Intelligent maintenance method, system and equipment for power equipment and storage medium

Abstract

The invention provides a power equipment intelligent maintenance method and system, equipment and a storage medium, and relates to the technical field of fault monitoring, and the method comprises the steps: obtaining the use duration and historical fault data of target power equipment, and the first operation data of the target power equipment in a preset duration; determining a safety index of the target power equipment in combination with the use duration and the historical fault data; determining a first abnormal index of the target power equipment according to the first operation data; acquiring second operation data of associated equipment of the target power equipment, and adjusting the first abnormal index according to the second operation data to generate a second abnormal index; and adjusting the second abnormal index through the safety index, generating a target abnormal index, and when the target abnormal index is greater than a preset threshold value, determining that the target power equipment has a fault risk, and triggering fault early warning. The technical effect of the invention is that the accuracy of power equipment fault early warning is improved.

Description

Technical Field

[0001] The present application relates to the technical field of fault monitoring, and in particular to an intelligent maintenance method, system, equipment and storage medium for power equipment. Background Art

[0002] With the continuous expansion of the scale of power systems and the continuous increase in the number of equipment, the maintenance and fault warning of power equipment have become more and more complicated. How to timely discover the potential failure risks of equipment and avoid large-scale power outages caused by equipment failure has become an important issue that needs to be urgently solved in the operation and maintenance of power systems.

[0003] At present, the common power equipment fault early warning method is mainly based on the analysis of the operating data of a single device, and determines whether the equipment has a risk of failure by monitoring the operating parameters of the equipment such as voltage, current, temperature, etc. However, due to the lack of comprehensive evaluation of the overall operating status of the equipment, it is easy to miss or report a false alarm, resulting in insufficient accuracy of the power equipment fault early warning. Summary of the invention

[0004] The present application provides an intelligent maintenance method, system, device and storage medium for power equipment, which are used to improve the accuracy of power equipment fault warning.

[0005] In a first aspect, the present application provides a method for intelligent maintenance of power equipment, the method comprising: obtaining the usage time and historical fault data of the target power equipment, and the first operating data of the target power equipment within a preset time; determining the safety index of the target power equipment in combination with the usage time and the historical fault data; determining the first abnormality index of the target power equipment based on the first operating data; obtaining the second operating data of the associated equipment of the target power equipment, and adjusting the first abnormality index based on the second operating data to generate a second abnormality index; adjusting the second abnormality index by the safety index to generate a target abnormality index, and when the target abnormality index is greater than a preset threshold, determining that the target power equipment is at risk of failure, and triggering a fault warning.

[0006] By adopting the above technical solution, by obtaining the usage time, historical fault data and first operation data of the target power equipment, combining the multiple evaluation mechanisms of the equipment safety index and the abnormality index, and considering the impact of the operating status of related equipment, a comprehensive analysis of the equipment operating status is achieved, and on this basis, fault risk assessment and early warning are carried out, effectively improving the accuracy of power equipment fault early warning.

[0007] Optionally, the safety index of the target power equipment is determined by combining the usage time and the historical fault data, including: calculating the ratio of the usage time to the entire life cycle of the target power equipment, and determining a first safety index of the target power equipment according to the size of the ratio; obtaining the number of failures of the target power equipment from the historical fault data, and comparing the number of failures with a preset failure reference number; when the number of failures is greater than the failure reference number, decreasing the first safety index according to a preset attenuation coefficient to determine the safety index of the target power equipment; when the number of failures is not greater than the failure reference number, setting the first safety index as the safety index of the target power equipment.

[0008] By adopting the above technical solution, the first safety index is determined by calculating the ratio of the usage time to the entire life cycle, and dynamically adjusted in combination with the comparison results of the historical failure times and the failure benchmark times, thus achieving accurate quantification of the safety status of the equipment. This solution not only takes into account the degree of equipment life consumption, but also reflects the impact of historical failures on equipment reliability through the attenuation coefficient, so that the final safety index can more accurately reflect the actual operating reliability level of the equipment, providing reliable basic data for subsequent abnormal status assessment.

