Threshold value dynamic calculation method for overload protection of intelligent charging box transformer substation and related equipment

Abstract

The invention relates to the technical field of box transformer substation control, and particularly discloses an intelligent charging box transformer substation overload protection threshold value dynamic calculation method and related equipment, and the method comprises the steps: continuously obtaining the load information, the in-box temperature information and the environment temperature information of an intelligent charging box transformer substation; identifying and acquiring a parameter set of the dynamic thermodynamic model to determine the dynamic thermodynamic model; the identified dynamic thermodynamic model is utilized to predict the temperature in the box at the next moment based on the load information and the environment temperature information; based on the predicted temperature in the box and a preset safe temperature threshold value, a temperature margin is obtained through calculation; adjusting an overload protection threshold according to the temperature margin; according to the method, the self-adaptive adjustment of the overload protection threshold is realized, so that the overload protection strategy can dynamically change according to the actual operation state of the box-type transformer substation, the safe operation of the box-type transformer substation under the working conditions of slow heat dissipation and load fluctuation is ensured, the overload capacity of the box-type transformer substation is fully utilized, and the continuity of the charging service is improved.

Description

Technical Field

[0001] This application relates to the technical field of box substation control. Specifically, it relates to a method for dynamically calculating the threshold of overload protection for an intelligent charging box substation and related equipment. Background Art

[0002] With the increase in the number of electric vehicles in the core area of the city, intelligent charging box substations in underground spaces have become key infrastructure to relieve the urban charging pressure. However, the enclosed environment of the underground space restricts the heat dissipation conditions of the charging box substation equipment. Being in a high-load operation state for a long time is extremely likely to cause the continuous increase of the internal temperature of the box substation, and then accumulate heat. Coupled with the volatility of the urban power grid load and the peak-valley effect of electric vehicle charging, the risk of thermal runaway faced by intelligent charging box substations increases significantly.

[0003] Currently, existing dynamic overload protection technologies mainly rely on predicting the load and adjusting the overload protection threshold based on this prediction result. However, when this method is applied to underground intelligent charging box substations, there are inherent technical defects: on the one hand, the thermal lag effect of the underground environment makes the change of the box substation temperature often lag behind the load fluctuation, resulting in traditional prediction models being difficult to accurately capture the complex non-linear thermal dynamic characteristics inside the box substation; on the other hand, in order to simplify the calculation complexity, existing models usually adopt linear models and ignore the coupling relationship between the box substation temperature field and the load. This leads to either a lag in the adjustment of the overload protection threshold and insufficient timeliness and accuracy, or frequent false triggers, which can neither effectively guarantee the safe and stable operation of intelligent charging box substations nor seriously affect the continuity of charging services for electric vehicle users, and the user experience is poor. Summary of the Invention

[0004] The purpose of this application is to provide a method for dynamically calculating the threshold of overload protection for an intelligent charging box substation and related equipment, so as to adjust the overload protection threshold on the premise of capturing the complex non-linear thermal dynamic characteristics inside the box substation and the coupling relationship between the box substation temperature field and the load, and improve the accuracy and timeliness of the overload protection threshold.

[0005] In the first aspect, this application provides a method for dynamically calculating the threshold of overload protection for an intelligent charging box substation, which is used to adjust the overload protection threshold to protect the intelligent charging box substation from overload. The method includes the following steps: S1. Continuously obtain the load information, the internal temperature information of the box, and the ambient temperature information of the intelligent charging box substation; S2. Based on the load information, the internal temperature information of the box, the ambient temperature information, and the historical operation information, identify and obtain a parameter set of a dynamic thermal model to determine the dynamic thermal model, and the dynamic thermal model is used to describe the dynamic relationship between the change of the box substation load and the ambient temperature change and the change of the internal temperature of the box; S3. Based on the identified dynamic thermal model, predict the temperature inside the box at the next moment based on the load information and the ambient temperature information; S4. Calculate and obtain the temperature margin based on the predicted temperature inside the box and the preset safe temperature threshold; S5. Adjust the overload protection threshold according to the temperature margin.

[0006] The threshold dynamic calculation method for overload protection of the intelligent charging box transformer in this application dynamically identifies the dynamic thermal model based on the load information, the temperature information inside the box, and the ambient temperature information to predict the temperature inside the box at the next moment, and calculates the temperature margin based on the predicted temperature inside the box at the next moment to adjust the overload protection threshold, achieving the adaptive adjustment of the overload protection threshold, enabling the overload protection strategy to dynamically change according to the actual operating state of the box transformer, ensuring the safe operation of the box transformer under the conditions of slow heat dissipation and load fluctuations, and making full use of the overload capacity of the box transformer, improving the continuity of the charging service. Compared with the traditional fixed-threshold overload protection method, the threshold dynamic calculation method for overload protection of the intelligent charging box transformer in this application can more effectively address the overload protection problem under the complex working conditions of the underground space intelligent charging box transformer, improving the safety and reliability of the system.

[0007] For the described threshold dynamic calculation method for overload protection of an intelligent charging box transformer, step S2 includes: S21. Based on the online parameter identification algorithm, identify the parameter set of the dynamic thermal model according to the load information, the temperature information inside the box, the ambient temperature information, and the historical operation information. The parameter set includes the reference thermal time constant, the load influence coefficient, the heat gain coefficient, the nonlinear dissipation coefficient, and the reference delay steps. The historical operation information includes historical load information, historical temperature information inside the box, and historical ambient temperature information.

[0008] Step S21 introduces historical operation information for identification, enhancing the accuracy of parameter identification because historical data can provide long-term trends and characteristic information of the box transformer operation. Thus, the established dynamic thermal model can more accurately predict the temperature of the box transformer, overcoming the problem that traditional models cannot accurately capture the nonlinear thermal dynamic characteristics, providing a more reliable model basis for subsequent adjustment of the overload protection threshold.

[0009] For the described threshold dynamic calculation method for overload protection of an intelligent charging box transformer, step S4 includes: S41. Calculate and obtain the initial temperature margin according to the difference between the predicted temperature inside the box and the preset safe temperature threshold; S42. Smooth the initial temperature margin using the first-order lag filtering algorithm to obtain the temperature margin.

[0010] Through this processing method, the sudden change of the temperature margin is effectively reduced, ensuring that the subsequent adjustment process of the overload protection threshold based on the temperature margin is more stable and reliable. The smoothed temperature margin can more accurately reflect the safety state of the temperature of the box-type transformer, providing a more reliable basis for adjusting the overload protection threshold in the subsequent step S5 and ensuring the stability of the overload protection threshold adjustment process.

