CN111830438B - Transformer fault detection method and transformer - Google Patents
Transformer fault detection method and transformer Download PDFInfo
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- CN111830438B CN111830438B CN201910319682.5A CN201910319682A CN111830438B CN 111830438 B CN111830438 B CN 111830438B CN 201910319682 A CN201910319682 A CN 201910319682A CN 111830438 B CN111830438 B CN 111830438B
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- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
- G01J5/0096—Radiation pyrometry, e.g. infrared or optical thermometry for measuring wires, electrical contacts or electronic systems
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
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Abstract
The invention discloses a transformer fault detection method, which comprises the following steps: collecting metal and nonmetal content data in a plurality of preset continuous time periods; determining whether a sleeve on the high-voltage side of the transformer is abnormal or not according to a display picture monitored by a thermal infrared imager arranged on the periphery of the transformer in a preset continuous time period; arranging state vectors in a time sequence according to the components; judging whether the vector distance between the time characteristic vector and a known fault characteristic frequency spectrum vector stored in a pre-stored characteristic frequency spectrum database is greater than a preset distance threshold value or not; judging whether the input current and the output current of the transformer are equal to the fault judgment starting current within a set time period, determining the intensity of the corrected vibration signal, reversely deducing the corrected input current according to the intensity of the corrected vibration signal, determining the time for reading the recording information generated in the transformer and collected within the set time period, and recording the recording information.
Description
Technical Field
The invention relates to the field of electric power, in particular to a transformer fault detection method and a transformer.
Background
The high-voltage transformer is a key device of the power system, and the reliable operation of the high-voltage transformer is crucial to the stable operation of the power system. Improving the reliability of the transformer can significantly improve the reliability of the power supply. In order to improve the reliability of the transformer, the current mainstream method is to perform online monitoring on some operation parameters of the transformer according to the national standard, monitor key operation parameters of the transformer, and alarm when the key operation parameters exceed a certain threshold. The method is used for online diagnosis of the fault of the transformer, when the online monitoring system of the transformer gives an alarm, the transformer usually has a remarkable abnormal state or has some faults, and the normal operation and the reliable operation of a power grid are influenced at the moment. In addition, a common method is to perform regular inspection tour and inspection, live detection and state maintenance on each transformer according to national standards, the method is an off-line detection method, compared with on-line monitoring, the method greatly improves the accuracy rate, and sometimes slight abnormality or early failure of the transformer can be found in advance. The live detection and the state detection of the transformer both have longer time intervals, the detection and the repair are carried out once every half year to one year on average, and if the abnormality or the fault occurs between the two time intervals of the detection and the repair of the transformer, the abnormality or the fault cannot be known in advance or avoided by an off-line detection method according to the running condition of the transformer.
Disclosure of Invention
An object of the present invention is to provide a transformer fault detection method and a transformer, which make the fault detection of the transformer more sensitive and efficient.
Specifically, the invention is realized by the following technical scheme:
a transformer fault detection method, the method comprising the steps of:
s1: selecting metal and nonmetal elements corresponding to the transformer fault;
s2: collecting metal and nonmetal content data in a plurality of preset continuous time periods;
s3: determining whether a sleeve on the high-voltage side of the transformer is abnormal or not according to a display picture monitored by a thermal infrared imager arranged on the periphery of the transformer in a preset continuous time period, if so, executing S4, and if not, executing S10;
s4: counting the failure times of the inner sleeve;
s5: manufacturing a component vector according to the number of times of the inner sleeve failure and the content data of metal and nonmetal;
s6: arranging state vectors in a time sequence according to the components;
s7: extracting time characteristic vectors such as amplitude, phase, energy and probability distribution from the state vector to represent the state of the transformer;
s8: judging whether the vector distance between the time characteristic vector and a known fault characteristic frequency spectrum vector stored in a pre-stored characteristic frequency spectrum database is larger than a preset distance threshold value, if so, executing S9, and if not, determining a fault according to a known fault characteristic frequency spectrum;
s9: establishing a concentration prediction model of the characteristic metal elements, and determining whether the transformer fails according to the corresponding relation between the concentrations of the characteristic metal elements and the historical state parameters;
s10: acquiring the intensity of a vibration signal of a transformer vibration signal and the input current and the output current value of the transformer;
s11: judging whether the input current and the output current of the transformer are equal to the fault judgment starting current within a set time period, if so, executing S12, and if not, executing S2;
s12: determining the intensity of the correction vibration signal, reversely deducing the correction input current according to the intensity of the correction vibration signal, determining the time for reading the recording information generated in the transformer and collected in the set time period, and recording the recording information;
s13: and performing N times of data acquisition on the recording information to form a one-dimensional vector, normalizing the formed one-dimensional vector to be used as a state vector, and then executing S7.
