Fault diagnosis method and system for current transformer

Abstract

The invention relates to a fault diagnosis method and system for a current transformer, and the method comprises the following steps: collecting electrical parameters of the current transformer in operation, drawing a trend curve of the electrical parameters, and determining slope change data based on the trend curve; judging whether the current transformer has a fault or not based on the slope change data, and if yes, performing fault analysis on the current transformer based on the slope change data to obtain a fault type result; determining a corresponding fault influence factor based on the fault type result, and performing electrical parameter verification on the current transformer based on the fault influence factor to obtain a parameter deviation table; and performing fault severity evaluation on the current transformer based on the parameter deviation table to obtain a fault severity level, and generating a fault diagnosis visualization graph based on the fault severity level and a fault type result, thereby solving the technical problem that an early symptom of performance degradation of the current transformer is difficult to effectively capture in a traditional technology.

Description

Technical Field

[0001] This invention relates to the field of electrical technology, and in particular to a fault diagnosis method and system for current transformers. Background Technology

[0002] In power systems, current transformers are critical measurement and protection devices, and their operating status directly affects the accuracy of relay protection and the safety and stability of the power grid. However, existing fault diagnosis methods mostly rely on threshold judgment or static parameter comparison, which is difficult to effectively capture early signs of current transformer performance degradation. Especially under complex operating conditions, they are prone to misjudgment or missed judgment, and lack the ability to dynamically perceive and quantitatively assess the fault evolution process. The above-mentioned technical solution collects electrical parameters and constructs trend curves, extracts slope change data to achieve dynamic identification and type determination of faults, and further combines fault influencing factors to verify parameter deviations and assess severity, ultimately generating visualized diagnostic results. The main problem it solves is how to integrate early dynamic identification, accurate classification, and quantitative assessment of severity of current transformer faults. Summary of the Invention

[0003] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, this invention proposes a fault diagnosis method for current transformers, comprising the following steps: Collect the electrical parameters of the current transformer in operation, plot the trend curves of the electrical parameters, and determine the slope change data based on the trend curves; The current transformer is determined to have a fault based on the slope change data. If a fault exists, the current transformer is analyzed based on the slope change data to obtain the fault type result. Based on the fault type results, the corresponding fault impact factors are determined, and the electrical parameters of the current transformer are verified based on the fault impact factors to obtain a parameter deviation table. The severity of faults in current transformers is assessed based on the parameter deviation table to obtain the fault severity level, and a fault diagnosis visualization is generated based on the fault severity level and fault type results.

[0004] Furthermore, the electrical parameters of the operating current transformer are collected, and trend curves of these electrical parameters are plotted. Based on these trend curves, the slope change data is determined, including: The original electrical parameters of the current transformer in operation are collected by a high-frequency sensing module, and the original electrical parameters are filtered and denoised to obtain the electrical parameters. The electrical parameters are sorted in ascending order according to the acquisition timestamp to obtain a time series parameter sequence, and a trend curve is obtained by plotting the time series parameter sequence using coordinate graphs. Divide the trend curve into fixed time intervals and calculate the slope of the line connecting the first and last points of each fixed time interval to obtain slope change data.

[0005] Furthermore, fault analysis of the current transformer is performed based on slope change data to obtain fault type results, including: The slope change data is divided into intervals according to the numerical range to obtain the slope distribution intervals. Statistical counting is then performed on the slope distribution intervals to obtain the slope frequency distribution map. Fault matching analysis of current transformers is performed based on slope frequency distribution map to obtain fault feature identifiers. The fault feature identifiers are then classified according to preset fault discrimination rules to obtain a candidate fault list. Calculate the confidence level for each fault type in the candidate fault list, and take the fault type with the highest confidence level as the fault type result.

[0006] Furthermore, based on the fault impact factor, the electrical parameters of the current transformer are verified, resulting in a parameter deviation table, including: Harmonic component analysis was performed on the fault influencing factors to obtain harmonic characteristic distribution information; Based on the harmonic characteristic distribution information, waveform distortion is calculated to obtain the distortion curve; Based on the distortion curve, load characteristic matching analysis is performed on the current transformer to obtain the load response signal; The electrical parameters of the current transformer are verified based on the load response signal to obtain a parameter deviation table.

[0007] Furthermore, based on the load response signal, the electrical parameters of the current transformer are verified to obtain a parameter deviation table, including: The amplitude characteristics of the load response signal are extracted to obtain the amplitude fluctuation curve, and the peak points of the amplitude fluctuation curve are marked to obtain the peak distribution map; Based on the peak distribution diagram, the original electrical parameters of the current transformer are matched at corresponding time points and the parameter values ​​are compared to obtain preliminary deviation data. Based on the preliminary deviation data, the parameter deviation range of the current transformer is defined to obtain the parameter deviation range interval, and a parameter deviation table is generated based on the parameter deviation range interval.

[0008] Furthermore, the severity of faults in the current transformer is assessed based on the parameter deviation table to obtain the fault severity level, including: The damage level of the key functional components of the current transformer is scored based on the parameter deviation table to obtain a component score list. The component score list is then weighted and summed to obtain a comprehensive score value. By using a grade mapping method, the severity of current transformers is classified based on comprehensive score values ​​to obtain the fault severity level.

