Transformer winding state evaluation method based on vibration signal energy matrix norm

Abstract

The invention relates to the field of high-voltage tests of electrical equipment, and discloses a transformer winding state evaluation method based on a vibration signal energy matrix norm. A transformer winding state evaluation device based on a vibration signal energy matrix norm comprises a vibration sensor installed at a corresponding position of a double-winding transformer or a three-winding transformer, and a processor in communication connection with the vibration sensor. Through the steps of vibration signal acquisition, vibration signal processing, transformer winding state evaluation and the like, on-line monitoring of the transformer winding state is realized, and the operation state of the transformer winding is reported to operation and maintenance personnel in time, so that the monitoring efficiency and the fault identification capability are improved, and greater flexibility and convenience are provided in operation and maintenance; the method has high practical value and application prospect.

Description

Technical Field

[0001] The invention relates to the field of high voltage testing of electrical equipment, and in particular to a transformer winding state assessment method based on a vibration signal energy matrix norm. Background Art

[0002] Transformer windings are an important part of the transformer, and their performance directly affects the safety and stability of the entire power system. However, in the actual operation of the transformer, the windings are often attacked by transient overvoltages and high short-circuit currents. These sudden changes in voltage and current may lead to a series of adverse consequences, especially having a serious impact on the winding's ability to withstand short circuits.

[0003] 1. Impact of transient overvoltage:

[0004] Transient overvoltages are usually caused by external environmental factors (such as lightning), system faults or switching operations. In transformers, transient overvoltages can rise rapidly to levels much higher than normal operating voltages. Such overvoltages may cause:

[0005] 1. Insulation breakdown: Winding insulation materials are prone to breakdown under transient overvoltage, resulting in short circuit or equipment failure.

[0006] 2 Thermal damage: The surge current caused by overvoltage will generate additional heat in the winding, which may cause aging or melting of the conductor material.

[0007] 3. Damage to cable connections: Transient overvoltage has a particularly significant impact on connection locations, which may cause the connection to become loose or fail.

[0008] Second, the impact of high short-circuit current:

[0009] When a short circuit occurs, the transformer winding will be subjected to a high short circuit current. In this case, the short circuit resistance of the winding becomes critical. Excessive short circuit current may cause:

[0010] 1 Mechanical stress: The magnetic field generated instantly by the short-circuit current will exert huge mechanical stress on the winding, which may cause the winding to deform or break.

[0011] 2. Vibration and noise: High-intensity short-circuit current will also cause abnormal vibration and noise in the winding, which not only affects the operating stability, but may also damage the equipment if accumulated over a long period of time.

[0012] 3. Local overheating: The flow of short-circuit current will cause local overheating in some parts, further accelerating the aging process of the insulation material.

[0013] 3. Limitations of traditional winding fault detection methods:

[0014] In order to maintain the safe operation of transformers, traditional winding fault detection methods such as short-circuit impedance method, frequency response method, low-voltage pulse method, etc. are widely used. However, these methods also have some significant limitations:

[0015] 1. Power outage detection requirements: Most traditional detection methods need to be performed under power outage conditions, which not only affects the normal operation of the transformer, but may also lead to long power outage periods and affect power supply reliability.

[0016] 2. Requires professional skills: The implementation of these detection methods usually requires technicians with certain professional knowledge and skills. It is difficult for ordinary maintenance personnel to operate independently, which increases maintenance costs and management difficulties.

[0017] 3. Timeliness and reliability of test results: Detection after a power outage is often not sensitive enough and cannot reflect the actual operating status of the winding in real time, which may lead to the inability to eliminate hidden dangers in a timely manner.

[0018] In summary, the current traditional transformer winding status detection method cannot be monitored online and has great difficulty in operation and maintenance, so there is an urgent need for an effective transformer winding status assessment method. Summary of the invention

[0019] In order to solve the problem that traditional transformer winding status detection methods cannot be monitored online and are difficult to operate and maintain, the present invention proposes a transformer winding status assessment method based on the vibration signal energy matrix norm. This method uses the vibration signal energy matrix norm of the transformer as a discriminant feature quantity, and divides the transformer winding status into three categories: normal, alarm and fault according to the feature quantity value.

