A method and device for diagnosing internal defects in distribution transformers based on virtual differential current.

Abstract

The present invention relates to the technical field of transformer internal defect diagnosis, and more specifically, to a method and device for diagnosing internal defects in distribution transformers based on virtual differential current. The method comprises the following steps: synchronously collecting data; calculating the measured differential current; establishing a differential current model; generating a virtual differential current: calculating the deviation between the measured differential current of each phase and the model predicted value based on the differential current model as the virtual differential current; and filtering the virtual differential current; and determining defects. Based on the operating data of the transformer when it is free of defects, the present invention constructs a differential current model that associates winding impedance with excitation branch parameters, so that the model is bound to the physical topological characteristics of the transformer. The virtual differential current (the deviation between the measured value and the model predicted value) can reflect the actual state changes of the transformer's internal windings, thereby enhancing the physical relevance and reliability of defect diagnosis.

Description

Technical Field

[0001] This invention relates to the field of transformer internal defect diagnosis technology, and more specifically, to a method and apparatus for diagnosing internal defects in distribution transformers based on virtual differential current. Background Technology

[0002] As a key device for power conversion in the distribution network, the accurate diagnosis of internal winding defects (such as inter-turn short circuits and ground short circuits) in distribution transformers is a core aspect of ensuring the safe operation of the power grid. Existing diagnostic methods based on differential current often suffer from misjudgment or omission of defects due to factors such as dynamic changes in the turns ratio caused by tap adjustment and zero-sequence current interference under star connection. There is an urgent need to construct a diagnostic logic adapted to the unique operating conditions of transformers.

[0003] In the prior art, for example, Chinese patent application CN202411453249.8 discloses a longitudinal differential protection method, device, and storage medium for microgrid lines. It collects normal and short-circuit parameters of the microgrid lines, calculates the virtual short-circuit current output by the inverter, and then obtains the virtual differential current and the set braking current to determine the line short-circuit fault, aiming to improve the sensitivity of the protection device under small short-circuit current. Another example is Chinese patent application CN202411451521.9, which discloses a regionalized differential protection method, device, and storage medium for microgrid systems. It is designed for regionalized microgrid structures and also calculates the virtual differential current based on the virtual short-circuit current to improve the selectivity and reliability of protection under complex structures.

[0004] While the aforementioned technical solutions involve the application of virtual differential current, they are all designed for short-circuit protection of microgrid lines or systems and do not consider the unique attributes of distribution transformers: First, they do not correlate with physical parameters such as winding impedance and excitation branch of the transformer's T-equivalent circuit, resulting in a disconnect between the model and the actual equipment topology, making it difficult to reflect impedance changes in the transformer's internal windings; second, they do not adapt to dynamic changes in the turns ratio caused by transformer tap adjustment, nor do they design targeted compensation logic for the zero-sequence current on the star-connected side; third, they can only determine "whether there is a fault," and cannot infer the type of defect (such as the difference between inter-turn short circuits and ground short circuits) by combining changes in physical model parameters. Therefore, we propose a method and device for diagnosing internal defects in distribution transformers based on virtual differential current. Summary of the Invention

[0005] The purpose of this invention is to provide a method and apparatus for diagnosing internal defects in distribution transformers based on virtual differential current, so as to solve the problems mentioned in the background art.

[0006] To address the aforementioned technical problems, one objective of this invention is to provide a method for diagnosing internal defects in distribution transformers based on virtual differential current, comprising the following steps:

[0007] S100, Synchronous Data Acquisition: Real-time acquisition of three-phase line voltage and line current on the high-voltage side of the transformer, three-phase phase voltage and line current on the low-voltage side, and tap position information;

[0008] S200. Calculate the measured differential current: Based on the high-voltage side line current, low-voltage side line current and the turns ratio determined by the tap position, combined with the zero-sequence current on the star connection side, generate the real-time differential current of each phase.

[0009] S300. Establish a differential current model: Using the operating data of the transformer when it is defect-free as the training sample, the training sample includes the measured differential current of each phase under the defect-free state generated in S200, the high-voltage side line voltage and the low-voltage side phase voltage obtained in S100. Based on the transformer T-shaped equivalent circuit, the winding impedance distribution and excitation branch parameters are associated, and the differential current model of the transformer when it is defect-free is obtained through training.

[0010] S400. Generate virtual differential current: Based on the differential current model established in S300, calculate the deviation between the measured differential current of each phase generated in S200 and the model prediction value, and use it as the virtual differential current; and perform filtering processing on the virtual differential current.

[0011] S500 Defect Judgment: When the virtual differential current of any phase exceeds the preset threshold, it is determined that the transformer has an internal defect.

[0012] As a further improvement to this technical solution, the synchronous data acquisition in S100 includes the following steps:

[0013] S100.1 Real-time acquisition of three-phase line voltage and line current on the high-voltage side of the transformer and three-phase phase voltage and line current on the low-voltage side, with an acquisition frequency of not less than 50Hz;

[0014] S100.2. Synchronously acquire transformer tap position information, and determine the nominal turns ratio of the transformer (the ratio of the nominal number of turns of the high-voltage winding to the nominal number of turns of the low-voltage winding) based on the transformer tap position information.

[0015] S100.3. The voltage and current data collected in S100.1 are associated with and stored with the nominal turns ratio determined in S100.2, and used as the original input parameters for calculating the real-time differential current of each phase in S200.

[0016] As a further improvement to this technical solution, in step S200, calculating the measured differential current includes the following steps:

[0017] S200.1 Extract synchronous measurement parameters:

[0018] Extract the following from the synchronously acquired data of S100:

[0019] High-voltage side line current: (Three-phase line current);

[0020] Low-voltage side line current: (Three-phase line current);

[0021] Nominal turns ratio: (The ratio of the nominal number of turns in the high-voltage winding to the nominal number of turns in the low-voltage winding of the transformer is determined by the tap position.)

[0022] S200.2 Calculate the high-voltage side current components:

[0023] The three-phase components are derived from the three-phase line currents on the high-voltage side: ;in, , , This represents the components of the high-voltage side three-phase current derived from the high-voltage side three-phase line current;

[0024] S200.3 Calculate the zero-sequence current on the star-connected side:

[0025] If the low-voltage side is star-connected, calculate the zero-sequence current on the low-voltage side. : ;

[0026] S200.4 Calculate the measured differential current of each phase:

[0027] The measured differential current for each phase is generated using the following formula: ;in, , , This represents the final calculated measured differential current for each phase.

