Online detection method and system for looseness of transformer winding, terminal and medium

Abstract

The invention discloses a transformer winding looseness online detection method, a transformer winding looseness online detection system, a terminal and a medium, and relates to the technical field of transformer detection. The association mapping model is established by analyzing the association relationship between the changing load current and the corresponding average amplitude, the reference load current is obtained through calculation, under the condition that the average amplitude is the same, the axis pretightening force change caused by the load current change is regarded as an equivalent loose force change value, the association mapping model is low in construction difficulty, and the construction efficiency is high. Detailed detection of the loosening condition of the transformer winding can be realized only by using a small amount of prior data; moreover, the average amplitude is used for replacing the load at a single moment, so that the interference of a system error on a detection result can be effectively avoided, the influence of part of nonlinear factors on the detection result can be effectively eliminated, and the accuracy of the looseness detection of the transformer winding is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer detection, and more specifically, to an online detection method, system, terminal and medium for transformer winding loosening. Background Art

[0002] Transformer winding loosening refers to the phenomenon that the fixed position of the transformer winding is offset due to mechanical stress or electromagnetic force. Winding loosening will reduce the winding's ability to resist short-circuit current shocks, posing a safety hazard to the power grid; in severe cases, the transformer may suffer a catastrophic failure, which will have a serious impact on the environment, such as fire, transformer oil leakage, etc. Therefore, transformer winding loosening detection is of great significance for ensuring the safe and stable operation of the power system, extending the service life of the transformer, reducing operation and maintenance costs, and improving the reliability and economy of power grid operation.

[0003] At present, the detection methods of transformer winding looseness mainly include vibration-based detection methods. For example, the detection method based on ratio analyzes the frequency components of transformer winding vibration and uses the ratio of 50Hz vibration component to 150Hz vibration component as the characteristic quantity for winding looseness diagnosis. When the ratio of 50Hz vibration component to 150Hz vibration component changes significantly, the transformer winding looseness can be judged. This method does not require prior data and the implementation process is relatively simple, but it is difficult to determine the specific situation of transformer winding looseness and cannot provide detailed reference opinions for subsequent power equipment management. For another example, the detection method based on relationship fitting simulates and analyzes the correlation model between vibration and influencing factors, and performs spectrum analysis on the vibration signal after Fourier transformation based on the correlation model. This method only requires less prior data, but it needs to consider multiple data when fitting the relationship between vibration and load, which easily leads to an increase in the complexity of the correlation model, making the model difficult to explain and understand. At the same time, the detection results obtained by this method can only be the relative state relative to the initial operation of the power transformer, and the specific transformer winding looseness cannot be obtained.

[0004] Therefore, how to research and design an online detection method, system, terminal and medium for transformer winding loosening that can overcome the above-mentioned defects is a problem that we urgently need to solve. Summary of the invention

[0005] In order to solve the deficiencies in the prior art, the purpose of the present invention is to provide an online detection method, system, terminal and medium for transformer winding loosening. In the initial operation stage, when the transformer winding axis preload force is known, a correlation mapping model is established by analyzing the correlation between the changing load current and the corresponding average amplitude, and the real-time average amplitude corresponding to the real-time load current is input into the correlation mapping model, and the reference load current is obtained by calculation. When the average amplitude is the same, the axis preload force change caused by the load current change is regarded as an equivalent loosening force change value. The construction of the correlation mapping model of this method is easy, and detailed detection of transformer winding loosening can be achieved with only a small amount of prior data.

[0006] The above technical objectives of the present invention are achieved through the following technical solutions: In a first aspect, a method for online detection of transformer winding looseness is provided, comprising the following steps: Obtain the vibration signal of the load current change process of the transformer in the initial operation stage, and process the vibration signal in sections according to the load current to obtain section signals of different load currents; The segmented signal is converted into a corresponding frequency domain signal by Fourier transform, and the vibration component of the set frequency is extracted from the frequency domain signal, and the corresponding average amplitude is calculated according to all vibration components of the same load current; Establishing a correlation mapping model based on the load current and the corresponding average amplitude; Collect the real-time load current and real-time vibration signal of the transformer, and calculate the real-time average amplitude corresponding to the real-time load current after segmentation processing, Fourier transformation and component extraction of the real-time vibration signal; The real-time average amplitude is input into the correlation mapping model to calculate and obtain the reference load current; Input the reference load current and the real-time load current into the correlation mapping model, and perform difference calculation to obtain the equivalent loose force change value; After comparing the equivalent loose force change value with the loose force change threshold, the detection result of transformer winding looseness is obtained.