[0020] The technical solution adopted by the present invention is:

[0021] A transformer winding state evaluation method based on a vibration signal energy matrix norm, a transformer winding state evaluation method based on a vibration signal energy matrix norm and a transformer winding state evaluation device;

[0022] The transformer winding state assessment device based on the vibration signal energy matrix norm includes a vibration sensor and a processor in communication connection with the vibration sensor;

[0023] The vibration sensor is installed on the transformer box on the low voltage side of the two-winding transformer, or on the medium voltage side of the three-winding transformer, which can collect vibration signals;

[0024] Two vibration sensors are installed at the 1 / 4 height and 3 / 4 height of each winding, so that the vibration sensor can monitor the vibration condition of the entire winding; the signal collected by the vibration sensor facing the winding can reflect the vibration state of the winding.

[0025] Furthermore, the transformer winding state assessment method determines the operating state of the winding by analyzing the stability of the transformer vibration signal.

[0026] Furthermore, the transformer winding state assessment method comprises the following steps:

[0027] Step 1, collection of vibration signals:

[0028] Install a vibration sensor at a corresponding position of the double-winding transformer or the three-winding transformer, collect a vibration signal of the transformer through the vibration sensor, and transmit it to the processor;

[0029] Step 2, vibration signal processing:

[0030] The processor obtains the normalized vibration energy matrix norm based on the transformer vibration signal window function processing, Fourier transform, and normalization processing;

[0031] Step 3, transformer winding condition assessment:

[0032] The processor performs calculations based on the vibration energy matrix to quantify the energy of the vibration signal; the processor generates a transformer winding judgment conclusion based on the state discrimination mechanism and the energy of the quantified vibration signal.

[0033] Furthermore, the transformer winding state assessment method further includes:

[0034] Step 4, result verification:

[0035] The evaluation results are manually verified by comparing historical data with actual operating conditions.

[0036] Furthermore, the transformer winding state assessment method further includes:

[0037] Step 5, regular calibration:

[0038] According to different operating conditions and vibration signal characteristics, the state judgment mechanism of the vibration sensor and processor is regularly calibrated and optimized.

[0039] Furthermore, in step 1, the collection time of the transformer vibration signal needs to be greater than 1 hour.

[0040] Further, in step 2, the processor performs window function processing based on the transformer vibration signal to obtain several frame sequences; the processor performs Fourier transform based on the frame sequence to obtain a time domain spectrum diagram of the vibration signal; the processor composes the time domain spectrum diagram into a vibration energy matrix based on the time domain spectrum diagram; the processor performs normalization processing based on the energy matrix to obtain the normalized vibration energy matrix norm.

[0041] Furthermore, in step 2, the calculation formula of the normalization process is:

[0042]

[0043] Where H is the normalized vibration energy matrix; Q i is the i-th element in the vibration energy matrix; N is the total number of elements in the vibration energy matrix, that is, all Q i the number of For all Q from i = 1 to N i The sum of the elements.

[0044] Furthermore, in step 3, the formula for calculating the vibration energy matrix norm is as follows:

[0045] E=||H||=(∑H i 2 ) 1 / 2

[0046] Where E is the norm of the normalized vibration energy matrix H; H i represents the i-th element in the normalized vibration energy matrix H; ∑H i 2 represents the sum of the squares of all elements in the normalized matrix H; (∑H i 2 ) 1 / 2 It means taking the square root of the sum result to get the final norm E.

[0047] Furthermore, the state determination mechanism in step 3 is:

[0048] If E < 0.62, the transformer winding is in normal condition;

[0049] 0.62≤K≤0.81 indicates that the transformer winding is in a warning state;

[0050] K>0.81 indicates that the transformer winding is in an abnormal state.

[0051] The beneficial effects of the present invention are:

[0052] The transformer winding condition assessment method based on the vibration signal energy matrix norm has the following significant beneficial effects:

[0053] 1. Online monitoring capabilities:

[0054] Real-time monitoring: Compared with traditional methods, the method of the present invention can realize online monitoring of the transformer winding status, reduce the maintenance time and cost caused by power outage detection, and at the same time improve the operating reliability of the transformer.

[0055] 2. Simple operation:

[0056] Lowering the technical threshold: This method installs vibration sensors and implements data collection, and uses more mature signal processing technology in processing and evaluation, which reduces the requirements for the professional skills of detection personnel and is relatively easier to operate.

[0057] 3. Improve detection accuracy:

[0058] Sufficient data volume: Long-term data collection (more than 1 hour) helps to accumulate sufficient vibration signals, improves the accuracy of the assessment, and makes the condition assessment more reliable.