[0009] Furthermore, based on the fault severity level and fault type results, a fault diagnosis visualization diagram is generated, including: The severity level of the fault is converted into a color code to obtain a level color identifier, and the fault type results are matched with icon symbols to obtain a type icon; By merging the level color indicators and type icons through layer overlay, a diagnostic graphical interface is obtained. Numerical labels and status indicators are then added to the diagnostic graphical interface to obtain a fault diagnosis visualization.

[0010] The present invention also provides a fault diagnosis system for a current transformer, comprising: The data acquisition module is used to collect the electrical parameters of the current transformer in operation, plot the trend curves of the electrical parameters, and determine the slope change data based on the trend curves. The judgment module is used to determine whether there is a fault in the current transformer based on the slope change data. If there is a fault, the current transformer is analyzed based on the slope change data to obtain the fault type result. The verification module is used to determine the corresponding fault influence factor based on the fault type result, and to verify the electrical parameters of the current transformer based on the fault influence factor to obtain a parameter deviation table. The evaluation module is used to assess the severity of faults in current transformers based on the parameter deviation table, obtain the fault severity level, and generate a fault diagnosis visualization based on the fault severity level and fault type results.

[0011] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the above methods.

[0012] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the above methods.

[0013] This invention provides a fault diagnosis method for current transformers, comprising the following steps: collecting electrical parameters of the operating current transformer and plotting trend curves of the electrical parameters, and determining slope change data based on the trend curves; determining whether the current transformer has a fault based on the slope change data, and if so, performing fault analysis on the current transformer based on the slope change data to obtain fault type results; determining the corresponding fault influencing factors based on the fault type results, and verifying the electrical parameters of the current transformer based on the fault influencing factors to obtain a parameter deviation table; assessing the severity of the fault based on the parameter deviation table to obtain a fault severity level, and generating a fault diagnosis visualization diagram based on the fault severity level and fault type results. This method solves the technical problem that traditional technologies struggle to effectively capture early signs of current transformer performance degradation, and achieves the quantification of fault impact through the parameter deviation table, thereby classifying fault severity levels. This provides a scientific basis for condition-based maintenance of equipment and helps optimize maintenance strategies and avoid over- or under-maintenance. Attached Figure Description

[0014] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a flowchart illustrating the steps of the fault diagnosis method for a current transformer in an embodiment of the present invention. Figure 2 This is a structural block diagram of the fault diagnosis system for the current transformer in an embodiment of the present invention; Figure 3 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0015] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0016] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0017] In the description of this invention, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0018] In the description of this invention, "several" means one or more, "more than" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0019] In the description of this invention, unless otherwise explicitly defined, terms such as "set up," "install," and "connect" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0020] The embodiments of this application will be further described below with reference to the accompanying drawings.

[0021] Reference Figure 1 This invention provides a fault diagnosis method for a current transformer, comprising the following steps: Step S1: Collect the electrical parameters of the current transformer in operation, plot the trend curve of the electrical parameters, and determine the slope change data based on the trend curve.

[0022] Specifically, when starting fault diagnosis of a current transformer, the first step is to collect the electrical parameters of the operating current transformer, including but not limited to key data such as voltage, current, and power factor. This process is usually accomplished through monitoring equipment connected to the secondary side of the current transformer, which can record the changes in these parameters in real time. Next, the collected data is imported into analysis software, which can be a tool specifically designed for power system monitoring and data analysis. Then, within this software, the function to plot trend curves of the electrical parameters is selected to graphically display how these parameters change over time. For example, in a substation scenario, if the focus is on the performance of the current transformer during peak load periods, then the trends in current and voltage during this period will be particularly noteworthy. Based on the plotted trend curve, the next step is to determine the slope change data. This means performing mathematical analysis on a specific time period on the trend curve to calculate the slope between different points, i.e., the rate of change. In practice, a representative time period can be selected, such as the period of most drastic load fluctuations, and the slope analysis tool provided by the software or multiple key points can be manually selected to calculate the slope values ​​between them. The purpose of this is to identify sudden increases or decreases in the rate of parameter change, as these can be early signs of current transformer failure. For example, if the slope of the current parameter deviates significantly from the normal range at a certain moment, it may indicate a potential problem inside the current transformer, such as localized overheating or accelerated aging of the insulation material. In this way, not only can the overall trend of electrical parameters be visualized, but also subtle changes that may foreshadow faults can be precisely captured, providing a solid foundation for subsequent fault diagnosis.

[0023] Step S2: Determine whether there is a fault in the current transformer based on the slope change data. If there is, perform fault analysis on the current transformer based on the slope change data to obtain the fault type result.

