A method and system for online evaluation of transformer mechanical condition

Abstract

The present invention relates to the technical field of transformers and discloses a method and system for online evaluation of the mechanical state of a transformer. The method comprises: installing M signal generators and N signal receivers on the oil tank wall of the transformer to be tested, and comparing correlation characteristic quantities of scattered signals in the current cycle and the next cycle to evaluate whether the mechanical state of the transformer to be tested has changed. The method can intuitively and accurately evaluate whether the mechanical state of the transformer has changed without relying on indirect parameters, significantly improving the intuitiveness and accuracy of the evaluation. At the same time, the method also enhances the ability to evaluate the mechanical state of the iron core without combining with other specialized methods, effectively solving the problems in the prior art of non-intuitive evaluation results, limited accuracy, and weak ability to evaluate the mechanical state of the iron core.

Description

Technical Field

[0001] The present invention relates to the technical field of transformers, and in particular to an online evaluation method and system for the mechanical state of a transformer. Background Art

[0002] Transformers are essential components of power systems, with their core function being the transmission and distribution of electrical energy. However, transformers can encounter a variety of faults during operation, particularly mechanical winding and core failures, which pose a serious threat to the stable operation of power systems.

[0003] Currently, the industry has developed a variety of transformer mechanical condition assessment methods, such as short-circuit impedance, vibration frequency response, and vibration detection. However, these methods rely on indirect evaluation of the transformer's electrical and mechanical parameters. This leads to limited intuitiveness and accuracy, and they are also weak in assessing the core's mechanical condition, often requiring integration with other specialized methods. Summary of the Invention

[0004] Based on this, it is necessary to address the above-mentioned problems and propose an online evaluation method and system for the mechanical state of a transformer, which can intuitively and accurately evaluate whether the mechanical state of the transformer has changed without relying on indirect parameters, significantly improving the intuitiveness and accuracy of the evaluation. At the same time, this method also enhances the ability to evaluate the mechanical state of the core without combining other specialized methods, effectively solving the problems of non-intuitive evaluation results, limited accuracy, and weak ability to evaluate the mechanical state of the core in the existing technology.

[0005] To achieve the above objectives, the present invention provides, in a first aspect, a method for online evaluation of a transformer mechanical state, the method comprising:

[0006] In a current cycle of operation of the transformer under test, M signal generators are controlled to emit M scattered signals of different frequencies, a scattered signal matrix received by N signal receivers is obtained, and the scattered signal matrix is used as a current scattered signal matrix, wherein the M signal generators and the N signal receivers are installed on the oil tank wall of the transformer under test;

[0007] Determining a scattered signal deviation matrix correlation according to the current scattered signal matrix and the reference scattered signal matrix, and using the scattered signal deviation matrix correlation as a correlation feature quantity of the current period;

[0008] In the next cycle of operation of the transformer to be tested, returning to the step of controlling the M signal generators to emit M scattered signals of different frequencies and obtaining the scattered signal matrices received by the N signal receivers until the correlation of the scattered signal deviation matrix is obtained, and using the correlation of the scattered signal phase deviation matrix as the correlation feature value of the next cycle;

[0009] Whether the mechanical state of the transformer to be tested has changed is evaluated based on the correlation feature quantity of the current cycle and the correlation feature quantity of the next cycle.

[0010] Optionally, determining the correlation of the scattered signal deviation matrix according to the current scattered signal matrix and the reference scattered signal matrix includes:

[0011] Determining a current average value of all elements in the current scattered signal matrix, and determining a reference average value of all elements in the reference scattered signal matrix;

[0012] The scattered signal deviation matrix correlation is determined based on the current average value and all elements in the current scattered signal matrix, and the reference average value and all elements in the reference scattered signal matrix.

[0013] Optionally, determining the scattered signal deviation matrix correlation according to the current average value and all elements in the current scattered signal matrix, and the reference average value and all elements in the reference scattered signal matrix, includes:

[0014] Using the formula Determining the scattered signal deviation matrix correlation;

[0015] in, is the scattered signal deviation matrix correlation, is the element in the nth row and mth column of the current scattered signal matrix, is the current average value, is the element in the nth row and mth column of the reference scattered signal matrix, is the reference average value.

[0016] Optionally, the evaluating whether the mechanical state of the transformer to be tested has changed based on the correlation feature quantity of the current cycle and the correlation feature quantity of the next cycle includes:

[0017] Determining a correlation feature change amount based on the correlation feature amount of the current period and the correlation feature amount of the next period;

[0018] Whether the mechanical state of the transformer to be tested has changed is evaluated based on the correlation feature change amount and the change amount threshold.

[0019] Optionally, the evaluating whether the mechanical state of the transformer to be tested has changed according to the correlation feature change amount and the change amount threshold includes:

[0020] When the change in the correlation feature is greater than the change threshold, evaluating that the mechanical state of the transformer to be tested has changed;

[0021] When the variation of the correlation feature is less than or equal to the variation threshold, it is assessed that the mechanical state of the transformer to be tested has not changed.

[0022] Optionally, the method further includes:

[0023] In any cycle when the transformer to be tested is not in operation, M signal generators are controlled to emit M scattered signals of different frequencies, a scattered signal matrix received by N signal receivers is obtained, and the scattered signal matrix is used as the reference scattered signal matrix.

[0024] Optionally, controlling M signal generators to emit scattered signals of M different frequencies and obtaining a scattered signal matrix received by N signal receivers includes:

[0025] Controlling M signal generators to emit M scattered signals of different frequencies, and obtaining N×M scattered signals received by N signal receivers;

[0026] Phase extraction is performed on each of the N×M scattered signals to obtain N×M scattered signal phases;

[0027] Determine an N×M scattered signal phase matrix according to the N×M scattered signal phases;

[0028] An N×M scattered signal phase matrix is used as the scattered signal matrix.

[0029] Optionally, controlling M signal generators to emit M scattered signals of different frequencies and obtaining N×M scattered signals received by N signal receivers includes:

[0030] Control M signal generators to emit M scattered signals of different frequencies, and obtain N×M intermediate scattered signals received by N signal receivers at the i-th time, where the initial value of i is 1;

[0031] Let i = i + 1, and return to the step of controlling the M signal generators to emit scattered signals of M different frequencies, and obtaining the N×M intermediate scattered signals received by the N signal receivers for the i-th time, until the intermediate scattered signal attenuation value corresponding one-to-one between the N×M intermediate scattered signals received for the i-th time and the N×M intermediate scattered signals received for the first time is less than the attenuation threshold, thereby obtaining the N×M intermediate scattered signals received multiple times;

[0032] Among the N×M intermediate scattered signals received multiple times, the N×M intermediate scattered signals received at any one time are regarded as the N×M scattered signals.

