Method, system, device and storage medium for intelligently detecting abnormal busbar temperature

Abstract

The present application provides a method, a system, a device and a storage medium for intelligently detecting abnormal busbar temperatures, relating to the field of monitoring technologies. The method includes: calculating the difference between the temperature parameters of each busbar to be monitored in the power system and the preset parameters, determining, according to the magnitude of the difference, the first busbar to be monitored in an abnormal state and the first abnormal index of the first busbar to be monitored; adjusting the first abnormal index according to the difference between the temperature parameters of the second busbar to be monitored and the preset parameters to generate a second abnormal index, where the second busbar to be monitored is adjacent to the first busbar to be monitored; adjusting the second abnormal index in combination with the historical monitoring data and current data of the first busbar to be monitored to generate a target abnormal index; if the target abnormal index exceeds the abnormal threshold, determining that there is a fault risk for the target busbar and sending an alarm message. The technical effect of the present application is: improving the accuracy of abnormal busbar temperature monitoring.

Description

Technical Field

[0001] This application relates to the field of monitoring technologies, and particularly to a method, system, device, and storage medium for intelligently detecting abnormal busbar temperatures. Background Art

[0002] A busbar is an important device for power transmission and distribution in a power system, and its stable operation is crucial for the safety and reliability of the entire power grid. However, due to the long-term exposure of the busbar to high temperatures and high currents, it is extremely prone to faults such as abnormal temperatures and even overheating damage, seriously threatening the safe operation of the power system. Therefore, there is an urgent practical need to timely and accurately monitor abnormal busbar temperatures.

[0003] In the prior art, a common method for monitoring abnormal busbar temperatures is to set temperature sensors at the busbar to collect busbar temperature data in real time. When the monitored temperature exceeds a preset threshold, it is determined as abnormal and a warning is issued. This method is simple to implement but difficult to comprehensively reflect the true state of the busbar temperature. Due to the uneven temperature distribution along the busbar and the influence of various factors, the temperature data of a single measurement point is insufficiently representative, thus affecting the accuracy of abnormal busbar temperature monitoring. Summary of the Invention

[0004] This application provides a method, system, device, and storage medium for intelligently detecting abnormal busbar temperatures, which is used to improve the accuracy of abnormal busbar temperature monitoring.

[0005] In a first aspect, this application provides a method for intelligently detecting abnormal busbar temperatures. The method includes: dividing a target busbar in a power system into multiple busbars to be monitored, and obtaining the temperature parameters of each busbar to be monitored; calculating the difference between the temperature parameter of each busbar to be monitored and a preset parameter, and determining, according to the magnitude of the difference, a first busbar to be monitored in an abnormal state and a first abnormal index of the first busbar to be monitored, where the difference between the temperature parameter of the first busbar to be monitored and the preset parameter is greater than a preset difference; adjusting the first abnormal index according to the difference between the temperature parameter of a second busbar to be monitored and the preset parameter to generate a second abnormal index, where the second busbar to be monitored is adjacent to the first busbar to be monitored; obtaining the historical monitoring data and current data of the first busbar to be monitored, and adjusting the second abnormal index in combination with the historical monitoring data and the current data to generate a target abnormal index; if the target abnormal index exceeds an abnormal threshold, determining that the target busbar has a fault risk and sending an alarm message.

[0006] By adopting the above technical solution, the target busbar is divided into multiple busbars to be monitored, and the temperature parameters of each segment are obtained, realizing the comprehensive monitoring of the busbar temperature distribution; by introducing the temperature influence of adjacent busbar segments to dynamically adjust the abnormal index, the technical defect that the traditional single-point temperature measurement method is difficult to reflect the overall temperature state is overcome; by combining historical monitoring data and current data to optimize and adjust the abnormal index, the interference influence of external factors such as load changes is effectively eliminated, and the accuracy of busbar temperature abnormal monitoring is improved.

[0007] Optionally, the determining the first abnormal index of the first busbar to be monitored according to the magnitude of the difference includes: substituting the difference into a preset formula to determine the first abnormal index of the first busbar to be monitored; wherein, the preset formula is: In the formula, I1 is the first abnormal index, α is the abnormal proportional coefficient, ΔT1 is the difference between the temperature parameter of the first busbar to be monitored and the preset parameter, and T is the preset difference.

[0008] By adopting the above technical solution, by calculating the first abnormal index using the above preset formula, the ratio of the temperature difference value of the first busbar to be monitored to the preset difference is combined with the abnormal proportional coefficient for quantitative calculation, realizing the precise quantification of the temperature abnormal degree; by introducing the abnormal proportional coefficient to calibrate the calculation result, the calculation result of the abnormal index is made more in line with the actual operation condition, improving the accuracy and scientificity of the temperature abnormal assessment.

[0009] Optionally, the adjusting the first abnormal index according to the difference between the temperature parameter of the second busbar to be monitored and the preset parameter to generate a second abnormal index includes: substituting the difference between the temperature parameter of the second busbar to be monitored and the preset parameter into a preset adjustment formula to obtain an adjustment coefficient; arithmetically multiplying the adjustment coefficient by the first abnormal index to obtain the second abnormal index.

[0010] By adopting the above technical solution, by introducing an adjustment coefficient to correct the first abnormal index and incorporating the temperature abnormal state of adjacent busbar segments into the evaluation scope, the correlation analysis of temperature abnormalities is realized. Using the arithmetic product of the adjustment coefficient and the first abnormal index as the second abnormal index not only retains the temperature abnormal characteristics of the first busbar to be monitored but also reflects the influence of the temperature state of adjacent busbar segments, making the calculation result of the abnormal index more comprehensive and accurate and effectively avoiding the problem of misjudgment of a single monitoring point.

