A transformer load regulation method based on transient hot-spot temperature calculation

Abstract

This invention relates to the field of transformer control technology, specifically to a transformer load regulation method based on transient hot spot temperature calculation. The method includes the following steps: obtaining a dataset of transformer hot spot temperature changes under multiple operating conditions; optimizing the parameters in the empirical formula for calculating transformer winding hot spot temperature using a genetic algorithm; calculating the transformer operating time under different ambient temperatures and different initial top-layer oil temperature rises; calculating the specific time when the transformer hot spot temperature exceeds a limit, and reducing the transformer load factor before the temperature exceeds the limit. This invention uses a genetic algorithm to obtain the optimal thermal parameters of the empirical formula for calculating transformer winding temperature, achieving accurate calculation of winding hot spot temperature. Using the winding hot spot temperature as a limiting factor, the load capacity of the transformer is assessed, and the sustainable operating time of the transformer under different operating conditions is calculated. This improves transformer utilization while ensuring safe transformer operation.

Description

Technical Field

[0001] This invention relates to the field of transformer control technology, specifically to a transformer load regulation method based on transient hot spot temperature calculation. Background Technology

[0002] As one of the most important and numerous pieces of electrical equipment in the power grid, the operating status and health of power transformers directly affect the safe and stable operation of the grid. Power transformers have long been a key focus for power operation and maintenance departments, power equipment manufacturers, and research institutions both domestically and internationally. To ensure the safe operation of transformers, their operating characteristics and practical value cannot be fully utilized. In certain situations where there is an urgent need to increase load capacity, transformers have become a bottleneck in further improving the power grid's supply capacity. According to relevant standards for transformer manufacturing and operation, transformer insulation aging is based on the transformer's hot spot temperature. Excessively high hot spot temperatures accelerate insulation aging and shorten insulation lifespan, affecting the normal use of the transformer. The transformer winding hot spot temperature is a major factor limiting the transformer's load-carrying capacity. Therefore, it is necessary to accurately calculate the winding hot spot temperature and use it as a limiting factor to assess the transformer's load capacity and improve its utilization rate.

[0003] Chinese invention application CN202011583796.X discloses a transformer-based power load control system and its usage method. This system addresses the problem of transformers operating under light loads most of the time, resulting in low average annual load rates, but experiencing strong bursts of load, leading to short-term heavy loads and overloads, causing frequent power outages, transformer burnouts, and other serious accidents, and resulting in poor performance. The system is electrically connected to both the first and second transformers. Two switching devices are provided, one between the first and second transformers and the other between the processor. The input terminals of both switching devices are electrically connected to the processor, and their output terminals are electrically connected to the first and second transformers, respectively. A circuit breaker is located between the first and second transformers and the electrical equipment. Load acquisition is located between the first and second transformers and the processor.

[0004] Chinese invention application CN202111554901.1 proposes a grid-transformer coordinated overload control method based on transformer safety margin. The method includes: acquiring monitoring information of transformers and controlled objects within a target area; clustering loads based on the monitoring information of the controlled objects and identifying the transformer power supply area to which each load point belongs; classifying the transformer's operating state into corresponding safety state levels based on the urgency of the control based on the operating status; calculating the transformer's safety margin based on the transformer's monitoring information and assessing the safety state level of the transformer's current operating state based on the safety margin; adopting corresponding control strategies based on the safety state level of the transformer's current operating state and generating corresponding control schemes based on the control strategies. This grid-transformer coordinated overload control method can ensure the safe operation of transformers and fully exploit their overload potential to provide support for system control, thereby improving the effectiveness of transformer overload protection.

[0005] Chinese invention application CN202011382786.X discloses a method and system for optimizing transformer load regulation. The method includes: acquiring factors affecting transformer load capacity and constructing an optimization model based on these factors; acquiring environmental parameters, temperature rise characteristic parameters of hot spots, and operating parameters of the transformer; inputting the environmental parameters, temperature rise characteristic parameters, and operating parameters into the optimization model; calculating the safe load rate increase space under the current operating conditions based on the optimization model; and increasing the transformer capacity utilization rate based on the safe increase space. This invention calculates the safe load rate increase space of the transformer under the current operating conditions, improves the transformer's operating capacity utilization rate, and avoids abnormal overload of some transformers caused by unreasonable load redistribution during power grid fluctuations.