[0033] Optionally, when M is greater than or equal to 2, N is greater than or equal to 1;

[0034] When M is greater than or equal to 1, N is greater than or equal to 2.

[0035] To achieve the above object, the present invention provides, in a second aspect, a transformer mechanical condition online evaluation system, the system comprising M signal generators, N signal receivers and a processor;

[0036] M signal generators and N signal receivers are installed on the oil tank wall of the transformer to be tested;

[0037] The processor is configured to execute the method as described in any one of the first aspects.

[0038] To achieve the above-mentioned object, the present invention provides, in a third aspect, a device for online evaluation of a transformer mechanical state, the device comprising:

[0039] a current control acquisition module, configured to control M signal generators to emit M scattered signals of different frequencies during a current cycle of operation of the transformer under test, obtain a scattered signal matrix received by N signal receivers, and use the scattered signal matrix as a current scattered signal matrix, wherein the M signal generators and N signal receivers are installed on the oil tank wall of the transformer under test;

[0040] a determination module, configured to determine a scattered signal deviation matrix correlation based on the current scattered signal matrix and a reference scattered signal matrix, and use the scattered signal deviation matrix correlation as a correlation feature value of the current period;

[0041] a next control acquisition module, configured to return to executing the step of controlling the M signal generators to emit M scattered signals of different frequencies and acquiring the scattered signal matrices received by the N signal receivers in the next cycle of the transformer under test, until the scattered signal deviation matrix correlation is obtained, and use the scattered signal phase deviation matrix correlation as the correlation feature value of the next cycle;

[0042] The evaluation module is used to evaluate whether the mechanical state of the transformer to be tested has changed based on the correlation feature quantity of the current cycle and the correlation feature quantity of the next cycle.

[0043] To achieve the above-mentioned objectives, the present invention provides, in a fourth aspect, a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the method as described in any one of the first aspects.

[0044] To achieve the above-mentioned objectives, the present invention provides a computer device in a fifth aspect, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the method as described in any one of the first aspects.

[0045] The embodiment of the present invention has the following beneficial effects: the above method controls M signal generators to emit M scattered signals of different frequencies in the current cycle of the transformer under test, obtains the scattered signal matrix received by N signal receivers, and uses the scattered signal matrix as the current scattered signal matrix, wherein the M signal generators and the N signal receivers have been installed on the oil tank wall of the transformer under test, and then determines the correlation of the scattered signal deviation matrix according to the current scattered signal matrix and the reference scattered signal matrix, and uses the correlation of the scattered signal deviation matrix as the correlation feature of the current cycle, and in the next cycle of the transformer under test, returns to execute the step of controlling the M signal generators to emit M scattered signals of different frequencies and obtaining the scattered signal matrix received by N signal receivers until the correlation of the scattered signal deviation matrix is obtained, and uses The correlation of the phase deviation matrix of the scattered signal is used as the correlation characteristic quantity of the next cycle, and finally, based on the correlation characteristic quantity of the current cycle and the correlation characteristic quantity of the next cycle, whether the mechanical state of the transformer to be tested has changed is evaluated; that is, by installing M signal generators and N signal receivers on the oil tank wall of the transformer to be tested, by comparing the correlation characteristic quantities of the scattered signals of the current cycle and the next cycle, whether the mechanical state of the transformer to be tested has changed is evaluated. This can intuitively and accurately evaluate whether the mechanical state of the transformer has changed without relying on indirect parameters, significantly improving the intuitiveness and accuracy of the evaluation. At the same time, this method also enhances the ability to evaluate the mechanical state of the iron core, without the need to combine other special methods, and effectively solves the problems of non-intuitive evaluation results, limited accuracy and weak ability to evaluate the mechanical state of the iron core in the existing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] in:

[0048] Figure 1 Schematic diagram of an online transformer mechanical condition evaluation method in an embodiment of the present application.

[0049] Figure 2 Schematic diagram of the installation relationship between the transformer to be tested, the signal generator and the signal receiver as exemplified in the embodiment of the present application;

[0050] Figure 3 This is a schematic diagram of an online transformer mechanical condition evaluation device according to an embodiment of the present application;

[0051] Figure 4 1 is a diagram of the internal structure of a computer device in some embodiments. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0053] Transformers are essential components of power systems, with their core function being the transmission and distribution of electrical energy. However, transformers can encounter a variety of faults during operation, particularly mechanical winding and core failures, which pose a serious threat to the stable operation of power systems.

[0054] Currently, the industry has developed a variety of transformer mechanical condition assessment methods, such as short-circuit impedance, vibration frequency response, and vibration detection. However, these methods rely on indirect evaluation of the transformer's electrical and mechanical parameters. This leads to limited intuitiveness and accuracy, and they are also weak in assessing the core's mechanical condition, often requiring integration with other specialized methods.

[0055] In response to the above problems, the present application proposes an online evaluation method and system for the mechanical state of a transformer, which can intuitively and accurately evaluate whether the mechanical state of the transformer has changed, without relying on indirect parameters, significantly improving the intuitiveness and accuracy of the evaluation. At the same time, this method also enhances the ability to evaluate the mechanical state of the core, without the need to combine other specialized methods, and effectively solves the problems in the prior art of non-intuitive evaluation results, limited accuracy, and weak ability to evaluate the mechanical state of the core. The specific implementation principle will be described in detail in the following embodiments.

[0056] In a first aspect, the present application provides a method for online evaluation of a transformer's mechanical condition.

[0057] See also Figure 1 , is a schematic diagram of a method for online evaluation of a transformer mechanical state in an embodiment of the present application, the method comprising:

[0058] Step 110: In the current cycle of operation of the transformer to be tested, control M signal generators to emit M scattered signals of different frequencies, obtain the scattered signal matrix received by N signal receivers, and use the scattered signal matrix as the current scattered signal matrix, wherein the M signal generators and N signal receivers have been installed on the oil tank wall of the transformer to be tested.

[0059] The transformer to be tested refers to a transformer that requires online mechanical condition evaluation; and the current cycle refers to the operating cycle of the transformer to be tested.

[0060] It should be noted that after controlling M signal generators to emit M scattered signals of different frequencies, the M scattered signals of different frequencies will be reflected in the oil tank of the transformer under test until they are received by N signal receivers to obtain a scattered signal matrix.

[0061] It should be further explained that the frequencies of the M signal generators must be different so as to emit scattered signals of M different frequencies, and the frequency band of each signal receiver must include the frequencies of the M signal generators so as to receive scattered signals of all frequencies.

