A Method for Suppressing Inrush Current of Transformer Based on Residual Magnetism Prediction

Abstract

The present invention discloses a method for suppressing inrush current of a transformer based on residual magnetism prediction, which relates to the technical field of transformer control and includes the following steps: establishing a residual magnetism prediction model, judging whether inrush current occurs when the transformer starts through the predicted residual magnetism intensity, and identifying the characteristics of magnetizing current caused by residual magnetism, designing an inrush current model applicable to the transformer, designing an algorithm for suppressing inrush current of the transformer according to the inrush current model, adopting an adaptive optimization method to optimize the inrush current situation in actual operation to obtain an optimized suppression algorithm, and performing online monitoring of the transformer based on the optimized suppression algorithm. This method for suppressing inrush current of a transformer based on residual magnetism prediction not only significantly reduces the amplitude of inrush current caused by residual magnetism through real-time prediction and optimized control of 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.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer control, and particularly to 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 directly relates 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. Such inrush current not only damages 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 excitation current, etc. However, these methods usually ignore the influence of the residual magnetism of the transformer on the inrush current generation. 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 affects the excitation current when the transformer is powered on again, thereby 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:

[0006] In a 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:

[0007] S1. Establish a residual magnetism prediction model to predict the residual magnetism intensity of the transformer;

[0008] S2. Based on the predicted residual magnetism intensity, determine whether inrush current is generated when the transformer starts, and identify the characteristics of the excitation current caused by residual magnetism, including high-frequency disturbance and the non-linear characteristics of the current waveform during the transient process;

[0009] S3. Based on the excitation current characteristics obtained in step S2, design an inrush current model applicable to the transformer;

[0010] S4. According to the inrush current model, design an inrush current suppression algorithm for the transformer;

[0011] S5. Based on the suppression algorithm in step S4, for the inrush current situation during actual operation, an adaptive optimization method is used to optimize and adjust the suppression algorithm to obtain an optimized suppression algorithm;

[0012] S6. Based on the optimized suppression algorithm, online monitoring of the transformer is carried out.

[0013] To further optimize this technical solution, in step S1, the residual magnetism prediction model is based on the relationship between the current characteristics of residual magnetism and the material characteristics of the transformer, and uses the known initial conditions and real-time changing data to predict the intensity of residual magnetism and its influence on inrush current through calculation;

[0014] In the residual magnetism prediction model, the residual magnetism intensity changes with time and is described by the following equation:

[0015] ;

[0016] where,

[0017] represents the differential operation and indicates the rate of change of the function with time;

[0018] represents the residual magnetism intensity, with the unit of tesla ;

[0019] is the residual magnetism decay constant, indicating the rate of residual magnetism decay with time, with the unit of ;

[0020] is the residual magnetism recovery coefficient, indicating the speed of residual magnetism recovery, with the unit of ;

[0021] is the excitation voltage, with the unit of ;

[0022] is the magnetic permeability of the transformer, with the unit of henry .

[0023] To further optimize this technical solution, in the residual magnetism prediction model:

[0024] represents the derivative of the residual magnetism intensity with respect to time , reflecting the increase and decrease of residual magnetism; if , it means the residual magnetism is increasing; if it means the residual magnetism is decreasing;

[0025] is the attenuation term, representing the natural attenuation of the residual magnetism over time. After the transformer is out of service, the intensity of the residual magnetism 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;

[0026] is the restoration term, representing the restoration process of the residual magnetism, which is proportional to the excitation voltage and inversely proportional to the magnetic permeability ; The restoration term is used to simulate the process in which the residual magnetism gradually recovers due to the action of the excitation voltage when the transformer is restarted.

[0027] Residual magnetism intensity Based on the initial residual magnetism and excitation voltage input conditions, the residual magnetism state at any time is calculated, and the state will have an impact when the transformer is restarted, thus affecting the inrush current.

