Method and system for detecting performance of transformer

Abstract

This application provides a method and system for transformer performance testing, belonging to the field of transformer testing technology. The method includes: acquiring electrical parameters, winding temperature, and mechanical parameters of the transformer within a first time period based on a first data acquisition frequency; the electrical parameters including voltage data and load data; extracting voltage fluctuation rate based on the voltage data; determining a first voltage fluctuation rate threshold based on the load data and a voltage fluctuation rate threshold lookup table; if the voltage fluctuation rate is greater than or equal to the first voltage fluctuation rate threshold, acquiring the electrical parameters, winding temperature, and mechanical parameters of the transformer within a second time period based on a second data acquisition frequency, and acquiring the partial discharge quantity of the transformer within the second time period; determining the transformer's performance indicators based on the electrical parameters, winding temperature, and mechanical parameters within the first time period, and the electrical parameters, winding temperature, mechanical parameters, and partial discharge quantity within the second time period. This application can improve the efficiency and accuracy of transformer performance testing.

Description

Technical Field

[0001] This application belongs to the field of transformer testing technology, and more specifically, relates to transformer performance testing methods and systems. Background Technology

[0002] Transformers are core equipment in power systems that realize power transmission and voltage transformation, and their performance stability is directly related to the safe operation of the power grid. Transformer performance testing typically includes the analysis and evaluation of indicators such as electrical performance and insulation performance, and then comprehensively assesses its operating status.

[0003] With the dynamic fluctuations in power load and the expansion of the power grid, transformers face increasingly complex operating environments and a greater risk of performance degradation, placing higher demands on the timeliness and accuracy of testing. Current technologies for transformer performance testing often employ fixed testing cycles and fixed data acquisition strategies, collecting preset parameters before performance evaluation.

[0004] However, such fixed strategies have significant drawbacks: on the one hand, when the transformer is in a stable operating state, high-frequency and long-span acquisition will generate a large amount of redundant data, resulting in low detection efficiency and increased data storage and processing costs; on the other hand, when the transformer encounters abnormal operating conditions, fixed frequencies cannot capture key performance changes, resulting in insufficient detection accuracy.

[0005] Therefore, how to balance the efficiency and accuracy of transformer performance testing, and avoid resource waste and data loss caused by blind data collection, has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0006] The purpose of this application is to provide a method and system for testing transformer performance, so as to improve the efficiency and accuracy of transformer performance testing.

[0007] A first aspect of this application provides a method for testing transformer performance, comprising:

[0008] The electrical parameters, winding temperature, and mechanical parameters of the transformer within a first time period are acquired based on the first data acquisition frequency. The electrical parameters include voltage data and load data.

[0009] Voltage fluctuation rate is extracted based on voltage data, and a first voltage fluctuation rate threshold is determined based on load data and a voltage fluctuation rate threshold lookup table; the voltage fluctuation rate threshold lookup table is constructed based on the transformer's historical load data and historical voltage data.

[0010] If the voltage fluctuation rate is greater than or equal to the first voltage fluctuation rate threshold, the electrical parameters, winding temperature and mechanical parameters of the transformer in the second time period are obtained based on the second data acquisition frequency, and the partial discharge of the transformer in the second time period is also obtained; the time span of the second time period is greater than the time span of the first time period.

[0011] The transformer's performance indicators are determined based on the transformer's electrical parameters, winding temperature, and mechanical parameters during the first time period, as well as the transformer's electrical parameters, winding temperature, mechanical parameters, and partial discharge quantity during the second time period.

[0012] A second aspect of this application provides a transformer performance testing system, comprising:

[0013] The first data acquisition module is used to acquire the electrical parameters, winding temperature and mechanical parameters of the transformer within a first time period based on the first data acquisition frequency. The electrical parameters include voltage data and load data.

[0014] The threshold determination module is used to extract voltage fluctuation rate based on voltage data and determine the first voltage fluctuation rate threshold based on load data and a voltage fluctuation rate threshold lookup table; the voltage fluctuation rate threshold lookup table is constructed based on the transformer's historical load data and historical voltage data.

[0015] The second data acquisition module is used to acquire the electrical parameters, winding temperature, and mechanical parameters of the transformer within a second time period based on the second data acquisition frequency if the voltage fluctuation rate is greater than or equal to the first voltage fluctuation rate threshold, and to acquire the partial discharge quantity of the transformer within the second time period; the time span of the second time period is greater than the time span of the first time period.

[0016] The performance testing module is used to determine the transformer's performance indicators based on the transformer's electrical parameters, winding temperature, and mechanical parameters during a first time period, and the transformer's electrical parameters, winding temperature, mechanical parameters, and partial discharge quantity during a second time period.

[0017] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the transformer performance detection method described above.

[0018] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the transformer performance detection method described above.

[0019] The beneficial effects of the transformer performance testing method and system provided in this application are as follows:

[0020] This application's embodiment avoids redundant data caused by fixed high-frequency, long-span acquisition through an initial acquisition and judgment mechanism in the first time period. This embodiment only initiates enhanced acquisition in the second time period when the voltage fluctuation rate exceeds the limit, reducing the amount of invalid data under stable operating conditions, lowering data storage, transmission, and processing costs, and improving the utilization efficiency of detection resources.

[0021] This application's embodiments utilize a voltage fluctuation rate threshold lookup table built based on historical load and voltage data. This allows the first voltage fluctuation rate threshold to adapt to different load conditions, avoiding misjudgments of complex operating states due to fixed thresholds and ensuring accurate identification of abnormal operating conditions. Simultaneously, the second time period employs higher frequency and longer data collection spans, supplemented with key parameters such as partial discharge, providing sufficient abnormal state data for performance evaluation and ensuring the accuracy of performance judgment.

