of strong correlation parameters; Indicated as the initial state s μ The mapping relationship between the strong correlation parameters of the q term and the comprehensive evaluation factors; q It is represented as the qth strong correlation parameter; x qmin It is expressed as the adjustable lower limit of the qth strong correlation parameter; x qmax It is represented as the adjustable upper limit of the qth strong correlation parameter; S is represented as the spatial set of initial states;

[0015] The optimization model is used to obtain the optimal values ​​of the strongly correlated parameters under different initial conditions, and the adjustable parameters at each stage of the drying process are set with the obtained optimal values, and the drying process is adaptively controlled according to the set adjustable parameters.

[0016] Transformer drying process control system based on artificial intelligence technology, including:

[0017] The data acquisition module is used to divide the drying process of the transformer equipment to be controlled into n stages and obtain the jth adjustable parameter x in the i-th stage. ij and real-time data during the drying process;

[0018] The data processing module is used to extract the index parameters used to evaluate the drying efficiency and quality, perform weighted processing, and determine the comprehensive evaluation factor Ce of the hth drying process. h ;

[0019] A data analysis module is used to analyze the adjustable parameter x ij The comprehensive evaluation factor Ce h Perform correlation analysis to calculate the correlation between the adjustable parameters and the comprehensive evaluation factors. ij , set the correlation threshold r min , filter r ij ≥r min The adjustable parameter of is used as a strong correlation parameter of the comprehensive evaluation factor;

[0020] A model building module is used to construct an adjustable parameter optimization model using the acquired comprehensive evaluation factors and historical data of strongly correlated parameters, and to use the optimization model to obtain the optimal values ​​of each of the strongly correlated parameters under different initial conditions;

[0021] The parameter setting module is used to set the adjustable parameters at each stage of the drying process according to the obtained optimal values, and to perform adaptive control on the drying process according to the set adjustable parameters.

[0022] The system further includes a memory, a processor, and an electronic program stored in the memory and capable of running on the processor.

[0023] Furthermore, the comprehensive evaluation factor Ce h The calculation formula is:

[0024]

[0025] In the formula, It is expressed as the distance between all index parameters and their optimal state in the hth drying process; It is expressed as the distance between all index parameters and their worst state in the hth drying process;

[0026] in, is represented as the weight of the kth indicator parameter, and z hk It is expressed as the normalized value of the kth index parameter in the hth drying process; It is expressed as the maximum value among the normalized values ​​of the kth indicator parameter;

[0027] It is expressed as the minimum value among the normalized values ​​of the kth indicator parameter.

[0028] Furthermore, the drying process is divided into a closed-cycle hot air stage, a preheating stage, a step-by-step vacuum stage, an intermittent vacuum stage, a high vacuum stage, and an endpoint judgment stage;

[0029] The index parameters include power consumption, drying time, humidity

[0030] The adjustable parameters of the closed-cycle hot air stage include a hot air temperature setting value and a hot air end temperature setting value;

[0031] The adjustable parameters of the preheating stage include a preheating vacuum setting value, a target temperature setting value, a vacuum waiting time setting value, and a preheating end temperature setting value;

[0032] The adjustable parameters of the step-by-step vacuum stage include 4-level vacuum setting values ​​and 4-level vacuum pumping intermittent waiting time setting values;

[0033] The adjustable parameters of the intermittent vacuum stage include the evacuation time setting value, the Roots vacuum setting value, the Roots temperature setting value, and the intermittent vacuum end setting value;

[0034] The adjustable parameters of the high vacuum stage include a high vacuum time setting value;

[0035] The adjustable parameters of the endpoint determination stage include a set value for the number of endpoint determinations.

[0036] Furthermore, the real-time data includes the initial state data of the core, the initial state data in the tank, and the process monitoring data;

[0037] The iron core initial state data includes the initial humidity and temperature of the iron core;

[0038] The initial state data in the tank include the initial temperature and gas pressure in the tank.

[0039] Furthermore, the correlation r ij The calculation process includes the following steps:

[0040] Step S21, using the comprehensive evaluation factor Ce of the h drying processes h Construct data sequence E = {Ce h}, with h adjustable parameters of the drying process Construct data sequence F = {F 1 , F 2 , ..., F ij}; F q ={x qh};

[0041] Step S22, using formula Standardize each data point in data series E and F;

[0042] Step S23, using formula Calculate the correlation coefficient between each data point in data sequence F and data sequence E Where ρ is the resolution coefficient, the value range is (0,1], and ρ=0.5; Δmin and Δmax are the minimum difference between the two levels and the maximum difference between the two levels of the data sequence respectively;

[0043] Step S24, using formula Calculate and obtain the correlation r between the adjustable parameter and the comprehensive evaluation factor ij ,Q is the type of adjustable parameter.