[0011] The described method for dynamically calculating the threshold of overload protection for an intelligent charging box-type transformer, wherein the filtering coefficient of the first-order lag filtering algorithm is adaptively adjusted according to the fluctuation degree of the load information of the box-type transformer, and this adaptive adjustment process includes: S421. Calculate and obtain the current load change rate according to the change situation of the load information; S422. Determine the filtering coefficient by using a piecewise function according to the current load change rate.

[0012] The described method for dynamically calculating the threshold of overload protection for an intelligent charging box-type transformer, wherein the method further includes the following steps: S6. Online monitor whether there are abnormal fluctuations in the voltage and current of the intelligent charging box-type transformer. When such abnormal fluctuations occur, generate an adaptive factor according to the fluctuation degree of the abnormal fluctuations, and compensate and adjust the overload protection threshold according to the adaptive factor.

[0013] The described method for dynamically calculating the threshold of overload protection for an intelligent charging box-type transformer, wherein step S6 includes: S61. Real-time monitor the voltage information and current information of the intelligent charging box-type transformer, and calculate the voltage fluctuation rate and current fluctuation rate respectively according to the voltage information and the current information; S62. Judge whether the voltage fluctuation rate and / or the current fluctuation rate exceed the corresponding preset fluctuation threshold. If so, it is determined that there are abnormal fluctuations in the voltage and current; S63. When such abnormal fluctuations occur, calculate the over-threshold ratio according to the voltage fluctuation rate and the current fluctuation rate; S64. Calculate the adaptive factor according to the over-threshold ratio and the preset adaptive coefficient; S65. Compensate and adjust the overload protection threshold according to the adaptive factor.

[0014] The described method for dynamically calculating the threshold of overload protection for an intelligent charging box-type transformer, wherein step S5 includes: S51. Determine the adjustment step size and adjustment direction according to the temperature margin, and calculate and obtain multiple candidate overload protection thresholds according to the adjustment step size, adjustment direction and the initial overload protection threshold; S52. Calculate the load margin of the box-type transformer according to the load information and each candidate overload protection threshold; S53. Obtain the historical load fluctuation frequency and historical overload duration according to the environmental temperature information and each transformer - substation load margin, and evaluate the risk level of the operation of the transformer - substation under each candidate overload protection threshold according to the historical load fluctuation frequency and historical overload duration; S54. Select the candidate overload protection threshold with the lowest risk level as the adjusted overload protection threshold.

[0015] In a second aspect, the present application also provides a device for dynamically calculating the threshold of intelligent charging transformer - substation overload protection, which is used to adjust the overload protection threshold to protect the intelligent charging transformer - substation from overload. The device includes: An acquisition module, configured to continuously acquire the load information, internal temperature information, and environmental temperature information of the intelligent charging transformer - substation; A model configuration module, configured to identify and obtain a parameter set of a dynamic thermal model based on the load information, internal temperature information, environmental temperature information, and historical operation information to determine the dynamic thermal model. The dynamic thermal model is used to describe the dynamic relationship between the change of the transformer - substation load, the change of the environmental temperature, and the change of the internal temperature of the transformer - substation; A temperature prediction module, configured to use the identified dynamic thermal model to predict the internal temperature of the next moment based on the load information and environmental temperature information; A margin calculation module, configured to calculate and obtain a temperature margin based on the predicted internal temperature of the transformer - substation and a preset safe temperature threshold; A threshold adjustment module, configured to adjust the overload protection threshold according to the temperature margin.

[0016] The device for dynamically calculating the threshold of intelligent charging transformer - substation overload protection in the present application dynamically identifies a dynamic thermal model based on the load information, internal temperature information, and environmental temperature information to predict the internal temperature of the next moment, and calculates a temperature margin based on the internal temperature of the next moment to adjust the overload protection threshold, realizing the adaptive adjustment of the overload protection threshold, so that the overload protection strategy can dynamically change according to the actual operation state of the transformer - substation. This not only ensures the safe operation of the transformer - substation under the conditions of slow heat dissipation and load fluctuation, but also makes full use of the overload capacity of the transformer - substation, improving the continuity of charging services. Compared with the traditional fixed - threshold overload protection device, the device for dynamically calculating the threshold of intelligent charging transformer - substation overload protection in the present application can more effectively address the overload protection problem under the complex working conditions of the underground space intelligent charging transformer - substation, improving the safety and reliability of the system.

[0017] In a third aspect, the present application also provides an electronic device, including a processor and a memory. The memory stores computer - readable instructions. When the computer - readable instructions are executed by the processor, the steps in the method provided in the first aspect above are run.

[0018] Fourthly, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it runs the steps in the method provided in the first aspect above.

[0019] As can be seen from the above, the present application provides a method for dynamically calculating the threshold of overload protection for an intelligent charging box transformer and related devices. Among them, the method dynamically identifies a dynamic thermal model based on load information, internal temperature information of the box, and ambient temperature information to predict the internal temperature of the box at the next moment, and calculates the temperature margin based on the internal temperature at the next moment to adjust the overload protection threshold, realizing the adaptive adjustment of the overload protection threshold, enabling the overload protection strategy to dynamically change according to the actual operating state of the box transformer, not only ensuring the safe operation of the box transformer under the conditions of slow heat dissipation and load fluctuations, but also making full use of the overload capacity of the box transformer, and improving the continuity of charging services. Compared with the traditional fixed-threshold overload protection method, the method for dynamically calculating the threshold of overload protection for the intelligent charging box transformer of the present application can more effectively address the overload protection problem under the complex working conditions of the underground space intelligent charging box transformer, improving the safety and reliability of the system. Description of the Drawings

[0020] Figure 1 It is a flowchart of the method for dynamically calculating the threshold of overload protection for the intelligent charging box transformer provided by the embodiment of the present application.

[0021] Figure 2 It is a schematic structural diagram of the device for dynamically calculating the threshold of overload protection for the intelligent charging box transformer provided by the embodiment of the present application.

[0022] Figure 3 It is a schematic structural diagram of the electronic device provided by the embodiment of the present application.

[0023] Reference Signs: 201, acquisition module; 202, model configuration module; 203, temperature prediction module; 204, margin calculation module; 205, threshold adjustment module; 301, processor; 302, memory; 303, communication bus. Detailed Embodiments

[0024] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Usually, the components of the embodiments of the present application described and shown here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the present application to be protected, but only represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0025] It should be noted that similar reference numerals and letters indicate similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present application, terms such as "first" and "second" are only used for differential description and cannot be construed as indicating or implying relative importance.