[0022] In summary, the embodiments of this application dynamically match the transformer operating status and data acquisition requirements through a phased strategy of initial judgment and on-demand reinforcement. This avoids resource waste under stable operating conditions and ensures detection depth under abnormal operating conditions, effectively solving the problem of difficulty in balancing efficiency and accuracy in existing fixed acquisition strategies. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 A schematic flowchart of a transformer performance testing method provided in an embodiment of this application;

[0025] Figure 2 This is a structural block diagram of a transformer performance testing system provided in an embodiment of this application;

[0026] Figure 3 This is a schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0027] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0028] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0029] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a transformer performance testing method provided in an embodiment of this application. The method can be executed by an electronic device, and specifically, the method may include S101 to S103.

[0030] S101: Based on the first data acquisition frequency, acquire the electrical parameters, winding temperature and mechanical parameters of the transformer within the first time period. The electrical parameters include voltage data and load data.

[0031] In this embodiment, the first data acquisition frequency is the number of times data is collected per unit time, used to control the density of data collection. The first time period is the duration of the initial data collection, used to acquire basic operating data. Electrical parameters are parameters reflecting the electrical characteristics of the transformer, and may include current data and power data in addition to voltage and load data. Winding temperature is the temperature value of the transformer windings, used to characterize the thermal state of the windings. Mechanical parameters are parameters reflecting the mechanical structural characteristics of the transformer, and may include, for example, core vibration acceleration and dominant vibration frequency. Voltage data is the voltage value during transformer operation, and load data is a parameter reflecting the magnitude of the load carried by the transformer.

[0032] This embodiment continuously collects the transformer's electrical parameters, winding temperature, and mechanical parameters within a first time period according to a set first data acquisition frequency, forming a dataset of the initial operating state. The considerations behind this embodiment are: controlling the acquisition rhythm through the first data acquisition frequency avoids data redundancy caused by high-frequency acquisition; setting the first time period ensures the acquisition of complete basic operating characteristics, providing a benchmark for subsequent testing; and simultaneously acquiring multiple types of parameters comprehensively reflects the transformer's initial state, covering not only electrical characteristics but also thermal state and mechanical stability information, laying a data foundation for subsequent performance evaluation.

[0033] For example, the specific implementation process of data acquisition within the first time period may include:

[0034] (1) Determine the first data acquisition frequency and the first time period. Specifically, this embodiment can be set in combination with the transformer model and operating scenario. For example, for a 10kV distribution transformer, the first data acquisition frequency is determined to be once every 1 minute based on historical operating data, and the first time period is 0.5 hours.

[0035] (2) Data Acquisition and Storage. Specifically, in this embodiment, voltage data can be acquired by voltage transformers installed on the high-voltage and low-voltage sides of the transformer, load-related current data can be acquired by current transformers, winding temperature can be acquired by fiber optic temperature sensors deployed at the winding ends, and mechanical vibration parameters can be acquired by accelerometers installed on the core casing. All sensors are connected to an edge data acquisition terminal, which can accept the first data acquisition frequency and first time period parameters pre-configured in this embodiment.

[0036] The edge data acquisition terminal can start data acquisition at the first data acquisition frequency, and record voltage data, load data in the form of current, winding temperature value and vibration acceleration value every minute. During the acquisition process, the time consistency of various parameters is ensured by time stamp synchronization. The acquired data is stored in real time to the terminal's local hard drive. After 0.5 hours, the terminal automatically stops acquisition, completing the data acquisition within the first time period.

[0037] S102: Extract voltage fluctuation rate based on voltage data, and determine the first voltage fluctuation rate threshold based on load data and a voltage fluctuation rate threshold lookup table; the voltage fluctuation rate threshold lookup table is constructed based on the transformer's historical load data and historical voltage data.

[0038] In this embodiment, the voltage fluctuation rate threshold lookup table is constructed based on the transformer's historical load data and historical voltage data, and is obtained in the following way:

[0039] Historical load rate is extracted from historical load data, and historical voltage fluctuation rate is extracted from historical voltage data.

[0040] By aligning historical load rates and historical voltage fluctuation rates over time, multiple pairs of historical data samples are obtained.

[0041] Clustering multiple historical data sample pairs yields multiple subsets of historical data sample pairs;

[0042] For each subset of historical data sample pairs, determine the load rate range and the voltage fluctuation rate threshold corresponding to the load rate range based on the subset of historical data sample pairs.

[0043] A voltage fluctuation threshold lookup table is constructed based on all load rate ranges and the corresponding voltage fluctuation thresholds for each load rate range.

[0044] In this embodiment, the historical load rate is the ratio of the actual load to the rated load calculated based on historical load data, used to characterize the historical load level. The historical voltage fluctuation rate is the degree of voltage fluctuation extracted from historical voltage data, used to reflect the historical voltage stability. Time alignment matches the historical load rate and historical voltage fluctuation rate on the same time dimension, ensuring data correspondence. A historical data sample pair is a set of historical load rates and historical voltage fluctuation rates after time alignment. Clustering is the process of grouping similar historical data sample pairs together. A subset of historical data sample pairs is each sample pair obtained after clustering. The load rate range is the range of historical load rate values ​​within the subset of historical data sample pairs. The voltage fluctuation rate threshold is the upper limit of the voltage fluctuation rate within the corresponding load rate range.

[0045] In this embodiment, historical load rate and historical voltage fluctuation rate are extracted from historical load data and historical voltage data, respectively. These are then time-aligned to form sample pairs, and clustering is used to group these pairs. For each group, a load rate range and corresponding voltage fluctuation rate threshold are determined, ultimately integrating them into a voltage fluctuation rate threshold lookup table. The underlying consideration for this embodiment is that the normal range of transformer voltage fluctuation rate varies with load rate, and fixed thresholds are difficult to adapt to different load conditions. Clustering based on historical data can uncover the inherent correlation between load rate and voltage fluctuation rate, ensuring that the threshold for each load rate range aligns with the actual fluctuation pattern of that range. This embodiment ensures the spatiotemporal consistency of sample pairs through time alignment, guarantees the representativeness of thresholds under similar load characteristics through clustering, and the final lookup table provides an accurate judgment benchmark for different load states, improving the accuracy of subsequent detection.

