Transformer fault ride-through monitoring, evaluation and short circuit model correction device and method

Abstract

The application belongs to the technical field of electric power engineering, and discloses a device and method for monitoring, evaluating and correcting a short-circuit model of a transformer transitive fault, which comprises a data acquisition device, a data processing module, a fault identification module, a state evaluation module and a correction module; the data acquisition device collects voltage and current signals of the transformer and the bus into digital signals; the data processing module analyzes, corrects or eliminates outlier and missing data of the collected voltage and current data; the fault identification module identifies the transitive fault of the transformer on the basis of the corrected data; the state evaluation module evaluates and early warns the state of the transformer; and the correction module checks and corrects impedance values of the short-circuit model. The application realizes automatic correction of an external transitive short-circuit model by real-time monitoring data, and can evaluate and early warn the transformer fault.

Description

Technical Field

[0001] This invention belongs to the field of transformer condition monitoring, specifically relating to a device and method for monitoring, evaluating, and correcting short-circuit models of transformer through-faults. Background Technology

[0002] As a key node and core equipment in the power system, the reliable operation of transformers directly affects the safety and stability of the power system. Transformer protection includes differential protection and gas protection, which mainly protect against internal or zone-specific faults. However, external short circuits causing through-faults to transformers can also affect their stable operation.

[0003] Current technologies primarily monitor the external operating status of transformers using fault recording devices. However, these devices have certain shortcomings that need improvement. First, existing fault recording devices cannot automatically identify through-faults such as external short circuits, requiring manual reading and judgment of waveforms, which increases the difficulty of frontline work. Second, existing fault recording devices cannot automatically correct external through-fault models based on real-time monitoring data, lacking transformer fault evaluation and early warning functions, and thus failing to effectively guide lean maintenance of transformers. Summary of the Invention

[0004] The technical problem to be solved by the present invention is that: existing fault recording devices cannot automatically identify through-faults such as external short circuits, and require manual reading and judgment of waveforms, which increases the difficulty of front-line work; secondly, existing transformer fault recording devices do not have the function of automatically correcting external through-fault models based on real-time monitoring data, do not have transformer fault evaluation and early warning functions, and cannot effectively guide the lean maintenance of transformers.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: a transformer through-fault monitoring, evaluation, and short-circuit model correction device, comprising a data acquisition device, a data processing module, a fault identification module, a status evaluation module, and a correction module; the data acquisition device is connected to the secondary terminals of the voltage transformer and current transformer on the outgoing side and busbar of the power transformer, and is used to acquire the voltage and current signals of the transformer and busbar into digital signals; the data processing module analyzes the acquired voltage and current data and corrects or removes outlier or missing data; the fault identification module identifies transformer through-faults based on the corrected data; the status evaluation module evaluates and provides early warning of the transformer status; and the correction module verifies and corrects the impedance value of the short-circuit model.

[0006] A method for monitoring, evaluating, and correcting short-circuit models of transformer through-faults includes the following steps:

[0007] Step 1: The data acquisition device samples and collects data from the secondary sides of the voltage transformers and current transformers on the outgoing side and busbar of the power transformer. The collected data is then transmitted to the data processing module for data processing.

[0008] Step 2: The data processing module determines that the data is out of the loop when the current signal or voltage signal exceeds the out-of-loop threshold compared with the predicted value for each data point of the acquired digital signal, calculates the out-of-loop interval, and corrects or removes the out-of-loop data.

[0009] Step 3: The fault identification module analyzes the corrected current and voltage data to identify transformer ride-through faults;

[0010] Step 4: The condition assessment module calculates the overvoltage and ride current of the transformer to evaluate and provide early warnings about the transformer's condition.

[0011] Step 5: Short-circuit model correction: Based on the through-short-circuit fault data, verify and correct the impedance value of the short-circuit model and the maximum short-circuit current model that can be withstood under dynamic and thermal stability.

[0012] Further optimization involves the following step: in step one, the sampling rate dynamically changes with the feedback data of waveform historical characteristics, outlier data, and through-fault identification. Depending on the feedback data, the data acquisition device has three modes: basic mode, monitoring mode, and fault recording mode.