[0006] Existing technologies often rely on evaluating the transformer's health status based on historical operating information when constructing load control strategies for transformers, and then carry out load control accordingly. However, it is difficult to accurately consider the winding hot spot temperature during the control process, which is an important factor affecting the transformer's load-carrying capacity. Therefore, it may cause inaccurate load control and make it difficult to fully utilize the transformer's load-carrying capacity. Summary of the Invention

[0007] To address the problem that the load-carrying capacity of transformers is limited by the winding hot spot temperature, making precise control difficult, a transformer load control method based on transient hot spot temperature calculation is proposed. The specific technical solution is as follows:

[0008] A transformer load control method based on transient hot spot temperature calculation includes the following steps:

[0009] Step S1: Obtain the temperature rise data of the transformer hot spot temperature and load changes, taking values ​​at fixed time intervals Δt to obtain a dataset of transformer hot spot temperature changes under multiple operating conditions (t). i ,T i ), i = 1, 2, ..., n; where t i T is the time corresponding to the i-th data point. i It is the winding hotspot temperature corresponding to the i-th data point; n is the total number of data sets collected.

[0010] Step S2: Based on the dataset obtained in Step S1, use a genetic algorithm to calculate the parameter k in the empirical formula for the transformer winding hot spot temperature. 11 k 21 k 22 τ o τ w Optimization is performed, and the optimized parameters are substituted into the empirical formula for calculating the hot spot temperature of the transformer winding to obtain the optimized empirical formula for calculating the hot spot temperature of the transformer winding; where k 11 k 21 k 22 τ is a parameter of the thermal model. o τ is the time constant of the transformer oil. w The transformer winding time constant;

[0011] Step S3: Set the temperature rise limit for hot spots in the transformer windings, and set the temperature θ for different ambient temperatures. a and the initial top oil temperature rise Δθ of different transformers oi Substituting the optimized empirical formula for calculating the hot spot temperature of the transformer winding, the results for different ambient temperatures θ are obtained. a and the initial top oil temperature rise Δθ of different transformers oi Transformer operating time;

[0012] Step S4: Calculate the specific time when the transformer hot spot temperature exceeds the limit based on the transformer operating time obtained in step S3, and reduce the transformer load factor in advance before the temperature exceeds the limit.

[0013] Preferably, in step S1, the transformer hot spot temperature and the temperature rise data of load change are obtained by multiphysics calculation or transformer temperature rise test.

[0014] Preferably, the fixed time Δt in step S1 is selected as 5 minutes.

[0015] Preferably, in step S2, a genetic algorithm is used to calculate the parameter k in the empirical formula for the transformer winding hot spot temperature. 11 k 21 k 22 τ o τw The optimization process specifically includes the following steps:

[0016] Step S21, set the relationship between the transformer winding hot spot temperature and time, and the thermal model parameters as T(t) = f(t, w), and the characteristic parameter vector w = (k 11 ,k 21 ,k 22 ,τ o ,τ w );

[0017] Step S22, in the transformer hot spot temperature change dataset (t) i ,T i When performing the calculation, the sum of squared residuals of hot spot temperatures, E(w), is used as the fitness function;

[0018] Step S23: Encode the feature parameter vector w into binary form and generate the initial population.

[0019] Step S24: Perform selection, crossover, and mutation operations on the binary encoding of the feature parameter vector w, calculate the fitness function, and determine whether the optimization termination condition is met. If the optimization termination condition is not met, continue to perform individual selection, crossover, and mutation operations. Repeat this process until the optimization termination condition is met, and determine the optimal feature parameter vector w.

[0020] Preferably, the fitness function in step S22 is specifically expressed as follows:

[0021] Preferably, step S23 further includes:

[0022] Five sets of binary codes are set for the feature parameter vector w, with a binary code length of 20 and a number of individuals in each binary code set to 50, and then an initial population is generated.

[0023] Preferably, step S24 further includes setting the crossover probability to 0.65 and the mutation probability to 0.05.