Preferably, the S4 includes:
s41: detecting the temperature of the end screen grounding wire of the abnormal sleeve; if the temperature of the end screen grounding wire is higher than the external environment temperature, determining that the abnormal sleeve has a fault;
s42: the number of failures of the sleeve during a plurality of preset continuous time periods within a day is counted.
Preferably, the S5 includes:
s51: making two-dimensional vector data of the failure times of the inner sleeve and the content data of metal and nonmetal in preset time;
s52: and carrying out segmentation processing on the two-dimensional vector according to time periods to obtain a component vector of each time period.
Preferably, the S6 includes:
s61: normalizing the numerical value of each dimension in each two-dimensional component vector, and inserting to form a new component vector;
s62: all new vectors are arranged into a one-dimensional vector in time sequence, and the one-dimensional vector is called a state vector.
Preferably, the S9 includes:
s91: establishing a concentration prediction model of the characteristic metal elements according to the collected historical concentrations and historical state parameters of the characteristic metal elements dissolved in the transformer oil;
s92: converting the collected metal and nonmetal content data in a plurality of continuous time periods preset for times into the concentration of a characteristic metal element, and determining the state parameter of the transformer according to the converted concentration of the characteristic metal element and the corresponding relation between the concentration of the characteristic metal element and the historical state parameter;
s93: and judging whether the transformer fails according to the state parameters of the transformer.
Preferably, the S12 includes:
s121: correcting the vibration signal intensity of the vibration signal of the transformer by using the input current value and the output current value of the transformer;
s122: and determining the corrected input current in a reverse direction by using the corrected vibration signal intensity, and reading the recording information generated in the transformer collected in the set time period when the corrected input current is smaller than the fault judgment starting current and the output current is continuously larger than or equal to the fault judgment starting current at any moment in the set time period.
Preferably, the S3 includes:
determining the temperatures of three high-voltage side bushings of a transformer, and determining the high-voltage side bushing with the highest temperature according to the temperatures of the three high-voltage side bushings of the transformer; and if the difference value between the temperature of the high-pressure side sleeve with the highest temperature and the temperature of any other high-pressure side sleeve is greater than the temperature of the other high-pressure side sleeve, determining that the high-pressure side sleeve with the highest temperature is abnormal.
Preferably, the S42 includes:
once a fault is detected, recording the fault in a storage inside the transformer, and randomly acquiring N times of data acquisition by the transformer controller for counting in the storage in a preset continuous time period, wherein N is the time for acquiring metal and nonmetal content data.
Preferably, the S52 includes:
and for each preset continuous time period, determining the number of the two-dimensional vector components according to the length of the preset continuous time period, wherein the number of the two-dimensional vector components = 10 times the total hours of the preset continuous time period.
A transformer uses the aforementioned transformer fault detection method.
