Intelligent cable fault monitoring and early warning method and system

Abstract

The present application relates to the field of cable fault monitoring, and in particular to an intelligent cable fault monitoring and early warning method and system, the method comprising: obtaining the voltage value of each cable section in the substation, screening out cables with abnormal voltage; collecting the thermal grayscale map of the screened cable, marking the cable area on the thermal grayscale map, setting a temperature detection window in the cable area, and taking the point with the fastest temperature rise among the pixels in the temperature detection window as the center point; for the pixels in the temperature detection window, taking the ratio of the difference between any pixel and two adjacent pixels as the gradient value of any pixel; calculating the abnormal evaluation of the gradient value; generating an early warning signal in response to the abnormal evaluation of the gradient value being greater than the preset gradient value evaluation threshold. The present application has the effect of improving the accuracy of judging the abnormal state of the cable.

Description

Technical Field

[0001] The present application relates to the field of cable fault monitoring, and in particular to an intelligent cable fault monitoring and early warning method and system. Background Art

[0002] Cables are widely used in substations, such as transmitting electrical energy within substations and between substations and other power equipment or power grids, or connecting different equipment within substations, such as transformers, circuit breakers, disconnectors, relay protection devices, etc.

[0003] Cables may overheat during use. The main reasons are: the current carrying capacity of the cable exceeds its rated capacity, long-term overload operation will cause the cable to overheat, or the cross-sectional area of ​​the cable conductor used is too small to meet the current transmission requirements, resulting in overload and heating. Cable overheating is a serious safety hazard and may cause the cable to catch fire, so the cable temperature needs to be monitored.

[0004] For example, a digital fire safety assessment and visual operation and maintenance method disclosed in a Chinese patent application document with publication number CN114257791A includes the following steps: risk level formulation; environmental indicator monitoring; including cable residual current monitoring, cable temperature monitoring or cracking quality monitoring, and initiating an early warning when the corresponding indicator is monitored to exceed the preset standard; cable temperature monitoring refers to real-time collection and monitoring of ambient temperature and cable temperature. During cable temperature monitoring, an early warning is issued when it is detected that the temperature exceeds the standard.

[0005] The existing technology does not make a comprehensive judgment based on the ambient temperature and the cable temperature. Under extreme ambient temperatures, even if the cable temperature does not exceed the standard, there may be a risk of overheating. Making judgments in a single dimension may lead to misjudgment of the cable status. Summary of the invention

[0006] In order to improve the accuracy of cable abnormal status judgment, the present application provides an intelligent cable fault monitoring and early warning method and system.

[0007] In a first aspect, the present application provides an intelligent cable fault monitoring and early warning method, which adopts the following technical solution:

[0008] An intelligent cable fault monitoring and early warning method comprises the following steps: obtaining the voltage value of each cable section in a substation, screening out cables with abnormal voltage; collecting a thermal grayscale image of the screened cables, marking the cable area on the thermal grayscale image, setting a temperature detection window in the cable area, and taking the point with the fastest temperature rise among the pixels in the temperature detection window as the center point; for the pixels in the temperature detection window, taking the ratio of the difference between any pixel and two adjacent pixels as the gradient value of any pixel; and calculating the abnormal evaluation of the gradient value, the calculation formula is: ,in, represents the abnormal evaluation of the gradient value, Indicates environmental factor bias, Indicates A collection moment, is the collection interval, Indicates The gradient value of the center point at each acquisition moment, Indicates The gradient value of the center point at time, represents a preset first hyperparameter; in response to the abnormal evaluation of the gradient value being greater than a preset gradient value evaluation threshold, a warning signal is generated.

[0009] The beneficial effects are: combining environmental factor bias and cable temperature factors, comprehensively judging the cable status, and quantifying the judgment results, that is, calculating the abnormal evaluation value of the gradient value. The accuracy of cable abnormal status judgment is improved, and the cable voltage value factor is considered in the early stage. The abnormal evaluation calculation of the gradient value of the cable with abnormal voltage is performed, which reduces the amount of calculation, speeds up the response time of the early warning, and provides a multi-dimensional monitoring method, which is more comprehensive than the method that relies solely on voltage or temperature.

