Lightning arrester health monitoring method, device and system

Abstract

The present application discloses a lightning arrester health monitoring method, device, and system. The method includes: obtaining working environment data, lightning arrester action data, lightning arrester electrical data, lightning arrester service data, and fault data of the lightning arrester; determining the environmental coefficient influencing factor of the lightning arrester based on the working environment data, determining the action state value of the lightning arrester based on the lightning arrester action data, determining the current growth rate of the lightning arrester based on the lightning arrester electrical data, determining the equipment utilization rate of the lightning arrester based on the lightning arrester service data, and determining the health status factor of the lightning arrester based on the fault data; inputting the environmental coefficient influencing factor, action state value, current growth rate, equipment utilization rate, and health status factor into a pre-constructed lightning arrester health status scoring model to obtain a lightning arrester health value; and performing health monitoring on the lightning arrester based on the lightning arrester health value and a preset health monitoring strategy.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of lightning arrester detection technology, and in particular to a lightning arrester health monitoring method, device, and system. Background Art

[0002] Metal oxide lightning arrester (MOA for short) has the advantages of good overvoltage protection characteristics, large flow capacity, fast action response, simple structure, small size and light weight. It has gradually replaced the old valve type lightning arrester and has been widely used in power systems.

[0003] Because the arrester is subjected to the system operating voltage for a long time, the MOA valve plate is prone to aging. In addition, environmental factors such as moisture and dirt, as well as factors such as overvoltage, will increase the resistive current and power of the arrester, thereby causing the MOA valve plate to deteriorate, resulting in the destruction of the insulation properties of the MOA, the loss of protection, causing thermal collapse, and even explosion in severe cases. Once an MOA accident occurs, the consequences are very serious.

[0004] In response to the above situation, the health status of MOAs can currently only be determined through manual observation and on-site testing. However, the large number and wide distribution of MOAs in the power system will lead to huge manpower consumption in daily operation and maintenance, power outage maintenance, and fault handling, further increasing the economic pressure on the power system. Summary of the Invention

[0005] The embodiments of the present application provide a lightning arrester health monitoring method, device, and system, which can effectively and accurately monitor the operating status of the lightning arrester in a non-manual manner.

[0006] In a first aspect, an embodiment of the present application provides a lightning arrester health monitoring method, the method comprising:

[0007] Acquiring operating information of the arrester, the operating information including working environment data of the arrester, arrester action data, arrester electrical data, arrester service data, and fault data;

[0008] determining an environmental coefficient influencing factor of the lightning arrester based on the working environment data, determining an action state value of the lightning arrester based on the lightning arrester action data, determining a current growth rate of the lightning arrester based on the lightning arrester electrical data, determining an equipment utilization rate of the lightning arrester based on the lightning arrester service data, and determining a health status factor of the lightning arrester based on the fault data;

[0009] Inputting the environmental coefficient influencing factor, the action state value, the current growth rate, the equipment utilization rate, and the health condition factor into a pre-constructed arrester health condition scoring model, and obtaining the arrester health value output by the arrester health condition scoring model;

[0010] The health of the arrester is monitored based on the arrester health value and a preset health monitoring strategy.

[0011] In a second aspect, an embodiment of the present application further provides a lightning arrester health monitoring device, the lightning arrester health monitoring device comprising:

[0012] An acquisition module is used to obtain operating information of the arrester, wherein the operating information includes working environment data of the arrester, arrester action data, arrester electrical data, arrester service data and fault data;

[0013] a processing module, configured to determine an environmental coefficient influencing factor of the lightning arrester based on the working environment data, determine an action state value of the lightning arrester based on the lightning arrester action data, determine a current growth rate of the lightning arrester based on the lightning arrester electrical data, determine an equipment utilization rate of the lightning arrester based on the lightning arrester service data, and determine a health status factor of the lightning arrester based on the fault data;

[0014] a health value calculation module, configured to input the environmental coefficient influencing factor, the action state value, the current growth rate, the equipment utilization rate, and the health condition factor into a pre-constructed arrester health condition scoring model, and obtain the arrester health value output by the arrester health condition scoring model;

[0015] A monitoring module is used to perform health monitoring on the arrester based on the arrester health value and a preset health monitoring strategy.

[0016] In a third aspect, an embodiment of the present application further provides a lightning arrester health monitoring system, the lightning arrester health monitoring system comprising:

[0017] The system includes: a multi-parameter acquisition module, a state assessment module and a human-computer interaction module;

[0018] The multi-parameter acquisition module is used to collect operating information of the arrester, wherein the operating information includes working environment data of the arrester, arrester action data, arrester electrical data, arrester service data and fault data;

[0019] The state assessment module is in communication with the multi-parameter acquisition module and is used to determine the health value of the arrester based on the working environment data, the arrester action data, the arrester electrical data, the arrester service data and the fault data of the arrester;

[0020] The human-computer interaction module is respectively communicated with the multi-parameter acquisition module and the status assessment module, and is used to upload the working environment data, lightning arrester action data, lightning arrester electrical data, lightning arrester service data, fault data and the lightning arrester health value of the lightning arrester to the cloud, and display the operating information and the lightning arrester health value.