[0046] For example, the process of constructing a voltage fluctuation rate threshold lookup table may include:

[0047] (1) Extract historical load rate and historical voltage fluctuation rate. Specifically, in this embodiment, the historical load data of the transformer for the past three months can be divided into time periods according to 24 hours per day. The ratio of the average load to the rated load for each time period can be calculated as the historical load rate. From the historical voltage data of the same period, the percentage of the difference between the maximum and minimum voltage values ​​in each time period to the rated voltage can be calculated as the historical voltage fluctuation rate for the corresponding time period.

[0048] (2) Time alignment of historical load rate and historical voltage fluctuation rate. Specifically, in this embodiment, the historical load rate and historical voltage fluctuation rate within the same hour can be matched one by one to form multiple historical data sample pairs, ensuring that each sample pair corresponds to the same time interval and eliminating invalid data with mismatched time.

[0049] (3) Clustering multiple historical data sample pairs. Specifically, this embodiment can use density clustering algorithm, with historical load rate and historical voltage fluctuation rate as features, to group similar sample pairs into a group, for example, clustering into three subsets of historical data sample pairs: light load, medium load and heavy load, with each subset containing multiple sample pairs under that load type.

[0050] (4) Determine the load rate range and voltage fluctuation threshold for each historical data sample subset. Specifically, for each subset, this embodiment can count the minimum and maximum historical load rates of the subset to determine the load rate range; this embodiment can calculate the 90th percentile of the historical voltage fluctuation rate within the subset and use it as the voltage fluctuation threshold corresponding to the load rate range to ensure that 90% of normal fluctuations do not exceed this value.

[0051] (5) Construct a voltage fluctuation rate threshold lookup table. Specifically, sort all load rate ranges and their corresponding voltage fluctuation rate thresholds in ascending order of load rate to form a structured table. For example, the voltage fluctuation rate threshold is 2.5% for the light load range of 0-30%, 1.8% for the medium load range of 30%-70%, and 1.2% for the heavy load range of 70%-100%. This completes the construction of the voltage fluctuation rate lookup table.

[0052] S103: If the voltage fluctuation rate is greater than or equal to the first voltage fluctuation rate threshold, the electrical parameters, winding temperature and mechanical parameters of the transformer in the second time period are obtained based on the second data acquisition frequency, and the partial discharge of the transformer in the second time period is obtained; the time span of the second time period is greater than the time span of the first time period.

[0053] In this embodiment, the second data acquisition frequency is the data acquisition frequency used when the voltage fluctuation rate exceeds the standard, which is used to improve the data acquisition density. The second time period is the duration of data acquisition after the voltage fluctuation rate exceeds the standard, which is used to acquire complete abnormal state data. Partial discharge quantity is the amount of charge of partial discharge inside or on the surface of the transformer insulation material, used to characterize the insulation performance state, and may include, for example, the discharge quantity value detected by an ultra-high frequency sensor.

[0054] In this embodiment, when the voltage fluctuation rate is greater than or equal to the first voltage fluctuation rate threshold, electrical parameters, winding temperature, and mechanical parameters can be collected at a second data acquisition frequency within a second time period, and partial discharge quantity can be acquired simultaneously. The considerations behind this embodiment are: excessive voltage fluctuation rate indicates that the transformer is in an abnormal state, requiring higher frequency and longer acquisition time to capture detailed changes; considering that voltage fluctuations can exacerbate insulation degradation, this embodiment supplements the partial discharge quantity to assess insulation performance; the second time period spans longer than the first time period, covering the development process of the abnormal state, providing comprehensive data for performance evaluation, and balancing detection accuracy with timely anomaly response.

[0055] For example, the data acquisition process after voltage fluctuation exceeds the limit may include: In this embodiment, the voltage fluctuation can be calculated based on the voltage data within a first time period, and compared with a first voltage fluctuation threshold. If the former is greater than or equal to the latter, the second stage of data acquisition is triggered. In this embodiment, parameters can be set according to the transformer type. For example, for a 35kV transformer, the second data acquisition frequency is set to once every 30 seconds, and the second time period is set to 2 hours to ensure that it is longer than the first time period. In this embodiment, electrical parameters such as voltage and load, and mechanical parameters such as winding temperature and core vibration can be acquired every 30 seconds. At the same time, the discharge signal is recorded by a partial discharge sensor installed on the transformer tank wall, and the discharge signal is converted into a partial discharge quantity value. All data is stored with timestamps. After 2 hours, the acquisition stops, completing the data acquisition for the second time period.

[0056] S104: Determine the transformer's performance indicators based on the transformer's electrical parameters, winding temperature, and mechanical parameters during the first time period, and the transformer's electrical parameters, winding temperature, mechanical parameters, and partial discharge quantity during the second time period.

[0057] In this embodiment, after extracting voltage fluctuation rate based on voltage data, the transformer performance testing method further includes:

[0058] If the voltage fluctuation rate is less than the first voltage fluctuation rate threshold, the electrical parameters, winding temperature and mechanical parameters of the transformer in the third time period are obtained based on the first data acquisition frequency; the time span of the third time period is less than the time span of the second time period, but greater than the time span of the first time period.

[0059] The transformer's performance indicators are determined based on its electrical parameters, winding temperature, and mechanical parameters during the third time period.

[0060] In this embodiment, the third time period is the data acquisition duration when the voltage fluctuation rate is less than the first voltage fluctuation rate threshold, which is used to obtain sufficient stable state data, and its time span is between the first time period and the second time period.