[0028] 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 excitation process through a mathematical equation, and is constructed based on the electrical parameters of the transformer, load changes, and external disturbance factors;

[0029] In the 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:

[0030] ;

[0031] Among them,

[0032] represents the change of the excitation current over time , with the unit of ;

[0033] is the initial coefficient of the excitation current, reflecting the initial influence of the excitation voltage on the current;

[0034] is the excitation voltage, with the unit of ;

[0035] is the time varying magnetic permeability of the transformer, with the unit of henry , which changes with the working state;

[0036] represents the residual magnetic flux density, with the unit of Tesla , which is predicted by step S1;

[0037] is the maximum residual magnetic value of the transformer, with the unit of Tesla , representing the possible maximum residual magnetic flux density at a certain moment;

[0038] is the coefficient of the influence of residual magnetic flux on the inrush current of the excitation current, indicating the non-linear influence of residual magnetic flux on the inrush current amplitude;

[0039] is the exponent of the influence of residual magnetic flux on the inrush current amplitude, controlling the weighting degree of the influence of residual magnetic flux on the inrush current;

[0040] is the correction coefficient of external disturbance on the excitation current;

[0041] is the external disturbance function, indicating the influence of external factors on the excitation current.

[0042] To further optimize this technical solution, in the inrush current model:

[0043] is the basic influence term of the excitation current, representing the excitation voltage and the magnetic permeability 's basic influence on the excitation current. This term calculates the initial change trend of the excitation current under the condition of no residual magnetic flux influence and external disturbance;

[0044] describes the influence of residual magnetic flux on the inrush current of the excitation current; the greater the residual magnetic flux density, the stronger the magnetic field of the transformer, and the greater the amplitude of the inrush current of the excitation current; by adjusting the coefficient and the exponent , the influence of residual magnetic flux can be flexibly described, reflecting the non-linear amplification effect of residual magnetic flux on the inrush current amplitude;

[0045] is used to consider the correction influence of external disturbance on the excitation 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 external disturbance.

[0046] To further optimize this technical solution, in step S4, the suppression algorithm includes:

[0047] By adjusting the excitation voltage waveform, it is used to avoid excessive instantaneous current during the transformer startup process;

[0048] Utilize soft start technology to gradually increase the excitation current, thereby reducing the inrush current amplitude;

[0049] Adopt a non-linear control method and adjust it in real time according to the actual change of the inrush current.

[0050] Further optimize this technical solution. 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;

[0051] The optimization model is as follows:

[0052] ;

[0053] Wherein,

[0054] is the optimization objective function, representing the total error of the inrush current suppression strategy;

[0055] is the actually calculated excitation current, with the unit of ;

[0056] is the target excitation current, representing the excitation current desired to be achieved according to the inrush current suppression strategy, with the unit of ;

[0057] is the optimization time range, representing the time period considered during the optimization process;

[0058] is the regularization coefficient, controlling the constraint intensity on the control parameter during the optimization process;

[0059] is the control parameter in the suppression algorithm, representing the th parameter in the algorithm;

[0060] is the total number of algorithm parameters, representing the number of control parameters that need to be adjusted during the optimization process.

[0061] Further optimize this technical solution. In the optimization model:

[0062] 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, thereby avoiding excessive inrush current;

[0063] is a regularization term used to constrain the control parameters in the optimization process , which is used to prevent excessive 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.

[0064] To further optimize this technical solution, when the optimization model is used, it includes:

[0065] Define the target excitation current;

[0066] Calculate the actual excitation current;

[0067] Parameter optimization;

[0068] Regularization control;

[0069] Real-time adjustment;

[0070] Optimization feedback and iteration.

[0071] To further optimize this technical solution, in step S6, the on-line monitoring of the transformer includes:

[0072] Real-time monitor the residual magnetic flux intensity and excitation current waveform of the transformer, and input the data into the optimized suppression algorithm for dynamic adjustment.

[0073] 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 inrush current of a transformer based on residual magnetic flux prediction as described in the first aspect of the present invention are implemented.

[0074] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program instructions are executed by the processor, the steps of a method for suppressing inrush current of a transformer based on residual magnetic flux prediction as described in the first aspect of the present invention are implemented.

[0075] Compared with the prior art, the present invention provides a method for suppressing inrush current of a transformer based on residual magnetic flux prediction, which has the following beneficial effects:

[0076] The method for suppressing inrush current of transformer based on residual magnetism prediction not only significantly reduces the amplitude of inrush current caused by residual magnetism through real-time prediction and optimized control of residual magnetism, but also can automatically adjust the inrush current suppression strategy under different loads and operating conditions to ensure the stable operation of the transformer under various working conditions. An adaptive algorithm based on an optimized 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 existing technology, 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative work, other drawings can be obtained according to these drawings.

