A method for diagnosing transformer winding faults

Abstract

The present invention relates to the technical field of power systems. An embodiment discloses a transformer winding fault diagnosis method, comprising: determining a Lissajous figure target fault set based on a historical measured health set of a Lissajous figure and a simulated fault set of a Lissajous figure variation, obtaining the Lissajous figure of the transformer to be tested at the current moment in real time, and determining the current moment's Lissajous figure status based on the current moment's Lissajous figure, the historical measured health set of the Lissajous figure, and the Lissajous figure target fault set, adding the current moment's Lissajous figure status to a Lissajous figure monitoring set, and determining the diagnosis result of the transformer to be tested in real time; the method can effectively eliminate the influence of external environmental factors on the Lissajous figure waveform, thereby improving the accuracy and reliability of transformer winding fault diagnosis using the Lissajous figure, and realizing real-time tracking and early diagnosis of the winding status.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a transformer winding fault diagnosis method. Background Art

[0002] As core equipment in power systems, power transformers carry the heavy responsibility of voltage conversion and power transmission and distribution. Their stable operation is crucial to ensuring power supply. However, power transformers face a variety of failure risks during operation, including insulation aging, short-circuit failures, overload, moisture, core failure, and improper maintenance. These factors can seriously affect the normal operation of the transformer. Therefore, transformer winding fault diagnosis is a key step in ensuring the safe and stable operation of the power system.

[0003] In recent years, Lissajous figures based on voltage and current signals have gradually attracted attention from the industry due to their high sensitivity in transformer winding fault diagnosis. Although the method of using Lissajous figures to diagnose transformer winding faults has been developed, in actual application, this method has the following main problems: it ignores the problem that the Lissajous figure waveform is easily disturbed by external environmental factors such as transformer grade and structure, service life, on-site noise, load fluctuations and different operating conditions, and is mistakenly diagnosed as a winding fault, which in turn affects the accuracy of the diagnosis results; there is a lack of tracking and early warning algorithms for winding faults, which is not conducive to the early diagnosis of winding faults. Summary of the Invention

[0004] Based on this, it is necessary to propose a transformer winding fault diagnosis method to address the above problems, which can effectively eliminate the influence of external environmental factors on the Lissajous figure waveform, thereby improving the accuracy and reliability of transformer winding fault diagnosis using Lissajous figure, and realizing real-time tracking and early diagnosis of winding status.

[0005] To achieve the above objectives, the present invention provides, in a first aspect, a transformer winding fault diagnosis method, the method comprising:

[0006] Obtain a historically measured Lissajous diagram health set of a healthy transformer and a simulated fault set of a Lissajous diagram variation of a simulated healthy transformer, and determine a target Lissajous diagram fault set based on the historically measured Lissajous diagram health set and the simulated fault set of the Lissajous diagram variation;

[0007] Obtaining the Lissajous diagram of the transformer under test at the current moment in real time, and determining the status of the Lissajous diagram at the current moment according to the Lissajous diagram at the current moment, the historical measured healthy set of the Lissajous diagram, and the target fault set of the Lissajous diagram, and determining whether the status is healthy or faulty;

[0008] Adding the current Lissajous figure to a Lissajous figure monitoring set, wherein the Lissajous figure monitoring set includes the Lissajous figures at all times within the monitoring period from a preset start time point to the current moment;

[0009] According to the Lissajous diagram monitoring set, a diagnosis result of the transformer to be tested is determined in real time.

[0010] Optionally, obtaining a historically measured Lissajous diagram of a healthy transformer includes:

[0011] Using a dynamic recording device, obtaining a measured voltage signal and a measured current signal of the healthy transformer at time t1, and determining a measured Lissajous figure at time t1 based on the measured voltage signal and the measured current signal at time t1, wherein t1 successively takes integers greater than 0 until t1 equals the total number of the first time, to obtain measured Lissajous figures at all times, and the dynamic recording device has been installed on the healthy transformer;

[0012] The Lissajous diagram historical measured health set is determined based on the measured Lissajous diagram at all moments.

[0013] Optionally, determining the Lissajous figure historical measured health set based on the measured Lissajous figure at all times includes:

[0014] Determine the confidence interval corresponding to each characteristic quantity based on each characteristic quantity of the measured Lissajous figure at all times;

[0015] Determine the confidence level of the measured Lissajous figure at each moment based on all confidence intervals and all characteristic quantities of the measured Lissajous figure at each moment;

[0016] The historical measured healthy set of the Lissajous figure is determined according to the confidence of the measured Lissajous figure at all moments.

[0017] Optionally, obtaining a simulated fault set of Lissajous figure variation of the healthy transformer simulation includes:

[0018] Constructing a three-dimensional finite element simulation model of the healthy transformer;

[0019] Solving the three-dimensional finite element simulation model of the transformer to obtain a healthy voltage signal and a healthy current signal;

[0020] determining a healthy simulation Lissajous figure according to the healthy voltage signal and the healthy current signal;

[0021] According to a preset fault type table, the fault types of the three-dimensional finite element simulation model of the transformer are adjusted in sequence, and after each adjustment, the adjusted three-dimensional finite element simulation model of the transformer is solved to obtain fault voltage signals and fault current signals of multiple fault types;

[0022] The fault simulation Lissajous diagram for each fault type is determined based on the fault voltage signal and fault current signal of each fault type;

[0023] Determine a change amount of the fault simulation Lissajous diagram for each fault type according to the fault simulation Lissajous diagram for each fault type and the healthy simulation Lissajous diagram;

[0024] The fault simulation Lissajous figure variations of all fault types are taken as the Lissajous figure variation simulation fault set.

[0025] Optionally, solving the three-dimensional finite element simulation model of the transformer to obtain a healthy voltage signal and a healthy current signal includes:

[0026] Constructing an equivalent circuit parameter simulation model of the three-dimensional finite element simulation model of the transformer;

[0027] Solving the three-dimensional finite element simulation model of the transformer to obtain characteristic parameters of a healthy transformer winding;

[0028] Inputting the healthy transformer winding characteristic parameters into the equivalent circuit parameter simulation model for simulation to obtain the healthy voltage signal and the healthy current signal;

[0029] According to the preset fault type table, the fault type of the transformer three-dimensional finite element simulation model is adjusted in sequence, and after each adjustment, the adjusted transformer three-dimensional finite element simulation model is solved to obtain fault voltage signals and fault current signals of multiple fault types, including:

[0030] According to the fault type table, the fault types of the three-dimensional finite element simulation model of the transformer are adjusted in sequence, and after each adjustment, the adjusted three-dimensional finite element simulation model of the transformer is solved to obtain characteristic parameters of the fault transformer windings of various fault types;

[0031] The fault transformer winding characteristic parameters of each fault type are sequentially input into the equivalent circuit parameter simulation model for simulation, and the fault voltage signals and fault current signals of various fault types are obtained.

[0032] Optionally, determining a Lissajous figure target fault set according to the Lissajous figure historical measured healthy set and the Lissajous figure variation simulated fault set includes:

[0033] Multiplying each characteristic quantity of the measured Lissajous figure at the t2 moment in the Lissajous figure historical measured health set by the corresponding characteristic quantity change quantity of the fault simulation Lissajous figure change quantity of each fault type in the Lissajous figure change quantity simulation fault set, to obtain the fault Lissajous figure of each fault type at the t2 moment, where t2 successively takes integers greater than 0 until t2 equals the total number of the second moment, to obtain the fault Lissajous figure of each fault type at all moments;

[0034] The fault Lissajous diagram of all fault types at all times is taken as the Lissajous diagram target fault set.