[0024] Preferably, the optimization termination condition in step S24 is: fitness function E(w) < ε, where ε is the minimum error value.

[0025] The beneficial effects of this invention are as follows: Based on the empirical formula for calculating transformer winding temperature, this invention uses a genetic algorithm to obtain the optimal calculation thermal parameters, thereby achieving accurate calculation of winding hot spot temperature. Based on the accurate calculation of winding hot spot temperature, the load capacity of the transformer is evaluated using the winding hot spot temperature as a limiting factor, and the sustainable operating time of the transformer under different operating conditions is calculated. This improves the transformer utilization rate while ensuring the safe operation of the transformer. Attached Figure Description

[0026] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0027] Figure 1 This is a schematic diagram of the process of the present invention;

[0028] Figure 2 This is a comparison curve of the hot spot temperature values ​​between the load control method of the present invention and existing control methods. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0031] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0032] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0033] To address the technical problem of accurately considering winding hot spot temperature when performing load regulation in existing technologies, the present invention provides a transformer load regulation method based on transient hot spot temperature calculation, comprising the following steps:

[0034] Step S1: Obtain transformer hot spot temperature and load change temperature rise data through multiphysics calculations or transformer temperature rise tests. Data is taken at fixed time intervals Δt = 5 min to obtain a dataset of transformer hot spot temperature changes under multiple operating conditions (t). i ,T i ), i = 1, 2, ..., n; where t i T is the time corresponding to the i-th data point. i It is the winding hotspot temperature corresponding to the i-th data point; n is the total number of data sets collected.

[0035] Step S2: Based on the dataset obtained in Step S1, use a genetic algorithm to calculate the parameter k in the empirical formula for the transformer winding hot spot temperature. 11 k 21 k 22 τ o τ w Optimization is performed, and the optimized parameters are substituted into the empirical formula for calculating the hot spot temperature of the transformer winding to obtain the optimized empirical formula for calculating the hot spot temperature of the transformer winding; where k 11 k 21 k 22 τ is a parameter of the thermal model. o τ is the time constant of the transformer oil. w This is the transformer winding time constant.

[0036] The parameter k in the empirical formula for calculating the hot spot temperature of transformer windings using a genetic algorithm is described. 11 k 21 k 22 τ o τ w The optimization process specifically includes the following steps:

[0037] Step S21, set the relationship between the transformer winding hot spot temperature and time, and the thermal model parameters as T(t) = f(t, w), and the characteristic parameter vector w = (k 11 ,k 21 ,k 22 ,τ o ,τ w ).

[0038] Step S22, in the transformer hot spot temperature change dataset (t) i ,T i When performing the calculation, the sum of squared residuals of hotspot temperatures, E(w), is used as the fitness function; the specific expression of the fitness function is: Step S23: Binary encoding is performed on the feature parameter vector w. Since the parameter vector w contains 5 key parameters, 5 sets of binary codes are set for the feature parameter vector w. To ensure accuracy, the length of the binary code is set to 20, and the number of individuals in each set of binary codes is set to 50. Then, the initial population is generated.

[0039] Step S24: Perform selection, crossover, and mutation operations on the binary encoding of the feature parameter vector w, setting the crossover probability to 0.65 and the mutation probability to 0.05. Calculate the fitness function and determine if the optimization termination condition is met. If the optimization termination condition is not met, continue the selection, crossover, and mutation operations on individuals, repeating this process until the optimization termination condition is met, thus determining the optimal feature parameter vector w. The optimization termination condition is fitness function E(w) < ε, where ε is the minimum error value.

[0040] GB / T 1094.7-2008 provides an empirical formula for calculating the hot spot temperature of windings, specifically:

[0041]

[0042]

[0043] In the formula, θ a For ambient temperature; Δθ oi For the initial top layer oil temperature rise; Δθ or The temperature rise of the top oil under rated loss; Δθ hi R represents the initial gradient between the hot spot and the top oil temperature; R is the ratio of transformer load loss to no-load loss; K is the transformer load factor; x is the transformer oil index; H is the winding hot spot coefficient; g r Let be the gradient of the average winding temperature with respect to the average oil temperature under rated current, and y be the winding exponent; f1(t), f2(t), and f3(t) are all exponential functions of time, and their expressions are as follows:

[0044]

[0045]

[0046]

[0047] When the transformer load factor increases, the winding hot spot temperature is calculated using formula (1); when the transformer load factor decreases, the winding hot spot temperature is calculated using formula (2).

