Method for detecting influence of frequent switching of power grid power flow on anti-short-circuit capability of transformer

Abstract

The invention relates to the technical field of power system equipment detection, in particular to a method for detecting the influence of frequent switching of power grid power flow on the anti-short-circuit capability of a transformer, comprising the following steps: screening a high-risk target transformer based on historical operation data of the transformer; performing non-power-off state detection on the target transformer; arranging a power-off maintenance window period, and carrying out deep detection; synthesizing the detection result, combining with a preset evaluation standard, diagnosing the anti-short-circuit capability grade of the transformer, and outputting a decision suggestion; compared with the prior art which mainly depends on regular maintenance and post fault analysis and lacks an effective monitoring means for accumulated degradation of the mechanical state of the transformer, the method has the advantages that a multi-stage diagnosis system of data screening, non-power-failure detection and power-failure verification is constructed; through multi-dimensional means such as historical impact data analysis, vibration spectrum monitoring and frequency response testing, dynamic evaluation and early warning of the anti-short-circuit capability of the transformer are realized, and the accuracy and timeliness of state sensing are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of power system equipment testing technology, and in particular to a method for detecting the impact of frequent power flow switching on the short-circuit withstand capability of transformers. Background Technology

[0002] With the increasing complexity of power grid structures and the large-scale integration of new energy power generation, power grid operation has become increasingly flexible, and frequent switching of power flow direction and magnitude on transmission lines has become the norm. This frequent operation leads to a significant increase in the number of short-circuit current impacts on transformers. Although the intensity of a single impact may not reach the extreme conditions of a standard short-circuit test, its cumulative and fatigue effects will cause progressive damage to the mechanical structure of the transformer, especially irreversible damage to key components such as windings and clamping devices, gradually reducing its short-circuit withstand capability and posing a serious safety hazard.

[0003] Currently, the testing of transformer short-circuit withstand capability mainly relies on traditional preventive testing and periodic maintenance. These methods have obvious limitations: conventional electrical tests are difficult to effectively detect hidden defects in mechanical structures; power outage inspections are time-consuming, costly, and destructive; existing condition monitoring methods are limited and lack the ability to assess cumulative effects and fatigue damage; more importantly, there is a lack of systematic testing schemes and evaluation standards for frequent power flow switching conditions, making it impossible to accurately assess the transformer's short-circuit withstand capability and perform predictive maintenance.

[0004] This invention provides a systematic detection and evaluation method. By combining historical operating data analysis, uninterrupted power supply monitoring, and power outage-specific testing, a multi-parameter integrated evaluation system is established. This system can quantitatively diagnose the mechanical condition deterioration of transformers caused by frequent power flow switching, promptly detect hidden defects, assess remaining short-circuit withstand capability, and provide a scientific basis for predictive maintenance and replacement decisions, ultimately realizing the transformation from passive maintenance to proactive prediction. Summary of the Invention

[0005] To overcome the problems mentioned in the background art, the present invention proposes a method for detecting the impact of frequent power flow switching on the short-circuit withstand capability of transformers.

[0006] The technical solution of this invention is: a method for detecting the impact of frequent power flow switching on the short-circuit withstand capability of transformers, comprising the following steps: S11: Screening high-risk target transformers based on historical transformer operation data; S12: Perform uninterrupted power-off detection on the target transformer to obtain its vibration characteristics, frequency response, and short-circuit impedance parameters; S13: Arrange a power outage maintenance window for the target transformer to conduct in-depth mechanical and physical condition testing; S14: Based on the results of the uninterrupted power supply detection and power outage detection, and combined with the preset evaluation criteria, diagnose the transformer's short-circuit withstand capability level and output decision recommendations.

[0007] As a preferred method, when screening high-risk target transformers based on historical transformer operating data, the specific methods include: S21: Data collection, collecting the switching operation records of the circuit breakers on each side associated with the target transformer, the short-circuit event data recorded by the fault recording device, and the operation information of the protection system; S22: Impact profile establishment, based on the collected data, quantifies and statistically analyzes the number, amplitude, and duration of short-circuit current impacts experienced by the transformer; S23: Risk level determination: Based on the impact file, calculate the risk level of the transformer by means of a preset risk assessment model or threshold comparison, and identify the high-risk transformer as the target transformer; S24: Priority ranking. All high-risk target transformers selected are ranked comprehensively based on their risk index, importance in the power grid, and years of operation, generating a list of transformers to be prioritized for testing.

