Transformer magnetizing inrush current suppression method based on residual magnetism prediction

Abstract

The invention discloses a transformer magnetizing inrush current suppression method based on residual magnetism prediction, which relates to the technical field of transformer control, and comprises the following steps of: establishing a residual magnetism prediction model, judging whether a transformer generates magnetizing inrush current or not when the transformer is started through predicted residual magnetism intensity, and if yes, judging whether the transformer generates the magnetizing inrush current; the method comprises the following steps: designing a transformer excitation inrush current suppression algorithm, identifying excitation current characteristics caused by residual magnetism, designing the transformer excitation inrush current suppression algorithm according to the excitation inrush current model, optimizing the inrush current condition in actual operation by adopting a self-adaptive optimization method to obtain an optimized suppression algorithm, and based on the optimized suppression algorithm, determining the excitation inrush current of the transformer. And on-line monitoring of the transformer is carried out. According to the transformer excitation surge current suppression method based on residual magnetism prediction, through real-time prediction and optimization control of residual magnetism, the amplitude of excitation surge current caused by the residual magnetism is remarkably reduced, the surge current suppression strategy can be automatically adjusted under different loads and operation states, and stable operation of the transformer under various working conditions is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer control, and specifically provides a method for suppressing inrush current of a transformer based on residual magnetism prediction. Background Art

[0002] As an important device in the power system, the operation stability of a transformer is directly related to the reliability of power supply. However, during the excitation process of the transformer, inrush current phenomena often occur. That is, when the power supply is connected to the transformer, due to the rapid change of the magnetic field, short-term high-amplitude current fluctuations are generated. This inrush current not only causes damage to the transformer itself, but may also cause overload and damage to other devices in the system, and may even affect the stability of the power network. Therefore, how to effectively suppress the inrush current of the transformer and ensure its safe and efficient operation is an urgent problem to be solved in the field of transformer technology.

[0003] In the prior art, although there are certain inrush current suppression methods, they mainly focus on reducing the inrush current by adjusting the starting method, optimizing the control of the exciting current, etc. However, these methods usually ignore the influence of the residual magnetism of the transformer on the generation of inrush current. Residual magnetism refers to the part of the magnetic field intensity that remains in the transformer after power-off due to the hysteresis characteristics of the iron core. It will affect the exciting current when the transformer is powered on again, thus generating a larger inrush current. Therefore, the prior art lacks a comprehensive suppression method for the influence of residual magnetism and cannot achieve precise control and real-time adjustment. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a method for suppressing inrush current of a transformer based on residual magnetism prediction to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions: In the first aspect, an embodiment of the present invention provides a method for suppressing inrush current of a transformer based on residual magnetism prediction, including the following steps: S1. Establish a residual magnetism prediction model to predict the residual magnetism intensity of the transformer; S2. Based on the predicted residual magnetism intensity, determine whether inrush current is generated when the transformer starts, and identify the characteristics of the exciting current caused by residual magnetism, including high-frequency disturbances and the non-linear characteristics of the current waveform during the transient process; S3. Based on the exciting current characteristics obtained in step S2, design an inrush current model suitable for the transformer; S4. According to the inrush current model, design an inrush current suppression algorithm for the transformer; S5. Based on the suppression algorithm in step S4, for the inrush current situation in actual operation, adopt an adaptive optimization method to optimize and adjust the suppression algorithm to obtain an optimized suppression algorithm; S6. Conduct on-line monitoring of the transformer based on the optimized suppression algorithm.

[0006] Further optimize the technical solution. In step S1, the residual magnetism prediction model is based on the relationship between the current characteristics of the residual magnetism and the characteristics of the transformer material. Using the known initial conditions and real-time changing data, the intensity of the residual magnetism and its influence on the inrush current are predicted through calculation. In the residual magnetism prediction model, the residual magnetism intensity changes with time and is described by the following equation: ; where represents the differential operation and indicates the rate of change of the function with time; represents the residual magnetism intensity, with the unit of tesla ; is the residual magnetism decay constant, indicating the rate of decay of the residual magnetism with time, with the unit of ; is the residual magnetism recovery coefficient, indicating the speed of residual magnetism recovery, with the unit of ; is the exciting voltage, with the unit of ; is the magnetic permeability of the transformer, with the unit of henry .

[0007] Further optimize the technical solution. In the residual magnetism prediction model: represents the derivative of the residual magnetism intensity with respect to time and reflects the increase or decrease of the residual magnetism; if , it indicates that the residual magnetism is increasing; if it indicates that the residual magnetism is decreasing; is the attenuation term, indicating the natural decay of the residual magnetism with time. After the transformer is out of service, the intensity of its residual magnetism will gradually weaken with time, and the decay rate is related to the magnetic material characteristics of the transformer. The faster the decay rate, the more rapid the disappearance of the residual magnetism; is the recovery term, indicating the recovery process of the residual magnetism, which is proportional to the exciting voltage and is proportional to the magnetic permeability inversely proportional; the restoration term is used to simulate the process in which the residual magnetism gradually recovers under the action of the exciting voltage when the transformer is restarted; Residual magnetism intensity According to the initial residual magnetism and exciting voltage input conditions, calculate the residual magnetism state at any moment which will have an impact when the transformer restarts, and further affect the inrush current.