[0078] Figure 1 FIG. is a schematic flow chart of a method for suppressing inrush current of transformer based on residual magnetism prediction proposed by the present invention;

[0079] Figure 2 FIG. is a schematic flow chart of an optimized model in a method for suppressing inrush current of transformer based on residual magnetism prediction proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0080] In order to make the above objects, features and advantages of the present invention more obvious and understandable, the following will describe the specific embodiments of the present invention in detail with reference to the drawings of the specification.

[0081] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also 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. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0082] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure or characteristic that can 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.

[0083] Embodiment 1:

[0084] Refer to Figures 1 to 2, which is the first embodiment of the present invention. This embodiment provides a method for suppressing transformer inrush current based on residual magnetism prediction, including the following steps:

[0085] S1. Establish a residual magnetism prediction model to predict the residual magnetism intensity of the transformer.

[0086] 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 operation data of the transformer, considering the materials, structure, and operating conditions of the transformer, a suitable residual magnetism prediction model is selected to predict the residual magnetism intensity.

[0087] In this embodiment, the residual magnetism prediction model is based on the relationship between the current characteristics of residual magnetism and the characteristics of transformer materials. Using known initial conditions and real-time changing data, it calculates and predicts the residual magnetism intensity and its impact on inrush current. Through this model, the impact degree of residual magnetism can be evaluated in advance before the transformer starts, preparing for the suppression of inrush current.

[0088] In the residual magnetism prediction model, the residual magnetism intensity changes with time and is described by the following equation:

[0089] ;

[0090] where

[0091] represents the differential operation and indicates the rate of change of the function with time;

[0092] represents the residual magnetism intensity, with the unit of Tesla ;

[0093] is the residual magnetism decay constant, indicating the rate of residual magnetism decay with time, with the unit of ;

[0094] is the residual magnetism recovery coefficient, indicating the speed of residual magnetism recovery, with the unit of ;

[0095] is the excitation voltage, with the unit of ;

[0096] is the magnetic permeability of the transformer, with the unit of Henry .

[0097] In the residual magnetism prediction model:

[0098] Represents the residual magnetic flux density With respect to time The derivative of, reflecting the increase or decrease of the residual magnetic flux density; if , it indicates that the residual magnetic flux density is increasing; if It indicates that the residual magnetic flux density is decreasing.

[0099] Is the attenuation term, representing the natural attenuation of the residual magnetic flux density over time. After the transformer is taken out of service, its residual magnetic flux density will gradually weaken over time, and the rate of attenuation Is related to the characteristics of the magnetic material of the transformer and is a constant related to the transformer material and its historical operating conditions. The faster the rate of attenuation, the more rapid the disappearance of the residual magnetic flux density, and vice versa;

[0100] Is the recovery term, representing the recovery process of the residual magnetic flux density, 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 magnetic flux density 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 magnetic flux density 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 magnetic flux density gradually recovering under the action of the excitation voltage when the transformer is restarted.

[0101] Residual magnetic flux density Based on the initial residual magnetic flux density and excitation voltage input conditions, the residual magnetic flux density state at any time Is calculated, and this state will have an impact when the transformer is restarted, and further affect the magnetizing inrush current.

[0102] When using this model, it includes:

[0103] Initial conditions: When the transformer is taken out of service, by measuring its residual magnetic flux density state (for example, deduced through magnetic flux or current characteristics), initial conditions are provided for the differential equation.

[0104] Input conditions: When restarting, the input excitation voltage Can be obtained in real time through the control system of the transformer, The change of which will directly affect the recovery speed and amplitude of the residual magnetic flux density.

[0105] Calculation process: According to the known initial residual magnetic flux density, 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 magnetic flux density 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 magnetizing current.

[0106] Influence prediction: Residual magnetism The predicted value provides a basis for suppressing inrush current in subsequent steps. By predicting the magnitude of the residual magnetism, it can be determined whether the transformer will generate an inrush current exceeding a certain specific current peak during startup. If the predicted residual magnetism intensity is large and the predicted magnetizing current is expected to exceed the set threshold, the soft start mechanism of the magnetizing current can be activated in advance or the current waveform can be adjusted to reduce the inrush current amplitude and ensure the safe startup of the transformer.