[0026] In a first aspect, please refer to Figure 1 , some embodiments of the present application provide a method for dynamically calculating the threshold of overload protection for an intelligent charging box transformer, which is used to adjust the overload protection threshold to perform overload protection on the intelligent charging box transformer. The method includes the following steps: S1. Continuously obtain the load information, the temperature information inside the box, and the ambient temperature information of the intelligent charging box transformer; S2. Based on the load information, the temperature information inside the box, the ambient temperature information, and the historical operation information, identify and obtain a parameter set of the dynamic thermal model to determine the dynamic thermal model, where the dynamic thermal model is used to describe the dynamic relationship between the change of the box transformer load and the change of the ambient temperature and the change of the temperature inside the box; S3. Use the identified dynamic thermal model to predict the temperature inside the box at the next moment based on the load information and the ambient temperature information; S4. Calculate and obtain the temperature margin based on the predicted temperature inside the box and the preset safe temperature threshold; S5. Adjust the overload protection threshold according to the temperature margin.

[0027] Specifically, in step S1, the load information can be obtained by real-time measurement of the current sensor and voltage sensor or power detection device installed in the box transformer, the temperature information inside the box can be obtained by real-time collection of the thermistor or temperature sensor deployed inside the box transformer, and the ambient temperature information can be obtained by real-time monitoring of the temperature sensor installed outside or near the box transformer. The real-time acquisition of these information provides a data basis for the subsequent identification of the dynamic thermal model and the adjustment of the overload protection threshold.

[0028] More specifically, step S2 is used to identify the parameter set of the dynamic thermal model based on the real-time information obtained in step S1 and the historical operation information to determine the dynamic thermal model. This identification process can adopt online parameter identification algorithms such as extended Kalman filtering and recursive least squares. The historical operation information can include historical load information, historical temperature information inside the box, and historical ambient temperature information. These historical operation information can be stored in a local database or a cloud platform for model parameter identification and update. The establishment of the dynamic thermal model realizes the accurate modeling of the thermal characteristics of the box transformer, overcomes the limitations of the traditional linear model, and provides a model support for subsequent temperature prediction and threshold adjustment.

[0029] More specifically, in step S3, the load information and ambient temperature information obtained in step S1 are substituted into the dynamic thermal model identified in step S2 for calculation to obtain the predicted value of the temperature inside the box at the next moment. The accuracy of temperature prediction directly affects the rationality of the adjustment of the overload protection threshold. Using the dynamic thermal model can more accurately predict the changing trend of the temperature inside the box and provide a reliable basis for subsequent threshold adjustment.

[0030] More specifically, in step S4, the predicted temperature inside the box at the next moment in step S3 is compared with a preset safe temperature threshold, and the difference between the two is the initial temperature margin. The safe temperature threshold can be preset according to factors such as the insulation level and heat dissipation conditions of the box substation equipment. For example, it can be set as the heat resistance level temperature of the insulation material of the box substation. The size of the temperature margin reflects the safety degree of the operation of the box substation. The larger the temperature margin, the safer the operation of the box substation and the greater the improvement space of the overload capacity.

[0031] More specifically, in step S5, the corresponding relationship between the temperature margin, the adjustment step size and the adjustment direction of the overload protection threshold can be preset according to the size of the temperature margin. For example, when the temperature margin is large, the overload protection threshold can be increased to allow the box substation to operate with overload within a certain range; when the temperature margin is small, the overload protection threshold is decreased to strengthen the overload protection. The dynamic adjustment of the overload protection threshold enables the overload protection strategy of the box substation to be adaptively adjusted according to the operation state of the box substation. While ensuring the safe operation of the box substation, it also takes into account the continuity of the charging service.

[0032] The method for dynamically calculating the threshold of the overload protection of the intelligent charging box substation in the embodiment of the present application predicts the temperature inside the box at the next moment by dynamically identifying the dynamic thermal model based on the load information, the temperature information inside the box and the ambient temperature information, and calculates the temperature margin based on the temperature inside the box at the next moment to adjust the overload protection threshold, realizing the adaptive adjustment of the overload protection threshold, so that the overload protection strategy can dynamically change according to the actual operation state of the box substation, which not only ensures the safe operation of the box substation under the conditions of slow heat dissipation and load fluctuation, but also makes full use of the overload capacity of the box substation and improves the continuity of the charging service. Compared with the traditional fixed-threshold overload protection method, the method for dynamically calculating the threshold of the overload protection of the intelligent charging box substation in the embodiment of the present application can more effectively cope with the overload protection problem under the complex working conditions of the underground space intelligent charging box substation and improve the safety and reliability of the system.

[0033] It should be noted that the overload protection threshold can be a current threshold, a power threshold or a temperature threshold, and it is preferably corresponding to the type of load information. In the embodiment of the present application, it is further preferably an overload current threshold.

[0034] In some preferred embodiments, step S2 includes: S21. Based on the online parameter identification algorithm, identify the parameter set of the dynamic thermal model according to the load information, the temperature information inside the box, the ambient temperature information, and the historical operation information. The parameter set includes the reference thermal time constant, the load influence coefficient, the heat gain coefficient, the nonlinear dissipation coefficient, and the reference delay steps. The historical operation information includes historical load information, historical temperature information inside the box, and historical ambient temperature information.

[0035] Specifically, the online parameter identification algorithm dynamically identifies the parameters of the dynamic thermal model according to the real-time data and historical data of the transformer operation. Among them, the real-time data refers to the load information, the real-time temperature information inside the box, and the real-time ambient temperature information, and the historical data refers to the historical operation information including historical load information, historical temperature information inside the box, and historical ambient temperature information. The parameter set of the dynamic thermal model specifically includes six key parameters: the reference thermal time constant, which characterizes the temperature response speed of the transformer; the load influence coefficient, which describes the influence degree of unit load change on the temperature inside the box; the heat gain coefficient, which reflects the effective influence ratio of the ambient temperature on the temperature inside the box; the nonlinear dissipation coefficient, which quantifies the nonlinear heat dissipation intensity; the reference delay steps, which represent the delay steps of the load influence. These parameters together constitute the core of the dynamic thermal model, which is used to accurately describe the thermal characteristics of the transformer, can characterize the thermal characteristics of the transformer from different dimensions, enables the dynamic thermal model to more comprehensively and precisely describe the thermal behavior of the transformer, and is used to accurately describe the thermal characteristics of the transformer.

[0036] More specifically, during the identification process, the algorithm continuously iterates and updates the parameter values to minimize the error between the model prediction output and the actual temperature inside the box. Through online identification, the parameters of the dynamic thermal model can be dynamically adjusted as the operation state of the transformer changes, so as to ensure that the model can accurately reflect the real-time thermal characteristics of the transformer.