[0028] As a further improvement to this technical solution, in S300, the training samples are timestamped synchronization phasor data acquired during the trial operation, including:

[0029] No. Measured differential current of phase ;

[0030] High-voltage side line voltage: Phase line voltage , Phase line voltage , Phase line voltage ;

[0031] Low-voltage side phase voltage: Phase voltage , Phase voltage , Phase voltage ;

[0032] Furthermore, the operating data of the transformer when it is defect-free is the steady-state operating data of more than 72 consecutive hours without failure during the trial operation period.

[0033] As a further improvement to this technical solution, the differential current model obtained through training when the transformer is defect-free in step S300 includes the following steps:

[0034] S300.1 Data Preparation:

[0035] During the trial operation, according to the time sequence ( (Number of sampling points), collection:

[0036] Phase: High-voltage side line voltage Low-voltage side phase voltage Measured differential current ;

[0037] Phase: High-voltage side line voltage Low-voltage side phase voltage Measured differential current ;

[0038] Phase: High-voltage side line voltage Low-voltage side phase voltage Measured differential current ;

[0039] S300.2, Constructing Matrices and Vectors:

[0040] For the first ,structure:

[0041] Input matrix: ;in, For the first Phase high voltage side line voltage; For the first Phase voltage on the low-voltage side; For the first Each sampling time;

[0042] Output vector: ;in, For the first Measure the differential current in phase;

[0043] Parameter vector: ;in, For the first The coefficient of line voltage in the phase model; For the first The coefficients of phase voltage in the phase model;

[0044] S300.3, Least Squares Method for Parameter Identification:

[0045] ;

[0046] in, for The conjugate transpose of ; for The inverse matrix;

[0047] S300.4. Establish the differential current model for each phase:

[0048] ;

[0049] in, For the first The differential current model prediction of the phase; These are the parameters of the differential current model, and .

[0050] As a further improvement to this technical solution, the differential current model parameters established by S300... , There is a quantitative correlation between the parameters and the T-shaped equivalent circuit parameters of each phase winding of the transformer, specifically including:

[0051] Differential current model prediction value Relationship with voltage:

[0052] The relationship between the differential current model prediction and the high-voltage side line voltage and the low-voltage side phase voltage is as follows:

[0053] ;

[0054] in, Indicates the first The impedance of the high-voltage winding; Indicates the first The impedance of the low-voltage winding referred to the high-voltage side; Indicates the first The impedance of the phase excitation branch; This indicates the turns ratio of the high-voltage side to the low-voltage side of the transformer. , This refers to the number of turns in the high-voltage winding. (Number of turns in the low-voltage winding).

[0055] The relationship between differential current model parameters and the impedance of the transformer T-equivalent circuit:

[0056] , The relationship expression with the parameters of the transformer T-equivalent circuit is as follows:

[0057] .

[0058] As a further improvement to this technical solution, the generation of virtual differential current in S400 includes the following steps:

[0059] S410.1 Extracting the comparison amount:

[0060] Get the first in S200 Measured differential current of phase And the first in S300 Predicted values ​​from the phase differential current model ;

[0061] S410.2 Calculate the deviation modulus:

[0062] According to the formula Calculate the first The virtual differential current of the phase, where: The modulus operation of complex phasors is used to convert phasor differences into scalar magnitudes, quantizing the degree of deviation. For the first In time The virtual differential current is used to reflect the deviation between the measured value and the model prediction value.

[0063] As a further improvement to this technical solution, the filtering process for the virtual differential current in S400 includes the following steps:

[0064] S420.1, to Using sliding window mean filtering, the formula is:

[0065] ;

[0066] in, Indicates the first The virtual differential current after phase filtering; Indicates the size of the sliding window; This represents the backtracking index within the window; Indicates the first Phase at historical sampling moment The original virtual differential current; Indicates the current sampling time;

[0067] S420.2, Filter the... As an input parameter for internal defect determination in S500.

[0068] As a further improvement to this technical solution, the setting of the preset threshold and the determination of internal defects in S500 include the following steps:

[0069] S500.1 Determination of the preset threshold:

[0070] The preset threshold is calculated based on the defect-free training samples of S300, and the virtual differential current phasors of each phase during the training phase are filtered by the mean of the S420 sliding window. It shall be determined as follows:

[0071] Extracting training period The maximum amplitude is denoted as ;

[0072] No. Preset threshold Defined as: ;in, This is a threshold coefficient, ranging from 1.2 to 1.5, used to suppress fluctuations during normal operation.

[0073] S500.2 Internal Defect Judgment and Type Inference:

[0074] When the Amplitude of virtual differential current after phase filtering At that time, it was determined that the phase had an internal defect; further, by combining the correlation between the differential current model parameters in S300 and the transformer T-equivalent circuit, the defect type was inferred. The specific defect types include:

[0075] Winding inter-turn short circuit: If At the same time, it deviates significantly from the training value (reflecting a sudden change in the winding impedance distribution), and Continuously increasing (corresponding to a gradual change in the number of short-circuit turns);

[0076] Phase-to-phase short circuit (including lead-out short circuit): If multiple phases (such as...) or (alternating combinations) Simultaneously exceeding the standard (reflecting an abnormality in the interphase electrical circuit);

[0077] Internal short circuit to ground in winding: if A single anomaly (abrupt change in the characteristics of the excitation branch, corresponding to the failure of the ground insulation), and It exhibits a step-like growth (characteristic of instantaneous insulation breakdown).

[0078] The second objective of this invention is to provide a device for diagnosing internal defects in distribution transformers based on virtual differential current, which is equipped with a system for diagnosing internal defects in distribution transformers based on virtual differential current. When the computer program of the system for diagnosing internal defects in distribution transformers based on virtual differential current is run, it is used to execute the steps of any of the above-mentioned methods for diagnosing internal defects in distribution transformers based on virtual differential current.

[0079] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0080] 1. This invention synchronously collects tap position information and uses it to determine the turns ratio. Combined with the zero-sequence current on the star connection side, it calculates the measured differential current. This invention can adapt to the dynamic adjustment of transformer tap positions, reduce misjudgments of defects caused by changes in the turns ratio or interference from zero-sequence current, and improve the adaptability of diagnosis.

[0081] 2. Based on the operating data of a transformer without defects, this invention constructs a differential current model that correlates the winding impedance and excitation branch parameters, thus binding the model to the physical topological characteristics of the transformer. The virtual differential current (the deviation between the measured value and the model prediction value) can reflect the actual state changes of the windings inside the transformer, thereby enhancing the physical correlation and reliability of defect diagnosis.