[0107] In practical applications:

[0108] Assume that the initial residual magnetism of the transformer is 0.05 , the magnetizing voltage is 220 , the magnetic permeability of the transformer is 1.5 , the residual magnetism decay constant is 0.02 , the residual magnetism recovery coefficient is 0.4 , then the residual magnetism intensity of the transformer at different time points can be predicted through numerical solution of the differential equation .

[0109] S2. Based on the predicted residual magnetism intensity, determine whether the transformer generates an inrush current during startup and identify the characteristics of the magnetizing current caused by the residual magnetism, including high-frequency disturbances and the non-linear characteristics of the current waveform during the transient process.

[0110] In this embodiment, after obtaining the predicted residual magnetism intensity, it enters the stage of analyzing the characteristics of the magnetizing current. By analyzing the waveform of the transformer magnetizing current, the characteristics of the current inrush caused by the residual magnetism 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 triggered by the residual magnetism. Through this analysis, data support can be provided for the design of subsequent current suppression algorithms.

[0111] S3. Based on the characteristics of the magnetizing current obtained in step S2, design an inrush current model suitable for the transformer.

[0112] In this embodiment, the inrush current model describes the influence of residual magnetism on the current waveform during the magnetization process through mathematical equations and is constructed based on factors such as the electrical parameters of the transformer, load changes and external disturbances.

[0113] In the described inrush current model, assume that during the magnetization process of the transformer, the inrush of the magnetizing current 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:

[0114] ;

[0115] Wherein,

[0116] represents the change of the exciting current with time , with the unit of ;

[0117] is the initial coefficient of the exciting current, reflecting the initial influence of the exciting voltage on the current;

[0118] is the exciting voltage, with the unit of ;

[0119] is the time varying magnetic permeability of the transformer, with the unit of henry , which varies with the working state;

[0120] represents the residual magnetism intensity, with the unit of tesla , which is predicted by step S1;

[0121] 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;

[0122] 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;

[0123] 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;

[0124] is the correction coefficient of external disturbance to the exciting current;

[0125] is the external disturbance function, representing the influence of external factors (such as temperature, load fluctuation, environmental change, etc.) on the exciting current.

[0126] In the described inrush current model:

[0127] is the basic influence term of the exciting current, representing that in the absence of residual magnetism and external disturbance, the exciting voltage and magnetic permeability The basic influence on the excitation current. This calculation is for the initial change trend of the excitation current without the influence of residual magnetism and external disturbances; excitation voltage directly affects the magnitude of the excitation current, while magnetic permeability determines the resistance to the flow of current, and the magnetic permeability changes dynamically with the operating state of the transformer.

[0128] Describe the residual magnetism The influence on the excitation inrush current; the greater the residual magnetism intensity, the stronger the magnetic field of the transformer, and the greater the amplitude of the excitation inrush current; since the residual magnetism will cause the magnetic field of the transformer to be abnormal, thus affecting the waveform and amplitude of the excitation 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.

[0129] Used to consider the corrective influence of external disturbances on the excitation current. External disturbances are caused by factors such as load changes and temperature changes. The correction coefficient is used to adjust the degree of influence of external disturbances.

[0130] When this model is used, it includes:

[0131] Input residual magnetism data: According to the residual magnetism prediction result in step S1 , input the residual magnetism value into this model to evaluate the change of the excitation current under different residual magnetism states.

[0132] Input the excitation voltage and magnetic permeability: According to the operating state of the actual transformer, obtain the excitation voltage and magnetic permeability in real time. These parameters will affect the calculation result of the excitation current.

[0133] Inrush current prediction and control: According to the parameters such as the input residual magnetism, excitation voltage, and magnetic permeability, the calculated excitation 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 such as soft start or current waveform control in subsequent steps.

[0134] External disturbance adjustment: By monitoring the external disturbance situation 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 excitation current through the correction coefficient .

[0135] In practical applications:

[0136] Assume the residual magnetism of the transformer is , the excitation voltage is , the magnetic permeability is , the maximum residual magnetism , the external disturbance correction coefficient , the disturbance function , and , , the change of the excitation current can be calculated according to the model, and then the amplitude of the inrush current can be predicted.