[0021] The technical solution of the embodiment of the present application obtains the operating information of the lightning arrester, determines the environmental coefficient influencing factor of the lightning arrester based on the working environment data, determines the action state value of the lightning arrester based on the action data of the lightning arrester, determines the current growth rate of the lightning arrester based on the electrical data of the lightning arrester, determines the equipment utilization rate of the lightning arrester based on the service data of the lightning arrester, determines the health status factor of the lightning arrester based on the fault data, inputs the environmental coefficient influencing factor, the action state value, the current growth rate, the equipment utilization rate and the health status factor into a pre-constructed lightning arrester health status scoring model, obtains the lightning arrester health value output by the lightning arrester health status scoring model, and performs health monitoring on the lightning arrester based on the lightning arrester health value and a preset health monitoring strategy. It can accurately monitor the operating condition of the lightning arrester, avoid the huge manpower consumption of daily operation and maintenance, power outage maintenance and fault handling through manual observation and on-site detection, and thus reduce the economic burden of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flowchart of a lightning arrester health monitoring method provided in Example 1 of the present application;

[0023] Figure 2 A flowchart of a method for determining an action state value provided in Example 1 of the present application;

[0024] Figure 3 A schematic flow chart of a method for determining a current growth rate provided in Example 1 of the present application;

[0025] Figure 4 This is a structural diagram of a lightning arrester health monitoring device provided in Example 2 of the present application;

[0026] Figure 5 A schematic structural diagram of a lightning arrester health monitoring system provided in Example 3 of the present application;

[0027] Figure 6 This is a schematic diagram of the installation structure of the lightning arrester health monitoring system provided in Example 3 of the present application;

[0028] Figure 7 This is a structural diagram of a telescopically adjustable lifting device provided in Example 3 of the present application;

[0029] Figure 8 This is a front schematic diagram of a lightning arrester bracket provided in Example 3 of the present application. DETAILED DESCRIPTION

[0030] The present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are intended only to illustrate the present application and are not intended to limit the present application. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions of the present application, not all of the structures.

[0031] Example 1

[0032] Figure 1 This is a flow chart of the arrester health monitoring method provided in Example 1 of the present application. This embodiment is applicable to the scenario of arrester health monitoring. The method can be performed by an arrester health monitoring device. The device can be implemented in hardware and / or software and can generally be integrated into an arrester health monitoring system such as a computer with data computing capabilities. Specifically, the method includes the following steps:

[0033] Step 101: Acquire operation information of the arrester, where the operation information includes working environment data of the arrester, action data of the arrester, electrical data of the arrester, service data of the arrester, and fault data.

[0034] In this step, the arrester operation information can be obtained through the arrester monitoring device. For example, the working environment data can be collected through the external environment sensing equipment, the arrester action data can be collected through the action number monitoring equipment, and the arrester electrical data can be collected through the voltage and current monitoring equipment.

[0035] Among them, the working environment data of the lightning arrester may include the temperature change of the surface of the lightning arrester, the humidity of the environment in which the lightning arrester is located, the number of thunderstorm days in a year, etc. The lightning arrester action data may include the action time corresponding to each time the lightning arrester is actuated. The lightning arrester electrical data may include the resistive current of the lightning arrester, the trend data of the third harmonic current and the voltage level of the lightning arrester. The service data may include the service life, the actual service life, the number of days used per year, the number of hours required for use per day, the actual number of hours used per day, etc. The lightning arrester fault data may include parameters such as fault time, operating load level, component status, lightning arrester fault condition and adverse working condition.

[0036] Step 102: determine the environmental coefficient influencing factor of the lightning arrester based on the working environment data, determine the action state value of the lightning arrester based on the lightning arrester action data, determine the current growth rate of the lightning arrester based on the lightning arrester electrical data, determine the equipment utilization rate of the lightning arrester based on the lightning arrester service data, and determine the health status factor of the lightning arrester based on the fault data.