[0007] Furthermore, the expression for segmenting the vibration signal according to the load current is specifically: ; in, Indicates The sampled load current; Indicates load current The corresponding segment signal, The starting time of the segment The corresponding signal, The end time of the segment The corresponding signal; Indicates the current threshold; Indicates the end time of the segment The corresponding current; Indicates the starting time of the segment The corresponding current; It represents the current function that characterizes the load current change during the initial operation stage.

[0008] Furthermore, the expression for segmenting the vibration signal according to the load current is specifically: ; in, Indicates The sampled load current; Indicates load current The corresponding segment signal, The starting time of the segment The corresponding signal, The end time of the segment The corresponding signal; Indicates the current threshold; Indicates the end time of the segment The corresponding current; Indicates the starting time of the segment The corresponding current; It represents the current function that characterizes the load current change during the initial operation stage; Represents a dynamic adjustment parameter; Indicates the time threshold.

[0009] Furthermore, the value range of the set frequency is [80 Hz, 120 Hz].

[0010] Furthermore, the expression of the association mapping model is specifically: ; in, Indicates the axial preload of the transformer winding during the initial operation stage; It represents the correlation parameter between load current and axis preload; Indicates load current; Represents the mapping function between the axis preload and the transformer winding vibration; Indicates the average amplitude.

[0011] Furthermore, the calculation formula of the loosening force change value is specifically: ; in, Indicates the equivalent loosening force change value; It represents the correlation parameter between load current and axis preload; Indicates real-time load current; Indicates the reference load current.

[0012] Furthermore, the process of obtaining the detection result of the transformer winding loosening is specifically as follows: Different loose force change thresholds are configured for different transformer winding looseness degrees, and adjacent loose force change thresholds constitute loose force change intervals for different transformer winding looseness levels; The equivalent loose force change value is matched with the loose force change interval, and the transformer winding looseness level in the matched loose force change interval is taken as the detection result.

[0013] In a second aspect, a transformer winding loosening online detection system is provided, the system is used to implement a transformer winding loosening online detection method as described in any one of the first aspects, including: The signal segmentation module is used to obtain the vibration signal of the load current change process of the transformer in the initial operation stage, and to process the vibration signal in segments according to the load current to obtain segmented signals of different load currents; A signal conversion module, used to convert the segmented signal into a corresponding frequency domain signal by Fourier transform, extract the vibration component of the set frequency from the frequency domain signal, and calculate the corresponding average amplitude according to all vibration components of the same load current; A model building module, used for building a correlation mapping model according to the load current and the corresponding average amplitude; The signal processing module is used to collect the real-time load current and real-time vibration signal of the transformer, and calculate the real-time average amplitude corresponding to the real-time load current after segmentation processing, Fourier transformation and component extraction of the real-time vibration signal; A current calculation module is used to input the real-time average amplitude into the correlation mapping model to calculate and obtain a reference load current; The difference operation module is used to input the reference load current and the real-time load current into the correlation mapping model, and obtain the equivalent loose force change value after performing the difference operation; The comparison detection module is used to compare the equivalent loose force change value with the loose force change threshold value to obtain the detection result of transformer winding looseness.

[0014] In a third aspect, a computer terminal is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, an online detection method for loose transformer windings as described in any one of the first aspects is implemented.