[0061] In this embodiment, when the voltage fluctuation rate is less than the first voltage fluctuation rate threshold, electrical parameters, winding temperature, and mechanical parameters can be collected within a third time period at the first data acquisition frequency. Based on this data, the transformer's performance indicators are then determined. The rationale behind this embodiment is that a voltage fluctuation rate within the acceptable range indicates that the transformer is in a relatively stable state, but short-term data from the first time period is insufficient for a comprehensive performance evaluation. The third time period has a longer span than the first time period, covering stable operating characteristics over a longer period and capturing potential slow changes; simultaneously, its shorter span than the second time period avoids unnecessary long-term data collection under stable conditions, reducing data redundancy. This embodiment maintains consistency in data collection by continuing the first data acquisition frequency, ensuring a balance between accuracy and efficiency in performance evaluation.

[0062] For example, the performance testing process when the voltage fluctuation rate does not exceed the limit may include:

[0063] (1) Determine whether the voltage fluctuation rate is less than the first voltage fluctuation rate threshold. Specifically, in this embodiment, the voltage fluctuation rate calculated in the first time period can be compared with the first voltage fluctuation rate threshold determined by load data and voltage fluctuation rate threshold lookup table. If the former is less than the latter, data collection in the third time period is started.

[0064] (2) Determine the parameters for the third time period and acquire data. Specifically, this embodiment can be set according to the operating characteristics of the transformer. For example, for a 110kV transformer, the first time period is 1 hour, the second time period is 4 hours, and the third time period is set to 2.5 hours, ensuring that its span is between the two, while maintaining the first data acquisition frequency of once per minute. In this embodiment, the acquisition of relevant data is started according to the first data acquisition frequency. Every minute, electrical parameters such as voltage and load, winding temperature value, and mechanical parameters such as core vibration acceleration and vibration frequency are recorded synchronously. The data is stored in the terminal database in real time. After the acquisition continues for 2.5 hours, the terminal automatically stops the acquisition and integrates all the data in the third time period.

[0065] (3) Performance index evaluation. Specifically, this embodiment evaluates the electrical stability, thermal stability and mechanical stability of the transformer by performing trend analysis (such as the trend of winding temperature change and the stability of vibration parameters) and threshold comparison (such as the deviation of each parameter from the rated value) on all data collected in the third time period, and comprehensively determines the performance index of the transformer.

[0066] As can be seen from the above, the embodiments of this application, through the initial acquisition and judgment mechanism in the first time period, avoid redundant data caused by fixed high-frequency, long-span acquisition. The embodiments of this application only initiate enhanced acquisition in the second time period when the voltage fluctuation rate exceeds the standard, reducing the amount of invalid data under stable operating conditions, lowering data storage, transmission, and processing costs, and improving the utilization efficiency of detection resources.

[0067] This application's embodiments utilize a voltage fluctuation rate threshold lookup table built based on historical load and voltage data. This allows the first voltage fluctuation rate threshold to adapt to different load conditions, avoiding misjudgments of complex operating states due to fixed thresholds and ensuring accurate identification of abnormal operating conditions. Simultaneously, the second time period employs higher frequency and longer data collection spans, supplemented with key parameters such as partial discharge, providing sufficient abnormal state data for performance evaluation and ensuring the accuracy of performance judgment.

[0068] In summary, the embodiments of this application dynamically match the transformer operating status and data acquisition requirements through a phased strategy of initial judgment and on-demand reinforcement. This avoids resource waste under stable operating conditions and ensures detection depth under abnormal operating conditions, effectively solving the problem of difficulty in balancing efficiency and accuracy in existing fixed acquisition strategies.

[0069] In one embodiment of this application, the first data acquisition frequency is determined as follows: an initial data acquisition frequency is determined based on the rated capacity and insulation structure type of the transformer; a first adjustment step size is determined based on the service life of the transformer, load rate, ambient temperature, and number of historical fault repairs; the service life, load rate, and number of historical fault repairs are all positively correlated with the first adjustment step size, and the difference between the ambient temperature and a preset temperature threshold is positively correlated with the first adjustment step size; the initial data acquisition frequency is adjusted based on the first adjustment step size to obtain the first data acquisition frequency.

[0070] In this embodiment, the initial data acquisition frequency is a base acquisition frequency determined based on the inherent properties of the transformer, serving as a benchmark for data acquisition. The first adjustment step size is used to correct the amplitude value of the initial data acquisition frequency, reflecting the degree of influence of the operating state on the acquisition frequency. The preset temperature threshold is a set ambient temperature benchmark value used to determine the degree of deviation from the ambient temperature. The historical fault repair count is the number of times the transformer has experienced a fault and completed repairs in the past, used to characterize equipment aging and fault risk.

[0071] The considerations behind this embodiment are: rated capacity and insulation structure determine the basic data requirements of transformers; different types of equipment require different initial frequencies; equipment with long service life, high load rate, large deviation of ambient temperature and many fault repairs has a higher risk of performance fluctuations, and the acquisition frequency needs to be increased by adjusting the step size in a positive correlation to ensure that the data can reflect the status changes in a timely manner, thus balancing acquisition efficiency and detection accuracy.

[0072] Examples of detailed technical implementation steps in practical applications

[0073] For example, the process of determining the first data acquisition frequency may include:

[0074] This embodiment can set the reference data acquisition frequency according to the rated capacity of the transformer. For example, the initial frequency for transformers with a rated capacity of less than 1000kVA is once every 3 minutes, the frequency for transformers with a rated capacity between 1000-5000kVA is once every 2 minutes, and the frequency for transformers with a rated capacity of more than 5000kVA is once every 1 minute. This embodiment can further adjust the reference data acquisition frequency in combination with the insulation structure type to obtain the initial data acquisition frequency. For example, for dry-type transformers, the frequency can be increased by 20% on the reference frequency, while for oil-immersed transformers, the reference data acquisition frequency is kept at the initial data acquisition frequency.

[0075] This embodiment can set the basic weights for various factors such as the service life of the transformer, load rate, ambient temperature, and number of historical fault repairs. For example, the step size increases by 0.2 minutes / time for every year the service life increases, by 0.5 minutes / time for every 10% increase in load rate above the rated value, by 0.2 minutes / time for every 5°C increase in ambient temperature above the preset threshold, and by 1 minute / time for every 2 increases in the number of historical fault repairs. The frequency adjustment data corresponding to each of the above factors are added together to obtain the first adjustment step size.