[0011] Optionally, the substituting the difference between the temperature parameter of the second busbar to be monitored and the preset parameter into a preset adjustment formula to obtain an adjustment coefficient includes: substituting the difference between the temperature parameter of the second busbar to be monitored and the preset parameter into a preset adjustment formula to obtain an adjustment coefficient; wherein, the preset adjustment formula is: Wherein, β is the adjustment coefficient, γ is the influence coefficient, ΔT2 is the difference between the temperature parameter of the second bus to be monitored and the preset parameter, and T is the preset difference.

[0012] By adopting the above technical solution, the adjustment coefficient is calculated by using the preset adjustment formula, and the ratio of the temperature parameter difference of the second bus to be monitored to the preset difference is combined with the influence coefficient for quantification, so that the reference value of the adjustment coefficient is 1. When the temperature of the adjacent bus section is abnormal, the adjustment coefficient increases accordingly; by introducing the influence coefficient to perform weighted adjustment on the influence degree of the temperature abnormality of the adjacent bus section, both the sensitivity of the adjustment is ensured and the excessive influence of the temperature fluctuation of the adjacent bus section on the abnormality index is avoided, thereby realizing the accurate assessment of the bus temperature abnormality.

[0013] Optionally, the adjusting the second abnormality index by combining the historical monitoring data and the current data to generate a target abnormality index includes: combining the current data and the historical monitoring data to predict the current change trend of the first bus to be monitored; generating a target adjustment coefficient according to the current change trend; and arithmetically multiplying the target adjustment coefficient by the second abnormality index to generate a target abnormality index.

[0014] By adopting the above technical solution, the current change trend of the first bus to be monitored is predicted by combining the historical monitoring data and the current data, and the target adjustment coefficient is generated accordingly, realizing the dynamic compensation of the influence of the load change; the second abnormality index is corrected by using the target adjustment coefficient, which not only considers the spatial correlation of the temperature abnormality but also incorporates the influence of the load change on the temperature, so that the finally generated target abnormality index can more accurately reflect the actual operating state of the bus, effectively avoiding the false alarm problem caused by the normal fluctuation of the load.

[0015] Optionally, the generating a target adjustment coefficient according to the current change trend includes: when the current change trend is an upward trend and the change amplitude is greater than the first preset upper limit, generating a first target coefficient greater than 1 as the target adjustment coefficient, wherein the first target coefficient is positively correlated with the change amplitude of the upward trend; when the current change trend is a downward trend and the change amplitude is greater than the second preset upper limit, generating a second target coefficient less than 1 as the target adjustment coefficient, wherein the second target coefficient is negatively correlated with the change amplitude of the downward trend; when the change amplitude of the current in the current change trend is within the range of the first preset upper limit or the second preset upper limit, setting the target adjustment coefficient to the preset reference value.

[0016] By adopting the above technical solution, a dynamic adjustment mechanism for the target adjustment coefficient is established through hierarchical judgment and differential processing of the current change trend: when the current shows a significant upward trend, a first target coefficient greater than 1 is used for amplification adjustment to avoid misjudging the temperature rise caused by the normal increase of the load as a fault; when the current shows a significant downward trend, a second target coefficient less than 1 is used for attenuation adjustment to prevent the abnormal temperature from being masked by the load decrease; when the current change range is within the normal range, the preset reference value is kept unchanged, ensuring the stability of the abnormal index. This adaptive adjustment method based on the current change characteristics effectively improves the adaptability of the temperature anomaly monitoring to the load change.

[0017] Optionally, after determining that the target bus has a fault risk and sending an alarm message if the target abnormal index exceeds the abnormal threshold, it further includes: monitoring the change trend of the target abnormal index; when the change rate of the target abnormal index is greater than the preset rate, raising the alarm level, and sending alarm messages to the terminal devices of different levels of management personnel according to different alarm levels.

[0018] By adopting the above technical solution, through dynamically monitoring the change trend of the target abnormal index and adaptively adjusting the alarm level according to its change rate, hierarchical early warning of the bus temperature anomaly is realized; by adopting a differentiated alarm strategy and accurately pushing alarm messages of different levels to the terminal devices of corresponding levels of management personnel, it not only ensures the timely transmission of alarm messages but also avoids the flooding of alarm messages, effectively improving the early warning effect and fault handling efficiency of the bus temperature anomaly, and providing a more reliable guarantee for the safe operation of the power system.

[0019] In a second aspect, the present application provides a system for intelligently detecting abnormal busbar temperatures. The system includes: an acquisition module, a calculation module, a first adjustment module, a second adjustment module, and an output module. Among them, the acquisition module is configured to divide a target busbar in a power system into multiple busbars to be monitored, and acquire the temperature parameters of each of the busbars to be monitored. The calculation module is configured to calculate the difference between the temperature parameter of each busbar to be monitored and a preset parameter, and determine a first busbar to be monitored in an abnormal state and a first abnormal index of the first busbar to be monitored according to the magnitude of the difference, where the difference between the temperature parameter of the first busbar to be monitored and the preset parameter is greater than a preset difference. The first adjustment module is configured to adjust the first abnormal index according to the difference between the temperature parameter of a second busbar to be monitored and the preset parameter to generate a second abnormal index, where the second busbar to be monitored is adjacent to the first busbar to be monitored. The second adjustment module is configured to acquire the historical monitoring data and current data of the first busbar to be monitored, and adjust the second abnormal index in combination with the historical monitoring data and the current data to generate a target abnormal index. The output module is configured to determine that there is a fault risk for the target busbar and send an alarm message if the target abnormal index exceeds an abnormal threshold.