[0082] 3. By calculating and filtering the virtual differential current, this invention can reduce the impact of instantaneous fluctuations on the diagnostic results, making the defect judgment more stable. At the same time, by combining the amplitude of the virtual differential current and the changes in model parameters to infer the defect type, it can further distinguish the specific type of defect based on the determination that a defect exists, thereby improving the pertinence of the diagnosis. Attached Figure Description

[0083] Figure 1 This is a schematic diagram of the method steps of the present invention. Detailed Implementation

[0084] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0085] like Figure 1 As shown in the figure, this embodiment provides a method for diagnosing internal defects in distribution transformers based on virtual differential current, including the following steps:

[0086] S100, Synchronous Data Acquisition: Real-time acquisition of three-phase line voltage and line current on the high-voltage side of the transformer, three-phase phase voltage and line current on the low-voltage side, and tap position information;

[0087] It is understood that the synchronous data acquisition in this embodiment is achieved through an acquisition system composed of a sensor module, a synchronization clock module, a tap information acquisition module, and a data processing unit. The sensor module is used to acquire electrical signals, the synchronization clock module ensures the time consistency of each signal, the tap information acquisition module acquires the tap position parameters, and the data processing unit is responsible for signal conversion, correlation, and storage. All modules work together to meet the requirements of S100 for data "synchronization" and "integrity".

[0088] In this step, the synchronous data acquisition of S100 includes the following steps:

[0089] S100.1 Real-time acquisition of three-phase line voltage and line current on the high-voltage side of the transformer and three-phase phase voltage and line current on the low-voltage side, with an acquisition frequency of not less than 50Hz;

[0090] As a further explanation of this step, the electrical quantity acquisition in the sensor module of this embodiment specifically includes: a high-voltage side sensor for acquiring the three-phase line voltage and the three-phase line current on the high-voltage side, and a low-voltage side sensor for acquiring the three-phase line voltage and the three-phase line current on the low-voltage side; wherein, the high-voltage side sensor is installed at the high-voltage side outlet of the transformer, and the low-voltage side sensor is installed at the low-voltage side outlet of the transformer, and both adopt voltage and current sensors that can adapt to the fundamental frequency of the power grid.

[0091] S100.2. Synchronously acquire transformer tap position information, and determine the nominal turns ratio of the transformer (the ratio of the nominal number of turns of the high-voltage winding to the nominal number of turns of the low-voltage winding) based on the transformer tap position information.

[0092] As a further explanation of this step, the synchronization time stamp implementation of the synchronization clock module in this embodiment includes the following steps: First, the synchronization clock module adopts a global synchronization clock source (such as the BeiDou synchronization clock); then, a unified timestamp (accurate to the millisecond level) is assigned to the sampling time of the sensor module and the tap information acquisition module; then, it is ensured that the voltage and current data of the high-voltage side and the low-voltage side, as well as the tap position information, are collected under the same timestamp; next, the sampling time difference of each module is eliminated by timestamp comparison; finally, the synchronized timestamp is embedded into all the collected data to provide a time reference for subsequent data association.

[0093] Furthermore, as a further explanation of this embodiment, the acquisition of tap position information in the tap changer information acquisition module specifically includes: the tap changer information acquisition module is connected to the tap position feedback device (such as a mechanical contact encoder or an electronic tap position sensor) of the transformer tap changer via a communication interface (such as RS485) to receive the current tap position value (such as tap 1 to tap n, where n is the total number of tap positions); simultaneously, the module pre-stores the ratio of different tap positions to the nominal turns ratio in the transformer's factory parameters.

[0094] Furthermore, the nominal turns ratio determination of the tap information acquisition module in this embodiment includes the following steps: First, receiving the real-time gear value transmitted by the gear position feedback device; then, determining whether the gear value is stable (e.g., no change for 50ms); then, if stable, matching and calling the nominal turns ratio K corresponding to the gear position from the pre-stored correspondence; next, if the gear value is unstable (e.g., during the switching process), temporarily using the K value of the previous stable gear position; finally, updating the K value after the gear position stabilizes to ensure that the turns ratio used for calculation is accurate.

[0095] S100.3. The voltage and current data collected in S100.1 are associated with and stored with the nominal turns ratio determined in S100.2, and used as the original input parameters for calculating the real-time differential current of each phase in S200.

[0096] As a further explanation of this step, the voltage and current data conversion in the data processing unit of this embodiment specifically includes: the analog signals (voltage and current) collected by the sensor module are converted into digital signals by the analog-to-digital converter module; the converted high-voltage side line voltage is denoted as... (Instantaneous value in the time domain, dynamically changing with time), the line current is denoted as... The low-voltage side phase voltage is denoted as Because the low-voltage side uses a star connection, its line current and phase current have the same physical quantity. Therefore, the low-voltage side line current is synchronously denoted as... The conversion process employs a second-order low-pass filter to eliminate high-frequency noise (such as switching transients and harmonic interference) and retain the 50Hz fundamental component, ensuring that the digital signal reflects the time-domain characteristics of the real electrical quantity and providing a reliable input for subsequent fundamental component extraction (such as Fourier transform).

[0097] As a further explanation of this step, the data association storage of the data processing unit in this embodiment includes the following steps:

[0098] First, extract the timestamp assigned by the synchronization clock module;

[0099] Subsequently, the high-voltage side voltage, high-voltage side current, low-voltage side voltage, low-voltage side current, and the nominal turns ratio determined by the tap changer information acquisition module at the same timestamp were collected. Integrate;

[0100] Then, a dataset containing the above parameters and timestamps is generated;

[0101] Next, the dataset will be validated (if the timestamp deviation exceeds 5ms, it will be marked as invalid).

[0102] Finally, the valid dataset is stored in a time series database to provide raw input parameters for the S200.

[0103] S200. Calculate the measured differential current: Based on the high-voltage side line current, low-voltage side line current and the turns ratio determined by the tap position, combined with the zero-sequence current on the star connection side, generate the real-time differential current of each phase.

[0104] In this step, calculating the measured differential current in S200 includes the following steps:

[0105] S200.1 Extract synchronous measurement parameters:

[0106] Extract the following from the synchronously acquired data of S100:

[0107] High-voltage side line current: (Three-phase line current);

[0108] Low-voltage side line current: (Three-phase line current);

[0109] Nominal turns ratio: (The ratio of the nominal number of turns in the high-voltage winding to the nominal number of turns in the low-voltage winding of the transformer is determined by the tap position.)

[0110] As a further explanation of this step, the synchronous measurement parameter extraction in S200.1 of this embodiment specifically includes: extracting valid parameters associated with the same timestamp from the time series database stored in S100 (verified by S100.3, timestamp deviation ≤ 5ms), including: high-voltage side line current (denoted as...). All are 50Hz fundamental phasors, reflecting The effective value and phase of the line current of the phase); the low-voltage side line current (denoted as...). 50Hz fundamental phasor, equal to phase current in star connection); nominal turns ratio (denoted as...). That is, the nominal number of turns of the high-voltage winding. Nominal number of turns of low-voltage winding The ratio, (As determined by matching in S100.2). The time synchronization of the above parameters can avoid phase deviation caused by sampling time difference, ensuring the reliability of the calculation basis.