[0037] In this step, since the working environment of the arrester directly affects its working quality and working condition, the environmental coefficient impact factor is introduced to describe the degree of influence of the environment on the key parameter data of the equipment. The environmental coefficient impact factor can be represented by ε. The specific environmental level can be corresponded to factors such as the maximum ambient temperature, the ambient temperature change rate, the average relative humidity, and the number of thunderstorm days per year. The value range of the impact factor is determined according to the specific environmental level. The specific determination method is shown in Table 1:

[0038] Table 1

[0039]

[0040] For example, when the maximum temperature of the environment in which the lightning arrester is located is 40°C, the temperature environment change rate is 10°C / h, the average relative humidity is 55%, the number of thunderstorm days in a year is 33 days, and the corresponding environmental level is level 3, then the environmental coefficient impact factor of the lightning arrester is 1.15~1.30.

[0041] In this step, when determining the action status value, please refer to Figure 2 , Figure 2 This is a flow chart of determining the action status value of a lightning arrester provided in the first embodiment of the present application.

[0042] like Figure 2 As shown, the process of determining the action state value of the arrester based on the arrester action data in this embodiment may include:

[0043] Step 201: Based on the action time, the number of actions of the arrester in a preset unit time period is counted, and a target rising rate of the number of actions of the arrester is determined according to the number of actions and the length of the preset unit time period.

[0044] The action time is the action time corresponding to each time the arrester is actuated. Specifically, the action time and the number of overvoltage actions of the arrester can be recorded by an action counter, and the number of actions within a preset unit time period can be counted based on the action time.

[0045] For example, if the length of the preset unit time period is 1 day, and the lightning arrester is activated at 8:00 am and 1:00 pm on January 1, 2022, then 8:00 and 1:00 pm on January 1, 2022 are the only two activation moments of the lightning arrester on January 1, 2022, and the lightning arrester is activated twice within the preset unit time period.

[0046] Under the premise that the arrester has no faults or is still able to operate despite having a fault, the more shocks the arrester receives within a preset unit time period, the faster the number of arrester operation counters will increase. Therefore, the increment of the number of arrester operations within a preset unit time period can be defined as the arrester operation number increase rate n', and n' can be expressed by the following formula:

[0047]

[0048] Wherein, Δn is the digital increment value of the arrester action counter, Δt is the length of the preset unit time period, and n1 and n2 are the number of actions displayed by the arrester action counter at time t1 and t2.

[0049] For example, at t1, the arrester action counter displays the number of actions as 20, at t2, the arrester action counter displays the number of actions as 22, and Δt is 1 day, then n′=2 times / day.

[0050] Step 202: Based on a preset mapping relationship between the rise rate and the action state value, determine the target action state value corresponding to the target rise rate, and determine the target action state value as the action state value of the arrester.

[0051] The mapping relationship between the preset rising rate and the action state value is shown in Table 2. The corresponding action state value is determined according to the rising rate of the number of actions, where the action state value is represented by A. For example, when the rising rate is 2, the corresponding action state value is 95.

[0052] Table 2

[0053]

[0054] In addition, the process of determining the current growth rate of the lightning arrester in this step can be referred to Figure 3 , Figure 3 This is a flow chart of determining the current growth rate of a lightning arrester provided in Example 1 of the present application.

[0055] like Figure 3 As shown, the process of determining the current growth rate of the lightning arrester based on the lightning arrester electrical data may include:

[0056] Step 301: Determine a target growth rate correction coefficient corresponding to a voltage level in electrical data of a lightning arrester according to a preset mapping relationship between voltage levels and growth rate correction coefficients.

[0057] The preset voltage levels can be divided according to the commonly used voltages of the arrester. The growth rate correction factor is related to the voltage level of the arrester and can be used to describe the degree of influence of the voltage level on the current growth rate. The mapping relationship between the preset voltage level and the growth rate correction factor can be expressed in Table 3:

[0058] Table 3

[0059]

[0060] For example, when the voltage level of the arrester is 10 kV, it can be seen from Table 3 that the correction coefficient is 0.80 to 0.96.

[0061] Step 302: Obtain the initial value of the resistive fundamental wave, and determine the resistive current fundamental wave growth rate based on the initial value of the resistive fundamental wave, the resistive current, and the target growth rate correction coefficient.

[0062] In this step, the fundamental wave growth rate of the resistive current can be obtained according to the following formula:

[0063]

[0064] Where, I r % is the fundamental wave growth rate of resistive current, I r初始 is the initial value of the resistive fundamental wave; I r is the current monitoring value of the resistive current; F 10 is the growth rate correction factor.

[0065] For example, assuming that the initial value of the resistive fundamental wave is 20A, the current monitored value of the resistive current is 22A, and the growth rate correction coefficient is 0.90, then the resistive current fundamental wave growth rate is

[0066] Step 303: Obtain the initial value of the third harmonic, and determine the third harmonic growth rate based on the initial value of the third harmonic, the third harmonic current, and the target growth rate correction coefficient.

