A transformer online monitoring and early warning prediction method based on vibration characteristics

Abstract

The present invention discloses a transformer online monitoring and early warning prediction method based on vibration characteristics, including: identifying the DC bias magnetization of the transformer based on vibration characteristics; identifying the overload of the transformer based on vibration characteristics; identifying the looseness of the iron core of the transformer based on vibration characteristics; identifying the looseness of the winding of the transformer based on vibration characteristics; identifying the interference signal vibration of the transformer based on vibration characteristics. The present invention is suitable for online monitoring and abnormal diagnosis of the health status of the transformer, and is also suitable for early warning prediction of the health status of the transformer; before a serious fault occurs, the current operating status of the transformer and the historical development trend of the fault that may be caused are identified through vibration characteristics to achieve early warning prediction, prevent it before it happens, avoid further evolution into a power grid accident, improve the automation level, and have the characteristics of high automation level; the present invention uses three different dimensions such as current data, historical data change trend, and the relationship between transformer winding current and vibration to extract the vibration characteristic data of the transformer respectively.

Description

Technical Field

[0001] The invention relates to a transformer online monitoring and early warning prediction method, and in particular to a transformer online monitoring and early warning prediction method based on vibration characteristics. Background Art

[0002] The transformer is one of the most important equipment in the power grid. Once a transformer fails, it will seriously affect the safe, stable and reliable operation of the power system. Therefore, real-time monitoring of the transformer and timely detection of potential faults are of great significance to ensuring the stable operation of the power system.

[0003] Currently, commonly used transformer diagnosis methods include short-circuit impedance method, frequency response analysis method, partial discharge detection method and oil chromatography analysis method. The degradation of the mechanical characteristics of the transformer is usually long-term and cumulative. In the early stage of mechanical stability degradation, the electrical performance of the transformer remains good, and it is difficult to detect potential threats to the transformer through the above methods.

[0004] The core and winding are the main components of the transformer. The vibration of the core laminations caused by magnetostriction and magnetic leakage, as well as the current electromotive force of the windings are the main causes of mechanical vibration of the transformer. When the windings are deformed or the core is loose, or other mechanical stability failures occur, the transformer will produce abnormal vibrations. If not discovered in time, the fault will evolve and worsen, and eventually the transformer will have a serious failure, causing the transformer to be damaged or even a large-scale power outage. The vibration signal can sensitively reflect the mechanical state of the transformer. By collecting the vibration signal of the transformer online in real time and analyzing the vibration characteristics and trends, the online monitoring of the transformer health status and the early warning and prediction of abnormal transformer failures can be achieved. Summary of the invention

[0005] Purpose of the invention: The purpose of the present invention is to provide a method for online monitoring and early warning prediction of transformers based on vibration characteristics, which is suitable for online monitoring and abnormal diagnosis of the health status of transformers, and is also suitable for early warning prediction of the health status of transformers.

[0006] Technical solution: The present invention includes: identifying the DC bias magnetization of the transformer based on vibration characteristics; identifying the overload of the transformer based on vibration characteristics; identifying the looseness of the iron core of the transformer based on vibration characteristics; identifying the looseness of the winding of the transformer based on vibration characteristics; identifying the interference signal vibration of the transformer based on vibration characteristics.

[0007] The method for identifying the DC bias of a transformer based on vibration characteristics includes: a method for identifying the DC bias based on a current state quantity, a method for identifying the DC bias based on a historical change quantity, and a method for jointly identifying the DC bias based on a winding current.

[0008] The method of identifying the DC bias magnetism of the transformer based on vibration characteristics specifically includes: reading the current vibration data of the transformer and extracting the vibration information characteristics of the current state; reading the historical vibration data of the transformer and extracting the vibration information characteristics of historical changes; reading the winding current on the power supply side and the load side of the transformer and analyzing it in combination with the vibration characteristic data; and completing the logical judgment.

[0009] The method for identifying transformer overload based on vibration characteristics includes: an overload identification method based on current state quantity, an overload identification method based on historical change quantity, and an overload identification method based on winding current combination.

