Transformer direct current magnetic bias test method and system based on electromagnetic feedback

Abstract

The invention discloses a transformer direct-current magnetic bias test method and system based on electromagnetic feedback, and aims to identify direct-current magnetic bias phenomena of a transformer under different direct-current current intensities and evaluate the working efficiency of the transformer. The method comprises the following steps: firstly, applying a direct-current bias magnetic source, testing a transformer, acquiring electromagnetic feedback signal data, and performing interference analysis; thirdly, identifying direct current magnetic bias characteristics and working characteristics, and constructing a corresponding simulation model; the direct current intensity data of the transformer is obtained in real time and imported into the simulation model for direct current magnetic bias simulation, so that the real-time working efficiency is evaluated. And finally, on the basis of the real-time efficiency, direct-current magnetic bias early warning operation is implemented. The invention provides an effective real-time monitoring and early warning mechanism, and has important practical application value.

Description

Technical Field

[0001] The invention relates to the technical field of transformer DC biasing technology, and in particular to a transformer DC biasing test method and system based on electromagnetic feedback. Background Art

[0002] As an important equipment in the power system, the performance of the transformer directly affects the efficiency and stability of power transmission and distribution. In actual operation, the transformer faces a variety of influencing factors, among which DC bias is a more serious influencing factor. DC bias refers to the bias phenomenon generated in the magnetic core of the transformer after being subjected to external DC current, which may cause magnetic saturation of the core, increased loss, and increased temperature, thus affecting the operating efficiency and safety of the transformer.

[0003] Traditional transformer detection methods mostly rely on manual inspections and simple electrical parameter measurements, which cannot comprehensively and accurately evaluate the real-time status of the transformer. This method is not only inefficient, but also has a certain degree of subjectivity in judging the bias state of the transformer, and is prone to missing potential fault risks. Therefore, there is an urgent need for a new detection method and system that can monitor the DC bias state of the transformer in real time and issue early warnings in time to ensure the safe and reliable operation of the transformer.

[0004] In recent years, the application of electromagnetic feedback technology in power equipment monitoring has gradually attracted attention. This technology can effectively identify the characteristic information of the equipment under different working conditions by acquiring the electromagnetic feedback signal of the equipment in real time. The analysis and modeling of the bias magnetic characteristics using the electromagnetic feedback signal can realize the accurate evaluation of the DC bias magnetic state and working efficiency of the transformer under different DC current intensities.

[0005] Therefore, this application proposes a transformer DC bias test method and system based on electromagnetic feedback, which aims to improve the monitoring accuracy and efficiency of the transformer, reduce potential fault risks, and provide strong guarantees for the stable operation of the power system by acquiring the electromagnetic feedback signal of the transformer in real time, analyzing its DC bias characteristics, and constructing a corresponding simulation model. Summary of the invention

[0006] In order to solve at least one of the above technical problems, the present invention proposes a transformer DC bias test method and system based on electromagnetic feedback.

[0007] A first aspect of the present invention provides a transformer DC bias test method based on electromagnetic feedback, comprising:

[0008] Apply a DC bias magnetic source to the transformer to perform a bias magnetic test, and obtain electromagnetic feedback signal data of the transformer during the bias magnetic test;

[0009] Determining the DC bias magnetic characteristics and working efficiency characteristics of the transformer under DC currents of different intensities according to the electromagnetic feedback signal data;

[0010] Constructing a DC bias simulation model of the transformer according to the DC bias characteristics and working efficiency characteristics of the transformer under DC currents of different intensities;

[0011] Acquire the current DC current intensity data of the transformer in real time, import the current DC current intensity data into the DC bias magnetic simulation model to perform DC bias magnetic simulation on the transformer, and evaluate the real-time working efficiency of the transformer according to the DC bias magnetic simulation;

[0012] A DC bias magnetic warning operation is performed on the transformer according to the real-time working efficiency.

[0013] In this solution, the DC bias source is applied to the transformer to perform a bias test, and the electromagnetic feedback signal data of the transformer in the bias test is obtained, specifically:

[0014] Acquire magnetic field distribution data of the transformer working state, and determine the layout position of the electromagnetic feedback signal sensor of the transformer according to the magnetic field distribution data;

[0015] A DC current is applied to the transformer through a DC power supply device, and the DC current intensity is gradually adjusted from low to high. The electromagnetic feedback signal data of the transformer at each DC current intensity is obtained according to the electromagnetic feedback signal sensor. The electromagnetic feedback signal data includes the transformer excitation current signal, the magnetic field strength of the transformer core, and the magnetic flux density signal.

[0016] In this solution, the DC bias magnetic characteristics and working efficiency characteristics of the transformer under different DC current intensities are determined according to the electromagnetic feedback signal data, specifically:

[0017] Analyze the transformer excitation current signal in the electromagnetic feedback signal data under different DC current intensities, determine the maximum signal frequency of the excitation current signal, and determine the sampling rate of the excitation current signal according to the maximum signal frequency;

[0018] Determine a sampling interval and a sampling duration according to the sampling rate, generate a time vector of a sampling moment according to the sampling interval and the sampling duration, perform a sampling operation on the excitation current signal according to the time vector, and obtain a discrete signal of the excitation current signal;

[0019] Performing window preprocessing on the discrete signal based on a Hanning window function to obtain a preprocessed discrete signal, and performing fast Fourier transform on the preprocessed discrete signal based on a Fourier transform method to obtain an excitation current frequency domain signal;

[0020] Calculating the spectrum amplitude and spectrum phase of the excitation current frequency domain signal, performing frequency offset detection on the excitation current frequency domain signal according to the spectrum amplitude and spectrum phase to obtain the frequency offset situation, and evaluating the distortion situation of the excitation current frequency domain signal according to the frequency offset situation;

[0021] Determine the DC bias state of the transformer according to the distortion condition. If a DC bias phenomenon occurs, extract the DC bias characteristics of each current intensity where the DC bias phenomenon occurs, wherein the DC bias characteristics include the spectrum amplitude characteristics of the excitation current frequency domain signal, the distortion degree characteristics, and the DC bias degree characteristics;

[0022] The magnetic saturation degree of the transformer core is determined based on the magnetic field strength and magnetic flux density signal of the transformer core in the electromagnetic feedback signal data under different DC current intensities. The working efficiency of the transformer under each current intensity is evaluated based on the magnetic saturation degree to obtain the working efficiency characteristics.

