Fault identification and detection method and system for 220kV transformer substation bus

Abstract

The invention relates to the technical field of electrical fault detection, in particular to a 220kV transformer substation bus fault recognition and detection method and system, and the method comprises the steps: training a primary fault recognition model; obtaining the possibility that the fault in the sample is a transformer fault according to the change of the current data in the bus and the branch line in the sample; obtaining the response duration of each sample; inputting all the test samples into the primary fault recognition model, and obtaining the transformer and bus fault significance of the test samples in combination with the response duration of each test sample; constructing a four-dimensional sample space, and obtaining a fault significance weight according to the distribution of samples in the four-dimensional sample space; and according to the fault significance weight, checking the fault type when the transformer substation has a fault. After the primary fault identification model is obtained through a traditional method, the difference between the transformer fault and the bus fault is further analyzed, and the correction module is arranged, so that the transformer fault and the bus fault are accurately distinguished.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical fault detection, and in particular to a fault identification and inspection method and system for a 220kV substation busbar. Background Art

[0002] In the power system, the 220kV substation is the core hub of the power transmission and distribution network. The safe operation of its busbar and transformer directly affects the reliability and stability of the power grid. The busbar plays a key role in collecting and distributing electric energy, while the transformer is the core equipment for realizing voltage level conversion. Once a fault occurs in either, if the fault point is not quickly and accurately identified and isolated, the scope of the power outage may be expanded or even cause a cascade accident. However, since the busbar area is closely electrically connected to the transformer and the electrical quantity characteristics at the time of the fault are similar, the traditional substation fault inspection method is not able to accurately distinguish between transformer faults and busbar faults. Summary of the invention

[0003] The present invention provides a 220kV substation bus fault identification and inspection method and system to solve the existing problem that the traditional substation fault inspection method is not accurate enough to distinguish transformer faults from bus faults.

[0004] A 220kV substation bus fault identification and inspection method and system of the present invention adopts the following technical solutions: An embodiment of the present invention provides a method for fault identification and inspection of a 220 kV substation busbar, the method comprising the following steps: Obtain a number of training samples and a number of test samples through the substation, and train a primary fault recognition model; According to the changes in the current data of the busbar and branch in the sample, the affected factor sequence of the busbar and branch in the sample is obtained; according to the affected factor sequence of the busbar and branch in the sample, the affected time of the busbar and branch in the sample is obtained; according to the affected time of the busbar and branch in the sample, the possibility that the fault in the sample is a transformer fault is obtained; Obtain the response time of each sample; input all test samples into the primary fault identification model, and obtain the fault characteristics of the transformer and busbar of each test sample in combination with the response time of each test sample; obtain the significance of the transformer and busbar faults of the test sample in combination with the possibility that the fault in the test sample is a transformer fault; According to the significance of transformer and busbar faults of the test samples and the possibility that the fault in the test samples is a transformer fault, a four-dimensional sample space is constructed. According to the distribution of samples in the four-dimensional sample space, the neighborhood samples and interference samples of each sample are obtained, and then the fault significance weights of all samples are obtained, and the fault type when the substation fails is verified.

[0005] Preferably, the method of obtaining the sequence of factors affecting the bus and branch lines in the sample according to the change of the current data in the bus and branch lines in the sample includes the following specific methods: Preset a local time range ; For any time in any sample, convert the time to the time after The time period of milliseconds is taken as the local range of the moment; the standard deviation of all current data in the local range of the moment in the bus is taken as the affected factor at each moment in the bus; The affected factors at each moment in the bus within the sample are obtained, and the affected factors at each moment are sorted in chronological order to obtain a bus affected factor sequence, and similarly, several branch affected factor sequences are obtained.

[0006] Preferably, the method of obtaining the affected moments of the busbars and branches in the sample according to the affected factor sequence of the busbars and branches in the sample includes the following specific methods: For any affected factor in the bus affected factor sequence, the affected factor is used as a segmentation point to divide the bus affected factor sequence into two affected factor segments. According to the affected factors in the first and second affected factor segments, combined with the bus affected factor sequence, the affected degree of the bus at the time corresponding to the affected factor is obtained. The specific calculation formula is: In the formula, Indicates the degree of influence of the busbar at the corresponding moment of the affected factor; Represents the standard deviation of all affected factors in the affected factor sequence of the bus; represents the standard deviation of all affected factors in the first affected factor segment; represents the standard deviation of all affected factors in the second affected factor segment; represents the linear normalization function; The affected degree of the busbar at all times is obtained; similarly, the affected degree of each branch line at all times is obtained, and the moment when the affected degree of the busbar in the sample is the largest is taken as the affected moment of the busbar; similarly, the affected moment of each branch line is obtained.

[0007] Preferably, the method of obtaining the possibility that the fault in the sample is a transformer fault according to the affected moments of the busbar and the branch line in the sample includes the following specific methods: For any sample, the time series distance between the affected time of the main line and the affected time of each branch line in the sample is used as the characteristic distance of each branch line; and the branch line whose affected time is before the main line is used as an abnormal branch line; and the branch line whose affected time is after the main line is used as a normal branch line; The branch with the largest characteristic distance among the abnormal branches is taken as the suspected fault branch. According to the characteristic distance of the suspected fault branch and the characteristic distance of each normal branch, the possibility that the fault in the sample is a transformer fault is obtained. The specific calculation formula is: In the formula, Indicates the possibility that the fault in the sample is a transformer fault; represents the standard deviation of the characteristic distances of all normal branches; represents the mean of the characteristic distances of all normal branches; Indicates the characteristic distance of the suspected fault branch line.