[0059] 4. Clear evaluation criteria:

[0060] Clear status classification: By setting a clear threshold of the vibration energy matrix norm E (such as E<0.62, 0.62≤E≤0.81, E>0.81), the status of the transformer winding can be effectively divided into three states: normal, warning and abnormal, which is convenient for maintenance personnel to quickly judge the health status of the equipment and take corresponding measures.

[0061] 5. Early fault identification:

[0062] Timely warning: In the warning state (0.62≤E≤0.81), maintenance personnel can conduct inspections and repairs in advance to reduce the risk of major failures, thereby reducing economic losses and safety hazards.

[0063] 6. Wide applicability:

[0064] Wide application: This method is applicable to different types of transformers (such as double-winding and three-winding transformers), and the installation position and number of sensors can be flexibly adjusted according to the actual situation on site, which increases the flexibility and adaptability of the method.

[0065] 7. Reduce maintenance costs

[0066] Reduce downtime: By implementing online monitoring, equipment maintenance can be performed while the equipment is running, reducing downtime caused by equipment inspection, thereby improving overall operating efficiency and economy.

[0067] In summary, the transformer winding state assessment method based on the vibration signal energy matrix norm can realize online monitoring of the transformer winding state and report the transformer winding operation state to the operation and maintenance personnel in a timely manner. It not only improves the monitoring efficiency and fault identification capability, but also provides greater flexibility and convenience in operation and maintenance. It has high practical value and application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0069] Figure 1 It is a flow chart of the transformer winding state evaluation method based on the vibration signal energy matrix norm of the present invention;

[0070] Figure 2 It is a diagram of the arrangement of vibration sensor measuring points on the high voltage side of a three-winding transformer according to the present invention;

[0071] Figure 3 This is a diagram of the arrangement of vibration sensor measuring points on the low-voltage side of a three-winding transformer according to the present invention. DETAILED DESCRIPTION

[0072] To make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be described clearly and completely in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them.

[0073] Therefore, the following detailed description of the embodiments of the present invention is not intended to limit the scope of the claimed invention, but merely represents some embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. It should be noted that the embodiments of the present invention and the features and technical solutions in the embodiments can be combined with each other without conflict. It should be noted that similar numbers and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0074] The traditional winding fault detection method can only be used for off-line detection, with a long power outage period, and the detection method requires the detection personnel to have a high level of testing skills, which brings difficulties to the operation and maintenance of the transformer. This embodiment provides a transformer winding state assessment method based on the vibration signal energy matrix norm. This method uses the vibration signal energy matrix norm of the transformer as a discriminant feature quantity, and divides the transformer winding state into three categories: normal, alarm and fault according to the feature quantity value. The method realizes online monitoring of the transformer winding state, and promptly reports the transformer winding operation state to the operation and maintenance personnel, which greatly improves the economic benefits of the operation and maintenance.

[0075] Specifically, the transformer winding state assessment method is based on a transformer winding state assessment device based on a vibration signal energy matrix norm; the transformer winding state assessment device based on a vibration signal energy matrix norm includes a vibration sensor and a processor that is communicatively connected to the vibration sensor; the vibration sensor is installed on a transformer box on the low-voltage side of a double-winding transformer, or on the medium-voltage side of a three-winding transformer, and is capable of collecting vibration signals.

[0076] Two vibration sensors are installed at the 1 / 4 height and 3 / 4 height of each winding, so that the vibration sensor can monitor the vibration condition of the entire winding; the signal collected by the vibration sensor facing the winding can reflect the vibration state of the winding.

[0077] The transformer winding state assessment method determines the operating state of the winding by analyzing the stability of the transformer vibration signal. Figure 1 As shown, the following steps are included:

[0078] Step 1, collection of vibration signals:

[0079] A vibration sensor is installed at a corresponding position of a double-winding transformer or a three-winding transformer, and a vibration signal of the transformer is collected by the vibration sensor and transmitted to a processor; specifically, the following steps are included:

[0080] Step 1.1, sensor selection and installation:

[0081] Choose the right vibration sensor: Choose a sensor with an appropriate frequency response range to accurately capture low-frequency and high-frequency vibration signals.

[0082] Installation location: Two-winding transformer: Installed on the transformer box on the low-voltage side. Three-winding transformer: Installed on the medium-voltage side to collect stronger vibration signals. Install two vibration sensors at the 1 / 4 height and 3 / 4 height of each winding to ensure that the vibration of the entire winding can be monitored. The sensor should face the winding to ensure that the collected signal can truly reflect the vibration status of the winding.