[0023] In this solution, the DC bias magnetic simulation model of the transformer is constructed according to the DC bias magnetic characteristics and working efficiency characteristics of the transformer under DC currents of different intensities, specifically:

[0024] Constructing a DC bias magnetic simulation model of the transformer based on the state space model, and defining the state vector, input vector, and output vector of the DC bias magnetic simulation model according to the DC bias magnetic characteristics and working efficiency characteristics of the transformer under DC currents of different intensities;

[0025] The state vector includes the transformer core magnetic flux density, magnetic field strength, and excitation current strength under different current intensities, the input vector is the DC current strength, and the output vector is the DC bias magnetic characteristics and the working efficiency characteristics;

[0026] Constructing a state equation and an output equation of a DC bias magnetic simulation model according to the state vector, the input vector, and the output vector to obtain a constructed DC bias magnetic simulation model, wherein the state equation includes a state matrix and an input matrix, and the output equation includes an output matrix and an input-output direct relationship matrix;

[0027] Solving the constructed DC bias magnetic simulation model under different DC current intensities based on a numerical method to obtain experimental DC bias magnetic characteristics and experimental work efficiency characteristics of the DC bias magnetic simulation model under different DC current intensities;

[0028] The experimental DC bias magnetic characteristics and experimental work efficiency characteristics are compared and verified with the DC bias magnetic characteristics and work efficiency characteristics, and the simulation accuracy of the DC bias magnetic simulation model is evaluated. If the simulation accuracy is lower than the preset value, the model parameters are adjusted, and the parameters include the state matrix and the input matrix, the output matrix and the input-output direct relationship matrix, until the simulation accuracy of the DC bias magnetic simulation model is not less than the preset value.

[0029] In this solution, the current DC current intensity data of the transformer is obtained in real time, and the current DC current intensity data is imported into the DC bias magnetic simulation model to perform DC bias magnetic simulation on the transformer, and the real-time working efficiency of the transformer is evaluated according to the DC bias magnetic simulation, specifically:

[0030] Based on the current sensor, the current DC current intensity data of the transformer is acquired in real time, and the current DC current intensity data is imported into the DC bias magnetic simulation model as a real-time input vector to perform real-time DC bias magnetic simulation;

[0031] Solving the state equation of the DC bias magnetic simulation model of the real-time DC bias magnetic simulation based on the numerical method, and updating the state vector of the DC bias magnetic simulation model according to the solution result;

[0032] The current DC bias magnetic characteristics and working efficiency characteristics of the transformer are calculated according to the updated state vector, and the real-time working efficiency of the transformer is evaluated according to the current DC bias magnetic characteristics and working efficiency characteristics.

[0033] In this solution, the DC bias magnetic warning operation of the transformer is performed according to the real-time working efficiency, specifically:

[0034] Acquire normal efficiency range data of the transformer, compare the real-time working efficiency with the normal efficiency range data, determine the influence of the DC bias on the real-time working efficiency of the transformer, and obtain influence data;

[0035] It is determined whether the transformer meets the current work efficiency requirement according to the impact data. If not, a DC bias magnetization warning operation is performed on the transformer, and DC bias magnetization warning information is generated and sent to operation and maintenance personnel.

[0036] The second aspect of the present invention further provides a transformer DC bias magnetic test system based on electromagnetic feedback, the system comprising: a memory, a processor, the memory comprising a transformer DC bias magnetic test method program based on electromagnetic feedback, the transformer DC bias magnetic test method program based on electromagnetic feedback when executed by the processor, implements the following steps:

[0037] Apply a DC bias magnetic source to the transformer to perform a bias magnetic test, and obtain electromagnetic feedback signal data of the transformer during the bias magnetic test;

[0038] Determining the DC bias magnetic characteristics and working efficiency characteristics of the transformer under DC currents of different intensities according to the electromagnetic feedback signal data;

[0039] Constructing a DC bias simulation model of the transformer according to the DC bias characteristics and working efficiency characteristics of the transformer under DC currents of different intensities;

[0040] Acquire the current DC current intensity data of the transformer in real time, import the current DC current intensity data into the DC bias magnetic simulation model to perform DC bias magnetic simulation on the transformer, and evaluate the real-time working efficiency of the transformer according to the DC bias magnetic simulation;

[0041] A DC bias magnetic warning operation is performed on the transformer according to the real-time working efficiency.