[0013] Further optimization, the processing procedure in step two is as follows:

[0014] The predicted value at time t is:

[0015]

[0016] Where t is time t, Fsig(t) and Sig(t) are the predicted and measured values ​​of the data at time t, respectively. s Let the sampling rate be set for the platform at time t, f0 be the rated frequency of the power system, and p be the cycle prediction factor, which is determined by the periodic fluctuation and calculated by the following formula:

[0017]

[0018] q is the sampling point number;

[0019] Let t q t represents the moment of a single outlier or the start of a collective outlier. r For any point within the collective out-of-group period, k s k e These are the outlier initiation value and the outlier regression value, respectively. When t exists... q Always satisfied: and Then determine tq Single-point outliers in time-series data; correction of outlier data.

[0020] When t exists q To t q +f0 / f s At any time t within the time period r satisfy:

[0021] and Then determine t q To t q +f0 / f s Collective outliers within the cycle, data within the collective outlier interval are corrected to...

[0022] Further optimization, the specific process of step three is as follows:

[0023] For three-phase voltage / current data, when the current / voltage mutation coefficients simultaneously satisfy the following:

[0024]

[0025]

[0026] In the formula, r u r is the voltage jump coefficient. i t is the coefficient of change in current. s U is the time when the fault begins. A / B / C and I A / B / C These are the voltages and currents of phases A, B, and C at the moment of fault initiation, ΔU k0 The voltage change threshold, ΔI k0 The threshold for sudden current change;

[0027] If the current or voltage signal corresponding to the analyzed data is found to be abnormal, the fault identification module will feed back the data to the data acquisition device, which will then switch from basic mode to monitoring mode, and adjust the sampling rate to be no less than:

[0028]

[0029] Where f w f a These are the reference frequency and the division frequency in the monitoring mode, respectively.

[0030] Further preferably, in monitoring mode, the fault identification module detects faults when the current and voltage on the high, medium, and low voltage sides of the transformer meet the specified conditions. and Then it is determined that the transformer has experienced a through-short circuit fault, where k coil For the transformer winding ratio, I HU is the high-voltage side current of the transformer. H / M / L I represents the voltage on any one of the high, medium, or low voltage sides of the transformer. M / L These are the currents on the medium and low voltage sides of the transformer.

[0031] After a through-short circuit occurs, the fault identification module sends a feedback signal to the data acquisition device, which then switches from basic mode to monitoring mode and adjusts the sampling rate to the maximum frequency.

[0032] Further optimization, the specific process of step four is as follows: the transformer suffers from the current through-fault risk coefficient k r Calculate k using the following formula: r =0.8k D ·[1+ln(n D1 +n D2 / 10+△t d / f0)]+0.2k T ·△t t / f0

[0033] Where n D1 n D2 Δt represents the number of times the historical dynamic stability current ratio exceeded 80% and 20%, respectively, including this instance. d , △t t For the duration of the dynamic and thermal stability currents, k D k is the dynamic stability current ratio of the short-circuit current during this through-fault. T The thermal stability current ratio of the short-circuit current during this through-fault is expressed as:

[0034]

[0035]

[0036] T max I represents the moment when the transformer current reaches its maximum. D To ensure the transformer can withstand the maximum short-circuit current under dynamic stability, I T This refers to the maximum short-circuit current that the transformer can withstand under thermal stability conditions.

[0037] Further preferred, in step five, the total impedance value Z of the short-circuit model a for:

[0038]

[0039] Among them, t max Δt represents the moment when the short-circuit current reaches its maximum instantaneous value. d U represents the short-circuit dynamic stability duration, U represents the short-circuit side voltage transient value, and I represents the short-circuit side current transient value.

[0040] The maximum short-circuit current that a transformer can withstand under dynamic and thermal stability conditions is corrected to be:

[0041]

[0042]

[0043] Among them, I D To ensure the transformer can withstand the maximum short-circuit current under dynamic stability, I T U is the maximum short-circuit current that the transformer can withstand under thermal stability conditions. m This is the highest operating voltage on the short-circuit side of the transformer.

[0044] The present invention also provides a non-volatile computer storage medium storing computer-executable instructions that can execute the transformer through-fault monitoring, evaluation, and short-circuit model correction method described in the above embodiments.