[0010] Optionally, the expression for the environmental factor bias is: ,in, Indicates environmental factor bias, Indicates The pixel value set of the pixel points in the thermal grayscale image at the acquisition time, represents the mean, Indicates The gradient value of the center point at each acquisition moment, represents the normalization function, represents the variance, Indicates The gradient sequence of the center point at each acquisition moment is obtained by arranging the gradient values ​​of the center pixel points in the order of the acquisition moments.

[0011] The beneficial effects are: Used to distinguish changes in indoor ambient temperature, It is used to distinguish abnormal temperature rise caused by non-cables, such as fire sources around cables, to further improve the accuracy of judging abnormal cable conditions.

[0012] Optionally, the voltage value of each cable section in the substation is obtained to screen out cables with abnormal voltage, including the steps of: ,in, Indicates Voltage anomaly evaluation of cable segments, express The collection time The voltage value of the cable segment, Indicates The maximum load voltage of the cable segment, represents the hyperbolic tangent function, express The collection time is except The voltage collection of other cable segments outside the segment cable, express The collection time The voltage value of the cable segment, Indicates The voltage value of the cable segment, represents the average value, Represents the preset second hyperparameter.

[0013] The beneficial effects are: It reflects whether the cable voltage is close to the maximum load. The larger the ratio is, the greater the possibility of voltage abnormality is. Reflects the Compared with the adjacent cables, the first Check whether the voltage value of the cable segment has a sudden change. The larger it is, the more likely it is to mutate.

[0014] Optionally, the voltage value of each cable section in the substation is obtained to screen out cables with abnormal voltage, including: obtaining the voltage value of each cable section in the substation, and screening out cables whose voltage values ​​are not within a preset voltage threshold range as cables with abnormal voltage.

[0015] Optionally, the early warning signal is to highlight the cable with abnormal voltage on the screen of the real-time monitoring video of the cable.

[0016] Optionally, the warning signal is a sound alarm or a warning text message.

[0017] Optionally, the temperature detection window is a rectangular window.

[0018] The beneficial effect is that the temperature detection window is rectangular in shape and the side plan view of the cable is also rectangular, so that the temperature detection window can move along the direction of the length extension of the cable to capture the temperature distribution.

[0019] In the second aspect, the present application provides an intelligent cable fault monitoring and early warning system, which adopts the following technical solution:

[0020] An intelligent cable fault monitoring and early warning system comprises: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the intelligent cable fault monitoring and early warning method described above is implemented.

[0021] The beneficial effect is: the above-mentioned intelligent cable fault monitoring and early warning method is generated into a computer program and stored in a memory so as to be loaded and executed by a processor, thereby making a system based on the memory and the processor for easy use.

[0022] This application has the following technical effects:

[0023] 1. Combined with environmental factor bias and cable temperature factors, the cable status is comprehensively judged, the judgment results are quantified, and the accuracy of cable abnormal status judgment is improved.

[0024] 2. The cable voltage value factor is considered first, and cables with abnormal voltage are screened out. Then, the abnormal gradient value of the cables with abnormal voltage is evaluated and calculated, which reduces the amount of data for subsequent calculation and analysis and speeds up the response time of the early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present application will become easily understood. In the accompanying drawings, several embodiments of the present application are shown in an exemplary and non-restrictive manner, and the same or corresponding numbers represent the same or corresponding parts.

[0026] Figure 1 It is a method flow chart of an intelligent cable fault monitoring and early warning method according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0028] It should be understood that when the terms "first", "second", etc. are used in the claims, specification and drawings of the present application, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the specification and claims of the present application indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their collections.

[0029] The present application embodiment discloses an intelligent cable fault monitoring and early warning method, referring to Figure 1 , including steps S1 to S5, which are specifically as follows:

[0030] S1: Obtain the voltage value of each cable section in the substation and filter out cables with abnormal voltage.

[0031] In one embodiment, the method for screening out cables with abnormal voltage is: calculating the voltage anomaly evaluation of each section of the cable, and screening out cables with voltage anomaly evaluations greater than a voltage evaluation threshold as cables with abnormal voltage.