[0042] In this solution, the DC bias magnetic simulation model of the transformer is constructed according to the DC bias magnetic characteristics and working efficiency characteristics of the transformer under DC currents of different intensities, specifically:

[0043] Constructing a DC bias magnetic simulation model of the transformer based on the state space model, and defining the state vector, input vector, and output vector of the DC bias magnetic simulation model according to the DC bias magnetic characteristics and working efficiency characteristics of the transformer under DC currents of different intensities;

[0044] The state vector includes the transformer core magnetic flux density, magnetic field strength, and excitation current strength under different current intensities, the input vector is the DC current strength, and the output vector is the DC bias magnetic characteristics and the working efficiency characteristics;

[0045] Constructing a state equation and an output equation of a DC bias magnetic simulation model according to the state vector, the input vector, and the output vector to obtain a constructed DC bias magnetic simulation model, wherein the state equation includes a state matrix and an input matrix, and the output equation includes an output matrix and an input-output direct relationship matrix;

[0046] Solving the constructed DC bias magnetic simulation model under different DC current intensities based on a numerical method to obtain experimental DC bias magnetic characteristics and experimental work efficiency characteristics of the DC bias magnetic simulation model under different DC current intensities;

[0047] The experimental DC bias magnetic characteristics and experimental work efficiency characteristics are compared and verified with the DC bias magnetic characteristics and work efficiency characteristics, and the simulation accuracy of the DC bias magnetic simulation model is evaluated. If the simulation accuracy is lower than the preset value, the model parameters are adjusted, and the parameters include the state matrix and the input matrix, the output matrix and the input-output direct relationship matrix, until the simulation accuracy of the DC bias magnetic simulation model is not less than the preset value.

[0048] In this solution, the current DC current intensity data of the transformer is obtained in real time, and the current DC current intensity data is imported into the DC bias magnetic simulation model to perform DC bias magnetic simulation on the transformer, and the real-time working efficiency of the transformer is evaluated according to the DC bias magnetic simulation, specifically:

[0049] Based on the current sensor, the current DC current intensity data of the transformer is acquired in real time, and the current DC current intensity data is imported into the DC bias magnetic simulation model as a real-time input vector to perform real-time DC bias magnetic simulation;

[0050] Solving the state equation of the DC bias magnetic simulation model of the real-time DC bias magnetic simulation based on the numerical method, and updating the state vector of the DC bias magnetic simulation model according to the solution result;

[0051] The current DC bias magnetic characteristics and working efficiency characteristics of the transformer are calculated according to the updated state vector, and the real-time working efficiency of the transformer is evaluated according to the current DC bias magnetic characteristics and working efficiency characteristics.

[0052] In this solution, the DC bias magnetic warning operation of the transformer is performed according to the real-time working efficiency, specifically:

[0053] Acquire normal efficiency range data of the transformer, compare the real-time working efficiency with the normal efficiency range data, determine the influence of the DC bias on the real-time working efficiency of the transformer, and obtain influence data;

[0054] It is determined whether the transformer meets the current work efficiency requirement according to the impact data. If not, a DC bias magnetization warning operation is performed on the transformer, and DC bias magnetization warning information is generated and sent to operation and maintenance personnel.

[0055] The present invention discloses a transformer DC bias test method and system based on electromagnetic feedback, which is intended to identify the DC bias phenomenon of the transformer under different DC current intensities and evaluate its working efficiency. The method first applies a DC bias source, tests the transformer, obtains electromagnetic feedback signal data and performs interference analysis. Next, the DC bias characteristics and working characteristics are identified, and a corresponding simulation model is constructed. By acquiring the DC current intensity data of the transformer in real time, it is imported into the simulation model for DC bias simulation, thereby evaluating the real-time working efficiency. Finally, based on the real-time efficiency, a DC bias warning operation is implemented. The present invention provides an effective real-time monitoring and early warning mechanism, which has important practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A flow chart of a transformer DC bias magnetic test method based on electromagnetic feedback according to the present invention is shown;

[0057] Figure 2 A flow chart of evaluating the real-time working efficiency of a transformer according to the present invention is shown;

[0058] Figure 3 A flow chart showing the DC bias magnetic warning operation of the transformer according to the present invention is shown;

[0059] Figure 4 A block diagram of a transformer DC bias magnetic test system based on electromagnetic feedback according to the present invention is shown. DETAILED DESCRIPTION

[0060] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0061] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0062] Figure 1 The flowchart of a transformer DC bias magnetic test method based on electromagnetic feedback of the present invention is shown.

[0063] like Figure 1 As shown, the first aspect of the present invention provides a transformer DC bias test method based on electromagnetic feedback, comprising:

[0064] S102, applying a DC bias source to the transformer to perform a bias test, and obtaining electromagnetic feedback signal data of the transformer during the bias test;

[0065] S104, determining the DC bias magnetic characteristics and working efficiency characteristics of the transformer under DC currents of different intensities according to the electromagnetic feedback signal data;

[0066] S106, constructing a DC bias simulation model of the transformer according to the DC bias characteristics and working efficiency characteristics of the transformer under DC currents of different intensities;

[0067] S108, acquiring the current DC current intensity data of the transformer in real time, importing the current DC current intensity data into the DC bias magnetic simulation model to perform DC bias magnetic simulation on the transformer, and evaluating the real-time working efficiency of the transformer according to the DC bias magnetic simulation;

[0068] S110, performing a DC bias magnetic warning operation on the transformer according to the real-time working efficiency.

[0069] It should be noted that by applying a DC bias source to the transformer for a bias test, the working state of the transformer under different current intensities can be stimulated, and its electromagnetic feedback signal can be obtained. These signal data can provide the real-time response of the transformer under the bias state; by analyzing the electromagnetic feedback signal data, the DC bias characteristics of the transformer under different DC current intensities can be clarified, including the frequency, amplitude and distortion of the excitation current. At the same time, by monitoring the magnetic field strength and flux density of the iron core, the working efficiency of the transformer can be evaluated; based on the DC bias characteristics and working efficiency characteristics under different DC current intensities, a corresponding simulation model is constructed. This model can accurately simulate the working state of the transformer under the DC bias phenomenon, which is helpful for analyzing the performance of the transformer under different operating conditions; by real-time monitoring of the DC current intensity of the transformer, the data can be timely input into the constructed simulation model for real-time simulation, and the real-time working efficiency of the transformer under the current DC current intensity can be calculated through the output of the simulation model; it can be timely judged whether the transformer has a DC bias risk and generate corresponding early warning information. This early warning mechanism can effectively reduce the probability of transformer failure and improve the safety and reliability of the power system.