[0010] The vibration feature-based identification of transformer overload specifically includes: reading the current vibration data of the transformer, extracting the vibration information features of the current state; reading the historical vibration data of the transformer, extracting the vibration information features of historical changes; reading the winding currents on the power supply side and the load side of the transformer, and analyzing them in combination with the vibration feature data; and completing the logical judgment.

[0011] The method for identifying the looseness of the transformer core based on vibration characteristics includes: a method for identifying the looseness of the core based on the current state quantity, a method for identifying the looseness of the core based on the historical change quantity, and a method for identifying the looseness of the core based on the combined winding current.

[0012] The vibration feature-based identification of transformer core loosening specifically includes: reading the current vibration data of the transformer, extracting the vibration information features of the current state; reading the historical vibration data of the transformer, extracting the vibration information features of historical changes; reading the winding currents on the power supply side and the load side of the transformer, and analyzing them in combination with the vibration feature data; and completing the logical judgment.

[0013] The method for identifying the loose winding of a transformer based on vibration characteristics includes: a method for identifying the loose winding based on the current state quantity, a method for identifying the loose core based on the historical change quantity, and a method for identifying the loose core based on the winding current; specifically includes: reading the current vibration data of the transformer, extracting the vibration information characteristics of the current state; reading the historical vibration data of the transformer, extracting the vibration information characteristics of the historical changes; reading the winding current on the power supply side and the load side of the transformer, and analyzing it in combination with the vibration characteristic data; and logical judgment is executed.

[0014] The vibration feature-based identification of the interference signal vibration of the transformer includes: a method for identifying the interference signal vibration based on the current state quantity, and a method for identifying the interference signal vibration based on the historical change quantity; specifically includes: reading the current vibration data of the transformer, extracting the vibration information characteristics of the current state; reading the historical vibration data of the transformer, extracting the vibration information characteristics of the historical changes; reading the winding current on the power supply side and the load side of the transformer, and analyzing it in combination with the vibration feature data; and logical judgment is executed.

[0015] A transformer online monitoring and early warning prediction device based on vibration characteristics, including: a transformer DC bias magnetization module based on vibration characteristics, a transformer overload module based on vibration characteristics, a transformer core loosening module based on vibration characteristics, a transformer winding loosening module based on vibration characteristics, a transformer interference signal vibration module based on vibration characteristics, and an early warning prediction module based on vibration characteristics to identify transformer internal faults.

[0016] Beneficial effects: The present invention is applicable to online monitoring and abnormal diagnosis of transformer health status, and is also applicable to early warning prediction of transformer health status; before a serious fault occurs, the vibration characteristics are used to identify the faults that may be caused by the current operating status of the transformer and the historical development trend, so as to realize early warning prediction, prevent them before they happen, and avoid further evolution into power grid accidents, thereby improving the level of automation and having the characteristics of high automation level; the present invention adopts three different dimensions, namely, current data, historical data change trend, and transformer winding current and vibration relationship, to extract the vibration characteristic data of the transformer respectively. The current method is applicable to the main transformer of the substation. Similarly, the present method is also applicable to the grounding transformer, plant transformer, etc. of the substation. The similar method evolved from the present method is even applicable to the reactor, GIS (Gas Insulated Switchgear, gas insulated metal enclosed switchgear), GIL (Gas Insulated Metal Enclosed Transmission Line, gas insulated metal enclosed transmission line), circuit breaker and other primary power grid equipment of the substation, and has the characteristics of wide application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart for online monitoring and diagnosis of transformer internal faults based on vibration characteristics;

[0018] Figure 2 It is a logic diagram of the transformer DC bias magnetic identification method based on vibration characteristics;

[0019] Figure 3 It is a logic diagram of a transformer overload identification method based on vibration characteristics;

[0020] Figure 4 It is a logic diagram of transformer core loosening identification method based on vibration characteristics;

[0021] Figure 5 It is a logic diagram of the transformer winding loosening identification method based on vibration characteristics. DETAILED DESCRIPTION

[0022] The present invention will be further described below in conjunction with the accompanying drawings.