[0045] The beneficial effects of this invention are as follows: the data processing module analyzes the collected voltage and current data, correcting or removing outliers and missing data; the fault identification module identifies transformer through-faults based on the corrected data; the status evaluation module evaluates and provides early warnings for the transformer status; and the correction module verifies and corrects the impedance value of the short-circuit model, enabling real-time monitoring data to automatically correct the external through-fault short-circuit model, allowing for transformer fault evaluation and early warning, and guiding lean maintenance of transformers. Attached Figure Description

[0046] Figure 1 This is a structural diagram of the present invention. Detailed Implementation

[0047] The invention will now be explained in further detail with reference to the accompanying drawings.

[0048] Reference Figure 1 A transformer through-fault monitoring, evaluation, and short-circuit model correction device is disclosed, comprising a data acquisition device, a data processing module, a fault identification module, a status evaluation module, and a correction module. The data acquisition device is connected to the secondary terminals of voltage transformers and current transformers on the outgoing side and busbar of the power transformer, and is used to acquire voltage and current signals of the transformer and busbar into digital signals. The data processing module analyzes the acquired voltage and current data and corrects or removes outliers and missing data. The fault identification module identifies through-faults in the transformer based on the corrected data. The status evaluation module evaluates and provides early warnings for the transformer status. The correction module verifies and corrects the impedance value of the short-circuit model.

[0049] A method for monitoring, evaluating, and correcting short-circuit models of transformer through-faults includes the following steps:

[0050] Step 1: The data acquisition device samples and collects data from the secondary sides of the voltage transformers and current transformers on the outgoing side and busbar of the power transformer. The sampling rate dynamically changes with the feedback data such as waveform history characteristics, outlier data, and through-fault identification. The minimum is 20kHz and the maximum is 800kHz. Depending on the feedback data, the data acquisition device has three modes: basic mode, monitoring mode, and fault recording mode. The collected data is transmitted to the data processing module for data processing.

[0051] Step 2: The data processing module determines that the data is out of the loop when the current signal or voltage signal exceeds the out-of-loop threshold compared with the predicted value for each data point of the acquired digital signal, calculates the out-of-loop interval, and corrects or removes the out-of-loop data.

[0052] The predicted value at time t is:

[0053]

[0054] Where t is time t, Fsig(t) and Sig(t) are the predicted and measured values ​​of the data at time t, respectively. s Let the sampling rate be set for the platform at time t, f0 be the rated frequency of the power system, and p be the cycle prediction factor, which is determined by the periodic fluctuation and calculated by the following formula:

[0055]

[0056] q is the sampling point number.

[0057] Let t q t represents the moment of a single outlier or the start of a collective outlier. r For any point within the collective out-of-group period, k s k e These are the outlier initiation value and the outlier regression value, respectively. When t exists... q Always satisfied: and Then determine t q Single-point outliers in time-series data; correction of outlier data.

[0058] When t exists q To t q +f0 / f s At any time t within the time period r satisfy:

[0059] and Then determine t q To t q +f0 / f sCollective outliers within the cycle, data within the collective outlier interval are corrected to...

[0060] Typically for current signal k s =0.9, k e =0.95, for voltage signal k s =0.8, k e =0.85.

[0061] Step 3: The fault identification module analyzes the corrected current and voltage data to identify transformer ride-through faults;

[0062] For three-phase voltage / current data, when the current / voltage mutation coefficients simultaneously satisfy the following:

[0063]

[0064]

[0065] In the formula, r u r is the voltage jump coefficient. i t is the coefficient of change in current. s U is the time when the fault begins. A / B / C and I A / B / C These are the voltages and currents of phases A, B, and C at the moment of fault initiation, ΔU k0 The voltage change threshold, ΔI k0 The threshold for sudden current change;

[0066] If the current or voltage signal corresponding to the analyzed data is found to be abnormal, the fault identification module will feed back the data to the data acquisition device, which will then switch from basic mode to monitoring mode, and adjust the sampling rate to be no less than:

[0067]

[0068] Where f w f a These are the reference frequency and the division frequency in the monitoring mode, respectively.

[0069] In monitoring mode, the fault identification module detects when the current and voltage on the high, medium, and low voltage sides of the transformer meet the requirements. and Then it is determined that the transformer has experienced a through-short circuit fault, where k coil For the transformer winding ratio, I H U is the high-voltage side current of the transformer. H / M / L I represents the voltage on any one of the high, medium, or low voltage sides of the transformer. M / L This refers to the current on the medium and low voltage sides of the transformer.

[0070] After a through-short circuit occurs, the fault identification module sends a feedback signal to the data acquisition device, which then switches from basic mode to monitoring mode and adjusts the sampling rate to the maximum frequency of 800kHz.