[0062] Regarding the method of obtaining the scattered signal matrix, in some embodiments, M signal generators are controlled to emit M scattered signals of different frequencies, and N×M scattered signals received by N signal receivers can be obtained. Then, an N×M scattered signal matrix is determined based on the N×M scattered signals, and finally the N×M scattered signal matrix is used as the scattered signal matrix.

[0063] Regarding the installation method of M signal generators and N signal receivers, in some embodiments, a preset number of windows can be cut out on the oil tank wall of the transformer to be tested, and these windows can be filled with insulating material to form insulation detection windows. Finally, the M signal generators and N signal receivers are installed on the oil tank wall of the transformer to be tested; wherein, the specific number of the preset windows can be obtained and pre-set by the operator based on a large amount of experience, experiments or statistics, and of course, can also be set by the operator according to actual needs.

[0064] In some embodiments, the specific number of preset windows can be N+M, N, or M, depending on the circumstances; for example, when N is less than or equal to M, the specific number of preset windows can be N+M, or M, and when N is greater than or equal to M, the specific number of preset windows can be N+M, or N.

[0065] In some embodiments, the M signal generators and the N signal receivers may be paired or not, that is, M and N may be equal or not.

[0066] In the present application, paired signal generators and signal receivers are preferably used, and more preferably, the number of pairs of signal generators and signal receivers is generally twice the number of windings of the transformer to be tested.

[0067] For example, assuming that the transformer to be tested is a three-winding transformer with 3 windings, the number of pairs of signal generators and signal receivers is 6. Figure 2 , is a schematic diagram of the installation relationship between the transformer to be tested, the signal generator and the signal receiver as illustrated in the embodiment of the present application. The schematic diagram shows 210 as the oil tank of the transformer to be tested, 220 as the signal generator, 230 as the signal receiver, and 240 as the insulation detection window.

[0068] Step 120: Determine the correlation of the scattered signal deviation matrix based on the current scattered signal matrix and the reference scattered signal matrix, and use the correlation of the scattered signal deviation matrix as the correlation feature of the current period.

[0069] The reference scattering signal matrix refers to the scattering signal matrix when there is no fault anomaly, which can be obtained and pre-set by the operator based on a large amount of experience, experiments or statistics. Of course, it can also be set by the operator according to actual needs.

[0070] Regarding the method of obtaining the reference scattered signal matrix, in some embodiments, when the transformer is operating normally, M signal generators can be controlled to emit M scattered signals of different frequencies, and the scattered signal matrix received by N signal receivers can be obtained, and the scattered signal matrix can be used as the reference scattered signal matrix; in other embodiments, when the transformer is not operating, M signal generators can be controlled to emit M scattered signals of different frequencies, and the scattered signal matrix received by N signal receivers can be obtained, and the scattered signal matrix can be used as the reference scattered signal matrix.

[0071] In some embodiments, when the current scattered signal matrix is the current scattered signal phase matrix and the reference scattered signal matrix is the reference scattered signal phase matrix, the scattered signal deviation matrix correlation is the scattered signal deviation phase matrix correlation; in other embodiments, the phase can also be replaced by amplitude, integral value or data point average value, etc. For example, when the current scattered signal matrix is the current scattered signal amplitude matrix and the reference scattered signal matrix is the reference scattered signal amplitude matrix, the scattered signal deviation matrix correlation is the scattered signal deviation amplitude matrix correlation; when the current scattered signal matrix is the current scattered signal integral value matrix and the reference scattered signal matrix is the reference scattered signal integral value matrix, the scattered signal deviation matrix correlation is the scattered signal deviation integral value matrix correlation; when the current scattered signal matrix is the current scattered signal data point average value matrix and the reference scattered signal matrix is the reference scattered signal data point average value matrix, the scattered signal deviation matrix correlation is the scattered signal deviation data point average value matrix correlation.

[0072] Step 130: In the next cycle of operation of the transformer to be tested, return to the step of controlling M signal generators to emit M scattered signals of different frequencies and obtaining the scattered signal matrix received by N signal receivers until the correlation of the scattered signal deviation matrix is obtained, and the correlation of the scattered signal phase deviation matrix is used as the correlation feature value of the next cycle.

[0073] The next cycle refers to the cycle next to the current cycle.

[0074] Step 140: Evaluate whether the mechanical state of the transformer to be tested has changed based on the correlation feature quantity of the current cycle and the correlation feature quantity of the next cycle.

[0075] It should be noted that since the scattered signal matrix is obtained by reflecting M scattered signals of different frequencies in the oil tank of the transformer to be tested, the scattered signal matrix can reflect the mechanical conditions in the oil tank of the transformer to be tested, and the correlation characteristic quantity is also obtained based on the scattered signal matrix. Therefore, by comparing the correlation characteristic quantity of the scattered signals in the current cycle and the next cycle, it is possible to evaluate whether the mechanical state of the transformer to be tested has changed.

[0076] In some embodiments, if the correlation characteristic quantity of the scattered signals between the current cycle and the next cycle differs greatly, it indicates that the mechanical state of the transformer to be tested has changed; if the correlation characteristic quantity of the scattered signals between the current cycle and the next cycle differs slightly, it indicates that the mechanical state of the transformer to be tested has not changed.

[0077] It should also be noted that, in addition to evaluating whether the mechanical state of the winding and the mechanical state or position of the core have changed, the method of the present application can also evaluate whether the mechanical state or position of other mechanical components in the transformer to be tested has changed; for example, the pads, clamps, fasteners, etc. of the transformer to be tested.

[0078] In an embodiment of the present application, by installing M signal generators and N signal receivers on the oil tank wall of the transformer to be tested, and by comparing the correlation characteristic quantities of the scattered signals in the current cycle and the next cycle, it is possible to evaluate whether the mechanical state of the transformer to be tested has changed. This allows for intuitive and accurate evaluation of whether the mechanical state of the transformer has changed without relying on indirect parameters, significantly improving the intuitiveness and accuracy of the evaluation. At the same time, this method also enhances the ability to evaluate the mechanical state of the iron core without the need to combine other specialized methods, effectively solving the problems in the prior art of non-intuitive evaluation results, limited accuracy, and weak ability to evaluate the mechanical state of the iron core.