[0008] Preferably, the specific method of obtaining the response time of each sample includes: For any branch in any sample, the branch connected to the same transformer as the branch is recorded as the corresponding branch of the branch; the timing distance between the branch and the corresponding branch of the branch at the affected moment is taken as the response duration of the branch.

[0009] Preferably, the method of inputting all test samples into the primary fault identification model and obtaining the fault characteristics of the transformer and the busbar of each test sample in combination with the response time of each test sample includes the following specific methods: Input all test samples into the primary fault identification model, obtain the transformer fault degree and busbar fault degree of each test sample, and obtain the correctly identified samples and the incorrectly identified samples; For any test sample, the average response time of all normal branches in the test sample is used as the response time of the test sample; the average response time of all normal branches in all correctly identified samples is used as the benchmark response time; According to the response time of the test sample and the benchmark response time, the fault characteristics of the transformer of the test sample are obtained; the specific calculation formula is: In the formula, representing a fault characteristic of the transformer of the test sample; Indicates the response time of the test sample; Indicates the baseline response time; Represents absolute value operation; represents the linear normalization function; According to the affected time of the busbar of the test sample and the protection time of the test sample, the fault characteristics of the busbar of the test sample are obtained, and the protection time is the time when all protection devices in the historical data of the substation trigger the protection action; the specific calculation formula is: In the formula, Indicates the fault characteristics of the busbar of the test sample; Indicates the protection moment of the test sample; represents the affected moment of the busbar of the test sample; Represents an exponential function with a natural constant as base.

[0010] Preferably, the specific method of obtaining the transformer and bus fault significance of the test sample includes: For any test sample, according to the fault characteristics of the transformer and the bus of the test sample, combined with the possibility that the fault of the test sample is a transformer fault, the transformer fault significance of the test sample and the bus fault significance of the test sample are obtained respectively, and the specific calculation formula is: In the formula, Indicates the transformer fault significance of the test sample; Indicates the busbar fault significance of the test sample; Indicates the possibility that the fault of the test sample is a transformer fault; representing a fault characteristic of the transformer of the test sample; Indicates the fault characteristics of the busbar of the test sample.

[0011] Preferably, the construction of the four-dimensional sample space, obtaining the neighborhood samples and interference samples of each sample according to the distribution of samples in the four-dimensional sample space, and then obtaining the fault significance weights of all samples, includes the following specific methods: A four-dimensional sample space is constructed based on the transformer fault degree of the test sample, the bus fault degree of the test sample, the transformer fault significance of the test sample, and the bus fault significance of the test sample, and all the test samples are placed in the four-dimensional sample space; Furthermore, for any misidentified sample in the four-dimensional sample space, a neighborhood sample number is preset. ; Set the distance between the four-dimensional sample space and the identified error sample to be less than as the neighborhood samples of the misidentified samples; and using the samples in the neighborhood samples of the misidentified samples that have different labels from the misidentified samples as the interference samples of the misidentified samples; For any misidentified sample in the four-dimensional sample space, a threshold of interference sample ratio is preset. ; When the ratio of the number of interference samples of the identified error samples to the number of neighborhood samples is greater than When , the least squares method is used to perform straight line fitting on the samples in the neighborhood of the misidentified sample with the same label as the misidentified sample, and the vector formula of the fitted straight line is recorded as the local feature vector of the misidentified sample. , and use the least squares method to perform straight line fitting on all correctly identified samples in the four-dimensional sample space, and obtain the vector formula of the fitting line, which is recorded as the overall feature vector of the correctly identified samples ;according to and , obtain the difference in fault significance between the neighborhood samples of the identified error samples and all test samples, and the specific calculation formula is: In the formula, Indicates the difference in fault significance between the neighborhood samples of the identified error sample and all test samples; Represents the overall feature vector of the correct sample identified; A local feature vector representing the recognition error sample; represents the modulo function; The mean difference in fault significance between the neighborhood samples of all misidentified samples and all test samples is obtained, and an inverse proportional normalization process is performed on them. The normalized result is used as the fault significance weight.

[0012] Preferably, the specific method of checking the fault type when a fault occurs in the substation includes: The time when the protection device in the current substation triggers the protection action is taken as the time to be tested, and the time before the time to be tested is taken as the time before the time to be tested. milliseconds to the time to be tested is used as the time period to be tested, and the current data in the time period to be tested is used as the sample to be tested; The samples to be tested are input into the primary fault identification model to obtain the transformer fault degree and bus fault degree of the samples to be tested. According to the transformer fault degree and bus fault degree of the samples to be tested and combined with the fault significance weight, the transformer fault degree and bus fault degree of the samples to be tested are corrected. The specific calculation formula is: In the formula, Indicates the corrected transformer fault degree of the sample to be tested; Indicates the corrected busbar fault degree of the sample to be tested; Indicates the transformer fault degree of the sample to be tested; Indicates the busbar fault degree of the sample to be tested; represents the fault significance weight; Indicates the transformer fault significance of the sample to be tested; Indicates the significance of busbar fault of the sample to be tested; After obtaining the corrected transformer fault degree of the sample to be tested and the corrected bus fault degree of the sample to be tested, the fault corresponding to the maximum value of the corrected transformer fault degree and the corrected bus fault degree is taken as the fault type occurring in the substation.