[0023] like Figure 1As shown, the transformer online monitoring and early warning prediction method based on vibration characteristics of the present invention collects the vibration data of the transformer, realizes the online monitoring and abnormal diagnosis of the health status of the transformer, quickly realizes the real-time alarm of abnormal operation of the transformer, and realizes early warning prediction based on the transformer internal fault diagnosis data of the historical change trend, and specifically includes the following steps:

[0024] S1. Identify the DC bias of the transformer based on vibration characteristics, including three methods: a method for identifying the DC bias based on the current state quantity, a method for identifying the DC bias based on the historical change quantity, and a method for identifying the DC bias based on the winding current. Figure 2 As shown, specifically including:

[0025] S11: Read the current vibration data of the transformer and extract the vibration information characteristics of the current state;

[0026] The specific conditions for identifying DC bias magnetic field based on the current state quantity are: the positive and negative half cycles of the current sampling waveform are obviously asymmetric, continuous harmonics appear above 1000Hz, and odd harmonics of 50Hz appear continuously.

[0027] S12: Read the historical vibration data of the transformer and extract the vibration information characteristics of the historical changes;

[0028] The specific discrimination conditions of the method for identifying DC bias magnetism based on historical changes are: the current sampling waveform changes from being basically symmetrical in the positive and negative half-cycles to being obviously asymmetrical in the positive and negative half-cycles; the vibration spectrum changes from being mainly 100-500Hz, with basically no frequency above 1000Hz, to a significant increase in the frequency of 500-1000Hz, and harmonics appearing in the frequency of 1000-1400Hz; the odd-order harmonics of 50Hz change from being basically absent to appearing continuously, and the proportion of the odd-order spectrum of 50Hz increases.

[0029] S13: Read the winding current on the power supply side and the load side of the transformer, and analyze it in combination with the vibration characteristic data;

[0030] The specific discrimination conditions of the method for jointly identifying DC bias based on winding current are: the current on the winding side where DC bias occurs is calculated, the DC bias current is collected, and the DC bias current or current saturation occurs in the winding current on the other side at the same time.

[0031] S14: logical judgment is completed;

[0032] Perform logical judgment according to the n value set by the logical judgment, and execute the "or logic" judgment condition, or the "three-out-of-two logic" judgment condition, or the "and logic" judgment condition.

[0033] S15: This module is executed; the logic judgment operation of this module is completed.

[0034] S2. Identify transformer overload based on vibration characteristics, including three methods: an overload identification method based on current state quantity, an overload identification method based on historical change quantity, and an overload identification method based on winding current. Figure 3 As shown, the specific steps include:

[0035] S21: Read the current vibration data of the transformer and extract the vibration information characteristics of the current state;

[0036] The specific judgment conditions of the overload identification method based on the current state quantity are: in the vibration spectrum composition analysis, the proportion of the main vibration frequency increases, and the proportion of non-integer frequencies of 50Hz and 100Hz decreases.

[0037] S22: Read the historical vibration data of the transformer and extract the vibration information characteristics of the historical changes;

[0038] The specific judgment conditions of the overload identification method based on historical changes are: compared with the previous vibration spectrum analysis, the amplitude of the 100Hz integer vibration frequency has increased, and the amplitude of the non-integer frequencies of 50Hz and 100Hz has decreased, remained unchanged or increased slightly.

[0039] S23: Read the winding current on the power supply side and the load side of the transformer, and analyze it in combination with the vibration characteristic data;

[0040] The specific judgment condition of the method for jointly identifying overload based on winding current is: the current on the power supply side and / or load side of the transformer meets the overload condition after delay.

[0041] S24: logical judgment is completed;

[0042] Perform logical judgment according to the n value set by the logical judgment, and execute the "or logic" judgment condition, or the "three-out-of-two logic" judgment condition, or the "and logic" judgment condition.

[0043] S25: This module is executed; the logic judgment operation of this module is completed.

[0044] S3, based on vibration characteristics to identify the transformer core loose, including three methods: based on the current state of the identification method, based on the historical change of the identification method, based on the winding current joint identification method. Figure 4 As shown, the specific steps include:

[0045] S31: Read the current vibration data of the transformer and extract the vibration information characteristics of the current state;

[0046] The specific conditions for identifying core looseness based on the current state quantity are: the main frequency is 100 Hz and its multiples, and the unit is day or hour. During the entire operation process, the main frequency amplitude does not change much, and the main frequency amplitude is fixed in a range with little change.

