Method and device for online monitoring of switchgear

Abstract

The present invention provides a method and device for online monitoring of a switchgear, wherein the switchgear comprises a plurality of monitored components, and the method comprises: obtaining a real-time current curve of a monitored component; determining a Hausdorff distance between the real-time current curve and a preset reference curve, wherein the reference curve is a current curve of the monitored component in a normal state; determining the similarity between the real-time current curve and the reference curve according to the Hausdorff distance; and judging whether the monitored component has a fault according to the similarity.

Description

Technical Field

[0001] The present invention relates to the field of switchgear, and in particular to a method and a device for online monitoring of switchgear. Background Art

[0002] Switchgear is an important power transmission and distribution device in the power system, so the operation of switchgear needs to be monitored in real time. For example, medium-voltage switchgear includes multiple components that need to be operated, such as chassis, opening and closing coils, grounding switches, energy storage motors, etc. These movable components need to be monitored in real time to determine whether they have faults as soon as possible. Summary of the invention

[0003] In view of this, the present invention provides a method for online monitoring of a switch device, wherein the switch device includes a plurality of monitored components, and the method includes:

[0004] Obtain a real-time current curve of a monitored component;

[0005] Determining a Hausdorff distance between the real-time current curve and a preset reference curve, wherein the reference curve is a current curve of the monitored component in a normal state;

[0006] Determining the similarity between the real-time current curve and the reference curve according to the Hausdorff distance;

[0007] It is determined whether the monitored component fails according to the similarity.

[0008] According to the method described above, optionally, determining the Hausdorff distance between the real-time current curve and a preset reference curve includes:

[0009] Obtain the Euclidean distances between the i-th sampling point on the real-time current curve and the i-th to i+n-th sampling points on the reference curve, select the smallest one as the first distance corresponding to the i-th sampling point, and select the largest one among the first distances corresponding to the sampling points as the first unidirectional distance between the real-time current curve and the reference curve, where i∈(1,2,3…,p), p is the total number of sampling points on the real-time current curve, and n is a positive integer;

[0010] Obtain the Euclidean distances between the jth sampling point on the reference curve and the jnth to j+nth sampling points on the real-time current curve, select the smallest one as the second distance, and select the largest one of the second distances corresponding to each sampling point as the second unidirectional distance between the reference curve and the real-time current curve, where j∈(1,2,3…,q), q is the total number of sampling points on the reference curve;

[0011] The largest one of the first one-way distance and the second one-way distance is selected as the Hausdorff distance.

[0012] According to the method as described above, optionally, before determining the Hausdorff distance between the real-time current curve and a preset reference curve, the method further includes:

[0013] Determine n according to the following formula:

[0014] n = [x * 0.1%], where x = (p + q) / 2;

[0015] If n=0, then set n=1.

[0016] According to the method as described above, optionally, determining the similarity between the real-time current curve and the reference curve according to the Hausdorff distance includes:

[0017] If the Hausdorff distance is 0, it is determined that the similarity between the real-time current curve and the reference curve is 100%; or

[0018] If the Hausdorff distance is greater than or equal to a preset threshold value, determining that the similarity between the real-time current curve and the reference curve is 0; or

[0019] If the Hausdorff distance is greater than 0 and less than a preset threshold value, a linear fit is performed on the real-time current curve and the reference curve to obtain similarity.

[0020] According to the method as described above, optionally, the starting point of the real-time current curve is the starting point of the operation of the monitored component, and the end point of the real-time current curve is the end point of the operation.

[0021] According to the method as described above, optionally, judging whether the monitored component fails according to the similarity includes:

[0022] It is determined whether the similarity is less than or equal to a preset threshold. If the determination result is yes, the monitored component fails.

[0023] The present invention also provides a device for online monitoring of a switchgear, wherein the switchgear comprises a plurality of monitored components, and the device comprises:

[0024] an acquisition unit, used for acquiring a real-time current curve of a monitored component;

[0025] a first determining unit, configured to determine a Hausdorff distance between the real-time current curve and a preset reference curve, wherein the reference curve is a current curve of the monitored component in a normal state;

[0026] a second determining unit, configured to determine the similarity between the real-time current curve and the reference curve according to the Hausdorff distance;

[0027] A judging unit is used to judge whether the monitored component fails according to the similarity.