[0009] Optionally, the first operating data includes voltage, current and temperature, and determining the first abnormality index of the target power equipment based on the first operating data includes: calculating a first difference between the voltage and a standard voltage, a second difference between the current and a standard current, and a third difference between the temperature and the standard temperature; obtaining a first weight coefficient corresponding to the voltage, a second weight coefficient corresponding to the current, and a third weight coefficient corresponding to the temperature; arithmetically multiplying the first difference by the first weight coefficient to generate a first index; arithmetically multiplying the second difference by the second weight coefficient to generate a second index; arithmetically multiplying the third difference by the third weight coefficient to generate a third index; and arithmetically adding the first index, the second index, and the third index to generate the first abnormality index of the target power equipment.

[0010] By adopting the above technical solution, the difference between the three key operating parameters of voltage, current and temperature and their standard values ​​is calculated, and the corresponding weight coefficients are introduced for weighted calculation, forming a multi-parameter fusion abnormal state assessment mechanism. This solution reflects the degree of parameter deviation through difference calculation, and reflects the importance of different parameters through weight coefficients. Finally, the first abnormal index obtained by arithmetic operation takes into account the actual fluctuation of each parameter and reflects the relative importance of the parameters, making the abnormal state assessment result more comprehensive and objective.

[0011] Optionally, the first abnormality index is adjusted according to the second operating data to generate a second abnormality index, including: judging whether the associated device is in an abnormal state according to the second operating data; if the associated device is in an abnormal state, obtaining the coupling coefficient between the associated device and the target power device, and arithmetically multiplying the first abnormality index by the coupling coefficient to generate a second abnormality index; if the associated device is in a normal state, setting the first abnormality index to the second abnormality index.

[0012] By adopting the above technical solution, a quantitative evaluation mechanism for the impact of fault correlation between devices is established by monitoring the operating status of associated devices and introducing coupling coefficients to correct the abnormality index. This solution selectively adjusts the abnormality index according to the status of the associated devices. When the associated devices are abnormal, the coupling coefficient reflects the degree of mutual influence between the devices, so that the second abnormality index finally generated can accurately reflect the propagation effect of the associated fault; when the associated devices are normal, the original abnormality index is kept unchanged, avoiding unnecessary interference, so that the fault risk assessment is more in line with the actual operation situation.

[0013] Optionally, obtaining the coupling coefficient between the associated device and the target power device includes: obtaining the physical distance and electrical connection relationship between the associated device and the target power device; determining a first coupling coefficient between the associated device and the target power device based on the physical distance, the first coupling coefficient being inversely proportional to the physical distance; determining a second coupling coefficient between the associated device and the target power device based on the electrical connection relationship, the second coupling coefficient decreasing with an increase in the number of transfer points in the electrical connection path; and weightedly summing the first coupling coefficient and the second coupling coefficient according to a preset ratio to obtain the coupling coefficient between the associated device and the target power device.

[0014] By adopting the above technical solution, the coupling coefficient is determined by considering both the physical distance and the electrical connection relationship, and a two-layer evaluation mechanism for the coupling relationship between devices is established. This solution uses the first coupling coefficient to reflect the attenuation effect of physical distance on fault propagation, and the second coupling coefficient to reflect the influence of electrical connection complexity on signal transmission. The influence of the two dimensions is organically combined through weighted summation, so that the final coupling coefficient reflects both the spatial layout characteristics and the electrical topological relationship, thereby more accurately quantifying the degree of mutual influence between devices.

[0015] Optionally, adjusting the second abnormality index by the safety index to generate a target abnormality index includes: obtaining an adjustment coefficient corresponding to the safety index, the adjustment coefficient being in direct proportion to the safety index; and arithmetically multiplying the second abnormality index by the adjustment coefficient to generate a target abnormality index.