[0015] In a fourth aspect, a computer-readable medium is provided, on which a computer program is stored, and the computer program is executed by a processor to implement an online detection method for loose transformer windings as described in any one of the first aspects.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention provides an online detection method for transformer winding loosening. In the initial operation stage, when the transformer winding axis preload force is known, a correlation mapping model is established by analyzing the correlation relationship between the changing load current and the corresponding average amplitude, and the real-time average amplitude corresponding to the real-time load current is input into the correlation mapping model to obtain a reference load current by calculation. When the average amplitude is the same, the axis preload force change caused by the load current change is regarded as an equivalent loose force change value. The construction difficulty of the correlation mapping model of this method is small, and detailed detection of transformer winding loosening can be achieved with only a small amount of prior data; 2. The present invention uses the average amplitude to replace the load at a single moment, which can effectively avoid the interference of system errors on the detection results, and can also effectively eliminate the influence of some nonlinear factors on the detection results, effectively improving the accuracy of transformer winding loosening detection; 3. When the present invention performs segmented processing on the vibration signal according to the load current size, the current width is flexibly adjusted by the time width of the segmented signal, which effectively ensures the balance between the current width and the time width in each segmented signal and ensures the rationality of the segmented processing of the vibration signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings: Figure 1 is a flow chart of Embodiment 1 of the present invention; Figure 2 It is a system block diagram in Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0018] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0019] Embodiment 1: A method for detecting transformer winding looseness online, such as Figure 1 As shown, the following steps are included: S1: Obtain the vibration signal of the load current change process of the transformer in the initial operation stage, and process the vibration signal in sections according to the load current to obtain section signals of different load currents; S2: converting the segmented signal into a corresponding frequency domain signal by Fourier transform, extracting the vibration component of the set frequency from the frequency domain signal, and calculating the corresponding average amplitude according to all vibration components of the same load current; S3: Establishing a correlation mapping model based on the load current and the corresponding average amplitude; S4: collecting the real-time load current and real-time vibration signal of the transformer, and calculating the real-time average amplitude corresponding to the real-time load current after segmentation processing, Fourier transformation and component extraction of the real-time vibration signal; S5: input the real-time average amplitude into the correlation mapping model to calculate and obtain the reference load current; S6: input the reference load current and the real-time load current into the correlation mapping model, and perform difference calculation to obtain an equivalent loose force change value; S7: After comparing the equivalent loose force change value with the loose force change threshold, a detection result of transformer winding looseness is obtained.

[0020] In step S1, the axis preload force of the transformer in the initial operation stage is known data. In addition to obtaining the priori data in the initial operation stage, the priori data can also be obtained in other cases where the axis preload force is known, which is not limited here.

[0021] In step S2, as an optional implementation, a fixed current threshold may be used to process the vibration signal in sections, and the specific expression is: ; in, Indicates The sampled load current; Indicates load current The corresponding segment signal, The starting time of the segment The corresponding signal, The end time of the segment The corresponding signal; Indicates the current threshold; Indicates the end time of the segment The corresponding current; Indicates the starting time of the segment The corresponding current; It represents the current function that characterizes the load current change during the initial operation stage.

[0022] It should be noted that the segmented signals can form a continuous signal or a signal with intervals, which is not limited here. However, in order to ensure the integrity of the data, a continuous segmented signal is generally used.

[0023] As another optional implementation, considering the randomness of the load current change, the load current changes too slowly at some times, so the current threshold can be flexibly adjusted according to the specific change situation. To this end, the expression for segmenting the vibration signal according to the load current size is specifically: ; in, Indicates The sampled load current; Indicates load current The corresponding segment signal, The starting time of the segment The corresponding signal, The end time of the segment The corresponding signal; Indicates the current threshold; Indicates the end time of the segment The corresponding current; Indicates the starting time of the segment The corresponding current; It represents the current function that characterizes the load current change during the initial operation stage; Represents a dynamic adjustment parameter; Indicates the time threshold.

[0024] The present invention flexibly adjusts the current width through the time width of the segmented signal, effectively ensures the balance between the current width and the time width in each segmented signal, and ensures the rationality of the segmented processing of the vibration signal.

[0025] In this embodiment, since 100 Hz is the characteristic frequency of winding looseness and the fundamental frequency of winding vibration fluctuates in a small range around 100 Hz, the value range of the set frequency is [80 Hz, 120 Hz], and 100 Hz is generally used.

[0026] In step S3, since the increase of load current will increase the vibration of transformer winding, the present application converts the change of load current into the force applied to the transformer winding, so as to obtain the correlation mapping model by fitting the relationship between the axial preload force and the average amplitude, and the specific expression is: ; in, Indicates the axial preload of the transformer winding during the initial operation stage; It represents the correlation parameter between load current and axis preload; Indicates load current; Represents the mapping function between the axis preload and the transformer winding vibration; Indicates the average amplitude.