[0047] S32: Read the historical vibration data of the transformer and extract the vibration information characteristics of the historical changes;

[0048] The specific judgment conditions of the method for identifying core looseness based on historical changes are: compared with no-load and load conditions, the main frequency does not change much and is less affected by the load current, so the main frequency amplitude does not change much in different time periods.

[0049] S33: Read the winding current on the power supply side and the load side of the transformer, and analyze it in combination with the vibration characteristic data;

[0050] The specific conditions for identifying core looseness based on winding current are: when running at no load, half load, and full load, the frequency amplitude of 100Hz and its multiples does not change much, and the amplitude of the main frequency is not closely related to the change of load current. The current size of the power supply side and the load side is collected, or the current changes, and the amplitude of the main frequency hardly changes.

[0051] S34: logical judgment is completed;

[0052] Perform logical judgment according to the n value set by the logical judgment, and execute the "or logic" judgment condition, or the "three-out-of-two logic" judgment condition, or the "and logic" judgment condition.

[0053] S35: This module is executed; the logic judgment operation of this module is completed.

[0054] S4. Identify transformer winding looseness based on vibration characteristics, including three methods: a method for identifying winding looseness based on current state quantity, a method for identifying core looseness based on historical change quantity, and a method for identifying core looseness based on winding current. Figure 5 As shown, the specific steps include:

[0055] S41: Read the current vibration data of the transformer and extract the vibration information characteristics of the current state;

[0056] The specific conditions for identifying winding looseness based on the current state quantity are: the main frequency is 100 Hz and its multiples, and the main frequency amplitude changes greatly during the entire operation process in days or hours.

[0057] S42: reading historical vibration data of the transformer and extracting vibration information characteristics of historical changes;

[0058] The specific conditions for identifying winding looseness based on historical changes are: when running at no load, the main frequency amplitude is almost zero, and when running at full load, the main frequency amplitude is the largest; the main frequency amplitude is related to the load current. The larger the load current, the larger the main frequency amplitude, and the smaller the load current, the smaller the main frequency amplitude; generally, the main frequency amplitude changes greatly in a day.

[0059] S43: Read the winding current on the power supply side and the load side of the transformer, and analyze it in combination with the vibration characteristic data;

[0060] The specific conditions for identifying winding looseness based on the winding current joint identification method are: when running at no-load, half-load and full-load, the frequency amplitude mainly 100Hz and its multiples changes greatly, which is related to the load current; the current size on the power supply side and the load side is collected, or the current changes, the amplitude of the main frequency also changes accordingly; the amplitude of the main frequency is almost proportional to the square of the load current.

[0061] S44: logical judgment is completed;

[0062] Perform logical judgment according to the n value set by the logical judgment, and execute the "or logic" judgment condition, or the "three-out-of-two logic" judgment condition, or the "and logic" judgment condition.

[0063] S45: This module is executed; the logic judgment operation of this module is completed.

[0064] S5. Identify the interference signal vibration of the transformer based on the vibration characteristics, including two methods: a method for identifying the interference signal vibration based on the current state quantity, and a method for identifying the interference signal vibration based on the historical change quantity. Specifically, the following steps are included:

[0065] S51: Read the current vibration data of the transformer and extract the vibration information characteristics of the current state;

[0066] Interference signal vibration includes but is not limited to the vibration of cooling system equipment. The specific judgment condition of the method for identifying core looseness based on the current state quantity is: the main frequency of the cooling system equipment is less than 100Hz, generally 50Hz. Therefore, by setting the main frequency of this type of vibration to be lower than a certain set value, the vibration of the cooling system equipment, etc. can be identified.

[0067] S52: Read the historical vibration data of the transformer and extract the vibration information characteristics of the historical changes;

[0068] Interference signal vibrations include but are not limited to vibrations caused by thunder, whistles, outdoor work vibrations, etc. The specific discrimination conditions of the method for identifying winding looseness based on historical changes are: this type of vibration is impact vibration, and this type of historical change is irregular, sometimes increasing, sometimes decreasing, and sometimes disappearing. Therefore, vibrations caused by thunder, whistles, outdoor work vibrations, etc. can be identified by setting the duration of this type of vibration.

