Wireless temperature measurement risk assessment method and system for power equipment

Abstract

The invention discloses a wireless temperature measurement risk assessment method and system for power equipment. The method comprises the following steps: acquiring an actually measured temperature value of each wireless temperature measurement sampling point in a station; compensating the actually measured temperature value of the wireless temperature measurement sampling point according to the environment temperature and humidity in the electrical cabinet where the wireless temperature measurement sampling point is located and the states of the electrical switch and the mechanical moving part so as to obtain a compensation temperature value T; and acquiring a safe temperature section set corresponding to the equipment type and the physical position of the wireless temperature measurement sampling point, and determining a corresponding risk level according to a safe temperature section to which the compensation temperature value T belongs in the safe temperature section set. The invention aims to solve the problem that the actually measured temperature value detection accuracy is insufficient due to the fact that the threshold value is complex and non-uniform and the environment temperature and humidity in an electrical cabinet and the states of an electrical switch and a mechanical moving part affect the heat radiation distribution condition in the electrical cabinet in a traditional wireless temperature measurement risk assessment method. And the accuracy of temperature detection and risk assessment of the power equipment is improved.

Description

Technical Field

[0001] This invention relates to the field of wireless temperature measurement of power equipment, and specifically to a method and system for risk assessment of wireless temperature measurement of power equipment. Background Technology

[0002] Abnormal temperature changes are often a precursor to power system accidents. Wireless temperature monitoring systems are widely used in power system monitoring systems. Therefore, timely temperature risk assessment of power equipment is of great significance for the safe operation of the power system. In the daily operation and maintenance of various power equipment such as distribution transformers, switchgear, busbars, and feeder joints, wireless temperature measurement and risk assessment based on temperature are commonly used. For example, common practices include: deploying passive / energy-self-collecting temperature sampling points or close-range read / write tags at heat-generating locations and periodically reading the temperature; maintaining a threshold table for each equipment or site and using the threshold method to determine whether the power equipment has malfunctioned; and displaying the status of the power equipment in the form of lists or dashboards at the display layer, with manual verification for anomalies to be explained or confirmed. However, the existing wireless temperature measurement risk assessment methods for power equipment mainly have the following technical problems: (1) There are many types of power equipment in the power system, and the wireless temperature sampling points are numerous and widespread. Different equipment has different danger thresholds. The traditional judgment method is inefficient and lacks the uniformity of assessment. There is a lack of a real-time, simple and uniform risk assessment method; (2) In the existing methods, the measured temperature values ​​of each wireless temperature sampling point often lack effective compensation or only have simple temperature compensation. The ambient temperature and humidity, electrical switches and mechanical moving parts in the electrical cabinet where the wireless temperature sampling point is located will affect the heat radiation distribution in the electrical cabinet, resulting in insufficient detection accuracy of the measured temperature values ​​of each wireless temperature sampling point, and it is difficult to accurately obtain the actual temperature of the power equipment. Summary of the Invention

[0003] The technical problem this invention aims to solve is as follows: Addressing the aforementioned problems in existing technologies, this invention provides a wireless temperature measurement risk assessment method and system for power equipment. This invention aims to resolve the issues of complex detection and low accuracy caused by the diverse types of power equipment in power systems, the numerous and widely distributed wireless temperature sampling points, and the varying danger thresholds for different equipment under traditional wireless temperature measurement risk assessment methods. Furthermore, it addresses the problem of insufficient accuracy in detecting the measured temperature values ​​of each wireless temperature sampling point due to the influence of ambient temperature and humidity, and the state of electrical switches and moving mechanical parts within the electrical cabinet where the wireless temperature sampling points are located on heat radiation distribution within the cabinet. This invention aims to improve the accuracy of temperature detection and risk assessment for power equipment.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for wireless temperature measurement risk assessment of power equipment includes the following steps: S101, acquire the measured temperature values ​​of each wireless temperature sampling point in the station; S102, based on the ambient temperature and humidity inside the electrical cabinet where the wireless temperature sampling point is located, and the status of electrical switches and mechanical moving parts, the measured temperature value of the wireless temperature sampling point is compensated to obtain the compensated temperature value T; S103, obtain the set of safe temperature zones corresponding to the device type and physical location of the wireless temperature measurement sampling point, and determine the corresponding risk level by the safe temperature zone to which the compensation temperature value T belongs in the set of safe temperature zones.

[0005] Optionally, when obtaining the measured temperature values ​​of each wireless temperature measurement sampling point in the station in step S101, the data records collected by each wireless temperature measurement sampling point include the collection time, equipment type, physical location and measured temperature value.