[0037] More specifically, step S21 introduces historical operation information for identification, which enhances the accuracy of parameter identification because historical data can provide long-term trends and characteristic information of the transformer operation. Thus, the established dynamic thermal model can more accurately predict the temperature of the transformer, overcomes the problem that the traditional model cannot accurately capture the nonlinear thermal dynamic characteristics, and provides a more reliable model basis for the subsequent adjustment of the overload protection threshold.

[0038] In some preferred embodiments, step S21 includes: S211. Obtain historical load information, historical temperature information inside the box, and historical ambient temperature information; S212. Based on the extended Kalman filter online parameter identification algorithm, identify the parameter set of the dynamic thermal model according to the historical load information, historical in-box temperature information, historical ambient temperature information, load information, real-time in-box temperature information, and real-time ambient temperature information. The dynamic thermal model is a dynamic thermal model considering nonlinear dissipation and delay effects, and its functional form is as follows: T in (k + 1)=e -Δt / τ T in (k)+K P ·P(k - dbase)+K G (1 - e -Δt / τ )T amb (k)-β(Tin(k)-T amb (k)) 2 (1) where Δt is the sampling time interval, τ is the reference thermal time constant, K P is the load influence coefficient; K G is the heat gain coefficient, β is the nonlinear dissipation coefficient, d base is the reference delay step number; where T in (k) is the in-box temperature information at time k, T in (k + 1) is the in-box temperature information at time k + 1, P(k) is the load information at time k, so P(k - d base ) is the load information at time k - d base and T amb (k) is the ambient temperature information at time k; S213. Output the identified parameter set to determine the dynamic thermal model.

[0039] Specifically, the identification process in step S212 uses the historical load information and load information as the observation data of P(k), uses the historical in-box temperature information and real-time in-box temperature information as the observation data of T in (k) and T in (k + 1), and uses the historical ambient temperature information and real-time ambient temperature information as the observation data of T amb (k) for identification, that is, based on the time series relationship, the historical load information, historical in-box temperature information, and historical ambient temperature information form one type of observation data group, and the continuously collected load information, real-time in-box temperature information, and real-time ambient temperature information form another type of observation data group to work as the observation data of the extended Kalman filter online parameter identification algorithm to identify the parameter set of the dynamic thermal model; among them, the extended Kalman filter online parameter identification algorithm belongs to the existing identification algorithm, and its identification process will not be elaborated here.

[0040] More specifically, in the above dynamic thermal model, e -Δt / τ T in Item (k) reflects the thermal inertia of the system and describes the self-attenuation characteristics of the box transformer temperature, K P The term P(k-dbase) reflects the effect of load on the temperature inside the box, and takes into account the delayed effect of load influence, K G (1-e -Δt / τ )T amb Term (k) represents the immediate effect of the environment on temperature, β(T in (k)-T amb (k)) 2 The term describes the nonlinear heat dissipation caused by the temperature difference; among them, the effect of the load has a reference delay step (d base ), indicating that the load change needs to take several time steps to fully act on the temperature; the dynamic thermal model also introduces a nonlinear relationship of the square of the temperature difference through a nonlinear dissipation term (β term) to simulate the complex heat exchange phenomenon in the actual heat dissipation process, and determines the speed of temperature response by introducing a reference thermal time constant (τ): the larger τ is, the slower the system temperature changes; in addition, the load influence coefficient (K P ) and ambient heat gain coefficient (K G ) quantifies the contribution weights of load and ambient temperature to the system respectively.

[0041] More specifically, the traditional linear model usually ignores the nonlinear dissipation term, while the above dynamic thermal model uses β(T in (k)-T amb (k)) 2 The quadratic term of more accurately describes the heat dissipation effect in high temperature difference scenarios, and also sets the benchmark delay step number (d base ) realizes the segmentation effect of time series and can predict the temperature information inside the box at the next moment by inputting the continuously acquired load information, ambient temperature information and temperature information inside the box.

[0042] More specifically, after the parameter set consisting of the reference thermal time constant, load influence coefficient, heat gain coefficient, nonlinear dissipation coefficient and reference delay step number is identified in step S212, the dynamic thermal model is determined accordingly in step S213. Through the above steps, the dynamic thermal model can more accurately reflect the thermal characteristics of the underground space intelligent charging box transformer, and improve the model identification accuracy and temperature prediction accuracy.

[0043] More specifically, the design of the above dynamic thermal model fully considers the characteristics of the operating conditions of the intelligent charging box transformer in the underground space. A non-linear dissipation term and a delay effect are introduced into the model to more comprehensively describe the thermal characteristics of the box transformer. The non-linear dissipation term can reflect the non-linear characteristics of heat dissipation, and the delay effect takes into account the hysteresis of the influence of the load on the temperature. These considerations enable the dynamic thermal model to more accurately capture the thermal behavior of the box transformer. Through the extended Kalman filter online parameter identification algorithm, the model parameters can be identified and updated online based on real-time operating data, ensuring that the model can adapt to changes in operating conditions and improving the identification accuracy and the robustness of the model. The finally determined dynamic thermal model can provide a more accurate basis for subsequent temperature prediction of the box transformer, thereby improving the accuracy and reliability of the adjustment of the overload protection threshold.

[0044] In some preferred embodiments, step S4 includes: S41. Calculate and obtain an initial temperature margin according to the difference between the predicted temperature inside the box and the preset safe temperature threshold; S42. Smooth the initial temperature margin by using a first-order lag filtering algorithm to obtain a temperature margin.

[0045] Specifically, in step S41, the initial temperature margin is calculated by subtracting the preset safe temperature threshold from the predicted temperature inside the box. This initial temperature margin can directly reflect the interval size between the predicted temperature inside the box and the safe temperature. To avoid sudden changes in the initial temperature margin, the threshold dynamic calculation method for the overload protection of the intelligent charging box transformer in the embodiments of the present application adds step S42 to smooth the initial temperature margin.

[0046] More specifically, in the embodiments of the present application, the formula of the first-order lag filtering algorithm is preferably: Margin(k)=α(k)*Margin(k - 1)+(1 - α(k))*Margin inikial (k) (2) where Margin(k) is the temperature margin at time k, that is, the temperature margin at the current moment, Margin(k - 1) is the temperature margin at time k - 1, that is, the temperature margin at the previous moment, Margin inikial (k) is the initial temperature margin at time k, that is, the initial temperature margin at the current moment, and α(k) is the filtering coefficient at time k, which can be a preset fixed value or an adaptive value set according to actual situations, and is used to set the intensity of the filtering adjustment, so as to control the smoothing degree of the temperature margin.

[0047] More specifically, the lag filtering of the above first-order lag filtering algorithm comprehensively considers the temperature margin at the previous moment and the current initial temperature margin to determine the current temperature margin, so that the fluctuation of the temperature margin can be effectively suppressed, thereby obtaining a smoother temperature margin.