[0008] To further optimize this technical solution, in step S3, the inrush current model describes the influence of residual magnetism on the current waveform during the exciting process through a mathematical equation, and is constructed based on the electrical parameters of the transformer, load changes and external disturbance factors; In the inrush current model, it is assumed that during the exciting process of the transformer, the inrush current of the exciting current is affected by the residual magnetism and due to external disturbances and load changes, the amplitude and waveform of the inrush current change dynamically; the mathematical equation of the inrush current model is as follows: ; where, represents the change of the exciting current with time with the unit of ; is the initial coefficient of the exciting current, reflecting the initial influence of the exciting voltage on the current; is the exciting voltage with the unit of ; is the time varying magnetic permeability of the transformer with the unit of henry and changes with the working state; represents the residual magnetism intensity with the unit of tesla , which is predicted by step S1; is the maximum residual magnetism value of the transformer with the unit of tesla , representing the possible maximum residual magnetism intensity at a certain moment; is the coefficient of the influence of residual magnetism on the inrush current of the exciting current, representing the non-linear influence of residual magnetism on the inrush current amplitude; is the exponent of the influence of residual magnetism on the inrush current amplitude, controlling the weighting degree of the influence of residual magnetism on the inrush current; is the correction coefficient of external disturbance on the exciting current; is an external disturbance function, representing the influence of external factors on the exciting current.

[0009] To further optimize this technical solution, in the exciting inrush current model: is the basic influence term of the exciting current, representing the exciting voltage and magnetic permeability 's basic influence on the exciting current. This term calculates the initial change trend of the exciting current under the conditions of no remanence influence and external disturbance; describes the influence of remanence on the exciting inrush current; the greater the remanence intensity, the stronger the transformer magnetic field, and the greater the amplitude of the exciting inrush current; by adjusting the coefficient and exponent , the influence of remanence can be flexibly described, reflecting the non-linear amplification effect of remanence on the current inrush amplitude; is used to consider the corrective influence of external disturbance on the exciting current. The external disturbance is caused by factors such as load change and temperature change, and the correction coefficient is used to adjust the influence degree of the external disturbance.

[0010] To further optimize this technical solution, in step S4, the suppression algorithm includes: By adjusting the exciting voltage waveform, it is used to avoid excessive instantaneous current during the transformer startup process; Using soft start technology, the exciting current is gradually increased, thereby reducing the inrush amplitude; Adopting a non-linear control method, it is adjusted in real time according to the actual change of the inrush current.

[0011] To further optimize this technical solution, in step S5, there is an optimization model in the adaptive optimization method, enabling the suppression algorithm to respond quickly under different working conditions. By dynamically adjusting the control parameters in the inrush current suppression strategy, the deviation between the inrush current and the transformer operating state is minimized; The optimization model is as follows: ; Among them, is the optimization objective function, representing the total error of the inrush current suppression strategy; is the actually calculated exciting current, with the unit of ; is the target exciting current, representing the exciting current that is expected to be achieved according to the inrush current suppression strategy, with the unit of ; is the optimized time range, representing the time period considered during the optimization process; is the regularization coefficient, controlling the constraint strength on the control parameters during the optimization process; is a control parameter in the suppression algorithm, representing the th parameter in the algorithm; is the total number of algorithm parameters, representing the number of control parameters that need to be adjusted during the optimization process.

[0012] To further optimize this technical solution, in the optimization model: is used to calculate the deviation between the actual excitation current and the target excitation current; by minimizing the deviation, the inrush current suppression algorithm accurately adjusts the excitation current to the ideal state, thus avoiding excessive excitation inrush current; is the regularization term, used to constrain the control parameters during the optimization process, to prevent excessive adjustment of the control parameters, ensure that the suppression algorithm cannot overly rely on specific parameter values, and maintain the stability and generalization ability of the algorithm.

[0013] To further optimize this technical solution, when the optimization model is used, it includes: Define the target excitation current; Calculate the actual excitation current; Parameter optimization; Regularization control; Real-time adjustment; Optimization feedback and iteration.

[0014] To further optimize this technical solution, in step S6, the online monitoring of the transformer includes: Real-time monitor the residual magnetic strength and excitation current waveform of the transformer, and input the data into the optimized suppression algorithm for dynamic adjustment.

[0015] In a second aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the computer program instructions are executed by the processor, the steps of a method for suppressing transformer excitation inrush current based on residual magnetic prediction as described in the first aspect of the present invention are implemented.

[0016] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, wherein: when the computer program instructions are executed by a processor, the steps of a method for suppressing inrush current of a transformer based on residual magnetism prediction as described in the first aspect of the present invention are implemented.