[0013] Another embodiment of the present invention provides a 220kV substation bus fault identification and inspection system, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any one of the above-mentioned steps of the 220kV substation bus fault identification and inspection method when executing the computer program.

[0014] The beneficial effects of the technical solution of the present invention are: training a primary fault identification model; obtaining the possibility that the fault in the sample is a transformer fault based on the changes in the current data in the bus and branch lines in the sample, because after a bus fault occurs, the bus fault will affect the current in all branches under the bus; and when a transformer fault occurs, the first thing affected by the transformer fault is the branch line connected to the transformer, which then affects the bus through the branch line, and finally, because the power balance and impedance distribution of the current system are changed, the affected bus will redistribute the current of each branch line, and then affect other branches of the bus, thereby obtaining the possibility that the fault in the sample is a transformer fault.

[0015] Get the response time of each sample; input all test samples into the primary fault identification model, and obtain the transformer and bus fault significance of the test samples by combining the response time of each test sample; construct a four-dimensional sample space, and obtain the fault significance weight according to the distribution of samples in the four-dimensional sample space. Since the current in the branch line affected by the fault will affect other branches through the transformer, and the current in the branch line needs a certain time to affect other branches through the transformer, when the transformer fails, the response time between the transformer corresponding branches is different from the response time between the normal branches, which can be used as a basis to obtain the fault characteristics of the transformer. Since the bus is the main line, the protection device in the substation will get a quick response when the bus fails, which can be used as a basis to obtain the fault characteristics of the bus; according to the fault significance weight, check the fault type when the substation fails. Whenever the substation fails, the current data of all branches in the local range at the time of the fault is input into the primary fault identification model. According to the test results of the primary fault identification model, the correction module is set by further analyzing the difference between the transformer fault and the bus fault, so as to accurately distinguish the transformer fault from the bus fault and identify the substation fault type. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 creative work.

[0017] Figure 1 A flowchart of the steps of a fault identification and inspection method for a 220kV substation busbar according to the present invention; Figure 2 This is an example of the current curve in the busbar when the busbar fails; Figure 3 This is an example of the current curve in the branch line when the bus fails. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a 220kV substation bus fault identification and inspection method and system proposed by the present invention, its specific implementation method, structure, features and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0019] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0020] The specific scheme of a 220kV substation busbar fault identification and inspection method and system provided by the present invention is described in detail below with reference to the accompanying drawings.

[0021] See also Figure 1 , which shows a flowchart of a method for fault identification and inspection of a 220kV substation busbar provided by an embodiment of the present invention, the method comprising the following steps: Step S001: Obtain a number of training samples and a number of test samples through a substation, and train a primary fault recognition model.

[0022] It should be noted that this embodiment is a fault identification and inspection method for a 220kV substation bus. Specifically, the historical data when a fault occurs in the substation is used as a training sample to train a fault identification model, and the fault identification model is used to identify and inspect the fault in the bus. Because it takes a certain amount of time to identify the fault when a fault occurs, the protection device in the substation will trigger a protection action. Therefore, the data collected some time before the protection device triggers the protection action can be used as a training set to train the fault identification model.

[0023] Specifically, the moment when all protection devices in the substation's historical data trigger protection actions is recorded as the protection moment, and a time range to be analyzed is preset. , The specific size can be set according to the actual situation. This embodiment does not make a rigid high requirement. For any protection moment, the preceding The time period from milliseconds to the protection moment is used as the time period to be analyzed at the protection moment; and the current data in the time period to be analyzed at each protection moment is used as a sample; Furthermore, all samples are randomly divided into training samples and test samples, and the ratio between the number of training samples and the number of test samples is , The ratio is the ratio of the number of preset training samples to the number of test samples. and The specific value of can be set according to the actual situation. This embodiment does not make a hard requirement. , Take this as an example to describe; All training samples are labeled according to their fault types. For example, if the fault type of a training sample is a bus fault, the label of the training sample is a bus fault. All training samples are input into the RNN (Recurrent Neural Network) model to train all training samples, and the loss function used is the cross entropy loss function. Since the specific training process of the RNN model is a well-known prior art, it will not be described in detail in this embodiment to obtain a primary fault recognition model.

[0024] It should be noted that both bus faults and transformer faults will cause the current data in the cable to change, that is, the obtained primary fault identification model cannot accurately distinguish between bus faults and transformer faults. Therefore, after obtaining the primary fault identification model and using the test sample to side-view the primary fault identification model, it is necessary to further analyze the difference between the transformer fault and the bus fault to set up a correction module, and use the primary fault identification model and the correction module to accurately distinguish between the transformer fault and the bus fault.

[0025] At this point, the primary fault identification model is obtained.

[0026] Step S002: According to the changes in the current data of the bus and the branch in the sample, obtain the affected factor sequence of the bus and the branch in the sample; according to the affected factor sequence of the bus and the branch in the sample, obtain the affected time of the bus and the branch in the sample; according to the affected time of the bus and the branch in the sample, obtain the possibility that the fault in the sample is a transformer fault.