[0083] Specific installation method: Take the three-winding transformer as an example for detailed description. Figure 2 As shown, for a three-winding transformer, three measuring points are set at the upper and lower parts of the three-phase high-voltage side of ABC, such as measuring point 1, measuring point 2, measuring point 3, measuring point 4, measuring point 5 and measuring point 6 in the figure; at the same time, Figure 3 As shown, there are three measuring points at the upper end and the lower end of the low-voltage side of the three-phase ABC, such as measuring points 7, 8, 9, 10, 11 and 12 in the figure; generally, the vibration value measured at the upper end is greater than that at the lower end, but the maximum vibration position is difficult to accurately locate in advance, so enough measuring points are selected on each side.

[0084] Selecting appropriate sensors and ensuring their effective installation locations can ensure that the collected data is highly representative and effective, thus providing an accurate basis for subsequent signal analysis.

[0085] Step 1.2, data collection time:

[0086] Collection time: The collection time of vibration signals must be greater than 1 hour to ensure the representativeness and accuracy of the data, which is helpful for subsequent signal analysis.

[0087] By continuously monitoring vibration signals, operators can adjust operating conditions or perform preventive maintenance in a timely manner, reducing unexpected downtime and equipment damage, thereby extending the service life of the transformer.

[0088] In step 1, by installing vibration sensors and collecting vibration signals in a timely manner, the status of the transformer can be effectively monitored and potential faults can be detected in a timely manner. For example, high-frequency vibrations may indicate mechanical wear or component imbalance, while low-frequency vibrations may be related to problems with the rotor or stator. Early detection can reduce the risk of equipment damage.

[0089] Vibration monitoring can reveal imbalances and other problems in transformer operation. By analyzing this data, system optimization can be performed to make the transformer operate in the best condition and improve overall operating efficiency. The collected vibration data can provide a scientific basis for improving transformer operation, formulating maintenance plans, and rationally allocating resources, making the decision-making process more accurate and efficient.

[0090] Step 2, vibration signal processing:

[0091] The processor performs window function processing, Fourier transform, and normalization processing on the transformer vibration signal to obtain the normalized vibration energy matrix norm. In step 2, the processor performs window function processing on the transformer vibration signal to obtain several frame sequences; the processor performs Fourier transform on the frame sequence to obtain the time domain spectrum of the vibration signal; the processor composes the time domain spectrum into a vibration energy matrix based on the time domain spectrum; the processor performs normalization processing on the energy matrix to obtain the normalized vibration energy matrix norm.

[0092] Step 2.1, signal framing:

[0093] Window function application: Use the window function to divide the obtained continuous vibration signal into N frame sequences for subsequent analysis and processing.

[0094] Selection of frame length: Select an appropriate frame length, such as a time window of 1 second to several seconds, based on the operating characteristics of the transformer and the changing characteristics of the vibration signal.

[0095] Step 2.2, Fourier transform:

[0096] Spectral analysis: Perform Fourier transform on each frame sequence to obtain the frequency domain representation of the signal and form a time domain spectrum diagram of the vibration signal, which lays the foundation for subsequent energy calculation.

[0097] Energy matrix construction: Based on the time domain spectrum, a vibration energy matrix Q is constructed. Each element of this matrix represents the energy of the corresponding frequency point.

[0098] Step 2.3, energy matrix normalization:

[0099] The vibration energy matrix Q is normalized, and the calculation formula of the normalization process is:

[0100]

[0101] Where H is the normalized vibration energy matrix; Q i is the i-th element in the vibration energy matrix; N is the total number of elements in the vibration energy matrix, that is, all Q i the number of For all Q from i = 1 to N i The sum of the elements.

[0102] By normalizing the energy values ​​under different conditions to the same level, horizontal comparison and evaluation can be better performed.