[0017] Compared with the prior art, the present invention provides a method for suppressing inrush current of a transformer based on residual magnetism prediction, having the following beneficial effects: This method for suppressing inrush current of a transformer based on residual magnetism prediction, through real-time prediction and optimized control of residual magnetism, not only significantly reduces the amplitude of inrush current caused by residual magnetism, but also can automatically adjust the inrush current suppression strategy under different loads and operating states to ensure the stable operation of the transformer under various working conditions. And an adaptive algorithm based on an optimization model is adopted, which can dynamically adjust the inrush current suppression strategy according to real-time data, so as to accurately suppress the inrush current under various working conditions. Compared with the prior art, this method has higher accuracy and adaptability, can effectively improve the working efficiency and reliability of the transformer, reduce equipment losses, extend the equipment life, and enhance the overall stability of the power system. Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 It is a schematic flowchart of a method for suppressing inrush current of a transformer based on residual magnetism prediction proposed by the present invention; Figure 2 It is a schematic flowchart of an optimization model in a method for suppressing inrush current of a transformer based on residual magnetism prediction proposed by the present invention. Detailed Embodiments

[0020] To make the above objects, features, and advantages of the present invention more obvious and understandable, the detailed embodiments of the present invention will be described in detail below with reference to the drawings in the specification.

[0021] Many specific details are set forth in the following description in order to provide a thorough understanding of the present invention, but the present invention may be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention, so the present invention is not limited by the specific embodiments disclosed below.

[0022] Secondly, the "one embodiment" or "embodiment" mentioned herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or selectively exclusive embodiments from other embodiments.

[0023] Embodiment 1: Referring to Figures 1 to 2 , this is the first embodiment of the present invention. This embodiment provides a method for suppressing inrush current of a transformer based on residual magnetism prediction, including the following steps: S1. Establish a residual magnetism prediction model to predict the residual magnetism intensity of the transformer.

[0024] After the transformer is out of service, residual magnetism is one of the main factors causing inrush current. The establishment of the residual magnetism prediction model is the core of the entire suppression method. In this step, first, by analyzing the operating data of the transformer, considering the materials, structure, and operating conditions of the transformer, etc., a suitable residual magnetism prediction model is selected to predict the intensity of the residual magnetism.

[0025] In this embodiment, the residual magnetism prediction model is based on the relationship between the current characteristics of the residual magnetism and the characteristics of the transformer materials. Using the known initial conditions and real-time changing data, it calculates and predicts the intensity of the residual magnetism and its influence on the inrush current. Through this model, the influence degree of the residual magnetism can be evaluated in advance before the transformer starts, preparing for the suppression of the inrush current.

[0026] In the residual magnetism prediction model, the residual magnetism intensity changes with time and is described by the following equation: ; where represents the differential operation, indicating the rate of change of the function with time; represents the residual magnetism intensity, with the unit of tesla ; is the residual magnetism decay constant, indicating the rate of decay of the residual magnetism with time, with the unit of ; is the residual magnetism recovery coefficient, indicating the speed of residual magnetism recovery, with the unit of ; is the exciting voltage, with the unit of ; is the magnetic permeability of the transformer, with the unit of henry .

[0027] In the residual magnetism prediction model: represents the residual magnetism intensity with respect to time The derivative of, reflecting the increase or decrease of the residual magnetism; if , it means the residual magnetism is increasing; if it means the residual magnetism is decreasing.

[0028] is the attenuation term, representing the natural attenuation of the residual magnetism over time. After the transformer is out of service, the residual magnetism intensity will gradually weaken over time, and the attenuation rate is related to the magnetic material characteristics of the transformer and is a constant related to the transformer material and its historical operating conditions. The faster the attenuation rate, the more rapid the disappearance of the residual magnetism, and vice versa; is the recovery term, representing the recovery process of the residual magnetism, which is proportional to the excitation voltage and inversely proportional to the magnetic permeability ; The recovery term is used to simulate the process of the residual magnetism gradually recovering under the action of the excitation voltage when the transformer is restarted. Under the action of the excitation voltage, the intensity of the residual magnetism will recover, and the speed and amplitude of the recovery depend on and the physical characteristics of the transformer. The role of this term is to simulate the process of the residual magnetism gradually recovering under the action of the excitation voltage when the transformer is restarted.

[0029] The residual magnetism intensity Based on the initial residual magnetism and excitation voltage input conditions, calculate the residual magnetism state at any time , and the state will have an impact when the transformer is restarted, thereby affecting the inrush current.

[0030] When using this model, it includes: Initial conditions: When the transformer is out of service, by measuring its residual magnetism state (for example, deduced from the magnetic flux or current characteristics), provide the initial conditions for the differential equation.

[0031] Input conditions: When restarting, the input excitation voltage can be obtained in real-time through the control system of the transformer, The change of will directly affect the recovery speed and amplitude of the residual magnetism.

[0032] Calculation process: According to the known initial residual magnetism, excitation voltage and the material characteristics of the transformer, use numerical integration methods (such as the Euler method or the Runge - Kutta method) to solve the differential equation to obtain the residual magnetism state when the transformer is restarted This step can be achieved through real-time monitoring, predicting the residual magnetic flux density in real time and adjusting the subsequent exciting current.