[0008] Preferably, data collection includes the following: Record the number of closing and opening operations of circuit breakers on each voltage level side of the transformer from the substation monitoring system and SCADA system, and count the switching frequency within a specific time period. Retrieve all fault recording files of lines and busbars associated with the transformer from the fault recording system, and extract waveform data where the transformer winding current exceeds a specified multiple of its rated current. The relay protection information management system collects protection action signals that are activated during abnormal operation of the transformer, including the activation and output action records of differential protection, overcurrent protection, and instantaneous overcurrent protection.

[0009] As a preferred option, the creation of impact files includes: For each short-circuit event, the following key parameters are extracted and recorded from the fault waveform data: peak short-circuit current, duration of short-circuit current, and current decay characteristics. All short-circuit events are statistically analyzed to form a list of impact events sorted by time series. Calculate the total number of short-circuit impact events within a specific time window and the distribution of impact number in different amplitude ranges.

[0010] Preferably, when determining the risk level, a weighted cumulative assessment model based on the number and intensity of impacts is used to calculate the risk index. The calculated risk index R is compared with multiple preset threshold intervals, and the transformer is classified into multiple risk levels based on the comparison results. The principle expression is as follows: ; in, As a risk index, The weighting coefficients correspond to the i-th amplitude interval, and these weighting coefficients increase non-linearly with the increase of the current amplitude. This represents the number of impacts where the short-circuit current amplitude falls within the i-th preset interval.

[0011] Preferably, the uninterrupted power-off state detection of the target transformer specifically includes: S31: High-frequency transient vibration measurement. Multiple vibration sensors are arranged on the surface of the transformer tank to record vibration signals during switching operations. The spectral characteristics are analyzed and compared with the fingerprint spectrum under healthy conditions to identify main frequency shift, amplitude change and low-frequency component anomalies. The sampling frequency of the vibration sensor is higher than 10kHz, and the spectrum analysis range covers 10Hz to 100kHz. S32: Frequency response analysis. When the transformer is out of service but the cover is not removed, a sweep frequency signal is injected into the beginning and end of the winding to measure the transfer function. The results of previous tests are compared laterally and the transfer function of the three-phase winding is compared longitudinally to identify the radial twist, axial twist and overall looseness of the winding. The frequency range of the sweep frequency signal is 100Hz to 10MHz. S33: Short-circuit impedance test. The short-circuit impedance of each pair of windings is measured using a low-voltage tester and compared with the factory value and the previous test value to determine the change in winding geometry.

[0012] Preferably, when performing high-frequency transient vibration measurements, the following are included: Sensor arrangement: At least one vibration sensor shall be arranged on the high-voltage side, low-voltage side and neutral point side of the outer wall of the transformer tank. Signal recording: When the transformer is closed under no-load and the on-load tap changer is operated, vibration signal recording is triggered synchronously to record the vibration signal within a 0.5s time window before and after the operation; Spectrum analysis: Perform a fast Fourier transform on the recorded vibration signal to obtain the vibration spectrum characteristics in the frequency band from 10Hz to 1000Hz; Spectrum comparison: Calculate the amplitude deviation and dominant frequency shift between the current vibration spectrum and the reference spectrum of the healthy state at characteristic frequency points.

[0013] Preferably, frequency response analysis includes: Test wiring: With the transformer off, connect the excitation terminal and the measurement terminal of the frequency response analyzer to the two ends of the winding under test, respectively; Frequency sweep test: Perform a logarithmic frequency sweep test in the frequency range of 100Hz to 10MHz and record the voltage transfer function H(f); Curve analysis steps: Use the correlation coefficient method to calculate the similarity between the current frequency response curve and the reference curve.

[0014] Preferably, when scheduling a power outage maintenance window for the target transformer to conduct in-depth mechanical and physical condition testing, the specific procedures include: S41: Visual inspection of windings and core. Enter the transformer oil tank and check whether the windings are deformed, whether the pads are missing or displaced, whether the pressure pins are loose, whether the pressure pin displacement indicator is working, and whether the core clamps and pull strips are cracked. S42: Winding clamping force measurement. Use a dedicated hydraulic device or torque wrench to measure the remaining preload of the clamping pins and determine whether it is lower than the minimum preload requirement specified by the manufacturer. S43: Auxiliary electrical testing, measuring insulation resistance and absorption ratio to assess insulation moisture condition, measuring DC resistance to check the contact status of wire connection points, leads and tap changer contacts.