[0079] In addition, the online evaluation method for the mechanical state of the transformer proposed in this application, in addition to the above-mentioned advantages of being able to intuitively and accurately evaluate the mechanical state of the transformer and enhance the evaluation ability of the mechanical state of the iron core, also has the following advantages: Real-time monitoring and early warning: This method can realize real-time mechanical state monitoring of the transformer. By continuously comparing the correlation characteristic quantities of scattered signals in different periods, it can timely detect slight changes in the mechanical state, providing strong support for transformer fault early warning. Real-time monitoring helps to intervene in the early stage of the fault, avoid the deterioration of the fault, and reduce maintenance costs and power outage time; Improve the stability of the power system: The transformer is a key equipment in the power system. The stability of its mechanical state directly affects the overall stability of the power system. By using this method to conduct online evaluation of the mechanical state of the transformer, potential faults can be discovered and handled in a timely manner, thereby improving the reliability and stability of the power system; Non-invasive detection: This method does not require disassembly or invasive detection of the transformer. It only needs to be carried out in the oil The signal generator and receiver are installed on the box wall, which greatly simplifies the detection process. Non-invasive detection avoids potential damage to the transformer due to detection, and also reduces human errors in the detection process; strong adaptability: this method is applicable to various types of transformers, whether single-phase transformers or three-phase transformers, whether dry-type transformers or oil-immersed transformers, this method can be used for mechanical condition assessment. By adjusting parameters such as the number of signal generators and receivers, it can adapt to the specific needs of different transformers; data-driven and intelligent: this method performs data analysis based on the scattered signal matrix, and is a data-driven evaluation method. With the development of big data and artificial intelligence technology, this method can be further combined with intelligent algorithms to achieve more accurate fault prediction and diagnosis; easy to implement and maintain: the installation of signal generators and receivers is relatively simple, and has little impact on the normal operation of the transformer. The implementation cost of this method is low and maintenance is convenient, making it suitable for widespread application in power systems.

[0080] It can be seen that the online evaluation method of the transformer mechanical condition proposed in this application not only improves the intuitiveness and accuracy of the evaluation, but also enhances the stability of the power system and realizes non-invasive detection. It has the advantages of strong adaptability, data-driven and intelligent, and easy implementation and maintenance.

[0081] In a feasible implementation, step 120 in the above embodiment determines the correlation of the scattered signal deviation matrix based on the current scattered signal matrix and the reference scattered signal matrix, including: determining the current average value of all elements in the current scattered signal matrix, and determining the reference average value of all elements in the reference scattered signal matrix; determining the correlation of the scattered signal deviation matrix based on the current average value and all elements in the current scattered signal matrix, and the reference average value and all elements in the reference scattered signal matrix.

[0082] In an embodiment of the present application, by calculating the average values of the current scattered signal matrix and the reference scattered signal matrix, and determining the correlation of the scattered signal deviation matrix based on these average values and all elements, not only can the calculation process be simplified and the accuracy and robustness of the evaluation be improved, but it is also easy to implement and deploy. This method has important application value in the online evaluation of the mechanical condition of the transformer and can further improve the performance and reliability of the evaluation system.

[0083] It can be understood that the calculation process is simplified: by calculating the average value of the current scattering signal matrix and the reference scattering signal matrix, the complex matrix comparison problem can be simplified to a comparison of the average values, thereby greatly simplifying the calculation process. The average value, as an overall characteristic of the matrix, can reflect the overall level of all elements in the matrix, making subsequent correlation calculations more intuitive and concise; improving evaluation accuracy: using the average value for comparison can reduce the impact of individual abnormal elements on the evaluation results and improve the accuracy of the evaluation. The average value can smooth out the noise and fluctuations in the matrix, making the evaluation results more stable and reliable; enhancing the robustness of the evaluation: by calculating the average value, the evaluation method is insensitive to small changes in the matrix, which enhances the robustness of the evaluation. This means that even if some elements in the matrix change slightly, it will not have a significant impact on the final evaluation results, thereby improving the stability and reliability of the evaluation method; easy to implement and deploy: calculating the average value is a relatively simple operation and is easy to implement in existing computing equipment and algorithms. This method does not require complex computing resources or special hardware support, making the deployment and maintenance of the evaluation system more convenient.

[0084] In a feasible implementation, the above embodiment determines the correlation of the scattered signal deviation matrix based on the current average value and all elements in the current scattered signal matrix, and the reference average value and all elements in the reference scattered signal matrix, including:

[0085] Using the formula Determine the correlation of the scattering signal deviation matrix;

[0086] in, is the scattering signal deviation matrix correlation, is the element in the nth row and mth column of the current scattered signal matrix, is the current average value, is the nth row and mth column element in the reference scattered signal matrix, is the reference average value.

[0087] In some embodiments, It can also be expressed as the n×mth scattered signal in the current scattered signal matrix, It can also be expressed as the n×mth scattered signal in the reference scattered signal matrix.

[0088] In the embodiment of the present application, by using the formula to determine the correlation of the scattered signal deviation matrix, not only a clear and quantitative standard is provided for evaluating the mechanical state of the transformer, but also the efficiency and accuracy of the evaluation are improved, the algorithm is easy to implement and automate, and it has strong adaptability. This method has important application value in the online evaluation of the mechanical state of the transformer, can further improve the performance and reliability of the evaluation system, and provide a strong guarantee for the stable operation of the power system.

[0089] It is understandable that the clear quantitative evaluation standard: Calculating the correlation of the scattered signal deviation matrix through a specific formula provides a clear and quantitative standard for evaluating the mechanical condition of the transformer. This quantitative evaluation method makes the evaluation results more objective and accurate, reducing the subjectivity of human judgment. Improved evaluation efficiency: The formulated calculation method can quickly derive the correlation of the scattered signal deviation matrix, improving the efficiency of the evaluation. In practical applications, this evaluation method can quickly make a judgment on the mechanical condition of the transformer, making it possible to take timely measures. Enhanced evaluation accuracy: The formula considers the relationship between each element and the corresponding average value in the current scattered signal matrix and the reference scattered signal matrix, making the evaluation results more accurate. By comparing the deviation of each element from the average value, it can more carefully reflect the changes in the mechanical condition of the transformer. Facilitated algorithm implementation and automation: The formulated evaluation method can be easily programmed and integrated into an automated monitoring system. This automated evaluation method can continuously and real-time monitor the mechanical condition of the transformer, providing a strong guarantee for the stable operation of the power system. Strong adaptability: The formula is applicable to different types of transformers and different evaluation scenarios, and only parameters such as the number of signal generators and receivers need to be adjusted. This adaptability makes this method have broad application prospects in power systems.

[0090] In a feasible implementation, step 140 in the above embodiment evaluates whether the mechanical state of the transformer to be tested has changed based on the correlation characteristic quantity of the current cycle and the correlation characteristic quantity of the next cycle, including: determining the correlation characteristic change amount based on the correlation characteristic quantity of the current cycle and the correlation characteristic quantity of the next cycle; and evaluating whether the mechanical state of the transformer to be tested has changed based on the correlation characteristic change amount and the change amount threshold.