[0006] Optionally, after step S101 and before step S102, three types of abnormal data processing are performed: duplicate records, missing fields, and abnormal readings. Duplicate record processing includes retaining the first data record that arrives and deleting the others if duplicate data records exist for the four fields of acquisition time, device type, physical location, and measured temperature value. Missing field processing involves reading a preset database of wireless temperature sampling points to complete the device type and physical location fields if the device type or physical location of the data record is empty. If the acquisition time is empty, it is determined whether the time difference between the wireless temperature sampling point's network entry time and access time is less than a preset threshold. If it is less than the preset threshold, the network entry time or access time of the wireless temperature sampling point is written into the missing acquisition time. If the acquisition time cannot be completed, the data record is deleted. Abnormal reading processing includes deleting data records where the measured temperature value changes beyond the limit or the measured temperature value exceeds the limit. The change in the measured temperature value is the difference or percentage change between the measured temperature values ​​of two consecutive data records from the same wireless temperature sampling point.

[0007] Optionally, the compensation for the measured temperature value of the wireless temperature sampling point in step S102 to obtain the compensated temperature value T includes: S201, acquire the ambient temperature and humidity, and the status of electrical switches and mechanical moving parts inside the electrical cabinet where the wireless temperature measurement sampling point is located; the status of the electrical switches and mechanical moving parts is 0 or 1, which respectively represent two different states of the electrical switches and mechanical moving parts inside the electrical cabinet; S202, by querying a preset temperature compensation table based on the ambient temperature and humidity, and the status of electrical switches and mechanical moving parts inside the electrical cabinet where the wireless temperature measurement sampling point is located, the temperature compensation value ΔT for the ambient temperature and humidity, and the status of electrical switches and mechanical moving parts inside the electrical cabinet is obtained; the preset temperature compensation table has a pre-established correspondence between the ambient temperature and humidity, the status of electrical switches and mechanical moving parts inside the electrical cabinet where the wireless temperature measurement sampling point is located, and the corresponding temperature compensation value ΔT; the status of electrical switches and mechanical moving parts is 0 or 1, which respectively represent two different states of electrical switches and mechanical moving parts inside the electrical cabinet; S203, the compensation temperature value T is calculated according to T=T0+ΔT, where T0 is the measured temperature value.

[0008] Optionally, the compensation for the measured temperature value of the wireless temperature sampling point in step S102 to obtain the compensated temperature value T includes: S301, acquire the ambient temperature and humidity, and the status of electrical switches and mechanical moving parts inside the electrical cabinet where the wireless temperature measurement sampling point is located. The ambient temperature and humidity are normalized to between 0 and 1. The status of the electrical switches and mechanical moving parts is 0 or 1, which respectively represent two different states of the electrical switches and mechanical moving parts inside the electrical cabinet. S302, the ambient temperature and humidity, and the states of electrical switches and mechanical moving parts inside the electrical cabinet where the wireless temperature measurement sampling point is located are concatenated to obtain an input vector. The input vector is then input into a pre-trained fully connected neural network to obtain the temperature compensation value ΔT for the ambient temperature and humidity, and the states of electrical switches and mechanical moving parts inside the electrical cabinet. The fully connected neural network consists of an input layer, a hidden layer, and an output layer. The input layer includes multiple neurons to input the state information in the input vector respectively, and the output layer includes one neuron to output the temperature compensation value ΔT. The fully connected neural network ML is pre-trained to establish the correspondence between the input vectors corresponding to the ambient temperature and humidity, and the states of electrical switches and mechanical moving parts inside the electrical cabinet where the wireless temperature measurement sampling point is located, and their corresponding temperature compensation values ​​ΔT. S303, the compensation temperature value T is calculated according to T=T0+ΔT, where T0 is the measured temperature value.

[0009] Optionally, when obtaining the set of safe temperature ranges corresponding to the device type and physical location of the wireless temperature measurement sampling points in step S103, the set of safe temperature ranges corresponding to the device type and physical location of the wireless temperature measurement sampling points includes the lowest dividing point T, which increases sequentially. l First intermediate dividing point T m1 Second intermediate dividing point T m2 and the highest dividing point T h Five safe temperature zones are formed by four temperature boundary points, where the compensation temperature value T satisfies T≤Tl When the risk level assessment result of the power equipment is "safe", and the compensation temperature value T meets the requirements of T... l <T≤T m1 At that time, the risk level assessment result of the power equipment is "low risk"; when the compensation temperature value T meets T m1 <T≤T m2 At that time, the risk level assessment result of the power equipment was "medium risk"; when the compensation temperature value T meets T m2 <T≤T h At that time, the risk level assessment result of the power equipment was "medium to high risk"; when the compensation temperature value T satisfies T>T h At that time, the risk level assessment result of the power equipment was "high risk".