[0076] In this embodiment, the first data acquisition frequency can be obtained by subtracting the value corresponding to the first adjustment step size from the initial data acquisition frequency. To avoid the adjusted frequency being too high or too low, this embodiment can set a minimum time interval and a maximum time interval. If the calculation result exceeds the boundary, the nearest boundary value is taken as the final time interval.

[0077] This embodiment sets the initial data acquisition frequency based on the inherent properties of the transformer to ensure that the basic acquisition is compatible with the characteristics of the equipment. This embodiment also dynamically adjusts the step size in combination with the overall operating status of the transformer, so that the frequency changes with the risk of performance fluctuations. When the risk is high, the frequency is increased to accurately capture the status changes, thus balancing detection efficiency and accuracy and improving the relevance and effectiveness of data acquisition.

[0078] In one embodiment of this application, the first time period is determined by: acquiring historical voltage data of the transformer, determining multiple voltage fluctuation data subsets based on the historical voltage data, calculating the voltage fluctuation rate of each voltage fluctuation data subset; selecting the voltage fluctuation data subset with a voltage fluctuation rate greater than or equal to a second voltage fluctuation rate threshold as the target voltage fluctuation data subset; and determining the first time period based on the duration of each voltage fluctuation data subset in the target voltage fluctuation data subset.

[0079] In this embodiment, the first time period is determined based on the duration of each voltage fluctuation data subset within the target voltage fluctuation data subset, specifically including:

[0080] The mean duration and standard deviation of duration are calculated based on the duration of each voltage fluctuation data subset in the target voltage fluctuation data subset.

[0081] Determine the confidence interval for duration based on the mean and standard deviation of duration;

[0082] The subset of voltage fluctuation data in the target voltage fluctuation data subset whose duration falls within the duration confidence interval is taken as the target analysis subset;

[0083] Duration sequences are extracted based on a subset of the target analysis; the duration sequences include multiple durations arranged in ascending order.

[0084] Extract the first quantile of the duration sequence and use the first quantile as the first time period.

[0085] In this embodiment, the mean duration is the average of all durations in the target voltage fluctuation data subset, reflecting the typical duration level. The standard deviation of duration is an indicator reflecting the dispersion of duration in the target subset, characterizing the range of data fluctuations. The confidence interval of duration is a reliable range determined based on the mean and standard deviation, used to screen representative data; for example, a range of mean ± 2 times the standard deviation can be used. The target analysis subset is the voltage fluctuation data subset whose duration falls within the confidence interval, used for subsequent analysis. The duration sequence is an ordered sequence formed by arranging the durations of the target analysis subset in ascending order. The first quantile is the value at a specific proportion position in the duration sequence, such as the 90th quantile, used to determine the first time period.

[0086] The considerations behind this embodiment are as follows: by constructing confidence intervals using the mean and standard deviation, extreme durations can be eliminated, retaining a representative subset of the target analysis; by sorting the durations and taking the first quantile, the first time period can be ensured to cover the complete process of most effective fluctuations, avoiding both insufficient data due to excessively short durations and redundancy due to excessively long durations, making the first time period more closely match the actual fluctuation characteristics.

[0087] For example, the process of determining the first time period based on its duration may include:

[0088] (1) Calculate the mean and standard deviation of the duration. Specifically, in this embodiment, all durations can be extracted from the target voltage fluctuation data subset. For example, 10 data points are obtained, which are 15, 20, 22, 25, 28, 30, 32, 35, 40, and 50 minutes respectively. The mean is 29.7 minutes and the standard deviation is 9.8 minutes.

[0089] (2) Determine the confidence interval for duration. Specifically, in this embodiment, the range of mean ± 2 standard deviations, i.e. 29.7 ± 19.6 minutes, can be used to obtain a confidence interval of 10.1 minutes to 49.3 minutes. All subsets of duration within this interval are selected to form the target analysis subset (all 10 data points above are met, so the target analysis subset is the original target subset).

[0090] (3) Extract the duration sequence and determine the first quantile. Specifically, in this embodiment, the duration of the target analysis subset can be arranged in ascending order to obtain the sequence 15, 20, 22, 25, 28, 30, 32, 35, 40, 50 minutes; if the first quantile is the 90th quantile, calculate the value of the 9th position (10 × 90% = 9), which is 40 minutes, and take it as the first time period.

[0091] This embodiment filters representative data through confidence intervals to eliminate interference from extreme values; this embodiment determines the first time period based on the first quantile to ensure coverage of the vast majority of valid fluctuations, thus ensuring data integrity, reducing time redundancy, and improving the rationality and detection efficiency of the first time period.

[0092] In one embodiment of this application, the electrical parameters include voltage data, current data, active power, and power factor; the mechanical parameters include core vibration acceleration and vibration frequency; and the transformer performance indicators include thermal stability sub-indicators, electrical stability sub-indicators, mechanical stability sub-indicators, and insulation performance sub-indicators.

[0093] The transformer's performance indicators are determined based on its electrical parameters, winding temperature, and mechanical parameters during the first time period, and its electrical parameters, winding temperature, mechanical parameters, and partial discharge level during the second time period. These indicators include:

[0094] The thermal stability sub-indices of the transformer are determined based on the winding temperatures of the transformer during the first and second time periods.

[0095] The electrical stability sub-indicators of the transformer are determined based on the voltage data, current data, active power and power factor of the transformer during the first and second time periods.

[0096] The mechanical stability sub-indices of the transformer are determined based on the core vibration acceleration and dominant vibration frequency of the transformer during the first and second time periods.

[0097] The insulation performance sub-indicators of the transformer are determined based on the partial discharge quantity of the transformer during the second time period.