[0091] The change threshold value may be obtained and pre-set by the operator based on a large amount of experience, experiments or statistics. Of course, it may also be set by the operator based on actual needs.

[0092] Regarding the method of determining the correlation feature change amount, in some embodiments, the difference between the correlation feature amount of the current period and the correlation feature amount of the next period can be used as the correlation feature change amount; in other embodiments, the absolute value of the difference between the correlation feature amount of the current period and the correlation feature amount of the next period can also be used as the correlation feature change amount.

[0093] In some embodiments, if the difference is used as the change in correlation feature, the change threshold may include two thresholds, one positive and one negative. When the change in correlation feature is positive, the positive threshold is used for evaluation, and when the change in correlation feature is negative, the negative threshold is used for evaluation. If the absolute value of the difference is used as the change in correlation feature, the change threshold may include one threshold, and the threshold is a positive threshold, and the positive threshold may be directly used for evaluation.

[0094] In an embodiment of the present application, by determining the change in the correlation feature and comparing it with the change threshold, it is evaluated whether the mechanical state of the transformer to be tested has changed. This method improves the accuracy, early warning capability and adaptability of the evaluation, simplifies the decision-making process, and provides a strong guarantee for the stable operation of the power system.

[0095] It can be understood that the quantitative evaluation standard provides a quantitative and objective standard for evaluating changes in the transformer's mechanical state by calculating the change in the correlation feature and comparing it with the change threshold. This quantitative method reduces the subjectivity and uncertainty of human judgment, making the evaluation results more accurate and reliable. Improving evaluation accuracy: By setting a reasonable change threshold, it is possible to accurately distinguish between small changes in the transformer's mechanical state and normal fluctuations, thereby improving the accuracy of the evaluation. This helps to detect and take measures in the early stages of a fault to prevent it from worsening. Enhancing early warning capabilities: When the change in the correlation feature exceeds the change threshold, an early warning mechanism can be triggered, promptly notifying operation and maintenance personnel to conduct inspections and maintenance. This early warning capability helps to detect potential faults in advance and reduce power outages and repair costs caused by faults. Strong adaptability: This method is applicable to different types of transformers and different operating environments. It only needs to adjust the change threshold according to the specific situation. This adaptability makes this method have broad application prospects in power systems. Simplifying the decision-making process: By clearly defining the change threshold, operation and maintenance personnel can make decisions quickly to determine whether further inspection or repair of the transformer is necessary, which simplifies the decision-making process and improves work efficiency.

[0096] In a feasible implementation method, the above embodiment evaluates whether the mechanical state of the transformer to be tested has changed based on the correlation feature change and the change threshold, including: when the correlation feature change is greater than the change threshold, evaluating that the mechanical state of the transformer to be tested has changed; when the correlation feature change is less than or equal to the change threshold, evaluating that the mechanical state of the transformer to be tested has not changed.

[0097] In other embodiments, the variation threshold may include multiple thresholds, and various mechanical states of the transformer to be tested may be evaluated by comparing the correlation feature variation with multiple thresholds in the variation threshold.

[0098] In an embodiment of the present application, by setting clear comparison rules between the correlation feature change and the change threshold, the method provides a simple, reliable, early warning and adaptive solution for evaluating whether the mechanical state of the transformer to be tested has changed, which helps to ensure the stable operation of the power system and improve the reliability and safety of the power system.

[0099] It can be understood that clarity: by setting specific numerical comparisons, this method provides a clear basis for evaluation results. Operation and maintenance personnel can quickly make decisions based on the numerical comparison results without relying on subjective judgment or complex analysis, which greatly improves the efficiency and accuracy of the evaluation. Simplicity: The judgment process is simple and clear. The evaluation result can be obtained by simply comparing the magnitude of the correlation feature change with the change threshold. This simplicity makes this method easy to promote and use in practical applications and reduces the requirements for the professional skills of operation and maintenance personnel. Reliability: Through quantitative comparison, the evaluation error caused by human factors is reduced. The change threshold can be set and adjusted according to actual conditions to ensure the reliability and accuracy of the evaluation results. Early warning: When the correlation feature change exceeds the change threshold, an early warning mechanism can be immediately triggered to remind operation and maintenance personnel to conduct timely inspection and maintenance. This early warning helps to detect problems in the early stages of the fault, avoid further deterioration of the fault, and reduce maintenance costs and power outage time. Adaptability: This method is applicable to different types of transformers and different operating environments. It only needs to adjust the change threshold according to the specific situation. This adaptability makes this method have broad application prospects in power systems and can meet the evaluation needs in different scenarios.

[0100] In a feasible implementation, the method in the above embodiment further includes: in any cycle in which the transformer to be tested is not in operation, controlling M signal generators to emit M scattered signals of different frequencies, obtaining a scattered signal matrix received by N signal receivers, and using the scattered signal matrix as a reference scattered signal matrix.

[0101] In an embodiment of the present application, it is preferred to obtain a reference scattered signal matrix in any cycle in which the transformer to be tested is not in operation, which not only improves the accuracy and reliability of the evaluation, but also simplifies the evaluation process and enhances the sensitivity and adaptability of the evaluation. This step is of great significance for ensuring the safe and stable operation of the transformer.

[0102] As can be understood, ensuring the accuracy of the reference data: Obtaining the reference scattered signal matrix when the transformer is not operating can eliminate various interference factors that may be generated during transformer operation, such as electromagnetic interference, temperature fluctuations, and mechanical vibration, thereby ensuring the purity and accuracy of the reference data, which provides a reliable benchmark for subsequent mechanical condition assessment; simplifying the assessment process: By obtaining reference data when the transformer is not operating, the complex data collection and processing work during normal operation of the transformer can be avoided, thereby simplifying the assessment process and improving assessment efficiency; improving the sensitivity of the assessment: Using the scattered signal matrix when the transformer is not operating as a reference can more easily detect subtle changes in the mechanical condition of the transformer, because any deviation from the reference data may indicate a change in the mechanical condition. This high sensitivity helps to promptly detect and take measures at the early stage of a fault; enhancing the reliability of the assessment: The reference data when the transformer is not operating provides a stable benchmark for comparison with the data when the transformer is operating. This stability enhances the reliability of the assessment, making the assessment results more accurate and reliable; adapting to different operating conditions: By obtaining reference data when the transformer is not operating, it can more easily adapt to the assessment needs of the transformer under different operating conditions. Because the reference data is obtained when the transformer is stationary, it is not affected by operating conditions and can serve as an assessment benchmark under various operating conditions.