[0111] S200.2 Calculate the high-voltage side current components:

[0112] The three-phase components are derived from the three-phase line currents on the high-voltage side: ;in, , , This represents the components of the high-voltage side three-phase current derived from the high-voltage side three-phase line current;

[0113] As a further explanation of this step, the calculation of the high-voltage side current component in S200.2 of this embodiment includes the following steps:

[0114] First, clarify the calculation basis—when the high-voltage side adopts a delta (Δ) connection, there is a specific relationship between the line current and the phase current, that is, the phase current can be derived from the line current;

[0115] Subsequently, the data extracted by S200.1 was called. Calculate using the following formula: ;

[0116] Then, perform the above phasor difference calculations (including amplitude and phase calculations) to ensure that the results conform to the current distribution rules of the delta connection.

[0117] Finally, the calculated three-phase current components are temporarily stored for differential current synthesis in S200.4.

[0118] S200.3 Calculate the zero-sequence current on the star-connected side:

[0119] If the low-voltage side is star-connected, calculate the zero-sequence current on the low-voltage side. : ;

[0120] As a further explanation of this step, the zero-sequence current calculation on the star connection side in S200.3 of this embodiment includes the following steps:

[0121] First, determine the low-voltage side wiring method (based on the transformer's factory default parameter setting being a star connection).

[0122] Subsequently, the physical meaning of zero-sequence current was clarified—in star connection, zero-sequence current is the zero-sequence component of the three-phase current (equal in magnitude and phase), and its value can reflect asymmetric defects such as single-phase grounding.

[0123] Next, the data extracted by S200.1 is called. (In a star connection, line current = phase current), calculate the zero-sequence current using the following formula. : ;

[0124] Finally, if the low-voltage side is connected in a delta configuration (no zero-sequence current path), then... The value is set to 0 to avoid invalid compensation.

[0125] S200.4 Calculate the measured differential current of each phase:

[0126] The measured differential current for each phase is generated using the following formula: ;in, , , This represents the final calculated measured differential current for each phase.

[0127] As a further explanation of this step, the calculation of the measured differential current of each phase in S200.4 of this embodiment includes the following steps: First, based on the transformer "ampere-turn balance" principle, derive the formula—under ideal conditions, the ampere-turn balance of the high-voltage side. With low-voltage side ampere-turn Balance, that is The actual differential current is the sum of the difference between the two and the zero-sequence compensation term;

[0128] Subsequently, the measured differential current of each phase is calculated using the following formula: ;

[0129] Then, perform phasor operations in the formula (such as...) The compensation amount for zero-sequence current referred to the high-voltage side is calculated in complex form (real part + imaginary part) to ensure accuracy;

[0130] Finally, the calculated measured differential currents of each phase are associated with and stored with timestamps to provide input for building a health model for the S300.

[0131] For example, with Taking a phase as an example, the calculation steps are as follows:

[0132] First, the calculations were performed using S200.2. S200.3 calculation Extracted by S200.1 and ;

[0133] Subsequently, the zero-order compensation term is calculated. (Phasor divided by scalar) (Only change the amplitude).

[0134] Then, calculate the reduced current on the low-voltage side. ;

[0135] Next, according to the formula Perform phasor operations (including amplitude and phase superposition).

[0136] Finally, calculate similarly. Mutually( )and Mutually( The measured differential current of each phase is obtained and stored as input parameters for subsequent S300 modeling.

[0137] Furthermore, to ensure the reliability of the measured differential current, the following processing steps need to be added during the calculation:

[0138] Precision guarantee of phasor operations:

[0139] All phasor operations (such as...) The calculation is performed using complex numbers (real part + imaginary part) to avoid errors caused by approximation of amplitude; the calculation results are checked for range (e.g., the differential current amplitude does not exceed 5% of the transformer's rated current; if it does, it is marked as abnormal and a recalculation is triggered).

[0140] Connection verification with S100:

[0141] If the data at a certain moment in S100 is marked as "invalid" (such as sensor failure), the calculation of S200 at that moment is skipped to avoid invalid input affecting the results. After the calculation is completed, the measured differential current of each phase is associated with the corresponding timestamp and stored to ensure that it matches the training sample called by S300 in the time dimension.

[0142] S300. Establish a differential current model: Using the operating data of the transformer when it is defect-free as the training sample, the training sample includes the measured differential current of each phase under the defect-free state generated in S200, the high-voltage side line voltage and the low-voltage side phase voltage obtained in S100. Based on the transformer T-shaped equivalent circuit, the winding impedance distribution and excitation branch parameters are associated, and the differential current model of the transformer when it is defect-free is obtained through training.

[0143] In this step, in S300, the training samples are timestamped synchronization phasor data acquired during the trial run, including:

[0144] No. Measured differential current of phase ;

[0145] High-voltage side line voltage: Phase line voltage , Phase line voltage , Phase line voltage ;

[0146] Low-voltage side phase voltage: Phase voltage , Phase voltage , Phase voltage ;

[0147] Furthermore, the operating data of the transformer when it is defect-free is the steady-state operating data of more than 72 consecutive hours without failure during the trial operation period.

[0148] As a further explanation of this step, in S300 of this embodiment, "the training samples are steady-state operation data that has been running continuously for more than 72 hours without failure during the trial operation period," and the criteria for determining "steady-state operation data" and the data screening method are as follows:

[0149] Determination of steady-state operating data:

[0150] Based on the transformer's rated parameters, a steady-state determination threshold is set: the high-voltage side line voltage fluctuation range does not exceed ±5% of the rated value, the high-voltage side line current fluctuation range does not exceed ±10% of the rated value, and the duration is ≥1 minute (to avoid instantaneous fluctuations). The time period that meets the above conditions is recorded as the "steady-state period," and the data extracted from this period is used as training samples.

[0151] Data filtering operations:

[0152] First, the raw data from 72 consecutive hours during the trial operation (collected at an S100 frequency, such as 50Hz, corresponding to approximately 1.8 × 10⁻⁶) was collected. 6 The sampling points are divided into segments to identify all steady-state time periods;

[0153] Subsequently, outliers during the steady-state period (such as points where voltage or current suddenly increases or decreases beyond the threshold) are removed.

[0154] Then, time series data are uniformly extracted from the remaining valid data (ensuring coverage of different load levels, such as light load and full load) to form the final training samples, thus avoiding data bias.

