Dynamic temperature rise deduction calculation method for transformer winding hot spot

Abstract

The invention relates to a transformer winding hot spot dynamic temperature rise deduction calculation method, which belongs to the technical field of transformers and comprises the following steps of S1, predicting transformer winding hot spot dynamic temperature rise data based on a differential evolution algorithm; s2, establishing a winding hot spot dynamic temperature rise calculation model associated with the load rate; s3, calculating parameter values in the winding hot spot dynamic temperature rise calculation model associated with the load rate, wherein the parameter values comprise a steady-state temperature rise coefficient, a steady-state temperature rise index, a dynamic temperature rise time constant coefficient and a dynamic temperature rise time constant index; and S4, based on the winding hot spot temperature rise calculation model associated with the load rate, solving to obtain a dynamic temperature rise change rule of the transformer winding hot spot under the large load current.

Description

Technical Field

[0001] The invention belongs to the technical field of transformers and relates to a method for calculating the dynamic temperature rise of transformer winding hot spots. Background Art

[0002] The dynamic information of transformer winding hot spot temperature can timely reflect the transformer operating status and is an important basis for measuring its load capacity and service life. Temperature rise test is an important verification item to ensure the quality of transformers. The high current temperature rise test under the standard conditions specified in the national standard GB / T1094.1-2013 faces on-site problems such as large volume of required test equipment, limited on-site power supply capacity, and long test time, which seriously restricts the on-site transformer quality detection level. Therefore, studying the calculation method of dynamic temperature rise deduction of transformer winding hot spots is an effective way to improve the level and efficiency of on-site transformer temperature rise detection.

[0003] In recent years, Liu Yunpeng and others from North China Electric Power University proposed a calculation model for the hot spot temperature and temperature distribution of the winding based on the temperature of the heat sink, which realized the fast and accurate calculation of the dynamic temperature of the hot spot of the winding. Sheng Gehao and others from Shanghai Jiaotong University established a dynamic inversion model of the hot spot temperature of the transformer based on multi-field coupling simulation and back propagation neural network, which improved the accuracy of transformer temperature detection. Zhou Lijun and others from Southwest Jiaotong University studied the dynamic temperature rise change law of the hot spot of the transformer under different load rates and pointed out that as the load rate increases, the temperature rise rate gradually becomes faster, and the temperature rise time constant decreases in inverse proportion. Liu Gang and others from North China Electric Power University proposed an efficient calculation method for the transient temperature rise of the winding of an oil-immersed power transformer, introduced dynamic modal decomposition into the transient calculation of the winding temperature rise, and approximated the subsequent system changes by extracting the change characteristics, which ultimately reduced the calculation time. At present, the existing results mainly focus on the inverse calculation of the dynamic temperature rise of transformer hot spots under rated large load rate. There are relatively few studies on the dynamic temperature rise law of transformer hot spots and the correlation between the dynamic temperature rise law of hot spots under different load rates. There is a lack of a calculation method for deducing temperature rise test data under large load rate from temperature rise test data under small load rate to solve the technical problems that temperature rise test is time-consuming and difficult to implement on site. Summary of the invention

[0004] In view of this, an object of the present invention is to provide a method for calculating the dynamic temperature rise of transformer winding hot spots.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] On the one hand, the present invention provides a method for calculating the dynamic temperature rise of transformer winding hot spots, comprising:

[0007] Based on the differential evolution algorithm, the dynamic temperature rise prediction curve of the winding hot spot under different load rates is obtained

[0008] Based on the prediction curves of the dynamic temperature rise of the hot spots of the windings under different load rates, a calculation model of the dynamic temperature rise of the hot spots of the windings associated with the load rate is established;

[0009] Solving the parameter values ​​in the winding hotspot dynamic temperature rise calculation model, wherein the parameter values ​​include a steady-state temperature rise coefficient, a steady-state temperature rise index, a dynamic temperature rise time constant coefficient, and a dynamic temperature rise time constant index;

[0010] Based on the winding hot spot temperature rise calculation model, the dynamic temperature rise variation law of the transformer winding hot spot under large load current is solved.

[0011] Further, the method of obtaining the dynamic temperature rise prediction curve of the winding hotspot under different load rates based on the differential evolution algorithm specifically includes the following steps:

[0012] S11: randomly initialize the population with the data of the temperature rise value of the hot spot of the winding under different load rates, and determine the substitution parameters of the objective function according to the dynamic temperature rise change law of the hot spot of the transformer winding;

[0013] S12: Perform fitness evaluation on the individuals in the population to determine whether the fitness is lower than the specified residual or reaches the maximum number of iterations; if so, output the optimal solution and draw the prediction curve of the temperature rise of the winding hot spot under each load rate; if not, use mutation, crossover, and selection operations to re-evaluate the fitness until the optimal solution is output, and finally obtain the dynamic temperature rise prediction curve of the winding hot spot under different load rates.