[0103] In step 2, after normalization, the energy values ​​under different frames or different experimental conditions will be converted to the same level, reducing the differences caused by changes in measurement conditions, making the data more consistent, and facilitating subsequent analysis and comparison. Through normalization, the vibration signal energy under different time periods, different devices or different operating conditions can be directly compared horizontally to assist in judging whether the equipment is operating normally or not, and the impact of different conditions on vibration characteristics. The normalization operation eliminates the impact of different signals on the dimensions, so that subsequent analysis and processing focus more on the change trend of the signal rather than the specific value, which helps the effectiveness of subsequent processing links such as machine learning and pattern recognition. Normalization can reduce the impact of extreme values ​​on the analysis results, making the analysis results more robust and less susceptible to interference from extreme data points. Normalization helps to improve the convergence speed and algorithm efficiency, because the algorithm is usually sensitive to the scale of the input data. After normalization, the value range of the input data is more consistent, which helps to improve the training effect. The normalized energy matrix can be more intuitively displayed graphically, so that users can more easily identify patterns and trends when performing data analysis.

[0104] Step 3, transformer winding condition assessment:

[0105] The processor performs calculations based on the vibration energy matrix to quantify the energy of the vibration signal; the processor generates a transformer winding judgment conclusion based on the state discrimination mechanism and the energy of the quantified vibration signal.

[0106] Step 3.1, calculate the vibration energy matrix norm:

[0107] Calculate the norm of the normalized vibration energy matrix H as the basis for judging the winding state:

[0108] The formula for calculating the vibration energy matrix norm is as follows:

[0109] E=||H||=(ΣH i 2 ) 1 / 2

[0110] Where E is the norm of the normalized vibration energy matrix H; H i represents the i-th element in the normalized vibration energy matrix H; ∑H i 2 represents the sum of the squares of all elements in the normalized matrix H; (∑H i 2 ) 1 / 2 It means taking the square root of the sum result to get the final norm E.

[0111] Step 3.2, state determination mechanism:

[0112] Judging criteria:

[0113] E<0.62: The transformer winding is in normal condition, indicating stable operation. 0.62≤K≤0.81: The transformer winding is in a warning state, there may be slight abnormalities, and regular monitoring is recommended. K>0.81 The transformer winding is in an abnormal state and needs to be checked immediately to avoid possible failures.

[0114] In step 3, by calculating the norm E, the vibration energy of the transformer winding can be quantified, providing a quantitative indicator to reflect the status of the equipment and helping technicians quickly evaluate the operation of the equipment. The state discrimination mechanism provides a clear judgment standard for transformer operation monitoring. In the early warning state, timely measures can be taken to avoid potential failures and reduce maintenance costs and downtime. Through effective state assessment, vibration anomalies can be discovered and handled in a timely manner, reducing equipment damage caused by winding failures, thereby improving the safety and reliability of equipment operation.

[0115] Step 4, result verification:

[0116] The evaluation results are validated by comparing historical data with actual operating conditions to ensure the effectiveness and accuracy of the method.

[0117] By comparing the evaluation results with historical data, the effectiveness of the evaluation method can be verified, thereby enhancing confidence in the monitoring results. Comparing the differences between actual operating conditions and historical data helps to promptly identify and correct possible model deviations and ensure the accuracy of the evaluation results. The verification of actual data provides a reliable basis to help operation and maintenance personnel make more scientific maintenance and management decisions and reduce the risk of misdiagnosis. By continuously verifying the evaluation results and forming a feedback mechanism, the monitoring methods and technologies can be continuously improved to enhance the overall operation and maintenance level. The verification process helps to clarify the applicability of the evaluation method under different working conditions, optimize the operating process, and reduce unnecessary waste of resources.

[0118] Step 5, regular calibration:

[0119] According to different operating conditions and vibration signal characteristics, the state judgment mechanism of the vibration sensor and processor is regularly calibrated and optimized to ensure the real-time and accuracy of monitoring.

[0120] Regularly calibrating vibration sensors and status discrimination mechanisms can maintain high monitoring accuracy and avoid a drop in accuracy due to equipment aging or environmental changes. Optimizing according to different operating conditions and vibration signal characteristics ensures that the monitoring system has good adaptability and can more accurately reflect the status of the equipment. By optimizing the correction mechanism, false alarms and missed alarms can be significantly reduced, and the accuracy of fault detection can be improved, thereby improving the safety of equipment operation. Regular calibration and optimization make the monitoring system more stable and reliable, able to operate effectively under various complex conditions, and ensure accurate monitoring of equipment status.

[0121] In summary, the transformer winding state assessment method based on the vibration signal energy matrix norm can realize online monitoring of the transformer winding state and report the transformer winding operation state to the operation and maintenance personnel in a timely manner, which not only improves the monitoring efficiency and fault identification capability, but also provides greater flexibility and convenience in operation and maintenance.