[0020] In a third aspect, the present application provides an electronic device, adopting the following technical solution: including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes a computer program of any one of the above methods for intelligently detecting abnormal busbar temperatures.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium, adopting the following technical solution: storing a computer program that can be loaded and executed by a processor for any one of the above methods for intelligently detecting abnormal busbar temperatures.

[0022] In summary, the present application includes at least one of the following beneficial technical effects:

[0023] 1. Dividing the target busbar into multiple busbars to be monitored and acquiring the temperature parameters of each segment realizes the comprehensive monitoring of the busbar temperature distribution; dynamically adjusting the abnormal index by introducing the temperature influence of adjacent busbar segments overcomes the technical defect that traditional single-point temperature measurement methods are difficult to reflect the overall temperature state; optimizing and adjusting the abnormal index in combination with historical monitoring data and current data effectively eliminates the interference effects of external factors such as load changes and improves the accuracy of busbar temperature abnormal monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a flowchart of a method for intelligently detecting abnormal busbar temperatures provided by an embodiment of the present application;

[0025] Figure 2 It is a schematic structural diagram of a system for intelligently detecting abnormal busbar temperature provided by an embodiment of the present application;

[0026] Figure 3 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

[0027] Description of reference numerals: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. Detailed implementation manners

[0028] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments.

[0029] In the description of the embodiments of the present application, words such as "exemplary", "for example" or "for illustration" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary", "for example" or "for illustration" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary", "for example" or "for illustration" is intended to present related concepts in a specific manner.

[0030] The method for intelligently detecting abnormal busbar temperature provided in this application can be applied to the scenario of monitoring the temperature of busbars at 35 kV and above in a substation. In actual application, the monitoring system collects temperature data through temperature sensors installed at different positions of the busbar, and divides it into several monitoring units according to the layout structure of the busbar. Taking a 500 kV substation as an example, the phase A busbar is divided into 12 monitoring units, and each monitoring unit is equipped with 2 temperature sensors. The system collects the temperature data of each monitoring unit every 5 minutes and compares it with the preset normal operating temperature parameters. When it is detected that the temperature of the 3rd monitoring unit exceeds the preset parameters, the system immediately calculates its first abnormal index, and at the same time conducts correlation analysis by combining the temperature data of the adjacent 2nd and 4th monitoring units, and corrects the abnormal index through an adjustment coefficient. In addition, the system also retrieves the temperature change records and current data of the 3rd monitoring unit in the past 24 hours, analyzes the load change trend, and further optimizes the abnormal index. If the final target abnormal index exceeds the set threshold, the system will send an alarm message to the operation and maintenance personnel. When the system detects that the abnormal index is rising rapidly, it will promptly raise the alarm level, and the relevant alarm messages will be synchronously pushed to the mobile terminals of different-level management personnel such as the on-duty dispatcher and the equipment supervisor, ensuring that the relevant personnel can take corresponding measures in a timely manner and effectively prevent the occurrence of busbar faults.

[0031] Figure 1 It is a flowchart showing a method for intelligently detecting abnormal busbar temperature provided by an embodiment of this application. As Figure 1 shown, the method includes S101 - S105:

[0032] S101, divide the target busbar in the power system into multiple busbars to be monitored, and obtain the temperature parameters of each busbar to be monitored.

[0033] In this embodiment, since the busbar in the power system is usually long, if the temperature is monitored only at a single position, it is easy to miss local overheating points and cannot accurately reflect the temperature distribution of the entire busbar. Therefore, it is necessary to divide the target busbar into multiple busbars to be monitored for segmented monitoring.

[0034] During specific implementation, according to the physical length, structural characteristics, and installation location of the busbar, a target busbar can be evenly divided into several monitored busbars with equal lengths. Taking the phase A busbar of a 500 kV substation as an example, the 24-meter target busbar can be divided into 12 monitored busbars, with each monitored busbar having a length of 2 meters. Two temperature sensors are installed on each monitored busbar, and the installation positions of the two temperature sensors are respectively at the head and tail ends of the monitored busbar for collecting the temperature parameters of the monitored busbar. The temperature sensors adopt fiber optic temperature measurement devices with high measurement accuracy, capable of achieving a temperature measurement accuracy of ±0.5 °C. The system collects the temperature parameters of each monitored busbar every 5 minutes through the data acquisition unit and transmits the collected temperature data to the monitoring center in real time.

[0035] This division method can not only comprehensively understand the temperature distribution of the target busbar, but also quickly locate the abnormally heated position through the comparison of the temperature parameters of adjacent monitored busbars, providing accurate data support for subsequent abnormality judgment and early warning. At the same time, by setting two temperature measurement points on each monitored busbar, it can effectively avoid misjudgment that may be caused by single-point temperature measurement and improve the reliability of temperature monitoring.

[0036] S102. Calculate the difference between the temperature parameter of each monitored busbar and the preset parameter. According to the size of the difference, determine the first monitored busbar in an abnormal state and the first abnormal index of the first monitored busbar. The difference between the temperature parameter of the first monitored busbar and the preset parameter is greater than the preset difference.

[0037] In this embodiment, to accurately judge whether there is an abnormal situation in the busbar temperature, it is necessary to compare and analyze the real-time collected temperature parameters with the preset normal operating temperature.

[0038] During specific implementation, taking the phase A busbar of a 500 kV substation as an example, according to the normal operating temperature requirements of the busbar stipulated by the electric power industry standard, considering local environmental temperature and busbar current-carrying capacity and other factors, the preset parameter is set to 65 °C. When the system detects that the temperature parameter of any monitored busbar exceeds the preset parameter, the difference between its temperature parameter and the preset parameter is calculated. If the difference between the temperature parameter of a monitored busbar and the preset parameter is greater than the preset difference of 10 °C, then this monitored busbar is determined as the first monitored busbar.