[0014] Furthermore, the random initialization of the population of the winding hot spot temperature rise data under different load rates specifically includes:

[0015] N individuals are randomly and uniformly generated in the solution space, and the dimension of each individual is m, as shown in formula (1):

[0016]

[0017] Where Y i (0) represents the state of the i-th individual at the initial moment, i = 1, 2, 3, ..., N; Represents the j-th parameter value of the i-th individual at the initial moment, j = 1, 2, 3, …, N.

[0018] Furthermore, the mutation operation includes: using the optimal solution and the random vector difference method to perform vector difference on the individual, for the i-th individual Y of the t-th generation i (t), the t+1th generation experimental individual H generated after differential mutation i (t+1), as shown in formula (2):

[0019] H i (t+1)=H i (t)+λtl ·[H t.best (t)-H j (t)]

[0020] +G·[H k (t)-H l (t)](i≠k≠l) (2)

[0021] In the formula, H t.best (t) represents the best individual in the tth generation population; G represents the amplification factor; H j (t), H k (t), H l (t) represents the other three different individuals in the current population except the i-th individual; λ tl Represents the greediness of the control algorithm.

[0022] Furthermore, the crossover and selection operations include: i (t+1) and the parent individual Y i (t) is crossed, and a random number is generated for each dimension j

[0023] like Or the current dimension j is a randomly selected dimension J rand , then the offspring individual V i (t+1) inherits the mutation vector value, otherwise select the current individual value;

[0024] The selection operation is shown in formula (3):

[0025]

[0026] In the formula, Represents the j-th gene position value of the i-th individual in the current population; represents the random number at the jth gene of the ith individual, between 0 and 1; C r represents the crossover rate, C r ∈[0,1]; Represents the j-th gene position value of the i-th individual in the offspring after mutation crossover; Represents the j-th gene position value of the i-th individual in the experimental individuals generated by differential mutation; Jrand represents a randomly selected dimension.

[0027] Further, the establishment of a dynamic temperature rise calculation model for the hot spots of the windings associated with the load rate specifically includes: based on the dynamic temperature rise change law of the hot spots of the transformer windings under different load rates as shown in formula (4), the temperature rise calculation model for the hot spots of the windings associated with the load rate is shown in formula (5):

[0028]

[0029] Δθ i is the temperature rise value, β is the transformer operating load rate, r i is the steady-state temperature rise coefficient, s i is the steady-state temperature rise index, p i is the dynamic temperature rise time constant coefficient, q i is the dynamic temperature rise time constant index, θS i is the temperature rise when the hot spot of the transformer winding reaches steady state, τ i is the dynamic temperature rise time constant of the transformer winding hot spot.

[0030] Further, the parameter values ​​in the winding hot spot dynamic temperature rise calculation model are solved, which specifically includes the following steps: jointly analyzing the prediction curve results of different load rates with the winding hot spot dynamic temperature rise calculation model associated with the load rate, and converting the known results of θS i , τ i Substitute into equation (5) and solve the equations separately to obtain the steady-state temperature rise coefficient r i , Steady-state temperature rise index s i , Dynamic temperature rise time constant coefficient p i , Dynamic temperature rise time constant index q i Parameter value.

[0031] Further, based on the winding hot spot temperature rise calculation model, the dynamic temperature rise change law of the transformer winding hot spot under large load current is solved, specifically including: for the rated load rate, the load rate size β and the winding hot spot temperature rise calculation model parameter value r associated with the load rate are converted into i 、s i 、p i ,q i Substitute them into equation (5) to obtain the temperature rise θS when the transformer hot spot reaches steady state under rated load rate. i And the dynamic temperature rise time constant τ of the transformer winding hot spot i , and finally bring it into equation (4) to solve the dynamic temperature rise variation law of the transformer winding hot spot under rated load rate.

[0032] On the other hand, the present invention provides a transformer winding hot spot dynamic temperature rise deduction and calculation device, comprising:

[0033] A determination module is used to obtain a dynamic temperature rise prediction curve of a winding hot spot under different load rates based on a differential evolution algorithm; based on the dynamic temperature rise prediction curve of a winding hot spot under different load rates, establish a dynamic temperature rise calculation model of a winding hot spot associated with the load rate;

[0034] A calculation module is used to solve the parameter values ​​in the dynamic temperature rise calculation model of the winding hotspot, wherein the parameter values ​​include a steady-state temperature rise coefficient, a steady-state temperature rise index, a dynamic temperature rise time constant coefficient, and a dynamic temperature rise time constant index; based on the winding hotspot temperature rise calculation model, the dynamic temperature rise change law of the transformer winding hotspot under large load current is solved.