[0010] Optionally, after step S103, the method further includes determining the safety score value Y of the power equipment based on the safe temperature range to which the compensated temperature value T belongs in the set of safe temperature ranges. S401, Obtain four safe temperature scoring segment boundary points that correspond one-to-one with the four temperature boundary points. The four safe temperature scoring segment boundary points include the lowest score Y. l First median score Y m1 Second median score Y m2 And the highest rating Y h The lowest dividing point T l The corresponding setting is the lowest score Y. l The first intermediate dividing point T m1 The corresponding setting is the first intermediate score Y. m1 The second intermediate dividing point T m2 The corresponding setting is the second intermediate score Y. m2 The highest dividing point T h The corresponding setting is the highest score Y. h ; S402, determine the safety score value Y of the power equipment based on the safe temperature range to which the compensated temperature value T belongs in the set of safe temperature ranges: if the current temperature value T is lower than the lowest threshold T l Then the safety score Y is uniformly set to the lowest score Y. l If the current temperature value T is located at the lowest dividing point T l and the first intermediate dividing point T m1 Between, located at the first intermediate dividing point T m1 Second intermediate dividing point T m2 Between, or located at the second intermediate dividing point T m2 and the highest dividing point T h Between these values, the safety score Y is processed using linear interpolation; if the current temperature value T is higher than the highest threshold T... hThe safety score Y is uniformly set to the highest score Y. h The current temperature value T is located at the lowest dividing point T. l and the first intermediate dividing point T m1 When the time interval is between 1 and 2, the corresponding safety score value Y is: ; In the above formula, T l <T≤T m1 ; The current temperature value T is located at the first intermediate dividing point T. m1 Second intermediate dividing point T m2 When the time interval is between 1 and 2, the corresponding safety score value Y is: ; In the above formula, T m1 <T≤T m2 ; The current temperature value T is located at the second intermediate dividing point T. m1 and the highest dividing point T h When the time interval is between 1 and 2, the corresponding safety score value Y is: ; In the above formula, T m2 <T≤T h .

[0011] The present invention also provides a wireless temperature measurement risk assessment system for power equipment, including a microprocessor and a memory interconnected thereto, the microprocessor being programmed or configured to execute the wireless temperature measurement risk assessment method for power equipment.

[0012] The present invention also provides a computer-readable storage medium storing a computer program or instructions that are programmed or configured to execute the wireless temperature measurement risk assessment method for power equipment via a processor.

[0013] The present invention also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the wireless temperature measurement risk assessment method for power equipment via a processor.

[0014] Compared with existing technologies, the present invention mainly achieves the following beneficial effects: 1. The present invention includes compensating the measured temperature value of the wireless temperature measurement sampling point based on the ambient temperature and humidity, and the state of electrical switches and mechanical moving parts within the electrical cabinet where the wireless temperature measurement sampling point is located, to obtain a compensated temperature value T. This solves the problem in traditional methods where the ambient temperature and humidity, and the state of electrical switches and mechanical moving parts within the electrical cabinet affect the heat radiation distribution within the electrical cabinet, leading to insufficient accuracy in the measured temperature value detection of each wireless temperature measurement sampling point. This improves the accuracy of temperature detection and risk assessment of power equipment. 2. The present invention does not include obtaining the set of safe temperature zones corresponding to the device type and physical location of the wireless temperature measurement sampling point. Instead, it determines the corresponding risk level by assigning the compensated temperature value T to the safe temperature zone within the set of safe temperature zones. By binding the set of safe temperature zones with the device type and physical location of the wireless temperature measurement sampling point, it solves the problems of complex detection and low accuracy caused by the diverse types of power equipment, numerous and widely distributed wireless temperature sampling points, and different danger thresholds for different devices in the power system under traditional wireless temperature measurement risk assessment methods. This improves the accuracy of temperature detection and risk assessment of power equipment. 3. This invention is applicable to various types of power equipment in power stations and has the advantage of good versatility. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the basic process of the method in an embodiment of the present invention.

[0016] Figure 2 This is a schematic diagram illustrating the process of determining the corresponding risk level in an embodiment of the present invention. Detailed Implementation

[0017] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings in the embodiments of the present invention.