[0070] According to an embodiment of the present invention, the DC bias source is applied to the transformer to perform a bias test, and the electromagnetic feedback signal data of the transformer in the bias test is obtained, specifically:

[0071] Acquire magnetic field distribution data of the transformer working state, and determine the layout position of the electromagnetic feedback signal sensor of the transformer according to the magnetic field distribution data;

[0072] A DC current is applied to the transformer through a DC power supply device, and the DC current intensity is gradually adjusted from low to high. The electromagnetic feedback signal data of the transformer at each DC current intensity is obtained according to the electromagnetic feedback signal sensor. The electromagnetic feedback signal data includes the transformer excitation current signal, the magnetic field strength of the transformer core, and the magnetic flux density signal.

[0073] It should be noted that based on the acquired magnetic field distribution data, the working state of the transformer is analyzed, and the areas with significant changes in magnetic field strength and specific magnetic field distribution patterns are identified. By understanding these characteristics, it can be inferred at what position the electromagnetic feedback signal sensor can obtain the most effective signal; according to the characteristics of the magnetic field distribution, the location of the sensor is selected, and the sensor is placed in the area where the magnetic field strength changes greatly, close to the iron core and winding position to ensure that the electromagnetic feedback signals such as the excitation current, iron core magnetic field strength and magnetic flux density can be accurately captured. These positions can effectively reflect the working state of the transformer under different DC current intensities; the electromagnetic feedback signal sensor includes a Hall effect sensor and a current transformer; the DC power supply equipment includes a battery and a rectifier.

[0074] According to an embodiment of the present invention, the DC bias magnetic characteristics and working efficiency characteristics of the transformer under DC currents of different intensities are determined according to the electromagnetic feedback signal data, specifically:

[0075] Analyze the transformer excitation current signal in the electromagnetic feedback signal data under different DC current intensities, determine the maximum signal frequency of the excitation current signal, and determine the sampling rate of the excitation current signal according to the maximum signal frequency;

[0076] Determine a sampling interval and a sampling duration according to the sampling rate, generate a time vector of a sampling moment according to the sampling interval and the sampling duration, perform a sampling operation on the excitation current signal according to the time vector, and obtain a discrete signal of the excitation current signal;

[0077] Performing window preprocessing on the discrete signal based on a Hanning window function to obtain a preprocessed discrete signal, and performing fast Fourier transform on the preprocessed discrete signal based on a Fourier transform method to obtain an excitation current frequency domain signal;

[0078] Calculating the spectrum amplitude and spectrum phase of the excitation current frequency domain signal, performing frequency offset detection on the excitation current frequency domain signal according to the spectrum amplitude and spectrum phase to obtain the frequency offset situation, and evaluating the distortion situation of the excitation current frequency domain signal according to the frequency offset situation;

[0079] Determine the DC bias state of the transformer according to the distortion condition. If a DC bias phenomenon occurs, extract the DC bias characteristics of each current intensity where the DC bias phenomenon occurs, wherein the DC bias characteristics include the spectrum amplitude characteristics of the excitation current frequency domain signal, the distortion degree characteristics, and the DC bias degree characteristics;

[0080] The magnetic saturation degree of the transformer core is determined based on the magnetic field strength and magnetic flux density signal of the transformer core in the electromagnetic feedback signal data under different DC current intensities. The working efficiency of the transformer under each current intensity is evaluated based on the magnetic saturation degree to obtain the working efficiency characteristics.

[0081] It should be noted that, first, the transformer excitation current signal in the electromagnetic feedback signal under different DC current intensities is sampled. By analyzing the highest signal frequency of the excitation current signal, the corresponding sampling rate is determined, and the sampling interval and sampling duration are calculated according to the sampling rate. Based on these parameters, a time vector of the sampling moment is generated, and a sampling operation is performed to convert the continuous excitation current signal into a discrete signal; before the discrete signal is analyzed in the frequency domain, the discrete signal is preprocessed by windowing using the Hanning window function. The windowing operation helps to reduce the spectrum leakage phenomenon of the signal, improve the spectrum resolution of the Fourier transform result, and ensure that the frequency domain signal more accurately reflects the actual situation; for the preprocessed discrete signal, the fast Fourier transform (FFT) method is used to convert the time domain signal to the frequency domain. Fourier transform can decompose the time series signal into different frequency components to obtain the frequency domain signal of the excitation current signal, that is, the performance of the signal on the spectrum; after obtaining the frequency domain signal, the amplitude and phase of the spectrum are calculated. The amplitude reflects the strength of each frequency component in the signal, while the phase reflects the relative position between the frequency components. By analyzing the spectrum amplitude and spectrum phase, the frequency offset of the excitation current frequency domain signal is detected. The frequency offset can reflect the abnormal phenomenon of the signal, thereby further evaluating the distortion of the signal. If the frequency offset and distortion degree exceed the normal range, it means that the transformer has a DC bias magnetization phenomenon. Once the DC bias magnetization phenomenon is identified, the DC bias magnetization characteristics related to each current intensity are extracted. In addition to the results of the frequency domain analysis, the magnetic field strength and magnetic flux density signals of the transformer core in the electromagnetic feedback signal are combined to further determine the magnetic saturation degree of the core. Magnetic saturation is closely related to the DC bias magnetization characteristics. This information can help more accurately evaluate the working state of the transformer. The higher the magnetic saturation degree, the lower the working efficiency.