[0035] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for calculating the dynamic temperature rise of a transformer winding hot spot as described in any one of the above items is implemented.

[0036] The beneficial effect of the present invention is that the present invention uses short-time temperature rise data to predict the hot spot temperature rise curve, which helps to improve the efficiency of transformer temperature rise performance detection. In addition, the method of using small current temperature rise data to obtain a large current temperature rise curve helps to solve the problem of large test equipment volume and difficult test implementation during large current temperature rise tests on transformers. This patented method provides important theoretical and engineering value references for improving the quality control level of on-site transformers and improving detection efficiency.

[0037] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0039] Figure 1 This is a flow chart of the calculation method for dynamic temperature rise deduction of transformer winding hot spots;

[0040] Figure 2 A flow chart for quickly acquiring the temperature rise data of winding hot spots based on the differential evolution algorithm;

[0041] Figure 3 It is the geometric model of oil-immersed transformer, where (a) is the three-dimensional model diagram and (b) is the two-dimensional cross-sectional diagram;

[0042] Figure 4 The dynamic temperature rise prediction curve of the winding hotspot based on the differential evolution algorithm, where (a) is the high-voltage winding and (b) is the low-voltage winding;

[0043] Figure 5 is the error percentage between the deduced calculation model and the temperature rise test at rated load rate, where (a) is the low voltage winding and (b) is the high voltage winding;

[0044] Figure 6 The following is a comparison chart of the dynamic temperature rise curves of the winding hot spots based on deductive calculation and temperature rise test at rated load rate. DETAILED DESCRIPTION

[0045] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0046] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0047] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0048] like Figure 1 As shown, the present invention provides a method for calculating the dynamic temperature rise of transformer winding hot spots, comprising the following steps:

[0049] Step 1: Rapid acquisition of dynamic temperature rise data of winding hot spots based on differential evolution algorithm. This embodiment takes the S11-M-630 / 10 oil-immersed distribution transformer as the research object, introduces the simulation to obtain the temperature rise data of the winding hot spots in the first 3 hours under the application of a small load rate, and explains the method of obtaining the full process prediction curve of the dynamic temperature rise of the winding hot spots under the corresponding load rate based on the differential evolution algorithm. The differential evolution algorithm (Differential Evolution, DE) is selected to predict the dynamic temperature rise of the transformer winding hot spots. The differential evolution calculation is mainly divided into four steps: population initialization operation, mutation operation, crossover operation, and selection operation. When the population is initialized, N individuals will be randomly and uniformly generated in the solution space, and the dimension of each individual is m, as shown in (1):

[0050]

[0051] Where Y i (0) represents the state of the i-th individual at the initial moment, i = 1, 2, 3, ...; Represents the j-th parameter value of the i-th individual at the initial moment, j = 1, 2, 3…

[0052] After the population is initialized, the individuals need to be mutated, crossovered, and selected until the optimal solution is obtained. The optimal solution and random vector difference method (DE / rand-to-best / 1) selects the best individual in the current population while making full use of the remaining individual information in the population (vector difference and multiplication by the greed factor), and selects any two different individuals for vector difference and multiplication by the amplification factor, and adds the result to the current individual, effectively avoiding falling into the local optimal situation. Therefore, the present invention selects DE / rand-to-best / 1 to perform vector difference on individuals. For the i-th individual Y of the t-th generation i (t), the t+1th generation experimental individual H generated after differential mutation i (t+1), as shown in (2):

[0053] H i (t+1)=H i (t)+λ tl ·[H t.best (t)-H j (t)]

[0054] +G·[H k (t)-H l (t)](i≠k≠l) (2)

[0055] In the formula, H t.best (t) represents the best individual in the tth generation population; G represents the amplification factor; H j (t), H k (t), H l (t) represents the other three different individuals in the current population except the i-th individual; λ tl Represents the "greedyness" of the control algorithm.