[0069] S53: Read the winding current on the power supply side and the load side of the transformer, and analyze it in combination with the vibration characteristic data;

[0070] The interference signal has nothing to do with the winding current on the power supply side and the load side.

[0071] S54: logical judgment is completed;

[0072] Perform logical judgment according to the n value set by the logical judgment, and execute the "or logic" judgment condition, or the "three-out-of-two logic" judgment condition, or the "and logic" judgment condition.

[0073] S55: This module is executed; the logic judgment operation of this module is completed.

[0074] The transformer online monitoring and early warning prediction device based on vibration characteristics of the present invention comprises:

[0075] Module 1: Identify transformer DC bias module based on vibration characteristics;

[0076] There are three methods for identifying the DC bias of a transformer based on vibration characteristics: a method for identifying the DC bias based on the current state quantity, a method for identifying the DC bias based on the historical change quantity, and a method for identifying the DC bias based on the combined winding current.

[0077] The specific discrimination conditions of the DC bias magnetic identification method based on the current state quantity are: the positive and negative half cycles of the current sampling waveform are obviously asymmetric, continuous harmonics appear above 1000Hz, and odd harmonics of 50Hz appear continuously.

[0078] The specific discrimination conditions of the DC bias magnetic identification method based on historical changes are: the current sampling waveform changes from basically symmetrical positive and negative half-cycles to obviously asymmetrical positive and negative half-cycles; the vibration spectrum changes from being mainly 100-500Hz, with basically no frequency above 1000Hz, to a significant increase in the frequency of 500-1000Hz, and harmonics appearing in the frequency of 1000-1400Hz; the odd-order harmonics of 50Hz change from basically no to continuous and persistent appearance, and the proportion of the odd-order spectrum of 50Hz increases.

[0079] The specific discrimination conditions of the method for jointly identifying DC bias based on winding current are: the current on the winding side where DC bias occurs is calculated, the DC bias current is collected, and the DC bias current or current saturation occurs in the winding current on the other side at the same time.

[0080] Module 2: Transformer overload identification module based on vibration characteristics;

[0081] There are three methods for identifying transformer overload based on vibration characteristics: an overload identification method based on current state quantity, an overload identification method based on historical change quantity, and an overload identification method based on winding current joint.

[0082] The specific judgment conditions of the overload identification method based on the current state quantity are: in the vibration spectrum composition analysis, the proportion of the main vibration frequency increases, and the proportion of non-integer frequencies of 50Hz and 100Hz decreases.

[0083] The specific judgment conditions of the overload identification method based on historical changes are: compared with the previous vibration spectrum analysis, the amplitude of the 100Hz integer vibration frequency has increased, and the amplitude of the non-integer frequencies of 50Hz and 100Hz has decreased, remained unchanged or increased slightly.

[0084] The specific judgment condition of the method for jointly identifying overload based on winding current is: the current on the power supply side and / or the load side of the transformer meets the overload condition after delay.

[0085] Module 3: Identify loose pieces in transformer core based on vibration characteristics;

[0086] There are three methods for identifying transformer core loosening based on vibration characteristics: a method for identifying core loosening based on current state quantity, a method for identifying core loosening based on historical change quantity, and a method for identifying core loosening based on winding current.

[0087] The specific conditions for identifying core looseness based on the current state quantity are: the main frequency is 100 Hz and its multiples, and the unit is day or hour. During the entire operation process, the main frequency amplitude does not change much, and the main frequency amplitude is fixed in a range with little change.

[0088] The specific judgment conditions of the method for identifying core looseness based on historical changes are: compared with no-load and load conditions, the main frequency does not change much and is less affected by the load current, so the main frequency amplitude does not change much in different time periods.

[0089] The specific conditions for the method of identifying core looseness based on winding current are: when running at no load, half load, and full load, the frequency amplitude of 100Hz and its multiples does not change much, and the amplitude of the main frequency is not closely related to the change of load current. The current size of the power supply side and the load side is collected, or the current changes, and the amplitude of the main frequency hardly changes.

[0090] Module 4: Identification of loose transformer windings based on vibration characteristics;

[0091] There are three methods for identifying transformer winding looseness based on vibration characteristics: a method for identifying winding looseness based on current state quantity, a method for identifying core looseness based on historical change quantity, and a method for identifying core looseness based on winding current.