[0015] Preferably, when diagnosing the transformer's short-circuit withstand capability level and outputting decision recommendations by comprehensively considering the results of uninterrupted power supply detection and power outage detection, and combining them with preset evaluation standards, the preset evaluation standards used include: A11: FRA Assessment: An abnormality is identified when either a spectrum deviation greater than 3.0 or a correlation coefficient less than 0.90 occurs, and the short-circuit impedance change is greater than 2%; a warning is issued when either a spectrum deviation greater than 2.0 or a correlation coefficient less than 0.96 occurs. A12: Short-circuit impedance assessment: A change in absolute value greater than 3% is considered a significant deformation; A13: Vibration spectrum assessment: When either the main vibration frequency point shift exceeds 10% or the amplitude change exceeds 30%, it is judged as an abnormal condition; A14: Clamping force assessment: Clamping failure is determined when the remaining preload is lower than the minimum requirement; A15: Combining multi-dimensional indicator correlation analysis, output four short-circuit withstand capability levels: healthy, alert, abnormal, and severe, and provide corresponding decision recommendations such as continue operation, shorten the detection cycle, perform maintenance within a specified period, or immediately withdraw from operation.

[0016] The beneficial effects of this invention are: 1. Compared with existing technologies that mainly rely on regular maintenance and post-fault analysis and lack effective monitoring methods for the cumulative deterioration of transformer mechanical condition, this invention constructs a multi-level diagnostic system of data screening, uninterrupted power-off detection, and power-off verification. Through multi-dimensional means such as historical impact data analysis, vibration spectrum monitoring, and frequency response testing, it realizes dynamic assessment and early warning of transformer short-circuit withstand capability, significantly improving the accuracy and timeliness of condition perception. 2. Compared with existing technologies that typically use a single electrical parameter or isolated detection method, which are difficult to fully reflect the mechanical structure status, this invention adopts a comprehensive diagnostic method that combines electrical characteristics and mechanical vibration. Through cross-validation of vibration spectrum and FRA curve, and correlation analysis of short-circuit impedance and clamping force, a multi-parameter fusion evaluation model is established, which effectively avoids misjudgment and omission, and greatly improves the reliability and scientific nature of the diagnostic results. 3. Compared with existing technologies that lack systematic evaluation standards and decision support and mainly rely on experience-based judgment, this invention establishes a four-level evaluation system based on quantitative indicators. By setting thresholds and correlation analysis rules, the detection data is transformed into clear health levels and operation and maintenance decisions, realizing a leap from qualitative judgment to quantitative evaluation. This provides accurate and reliable decision-making basis for equipment maintenance and significantly improves the guarantee rate of power grid safe operation. Attached Figure Description

[0017] Figure 1 The diagram shows a flowchart of the method for detecting the impact of frequent power flow switching on the transformer's short-circuit withstand capability according to the present invention. Detailed Implementation

[0018] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0019] Please see Figure 1 This invention provides an embodiment of a method for detecting the impact of frequent power flow switching on the short-circuit withstand capability of transformers, comprising the following steps: S11: Screening high-risk target transformers based on historical transformer operation data; S12: Perform uninterrupted power-off detection on the target transformer to obtain its vibration characteristics, frequency response, and short-circuit impedance parameters; S13: Arrange a power outage maintenance window for the target transformer to conduct in-depth mechanical and physical condition testing; S14: Based on the results of the uninterrupted power supply detection and power outage detection, and combined with the preset evaluation criteria, diagnose the transformer's short-circuit withstand capability level and output decision recommendations.

[0020] As described above, this technical solution first identifies high-risk transformers based on historical operating data (such as the number of circuit breaker operations and short-circuit impact records). Then, it sequentially performs uninterrupted operation monitoring (including high-frequency transient vibration measurement, frequency response analysis, and short-circuit impedance testing) and in-depth outage monitoring (including winding mechanical inspection, clamping force measurement, and electrical testing). Finally, it integrates multi-dimensional monitoring data and diagnoses the degree of mechanical degradation and remaining short-circuit withstand capability of the transformer based on quantitative evaluation standards, generating differentiated maintenance decision recommendations. This achieves a shift from "post-event maintenance" to "proactive prediction." Through a comprehensive diagnostic chain combining electrical and mechanical characteristics, it can detect latent mechanical defects caused by accumulated electrodynamic forces at an early stage, significantly improving the ability to perceive changes in the transformer's internal state and the accuracy of risk assessment. This effectively prevents sudden short-circuit damage accidents, ensures the safe operation of the power grid, optimizes maintenance resource allocation, and improves the economic efficiency of equipment operation and maintenance.