[0039] For example, when the temperature parameter of the 3rd monitored busbar is detected to be 78 °C, the difference between it and the preset parameter of 65 °C is 13 °C, exceeding the preset difference of 10 °C, then the 3rd monitored busbar is determined as the first monitored busbar. To quantify the degree of abnormality, the system uses a preset formula Calculate the first abnormal index, where α is taken as 1.2 (considering the sensitivity of the busbar heating at the 500 kV voltage level), ΔT1 is 13 °C (the difference between the temperature parameter of the first busbar to be monitored and the preset parameter), and T is 10 °C (the preset difference). Substitute the data into the calculation to obtain the first abnormal index I1 = 1.56. In this way, not only can the busbar segment with temperature abnormality be quickly identified, but also the severity of the abnormality can be intuitively reflected by the magnitude of the abnormal index, providing a quantitative basis for subsequent early warning decisions.

[0040] In this embodiment, the formula is set based on the following technical principles and practical considerations: First, select the ratio form as the basic calculation term, where ΔT1 represents the actual deviation value of the temperature exceeding the preset parameter, and T represents the allowed preset difference. This ratio calculation method can standardize the degree of temperature abnormality, enabling the abnormal index to intuitively reflect the degree of temperature deviation from the allowed range. For example, when it means that the temperature deviation just reaches the allowed preset difference; when the ratio is greater than 1, it means that the temperature deviation has exceeded the allowed range, and the larger the ratio, the more serious the deviation degree.

[0041] Secondly, the purpose of introducing the abnormal proportion coefficient α is to perform differential processing according to the characteristics of busbars at different voltage levels. In the 500 kV substation scenario, considering the characteristics of high operating voltage level and large current-carrying capacity of the busbar, its temperature abnormality may lead to more serious consequences. Therefore, α = 1.2 is set to appropriately amplify the abnormal index and improve the sensitivity of the system.

[0042] Specifically, when the temperature parameter is 78 °C, ΔT1 = 13 °C, and T = 10 °C, substituting into the formula for calculation gives I1 = 1.2×13 / 10 = 1.56. This calculation result not only reflects the degree of temperature overrun but also reflects the strict requirements for high-voltage busbar temperature monitoring through the adjustment of the α value.

[0043] When the temperature is close to the warning value (such as when ΔT1 is approximately equal to T), the abnormal index is close to the α value, providing sufficient warning time for the operation and maintenance personnel; when the temperature significantly exceeds the standard (such as when ΔT1 is much greater than T), the abnormal index will increase rapidly, prompting the system to respond quickly. Through actual operation verification, this formula can accurately reflect the abnormal state of the busbar temperature, neither causing frequent false alarms due to excessive sensitivity nor delaying the fault handling time due to sluggishness. At the same time, this calculation method has good scalability and can adapt to the monitoring requirements of different voltage levels and operating environments by adjusting the α value.

[0044] S103. Adjust the first abnormal index according to the difference between the temperature parameter of the second busbar to be monitored and the preset parameter to generate a second abnormal index, where the second busbar to be monitored is adjacent to the first busbar to be monitored.

[0045] In this embodiment, since the abnormal temperature of the power system bus usually has the characteristics of continuity and correlation, relying solely on the temperature anomaly index of a single bus section may not be able to fully reflect the actual fault state. Therefore, it is necessary to incorporate the temperature conditions of adjacent buses into the evaluation system.

[0046] Specifically, taking the No. 3 bus to be monitored (i.e., the first bus to be monitored) of a 500 kV substation as an example, after determining that its temperature is 78 °C (the difference ΔT1 from the preset parameter of 65 °C is 13 °C), the temperature data of the adjacent No. 2 and No. 4 buses to be monitored (i.e., the second buses to be monitored) are obtained simultaneously. The temperature of the No. 2 bus to be monitored is measured as 71 °C, and the temperature of the No. 4 bus to be monitored is 73 °C. Calculate their differences from the preset parameter of 65 °C as ΔT2-1 = 6 °C and ΔT2-2 = 8 °C respectively, and select the larger value ΔT2 = 8 °C as the adjustment basis. According to the temperature influence factor calculation formula β = 1 + γ(ΔT2) / T, where γ takes a value of 0.3 (the influence weight coefficient determined based on on-site operation experience), and T is the preset difference of 10 °C, calculate β = 1 + 0.3×(8 / 10) = 1.24. Multiply the first anomaly index I1 = 1.56 by the temperature influence factor β to obtain the second anomaly index I2 = 1.56×1.24 = 1.93.

[0047] This adjustment mechanism considering the influence of adjacent bus temperatures has significant advantages: when the temperature of the adjacent bus section also rises, the anomaly index is increased through the amplification effect of the β value, effectively reflecting the spread trend of the temperature anomaly; when the temperature of the adjacent bus section is normal, the β value is close to 1, maintaining the original anomaly index level and accurately reflecting the local characteristics of the anomaly. This adjustment method not only improves the accuracy of temperature anomaly assessment but also provides a reliable quantitative basis for maintenance personnel to judge the fault range and development trend.

[0048] The preset adjustment formula is: Among them, the base number 1 ensures the reference value of the temperature influence factor β. This setting makes β approach 1 when the temperature of the adjacent bus is normal (i.e., ΔT2 approaches 0), thus maintaining the original level of the first anomaly index. This conforms to the physical characteristics of temperature anomalies in actual operation and avoids unnecessary interference with the original anomaly index when the adjacent bus is normal.