[0035] Optionally, determining the status of the Lissajous diagram at the current moment according to the Lissajous diagram at the current moment, the historically measured healthy set of the Lissajous diagram, and the target fault set of the Lissajous diagram includes:

[0036] Determine each mean data point of the measured Lissajous figure in the historical measured health set according to each data point of the measured Lissajous figure at all times in the historical measured health set;

[0037] Determining a health correlation between the Lissajous figure at the current moment and the Lissajous figure measured in the historical measured health set based on a plurality of data points of the Lissajous figure at the current moment and a plurality of mean data points of the Lissajous figure measured in the historical measured health set;

[0038] Determine, according to each data point of the fault Lissajous diagram of each fault type in the Lissajous diagram target fault set at all times, each mean data point of the fault Lissajous diagram of each fault type in the Lissajous diagram target fault set;

[0039] Determining, based on a plurality of data points of the Lissajous diagram at a current moment and a plurality of mean data points of the fault Lissajous diagram of each fault type in the Lissajous diagram target fault set, a fault correlation between the Lissajous diagram at a current moment and the fault Lissajous diagram of each fault type in the Lissajous diagram target fault set;

[0040] determining a maximum fault correlation based on the fault correlations of all fault types;

[0041] The status of the Lissajous diagram at the current moment is determined according to the health correlation and the maximum fault correlation.

[0042] Optionally, determining the Lissajous diagram at the current moment according to the health correlation and the maximum fault correlation includes:

[0043] If the absolute value of the difference between the healthy correlation and the maximum fault correlation is greater than or equal to the minimum correlation determination threshold, and the healthy correlation is greater than the maximum fault correlation, the Lissajous diagram at the current moment is considered healthy.

[0044] When the absolute value of the difference between the healthy correlation and the maximum fault correlation is greater than or equal to the minimum correlation determination threshold, and the healthy correlation is less than the maximum fault correlation, the Lissajous diagram at the current moment is in a fault state;

[0045] When the absolute value of the difference between the health correlation and the maximum fault correlation is less than the minimum correlation judgment threshold, the Lissajous diagram at the current moment, the Lissajous diagram historical measured health set and the Lissajous diagram target fault set are input into the preset Lissajous diagram affiliation judgment model to obtain the affiliation of the Lissajous diagram at the current moment.

[0046] Optionally, determining the diagnosis result of the transformer to be tested in real time according to the Lissajous diagram monitoring set includes:

[0047] If, within the monitoring time of the Lissajous figure monitoring set, the proportion of the Lissajous figure belonging to the situation of failure in the first continuous time period is greater than or equal to a first preset proportion, the diagnosis result is determined to be a sudden failure;

[0048] If, during the monitoring time of the Lissajous figure monitoring set, there is no Lissajous figure in the first continuous time period with a ratio of fault conditions greater than or equal to the first preset ratio, and there is a Lissajous figure in the second continuous time period with a ratio of fault conditions less than the second preset ratio, then the diagnosis result is determined to be normal and without faults;

[0049] If, during the monitoring period of the Lissajous diagram monitoring set, there is no Lissajous diagram in the first continuous time period whose proportion of fault conditions is greater than or equal to the first preset proportion, and there is a Lissajous diagram in the second continuous time period whose proportion of fault conditions is greater than or equal to the second preset proportion, then the diagnostic result is determined to be a fault that needs to be monitored.

[0050] Optionally, the method further includes:

[0051] If the diagnostic result is the fault to be monitored, and within the monitoring period of the Lissajous diagram monitoring set, there is a third consecutive time period in which the slope value of the proportion of Lissajous diagrams belonging to the fault changes over time is greater than 0, then the diagnostic result is updated to a progressive fault.

[0052] To achieve the above object, the present invention provides, in a second aspect, a transformer winding fault diagnosis device, the device comprising:

[0053] an acquisition and determination module, configured to acquire a historically measured health set of a healthy transformer according to a Lissajous diagram, and a simulated fault set of a variation of a healthy transformer according to a simulation of the healthy transformer according to a Lissajous diagram, and determine a target fault set of the Lissajous diagram according to the historically measured health set of the Lissajous diagram and the simulated fault set of the variation of the Lissajous diagram;

[0054] A real-time acquisition and determination module is used to obtain the Lissajous diagram of the transformer under test at the current moment in real time, and determine the status of the Lissajous diagram at the current moment based on the Lissajous diagram, the historical measured healthy set of the Lissajous diagram, and the target fault set of the Lissajous diagram, and determine whether the status is healthy or faulty;

[0055] Add a monitoring module, for adding the current moment's Lissajous figure status to a Lissajous figure monitoring set, wherein the Lissajous figure monitoring set includes the Lissajous figure status of all moments within the monitoring duration from a preset start time point to the current moment;

[0056] A determination module is used to determine the diagnosis result of the transformer to be tested in real time based on the Lissajous diagram monitoring set.

[0057] To achieve the above-mentioned object, the present invention provides, in a third aspect, a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the method as described in any one of the first aspects.

[0058] To achieve the above-mentioned objectives, the present invention provides a computer device in a fourth aspect, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the method as described in any one of the first aspects.

[0059] The embodiment of the present invention has the following beneficial effects: the above method obtains the historical measured health set of the Lissajous diagram of the healthy transformer and the simulated fault set of the Lissajous diagram variation of the simulated healthy transformer, and determines the target fault set of the Lissajous diagram based on the historical measured health set of the Lissajous diagram and the simulated fault set of the Lissajous diagram variation, obtains the Lissajous diagram of the transformer to be tested at the current moment in real time, and determines the status of the Lissajous diagram at the current moment based on the Lissajous diagram at the current moment, the historical measured health set of the Lissajous diagram and the target fault set of the Lissajous diagram, and whether the status is healthy or faulty, and adds the status of the Lissajous diagram at the current moment to the Lissajous diagram monitoring set The Lissajous diagram monitoring set includes the conditions of the Lissajous diagram at all moments within the monitoring period from a preset start time point to the current moment. According to the Lissajous diagram monitoring set, the diagnostic result of the transformer to be tested is determined in real time; that is, the real fault Lissajous diagram is determined by combining the healthy Lissajous diagram of the healthy transformer measured historically and the change amount of the fault Lissajous diagram of the healthy transformer simulated, and the real fault Lissajous diagram is used to perform transformer winding fault diagnosis, which effectively eliminates the influence of external environmental factors on the Lissajous diagram waveform, thereby improving the accuracy and reliability of transformer winding fault diagnosis using the Lissajous diagram, and realizing real-time tracking and early diagnosis of winding status. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0061] in:

[0062] Figure 1 A schematic diagram of a transformer winding fault diagnosis method according to an embodiment of the present application;

[0063] Figure 2 This is a schematic diagram of a Lissajous diagram in an embodiment of the present application;

[0064] Figure 3 Schematic diagram of a transformer winding fault diagnosis device according to an embodiment of the present application;

[0065] Figure 4 1 is a diagram of the internal structure of a computer device in some embodiments. DETAILED DESCRIPTION

[0066] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0067] In a first aspect, the present application provides a transformer winding fault diagnosis method.