[0033] Influence on prediction: Residual magnetic flux The predicted value provides a basis for suppressing the inrush current in the subsequent steps. By predicting the magnitude of the residual magnetic flux, it can be determined whether the transformer will generate an inrush current exceeding a certain specific current peak during startup. If the predicted residual magnetic flux density is large and the expected exciting current is expected to exceed the set threshold, the soft startup mechanism of the exciting current can be started in advance or the current waveform can be adjusted to reduce the inrush current amplitude and ensure the safe startup of the transformer.

[0034] In practical applications: Assume that the initial residual magnetic flux of the transformer is 0.05 , the exciting voltage is 220 , the magnetic permeability of the transformer is 1.5 , the residual magnetic flux decay constant is 0.02 , the residual magnetic flux recovery coefficient is 0.4 , then the residual magnetic flux density of the transformer at different time points can be predicted by numerically solving the differential equation .

[0035] S2. Based on the predicted residual magnetic flux density, determine whether the transformer generates an inrush current during startup and identify the characteristics of the exciting current caused by the residual magnetic flux, including high-frequency disturbances and the non-linear characteristics of the current waveform during the transient process.

[0036] In this embodiment, after obtaining the predicted residual magnetic flux density, it enters the stage of analyzing the characteristics of the exciting current. By analyzing the waveform of the transformer exciting current, the characteristics of the current inrush caused by the residual magnetic flux can be identified. This analysis needs to be carried out under different operating conditions, such as tests under different loads, different temperatures and other environmental conditions, to ensure the comprehensiveness of the analysis results. The non-linear characteristics of the current waveform need to be focused on, especially the high-frequency disturbances and the sudden changes during the transient process. These fluctuations are caused by the inrush current effect caused by the residual magnetic flux. Through this analysis, data support can be provided for the design of subsequent current suppression algorithms.

[0037] S3. Based on the characteristics of the exciting current obtained in step S2, design an inrush current model suitable for the transformer.

[0038] In this embodiment, the inrush current model describes the influence of the residual magnetic flux on the current waveform during the exciting process through mathematical equations and is constructed based on the electrical parameters of the transformer, load changes and external disturbance factors.

[0039] In the described inrush current model, it is assumed that during the excitation process of the transformer, the inrush current of the excitation current is affected by the residual magnetism , and due to external disturbances and load changes, the amplitude and waveform of the inrush current change dynamically; the mathematical equation of the inrush current model is as follows: ; Among them, represents the change of the excitation current with time , and the unit is ; is the initial coefficient of the excitation current, reflecting the preliminary influence of the excitation voltage on the current; is the excitation voltage, and the unit is ; is the time varying magnetic permeability of the transformer, and the unit is henry , which changes with the change of the working state; represents the residual magnetism intensity, and the unit is tesla , which is predicted by step S1; is the maximum residual magnetism value of the transformer, and the unit is tesla , representing the possible maximum residual magnetism intensity at a certain moment; is the coefficient of the influence of the residual magnetism on the inrush current of the excitation current, representing the non-linear influence of the residual magnetism on the inrush current amplitude; is the exponent of the influence of the residual magnetism on the inrush current amplitude, controlling the weighting degree of the influence of the residual magnetism on the inrush current; is the correction coefficient of the external disturbance to the excitation current; is the external disturbance function, representing the influence of external factors (such as temperature, load fluctuation, environmental change, etc.) on the excitation current.

[0040] In the described inrush current model: is the basic influence term of the excitation current, representing the basic influence of the excitation voltage and the magnetic permeability on the excitation current in the absence of residual magnetism and external disturbances. This term calculates the initial change trend of the excitation current in the absence of the influence of residual magnetism and external disturbances; the excitation voltage directly affects the magnitude of the excitation current, while the magnetic permeability determines the resistance to the flow of current, and the magnetic permeability changes dynamically with the operating state of the transformer.

[0041] Describe the residual magnetism The influence on the inrush current of the exciting current; the greater the residual magnetism intensity, the stronger the magnetic field of the transformer, and the greater the amplitude of the inrush current of the exciting current; since the residual magnetism will cause abnormal magnetic fields in the transformer, thereby affecting the waveform and amplitude of the exciting current, a non-linear relationship is used to describe the influence of the residual magnetism. By adjusting the coefficient and the exponent , the influence of the residual magnetism can be flexibly described, reflecting the non-linear amplification effect of the residual magnetism on the amplitude of the inrush current.

[0042] Used to consider the corrective influence of external disturbances on the exciting current. The external disturbance is caused by factors such as load changes and temperature changes. The correction coefficient is used to adjust the degree of influence of the external disturbance.

[0043] When using this model, it includes: Input the residual magnetism data: According to the residual magnetism prediction result in step S1 , input the residual magnetism value into this model to evaluate the changes in the exciting current under different residual magnetism states.

[0044] Input the exciting voltage and magnetic permeability: According to the operating state of the actual transformer, obtain the exciting voltage and the magnetic permeability in real time. These parameters will affect the calculation result of the exciting current.