[0024] Specifically, after obtaining the electrical parameter trend curve of the current transformer and calculating the slope change data, the slope change data is first compared point by point or segment by segment with the preset normal operating slope threshold range. If the slope change data is found to continuously exceed the threshold range within a certain period, such as a sudden increase in the absolute value of the slope or a non-periodic violent fluctuation, the current transformer is determined to be faulty. Subsequently, based on the confirmed abnormal slope change data, a pre-built fault feature mapping rule library is further invoked. This rule library stores typical slope change patterns corresponding to different fault types (such as core saturation, inter-turn short circuit, insulation degradation, etc.). By matching the morphological characteristics (including change direction, amplitude, duration, and frequency) of the current slope change data with the patterns in the rule library, the most suitable fault type result is determined. For example, during a sudden increase in substation load, if the slope of the current trend curve on the secondary side of the current transformer changes from flat to steep in a short period of time and cannot recover to a stable state, and the slope change characteristic is highly consistent with the "rapid positive rise followed by a plateau" pattern corresponding to core saturation in the rule base, then "core saturation" can be output as the fault type result.

[0025] Step S3: Determine the corresponding fault influence factor based on the fault type result, and verify the electrical parameters of the current transformer based on the fault influence factor to obtain the parameter deviation table.

[0026] Specifically, after obtaining the electrical parameter trend curve of the current transformer and calculating the slope change data, the slope change data is first compared point by point or segment by segment with the preset normal operating slope threshold range. If the slope change data is found to continuously exceed the threshold range within a certain period, such as a sudden increase in the absolute value of the slope or a non-periodic violent fluctuation, the current transformer is determined to be faulty. Subsequently, based on the confirmed abnormal slope change data, a pre-built fault feature mapping rule library is further invoked. This rule library stores typical slope change patterns corresponding to different fault types (such as core saturation, inter-turn short circuit, insulation degradation, etc.). By matching the morphological characteristics (including change direction, amplitude, duration, and frequency) of the current slope change data with the patterns in the rule library, the most suitable fault type result is determined. For example, during a sudden increase in substation load, if the slope of the current trend curve on the secondary side of the current transformer changes from flat to steep in a short period of time and cannot recover to a stable state, and the slope change characteristic is highly consistent with the "rapid positive rise followed by a plateau" pattern corresponding to core saturation in the rule base, then "core saturation" can be output as the fault type result.

[0027] Step S4: Based on the parameter deviation table, assess the severity of the fault in the current transformer to obtain the fault severity level, and generate a fault diagnosis visualization based on the fault severity level and fault type results.

[0028] Specifically, after obtaining the parameter deviation table, the first step is to compare each electrical parameter's actual measured value with the standard reference value against a pre-set fault severity grading standard. This standard divides the deviation range into multiple intervals, corresponding to fault severity levels such as "minor," "moderate," "serious," and "critical." Then, by combining the deviation levels of all parameters, a weighted average or maximum value priority principle is used to determine the overall fault severity level of the current transformer. For example, if the parameter deviation table shows that the current ratio error exceeds the allowable limit by 1.5 times and the phase angle deviation also exceeds 2 degrees, it is judged as "serious" according to the standard. Next, this fault severity level is compared with the fault type result obtained in the previous step (e.g., "iron core"). The system performs data fusion on fault types (e.g., core saturation) and inputs it into the visualization generation module. This module automatically draws a fault diagnosis visualization diagram according to a preset template. The diagram uses color coding to indicate the severity level (e.g., red represents "severe") and uses text labels to indicate the specific fault type. It can also overlay and display relevant parameter deviation values ​​and trend curve segments. For example, in a substation operation monitoring scenario, when the system detects that a current transformer used for main transformer protection has core saturation due to long-term overload, and the parameter deviation table shows that both the ratio difference and angle difference are significantly exceeded, it can automatically generate a visualization diagram containing "Fault Type: Core Saturation", "Severity Level: Severe" and corresponding deviation data, and push it to the operation and maintenance personnel's terminal so that they can quickly grasp the equipment status and make decisions on handling measures.

[0029] In a specific embodiment, the electrical parameters of the operating current transformer are collected, and trend curves of the electrical parameters are plotted. Based on the trend curves, slope change data are determined, including: The original electrical parameters of the current transformer in operation are collected by a high-frequency sensing module, and the original electrical parameters are filtered and denoised to obtain the electrical parameters. The electrical parameters are sorted in ascending order according to the acquisition timestamp to obtain a time series parameter sequence, and a trend curve is obtained by plotting the time series parameter sequence using coordinate graphs. Divide the trend curve into fixed time intervals and calculate the slope of the line connecting the first and last points of each fixed time interval to obtain slope change data.