[0021] As a preferred method, when screening high-risk target transformers based on historical transformer operating data, the specific methods include: S21: Data collection, collecting the switching operation records of the circuit breakers on each side associated with the target transformer, the short-circuit event data recorded by the fault recording device, and the operation information of the protection system; S22: Impact profile establishment, based on the collected data, quantifies and statistically analyzes the number, amplitude, and duration of short-circuit current impacts experienced by the transformer; S23: Risk level determination: Based on the impact file, calculate the risk level of the transformer by means of a preset risk assessment model or threshold comparison, and identify the high-risk transformer as the target transformer; S24: Priority ranking. All high-risk target transformers selected are ranked comprehensively based on their risk index, importance in the power grid, and years of operation, generating a list of transformers to be prioritized for testing.

[0022] As described above, the system first collects the switching frequency of circuit breakers on each side of the transformer, details of short-circuit events (such as current amplitude and duration) from fault recordings, and protection action information. Based on this data, a detailed "impact profile" is established, quantifying and statistically analyzing the history of short-circuit impacts. A weighted cumulative model is used to calculate the risk index, accurately determining the transformer's risk level. Finally, priority is ranked based on equipment importance and years of operation, generating an optimized inspection list. This achieves a shift from "experience-based judgment" to "data-driven" approaches. Through structured analysis and quantitative evaluation of multi-dimensional historical data, the accuracy of identifying high-risk transformers and early warning capabilities are significantly improved, ensuring the precise allocation of limited inspection resources and providing a scientific basis for subsequent key inspections. This fundamentally enhances the targeting and efficiency of power grid safety protection.

[0023] Preferably, data collection includes the following: Record the number of closing and opening operations of circuit breakers on each voltage level side of the transformer from the substation monitoring system and SCADA system, and count the switching frequency within a specific time period. Retrieve all fault recording files of lines and busbars associated with the transformer from the fault recording system, and extract waveform data where the transformer winding current exceeds a specified multiple of its rated current. The relay protection information management system collects protection action signals that are activated during abnormal operation of the transformer, including the activation and output action records of differential protection, overcurrent protection, and instantaneous overcurrent protection.

[0024] As described above, by acquiring circuit breaker operation frequency data from substation monitoring and SCADA systems, extracting short-circuit waveform data exceeding multiples of rated current from fault recording systems, and collecting various protection action signals from relay protection information management systems, a comprehensive data acquisition system covering electrical operation, transient processes, and protection responses has been constructed. This system overcomes the limitations of traditional single data sources. Through cross-validation and complementarity of data from multiple systems, it achieves a precise profile of the electromagnetic-mechanical shocks experienced by transformers. This not only significantly improves the completeness and accuracy of historical shock event records, providing a solid data foundation for subsequent risk assessment, but also significantly enhances the sensitivity of early hazard identification, providing crucial data support for building a predictive maintenance system.

[0025] As a preferred option, the creation of impact files includes: For each short-circuit event, the following key parameters are extracted and recorded from the fault waveform data: peak short-circuit current, duration of short-circuit current, and current decay characteristics. All short-circuit events are statistically analyzed to form a list of impact events sorted by time series. Calculate the total number of short-circuit impact events within a specific time window and the distribution of impact number in different amplitude ranges.

[0026] As stated above Preferably, when determining the risk level, a weighted cumulative assessment model based on the number and intensity of impacts is used to calculate the risk index. The calculated risk index R is compared with multiple preset threshold intervals, and the transformer is classified into multiple risk levels based on the comparison results. The principle expression is as follows: ; in, As a risk index, The weighting coefficients correspond to the i-th amplitude interval, and these weighting coefficients increase non-linearly with the increase of the current amplitude. This represents the number of impacts where the short-circuit current amplitude falls within the i-th preset interval.