[0068] See also Figure 1 , is a schematic diagram of a transformer winding fault diagnosis method according to an embodiment of the present application, the method comprising:

[0069] Step 110: Obtain a historical measured health set of the Lissajous figure of the healthy transformer and a simulated fault set of the Lissajous figure variation of the healthy transformer simulation, and determine a target fault set of the Lissajous figure based on the historical measured health set and the simulated fault set of the Lissajous figure variation.

[0070] Among them, a healthy transformer refers to a transformer in good operating condition; the Lissajous figure historical measured health set includes multiple measured Lissajous figures of historical measurements of healthy transformers; the Lissajous figure variation simulation fault set includes multiple fault simulation Lissajous figure variations of healthy transformer simulations; and the Lissajous figure target fault set includes multiple real and actual fault Lissajous figures.

[0071] Regarding the selection of healthy transformers, in some embodiments, transformers with long-term stable operation records and good working conditions can be preferentially selected as healthy transformers to ensure the accuracy and representativeness of the obtained Lissajous diagram historical measured health set.

[0072] Regarding the method for determining the change amount of the fault simulation Lissajous diagram, in some embodiments, the healthy transformer simulation can be simulated to obtain the healthy simulation Lissajous diagram and the fault simulation Lissajous diagram respectively, and then the change amount of the fault simulation Lissajous diagram is determined based on the healthy simulation Lissajous diagram and the fault simulation Lissajous diagram; it can be understood that the change amount of the fault simulation Lissajous diagram is the change amount of the fault simulation Lissajous diagram relative to the healthy simulation Lissajous diagram.

[0073] It should be noted that simulation is an approximate description of reality. Since simplifications and assumptions are made in the simulation process of healthy transformers, the fault simulation Lissajous diagram obtained by the healthy transformer simulation will ignore some external environmental factors. The Lissajous diagram waveform is easily affected by external environmental factors such as transformer grade and structure, service life, on-site noise, load fluctuations and different operating conditions. Therefore, these external environmental factors will cause the fault simulation Lissajous diagram to deviate from the actual fault Lissajous diagram, thereby affecting the accuracy of the diagnosis results.

[0074] In this regard, in order to eliminate the interference of external environmental factors on the diagnosis results, the present application, in some embodiments, determines the target fault set of the Lissajous diagram based on the historical measured healthy set of the Lissajous diagram and the simulated fault set of the Lissajous diagram variation. That is, the actual fault Lissajous diagram is determined by using the variation between the measured Lissajous diagram and the fault simulation Lissajous diagram relative to the healthy simulation Lissajous diagram, thereby avoiding the interference of external environmental factors on the diagnosis results.

[0075] Step 120: Obtain the Lissajous diagram of the transformer under test at the current moment in real time, and determine the status of the Lissajous diagram at the current moment based on the Lissajous diagram, the historical measured healthy set of the Lissajous diagram, and the target fault set of the Lissajous diagram, which is healthy or faulty.

[0076] The transformer under test refers to a transformer that requires winding fault diagnosis, and the transformer under test and the healthy transformer are transformers of the same specification type.

[0077] Regarding the method of determining the Lissajous diagram at the current moment, in some embodiments, based on the Lissajous diagram at the current moment, the historically measured healthy set of the Lissajous diagram, and the target fault set of the Lissajous diagram, it can be determined whether the Lissajous diagram at the current moment belongs to the historically measured healthy set of the Lissajous diagram or the target fault set of the Lissajous diagram; when the Lissajous diagram at the current moment belongs to the historically measured healthy set of the Lissajous diagram, it can be determined that the condition of the Lissajous diagram at the current moment is healthy; otherwise, it can be determined that the condition of the Lissajous diagram at the current moment is faulty.

[0078] Step 130: Add the current Lissajous figure status to the Lissajous figure monitoring set, which includes the Lissajous figure status at all times within the monitoring period from the preset start time point to the current moment.

[0079] The preset start time point is the time point at which the Lissajous figure of the transformer to be tested is first obtained in real time.

[0080] It should be noted that, since the present application is to obtain the Lissajous diagram of the transformer to be tested in real time and perform real-time diagnosis based on all Lissajous diagrams, the Lissajous diagram monitoring set includes the status of the Lissajous diagrams at all moments within the monitoring period from the time point at which the real-time acquisition of the Lissajous diagram of the transformer to be tested is just begun to the current moment.

[0081] Step 140: Determine the diagnosis result of the transformer to be tested in real time based on the Lissajous diagram monitoring set.

[0082] Regarding the method of determining the diagnostic result, in some embodiments, the diagnostic result of the transformer to be tested can be determined in real time based on the comparison between the proportion of the Lissajous figure conditions in the Lissajous figure monitoring set that are faults and the intermediate preset proportion; for example, the intermediate preset proportion is 50%. At this time, when the proportion of the Lissajous figure conditions in the Lissajous figure monitoring set that are faults is greater than 50%, it means that the diagnostic result of the transformer to be tested is a fault.

[0083] In an embodiment of the present application, a real fault Lissajous diagram is determined by combining the historically measured healthy Lissajous diagram of a healthy transformer and the changes in the fault Lissajous diagram of a simulated healthy transformer, and the real fault Lissajous diagram is used to diagnose transformer winding faults, thereby effectively eliminating the influence of external environmental factors on the waveform of the Lissajous diagram, thereby improving the accuracy and reliability of transformer winding fault diagnosis using the Lissajous diagram, and realizing real-time tracking and early diagnosis of winding status.

[0084] In addition, the method of the present application also has the following advantages: by obtaining the Lissajous diagram of the transformer to be tested in real time and performing continuous monitoring and diagnosis based on these data, potential faults and early faults of the transformer windings can be discovered in a timely manner, thereby enhancing the dynamic monitoring and early warning capabilities of the power system, which is of great significance for preventing sudden faults, reducing power outages and improving power supply quality; by continuously monitoring the Lissajous diagram of the transformer and determining its status (i.e., healthy or faulty) in real time, operation and maintenance personnel can formulate maintenance plans more accurately. For transformers in a healthy state, the number of unnecessary inspections and repairs can be reduced, thereby saving operation and maintenance costs. For transformers showing signs of failure, timely measures can be taken to repair them to prevent the failure from further deteriorating, reducing maintenance costs and downtime; by real-time monitoring and diagnosis of the Lissajous diagram of the transformer, strong support is provided for the intelligent diagnosis of the transformer, which helps to discover potential faults in advance and improve the reliability and operation efficiency of the transformer.

[0085] In a feasible implementation, step 110 in the above embodiment, obtaining the historical measured health set of the Lissajous figure of the healthy transformer, includes: using a dynamic recording device to obtain the measured voltage signal and the measured current signal of the healthy transformer at the moment t1, and determining the measured Lissajous figure at the moment t1 based on the measured voltage signal and the measured current signal at the moment t1, where t1 successively takes integers greater than 0 until t1 is equal to the total number of the first moment, and the measured Lissajous figures at all moments are obtained, and the dynamic recording device has been installed to the healthy transformer; determining the historical measured health set of the Lissajous figure based on the measured Lissajous figures at all moments.

[0086] Among them, the dynamic recording equipment refers to the equipment used to record the changes in voltage and current waveforms in real time; the total number at the first moment can be obtained and set by the operator based on a lot of experience, experiments or statistics, and of course, it can also be set by the operator according to actual needs.