[0056] After the mutation operation, in order to maintain the diversity of the population, it is necessary to perform crossover and selection operations on the individuals. i (t+1) and the parent individual Y i (t) is crossed, and a random number is generated for each dimension j like Or the current dimension j is a randomly selected dimension J rand , then the offspring individual V i(t+1) will inherit the mutation vector value, otherwise select the current individual Value. It is particularly noteworthy that the selection operation is to mix the current individual and the mutation vector to ensure that at least one position value comes from the mutation vector to improve the diversity of the population, as shown in (3):

[0057]

[0058] In the formula, Represents the j-th gene position value of the i-th individual in the current population; represents the random number at the jth gene of the ith individual, between 0 and 1; C r represents the crossover rate, C r ∈[0,1]; Represents the j-th gene position value of the i-th individual in the offspring after mutation crossover; Represents the j-th gene position value of the i-th individual in the experimental individuals generated by differential mutation; Jrand represents a randomly selected dimension.

[0059] Taking the S11-M-630 / 10 oil-immersed distribution transformer as the research object, the temperature rise data of the hot spots of the windings in the first 3 hours under the application of a small load rate are obtained by simulation. Based on the differential evolution algorithm, the dynamic temperature rise prediction curve under the corresponding load rate is obtained, and the accuracy of the prediction curve is verified. The specific process is as follows Figure 2 shown.

[0060] Step 1: Use the simulation data of the temperature rise of the hot spot of the winding at 40%, 50%, and 60% load rates before 3 hours as the true value of the prediction model to randomly initialize the population, and determine the substitution parameters of the objective function according to the dynamic temperature rise change law of the hot spot of the transformer winding.

[0061] Step 2: Evaluate the fitness of the individuals in the population to determine whether the fitness is lower than the specified residual or reaches the maximum number of iterations. If so, output the optimal solution and draw the prediction curve of the hot spot temperature rise of the winding under 40%, 50%, and 60% load rates; if not, use mutation, crossover, and selection operations to re-evaluate the fitness until the optimal solution is output.

[0062] Step 3: Verify the accuracy of the hotspot temperature rise prediction curves at 40%, 50%, and 60% load rates.

[0063] Step 2: Establish a dynamic temperature rise calculation model for the winding hotspot associated with the load rate. Based on the change law of the winding hotspot temperature rise with the load application time, a dynamic temperature rise calculation model for the winding hotspot is proposed. The input parameters of the model are the transformer operating load rate β, the steady-state temperature rise coefficient r i , Steady-state temperature rise index s i , Dynamic temperature rise time constant coefficient pi , Dynamic temperature rise time constant index q i The output parameter is the temperature rise θS when the transformer hot spot reaches steady state i , transformer hot spot dynamic temperature rise time constant τ i Based on the dynamic temperature rise change law of transformer winding hotspot under different load rates as shown in formula (4), the present invention proposes a winding hotspot temperature rise calculation model associated with the load rate, as shown in formula (5). i is the temperature rise value, β is the transformer operating load rate, r i is the steady-state temperature rise coefficient, s i is the steady-state temperature rise index, p i is the dynamic temperature rise time constant coefficient, q i is the dynamic temperature rise time constant index, θS i is the temperature rise when the hot spot of the transformer winding reaches steady state, τ i is the dynamic temperature rise time constant of the transformer winding hot spot. It can be seen from formula (5) that the steady-state temperature rise value and the dynamic temperature rise time constant of the transformer hot spot both show a power function change with the change of load rate.

[0064]

[0065] Step 3: Solve the relevant parameters in the winding hot spot temperature rise calculation model associated with the load rate. Based on the dynamic temperature rise prediction curve results of the winding hot spot under different small load rates, the parameter values ​​of the winding hot spot temperature rise calculation model associated with the load rate are obtained by solving the simultaneous equations. Among them, the solved parameter values ​​include the steady-state temperature rise coefficient r i , Steady-state temperature rise index s i , Dynamic temperature rise time constant coefficient p i , Dynamic temperature rise time constant index q i The specific solution steps are as follows:

[0066] Step 1: Combine the prediction curve results of the load rate of 40% / 50% with the dynamic temperature rise calculation model of the winding hot spot associated with the load rate, and convert the known results of θS i (40%), τ i (40%) and θS i (50%), τ i (50%) is substituted into formula (5), and the simultaneous equations are solved to obtain r i 、s i 、p i ,q i Parameter value.

[0067] Step 2: Repeat the operation of step 1 for the prediction curve results with load rates of 40% / 60% and 50% / 60% to characterize the validity of the calculation results of this method.