[0137] S4. Design an algorithm for suppressing the excitation inrush current of the transformer according to the excitation inrush current model.

[0138] In this embodiment, the suppression algorithm includes:

[0139] By adjusting the waveform of the excitation voltage, that is, adjusting the magnitude and change characteristics of the excitation voltage, it is used to avoid excessive instantaneous current during the startup process of the transformer.

[0140] 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 excitation voltage. Specifically, after the transformer is powered off, the magnitude and direction of the residual magnetism will directly affect the excitation 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 to make the core magnetic flux form a reasonable match with the excitation voltage, thereby reducing the inrush current peak caused by the residual magnetism.

[0141] It is applicable to the transformer scenario where the influence of residual magnetism is significant, especially in the working condition of frequent power-off and restart. This algorithm can significantly reduce the inrush current amplitude and improve the stability of the startup process.

[0142] Utilize soft start technology to gradually increase the excitation current, thereby reducing the inrush current amplitude.

[0143] By gradually increasing the excitation voltage of the transformer, starting from zero and slowly increasing until reaching the rated value, to control the rising speed of the excitation current and avoid the occurrence of instantaneous high-amplitude inrush current. This algorithm makes the change of the excitation current smoother by setting the voltage change curve during the startup process. At the same time, according to the characteristics of different transformers, the startup time and voltage rising rate can be dynamically adjusted to achieve the best control effect.

[0144] It is applicable to the working condition that requires strict control of the inrush current peak, especially for transformers where the startup current has a greater impact on the system. This algorithm can improve the smoothness of startup, reduce equipment wear, and extend the service life.

[0145] Adopt a non-linear control method and adjust it in real time according to the actual change of the inrush current.

[0146] Utilize the non - linear characteristics of inrush current and the real - time acquired excitation current data to dynamically adjust the excitation voltage and related control parameters. Through real - time monitoring and calculation, the algorithm optimizes the excitation voltage according to the current working conditions, keeping the inrush current within a reasonable range at all times. This adaptive control method can dynamically optimize the inrush current suppression strategy under different loads and operating conditions.

[0147] 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, ensuring that the inrush current amplitude is always under control.

[0148] 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.

[0149] 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.

[0150] The optimization model is as follows:

[0151] ;

[0152] Where,

[0153] is the optimization objective function, representing the total error of the inrush current suppression strategy.

[0154] is the actually calculated excitation current, with the unit of .

[0155] is the target excitation current, representing the excitation current that is desired to be achieved according to the inrush current suppression strategy, with the unit of .

[0156] is the optimization time range, representing the time period considered during the optimization process.

[0157] is the regularization coefficient, controlling the constraint strength on the control parameter during the optimization process.

[0158] is the control parameter in the suppression algorithm, represents the th parameter in the algorithm, used to represent the control parameters. 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., which are used to dynamically adjust the current and voltage during the transformer startup process. It may represent an adjustment ratio, coefficient, or phase, etc. The control parameters are 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.

[0159] is the total number of algorithm parameters, indicating the number of control parameters that need to be adjusted during the optimization process.

[0160] In the optimization model:

[0161] 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 the occurrence of excessive excitation inrush current;

[0162] is the regularization term, which is used to constrain the control parameters during the optimization process to prevent the over-adjustment of control parameters, ensure that the suppression algorithm cannot overly rely on specific parameter values, and maintain the stability and generalization ability of the algorithm.

[0163] During the optimization process, the goal is to minimize the objective function by adjusting the parameters in the inrush current suppression algorithm.

[0164] is the desired excitation current, usually preset based on the design parameters of the transformer and the current working conditions. Its change is set according to the requirements of the inrush current suppression strategy.

[0165] is the excitation current obtained through actual operation calculations and can be obtained through real-time data.

[0166] The regularization coefficient functions to control the adjustment amplitude of parameters during the optimization process. A larger will force the optimization process to minimize the change of control parameters and avoid overfitting; a smaller allows 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.

[0167] Each control parameter All represent a control variable (such as gain, delay, feedback coefficient, etc.) in the inrush current suppression strategy, which will be dynamically adjusted during the optimization process to adapt to different operating conditions of the transformer.