[0082] According to an embodiment of the present invention, the DC bias magnetic simulation model of the transformer is constructed according to the DC bias magnetic characteristics and working efficiency characteristics of the transformer under DC currents of different intensities, specifically:

[0083] Constructing a DC bias magnetic simulation model of the transformer based on the state space model, and defining the state vector, input vector, and output vector of the DC bias magnetic simulation model according to the DC bias magnetic characteristics and working efficiency characteristics of the transformer under DC currents of different intensities;

[0084] The state vector includes the transformer core magnetic flux density, magnetic field strength, and excitation current strength under different current intensities, the input vector is the DC current strength, and the output vector is the DC bias magnetic characteristics and the working efficiency characteristics;

[0085] Constructing a state equation and an output equation of a DC bias magnetic simulation model according to the state vector, the input vector, and the output vector to obtain a constructed DC bias magnetic simulation model, wherein the state equation includes a state matrix and an input matrix, and the output equation includes an output matrix and an input-output direct relationship matrix;

[0086] Solving the constructed DC bias magnetic simulation model under different DC current intensities based on a numerical method to obtain experimental DC bias magnetic characteristics and experimental work efficiency characteristics of the DC bias magnetic simulation model under different DC current intensities;

[0087] The experimental DC bias magnetic characteristics and experimental work efficiency characteristics are compared and verified with the DC bias magnetic characteristics and work efficiency characteristics, and the simulation accuracy of the DC bias magnetic simulation model is evaluated. If the simulation accuracy is lower than the preset value, the model parameters are adjusted, and the parameters include the state matrix and the input matrix, the output matrix and the input-output direct relationship matrix, until the simulation accuracy of the DC bias magnetic simulation model is not less than the preset value.

[0088] It should be noted that the specific process of constructing a DC bias simulation model through a state space model is as follows: First, determine the state vector, input vector and output vector of the system. The state vector includes the magnetic flux density, magnetic field intensity and excitation current intensity of the transformer core, the input vector is the DC current intensity applied to the transformer, and the output vector includes DC bias characteristics (such as excitation current spectrum amplitude, distortion degree, DC bias degree, etc.) and transformer working efficiency characteristics. Then, based on these vectors, construct state equations and output equations. The state equation is used to describe the dynamic changes of the system, linking the input vector with the state vector, and the output equation is used to describe the relationship between the output and the state and input of the system. Specifically, the state matrix and input matrix in the state equation describe the internal dynamics of the system and the influence of the input on the state, respectively, while the output matrix and input-output direct relationship matrix in the output equation represent the influence of the state and input on the output, respectively. Next, these equations are solved by numerical methods, and the results of the model are calculated under different DC current intensities, so as to obtain the DC bias characteristics and working efficiency characteristics of the transformer under these conditions. Finally, the simulation results are compared and verified with the actual measured characteristics to evaluate the accuracy of the model, and the parameters such as the state matrix, input matrix, and output matrix are adjusted until the accuracy of the simulation model reaches the preset value; the state variables are used to describe the internal dynamic behavior of the transformer; the state matrix describes the relationship between the transformer core flux density, magnetic field strength, and excitation current strength; the input matrix describes the changing relationship between the transformer core flux density, magnetic field strength, and excitation current strength under different DC current intensities; the output matrix is ​​the influence relationship between the DC bias magnetic characteristics and the work efficiency characteristics under different DC current intensities; the input-output direct relationship matrix is ​​non-zero if there is a situation in the simulation model where the input directly affects the output; the numerical methods include the Euler method and the Runge-Kutta method.

[0089] Figure 2 A flow chart of evaluating the real-time working efficiency of a transformer according to the present invention is shown.

[0090] According to an embodiment of the present invention, the current DC current intensity data of the transformer is acquired in real time, the current DC current intensity data is imported into the DC bias magnetic simulation model to perform DC bias magnetic simulation on the transformer, and the real-time working efficiency of the transformer is evaluated according to the DC bias magnetic simulation, specifically:

[0091] S202, obtaining current DC current intensity data of the transformer in real time based on the current sensor, and importing the current DC current intensity data into the DC bias magnetic simulation model as a real-time input vector to perform real-time DC bias magnetic simulation;

[0092] S204, solving the state equation of the DC bias magnetic simulation model of the real-time DC bias magnetic simulation based on a numerical method, and updating the state vector of the DC bias magnetic simulation model according to the solution result;

[0093] S206, calculating the current DC bias magnetic characteristics and working efficiency characteristics of the transformer according to the updated state vector, and evaluating the real-time working efficiency of the transformer according to the current DC bias magnetic characteristics and working efficiency characteristics.

[0094] It should be noted that by importing the obtained current DC current intensity data of the transformer into the DC bias simulation model for real-time DC bias simulation, the state vector of the transformer (including magnetic flux density, magnetic field intensity, excitation current, etc.) can be updated in real time. This precise real-time simulation can reflect the actual working state of the transformer and its DC bias characteristics. By updating and calculating the state vector of the DC bias simulation model, the current DC bias characteristics and working efficiency characteristics of the transformer can be obtained. This dynamic evaluation can instantly determine the impact of DC bias on the working performance of the transformer, especially the degree to which the change in the electromagnetic characteristics of the transformer weakens its efficiency; since the DC bias effect of the transformer can be simulated and evaluated in real time during operation, the working state of the transformer can be fed back instantly. In this way, when the bias effect affects the efficiency of the transformer or causes abnormal operation, the system can take adjustment measures in time to effectively prevent equipment damage and ensure the safe operation of the power system.

[0095] Figure 3 The flowchart of the present invention for performing DC bias magnetic warning operation on the transformer is shown.

[0096] According to an embodiment of the present invention, the DC bias magnetic warning operation on the transformer is performed according to the real-time working efficiency, specifically:

[0097] S302, obtaining normal efficiency range data of the transformer, comparing the real-time working efficiency with the normal efficiency range data, determining the influence of the DC bias on the real-time working efficiency of the transformer, and obtaining influence data;

[0098] S304, judging whether the transformer meets the current work efficiency requirement according to the impact data, and if not, performing a DC bias magnetization warning operation on the transformer, generating DC bias magnetization warning information, and sending the DC bias magnetization warning information to operation and maintenance personnel.

