Mutual inductor characteristic detection method and system, storage medium and electronic equipment

Abstract

The invention provides a mutual inductor characteristic detection method and system, a storage medium and electronic equipment, and relates to the technical field of mutual inductor detection. Comprising the steps of obtaining a parameter data set containing basic information of a target mutual inductor, and determining a test loop of the target mutual inductor; in different preset temperature intervals, excitation characteristic detection is carried out on the target mutual inductor through the test loop to obtain excitation characteristic detection results of the different temperature intervals, and the excitation characteristic detection results comprise excitation curve data, hysteresis characteristic curve data and eddy current loss data; and integrating the excitation characteristic detection results of different temperature intervals into a temperature-excitation characteristic associated data set, and generating an excitation characteristic detection report of the target transformer according to the temperature-excitation characteristic associated data set. According to the invention, the relevance between the excitation characteristic and the temperature change of the mutual inductor can be comprehensively reflected, and a scientific basis is provided for the performance evaluation of the mutual inductor; through comprehensive detection of excitation characteristics, hysteresis characteristics and eddy-current loss, the stability and reliability of the operation of the mutual inductor are ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of mutual inductor detection, and in particular relates to a mutual inductor characteristic detection method, system, storage medium and electronic equipment. Background Art

[0002] As a key device in the power system, the instrument transformer is widely used in voltage and current measurement and protection devices. The accurate detection of its performance and characteristics is crucial for the safe and stable operation of the power system. The main function of the instrument transformer is to convert high voltage or high current into low voltage or low current signals for use by metering instruments and protection devices. However, the instrument transformer will be significantly affected by temperature changes in the actual operating environment. This effect is not only reflected in the excitation characteristics of the instrument transformer, but may also cause fluctuations in its measurement accuracy.

[0003] Most existing transformer detection methods are limited to characteristic detection under normal temperature environment, which is difficult to fully reflect the performance of the transformer under actual operating conditions. Especially in the actual operation of the power system, the transformer may experience a large range of temperature changes. If the temperature factor is not fully considered, the accuracy and reliability of the detection results will be low. Summary of the invention

[0004] In order to overcome the deficiencies of the above-mentioned prior art, the present invention provides a method, system, storage medium and electronic device for detecting characteristics of a mutual inductor. By performing detection in different temperature ranges, the correlation between the excitation characteristics of the mutual inductor and temperature changes can be fully reflected. By comprehensively detecting the excitation characteristics, hysteresis characteristics and eddy current losses, the stability and reliability of the mutual inductor in actual operation are ensured, so as to solve the technical problem that the existing mutual inductor detection method is limited to characteristic detection under normal temperature environment, resulting in low accuracy and reliability of the detection results.

[0005] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: A first aspect of the present invention provides a method for detecting characteristics of a mutual inductor.

[0006] The method for detecting characteristics of a mutual inductor comprises the following steps: Acquire a parameter data set containing basic information of a target transformer, and determine a test loop of the target transformer based on the parameter data set; Under different preset temperature ranges, the target mutual inductor is tested for excitation characteristics through the test circuit to obtain the excitation characteristics test results of the different temperature ranges, wherein the excitation characteristics test results include excitation curve data, hysteresis characteristic curve data and eddy current loss data; The excitation characteristic detection results in different temperature ranges are integrated into a temperature-excitation characteristic associated data set, and an excitation characteristic detection report of a target transformer is generated according to the temperature-excitation characteristic associated data set.

[0007] As an optional technical solution, the excitation characteristics of the target transformer are tested through a test circuit, specifically including: According to the parameter data set, a programmable power supply corresponding to the test loop of the target transformer is controlled to output a multi-level voltage to obtain a voltage-current data pair of the primary winding of the target transformer, and excitation curve data of the target transformer is generated according to the voltage-current data pair; According to the obtained excitation curve data, the hysteresis characteristics of the target transformer are reconstructed and feature extracted to obtain a hysteresis characteristic parameter set including saturation magnetization, remanence and coercive force; according to the hysteresis characteristic parameter set, the original hysteresis loop is subjected to curve fitting processing to obtain the hysteresis characteristic curve data of the target transformer; According to the obtained hysteresis characteristic parameter set, the eddy current loss data of the target transformer is calculated.

[0008] As an optional technical solution, the process of generating the excitation curve data of the target transformer specifically includes: According to the parameter data set, a programmable power supply corresponding to the target transformer test loop is controlled to output a multi-level voltage to obtain a series of test voltages; At each test voltage, the voltage and current of the primary winding of the target transformer are synchronously sampled to obtain multiple sets of voltage-current data pairs; Performing digital filtering and normalization processing on multiple sets of voltage-current data pairs, and performing interpolation processing on the multiple sets of voltage-current data pairs after the digital filtering and normalization processing to obtain continuous voltage-current curves; The continuous voltage-current curve is numerically differentiated to obtain the excitation curve data of the target transformer.

[0009] As an optional technical solution, the process of acquiring the hysteresis characteristic curve data of the target transformer specifically includes: Based on the basic principles of electromagnetics, the magnetic field strength and magnetic induction strength of the target mutual inductor are calculated according to the excitation curve data, and the time series of the magnetic induction strength and magnetic field strength are obtained; Performing phase calibration on the time series of magnetic induction intensity and magnetic field intensity to obtain synchronized magnetic induction intensity-magnetic field intensity data pairs; A closed loop is constructed for the magnetic induction intensity-magnetic field intensity data pair to obtain a hysteresis loop function of the original hysteresis loop, and characteristic points are extracted for the hysteresis loop function to obtain a hysteresis characteristic parameter set; The original hysteresis loop is subjected to mathematical model fitting processing according to the hysteresis characteristic parameter set to obtain a continuous hysteresis characteristic function, and the continuous hysteresis characteristic function is subjected to discretization sampling processing to obtain hysteresis characteristic curve data of the target mutual inductor; or, The closed loop is constructed for the magnetic induction intensity-magnetic field intensity data pair to obtain the hysteresis loop function of the original hysteresis loop, and the feature point extraction process is performed on the hysteresis loop function to obtain the hysteresis characteristic parameter set, which specifically includes: Performing data interpolation processing on the magnetic induction intensity-magnetic field intensity data pairs to obtain equally spaced magnetic induction intensity-magnetic field intensity data points, and performing curve fitting processing on the equally spaced magnetic induction intensity-magnetic field intensity data points to obtain a hysteresis loop function of the original hysteresis loop; The extreme value points of the hysteresis loop function are calculated to obtain the maximum magnetic induction intensity point and the maximum magnetic field intensity point of the original hysteresis loop; the zero point analysis of the hysteresis loop function is performed to obtain the remanence point and coercive force point of the original hysteresis loop; Parameters of the maximum magnetic induction intensity point, the maximum magnetic field intensity point, the remanence point and the coercive force point are extracted to obtain a hysteresis characteristic parameter set including saturation magnetization intensity, remanence and coercive force.