[0044] The calculation formulas for the two-level minimum difference and the two-level maximum difference of the data sequence are:

[0045]

[0046]

[0047] in, Indicates first-take Calculate the minimum value in the result, and then take the minimum value of all minimum values;

[0048] Indicates first-take Calculate the maximum value among the results, and then take the maximum value of all maximum values.

[0049] The present invention has the following beneficial effects:

[0050] Compared with the prior art, the drying process of the transformer equipment to be controlled is divided into n stages, and the jth adjustable parameter x of the i-th stage is obtained. ij , collect real-time data during the drying process, extract index parameters used to evaluate drying efficiency and quality, perform weighted processing, and determine the comprehensive evaluation factor Ce of the hth drying process h , for the adjustable parameter x ijThe comprehensive evaluation factor Ce h Perform correlation analysis to calculate the correlation between the adjustable parameters and the comprehensive evaluation factors. ij , set the correlation threshold r min , filter r ij ≥r min The adjustable parameters are used as strongly correlated parameters of the comprehensive evaluation factors. The historical data of the comprehensive evaluation factors and the strongly correlated parameters are used to construct an adjustable parameter optimization model. The optimization model is used to obtain the optimal values ​​of each of the strongly correlated parameters under different initial conditions. The adjustable parameters of each stage in the drying process are set with the obtained optimal values. The drying process is adaptively controlled according to the set adjustable parameters, so as to improve the efficiency and accuracy of the transformer in the closed-cycle hot air-transformer vacuum drying process and reduce unnecessary energy consumption in the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A schematic diagram of a transformer drying process method based on artificial intelligence technology according to the present invention;

[0052] Figure 2 It is a structural schematic diagram of the transformer drying process control system based on artificial intelligence technology of the present invention. DETAILED DESCRIPTION

[0053] The present invention will be further described below in conjunction with specific implementation methods, wherein the accompanying drawings are only used for exemplary descriptions and represent only schematic diagrams rather than actual drawings, and should not be understood as limiting the present invention. In order to better illustrate the specific implementation methods of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product.

[0054] The specific implementation process of the technical solution of the present invention includes the following steps:

[0055] Step 1: The drying process of the transformer equipment to be controlled is divided into a closed-cycle hot air stage, a preheating stage, a step-by-step vacuum stage, an intermittent vacuum stage, a high vacuum stage, and an endpoint judgment stage;

[0056] Specifically, the closed-cycle hot air stage includes two sub-stages, which are used to increase the core temperature and condense moisture. The adjustable parameters of this stage include the hot air temperature setting and the hot air end temperature setting.

[0057] The preheating stage is divided into two sub-stages. The vacuum degree is adjusted and improved by cyclic exhaust and filling, and the core temperature reaches the set value. The adjustable parameters in this stage include preheating vacuum setting, target temperature setting, vacuum waiting time setting, and preheating end temperature setting.

[0058] Step-by-step vacuum stage: gradually adjust and improve the vacuum degree to make the core temperature and vacuum degree meet the requirements of the next stage. The adjustable parameters in this stage include 4-level vacuum settings and 4-level vacuum interval waiting time settings.

[0059] Intermittent vacuum stage: use ordinary vacuum pump and Roots pump to perform intermittent and relay vacuum pumping to further reduce the vacuum degree in the tank. The adjustable parameters in this stage include the setting of vacuum time, the setting of starting Roots vacuum, the setting of starting Roots temperature, and the setting of ending intermittent vacuum.

[0060] High vacuum stage: Continuously evacuate to achieve the high vacuum conditions required for the drying end point. The adjustable parameter in this stage is the high vacuum time setting.

[0061] End point judgment stage: intermittent vacuuming, judging whether the drying is complete by the increase in vacuum degree. The adjustable parameter in this stage is the number of endpoint judgment settings.

[0062] Step 2: Get the jth adjustable parameter x of the i-th stage ij , collect real-time data during the drying process; wherein the real-time data includes the initial state data of the iron core, the initial state data in the tank, and the process monitoring data; the initial state data of the iron core includes the initial humidity and temperature of the iron core; the initial state data in the tank includes the initial temperature and air pressure in the tank.