[0030] Specifically, in the initial stage of current transformer fault diagnosis, the raw electrical parameters of the operating current transformer are first collected using a high-frequency sensing module. This high-frequency sensing module is deployed in the secondary circuit of the current transformer and can continuously acquire raw electrical parameters, including the primary equivalent current, secondary output current, and voltage phase difference, at a sampling frequency of not less than 10 kHz. Due to the complex electromagnetic environment at the site, the collected raw electrical parameters usually contain high-frequency interference, power frequency harmonics, and sensor noise. Therefore, the raw electrical parameters need to be filtered and denoised immediately. Specifically, a low-pass digital filter or wavelet denoising algorithm is used to remove frequencies exceeding the normal frequency band of the power system (such as 50 Hz ± 2 kHz). The system removes invalid components (Hz) to obtain stable and reliable electrical parameters. These filtered and denoised electrical parameters are then arranged in ascending order according to their corresponding acquisition timestamps, forming a time-series parameter sequence with strict temporal order. Each data point in this sequence contains a timestamp and a corresponding electrical parameter value, ensuring temporal continuity and logical consistency in subsequent analysis. Based on this, a trend curve is plotted using a coordinate graph method, with the horizontal axis representing time (in seconds or minutes) and the vertical axis representing electrical parameter values ​​(such as RMS current or percentage difference). Connecting each pair of time-parameter values ​​in the time-series parameter sequence as coordinate points generates a continuous trend curve that visually reflects the time sequence. The dynamic characteristics of electrical parameters evolving over time are analyzed. Next, fixed time intervals are divided within the generated trend curves, for example, every 5 minutes. If the total monitoring time is 60 minutes, this results in 12 consecutive and non-overlapping fixed time intervals. For each fixed time interval, the trend curve points corresponding to the start and end times are extracted, and the slope of the line connecting these two points is calculated. The formula for the slope is (end point ordinate - start point ordinate) ÷ (end point abscissa - start point abscissa). The result is the slope value of that fixed time interval. Arranging the slope values ​​corresponding to all fixed time intervals in chronological order constitutes complete slope change data. For example, in a certain 220... In the protection circuit of the main transformer in a kV substation, a current transformer used for differential protection operates continuously during peak load periods. The high-frequency sensing module collects its secondary current signal at a frequency of 12 kHz. After filtering, a clean sequence of effective current values ​​is obtained. After sorting by timestamp, a trend curve is plotted. It is found that the current rises slowly from 14:00 to 14:05, while the curve rises sharply from 14:05 to 14:10. By dividing the time into fixed 5-minute intervals and calculating the slope for each interval, the slope of the first interval is 0.02 A / min, and the slope of the second interval increases sharply to 0.35 A / min. This significantly changed slope data is recorded as part of the slope change data and used for subsequent fault diagnosis.

[0031] In a specific embodiment, fault analysis of the current transformer is performed based on slope change data to obtain fault type results, including: The slope change data is divided into intervals according to the numerical range to obtain the slope distribution intervals. Statistical counting is then performed on the slope distribution intervals to obtain the slope frequency distribution map. Fault matching analysis of current transformers is performed based on slope frequency distribution map to obtain fault feature identifiers. The fault feature identifiers are then classified according to preset fault discrimination rules to obtain a candidate fault list. Calculate the confidence level for each fault type in the candidate fault list, and take the fault type with the highest confidence level as the fault type result.

[0032] Specifically, after obtaining the slope change data, the data is first divided into intervals according to the numerical range. That is, all slope values ​​are divided into several slope distribution intervals according to preset equal-width or non-equal-width rules. For example, the slope values ​​are divided from... The range from 1.0 to +1.0 is divided into 20 intervals, each with a width of 0.1, thus forming a sequence like [...]. 1.0, 0.9), [ 0.9, The slope distribution intervals are 0.8)……[0.9, 1.0]. Then, statistical counting is performed on each slope distribution interval, that is, all slope change data are traversed, the number of slope values ​​falling into each interval is counted, and the statistical results are presented in the form of a bar chart to generate a slope frequency distribution chart. The horizontal axis of the chart is the slope distribution interval, and the vertical axis is the number of times the slope appears in the corresponding interval, which intuitively reflects the concentration and dispersion characteristics of the slope values. Subsequently, based on the slope frequency distribution chart, fault matching analysis is performed on the current transformer. Specifically, the peak position, distribution shape (such as single peak, double peak, right skew, left skew) and high frequency interval combination in the slope frequency distribution chart are compared with the pre-established fault mode template library. The template library stores the typical slope frequency distribution characteristics corresponding to different fault types. For example, "core saturation" is usually manifested as a positive high slope interval (such as [0.3, The frequency of "0.5" is significantly higher than other intervals, while "inter-turn short circuit" may show abnormal spikes in the negative slope interval. By matching these features, the fault feature identifier that best matches the current distribution is extracted. Then, the extracted fault feature identifiers are classified according to preset fault discrimination rules. These rules exist in the form of logical conditions or decision trees, such as "if the slope frequency is in [0.4, If the percentage of faults in the interval [0.6] exceeds 60% and there are no high-frequency points with negative slopes, then it is classified as "core saturation". This generates a candidate fault list containing several possibilities, which may include options such as "core saturation", "insulation degradation", and "secondary open circuit". Next, the confidence level of each fault type in the candidate fault list is calculated. The confidence level is calculated based on the similarity score between the slope frequency distribution and the template, the prior probability of historical similar equipment faults, and the correction coefficient of the current operating conditions (such as load rate and ambient temperature). For example, the weighted Euclidean distance is used to measure the distribution similarity, and the posterior probability is updated by combining the Bayesian formula. Finally, a confidence level value between 0 and 1 is assigned to each candidate fault type. Finally, the fault type corresponding to the highest confidence level after calculation is output as the final fault type result. For example, in the main transformer protection circuit of a 220 kV substation, the slope change data obtained by a current transformer during continuous monitoring is divided into intervals, and 70% of the slope values ​​are concentrated in [0.35, In the interval [0.45], the slope frequency distribution plot shows a clear right-skewed single-peak shape. After comparison with the template library, this feature is highly consistent with the typical pattern of "core saturation". The fault feature is marked as "high positive slope concentration type" and classified into the "core saturation" category according to the preset discrimination rules. At the same time, the candidate list also includes "load surge" but its confidence level is only 0.32, while the confidence level of "core saturation" reaches 0.89 after comprehensive calculation. Therefore, "core saturation" is finally output as the fault type result.