[0067] The third harmonic growth rate can be obtained according to the following formula:

[0068]

[0069] Where, I r3 % is the third harmonic growth rate, I r3初始 is the initial value of the third harmonic; I r3 It is the current monitoring value of the third harmonic current.

[0070] For example, assuming that the initial value of the third harmonic is 5A, the current monitored value of the third harmonic current is 5.2A, and the growth rate correction coefficient is 0.90, then the third harmonic growth rate is The fundamental wave growth rate of the resistive current and the third harmonic growth rate are determined as the current growth rate of the lightning arrester.

[0071] In this step, it can be understood that the current growth rate of the arrester includes the fundamental wave growth rate of the resistive current and the third harmonic growth rate. For example, when the fundamental wave growth rate of the resistive current is 9% and the third harmonic growth rate is 3.6%, the current growth rates are 9% and 3.6%.

[0072] In this step, the less time the arrester spends on faults, power outages, regular repairs, and maintenance, the more time it's effectively working, indicating a higher degree of device utilization and better health. Therefore, the health of the equipment can be expressed by determining its utilization rate. This utilization rate can be represented by δ, which is determined as follows:

[0073]

[0074] Where T end is the service life, T1 is the actual service life, T′ is the number of days used per year, t is the actual number of hours used per day, t′ is the number of hours expected to be used per day, and F 11 is the utilization correction factor.

[0075] The utilization rate correction factor is related to the service life of the equipment and can be used to describe the impact of the service life of the equipment on the equipment utilization rate. 11 This can be determined from Table 4:

[0076] Table 4

[0077]

[0078] For example, assuming that the arrester has a service life of 10 years and the environmental coefficient impact factor is 1.20, the correction coefficient range is 0.90 to 0.98, which can be taken as 0.94. The actual service life is Assuming that the number of days of use per year is 300, the actual use hours per day are 22 hours, and the number of hours of use per day should be 24 hours, the equipment utilization rate is

[0079] In this step, the health factor of the arrester can also be determined based on the component status and the frequency of occurrence of the bad condition. Specifically, the arrester fault level coefficient can be first determined based on the component status and the frequency of occurrence of the bad condition, and then the health factor can be determined based on the arrester fault level coefficient.

[0080] Among them, when determining the fault level coefficient, you can first query the historical operation data of the lightning arrester, which includes the status of the components and the poor condition. Then, determine the type of fault based on the status of the components and the poor condition (that is, its own quality reasons), and count the number of various types of faults that occurred due to its own quality reasons during the past operation.

[0081] Generally, the failures of the arrester due to its own quality during the past operation can be divided into the following four categories: minor failure, general failure, serious failure and emergency failure, and their failure base numbers are 1, 2, 4 and 6 respectively. The specific classification of arrester failure levels is shown in Table 5:

[0082] Table 5

[0083]

[0084] The method for calculating the arrester fault level coefficient is: Fault level coefficient = number of minor faults × general fault base + number of general faults × general fault base + number of serious faults × serious fault base + number of emergency faults × emergency fault base.

[0085] For example, the arrester has 3 minor faults, 2 general faults, 1 serious fault, and 0 emergency faults, and the fault level coefficient = 3×2+2×2+1×4+0×6=14.

[0086] When determining the health factor based on the arrester's fault level coefficient, the health factor can be determined based on a mapping relationship between the health factor and the arrester's fault level coefficient. The mapping relationship between the arrester's fault level coefficient and the health factor can be shown in Table 6:

[0087] Table 6

[0088]

[0089] For example, when the arrester failure level coefficient is 14, the health factor G is less than 0.25.

[0090] Step 103: Input the environmental coefficient influencing factor, the action state value, the current growth rate, the equipment utilization rate, and the health status factor into a pre-constructed arrester health status scoring model, and obtain the arrester health value output by the arrester health status scoring model.

[0091] The environmental coefficient influencing factor, action state value, current growth rate, equipment utilization rate and health status factors obtained through the above steps are input into the pre-constructed arrester health status score model to obtain the arrester health value K.

[0092] The pre-constructed arrester health score model is

[0093] For example, when the action state value A is 95, the fundamental wave growth rate of the resistive current I r % is 9%, the third harmonic growth rate is I r3% is 3.6%, the environmental coefficient impact factor ε is 1.20, the equipment utilization rate δ is 71.81%, and the health factor G is 0.2, the arrester health value output is as follows:

[0094]

[0095] Step 104: Perform health monitoring on the arrester based on the arrester health value and a preset health monitoring strategy.

[0096] In this step, as shown in Table 7, the health level of the arrester can be divided according to the health level score. Further, whether maintenance is required is determined based on the maintenance suggestion corresponding to the health level to achieve health monitoring of the arrester.