[0010] As an optional technical solution, the eddy current loss data of the target transformer is calculated in the following specific processes: According to the parameter data set, a programmable power supply corresponding to the target transformer test loop is subjected to multi-frequency control to obtain an excitation signal at a series of preset frequency points; The total loss of the target mutual inductor under the excitation signal of each preset frequency point is measured to obtain multiple sets of frequency-total loss data pairs; Perform loss separation processing on the obtained multiple sets of frequency-total loss data pairs and hysteresis characteristic parameter sets to obtain the first eddy current loss data of each frequency point; Performing frequency relationship fitting processing on the first eddy current loss data of each frequency point to obtain an eddy current loss-frequency characteristic curve; Inputting the eddy current loss-frequency characteristic curve into a pre-trained machine learning model to obtain second eddy current loss data of the target mutual inductor within an extended frequency range; The first eddy current loss data and the second eddy current loss data are combined to serve as eddy current loss data of the target transformer.

[0011] As an optional technical solution, loss separation processing is performed on the obtained multiple sets of frequency-total loss data pairs and hysteresis characteristic parameter sets to obtain the first eddy current loss data of each frequency point, specifically including: Acquire a corresponding hysteresis loop function according to the hysteresis characteristic parameter set, perform area calculation on the hysteresis loop function, and obtain hysteresis loss energy data per unit volume; Performing a product operation on the hysteresis loss energy data per unit volume and the effective core volume of the target transformer to obtain hysteresis loss data per cycle; Performing a product operation on the hysteresis loss data of each cycle and each preset frequency point to obtain hysteresis loss data, and then obtaining multiple sets of frequency-hysteresis loss data pairs corresponding to the frequency; Based on the difference between multiple sets of frequency-total loss data pairs and corresponding frequency-hysteresis loss data pairs, first eddy current loss data of each frequency point is obtained; or, The specific generation process of the target transformer excitation characteristic detection report includes: The excitation curve data in different temperature ranges are normalized to obtain a standardized temperature-excitation curve data set; Performing temperature sensitivity analysis and processing according to the standardized temperature-excitation curve data set to obtain a temperature coefficient of the excitation curve; Perform feature extraction on the hysteresis characteristic curve data in different temperature ranges to obtain a temperature-hysteresis parameter correlation data set; Performing mathematical model fitting processing on the temperature-hysteresis parameter correlation data set to obtain a functional relationship between hysteresis characteristics and temperature; The eddy current loss data in different temperature ranges are subjected to frequency-temperature correlation analysis to obtain the temperature-frequency dependence model of eddy current loss. The mutual relationship and comprehensive influence among the temperature coefficient of the excitation curve, the functional relationship of the hysteresis characteristic with temperature and the temperature-frequency dependence model of the eddy current loss are analyzed to obtain an excitation characteristic detection report of the target transformer.

[0012] As an optional technical solution, determining a test loop of a target transformer based on a parameter data set specifically includes: According to the rated voltage and current of the transformer, select the voltage source and current source of the appropriate range; According to the accuracy level of the transformer, select the measuring instrument with corresponding accuracy; Consider temperature factors and design a test circuit including temperature control and monitoring devices; The parameter data set includes the rated primary voltage, rated secondary voltage, rated primary current, and rated secondary current of the target transformer.

[0013] A second aspect of the present invention provides a transformer characteristic detection system.

[0014] Transformer characteristic detection system, including: An acquisition module is configured to: acquire a parameter data set containing basic information of a target transformer, and determine a test loop of the target transformer based on the parameter data set; The detection module is configured to: perform excitation characteristic detection on the target mutual inductor through the test circuit in different preset temperature ranges, respectively, to obtain the excitation characteristic detection results in different temperature ranges, wherein the excitation characteristic detection results include excitation curve data, hysteresis characteristic curve data and eddy current loss data; The report generation module is configured to: integrate the excitation characteristic detection results of different temperature ranges into a temperature-excitation characteristic associated data set, and generate an excitation characteristic detection report of the target transformer according to the temperature-excitation characteristic associated data set.

[0015] The third aspect of the present invention provides a storage medium on which a program is stored. When the program is executed by a processor, the steps in the mutual inductor characteristic detection method as described in the first aspect of the present invention are implemented.

[0016] The fourth aspect of the present invention provides an electronic device, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the mutual inductor characteristic detection method as described in the first aspect of the present invention are implemented.

[0017] One or more of the above technical solutions have the following beneficial effects: The present invention provides a method, system, storage medium and electronic device for detecting the characteristics of a mutual inductor. By acquiring a parameter data set of a target mutual inductor, the corresponding test loop is determined, and the excitation characteristics of the mutual inductor are detected under different temperature ranges. The method can not only obtain the excitation characteristics detection results of different temperature ranges, but also integrate these results to generate a temperature-excitation characteristics correlation data set, thereby generating an excitation characteristics detection report of the target mutual inductor, which can provide more accurate basic data for the design and improvement of the mutual inductor, thereby optimizing product design and reducing the frequency of later maintenance. At the same time, accurate detection data helps to improve the overall energy efficiency of the power system and reduce operating costs. In addition, complex data is integrated for report generation, manual intervention is reduced, the efficiency and accuracy of the detection process are improved, and the mutual inductor detection process is promoted to intelligent transformation. By performing detection under different temperature ranges, the correlation between the excitation characteristics of the mutual inductor and the temperature change can be fully reflected, which provides a more scientific basis for the performance evaluation of the mutual inductor. In addition, it can also dynamically adapt to the temperature changes in various complex operating environments, which enables the mutual inductor to maintain its performance stability under extreme climates or sudden working conditions, and enhances the ability of the mutual inductor to cope with a changing external environment. At the same time, through comprehensive testing of excitation characteristics, hysteresis characteristics and eddy current losses, the stability and reliability of the transformer in actual operation are ensured.

[0018] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0020] Figure 1 This is a flow chart of the method of the first embodiment.

[0021] Figure 2 It is a system structure diagram of the second embodiment.

[0022] Figure 3 It is a structural diagram of the characteristic detection equipment of the transformer of the fourth embodiment. DETAILED DESCRIPTION

[0023] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0024] It should be noted that the terms used herein are for describing specific embodiments only and are not intended to be limiting of exemplary embodiments according to the present invention.

[0025] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0026] Embodiment 1 This embodiment discloses a method for detecting characteristics of a mutual inductor.

[0027] like Figure 1 As shown, the method for detecting the characteristics of a mutual inductor comprises the following steps: Acquire a parameter data set containing basic information of a target transformer, and determine a test loop of the target transformer based on the parameter data set; Under different preset temperature ranges, the excitation characteristics of the target mutual inductor are detected through the test circuit to obtain the excitation characteristics detection results of different temperature ranges. The excitation characteristics detection results include excitation curve data, hysteresis characteristic curve data and eddy current loss data; The excitation characteristic detection results in different temperature ranges are integrated into a temperature-excitation characteristic correlation data set, and an excitation characteristic detection report of the target transformer is generated based on the temperature-excitation characteristic correlation data set.