[0018] like Figure 1 As shown, the wireless temperature measurement risk assessment method for power equipment in this embodiment includes the following steps: S101, acquire the measured temperature values ​​of each wireless temperature sampling point in the station; S102, based on the ambient temperature and humidity inside the electrical cabinet where the wireless temperature sampling point is located, and the status of electrical switches and mechanical moving parts, the measured temperature value of the wireless temperature sampling point is compensated to obtain the compensated temperature value T; S103, obtain the set of safe temperature zones corresponding to the device type and physical location of the wireless temperature measurement sampling point, and determine the corresponding risk level by the safe temperature zone to which the compensation temperature value T belongs in the set of safe temperature zones.

[0019] In this embodiment, when obtaining the measured temperature values ​​of each wireless temperature measurement sampling point in the station in step S101, the data records collected by each wireless temperature measurement sampling point include the collection time, device type, physical location, and measured temperature value, which can be represented as: rec={time,dt,location,T}; In this data set, rec represents a single data record, time represents the actual acquisition time, dt represents the device type, location represents the physical location, and T represents the measured temperature value. The physical location is represented by longitude and latitude.

[0020] Considering the harsh environment of wireless temperature measurement for power equipment, problems such as duplicate recordings, missing fields, and abnormal readings are prone to occur. To address these issues and improve the accuracy and effectiveness of wireless temperature measurement risk assessment for power equipment, this embodiment includes three types of abnormal data processing after step S101 and before step S102: ① Duplicate recording processing includes handling duplicate data records in the four fields of acquisition time, device type, physical location, and measured temperature value, retaining the first arriving record and deleting the rest; ② Missing field processing includes, if the device type or physical location of a data record is empty, reading from a preset database of wireless temperature measurement sampling points to complete the device type and physical location fields; if the acquisition time is... If the interval is empty, it is determined whether the time difference between the network entry time and access time of the wireless temperature measurement sampling point is less than a preset threshold. If it is less than the preset threshold, the network entry time or access time of the wireless temperature measurement sampling point is written into the missing collection time. If the collection time cannot be filled in, the data record is deleted. Through the above method, the integrity of the data collected under the circumstances of wireless temperature measurement sampling point restart or power-on can be solved, so as to accurately collect the temperature anomaly problem during the initialization of power equipment; ③ Abnormal data processing of reading abnormalities includes deleting data records where the change range of the measured temperature value exceeds the limit or the measured temperature value exceeds the limit. The change range of the measured temperature value is the difference or change ratio between the measured temperature values ​​of two data records before and after the same wireless temperature measurement sampling point.

[0021] To address the problem of insufficient accuracy in measuring temperature values ​​at various wireless temperature sampling points due to the influence of ambient temperature and humidity, the state of electrical switches and moving mechanical parts within the electrical cabinet on heat radiation distribution within the cabinet, and the influence of these factors on traditional methods, and to improve the accuracy of temperature detection and risk assessment of power equipment, as an optional implementation method, step S102 of this embodiment involves compensating the measured temperature values ​​of the wireless temperature sampling points to obtain a compensated temperature value T. This includes: S201, acquire the ambient temperature and humidity, and the status of electrical switches and mechanical moving parts inside the electrical cabinet where the wireless temperature measurement sampling point is located; the status of the electrical switches and mechanical moving parts is 0 or 1, representing two different states of the electrical switches and mechanical moving parts inside the electrical cabinet; the electrical cabinet can be a high-voltage, medium-voltage, or low-voltage electrical cabinet, and common electrical switches include circuit breakers, disconnect switches, contactors, thermal relays, fuses, push-button switches, changeover switches, relays, and surge protectors, etc., and common mechanical moving parts include cabinet doors, partitions, operating handles, interlocking mechanisms, door locks, hinges, drawer-type unit slide rails, mechanical indicators, spring energy storage mechanisms, and fan brackets, etc. S202, by querying a preset temperature compensation table based on the ambient temperature and humidity, and the status of electrical switches and mechanical moving parts inside the electrical cabinet where the wireless temperature measurement sampling point is located, the temperature compensation value ΔT for the ambient temperature and humidity, and the status of electrical switches and mechanical moving parts inside the electrical cabinet is obtained; the preset temperature compensation table has a pre-established correspondence between the ambient temperature and humidity, the status of electrical switches and mechanical moving parts inside the electrical cabinet where the wireless temperature measurement sampling point is located, and the corresponding temperature compensation value ΔT; the status of electrical switches and mechanical moving parts is 0 or 1, which respectively represent two different states of electrical switches and mechanical moving parts inside the electrical cabinet; S203, the compensation temperature value T is calculated according to T=T0+ΔT, where T0 is the measured temperature value.