[0027] As described above, this invention employs a multi-parameter quantitative analysis method based on fault waveform data when establishing the impact profile. Specifically, this includes: accurately extracting key parameters such as the peak short-circuit current (expressed in per-unit value), duration, and attenuation characteristics for each short-circuit event; constructing a complete list of impact events according to time series; and statistically analyzing the total number of impacts within a specific period and the impact distribution patterns across different current amplitude ranges. This achieves a refined quantitative description of the short-circuit impacts on the transformer. By establishing an "impact fingerprint" that includes time, intensity, and waveform characteristics, it not only accurately reflects the evolution of cumulative effects and fatigue damage but also provides a reliable quantitative basis for subsequent risk assessment. This significantly improves the objectivity and accuracy of condition evaluation and provides scientific support for the formulation of differentiated operation and maintenance strategies.

[0028] Preferably, the uninterrupted power-off state detection of the target transformer specifically includes: S31: High-frequency transient vibration measurement. Multiple vibration sensors are arranged on the surface of the transformer tank to record vibration signals during switching operations. The spectral characteristics are analyzed and compared with the fingerprint spectrum under healthy conditions to identify main frequency shift, amplitude change and low-frequency component anomalies. The sampling frequency of the vibration sensor is higher than 10kHz, and the spectrum analysis range covers 10Hz to 100kHz. S32: Frequency response analysis. When the transformer is out of service but the cover is not removed, a sweep frequency signal is injected into the beginning and end of the winding to measure the transfer function. The results of previous tests are compared laterally and the transfer function of the three-phase winding is compared longitudinally to identify the radial twist, axial twist and overall looseness of the winding. The frequency range of the sweep frequency signal is 100Hz to 10MHz. S33: Short-circuit impedance test. The short-circuit impedance of each pair of windings is measured using a low-voltage tester and compared with the factory value and the previous test value to determine the change in winding geometry.

[0029] As described above, this invention captures the mechanical vibration response during switching operations through high-frequency transient vibration measurement (sampling frequency > 10kHz, analysis bandwidth 10Hz-100kHz) to identify signs of winding loosening; it accurately diagnoses winding deformation and displacement through wideband frequency response analysis (100Hz-10MHz); and it detects changes in winding geometry through short-circuit impedance testing. This achieves multi-dimensional integrated diagnosis of electrical characteristics and mechanical vibration. Through cross-validation of vibration spectrum and electrical transfer function, it significantly improves the early detection capability of winding mechanical condition degradation. It can accurately assess the degree of short-circuit withstand capability attenuation without power interruption, providing key technical support for predictive maintenance. This effectively avoids the limitations of traditional single-detection methods and greatly improves the reliability and accuracy of the detection results.

[0030] Preferably, when performing high-frequency transient vibration measurements, the following are included: Sensor arrangement: At least one vibration sensor shall be arranged on the high-voltage side, low-voltage side and neutral point side of the outer wall of the transformer tank. Signal recording: When the transformer is closed under no-load and the on-load tap changer is operated, vibration signal recording is triggered synchronously to record the vibration signal within a 0.5s time window before and after the operation; Spectrum analysis: Perform a fast Fourier transform on the recorded vibration signal to obtain the vibration spectrum characteristics in the frequency band from 10Hz to 1000Hz; Spectrum comparison: Calculate the amplitude deviation and dominant frequency shift between the current vibration spectrum and the reference spectrum of the healthy state at characteristic frequency points.

[0031] As described above, this technical solution employs a multi-point synchronous trigger acquisition and intelligent spectrum comparison method in the high-frequency transient vibration measurement stage. Specifically, this includes: deploying a high-frequency vibration sensor array at key locations in the transformer tank (high-voltage side, low-voltage side, and neutral point side); accurately capturing transient vibration signals during no-load closing and on-load voltage regulation, recording complete time windows of 0.5 seconds before and after each operation; extracting the characteristic frequency band spectrum from 10Hz to 1000Hz through Fast Fourier Transform; and finally, quantitatively comparing the amplitude deviation and main frequency offset at characteristic frequency points with the healthy reference spectrum. This achieves non-intrusive and precise monitoring of the transformer's mechanical condition. The multi-point deployment effectively captures the vibration characteristics of different parts, and the specific operation triggering mechanism ensures the relevance and comparability of the detection. Combined with intelligent spectrum analysis, it can detect mechanical defects such as decreased winding clamping force and loose insulation components at an early stage, providing reliable mechanical condition indicators for assessing the transformer's short-circuit withstand capability and significantly improving the sensitivity and accuracy of the detection.