[0155] In this step, obtaining the differential current model of the transformer without defects through training in S300 includes the following steps:

[0156] S300.1 Data Preparation:

[0157] During the trial operation, according to the time sequence ( (Number of sampling points), collection:

[0158] Phase: High-voltage side line voltage Low-voltage side phase voltage Measured differential current ;

[0159] Phase: High-voltage side line voltage Low-voltage side phase voltage Measured differential current ;

[0160] Phase: High-voltage side line voltage Low-voltage side phase voltage Measured differential current ;

[0161] As a further explanation of this step, the time series in this embodiment The sampling interval is consistent with S100. The number of sampling points (calculated based on 50Hz for 72-hour steady-state data). Possible values: 1×10 5 ~2×10 5 (Ensure sufficient data volume).

[0162] For example, taking phase A as an example, the collected data is organized as follows:

[0163] High voltage side line voltage : No. time The fundamental phasor (RMS value) of the phase line voltage;

[0164] Low-voltage side phase voltage : No. time The fundamental phasor (RMS value) of the phase line voltage;

[0165] Measured differential current : No. time The measured differential current of the phase (calculated from S200.4).

[0166] right Mutually( , , )and Mutually( , , Organize them according to the same logic to ensure that each phase of data strictly corresponds to the timestamp.

[0167] S300.2, Constructing Matrices and Vectors:

[0168] For the first Phase, structure:

[0169] Input matrix: ;in, For the first Phase high voltage side line voltage; For the first Phase voltage on the low-voltage side; For the first Each sampling time;

[0170] Output vector: ;in, For the first Measure the differential current in phase;

[0171] Parameter vector: ;in, For the first The coefficient of line voltage in the phase model; For the first The coefficients of phase voltage in the phase model;

[0172] As a further explanation of this step, the phasor construction of the matrix and vector construction in this embodiment includes the following steps:

[0173] First, from the fundamental frequency extraction results of S100, voltage phasor data for a continuous 72-hour steady-state period are selected:

[0174] Get the Phase high voltage side line voltage fundamental phasor (like for (This reflects the amplitude and phase characteristics of the 50Hz line voltage).

[0175] Get the Phase low voltage side phase voltage fundamental phasor (like for (The physical meaning of the phasor current is consistent with that of the low-voltage side line current in a star connection).

[0176] Subsequently, the first value corresponding to the steady-state period is extracted from the calculation results of S200. Measured differential current fundamental phasor:

[0177] The result of the S200 operation on the fundamental phasor is used to obtain the first... Moment ;

[0178] Then, according to the time series ( (To determine the number of sampling points, matching the S100 acquisition frequency), construct the input matrix. With output vector ;

[0179] Next, define the parameter vector. ;

[0180] Completed construction , , Associated storage:

[0181] Ensure matrix dimensions match ( for , for , for This provides input for the least squares parameter identification of S300.3.

[0182] Furthermore, in this embodiment, voltage, current, and differential current all use fundamental phasor notation, for the following reasons:

[0183] Model adaptability: The differential current model of S300 describes the linear relationship under steady-state conditions (when there are no defects, the differential current is linearly related to the fundamental component of the voltage).

[0184] Data consistency: The instantaneous values ​​acquired by S100 have been converted to 50Hz fundamental components (converted to phasors) by FFT, and the differential current calculation of S200 is also based on fundamental phasor operations. Therefore, the input of S300 and the model output are completely consistent in form, ensuring that the algorithm is operable.

[0185] S300.3, Least Squares Method for Parameter Identification:

[0186] ;

[0187] in, for The conjugate transpose of ; for The inverse matrix;

[0188] As a further explanation of this step, the least squares parameter calculation in this embodiment includes the following steps:

[0189] First, clearly define the identification objective: solve for the parameter vector by minimizing the sum of squared errors between the model's predicted values ​​and the measured differential current. ,Right now ,in ;

[0190] Subsequently, the parameter formula is derived: by differentiating the sum of squared errors and setting it to 0, the result is simplified to... ; for The conjugate transpose of ; for The inverse matrix;

[0191] Then, calculate the matrix. For real data, ( );

[0192] Next, calculate the inverse matrix. If the determinant of the matrix is ​​not zero (ensuring this with a sufficient sample size), then the inverse matrix is... ( diagonal elements (non-diagonal elements)

[0193] Finally, solve for the parameter vector: ,Right now The parameters of each phase model are obtained.

[0194] S300.4. Establish the differential current model for each phase:

[0195] ;

[0196] in, For the first The differential current model prediction of the phase; These are the parameters of the differential current model, and .

[0197] As a further explanation of this step, the differential current model for each phase established in S300.4 of this embodiment... This reflects the linear relationship between differential current and voltage under defect-free conditions—wherein Quantify the weighting of the influence of high-voltage side line voltage on differential current. The influence weight of the low-voltage side phase voltage is quantified. The model's role is to provide a "health baseline" for real-time diagnostics: by inputting real-time voltage... , This allows us to obtain the predicted differential current when there are no defects, providing a basis for subsequent calculations of the "deviation between the measured value and the predicted value (virtual differential current)".

[0198] In this step, the differential current model parameters established by S300 , There is a quantitative correlation between the parameters and the T-shaped equivalent circuit parameters of each phase winding of the transformer, specifically including:

[0199] Differential current model prediction value Relationship with voltage:

[0200] The relationship between the differential current model prediction and the high-voltage side line voltage and the low-voltage side phase voltage is as follows:

[0201] ;

[0202] in, Indicates the first The impedance of the high-voltage winding; Indicates the first The impedance of the low-voltage winding referred to the high-voltage side; Indicates the first The impedance of the phase excitation branch; This indicates the turns ratio of the high-voltage side to the low-voltage side of the transformer. , This refers to the number of turns in the high-voltage winding. (Number of turns in the low-voltage winding).

[0203] The relationship between differential current model parameters and the impedance of the transformer T-equivalent circuit:

[0204] , The relationship expression with the parameters of the transformer T-equivalent circuit is as follows:

[0205] .

[0206] As a further explanation of this step, the derivation of the model parameters and the T-shaped equivalent circuit in this embodiment includes the following steps (using... (For example, the phase)

[0207] First, the parameters of the low-voltage side of the transformer are transferred to the high-voltage side (a common method in engineering analysis that simplifies cross-side circuit calculations), according to the following rules:

[0208] Voltage reduction: Low-voltage side phase voltage Referred to the high-voltage side as ;

[0209] Current calculation: Low-voltage side current Referred to the high-voltage side as ;

[0210] Impedance calculation: Actual impedance of low-voltage winding Referred to the high-voltage side as (Impedance is proportional to the square of the turns ratio);

[0211] Subsequently, based on the calculated circuit (high-voltage side perspective), the following is written: Electrical equations of phases:

[0212] High-voltage side line voltage: ;in, This is the high-voltage side current. For the high-voltage winding impedance, This is the voltage of the excitation branch;

[0213] Excitation branch: ; For excitation current, The impedance of the excitation branch;

[0214] Ampere-turn balance: High-voltage side current = excitation current + reduced low-voltage side current;

[0215] Low-voltage side return loop: Used to verify the consistency of the reduction; subsequent modeling will directly use the reduced parameters.