[0033] In a specific embodiment, the electrical parameters of the current transformer are verified based on the fault impact factor to obtain a parameter deviation table, including: Harmonic component analysis was performed on the fault influencing factors to obtain harmonic characteristic distribution information; Based on the harmonic characteristic distribution information, waveform distortion is calculated to obtain the distortion curve; Based on the distortion curve, load characteristic matching analysis is performed on the current transformer to obtain the load response signal; The electrical parameters of the current transformer are verified based on the load response signal to obtain a parameter deviation table.

[0034] Specifically, after obtaining the fault influence factors of the current transformer, the first step is to analyze the harmonic components of these factors. This step requires using Fast Fourier Transform (FFT) or other suitable algorithms to convert the fault influence factor data in the time domain into frequency domain information, in order to identify the various frequency components and their corresponding amplitude and phase information, thereby obtaining detailed harmonic characteristic distribution information. For example, when analyzing a specific model of current transformer, by collecting the voltage and current signals generated during its operation as inputs for the fault influence factors, and using software tools to perform FFT calculations, the fundamental frequency and the second, third, and even higher order harmonic components can be accurately extracted, and the harmonic characteristics distribution can be clearly identified. The intensity and phase relationship of each harmonic is used to form a table or chart containing the characteristics of all key harmonic components; this is the harmonic characteristic distribution information. Based on this harmonic characteristic distribution information, waveform distortion is further calculated. Specifically, the original waveform is reconstructed based on the amplitude and phase information of each harmonic component and compared with an ideal distortion-free sine wave. The difference between the two is calculated, and this difference is usually represented by the total harmonic distortion (THD). A distortion curve describing the degree of distortion over time under different load conditions is generated. For example, in a certain application scenario, when the current transformer is under light load, its output signal THD may be low, but as the load increases to full load... With increasing load, the THD value rises significantly, and this distortion curve visually illustrates this trend. Next, based on the obtained distortion curve, load characteristic matching analysis is performed on the current transformer. This means simulating the current transformer's response behavior under different loads by combining the current transformer's design parameters (such as rated capacity and rated current) and the actual load conditions. For example, considering a 100A rated current current transformer, the response performance at 50%, 75%, and 100% load rates is analyzed. The corresponding load response signals are obtained through simulation or experimentation. These signals reflect the current transformer's ability to handle non-ideal inputs under various operating conditions. Finally, based on the above... The load response signal is used to verify the electrical parameters of the current transformer. This involves comparing the actual measured electrical parameters (such as ratio error, phase angle error, etc.) with the design standard or historical data during normal operation, calculating the deviation between the two, and listing a detailed parameter deviation table. This deviation table not only records the actual measured values ​​of each key parameter, but also clearly indicates the degree of deviation from the standard value. For example, the ratio error of a current transformer under full load operation changes from the design requirement of ±0.5% to +1.2% in actual testing, and the phase angle error changes from ≤10' to 15'. Such a parameter deviation table helps to accurately locate the problems of the current transformer and provides a scientific basis for further maintenance, adjustment, or replacement.

[0035] In a specific embodiment, the electrical parameters of the current transformer are verified based on the load response signal to obtain a parameter deviation table, including: The amplitude characteristics of the load response signal are extracted to obtain the amplitude fluctuation curve, and the peak points of the amplitude fluctuation curve are marked to obtain the peak distribution map; Based on the peak distribution diagram, the original electrical parameters of the current transformer are matched at corresponding time points and the parameter values ​​are compared to obtain preliminary deviation data. Based on the preliminary deviation data, the parameter deviation range of the current transformer is defined to obtain the parameter deviation range interval, and a parameter deviation table is generated based on the parameter deviation range interval.