[0045] Inrush current prediction and control: According to the input parameters such as residual magnetism, exciting voltage, and magnetic permeability, the calculated exciting current can be used to predict the amplitude and waveform of the inrush current. If a large inrush current is calculated, it may be necessary to apply inrush current suppression strategies in subsequent steps, such as soft start or current waveform control.

[0046] External disturbance adjustment: By monitoring the external disturbance conditions of the transformer in real time (such as load changes, temperature changes, etc.), dynamically adjust , ensure that the model can adapt to the actual changes under different working conditions, and adjust the influence of external factors on the exciting current through the correction coefficient .

[0047] In practical applications: Assume that the residual magnetism of the transformer is , the exciting voltage is , the magnetic permeability is , the maximum residual magnetism , the external disturbance correction coefficient , the perturbation function , and , , the change of the exciting current can be calculated according to the model, and then the amplitude of the inrush current can be predicted.

[0048] S4. Design an algorithm for suppressing the exciting inrush current of the transformer according to the exciting inrush current model.

[0049] In this embodiment, the suppression algorithm includes: By adjusting the waveform of the exciting voltage, that is, adjusting the magnitude and change characteristics of the exciting voltage, it is used to avoid excessive instantaneous current during the startup process of the transformer.

[0050] Based on the prediction result of the residual magnetism intensity of the transformer, the influence of the residual magnetism is offset by adjusting the phase and amplitude of the exciting voltage. Specifically, after the transformer is powered off, the magnitude and direction of the residual magnetism will directly affect the exciting inrush current during the next power-on. Using the predicted residual magnetism value, adjust the voltage characteristics (such as amplitude and phase) at the initial power-on, so that the core magnetic flux forms a reasonable match with the exciting voltage, thereby reducing the inrush current peak caused by the residual magnetism.

[0051] It is applicable to the transformer scenarios where the influence of residual magnetism is significant, especially in the working conditions of frequent power-off and restart. This algorithm can significantly reduce the inrush current amplitude and improve the stability of the startup process.

[0052] Utilize soft start technology to gradually increase the exciting current, thereby reducing the inrush current amplitude.

[0053] By gradually increasing the exciting voltage of the transformer, starting from zero and slowly increasing until reaching the rated value, to control the rising speed of the exciting current and avoid the appearance of instantaneous high-amplitude inrush current. This algorithm makes the change of the exciting current smoother by setting the voltage change curve during the startup process. At the same time, for the characteristics of different transformers, the startup time and voltage rising rate can be dynamically adjusted to achieve the best control effect.

[0054] It is applicable to the working conditions that require strict control of the inrush current peak, especially for transformers where the starting current has a greater impact on the system. This algorithm can improve the smoothness of startup, reduce equipment wear, and extend the service life.

[0055] Adopt a non-linear control method and adjust it in real time according to the actual change of the inrush current.

[0056] Utilize the non-linear characteristics of the exciting inrush current and the real-time collected exciting current data to dynamically adjust the exciting voltage and related control parameters. Through real-time monitoring and calculation, the algorithm optimizes the exciting voltage according to the current working condition, so that the exciting inrush current is always kept within a reasonable range. This adaptive control method can dynamically optimize the inrush current suppression strategy under different loads and operating conditions.

[0057] It is applicable to complex and dynamic operating environments, such as power systems with frequent load changes. This algorithm has a high level of intelligence and can quickly respond to changes in the system state to ensure that the inrush current amplitude is always controlled.

[0058] S5. Based on the suppression algorithm in step S4, for the inrush current situation in actual operation, an adaptive optimization method is used to optimize and adjust the suppression algorithm to obtain an optimized suppression algorithm.

[0059] In this embodiment, an optimization model is set in the adaptive optimization method, enabling the suppression algorithm to quickly respond under different working conditions. By dynamically adjusting the control parameters in the inrush current suppression strategy, the deviation between the inrush current and the transformer operating state is minimized, while ensuring that the algorithm can adapt to multiple operating conditions.

[0060] The optimization model is as follows: ; where is the optimization objective function, representing the total error of the inrush current suppression strategy.

[0061] is the actually calculated exciting current, with the unit of .

[0062] is the target exciting current, representing the exciting current that is desired to be achieved according to the inrush current suppression strategy, with the unit of .

[0063] is the optimization time range, representing the time period considered during the optimization process.

[0064] is the regularization coefficient, controlling the constraint strength on the control parameter during the optimization process.

[0065] are the control parameters in the suppression algorithm, representing the th parameter in the algorithm, used to represent the th control parameter in the suppression algorithm. These control parameters are defined according to different calculation formulas or control strategies and can be the voltage adjustment amplitude, starting time, non-linear control coefficient, etc., for dynamically adjusting the current and voltage during the transformer startup process. It may represent the adjustment ratio, coefficient, or phase, etc. The control parameter are the dynamic variables during the algorithm execution process, and they will be adjusted according to the inrush current prediction results to optimize the actual control effect.

[0066] is the total number of algorithm parameters, representing the number of control parameters to be adjusted during the optimization process.