[0022] As another optional implementation, in step S102 of this embodiment, compensating the measured temperature value of the wireless temperature sampling point to obtain the compensated temperature value T includes: S301, acquire the ambient temperature and humidity, and the status of electrical switches and mechanical moving parts inside the electrical cabinet where the wireless temperature measurement sampling point is located. The ambient temperature and humidity are normalized to between 0 and 1. The status of the electrical switches and mechanical moving parts is 0 or 1, representing two different states of the electrical switches and mechanical moving parts inside the electrical cabinet. Similarly, the electrical cabinet can be a high-voltage, medium-voltage, or low-voltage electrical cabinet. Common electrical switches include circuit breakers, disconnect switches, contactors, thermal relays, fuses, push-button switches, changeover switches, relays, and surge protectors. Common mechanical moving parts include cabinet doors, partitions, operating handles, interlocking mechanisms, door locks, hinges, drawer-type unit slide rails, mechanical indicators, spring energy storage mechanisms, and fan brackets. S302, the ambient temperature and humidity, and the states of electrical switches and mechanical moving parts within the electrical cabinet where the wireless temperature measurement sampling point is located are concatenated to obtain an input vector. This input vector is then fed into a pre-trained fully connected neural network to obtain a temperature compensation value ΔT based on the ambient temperature and humidity, and the states of electrical switches and mechanical moving parts within the electrical cabinet. The fully connected neural network consists of an input layer, a hidden layer, and an output layer. The input layer includes multiple neurons to input the state information from the input vector, and the output layer includes one neuron to output the temperature compensation value ΔT. The fully connected neural network ML is pre-trained to establish the ambient temperature and humidity, and the states of electrical switches and mechanical moving parts within the electrical cabinet where the wireless temperature measurement sampling point is located. The system identifies the input vector corresponding to the state of the component and its corresponding temperature compensation value ΔT. Alternatively, other neural networks, such as multilayer perceptrons (MLPs), can be selected based on actual needs to achieve the same learning and reasoning of the aforementioned correspondence. Furthermore, a transmission temperature can be added to the wireless temperature sampling point based on the mechanical connection relationship between the electrical equipment within the electrical cabinet. This transmission temperature quantifies the temperature transmission between different electrical equipment's wireless temperature sampling points, and after normalization, serves as part of the input vector. This further enhances the correlation between the input vector corresponding to the ambient temperature and humidity, the state of electrical switches, and the mechanical moving parts within the electrical cabinet where the aforementioned wireless temperature sampling point is located, and their corresponding temperature compensation value ΔT. S303, the compensation temperature value T is calculated according to T=T0+ΔT, where T0 is the measured temperature value.

[0023] like Figure 2 As shown, in step S103 of this embodiment, when obtaining the set of safe temperature ranges corresponding to the device type and physical location of the wireless temperature sampling points, the set of safe temperature ranges corresponding to the device type and physical location of the wireless temperature sampling points includes the lowest dividing point T, which increases sequentially. l First intermediate dividing point T m1 Second intermediate dividing point T m2 and the highest dividing point T h Five safe temperature zones consisting of four temperature boundary points: When the compensation temperature value T satisfies T≤T l At that time, the risk level assessment result of the power equipment was "safe"; When the compensation temperature value T satisfies T l <T≤T m1 At that time, the risk level assessment result of the power equipment was "low risk"; When the compensation temperature value T satisfies T m1 <T≤T m2 At that time, the risk level assessment result of the power equipment was "medium risk"; When the compensation temperature value T satisfies T m2 <T≤Th At that time, the risk level assessment result of the power equipment was "medium to high risk"; When the compensation temperature value T satisfies T>T h At that time, the risk level assessment result of the power equipment was "high risk".