[0097] Table 7

[0098]

[0099] For example, when the health value of the lightning arrester is 38.9, the corresponding health level is 3, which is in a serious warning state and requires further repair and inspection as soon as possible.

[0100] In this embodiment, by obtaining the operating information of the lightning arrester, determining the environmental coefficient impact factor of the lightning arrester based on the working environment data, determining the action state value of the lightning arrester based on the lightning arrester action data, determining the current growth rate of the lightning arrester based on the lightning arrester electrical data, determining the equipment utilization rate of the lightning arrester based on the lightning arrester service data, and determining the health status factor of the lightning arrester based on the fault data, the environmental coefficient impact factor, the action state value, the current growth rate, the equipment utilization rate, and the health status factor are input into a pre-constructed lightning arrester health score model, obtaining the lightning arrester health value output by the lightning arrester health score model, and performing health monitoring on the lightning arrester based on the lightning arrester health value and the preset health monitoring strategy, the operating status of the lightning arrester can be accurately monitored, avoiding the huge manpower consumption of daily operation and maintenance, power outage maintenance, and fault handling caused by manual observation and on-site detection, thereby alleviating the economic pressure on the power system. At the same time, combining the multi-parameter monitoring data to construct an online lightning arrester status assessment model to comprehensively analyze the health status of the lightning arrester can effectively prevent the occurrence of accidents and achieve the effect of timely warning.

[0101] Example 2

[0102] See also Figure 4 , Figure 4 This is a schematic diagram of the structure of a lightning arrester health monitoring device provided in the second embodiment of the present application. The lightning arrester health monitoring device provided in the embodiment of the present application can execute the lightning arrester health monitoring method provided in any embodiment of the present application, and has the corresponding functional modules and beneficial effects of the execution method. The device can be implemented in software and / or hardware, such as Figure 4 As shown, the arrester health monitoring device specifically includes: a collection module 401 , a processing module 402 , a health value calculation module 403 , and a monitoring module 404 .

[0103] The acquisition module is used to obtain the operating information of the arrester, which includes the working environment data of the arrester, the action data of the arrester, the electrical data of the arrester, the service data of the arrester and the fault data.

[0104] A processing module is used to determine the environmental coefficient influencing factor of the lightning arrester based on the working environment data, determine the action state value of the lightning arrester based on the lightning arrester action data, determine the current growth rate of the lightning arrester based on the lightning arrester electrical data, determine the equipment utilization rate of the lightning arrester based on the lightning arrester service data, and determine the health status factor of the lightning arrester based on the fault data.

[0105] The health value calculation module is used to input the environmental coefficient influencing factor, action state value, current growth rate, equipment utilization rate and health status factor into the pre-constructed arrester health status score model, and obtain the arrester health value output by the arrester health status score model.

[0106] The monitoring module is used to perform health monitoring on the arrester based on the arrester health value and the preset health monitoring strategy.

[0107] In this embodiment, by obtaining the operating information of the lightning arrester, determining the environmental coefficient impact factor of the lightning arrester based on the working environment data, determining the action state value of the lightning arrester based on the lightning arrester action data, determining the current growth rate of the lightning arrester based on the lightning arrester electrical data, determining the equipment utilization rate of the lightning arrester based on the lightning arrester service data, and determining the health status factor of the lightning arrester based on the fault data, the environmental coefficient impact factor, the action state value, the current growth rate, the equipment utilization rate, and the health status factor are input into a pre-constructed lightning arrester health score model, obtaining the lightning arrester health value output by the lightning arrester health score model, and performing health monitoring on the lightning arrester based on the lightning arrester health value and the preset health monitoring strategy, the operating status of the lightning arrester can be accurately monitored, avoiding the huge manpower consumption of daily operation and maintenance, power outage maintenance, and fault handling caused by manual observation and on-site detection, thereby alleviating the economic pressure on the power system. At the same time, combining the multi-parameter monitoring data to construct an online lightning arrester status assessment model to comprehensively analyze the health status of the lightning arrester can effectively prevent the occurrence of accidents and achieve the effect of timely warning.

[0108] Example 3

[0109] Figure 5This is a structural diagram of a lightning arrester health monitoring system provided in Example 3 of this application. The lightning arrester health monitoring device provided in this embodiment of the application can execute the lightning arrester health monitoring method provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects of the execution method. The device can be implemented in software and / or hardware, such as Figure 5 As shown in the figure, the arrester health monitoring device specifically includes: a multi-parameter acquisition module, a status assessment module, and a human-computer interaction module.

[0110] The multi-parameter acquisition module is used to collect the operating information of the lightning arrester, which includes the working environment data of the lightning arrester, the lightning arrester action data, the lightning arrester electrical data, the lightning arrester service data and the fault data.