[0098] In this embodiment, the thermal stability sub-index characterizes the transformer winding's tolerance to temperature changes and its heat dissipation capacity, reflecting thermal stability. Examples include parameters such as the duration of temperature exceeding the threshold and the rate of temperature rise. The electrical stability sub-index reflects the stability of the transformer's electrical parameters, including parameters such as voltage fluctuation rate, current fluctuation amplitude, and power factor deviation. The mechanical stability sub-index characterizes the stability of the transformer's mechanical structure, including parameters such as the number of vibration acceleration exceedances and the dominant frequency offset. The insulation performance sub-index evaluates the performance of the transformer's insulation materials, including parameters such as the partial discharge exceedance multiple and the discharge frequency.

[0099] The considerations behind this embodiment are as follows: winding temperature is directly related to the risk of thermal degradation, thus serving as a core parameter for thermal stability; electrical parameters reflect the stable state of power conversion and are key evidence of electrical stability; mechanical parameters are closely related to structural integrity, supporting mechanical stability assessment; and partial discharge is a direct characterization of insulation degradation, used to judge insulation performance. This embodiment evaluates parameters according to different dimensions, which can specifically capture the state of each performance dimension, avoid evaluation bias caused by parameter confusion, and achieve comprehensive and accurate performance characterization.

[0100] For example, the process of determining transformer performance indicators may include:

[0101] (1) Determine the thermal stability sub-indicators. Specifically, in this embodiment, the winding temperature data of the first time period and the second time period can be extracted, and the ratio of the highest temperature to the rated highest temperature and the duration of the temperature exceeding 80% of the rated value can be calculated. If the ratio of the highest temperature is less than 0.9 and the duration of the temperature exceedance is less than 30 minutes, the thermal stability sub-indicator is judged to be excellent; otherwise, it is judged to be qualified or need attention based on the degree of exceedance.

[0102] (2) Determine the electrical stability sub-indicators. Specifically, in this embodiment, the voltage fluctuation rate, current fluctuation amplitude, active power deviation rate, and power factor difference from the standard value (0.9) can be calculated for the first time period and the second time period. If all indicators are within the preset normal range (e.g., voltage fluctuation rate is less than 2%), the electrical stability sub-indicators are judged to be excellent; if one indicator slightly exceeds the standard, it is considered qualified; if multiple indicators exceed the standard, it is considered to require attention.

[0103] (3) Determine the mechanical stability sub-indicators. Specifically, in this embodiment, the deviation of the core vibration acceleration from the reference value and the offset of the vibration main frequency from the standard main frequency during the first and second time periods can be analyzed. If the deviation is less than 10% and the offset is less than 5Hz, the mechanical stability sub-indicator is judged to be excellent; if the deviation or offset exceeds the standard, it is judged to be qualified or need attention according to the degree.

[0104] (4) Determine the insulation performance sub-indicators. Specifically, in this embodiment, the partial discharge amount in the second time period is compared with the safety threshold. If the discharge amount is less than 80% of the threshold, the insulation performance sub-indicator is judged to be excellent; if it exceeds the threshold, it is judged to be qualified or require maintenance according to the excess multiple. The results of the four sub-indicators are combined to form the overall performance index of the transformer.

[0105] This embodiment determines sub-indicators by corresponding parameters across different dimensions, which can specifically capture the status of each performance dimension, avoid deviations caused by parameter confusion, improve the accuracy and comprehensiveness of performance evaluation, and provide a reliable basis for transformer condition-based maintenance.

[0106] Corresponding to the transformer performance testing method in the above embodiment, Figure 2 This is a structural block diagram of a transformer performance testing system provided according to an embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown. References Figure 2 The transformer performance testing system 20 includes: a first data acquisition module 21, a threshold determination module 22, a second data acquisition module 23, and a performance testing module 24.

[0107] The first data acquisition module 21 is used to acquire the electrical parameters, winding temperature and mechanical parameters of the transformer within a first time period based on the first data acquisition frequency. The electrical parameters include voltage data and load data.

[0108] The threshold determination module 22 is used to extract voltage fluctuation rate based on voltage data and determine the first voltage fluctuation rate threshold based on load data and a voltage fluctuation rate threshold lookup table; the voltage fluctuation rate threshold lookup table is constructed based on the transformer's historical load data and historical voltage data;

[0109] The second data acquisition module 23 is used to acquire the electrical parameters, winding temperature and mechanical parameters of the transformer in the second time period based on the second data acquisition frequency if the voltage fluctuation rate is greater than or equal to the first voltage fluctuation rate threshold, and to acquire the partial discharge amount of the transformer in the second time period; the time span of the second time period is greater than the time span of the first time period.

[0110] The performance testing module 24 is used to determine the performance indicators of the transformer based on the electrical parameters, winding temperature and mechanical parameters of the transformer in the first time period, and the electrical parameters, winding temperature, mechanical parameters and partial discharge quantity of the transformer in the second time period.

[0111] In one embodiment of this application, the transformer performance testing system 20 further includes: a third data acquisition module, configured to: if the voltage fluctuation rate is less than a first voltage fluctuation rate threshold, acquire the electrical parameters, winding temperature, and mechanical parameters of the transformer within a third time period based on a first data acquisition frequency; the time span of the third time period is less than the time span of the second time period and greater than the time span of the first time period; and determine the performance indicators of the transformer based on the electrical parameters, winding temperature, and mechanical parameters of the transformer within the third time period.

[0112] In one embodiment of this application, the first data acquisition module 21 is specifically used for:

[0113] The initial data acquisition frequency is determined based on the transformer's rated capacity and insulation structure type;

[0114] The first adjustment step size is determined based on the transformer's service life, load rate, ambient temperature, and number of historical fault repairs. The service life, load rate, and number of historical fault repairs are all positively correlated with the first adjustment step size, and the difference between the ambient temperature and the preset temperature threshold is also positively correlated with the first adjustment step size.

[0115] The initial data acquisition frequency is adjusted based on the first adjustment step size to obtain the first data acquisition frequency.