[0036] Specifically, after obtaining the load response signal, the amplitude feature is first extracted. This involves iterating through all data points of the load response signal, reading the instantaneous amplitude point by point, and arranging these amplitudes in chronological order to form a continuous amplitude fluctuation curve. This curve, with time on the horizontal axis and signal amplitude on the vertical axis, fully reflects the dynamic changes in the output amplitude of the current transformer under different load conditions. Next, peak points are marked on the amplitude fluctuation curve. This is done using first-order derivative zero-crossing detection or a sliding window local maximum algorithm to identify all local maxima points on the curve and record the timestamp and amplitude value corresponding to each peak point, thereby generating a peak distribution map. The graph marks the location and intensity of all significant peaks as discrete points for subsequent precise location of anomalies. Then, based on the peak distribution graph, the original electrical parameters of the current transformer are matched to corresponding time points and their values ​​compared. This involves precisely aligning the timestamp of each peak point in the peak distribution graph with the time series in the original electrical parameter database, extracting the original electrical parameters of the current transformer at that moment (such as the effective value of the secondary current, ratio difference, angle difference, etc.), and comparing each parameter with the theoretical parameter values ​​or historical normal operating parameter values ​​of the device under standard operating conditions. The absolute or relative deviation between the two is calculated, thus obtaining preliminary deviation data. For example, in a certain 220... In the main transformer protection circuit of a kV substation, the load response signal of a current transformer during a load surge, after amplitude extraction, showed a significant peak value of 5.82 A at 14:07:23. However, the original electrical parameter record for that moment indicated a secondary current of 5.00 A (corresponding to the converted value of the primary side rated current). The theoretical allowable value for the ratio error is ±0.5%, but the measured value reached +16.4%. This deviation was recorded as an item in the preliminary deviation data. Next, based on this preliminary deviation data, the parameter deviation range of the current transformer was defined. This involved statistically analyzing the maximum, minimum, and distribution trends of all preliminary deviation data, and combining this with the power industry standards (such as IEC 61869 or DL / T 725) regarding the accuracy class of current transformers, to determine a reasonable parameter deviation range. For example, the ratio error deviation was divided into […]. [0.5%, +0.5%] is the acceptable range, [+0.5%, +1.0%] is slightly out of tolerance, and [+1.0%, +2.0%] is seriously out of tolerance. Finally, a parameter deviation table is generated based on the parameter deviation range. This table lists each electrical parameter (such as ratio difference, angle difference, excitation current, etc.), its measured value, standard reference value, deviation value, deviation range, and corresponding level identifier in a structured format. For example, "Ratio difference: measured +1.2%, standard ±0.5%, deviation +0.7%, range [+1.0%, +2.0%), level: serious".

[0037] In a specific embodiment, the severity of a fault in the current transformer is assessed based on a parameter deviation table to obtain a fault severity level, including: The damage level of the key functional components of the current transformer is scored based on the parameter deviation table to obtain a component score list. The component score list is then weighted and summed to obtain a comprehensive score value. By using a grade mapping method, the severity of current transformers is classified based on comprehensive score values ​​to obtain the fault severity level.

[0038] Specifically, after obtaining the parameter deviation table, the first step is to score the damage level of the key functional components of the current transformer based on this table. This process relies on the mapping relationship between the deviation values ​​of each electrical parameter in the parameter deviation table and the corresponding functional components. Key functional components of a current transformer typically include the core, primary winding, secondary winding, insulation structure, and shielding layer. The deviation of each electrical parameter (such as ratio difference, phase difference, excitation current, harmonic content, etc.) is closely related to the state of a specific component. For example, a significantly large ratio difference mainly reflects the deterioration or saturation of the core's magnetic properties, while an abnormal phase difference may indicate insulation aging or winding deformation. Therefore, based on each deviation data listed in the parameter deviation table, and referring to the preset "parameter-component-scoring rule table," each key functional component is assigned a damage level score between 0 and 10, where 0 indicates no damage and 10 indicates severe failure. For example, in a certain 220... In the main transformer protection circuit of a kV substation, the parameter deviation table of a current transformer shows a ratio error of +1.3% (exceeding the ±0.5% standard), an angle error of 18′ (exceeding the ≤10′ limit), and a third harmonic content reaching 8% of the fundamental frequency. According to the rule table, the core is scored 7.5 points due to the ratio error and excessive third harmonics, the insulation structure is scored 6.0 points due to the angle error, and the secondary winding is scored 5.0 points due to the harmonic distortion. This forms a component score list containing each component and its corresponding score. Subsequently, a weighted summation calculation is performed on this component score list. The weight coefficients are pre-set according to the importance of each component in the overall function of the current transformer. For example, the core weight is 0.4, the insulation structure weight is 0.3, the winding weight is 0.2, and the shielding layer weight is 0.1. The calculation method is: Comprehensive score value = Σ (component score × corresponding weight). Taking the above example, the comprehensive score value = 7.5 × 0.4 + 6.0 × 0.3 + 5.0 × 0.2 + 0 × 0.1 = 3.0 + 1.8 + 1.0 + 0 = 5.8; Next, using a grade mapping method, the current transformer is classified into severity levels based on this comprehensive score value. This grade mapping method corresponds one-to-one with the severity level of the fault using a preset numerical range. For example: 0.0–2.9 is “minor”, ​​3.0–5.9 is “moderate”, 6.0–7.9 is “serious”, and 8.0–10.0 is “critical”; Since the calculated comprehensive score value is 5.8, it falls within the 3.0–5 range.Within range 9, the fault severity level of the current transformer was therefore determined to be "moderate." The entire evaluation process strictly followed the sequence of "parameter deviation table → component mapping scoring → generating component scoring list → weighted summation → comprehensive score value → level mapping → outputting fault severity level." All scoring rules, weight allocations, and level thresholds were derived from statistical analysis results of equipment technical specifications, industry standards (such as DL / T 725, IEC 61869), and historical fault databases, without incorporating subjective experience or vague judgments. For example, in the aforementioned substation scenario, although the current transformer showed signs of core saturation, it had not yet caused protection malfunctions or measurement inaccuracies. The comprehensive score was 5.8, corresponding to the "moderate" level. Based on this, the maintenance system scheduled replacement during the next planned power outage window, rather than immediately shutting it down, thus ensuring grid safety and avoiding unnecessary emergency operations.