[0122] At the same time, it should be pointed out that the above description is only a typical embodiment of the present invention and does not limit the present invention in any form. Any changes made to the above embodiments based on the ideas and technical essence of the present invention are within the protection scope of the present invention.

Claims

1. A transformer winding state assessment method based on the vibration signal energy matrix norm, characterized by: The transformer winding state assessment method is a transformer winding state assessment device based on the vibration signal energy matrix norm; The transformer winding state assessment device based on the vibration signal energy matrix norm includes a vibration sensor and a processor in communication connection with the vibration sensor; The vibration sensor is installed on the transformer box on the low voltage side of the two-winding transformer, or on the medium voltage side of the three-winding transformer, which can collect vibration signals; Two vibration sensors are installed at the 1 / 4 height and 3 / 4 height of each winding, so that the vibration sensor can monitor the vibration condition of the entire winding; the signal collected by the vibration sensor facing the winding can reflect the vibration state of the winding.

2. The transformer winding state assessment method based on the vibration signal energy matrix norm according to claim 1 is characterized in that: The transformer winding state evaluation method judges the operating state of the winding by analyzing the stability of the transformer vibration signal.

3. The transformer winding state assessment method based on the vibration signal energy matrix norm according to claim 1 is characterized in that: The transformer winding condition assessment method comprises the following steps: Step 1, collection of vibration signals: Install a vibration sensor at a corresponding position of the double-winding transformer or the three-winding transformer, collect a vibration signal of the transformer through the vibration sensor, and transmit it to the processor; Step 2, vibration signal processing: The processor obtains the normalized vibration energy matrix norm based on the transformer vibration signal window function processing, Fourier transform, and normalization processing; Step 3, transformer winding condition assessment: The processor performs calculations based on the vibration energy matrix to quantify the energy of the vibration signal; the processor generates a transformer winding judgment conclusion based on the state discrimination mechanism and the energy of the quantified vibration signal.

4. The transformer winding state assessment method based on the vibration signal energy matrix norm according to claim 3 is characterized in that: The transformer winding condition assessment method further includes: Step 4, result verification: The evaluation results are manually verified by comparing historical data with actual operating conditions.

5. The transformer winding state assessment method based on the vibration signal energy matrix norm according to claim 3 is characterized in that: The transformer winding condition assessment method further includes: Step 5, regular calibration: According to different operating conditions and vibration signal characteristics, the state judgment mechanism of the vibration sensor and processor is regularly calibrated and optimized.

6. The transformer winding state assessment method based on the vibration signal energy matrix norm according to claim 3 is characterized in that: In step 1, the collection time of the transformer vibration signal must be greater than 1 hour.

7. The transformer winding state assessment method based on the vibration signal energy matrix norm according to claim 3 is characterized in that: In step 2, the processor performs window function processing based on the transformer vibration signal to obtain several frame sequences; the processor performs Fourier transform based on the frame sequence to obtain a time domain spectrum diagram of the vibration signal; the processor composes the time domain spectrum diagram into a vibration energy matrix based on the time domain spectrum diagram; the processor performs normalization processing based on the energy matrix to obtain the normalized vibration energy matrix norm.

8. The transformer winding state assessment method based on the vibration signal energy matrix norm according to claim 3 is characterized in that: In step 2, the calculation formula for the normalization process is: Where H is the normalized vibration energy matrix; Q i is the i-th element in the vibration energy matrix; N is the total number of elements in the vibration energy matrix, that is, all Q i the number of For all Q from i = 1 to N i The sum of the elements.

9. The transformer winding state assessment method based on the vibration signal energy matrix norm according to claim 3 is characterized in that: In step 3, the formula for calculating the vibration energy matrix norm is as follows: E=||H||=(∑H i 2 ) 1 / 2 Where E is the norm of the normalized vibration energy matrix H; H i represents the i-th element in the normalized vibration energy matrix H; ∑H i 2 represents the sum of the squares of all elements in the normalized matrix H; (∑H i 2 ) 1 / 2 It means taking the square root of the sum result to get the final norm E.

10. The transformer winding state assessment method based on vibration signal energy matrix norm according to claim 3 is characterized in that: The state determination mechanism in step 3 is: If E < 0.62, the transformer winding is in normal condition; 0.62≤K≤0.81 indicates that the transformer winding is in a warning state; K>0.81 indicates that the transformer winding is in an abnormal state.

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