[0016] By adopting the above technical solution, a dynamic adjustment mechanism for abnormal conditions based on equipment reliability is realized by establishing a proportional mapping relationship between the safety index and the adjustment coefficient. The solution uses the adjustment coefficient to correct the second abnormal index. When the equipment safety is high, a smaller adjustment coefficient is used to reduce the impact of the abnormal condition; when the equipment safety is low, a larger adjustment coefficient is used to amplify the impact of the abnormal condition, so that the target abnormal index generated in the end can be adaptively adjusted according to the historical reliability level of the equipment, thereby improving the accuracy of fault warning.

[0017] Optionally, after the fault warning is triggered, it also includes: obtaining the backup plan information and maintenance records of the target power equipment; determining the backup switching strategy of the target power equipment based on the backup plan information; generating maintenance suggestions for the target power equipment based on the maintenance records; and sending the backup switching strategy and the maintenance suggestions to the terminal device of the maintenance personnel so that the maintenance personnel can perform fault handling.

[0018] By adopting the above technical solution, a response strategy is formulated by combining the backup plan information and historical maintenance records, and a rapid response mechanism after fault warning is established. The solution generates a switching strategy based on the backup plan information, provides maintenance suggestions based on maintenance records, and pushes information to maintenance personnel in a timely manner through terminal devices, achieving a seamless connection from fault warning to treatment plan, enabling maintenance personnel to quickly obtain key information and take effective response measures, improving the efficiency and accuracy of fault handling.

[0019] In a second aspect, the present application provides an intelligent maintenance system for power equipment, the system comprising: an acquisition module, a combination module, a determination module, a generation module and an adjustment module; wherein the acquisition module is used to acquire the usage time and historical fault data of the target power equipment, and the first operation data of the target power equipment within a preset time; the combination module is used to determine the safety index of the target power equipment in combination with the usage time and the historical fault data; the determination module is used to determine the first abnormality index of the target power equipment based on the first operation data; the generation module is used to acquire the second operation data of the associated equipment of the target power equipment, and adjust the first abnormality index according to the second operation data to generate a second abnormality index; the adjustment module is used to adjust the second abnormality index through the safety index to generate a target abnormality index, and when the target abnormality index is greater than a preset threshold, it is determined that the target power equipment has a failure risk and a fault warning is triggered.

[0020] In the 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 such as any of the above-mentioned intelligent maintenance methods for power equipment.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: storing a computer program that can be loaded by a processor and execute any of the above-mentioned intelligent maintenance methods for power equipment.

[0022] In summary, the present application includes at least one of the following beneficial technical effects: By obtaining the usage time, historical fault data and first operation data of the target power equipment, combining the multiple evaluation mechanisms of equipment safety index and abnormality index, and considering the impact of the operating status of related equipment, a comprehensive analysis of the equipment operating status is achieved. On this basis, fault risk assessment and early warning are carried out, effectively improving the accuracy of power equipment fault early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flowchart of an intelligent maintenance method for power equipment provided in an embodiment of the present application; Figure 2 It is a structural diagram of an intelligent maintenance system for electric power equipment provided in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0024] Description of reference numerals: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. DETAILED DESCRIPTION

[0025] 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 drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0026] In the description of the embodiments of the present application, words such as "illustrative", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "illustrative", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "illustrative", "for example" or "for example" is intended to present related concepts in a concrete way.

[0027] Figure 1 Schematic diagram of a flow chart of a method for intelligent maintenance of electric power equipment provided in an embodiment of the present application. Figure 1 As shown, the method includes S101-S105: S101, obtaining the usage time and historical fault data of the target electric power equipment, and the first operation data of the target electric power equipment within a preset time.

[0028] When implementing the present invention, it is first necessary to obtain the basic information and operation data of the target power equipment. The usage time can be obtained by calling the difference between the installation time and the current time of the target power equipment, and the usage time data reflects the service life of the equipment; the historical fault data contains detailed records of the time, type, severity, etc. of the equipment's previous faults, which can be extracted from the database of the power equipment management system.