[0168] These control parameters The optimization depends on the actual operating data of the transformer, and each parameter is adaptively adjusted through real-time feedback algorithms (such as Q-learning, deep learning, etc.).

[0169] When the optimization model is used, it includes:

[0170] Define the target excitation current;

[0171] 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.

[0172] Calculate the actual excitation current;

[0173] In actual operation, the excitation current of the transformer is measured in real time , and calculate the error between it and the target current .

[0174] Parameter optimization;

[0175] 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.

[0176] Regularization control;

[0177] During the optimization process, a regularization term is used to prevent overfitting, ensure the stability of the algorithm, and make the changes in the control parameters not too drastic.

[0178] Real-time adjustment;

[0179] According to the transformer operation data, the control parameters in the optimization model are adjusted in real time, so that the suppression strategy can quickly adapt to different operating conditions and minimize the impact of inrush current to the greatest extent.

[0180] Optimization feedback and iteration;

[0181] 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.

[0182] In practical applications: Suppose in a certain optimization process, the target excitation current of the transformer is set to , 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 an optimization algorithm , so as to adjust the actual excitation current to the target value.

[0183] S6. Based on the optimized suppression algorithm, online monitoring of the transformer is carried out.

[0184] In this embodiment, the online monitoring of the transformer includes:

[0185] Real-time monitoring of the residual magnetic intensity and excitation current waveform of the transformer, and inputting the data into the optimized suppression algorithm for dynamic adjustment.

[0186] Furthermore, the dynamic adjustment includes:

[0187] Real-time adjustment of the residual magnetic intensity;

[0188] Real-time acquisition of the residual magnetic state in the transformer core, and dynamically adjusting the residual magnetic compensation algorithm according to the acquired 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 excitation 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 excitation 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 excitation current matches the characteristics of the residual magnetic, thereby effectively weakening the inrush current caused by the residual magnetic.

[0189] Adjustment mechanism 1:

[0190] Obtain the change of the residual magnetic through high-frequency sampling;

[0191] Dynamically adjust the compensation amount according to the magnitude of the residual magnetic and correct the suppression parameters.

[0192] Waveform tracking and smoothing control of the excitation current;

[0193] Real-time analysis of the excitation current waveform to judge whether abnormal inrush current occurs. Combining the waveform characteristics, the soft start algorithm will dynamically adjust the voltage boost curve according to the actual change of the excitation current, such as extending the voltage rise time or slowing down the voltage rise rate, so as to achieve a smoother start process.

[0194] Adjustment mechanism 2:

[0195] Real-time calculation of the change rate of the excitation current;

[0196] Adjust the slope of the voltage rise curve or delay the start-up time.

[0197] Involve parameter optimization of non-linear control;

[0198] Under complex working conditions, the non-linear control algorithm will dynamically update the control parameters according to the real-time monitored residual magnetic flux density and excitation current waveform. Input these parameters into the optimization model, and the model calculates the new excitation voltage and target current values. This adjustment process can adapt to the changes in the system operation state and ensure that the inrush current is always within a reasonable range.

[0199] Adjustment mechanism 3:

[0200] Update the initial parameters of the non-linear model according to the real-time collected data;

[0201] Optimize the matching relationship between the excitation voltage and the target current.

[0202] Strategy adjustment under abnormal working conditions;

[0203] When detecting an abnormal state beyond expectation (such as excessive residual magnetic flux density fluctuation or sharp increase in excitation current), an abnormal adjustment strategy will be triggered. For example, by temporarily reducing the excitation voltage or adjusting the compensation phase, quickly suppress the abnormal inrush current. At the same time, record the abnormal state for subsequent analysis.

[0204] Adjustment mechanism 4:

[0205] Automatically switch to the protection mode and limit the excitation voltage;

[0206] Store and alarm the abnormal state.

[0207] Real-time feedback and self-learning optimization;

[0208] By comparing the real-time monitored data with the suppression effect, continuously optimize the algorithm parameters. Adopt a self-learning mechanism to enable the suppression algorithm to continuously adjust the compensation strategy and parameter setting during actual operation, thereby improving the intelligent level of the system.