[0028] To facilitate understanding of this embodiment, the mutual inductor characteristic detection method disclosed in the embodiment is described in detail below. Figure 1 As shown, the method comprises the following steps: 101. Obtain a parameter data set of a target transformer, and determine a test loop corresponding to the target transformer according to the parameter data set.

[0029] In one embodiment of the present invention, the parameter data set generally includes basic information of the transformer, such as the rated primary voltage, rated secondary voltage, rated primary current, rated secondary current, rated load, accuracy level, core material type, winding structure, etc. These parameters can be obtained in a variety of ways, including reading directly from the transformer nameplate, querying the technical documents provided by the manufacturer, or using a special parameter identification device for rapid scanning.

[0030] After obtaining the parameter data set, the system will automatically configure the test loop according to these parameters. The test loop determination process involves several considerations: 1) First, according to the rated voltage and current of the transformer, the system will select the voltage source and current source of the appropriate range. For high-voltage transformers, a step-up transformer is required to provide sufficient test voltage; for high-current transformers, a current amplifier is required to provide the required test current.

[0031] 2) Secondly, according to the accuracy level of the transformer, the system will select a measuring instrument with corresponding accuracy, such as a high-precision digital multimeter or power analyzer, to ensure that the measurement results meet the accuracy requirements.

[0032] 3) In addition, the core material type and winding structure of the transformer will also affect the design of the test loop. For example, for a transformer using an amorphous alloy core, due to its high initial magnetic permeability and low loss, the test loop requires a higher excitation voltage to reach saturation. For a transformer with a multi-winding structure, the test loop needs to be able to measure the output of multiple secondary windings at the same time, which requires the use of a multi-channel data acquisition system.

[0033] 4) The determination of the test loop also needs to consider the temperature factor. Since this solution requires testing at different temperature ranges, the test loop must include temperature control and monitoring devices. This involves the use of a constant temperature box or temperature circulation system, as well as a high-precision temperature sensor to accurately control and record temperature changes during the test.

[0034] More specifically, the determination of the test loop also includes the software configuration part. The system will automatically generate a test sequence based on the parameters of the transformer, including the range of voltage and current changes, step value, number of measurement points, etc. For the excitation characteristic test, the system will set a series of test voltage points according to the rated voltage of the transformer, usually gradually increasing from zero to about 120% of the rated voltage. For the hysteresis characteristic test, the system will set an appropriate magnetization period and sampling frequency to ensure that the characteristics of the hysteresis loop can be accurately captured. For the eddy current loss test, the system will set multiple frequency points for testing according to the operating frequency range of the transformer.

[0035] 102. Perform excitation characteristic detection on the target mutual inductor through the test circuit in different preset temperature ranges to obtain the excitation characteristic detection results in different temperature ranges.

[0036] In one embodiment of the present invention, the excitation characteristic detection result includes excitation curve data, hysteresis characteristic curve data and eddy current loss data.

[0037] The steps of detecting the excitation characteristics of the target transformer through the test circuit include: According to the parameter data set, a programmable power supply corresponding to the target transformer test loop is controlled to output a multi-level voltage to obtain a voltage-current data pair, and excitation curve data of the target transformer is generated according to the voltage-current data pair; Reconstructing and extracting features of the hysteresis characteristics of the target transformer according to the excitation curve data to obtain a hysteresis characteristic parameter set, and performing curve fitting processing on the hysteresis characteristic parameter set to obtain hysteresis characteristic curve data of the target transformer; According to the obtained hysteresis characteristic parameter set, the eddy current loss data of the target transformer is calculated.

[0038] Specific: (1) The excitation curve data reflects the magnetization characteristics of the transformer core. It is usually expressed as the relationship between the magnetic field intensity H and the magnetic induction intensity B, also known as the BH curve. In actual testing, by changing the voltage applied to the primary winding of the transformer and measuring the corresponding current, the voltage-current relationship is obtained and then converted into a BH relationship.

[0039] The excitation curve data can reflect the magnetization process of the transformer core, including the initial magnetization, linear region, saturation region and other characteristics. This information is very important for understanding the response characteristics of the transformer under different working conditions, especially in various abnormal conditions that may occur in the power system.

[0040] (2) The hysteresis characteristic curve data describes the magnetization and demagnetization process of the transformer core under the action of the alternating magnetic field. It is usually expressed as a closed loop, called a hysteresis loop. The area of ​​the hysteresis loop represents the energy loss in each magnetization cycle, that is, the hysteresis loss. The hysteresis characteristic curve data contains important parameters such as residual magnetism, coercive force, and maximum magnetic induction intensity. These parameters directly affect the measurement accuracy and dynamic response characteristics of the transformer. For example, a larger residual magnetism may cause the transformer to have an increased nonlinear error under small signal conditions, while a higher coercive force may affect the transformer's ability to follow rapidly changing signals.

[0041] (3) Eddy current loss data reflects the energy loss caused by the induced current in the transformer core due to the alternating magnetic field. Eddy current loss is related to the resistivity of the core material, the frequency of magnetic field change and the geometric structure of the core. In the transformer, eddy current loss is another major loss source besides hysteresis loss. Eddy current loss data is usually expressed as the relationship between loss power and frequency.

[0042] These data are essential for evaluating the performance of the transformer under different frequency conditions, especially in modern power systems, where the increase in harmonic components makes the performance of the transformer over a wide frequency band increasingly important. These three types of data are interrelated and together constitute a complete description of the magnetic properties of the transformer. The excitation curve reflects the overall magnetization characteristics of the core, while the hysteresis characteristics describe in detail the energy conversion process in each magnetization cycle, and the eddy current loss data supplements the frequency-related loss information.

[0043] Further, according to the parameter data set, a programmable power supply corresponding to the target transformer test loop is controlled to output a multi-level voltage to obtain a voltage-current data pair, and excitation curve data of the target transformer is generated according to the voltage-current data pair, including: According to the parameter data set, a programmable power supply corresponding to a target transformer test loop is controlled to output a multi-level voltage to obtain a series of test voltages; the voltage and current of the primary winding of the target transformer under each test voltage are synchronously sampled to obtain multiple groups of voltage-current data pairs; digital filtering and normalization are performed on the multiple groups of voltage-current data pairs, and interpolation is performed on the multiple groups of voltage-current data pairs after digital filtering and normalization to obtain a continuous voltage-current curve; the continuous voltage-current curve is numerically differentiated to obtain excitation curve data of the target transformer.

[0044] Specific: Based on these parameters, especially the rated voltage and current, the system designs a series of test voltage points, which usually start from near zero and gradually increase until they exceed the rated voltage to fully detect the linear region and saturation characteristics of the transformer. Under the control of the system, the programmable power supply outputs these voltage points according to the preset test sequence. This process requires precise control and stable output because the excitation characteristics of the transformer are very sensitive to voltage changes. At each voltage point, the system will maintain a certain stabilization time to ensure that the transformer reaches a steady-state working state.