[0028] According to the above device, optionally, the first determining unit is specifically configured to:

[0029] Obtain the Euclidean distances between the i-th sampling point on the real-time current curve and the i-th to i+n-th sampling points on the reference curve, select the smallest one as the first distance corresponding to the i-th sampling point, and select the largest one among the first distances corresponding to the sampling points as the first unidirectional distance between the real-time current curve and the reference curve, where i∈(1,2,3…,p), p is the total number of sampling points on the real-time current curve, and n is a positive integer;

[0030] Obtain the Euclidean distances between the jth sampling point on the reference curve and the jnth to j+nth sampling points on the real-time current curve, select the smallest one as the second distance, and select the largest one of the second distances corresponding to each sampling point as the second unidirectional distance between the reference curve and the real-time current curve, where j∈(1,2,3…,q), q is the total number of sampling points on the reference curve;

[0031] The largest one of the first one-way distance and the second one-way distance is selected as the Hausdorff distance.

[0032] According to the device as described above, optionally, it further comprises:

[0033] A third determining unit, configured to determine n according to the following formula:

[0034] n = [x * 0.1%], where x = (p + q) / 2;

[0035] If n=0, then set n=1.

[0036] According to the device as described above, optionally, the second determining unit is used to:

[0037] If the Hausdorff distance is 0, it is determined that the similarity between the real-time current curve and the reference curve is 100%; or

[0038] If the Hausdorff distance is greater than or equal to a preset threshold value, determining that the similarity between the real-time current curve and the reference curve is 0; or

[0039] If the Hausdorff distance is greater than 0 and less than a preset threshold value, a linear fit is performed on the real-time current curve and the reference curve to obtain similarity.

[0040] According to the above device, optionally, the judging unit is used to:

[0041] It is determined whether the similarity is less than or equal to a preset threshold. If the determination result is yes, the monitored component fails.

[0042] The present invention further provides a device for online monitoring of a switchgear, wherein the switchgear comprises a plurality of monitored components, and the device comprises:

[0043] at least one memory for storing instructions;

[0044] At least one processor is configured to execute the method for online monitoring of a switchgear according to any one of the above items according to the instructions stored in the memory.

[0045] The present invention further provides a readable storage medium, wherein the readable storage medium stores machine-readable instructions. When the machine-readable instructions are executed by a machine, the machine executes the method for online monitoring of a switch device as described in any one of the above items.

[0046] It can be seen from the above scheme that the present invention compares the real-time current curve with the reference curve under normal conditions, obtains the similarity between the two to determine whether the monitored component has a fault, and the similarity algorithm based on the Hausdorff distance in the present invention can ensure a faster calculation speed, thereby being able to promptly determine whether the monitored component has a fault. This method does not require processing of the sampled data, has a sensitive determination capability for abnormal sampling points, can accurately determine abnormal states, and avoid omissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings so that the above and other features and advantages of the present invention will be more apparent to those skilled in the art. In the accompanying drawings:

[0048] Figure 1 The figure is a flow chart of a method for online monitoring of a switchgear according to an embodiment of the present invention.

[0049] Figure 2 The figure is a flow chart of a method for online monitoring of a switchgear according to another embodiment of the present invention.

[0050] Figure 3A is a reference curve of the opening and closing coil according to an embodiment of the present invention.

[0051] Figure 3BFIG. 4 is a real-time current curve of a closing and opening coil according to an embodiment of the present invention.

[0052] Figure 3C FIG. 4 is another real-time current curve of the opening and closing coil according to an embodiment of the present invention.

[0053] Figure 4 Schematic diagram of the structure of an apparatus for online monitoring of switchgear according to an embodiment of the present invention.

[0054] Figure 5 Schematic diagram of the structure of an apparatus for online monitoring of switchgear according to another embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is further described in detail with reference to the following embodiments.