[0048] More specifically, through this processing method, the mutation of the temperature margin is effectively reduced, ensuring that the subsequent process of adjusting the overload protection threshold based on the temperature margin is more stable and reliable. The smoothed temperature margin can more accurately reflect the safety state of the temperature of the box transformer, providing a more reliable basis for adjusting the overload protection threshold in the subsequent step S5 and ensuring the stability of the overload protection threshold adjustment process.

[0049] In some preferred embodiments, the filtering coefficient of the first-order lag filtering algorithm is adaptively adjusted according to the fluctuation degree of the load information of the box transformer, and this adaptive adjustment process includes: S421. Calculate and obtain the current load change rate according to the change situation of the load information; S422. Determine the filtering coefficient by using a piecewise function according to the current load change rate.

[0050] Specifically, step S421 calculates the current load change rate to quantify the load fluctuation degree. The calculation process can be to calculate the difference between the load value at the current moment and the load value at the previous moment, and then divide this difference by the load value at the previous moment to obtain the load change rate; step S422 determines the filtering coefficient based on the piecewise function according to the load change rate. For example, the load change rate is divided into multiple intervals, and each interval corresponds to a specific filtering coefficient. When the calculated load change rate falls into a certain interval, the piecewise function outputs the filtering coefficient corresponding to this interval as the current filtering coefficient; among them, when the load changes violently, a smaller filtering coefficient is used to accelerate the filtering response speed; when the load changes gently, a larger filtering coefficient is used to enhance the filtering smoothness. The adaptive adjustment of the filtering coefficient enables the first-order lag filtering algorithm to balance quickly tracking the load change and suppressing noise interference, improving the accuracy and reliability of the temperature margin calculation, and providing a more accurate temperature margin basis for the subsequent adaptive adjustment of the overload protection threshold. Thus, through the adaptive adjustment of the filtering coefficient, the calculation of the temperature margin can not only quickly track the load change but also effectively suppress noise, thereby providing a more reliable and accurate temperature margin basis for the subsequent adaptive adjustment of the overload protection threshold.

[0051] More specifically, the interval division and coefficient values of the piecewise function can be adjusted according to the actual application scenario and requirements.

[0052] In some preferred embodiments, step S421 calculates the load change rate based on the following formula: ΔL(k)=|L(k)-L(k - 1)| / L max (3) where ΔL(k) is the load change rate at time k, L(k) is the load information at time k, L(k - 1) is the load information at time k - 1, and L maxIt is the maximum load capacity of the box transformer.

[0053] Specifically, the load change rate ΔL(t) can directly reflect the fluctuation range of the current load information relative to the previous load information.

[0054] In some preferred embodiments, the piecewise function is expressed as follows: when ΔL(k)≤θ1, α(k)=α max ;When θ1<ΔL(k)<θ2, α(k)=α max -(α max -α min )*(ΔL(k)-θ1) / (θ2-θ1); when ΔL(k)≥θ2, α(k)=α min ; Among them, α max is the maximum filter coefficient, α min is the minimum filtering coefficient, θ1 and θ2 are the first boundary and the second boundary of the preset load change rate threshold, and θ1<θ2.

[0055] Specifically, in this embodiment, the determination of the filter coefficient α(k) is based on a piecewise function of the load change rate ΔL(k). When ΔL(k) is less than or equal to the threshold θ1, the filter coefficient α(k) is set to the maximum filter coefficient α max , at this time, the filtering effect is the strongest and the temperature margin is the smoothest. When ΔL(k) is between the thresholds θ1 and θ2, the filter coefficient α(k) changes from α max Linear transition to the minimum filter coefficient α min , achieving a gradual weakening of the filtering effect. When ΔL(k) is greater than or equal to the threshold θ2, the filter coefficient α(k) is set to the minimum filter coefficient α min , at this time, the filtering effect is the weakest and the response speed to load changes is the fastest. Through this piecewise function method, the filter coefficient α(k) can be adaptively adjusted according to the degree of load fluctuation, thereby ensuring the smoothness of the temperature margin while taking into account the rapid response to load changes.

[0056] In some preferred embodiments, the method further comprises the following steps: S6. Online monitoring is performed to determine whether the voltage and current of the smart charging box transformer fluctuate abnormally. When abnormal fluctuations occur, an adaptive factor is generated according to the degree of fluctuation of the abnormal fluctuations, and the overload protection threshold value adjusted by the adaptive factor compensation is used.

[0057] Specifically, affected by weather conditions or other irresistible factors, the voltage and current input to the intelligent charging substation may fluctuate violently and frequently. The fluctuations in voltage and current will cause corresponding fluctuations in the load information, thereby enhancing the randomness and suddenness of the load, and greatly increasing the difficulty of predicting the temperature inside the box at the next moment, and significantly reducing the prediction accuracy. Therefore, the threshold dynamic calculation method for overload protection of the intelligent charging substation in the embodiments of the present application introduces step S6 to monitor whether there are abnormal fluctuations in the voltage and current input to the intelligent charging substation, and temporarily take specific safety measures when abnormal fluctuations occur.

[0058] More specifically, the design purpose of step S6 is to address the problem that abnormal fluctuations in voltage and current may lead to untimely overload protection, and further adjust the overload protection threshold to achieve faster and more accurate overload protection. Among them, the monitoring of voltage and current can be achieved through devices such as voltage sensors and current sensors, and these sensors are configured to collect voltage and current data during the operation of the substation in real time.

[0059] More specifically, the generation of the adaptive factor can be determined based on the relationship between the preset fluctuation threshold and the actual fluctuation degree. For example, the greater the fluctuation degree, the greater the adaptive factor. The adaptive factor is used to compensate and adjust the overload protection threshold previously adjusted based on the temperature margin. The compensation adjustment process aims to enable the overload protection threshold to respond more quickly to abnormal fluctuations in voltage and current, thereby starting the overload protection mechanism more timely. In this way, even when the temperature change lags behind the load fluctuation, or when the simplified model fails to fully consider the voltage and current mutations, the system can improve the response speed and accuracy of overload protection through real-time monitoring and adaptive adjustment of voltage and current, ensure the safe operation of the intelligent charging substation, effectively avoid potential risks caused by abnormal fluctuations in voltage and current, and ensure the safety and continuity of the charging service.