[0024] As an optional implementation, to further quantify the risk status of power equipment, this embodiment further includes, after step S103, determining the safety score value Y of the power equipment based on the safe temperature range to which the compensation temperature value T belongs in the set of safe temperature ranges: S401, Obtain four safe temperature scoring segment boundary points that correspond one-to-one with the four temperature boundary points. The four safe temperature scoring segment boundary points include the lowest score Y. l First median score Y m1 Second median score Y m2 And the highest rating Y h The lowest dividing point T l The corresponding setting is the lowest score Y. l The first intermediate dividing point T m1 The corresponding setting is the first intermediate score Y. m1 The second intermediate dividing point T m2 The corresponding setting is the second intermediate score Y. m2 The highest dividing point T h The corresponding setting is the highest score Y. h ; S402, determine the safety score value Y of the power equipment based on the safe temperature range to which the compensated temperature value T belongs in the set of safe temperature ranges: if the current temperature value T is lower than the lowest threshold T l Then the safety score Y is uniformly set to the lowest score Y. l If the current temperature value T is located at the lowest dividing point T l and the first intermediate dividing point T m1 Between, located at the first intermediate dividing point T m1 Second intermediate dividing point T m2 Between, or located at the second intermediate dividing point T m2 and the highest dividing point T h Between these values, the safety score Y is processed using linear interpolation; if the current temperature value T is higher than the highest threshold T... h The safety score Y is uniformly set to the highest score Y. h The current temperature value T is located at the lowest dividing point T. l and the first intermediate dividing point T m1 When the time interval is between 1 and 2, the corresponding safety score value Y is: ; In the above formula, Tl <T≤T m1 ; The current temperature value T is located at the first intermediate dividing point T. m1 Second intermediate dividing point T m2 When the time interval is between 1 and 2, the corresponding safety score value Y is: ; In the above formula, T m1 <T≤T m2 ; The current temperature value T is located at the second intermediate dividing point T. m1 and the highest dividing point T h When the time interval is between 1 and 2, the corresponding safety score value Y is: ; In the above formula, T m2 <T≤T h .

[0025] In this embodiment, the preset safe temperature range and corresponding risk score value of a certain type of transformer are shown in Table 1, and the preset safe temperature range and corresponding risk score value of a certain type of power distribution cable are shown in Table 2.

[0026] Table 1. Temperature thresholds and corresponding rating values ​​for a certain type of transformer.

[0027] For transformers, the rating is: 1) The risk score for a current temperature T below 90℃ is uniformly set to 60 points, and the safe temperature range information is "Safe"; 2) When the current temperature T is between 90℃ and 105℃, its corresponding safety score Y is:

[0028] The safe temperature range is classified as "low risk". 3) When the current temperature value T is between 105℃ and 130℃, the corresponding safety score value Y is:

[0029] The safe temperature range is classified as "medium risk". 4) When the current temperature value T is between 130℃ and 150℃, the corresponding safety score value Y is:

[0030] The safe temperature range is classified as "medium to high risk". 5) The risk score for a current temperature value T above 150℃ is uniformly set to 90 points, and the safe temperature range information is "high risk".

[0031] Table 2. Temperature thresholds and corresponding rating values ​​for a certain type of power distribution cable

[0032] For power distribution cables, the scoring rules are as follows: 1) The risk score for a current temperature T below 50℃ is uniformly set to 60 points, and the safe temperature range information is "Safe"; 2) When the current temperature T is between 50℃ and 70℃, its corresponding safety score Y is:

[0033] The safe temperature range is classified as "low risk". 3) When the current temperature value T is between 70℃ and 90℃, the corresponding safety score value Y is:

[0034] The safe temperature range is classified as "medium risk". 4) When the current temperature value T is between 90℃ and 100℃, the corresponding safety score value Y is:

[0035] The safe temperature range information is "medium to high risk"; 5) The risk score for the current temperature value T above 100℃ is uniformly set to 90 points, and the safe temperature range information is "high risk".

[0036] At the same time, sampling information from sampling point 1 and sampling point 2 was acquired, as follows: 1) Sampling point 1: The type of power equipment acquired is a transformer, the current temperature value is T=85℃, and the physical location information is transformer No. 3 in substation A; the temperature range is T. <T l If the safe temperature zone information is "safe", the risk score is 60 points; 2) Sampling point 2, the type of power equipment obtained is power distribution cable, the current temperature value is T=84℃, and the physical location information is section 1 of power distribution line in area B. The temperature range is T l <T<T m1 The safe temperature range is classified as "medium risk," and the corresponding safety score Y is: ; At this moment, the system displays: "Transformer No. 3 in Substation A - Temperature sampling value T=85℃ - Risk score 60 points - Safe temperature zone information: Safe"; "Distribution line section 1 in Area B - Temperature sampling value T=84℃ - Risk score 77 points - Safe temperature zone information: Medium risk". For different locations and different equipment, this embodiment's method can unify different temperature sampling values ​​into safe zones and obtain risk scores using a normalized scoring method. This allows for a direct and rapid assessment of the operational risks of different power equipment, reducing workload, improving efficiency, and ensuring the real-time nature of risk assessment.

[0037] This embodiment also provides a wireless temperature measurement risk assessment system for power equipment, including a microprocessor and a memory interconnected, wherein the microprocessor is programmed or configured to execute the wireless temperature measurement risk assessment method for power equipment.

[0038] This embodiment also provides a computer-readable storage medium storing a computer program or instructions that are programmed or configured to execute the wireless temperature measurement risk assessment method for power equipment via a processor.