[0039] In a specific embodiment, a fault diagnosis visualization diagram is generated based on the fault severity level and fault type results, including: The severity level of the fault is converted into a color code to obtain a level color identifier, and the fault type results are matched with icon symbols to obtain a type icon; By merging the level color indicators and type icons through layer overlay, a diagnostic graphical interface is obtained. Numerical labels and status indicators are then added to the diagnostic graphical interface to obtain a fault diagnosis visualization.

[0040] Specifically, after obtaining the fault severity level and fault type results, the fault severity level is first converted into a color code. This conversion is performed according to a predefined color mapping rule. For example, "minor" corresponds to green (#00FF00), "moderate" corresponds to yellow (#FFFF00), "severe" corresponds to orange (#FFA500), and "critical" corresponds to red (#FF0000). The fault severity level obtained from the current diagnosis (such as "moderate") is directly replaced with its corresponding hexadecimal color code through table lookup or program logic, thus obtaining the level color identifier. At the same time, the fault type results are matched with icon symbols. This matching is based on a preset icon library. The icon library assigns a unique graphic symbol to each possible fault type (such as "core saturation", "inter-turn short circuit", "insulation degradation", "secondary open circuit", etc.). For example, "core saturation" corresponds to a symbol with a hysteresis loop pattern. The system uses a circular icon; for "inter-turn short circuit," a coil icon with a lightning bolt symbol is used. Based on the current fault type output (e.g., "core saturation"), the system retrieves and calls the corresponding graphic file or vector symbol from the icon library to obtain the type icon. Then, the level color identifier and type icon are blended and drawn using a layer overlay method. Specifically, two layers are created in the graphics rendering engine: the bottom layer is a background area filled with the level color identifier (e.g., a circular or rectangular color block), and the top layer is the type icon with a transparent background. The type icon is centered and overlaid on the colored background, ensuring the icon outline is clearly distinguishable and the background color is fully presented, thus generating a preliminary diagnostic graphical interface. Next, numerical annotations and status indicators are added to this diagnostic graphical interface. The numerical annotations include key data from the parameter deviation table (e.g., ratio difference +1.3%, angle difference 18′), the comprehensive score value (e.g., 5.8), and the peak value of the slope change (e.g., 0.42). These values ​​(A / min) are labeled in text form with a fixed font and size at designated locations on the graphical interface (such as the lower right corner or bottom bar). Status indicators are displayed using concise text labels (such as "Abnormal operation, planned maintenance recommended") above or to the side of the graphic to describe the current status of the equipment. For example, in the main transformer protection circuit of a 220 kV substation, a current transformer is diagnosed as having a fault type of "core saturation" and a fault severity level of "moderate" after the aforementioned steps. The system first converts "moderate" to yellow (#FFFF00) as a level color identifier, then retrieves the hysteresis loop icon corresponding to "core saturation" from the icon library as the type icon, and generates a circular diagnostic graphical interface with a yellow background and a black hysteresis icon superimposed on it through layer overlay. Subsequently, the text label "Ratio difference: +1.3% | Angle difference: 18′ | Overall score: 5.8" is added below the interface, and the status indicator "Abnormal operation, planned maintenance recommended" is displayed at the top, ultimately forming a clear and complete fault diagnosis visualization.

[0041] The above describes the fault diagnosis method for current transformers in embodiments of the present invention. The following describes the fault diagnosis system for current transformers in embodiments of the present invention. Please refer to [link / reference]. Figure 2 One embodiment of the fault diagnosis system for current transformers in this invention includes: The acquisition module 21 is used to acquire the electrical parameters of the current transformer in operation, plot the trend curve of the electrical parameters, and determine the slope change data based on the trend curve. The judgment module 22 is used to determine whether there is a fault in the current transformer based on the slope change data. If there is a fault, the current transformer is analyzed based on the slope change data to obtain the fault type result. Verification module 23 is used to determine the corresponding fault influence factor based on the fault type result, and to perform electrical parameter verification on the current transformer based on the fault influence factor to obtain a parameter deviation table. The evaluation module 24 is used to evaluate the severity of faults of current transformers based on the parameter deviation table, obtain the fault severity level, and generate a fault diagnosis visualization based on the fault severity level and fault type results.

[0042] In this embodiment, the specific implementation of each unit in the above system embodiment is the same as that in the above method embodiment, and will not be repeated here.

[0043] like Figure 3 As shown in the diagram, this embodiment of the invention provides a structural schematic block diagram of a computer device, including: At least one processor; At least one memory for storing at least one program; When at least one program is executed by at least one processor, the at least one processor implements the above-described fault diagnosis method for current transformers.