The invention has the beneficial effects that: the fault detection of the transformer is more sensitive and efficient.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic diagram of a transformer fault detection method according to the present invention;
FIG. 2 is a schematic view of a detailed process of S4 in FIG. 1;
FIG. 3 is a schematic view of a detailed process of S5 in FIG. 1;
FIG. 4 is a schematic view of a detailed process of S6 in FIG. 1;
FIG. 5 is a schematic view of a detailed process of S9 in FIG. 1;
fig. 6 is a schematic diagram of a specific flow of S12 in fig. 1.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present invention. The word "if" as used herein may be interpreted as "at" \8230; "or" when 8230; \8230; "or" in response to a determination ", depending on the context.
The present invention will be described in detail below by way of examples.
The invention provides a transformer fault detection method, as shown in fig. 1, the method comprises the following steps:
s1: and selecting metal and nonmetal elements corresponding to the transformer faults.
The transformer is one of the most important devices in the power system, and plays a role in voltage transformation, power distribution and transmission, and the operation condition of the transformer is related to the safety and stability of the whole power system. Whether the transformer has faults or not is accurately detected and judged, and the method is of great importance for improving the safety and reliability of a power system. With the continuous development of sensor technology, artificial intelligence technology and distributed data processing technology, intelligent diagnosis technology can be used for fault diagnosis of transformers and can also find faults of the transformers.
At present, transformer faults can be classified into short-circuit faults, discharge faults, insulation faults, iron core faults and the like, and methods for detecting the transformer faults mainly comprise a three-ratio method, a dissolved gas analysis method, a chromatographic analysis method and the like. When the oil-immersed transformer operates, the oil-immersed transformer is gradually aged due to the action of various factors such as electricity, heat, local electric arcs and the like, and is cracked into gas, and when latent local overheating or local discharge exists in the power transformer, the gas generation speed is accelerated. Therefore, the fault diagnosis of the power transformer can be carried out by analyzing the dissolved gas in the oil, and the related quantitative and qualitative analysis has already been experienced in practical application. However, there are some disadvantages in detecting the nature of the internal failure of the transformer (overheating or discharging) from the gas components dissolved in the transformer oil. For example: the transformer faults discovered through analysis of dissolved gas in oil are mostly after the transformer is obviously abnormal, but the transformer faults are more serious at the moment; if the abnormality is not obvious after the gas is dissolved, the insulation condition of the transformer cannot be accurately judged, and the overhaul of a fault transformer can be delayed, so that more serious faults occur; some transformers are in the trouble discovery and hang the cover and overhaul the back, and the problem appears once more, leads to the maintenance of relapse, but can't solve the transformer problem. The transformer faults discovered through analysis of the dissolved gas in the oil are mostly after the transformer is obviously abnormal, but the transformer faults are more serious at the moment; if the abnormality is not obvious after the gas is dissolved, the insulation condition of the transformer cannot be accurately judged, and the overhaul of a fault transformer can be delayed, so that more serious faults occur; some transformers are after finding trouble and hanging the cover and overhaul, and the problem appears once more, leads to technical problem such as relapse maintenance.
Specifically, cu, fe, al, mn and Sn, which are metals closely related to fault types, are selected as characteristic metals, and Si, which is a nonmetal closely related to faults, is selected as a characteristic nonmetal.
S2: collecting metal and nonmetal content data at a preset time for a plurality of continuous time periods.
The preset continuous time period is a user-defined time period, and generally, a time period in which the transformer is most prone to failure, such as a time period with a high peak point, or a time period requiring power guarantee, can be selected. Such as at 12 noon: and (5) acquiring data in three time periods of 00-22. Specifically, in a preset continuous time period, data acquisition is randomly performed for N times, for example, at 12 to 13 pm, at 17 to 19 pm: and in the three time periods of 00-22.
S3: and in a preset continuous time period, determining whether the high-voltage side sleeve of the transformer is abnormal according to a display picture monitored by a thermal infrared imager arranged on the periphery of the transformer, if so, executing S4, and otherwise, executing S10.