[0071] Step 4: The condition assessment module calculates the overvoltage and ride current of the transformer to evaluate and provide early warnings about the transformer's condition.

[0072] Transformer suffers current through-fault risk coefficient k r Calculate k using the following formula: r =0.8k D ·[1+ln(n D1 +n D2 / 10+△t d / f0)]+0.2k T ·△t t / f0

[0073] Where n D1 n D2 Δt represents the number of times the historical dynamic stability current ratio exceeded 80% and 20%, respectively, including this instance. d , △t t For the duration of the dynamic and thermal stability currents, k D k is the dynamic stability current ratio of the short-circuit current during this through-fault. T The thermal stability current ratio of the short-circuit current during this through-fault is expressed as:

[0074]

[0075]

[0076] T max I represents the moment when the transformer current reaches its maximum. D To ensure the transformer can withstand the maximum short-circuit current under dynamic stability, I T This refers to the maximum short-circuit current that the transformer can withstand under thermal stability conditions.

[0077] Based on the transformer's current through-fault risk coefficient k r The risk of transformer short circuits is classified into four levels, and the classification criteria and corresponding handling measures for transformers are shown in the table below:

[0078]

[0079] Step 5: Short-circuit model correction: Based on the through-short-circuit fault data, verify and correct the impedance value of the short-circuit model and the maximum short-circuit current model that can be withstood under dynamic and thermal stability.

[0080] The total impedance value Z of the short-circuit model a for:

[0081]

[0082] Among them, t max Δt represents the moment when the short-circuit current reaches its maximum instantaneous value. d U represents the short-circuit dynamic stability duration, U represents the short-circuit side voltage transient value, and I represents the short-circuit side current transient value.

[0083] The maximum short-circuit current that a transformer can withstand under dynamic and thermal stability conditions is corrected to be:

[0084]

[0085]

[0086] Among them, I D To ensure the transformer can withstand the maximum short-circuit current under dynamic stability, I T U is the maximum short-circuit current that the transformer can withstand under thermal stability conditions. m This is the highest operating voltage on the short-circuit side of the transformer.

[0087] This embodiment also provides a non-volatile computer storage medium storing computer-executable instructions that can execute the transformer through-fault monitoring, evaluation, and short-circuit model correction method described in the above embodiment.

[0088] Finally, it should be noted that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for monitoring, evaluating, and correcting short-circuit models of transformer through-faults, characterized by the following steps: as follows: Step 1: The data acquisition device samples and collects data from the secondary sides of the voltage transformers and current transformers on the outgoing side and busbar of the power transformer. The collected data is then transmitted to the data processing module for data processing. Step 2: The data processing module determines that the data is out of the loop when the current signal or voltage signal exceeds the out-of-loop threshold compared with the predicted value for each data point of the acquired digital signal, calculates the out-of-loop interval, and corrects or removes the out-of-loop data. Step 3: The fault identification module analyzes the corrected current and voltage data to identify transformer ride-through faults; In monitoring mode, the fault identification module detects when the current and voltage on the high, medium, and low voltage sides of the transformer meet the requirements. and If so, it is determined that the transformer has experienced a through-circuit short circuit fault, where k coil For the transformer winding ratio, I H U is the high-voltage side current of the transformer. H / M / L I represents the voltage on any one of the high, medium, or low voltage sides of the transformer. M / L For the medium and low voltage sides of the transformer; f s Set the sampling rate for the platform at time t, where f0 is the rated frequency of the power system, q is the sampling point number, and t is the sampling rate. s This is the time when the fault begins; Step 4: The condition assessment module calculates the transformer's overvoltage and through-current to evaluate and provide early warnings regarding the transformer's condition; among which, the transformer's risk coefficient k for current through-fault is calculated. r Calculate using the following formula: ， in , For the number of times the historical dynamic stability current ratio exceeded 80% and 20%, respectively, including this instance, △t d , △t t For the duration of the dynamic and thermal stability currents, k D k is the dynamic stability current ratio of the short-circuit current during this through-fault. T This is the ratio of the short-circuit current to the thermally stable current during this through-fault. Step 5: Short-circuit model correction: Based on through-short-circuit fault data, verify and correct the impedance value and the maximum short-circuit current model that can withstand under dynamic and thermal stability conditions of the short-circuit model; wherein, the total impedance value Z of the short-circuit model is... a for: ， Among them, t max Δt represents the moment when the short-circuit current reaches its maximum instantaneous value. d U represents the short-circuit dynamic stability duration, U represents the short-circuit side voltage transient value, and I represents the short-circuit side current transient value.