[0056] Embodiment 1

[0057] This embodiment provides a method for online monitoring of a switchgear, wherein the switchgear includes multiple monitored components, such as a chassis, a closing coil, a grounding switch, an energy storage motor, etc., that is, any component that needs to be monitored in real time can be used as a monitored component of this application. The executor of this embodiment is a device for online monitoring of the switchgear, which can be set separately or integrated in a relay protection device, and will not be described in detail here.

[0058] like Figure 1 FIG. 1 is a flow chart of a method for online monitoring of a switchgear according to this embodiment, the method comprising:

[0059] Step 101, obtaining a real-time current curve of a monitored component.

[0060] The real-time current curve can be obtained by a device capable of recording waves. For example, if the relay protection device can perform the recording function, the real-time current curve can be obtained by the relay protection device. Specifically, when the monitored component starts an operation, the relay protection device starts recording waves. When the operation of the monitored component is completed, the recording ends. The current curve of the recording can be used as the real-time current curve in step 101. For example, assuming that the monitored component is a chassis vehicle, the current recording of the operation of the chassis vehicle moving from the working position to the disconnected position can be used as the real-time current curve in step 101.

[0061] Of course, the starting point and the end point of the real-time current curve can also be determined according to actual needs, which will not be described in detail here.

[0062] Specifically, the corresponding real-time current curve can be obtained by periodically sampling the current of the monitored component, that is, the real-time current curve includes multiple sampling points.

[0063] Step 102, determining the Hausdorff distance between the real-time current curve and a preset reference curve, where the reference curve is the current curve of the monitored component in a normal state.

[0064] The Hausdorff distance is a measure that describes the similarity between two sets of points. Specifically, each sampling point on the real-time current curve can be selected, and the distance between each sampling point and a number of sampling points on the reference curve can be determined. The smallest one of the distances can be selected as the first distance corresponding to the sampling point, and the largest one of the first distances can be selected as the one-way Hausdorff distance between the real-time current curve and the reference curve. The distance between two sampling points is, for example, the Euclidean distance.

[0065] The one-way Hausdorff distance between the reference curve and the real-time current curve can also be obtained in a similar manner, that is, each sampling point on the reference curve is selected, and the distance between each sampling point and several sampling points on the real-time current curve is determined, and the smallest one is selected as the second distance corresponding to the sampling point, and the largest one is selected from each second distance as the one-way Hausdorff distance between the reference curve and the real-time current curve.

[0066] A larger one of the two one-way Hausdorff distances is selected as the Hausdorff distance between the real-time current curve and a preset reference curve.

[0067] The specific number and position represented by the several sampling points in this step can be determined according to actual needs, for example, the position close to the corresponding sampling point on another curve, and the number can be determined according to the total number of sampling points of the real-time current curve.

[0068] Step 103: Determine the similarity between the real-time current curve and the reference curve according to the Hausdorff distance.

[0069] The larger the value of the Hausdorff distance is, the lower the similarity is. Specifically, the similarity between the real-time current curve and the reference curve can be determined according to the size of the Hausdorff distance.

[0070] Step 104: Determine whether the monitored component fails based on the similarity.

[0071] For example, it is determined whether the similarity is less than or equal to a preset threshold. If the determination result is yes, the monitored component fails. Otherwise, it can be confirmed that the monitored component does not fail.

[0072] When it is determined that a monitored component fails, an alarm may be issued, for example, by sending a message or emitting an alarm sound. The specific method may be selected according to actual needs and will not be described in detail here.

[0073] According to this embodiment, by comparing the real-time current curve with the reference curve under normal conditions, the similarity between the two is obtained to determine whether the monitored component has a fault. The similarity algorithm based on the Hausdorff distance in this embodiment can ensure a faster calculation speed, and thus can promptly determine whether the monitored component has a fault. This method does not require processing of the sampled data, has a sensitive determination capability for abnormal sampling points, can accurately determine abnormal states, and avoid omissions.