The infrared temperature measurement technology is applied to the live-line detection of the power equipment, is one of routine live-line detection items of the transformer, and can successfully detect various heating defects of the transformer, including dielectric loss heating of a high-voltage bushing, abnormal oil level in the bushing, poor contact of a bushing joint, body magnetic leakage heating and the like. The infrared temperature measurement is realized by an infrared thermal imager.
Specifically, the temperature of three high-voltage side bushings of a transformer is determined, and the high-voltage side bushing with the highest temperature is determined according to the temperature of the three high-voltage side bushings of the transformer; and if the difference value between the temperature of the high-pressure side sleeve with the highest temperature and the temperature of any other high-pressure side sleeve is greater than the temperature of the other high-pressure side sleeve, determining that the high-pressure side sleeve with the highest temperature is abnormal.
S4: and counting the failure times of the inner sleeve.
S5: and manufacturing a component vector according to the number of times of faults of the inner sleeve and the content data of the metal and the nonmetal.
S6: and arranging the state vectors in a time sequence according to the branch vectors.
S7: and extracting time characteristic vectors such as amplitude, phase, energy and probability distribution from the state vector to represent the state of the transformer.
And taking each numerical value in the state vector as a discrete digital signal numerical value, and acquiring time characteristic vectors such as amplitude, phase, energy, probability distribution and the like corresponding to the state vector by utilizing the conventional digital signal processing method.
The transformer fault is the result of the comprehensive action and long-term accumulation of the transformer and the application environment thereof, so that the symptoms of the transformer fault are various, and the connection between the symptoms of the fault and the fault mechanism is complicated, thereby causing great difficulty in establishing a universal transformer fault control method. According to the invention, after the metal and nonmetal content data and the failure times of the inner sleeve are collected for a long time and rearranged, a state vector uniquely corresponding to the working state of the transformer is formed, and the current failure state of the transformer can be determined from the aspect that the transformer failure is most easily generated.
S8: and judging whether the vector distance between the time characteristic vector and a known fault characteristic frequency spectrum vector stored in a pre-stored characteristic frequency spectrum database is greater than a preset distance threshold, if so, executing S9, and if not, determining the fault according to the known fault characteristic frequency spectrum.
If the vector distance is greater than the preset distance threshold, it is indicated that the difference between the transformer state corresponding to the time characteristic vector and the transformer state corresponding to the normal working characteristic frequency spectrum vector is large, it is indicated that the current state belongs to a predefined fault state, and a new fault state needs to be determined. If the vector distance is smaller than the preset distance threshold value, the current transformer fault belongs to a predefined fault state.
S9: and establishing a concentration prediction model of the characteristic metal elements, and determining whether the transformer fails according to the corresponding relation between the concentrations of the characteristic metal elements and the historical state parameters.
S10: and acquiring the vibration signal intensity of the vibration signal of the transformer and the input current and the output current value of the transformer.
According to the transformer fault detection method, the intensity of the vibration signal is corrected according to the output current value of the transformer and the preset rated output current value of the transformer, and whether the transformer has a fault or hidden trouble is judged according to the intensity of the corrected vibration signal. In the operation process of the transformer, the voltage basically keeps unchanged, but the output current of the transformer, namely the working current of the transformer, can change along with the change of the load driven by the transformer, and the magnitude of the output current has an influence on the vibration signal, so that the influence brought by the output current of the transformer is brought in the intensity of the vibration signal obtained in the way, and the change of the mechanical structure of the transformer winding cannot be accurately represented. Based on the above, the present embodiment considers the influence of different loads on the output current of the transformer, and corrects the vibration signal intensity according to the transformer output current value and the rated current value, so that the corrected vibration signal intensity can more accurately represent the state of the transformer, and thus the diagnosis result is more accurate. The method provided by the embodiment can accurately diagnose the state of the transformer while the transformer is put into operation, so that workers can find and repair the fault or the hidden fault trouble of the transformer in time, and the stable operation of the power system is ensured.
S11: and judging whether the input current and the output current of the transformer are equal to the fault judgment starting current within a set time period, if so, executing S12, and if not, executing S2.