[0049] Secondly, the correction term In, ΔT2 represents the actual deviation value of the temperature of the second bus to be monitored exceeding the preset parameter, and T is the preset difference. The ratio of the two reflects the relative degree of temperature anomaly of the adjacent bus. When When it is [a certain value], it indicates that the temperature deviation between adjacent busbars reaches the preset difference; when this ratio is less than 1, it indicates that the degree of abnormal temperature of adjacent busbars is relatively low; when this ratio is greater than 1, it indicates that serious temperature anomalies have occurred in adjacent busbars. This ratio calculation method realizes the standardized expression of the degree of temperature anomaly.

[0050] The purpose of introducing the influence weight coefficient γ is to reasonably control the influence degree of the temperature of adjacent busbars on the anomaly index. The value of this coefficient needs to balance two aspects: on the one hand, it cannot be too large to avoid drastic changes in the anomaly index caused by temperature fluctuations of adjacent busbars; on the other hand, it should maintain sufficient sensitivity to reflect the spatial correlation of temperature anomalies. The value of γ is determined through a large amount of on-site data statistics and operation experience analysis.

[0051] For example, when the temperature of the second busbar to be monitored is detected to be 73°C, ΔT2 = 8°C, and T = 10°C, substituting into the formula gives β = 1 + 0.3×(8 / 10) = 1.24. This result indicates that the temperature anomaly of adjacent busbars increases the original anomaly index by 24%, and this increase neither overstates the adjacent influence nor fails to timely reflect the expansion trend of temperature anomalies.

[0052] Through actual operation verification, this formula setting not only maintains the continuity and stability of the anomaly index calculation but also realizes the accurate quantification of the spatial distribution characteristics of temperature anomalies. When temperature anomalies occur in adjacent busbars, the moderate increase in the β value prompts the operation and maintenance personnel to pay attention to the risk of anomaly expansion; when the temperatures of adjacent busbars are normal, the β value is close to 1, maintaining the original anomaly level, which helps to accurately judge the fault range. The rationality of this calculation method is also reflected in its good scalability. By adjusting the γ value, it can adapt to the monitoring requirements under different voltage levels and operating environments.

[0053] S104, Obtain the historical monitoring data and current data of the first busbar to be monitored, and adjust the second anomaly index in combination with the historical monitoring data and current data to generate a target anomaly index.

[0054] The reason for adjusting the second anomaly index is that the second anomaly index only reflects the temperature anomaly state of the busbar at the current moment, and it is also necessary to combine historical monitoring data and current data for trend prediction and adjustment, so as to realize the dynamic early warning of busbar temperature anomalies.

[0055] Specifically, the development of busbar temperature anomalies is often related to the change trend of load current. When it is predicted that the future load current will continue to rise, even if the current degree of temperature anomaly is not high, the early warning level needs to be increased because the increase in load may exacerbate the development of temperature anomalies. For example, the current temperature anomaly of a certain busbar is not serious, but if the historical data shows that the load current continues to rise and it is predicted that it will still rise in the future, then the anomaly index needs to be increased through an adjustment coefficient to timely warn of the possible deterioration of temperature anomalies.

[0056] Through this dynamic adjustment mechanism based on historical data analysis and trend prediction, the abnormal index can not only reflect the current state but also predict the possible future development trend, thus improving the warning effect of bus temperature monitoring.

[0057] During specific implementation, taking the No. 3 bus to be monitored (i.e., the first bus to be monitored) in a 500 kV substation as an example, first obtain its historical temperature monitoring data and current data for the past 24 hours, and use time series analysis method to conduct trend analysis on the current data. It is found through calculation that the current of this bus has shown a continuous upward trend in the past 6 hours, gradually rising from 1800 A to the current 2100 A, and it is predicted according to the load curve that the current will continue to rise to 2300 A within the next 4 hours.

[0058] Based on the prediction result of the current upward trend, determine the calculation method of the target adjustment coefficient σ: when the predicted current shows an upward trend, take σ = 1.2, indicating that the warning level of the abnormal index needs to be increased; when the predicted current shows a downward trend, take σ = 0.8, indicating that the warning level of the abnormal index can be appropriately reduced; when the predicted current remains stable, take σ = 1.0, keeping the abnormal index unchanged.

[0059] In this example, if the predicted current shows an upward trend, take σ = 1.2, multiply the target adjustment coefficient σ by the second abnormal index I2 to obtain the target abnormal index I = I2 × σ = 1.93 × 1.2 = 2.32. This dynamic adjustment method based on the current change trend can reflect in advance the temperature anomaly risk that may be brought by the load change, and provide a decision-making basis for the operation and maintenance personnel to take preventive measures.

[0060] On the basis of the above embodiments, as an optional implementation manner, in S104, adjusting the second abnormal index by combining historical monitoring data and current data to generate the target abnormal index specifically includes S401 - S403:

[0061] S401, combining the current data and historical monitoring data to predict the current change trend of the first bus to be monitored.

[0062] In this embodiment, since the bus temperature anomaly is closely related to the change trend of the load current, it is necessary to predict the future current change trend through historical monitoring data.

[0063] The current data refers to the real-time operating current value of the first bus to be monitored, which is usually collected in real time by a current transformer. For example, the current operating current of the 3rd bus to be monitored in a 500 kV substation is 2100 A; the historical monitoring data includes two parts: one is the current values recorded at 10-minute intervals within the past n hours (where n can be set according to actual needs), such as the current values in the past 24 hours are 1800 A, 1850 A, 1900 A, 1950 A, 2000 A, 2100 A, etc. for the first 6 hours; the other is the temperature data collected during the same period, which records the temperature value of the bus at each time point.