[0111] The status assessment module is communicatively connected to the multi-parameter acquisition module and is used to determine the health value of the arrester based on the arrester's working environment data, arrester action data, arrester electrical data, arrester service data, and fault data.

[0112] The human-computer interaction module is respectively connected to the multi-parameter acquisition module and the status assessment module, and is used to upload the arrester's working environment data, arrester action data, arrester electrical data, arrester service data, fault data and arrester health value to the cloud, and display the operating information and arrester health value.

[0113] Furthermore, the multi-parameter acquisition module includes equipment basic data acquisition equipment, voltage and current monitoring equipment, action number monitoring equipment, external environment sensing equipment and Beidou navigation and positioning equipment.

[0114] The status assessment module includes a health status judgment unit, a self-diagnosis unit and a fault alarm unit.

[0115] The human-computer interaction module includes signal display equipment, intelligent duplex equipment and wireless communication equipment.

[0116] The multi-parameter acquisition module is used to obtain the operating information of the lightning arrester, which includes the working environment data of the lightning arrester, the lightning arrester action data, the lightning arrester electrical data, the lightning arrester service data and the fault data.

[0117] The status assessment module is used to determine the health status value of the lightning arrester based on the acquired lightning arrester operation information, and perform health monitoring on the lightning arrester based on the lightning arrester health value and the preset health monitoring strategy.

[0118] Among them, the self-diagnostic unit is also used to check the status of the monitoring device itself, including self-detection of whether the power is sufficient before installation, self-detection of the power level during use, and heartbeat packet detection of whether the device itself is normal.

[0119] The human-computer interaction module includes a signal display module, an intelligent duplex module, and a wireless communication module. The signal display module includes a power indicator, a communication indicator, a positioning signal light, and a health status indicator. The power indicator is used to display the working status of the working power supply, the communication indicator is used to indicate whether communication with the IoT platform has been established, the positioning signal light is used to indicate whether the navigation and positioning module is normally enabled, and the health status indicator displays different colors based on the health level. When the health level is 0, the status display light is green, when the health level is 1, the status display light is yellow, when the health level is 2, the status display light is orange, and when the health level is 3, the status display light is red.

[0120] Intelligent duplex equipment is used to remotely set parameters such as thresholds, warning values, alarm values, and communication times in each unit.

[0121] Wireless communication equipment includes three communication modes: wireless ad hoc network, full network data transmission, and radio frequency communication transmission. It can upload various terminal data to the cloud platform in real time.

[0122] It should be noted that the specific installation structure of the arrester health monitoring system can be found in Figure 6 , Figure 6 This is a schematic diagram of the installation structure of the lightning arrester health monitoring system provided in Example 3 of the present application.

[0123] like Figure 6 As shown, the arrester health monitoring system further includes an arrester bracket 2 , a main housing 6 , a secondary housing 3 , a lifting bracket 8 and a solar power supply module 9 .

[0124] The arrester bracket is used to rotate and fix the arrester 1 , and the sub-housing is used to set a multi-parameter acquisition module. The arrester is connected to the multi-parameter acquisition module through a connecting device 4 .

[0125] The main housing is used to set the status assessment module, the human-computer interaction module and the power supply components of the solar power supply module.

[0126] The solar panels of the solar power supply module are arranged on the lifting bracket.

[0127] The main shell and the auxiliary shell are fixed by a fixing tube 5, and the connecting lines between the state assessment module, human-computer interaction module and solar power supply module in the main shell and the multi-parameter acquisition module in the auxiliary shell extend from the main shell into the auxiliary shell through the fixing tube.

[0128] The display panel 7 of the signal display device in the human-computer interaction module is arranged on the outer surface of the main housing.

[0129] Among them, the display panel can be a signal light, and the signal light includes a power indicator light, a communication indicator light, a positioning signal light, and a health status display light. Among them, the power indicator light is used to display the working status of the working power supply, the communication indicator light is used to display whether communication is established with the Internet of Things platform, the positioning signal light is used to display whether the navigation and positioning module is enabled normally, and the health status display light is used to display different colors according to the health level. When the health level is 0, the status display light is green, when the health level is 1, the status display light is yellow, when the health level is 2, the status display light is orange, and when the health level is 3, the status display light is red.

[0130] In addition, the specific structure of the lifting bracket can be found in Figure 7 , Figure 7 This is a structural schematic diagram of a telescopically adjustable lifting bracket provided in Example 3 of the present application.