[0045] At the same time, the high-precision voltmeter and ammeter (or integrated power analyzer) in the test loop synchronously sample the voltage and current of the transformer primary winding. "Synchronization" is the key here, because it ensures that the collected voltage and current data are accurately corresponding in time. The sampling process uses a high-speed analog-to-digital converter (ADC), and the sampling rate usually needs to reach thousands or even tens of thousands of times per second to capture the details of the voltage and current waveforms. Such high-frequency sampling can not only accurately reflect the steady-state operating point, but also capture possible transient phenomena. In this way, the system obtains a series of voltage-current data pairs corresponding to different test voltages.

[0046] However, the original sampled data often contains various noises and interferences, which may come from the measurement system itself or from the external environment. In order to improve the data quality, the system will perform digital filtering on these raw data. Common filtering methods include moving average filtering, median filtering, low-pass filtering, etc. The purpose of filtering is to remove high-frequency noise and retain the main characteristics of the signal. The filtered data is smoother and better reflects the true electrical characteristics of the transformer.

[0047] Next, the system normalizes the filtered data. The purpose of normalization is to convert data of different magnitudes to a unified standard scale, which not only facilitates subsequent data processing and analysis, but also allows the test results of transformers of different specifications to be directly compared. Normalization usually divides the voltage and current values ​​by their rated values ​​or maximum values ​​to obtain dimensionless relative values.

[0048] However, even after filtering and normalization, the data points are still discrete. In order to obtain a continuous voltage-current characteristic curve, the system will interpolate these discrete data points. Common interpolation methods include linear interpolation, polynomial interpolation, spline interpolation, etc. The choice of interpolation requires a trade-off between computational complexity and fitting accuracy. Through interpolation, the system obtains a continuous and smooth voltage-current curve, which can better reflect the electrical characteristics of the transformer over the entire working range.

[0049] Finally, in order to obtain the excitation curve data, the system numerically differentiates the continuous voltage-current curve. The "excitation curve" here actually refers to the BH curve of the transformer core, where B (magnetic induction intensity) is related to voltage and H (magnetic field intensity) is related to current. By appropriately transforming and differentiating the voltage-current curve, the BH relationship, that is, the excitation curve, can be obtained. In addition, since the differential operation will amplify the noise in the data. Therefore, some special numerical differentiation techniques, such as the central difference method or higher-order difference methods, are usually used to obtain smoother and more accurate results.

[0050] Further, the hysteresis characteristics of the target transformer are reconstructed and feature extracted according to the excitation curve data to obtain a hysteresis characteristic parameter set, and the hysteresis characteristic parameter set is subjected to curve fitting processing to obtain hysteresis characteristic curve data of the target transformer, including: The magnetic field intensity and magnetic induction intensity of the target mutual inductor are calculated according to the excitation curve data to obtain the time series of the magnetic induction intensity and the magnetic field intensity; the time series of the magnetic induction intensity and the magnetic field intensity are subjected to phase calibration processing to obtain a synchronized magnetic induction intensity-magnetic field intensity data pair; the magnetic induction intensity-magnetic field intensity data pair is subjected to closed loop construction processing to obtain an original hysteresis loop, and the original hysteresis loop is subjected to feature point extraction processing to obtain a hysteresis characteristic parameter set; the original hysteresis loop is subjected to mathematical model fitting processing according to the hysteresis characteristic parameter set to obtain a continuous hysteresis characteristic function, and the continuous hysteresis characteristic function is subjected to discrete sampling processing to obtain the hysteresis characteristic curve data of the target mutual inductor.

[0051] Specific: The magnetic field strength and magnetic induction strength of the target transformer are calculated based on the excitation curve data obtained in the previous step. This calculation process is based on the basic principles of electromagnetism, where the magnetic field strength H is usually proportional to the current of the transformer primary winding, while the magnetic induction strength B is proportional to the integral of the voltage of the primary winding. This conversion takes into account the geometric parameters of the transformer, such as the effective magnetic path length and the cross-sectional area of ​​the core. In this way, the system obtains the time series of magnetic induction strength B and magnetic field strength H, which reflect the magnetic changes of the transformer core during the entire magnetization process.

[0052] However, due to the inherent delay of the measurement system and possible phase errors, the obtained B and H time series may have phase asynchrony problems. To solve this problem, the system will perform phase calibration on these time series. This process usually involves signal processing techniques such as Fourier transform and phase compensation. By analyzing the spectral characteristics of the B and H signals, the system can identify the phase difference and perform appropriate time shift or phase adjustment on one of the signals to obtain a synchronized BH data pair. This synchronization is critical for accurately reconstructing the hysteresis loop, because the hysteresis phenomenon is essentially the phase relationship between B and H.

[0053] Next, the system constructs a closed loop for the synchronized BH data pair to obtain the original hysteresis loop, extracts feature points from it, and obtains the hysteresis characteristic parameter set. This process involves complex data processing and feature recognition algorithms, which need to consider the shape characteristics and key point positions of the hysteresis loop.

[0054] With the hysteresis characteristic parameter set, the system will perform mathematical model fitting on the original hysteresis loop. The purpose of this step is to obtain a continuous hysteresis characteristic function that can be described mathematically. Common hysteresis models include the Jiles-Atherton model, the Preisach model, etc. These models can describe the hysteresis phenomenon using mathematical expressions, including the characteristics of the main loop and the sub-loop. The selection of the model and the determination of the parameters need to comprehensively consider factors such as computational complexity, fitting accuracy, and physical significance. The fitting process usually uses nonlinear optimization algorithms, such as the Levenberg-Marquardt algorithm or the genetic algorithm, to minimize the error between the model prediction value and the measured data. Through model fitting, the system obtains a continuous hysteresis characteristic function. This function can not only accurately describe the measured hysteresis loop, but also predict the hysteresis behavior under other magnetization conditions.

[0055] However, in order to facilitate subsequent analysis and application, the system also needs to discretize and sample this continuous function. The sampling process needs to consider the distribution and density of the sampling points to ensure that the key features of the hysteresis characteristics, such as the characteristics of the saturation region, linear region, and initial magnetization region, can be accurately reflected. Usually, denser sampling is used near the inflection point of the hysteresis loop to capture the rapid changes in the curve. Finally, through this series of complex data processing and analysis steps, the system obtains the hysteresis characteristic curve data of the target transformer. These data are expressed in the form of discrete points, but can fully reflect the hysteresis characteristics of the transformer core. These data contain rich information, such as key parameters such as remanence, coercive force, maximum magnetic induction intensity, and changes in the BH relationship during the entire magnetization process.