[0039] This embodiment also provides a computer program product, including a computer program or instructions that are programmed or configured to execute the wireless temperature measurement risk assessment method for power equipment via a processor.

[0040] Those skilled in the art will understand that the technical solutions provided by this invention may take the form of a method, system, or computer program product. Therefore, this invention may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this invention may take the form of a computer program product embodied on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, produce an implementation of the flowchart... Figure 1 One or more processes and / or boxes Figure 1The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0041] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for wireless temperature measurement risk assessment of power equipment, characterized in that, Includes the following steps: S101, acquire the measured temperature values ​​of each wireless temperature sampling point in the station; S102, based on the ambient temperature and humidity inside the electrical cabinet where the wireless temperature sampling point is located, and the status of electrical switches and mechanical moving parts, the measured temperature value of the wireless temperature sampling point is compensated to obtain the compensated temperature value T; S103, obtain the set of safe temperature zones corresponding to the device type and physical location of the wireless temperature measurement sampling point, and determine the corresponding risk level by the safe temperature zone to which the compensation temperature value T belongs in the set of safe temperature zones.

2. The wireless temperature measurement risk assessment method for power equipment according to claim 1, characterized in that, When obtaining the measured temperature values ​​of each wireless temperature measurement sampling point in the station in step S101, the data records collected by each wireless temperature measurement sampling point include the collection time, equipment type, physical location and measured temperature value.

3. The wireless temperature measurement risk assessment method for power equipment according to claim 2, characterized in that, After step S101 and before step S102, three types of abnormal data processing are performed: duplicate records, missing fields, and abnormal readings. The abnormal data processing for duplicate records includes retaining the first data record that is duplicated in the four fields of acquisition time, device type, physical location, and measured temperature value, and deleting the rest. The abnormal data processing for missing fields includes reading the preset database of wireless temperature measurement sampling points to complete the fields of device type and physical location for the data record if the device type or physical location of the data record is empty. If the acquisition time is empty, it is determined whether the time difference between the network entry time and access time of the wireless temperature measurement sampling point is less than a preset threshold. If it is less than the preset threshold, the network entry time or access time of the wireless temperature measurement sampling point is written into the missing acquisition time. If the acquisition time cannot be filled in, the data record is deleted. Abnormal data processing for abnormal readings includes deleting data records where the variation range of the measured temperature value exceeds the limit or the measured temperature value exceeds the limit. The variation range of the measured temperature value is the difference or change ratio between the measured temperature values ​​of two data records before and after the same wireless temperature measurement sampling point.

4. The wireless temperature measurement risk assessment method for power equipment according to claim 1, characterized in that, Step S102, which compensates for the measured temperature value of the wireless temperature sampling point to obtain the compensated temperature value T, includes: S201, acquire the ambient temperature and humidity, and the status of electrical switches and mechanical moving parts inside the electrical cabinet where the wireless temperature measurement sampling point is located; the status of the electrical switches and mechanical moving parts is 0 or 1, which respectively represent two different states of the electrical switches and mechanical moving parts inside the electrical cabinet; S202, by querying a preset temperature compensation table based on the ambient temperature and humidity, and the status of electrical switches and mechanical moving parts inside the electrical cabinet where the wireless temperature measurement sampling point is located, the temperature compensation value ΔT for the ambient temperature and humidity, and the status of electrical switches and mechanical moving parts inside the electrical cabinet is obtained; the preset temperature compensation table has a pre-established correspondence between the ambient temperature and humidity, the status of electrical switches and mechanical moving parts inside the electrical cabinet where the wireless temperature measurement sampling point is located, and the corresponding temperature compensation value ΔT; the status of electrical switches and mechanical moving parts is 0 or 1, which respectively represent two different states of electrical switches and mechanical moving parts inside the electrical cabinet; S203, the compensation temperature value T is obtained by calculating T=T0+ΔT, where T0 is the measured temperature value.