[0060] In some preferred embodiments, step S6 includes: S61. Real-time monitor the voltage information and current information of the intelligent charging substation, and calculate the voltage volatility and current volatility respectively according to the voltage information and current information; S62. Determine whether the voltage volatility and / or current volatility exceed the corresponding preset fluctuation threshold. If so, it is determined that there are abnormal fluctuations in voltage and current; S63. When abnormal fluctuations occur, calculate the over-threshold ratio according to the voltage volatility and current volatility; S64. Calculate the adaptive factor according to the over-threshold ratio and the preset adaptive coefficient; S65. Compensate and adjust the overload protection threshold according to the adaptive factor.

[0061] Specifically, in step S61, the voltage volatility is the standard deviation of voltage changes per unit time, and the current volatility is the standard deviation of current changes per unit time. The calculation of voltage volatility and current volatility can be achieved by using the standard deviation calculation method of a sliding window. For example, a time window is set, such as 30 seconds, and then the standard deviation of voltage sampling values and current sampling values within this 30 seconds is calculated, which is used as the voltage volatility and current volatility.

[0062] More specifically, in step S62, the preset volatility threshold is a benchmark set in advance for determining whether the voltage and current fluctuations are abnormal. The preset volatility threshold can be set according to the historical operation data and actual operation conditions of the intelligent charging box transformer.

[0063] More specifically, in step S63, the over-threshold ratio is used to quantify the degree to which the voltage volatility and current volatility exceed the preset volatility threshold. It can calculate the degree to which the voltage volatility exceeds the preset voltage volatility threshold and the degree to which the current volatility exceeds the preset current volatility threshold respectively, and then take the larger value as the over-threshold ratio.

[0064] More specifically, in step S64, the adaptation coefficient can be a preset value, which can be set according to actual needs and is used to combine with the over-threshold ratio to determine the adaptation factor. In the embodiments of the present application, it is preferably to use the product of the over-threshold ratio and the adaptation coefficient as the adaptation factor, and this adaptation factor reflects the severity of abnormal voltage and current fluctuations.

[0065] More specifically, in step S65, in the process of compensating and adjusting the overload protection threshold according to the adaptation factor, the compensation and adjustment method is not limited to multiplication or subtraction, and non-linear methods such as look-up table method and function fitting method can also be used to establish the mapping relationship between the adaptation factor and the overload protection threshold to achieve more refined threshold adjustment; in the embodiments of the present application, it is preferably to subtract the adaptation factor from the original overload protection threshold to obtain the compensated and adjusted overload protection threshold. In this way, when abnormal voltage and current fluctuations occur, the overload protection threshold can be adaptively lowered, so that the intelligent charging box transformer can enter the overload protection mode faster, thus ensuring the safe operation of the equipment.

[0066] In some preferred embodiments, the preset volatility threshold includes a preset voltage volatility threshold and a preset current volatility threshold. The steps of calculating the over-threshold ratio according to the voltage volatility and current volatility include: S631. When the voltage volatility exceeds the preset voltage volatility threshold, calculate the difference between the voltage volatility and the preset voltage volatility threshold to obtain the voltage over-threshold difference; when the current volatility exceeds the preset current volatility threshold, calculate the difference between the current volatility and the preset current volatility threshold to obtain the current over-threshold difference; S632. Obtain the voltage over-threshold ratio by dividing the voltage over-threshold difference by the preset voltage fluctuation threshold; obtain the current over-threshold ratio by dividing the current over-threshold difference by the preset current fluctuation threshold. S633. Take the larger value of the voltage over-threshold ratio and the current over-threshold ratio as the over-threshold ratio.

[0067] Specifically, in step S631, when the voltage volatility does not exceed the preset voltage fluctuation threshold, the calculation of the voltage over-threshold difference is skipped; when the current volatility does not exceed the preset current fluctuation threshold, the calculation of the current over-threshold difference is skipped.

[0068] More specifically, step S632 calculates the voltage over-threshold ratio by dividing the voltage over-threshold difference obtained in step S631 by the preset voltage fluctuation threshold, and calculates the current over-threshold ratio by dividing the current over-threshold difference obtained in step S631 by the preset current fluctuation threshold.

[0069] More specifically, step S633 selects the larger value as the final over-threshold ratio by comparing the voltage over-threshold ratio and the current over-threshold ratio. This over-threshold ratio will be used for subsequent adaptive factor calculation, thereby affecting the adjustment of the overload protection threshold.

[0070] More specifically, step S631 quantifies the degree to which the voltage volatility and current volatility exceed the safety threshold by calculating the over-threshold difference, avoiding the accuracy loss that may be caused by using only simple Boolean judgments. Step S632 introduces normalization processing, converting the over-threshold differences of voltage and current into ratios relative to their respective preset fluctuation thresholds, enabling direct comparison of the over-threshold degrees between different physical quantities. Step S633 selects the larger value as the final over-threshold ratio, ensuring that the system responds to the more severe abnormal fluctuations in both the voltage and current dimensions, improving the sensitivity and reliability of triggering the fast overload protection mode. Thus, the obtained over-threshold ratio can more comprehensively and accurately reflect the comprehensive abnormal fluctuations of the voltage and current of the intelligent charging substation, providing more effective data support for subsequent adaptive factor generation and overload protection threshold adjustment, solving the problem in the prior art that the over-threshold ratio calculation method is one-sided and may lead to a decrease in the accuracy and timeliness of the fast overload protection mode, and improving the safe operation performance of the intelligent charging substation under abnormal voltage and current fluctuation conditions.

[0071] In some preferred embodiments, step S5 includes: S51. Determine the adjustment step size and adjustment direction according to the temperature margin, and calculate and obtain multiple candidate overload protection thresholds based on the adjustment step size, adjustment direction, and the initial overload protection threshold; S52. Calculate the substation load margin according to the load information and each candidate overload protection threshold; S53. Obtain the historical load fluctuation frequency and historical overload duration according to the load margin of each transformer substation, and evaluate the risk level of the operation of the transformer substation under each candidate overload protection threshold according to the ambient temperature information, historical load fluctuation frequency and historical overload duration; S54. Select the candidate overload protection threshold with the lowest risk level as the adjusted overload protection threshold.

[0072] Specifically, step S51 can divide the range from zero to the temperature margin into multiple intervals according to a preset quantity, and use the upper limit of each interval to correspond to different adjustment steps, respectively for adjusting the initial overload protection threshold to obtain multiple candidate overload protection thresholds.

[0073] More specifically, in step S52, the load margin of the transformer substation can be understood as the remaining space between the actual load of the transformer substation and the overload protection threshold under the current candidate overload protection threshold. The larger its value, the higher the operating safety of the transformer substation, but at the same time, it may also limit the full utilization of the load capacity of the transformer substation.