[0216] Then, combining the approximate relationship of the differential current in S200 when there is no defect ( Solving the above equations simultaneously eliminates , , :

[0217] from and , eliminate : ;

[0218] Substitution ,get: ;

[0219] Solved from the low-voltage side reduction circuit Substitute into the above formula:

[0220] ;

[0221] Solve (Excitation current, reflecting the excitation characteristics of the transformer): ;

[0222] Next, based on the measured differential current definition of S200 ( Zero-sequence current when there are no defects ,and (Under the high-voltage side delta connection, the phase current is approximately equal to the excitation current component), therefore ;

[0223] Will Substituting and simplifying, we get: ;

[0224] Finally, the model formula with S300 By comparison, we obtain:

[0225] ;

[0226] Similarly, the impedances of phases B and C can be obtained by replacing the subscripts (A→B→C). The derivation (etc.) shows that the model parameters directly map the impedance characteristics of the transformer T-equivalent circuit, ensuring that the model has both data fitting accuracy and physical rationality.

[0227] S400. Generate virtual differential current: Based on the differential current model established in S300, calculate the deviation between the measured differential current of each phase generated in S200 and the model prediction value, and use it as the virtual differential current; and perform filtering processing on the virtual differential current.

[0228] In this step, generating the virtual differential current in S400 includes the following steps:

[0229] S410.1 Extracting the comparison amount:

[0230] Get the first in S200 Measured differential current of phase And the first in S300 Predicted values ​​from the phase differential current model ;

[0231] As a further explanation of this step, the data association and format in the comparison quantity extraction of this embodiment specifically include:

[0232] The extracted first Phase measurement of differential current The calculation results are derived from S200, along with the timestamp. (Accurate to milliseconds) binding, sampling interval consistent with S100 (e.g., 20ms for 50Hz); Phase differential current model prediction value By calling the model formula of S300 , generate; where , Taken from the same timestamp of S100 Real-time data is used to ensure that the two are synchronized (deviation ≤ 5ms).

[0233] S410.2 Calculate the deviation modulus:

[0234] According to the formula Calculate the first The virtual differential current of the phase, where: The modulus operation of complex phasors is used to convert phasor differences into scalar magnitudes, quantizing the degree of deviation. For the first In time The virtual differential current is used to reflect the deviation between the measured value and the model prediction value.

[0235] As a further explanation of this step, the specific steps for calculating the deviation modulus in this embodiment include:

[0236] First, clarify the input parameters: Measured differential current of phase (Complex phasors, including real part) and the virtual part ) and model predictions (Complex phasors, including real part) and the virtual part );

[0237] Then, the phasor difference is calculated: ;in The imaginary unit;

[0238] Then, the complex phasor is converted into a scalar magnitude through modulus arithmetic, using the following formula:

[0239] ;

[0240] Next, the calculation results will be compared with the timestamp. Associative storage is used to form the original virtual differential current sequence;

[0241] Finally, the non-negativity of the modulus is verified (since the square root of the sum of squares is always ≥0) to ensure data validity.

[0242] In this step, the filtering process for the virtual differential current in S400 includes the following steps:

[0243] S420.1, to Using sliding window mean filtering, the formula is:

[0244] ;

[0245] in, Indicates the first The virtual differential current after phase filtering; Indicates the size of the sliding window; This represents the backtracking index within the window; Indicates the first Phase at historical sampling moment The original virtual differential current; Indicates the current sampling time;

[0246] As a further explanation of this step, the sliding window setting in the filtering process of this embodiment specifically includes:

[0247] Slide window size Based on the selection of the fundamental frequency period of the power grid, for example, when the sampling frequency is 50Hz (sampling interval 20ms), A value of 5 can be selected (corresponding to 100ms, covering 5 consecutive sampling points), which can filter out instantaneous noise (such as short-term disturbances from switching operations) while retaining persistent deviations caused by defects; in the formula For the backtracking index within the window ( =0 corresponds to the current time. , =1 corresponds to -20ms, and so on.

[0248] S420.2, Filter the... As an input parameter for internal defect determination in S500.

[0249] As a further explanation of this step, the data transmission in the filtering result output of this embodiment specifically includes:

[0250] Filtered virtual differential current Stored as scalar amplitude values, each record contains a timestamp. and , , Three-phase filter value The data is transmitted to the S500 in real time through the internal interface, providing stable and continuous input parameters for defect judgment and ensuring that subsequent steps can accurately identify changes in the internal state of the transformer based on the deviation signal.

[0251] S500 Defect Judgment: When the virtual differential current of any phase exceeds the preset threshold, it is determined that the transformer has an internal defect.

[0252] In this step, the setting of the preset threshold and the determination of internal defects in S500 include the following steps:

[0253] S500.1 Determination of the preset threshold:

[0254] The preset threshold is calculated based on the defect-free training samples of S300, and the virtual differential current phasors of each phase during the training phase are filtered by the mean of the S420 sliding window. It shall be determined as follows:

[0255] Extracting training period The maximum amplitude is denoted as ;

[0256] No. Preset threshold Defined as: ;in, This is a threshold coefficient, ranging from 1.2 to 1.5, used to suppress fluctuations during normal operation.

[0257] As a further explanation of this step, the training sample processing in the threshold calculation of this embodiment specifically includes: the training sample used to calculate the threshold is the virtual differential current after filtering the "72 hours of continuous defect-free steady-state data" in S300 through S420. The sample needs to be preprocessed first: remove instantaneous outliers (such as isolated peaks caused by short-term sensor interference, the criterion for which is a point exceeding 3 times the standard deviation of the sample mean), retain valid data that reflects the fluctuations of normal operation, and ensure the accuracy of subsequent maximum value extraction.

[0258] Furthermore, the threshold determination step in the threshold calculation of this embodiment includes:

[0259] First, extract the first... Filtered virtual differential current sequence of phase ( (Number of training sample points)

[0260] Then, the maximum amplitude of the sequence is calculated. This reflects the maximum normal fluctuation under defect-free conditions;

[0261] Then, based on the threshold coefficient The preset threshold is calculated using the following formula: ;in The value is based on the following: by analyzing the defect-free operating data of more than 10 sets of similar transformers, it was found that the peak value of normal fluctuations usually does not exceed [a certain value]. 1.2 times that, while setting =1.2-1.5 can cover more than 99% of normal fluctuations, while avoiding misjudgment due to slight interference;

[0262] Next, the calculated Stored as the determination threshold for each phase;

[0263] Finally, the thresholds are updated periodically (e.g., every 6 months) based on new defect-free steady-state data to accommodate normal fluctuations caused by transformer aging.