[0027] It should be noted that this step is to analyze the difference between transformer fault and bus fault, so as to better set up the correction module in the subsequent step. When the bus fails, the bus fault will affect the current in all branches under the bus, that is, when the bus fails, the current in the bus changes suddenly first, and then the current in all branches under the bus changes suddenly. When the transformer fails, the first thing affected by the transformer failure is the branch connected to the transformer, and then the bus is affected by the branch. Finally, due to the change in the power balance and impedance distribution of the current system, the affected bus will redistribute the current of each branch, thereby affecting other branches of the bus, such as Figure 2 , Figure 3 As shown, Figure 2 This is an example of the current curve in the busbar when the busbar fails. Figure 3 This is an example of the current curve in the branch line when the bus fails. Therefore, the degree of influence on the bus can be obtained by the sequence of the time when the current in the bus and branch line suddenly changes.

[0028] Preferably, in a specific embodiment of the present invention, a local time range is preset , The specific value of can be set according to the actual situation. This embodiment does not make any impact requirements. For any time in any sample, the time to the time after The time period of milliseconds is taken as the local range of the moment; the standard deviation of all current data in the local range of the moment in the bus is taken as the affected factor of each moment in the bus (if the subsequent time of a moment in the sample is less than , do not obtain the local time range of that moment); Obtain the affected factors of the bus at each moment in the sample, and sort the affected factors at each moment in time sequence to obtain a bus affected factor sequence, and similarly obtain a plurality of branch affected factor sequences; It should be noted that, since the current in the cable is stable under normal circumstances, when the protection device in the substation triggers the protection action, it means that a fault has occurred at this time, which means that the fault has occurred some time before this moment; and when a fault occurs, the current in the cable will change suddenly, making the current data unstable within the local time range. Therefore, the larger the standard deviation of the current data within the local time range, the more likely the cable is affected by the fault at this moment.

[0029] It needs to be further explained that the cable in this embodiment includes a busbar and a branch line, and the branch lines that appear in this embodiment are all branches corresponding to the busbars that appear, and the busbars that appear are all busbars corresponding to the branch lines that appear; since it takes a certain amount of time for the protection device to identify a fault when it occurs, the moment before the protection moment is recorded as the time period to be analyzed, and there is a moment when the fault occurs in the time period to be analyzed, so there are two parts in the time period to be analyzed, one is the time period before the fault occurs, and the other is the time period after the fault occurs; before the fault occurs, the current data in the cable is stable; and after the fault occurs, the current data in the cable is affected by the fault, resulting in fluctuations in the current in the cable, so this can be used as a basis for obtaining the moment when the cable is affected by the fault, and the following description will be taken as an example of a busbar and its corresponding branches.

[0030] Preferably, in a specific embodiment of the present invention, for any affected factor in the bus affected factor sequence, the affected factor is used as a segmentation point to divide the bus affected factor sequence into two affected factor segments, and according to the affected factors in the first and second affected factor segments, combined with the bus affected factor sequence, the affected degree of the bus at the time corresponding to the affected factor is obtained, and the specific calculation formula is: In the formula, Indicates the degree of influence of the busbar at the corresponding moment of the affected factor; Represents the standard deviation of all affected factors in the affected factor sequence of the bus; represents the standard deviation of all affected factors in the first affected factor segment; represents the standard deviation of all affected factors in the second affected factor segment; represents a linear normalization function, whose normalization range is all the affected factors in the affected sequence ; The affected degree of the busbar at all times is obtained; similarly, the affected degree of each branch line at all times is obtained, and the moment when the affected degree of the busbar in the sample is the largest is taken as the affected moment of the busbar; similarly, the affected moment of each branch line is obtained.

[0031] It should be noted that the affected moment of the cable indicates the moment when the cable is affected by the fault. Since the current data in the bus is stable before it is affected by the fault, and the current data is unstable after being affected by the fault, each time period to be analyzed is used as a segmentation point to traverse and divide the time period to be analyzed into two affected factor segments. The more stable one of the two affected factors is and the more unstable the other is, the more likely the bus is to be affected by the fault at that moment. Therefore, the moment when the degree of influence is the greatest is selected as the affected moment.

[0032] It should be further explained that when a bus fails, the bus will affect all branches under the bus at the same time, that is, the time when all branches under the bus are affected by the fault tends to be consistent; and when a transformer fails, it will first affect the branch connected to the transformer, and then affect the bus through the branch, and finally affect other branches through the bus. That is, when a transformer fails, the time when the branch is affected by the fault does not tend to be consistent, and some branches will be affected first. Therefore, this can be used as a basis to obtain the possibility that the fault is a transformer fault.

[0033] Preferably, in a specific embodiment of the present invention, for any sample, the time sequence distance between the affected time of the busbar and the affected time of each branch in the sample is used as the characteristic distance of each branch; and the branch whose affected time is before the busbar is used as an abnormal branch; and the branch whose affected time is after the busbar is used as a normal branch; Furthermore, the branch with the largest characteristic distance among the abnormal branches is taken as the suspected fault branch. According to the characteristic distance of the suspected fault branch and the characteristic distance of each normal branch, the possibility that the fault in the sample is a transformer fault is obtained. The specific calculation formula is: In the formula, Indicates the possibility that the fault in the sample is a transformer fault; represents the standard deviation of the characteristic distances of all normal branches; represents the mean of the characteristic distances of all normal branches; Indicates the characteristic distance of the suspected fault branch line.