[0068] Step 4: Calculation and analysis of the dynamic temperature rise of the winding hotspot under a specific load rate. Based on the winding hotspot temperature rise calculation model associated with the load rate, the dynamic temperature rise calculation formula of the transformer winding hotspot under large load current is solved to achieve dynamic temperature rise defect mapping under large load current and support transformer temperature rise performance assessment. Taking the rated load rate as an example, the load rate β and the parameter value r of the winding hotspot temperature rise calculation model associated with the load rate are calculated. i 、s i 、p i ,q i Substitute them into equation (5) to obtain the temperature rise θS when the transformer hot spot reaches steady state under rated load rate. i And the dynamic temperature rise time constant τ of the transformer winding hot spot i , and finally bring it into equation (4) to solve the dynamic temperature rise variation law of the transformer winding hot spot under rated load rate.

[0069] Embodiment 1:

[0070] (I) Rapid acquisition of dynamic temperature rise data of winding hot spots based on differential evolution algorithm and implementation effect

[0071] This embodiment takes a S11-M-630 / 10 oil-immersed distribution transformer as the research object, and the geometric model is as follows: Figure 3 First, based on the electromagnetic-fluid-heat multi-field coupling simulation calculation results, the dynamic temperature rise variation law of the transformer high-voltage winding hot spots and low-voltage winding hot spots at 40%, 50%, and 60% load rates from 0 to 8 hours is obtained by fitting.

[0072] Secondly, the simulation data of the hot spot temperature rise of the winding at 40%, 50%, and 60% load rates before 3 hours are used as the initial value of the differential evolution algorithm, and the prediction curves of the hot spot temperature rise at 40%, 50%, and 60% load rates are obtained, as shown in Figure 4 The simulation data (provided once every hour) of the winding hot spot temperature rise after 5 hours at 40%, 50%, and 60% load rates are compared with the prediction curve. The error is shown in Table 1, verifying the accuracy of the prediction curve.

[0073] Table 1

[0074]

[0075] (II) Establishment of the calculation model of dynamic temperature rise of winding hot spots associated with load rate

[0076] Based on the hot spot temperature rise prediction curves at 40%, 50%, and 60% load rates obtained in (1), the general formula of the dynamic temperature rise quantitative calculation model is fitted as shown in (4). Based on this, the present invention proposes a winding hot spot temperature rise calculation model associated with the load rate, as shown in formula (5). Wherein, Δθ i is the temperature rise value, β is the transformer operating load rate, r i is the steady-state temperature rise coefficient, s i is the steady-state temperature rise index, p i is the dynamic temperature rise time constant coefficient, q i is the dynamic temperature rise time constant index, θS i is the temperature rise when the hot spot of the transformer winding reaches steady state, τ i is the dynamic temperature rise time constant of the transformer winding hot spot. It can be seen from formula (5) that the steady-state temperature rise value and the dynamic temperature rise time constant of the transformer hot spot both show a power function change with the change of load rate.

[0077] (III) Solution of relevant parameters in the calculation model of winding hot spot temperature rise associated with load rate

[0078] The three sets of prediction curves with load rates of 40% / 50%, 40% / 60%, and 50% / 60% are solved simultaneously to obtain the associated calculation model parameters r of the high-voltage winding and the low-voltage winding in each case. i 、s i 、p i ,q i , as shown in Table 2. The deduction model data is compared with the temperature rise test data. The results are as follows Figure 5 . Transformer low voltage winding hot spot test and deduction calculation parameter value θS 1 The maximum error percentage is only 2.83%, and the high-voltage winding hot spot test and deduction calculation parameter value θS 2 The maximum error percentage is only 2.58%, and the deduction calculation and the measured results are in good agreement, meeting the engineering requirements. In addition, the hot spot test and deduction calculation parameter value τ of the transformer low-voltage winding are 1 The maximum error percentage is only 4.76%, and the high-voltage winding hot spot test and deduction calculation parameter value τ 2 The maximum error percentage is only 0.96%. The deduced calculation results are consistent with the measured results and meet the engineering requirements.

[0079] (IV) Calculation and analysis of dynamic temperature rise of winding hot spots under specific load rate

[0080] The temperature rise of the hot spot of the low voltage winding at the rated load rate is obtained by the test as Δθ 1 =67.67(1-e -t / 0.84 ), the high voltage winding hot spot temperature rise is Δθ 2 =60.41(1-e-t / 1.04 ). Taking the calculation results of the 0.4 / 0.6 load rate combination as an example, the dynamic temperature rise curves of the high and low voltage winding hot spots are finally solved as follows: Figure 6 As shown in the figure, the maximum temperature rise error is only 0.69°C, which proves the accuracy of this method. This patent uses short-term temperature rise data to predict the hot spot temperature rise curve, which helps to improve the efficiency of transformer temperature rise performance detection on the basis of greatly reducing power loss. In addition, the method of using small current temperature rise data to obtain large current temperature rise curve helps to solve the problem of large test equipment size and difficult test implementation during large current temperature rise test of transformer on-site.