When a line in the transformer fails, the input current of the transformer is generally normal, and the output current of the transformer is often abnormal. In the scheme, the lines at the positions where the current needs to be collected are all provided with the fault judgment starting current corresponding to the line current, and the corresponding fault judgment starting currents on the lines at each position are different. The failure determination starting current mentioned in the present embodiment is set to a rated current greater than 2 times its corresponding current. When the transformer works, if more electric equipment is connected to the transformer, the phenomenon of overload can occur, and at the moment, the power and the working current of the transformer can fluctuate within a small range under a rated condition. If the input current and the output current are equal to the fault judgment starting current within the set time period, carrying out next fault judgment, if not, judging that the current transformer has no fault condition, and returning to S2.
S12: determining the intensity of the correction vibration signal, reversely deducing the correction input current according to the intensity of the correction vibration signal, determining the time for reading the recording information generated in the transformer collected in the set time period, and recording the recording information.
S13: and carrying out data acquisition on the recording information for N times to form a one-dimensional vector, normalizing the formed one-dimensional vector to be used as a state vector, and then executing S7.
And the N is the same as the times of acquiring the metal and nonmetal content data.
By adopting the transformer fault detection method provided by the embodiment of the invention, the source of the transformer fault can be obtained from multiple dimensions in a comprehensive judgment mode, and the fault characteristics are compared, so that the fault state of the transformer can be more accurately obtained.
Further, as shown in fig. 2, the S4 specifically includes:
s41: detecting the temperature of the end screen grounding wire of the abnormal casing pipe; and if the temperature of the end screen grounding wire is higher than the external environment temperature, determining that the abnormal sleeve has a fault.
For the capacitive bushing on the high voltage side, the detection of voltage-induced thermal defects is easily disturbed by the environment. Under the condition that an obstacle exists near the transformer, the heat of the transformer body is easy to accumulate among the obstacles, when the thermal infrared imager measures the temperature, part of high-voltage side sleeves can obviously generate heat under the influence of air heat convection, a detector can easily misjudge that the medium loss generates heat, voltage type heating caused by the medium loss is often a critical defect, and a maintainer is easily misled to make wrong treatment measures, so that manpower and material resources are wasted. Therefore, even if it is determined that the casing having the abnormality has a failure, it may be caused by erroneous judgment, and in order to eliminate such erroneous judgment, it is necessary to perform the operation of S5 and thereafter for erroneous judgment elimination.
S42: the number of failures of the sleeve during a plurality of preset continuous time periods within a day is counted.
Specifically, once a fault is detected, the fault is recorded in the internal memory of the transformer, so that the number of times of faults occurring in one day is accumulated in the internal memory of the transformer, and the transformer controller randomly performs N times of data acquisition on the count in the memory in a preset continuous time period, namely the number of times is the same as the number of times of acquiring metal and nonmetal content data.
Further, as shown in fig. 3, the S5 specifically includes:
s51: and (4) making two-dimensional vector data of the failure times of the inner sleeve and the content data of the metal and the nonmetal in preset time.
According to the method of S41 and S2, a series of values of the failure times and a series of values of the metal and nonmetal content data can be obtained, the number of the values is consistent and is N, the obtained series of values of the failure times are used as a first dimension part of a two-dimensional vector, and the obtained metal and nonmetal content data are used as a second dimension part of the two-dimensional vector.
S52: and carrying out segmentation processing on the two-dimensional vector according to time periods to obtain a sub-vector of each time period.
The method includes the steps that a two-dimensional vector is segmented into a plurality of two-dimensional sub-vectors with fixed lengths according to a mode that the two-dimensional vector is segmented according to time periods, specifically, the number of the two-dimensional sub-vectors is determined according to the length of each preset continuous time period, and the number of the two-dimensional sub-vectors = 10 times the total hours of the preset continuous time period.