[0074] More specifically, in step S53, the risk level evaluation process is as follows: First, query the preset corresponding relationship table between ambient temperature and heat dissipation capacity according to the ambient temperature information to obtain the heat dissipation capacity level corresponding to the current ambient temperature. Then, according to the historical load data, find and count the historical load fluctuation frequency and historical overload duration within a preset time window when the intelligent charging transformer substation has different load margins. The historical load fluctuation frequency is obtained by counting the number of times the load change exceeds the preset threshold per unit time, and the historical overload duration is obtained by accumulating the duration when the load exceeds the candidate overload protection threshold. Finally, based on the heat dissipation capacity level, historical load fluctuation frequency and historical overload duration, use a pre-trained risk assessment model or a preset risk assessment table to obtain the risk level of the operation of the transformer substation under each candidate overload protection threshold.

[0075] More specifically, in step S54, by comparing the risk levels corresponding to each candidate overload protection threshold, select the candidate overload protection threshold with the lowest risk level as the finally adjusted overload protection threshold.

[0076] More specifically, selecting the candidate overload protection threshold with the lowest risk level as the final adjustment result realizes the optimized adjustment of the overload protection threshold. Through the introduction of the risk assessment mechanism, the adjustment of the overload protection threshold is no longer simply determined by a single factor of temperature margin, but comprehensively considers multiple factors such as environment and load, making the adjustment result more scientific and reasonable, and achieving a balance between safety and load demand.

[0077] In some preferred embodiments, step S53 includes: S531. Query the preset correspondence table between ambient temperature and heat dissipation capacity according to the ambient temperature information, and obtain the heat dissipation capacity level corresponding to the current ambient temperature; S532. According to the load margin of the box-type substation, combined with historical load data, count the historical load fluctuation frequency and historical overload duration within a preset time window when setting different load margins of the box-type substation. Among them, the historical load fluctuation frequency is obtained by counting the number of times the load change exceeds the preset threshold per unit time, and the historical overload duration is obtained by accumulating the duration when the load exceeds the candidate overload protection threshold. The preset threshold is positively correlated with the load margin of the box-type substation; S533. Based on the heat dissipation capacity level, historical load fluctuation frequency, and historical overload duration, use the pre-trained risk assessment model to calculate the risk level of the box-type substation operation under each candidate overload protection threshold. The risk assessment model takes the heat dissipation capacity level, historical load fluctuation frequency, and historical overload duration as inputs and the risk level as the output, and the risk assessment model is periodically updated according to historical data to adapt to the change of load characteristics.

[0078] Specifically, in step S531, the correspondence table between ambient temperature and heat dissipation capacity can be preset as a lookup table, and this lookup table records the heat dissipation capacity levels of the box-type substation at different ambient temperatures. Ambient temperature is an important factor affecting the heat dissipation capacity of the box-type substation. A higher ambient temperature usually means a decrease in the heat dissipation capacity of the box-type substation and an increase in the overload risk. By considering the influence of ambient temperature on the heat dissipation capacity, the risk assessment can be more in line with the actual operating conditions.

[0079] More specifically, step S532 combines the load margin of the box-type substation and refers to the historical load data to calculate the historical load fluctuation frequency and historical overload duration within a preset time window. The historical load fluctuation frequency reflects the severity of the load change, and the historical overload duration reflects the duration of the box-type substation operating under high load. These two parameters characterize the operating pressure and potential risks of the box-type substation from the load side.

[0080] More specifically, the risk assessment model is trained by using historical operation data to learn the mapping relationship between the heat dissipation capacity level, historical load fluctuation frequency, historical overload duration, and the risk level of the box-type substation operation. The risk assessment model comprehensively considers multiple factors such as ambient temperature, load fluctuation, and overload duration, and realizes the comprehensive assessment of the risk of the box-type substation operation. By selecting the candidate overload protection threshold with the lowest risk level as the adjusted overload protection threshold, the overload capacity of the box-type substation can be fully utilized while ensuring the safe operation of the box-type substation.

[0081] In the second aspect, please refer to Figure 2, some embodiments of the present application further provide a device for dynamically calculating the threshold of overload protection for an intelligent charging box transformer, which is used to adjust the overload protection threshold to protect the intelligent charging box transformer from overload. The device includes: An acquisition module 201, configured to continuously acquire the load information, the temperature information inside the box, and the ambient temperature information of the intelligent charging box transformer; A model configuration module 202, configured to identify and obtain a parameter set of a dynamic thermal model based on the load information, the temperature information inside the box, the ambient temperature information, and the historical operation information to determine the dynamic thermal model. The dynamic thermal model is used to describe the dynamic relationship between the change of the box transformer load and the ambient temperature change and the change of the temperature inside the box; A temperature prediction module 203, configured to use the identified dynamic thermal model to predict the temperature inside the box at the next moment based on the load information and the ambient temperature information; A margin calculation module 204, configured to calculate and obtain a temperature margin based on the predicted temperature inside the box and a preset safe temperature threshold; A threshold adjustment module 205, configured to adjust the overload protection threshold according to the temperature margin.

[0082] The device for dynamically calculating the threshold of overload protection for the intelligent charging box transformer according to the embodiments of the present application dynamically identifies the dynamic thermal model based on the load information, the temperature information inside the box, and the ambient temperature information to predict the temperature inside the box at the next moment, and calculates the temperature margin based on the temperature inside the box at the next moment to adjust the overload protection threshold, realizing the adaptive adjustment of the overload protection threshold, so that the overload protection strategy can dynamically change according to the actual operation state of the box transformer, which not only ensures the safe operation of the box transformer under the conditions of slow heat dissipation and load fluctuation, but also makes full use of the overload capacity of the box transformer, improving the continuity of charging services. Compared with the traditional fixed-threshold overload protection device, the device for dynamically calculating the threshold of overload protection for the intelligent charging box transformer according to the embodiments of the present application can more effectively cope with the overload protection problem under the complex working conditions of the underground space intelligent charging box transformer, improving the safety and reliability of the system.

[0083] In some preferred embodiments, the threshold adjustment module 205 is further configured to online monitor whether there is abnormal fluctuation in the voltage and current of the intelligent charging box transformer. When there is abnormal fluctuation, an adaptive factor is generated according to the degree of the abnormal fluctuation, and the adjusted overload protection threshold is compensated according to the adaptive factor.

[0084] In some preferred embodiments, the device for dynamically calculating the threshold of overload protection for the intelligent charging box transformer according to the embodiments of the present application is used to execute the method for dynamically calculating the threshold of overload protection for the intelligent charging box transformer provided in the first aspect above.