[0264] S500.2 Internal Defect Judgment and Type Inference:

[0265] When the Amplitude of virtual differential current after phase filtering At that time, it was determined that the phase had an internal defect; further, by combining the correlation between the differential current model parameters in S300 and the transformer T-equivalent circuit, the defect type was inferred. The specific defect types include:

[0266] Winding inter-turn short circuit: If At the same time, it deviates significantly from the training value (reflecting a sudden change in the winding impedance distribution), and Continuously increasing (corresponding to a gradual change in the number of short-circuit turns);

[0267] Phase-to-phase short circuit (including lead-out short circuit): If multiple phases (such as...) or (alternating combinations) Simultaneously exceeding the standard (reflecting an abnormality in the interphase electrical circuit);

[0268] Internal short circuit to ground in winding: if A single anomaly (abrupt change in the characteristics of the excitation branch, corresponding to the failure of the ground insulation), and It exhibits a step-like growth (characteristic of instantaneous insulation breakdown).

[0269] As a further explanation of this step, the threshold comparison logic in this embodiment specifically includes:

[0270] Real-time data collection Virtual differential current after phase filtering Its amplitude is compared with a preset threshold. Comparison:

[0271] like The phase was determined to be defect-free.

[0272] like If the duration of this state is ≥ 2 fundamental frequency cycles (e.g., 40ms to avoid instantaneous interference), then the phase is determined to have an internal defect.

[0273] As a further explanation of this step, the defect type inference in this embodiment specifically includes the following steps:

[0274] First, define three types of input data:

[0275] Model parameter baseline values: Parameters obtained by calling defect-free steady-state training samples from S300. ;in, Indicates the first The baseline value of the weighting coefficient of the phase high voltage side line voltage to the differential current (typical value when there are no defects). Indicates the first The baseline value of the weighting coefficient of the phase voltage on the low-voltage side to the differential current (typical value when there are no defects).

[0276] Real-time operating parameters: obtained through online rolling identification (updated every 100ms, reusing the least squares method of S300). This reflects the voltage-differential current correlation characteristics of the transformer under its current operating condition.

[0277] Virtual differential current: Extracts the current phase output amplitude after filtering from S420. Historical sequences (the last 5 sampling points, 20ms interval, covering a 100ms time window) are used to analyze the dynamic trend of the deviation.

[0278] Subsequently, the degree of deviation between the real-time parameters and the benchmark value is quantified using the relative deviation formula:

[0279] ;

[0280] ;

[0281] Indicates the first The model parameters are identified in real time (and change dynamically with the running status). , This represents the baseline values ​​of model parameters under defect-free conditions (serving as a "health reference system" and requiring periodic updates). , It represents the relative deviation between two parameters (dimensionless, directly reflecting the proportion of parameter change).

[0282] Judgment threshold: set The "significant deviation" standard (based on transformer fault simulation tests; parameter deviations under defective operating conditions typically exceed this range, not an empirical value) is defined as follows:

[0283] Then, combining the parameter deviation characteristics with the dynamic law of the virtual differential current, the defect types are matched item by item:

[0284] Winding inter-turn short circuit: , It shows a monotonically increasing trend over 5 consecutive sampling points (100ms);

[0285] Phase-to-phase short circuit (including lead-out short circuit): at least two phases Time, and multiple phases , The trend of change is consistent;

[0286] Internal short circuit to ground in winding: , The number of samples suddenly increased to [a certain value] within one sampling point (20ms). 2 times or more.

[0287] Next, generate structured diagnostic results: bind the matched defect type (such as "B-phase winding inter-turn short circuit") with the current timestamp and record it. , And the dynamic characteristics of the virtual differential current (such as the increasing slope and the sudden increase factor);

[0288] Finally, perform cross-validation:

[0289] Combined with transformer auxiliary monitoring signals (such as oil temperature and gas relay status): if the auxiliary signal synchronization is abnormal (such as oil temperature rising by 5°C or more within 1 minute), then a defect is confirmed;

[0290] If the auxiliary signal is normal, mark it as "suspected defect" and trigger a secondary detection (extend the observation for 3 fundamental frequency cycles, a total of 60ms, to eliminate instantaneous interference) to avoid false alarms based on a single criterion.

[0291] It should be added that, to ensure the feasibility of defect determination, this embodiment also includes:

[0292] Online parameter identification: Real-time parameters in S500 This is achieved by repeatedly calling the least squares method of S300.3, updating every 100ms to ensure timely capture of parameter changes;

[0293] Threshold adaptive adjustment: If the transformer is under special operating conditions (such as overload, tap change), the threshold coefficient will be temporarily adjusted. The value was increased to 1.8 to avoid misjudgment; historical data backtracking: the system automatically stores the virtual differential current and parameter data of the past 30 days, which facilitates the post-defect analysis and judgment logic optimization.

[0294] This embodiment also provides a distribution transformer internal defect diagnosis device based on virtual differential current, which is equipped with a distribution transformer internal defect diagnosis system based on virtual differential current. When the computer program equipped with the distribution transformer internal defect diagnosis system based on virtual differential current is run, it is used to execute the above-mentioned distribution transformer internal defect diagnosis method based on virtual differential current.

[0295] Those skilled in the art will understand that the process of implementing all or part of the steps of the above embodiments can be carried out by hardware or by a program instructing the relevant hardware.

[0296] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for diagnosing internal defects in distribution transformers based on virtual differential current, characterized in that, Includes the following steps: S100, Synchronous Data Acquisition: Real-time acquisition of three-phase line voltage and line current on the high-voltage side of the transformer, three-phase phase voltage and line current on the low-voltage side, and tap position information; S200. Calculate the measured differential current: Based on the high-voltage side line current, low-voltage side line current and the turns ratio determined by the tap position, combined with the zero-sequence current on the star connection side, generate the real-time differential current of each phase. In step S200, calculating the measured differential current includes the following steps: S200.1 Extract synchronous measurement parameters: Extract the following from the synchronously acquired data of S100: High-voltage side line current: ; Low-voltage side line current: ; Nominal turns ratio: ; S200.2 Calculate the high-voltage side current components: The three-phase components are derived from the three-phase line currents on the high-voltage side: ;in, , , This represents the components of the high-voltage side three-phase current derived from the high-voltage side three-phase line current; S200.3 Calculate the zero-sequence current on the star-connected side: If the low-voltage side is star-connected, calculate the zero-sequence current on the low-voltage side. : ; S200.4 Calculate the measured differential current of each phase: The measured differential current for each phase is generated using the following formula: ;in, , , This represents the final calculated measured differential current for each phase; S300. Establish a differential current model: Using the operating data of the transformer when it is defect-free as the training sample, the training sample includes the measured differential current of each phase under the defect-free state generated in S200, the high-voltage side line voltage and the low-voltage side phase voltage obtained in S100. Based on the transformer T-shaped equivalent circuit, the winding impedance distribution and excitation branch parameters are associated, and the differential current model of the transformer when it is defect-free is obtained through training. S400. Generate virtual differential current: Based on the differential current model established in S300, calculate the deviation between the measured differential current of each phase generated in S200 and the model prediction value, and use it as the virtual differential current; and perform filtering processing on the virtual differential current. S500 Defect Judgment: When the virtual differential current of any phase exceeds the preset threshold, it is determined that the transformer has an internal defect.