Further, as shown in fig. 4, the S6 specifically includes:
s61: and normalizing the numerical value of each dimension in each two-dimensional component vector, and inserting to form a new component vector.
And normalizing the numerical value of each dimension of each two-dimensional component vector, and then alternately arranging the numerical value normalized vector values in the two-dimensional component vectors to form a corresponding number of new component vectors.
S62: all new vectors are arranged into a one-dimensional vector in time sequence, and the one-dimensional vector is called a state vector.
Further, as shown in fig. 5, the S9 specifically includes:
s91: and establishing a concentration prediction model of the characteristic metal elements according to the collected historical concentrations and historical state parameters of the characteristic metal elements dissolved in the transformer oil.
And acquiring historical concentration and historical state parameters of the characteristic metal elements in the preset time of the transformer, and performing data association by taking the time and the equipment ID as identifiers. In the embodiment, the preset time is set to 10 days before the prediction day, the historical concentrations, the historical oil temperatures and the historical load data of the characteristic metal elements of the transformer equipment on 10 days before the prediction day are acquired, and the maximum value of the oil temperature of the transformer equipment every day is counted and recorded. And standardizing the historical concentration and the historical state parameters of the metal elements to obtain a correlation cross table, namely obtaining the corresponding relation between the concentration of the characteristic metal elements and the historical state parameters, wherein the historical oil temperature and the historical load data are the historical state parameters.
S92: and converting the collected metal and nonmetal content data in a plurality of preset continuous time periods into the concentration of the characteristic metal element, and determining the state parameter of the transformer according to the converted concentration of the characteristic metal element and the corresponding relation between the concentration of the characteristic metal element and the historical state parameter.
S93: and judging whether the transformer fails according to the state parameters of the transformer.
Further, as shown in fig. 6, the S12 specifically includes:
s121: and correcting the vibration signal intensity of the transformer vibration signal by using the input current value and the output current value of the transformer.
Specifically, a ratio of the input current to the output current of the transformer is calculated, and the ratio is multiplied by the original vibration signal intensity to obtain the corrected vibration signal intensity. Because the ratio of the input current to the output current is a constant value which changes in a small range when the transformer works normally,
s122: and determining the corrected input current in a reverse direction by using the corrected vibration signal intensity, and reading the recording information generated in the transformer collected in the set time period when the corrected input current is smaller than the fault judgment starting current and the output current is continuously larger than or equal to the fault judgment starting current at any moment in the set time period.
Wherein, the determining the corrected input current in the reverse direction by the corrected vibration signal intensity may be: the method comprises the steps of storing a corresponding relation table of vibration signal intensity and input current in a memory inside the transformer in advance, inquiring an input current value corresponding to the corrected vibration signal intensity in the corresponding relation table, and using the inquired input current value as a corrected input current.
The invention also provides a transformer using the transformer fault detection method.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method of detecting a fault in a transformer, the method comprising the steps of:
s1: selecting metal and nonmetal elements corresponding to the transformer fault;
s2: collecting metal and nonmetal content data in a plurality of preset continuous time periods;
s3: determining whether a sleeve on the high-voltage side of the transformer is abnormal or not according to a display picture monitored by a thermal infrared imager arranged on the periphery of the transformer in a preset continuous time period, if so, executing S4, and if not, executing S10;
s4: counting the failure times of the inner sleeve;
s5: manufacturing a component vector according to the number of times of the inner sleeve failure and the content data of metal and nonmetal;
s6: arranging state vectors in a time sequence according to the vectors;
s7: extracting time characteristic vectors related to amplitude, phase, energy and probability distribution from the state vectors, and using the time characteristic vectors to represent the state of the transformer;
s8: judging whether the vector distance between the time characteristic vector and a known fault characteristic frequency spectrum vector stored in a pre-stored characteristic frequency spectrum database is larger than a preset distance threshold value, if so, executing S9, and if not, determining a fault according to a known fault characteristic frequency spectrum;
s9: establishing a concentration prediction model of the characteristic metal elements, and determining whether the transformer fails according to the corresponding relation between the concentrations of the characteristic metal elements and the historical state parameters;
s10: acquiring the intensity of a vibration signal of a transformer vibration signal and the input current and the output current value of the transformer;
s11: judging whether the input current and the output current of the transformer are equal to the fault judgment starting current within a set time period, if so, executing S12, and if not, executing S2;
s12: determining the intensity of the correction vibration signal, reversely deducing the correction input current according to the intensity of the correction vibration signal, determining the time for reading the recording information generated in the transformer and collected in the set time period, and recording the recording information;
s13: and performing N times of data acquisition on the recording information to form a one-dimensional vector, normalizing the formed one-dimensional vector to be used as a state vector, and then executing S7.