[0067] In the optimization model: is used to calculate the deviation between the actual excitation current and the target excitation current; by minimizing the deviation, the inrush current suppression algorithm accurately adjusts the excitation current to the ideal state, thus avoiding excessive inrush current; is the regularization term, used to constrain the control parameters during the optimization process , which is used to prevent excessive adjustment of the control parameters, ensuring that the suppression algorithm cannot overly rely on specific parameter values and maintaining the stability and generalization ability of the algorithm.

[0068] During the optimization process, the goal is to minimize the objective function by adjusting the parameters in the inrush current suppression algorithm to .

[0069] is the desired excitation current, usually preset based on the design parameters of the transformer and the current operating conditions. Its change is set according to the requirements of the inrush current suppression strategy.

[0070] is the excitation current obtained through actual operation calculations and can be acquired through real-time data.

[0071] Regularization coefficient functions to control the adjustment amplitude of the parameters during the optimization process. A larger will force the optimization process to minimize the change in control parameters and avoid overfitting; a smaller will allow more changes, which may bring higher flexibility but may also lead to instability; usually, it is adjusted through methods such as cross-validation to achieve the optimal optimization effect.

[0072] Each control parameter represents a control variable in the inrush current suppression strategy (such as gain, delay, feedback coefficient, etc.), and they will be dynamically adjusted during the optimization process to adapt to different operating conditions of the transformer.

[0073] These control parameters will be optimized depending on the actual operation data of the transformer, and each parameter will be adaptively adjusted through real-time feedback algorithms (such as Q-learning, deep learning, etc.).

[0074] When the optimization model is used, it includes: Defining the target excitation current; Set the target excitation current according to the design parameters and current operating status of the transformer , which reflects the ideal inrush current suppression effect.

[0075] Calculate the actual excitation current; In actual operation, measure the excitation current of the transformer in real time , and calculate the error between it and the target current .

[0076] Parameter optimization; Adjust the control parameters through machine learning or optimization algorithms (such as gradient descent, genetic algorithms, etc.) to minimize the objective function . This process is dynamically adjusted based on real-time data feedback.

[0077] Regularization control; In the optimization process, use regularization terms to prevent overfitting, ensure the stability of the algorithm, and make the changes in control parameters not too drastic.

[0078] Real-time adjustment; According to the transformer operation data, adjust the control parameters in the optimization model in real time, so that the suppression strategy can quickly adapt to different working conditions and minimize the impact of inrush current to the greatest extent.

[0079] Optimization feedback and iteration; The optimization process is an iterative process. By continuously collecting real-time feedback data, the optimization algorithm will gradually adjust the parameters in different operating cycles, and finally achieve the optimal inrush current suppression effect.

[0080] In practical applications: Assume that in a certain optimization process, the target excitation current of the transformer is set to , and the actual excitation current is . By calculating the error , we can obtain the current error and adjust the control parameters in the inrush current suppression strategy through the optimization algorithm , so as to adjust the actual excitation current to the target value.

[0081] S6. Based on the optimized suppression algorithm, conduct on-line monitoring of the transformer.

[0082] In this embodiment, the on-line monitoring of the transformer includes: Real-time monitor the residual magnetic flux density and excitation current waveform of the transformer, and input the data into the optimized suppression algorithm for dynamic adjustment.

[0083] Furthermore, the dynamic adjustment includes: Real-time adjustment of the residual magnetic flux density; Collect the residual magnetic state in the transformer core in real time and dynamically adjust the residual magnetic compensation algorithm according to the collected data. The residual magnetic compensation algorithm is based on the prediction result of the residual magnetic state of the transformer, and cancels the influence of the residual magnetic by adjusting the phase and amplitude of the exciting voltage. Specifically, after the transformer is powered off, the magnitude and direction of the residual magnetic will directly affect the inrush current during the next power-on. Using the predicted residual magnetic value, adjust the voltage characteristics (such as amplitude and phase) at the initial power-on, so that the core magnetic flux and the exciting voltage form a reasonable match, thereby reducing the inrush current peak caused by the residual magnetic. For example, when it is monitored that the residual magnetic intensity exceeds the preset threshold, the algorithm will recalculate the initial phase and amplitude of the compensation voltage to ensure that the waveform of the exciting current matches the characteristics of the residual magnetic, thereby effectively weakening the inrush current caused by the residual magnetic.

[0084] Adjustment mechanism 1: Obtain the change of the residual magnetic through high-frequency sampling; Dynamically adjust the compensation amount according to the magnitude of the residual magnetic and correct the suppression parameter.

[0085] Waveform tracking and smooth control of the exciting current; Perform real-time analysis on the waveform of the exciting current to judge whether abnormal inrush current occurs. Combining with the waveform characteristics, the soft start algorithm will dynamically adjust the voltage boost curve according to the actual change of the exciting current, such as extending the voltage rise time or slowing down the voltage rise rate, so as to achieve a smoother start-up process.

[0086] Adjustment mechanism 2: Calculate the change rate of the exciting current in real time; Adjust the slope of the voltage rise curve or delay the start-up time.

[0087] Parameter optimization involving non-linear control; Under complex working conditions, the non-linear control algorithm will dynamically update the control parameters according to the real-time monitored residual magnetic intensity and the waveform of the exciting current. Input these parameters into the optimization model, and the model will calculate the new exciting voltage and target current values. This adjustment process can adapt to the change of the system operation state and ensure that the inrush current is always within a reasonable range.