[0032] Specifically, the voltage anomaly evaluation calculation formula is:

[0033] ,in, Indicates Voltage anomaly evaluation of cable segments, express The collection time The voltage value of the cable segment, Indicates The maximum load voltage of the cable segment, represents the hyperbolic tangent function, express The collection time is except The voltage collection of other cable segments outside the segment cable, express The collection time The voltage value of the cable segment, Indicates The voltage value of the cable segment, Indicates the average value. Represents the preset second hyperparameter, in this application The value of is set to 3 or 4. is to make The value range is as close as possible to , which is close to the domain.

[0034] In the formula Calculated the Section cable The ratio of the voltage value at each collection moment to its maximum load voltage reflects whether the cable voltage is close to the maximum load. The larger the ratio, the greater the possibility of voltage abnormality.

[0035] The function will Map to , Reflects the Compared with the adjacent cables, the first Check whether the voltage value of the cable segment has a sudden change. The larger it is, the more likely it is to mutate.

[0036] In other embodiments, the method for screening out cables with abnormal voltage may also be: obtaining the voltage value of each cable section in the substation, and screening out cables whose voltage values ​​are not within a preset voltage threshold range as cables with abnormal voltage. The voltage threshold range can be set according to the application scenario. For example, assuming that the rated voltage of the substation cable is 10kV, the voltage threshold range can be set to 9.5kV to 10.5kV, including 9.5kV and 10.5kV.

[0037] The main purpose of screening is to find cables with abnormal voltages and use these cables as analysis objects to reduce the amount of data processing for subsequent calculations and analysis.

[0038] S2: Collect the thermal grayscale image of the screened cable, mark the cable area on the thermal grayscale image, set a temperature detection window in the cable area, and use the point with the fastest temperature rise among the pixels in the temperature detection window as the center point.

[0039] A thermal imaging camera is used to capture the thermal radiation image of the cable. The thermal radiation image is usually represented in the form of a thermal grayscale image, where different grayscale values ​​correspond to different temperatures.

[0040] According to the semantic segmentation model, the cable area is separated from the background and marked on the thermal grayscale map.

[0041] The temperature detection window is set as a rectangular window, extending along the length of the cable to capture the temperature distribution. The length of the detection window can be set according to the specific application scenario. For example, it can be 20 pixels long, that is, the window will cover an area 20 pixels wide on the center line of the cable.

[0042] S3: For the pixel points in the temperature detection window, the ratio of the difference between any pixel point and two adjacent pixel points is used as the gradient value of any pixel point.

[0043] For example, the pixel points along the power transmission direction are set to be point A, point B and point C, point B is any pixel point, point A and point B are two adjacent pixel points, point A is on the left side of point B, and point C is on the right side of point B. In one embodiment, the ratio of the difference between any pixel point and the two adjacent pixel points can be the ratio of the pixel value difference between point B and point A to the pixel value difference between point B and point C, or the ratio of the pixel value difference between point B and point C to the pixel value difference between point B and point A.

[0044] S4: Calculate the abnormal evaluation of the gradient value.

[0045] The calculation formula for the abnormal evaluation of the gradient value is: ,in, represents the abnormal evaluation of the gradient value, Indicates environmental factor bias, Indicates A collection moment, is the collection interval, Indicates The gradient value of the center point at each acquisition moment, Indicates The gradient value of the center point at the moment. Represents the first preset hyperparameter. The value of is set to 0.0001, and the hyperparameter is to avoid the denominator from being zero.

[0046] In one embodiment, the expression of environmental factor bias is: ,in, Indicates environmental factor bias, Indicates The pixel value set of the pixel points in the thermal grayscale image at the acquisition time, represents the mean, Indicates The gradient value of the center point at each acquisition moment, represents the normalization function, represents the variance, Indicates The gradient sequence of the center point of each acquisition moment.

[0047] Arrange the gradient values ​​of the central pixel points in the order of acquisition time to obtain the gradient sequence of the central point.

[0048] In other embodiments, the value of the environmental factor bias can be set by the operation and maintenance personnel according to the specific application scenario. For example, the environmental factor bias in summer is set to 0.8, and the environmental factor bias in winter is set to 1.2. The ambient temperature in summer is usually higher than that in winter, so the environmental factor bias in winter is greater than that in summer, thereby reducing the impact of seasonal ambient temperature changes on the abnormal evaluation value of the gradient value.

[0049] S5: In response to the abnormal evaluation of the gradient value being greater than a preset gradient value evaluation threshold, generating a warning signal.