[0048] Step S3: Set the temperature rise limit for hot spots in the transformer windings, and set the temperature θ for different ambient temperatures. a and the initial top oil temperature rise Δθ of different transformers oiSubstituting the optimized empirical formula for calculating the hot spot temperature of the transformer winding, the results for different ambient temperatures θ are obtained. a and the initial top oil temperature rise Δθ of different transformers oi The transformer's operating time.

[0049] Based on the optimized empirical formula for calculating transformer winding hot spot temperature determined in step S2, the transient winding hot spot temperature of the transformer is calculated. Considering that the Class A insulation withstand temperature is 105℃, 105℃ is used as the winding hot spot temperature rise limit. Taking a 10kV oil-immersed distribution transformer as an example, different ambient temperatures θ are considered. a and the initial top oil temperature rise Δθ of different transformers oi Substituting these values ​​into formulas (1) and (2) after optimizing the characteristic parameters in step one, the values ​​θ for different ambient temperatures are calculated. a and the initial top oil temperature rise Δθ of different transformers oi The operating time of the transformers is shown in Table 1.

[0050] Table 1

[0051]

[0052] In step S4, when the transformer load changes in multiple steps, in order to make full use of the transformer capacity and avoid the transformer winding hot spot temperature from exceeding the limit, the specific time when the transformer hot spot temperature exceeds the limit is calculated based on the transformer running time obtained in step S3. The transformer load factor is reduced in advance before the temperature exceeds the limit. In this way, the load control strategy can be prepared in advance to improve the transformer utilization rate on the basis of safe transformer operation.

[0053] Taking a 10kV oil-immersed transformer as an example, the load change of the transformer within 24 hours is shown in Table 2.

[0054] Table 2

[0055] 0-360 0.8 361-840 1.0 841-1080 1.1 1081-1200 1.2 1201-1260 1.0 1261-1440 0.9

[0056] Assuming the initial top oil temperature of the transformer reached 15.6℃ at the end of the previous day, the temperature gradient between the transformer hot spot temperature and the top oil temperature was 5.9℃, and the ambient temperature was 28℃, the transformer winding hot spot temperature changes with the original load factor as follows: Figure 2 As shown, under the original load factor, when the transformer load increases from 1.1 to 1.2 in the 18th hour of operation, the winding hot spot temperature will quickly exceed the limit of 105℃. To ensure the safe operation of the transformer, the following two methods can be adopted:

[0057] (1) It can directly reduce the load factor of the transformer after 18 hours of operation;

[0058] (2) The hot spot temperature curve obtained by the optimized transformer winding hot spot temperature calculation empirical formula of the present invention can be used to interpolate and solve the specific time when the transformer hot spot temperature exceeds the limit, and reduce the transformer load factor in advance before the temperature exceeds the limit.

[0059] Assuming both methods reduce the transformer load factor from 1.2 to 1.1, the hot spot temperature values ​​under the two adjustment methods are as follows: Figure 2 As shown, Figure 2 In the diagram, the solid black line represents the change in winding hot spot temperature during operation according to the operating load factor in Table 2. It can be seen that after the load factor increased from 1.1 to 1.2 in the 18th hour (1080 minutes in Table 2), the hot spot temperature quickly exceeded the operating limit of 105℃, thus requiring load adjustment. The dashed black line represents the situation where the transformer's load factor was not increased in the 18th hour (1080 minutes in Table 2), and the load factor remained constant throughout the 18th hour. At this time, the hot spot temperature did not exceed the operating limit of 105℃. Figure 2 In the diagram, the curve represented by "line segment + point" indicates that during the 18th hour (1080th minute in Table 2), the transformer's load rate was maintained at 1.2. After running for a period, the load rate was reduced to 1.1 before the hot spot temperature exceeded the limit. During this period, the transformer's load rate was increased, thereby improving the transformer's utilization rate. Therefore, based on accurate calculation of the transformer's transient hot spot temperature, adjusting the transformer's operating load curve using the hot spot temperature interpolation method can, to a certain extent, improve the transformer's overload capacity and increase its utilization rate.