2. The method for monitoring, evaluating, and correcting short-circuit models of transformer through-faults according to claim 1, characterized in that, In step one, the sampling rate changes dynamically with the feedback data of waveform historical characteristics, outlier data, and through-fault identification. Depending on the feedback data, the data acquisition device has three modes: basic mode, monitoring mode, and fault recording mode.

3. The method for monitoring, evaluating, and correcting short-circuit models of transformer through-faults according to claim 1, characterized in that, The specific processing steps in step two are as follows: The predicted value at time t is: ； Where t is time t, Fsig(t) and Sig(t) are the predicted and measured values ​​of the data at time t, respectively, and p is the cycle prediction factor, which is determined by the cycle fluctuation and is calculated by the following formula: ； Let t q t represents the moment of a single outlier or the start of a collective outlier. r For any point within the collective out-of-group period, k s k e These are the outlier initiation value and the outlier regression value, respectively. When t exists... q Always satisfied: Then determine t q Single-point outliers in time-series data; correction of outlier data. ; When t exists q To t q +f0 / f s At any time t within the time period r satisfy: Then determine t q To t q +f0 / f s Collective outliers within the cycle, data within the collective outlier interval are corrected to... .

4. The method for monitoring, evaluating, and correcting short-circuit models of transformer through-faults according to claim 3, characterized in that, The specific process of step three is as follows: For three-phase voltage / current data, when the current / voltage mutation coefficients simultaneously satisfy the following: ； In the formula, r u r is the voltage jump coefficient. i U is the coefficient of change in current. A / B / C and I A / B / C These are the voltages and currents of phases A, B, and C at the moment of fault initiation. The voltage change threshold, The threshold for sudden current change; If the current or voltage signal corresponding to the analyzed data is found to be abnormal, the fault identification module will feed back the data to the data acquisition device, which will then switch from basic mode to monitoring mode, and adjust the sampling rate to be no less than: ； Where f w f a These are the reference frequency and the division frequency in the monitoring mode, respectively.

5. The method for monitoring, evaluating, and correcting short-circuit models of transformer through-faults according to claim 4, characterized in that, After a through-short circuit occurs, the fault identification module sends a feedback signal to the data acquisition device, which then switches from basic mode to monitoring mode and adjusts the sampling rate to the maximum frequency.

6. The method for monitoring, evaluating, and correcting short-circuit models of transformer through-faults according to claim 5, characterized in that, In step four, k D and k T The calculation method is as follows: ； ； T max I represents the moment when the transformer current reaches its maximum. D To ensure the transformer can withstand the maximum short-circuit current under dynamic stability, I T This refers to the maximum short-circuit current that the transformer can withstand under thermal stability conditions.

7. The method for monitoring, evaluating, and correcting short-circuit models of transformer through-faults according to claim 1, characterized in that the transformer... The maximum short-circuit current that can be withstood under dynamic and thermal stability conditions is corrected to be: ； Among them, I D To ensure the transformer can withstand the maximum short-circuit current under dynamic stability, I T U is the maximum short-circuit current that the transformer can withstand under thermal stability conditions. m This is the highest operating voltage on the short-circuit side of the transformer.

8. An apparatus for implementing the transformer through-fault monitoring, evaluation, and short-circuit model correction method according to any one of claims 1-7, characterized in that, The system includes a data acquisition device, a data processing module, a fault identification module, a status evaluation module, and a correction module. The data acquisition device connects to the secondary terminals of the voltage and current transformers on the outgoing side and busbar of the power transformer, and is used to acquire the voltage and current signals of the transformer and busbar into digital signals. The data processing module analyzes the acquired voltage and current data and corrects or removes outliers and missing data. The fault identification module identifies transformer through-faults based on the corrected data. The status evaluation module evaluates and provides early warnings for the transformer status. The correction module verifies and corrects the impedance value of the short-circuit model.

9. A non-volatile computer storage medium storing computer-executable instructions, characterized in that, The computer-executable instructions can execute the transformer through-fault monitoring, evaluation and short-circuit model correction method according to any one of claims 1-7.

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