[0032] Preferably, frequency response analysis includes: Test wiring: With the transformer off, connect the excitation terminal and the measurement terminal of the frequency response analyzer to the two ends of the winding under test, respectively; Frequency sweep test: Perform a logarithmic frequency sweep test in the frequency range of 100Hz to 10MHz and record the voltage transfer function H(f); Curve analysis steps: Use the correlation coefficient method to calculate the similarity between the current frequency response curve and the reference curve.

[0033] As described above, with the transformer out of service, a complete test circuit is formed by connecting the frequency response analyzer and the winding under test using a professional wiring method. The voltage transfer function is accurately measured using a logarithmic sweep frequency method within a wide frequency range of 100Hz to 10MHz. Finally, the similarity between the current curve and the reference curve is quantitatively analyzed using a correlation coefficient algorithm. This achieves a precise quantitative assessment of the mechanical state of the transformer winding. The wideband testing can comprehensively capture the response characteristics of different parts of the winding, and the correlation coefficient analysis method provides an objective quantitative basis for judgment, effectively identifying mechanical defects such as winding deformation, displacement, and loosening. This provides a high-precision diagnostic method for assessing the transformer's short-circuit withstand capability, significantly improving the reliability and comparability of the test results.

[0034] Preferably, when scheduling a power outage maintenance window for the target transformer to conduct in-depth mechanical and physical condition testing, the specific procedures include: S41: Visual inspection of windings and core. Enter the transformer oil tank and check whether the windings are deformed, whether the pads are missing or displaced, whether the pressure pins are loose, whether the pressure pin displacement indicator is working, and whether the core clamps and pull strips are cracked. S42: Winding clamping force measurement. Use a dedicated hydraulic device or torque wrench to measure the remaining preload of the clamping pins and determine whether it is lower than the minimum preload requirement specified by the manufacturer. S43: Auxiliary electrical testing, measuring insulation resistance and absorption ratio to assess insulation moisture condition, measuring DC resistance to check the contact status of wire connection points, leads and tap changer contacts.

[0035] As described above, a comprehensive mechanical condition inspection of the windings, core, and fasteners is conducted through visual inspection and specialized tools. A dedicated hydraulic device is used to quantitatively measure the remaining preload of the winding clamping system. Simultaneously, electrical tests such as insulation resistance, absorption ratio, and DC resistance are performed to assess the insulation condition and the integrity of the conductive circuit. This approach achieves a deeper level of inspection, moving from indirect external detection to direct internal inspection. Through quantitative measurement of mechanical parameters and comprehensive verification of electrical characteristics, potential defects such as winding deformation, clamping force attenuation, and insulation degradation can be accurately identified. This provides direct evidence for the accurate assessment of the transformer's short-circuit withstand capability, significantly improves the reliability of diagnostic results, provides key technical support for maintenance decisions, and effectively avoids the risk of operating equipment with defects.

[0036] Preferably, when diagnosing the transformer's short-circuit withstand capability level and outputting decision recommendations by comprehensively considering the results of uninterrupted power supply detection and power outage detection, and combining them with preset evaluation standards, the preset evaluation standards used include: A11: FRA Assessment: An abnormality is identified when either a spectrum deviation greater than 3.0 or a correlation coefficient less than 0.90 occurs, and the short-circuit impedance change is greater than 2%; a warning is issued when either a spectrum deviation greater than 2.0 or a correlation coefficient less than 0.96 occurs. A12: Short-circuit impedance assessment: A change in absolute value greater than 3% is considered a significant deformation; A13: Vibration spectrum assessment: When either the main vibration frequency point shift exceeds 10% or the amplitude change exceeds 30%, it is judged as an abnormal condition; A14: Clamping force assessment: Clamping failure is determined when the remaining preload is lower than the minimum requirement; A15: Combining multi-dimensional indicator correlation analysis, output four short-circuit withstand capability levels: healthy, alert, abnormal, and severe, and provide corresponding decision recommendations such as continue operation, shorten the detection cycle, perform maintenance within a specified period, or immediately withdraw from operation.