[0060] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0061] In the embodiments provided in this application, it should be understood that the division of units is only a logical functional division. In actual implementation, there may be other division methods, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A transformer load control method based on transient hot spot temperature calculation, characterized in that, Includes the following steps: Step S1: Obtain the temperature rise data of the transformer hot spot temperature and load changes, taking values ​​at fixed time intervals Δt to obtain a dataset of transformer hot spot temperature changes under multiple operating conditions (t). i ,T i ), i = 1, 2, ..., n; where t i T is the time corresponding to the i-th data point. i It is the winding hotspot temperature corresponding to the i-th data point; n is the total number of data sets collected. Step S2: Based on the dataset obtained in Step S1, use a genetic algorithm to calculate the parameter k in the empirical formula for the transformer winding hot spot temperature. 11 k 21 k 22 τ o τ w Optimization is performed, and the optimized parameters are substituted into the empirical formula for calculating the hot spot temperature of the transformer winding to obtain the optimized empirical formula for calculating the hot spot temperature of the transformer winding; where k 11 k 21 k 22 τ is a parameter of the thermal model. o τ is the time constant of the transformer oil. w The transformer winding time constant; Step S3: Set the temperature rise limit for hot spots in the transformer windings, and set the temperature θ for different ambient temperatures. a and the initial top oil temperature rise Δθ of different transformers oi Substituting the optimized empirical formula for calculating the hot spot temperature of the transformer winding, the results for different ambient temperatures θ are obtained. a and the initial top oil temperature rise Δθ of different transformers oi Transformer operating time; Step S4: Calculate the specific time when the transformer hot spot temperature exceeds the limit based on the transformer operating time obtained in step S3, and reduce the transformer load factor in advance before the temperature exceeds the limit.

2. The transformer load control method based on transient hot spot temperature calculation according to claim 1, characterized in that, In step S1, the transformer hot spot temperature and load change temperature rise data are obtained through multiphysics calculation or transformer temperature rise test.

3. The transformer load control method based on transient hot spot temperature calculation according to claim 1, characterized in that, The fixed time Δt in step S1 is selected as 5 minutes.

4. The transformer load control method based on transient hot spot temperature calculation according to claim 1, characterized in that, In step S2, a genetic algorithm is used to calculate the parameter k in the empirical formula for the transformer winding hot spot temperature. 11 k 21 k 22 τ o τ w The optimization process specifically includes the following steps: Step S21, set the relationship between the transformer winding hot spot temperature and time, and the thermal model parameters as T(t) = f(t, w), where the characteristic parameter vector w = (k 11 ,k 21 ,k 22 ,τ o ,τ w ); Step S22, in the transformer hot spot temperature change dataset (t) i ,T i When performing the calculation, the sum of squared residuals of hot spot temperatures, E(w), is used as the fitness function; Step S23: Encode the feature parameter vector w into binary form and generate an initial population; Step S24: Perform selection, crossover, and mutation operations on the binary encoding of the feature parameter vector w, calculate the fitness function, and determine whether the optimization termination condition is met. If the optimization termination condition is not met, continue to perform individual selection, crossover, and mutation operations. Repeat this process until the optimization termination condition is met, and determine the optimal feature parameter vector w.

5. A transformer load control method based on transient hotspot temperature calculation according to claim 4, characterized in that, The specific expression for the fitness function in step S22 is as follows:

6. The transformer load control method based on transient hotspot temperature calculation according to claim 4, characterized in that, Step S23 also includes: Five sets of binary codes are set for the feature parameter vector w, with a binary code length of 20 and a number of individuals in each binary code set to 50, and then an initial population is generated.

7. A transformer load control method based on transient hotspot temperature calculation according to claim 4, characterized in that, Step S24 further includes setting the crossover probability to 0.65 and the mutation probability to 0.

05.

8. A transformer load control method based on transient hotspot temperature calculation according to claim 4, characterized in that, The optimization termination condition in step S24 is: fitness function E(w) < ε, where ε is the minimum error value.

Images