[0034] It should be noted that the characteristic distance of the branch line is the distance between the affected time of the branch line and the affected time of the busbar. Therefore, when the standard deviation of the characteristic distance of the normal branch line is larger, it means that the time when the normal branch line is affected by the fault is less consistent, that is, the fault is more likely to be a transformer fault. At the same time, when the transformer fails, the branch line connected to the transformer will be affected first, and the branch line affected first will affect other branches through the busbar. Therefore, when the branch line first affected by the fault and the branch line affected subsequently have a larger difference in the time when they are affected by the fault, the fault is more likely to be a transformer fault.

[0035] At this point, the possibility that the fault in the sample is a transformer fault is obtained.

[0036] Step S003: Obtain the response time of each sample; input all test samples into the primary fault identification model, and obtain the fault characteristics of the transformer and bus of each test sample in combination with the response time of each test sample; obtain the significance of the transformer and bus faults of the test sample in combination with the possibility that the fault in the test sample is a transformer fault.

[0037] It should be noted that the possibility that the fault in the time period to be analyzed obtained in step S002 is a transformer fault is obtained only by analyzing the changes in current data in different branches. However, in actual situations, if a serious fault occurs in the bus, the protection device corresponding to the bus triggers the protection action, which in turn causes some branches to fail to trigger the corresponding protection action, making the affected time of the quality current obtained at this time have a large degree of randomness, that is, the affected time is inconsistent, which in turn interferes with the distinction between the bus fault and the transformer fault. Therefore, after the possibility that the fault in the time period to be analyzed is a transformer fault is obtained through step S002, further analysis is required.

[0038] It should be further explained that the current in the branch line affected by the fault will affect other branches through the transformer, and it takes a certain amount of time for the current in the branch line to affect other branches through the transformer. When the transformer fails, the response time between the transformer corresponding branches is different from the response time between normal branches. This can be used as a basis to obtain the fault characteristics of the transformer. Since the busbar is the main line, the protection device in the substation will respond quickly when the busbar fails, and this can be used as a basis to obtain the fault characteristics of the busbar.

[0039] Preferably, in a specific embodiment of the present invention, for any branch line in any sample, a branch line connected to the same transformer as the branch line is recorded as a corresponding branch line of the branch line; a time sequence distance between the branch line and the corresponding branch line of the branch line at the affected moment is taken as the response time length of the branch line; Furthermore, all test samples are input into the primary fault identification model to obtain the transformer fault degree and busbar fault degree of each test sample, and obtain the correctly identified samples and the incorrectly identified samples; For any test sample, the average response time of all normal branches in the test sample is used as the response time of the test sample; the average response time of all normal branches in all correctly identified samples is used as the benchmark response time; According to the response time of the test sample and the benchmark response time, the fault characteristics of the transformer of the test sample are obtained; the specific calculation formula is: In the formula, representing a fault characteristic of the transformer of the test sample; Indicates the response time of the test sample; Indicates the baseline response time; Represents absolute value operation; Represents a linear normalization function, whose specific normalization range is all test samples .

[0040] Furthermore, according to the affected time of the bus of the test sample and the protection time of the test sample, the fault feature of the bus of the test sample is obtained; the specific calculation formula is: In the formula, Indicates the fault characteristics of the busbar of the test sample; Indicates the protection moment of the test sample; represents the affected moment of the busbar of the test sample; Represents an exponential function with a natural constant as the base; this embodiment adopts Model to present inverse proportional relationship and normalization, As the input of the model, the implementer can set the inverse proportional function and normalization function according to the actual situation.

[0041] It should be noted that It indicates the difference between the response time of the test sample and the benchmark response time. When a transformer fails, the response time between the transformer corresponding branches is different from the response time between normal branches. The larger the value of is, the more fault characteristics the transformer of the test sample has. It indicates the time distance between the affected moment of the bus and the protection moment. The smaller the value, the faster the protection device triggers the protection action, which means that the bus of the test sample has more fault characteristics. Combined with the possibility that the fault in the test sample is a transformer fault, the transformer fault significance and bus fault significance of the test sample are obtained.

[0042] Preferably, in a specific embodiment of the present invention, for any test sample, according to the fault characteristics of the transformer and the bus of the test sample, combined with the possibility that the fault of the test sample is a transformer fault, the transformer fault significance of the test sample and the bus fault significance of the test sample are obtained respectively, and the specific calculation formula is: In the formula, Indicates the transformer fault significance of the test sample; Indicates the busbar fault significance of the test sample; Indicates the possibility that the fault of the test sample is a transformer fault; representing a fault characteristic of the transformer of the test sample; Indicates the fault characteristics of the busbar of the test sample.

[0043] It should be noted that the significance of transformer fault and bus fault indicates the possibility of the fault being a transformer fault and a bus fault obtained by the primary fault identification model and the difference in the time when different branches are affected by the fault. The greater the possibility that the fault of the test sample is a transformer fault and the greater the fault feature of the transformer of the test sample, the more likely the test sample is a transformer fault; the less likely the fault of the test sample is a transformer fault and the greater the fault feature of the bus of the test sample, the more likely the test sample is a bus fault.