[0081] Table 2

[0082]

[0083] In the above embodiments, the description's reference to "this embodiment" indicates that a particular feature, structure, or characteristic described in conjunction with the embodiment is included in at least some embodiments, but not necessarily all embodiments. Multiple occurrences of "this embodiment" do not necessarily all refer to the same embodiment.

[0084] In the above-described embodiments, although the invention has been described in conjunction with specific embodiments of the invention, many substitutions, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art based on the foregoing description. For example, other storage structures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed. Embodiments of the invention are intended to encompass all such substitutions, modifications, and variations that fall within the broad scope of the appended claims.

[0085] The present invention also provides a transformer winding hot spot dynamic temperature rise deduction and calculation device, comprising:

[0086] A determination module is used to obtain a dynamic temperature rise prediction curve of a winding hot spot under different load rates based on a differential evolution algorithm; based on the dynamic temperature rise prediction curve of a winding hot spot under different load rates, establish a dynamic temperature rise calculation model of a winding hot spot associated with the load rate;

[0087] A calculation module is used to solve the parameter values ​​in the winding hot spot dynamic temperature rise calculation model, wherein the parameter values ​​include a steady-state temperature rise coefficient, a steady-state temperature rise index, a dynamic temperature rise time constant coefficient, and a dynamic temperature rise time constant index; based on the winding hot spot temperature rise calculation model, the dynamic temperature rise change law of the transformer winding hot spot under a large load current is solved;

[0088] In the determination module, the method of obtaining the dynamic temperature rise prediction curve of the winding hotspot under different load rates based on the differential evolution algorithm specifically includes the following steps:

[0089] S11: randomly initialize the population with the data of the temperature rise value of the hot spot of the winding under different load rates, and determine the substitution parameters of the objective function according to the dynamic temperature rise change law of the hot spot of the transformer winding;

[0090] S12: Evaluate the fitness of the individuals in the population to determine whether the fitness is lower than the specified residual or reaches the maximum number of iterations; if so, output the optimal solution and draw the prediction curve of the temperature rise of the hot spot of the winding under each load rate; if not, re-evaluate the fitness by using mutation, crossover and selection operations until the optimal solution is output, and finally obtain the dynamic temperature rise prediction curve of the hot spot of the winding under different load rates;

[0091] The method of randomly initializing a population of the hot spot temperature rise data of the windings under different load rates specifically includes:

[0092] N individuals are randomly and uniformly generated in the solution space, and the dimension of each individual is m, as shown in formula (1):

[0093]

[0094] Where Y i (0) represents the state of the i-th individual at the initial moment, i = 1, 2, 3, ..., N; represents the jth parameter value of the i-th individual at the initial moment, j = 1, 2, 3, ..., N;

[0095] The mutation operation includes: using the optimal solution and random vector difference method to perform vector difference on individuals, for the i-th individual Y of the t-th generation i (t), the t+1th generation experimental individual H generated after differential mutation i (t+1), as shown in formula (2):

[0096] H i (t+1)=H i (t)+λ tl ·[H t.best (t)-H j (t)]

[0097] +G·[H k (t)-H l (t)](i≠k≠l) (2)

[0098] In the formula, H t.best (t) represents the best individual in the tth generation population; G represents the amplification factor; H j (t), H k (t), H l (t) represents the other three different individuals in the current population except the i-th individual; λ tl Represents the greediness of the control algorithm;

[0099] The crossover and selection operations include: i (t+1) and the parent individual Y i (t) is crossed, and a random number is generated for each dimension j

[0100] like Or the current dimension j is a randomly selected dimension J rand , then the offspring individual V i (t+1) inherits the mutation vector value, otherwise select the current individual value;

[0101] The selection operation is shown in formula (3):

[0102]

[0103] In the formula, Represents the j-th gene position value of the i-th individual in the current population; represents the random number at the jth gene of the ith individual, between 0 and 1; C r represents the crossover rate, C r ∈[0,1]; Represents the j-th gene position value of the i-th individual in the offspring after mutation crossover; represents the j-th gene position value of the ith individual in the experimental individuals generated by differential mutation; Jrand represents a randomly selected dimension;

[0104] The establishment of a dynamic temperature rise calculation model for the hot spot of the winding associated with the load rate specifically includes: based on the dynamic temperature rise change law of the hot spot of the transformer winding under different load rates as shown in formula (4), the temperature rise calculation model for the hot spot of the winding associated with the load rate is shown in formula (5):