[0087] Regarding the selection method of dynamic recording equipment, in some embodiments, the dynamic recording equipment can give priority to equipment with higher sampling frequency and anti-interference ability to adapt to the voltage and current waveform changes of the transformer under different operating conditions, and can filter out interference from high-order harmonics and noise.

[0088] Regarding the selection method of the total number of the first moment, in some embodiments, the total number of the first moment can be preferentially selected as three months; it can be understood that the measured Lissajous diagrams of all moments within three months can cover various operating modes of the transformer, including load fluctuations, startup and shutdown states, thereby obtaining a complete and reliable Lissajous diagram historical measured health set.

[0089] Regarding the method for determining the measured Lissajous figure at the moment t1, in some embodiments, the mode of the oscilloscope can be adjusted to the XY mode, and then the measured voltage signal and the measured current signal at the moment t1 are input into the oscilloscope to generate the measured Lissajous figure at the moment t1; wherein the measured voltage signal and the measured current signal can be respectively connected to the Y-axis access terminal and the X-axis access terminal of the oscilloscope.

[0090] In an embodiment of the present application, the voltage and current signals of a healthy transformer are recorded in real time by a dynamic recording device over a continuous time period (such as three months), and measured Lissajous diagrams at multiple moments are generated based on this. This ensures that the collected Lissajous diagram data comprehensively covers various operating modes and states of the transformer, which helps to build a more complete and reliable historical measured health set of Lissajous diagrams, providing a solid foundation for subsequent fault diagnosis. Since measured data over a longer period of time is collected, Lissajous diagram anomalies caused by accidental factors (such as short-term load fluctuations, transient noise, etc.) can be effectively eliminated, thereby improving the quality and representativeness of the historical measured health set of Lissajous diagrams, which is crucial for accurately identifying the true fault state of the transformer winding. Using a dynamic recording device with a high sampling frequency and strong anti-interference ability can adapt to the voltage and current waveform changes of the transformer under different operating conditions, and effectively filter out the interference of high-order harmonics and noise, which makes the collected historical measured health set of Lissajous diagrams more accurate and clear, which helps to improve the accuracy and efficiency of fault diagnosis.

[0091] In a feasible implementation method, the above embodiment determines the historical measured health set of the Lissajous figure based on the measured Lissajous figure at all times, including: determining the confidence interval corresponding to each characteristic quantity based on each characteristic quantity of the measured Lissajous figure at all times; determining the confidence status of the measured Lissajous figure at each moment based on all confidence intervals and all characteristic quantities of the measured Lissajous figure at each moment; and determining the historical measured health set of the Lissajous figure based on the confidence status of the measured Lissajous figure at all times.

[0092] Among them, multiple characteristic quantities of the measured Lissajous figure include but are not limited to major axis / semi-major axis, minor axis / semi-minor axis, eccentricity (i.e. eccentricity), inclination and area.

[0093] For examples, see Figure 2 , is a schematic diagram of a Lissajous figure in an embodiment of the present application, in which a is the semi-major axis, b is the semi-minor axis, and c is the eccentricity. is the inclination angle, and s is the area.

[0094] Regarding the determination of the confidence interval, in some embodiments, the formula Determine the confidence interval corresponding to each feature; where, is the confidence interval corresponding to the nth feature quantity, is the average value of the nth characteristic quantity in the measured Lissajous figure at all times, is the confidence level key value (for example, for a 95% confidence level, ), is the standard error of the nth characteristic quantity in the measured Lissajous figure at all times.

[0095] Regarding the method for determining the confidence situation and the method for determining the historical measured health set of the Lissajous diagram, in some embodiments, when all the feature quantities of the measured Lissajous diagram are within the confidence interval of the corresponding feature quantities, the confidence situation of the measured Lissajous diagram is true; otherwise, the confidence situation of the measured Lissajous diagram is false; further, all measured Lissajous diagrams with true confidence situations can be used as the historical measured health set of the Lissajous diagram.

[0096] It should be noted that by taking all measured Lissajous figures with true confidence as the historical measured healthy set of Lissajous figures, irrelevant Lissajous figure data caused by accidental environmental interference or transient fluctuations can be further eliminated.

[0097] In an embodiment of the present application, by determining the confidence interval and judging the confidence situation, irrelevant or abnormal data caused by accidental environmental interference or transient fluctuations can be effectively eliminated, thereby improving the accuracy and representativeness of the historical measured health set of the Lissajous diagram; fault diagnosis based on an accurate and reliable historical measured health set of the Lissajous diagram can more accurately identify the true fault state of the transformer winding, reduce the possibility of misjudgment and missed judgment, and improve the efficiency and accuracy of fault diagnosis.

[0098] In a feasible implementation, step 110 in the above embodiment, obtaining a simulated fault set of Lissajous figure variation of a healthy transformer simulation, includes: constructing a three-dimensional finite element simulation model of a healthy transformer; solving the three-dimensional finite element simulation model of the transformer to obtain a healthy voltage signal and a healthy current signal; determining a healthy simulation Lissajous figure based on the healthy voltage signal and the healthy current signal; adjusting the fault type of the three-dimensional finite element simulation model of the transformer in sequence according to a preset fault type table, and after each adjustment, solving the adjusted three-dimensional finite element simulation model of the transformer to obtain fault voltage signals and fault current signals of multiple fault types; determining a fault simulation Lissajous figure for each fault type based on the fault voltage signal and fault current signal of each fault type; determining a fault simulation Lissajous figure variation of each fault type based on the fault simulation Lissajous figure and the healthy simulation Lissajous figure of each fault type; and treating the fault simulation Lissajous figure variation of all fault types as a Lissajous figure variation simulation fault set.

[0099] Among them, the preset fault type table can be obtained and set by the operator based on a large amount of experience, experiments or statistics. The fault type table includes the adjustment data of the transformer under different winding fault types. The winding fault types include but are not limited to winding short circuit fault, radial deformation fault, axial deformation fault, inter-pancake spacing change and axial displacement fault.

[0100] Regarding the method of constructing a three-dimensional finite element simulation model of a transformer, in some embodiments, a three-dimensional finite element simulation model of a healthy transformer can be constructed based on the actual structure and electrical parameters of a healthy transformer; wherein the actual structure and electrical parameters of a healthy transformer include a winding connection method, a winding winding method, nameplate parameters, and structural parameters, and its structural parameters include the number of coil turns of the high-voltage winding, the number of coil turns of the low-voltage winding, the number of coils, the core radius, the inner radius of the winding, the outer radius of the winding, and the winding height.

[0101] Regarding the method for determining the change amount of the fault simulation Lissajous diagram, in some embodiments, the change amount of each characteristic quantity of the fault simulation Lissajous diagram of each fault type relative to the corresponding characteristic quantity of the healthy simulation Lissajous diagram can be determined based on each characteristic quantity of the fault simulation Lissajous diagram of each fault type and the corresponding characteristic quantity of the healthy simulation Lissajous diagram, so as to obtain the change amount of the fault simulation Lissajous diagram of each fault type; further, in other embodiments, the change amount of each data point of the fault simulation Lissajous diagram of each fault type relative to the corresponding data point of the healthy simulation Lissajous diagram can be determined based on each data point of the fault simulation Lissajous diagram of each fault type and the corresponding data point of the healthy simulation Lissajous diagram, so as to obtain the change amount of the fault simulation Lissajous diagram of each fault type.