[0074] Embodiment 2

[0075] This embodiment further supplements the method for online monitoring of the switchgear in the first embodiment. Figure 2 FIG. 1 is a flow chart of a method for online monitoring of a switchgear according to this embodiment, the method comprising:

[0076] Step 201, obtaining a real-time current curve of a monitored component.

[0077] The starting point of the real-time current curve is the starting point of the operation of the monitored component, and the end point of the real-time current curve is the end point of the operation. For example, the current in an action of the monitored component is recorded, and the recording result is used as the real-time current curve.

[0078] Step 202, obtain the Euclidean distances between the i-th sampling point on the real-time current curve and the i-th to i+n-th sampling points on the reference curve, and select the smallest one as the first distance corresponding to the i-th sampling point, and select the largest one among the first distances corresponding to each sampling point as the first unidirectional distance between the real-time current curve and the reference curve, where i∈(1,2,3…,p), p is the total number of sampling points on the real-time current curve, and n is a positive integer.

[0079] For example, the Euclidean distances between the first sampling point on the real-time current curve and the first sampling point, the second sampling point, the third sampling point, ... the 1+nth sampling point on the reference curve are obtained, and the smallest of these Euclidean distances is obtained as the first distance corresponding to the first sampling point, ..., the Euclidean distances between the 16th sampling point on the real-time current curve and the 16-nth sampling points, the 17-nth sampling points, the 18-nth sampling points, ... the 16+nth sampling points on the reference curve are obtained, and the smallest of these Euclidean distances is obtained as the first distance corresponding to the 16th sampling point, the Euclidean distances between the 17th sampling point on the real-time current curve and the 17-nth sampling points, the 18-nth sampling points, ... the 19-nth sampling points on the reference curve are obtained, and the smallest of these Euclidean distances is obtained as the first distance corresponding to the 17th sampling point, and so on, until the first distances corresponding to all the sampling points on the real-time current curve are obtained. The largest one of the first distances is used as the first unidirectional distance between the real-time current curve and the reference curve.

[0080] In this embodiment, n can be dynamically selected according to actual needs, for example, n is determined according to the total number p of sampling points on the real-time current curve. Specifically, when the total number of sampling points on the real-time current curve is large, for example, there are tens of thousands of sampling points, the value of n is relatively large, and when the total number of sampling points on the real-time current curve is small, for example, there are dozens of points, the value of n is relatively small; or, it can be obtained according to the formula n = [x*0.1%], that is, the result of x*0.1% is an integer, where x = (p+q) / 2, p is the total number of sampling points on the real-time current curve, q is the total number of sampling points on the reference curve, and if the calculation result of n according to the formula is 0, then n = 1. Of course, n can also be determined as a fixed value, for example, the value of n is 15.

[0081] How to obtain the Euclidean distance between two points belongs to the prior art and will not be described in detail here.

[0082] Step 203, obtain the Euclidean distances between the jth sampling point on the reference curve and the jnth to j+nth sampling points on the real-time current curve, select the smallest one as the second distance, and select the largest one of the second distances corresponding to each sampling point as the second unidirectional distance between the reference curve and the real-time current curve, where j∈(1,2,3…,q), q is the total number of sampling points on the reference curve.

[0083] The specific implementation of step 203 is similar to step 202, that is, each sampling point on the reference curve is selected and each Euclidean distance between the sampling point and the jnth to j+nth sampling points on the real-time current curve is obtained.

[0084] There is no sequence between step 202 and step 203. Step 203 may be performed first and then step 202, or they may be performed simultaneously. There is no specific limitation.

[0085] Step 204 : selecting the largest one of the first one-way distance and the second one-way distance as the Hausdorff distance between the real-time current curve and a preset reference curve.

[0086] For example, if the first one-way distance is larger, the value of the first one-way distance is used as the Hausdorff distance between the two curves; if the second one-way distance is larger, the value of the second one-way distance is used as the Hausdorff distance between the two curves.

[0087] Step 205 : determining the similarity between the real-time current curve and the reference curve according to the Hausdorff distance.