[0063] Step 3: Extract the index parameters used to evaluate the drying efficiency and quality, including power consumption, drying time, and humidity, and perform weighted processing on the obtained index parameters to determine the comprehensive evaluation factor Ce of the hth drying process h ; The calculation formula is:

[0064]

[0065] In the formula, It is expressed as the distance between all index parameters and their optimal state in the hth drying process; It is expressed as the distance between all index parameters and their worst state in the hth drying process;

[0066] in, is represented as the weight of the kth indicator parameter, and z hk It is expressed as the normalized value of the kth index parameter in the hth drying process; It is expressed as the maximum value among the normalized values ​​of the kth indicator parameter;

[0067] It is expressed as the minimum value among the normalized values ​​of the kth indicator parameter.

[0068] Step 4: Adjust the adjustable parameter xij and comprehensive evaluation factor Ce h Perform correlation analysis and calculate the correlation between adjustable parameters and comprehensive evaluation factors. ij ; The calculation process includes the following steps:

[0069] Step S41, using the comprehensive evaluation factor Ce of the h drying processes h Construct data sequence E = {Ce h}, with h adjustable parameters of the drying process Construct data sequence F = {F 1 , F 2 , ..., F ij}; F q ={x qh};

[0070] Step S42, using formula Standardize each data point in data series E and F;

[0071] Step S43, using formula Calculate the correlation coefficient between each data point in data sequence F and data sequence E Where ρ is the resolution coefficient, the value range is (0,1], and ρ=0.5; Δmin and Δmax are the minimum difference between the two levels and the maximum difference between the two levels of the data sequence respectively; the calculation formulas are:

[0072]

[0073]

[0074] in, Indicates first-take Calculate the minimum value in the result, and then take the minimum value of all minimum values;

[0075] Indicates first-take Calculate the maximum value among the results, and then take the maximum value of all maximum values.

[0076] Step S44, using formula Calculate the correlation between adjustable parameters and comprehensive evaluation factors r ij ,Q is the type of adjustable parameter.

[0077] Step 5: Set the correlation threshold r min , filter r ij ≥r min The adjustable parameters are used as strong correlation parameters of comprehensive evaluation factors;

[0078] Step 6: Use the obtained comprehensive evaluation factors and historical data of strongly correlated parameters to construct an adjustable parameter optimization model, where the expression of the optimization model is:

[0079]

[0080]

[0081] In the formula, A represents the adjustable space of strong correlation parameters; Indicated as the initial state s μ The mapping relationship between the strong correlation parameters of the q term and the comprehensive evaluation factors; q It is represented as the qth strong correlation parameter; x qmin It is expressed as the adjustable lower limit of the qth strong correlation parameter; x qmax It is represented as the adjustable upper limit of the qth strong correlation parameter; S is represented as the spatial set of initial states;

[0082] Step 7: Use the optimization model to obtain the optimal values ​​of each strongly correlated parameter under different initial conditions, and use the obtained optimal values ​​to set the adjustable parameters of each stage in the drying process, and adaptively control the drying process according to the set adjustable parameters.

[0083] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A transformer drying process control method based on artificial intelligence technology, characterized in that: include: The drying process of the transformer equipment to be controlled is divided into n stages, and the jth adjustable parameter x of the i-th stage is obtained. ij , collect real-time data during the drying process, extract index parameters used to evaluate drying efficiency and quality, perform weighted processing, and determine the comprehensive evaluation factor Ce of the hth drying process h ; For the adjustable parameter x ij The comprehensive evaluation factor Ce h Perform correlation analysis to calculate the correlation between the adjustable parameters and the comprehensive evaluation factors. ij , set the correlation threshold r min , filter r ij ≥r min The adjustable parameter of is used as a strong correlation parameter of the comprehensive evaluation factor; The obtained comprehensive evaluation factors and historical data of strongly correlated parameters are used to construct an adjustable parameter optimization model, wherein the expression of the optimization model is: In the formula, A represents the adjustable space of strong correlation parameters; Indicated as the initial state s μ The mapping relationship between the strong correlation parameters of the q term and the comprehensive evaluation factors; q It is represented as the qth strong correlation parameter; x qmin It is expressed as the adjustable lower limit of the qth strong correlation parameter; x qmax It is represented as the adjustable upper limit of the qth strong correlation parameter; S is represented as the spatial set of initial states; The optimization model is used to obtain the optimal values ​​of the strongly correlated parameters under different initial conditions, and the adjustable parameters at each stage of the drying process are set with the obtained optimal values, and the drying process is adaptively controlled according to the set adjustable parameters.