[0056] Furthermore, a closed loop is constructed for the magnetic induction intensity-magnetic field intensity data pair to obtain an original hysteresis loop, and feature point extraction is performed on the original hysteresis loop to obtain a hysteresis characteristic parameter set, including: The magnetic induction intensity-magnetic field intensity sequence is subjected to data interpolation processing to obtain equally spaced magnetic induction intensity-magnetic field intensity data points, and the equally spaced magnetic induction intensity-magnetic field intensity data points are subjected to curve fitting processing to obtain the hysteresis loop function of the original hysteresis loop; the extreme point calculation is performed according to the hysteresis loop function to obtain the maximum magnetic induction intensity point and the maximum magnetic field intensity point of the original hysteresis loop, and the zero point analysis is performed on the hysteresis loop function to obtain the remanence point and the coercive force point of the original hysteresis loop; the parameters of the maximum magnetic induction intensity point, the maximum magnetic field intensity point, the remanence point and the coercive force point are extracted to obtain a hysteresis characteristic parameter set including saturation magnetization intensity, remanence and coercive force.

[0057] Specific: The obtained magnetic induction intensity-magnetic field intensity sequence is subjected to data interpolation processing. The purpose of this step is to obtain equally spaced magnetic induction intensity-magnetic field intensity data points. Interpolation processing usually uses high-order interpolation methods, such as cubic spline interpolation or Hermite interpolation, to ensure that the interpolated data points can accurately reflect the changing trend of the original data while avoiding the introduction of unnecessary fluctuations. This equally spaced data point distribution is helpful for subsequent numerical calculations and analysis, especially when performing differential and integral operations, it can improve the stability and accuracy of the calculation.

[0058] After obtaining the equally spaced data points, the system will perform curve fitting on these points to obtain the hysteresis loop function of the original hysteresis loop. This fitting process usually uses nonlinear fitting methods, such as polynomial fitting or Fourier series fitting. Choosing a suitable fitting method requires considering the characteristics of the hysteresis loop, including its nonlinearity and symmetry. The goal of fitting is to obtain a continuous function that can accurately describe the entire hysteresis loop. This function must not only accurately represent the known data points, but also be able to reasonably interpolate and extrapolate. During the fitting process, the system will determine the parameters of the fitting function by minimizing the sum of squared errors or other optimization criteria.

[0059] After obtaining the hysteresis loop function, the system will perform a series of characteristic point analyses: The first step is to calculate the extreme points, with the goal of finding the points of maximum magnetic induction intensity and maximum magnetic field intensity of the original hysteresis loop. This process involves the numerical calculation of the extreme values ​​of the function, usually using the derivative method or the search method. In the derivative method, the system calculates the first-order derivative of the hysteresis loop function and looks for the point where the derivative is zero; in the search method, the system uses algorithms such as the bisection method or the golden section method to gradually approach the extreme points. These extreme points reflect the saturation characteristics of the material and are important parameters for evaluating the performance of the transformer.

[0060] Next, the system will perform a zero-point analysis on the hysteresis loop function to obtain the remanence point and coercivity point of the original hysteresis loop. The remanence point is the value of the magnetic induction intensity when the magnetic field intensity is zero, and the coercivity point is the value of the magnetic field intensity when the magnetic induction intensity is zero. This analysis process usually involves numerical methods, such as Newton iteration method or bisection method, to locate these characteristic points with high precision. Remanence and coercivity are key parameters to describe the magnetic properties of materials, which directly affect the measurement accuracy and dynamic response characteristics of the transformer.

[0061] After obtaining these characteristic points, the system will perform parameter extraction to obtain a set of hysteresis characteristic parameters including saturation magnetization, remanence and coercivity. Saturation magnetization is usually determined by the point of maximum magnetic induction, which reflects the limit of material magnetization. Remanence and coercivity are directly given by the corresponding characteristic points. In addition, the system can also calculate other parameters, such as initial permeability (determined by the slope near the origin), hysteresis loss (calculated by the area of ​​the hysteresis loop), etc.

[0062] Furthermore, according to the obtained hysteresis characteristic parameter set, the eddy current loss data of the target transformer is calculated, including: According to the parameter data set, multi-frequency control is performed on the programmable power supply corresponding to the target transformer test loop to obtain a series of excitation signals at preset frequency points; the total loss of the target transformer at each preset frequency point is measured according to the excitation signal to obtain multiple sets of frequency-total loss data pairs; loss separation processing is performed based on the multiple sets of frequency-total loss data pairs and the hysteresis characteristic parameter set to obtain the first eddy current loss data of each frequency point; frequency relationship analysis processing is performed on the eddy current loss data of each frequency point to obtain an eddy current loss-frequency characteristic curve; the eddy current loss-frequency characteristic curve is input into a pre-trained machine learning model to obtain the second eddy current loss data of the target transformer within the extended frequency range, and the first eddy current loss data and the second eddy current loss data are combined as the eddy current loss data of the target transformer.

[0063] Specific: According to the parameter data set, the programmable power supply corresponding to the target transformer test loop is multi-frequency controlled to generate a series of excitation signals at preset frequency points. The selection of these frequency points usually covers the entire operating frequency range of the transformer, starting from the power frequency (such as 50Hz or 60Hz) and can extend to several thousand Hertz to fully capture the performance characteristics of the transformer under various frequency conditions. The amplitude of the excitation signal is usually kept constant to isolate the effect of frequency on loss.

[0064] After generating the excitation signal, the system measures the total loss of the target transformer at each preset frequency point. This measurement process usually uses a high-precision power analyzer or a specially designed loss measurement circuit. During the measurement, the system records the input power and output power at the same time, and the difference is the total loss of the transformer. To ensure the accuracy of the measurement, the measurement of each frequency point is repeated multiple times and the average value is taken. In this way, the system obtains a set of frequency-total loss data pairs, which intuitively reflect the trend of transformer loss changing with frequency.

[0065] Next, the system performs loss separation processing based on the obtained frequency-total loss data and the previously measured hysteresis characteristic parameter set to obtain the first eddy current loss data at each frequency point. This process involves complex calculations and theoretical models, and needs to consider the different contributions of hysteresis loss and eddy current loss to the total loss.

[0066] After obtaining the first eddy current loss data, the system performs frequency relationship analysis on these data to obtain the eddy current loss-frequency characteristic curve. This analysis process usually involves curve fitting techniques, such as polynomial fitting or power function fitting. In theory, eddy current loss is proportional to the square of the frequency, but the actual situation may deviate due to material properties and structural factors. Through this analysis, the system can obtain a mathematical model that describes the change of eddy current loss with frequency. This model can not only accurately represent the data of the measurement point, but also reasonably predict the eddy current loss of the unmeasured frequency point.

[0067] However, relying solely on mathematical models may not be able to fully capture the complex behavior of the transformer over a wider frequency range. Therefore, the system inputs the obtained eddy current loss-frequency characteristic curve into a pre-trained machine learning model. This machine learning model is a neural network or other advanced regression algorithm that has been trained with a large amount of historical data and can take into account various factors that affect eddy current loss, such as material nonlinearity, geometric structure effects, etc. Through this model, the system can obtain the second eddy current loss data of the target transformer within the extended frequency range. This extended frequency range can exceed the actual measured frequency range, providing an important basis for the performance evaluation of the transformer under extreme working conditions.