[0027] In step S4, the steps of segmentation processing, Fourier transformation and component extraction of the real-time vibration signal are consistent with the steps of processing the segmented signal in the priori data.

[0028] In steps S5 and S6, since the real-time average amplitude corresponds to the real-time load current, there is a difference between the reference load current obtained by inputting the real-time average amplitude into the correlation mapping model and the real-time load current. The present invention converts the axial preload force change caused by part of the difference into the loosening force change.

[0029] Therefore, the calculation formula of the loose force change value is as follows: ; in, Indicates the equivalent loosening force change value; It represents the correlation parameter between load current and axis preload; Indicates real-time load current; Indicates the reference load current.

[0030] In step S7, the process of obtaining the detection result of transformer winding looseness is specifically as follows: different loose force change thresholds are configured for different transformer winding looseness degrees, and adjacent loose force change thresholds constitute loose force change intervals for different transformer winding looseness levels; the loose force change interval is matched to the equivalent loose force change value, and the transformer winding looseness level of the matched loose force change interval is used as the detection result.

[0031] Embodiment 2: A transformer winding loosening online detection system, the system is used to implement a transformer winding loosening online detection method as described in Embodiment 1, such as Figure 2 As shown, it includes a signal segmentation module, a signal transformation module, a model building module, a signal processing module, a current operation module, a difference operation module and a comparison detection module.

[0032] Among them, the signal segmentation module is used to obtain the vibration signal of the load current change process of the transformer in the initial operation stage, and to process the vibration signal in segments according to the load current to obtain segmented signals of different load currents; the signal conversion module is used to convert the segmented signal into the corresponding frequency domain signal by Fourier transform, and to extract the vibration component of the set frequency from the frequency domain signal, and to calculate the corresponding average amplitude according to all vibration components of the same load current; the model construction module is used to establish an association mapping model according to the load current and the corresponding average amplitude; the signal processing module is used to collect the real-time load current and real-time vibration signal of the transformer, and to calculate the real-time average amplitude corresponding to the real-time load current after segmentation processing, Fourier transform and component extraction of the real-time vibration signal; the current calculation module is used to input the real-time average amplitude into the association mapping model to calculate and obtain the reference load current; the difference calculation module is used to input the reference load current and the real-time load current into the association mapping model, and to obtain the equivalent loose force change value after difference calculation; the comparison detection module is used to compare the equivalent loose force change value with the loose force change threshold value to obtain the detection result of transformer winding looseness.

[0033] The present invention also records a computer terminal, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, an online detection method for transformer winding loosening as described in Example 1 is implemented.

[0034] The present invention also describes a computer-readable medium on which a computer program is stored. The computer program is executed by a processor to implement an online detection method for transformer winding looseness as described in Example 1.

[0035] Working principle: In the initial operation stage, when the axial preload force of the transformer winding is known, the present invention establishes an association mapping model by analyzing the correlation between the changing load current and the corresponding average amplitude, and inputs the real-time average amplitude corresponding to the real-time load current into the association mapping model, and calculates to obtain the reference load current. When the average amplitude is the same, the change in the axial preload force caused by the change in the load current is regarded as an equivalent loosening force change value. The construction of the association mapping model of this method is easy, and detailed detection of the looseness of the transformer winding can be achieved with only a small amount of prior data; in addition, the present invention replaces the load at a single moment with the average amplitude, which can effectively avoid the interference of system errors on the detection results, and can also effectively eliminate the influence of some nonlinear factors on the detection results, effectively improving the accuracy of transformer winding looseness detection.

[0036] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0037] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0038] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0039] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0040] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for online detection of transformer winding looseness, characterized in that: The following steps are involved: Obtain the vibration signal of the load current change process of the transformer in the initial operation stage, and process the vibration signal in sections according to the load current to obtain section signals of different load currents; The segmented signal is converted into a corresponding frequency domain signal by Fourier transform, and the vibration component of the set frequency is extracted from the frequency domain signal, and the corresponding average amplitude is calculated according to all vibration components of the same load current; Establishing a correlation mapping model based on the load current and the corresponding average amplitude; Collect the real-time load current and real-time vibration signal of the transformer, and calculate the real-time average amplitude corresponding to the real-time load current after segmentation processing, Fourier transformation and component extraction of the real-time vibration signal; The real-time average amplitude is input into the correlation mapping model to calculate and obtain the reference load current; Input the reference load current and the real-time load current into the correlation mapping model, and perform difference calculation to obtain the equivalent loose force change value; After comparing the equivalent loose force change value with the loose force change threshold, the detection result of transformer winding looseness is obtained.