[0209] Adjustment mechanism 5:

[0210] Correct the algorithm according to the historical monitored data;

[0211] Introduce a feedback mechanism to dynamically optimize the strategy effect.

[0212] The relationship between the five adjustment mechanisms is parallel, that is, they are not executed sequentially but simultaneously. Each adjustment mechanism makes independent adjustments according to the real-time data and operation state, and cooperates with each other to achieve the best inrush current suppression effect.

[0213] In this step, through real-time data acquisition, dynamic optimization is carried out on residual magnetism compensation, excitation current control, and nonlinear optimization, and an emergency adjustment strategy is provided under abnormal conditions. 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.

[0214] Embodiment 2:

[0215] This embodiment also provides a computer device applicable to a method for suppressing inrush current of a transformer 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 inrush current of a transformer based on residual magnetism prediction as proposed in the above embodiment.

[0216] 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 inrush current of a transformer based on residual magnetism prediction as proposed in the above embodiment.

[0217] The computer device can be a terminal, which includes a processor, a memory, a communication interface, a display screen, and an input device connected through 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 a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved 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, and 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, a touchpad, or a mouse, etc.

[0218] 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 may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0219] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a predefined sequence list of executable instructions for implementing logical functions. It 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 and execute instructions from the instruction execution system, apparatus, or device), 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.

[0220] More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection part with one or more wirings (electronic device), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber device, and portable compact disc read-only memory (CDROM). Additionally, a computer-readable medium 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, then editing, interpreting, or processing it in other suitable ways if necessary, and then storing it in a computer memory.

[0221] It should be understood that each part 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 well known in the art or a combination thereof can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application specific integrated circuits with suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0222] 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 within the scope of the claims of the present invention.

Claims

1. A method for suppressing transformer excitation inrush current based on residual magnetism prediction, characterized in that: The following steps are involved: S1. Establish a residual magnetism prediction model to predict the residual magnetism intensity of the transformer; S2. Determine whether the transformer generates an inrush current during startup based on the predicted residual magnetism intensity and identify the characteristics of the excitation current caused by residual magnetism, including high-frequency disturbances and nonlinear characteristics of the current waveform during the transition process. S3. Based on the excitation current characteristics obtained in step S2, a magnetizing inrush current model suitable for the transformer is designed. The magnetizing inrush current model describes the effect of residual magnetism on the current waveform during the excitation process through a mathematical equation and is constructed based on the electrical parameters of the transformer, load changes, and external disturbance factors. In the excitation inrush current model, it is assumed that during the transformer excitation process, the inrush current of the excitation current is Remanent magnetism Due to the influence of external disturbances and load changes, the amplitude and waveform of the inrush current change dynamically; the mathematical equation of the excitation inrush current model is as follows: ； in, The excitation current changes with time The change in A; It is the initial coefficient of the excitation current, reflecting the initial effect of the excitation voltage on the current; is the excitation voltage, in V; It's transformer time Variable permeability, measured in Henry H, changes with operating conditions; Represents the residual magnetic intensity, in Tesla T, which is predicted in step S1; It is the maximum residual magnetism value of the transformer, in Tesla T, which indicates the maximum residual magnetism intensity possible at a certain moment; It is the coefficient of the effect of residual magnetism on the inrush current of the excitation current, indicating the nonlinear effect of residual magnetism on the inrush current amplitude; It is the index of the effect of residual magnetism on the inrush current amplitude, which controls the weighted degree of the effect of residual magnetism on the inrush current; is the correction coefficient of external disturbance on the excitation current; is the external disturbance function, which represents the influence of external factors on the excitation current; S4. Design an algorithm to suppress transformer magnetizing inrush current based on the magnetizing inrush current model. S5. Based on the suppression algorithm in step S4, an adaptive optimization method is used to optimize and adjust the suppression algorithm according to the inrush current situation in actual operation to obtain an optimized suppression algorithm; the adaptive optimization method is provided with an optimization model so that the suppression algorithm responds quickly under different operating conditions, and the control parameters in the inrush current suppression strategy are dynamically adjusted to minimize the deviation between the inrush current and the operating state of the transformer; The optimization model is as follows: ； in, is the optimization objective function, which represents the total error of the inrush current suppression strategy; is the actual calculated excitation current, in A; is the target excitation current, which indicates the excitation current that is expected to be achieved according to the inrush current suppression strategy, in A; is the time range of optimization, which indicates the time period examined during the optimization process; is the regularization coefficient, which controls the control parameters during the optimization process. The strength of the constraint; is the control parameter in the suppression algorithm, Indicates the algorithm parameters; is the total number of algorithm parameters, indicating the number of control parameters that need to be adjusted during the optimization process; S6. Based on the optimized suppression algorithm, perform online monitoring of the transformer.