[0088] Specifically, the similarity can be obtained by at least one of the following methods:

[0089] Method 1: If the Hausdorff distance is 0, the similarity between the real-time current curve and the reference curve is determined to be 100%;

[0090] Method 2: If the Hausdorff distance is greater than or equal to a preset threshold value, the similarity between the real-time current curve and the reference curve is determined to be 0;

[0091] Method 3: If the Hausdorff distance is greater than 0 and less than a preset threshold value, a linear fit is performed on the real-time current curve and the reference curve to obtain the similarity.

[0092] Regarding the third method, there are many methods in the prior art for performing linear fitting on the Hausdorff distance and the similarity. This embodiment may adopt a univariate linear fitting method based on a gradient descent algorithm.

[0093] Step 206: Determine whether the monitored component fails based on the similarity.

[0094] Specifically, it is determined whether the similarity is less than or equal to a preset threshold. If the determination result is yes, the monitored component fails, otherwise the monitored component does not fail. The preset threshold can be determined based on the total number of sampling points of the real-time current curve. When the total number of sampling points is large, the preset threshold is lower, and when the total number of sampling points is small, the preset threshold is higher. For example, when the monitored component is a chassis vehicle, the sampling points of the corresponding real-time current curve may reach tens of thousands. At this time, the preset threshold can be set to 80%. When the monitored component is a closing coil, the sampling points of the corresponding real-time current curve are generally dozens. At this time, the preset threshold can be set to about 90%.

[0095] According to the method of this embodiment, since several sampling points on another curve are selected to determine the Hausdorff distance, the complexity of the calculation is greatly reduced. The method can also better obtain the similarity of the contours of the two curves, and the accuracy of the calculation will not be affected by the inconsistency of the horizontal coordinates of the curves, the delay of the starting point, etc.

[0096] Embodiment 3

[0097] This embodiment further supplements the method for online monitoring of the switch device in the above embodiment in the form of specific distance.

[0098] In this embodiment, the monitored component is the opening and closing coil. Its preset reference curve is as follows: Figure 3A As shown, the reference curve is a current curve of the monitored component in normal operation obtained based on historical data. In this embodiment, n is 15, and the preset threshold is 90%.

[0099] like Figure 3B As shown in the figure, it is the real-time current curve under normal working condition; Figure 3C As shown, it is the real-time current curve under abnormal conditions. FIG. 3A to FIG. 3C The horizontal axis T is time, the unit is ms, and the vertical axis C is current value, the unit is A.

[0100] for Figure 3B The real-time current curve shown in FIG. 1 has a total of 33 sampling data. Obtain the i-th sampling point on the real-time current curve and Figure 3A The Euclidean distance between the i-15th and i+15th sampling points on the reference curve, that is, the distance between the first sampling point on the real-time current curve and Figure 3A The Euclidean distances between the first, second, third and sixteenth sampling points on the reference curve are selected, that is, the first sampling point on the real-time current curve corresponds to 16 Euclidean distances, and the smallest one is selected from the Euclidean distances as the first distance corresponding to the first sampling point. The second sampling point corresponds to the Euclidean distances between the first, second, third and seventeenth sampling points on the reference curve, and the smallest one is selected from the Euclidean distances corresponding to the second sampling point as the first distance corresponding to the second sampling point. . . , the sixteenth sampling point corresponds to the Euclidean distances between the first, second, third and thirty-first sampling points on the reference curve, and the smallest one is selected from the Euclidean distances corresponding to the sixteenth sampling point as the first distance corresponding to the sixteenth sampling point. By analogy, each sampling point on the real-time current curve corresponds to multiple Euclidean distances, and the smallest one is selected from the Euclidean distances as the first distance corresponding to the sampling point.

[0101] Accordingly, obtain Figure 3A The first sampling point on the reference curve is Figure 3BThe Euclidean distances between the first, second, third to 16th sampling points on the real-time current curve are calculated. That is, the first sampling point on the reference curve corresponds to 16 Euclidean distances, and the second sampling point corresponds to the Euclidean distances between the first, second, third to 17th sampling points on the real-time current curve. By analogy, each sampling point on the reference curve corresponds to multiple Euclidean distances, and the largest one is selected from each Euclidean distance as the second distance. The largest one between the first distance and the second distance is taken as the final Hausdorff distance.