[0116] In one embodiment of this application, the first data acquisition module 21 is further configured to:

[0117] Obtain historical voltage data of the transformer, determine multiple voltage fluctuation data subsets based on the historical voltage data, and calculate the voltage fluctuation rate of each voltage fluctuation data subset;

[0118] The subset of voltage fluctuation data with voltage fluctuation rates greater than or equal to the second voltage fluctuation rate threshold is taken as the target voltage fluctuation data subset.

[0119] The first time period is determined based on the duration of each voltage fluctuation data subset in the target voltage fluctuation data subset.

[0120] In one embodiment of this application, the first data acquisition module 21 is further configured to:

[0121] The mean duration and standard deviation of duration are calculated based on the duration of each voltage fluctuation data subset in the target voltage fluctuation data subset.

[0122] Determine the confidence interval for duration based on the mean and standard deviation of duration;

[0123] The subset of voltage fluctuation data in the target voltage fluctuation data subset whose duration falls within the duration confidence interval is taken as the target analysis subset;

[0124] Duration sequences are extracted based on a subset of the target analysis; the duration sequences include multiple durations arranged in ascending order.

[0125] Extract the first quantile of the duration sequence and use the first quantile as the first time period.

[0126] In one embodiment of this application, the threshold determination module 22 is specifically used to extract the historical load rate based on historical load data and extract the historical voltage fluctuation rate based on historical voltage data.

[0127] By aligning historical load rates and historical voltage fluctuation rates over time, multiple pairs of historical data samples are obtained.

[0128] Clustering multiple historical data sample pairs yields multiple subsets of historical data sample pairs;

[0129] For each subset of historical data sample pairs, determine the load rate range and the voltage fluctuation rate threshold corresponding to the load rate range based on the subset of historical data sample pairs.

[0130] A voltage fluctuation threshold lookup table is constructed based on all load rate ranges and the corresponding voltage fluctuation thresholds for each load rate range.

[0131] In one embodiment of this application, the electrical parameters include voltage data, current data, active power, and power factor; the mechanical parameters include core vibration acceleration and dominant vibration frequency; the transformer performance indicators include thermal stability sub-indicators, electrical stability sub-indicators, mechanical stability sub-indicators, and insulation performance sub-indicators; the performance detection module 24 is specifically used for:

[0132] The thermal stability sub-indices of the transformer are determined based on the winding temperatures of the transformer during the first and second time periods.

[0133] The electrical stability sub-indicators of the transformer are determined based on the voltage data, current data, active power and power factor of the transformer during the first and second time periods.

[0134] The mechanical stability sub-indices of the transformer are determined based on the core vibration acceleration and dominant vibration frequency of the transformer during the first and second time periods.

[0135] The insulation performance sub-indicators of the transformer are determined based on the partial discharge quantity of the transformer during the second time period.

[0136] See Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided according to an embodiment of this application. Figure 3The electronic device 300 in this embodiment may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memories 304 store computer programs, including program instructions. The processors 301 execute the program instructions stored in the memories 304. Specifically, the processors 301 are configured to invoke the program instructions to perform the functions of the modules in the aforementioned system embodiments, for example... Figure 2 The functions of the first data acquisition module 21, the threshold determination module 22, the second data acquisition module 23, and the performance detection module 24 are shown.

[0137] It should be understood that, in the embodiments of this application, the processor 301 may be a central processing unit (CPU), but it may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0138] Input device 302 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 303 may include a display (LCD, etc.), a speaker, etc.

[0139] The memory 304 may include read-only memory and random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store transformer type information.

[0140] In specific implementations, the processor 301, input device 302, and output device 303 described in the embodiments of this application can execute the implementation methods described in the embodiments of the transformer performance detection method provided in this application, or they can execute the implementation methods of the electronic device 300 described in the embodiments of this application, which will not be repeated here.

[0141] In another embodiment of this application, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the methods described above. Alternatively, the computer program can instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0142] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD) card, flash card, etc., equipped on the electronic device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0143] Those skilled in the art will recognize that the modules / units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0144] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic devices and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0145] In the embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules, units, or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces or modules / units, or it may be an electrical, mechanical, or other form of connection.

[0146] The modules / units described as separate components may or may not be physically separate. Similarly, the components shown as modules / units may or may not be physical modules / units; they may be located in one place or distributed across multiple network modules / units. Some or all of the modules / units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0147] Furthermore, the functional modules / units in the various embodiments of this application can be integrated into one processing module / unit, or each module / unit can exist physically separately, or two or more modules / units can be integrated into one module / unit. The integrated modules / units described above can be implemented in hardware or in the form of software functional modules / units.

[0148] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for testing transformer performance, characterized in that, include: The electrical parameters, winding temperature, and mechanical parameters of the transformer within a first time period are acquired based on a first data acquisition frequency. The electrical parameters include voltage data and load data. Voltage fluctuation rate is extracted based on the voltage data, and a first voltage fluctuation threshold is determined based on the load data and a voltage fluctuation rate threshold lookup table. If the voltage fluctuation rate is greater than or equal to the first voltage fluctuation rate threshold, then the electrical parameters, winding temperature and mechanical parameters of the transformer in the second time period are obtained based on the second data acquisition frequency, and the partial discharge of the transformer in the second time period is obtained; the time span of the second time period is greater than the time span of the first time period; The transformer's performance indicators are determined based on the transformer's electrical parameters, winding temperature, and mechanical parameters during the first time period, as well as the transformer's electrical parameters, winding temperature, mechanical parameters, and partial discharge quantity during the second time period. The first time period is determined by: acquiring historical voltage data of the transformer, determining multiple voltage fluctuation data subsets based on the historical voltage data, and calculating the voltage fluctuation rate of each voltage fluctuation data subset; The subset of voltage fluctuation data with voltage fluctuation rates greater than or equal to the second voltage fluctuation rate threshold is taken as the target voltage fluctuation data subset. The mean and standard deviation of duration are calculated based on the duration of each voltage fluctuation data subset in the target voltage fluctuation data subset; a confidence interval for duration is determined based on the mean and standard deviation of duration; the voltage fluctuation data subset in the target voltage fluctuation data subset whose duration falls within the confidence interval of duration is selected as the target analysis subset; a duration sequence is extracted based on the target analysis subset; the duration sequence includes multiple durations arranged in ascending order. Extract the first quantile of the duration sequence and use the first quantile as the first time period; The voltage fluctuation rate threshold lookup table is constructed based on the transformer's historical load data and historical voltage data, and is obtained by: extracting the historical load rate based on the historical load data, and extracting the historical voltage fluctuation rate based on the historical voltage data; and aligning the historical load rate and the historical voltage fluctuation rate over time to obtain multiple historical data sample pairs. Cluster the multiple historical data sample pairs to obtain multiple historical data sample pair subsets; for each historical data sample pair subset, determine the load rate interval and the voltage fluctuation threshold corresponding to the load rate interval based on the historical data sample pair subset; construct the voltage fluctuation threshold lookup table based on all load rate intervals and the voltage fluctuation threshold corresponding to the load rate interval.