[0092] The specific conditions for identifying winding looseness based on the current state quantity are: the main frequency is 100 Hz and its multiples, and the main frequency amplitude changes greatly during the entire operation process in days or hours.

[0093] The specific conditions for identifying winding looseness based on historical changes are: when running at no load, the main frequency amplitude is almost zero, and when running at full load, the main frequency amplitude is the largest; the main frequency amplitude is related to the load current. The larger the load current, the larger the main frequency amplitude, and the smaller the load current, the smaller the main frequency amplitude; generally, the main frequency amplitude changes greatly in a day.

[0094] The specific conditions for identifying winding looseness based on winding current are: when running at no-load, half-load and full-load, the frequency amplitude mainly composed of 100Hz and its multiples changes greatly, which is related to the load current; the current on the power supply side and the load side is collected, or when the current changes, the amplitude of the main frequency also changes accordingly; the amplitude of the main frequency is almost proportional to the square of the load current.

[0095] Module 5: Identify transformer interference signal vibration module based on vibration characteristics;

[0096] There are two methods for identifying the interference signal vibration of the transformer based on the vibration characteristics: a method for identifying the interference signal vibration based on the current state quantity, and a method for identifying the interference signal vibration based on the historical change quantity.

[0097] The interference signal vibration includes but is not limited to the vibration of the cooling system equipment. The specific judgment condition of the method for identifying the loose core based on the current state quantity is: the main frequency of the cooling system equipment is less than 100Hz, generally 50Hz. Therefore, by setting the main frequency of this type of vibration to be lower than a certain set value, the vibration of the cooling system equipment, etc. can be identified.

[0098] The interference signal vibration includes but is not limited to vibrations such as thunder, whistle, outdoor operation vibration, etc. The specific judgment conditions of the method for identifying winding looseness based on historical changes are: this type of vibration is impact vibration, and this type of historical change is irregular, sometimes larger, sometimes smaller, and sometimes absent. Therefore, by setting the duration of this type of vibration, vibrations such as thunder, whistle, outdoor operation vibration, etc. can be identified.

[0099] Module 6: Early warning and prediction module for identifying internal faults of transformers based on vibration characteristics;

[0100] Online monitoring of the transformer health status based on vibration characteristics, in addition to realizing health monitoring and abnormal diagnosis of the current status, is also based on the status trend analysis of the monitored vibration characteristic quantities. It is also applicable to early warning prediction of the transformer health status through comparison with set fixed trend threshold values ​​or floating threshold values.

[0101] The online monitoring of the transformer health status based on vibration characteristics of the present invention, in addition to realizing the health monitoring and abnormal diagnosis of the current state, is also based on the state trend analysis of the monitored vibration characteristic quantity, through the comparison of the set fixed trend threshold value or the floating threshold value, which is also applicable to the early warning prediction of the transformer health status. The method described for the transformer main equipment of the substation, based on the current vibration data, the trend of historical data changes, and the vibration feature extraction based on the relationship between current and vibration and the method of identifying equipment abnormalities based on vibration, is also similarly applicable to primary power grid equipment such as reactors, GIS, GIL, and circuit breakers.

[0102] The present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the steps of the method are implemented when the processor executes the computer program.

[0103] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described are implemented.