[0102] Next, the similarity between the real-time current curve and the reference curve is determined based on the Hausdorff distance. The similarity is 98.5%, which is greater than the preset threshold, which further confirms that Figure 3B There is no fault in the opening and closing coil under the real-time current curve.

[0103] The following is obtained according to the method of embodiment 1 and embodiment 2 Figure 3C The similarity of the real-time current curves shown in the figure is 83.3%, which is less than the preset threshold. Figure 3C The closing and opening coil under the real-time current curve is faulty.

[0104] Embodiment 4

[0105] This embodiment provides a device for online monitoring of a switchgear, which is used to execute the method for online monitoring of a switchgear of the first embodiment. Figure 4 , which is a schematic structural diagram of a device for online monitoring of a switchgear according to this embodiment.

[0106] The switchgear online monitoring device comprises an acquisition unit 401, a first determination unit 402, a second determination unit 403 and a judgment unit 404. The acquisition unit 401 is used to acquire a real-time current curve of a monitored component; the first determination unit 402 is used to determine the Hausdorff distance between the real-time current curve and a preset reference curve, the reference curve being the current curve of the monitored component in a normal state; the second determination unit 403 is used to determine the similarity between the real-time current curve and the reference curve according to the Hausdorff distance; the judgment unit 404 is used to judge whether the monitored component has a fault according to the similarity.

[0107] The starting point of the real-time current curve is the starting point of the operation of the monitored component, and the end point of the real-time current curve is the end point of the operation.

[0108] Optionally, the second determining unit 403 is configured to:

[0109] If the Hausdorff distance is 0, it is determined that the similarity between the real-time current curve and the reference curve is 100%; or

[0110] If the Hausdorff distance is greater than or equal to a preset threshold value, then the similarity between the real-time current curve and the reference curve is determined to be 0; or

[0111] If the Hausdorff distance is greater than 0 and less than a preset threshold value, a linear fit is performed on the real-time current curve and the reference curve to obtain the similarity.

[0112] Optionally, the determining unit 404 is configured to:

[0113] It is determined whether the similarity is less than or equal to a preset threshold. If the determination result is yes, the monitored component fails.

[0114] The working methods of each unit in this embodiment are the same as those in the previous embodiment and will not be described again here.

[0115] According to this embodiment, by comparing the real-time current curve with the reference curve under normal conditions, the similarity between the two is obtained to determine whether the monitored component has a fault. The similarity algorithm based on the Hausdorff distance in this embodiment can ensure a faster calculation speed, and thus can promptly determine whether the monitored component has a fault. This method does not require processing of the sampled data, has a sensitive determination capability for abnormal sampling points, can accurately determine abnormal states, and avoid omissions.

[0116] Embodiment 5

[0117] This embodiment further supplements the device for online monitoring of switchgear of the fourth embodiment.

[0118] The first determining unit 402 of this embodiment is specifically configured to:

[0119] Obtain the Euclidean distances between the i-th sampling point on the real-time current curve and the i-th to i+n-th sampling points on the reference curve, select the smallest one as the first distance corresponding to the i-th sampling point, and select the largest one among the first distances corresponding to each sampling point as the first unidirectional distance between the real-time current curve and the reference curve, where i∈(1,2,3…,p), p is the total number of sampling points on the real-time current curve, and n is a positive integer;

[0120] Obtain the Euclidean distances between the jth sampling point on the reference curve and the jnth to j+nth sampling points on the real-time current curve, select the smallest one as the second distance, and select the largest one of the second distances corresponding to each sampling point as the second unidirectional distance between the reference curve and the real-time current curve, where j∈(1,2,3…,q), q is the total number of sampling points on the reference curve;

[0121] The largest one of the first one-way distance and the second one-way distance is selected as the Hausdorff distance.

[0122] Alternatively, if Figure 5 As shown, the device for online monitoring of a switchgear in this embodiment further includes a third determining unit 501. The third determining unit 501 is specifically configured to determine n according to the following formula:

[0123] n = [x * 0.1%], where x = (p + q) / 2;

[0124] If n=0, then set n=1.