[0102] In an embodiment of the present application, the internal structure and electrical characteristics of the transformer can be accurately simulated through a three-dimensional finite element simulation model, thereby generating accurate fault simulation Lissajous figure variations, which helps to improve the accuracy and reliability of fault diagnosis; the preset fault type table contains various winding fault types that the transformer may encounter. By adjusting the fault types of the transformer three-dimensional finite element simulation model in turn, these faults can be fully covered and simulated, thereby generating a complete Lissajous figure variation simulation fault set, which not only improves the accuracy and reliability of fault diagnosis, but also provides an important reference basis for the maintenance and overhaul of the transformer.

[0103] In a feasible implementation method, the three-dimensional finite element simulation model of the transformer in the above embodiment is solved to obtain a healthy voltage signal and a healthy current signal, including: constructing an equivalent circuit parameter simulation model of the three-dimensional finite element simulation model of the transformer; solving the three-dimensional finite element simulation model of the transformer to obtain healthy transformer winding characteristic parameters; inputting the healthy transformer winding characteristic parameters into the equivalent circuit parameter simulation model for simulation to obtain a healthy voltage signal and a healthy current signal.

[0104] In the above embodiment, according to a preset fault type table, the fault type of the three-dimensional finite element simulation model of the transformer is adjusted in sequence, and after each adjustment, the adjusted three-dimensional finite element simulation model of the transformer is solved to obtain fault voltage signals and fault current signals of multiple fault types, including: according to the fault type table, the fault type of the three-dimensional finite element simulation model of the transformer is adjusted in sequence, and after each adjustment, the adjusted three-dimensional finite element simulation model of the transformer is solved to obtain fault transformer winding characteristic parameters of multiple fault types; the fault transformer winding characteristic parameters of each fault type are input into the equivalent circuit parameter simulation model in sequence for simulation to obtain fault voltage signals and fault current signals of multiple fault types.

[0105] Regarding the method for determining the equivalent circuit parameter simulation model, in some embodiments, an existing equivalent circuit parameter simulation model construction method can be used to construct an equivalent circuit parameter simulation model of the transformer three-dimensional finite element simulation model.

[0106] Regarding the method for determining the characteristic parameters of healthy transformer windings and faulty transformer windings, in some embodiments, the finite element method can be used to solve the three-dimensional finite element simulation model of the transformer to obtain the characteristic parameters of healthy transformer windings, and the adjusted three-dimensional finite element simulation model of the transformer can be solved to obtain the characteristic parameters of faulty transformer windings of various fault types; wherein the characteristic parameters of healthy transformer windings and the characteristic parameters of faulty transformer windings include but are not limited to the ground capacitance, mutual capacitance, self-inductance, mutual inductance of each level of high and low voltage windings, as well as the mutual capacitance of the same level units of high and low voltage windings, and the mutual inductance between each level.

[0107] In the embodiment of the present application, by constructing an equivalent circuit parameter simulation model of the transformer three-dimensional finite element simulation model, the electrical characteristics of the transformer can be simulated more accurately, which helps to more accurately reflect the actual operation of the transformer during the simulation process, thereby improving the accuracy and reliability of the simulation; using the finite element method to solve the transformer three-dimensional finite element simulation model, detailed healthy transformer winding characteristic parameters can be obtained, including key parameters such as capacitance and inductance. These parameters are the basis for subsequent simulation analysis, ensuring the accuracy of the simulation results, and after adjusting the fault type of the transformer three-dimensional finite element simulation model, the finite element method is also used for solving, and a variety of fault types can be obtained. The fault transformer winding characteristic parameters are obtained by this solution method, which can comprehensively cover and accurately simulate various fault types, and provide reliable data support for fault diagnosis. The healthy transformer winding characteristic parameters and the fault transformer winding characteristic parameters are input into the equivalent circuit parameter simulation model for simulation, and healthy voltage signals and healthy current signals, as well as fault voltage signals and fault current signals of various fault types can be obtained. These signals are the basis for the subsequent generation of Lissajous diagrams, which helps to more accurately identify the fault status of the transformer. Compared with the three-dimensional finite element simulation model of the transformer to generate voltage signals and current signals, the equivalent circuit parameter simulation model is more efficient.

[0108] In a feasible implementation, step 110 in the above embodiment determines the Lissajous figure target fault set based on the historical measured healthy set of the Lissajous figure and the simulated fault set of the Lissajous figure variation, including: multiplying each characteristic quantity of the measured Lissajous figure at the t2th moment in the historical measured healthy set of the Lissajous figure with the corresponding characteristic quantity change of the fault simulated Lissajous figure change of each fault type in the simulated fault set of the Lissajous figure variation, to obtain the fault Lissajous figure of each fault type at the t2th moment, where t2 successively takes integers greater than 0 until t2 is equal to the total number at the second moment, to obtain the fault Lissajous figure of each fault type at all moments; and taking the fault Lissajous figures of all fault types at all moments as the target fault set of the Lissajous figure.

[0109] In an embodiment of the present application, by combining the measured Lissajous figure feature quantities in the historical measured health set of the Lissajous figure with the corresponding feature quantity changes of the fault simulation Lissajous figure changes, a fault Lissajous figure that is closer to the actual fault can be simulated. This method not only takes into account the electrical characteristics under the healthy state, but also incorporates the characteristic changes caused by the fault, making the simulated fault state more realistic and reliable; since the fault simulation Lissajous figure changes of multiple fault types are taken into account, and the fault Lissajous figure of each fault type at all times is generated separately, this method can comprehensively cover various winding fault types that the transformer may encounter, which also helps to more accurately identify the specific fault type during the fault diagnosis process and improve the comprehensiveness and accuracy of the diagnosis; by combining the measured data with the simulation data, the influence of external environmental factors on the Lissajous figure waveform can be effectively eliminated. This method can more accurately reflect the changes in the electrical characteristics of the transformer winding under the fault state, thereby improving the accuracy and reliability of the diagnosis results, which is of great significance for timely detection and processing of transformer faults and ensuring the safe and stable operation of the power system.

[0110] In a feasible implementation, step 120 in the above embodiment determines the situation of the Lissajous diagram at the current moment based on the Lissajous diagram at the current moment, the historical measured health set of the Lissajous diagram, and the target fault set of the Lissajous diagram, including: determining each mean data point of the measured Lissajous diagram in the historical measured health set of the Lissajous diagram based on each data point of the measured Lissajous diagram at all moments in the historical measured health set of the Lissajous diagram; determining the health correlation between the Lissajous diagram at the current moment and the measured Lissajous diagram in the historical measured health set of the Lissajous diagram based on multiple data points of the Lissajous diagram at the current moment and multiple mean data points of the measured Lissajous diagram in the historical measured health set of the Lissajous diagram. ; According to each data point of the fault Lissajous diagram of each fault type in the Lissajous diagram target fault set at all times, determine each mean data point of the fault Lissajous diagram of each fault type in the Lissajous diagram target fault set; According to multiple data points of the Lissajous diagram at the current moment and multiple mean data points of the fault Lissajous diagram of each fault type in the Lissajous diagram target fault set, determine the fault correlation between the Lissajous diagram at the current moment and the fault Lissajous diagram of each fault type in the Lissajous diagram target fault set; Determine the maximum fault correlation based on the fault correlations of all fault types; Determine the situation of the Lissajous diagram at the current moment based on the health correlation and the maximum fault correlation.