[0103] In a feasible implementation, step 110 in the above embodiment, controlling M signal generators to emit M scattered signals of different frequencies and obtaining a scattered signal matrix received by N signal receivers, includes: controlling M signal generators to emit M scattered signals of different frequencies and obtaining N×M scattered signals received by N signal receivers; performing phase extraction on each of the N×M scattered signals to obtain N×M scattered signal phases; determining an N×M scattered signal phase matrix based on the N×M scattered signal phases; and using the N×M scattered signal phase matrix as the scattered signal matrix.

[0104] In the present application, the phase of the scattered signal is preferably used for evaluation.

[0105] In an embodiment of the present application, a signal generator is controlled to emit scattered signals of different frequencies, and these signals are received to extract phase information, thereby constructing a scattered signal phase matrix as an evaluation basis. This method not only improves the accuracy and stability of the evaluation, but also enhances the sensitivity of the evaluation and the ability to identify fault types, providing an effective means for online evaluation of the mechanical condition of the transformer.

[0106] It can be understood that improving assessment accuracy: phase information, as an important feature of the signal, can accurately reflect the changes in the signal during transmission. By comparing the phase matrix of the scattered signal in different cycles, the changes in the mechanical state of the transformer can be accurately judged. This phase-based assessment method improves the accuracy of the assessment and helps to detect potential faults in a timely manner; enhancing assessment stability: phase information is insensitive to the amplitude change of the signal and mainly reflects the time delay and waveform change of the signal. Therefore, when the signal amplitude fluctuates, the phase information can still remain relatively stable. This stability makes the assessment results more reliable; improving assessment sensitivity: phase changes often appear before amplitude changes. Therefore, by monitoring phase information, small changes in the mechanical state of the transformer can be detected earlier. This high sensitivity helps to take intervention measures in the early stage of the fault and avoid the deterioration of the fault; enhancing the ability to identify the fault type: different types of mechanical faults may cause different phase change patterns. By analyzing the change characteristics of the phase matrix, the fault type can be identified, which provides strong support for fault location and cause analysis; simplifying the data processing process: the extraction of phase information is relatively simple and does not require complex signal processing algorithms. This simplifies the data processing process, improves the evaluation efficiency, and makes the method easier to promote in practical applications.

[0107] In a feasible implementation, in the above embodiment, controlling M signal generators to emit scattered signals of M different frequencies to obtain N×M scattered signals received by N signal receivers includes: controlling the M signal generators to emit scattered signals of M different frequencies to obtain N×M intermediate scattered signals received by the N signal receivers for the i-th time, where the initial value of i is 1; setting i=i+1, and returning to execute the step of controlling the M signal generators to emit scattered signals of M different frequencies to obtain the N×M intermediate scattered signals received by the N signal receivers for the i-th time, until an intermediate scattered signal attenuation value corresponding one-to-one between the N×M intermediate scattered signals received for the i-th time and the N×M intermediate scattered signals received for the first time is less than an attenuation threshold, thereby obtaining N×M intermediate scattered signals received multiple times; and using the N×M intermediate scattered signals received at any one time among the N×M intermediate scattered signals received multiple times as the N×M scattered signals.

[0108] The attenuation threshold may be obtained and pre-set by the operator based on a large amount of experience, experiments or statistics. Of course, the operator may also set it based on actual needs.

[0109] In some embodiments, the attenuation threshold may be set to half the scattered signal emitted by the signal generator.

[0110] It should be noted that, since the M scattered signals emitted by the M signal generators are of different frequencies, each signal receiver can identify the number of receptions when receiving the M scattered signals reflected by the oil tank of the transformer under test.

[0111] It should be further explained that the scattered signal emitted by the signal generator will gradually attenuate when reflected in the oil tank of the transformer to be tested; in this application, the scattered signal emitted by the signal generator decays to 0 by default within one operating cycle.

[0112] In an embodiment of the present application, by introducing attenuation threshold control in the process of acquiring scattered signals and selecting N×M intermediate scattered signals received at the same time as N×M scattered signals, not only the data quality and the accuracy of the evaluation are improved, but also the consistency of the evaluation is ensured, the data acquisition process is optimized, the robustness and adaptability of the evaluation are enhanced, the evaluation process is simplified, and a more efficient and reliable method is provided for online evaluation of the mechanical condition of the transformer.

[0113] It can be understood that improving data quality: by introducing attenuation threshold control, ensuring that the acquired scattering signal has sufficient intensity and quality, only when the signal decays to below a certain threshold, stopping receiving new scattering signals, which can avoid receiving signals that are too weak or have large noise interference, thereby improving the accuracy and reliability of subsequent data analysis; ensuring consistency of evaluation: among the N×M intermediate scattering signals received multiple times, selecting the signal received at the same time as the evaluation basis can ensure the consistency and comparability of the evaluation, because each received signal has undergone reflection attenuation, selecting the signal received at the same time can avoid evaluation errors caused by differences between different received signals; optimizing the data acquisition process: by receiving scattered signals multiple times and stopping receiving when the signal decays to a certain extent, the data acquisition process can be optimized. This method can reduce unnecessary data collection while ensuring data quality and improve data acquisition efficiency; enhancing the robustness of the evaluation: since the scattered signal received each time may be affected by external factors, Interference from environmental factors, such as electromagnetic interference and temperature fluctuations, can be enhanced by receiving and selecting appropriate signals multiple times as the basis for evaluation. Even if a signal received at a certain time is interfered with, it can be compensated or corrected by signals received at other times; Adapt to different operating conditions: The attenuation speed and degree of the scattered signal may be different for different transformers and different operating conditions. By introducing attenuation threshold control, it can adapt to different situations and ensure that high-quality scattered signals can be obtained under different conditions; Improve the sensitivity and accuracy of the evaluation: By selecting the intermediate scattered signal that has attenuated to a certain extent but has not completely disappeared as the basis for evaluation, the sensitivity and accuracy of the evaluation can be improved, because at this time the signal still contains sufficient mechanical state information, but the noise and interference are relatively small; Simplify the evaluation process: After introducing the attenuation threshold control, there is no need to complete the signal reception and processing for each operating cycle. It is only necessary to stop receiving when the signal attenuates to a certain extent, thereby simplifying the evaluation process and improving the evaluation efficiency.

[0114] In a feasible implementation, when M is greater than or equal to 2, N is greater than or equal to 1; when M is greater than or equal to 1, N is greater than or equal to 2.

[0115] In the embodiment of the present application, the configuration conditions for the number of signal generators and signal receivers (when M is greater than or equal to 2, N is greater than or equal to 1; when M is greater than or equal to 1, N is greater than or equal to 2) provide flexibility and scalability for the online assessment system of the transformer mechanical condition, improve the accuracy, robustness, and adaptability of the assessment, and optimize the system cost. This configuration condition enables the method to provide effective solutions under different assessment scenarios and requirements.