[0131] Specifically, the lifting bracket is provided with a screw rod 81, a fixing plate 82, a fixing mounting hole 83 and a hinge 84. The lower end of the screw rod 81 is connected to the motor transmission device, and the height and angle of the solar panel can be controlled by controlling the up and down sliding of the screw rod 81. The upper end of the screw rod 81 is hinged to the bottom of the fixing plate 82 through a hinge 84. There are four fixing mounting holes 83 on the fixing plate 82 for fixed connection with the solar panel.

[0132] In addition, for the structure of the arrester bracket, please refer to Figure 8 , Figure 8 This is a front schematic diagram of a lightning arrester bracket provided in Example 3 of the present application.

[0133] Specifically, there are three mounting holes 21 evenly distributed on the edge of the lightning arrester bracket, and the bracket is fixedly installed above the detection device through the cooperation of screws and nuts. There is a spiral through-hole 22 in the center of the bracket 2, and the lightning arrester is screwed to the bracket through the spiral through-hole.

[0134] In this embodiment, by obtaining the operating information of the lightning arrester, determining the environmental coefficient impact factor of the lightning arrester based on the working environment data, determining the action state value of the lightning arrester based on the lightning arrester action data, determining the current growth rate of the lightning arrester based on the lightning arrester electrical data, determining the equipment utilization rate of the lightning arrester based on the lightning arrester service data, and determining the health status factor of the lightning arrester based on the fault data, the environmental coefficient impact factor, the action state value, the current growth rate, the equipment utilization rate, and the health status factor are input into a pre-constructed lightning arrester health score model, obtaining the lightning arrester health value output by the lightning arrester health score model, and performing health monitoring on the lightning arrester based on the lightning arrester health value and the preset health monitoring strategy, the operating status of the lightning arrester can be accurately monitored, avoiding the huge manpower consumption of daily operation and maintenance, power outage maintenance, and fault handling caused by manual observation and on-site detection, thereby alleviating the economic pressure on the power system. At the same time, combining the multi-parameter monitoring data to construct an online lightning arrester status assessment model to comprehensively analyze the health status of the lightning arrester can effectively prevent the occurrence of accidents and achieve the effect of timely warning.

[0135] It is worth noting that in the embodiment of the above-mentioned search device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application.

[0136] Note that the above are only preferred embodiments of the present application and the technical principles employed. Those skilled in the art will understand that the present application is not limited to the specific embodiments herein, and that various obvious changes, readjustments, and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. Therefore, although the present application has been described in more detail through the above embodiments, the present application is not limited to the above embodiments and may include many other equivalent embodiments without departing from the scope of the present application. The scope of the present application is determined by the scope of the appended claims.

Claims

1. A lightning arrester health monitoring method, characterized in that: The method comprises: Acquiring operating information of the arrester, the operating information including working environment data of the arrester, arrester action data, arrester electrical data, arrester service data, and fault data; determining an environmental coefficient influencing factor of the lightning arrester based on the working environment data, determining an action state value of the lightning arrester based on the lightning arrester action data, determining a current growth rate of the lightning arrester based on the lightning arrester electrical data, determining an equipment utilization rate of the lightning arrester based on the lightning arrester service data, and determining a health status factor of the lightning arrester based on the fault data; Inputting the environmental coefficient influencing factor, the action state value, the current growth rate, the equipment utilization rate, and the health condition factor into a pre-constructed arrester health condition scoring model, and obtaining the arrester health value output by the arrester health condition scoring model; Performing health monitoring on the arrester based on the arrester health value and a preset health monitoring strategy; The arrester action data includes an action time corresponding to each action of the arrester; and determining the action state value of the arrester based on the arrester action data includes: Based on the action moment, counting the number of actions of the arrester within a preset unit time period, and determining a target rising rate of the number of actions of the arrester according to the number of actions and the length of the preset unit time period; Based on a preset mapping relationship between the rise rate and the action state value, a target action state value corresponding to the target rise rate is determined, and the target action state value is determined as the action state value of the arrester.

2. The method according to claim 1, characterized in that The arrester electrical data includes the resistive current, third harmonic current trend data and voltage level of the arrester; Determining the current growth rate of the lightning arrester based on the lightning arrester electrical data includes: Determine the target growth rate correction coefficient corresponding to the voltage level in the arrester electrical data according to a preset mapping relationship between the voltage level and the growth rate correction coefficient; Obtaining an initial value of a resistive fundamental wave, and determining a resistive current fundamental wave growth rate based on the initial value of the resistive fundamental wave, the resistive current, and the target growth rate correction coefficient; Obtaining a third harmonic initial value, and determining a third harmonic growth rate based on the third harmonic initial value, the third harmonic current, and the target growth rate correction coefficient; The fundamental wave growth rate of the resistive current and the third harmonic growth rate are determined as the current growth rate of the arrester.