2. The method for online detection of transformer winding looseness according to claim 1 is characterized in that: The expression for segmenting the vibration signal according to the load current is specifically: ; in, Indicates The sampled load current; Indicates load current The corresponding segment signal, The starting time of the segment The corresponding signal, The end time of the segment The corresponding signal; Indicates the current threshold; Indicates the end time of the segment The corresponding current; Indicates the starting time of the segment The corresponding current; It represents the current function that characterizes the load current change during the initial operation stage.

3. The method for online detection of transformer winding looseness according to claim 1 is characterized in that: The expression for segmenting the vibration signal according to the load current is specifically: ; in, Indicates The sampled load current; Indicates load current The corresponding segment signal, The starting time of the segment The corresponding signal, The end time of the segment The corresponding signal; Indicates the current threshold; Indicates the end time of the segment The corresponding current; Indicates the starting time of the segment The corresponding current; It represents the current function that characterizes the load current change during the initial operation stage; Represents a dynamic adjustment parameter; Indicates the time threshold.

4. The method for online detection of transformer winding looseness according to claim 1 is characterized in that: The value range of the set frequency is [80Hz, 120Hz].

5. The method for online detection of transformer winding looseness according to claim 1, characterized in that: The expression of the association mapping model is specifically: ; in, Indicates the axial preload of the transformer winding during the initial operation stage; It represents the correlation parameter between load current and axis preload; Indicates load current; Represents the mapping function between the axis preload and the transformer winding vibration; Indicates the average amplitude.

6. The method for online detection of transformer winding looseness according to claim 1, characterized in that: The calculation formula of the loose force change value is specifically: ; in, Indicates the equivalent loosening force change value; It represents the correlation parameter between load current and axis preload; Indicates real-time load current; Indicates the reference load current.

7. The method for online detection of transformer winding looseness according to claim 1, characterized in that: The process of obtaining the detection result of the transformer winding loosening is specifically as follows: Different loose force change thresholds are configured for different transformer winding looseness degrees, and adjacent loose force change thresholds constitute loose force change intervals for different transformer winding looseness levels; The equivalent loose force change value is matched with the loose force change interval, and the transformer winding looseness level in the matched loose force change interval is taken as the detection result.

8. A transformer winding loosening online detection system, characterized in that: The system is used to implement an online detection method for transformer winding loosening according to any one of claims 1 to 7, comprising: The signal segmentation module is used to obtain the vibration signal of the load current change process of the transformer in the initial operation stage, and to process the vibration signal in segments according to the load current to obtain segmented signals of different load currents; A signal conversion module, used to convert the segmented signal into a corresponding frequency domain signal by Fourier transform, extract the vibration component of the set frequency from the frequency domain signal, and calculate the corresponding average amplitude according to all vibration components of the same load current; A model building module, used for building a correlation mapping model according to the load current and the corresponding average amplitude; The signal processing module is used to collect the real-time load current and real-time vibration signal of the transformer, and calculate the real-time average amplitude corresponding to the real-time load current after segmentation processing, Fourier transformation and component extraction of the real-time vibration signal; A current calculation module is used to input the real-time average amplitude into the correlation mapping model to calculate and obtain a reference load current; The difference operation module is used to input the reference load current and the real-time load current into the correlation mapping model, and obtain the equivalent loose force change value after performing the difference operation; The comparison detection module is used to compare the equivalent loose force change value with the loose force change threshold value to obtain the detection result of transformer winding looseness.

9. A computer terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, an online detection method for transformer winding loosening as described in any one of claims 1 to 7 is implemented.

10. A computer readable medium having a computer program stored thereon, characterized in that: The computer program is executed by a processor to implement an online detection method for loose transformer windings as described in any one of claims 1 to 7.

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