[0088] Adjustment mechanism 3: Update the initial parameters of the non-linear model according to the real-time collected data; Optimize the matching relationship between the exciting voltage and the target current.

[0089] Strategy adjustment under abnormal working conditions; When an unexpected abnormal state (such as excessive residual magnetic fluctuation or sharp increase in exciting current) is detected, an abnormal adjustment strategy will be triggered. For example, by temporarily reducing the exciting voltage or adjusting the compensation phase, quickly suppress the abnormal inrush current, and at the same time, record the abnormal state for subsequent analysis.

[0090] Adjustment mechanism 4: Automatically switch to the protection mode and limit the excitation voltage; Store and alarm for abnormal states.

[0091] Real-time feedback and self-learning optimization; By comparing the real-time monitored data with the suppression effect, continuously optimize the algorithm parameters. Adopt a self-learning mechanism so that the suppression algorithm can continuously adjust the compensation strategy and parameter settings during actual operation, thereby improving the intelligent level of the system.

[0092] Adjustment mechanism 5: Correct the algorithm according to historical monitoring data; Introduce a feedback mechanism to dynamically optimize the strategy effect.

[0093] The relationship between the five adjustment mechanisms is parallel, that is, they do not execute sequentially but simultaneously. Each adjustment mechanism makes independent adjustments according to real-time data and operating status, and cooperates with each other to achieve the best inrush current suppression effect.

[0094] In this step, through real-time data acquisition, dynamic optimization of residual magnetism compensation, excitation current control, and nonlinear optimization is carried out, and an emergency adjustment strategy is provided in abnormal states. Through this closed-loop control method, accurate suppression of inrush current can be achieved under various complex working conditions, ensuring the safety and stability of transformer operation.

[0095] Embodiment 2: This embodiment also provides a computer device, applicable to a situation of a method for suppressing transformer inrush current based on residual magnetism prediction, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement a method for suppressing transformer inrush current based on residual magnetism prediction as proposed in the above embodiment.

[0096] This embodiment also provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements a method for suppressing transformer inrush current based on residual magnetism prediction as proposed in the above embodiment.

[0097] The computer device can be a terminal, which includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be implemented through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the outer shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0098] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs, etc., which are various media that can store program codes.

[0099] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing a logical function, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device.

[0100] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which a program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then storing it in a computer memory.

[0101] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following techniques known in the art or a combination thereof can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0102] 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 preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A method for suppressing inrush current of transformer based on residual magnetism prediction, characterized in that Including the following steps: S1. Establish a residual magnetism prediction model to predict the residual magnetism intensity of the transformer; S2. Based on the predicted residual magnetism intensity, determine whether inrush current is generated when the transformer starts, and identify the characteristics of the exciting current caused by residual magnetism, including high-frequency disturbance and the non-linear characteristics of the current waveform during the transient process; S3. Design an inrush current model applicable to the transformer based on the exciting current characteristics obtained in step S2; S4. Design an inrush current suppression algorithm for the transformer according to the inrush current model; S5. Based on the suppression algorithm in step S4, for the inrush current situation in actual operation, adopt an adaptive optimization method to optimize and adjust the suppression algorithm to obtain an optimized suppression algorithm; S6. Based on the optimized suppression algorithm, conduct on-line monitoring of the transformer.

2. The method for suppressing inrush current of transformer based on residual magnetism prediction according to claim 1, characterized in that In step S1, the residual magnetism prediction model is based on the relationship between the current characteristics of residual magnetism and the characteristics of transformer materials, and uses known initial conditions and real-time changing data to predict the intensity of residual magnetism and its influence on inrush current through calculation; In the residual magnetism prediction model, the residual magnetism intensity changes with time and is described by the following equation: ； Among them, represents a differential operation, indicating the rate of change of a function with respect to time; Indicates the remanence intensity, in tesla ; is the residual magnetism decay constant, representing the rate of residual magnetism decay over time, with the unit of ; is the residual magnetism recovery coefficient, indicating the speed of residual magnetism recovery, with the unit of ; is the exciting voltage, with the unit of ; is the magnetic permeability of the transformer, in henries .