[0111] Regarding the method of determining the health correlation and the fault correlation, in some embodiments, the Pearson correlation coefficient can be used to determine the health correlation between the Lissajous diagram at the current moment and the measured Lissajous diagram in the historical measured health set based on multiple data points of the Lissajous diagram at the current moment and multiple mean data points of the measured Lissajous diagram in the historical measured health set of the Lissajous diagram, and to determine the fault correlation between the Lissajous diagram at the current moment and the fault Lissajous diagram of each fault type in the target fault set of the Lissajous diagram based on multiple data points of the Lissajous diagram at the current moment and multiple mean data points of the fault Lissajous diagram of each fault type in the target fault set of the Lissajous diagram.

[0112] Specifically, we can use the formula Determine the health correlation and fault correlation; where, is the total number of data points, is the coordinate of the jth data point of the Lissajous diagram at the current moment, In the case of is the health correlation between the Lissajous diagram at the current moment and the Lissajous diagram in the historical measured health set, is the coordinate of the jth mean data point of the Lissajous figure in the historical measured health set. In the case of is the fault correlation between the Lissajous diagram at the current moment and the fault Lissajous diagram of the i-th fault type in the target fault set of the Lissajous diagram, is the coordinate of the jth mean data point of the fault Lissajous diagram of the i-th fault type in the Lissajous diagram target fault set.

[0113] Regarding the method for determining the situation of the Lissajous diagram at the current moment, in some embodiments, the situation of the Lissajous diagram at the current moment can be determined based on the comparison relationship between the health correlation and the maximum fault correlation.

[0114] In an embodiment of the present application, by calculating the health correlation between the Lissajous diagram at the current moment and the historical measured health set of the Lissajous diagram, as well as the fault correlation between the fault Lissajous diagrams of various fault types in the target fault set of the Lissajous diagram, the situation of the Lissajous diagram at the current moment can be evaluated more accurately. This method comprehensively considers the health status and multiple fault states, thereby improving the accuracy of fault diagnosis.

[0115] In a feasible implementation method, the above embodiment determines the situation of the Lissajous diagram at the current moment based on the health correlation and the maximum fault correlation, including: when the absolute value of the difference between the health correlation and the maximum fault correlation is greater than or equal to the minimum correlation judgment threshold, and the health correlation is greater than the maximum fault correlation, the situation of the Lissajous diagram at the current moment is healthy; when the absolute value of the difference between the health correlation and the maximum fault correlation is greater than or equal to the minimum correlation judgment threshold, and the health correlation is less than the maximum fault correlation, the situation of the Lissajous diagram at the current moment is faulty; when the absolute value of the difference between the health correlation and the maximum fault correlation is less than the minimum correlation judgment threshold, the Lissajous diagram at the current moment, the historical measured health set of the Lissajous diagram, and the target fault set of the Lissajous diagram are input into the preset Lissajous diagram affiliation judgment model to obtain the situation of the Lissajous diagram at the current moment.

[0116] Among them, the minimum correlation judgment threshold can be obtained and set by the operator based on a large amount of experience, experiments or statistics. Of course, it can also be set by the operator according to actual needs; the preset Lissajous diagram judgment model can be obtained by pre-training by the operator.

[0117] Regarding the value determination method of the minimum correlation determination threshold, in some embodiments, the minimum correlation determination threshold may be set to 0.005; further, the minimum correlation determination threshold may also be set to 0.

[0118] Regarding the method for determining the situation of the Lissajous diagram at the current moment, in some embodiments, multiple data points of the Lissajous diagram at the current moment, multiple data points of the measured Lissajous diagram at each moment in the historical measured health set of the Lissajous diagram, and multiple data points of the fault Lissajous diagram of each fault type at each moment in the target fault set of the Lissajous diagram can be input into a preset Lissajous diagram affiliation determination model to obtain the situation of the Lissajous diagram at the current moment; further, in other embodiments, multiple feature quantities of the Lissajous diagram at the current moment, multiple feature quantities of the measured Lissajous diagram at each moment in the historical measured health set of the Lissajous diagram, and multiple feature quantities of the fault Lissajous diagram of each fault type at each moment in the target fault set of the Lissajous diagram can also be input into a preset Lissajous diagram affiliation determination model to obtain the situation of the Lissajous diagram at the current moment.

[0119] Regarding the method for obtaining the determination model of the Lissajous figure, in some embodiments, the determination model of the Lissajous figure can be obtained by training a convolutional neural network (CNN).

[0120] In an embodiment of the present application, by comparing the health correlation with the maximum fault correlation and setting a minimum correlation judgment threshold, it is possible to more accurately judge whether the Lissajous diagram at the current moment is in a normal state or a fault state. This method comprehensively considers the similarity between the Lissajous diagram and the historical health data set and the target fault data set, thereby improving the accuracy of the diagnosis; when the difference between the health correlation and the maximum fault correlation is not significant (that is, the absolute value of the difference is less than the minimum correlation judgment threshold), the preset Lissajous diagram judgment model is introduced for further judgment. This method enhances the robustness of the diagnosis and can make more reliable decisions in complex or ambiguous situations.

[0121] In a feasible implementation, step 140 in the above embodiment determines the diagnostic result of the transformer to be tested in real time based on the Lissajous diagram monitoring set, including: if within the monitoring time of the Lissajous diagram monitoring set, the proportion of Lissajous diagrams in a first continuous time period that are faults is greater than or equal to a first preset proportion, then the diagnostic result is determined to be a sudden fault; if within the monitoring time of the Lissajous diagram monitoring set, there is no Lissajous diagram in a first continuous time period that is faulty and the proportion of Lissajous diagrams in a second continuous time period that are faults is less than a second preset proportion, then the diagnostic result is determined to be normal and fault-free; if within the monitoring time of the Lissajous diagram monitoring set, there is no Lissajous diagram in a first continuous time period that is faulty and the proportion of Lissajous diagrams in a second continuous time period that are faults is greater than or equal to the first preset proportion, then the diagnostic result is determined to be a fault that needs to be monitored.

[0122] The first continuous time period, the second continuous time period, the first preset ratio and the second preset ratio can all be obtained and set by the operator based on a large amount of experience, experiments or statistics. Of course, they can also be set by the operator according to actual needs.

[0123] Regarding the value selection method of the first continuous time period, the second continuous time period, the first preset ratio and the second preset ratio, in some embodiments, the first continuous time period can be set to 1 hour, the second continuous time period can be set to one day, the first preset ratio can be set to 50%, and the second preset ratio can be set to 10%.

[0124] In an embodiment of the present application, by setting a first continuous time period and a first preset ratio, sudden faults can be quickly identified. When the fault ratio of the Lissajous figure exceeds a set threshold (such as 50%) within a short period of time (such as 1 hour), it can be immediately determined as a sudden fault, which helps operation and maintenance personnel to respond quickly and take measures to prevent the fault from expanding. In the absence of a sudden fault, the second continuous time period and the second preset ratio are further analyzed. This multi-level analysis method helps to reduce false alarms and missed alarms and improve the robustness of the diagnosis. When the fault ratio of the Lissajous figure is less than 10% within a day, it is determined to be normal and fault-free, which helps to avoid misjudgment caused by short-term fluctuations or accidental factors. When there is no sudden fault, but the fault ratio of the Lissajous figure continues to be high for a long period of time (such as the fault ratio reaches or exceeds 10% within a day), the diagnosis result is determined to be a fault that needs to be monitored. This provides an early warning mechanism, allowing operation and maintenance personnel to pay attention to the operating status of the transformer in advance and take necessary preventive measures to avoid the occurrence or deterioration of faults.