2. The transformer drying process control method based on artificial intelligence technology according to claim 1 is characterized in that: Comprehensive evaluation factor Ce h The calculation formula is: In the formula, It is expressed as the distance between all index parameters and their optimal state in the hth drying process; It is expressed as the distance between all index parameters and their worst state in the hth drying process; in, is represented as the weight of the kth indicator parameter, and z hk It is expressed as the normalized value of the kth index parameter in the hth drying process; It is expressed as the maximum value among the normalized values ​​of the kth indicator parameter; It is expressed as the minimum value among the normalized values ​​of the kth indicator parameter.

3. The transformer drying process control method based on artificial intelligence technology according to claim 1 is characterized in that: The drying process is divided into a closed-cycle hot air stage, a preheating stage, a step-by-step vacuum stage, an intermittent vacuum stage, a high vacuum stage, and an endpoint judgment stage; The index parameters include power consumption, drying time, humidity The adjustable parameters of the closed-cycle hot air stage include a hot air temperature setting value and a hot air end temperature setting value; The adjustable parameters of the preheating stage include a preheating vacuum setting value, a target temperature setting value, a vacuum waiting time setting value, and a preheating end temperature setting value; The adjustable parameters of the step-by-step vacuum stage include 4-level vacuum setting values ​​and 4-level vacuum pumping intermittent waiting time setting values; The adjustable parameters of the intermittent vacuum stage include the evacuation time setting value, the Roots vacuum setting value, the Roots temperature setting value, and the intermittent vacuum end setting value; The adjustable parameters of the high vacuum stage include a high vacuum time setting value; The adjustable parameters of the endpoint determination stage include a set value for the number of endpoint determinations.

4. The transformer drying process control method based on artificial intelligence technology according to claim 1 is characterized in that: The real-time data includes the initial state data of the core, the initial state data in the tank, and the process monitoring data; The iron core initial state data includes the initial humidity and temperature of the iron core; The initial state data in the tank include the initial temperature and gas pressure in the tank.

5. The transformer drying process control method based on artificial intelligence technology according to claim 1 is characterized in that: Correlation r ij The calculation process includes the following steps: Step S21, using the comprehensive evaluation factor Ce of the h drying processes h Construct data sequence E = {Ce h }, with h adjustable parameters of the drying process Construct a data sequence F = {F1, F2, ..., F ij }; F q ={x qh }; Step S22, using formula Standardize each data point in data series E and F; Step S23, using formula Calculate the correlation coefficient between each data point in data sequence F and data sequence E Where ρ is the resolution coefficient, the value range is (0,1], and ρ=0.5; Δmin and Δmax are the minimum difference between the two levels and the maximum difference between the two levels of the data sequence respectively; Step S24, using formula Calculate and obtain the correlation r between the adjustable parameter and the comprehensive evaluation factor ij ,Q is the type of adjustable parameter.

6. The transformer drying process control method based on artificial intelligence technology according to claim 5 is characterized in that: The calculation formulas for the two-level minimum difference and the two-level maximum difference of the data sequence are: in, Indicates first-take Calculate the minimum value in the result, and then take the minimum value of all minimum values; Indicates first-take Calculate the maximum value among the results, and then take the maximum value of all maximum values.

7. Transformer drying process control system based on artificial intelligence technology, characterized in that: The system is used to implement the steps of the transformer drying process control method based on artificial intelligence technology according to any one of claims 1 to 6, including: The data acquisition module is used to divide the drying process of the transformer equipment to be controlled into n stages and obtain the jth adjustable parameter x in the i-th stage. ij and real-time data during the drying process; The data processing module is used to extract the index parameters used to evaluate the drying efficiency and quality, perform weighted processing, and determine the comprehensive evaluation factor Ce of the hth drying process. h ; A data analysis module is used to analyze the adjustable parameter x ij The comprehensive evaluation factor Ce h Perform correlation analysis to calculate the correlation between the adjustable parameters and the comprehensive evaluation factors. ij , set the correlation threshold r min , filter r ij ≥r min The adjustable parameter of is used as a strong correlation parameter of the comprehensive evaluation factor; A model building module is used to construct an adjustable parameter optimization model using the acquired comprehensive evaluation factors and historical data of strongly correlated parameters, and to use the optimization model to obtain the optimal values ​​of each of the strongly correlated parameters under different initial conditions; The parameter setting module is used to set the adjustable parameters at each stage of the drying process according to the obtained optimal values, and to perform adaptive control on the drying process according to the set adjustable parameters.

8. The transformer drying process control system based on artificial intelligence technology according to claim 7 is characterized in that: The system also includes a memory, a processor, and an electronic program stored in the memory and capable of running on the processor, wherein the processor can implement the steps of the transformer drying process control method based on artificial intelligence technology as described in any one of claims 1-6 when running the electronic program.

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