3. A method for suppressing inrush current of a transformer based on residual magnetism prediction according to claim 2, characterized in that, In the residual magnetism prediction model: Indicates the remanence intensity With respect to time The derivative of, reflecting the increase or decrease of the remanence; if , it indicates that the remanence is increasing; if it indicates that the remanence is decreasing; is the attenuation term, representing the natural attenuation of the residual magnetism over time. After the transformer is out of service, the residual magnetism intensity will gradually weaken over time, and the attenuation rate is related to the characteristics of the magnetic material of the transformer. The faster the attenuation rate, the more rapid the disappearance of the residual magnetism; It is a recovery term, representing the recovery process of residual magnetism, which is directly proportional to the exciting voltage and inversely proportional to the magnetic permeability; the recovery term is used to simulate the process in which the residual magnetism gradually recovers due to the action of the exciting voltage when the transformer is re - put into operation; Residual magnetic flux density According to the initial residual magnetic flux density and the input condition of exciting voltage, calculate the residual magnetic flux density state at any time This state will have an impact when the transformer restarts, and further affect the inrush current 4. A method for suppressing inrush current of a transformer based on residual magnetism prediction according to claim 1, characterized in that In step S3, the inrush current model describes the influence of residual magnetism on the current waveform during the exciting process through mathematical equations, and is constructed based on factors such as the electrical parameters of the transformer, load changes, and external disturbances; In the described inrush current model, it is assumed that during the excitation process of the transformer, the inrush current of the exciting current is affected by residual magnetism , and due to external disturbances and load changes, the amplitude and waveform of the inrush current change dynamically; the mathematical equation of the inrush current model is as follows: ； Among them, Indicates the change of the exciting current over time , with the unit of ; is the initial coefficient of the exciting current, reflecting the preliminary influence of the exciting voltage on the current; is the exciting voltage, with the unit of ; is the time of the transformer Variable permeability, in henries , which changes with the change of the working state; Indicates the residual magnetic flux density, in tesla , predicted from step S1; is the maximum residual magnetic value of the transformer, with the unit of Tesla , representing the maximum possible residual magnetic intensity at a certain moment; is the coefficient of the influence of residual magnetism on the inrush current of excitation, indicating the non-linear influence of residual magnetism on the inrush current amplitude; is the exponent of the influence of residual magnetism on the inrush current amplitude, controlling the weighting degree of the influence of residual magnetism on the inrush current; is the correction coefficient of the external disturbance to the excitation current; is an external disturbance function, representing the influence of external factors on the exciting current.

5. A method for suppressing inrush current of a transformer based on residual magnetism prediction according to claim 4, characterized in that, In the inrush current model: is the basic influencing term of the exciting current, representing the exciting voltage and the magnetic permeability on the basic influence of the exciting current. This term calculates the initial change trend of the exciting current under the conditions without the influence of residual magnetism and external disturbances; Describe the residual magnetism The influence on the inrush current of the exciting current; The greater the residual magnetic intensity, the stronger the magnetic field of the transformer, and the greater the amplitude of the inrush current; by adjusting the coefficient and the exponent , the influence of the residual magnetic can be flexibly described, and the non-linear amplification effect of the residual magnetic on the inrush current amplitude can be reflected; For considering the corrective influence of external disturbances on the excitation current, the external disturbances are caused by factors such as load changes and temperature changes, and the correction coefficient is used to adjust the influence degree of external disturbances.

6. A method for suppressing inrush current of a transformer based on residual magnetism prediction according to claim 1, characterized in that In step S4, the suppression algorithm includes: Adjust the exciting voltage waveform to avoid excessive instantaneous current during the transformer startup process; Utilize soft start technology to gradually increase the exciting current, thereby reducing the inrush current amplitude; Adopt a non-linear control method to adjust in real time according to the changes in the actual inrush current.

7. A method for suppressing inrush current of a transformer based on residual magnetism prediction according to claim 1, characterized in that, In step S5, an optimization model is set in the adaptive optimization method, enabling the suppression algorithm to respond quickly under different working conditions. By dynamically adjusting the control parameters in the inrush current suppression strategy, the deviation between the inrush current and the transformer operating state is minimized; The optimization model is as follows: ； Among them, is the optimization objective function, representing the total error of the inrush current suppression strategy; is the actually calculated exciting current, with the unit of ; is the target exciting current, representing the exciting current desired to be achieved according to the inrush current suppression strategy, with the unit of ; is the optimized time range, representing the time period considered during the optimization process; is the regularization coefficient that controls the constraint strength on the control parameter during the optimization process; is a control parameter in the suppression algorithm, indicating the th parameter in the algorithm; It is the total number of algorithm parameters, representing the number of control parameters that need to be adjusted during the optimization process.

8. A method for suppressing inrush current of a transformer based on residual magnetism prediction according to claim 7, characterized in that In the optimization model: for calculating a deviation between an actual excitation current and a target excitation current; By minimizing the deviation, the inrush current suppression algorithm accurately adjusts the exciting current to the ideal state, thereby avoiding excessive inrush current; is a regularization term used to constrain the control parameters in the optimization process , which is used to prevent the over-adjustment of the control parameters, ensure that the suppression algorithm does not overly rely on specific parameter values, and maintain the stability and generalization ability of the algorithm.

9. A method for suppressing inrush current of a transformer based on residual magnetism prediction according to claim 7, characterized in that When the optimization model is used, it includes: Define the target exciting current; Calculate the actual exciting current; Parameter optimization; Regularization control; Real-time adjustment; Optimization feedback and iteration.

10. A method for suppressing inrush current of a transformer based on residual magnetism prediction according to claim 1, characterized in that, In step S6, the on-line monitoring of the transformer includes: Real-time monitor the residual magnetism intensity and exciting current waveform of the transformer, and input the data into the optimized suppression algorithm for dynamic adjustment.

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