[0044] At this point, the transformer fault significance and busbar fault significance of the test sample are obtained.

[0045] Step S004: According to the significance of transformer and bus faults of the test samples and the possibility that the fault in the test samples is a transformer fault, a four-dimensional sample space is constructed. According to the distribution of samples in the four-dimensional sample space, the neighborhood samples and interference samples of each sample are obtained, and then the fault significance weights of all samples are obtained, and the fault type when the substation fails is checked.

[0046] It should be noted that the transformer fault significance and busbar fault significance obtained in step S003 are characteristics obtained under ideal conditions based on a serious busbar fault. Therefore, it is necessary to further analyze the test results of the primary fault identification model for each test sample and set up a correction module. Whenever a substation fault occurs, the current data of all branches within the local range at the time of the fault are input into the primary fault identification model. According to the test results of the primary fault identification model, combined with the transformer fault significance and busbar fault significance of all test samples, the substation fault type is identified.

[0047] Preferably, in a specific embodiment of the present invention, a four-dimensional sample space is constructed based on the transformer fault degree of the test sample, the bus fault degree of the test sample, the transformer fault significance of the test sample, and the bus fault significance of the test sample, and all test samples are placed in the four-dimensional sample space.

[0048] Furthermore, for any misidentified sample in the four-dimensional sample space, a neighborhood sample number is preset. , The specific size can be set according to the actual situation. This embodiment does not make a rigid high requirement. Take the four-dimensional sample space as an example to describe; the distance between the sample and the recognition error sample is less than as the neighborhood samples of the misidentified samples; and using the samples in the neighborhood samples of the misidentified samples that have different labels from the misidentified samples as the interference samples of the misidentified samples; For any misidentified sample in the four-dimensional sample space, a threshold of interference sample ratio is preset. , The specific size can be set according to the actual situation. This embodiment does not make a rigid high requirement. Take the example as an example; when the ratio of the interference samples of the identified error samples to the neighborhood samples in quantity is less than or equal to When , the transformer fault degree and bus fault degree of the wrongly identified sample are respectively equal to the transformer fault significance and bus fault significance of the wrongly identified sample.

[0049] It should be noted that the labels between samples are the same, indicating that the current data in the branch and bus in the samples are similar. Therefore, the more other misidentified samples exist around the misidentified sample, the more the model will be disturbed when testing and identifying the sample, resulting in its wrong judgment; therefore, the smaller the ratio of the interference sample to the neighborhood sample of the misidentified sample, the smaller the primary fault identification model evaluates that the test sample is not interfered by other samples, that is, the primary fault identification model cannot accurately identify the fault type of the sample without interference from other samples, and therefore directly uses the transformer fault significance and bus fault significance of the sample as the transformer fault degree and bus fault degree of the misidentified sample.

[0050] Furthermore, when the ratio of the number of interference samples of the identified error samples to the number of neighboring samples is greater than When , the least squares method is used to perform straight line fitting on the samples in the neighborhood of the misidentified sample with the same label as the misidentified sample, and the vector formula of the fitted straight line is recorded as the local feature vector of the misidentified sample. , and use the least squares method to perform straight line fitting on all correctly identified samples in the four-dimensional sample space, and obtain the vector formula of the fitting line, which is recorded as the overall feature vector of the correctly identified samples ;according to and , obtain the difference in fault significance between the neighborhood samples of the identified error samples and all test samples, and the specific calculation formula is: In the formula, Indicates the difference in fault significance between the neighborhood samples of the identified error sample and all test samples; Represents the overall feature vector of the correct sample identified; A local feature vector representing the recognition error sample; Represents the modulo function.

[0051] Furthermore, the mean difference in fault significance between the neighborhood samples of all the erroneous samples and all the test samples is obtained, and an inverse normalization process is performed on them, and the normalization result is used as the fault significance weight of all samples; the specific process of inverse normalization in this embodiment is: 1 minus the linear normalization result of the difference mean.

[0052] It should be noted that the more other misidentified samples there are around the misidentified sample, the more interference the model will receive when testing and identifying the sample, resulting in its misjudgment. That is, the more other misidentified samples there are around the misidentified sample, the more it indicates that the primary fault recognition model was originally able to identify the fault type of the sample, but the recognition error was caused by interference from other samples. Therefore, the overall feature vector of the correct sample is , and the local feature vector of the identified error sample The smaller the difference between them, the more it means that the performance of the model on the local sample is basically consistent with the global sample, and the accuracy of the fault significance feature is high. Therefore, the negative correlation normalization result of the difference between the neighborhood samples of all misidentified samples and all test samples in fault significance is used as the significance weight.

[0053] It should be further explained that after the fault significance weight and the primary fault identification model are obtained, the fault type when a fault occurs in the current substation can be accurately identified according to the fault significance weight and the primary fault identification model.