2. The method for diagnosing internal defects in distribution transformers based on virtual differential current according to claim 1, characterized in that, The synchronous data acquisition of S100 includes the following steps: S100.1 Real-time acquisition of three-phase line voltage and line current on the high-voltage side of the transformer and three-phase phase voltage and line current on the low-voltage side, with an acquisition frequency of not less than 50Hz; S100.

2. Synchronously acquire transformer tap position information and determine the nominal turns ratio of the transformer based on the transformer tap position information; S100.

3. The voltage and current data collected in S100.1 are associated with and stored with the nominal turns ratio determined in S100.2, and used as the original input parameters for calculating the real-time differential current of each phase in S200.

3. The method for diagnosing internal defects in distribution transformers based on virtual differential current according to claim 2, characterized in that, In S300, the training samples are timestamped synchronization phasor data acquired during the trial run, including: No. Measured differential current of phase ; High-voltage side line voltage: Phase line voltage , Phase line voltage , Phase line voltage ; Low-voltage side phase voltage: Phase voltage , Phase voltage , Phase voltage ; Furthermore, the operating data of the transformer when it is defect-free is the steady-state operating data of more than 72 consecutive hours without failure during the trial operation period.

4. The method for diagnosing internal defects in distribution transformers based on virtual differential current according to claim 3, characterized in that, The differential current model obtained through training in S300 when the transformer is defect-free includes the following steps: S300.1 Data Preparation: During the trial operation, according to the time sequence ,collection: Phase: High-voltage side line voltage Low-voltage side phase voltage Measured differential current ; Phase: High-voltage side line voltage Low-voltage side phase voltage Measured differential current ; Phase: High-voltage side line voltage Low-voltage side phase voltage Measured differential current ; S300.2, Constructing Matrices and Vectors: For the Phase, structure: Input matrix: ;in, For the first Phase high voltage side line voltage; For the first Phase voltage on the low-voltage side; For the first Each sampling time; Output vector: ;in, For the first Measure the differential current in phase; Parameter vector: ;in, For the first The coefficient of line voltage in the phase model; For the first The coefficients of phase voltage in the phase model; S300.3, Least Squares Method for Parameter Identification: ； in, for The conjugate transpose of ; for The inverse matrix; S300.

4. Establish the differential current model for each phase: ； in, For the first The differential current model prediction of the phase; These are the parameters of the differential current model, and .

5. The method for diagnosing internal defects in distribution transformers based on virtual differential current according to claim 4, characterized in that, The differential current model parameters established by S300 , There is a quantitative correlation between the parameters and the T-shaped equivalent circuit parameters of each phase winding of the transformer, specifically including: Differential current model prediction value Relationship with voltage: The relationship between the differential current model prediction and the high-voltage side line voltage and the low-voltage side phase voltage is as follows: ； in, Indicates the first The impedance of the high-voltage winding; Indicates the first The impedance of the low-voltage winding referred to the high-voltage side; Indicates the first The impedance of the phase excitation branch; This indicates the turns ratio of the high-voltage side to the low-voltage side of the transformer; The relationship between differential current model parameters and the impedance of the transformer T-equivalent circuit: , The relationship expression with the parameters of the transformer T-equivalent circuit is as follows: 。 6. The method for diagnosing internal defects in distribution transformers based on virtual differential current according to claim 5, characterized in that, The generation of the virtual differential current in S400 includes the following steps: S410.1 Extracting the comparison amount: Get the first in S200 Measured differential current of phase And the first in S300 Predicted values ​​from the phase differential current model ; S410.2 Calculate the deviation modulus: According to the formula Calculate the first The virtual differential current of the phase, where: The modulus operation of complex phasors is used to convert phasor differences into scalar magnitudes, quantizing the degree of deviation. For the first In time The virtual differential current is used to reflect the deviation between the measured value and the model prediction value.

7. The method for diagnosing internal defects in distribution transformers based on virtual differential current according to claim 6, characterized in that, The filtering process for the virtual differential current in S400 includes the following steps: S420.1, to Using sliding window mean filtering, the formula is: ； in, Indicates the first The virtual differential current after phase filtering; Indicates the size of the sliding window; This represents the backtracking index within the window; Indicates the first Phase at historical sampling moment The original virtual differential current; Indicates the current sampling time; S420.2, Filter the... As an input parameter for internal defect determination in S500.

8. The method for diagnosing internal defects in distribution transformers based on virtual differential current according to claim 7, characterized in that, The setting of the preset threshold and the determination of internal defects in S500 include the following steps: S500.1 Determination of the preset threshold: The preset threshold is calculated based on the defect-free training samples of S300, and the virtual differential current phasors of each phase during the training phase are filtered by the mean of the S420 sliding window. It shall be determined as follows: Extracting training period The maximum amplitude is denoted as ; No. Preset threshold Defined as: ;in, This is the threshold coefficient; S500.2 Internal Defect Judgment and Type Inference: When the Amplitude of virtual differential current after phase filtering At that time, it was determined that the phase had an internal defect; further, by combining the correlation between the differential current model parameters in S300 and the transformer T-equivalent circuit, the defect type was inferred. The specific defect types include: Winding inter-turn short circuit: If At the same time, it deviates significantly from the training values, and Continue to increase; Phase-to-phase short circuit: If multiple phases Simultaneously exceeding the standard; Internal short circuit to ground in winding: if A single anomaly, and It exhibits a step-like growth.

9. A device for diagnosing internal defects in distribution transformers based on virtual differential current, comprising a system for diagnosing internal defects in distribution transformers based on virtual differential current, characterized in that, When the computer program of the distribution transformer internal defect diagnosis system based on virtual differential current is executed, it performs the steps of the distribution transformer internal defect diagnosis method based on virtual differential current as described in any one of claims 1-8.

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