[0099] It should be noted that by obtaining the normal efficiency range data of the transformer and comparing the real-time working efficiency with the predetermined normal range, the impact of DC bias on the real-time working efficiency of the transformer can be accurately monitored. This comparison process helps to quickly identify the cause of abnormal efficiency, especially the efficiency drop under the influence of DC bias; if the real-time working efficiency of the transformer fails to meet the current operating requirements, the system will automatically trigger the early warning mechanism and generate DC bias early warning information. This automated early warning operation can quickly respond to efficiency issues, avoid the lag of manual monitoring, and ensure that operation and maintenance personnel can understand potential bias problems in a timely manner; through the early warning system, the transformer can be warned in time when efficiency abnormalities or bias phenomena are serious, and the operation and maintenance personnel can take effective intervention measures before the problem deteriorates further, avoiding the transformer from being in an unhealthy working state for a long time and reducing the risk of equipment damage caused by bias phenomena.

[0100] Figure 4 A block diagram of a transformer DC bias magnetic test system based on electromagnetic feedback according to the present invention is shown.

[0101] The second aspect of the present invention further provides a transformer DC bias magnetic test system 4 based on electromagnetic feedback, the system comprising: a memory 41, a processor 42, the memory comprising a transformer DC bias magnetic test method program based on electromagnetic feedback, the transformer DC bias magnetic test method program based on electromagnetic feedback being executed by the processor, implementing the following steps:

[0102] Apply a DC bias magnetic source to the transformer to perform a bias magnetic test, and obtain electromagnetic feedback signal data of the transformer during the bias magnetic test;

[0103] Determining the DC bias magnetic characteristics and working efficiency characteristics of the transformer under DC currents of different intensities according to the electromagnetic feedback signal data;

[0104] Constructing a DC bias simulation model of the transformer according to the DC bias characteristics and working efficiency characteristics of the transformer under DC currents of different intensities;

[0105] Acquire the current DC current intensity data of the transformer in real time, import the current DC current intensity data into the DC bias magnetic simulation model to perform DC bias magnetic simulation on the transformer, and evaluate the real-time working efficiency of the transformer according to the DC bias magnetic simulation;

[0106] A DC bias magnetic warning operation is performed on the transformer according to the real-time working efficiency.

[0107] According to an embodiment of the present invention, the DC bias magnetic simulation model of the transformer is constructed according to the DC bias magnetic characteristics and working efficiency characteristics of the transformer under DC currents of different intensities, specifically:

[0108] Constructing a DC bias magnetic simulation model of the transformer based on the state space model, and defining the state vector, input vector, and output vector of the DC bias magnetic simulation model according to the DC bias magnetic characteristics and working efficiency characteristics of the transformer under DC currents of different intensities;

[0109] The state vector includes the transformer core magnetic flux density, magnetic field strength, and excitation current strength under different current intensities, the input vector is the DC current strength, and the output vector is the DC bias magnetic characteristics and the working efficiency characteristics;

[0110] Constructing a state equation and an output equation of a DC bias magnetic simulation model according to the state vector, the input vector, and the output vector to obtain a constructed DC bias magnetic simulation model, wherein the state equation includes a state matrix and an input matrix, and the output equation includes an output matrix and an input-output direct relationship matrix;

[0111] Solving the constructed DC bias magnetic simulation model under different DC current intensities based on a numerical method to obtain experimental DC bias magnetic characteristics and experimental work efficiency characteristics of the DC bias magnetic simulation model under different DC current intensities;

[0112] The experimental DC bias magnetic characteristics and experimental work efficiency characteristics are compared and verified with the DC bias magnetic characteristics and work efficiency characteristics, and the simulation accuracy of the DC bias magnetic simulation model is evaluated. If the simulation accuracy is lower than the preset value, the model parameters are adjusted, and the parameters include the state matrix and the input matrix, the output matrix and the input-output direct relationship matrix, until the simulation accuracy of the DC bias magnetic simulation model is not less than the preset value.

[0113] According to an embodiment of the present invention, the current DC current intensity data of the transformer is acquired in real time, the current DC current intensity data is imported into the DC bias magnetic simulation model to perform DC bias magnetic simulation on the transformer, and the real-time working efficiency of the transformer is evaluated according to the DC bias magnetic simulation, specifically:

[0114] Based on the current sensor, the current DC current intensity data of the transformer is acquired in real time, and the current DC current intensity data is imported into the DC bias magnetic simulation model as a real-time input vector to perform real-time DC bias magnetic simulation;

[0115] Solving the state equation of the DC bias magnetic simulation model of the real-time DC bias magnetic simulation based on the numerical method, and updating the state vector of the DC bias magnetic simulation model according to the solution result;

[0116] The current DC bias magnetic characteristics and working efficiency characteristics of the transformer are calculated according to the updated state vector, and the real-time working efficiency of the transformer is evaluated according to the current DC bias magnetic characteristics and working efficiency characteristics.

[0117] According to an embodiment of the present invention, the DC bias magnetic warning operation on the transformer is performed according to the real-time working efficiency, specifically:

[0118] Acquire normal efficiency range data of the transformer, compare the real-time working efficiency with the normal efficiency range data, determine the influence of the DC bias on the real-time working efficiency of the transformer, and obtain influence data;

[0119] It is determined whether the transformer meets the current work efficiency requirement according to the impact data. If not, a DC bias magnetization warning operation is performed on the transformer, and DC bias magnetization warning information is generated and sent to operation and maintenance personnel.