[0064] In specific implementation, taking the 3rd bus to be monitored (i.e., the first bus to be monitored) in a 500 kV substation as an example, obtain its historical monitoring data for the past 24 hours, including temperature data and current data at 10-minute intervals. Through time series analysis of the current data, the sliding average method is used to calculate the current change rate within the past 6 hours. If the analysis result shows that the current of this bus gradually rises from 1800 A to the current 2100 A, with an average increase of 50 A per hour. Based on the historical load characteristics and daily cycle rules, combined with the operating characteristics of the current period, a linear regression model is used to predict the current change trend in the next 4 hours. The prediction result shows that under the condition of maintaining the current load growth rate, the bus current will continue to rise to about 2300 A.

[0065] This method of predicting the current trend based on historical data can provide a reliable basis for subsequent adjustment of the anomaly index and help to detect potential temperature anomaly risks in advance.

[0066] S402, generate a target adjustment coefficient according to the current change trend.

[0067] In this embodiment, in order to enable the anomaly index to reflect the potential impact of load changes on the bus temperature, it is necessary to generate a target adjustment coefficient σ according to the predicted current change trend. In specific implementation, based on the analysis result of the current data of the first bus to be monitored within the time period [t - 6h, t], the current rises from I(t - 6h) = 1800 A to I(t) = 2100 A, and it is predicted that the current will continue to rise to I(t + 4h) = 2300 A within the next 4 hours. Determine the current change trend according to the positive and negative values of the current change rate ΔI / Δt: when ΔI / Δt > 0, it indicates that the current is on the rise, and at this time, set the target adjustment coefficient σ = 1.2, because the increase in load will exacerbate the development risk of temperature anomalies; when ΔI / Δt < 0, it indicates that the current is on the decline, and at this time, set σ = 0.8, because the decrease in load helps to relieve temperature anomalies; when |ΔI / Δt| ≤ ε (ε is a preset change rate threshold), it indicates that the current is basically stable, and at this time, set σ = 1.0.

[0068] In this example, since ΔI / Δt = (2100 - 1800) / (6×3600) = 0.014 A / s > 0, it is determined that the current shows an upward trend, so the target adjustment coefficient σ = 1.2 is generated. This dynamic adjustment method based on the current change trend can enable the anomaly index to better reflect the potential risks brought by load changes and improve the early warning effect of temperature anomaly monitoring.

[0069] Based on the above embodiments, as an optional implementation manner, in S402, generating the target adjustment coefficient according to the current change trend specifically includes S4021 - S4023:

[0070] S4021, when the current change trend is an upward trend and the change amplitude is greater than the first preset upper limit, generate a first target coefficient greater than 1 as the target adjustment coefficient, where the first target coefficient is positively correlated with the change amplitude of the upward trend.

[0071] S4022, when the current change trend is a downward trend and the change amplitude is greater than the second preset upper limit, generate a second target coefficient less than 1 as the target adjustment coefficient, where the second target coefficient is negatively correlated with the change amplitude of the downward trend.

[0072] S4023, when the change amplitude of the current in the current change trend is within the range of the first preset upper limit or the second preset upper limit, set the target adjustment coefficient to the preset reference value.

[0073] In this embodiment, in order to enable the anomaly index to accurately reflect the potential impact of load changes on the bus temperature, a method for generating a target adjustment coefficient based on the current change trend is proposed.

[0074] Specifically in implementation, based on the current data analysis result of the first bus to be monitored within the time period [t - 6h, ], the current changes from I(t - 6h) = 1800 A to I(t) = 2100 A. First, calculate the current change amplitude |ΔI| = |I(t) - I(t - 6h)| / I(t - 6h)×100% = 16.7%, set the first preset upper limit α1 = 15% and the second preset upper limit α2 = 10%, and the preset reference value β = 1.0.

[0075] In S4021, when the current shows an upward trend (ΔI>0) and the change amplitude |ΔI|>α1, it indicates that a significant increase in the load will exacerbate the risk of temperature anomalies. At this time, the formula σ = 1 + k1×(|ΔI| - α1) is used to calculate the first target coefficient as the target adjustment coefficient, where k1 = 2 is a positive correlation coefficient. In this example, σ = 1 + 2×(16.7% - 15%) = 1.034; in S4022, when the current shows a downward trend (ΔI<0) and the change amplitude |ΔI|>α2, it indicates that a decrease in the load is beneficial to alleviating temperature anomalies. At this time, the formula σ = 1 - k2×(|ΔI| - α2) is used to calculate the second target coefficient as the target adjustment coefficient, where k2 = 1.5 is a negative correlation coefficient; in S4023, when the current change amplitude |ΔI|≤α1 (upward trend) or |ΔI|≤α2 (downward trend), it indicates that the load change is within the allowable range. At this time, the target adjustment coefficient σ is set to the preset reference value β = 1.0. This hierarchical adjustment method fully considers the influence of the direction and amplitude of current changes on temperature anomalies. Through the generation of dynamic adjustment coefficients, precise adjustment of the anomaly index is achieved, improving the accuracy and reliability of temperature anomaly monitoring.

[0076] S403, arithmetically multiply the target adjustment coefficient by the second anomaly index to generate the target anomaly index.

[0077] S105, if the target anomaly index exceeds the anomaly threshold, it is determined that there is a risk of failure in the target busbar and an alarm message is sent.

[0078] In this embodiment, in order to detect the risk of busbar failure in a timely manner and take preventive measures, a reasonable anomaly threshold needs to be set and an alarm needs to be issued. Specifically, when implementing, taking the No. 3 busbar to be monitored (i.e., the target busbar) in a 500 kV substation as an example, according to the operation regulations of power equipment and historical operation experience, the anomaly threshold is set to 2.0. When the calculated target anomaly index I = 2.32 is greater than the anomaly threshold 2.0, the system automatically determines that there is a risk of failure in the target busbar and immediately generates an alarm message containing key information such as the busbar number, location information, current temperature value, and target anomaly index, and pushes it to the mobile terminal of the operation and maintenance personnel through the substation monitoring system. This threshold-based alarm mechanism can notify relevant personnel in a timely manner before the busbar temperature anomaly develops to a dangerous level, winning precious time for fault prevention and handling.