[0037] As described above, this invention innovatively establishes a multi-parameter correlation-based quantitative diagnostic system during the comprehensive evaluation phase. Specifically, it employs five evaluation criteria: dual-indicator judgment based on spectral deviation and correlation coefficient of the FRA curve, short-circuit impedance change rate assessment, vibration spectrum characteristic analysis, and quantitative measurement of clamping force. Through multi-dimensional index correlation analysis, the transformer's short-circuit withstand capability is precisely divided into four levels: healthy, alert, abnormal, and severe, generating corresponding differentiated operation and maintenance decisions. This overcomes the limitations of traditional single-threshold judgments. By establishing a multi-parameter cross-validation mechanism, the accuracy and reliability of condition assessment are significantly improved. The graded evaluation system achieves a shift from qualitative judgment to quantitative grading, providing a precise basis for operation and maintenance decisions. The correlation analysis mechanism effectively avoids misjudgments and omissions, ensuring the scientific nature of risk assessment results. Ultimately, this forms a complete and reliable transformer short-circuit withstand capability assessment and decision support system.

[0038] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A method for detecting the impact of frequent power flow switching on the short-circuit withstand capability of transformers, characterized in that: Includes the following steps: S11: Screening high-risk target transformers based on historical transformer operation data; S12: Perform uninterrupted power-off detection on the target transformer to obtain its vibration characteristics, frequency response, and short-circuit impedance parameters; S13: Arrange a power outage maintenance window for the target transformer to conduct in-depth mechanical and physical condition testing; S14: Based on the combined results of the uninterrupted power supply detection and power outage detection, and in accordance with the preset evaluation criteria, diagnose the transformer's short-circuit withstand capability level and output decision recommendations.

2. The method for detecting the impact of frequent power flow switching on the short-circuit withstand capability of transformers according to claim 1, characterized in that: When screening high-risk target transformers based on historical transformer operating data, the specific steps include: S21: Data collection, collecting the switching operation records of the circuit breakers on each side associated with the target transformer, the short-circuit event data recorded by the fault recording device, and the operation information of the protection system; S22: Impact profile establishment, based on the collected data, quantifies and statistically analyzes the number, amplitude, and duration of short-circuit current impacts experienced by the transformer; S23: Risk level determination: Based on the impact file, calculate the risk level of the transformer by comparing it with a preset risk assessment model or threshold, and identify the high-risk transformer as the target transformer; S24: Priority ranking. All high-risk target transformers selected are ranked comprehensively based on their risk index, importance in the power grid, and years of operation, generating a list of transformers to be prioritized for testing.

3. The method for detecting the impact of frequent power flow switching on the short-circuit withstand capability of transformers according to claim 2, characterized in that: Data collection specifically includes: Record the number of closing and opening operations of circuit breakers on each voltage level side of the transformer from the substation monitoring system and SCADA system, and count the switching frequency within a specific time period. Retrieve all fault recording files of lines and busbars associated with the transformer from the fault recording system, and extract waveform data where the transformer winding current exceeds a specified multiple of its rated current. Collect protection action signals initiated during abnormal transformer operation from the relay protection information management system, including the initiation and output action records of differential protection, overcurrent protection, and instantaneous overcurrent protection.

4. The method for detecting the impact of frequent power flow switching on the transformer's short-circuit withstand capability according to claim 3, characterized in that: When creating an impact file, the following are included: For each short-circuit event, the following key parameters are extracted and recorded from the fault waveform data: peak short-circuit current, duration of short-circuit current, and current decay characteristics. All short-circuit events are statistically analyzed to form a list of impact events sorted by time series. Calculate the total number of short-circuit impact events within a specific time window and the distribution of impact number in different amplitude ranges.

5. The method for detecting the impact of frequent power flow switching on the transformer's short-circuit withstand capability according to claim 4, characterized in that: When determining the risk level, a weighted cumulative assessment model based on the number and intensity of impacts is used to calculate the risk index. The calculated risk index R is compared with multiple preset threshold intervals, and the transformer is classified into multiple risk levels based on the comparison results. The principle expression is as follows: ； in, As a risk index, The weighting coefficients correspond to the i-th amplitude interval, and these weighting coefficients increase non-linearly with the increase of the current amplitude. This represents the number of impacts where the short-circuit current amplitude falls within the i-th preset interval.