3. The method according to claim 1, characterized in that The pre-constructed arrester health score model is Among them, ε is the environmental coefficient influence factor, A is the action state value, I r % and I r3 % is the current growth rate, δ is the equipment utilization rate, and G is the health status factor.

4. A lightning arrester health monitoring device, characterized in that: The device comprises: An acquisition module is used to obtain operating information of the arrester, wherein the operating information includes working environment data of the arrester, arrester action data, arrester electrical data, arrester service data and fault data; a processing module, configured to determine an environmental coefficient influencing factor of the lightning arrester based on the working environment data, determine an action state value of the lightning arrester based on the lightning arrester action data, determine a current growth rate of the lightning arrester based on the lightning arrester electrical data, determine an equipment utilization rate of the lightning arrester based on the lightning arrester service data, and determine a health status factor of the lightning arrester based on the fault data; a health value calculation module, configured to input the environmental coefficient influencing factor, the action state value, the current growth rate, the equipment utilization rate, and the health condition factor into a pre-constructed arrester health condition scoring model, and obtain the arrester health value output by the arrester health condition scoring model; A monitoring module, configured to perform health monitoring on the arrester based on the arrester health value and a preset health monitoring strategy; The arrester action data includes the action time corresponding to each action of the arrester; the processing module is specifically used to: Based on the action moment, counting the number of actions of the arrester within a preset unit time period, and determining a target rising rate of the number of actions of the arrester according to the number of actions and the length of the preset unit time period; Based on a preset mapping relationship between the rise rate and the action state value, a target action state value corresponding to the target rise rate is determined, and the target action state value is determined as the action state value of the arrester.

5. A lightning arrester health monitoring system, characterized in that: The system includes: a multi-parameter acquisition module, a state assessment module and a human-computer interaction module; The multi-parameter acquisition module is used to collect operating information of the arrester, wherein the operating information includes working environment data of the arrester, arrester action data, arrester electrical data, arrester service data and fault data; The state assessment module is communicatively connected with the multi-parameter acquisition module, and is used to determine the health value of the arrester according to the working environment data, action data, electrical data, service data and fault data of the arrester; when determining the health value, the environmental coefficient influencing factor of the arrester is determined based on the working environment data, the action state value of the arrester is determined based on the action data, the current growth rate of the arrester is determined based on the electrical data, the equipment utilization rate of the arrester is determined based on the service data and the health condition factor of the arrester is determined based on the fault data; the environmental coefficient influencing factor, the action state value, the current growth rate, the equipment utilization rate and the fault data are combined into a single matrix. The health condition factor is input into a pre-constructed arrester health condition score model, and the arrester health value output by the arrester health condition score model is obtained; the arrester action data includes an action time corresponding to each time the arrester takes action; the action state value of the arrester is determined based on the arrester action data, including: based on the action time, counting the number of actions of the arrester in a preset unit time period, and determining a target increase rate of the number of arrester actions according to the number of actions and the length of the preset unit time period; based on a mapping relationship between a preset increase rate and an action state value, determining a target action state value corresponding to the target increase rate, and determining the target action state value as the action state value of the arrester; The human-computer interaction module is respectively communicated with the multi-parameter acquisition module and the status assessment module, and is used to upload the working environment data, lightning arrester action data, lightning arrester electrical data, lightning arrester service data, fault data and the lightning arrester health value of the lightning arrester to the cloud, and display the operating information and the lightning arrester health value.

6. The system according to claim 5, characterized in that The multi-parameter acquisition module includes equipment basic data acquisition equipment, voltage and current monitoring equipment, action number monitoring equipment, external environment sensing equipment and Beidou navigation and positioning equipment.

7. The system according to claim 6, characterized in that The status assessment module includes a health status judgment unit, a self-diagnosis unit and a fault alarm unit.

8. The system according to claim 7, characterized in that The human-computer interaction module includes a signal display device, an intelligent duplex device and a wireless communication device.

9. The system according to claim 8, characterized in that The system also includes a lightning arrester bracket, a main housing, a secondary housing, a lifting bracket and a solar power supply module; The arrester bracket is used to rotate and fix the arrester, the secondary housing is used to set the multi-parameter acquisition module, and the arrester is connected to the multi-parameter acquisition module through a connecting device; The main housing is used to set the status assessment module, the human-computer interaction module and the power supply component of the solar power supply module; The solar panels of the solar power supply module are arranged on the lifting bracket; The main housing and the auxiliary housing are fixed by a fixing tube, and the connection lines between the state assessment module, the human-computer interaction module, and the solar power supply module in the main housing and the multi-parameter acquisition module in the auxiliary housing extend from the main housing into the auxiliary housing through the fixing tube; The display panel of the signal display device in the human-computer interaction module is arranged on the outer surface of the main housing.

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