2. The transformer performance testing method as described in claim 1, characterized in that, After extracting the voltage fluctuation rate based on the voltage data, the method further includes: If the voltage fluctuation rate is less than the first voltage fluctuation rate threshold, then the electrical parameters, winding temperature and mechanical parameters of the transformer in the third time period are obtained based on the first data acquisition frequency; the time span of the third time period is less than the time span of the second time period and greater than the time span of the first time period. The transformer's performance indicators are determined based on the transformer's electrical parameters, winding temperature, and mechanical parameters during the third time period.

3. The transformer performance testing method as described in claim 1, characterized in that, The first data acquisition frequency is determined in the following way: The initial data acquisition frequency is determined based on the transformer's rated capacity and insulation structure type; The first adjustment step size is determined based on the transformer's service life, load rate, ambient temperature, and number of historical fault repairs; the service life, load rate, and number of historical fault repairs are all positively correlated with the first adjustment step size, and the difference between the ambient temperature and a preset temperature threshold is positively correlated with the first adjustment step size. The initial data acquisition frequency is adjusted based on the first adjustment step size to obtain the first data acquisition frequency.

4. The transformer performance testing method as described in claim 1, characterized in that, The electrical parameters include voltage data, current data, active power, and power factor; the mechanical parameters include core vibration acceleration and dominant vibration frequency; the transformer performance indicators include thermal stability sub-indicators, electrical stability sub-indicators, mechanical stability sub-indicators, and insulation performance sub-indicators. The determination of transformer performance indicators based on the transformer's electrical parameters, winding temperature, and mechanical parameters during the first time period, and the transformer's electrical parameters, winding temperature, mechanical parameters, and partial discharge quantity during the second time period, includes: The thermal stability sub-indices of the transformer are determined based on the winding temperature of the transformer during the first and second time periods. The electrical stability sub-indicators of the transformer are determined based on the voltage data, current data, active power and power factor of the transformer during the first time period and the second time period. The mechanical stability sub-indices of the transformer are determined based on the core vibration acceleration and dominant vibration frequency of the transformer during the first and second time periods. The insulation performance sub-indicators of the transformer are determined based on the partial discharge quantity of the transformer during the second time period.

5. A transformer performance testing system, characterized in that, include: The first data acquisition module is used to acquire the electrical parameters, winding temperature and mechanical parameters of the transformer within a first time period based on a first data acquisition frequency. The electrical parameters include voltage data and load data. The first time period is determined by: acquiring historical voltage data of the transformer, determining multiple voltage fluctuation data subsets based on the historical voltage data, and calculating the voltage fluctuation rate of each voltage fluctuation data subset; The subset of voltage fluctuation data with voltage fluctuation rates greater than or equal to the second voltage fluctuation rate threshold is taken as the target voltage fluctuation data subset. The mean and standard deviation of duration are calculated based on the duration of each voltage fluctuation data subset in the target voltage fluctuation data subset; a confidence interval for duration is determined based on the mean and standard deviation of duration; the voltage fluctuation data subset in the target voltage fluctuation data subset whose duration falls within the confidence interval of duration is selected as the target analysis subset; a duration sequence is extracted based on the target analysis subset; the duration sequence includes multiple durations arranged in ascending order. Extract the first quantile of the duration sequence and use the first quantile as the first time period; The threshold determination module is used to extract voltage fluctuation rate based on the voltage data and determine a first voltage fluctuation rate threshold based on the load data and a voltage fluctuation rate threshold lookup table. The voltage fluctuation rate threshold lookup table is constructed based on the transformer's historical load data and historical voltage data in the following way: extracting historical load rate based on the historical load data and extracting historical voltage fluctuation rate based on the historical voltage data; and aligning the historical load rate and the historical voltage fluctuation rate over time to obtain multiple historical data sample pairs. Cluster the multiple historical data sample pairs to obtain multiple subsets of historical data sample pairs; for each subset of historical data sample pairs, determine the load rate interval and the voltage fluctuation threshold corresponding to the load rate interval based on the subset of historical data sample pairs; construct the voltage fluctuation threshold lookup table based on all load rate intervals and the voltage fluctuation threshold corresponding to the load rate interval; The second data acquisition module is used to acquire the electrical parameters, winding temperature, and mechanical parameters of the transformer within a second time period based on a second data acquisition frequency if the voltage fluctuation rate is greater than or equal to a first voltage fluctuation rate threshold, and to acquire the partial discharge amount of the transformer within the second time period; the time span of the second time period is greater than the time span of the first time period. The performance testing module is used to determine the performance indicators of the transformer based on the electrical parameters, winding temperature, and mechanical parameters of the transformer during the first time period, and the electrical parameters, winding temperature, mechanical parameters, and partial discharge quantity of the transformer during the second time period.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 4.

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