6. The method for detecting the impact of frequent power flow switching on the short-circuit withstand capability of transformers according to claim 5, characterized in that: The specific steps for detecting the target transformer without power interruption include: S31: High-frequency transient vibration measurement. Multiple vibration sensors are arranged on the surface of the transformer tank to record vibration signals during switching operations. The spectral characteristics are analyzed and compared with the fingerprint spectrum under healthy conditions to identify main frequency shift, amplitude change and low-frequency component anomalies. The sampling frequency of the vibration sensor is higher than 10kHz, and the spectrum analysis range covers 10Hz to 100kHz. S32: Frequency response analysis. When the transformer is out of service but the cover is not removed, a sweep frequency signal is injected into the beginning and end of the winding to measure the transfer function. The results of previous tests are compared laterally and the transfer function of the three-phase winding is compared longitudinally to identify the radial twist, axial twist and overall looseness of the winding. The frequency range of the sweep frequency signal is 100Hz to 10MHz. S33: Short-circuit impedance test. The short-circuit impedance of each pair of windings is measured using a low-voltage tester and compared with the factory value and the previous test value to determine the change in winding geometry.

7. The method for detecting the impact of frequent power flow switching on the short-circuit withstand capability of transformers according to claim 6, characterized in that: When performing high-frequency transient vibration measurements, the following are included: Sensor arrangement: At least one vibration sensor shall be arranged on the high-voltage side, low-voltage side and neutral point side of the outer wall of the transformer tank. Signal recording: When the transformer is closed under no-load and the on-load tap changer is operated, vibration signal recording is triggered synchronously to record the vibration signal within a 0.5s time window before and after the operation; Spectrum analysis: Perform a fast Fourier transform on the recorded vibration signal to obtain the vibration spectrum characteristics in the frequency band from 10Hz to 1000Hz; Spectrum comparison: Calculate the amplitude deviation and dominant frequency shift between the current vibration spectrum and the reference spectrum of the healthy state at characteristic frequency points.

8. The method for detecting the impact of frequent power flow switching on the short-circuit withstand capability of transformers according to claim 7, characterized in that: Frequency response analysis includes: Test wiring: With the transformer off, connect the excitation terminal and the measurement terminal of the frequency response analyzer to the two ends of the winding under test, respectively; Frequency sweep test: Perform a logarithmic frequency sweep test in the frequency range of 100Hz to 10MHz and record the voltage transfer function H(f); Curve analysis steps: Use the correlation coefficient method to calculate the similarity between the current frequency response curve and the reference curve.

9. The method for detecting the impact of frequent power flow switching on the short-circuit withstand capability of transformers according to claim 8, characterized in that: When scheduling a power outage for maintenance of the target transformer and conducting in-depth mechanical and physical condition testing, the specific procedures include: S41: Visual inspection of windings and core. Enter the transformer oil tank and check whether the windings are deformed, whether the pads are missing or displaced, whether the pressure pins are loose, whether the pressure pin displacement indicator is working, and whether the core clamps and pull strips are cracked. S42: Winding clamping force measurement. Use a dedicated hydraulic device or torque wrench to measure the remaining preload of the clamping pins and determine whether it is lower than the minimum preload requirement specified by the manufacturer. S43: Auxiliary electrical testing, measuring insulation resistance and absorption ratio to assess insulation moisture condition, measuring DC resistance to check the contact status of wire connection points, leads and tap changer contacts.

10. The method for detecting the impact of frequent power flow switching on the short-circuit withstand capability of transformers according to claim 9, characterized in that: When diagnosing the transformer's short-circuit withstand capability level and outputting decision recommendations based on the combined results of uninterrupted power supply testing and power outage testing, and in accordance with preset evaluation standards, the preset evaluation standards used include: A11: FRA Assessment: An abnormality is identified when either a spectrum deviation greater than 3.0 or a correlation coefficient less than 0.90 occurs, and the short-circuit impedance change is greater than 2%; a warning is issued when either a spectrum deviation greater than 2.0 or a correlation coefficient less than 0.96 occurs. A12: Short-circuit impedance assessment: A change in absolute value greater than 3% is considered a significant deformation; A13: Vibration spectrum assessment: When either the main vibration frequency point shift exceeds 10% or the amplitude change exceeds 30%, it is judged as an abnormal condition; A14: Clamping force assessment: Clamping failure is determined when the remaining preload is lower than the minimum requirement; A15: Combining multi-dimensional indicator correlation analysis, output four short-circuit withstand capability levels: healthy, alert, abnormal, and severe, and provide corresponding decision recommendations such as continue operation, shorten the detection cycle, perform maintenance within a specified period, or immediately withdraw from operation.

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