5. The wireless temperature measurement risk assessment method for power equipment according to claim 1, characterized in that, Step S102, which compensates for the measured temperature value of the wireless temperature sampling point to obtain the compensated temperature value T, includes: S301, acquire the ambient temperature and humidity, and the status of electrical switches and mechanical moving parts inside the electrical cabinet where the wireless temperature measurement sampling point is located. The ambient temperature and humidity are normalized to between 0 and 1. The status of the electrical switches and mechanical moving parts is 0 or 1, which respectively represent two different states of the electrical switches and mechanical moving parts inside the electrical cabinet. S302, the ambient temperature and humidity, and the states of electrical switches and mechanical moving parts inside the electrical cabinet where the wireless temperature measurement sampling point is located are concatenated to obtain an input vector. The input vector is then input into a pre-trained fully connected neural network to obtain the temperature compensation value ΔT for the ambient temperature and humidity, and the states of electrical switches and mechanical moving parts inside the electrical cabinet. The fully connected neural network consists of an input layer, a hidden layer, and an output layer. The input layer includes multiple neurons to input the state information in the input vector respectively, and the output layer includes one neuron to output the temperature compensation value ΔT. The fully connected neural network ML is pre-trained to establish the correspondence between the input vectors corresponding to the ambient temperature and humidity, and the states of electrical switches and mechanical moving parts inside the electrical cabinet where the wireless temperature measurement sampling point is located, and their corresponding temperature compensation values ​​ΔT. S303, the compensation temperature value T is calculated according to T=T0+ΔT, where T0 is the measured temperature value.

6. The wireless temperature measurement risk assessment method for power equipment according to claim 1, characterized in that, In step S103, when obtaining the set of safe temperature ranges corresponding to the device type and physical location of the wireless temperature measurement sampling points, the set of safe temperature ranges corresponding to the device type and physical location of the wireless temperature measurement sampling points includes the lowest dividing point T, which increases sequentially. l First intermediate dividing point T m1 Second intermediate dividing point T m2 and the highest dividing point T h Five safe temperature zones are formed by four temperature boundary points, where the compensation temperature value T satisfies T≤T l When the risk level assessment result of the power equipment is "safe", and the compensation temperature value T meets the requirements of T... l <T≤T m1 At that time, the risk level assessment result of the power equipment is "low risk"; when the compensation temperature value T meets T m1 <T≤T m2 At that time, the risk level assessment result of the power equipment was "medium risk"; when the compensation temperature value T meets T m2 <T≤T h At that time, the risk level assessment result of the power equipment was "medium to high risk"; when the compensation temperature value T satisfies T>T h At that time, the risk level assessment result of the power equipment was "high risk".

7. The wireless temperature measurement risk assessment method for power equipment according to claim 6, characterized in that, Step S103 is followed by determining the safety score value Y of the power equipment based on the safe temperature range to which the compensation temperature value T belongs in the set of safe temperature ranges. S401, Obtain four safe temperature scoring segment boundary points that correspond one-to-one with the four temperature boundary points. The four safe temperature scoring segment boundary points include the lowest score Y. l First median score Y m1 Second median score Y m2 And the highest rating Y h The lowest dividing point T l The corresponding setting is the lowest score Y. l The first intermediate dividing point T m1 The corresponding setting is the first intermediate score Y. m1 The second intermediate dividing point T m2 The corresponding setting is the second intermediate score Y. m2 The highest dividing point T h The corresponding setting is the highest score Y. h ; S402, determine the safety score value Y of the power equipment based on the safe temperature range to which the compensated temperature value T belongs in the set of safe temperature ranges: if the current temperature value T is lower than the lowest threshold T l Then the safety score Y is uniformly set to the lowest score Y. l If the current temperature value T is located at the lowest dividing point T l and the first intermediate dividing point T m1 Between, located at the first intermediate dividing point T m1 Second intermediate dividing point T m2 Between, or located at the second intermediate dividing point T m2 and the highest dividing point T h Between these values, the safety score Y is processed using linear interpolation; if the current temperature value T is higher than the highest threshold T... h The safety score Y is uniformly set to the highest score Y. h The current temperature value T is located at the lowest dividing point T. l and the first intermediate dividing point T m1 When the time interval is between 1 and 2, the corresponding safety score value Y is: ； In the above formula, T l <T≤T m1 ; The current temperature value T is located at the first intermediate dividing point T. m1 Second intermediate dividing point T m2 When the time interval is between 1 and 2, the corresponding safety score value Y is: ； In the above formula, T m1 <T≤T m2 ; The current temperature value T is located at the second intermediate dividing point T. m1 and the highest dividing point T h When the time interval is between 1 and 2, the corresponding safety score value Y is: ； In the above formula, T m2 <T≤T h .

8. A wireless temperature measurement risk assessment system for power equipment, comprising a microprocessor and a memory interconnected, characterized in that, The microprocessor is programmed or configured to perform the wireless temperature measurement risk assessment method for power equipment as described in any one of claims 1 to 7.

9. A computer-readable storage medium storing a computer program or instructions, characterized in that, The computer program or instructions are programmed or configured to execute, via a processor, the wireless temperature measurement risk assessment method for power equipment as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program or instructions, characterized in that, The computer program or instructions are programmed or configured to execute, via a processor, the wireless temperature measurement risk assessment method for power equipment as described in any one of claims 1 to 7.

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