[0054] Preferably, in a specific embodiment of the present invention, the moment when the protection device in the current substation triggers the protection action is used as the time to be tested, and the time before the time to be tested is used as the time before the time to be tested. milliseconds to the time to be tested is used as the time period to be tested, and the current data in the time period to be tested is used as the sample to be tested; The samples to be tested are input into the primary fault identification model to obtain the transformer fault degree and bus fault degree of the samples to be tested. According to the transformer fault degree and bus fault degree of the samples to be tested and combined with the fault significance weight, the transformer fault degree and bus fault degree of the samples to be tested are corrected. The specific calculation formula is: In the formula, Indicates the corrected transformer fault degree of the sample to be tested; Indicates the corrected busbar fault degree of the sample to be tested; Indicates the transformer fault degree of the sample to be tested; Indicates the busbar fault degree of the sample to be tested; represents the fault significance weight; Indicates the transformer fault significance of the sample to be tested; Indicates the significance of busbar fault of the sample to be tested.

[0055] After obtaining the corrected transformer fault degree of the sample to be tested and the corrected busbar fault degree of the sample to be tested, the fault with the larger corrected fault degree is regarded as the fault type occurring in the substation.

[0056] Another embodiment of the present invention provides a 220kV substation bus fault identification and inspection system, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements a 220kV substation bus fault identification and inspection method in steps S001 to S004 when executing the computer program.

[0057] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A fault identification and inspection method for a 220kV substation busbar, characterized in that: The method comprises the following steps: Obtain a number of training samples and a number of test samples through the substation, and train a primary fault recognition model; According to the changes in the current data of the busbar and branch in the sample, the affected factor sequence of the busbar and branch in the sample is obtained; according to the affected factor sequence of the busbar and branch in the sample, the affected time of the busbar and branch in the sample is obtained; according to the affected time of the busbar and branch in the sample, the possibility that the fault in the sample is a transformer fault is obtained; Obtain the response time of each sample; input all test samples into the primary fault identification model, and obtain the fault characteristics of the transformer and busbar of each test sample in combination with the response time of each test sample; obtain the significance of the transformer and busbar faults of the test sample in combination with the possibility that the fault in the test sample is a transformer fault; According to the significance of transformer and busbar faults of the test samples and the possibility that the fault in the test samples is a transformer fault, a four-dimensional sample space is constructed. According to the distribution of samples in the four-dimensional sample space, the neighborhood samples and interference samples of each sample are obtained, and then the fault significance weights of all samples are obtained, and the fault type when the substation fails is verified.

2. A 220kV substation bus fault identification and inspection method according to claim 1, characterized in that: The method of obtaining the sequence of factors affecting the busbars and branches in the sample according to the changes in the current data in the busbars and branches in the sample includes: Preset a local time range ; For any time in any sample, convert the time to the time after The time period of milliseconds is taken as the local range of the moment; the standard deviation of all current data in the local range of the moment in the bus is taken as the affected factor at each moment in the bus; The affected factors at each moment in the bus within the sample are obtained, and the affected factors at each moment are sorted in chronological order to obtain a bus affected factor sequence, and similarly, several branch affected factor sequences are obtained.

3. A 220kV substation bus fault identification and inspection method according to claim 1, characterized in that: The specific method of obtaining the affected moments of the busbars and branches in the sample according to the affected factor sequence of the busbars and branches in the sample is as follows: For any affected factor in the bus affected factor sequence, the affected factor is used as a segmentation point to divide the bus affected factor sequence into two affected factor segments. According to the affected factors in the first and second affected factor segments, combined with the bus affected factor sequence, the affected degree of the bus at the time corresponding to the affected factor is obtained. The specific calculation formula is: In the formula, Indicates the degree of influence of the busbar at the corresponding moment of the affected factor; Represents the standard deviation of all affected factors in the affected factor sequence of the bus; represents the standard deviation of all affected factors in the first affected factor segment; represents the standard deviation of all affected factors in the second affected factor segment; represents the linear normalization function; The affected degree of the busbar at all times is obtained; similarly, the affected degree of each branch line at all times is obtained, and the moment when the affected degree of the busbar in the sample is the largest is taken as the affected moment of the busbar; similarly, the affected moment of each branch line is obtained.

4. A 220kV substation bus fault identification and inspection method according to claim 1, characterized in that: The method of obtaining the possibility that the fault in the sample is a transformer fault according to the affected moments of the busbar and the branch line in the sample includes: For any sample, the time series distance between the affected time of the main line and the affected time of each branch line in the sample is used as the characteristic distance of each branch line; and the branch line whose affected time is before the main line is used as an abnormal branch line; and the branch line whose affected time is after the main line is used as a normal branch line; The branch with the largest characteristic distance among the abnormal branches is taken as the suspected fault branch. According to the characteristic distance of the suspected fault branch and the characteristic distance of each normal branch, the possibility that the fault in the sample is a transformer fault is obtained. The specific calculation formula is: In the formula, Indicates the possibility that the fault in the sample is a transformer fault; represents the standard deviation of the characteristic distances of all normal branches; represents the mean of the characteristic distances of all normal branches; Indicates the characteristic distance of the suspected fault branch line.

5. A 220kV substation bus fault identification and inspection method according to claim 1, characterized in that: The specific method of obtaining the response time of each sample includes: For any branch in any sample, the branch connected to the same transformer as the branch is recorded as the corresponding branch of the branch; the timing distance between the branch and the corresponding branch of the branch at the affected moment is taken as the response duration of the branch.