[0050] Exemplarily, the gradient value evaluation threshold may be 0.3, and the gradient value evaluation threshold may be adjusted according to actual application scenarios.

[0051] The early warning signal is to highlight the cable with abnormal voltage on the real-time monitoring video of the cable. At the same time, a sound alarm and / or a warning text message can be sent.

[0052] For example, in the monitoring center of a substation, when the system detects that the gradient value of a certain cable exceeds the threshold, the cable is automatically highlighted on the monitoring screen with a red flashing border and a continuous alarm. The operation and maintenance team's mobile application receives a push notification containing the location, time and recommended inspection steps of the abnormal cable. For example, the operation and maintenance personnel's mobile phone receives an early warning text message with the content "The cable voltage is abnormal, please check immediately."

[0053] The embodiment of the present application also discloses an intelligent cable fault monitoring and early warning system, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the intelligent cable fault monitoring and early warning method according to the present application is implemented.

[0054] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0055] In the present application, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high bandwidth memory HBM (High Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device.

[0056] Although this specification has shown and described a plurality of embodiments of the present application, it is obvious to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will think of many changes, modifications and alternatives without departing from the thought and spirit of the present application. It should be understood that in the process of practicing the present application, various alternatives to the embodiments of the present application described herein may be adopted.

[0057] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. An intelligent cable fault monitoring and early warning method, characterized in that: Includes steps: Obtain the voltage value of each cable section in the substation and filter out cables with abnormal voltage; Collect the thermal grayscale image of the screened cable, mark the cable area on the thermal grayscale image, set a temperature detection window in the cable area, and use the point with the fastest temperature rise among the pixels in the temperature detection window as the center point; For the pixel points in the temperature detection window, the ratio of the difference between any pixel point and two adjacent pixel points is taken as the gradient value of any pixel point; Calculate the abnormal evaluation of the gradient value, the calculation formula is: ,in, represents the abnormal evaluation of the gradient value, Indicates environmental factor bias, Indicates A collection moment, is the collection interval, Indicates The gradient value of the center point at each acquisition moment, Indicates The gradient value of the center point at time, represents the preset first hyperparameter; In response to the abnormal evaluation of the gradient value being greater than the preset gradient value evaluation threshold, an early warning signal is generated; the expression of the environmental factor bias is: ,in, Indicates The pixel value set of the pixel points in the thermal grayscale image at the acquisition time, represents the mean, represents the normalization function, represents the variance, Indicates The gradient sequence of the center point at each acquisition moment is obtained by arranging the gradient values ​​of the center pixel points in the order of the acquisition moments.

2. The intelligent cable fault monitoring and early warning method according to claim 1 is characterized in that: Obtain the voltage value of each cable section in the substation and filter out cables with abnormal voltage, including the following steps: Calculate the voltage anomaly evaluation of each cable section using the following formula: ,in, Indicates Voltage anomaly evaluation of cable segments, express The collection time The voltage value of the cable segment, Indicates The maximum load voltage of the cable segment, represents the hyperbolic tangent function, express The collection time is except The voltage collection of other cable segments outside the segment cable, express The collection time The voltage value of the cable segment, Indicates The voltage value of the cable segment, represents the preset second hyperparameter; Cables whose voltage anomaly evaluation is greater than the voltage evaluation threshold are screened out as cables with voltage anomaly.

3. The intelligent cable fault monitoring and early warning method according to claim 1 is characterized in that: The voltage value of each cable section in the substation is obtained, and cables with abnormal voltage are screened out, including: obtaining the voltage value of each cable section in the substation, and screening out cables whose voltage values ​​are not within a preset voltage threshold range as cables with abnormal voltage.

4. The intelligent cable fault monitoring and early warning method according to claim 1 is characterized in that: The early warning signal is to highlight the cables with abnormal voltage on the real-time monitoring video of the cables.

5. The intelligent cable fault monitoring and early warning method according to claim 1 is characterized in that: The warning signal is a sound alarm or a warning text message.

6. The intelligent cable fault monitoring and early warning method according to claim 1 is characterized in that: The temperature detection window is a rectangular window.

7. An intelligent cable fault monitoring and early warning system, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the intelligent cable fault monitoring and early warning method according to any one of claims 1 to 6 is implemented.

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