[0044] It is evident that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented in this device embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0045] Furthermore, this application also discloses a computer program product or computer program stored in a computer-readable storage medium. A processor of a computer device can read the computer program from the computer-readable storage medium and execute the computer program, causing the computer device to perform the aforementioned fault diagnosis method for current transformers. Similarly, the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0046] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A fault diagnosis method for a current transformer, characterized in that, Includes the following steps: Collect the electrical parameters of the current transformer in operation, plot the trend curves of the electrical parameters, and determine the slope change data based on the trend curves; The current transformer is determined to have a fault based on the slope change data. If a fault exists, the current transformer is analyzed based on the slope change data to obtain the fault type result. Based on the fault type results, the corresponding fault impact factors are determined, and the electrical parameters of the current transformer are verified based on the fault impact factors to obtain a parameter deviation table. The severity of faults in current transformers is assessed based on the parameter deviation table to obtain the fault severity level, and a fault diagnosis visualization is generated based on the fault severity level and fault type results.

2. The fault diagnosis method for a current transformer according to claim 1, characterized in that, Collect the electrical parameters of the operating current transformer, plot the trend curves of the electrical parameters, and determine the slope change data based on the trend curves, including: The original electrical parameters of the current transformer in operation are collected by a high-frequency sensing module, and the original electrical parameters are filtered and denoised to obtain the electrical parameters. The electrical parameters are sorted in ascending order according to the acquisition timestamp to obtain a time series parameter sequence, and a trend curve is obtained by plotting the time series parameter sequence using coordinate graphs. Divide the trend curve into fixed time intervals and calculate the slope of the line connecting the first and last points of each fixed time interval to obtain slope change data.

3. The fault diagnosis method for a current transformer according to claim 1, characterized in that, Fault analysis of current transformers based on slope change data yields fault type results, including: The slope change data is divided into intervals according to the numerical range to obtain the slope distribution intervals. Statistical counting is then performed on the slope distribution intervals to obtain the slope frequency distribution map. Fault matching analysis of current transformers is performed based on slope frequency distribution map to obtain fault feature identifiers. The fault feature identifiers are then classified according to preset fault discrimination rules to obtain a candidate fault list. Calculate the confidence level for each fault type in the candidate fault list, and take the fault type with the highest confidence level as the fault type result.

4. The fault diagnosis method for a current transformer according to claim 1, characterized in that, Electrical parameters of the current transformer are verified based on fault impact factors, resulting in a parameter deviation table, including: Harmonic component analysis was performed on the fault influencing factors to obtain harmonic characteristic distribution information; Based on the harmonic characteristic distribution information, waveform distortion is calculated to obtain the distortion curve; Based on the distortion curve, load characteristic matching analysis is performed on the current transformer to obtain the load response signal; The electrical parameters of the current transformer are verified based on the load response signal to obtain a parameter deviation table.

5. The fault diagnosis method for a current transformer according to claim 4, characterized in that, Based on the load response signal, the electrical parameters of the current transformer are verified to obtain a parameter deviation table, including: The amplitude characteristics of the load response signal are extracted to obtain the amplitude fluctuation curve, and the peak points of the amplitude fluctuation curve are marked to obtain the peak distribution map; Based on the peak distribution diagram, the original electrical parameters of the current transformer are matched at corresponding time points and the parameter values ​​are compared to obtain preliminary deviation data. Based on the preliminary deviation data, the parameter deviation range of the current transformer is defined to obtain the parameter deviation range interval, and a parameter deviation table is generated based on the parameter deviation range interval.

6. The fault diagnosis method for a current transformer according to claim 1, characterized in that, The severity of faults in current transformers is assessed based on a parameter deviation table to obtain a fault severity level, including: The damage level of the key functional components of the current transformer is scored based on the parameter deviation table to obtain a component score list. The component score list is then weighted and summed to obtain a comprehensive score value. By using a grade mapping method, the severity of current transformers is classified based on comprehensive score values ​​to obtain the fault severity level.

7. The fault diagnosis method for a current transformer according to claim 1, characterized in that, A fault diagnosis visualization is generated based on the fault severity level and fault type results, including: The severity level of the fault is converted into a color code to obtain a level color identifier, and the fault type results are matched with icon symbols to obtain a type icon; By merging the level color indicators and type icons through layer overlay, a diagnostic graphical interface is obtained. Numerical labels and status indicators are then added to the diagnostic graphical interface to obtain a fault diagnosis visualization.

8. A fault diagnosis system for a current transformer, characterized in that, include: The data acquisition module is used to collect the electrical parameters of the current transformer in operation, plot the trend curves of the electrical parameters, and determine the slope change data based on the trend curves. The judgment module is used to determine whether there is a fault in the current transformer based on the slope change data. If there is a fault, the current transformer is analyzed based on the slope change data to obtain the fault type result. The verification module is used to determine the corresponding fault influence factor based on the fault type result, and to verify the electrical parameters of the current transformer based on the fault influence factor to obtain a parameter deviation table. The evaluation module is used to assess the severity of faults in current transformers based on the parameter deviation table, obtain the fault severity level, and generate a fault diagnosis visualization based on the fault severity level and fault type results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, The processor executes the steps of any one of claims 1 to 7 when executing a computer program.

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

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