[0120] The present invention discloses a transformer DC bias test method and system based on electromagnetic feedback, which is intended to identify the DC bias phenomenon of the transformer under different DC current intensities and evaluate its working efficiency. The method first applies a DC bias source, tests the transformer, obtains electromagnetic feedback signal data and performs interference analysis. Next, the DC bias characteristics and working characteristics are identified, and a corresponding simulation model is constructed. By acquiring the DC current intensity data of the transformer in real time, it is imported into the simulation model for DC bias simulation, thereby evaluating the real-time working efficiency. Finally, based on the real-time efficiency, a DC bias warning operation is implemented. The present invention provides an effective real-time monitoring and early warning mechanism, which has important practical application value.

[0121] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0122] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0123] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0124] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks or optical disks, and other media that can store program codes.

[0125] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0126] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A transformer DC bias test method based on electromagnetic feedback, characterized in that: The following steps are involved: Apply a DC bias magnetic source to the transformer to perform a bias magnetic test, and obtain electromagnetic feedback signal data of the transformer during the bias magnetic test; Determining the DC bias magnetic characteristics and working efficiency characteristics of the transformer under DC currents of different intensities according to the electromagnetic feedback signal data; Constructing a DC bias simulation model of the transformer according to the DC bias characteristics and working efficiency characteristics of the transformer under DC currents of different intensities; Acquire the current DC current intensity data of the transformer in real time, import the current DC current intensity data into the DC bias magnetic simulation model to perform DC bias magnetic simulation on the transformer, and evaluate the real-time working efficiency of the transformer according to the DC bias magnetic simulation; A DC bias magnetic warning operation is performed on the transformer according to the real-time working efficiency.

2. A transformer DC bias test method based on electromagnetic feedback according to claim 1, characterized in that: The step of applying a DC bias source to the transformer to perform a bias test and obtaining electromagnetic feedback signal data of the transformer in the bias test is specifically as follows: Acquire magnetic field distribution data of the transformer working state, and determine the layout position of the electromagnetic feedback signal sensor of the transformer according to the magnetic field distribution data; A DC current is applied to the transformer through a DC power supply device, and the DC current intensity is gradually adjusted from low to high. The electromagnetic feedback signal data of the transformer at each DC current intensity is obtained according to the electromagnetic feedback signal sensor. The electromagnetic feedback signal data includes the transformer excitation current signal, the magnetic field strength of the transformer core, and the magnetic flux density signal.

3. The transformer DC bias test method based on electromagnetic feedback according to claim 1 is characterized in that: The DC bias magnetic characteristics and working efficiency characteristics of the transformer under different DC current intensities are determined according to the electromagnetic feedback signal data, specifically: Analyze the transformer excitation current signal in the electromagnetic feedback signal data under different DC current intensities, determine the maximum signal frequency of the excitation current signal, and determine the sampling rate of the excitation current signal according to the maximum signal frequency; Determine a sampling interval and a sampling duration according to the sampling rate, generate a time vector of a sampling moment according to the sampling interval and the sampling duration, perform a sampling operation on the excitation current signal according to the time vector, and obtain a discrete signal of the excitation current signal; Performing window preprocessing on the discrete signal based on a Hanning window function to obtain a preprocessed discrete signal, and performing fast Fourier transform on the preprocessed discrete signal based on a Fourier transform method to obtain an excitation current frequency domain signal; Calculating the spectrum amplitude and spectrum phase of the excitation current frequency domain signal, performing frequency offset detection on the excitation current frequency domain signal according to the spectrum amplitude and spectrum phase to obtain the frequency offset situation, and evaluating the distortion situation of the excitation current frequency domain signal according to the frequency offset situation; Determine the DC bias state of the transformer according to the distortion condition. If a DC bias phenomenon occurs, extract the DC bias characteristics of each current intensity where the DC bias phenomenon occurs, wherein the DC bias characteristics include the spectrum amplitude characteristics of the excitation current frequency domain signal, the distortion degree characteristics, and the DC bias degree characteristics; The magnetic saturation degree of the transformer core is determined based on the magnetic field strength and magnetic flux density signal of the transformer core in the electromagnetic feedback signal data under different DC current intensities. The working efficiency of the transformer under each current intensity is evaluated based on the magnetic saturation degree to obtain the working efficiency characteristics.

4. The transformer DC bias test method based on electromagnetic feedback according to claim 1, characterized in that: The DC bias simulation model of the transformer is constructed according to the DC bias characteristics and working efficiency characteristics of the transformer under DC currents of different intensities, specifically: Constructing a DC bias magnetic simulation model of the transformer based on the state space model, and defining the state vector, input vector, and output vector of the DC bias magnetic simulation model according to the DC bias magnetic characteristics and working efficiency characteristics of the transformer under DC currents of different intensities; The state vector includes the transformer core magnetic flux density, magnetic field strength, and excitation current strength under different current intensities, the input vector is the DC current strength, and the output vector is the DC bias magnetic characteristics and the working efficiency characteristics; Constructing a state equation and an output equation of a DC bias magnetic simulation model according to the state vector, the input vector, and the output vector to obtain a constructed DC bias magnetic simulation model, wherein the state equation includes a state matrix and an input matrix, and the output equation includes an output matrix and an input-output direct relationship matrix; Solving the constructed DC bias magnetic simulation model under different DC current intensities based on a numerical method to obtain experimental DC bias magnetic characteristics and experimental work efficiency characteristics of the DC bias magnetic simulation model under different DC current intensities; The experimental DC bias magnetic characteristics and experimental work efficiency characteristics are compared and verified with the DC bias magnetic characteristics and work efficiency characteristics, and the simulation accuracy of the DC bias magnetic simulation model is evaluated. If the simulation accuracy is lower than the preset value, the model parameters are adjusted, and the parameters include the state matrix and the input matrix, the output matrix and the input-output direct relationship matrix, until the simulation accuracy of the DC bias magnetic simulation model is not less than the preset value.