[0116] In a second aspect, the present application provides a transformer mechanical state online evaluation system, which includes M signal generators, N signal receivers and a processor (not shown in the figure, which can be referred to as Figure 2 Schematic diagram shown as an example).

[0117] In a feasible implementation, M signal generators and N signal receivers are installed on the oil tank wall of the transformer to be tested, and the processor is used to execute any method in the first aspect.

[0118] In the embodiments of the present application, the online evaluation system for the mechanical condition of a transformer proposed in the present application provides a strong guarantee for the stable operation of the power system through the beneficial effects of integrated design, real-time online monitoring, flexible scalability, efficient data processing capabilities, easy deployment and maintenance, and improved evaluation accuracy and reliability. The system not only solves the problems of non-intuitive evaluation results, limited accuracy, and weak ability to evaluate the mechanical condition of the core in the prior art, but also reduces operation and maintenance costs and improves the overall benefits of the power system.

[0119] It can be understood that system integration and automation: by integrating M signal generators, N signal receivers and processors into one system, the online evaluation of the mechanical state of the transformer is automated. This integrated design makes the evaluation process more efficient and convenient, reduces the need for manual intervention, and improves the accuracy and reliability of the evaluation; real-time and online monitoring: the system can collect and process scattered signal data in real time, and realize online monitoring of the mechanical state of the transformer through real-time analysis of the data by the processor. This real-time performance enables operation and maintenance personnel to understand the operating status of the transformer in a timely manner, discover and deal with potential faults in a timely manner, and avoid greater losses caused by the deterioration of faults; flexibility and scalability: the number of signal generators and signal receivers in the system can be flexibly configured according to actual needs. This flexibility enables the system to adapt to different types of transformers and different evaluation needs. At the same time, with the continuous development of technology, the system can also be easily expanded and upgraded to adapt to new evaluation methods and technologies that may emerge in the future. Technology; Efficient data processing capability: As the core component of the system, the processor has powerful data processing capabilities. It can quickly process large amounts of scattered signal data and extract useful information for evaluating the mechanical condition of the transformer. This efficient data processing capability improves the efficiency and accuracy of the evaluation, enabling the system to obtain reliable evaluation results in a short period of time; Easy to deploy and maintain: The system is relatively simple to deploy. It only requires installing a signal generator and a signal receiver on the oil tank wall of the transformer to be tested and connecting the processor. At the same time, the system is also very convenient to maintain. Due to the high degree of integration and automation of the system, operation and maintenance personnel only need to perform regular inspections and maintenance to ensure the normal operation of the system; Improve the accuracy and reliability of the evaluation: Through the processor's precise analysis and processing of scattered signal data, the system can more accurately evaluate the mechanical condition of the transformer. Compared with traditional evaluation methods, this system does not rely on indirect parameters and can directly reflect the actual operating status of the transformer, thereby improving the accuracy and reliability of the evaluation.

[0120] In a third aspect, the present application provides an online evaluation device for the mechanical state of a transformer.

[0121] See also Figure 3 , is a schematic diagram of an online transformer mechanical condition evaluation device according to an embodiment of the present application, wherein the device 310 includes:

[0122] The current control acquisition module 311 is used to control M signal generators to emit M scattered signals of different frequencies during the current operation cycle of the transformer under test, obtain the scattered signal matrix received by N signal receivers, and use the scattered signal matrix as the current scattered signal matrix, wherein the M signal generators and N signal receivers are installed on the oil tank wall of the transformer under test;

[0123] A determination module 312 is configured to determine a scattered signal deviation matrix correlation based on the current scattered signal matrix and the reference scattered signal matrix, and use the scattered signal deviation matrix correlation as a correlation feature value of the current period;

[0124] The next control acquisition module 313 is configured to return to the step of controlling the M signal generators to emit M scattered signals of different frequencies and acquiring the scattered signal matrices received by the N signal receivers in the next cycle of the transformer under test, until the scattered signal deviation matrix correlation is obtained, and use the scattered signal phase deviation matrix correlation as the correlation feature value of the next cycle;

[0125] The evaluation module 314 is configured to evaluate whether the mechanical state of the transformer to be tested has changed based on the correlation feature quantity of the current cycle and the correlation feature quantity of the next cycle.

[0126] In the embodiment of the present application, the relevant contents of the above-mentioned current control acquisition module 311, determination module 312, next control acquisition module 313 and evaluation module 314 can be referred to. Figure 1 The contents of the illustrated embodiments are not described in detail here.

[0127] It should be noted that the device 310 of the present application also includes some other modules. It can be understood that the method of the present application and the device 310 have a one-to-one correspondence. Therefore, the other modules of the device 310 of the present application are the contents corresponding to the method of the present application in the above embodiment.

[0128] In an embodiment of the present application, by installing M signal generators and N signal receivers on the oil tank wall of the transformer to be tested, and by comparing the correlation characteristic quantities of the scattered signals in the current cycle and the next cycle, it is possible to evaluate whether the mechanical state of the transformer to be tested has changed. This allows for intuitive and accurate evaluation of whether the mechanical state of the transformer has changed without relying on indirect parameters, significantly improving the intuitiveness and accuracy of the evaluation. At the same time, this method also enhances the ability to evaluate the mechanical state of the iron core without the need to combine with other specialized devices, effectively solving the problems in the prior art of non-intuitive evaluation results, limited accuracy, and weak ability to evaluate the mechanical state of the iron core.

[0129] In addition, the online evaluation device for the mechanical state of the transformer proposed in this application, in addition to the above-mentioned ability to intuitively and accurately evaluate the mechanical state of the transformer and enhance the evaluation ability of the mechanical state of the iron core, also has the following advantages: Real-time monitoring and early warning: The device can realize real-time mechanical state monitoring of the transformer. By continuously comparing the correlation characteristic quantities of scattered signals in different periods, it can timely detect slight changes in the mechanical state, providing strong support for transformer fault early warning. Real-time monitoring helps to intervene in the early stage of the fault, avoid the deterioration of the fault, and reduce maintenance costs and power outage time; Improve the stability of the power system: The transformer is a key equipment in the power system. The stability of its mechanical state directly affects the overall stability of the power system. By using this device to perform online evaluation of the mechanical state of the transformer, potential faults can be discovered and handled in time, thereby improving the reliability and stability of the power system; Non-invasive detection: The device does not require disassembly or invasive detection of the transformer. It only needs to be installed in the oil tank. The signal generator and receiver are installed on the box wall, which greatly simplifies the detection process. Non-invasive detection avoids potential damage to the transformer due to detection, and also reduces human errors in the detection process; strong adaptability: the device is suitable for various types of transformers, whether single-phase transformers or three-phase transformers, whether dry-type transformers or oil-immersed transformers, the device can be used for mechanical condition assessment, and by adjusting parameters such as the number of signal generators and receivers, it can adapt to the specific needs of different transformers; data-driven and intelligent: the device performs data analysis based on the scattered signal matrix and is a data-driven evaluation device. With the development of big data and artificial intelligence technology, the device can be further combined with intelligent algorithms to achieve more accurate fault prediction and diagnosis; easy implementation and maintenance: the installation of the signal generator and receiver is relatively simple, and has little impact on the normal operation of the transformer. The implementation cost of the device is low and maintenance is convenient, making it suitable for widespread use in power systems.