[0068] Finally, the system combines the first eddy current loss data (obtained directly by measurement) and the second eddy current loss data (predicted by the machine learning model) as the complete eddy current loss data for the target transformer. This combination takes advantage of the accuracy of direct measurement and the predictive power of the machine learning model to provide a comprehensive and detailed description of the eddy current loss. The advantage of this entire process is that it can provide the eddy current loss characteristics of the transformer over a wide frequency range, not just limited to the actual measured frequency points. This is critical for evaluating the performance of transformers in complex power systems, especially in modern power systems with a large number of harmonic components. In addition, through the application of machine learning models, this method is able to capture complex nonlinear effects that may be ignored by simple mathematical models, thereby providing more accurate loss predictions.

[0069] Furthermore, loss separation processing is performed based on multiple sets of frequency-total loss data pairs and hysteresis characteristic parameter sets to obtain first eddy current loss data at each frequency point, including: According to the hysteresis characteristic parameter set, the corresponding hysteresis loop function is obtained, and the area of ​​the hysteresis loop function is calculated to obtain the hysteresis loss energy data per unit volume; the hysteresis loss energy data per unit volume and the effective core volume of the target transformer are multiplied to obtain the hysteresis loss data per cycle; the hysteresis loss data per cycle and each preset frequency point are multiplied to obtain multiple groups of frequency-hysteresis loss data pairs; the difference calculation is performed based on the multiple groups of frequency-hysteresis loss data pairs and the multiple groups of frequency-total loss data pairs to obtain the first eddy current loss data of each frequency point.

[0070] Specific: The hysteresis loop function is reconstructed based on the previously obtained hysteresis characteristic parameter set. This function is usually a nonlinear closed curve that describes the relationship between the magnetic induction intensity B and the magnetic field intensity H of the material under the action of the alternating magnetic field. Reconstructing this function involves complex mathematical models, such as the Jiles-Atherton model or the Preisach model, which can generate complete hysteresis loops based on key parameters (such as remanence, coercive force, saturation magnetization, etc.).

[0071] After obtaining the hysteresis loop function, the system will calculate the area of ​​this function to obtain the hysteresis loss energy data per unit volume. This calculation process is actually an integration operation on the hysteresis loop, usually using numerical integration methods such as the trapezoidal method or the Simpson method. The result of the integration represents the energy lost by the material in a complete magnetization cycle, and this energy is expressed in the form of per unit volume. The accuracy of this step directly affects the accuracy of the subsequent eddy current loss calculation, so the system usually uses high-order numerical methods to ensure the calculation accuracy.

[0072] Next, the system multiplies the hysteresis loss energy per unit volume by the effective core volume of the target transformer to obtain the hysteresis loss per cycle data. This step converts the microscopic unit volume loss into a macroscopic overall loss, taking into account the actual size of the transformer. The accurate determination of the effective core volume is the key to this step, which needs to take into account the geometric structure of the transformer, such as lamination thickness, air gap and other factors. This calculation represents the total hysteresis loss of the transformer in a complete magnetization cycle.

[0073] Then, the system multiplies the hysteresis loss data of each cycle with each preset frequency point to obtain multiple sets of frequency-hysteresis loss data pairs. This step converts the cycle loss into time loss at different frequencies based on the theoretical relationship that hysteresis loss is proportional to frequency. This process needs to take into account the possible nonlinear changes of hysteresis loss at different frequencies, especially in the high frequency band, which requires the introduction of correction factors to adjust the simple linear relationship.

[0074] Finally, the system performs difference calculation based on the obtained multiple frequency-hysteresis loss data pairs and the previously measured multiple frequency-total loss data pairs to obtain the first eddy current loss data at each frequency point. This difference calculation is based on the principle that the total loss is equal to the hysteresis loss plus the eddy current loss. By subtracting the calculated hysteresis loss from the total loss, the eddy current loss can be obtained. This calculation process requires special attention to data alignment to ensure that the total loss and hysteresis loss data at each frequency point accurately correspond.

[0075] 103. Integrate the excitation characteristic detection results in different temperature ranges into a temperature-excitation characteristic correlation data set, and generate an excitation characteristic detection report of the target transformer based on the temperature-excitation characteristic correlation data set.

[0076] In one embodiment of the present invention, the excitation characteristic detection results of different temperature ranges are integrated into a temperature-excitation characteristic correlation data set, and an excitation characteristic detection report of a target transformer is generated according to the temperature-excitation characteristic correlation data set, including: The excitation curve data of different temperature ranges are normalized to obtain a standardized temperature-excitation curve data set; temperature sensitivity analysis is performed on the standardized temperature-excitation curve data set to obtain the temperature coefficient of the excitation curve; feature extraction is performed on the hysteresis characteristic curve data of different temperature ranges to obtain a temperature-hysteresis parameter correlation data set; mathematical model fitting is performed on the temperature-hysteresis parameter correlation data set to obtain the functional relationship between the hysteresis characteristic and temperature; frequency-temperature correlation analysis is performed on the eddy current loss data of different temperature ranges to obtain the temperature-frequency dependence model of the eddy current loss; comprehensive analysis is performed based on the temperature coefficient of the excitation curve, the functional relationship between the hysteresis characteristic and the temperature, and the temperature-frequency dependence model of the eddy current loss to obtain the excitation characteristic detection report of the target transformer.

[0077] Specific: Normalization usually involves dividing the magnetic induction intensity B and magnetic field intensity H of each curve by their maximum values, which can eliminate the possible differences in the measurement range at different temperatures and make the curves at different temperatures comparable. This standardized data set lays the foundation for subsequent temperature sensitivity analysis.

[0078] Next, the system performs temperature sensitivity analysis based on the standardized temperature-excitation curve data set to obtain the temperature coefficient of the excitation curve. This analysis process usually involves fitting the trend of magnetic induction intensity at each magnetic field intensity point with temperature, and linear regression or more complex nonlinear models can be used. In this way, the system can obtain quantitative relationships that describe the changes in temperature of each characteristic point of the excitation curve (such as initial magnetic susceptibility, saturation magnetization, etc.). These relationships are usually expressed in the form of temperature coefficients, reflecting the sensitivity of the magnetic properties of the transformer core material to temperature.

[0079] At the same time, the system performs feature extraction processing on the hysteresis characteristic curve data in different temperature ranges to obtain a temperature-hysteresis parameter correlation data set. This process involves extracting key parameters from the hysteresis loop at each temperature point, such as remanence, coercive force, maximum magnetic induction intensity, etc. The trend of these parameters changing with temperature directly reflects the temperature dependence of the hysteresis characteristics of the transformer core material. Based on this correlation data set, the system performs mathematical model fitting processing to obtain the functional relationship of the hysteresis characteristics changing with temperature. Through this functional relationship, the hysteresis characteristics of the transformer at any temperature can be predicted, providing an important basis for the performance evaluation of the transformer under different environmental conditions.