[0085] For the third aspect, please refer to Figure 3, some embodiments of the present application also provide a schematic structural diagram of an electronic device. The present application provides an electronic device, including: a processor 301 and a memory 302. The processor 301 and the memory 302 are interconnected and communicate with each other through a communication bus 303 and / or other forms of connection mechanisms (not shown). The memory 302 stores computer-readable instructions executable by the processor 301. When the electronic device runs, the processor 301 executes the computer-readable instructions to execute the methods in any optional implementation manner of the above embodiments.

[0086] In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it executes the methods in any optional implementation manner of the above embodiments. Among them, the computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, abbreviated as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, abbreviated as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, abbreviated as EPROM), programmable read-only memory (Programmable Red-Only Memory, abbreviated as PROM), read-only memory (Read-OnlyMemory, abbreviated as ROM), magnetic memory, flash memory, magnetic disk or optical disc.

[0087] In the embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some communication interfaces. The indirect coupling or communication connection of the devices or units can be in an electrical, mechanical or other form.

[0088] In addition, the units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0089] Furthermore, each functional module in various embodiments of the present application may be integrated together to form an independent part, or each module may exist alone, or two or more modules may be integrated to form an independent part.

[0090] In this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

[0091] The above are only embodiments of the present application and are not used to limit the protection scope of the present application. For those skilled in the art, the present application may have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for dynamically calculating the threshold of overload protection for an intelligent charging box transformer, which is used to adjust the overload protection threshold to perform overload protection on the intelligent charging box transformer, characterized in that The method includes the following steps: S1. Continuously obtain the load information, the temperature information inside the box, and the ambient temperature information of the intelligent transformer substation with charging function; S2. Based on the load information, the temperature information inside the box, the ambient temperature information, and the historical operation information, identify and obtain the parameter set of the dynamic thermal model to determine the dynamic thermal model, which is used to describe the dynamic relationship between the load change of the transformer substation, the ambient temperature change, and the temperature change inside the box; S3. Use the identified dynamic thermal model to predict the temperature inside the box at the next moment based on the load information and the ambient temperature information; S4. Calculate and obtain the temperature margin based on the predicted temperature inside the box and the preset safe temperature threshold; S5. Adjust the overload protection threshold according to the temperature margin.

2. The threshold dynamic calculation method for overload protection of an intelligent charging box substation according to claim 1, characterized in that, Step S2 includes: S21. Based on the online parameter identification algorithm, identify the parameter set of the dynamic thermal model according to the load information, the temperature information inside the box, the ambient temperature information, and the historical operation information. The parameter set includes the reference thermal time constant, the load influence coefficient, the heat gain coefficient, the nonlinear dissipation coefficient, and the reference delay steps. The historical operation information includes historical load information, historical temperature information inside the box, and historical ambient temperature information.

3. A method for dynamically calculating the threshold of overload protection of an intelligent charging box transformer according to claim 1, characterized in that Step S4 includes: S41. Calculate and obtain the initial temperature margin according to the difference between the predicted temperature inside the box and the preset safe temperature threshold; S42. Smooth the initial temperature margin using the first-order lag filtering algorithm to obtain the temperature margin.

4. The threshold dynamic calculation method for intelligent charging substation overload protection according to claim 3, characterized in that, The filtering coefficient of the first-order lag filtering algorithm is adaptively adjusted according to the fluctuation degree of the load information of the transformer substation with charging function. This adaptive adjustment process includes: S421. Calculate and obtain the current load change rate according to the change situation of the load information; S422. Determine the filtering coefficient using a piecewise function according to the current load change rate.

5. The threshold dynamic calculation method for overload protection of an intelligent charging box transformer according to claim 1, characterized in that, The method further includes the following steps: S6. Online monitor whether there are abnormal fluctuations in the voltage and current of the intelligent transformer substation with charging function. When such abnormal fluctuations occur, generate an adaptive factor according to the fluctuation degree of the abnormal fluctuations, and compensate and adjust the overload protection threshold according to the adaptive factor.

6. The threshold dynamic calculation method for intelligent charging substation overload protection according to claim 5, characterized in that, Step S6 includes: S61. Real-time monitor the voltage information and current information of the intelligent transformer substation with charging function, and calculate the voltage fluctuation rate and the current fluctuation rate respectively according to the voltage information and the current information; S62. Determine whether the voltage fluctuation rate and / or the current fluctuation rate exceed the corresponding preset fluctuation thresholds. If so, it is determined that there are abnormal fluctuations in the voltage and current; S63. When such abnormal fluctuations occur, calculate the over-threshold ratio according to the voltage fluctuation rate and the current fluctuation rate; S64. Calculate the adaptive factor according to the over-threshold ratio and the preset adaptive coefficient; S65. Compensate and adjust the overload protection threshold according to the adaptive factor.

7. A method for dynamically calculating the threshold of overload protection of an intelligent charging box transformer according to claim 1, characterized in that, Step S5 includes: S51. Determine the adjustment step size and adjustment direction according to the temperature margin, and calculate and obtain multiple candidate overload protection thresholds according to the adjustment step size, the adjustment direction, and the initial overload protection threshold; S52. Calculate the load margin of the transformer substation according to the load information and each candidate overload protection threshold; S53. Obtain the historical load fluctuation frequency and historical overload duration according to the environmental temperature information and each transformer - substation load margin, and evaluate the risk level of the operation of the transformer - substation under each candidate overload protection threshold according to the historical load fluctuation frequency and historical overload duration; S54. Select the candidate overload protection threshold with the lowest risk level as the adjusted overload protection threshold.

8. A threshold dynamic calculation device for overload protection of an intelligent charging box transformer, which is used to adjust the overload protection threshold to perform overload protection on the intelligent charging box transformer, characterized in that, The device includes: An acquisition module, configured to continuously acquire the load information, the temperature information inside the box, and the environmental temperature information of the intelligent charging transformer - substation; A model configuration module, configured to identify and obtain a parameter set of a dynamic thermal model based on the load information, the temperature information inside the box, the environmental temperature information, and the historical operation information to determine the dynamic thermal model, where the dynamic thermal model is used to describe the dynamic relationship between the change of the transformer - substation load, the change of the environmental temperature, and the change of the temperature inside the box; A temperature prediction module, configured to use the identified dynamic thermal model to predict the temperature inside the box at the next moment based on the load information and the environmental temperature information; A margin calculation module, configured to calculate and obtain a temperature margin based on the predicted temperature inside the box and a preset safe temperature threshold; A threshold adjustment module, configured to adjust the overload protection threshold according to the temperature margin.

9. An electronic device, characterized in that, It includes a processor and a memory, and the memory stores computer - readable instructions. When the computer - readable instructions are executed by the processor, the steps in the method according to any one of claims 1 - 7 are run.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps in the method according to any one of claims 1 - 7 are run.

Images