[0105]

[0106] Δθ i is the temperature rise value, β is the transformer operating load rate, r i is the steady-state temperature rise coefficient, s i is the steady-state temperature rise index, p i is the dynamic temperature rise time constant coefficient, q i is the dynamic temperature rise time constant index, θS i is the temperature rise when the hot spot of the transformer winding reaches steady state, τ i is the dynamic temperature rise time constant of the transformer winding hot spot;

[0107] In the calculation module, the parameter values ​​in the dynamic temperature rise calculation model of the winding hotspot are solved, which specifically includes the following steps: jointly analyzing the prediction curve results of different load rates with the dynamic temperature rise calculation model of the winding hotspot associated with the load rate, and converting the known results of θS i , τ i Substitute into equation (5) and solve the equations separately to obtain the steady-state temperature rise coefficient r i , Steady-state temperature rise index s i , Dynamic temperature rise time constant coefficient p i , Dynamic temperature rise time constant index q i Parameter value of

[0108] Based on the winding hot spot temperature rise calculation model, the dynamic temperature rise change law of the transformer winding hot spot under large load current is solved, specifically including: for the rated load rate, the load rate size β and the winding hot spot temperature rise calculation model parameter value r associated with the load rate are calculated. i 、s i 、p i ,q i Substitute them into equation (5) to obtain the temperature rise θS when the transformer hot spot reaches steady state under rated load rate. i And the dynamic temperature rise time constant τ of the transformer winding hot spot i , and finally bring it into equation (4) to solve the dynamic temperature rise variation law of the transformer winding hot spot under rated load rate.

[0109] This embodiment further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, any one of the methods in this embodiment is implemented.

[0110] This embodiment also provides an electronic terminal, including: a processor and a memory;

[0111] The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal executes any one of the methods in this embodiment.

[0112] The computer-readable storage medium in this embodiment can be understood by ordinary technicians in this field: all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to the computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes.

[0113] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication with each other. The memory is used to store computer programs, the communication interface is used to communicate, and the processor and the transceiver are used to run computer programs so that the electronic terminal executes each step of the above method.

[0114] In this embodiment, the memory may include a random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0115] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0116] The present invention can be used in many general or special computing system environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like.

[0117] The present invention may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A method for calculating the dynamic temperature rise of transformer winding hot spots, characterized by: include: Based on the differential evolution algorithm, the dynamic temperature rise prediction curve of the winding hotspot under different load rates is obtained; Based on the prediction curves of the dynamic temperature rise of the hot spots of the windings under different load rates, a calculation model of the dynamic temperature rise of the hot spots of the windings associated with the load rate is established; Solving the parameter values ​​in the winding hotspot dynamic temperature rise calculation model, wherein the parameter values ​​include a steady-state temperature rise coefficient, a steady-state temperature rise index, a dynamic temperature rise time constant coefficient, and a dynamic temperature rise time constant index; Based on the winding hot spot temperature rise calculation model, the dynamic temperature rise variation law of the transformer winding hot spot under large load current is solved.

2. The transformer winding hot spot dynamic temperature rise deduction and calculation method according to claim 1 is characterized in that: The method of obtaining the dynamic temperature rise prediction curve of the winding hotspot under different load rates based on the differential evolution algorithm specifically includes the following steps: S11: randomly initialize the population with the data of the temperature rise value of the hot spot of the winding under different load rates, and determine the substitution parameters of the objective function according to the dynamic temperature rise change law of the hot spot of the transformer winding; S12: Perform fitness evaluation on the individuals in the population to determine whether the fitness is lower than the specified residual or reaches the maximum number of iterations; if so, output the optimal solution and draw the prediction curve of the temperature rise of the winding hot spot under each load rate; if not, use mutation, crossover, and selection operations to re-evaluate the fitness until the optimal solution is output, and finally obtain the dynamic temperature rise prediction curve of the winding hot spot under different load rates.

3. The transformer winding hot spot dynamic temperature rise deduction and calculation method according to claim 2 is characterized by: The method of randomly initializing a population of the winding hot spot temperature rise data under different load rates specifically includes: N individuals are randomly and uniformly generated in the solution space, and the dimension of each individual is m, as shown in formula (1): Where Y i (0) represents the state of the i-th individual at the initial moment, i = 1, 2, 3, ..., N; Represents the j-th parameter value of the i-th individual at the initial moment, j = 1, 2, 3, …, N.