[0080] In addition, the system also performs frequency-temperature correlation analysis on eddy current loss data in different temperature ranges to obtain a temperature-frequency dependence model of eddy current loss. This analysis process needs to consider the dual effects of temperature and frequency on eddy current loss. Generally, eddy current loss increases with increasing frequency, but its relationship with temperature may be more complicated because temperature affects the resistivity of the material, which in turn affects the generation and distribution of eddy currents. By establishing a mathematical model containing two independent variables, temperature and frequency, the system can accurately describe the variation of eddy current loss under different temperature and frequency conditions.

[0081] Finally, the system performs comprehensive analysis and processing based on the previously obtained temperature coefficient of the excitation curve, the functional relationship of the hysteresis characteristics with temperature, and the temperature-frequency dependence model of the eddy current loss, and generates an excitation characteristic detection report for the target transformer. This comprehensive analysis process needs to consider the interrelationship and comprehensive impact of these three aspects. For example, an increase in temperature may reduce the saturation magnetization of the material (reflected by the temperature coefficient of the excitation curve), while increasing eddy current losses (reflected by the temperature-frequency dependence model of eddy current losses), but may reduce hysteresis losses (reflected by the temperature function relationship of the hysteresis characteristics). The system needs to weigh these factors and provide a comprehensive performance evaluation of the transformer under different temperature and frequency conditions.

[0082] In this embodiment, by obtaining the parameter data set of the target transformer, its corresponding test loop is determined, and the excitation characteristics are detected in different temperature ranges. This method can not only obtain the excitation characteristics detection results in different temperature ranges, but also integrate these results to generate a temperature-excitation characteristics correlation data set, thereby generating an excitation characteristics detection report for the target transformer. By performing detection in different temperature ranges, the correlation between the excitation characteristics of the transformer and the temperature change can be fully reflected, providing a more scientific basis for the performance evaluation of the transformer. At the same time, through the comprehensive detection of excitation characteristics, hysteresis characteristics and eddy current losses, the stability and reliability of the transformer in actual operation are ensured.

[0083] Embodiment 2 This embodiment discloses a transformer characteristic detection system.

[0084] like Figure 2 As shown, the transformer characteristic detection system includes: The acquisition module 201 is configured to: acquire a parameter data set containing basic information of a target transformer, and determine a test loop of the target transformer based on the parameter data set; The detection module 202 is configured to: perform excitation characteristic detection on the target mutual inductor through the test circuit in different preset temperature ranges, and obtain the excitation characteristic detection results in different temperature ranges, wherein the excitation characteristic detection results include excitation curve data, hysteresis characteristic curve data and eddy current loss data; The report generating module 203 is configured to: integrate the excitation characteristic detection results of different temperature intervals into a temperature-excitation characteristic associated data set, and generate an excitation characteristic detection report of the target transformer according to the temperature-excitation characteristic associated data set. Among them, the excitation characteristic detection results include excitation curve data, hysteresis characteristic curve data and eddy current loss data; The excitation characteristics of the target transformer are tested through the test circuit, including: According to the parameter data set, a programmable power supply corresponding to the target transformer test loop is controlled to output a multi-level voltage to obtain a voltage-current data pair of the primary winding of the target transformer, and excitation curve data of the target transformer is generated according to the voltage-current data pair; According to the obtained excitation curve data, the hysteresis characteristics of the target transformer are reconstructed and feature extracted to obtain a hysteresis characteristic parameter set including saturation magnetization, remanence and coercive force; according to the hysteresis characteristic parameter set, the original hysteresis loop is subjected to curve fitting processing to obtain the hysteresis characteristic curve data of the target transformer; According to the obtained hysteresis characteristic parameter set, the eddy current loss data of the target transformer is calculated.

[0085] Embodiment 3 The purpose of this embodiment is to provide a storage medium.

[0086] A storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the mutual inductor characteristic detection method as described in Embodiment 1 of the present disclosure.

[0087] Embodiment 4 The purpose of this embodiment is to provide an electronic device.

[0088] An electronic device comprises a memory, a processor and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the mutual inductor characteristic detection method as described in Embodiment 1 of the present disclosure are implemented.

[0089] like Figure 3 As shown, the electronic device provided in this embodiment is Figure 3The characteristic detection device of the mutual inductor in the invention. The characteristic detection device 300 of the mutual inductor may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 310 (for example, one or more processors) and a memory 320, and one or more storage media 330 (for example, one or more mass storage device terminals) storing application programs 333 or data 332. Among them, the memory 320 and the storage medium 330 may be temporary storage or permanent storage. The program stored in the storage medium 330 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the characteristic detection device 300 of the mutual inductor. Furthermore, the processor 310 may be configured to communicate with the storage medium 330, and execute a series of instruction operations in the storage medium 330 on the characteristic detection device 300 of the mutual inductor to implement the steps of the characteristic detection method of the mutual inductor described above.

[0090] The characteristic detection device 300 of the transformer may further include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input and output interfaces 360, and / or one or more operating systems 331, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. It will be appreciated by those skilled in the art that Figure 3 The structure of the characteristic detection device of the mutual inductor shown does not constitute a limitation on the characteristic detection device of the mutual inductor provided by the present invention, and may include more or less components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0091] The steps involved in the apparatuses of the above embodiments 2, 3 and 4 correspond to the method embodiment 1, and the specific implementation methods can refer to the relevant description part of embodiment 1. The term "storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode or carry an instruction set for execution by a processor and enable the processor to execute any method of the present invention.

[0092] Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0093] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A method for detecting characteristics of a mutual inductor, characterized in that: The following steps are involved: Acquire a parameter data set containing basic information of a target transformer, and determine a test loop of the target transformer based on the parameter data set; Under different preset temperature ranges, the excitation characteristics of the target mutual inductor are detected through the test circuit to obtain the excitation characteristics detection results of the different temperature ranges, wherein the excitation characteristics detection results include excitation curve data, hysteresis characteristic curve data and eddy current loss data; The excitation characteristic detection results in different temperature ranges are integrated into a temperature-excitation characteristic associated data set, and an excitation characteristic detection report of a target transformer is generated according to the temperature-excitation characteristic associated data set.

2. The method for detecting mutual inductor characteristics according to claim 1, characterized in that: The excitation characteristics of the target transformer are tested through the test circuit, including: According to the parameter data set, a programmable power supply corresponding to the test loop of the target transformer is controlled to output a multi-level voltage to obtain a voltage-current data pair of the primary winding of the target transformer, and excitation curve data of the target transformer is generated according to the voltage-current data pair; According to the obtained excitation curve data, the hysteresis characteristics of the target transformer are reconstructed and feature extracted to obtain a hysteresis characteristic parameter set including saturation magnetization, remanence and coercive force; according to the hysteresis characteristic parameter set, the original hysteresis loop is subjected to curve fitting processing to obtain the hysteresis characteristic curve data of the target transformer; According to the obtained hysteresis characteristic parameter set, the eddy current loss data of the target transformer is calculated.