[0125] In summary, this method sets a reasonable continuous time period and preset ratio, and combines it with the Lissajous diagram monitoring set for real-time diagnosis. It not only improves the accuracy and timeliness of diagnosis, but also enhances the robustness and reliability of diagnosis, providing strong technical support for the operation and maintenance of transformers.

[0126] In a feasible implementation, the method in the above embodiment also includes: if the diagnosis result is a fault that needs to be monitored, and within the monitoring period of the Lissajous diagram monitoring set, there is a third consecutive time period in which the slope value of the proportion of Lissajous diagrams belonging to the fault changes over time is greater than 0, then the diagnosis result is updated to a progressive fault.

[0127] The third continuous time period may be obtained and set by the operator based on a large amount of experience, experiments or statistics. Of course, it may also be set by the operator based on actual needs.

[0128] Regarding the way of setting the value of the third continuous time period, in some embodiments, the third continuous time period may be set to 30 days.

[0129] In some embodiments, when the diagnosis result is a sudden fault, a fault that needs to be monitored, or a progressive fault, the specific winding fault type of the transformer to be tested can be determined based on the proportion of the Lissajous diagram of all fault types in the corresponding time period; further, the fault type with the largest proportion can be used as the specific winding fault type of the transformer to be tested.

[0130] In an embodiment of the present application, by monitoring the slope of the change in the fault ratio of the Lissajous figure over time within a third continuous time period (such as 30 days), the fault trend can be identified in advance. When the slope value is greater than 0, it means that the fault ratio is gradually increasing, indicating that the transformer to be tested may have a progressive fault. This early warning mechanism enables operation and maintenance personnel to take measures before the fault develops further, avoiding the sudden deterioration of the fault and the possible power system failure; by updating the diagnostic results in real time, especially upgrading the monitored fault to a progressive fault, operation and maintenance personnel can obtain important information about the transformer status more quickly, which helps them make decisions in a timely manner and reduce power outages and maintenance costs caused by the fault; combined with the Lissajous figure monitoring set and the slope analysis of the fault ratio over time, this method provides a more comprehensive diagnostic basis, which can not only improve the accuracy of the diagnosis, but also reduce misjudgments caused by accidental factors or short-term fluctuations.

[0131] In a second aspect, the present application provides a transformer winding fault diagnosis device.

[0132] See also Figure 3 , is a schematic diagram of a transformer winding fault diagnosis device according to an embodiment of the present application, wherein the device 310 includes:

[0133] An acquisition and determination module 311 is configured to acquire a historically measured healthy set of Lissajous diagrams of healthy transformers and a simulated fault set of Lissajous diagram variation values of simulated healthy transformers, and determine a target fault set of Lissajous diagrams based on the historically measured healthy set of Lissajous diagrams and the simulated fault set of Lissajous diagram variation values;

[0134] A real-time acquisition and determination module 312 is configured to acquire the Lissajous diagram of the transformer under test at the current moment in real time, and determine the current state of the Lissajous diagram based on the current moment's Lissajous diagram, the historically measured healthy set of the Lissajous diagram, and the target fault set of the Lissajous diagram, to determine whether the current state is healthy or faulty.

[0135] Add monitoring module 313, for adding the current moment's Lissajous figure status to the Lissajous figure monitoring set, the Lissajous figure monitoring set including the Lissajous figure status of all moments within the monitoring time from the preset start time point to the current moment;

[0136] The determination module 314 is used to determine the diagnosis result of the transformer to be tested in real time according to the Lissajous diagram monitoring set.

[0137] In the embodiment of the present application, the relevant contents of the acquisition and determination module 311, the real-time acquisition and determination module 312, the addition monitoring module 313 and the determination module 314 can be referred to. Figure 1 The contents of the illustrated embodiments are not described in detail here.

[0138] It should be noted that the device 310 of the present application also includes some other modules. It can be understood that the method of the present application and the device 310 have a one-to-one correspondence. Therefore, the other modules of the device 310 of the present application are the contents corresponding to the method of the present application in the above embodiment.

[0139] In an embodiment of the present application, a true fault Lissajous diagram is determined by combining the historically measured healthy Lissajous diagram of a healthy transformer and the changes in the fault Lissajous diagram of a simulated healthy transformer, and the true fault Lissajous diagram is used to diagnose transformer winding faults, thereby effectively eliminating the influence of external environmental factors on the waveform of the Lissajous diagram, thereby improving the accuracy and reliability of transformer winding fault diagnosis using the Lissajous diagram.

[0140] In addition, the method of the present application also has the following advantages: by obtaining the Lissajous diagram of the transformer to be tested in real time and performing continuous monitoring and diagnosis based on these data, potential faults and early faults of the transformer windings can be discovered in a timely manner, thereby enhancing the dynamic monitoring and early warning capabilities of the power system, which is of great significance for preventing sudden faults, reducing power outages and improving power supply quality; by continuously monitoring the Lissajous diagram of the transformer and determining its status (i.e., healthy or faulty) in real time, operation and maintenance personnel can formulate maintenance plans more accurately. For transformers in a healthy state, the number of unnecessary inspections and repairs can be reduced, thereby saving operation and maintenance costs. For transformers showing signs of failure, timely measures can be taken to repair them to prevent the failure from further deteriorating, reducing maintenance costs and downtime; by real-time monitoring and diagnosis of the Lissajous diagram of the transformer, strong support is provided for the intelligent diagnosis of the transformer, which helps to discover potential faults in advance and improve the reliability and operation efficiency of the transformer.

[0141] In a third aspect, the present application further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes a transformer winding fault diagnosis method in the above method embodiment.

[0142] In a fourth aspect, the present application further provides a computer device including a memory and a processor, wherein the memory stores a computer program. When the computer program is executed by the processor, the processor executes a transformer winding fault diagnosis method in the above method embodiment.

[0143] Figure 4 The internal structure diagram of the computer device in some embodiments is shown. The computer device can be a terminal, a server, or a gateway. Figure 4 As shown, the computer device includes a processor, a memory, and a network interface connected via a system bus.

[0144] The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor can implement the various steps in the above method embodiment. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor can implement the various steps in the above method embodiment. It will be understood by those skilled in the art that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0145] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods.

[0146] Among them, any reference to memory, storage, database or other media used in the various embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0147] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0148] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A transformer winding fault diagnosis method, characterized in that: The method comprises: Obtain a historically measured Lissajous diagram health set of a healthy transformer and a simulated fault set of a Lissajous diagram variation of a simulated healthy transformer, and determine a target Lissajous diagram fault set based on the historically measured Lissajous diagram health set and the simulated fault set of the Lissajous diagram variation; Obtaining the Lissajous diagram of the transformer under test at the current moment in real time, and determining the status of the Lissajous diagram at the current moment according to the Lissajous diagram at the current moment, the historical measured healthy set of the Lissajous diagram, and the target fault set of the Lissajous diagram, and determining whether the status is healthy or faulty; Adding the current Lissajous figure to a Lissajous figure monitoring set, wherein the Lissajous figure monitoring set includes the Lissajous figures at all times within the monitoring period from a preset start time point to the current moment; Determining the diagnostic result of the transformer to be tested in real time according to the Lissajous diagram monitoring set; The determining of the current moment's Lissajous diagram's status according to the current moment's Lissajous diagram, the Lissajous diagram's historically measured healthy set, and the Lissajous diagram's target fault set includes: Determine each mean data point of the measured Lissajous figure in the historical measured health set according to each data point of the measured Lissajous figure at all times in the historical measured health set; Determining a health correlation between the Lissajous figure at the current moment and the Lissajous figure measured in the historical measured health set based on a plurality of data points of the Lissajous figure at the current moment and a plurality of mean data points of the Lissajous figure measured in the historical measured health set; Determine, according to each data point of the fault Lissajous diagram of each fault type in the Lissajous diagram target fault set at all times, each mean data point of the fault Lissajous diagram of each fault type in the Lissajous diagram target fault set; Determining, based on a plurality of data points of the Lissajous diagram at a current moment and a plurality of mean data points of the fault Lissajous diagram of each fault type in the Lissajous diagram target fault set, a fault correlation between the Lissajous diagram at a current moment and the fault Lissajous diagram of each fault type in the Lissajous diagram target fault set; determining a maximum fault correlation based on the fault correlations of all fault types; The status of the Lissajous diagram at the current moment is determined according to the health correlation and the maximum fault correlation.