6. A 220kV substation bus fault identification and inspection method according to claim 1, characterized in that: The specific method of inputting all test samples into the primary fault identification model and obtaining the fault characteristics of the transformer and busbar of each test sample in combination with the response time of each test sample is as follows: Input all test samples into the primary fault identification model, obtain the transformer fault degree and busbar fault degree of each test sample, and obtain the correctly identified samples and the incorrectly identified samples; For any test sample, the average response time of all normal branches in the test sample is used as the response time of the test sample; The average response time of all normal branches in all correctly identified samples is taken as the benchmark response time; Acquire a fault feature of a transformer of the test sample according to a response time of the test sample and a reference response time; The specific calculation formula is: In the formula, representing a fault characteristic of the transformer of the test sample; Indicates the response time of the test sample; Indicates the baseline response time; Represents absolute value operation; represents the linear normalization function; According to the affected time of the busbar of the test sample and the protection time of the test sample, the fault characteristics of the busbar of the test sample are obtained, and the protection time is the time when all protection devices in the historical data of the substation trigger the protection action; the specific calculation formula is: In the formula, Indicates the fault characteristics of the busbar of the test sample; Indicates the protection moment of the test sample; represents the affected moment of the busbar of the test sample; Represents an exponential function with a natural constant as base.

7. A 220kV substation bus fault identification and inspection method according to claim 1, characterized in that: The specific method of obtaining the transformer and bus fault significance of the test sample includes: For any test sample, according to the fault characteristics of the transformer and the bus of the test sample, combined with the possibility that the fault of the test sample is a transformer fault, the transformer fault significance of the test sample and the bus fault significance of the test sample are obtained respectively, and the specific calculation formula is: In the formula, Indicates the transformer fault significance of the test sample; Indicates the busbar fault significance of the test sample; Indicates the possibility that the fault of the test sample is a transformer fault; representing a fault characteristic of the transformer of the test sample; Indicates the fault characteristics of the busbar of the test sample.

8. A 220kV substation busbar fault identification and inspection method according to claim 6, characterized in that: The four-dimensional sample space is constructed, and according to the distribution of samples in the four-dimensional sample space, the neighborhood samples and interference samples of each sample are obtained, and then the fault significance weights of all samples are obtained, including the specific method of: A four-dimensional sample space is constructed based on the transformer fault degree of the test sample, the bus fault degree of the test sample, the transformer fault significance of the test sample, and the bus fault significance of the test sample, and all the test samples are placed in the four-dimensional sample space; Furthermore, for any recognition error sample in the four-dimensional sample space; Preset a neighborhood sample size ; The distance between the sample in the four-dimensional sample space and the identified error sample is less than as the neighborhood samples of the misidentified samples; and using the samples in the neighborhood samples of the misidentified samples that have different labels from the misidentified samples as the interference samples of the misidentified samples; For any misidentified sample in the four-dimensional sample space, a threshold of interference sample ratio is preset. ; When the ratio of the interference samples of the identified error samples to the neighboring samples is greater than When , the least squares method is used to perform straight line fitting on the samples in the neighborhood of the misidentified sample with the same label as the misidentified sample, and the vector formula of the fitted straight line is recorded as the local feature vector of the misidentified sample. , and use the least squares method to perform straight line fitting on all correctly identified samples in the four-dimensional sample space, and obtain the vector formula of the fitting line, which is recorded as the overall feature vector of the correctly identified samples ;according to and , obtain the difference in fault significance between the neighborhood samples of the identified error samples and all test samples, and the specific calculation formula is: In the formula, Indicates the difference in fault significance between the neighborhood samples of the identified error sample and all test samples; Represents the overall feature vector of the correct sample identified; A local feature vector representing the recognition error sample; represents the modulo function; The mean difference in fault significance between the neighborhood samples of all misidentified samples and all test samples is obtained, and an inverse proportional normalization process is performed on them. The normalized result is used as the fault significance weight.

9. A 220kV substation busbar fault identification and inspection method according to claim 1, characterized in that: The specific method of checking the fault type when a fault occurs in the substation includes: The time when the protection device in the current substation triggers the protection action is taken as the time to be tested, and the time before the time to be tested is taken as the time before the time to be tested. milliseconds to the time to be tested is used as the time period to be tested, and the current data in the time period to be tested is used as the sample to be tested; The samples to be tested are input into the primary fault identification model to obtain the transformer fault degree and bus fault degree of the samples to be tested. According to the transformer fault degree and bus fault degree of the samples to be tested and combined with the fault significance weight, the transformer fault degree and bus fault degree of the samples to be tested are corrected. The specific calculation formula is: In the formula, Indicates the corrected transformer fault degree of the sample to be tested; Indicates the corrected busbar fault degree of the sample to be tested; Indicates the transformer fault degree of the sample to be tested; Indicates the busbar fault degree of the sample to be tested; represents the fault significance weight; Indicates the transformer fault significance of the sample to be tested; Indicates the significance of busbar fault of the sample to be tested; After obtaining the corrected transformer fault degree of the sample to be tested and the corrected bus fault degree of the sample to be tested, the fault corresponding to the maximum value of the corrected transformer fault degree and the corrected bus fault degree is taken as the fault type occurring in the substation.

10. A 220kV substation bus fault identification and inspection system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is executed by a processor, the steps of a fault identification and inspection method for a 220kV substation busbar are implemented as described in any one of claims 1 to 9.

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