[0130] In a fourth aspect, the present application further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes a method for online evaluation of the mechanical state of a transformer in the above method embodiment.

[0131] In a fifth aspect, the present application further provides a computer device including a memory and a processor, wherein the memory stores a computer program. When the computer program is executed by the processor, the processor executes a transformer mechanical state online evaluation method in the above method embodiment.

[0132] Figure 4 The internal structure diagram of the computer device in some embodiments is shown. The computer device can be a terminal, a server, or a gateway. Figure 4 As shown, the computer device includes a processor, a memory, and a network interface connected via a system bus.

[0133] The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor can implement the various steps in the above method embodiment. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor can implement the various steps in the above method embodiment. It will be understood by those skilled in the art that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0134] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods.

[0135] Among them, any reference to memory, storage, database or other media used in the various embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0136] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0137] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for online evaluation of transformer mechanical status, characterized in that: The method comprises: In a current cycle of operation of the transformer under test, M signal generators are controlled to emit M scattered signals of different frequencies, a scattered signal matrix received by N signal receivers is obtained, and the scattered signal matrix is used as a current scattered signal matrix, wherein the M signal generators and the N signal receivers are installed on the oil tank wall of the transformer under test; Determining a scattered signal deviation matrix correlation according to the current scattered signal matrix and the reference scattered signal matrix, and using the scattered signal deviation matrix correlation as a correlation feature quantity of the current period; In the next cycle of operation of the transformer to be tested, returning to the step of controlling the M signal generators to emit M scattered signals of different frequencies and obtaining the scattered signal matrices received by the N signal receivers until the correlation of the scattered signal deviation matrix is obtained, and using the correlation of the scattered signal phase deviation matrix as the correlation feature value of the next cycle; Evaluate whether the mechanical state of the transformer to be tested has changed based on the correlation feature quantity of the current cycle and the correlation feature quantity of the next cycle; The method further comprises: In any cycle when the transformer to be tested is not in operation, M signal generators are controlled to emit M scattered signals of different frequencies, a scattered signal matrix received by N signal receivers is obtained, and the scattered signal matrix is used as the reference scattered signal matrix.

2. The online evaluation method for transformer mechanical status according to claim 1, characterized in that: The determining, according to the current scattered signal matrix and the reference scattered signal matrix, a scattered signal deviation matrix correlation comprises: Determining a current average value of all elements in the current scattered signal matrix, and determining a reference average value of all elements in the reference scattered signal matrix; The scattered signal deviation matrix correlation is determined based on the current average value and all elements in the current scattered signal matrix, and the reference average value and all elements in the reference scattered signal matrix.

3. The online evaluation method for transformer mechanical status according to claim 2, characterized in that: Determining the scattered signal deviation matrix correlation according to the current average value and all elements in the current scattered signal matrix, and the reference average value and all elements in the reference scattered signal matrix, includes: Using the formula Determining the scattered signal deviation matrix correlation; in, is the scattered signal deviation matrix correlation, is the element in the nth row and mth column of the current scattered signal matrix, is the current average value, is the element in the nth row and mth column of the reference scattered signal matrix, is the reference average value.

4. The online evaluation method for transformer mechanical status according to claim 1, characterized in that: The step of evaluating whether the mechanical state of the transformer to be tested has changed based on the correlation feature quantity of the current cycle and the correlation feature quantity of the next cycle includes: Determining a correlation feature change amount based on the correlation feature amount of the current period and the correlation feature amount of the next period; Whether the mechanical state of the transformer to be tested has changed is evaluated based on the correlation feature change amount and the change amount threshold.

5. The method for online evaluation of transformer mechanical status according to claim 4, characterized in that: The step of evaluating whether the mechanical state of the transformer to be tested has changed according to the correlation feature change amount and the change amount threshold includes: When the change in the correlation feature is greater than the change threshold, evaluating that the mechanical state of the transformer to be tested has changed; When the variation of the correlation feature is less than or equal to the variation threshold, it is assessed that the mechanical state of the transformer to be tested has not changed.

6. The method for online evaluation of transformer mechanical status according to claim 1, characterized in that: The controlling M signal generators to emit M scattered signals of different frequencies and obtaining a scattered signal matrix received by N signal receivers includes: Controlling M signal generators to emit M scattered signals of different frequencies, and obtaining N×M scattered signals received by N signal receivers; Phase extraction is performed on each of the N×M scattered signals to obtain N×M scattered signal phases; Determine an N×M scattered signal phase matrix according to the N×M scattered signal phases; An N×M scattered signal phase matrix is used as the scattered signal matrix.

7. The method for online evaluation of transformer mechanical status according to claim 6, characterized in that: The controlling M signal generators to emit M scattered signals of different frequencies and obtaining N×M scattered signals received by N signal receivers includes: Control M signal generators to emit M scattered signals of different frequencies, and obtain N×M intermediate scattered signals received by N signal receivers at the i-th time, where the initial value of i is 1; Let i = i + 1, and return to the step of controlling the M signal generators to emit scattered signals of M different frequencies, and obtaining the N×M intermediate scattered signals received by the N signal receivers for the i-th time, until the intermediate scattered signal attenuation value corresponding one-to-one between the N×M intermediate scattered signals received for the i-th time and the N×M intermediate scattered signals received for the first time is less than the attenuation threshold, thereby obtaining the N×M intermediate scattered signals received multiple times; Among the N×M intermediate scattered signals received multiple times, the N×M intermediate scattered signals received at any one time are regarded as the N×M scattered signals.

8. The method for online evaluation of transformer mechanical status according to claim 1, characterized in that: When M is greater than or equal to 2, N is greater than or equal to 1; When M is greater than or equal to 1, N is greater than or equal to 2.

9. A transformer mechanical condition online evaluation system, characterized in that: The system includes M signal generators, N signal receivers and a processor; M signal generators and N signal receivers are installed on the oil tank wall of the transformer to be tested; The processor is configured to execute the online transformer mechanical condition assessment method according to any one of claims 1 to 8.

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