4. The transformer winding hot spot dynamic temperature rise deduction and calculation method according to claim 2 is characterized in that: The mutation operation includes: using the optimal solution and random vector difference method to perform vector difference on individuals, for the i-th individual Y of the t-th generation i (t), the t+1th generation experimental individual H generated after differential mutation i (t+1), as shown in formula (2): H i (t+1)=H i (t)+λ tl ·[H t.best (t)-H j (t)] +G·[H k (t)-H l (t)](i≠k≠l) (2) In the formula, H t.best (t) represents the best individual in the tth generation population; G represents the amplification factor; H j (t), H k (t), H l (t) represents the other three different individuals in the current population except the i-th individual; λ tl Represents the greediness of the control algorithm.

5. The transformer winding hot spot dynamic temperature rise deduction and calculation method according to claim 2 is characterized in that: The crossover and selection operations include: i (t+1) and the parent individual Y i (t) is crossed, and a random number is generated for each dimension j like Or the current dimension j is a randomly selected dimension J rand , then the offspring individual V i (t+1) inherits the mutation vector value, otherwise select the current individual value; The selection operation is shown in formula (3): In the formula, Represents the j-th gene position value of the i-th individual in the current population; represents the random number at the jth gene of the ith individual, between 0 and 1; C r represents the crossover rate, C r ∈[0,1]; Represents the j-th gene position value of the i-th individual in the offspring after mutation crossover; Represents the j-th gene position value of the i-th individual in the experimental individuals generated by differential mutation; Jrand represents a randomly selected dimension.

6. The transformer winding hot spot dynamic temperature rise deduction and calculation method according to claim 1 is characterized in that: The establishment of a dynamic temperature rise calculation model for the hot spot of the winding associated with the load rate specifically includes: based on the dynamic temperature rise change law of the hot spot of the transformer winding under different load rates as shown in formula (4), the temperature rise calculation model for the hot spot of the winding associated with the load rate is shown in formula (5): Δθ i is the temperature rise value, β is the transformer operating load rate, r i is the steady-state temperature rise coefficient, s i is the steady-state temperature rise index, p i is the dynamic temperature rise time constant coefficient, q i is the dynamic temperature rise time constant index, θS i is the temperature rise when the hot spot of the transformer winding reaches steady state, τ i is the dynamic temperature rise time constant of the transformer winding hot spot.

7. The transformer winding hot spot dynamic temperature rise deduction and calculation method according to claim 6 is characterized in that: Solving the parameter values ​​in the winding hot spot dynamic temperature rise calculation model specifically includes the following steps: jointly analyzing the prediction curve results of different load rates with the winding hot spot dynamic temperature rise calculation model associated with the load rate, and converting the known results of θS i , τ i Substitute into equation (5) and solve the equations separately to obtain the steady-state temperature rise coefficient r i , Steady-state temperature rise index s i , Dynamic temperature rise time constant coefficient p i , Dynamic temperature rise time constant index q i Parameter value.

8. The transformer winding hot spot dynamic temperature rise deduction and calculation method according to claim 6 is characterized by: Based on the winding hot spot temperature rise calculation model, the dynamic temperature rise change law of the transformer winding hot spot under large load current is solved, specifically including: for the rated load rate, the load rate size β and the winding hot spot temperature rise calculation model parameter value r associated with the load rate are converted to i 、s i 、p i ,q i Substitute them into equation (5) to obtain the temperature rise θS when the transformer hot spot reaches steady state under rated load rate. i And the dynamic temperature rise time constant τ of the transformer winding hot spot i , and finally bring it into equation (4) to solve the dynamic temperature rise variation law of the transformer winding hot spot under rated load rate.

9. A transformer winding hot spot dynamic temperature rise deduction and calculation device, characterized in that: include: A determination module is used to obtain a dynamic temperature rise prediction curve of a winding hot spot under different load rates based on a differential evolution algorithm; Based on the prediction curves of the dynamic temperature rise of the hot spots of the windings under different load rates, a calculation model of the dynamic temperature rise of the hot spots of the windings associated with the load rate is established; A calculation module is used to solve the parameter values ​​in the dynamic temperature rise calculation model of the winding hotspot, wherein the parameter values ​​include a steady-state temperature rise coefficient, a steady-state temperature rise index, a dynamic temperature rise time constant coefficient, and a dynamic temperature rise time constant index; based on the winding hotspot temperature rise calculation model, the dynamic temperature rise change law of the transformer winding hotspot under large load current is solved.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method for calculating the dynamic temperature rise of a transformer winding hot spot as described in any one of claims 1 to 8 is implemented.

Images