[0125] The working methods of each unit in this embodiment are the same as those in the previous embodiment and will not be described again here.

[0126] According to the method of this embodiment, since several sampling points on another curve are selected to determine the Hausdorff distance, the complexity of the calculation is greatly reduced. The method can also better obtain the similarity of the contours of the two curves, and the accuracy of the calculation will not be affected by the inconsistency of the horizontal coordinates of the curves, the delay of the starting point, etc.

[0127] The present invention also provides a device for online monitoring of a switchgear, wherein the switchgear includes a plurality of monitored components, and the device includes at least one memory and at least one processor. The memory is used to store instructions. The processor is used to execute the method for online monitoring of a switchgear described in any of the above embodiments according to the instructions stored in the memory.

[0128] An embodiment of the present invention further provides a readable storage medium in which machine-readable instructions are stored, and when the machine-readable instructions are executed by a machine, the machine executes the apparatus for online monitoring of switchgear described in any of the above embodiments.

[0129] The readable medium stores machine-readable instructions, which, when executed by the processor, cause the processor to execute any of the aforementioned methods. Specifically, a system or device equipped with a readable storage medium may be provided, on which a software program code implementing the functions of any of the above-mentioned embodiments is stored, and a computer or processor of the system or device reads and executes the machine-readable instructions stored in the readable storage medium.

[0130] In this case, the program code itself read from the machine-readable medium can realize the function of any one of the above-mentioned embodiments, and thus the machine-readable code and the machine-readable storage medium storing the machine-readable code constitute a part of the present invention.

[0131] Examples of readable storage media include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, the program code may be downloaded from a server computer or a cloud via a communication network.

[0132] Those skilled in the art should understand that the various embodiments disclosed above can be modified and altered in various ways without departing from the essence of the invention. Therefore, the protection scope of the present invention should be defined by the appended claims.

[0133] It should be noted that not all steps and units in the above-mentioned processes and system structure diagrams are necessary, and some steps or units can be ignored according to actual needs. The execution order of each step is not fixed and can be adjusted as needed. The device structure described in the above-mentioned embodiments can be a physical structure or a logical structure, that is, some units may be implemented by the same physical entity, or some units may be implemented by multiple physical entities, or some components in multiple independent devices may be implemented together.

[0134] In the above embodiments, the hardware unit can be realized by mechanical means or electrical means. For example, a hardware unit or a processor can include permanent dedicated circuits or logic (such as special processors, FPGA or ASIC) to complete the corresponding operation. The hardware unit or the processor can also include programmable logic or circuits (such as general-purpose processors or other programmable processors), which can be temporarily set by software to complete the corresponding operation. Concrete implementation (mechanical means or dedicated permanent circuits or temporarily set circuits) can be determined based on cost and time considerations.

[0135] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for online monitoring of a switchgear, wherein the switchgear comprises a plurality of monitored components, characterized in that: The method comprises: Obtain a real-time current curve of a monitored component; Determining a Hausdorff distance between the real-time current curve and a preset reference curve, wherein the reference curve is a current curve of the monitored component in a normal state; Determining the similarity between the real-time current curve and the reference curve according to the Hausdorff distance; Determining whether the monitored component fails according to the similarity; Determining the Hausdorff distance between the real-time current curve and a preset reference curve includes: Obtain the Euclidean distances between the i-th sampling point on the real-time current curve and the i-th to i+n-th sampling points on the reference curve, select the smallest one as the first distance corresponding to the i-th sampling point, and select the largest one among the first distances corresponding to the sampling points as the first unidirectional distance between the real-time current curve and the reference curve, where i∈(1,2,3…,p), p is the total number of sampling points on the real-time current curve, and n is a positive integer; Obtain the Euclidean distances between the jth sampling point on the reference curve and the jnth to j+nth sampling points on the real-time current curve, select the smallest one as the second distance, and select the largest one of the second distances corresponding to each sampling point as the second unidirectional distance between the reference curve and the real-time current curve, where j∈(1,2,3…,q), q is the total number of sampling points on the reference curve; The largest one of the first one-way distance and the second one-way distance is selected as the Hausdorff distance.