[0079] After determining that there is a risk of failure in the target busbar and sending an alarm message if the target anomaly index exceeds the anomaly threshold, it further includes:

[0080] Monitoring the change trend of the target anomaly index; when the change rate of the target anomaly index is greater than the preset rate, the alarm level is increased, and alarm messages are sent to the terminal devices of different levels of management personnel according to different alarm levels.

[0081] In this embodiment, in order to timely discover the development trend of the busbar temperature anomaly and take corresponding preventive measures, after the target anomaly index H exceeds the anomaly threshold θ, it is necessary to continuously monitor the change trend of the target anomaly index.

[0082] In specific implementation, the change rate v = ΔH / Δt of the target abnormality index is calculated with a time interval Δt of 10 minutes, where ΔH is the difference between two adjacent target abnormality indices. For example, when the target abnormality index H1 = 1.2 exceeds the abnormal threshold θ = 1.0 at time t1, the alarm is triggered for the first time. The system determines the alarm as a level 3 alarm and sends an alarm message to the mobile terminal of the on-site duty personnel; 10 minutes later, at time t2, the target abnormality index H2 = 1.5 is measured, and the calculated change rate v = (1.5-1.2) / 600 = 0.0005 / s, which exceeds the preset rate threshold v0 = 0.0003 / s, indicating that the temperature anomaly is rapidly deteriorating. At this time, the system automatically upgrades the alarm level to level 2 alarm. In addition to sending alarm information to the on-site duty personnel, it also pushes alarm information to the terminal device of the substation operation and maintenance supervisor; if the target abnormality index H3=2.0 is measured at time t3 after another 10 minutes, and the change rate v=(2.0-1.5) / 600=0.00083 / s>v0 is calculated, the system will further upgrade the alarm level to level 1 alarm, and send alarm information to the terminal devices of the substation manager and the head of the operation and maintenance department.

[0083] This multi-level alarm mechanism based on the change rate of the target abnormality index can automatically adjust the alarm level according to the development trend of the temperature anomaly and notify the management personnel at the corresponding level, effectively improving the efficiency of handling fault risks and providing more reliable protection for the safe operation of equipment.

[0084] Based on the above method, the present application also discloses a system for intelligently measuring busbar temperature anomalies, such as Figure 2 As shown, Figure 2 : is a structural diagram of a system for intelligently measuring abnormal bus temperature provided by an embodiment of the present application, the system comprises: an acquisition module, a calculation module, a first adjustment module, a second adjustment module and an output module; wherein,

[0085] An acquisition module is used to divide a target busbar in a power system into multiple busbars to be monitored and acquire the temperature parameters of each busbar to be monitored; a calculation module is used to calculate the difference between the temperature parameter of each busbar to be monitored and a preset parameter, and determine a first busbar to be monitored in an abnormal state and a first abnormal index of the first busbar to be monitored according to the magnitude of the difference, and the difference between the temperature parameter of the first busbar to be monitored and the preset parameter is greater than a preset difference; a first adjustment module is used to adjust the first abnormal index according to the difference between the temperature parameter of a second busbar to be monitored and the preset parameter to generate a second abnormal index, and the second busbar to be monitored is adjacent to the first busbar to be monitored; a second adjustment module is used to acquire the historical monitoring data and current data of the first busbar to be monitored, and adjust the second abnormal index in combination with the historical monitoring data and current data to generate a target abnormal index; an output module is used to determine that there is a fault risk in the target busbar and send an alarm message if the target abnormal index exceeds an abnormal threshold.

[0086] It should be noted that when the device provided in the above embodiment realizes its functions, only the division of the above-mentioned functional modules is used for illustration. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be seen in the method embodiments, which will not be elaborated here.

[0087] Please refer to Figure 3 , which is a schematic structural diagram of an electronic device provided by an embodiment of the present application. As Figure 3 shown, the electronic device 1000 may include: at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, and at least one communication bus 1002.

[0088] Among them, the communication bus 1002 is used to realize the connection and communication between these components.

[0089] Among them, the user interface 1003 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 1003 may further include a standard wired interface and a wireless interface.

[0090] Among them, the network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0091] Among them, the processor 1001 may include one or more processing cores. The processor 1001 connects various parts within the entire server through various interfaces and lines, and executes various functions of the server and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and by calling the data stored in the memory 1005. Optionally, the processor 1001 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 1001 may integrate a combination of one or several of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 1001 and may be implemented separately by a single chip.

[0092] Among them, the memory 1005 may include random access memory (RAM) and may also include read-only memory. Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 1005 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store the data involved in the above-mentioned various method embodiments. Optionally, the memory 1005 may also be at least one storage device located far from the aforementioned processor 1001. As Figure 3 shown, the memory 1005, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a method of intelligently detecting abnormal busbar temperature.

[0093] In Figure 3In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an interface for the user to input and obtain the data input by the user; and the processor 1001 can be used to call the application program stored in the memory 1005 for a method of intelligently detecting abnormal busbar temperature. When executed by one or more processors, the electronic device is caused to execute one or more of the methods as described in the above embodiments.

[0094] An electronic device-readable storage medium stores instructions. When executed by one or more processors, the electronic device is caused to execute one or more of the methods as described in the above embodiments.