3. The method for detecting mutual inductor characteristics according to claim 2, characterized in that: The process of generating the excitation curve data of the target transformer specifically includes: According to the parameter data set, a programmable power supply corresponding to the target transformer test loop is controlled to output a multi-level voltage to obtain a series of test voltages; At each test voltage, the voltage and current of the primary winding of the target transformer are synchronously sampled to obtain multiple sets of voltage-current data pairs; Performing digital filtering and normalization processing on multiple sets of voltage-current data pairs, and performing interpolation processing on the multiple sets of voltage-current data pairs after the digital filtering and normalization processing to obtain continuous voltage-current curves; The continuous voltage-current curve is numerically differentiated to obtain the excitation curve data of the target transformer.

4. The method for detecting mutual inductor characteristics according to claim 2, characterized in that: The process of acquiring the hysteresis characteristic curve data of the target transformer specifically includes: Based on the basic principles of electromagnetics, the magnetic field strength and magnetic induction strength of the target mutual inductor are calculated according to the excitation curve data, and the time series of the magnetic induction strength and magnetic field strength are obtained; Performing phase calibration on the time series of magnetic induction intensity and magnetic field intensity to obtain synchronized magnetic induction intensity-magnetic field intensity data pairs; A closed loop is constructed for the magnetic induction intensity-magnetic field intensity data pair to obtain a hysteresis loop function of the original hysteresis loop, and characteristic points are extracted for the hysteresis loop function to obtain a hysteresis characteristic parameter set; The original hysteresis loop is subjected to mathematical model fitting processing according to the hysteresis characteristic parameter set to obtain a continuous hysteresis characteristic function, and the continuous hysteresis characteristic function is subjected to discretization sampling processing to obtain hysteresis characteristic curve data of the target mutual inductor; or, The closed loop is constructed for the magnetic induction intensity-magnetic field intensity data pair to obtain the hysteresis loop function of the original hysteresis loop, and the feature point extraction process is performed on the hysteresis loop function to obtain the hysteresis characteristic parameter set, which specifically includes: Performing data interpolation processing on the magnetic induction intensity-magnetic field intensity data pairs to obtain equally spaced magnetic induction intensity-magnetic field intensity data points, and performing curve fitting processing on the equally spaced magnetic induction intensity-magnetic field intensity data points to obtain a hysteresis loop function of the original hysteresis loop; The extreme value points of the hysteresis loop function are calculated to obtain the maximum magnetic induction intensity point and the maximum magnetic field intensity point of the original hysteresis loop; the zero point analysis of the hysteresis loop function is performed to obtain the remanence point and coercive force point of the original hysteresis loop; Parameters of the maximum magnetic induction intensity point, the maximum magnetic field intensity point, the remanence point and the coercive force point are extracted to obtain a hysteresis characteristic parameter set including saturation magnetization intensity, remanence and coercive force.

5. The method for detecting mutual inductor characteristics according to claim 4, characterized in that: The eddy current loss data of the target transformer, the specific calculation process includes: According to the parameter data set, a programmable power supply corresponding to the target transformer test loop is subjected to multi-frequency control to obtain an excitation signal at a series of preset frequency points; The total loss of the target mutual inductor under the excitation signal of each preset frequency point is measured to obtain multiple sets of frequency-total loss data pairs; Perform loss separation processing on the obtained multiple sets of frequency-total loss data pairs and hysteresis characteristic parameter sets to obtain the first eddy current loss data of each frequency point; Performing frequency relationship fitting processing on the first eddy current loss data of each frequency point to obtain an eddy current loss-frequency characteristic curve; Inputting the eddy current loss-frequency characteristic curve into a pre-trained machine learning model to obtain second eddy current loss data of the target mutual inductor within an extended frequency range; The first eddy current loss data and the second eddy current loss data are combined to serve as eddy current loss data of the target transformer.

6. The method for detecting mutual inductor characteristics according to claim 5, characterized in that: The obtained multiple sets of frequency-total loss data pairs and hysteresis characteristic parameter sets are subjected to loss separation processing to obtain the first eddy current loss data of each frequency point, specifically including: Acquire a corresponding hysteresis loop function according to the hysteresis characteristic parameter set, perform area calculation on the hysteresis loop function, and obtain hysteresis loss energy data per unit volume; Performing a product operation on the hysteresis loss energy data per unit volume and the effective core volume of the target transformer to obtain hysteresis loss data per cycle; Performing a product operation on the hysteresis loss data of each cycle and each preset frequency point to obtain hysteresis loss data, and then obtaining multiple sets of frequency-hysteresis loss data pairs corresponding to the frequency; Based on the difference between multiple sets of frequency-total loss data pairs and corresponding frequency-hysteresis loss data pairs, first eddy current loss data of each frequency point is obtained; or, The specific generation process of the target transformer excitation characteristic detection report includes: The excitation curve data in different temperature ranges are normalized to obtain a standardized temperature-excitation curve data set; Performing temperature sensitivity analysis and processing according to the standardized temperature-excitation curve data set to obtain a temperature coefficient of the excitation curve; Perform feature extraction on the hysteresis characteristic curve data in different temperature ranges to obtain a temperature-hysteresis parameter correlation data set; Performing mathematical model fitting processing on the temperature-hysteresis parameter correlation data set to obtain a functional relationship between hysteresis characteristics and temperature; The eddy current loss data in different temperature ranges are subjected to frequency-temperature correlation analysis to obtain the temperature-frequency dependence model of eddy current loss. The mutual relationship and comprehensive influence among the temperature coefficient of the excitation curve, the functional relationship of the hysteresis characteristic with temperature and the temperature-frequency dependence model of the eddy current loss are analyzed to obtain an excitation characteristic detection report of the target transformer.

7. The method for detecting mutual inductor characteristics according to claim 1, characterized in that: Determine the test loop of the target transformer based on the parameter data set, specifically including: According to the rated voltage and current of the transformer, select the voltage source and current source of the appropriate range; According to the accuracy level of the transformer, select the measuring instrument with corresponding accuracy; Consider temperature factors and design a test circuit including temperature control and monitoring devices; The parameter data set includes the rated primary voltage, rated secondary voltage, rated primary current, and rated secondary current of the target transformer.

8. A transformer characteristic detection system, characterized in that: include: An acquisition module is configured to: acquire a parameter data set containing basic information of a target transformer, and determine a test loop of the target transformer based on the parameter data set; The detection module is configured to: perform excitation characteristic detection on the target mutual inductor through the test circuit in different preset temperature ranges, respectively, to obtain the excitation characteristic detection results in different temperature ranges, wherein the excitation characteristic detection results include excitation curve data, hysteresis characteristic curve data and eddy current loss data; The report generation module is configured to: integrate the excitation characteristic detection results of different temperature ranges into a temperature-excitation characteristic associated data set, and generate an excitation characteristic detection report of the target transformer according to the temperature-excitation characteristic associated data set.

9. A storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps in the mutual inductor characteristic detection method as described in any one of claims 1 to 7 are implemented.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the mutual inductor characteristic detection method according to any one of claims 1 to 7 are implemented.

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