5. The transformer DC bias test method based on electromagnetic feedback according to claim 1, characterized in that: The real-time acquisition of the current DC current intensity data of the transformer, the importation of the current DC current intensity data into the DC bias simulation model to perform DC bias simulation on the transformer, and the evaluation of the real-time working efficiency of the transformer according to the DC bias simulation are specifically as follows: Based on the current sensor, the current DC current intensity data of the transformer is acquired in real time, and the current DC current intensity data is imported into the DC bias magnetic simulation model as a real-time input vector to perform real-time DC bias magnetic simulation; Solving the state equation of the DC bias magnetic simulation model of the real-time DC bias magnetic simulation based on the numerical method, and updating the state vector of the DC bias magnetic simulation model according to the solution result; The current DC bias magnetic characteristics and working efficiency characteristics of the transformer are calculated according to the updated state vector, and the real-time working efficiency of the transformer is evaluated according to the current DC bias magnetic characteristics and working efficiency characteristics.

6. The transformer DC bias test method based on electromagnetic feedback according to claim 1, characterized in that: The DC bias magnetic warning operation on the transformer according to the real-time working efficiency is specifically: Acquire normal efficiency range data of the transformer, compare the real-time working efficiency with the normal efficiency range data, determine the influence of the DC bias on the real-time working efficiency of the transformer, and obtain influence data; It is determined whether the transformer meets the current work efficiency requirement according to the impact data. If not, a DC bias magnetization warning operation is performed on the transformer, and DC bias magnetization warning information is generated and sent to operation and maintenance personnel.

7. A transformer DC bias test system based on electromagnetic feedback, characterized in that: The transformer DC bias test system based on electromagnetic feedback includes a storage device and a processor. The storage device includes a transformer DC bias test method program based on electromagnetic feedback. When the transformer DC bias test method program based on electromagnetic feedback is executed by the processor, the following steps are implemented: Apply a DC bias magnetic source to the transformer to perform a bias magnetic test, and obtain electromagnetic feedback signal data of the transformer during the bias magnetic test; Determining the DC bias magnetic characteristics and working efficiency characteristics of the transformer under DC currents of different intensities according to the electromagnetic feedback signal data; Constructing a DC bias simulation model of the transformer according to the DC bias characteristics and working efficiency characteristics of the transformer under DC currents of different intensities; Acquire the current DC current intensity data of the transformer in real time, import the current DC current intensity data into the DC bias magnetic simulation model to perform DC bias magnetic simulation on the transformer, and evaluate the real-time working efficiency of the transformer according to the DC bias magnetic simulation; A DC bias magnetic warning operation is performed on the transformer according to the real-time working efficiency.

8. The transformer DC bias test system based on electromagnetic feedback according to claim 7, characterized in that: The DC bias simulation model of the transformer is constructed according to the DC bias characteristics and working efficiency characteristics of the transformer under DC currents of different intensities, specifically: Constructing a DC bias magnetic simulation model of the transformer based on the state space model, and defining the state vector, input vector, and output vector of the DC bias magnetic simulation model according to the DC bias magnetic characteristics and working efficiency characteristics of the transformer under DC currents of different intensities; The state vector includes the transformer core magnetic flux density, magnetic field strength, and excitation current strength under different current intensities, the input vector is the DC current strength, and the output vector is the DC bias magnetic characteristics and the working efficiency characteristics; Constructing a state equation and an output equation of a DC bias magnetic simulation model according to the state vector, the input vector, and the output vector to obtain a constructed DC bias magnetic simulation model, wherein the state equation includes a state matrix and an input matrix, and the output equation includes an output matrix and an input-output direct relationship matrix; Solving the constructed DC bias magnetic simulation model under different DC current intensities based on a numerical method to obtain experimental DC bias magnetic characteristics and experimental work efficiency characteristics of the DC bias magnetic simulation model under different DC current intensities; The experimental DC bias magnetic characteristics and experimental work efficiency characteristics are compared and verified with the DC bias magnetic characteristics and work efficiency characteristics, and the simulation accuracy of the DC bias magnetic simulation model is evaluated. If the simulation accuracy is lower than the preset value, the model parameters are adjusted, and the parameters include the state matrix and the input matrix, the output matrix and the input-output direct relationship matrix, until the simulation accuracy of the DC bias magnetic simulation model is not less than the preset value.

9. The transformer DC bias test system based on electromagnetic feedback according to claim 7, characterized in that: The real-time acquisition of the current DC current intensity data of the transformer, the importation of the current DC current intensity data into the DC bias simulation model to perform DC bias simulation on the transformer, and the evaluation of the real-time working efficiency of the transformer according to the DC bias simulation are specifically as follows: Based on the current sensor, the current DC current intensity data of the transformer is acquired in real time, and the current DC current intensity data is imported into the DC bias magnetic simulation model as a real-time input vector to perform real-time DC bias magnetic simulation; Solving the state equation of the DC bias magnetic simulation model of the real-time DC bias magnetic simulation based on the numerical method, and updating the state vector of the DC bias magnetic simulation model according to the solution result; The current DC bias magnetic characteristics and working efficiency characteristics of the transformer are calculated according to the updated state vector, and the real-time working efficiency of the transformer is evaluated according to the current DC bias magnetic characteristics and working efficiency characteristics.

10. The transformer DC bias test system based on electromagnetic feedback according to claim 7, characterized in that: The DC bias magnetic warning operation on the transformer according to the real-time working efficiency is specifically: Acquire normal efficiency range data of the transformer, compare the real-time working efficiency with the normal efficiency range data, determine the influence of the DC bias on the real-time working efficiency of the transformer, and obtain influence data; It is determined whether the transformer meets the current work efficiency requirement according to the impact data. If not, a DC bias magnetization warning operation is performed on the transformer, and DC bias magnetization warning information is generated and sent to operation and maintenance personnel.

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