Claims

1. A transformer online monitoring and early warning prediction method based on vibration characteristics, characterized in that: include: The DC bias magnetization of the transformer is identified based on vibration characteristics, including: a method for identifying the DC bias magnetization based on the current state quantity, a method for identifying the DC bias magnetization based on the historical change quantity, and a method for jointly identifying the DC bias magnetization based on the winding current; the specific discrimination conditions of the method for identifying the DC bias magnetization based on the current state quantity are: the positive and negative half-cycles of the current sampling waveform are obviously asymmetric, continuous harmonics appear above 1000Hz, and odd harmonics of 50Hz appear continuously; the specific discrimination conditions of the method for identifying the DC bias magnetization based on the historical change quantity are: the current sampling waveform changes from being basically symmetrical in the positive and negative half-cycles to being obviously asymmetric in the positive and negative half-cycles; the vibration spectrum is mainly 100~500Hz, and the frequency above 1000Hz is Basically no, the change is that the frequency of 500~1000Hz increases significantly, and harmonics appear at 1000~1400Hz; the odd-order harmonics of 50Hz change from basically no to continuous, and the odd-order spectrum proportion of 50Hz increases; the specific discrimination conditions of the method of joint identification of DC bias based on winding current are: the current on the winding side where DC bias occurs is calculated, and the DC bias current is collected, and the current on the other side of the winding is DC biased or current saturation occurs at the same time; the vibration characteristic data of the transformer are extracted respectively using three different dimensions: current data, historical data change trend, and the relationship between transformer winding current and vibration; the above conditions are executed and logical judgment conditions are made; Identifying transformer overload based on vibration characteristics, including: a method for identifying overload based on current state quantity, a method for identifying overload based on historical change quantity, and a method for jointly identifying overload based on winding current; identifying transformer core loosening based on vibration characteristics, including: a method for identifying core loosening based on current state quantity, a method for identifying core loosening based on historical change quantity, and a method for jointly identifying core loosening based on winding current; identifying transformer winding loosening based on vibration characteristics, including: a method for identifying winding loosening based on current state quantity, a method for identifying core loosening based on historical change quantity, and a method for jointly identifying core loosening based on winding current; identifying transformer interference signal vibration based on vibration characteristics, including: a method for identifying interference signal vibration based on current state quantity, and a method for identifying interference signal vibration based on historical change quantity.

2. The transformer online monitoring and early warning prediction method based on vibration characteristics according to claim 1 is characterized in that: The method of identifying the DC bias magnetism of the transformer based on vibration characteristics specifically includes: reading the current vibration data of the transformer and extracting the vibration information characteristics of the current state; reading the historical vibration data of the transformer and extracting the vibration information characteristics of historical changes; reading the winding current on the power supply side and the load side of the transformer and analyzing it in combination with the vibration characteristic data; and completing the logical judgment.

3. The transformer online monitoring and early warning prediction method based on vibration characteristics according to claim 1 is characterized in that: The vibration feature-based identification of transformer overload specifically includes: reading the current vibration data of the transformer, extracting the vibration information features of the current state; reading the historical vibration data of the transformer, extracting the vibration information features of historical changes; reading the winding currents on the power supply side and the load side of the transformer, and analyzing them in combination with the vibration feature data; and completing the logical judgment.

4. The transformer online monitoring and early warning prediction method based on vibration characteristics according to claim 1 is characterized in that: The vibration feature-based identification of transformer core loosening specifically includes: reading the current vibration data of the transformer, extracting the vibration information features of the current state; reading the historical vibration data of the transformer, extracting the vibration information features of historical changes; reading the winding currents on the power supply side and the load side of the transformer, and analyzing them in combination with the vibration feature data; and completing the logical judgment.

5. The transformer online monitoring and early warning prediction method based on vibration characteristics according to claim 1 is characterized in that: The method for identifying loose windings of a transformer based on vibration characteristics specifically includes: reading current vibration data of the transformer, extracting vibration information characteristics of the current state; reading historical vibration data of the transformer, extracting vibration information characteristics of historical changes; reading winding currents on the power supply side and the load side of the transformer, and analyzing them in combination with vibration characteristic data; and logical judgment is executed.

6. A transformer online monitoring and early warning prediction method based on vibration characteristics according to claim 5, characterized in that: The vibration characteristics-based identification of the interference signal vibration of the transformer specifically includes: reading the current vibration data of the transformer, extracting the vibration information characteristics of the current state; reading the historical vibration data of the transformer, extracting the vibration information characteristics of the historical changes; reading the winding currents on the power supply side and the load side of the transformer, and analyzing them in combination with the vibration characteristic data; and the logical judgment is executed.

7. A device for transformer online monitoring and early warning prediction method based on the vibration characteristics of any one of claims 1 to 6, characterized in that: include: A module for identifying transformer DC bias magnetization based on vibration characteristics, a module for identifying transformer overload based on vibration characteristics, a module for identifying transformer core loosening based on vibration characteristics, a module for identifying transformer winding loosening based on vibration characteristics, a module for identifying transformer interference signal vibration based on vibration characteristics, and a warning and prediction module for identifying transformer internal faults based on vibration characteristics.

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