2. The method according to claim 1, characterized in that The method of obtaining the historically measured Lissajous diagram of the health transformer includes: Using a dynamic recording device, obtaining a measured voltage signal and a measured current signal of the healthy transformer at time t1, and determining a measured Lissajous figure at time t1 based on the measured voltage signal and the measured current signal at time t1, wherein t1 successively takes integers greater than 0 until t1 equals the total number of the first time, to obtain measured Lissajous figures at all times, and the dynamic recording device has been installed on the healthy transformer; The Lissajous diagram historical measured health set is determined based on the measured Lissajous diagram at all moments.

3. The method according to claim 2, characterized in that Determining the Lissajous diagram historical measured health set based on the measured Lissajous diagram at all times includes: Determine the confidence interval corresponding to each characteristic quantity based on each characteristic quantity of the measured Lissajous figure at all times; Determine the confidence level of the measured Lissajous figure at each moment based on all confidence intervals and all characteristic quantities of the measured Lissajous figure at each moment; The historical measured healthy set of the Lissajous figure is determined according to the confidence of the measured Lissajous figure at all moments.

4. The method according to claim 1, wherein Obtaining a simulated fault set of Lissajous figure variation of the healthy transformer simulation, including: Constructing a three-dimensional finite element simulation model of the healthy transformer; Solving the three-dimensional finite element simulation model of the transformer to obtain a healthy voltage signal and a healthy current signal; determining a healthy simulation Lissajous figure according to the healthy voltage signal and the healthy current signal; According to a preset fault type table, the fault types of the three-dimensional finite element simulation model of the transformer are adjusted in sequence, and after each adjustment, the adjusted three-dimensional finite element simulation model of the transformer is solved to obtain fault voltage signals and fault current signals of multiple fault types; The fault simulation Lissajous diagram for each fault type is determined based on the fault voltage signal and fault current signal of each fault type; Determine a change amount of the fault simulation Lissajous diagram for each fault type according to the fault simulation Lissajous diagram for each fault type and the healthy simulation Lissajous diagram; The fault simulation Lissajous figure variations of all fault types are taken as the Lissajous figure variation simulation fault set.

5. The method according to claim 4, characterized in that Solving the three-dimensional finite element simulation model of the transformer to obtain a healthy voltage signal and a healthy current signal includes: Constructing an equivalent circuit parameter simulation model of the three-dimensional finite element simulation model of the transformer; Solving the three-dimensional finite element simulation model of the transformer to obtain characteristic parameters of a healthy transformer winding; Inputting the healthy transformer winding characteristic parameters into the equivalent circuit parameter simulation model for simulation to obtain the healthy voltage signal and the healthy current signal; According to the preset fault type table, the fault type of the transformer three-dimensional finite element simulation model is adjusted in sequence, and after each adjustment, the adjusted transformer three-dimensional finite element simulation model is solved to obtain fault voltage signals and fault current signals of multiple fault types, including: According to the fault type table, the fault types of the three-dimensional finite element simulation model of the transformer are adjusted in sequence, and after each adjustment, the adjusted three-dimensional finite element simulation model of the transformer is solved to obtain characteristic parameters of the fault transformer windings of various fault types; The fault transformer winding characteristic parameters of each fault type are sequentially input into the equivalent circuit parameter simulation model for simulation, and the fault voltage signals and fault current signals of various fault types are obtained.

6. The method according to claim 1, characterized in that Determining the Lissajous diagram target fault set based on the Lissajous diagram historical measured health set and the Lissajous diagram variation simulated fault set includes: Multiplying each characteristic quantity of the measured Lissajous figure at the t2 moment in the Lissajous figure historical measured health set by the corresponding characteristic quantity change quantity of the fault simulation Lissajous figure change quantity of each fault type in the Lissajous figure change quantity simulation fault set, to obtain the fault Lissajous figure of each fault type at the t2 moment, where t2 successively takes integers greater than 0 until t2 equals the total number of the second moment, to obtain the fault Lissajous figure of each fault type at all moments; The fault Lissajous diagram of all fault types at all times is taken as the Lissajous diagram target fault set.

7. The method according to claim 6, characterized in that The determining of the Lissajous figure at the current moment according to the health correlation and the maximum fault correlation includes: If the absolute value of the difference between the healthy correlation and the maximum fault correlation is greater than or equal to the minimum correlation determination threshold, and the healthy correlation is greater than the maximum fault correlation, the Lissajous diagram at the current moment is considered healthy. When the absolute value of the difference between the healthy correlation and the maximum fault correlation is greater than or equal to the minimum correlation determination threshold, and the healthy correlation is less than the maximum fault correlation, the Lissajous diagram at the current moment is in a fault state; When the absolute value of the difference between the health correlation and the maximum fault correlation is less than the minimum correlation judgment threshold, the Lissajous diagram at the current moment, the Lissajous diagram historical measured health set and the Lissajous diagram target fault set are input into the preset Lissajous diagram affiliation judgment model to obtain the affiliation of the Lissajous diagram at the current moment.

8. The method according to claim 1, characterized in that Determining the diagnostic result of the transformer to be tested in real time based on the Lissajous diagram monitoring set includes: If, within the monitoring time of the Lissajous figure monitoring set, the proportion of the Lissajous figure belonging to the situation of failure in the first continuous time period is greater than or equal to a first preset proportion, the diagnosis result is determined to be a sudden failure; If, during the monitoring time of the Lissajous figure monitoring set, there is no Lissajous figure in the first continuous time period with a ratio of fault conditions greater than or equal to the first preset ratio, and there is a Lissajous figure in the second continuous time period with a ratio of fault conditions less than the second preset ratio, then the diagnosis result is determined to be normal and without faults; If, during the monitoring period of the Lissajous diagram monitoring set, there is no Lissajous diagram in the first continuous time period whose proportion of fault conditions is greater than or equal to the first preset proportion, and there is a Lissajous diagram in the second continuous time period whose proportion of fault conditions is greater than or equal to the second preset proportion, then the diagnostic result is determined to be a fault that needs to be monitored.

9. The method according to claim 8, characterized in that The method further comprises: If the diagnostic result is the fault to be monitored, and within the monitoring period of the Lissajous diagram monitoring set, there is a third consecutive time period in which the slope value of the proportion of Lissajous diagrams belonging to the fault changes over time is greater than 0, then the diagnostic result is updated to a progressive fault.

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