2. The method according to claim 1, characterized in that Before determining the Hausdorff distance between the real-time current curve and a preset reference curve, the method further includes: Determine n according to the following formula: n = [x * 0.1%], where x = (p + q) / 2; If n=0, then set n=1.

3. The method according to claim 1, characterized in that Determining the similarity between the real-time current curve and the reference curve according to the Hausdorff distance includes: If the Hausdorff distance is 0, it is determined that the similarity between the real-time current curve and the reference curve is 100%; or If the Hausdorff distance is greater than or equal to a preset threshold value, determining that the similarity between the real-time current curve and the reference curve is 0; or If the Hausdorff distance is greater than 0 and less than a preset threshold value, a linear fit is performed on the real-time current curve and the reference curve to obtain a similarity.

4. The method according to claim 1, characterized in that The starting point of the real-time current curve is the starting point of the operation of the monitored component, and the end point of the real-time current curve is the end point of the operation.

5. The method according to any one of claims 1 to 4, characterized in that Judging whether the monitored component fails according to the similarity includes: It is determined whether the similarity is less than or equal to a preset threshold. If the determination result is yes, the monitored component fails.

6. A device for online monitoring of a switchgear, the switchgear comprising a plurality of monitored components, characterized in that: The device comprises: an acquisition unit, used for acquiring a real-time current curve of a monitored component; a first determining unit, configured to determine a Hausdorff distance between the real-time current curve and a preset reference curve, wherein the reference curve is a current curve of the monitored component in a normal state; a second determining unit, configured to determine the similarity between the real-time current curve and the reference curve according to the Hausdorff distance; a judgment unit, configured to judge whether the monitored component fails according to the similarity; The first determining unit is specifically configured to: Obtain the Euclidean distances between the i-th sampling point on the real-time current curve and the i-th to i+n-th sampling points on the reference curve, select the smallest one as the first distance corresponding to the i-th sampling point, and select the largest one among the first distances corresponding to the sampling points as the first unidirectional distance between the real-time current curve and the reference curve, where i∈(1,2,3…,p), p is the total number of sampling points on the real-time current curve, and n is a positive integer; Obtain the Euclidean distances between the jth sampling point on the reference curve and the jnth to j+nth sampling points on the real-time current curve, select the smallest one as the second distance, and select the largest one of the second distances corresponding to each sampling point as the second unidirectional distance between the reference curve and the real-time current curve, where j∈(1,2,3…,q), q is the total number of sampling points on the reference curve; The largest one of the first one-way distance and the second one-way distance is selected as the Hausdorff distance.

7. The device according to claim 6, characterized in that Also includes: A third determining unit, configured to determine n according to the following formula: n = [x * 0.1%], where x = (p + q) / 2; If n=0, then set n=1.

8. The device according to claim 6, characterized in that The second determining unit is used for: If the Hausdorff distance is 0, it is determined that the similarity between the real-time current curve and the reference curve is 100%; or If the Hausdorff distance is greater than or equal to a preset threshold value, determining that the similarity between the real-time current curve and the reference curve is 0; or If the Hausdorff distance is greater than 0 and less than a preset threshold value, a linear fit is performed on the real-time current curve and the reference curve to obtain a similarity.

9. The device according to any one of claims 6 to 8, characterized in that The judging unit is used for: It is determined whether the similarity is less than or equal to a preset threshold. If the determination result is yes, the monitored component fails.

10. A device for online monitoring of a switchgear, the switchgear comprising a plurality of monitored components, characterized in that: The device comprises: at least one memory for storing instructions; At least one processor is configured to execute the method for online monitoring of a switchgear according to any one of claims 1 to 5 according to the instructions stored in the memory.

11. A readable storage medium, characterized in that: The readable storage medium stores machine-readable instructions. When the machine-readable instructions are executed by a machine, the machine executes the method for online monitoring of a switch device according to any one of claims 1 to 5.

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