2. The method for suppressing transformer excitation inrush current 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 the residual magnetism and the material characteristics of the transformer, and uses known initial conditions and real-time changing data to predict the intensity of the residual magnetism and its impact on the excitation inrush current through calculation; In the remanence prediction model, the remanence intensity Over time The change in is described by the following equation: ； in, represents the differential operation, which indicates the rate at which a function changes over time; Indicates the residual magnetic intensity, the unit is Tesla T; is the remanence decay constant, which indicates the rate at which remanence decays over time, and its unit is ; Is the remanence recovery coefficient, which indicates how fast the remanence is recovered, and the unit is ; is the excitation voltage, in V; is the magnetic permeability of the transformer, in Henry H.

3. The method for suppressing transformer excitation inrush current based on residual magnetism prediction according to claim 2, characterized in that: In the remanence prediction model: Indicates the residual magnetic strength Relative to time The derivative of reflects the increase or decrease of remanence; if , it means that the remanence is increasing; if , it means that the remanence is decreasing; The attenuation term represents the natural attenuation of residual magnetism over time. After the transformer is shut down, its residual magnetism will gradually weaken over time. The attenuation rate It is related to the magnetic material properties of the transformer. The faster the decay rate, the faster the residual magnetism disappears. is the recovery term, which represents the recovery process of residual magnetism and is related to the excitation voltage is 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 excitation voltage when the transformer is put back into operation. Remanent magnetic strength According to the initial residual magnetism and excitation voltage input conditions, calculate the The residual magnetism state will have an impact when the transformer is restarted, thereby affecting the excitation inrush current.

4. The method for suppressing transformer excitation inrush current based on residual magnetism prediction according to claim 1, characterized in that: In the magnetizing inrush current model: is the basic influencing term of the excitation current, indicating the excitation voltage and magnetic permeability Basic influence on the excitation current. This item calculates the initial change trend of the excitation current under the conditions of no residual magnetism and external disturbance. Describing remanence Impact on excitation current inrush; The greater the residual magnetic intensity, the stronger the transformer magnetic field, and the greater the amplitude of the excitation current surge; by adjusting the coefficient and index , which can flexibly describe the influence of residual magnetism and reflect the nonlinear amplification effect of residual magnetism on the current inrush amplitude; Used to consider the correction effect of external disturbance on the excitation current. Caused by load changes, temperature changes, correction coefficient Used to adjust the impact of external disturbances.

5. The method for suppressing transformer excitation inrush current based on residual magnetism prediction according to claim 1, characterized in that: In step S4, the suppression algorithm includes: By adjusting the excitation voltage waveform, it is used to avoid excessive transient current during the transformer startup process; Utilize soft start technology to gradually increase the excitation current, thereby reducing the inrush current amplitude; A nonlinear control method is used to make real-time adjustments based on changes in actual inrush current.

6. The method for suppressing transformer excitation inrush current based on residual magnetism prediction according to claim 1, characterized in that: In the optimization model: 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 a regularization term used to constrain the control parameters in the optimization process , which is used to prevent over-adjustment of control parameters, ensure that the suppression algorithm cannot over-rely on specific parameter values, and maintain the stability and generalization ability of the algorithm.

7. The method for suppressing transformer excitation inrush current based on residual magnetism prediction according to claim 6, characterized in that: When used, the optimization model includes: Define the target excitation current; Calculate the actual excitation current; Parameter optimization; Regularization control; Real-time adjustments; Optimize feedback and iteration.

8. The method for suppressing transformer excitation inrush current based on residual magnetism prediction according to claim 1, characterized in that: In step S6, the online monitoring of the transformer includes: The residual magnetism intensity and excitation current waveform of the transformer are monitored in real time, and the data are input into the optimized suppression algorithm for dynamic adjustment.

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