2. The transformer fault detection method of claim 1, wherein the S4 comprises:
s41: detecting the temperature of the end screen grounding wire of the abnormal sleeve; if the temperature of the end screen grounding wire is higher than the external environment temperature, determining that the abnormal casing pipe has a fault;
s42: the number of failures of the sleeve during a plurality of preset continuous time periods within a day is counted.
3. The transformer fault detection method according to claim 2, wherein the S5 includes:
s51: making two-dimensional vector data of the failure times of the inner sleeve and the content data of metal and nonmetal in preset time;
s52: and carrying out segmentation processing on the two-dimensional vector according to time periods to obtain a component vector of each time period.
4. The transformer fault detection method of claim 1, wherein the S6 comprises:
s61: normalizing the numerical value of each dimension in each two-dimensional component vector, and then inserting to form a new component vector;
s62: and arranging all the new vectors into a one-dimensional vector, called a state vector, in chronological order.
5. The transformer fault detection method of claim 1, wherein the S9 comprises:
s91: establishing a concentration prediction model of the characteristic metal elements according to the collected historical concentration and historical state parameters of the characteristic metal elements dissolved in the transformer oil;
s92: converting the collected metal and nonmetal content data in a plurality of continuous time periods preset for times into the concentration of a characteristic metal element, and determining the state parameter of the transformer according to the converted concentration of the characteristic metal element and the corresponding relation between the concentration of the characteristic metal element and the historical state parameter;
s93: and judging whether the transformer fails according to the state parameters of the transformer.
6. The transformer fault detection method according to claim 1, wherein the S12 includes:
s121: correcting the intensity of the vibration signal of the transformer vibration signal by using the input current value and the output current value of the transformer;
s122: and determining the corrected input current in a reverse direction by using the corrected vibration signal intensity, and reading the recording information generated in the transformer collected in the set time period when the corrected input current is smaller than the fault judgment starting current and the output current is continuously larger than or equal to the fault judgment starting current at any moment in the set time period.
7. The transformer fault detection method of claim 1, wherein the S3 comprises:
determining the temperatures of three high-voltage side bushings of a transformer, and determining the high-voltage side bushing with the highest temperature according to the temperatures of the three high-voltage side bushings of the transformer; and if the difference value between the temperature of the high-pressure side sleeve with the highest temperature and the temperature of any other high-pressure side sleeve is greater than the temperature of the other high-pressure side sleeve, determining that the high-pressure side sleeve with the highest temperature is abnormal.
8. The transformer fault detection method of claim 2, wherein the S42 comprises:
once a fault is detected, recording the fault in an internal memory of the transformer, and randomly acquiring N times of data by the transformer controller according to the count in the memory in a preset continuous time period, wherein N is the time for acquiring the metal and nonmetal content data.
9. The transformer fault detection method of claim 3, wherein the S52 comprises:
and for each preset continuous time period, determining the number of the two-dimensional component vectors according to the length of the preset continuous time period, wherein the number of the two-dimensional component vectors = 10 times the total hours of the preset continuous time period.
10. A transformer using the transformer fault detection method according to any one of claims 1 to 9.
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