[0095] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0096] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0097] In several embodiments provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some service interfaces, and the indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0098] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0099] In addition, in each embodiment of the present application, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0100] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The aforementioned memory includes: various media such as USB flash drives, mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0101] The above are only exemplary embodiments of the present disclosure, and the scope of the present disclosure cannot be limited thereby. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. After considering the specification and practicing the present disclosure herein, those skilled in the art will readily think of other implementation manners of the present disclosure. The present application aims to cover any variations, uses, or adaptation changes of the present disclosure, and these variations, uses, or adaptation changes follow the general principles of the present disclosure and include the common general knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and the embodiments are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for intelligently detecting abnormal busbar temperature, characterized in that The method includes: Dividing a target busbar in a power system into multiple monitored busbars, and obtaining temperature parameters of each of the monitored busbars; Calculating the difference between the temperature parameter of each monitored busbar and a preset parameter, and determining a first monitored busbar in an abnormal state and a first abnormal index of the first monitored busbar according to the magnitude of the difference, where the difference between the temperature parameter of the first monitored busbar and the preset parameter is greater than a preset difference; Adjusting the first abnormal index according to the difference between the temperature parameter of a second monitored busbar and the preset parameter to generate a second abnormal index, where the second monitored busbar is adjacent to the first monitored busbar; Obtaining historical monitoring data and current data of the first monitored busbar, and adjusting the second abnormal index by combining the historical monitoring data and the current data to generate a target abnormal index; If the target abnormal index exceeds an abnormal threshold, it is determined that there is a fault risk in the target busbar and an alarm message is sent.

2. The method for intelligently detecting abnormal busbar temperature according to claim 1, wherein, The determining the first abnormal index of the first monitored busbar according to the magnitude of the difference includes: Substitute the difference into a preset formula to determine a first anomaly index of the first bus to be monitored; wherein, the preset formula is: In the formula, I1 is the first abnormal index, α is an abnormal proportionality coefficient, ΔT1 is the difference between the temperature parameter of the first monitored busbar and the preset parameter, and T is the preset difference.

3. The method for intelligently detecting abnormal busbar temperature according to claim 1, wherein The adjusting the first abnormal index according to the difference between the temperature parameter of the second monitored busbar and the preset parameter to generate a second abnormal index includes: Substituting the difference between the temperature parameter of the second monitored busbar and the preset parameter into a preset adjustment formula to obtain an adjustment coefficient; arithmetically multiplying the adjustment coefficient by the first abnormal index to obtain the second abnormal index.

4. The method for intelligently detecting abnormal busbar temperature according to claim 3, characterized in that, The substituting the difference between the temperature parameter of the second monitored busbar and the preset parameter into a preset adjustment formula to obtain an adjustment coefficient includes: Substituting the difference between the temperature parameter of the second monitored busbar and the preset parameter into a preset adjustment formula to obtain an adjustment coefficient; where The preset adjustment formula is as follows: In the formula, β is the adjustment coefficient, γ is the influence coefficient, ΔT2 is the difference between the temperature parameter of the second bus to be monitored and the preset parameter, and T is the preset difference value.

5. The method for intelligently detecting abnormal busbar temperature according to claim 1, characterized in that, The adjusting the second abnormal index by combining the historical monitoring data and the current data to generate a target abnormal index includes: predicting the current change trend of the first monitored busbar by combining the current data and the historical monitoring data; Generating a target adjustment coefficient according to the current change trend; Arithmetically multiplying the target adjustment coefficient by the second abnormal index to generate a target abnormal index.

6. The method for intelligently detecting abnormal busbar temperature according to claim 5, characterized in that, The generating a target adjustment coefficient according to the current change trend includes: When the current change trend is an upward trend and the change amplitude is greater than a first preset upper limit, generating a first target coefficient greater than 1 as the target adjustment coefficient, where the first target coefficient is positively correlated with the change amplitude of the upward trend; When the current change trend is a downward trend and the change amplitude is greater than a second preset upper limit, generating a second target coefficient less than 1 as the target adjustment coefficient, where the second target coefficient is negatively correlated with the change amplitude of the downward trend; When the change amplitude of the current in the current change trend is within the range of the first preset upper limit or the second preset upper limit, setting the target adjustment coefficient to a preset reference value.

7. The method for intelligently detecting abnormal busbar temperature according to claim 1, characterized in that, After determining that there is a risk of failure of the target busbar and sending an alarm message if the target abnormal index exceeds the abnormal threshold, the following steps are further included: Monitoring the change trend of the target abnormal index; When the change rate of the target abnormal index is greater than the preset rate, increasing the alarm level and sending alarm messages to the terminal devices of different levels of management personnel according to different alarm levels.

8. An intelligent system for detecting abnormal busbar temperature, characterized in that, The system includes: an acquisition module, a calculation module, a first adjustment module, a second adjustment module, and an output module; wherein, The acquisition module is used to divide the target busbar in the power system into multiple busbars to be monitored and acquire the temperature parameters of each busbar to be monitored; The calculation module is used to calculate the difference between the temperature parameter of each busbar to be monitored and the preset parameter, and determine the first busbar to be monitored in an abnormal state and the first abnormal index of the first busbar to be monitored according to the magnitude of the difference, where the difference between the temperature parameter of the first busbar to be monitored and the preset parameter is greater than the preset difference; The first adjustment module is used to adjust the first abnormal index according to the difference between the temperature parameter of the second busbar to be monitored and the preset parameter to generate a second abnormal index, where the second busbar to be monitored is adjacent to the first busbar to be monitored; the second adjustment module is used to acquire the historical monitoring data and current data of the first busbar to be monitored and adjust the second abnormal index in combination with the historical monitoring data and the current data to generate a target abnormal index; The output module is used to determine that there is a risk of failure of the target busbar and send an alarm message if the target abnormal index exceeds the abnormal threshold.

9. An electronic device, characterized in that, It includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions. The user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory so that the electronic device executes the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, A computer program is stored that can be loaded and executed by a processor to execute the method according to any one of claims 1-7.

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