[0029] For example, a transformer has been in use for 5 years, during which time it has experienced 2 overheating failures and 1 insulation aging failure. At the same time, the first operating data of the equipment within a preset time (such as nearly 24 hours) is collected in real time through sensors deployed on the target power equipment. The operating data includes but is not limited to key operating parameters such as voltage, current, and temperature. These data are collected by the data acquisition unit at a predetermined sampling frequency (such as once every 5 minutes) and transmitted to the data processing unit for storage. The purpose of obtaining these data is to establish a data foundation for equipment status evaluation, in which the service life reflects the degree of aging of the equipment, the historical fault data reflects the reliability level of the equipment, and the real-time operation data reflects the current operating status of the equipment.

[0030] By comprehensively analyzing these data, the operation status of the equipment can be comprehensively evaluated from the time dimension and status dimension, providing the necessary data support for the subsequent calculation of safety index and abnormality index, thereby improving the accuracy and reliability of fault prediction.

[0031] S102, determining a safety index of the target power equipment by combining the usage time and historical fault data.

[0032] During operation, the performance of power equipment will degrade as the usage time increases. At the same time, the occurrence of historical failures will also have a cumulative impact on the reliability of the equipment. Therefore, it is necessary to combine the usage time and historical failure data to determine the safety index of the target power equipment.

[0033] In the specific implementation, it is first necessary to calculate the ratio of the use time of the target power equipment to its full life cycle. For example, if the design service life of a transformer is 30 years and it has been used for 15 years, the ratio is 0.5. Based on this ratio, a linear decreasing function is used to calculate the first safety index of 0.8. Secondly, extract historical fault data from the power equipment management system, obtain the number of failures of the target power equipment, and compare it with the preset fault reference number. When the actual number of failures of the target power equipment is greater than the fault reference number, it indicates that the reliability of the equipment is poor. At this time, it is necessary to introduce a preset attenuation coefficient (such as 0.9) to reduce the first safety index, and finally obtain the safety index of the target power equipment; if the number of failures is not greater than the fault reference number, the first safety index is directly used as the final safety index.

[0034] The safety index calculated in this way can comprehensively reflect the degree of service life consumption and historical operating reliability of the equipment, provide an important reference for subsequent abnormal state assessment, and help improve the accuracy of fault prediction. For example, when the first safety index of a transformer is 0.8, and the number of its faults exceeds the benchmark value, after adjustment by an attenuation coefficient of 0.9, the final safety index is 0.72, which more accurately reflects the actual safety status of the equipment.

[0035] Based on the above embodiment, as an optional implementation, in S102, combining the usage time and the historical fault data to determine the safety index of the target power equipment specifically includes S21-S23: S21, calculating the ratio of the usage time to the entire life cycle of the target power equipment, and determining the first safety index of the target power equipment according to the size of the ratio.

[0036] During its life cycle, power equipment will experience a gradual performance degradation process. The ratio of usage time to the entire life cycle directly reflects the degree of life consumption of the equipment, which has an important impact on the safe operation status of the equipment.

[0037] In the specific implementation, first obtain the usage time data of the target power equipment and the full life cycle data specified in the equipment manual from the equipment management system, and calculate the ratio of the two. For example, the design service life (full life cycle) of a certain type of transformer is 30 years, and it has been used for 15 years, so its life consumption ratio is 0.5. Based on this ratio, a pre-set functional relationship is used to determine the first safety index, which can be a piecewise linear function or an exponential decay function.

[0038] Specifically, when the ratio is in the interval [0,0.3], the first safety index takes a value of 1.0; when the ratio is in the interval (0.3,0.6], the first safety index decreases linearly with the increase of the ratio, which can be expressed as 1.0-0.5*(ratio-0.3); when the ratio is in the interval (0.6,1.0], the first safety index shows an accelerated decreasing trend, which can be expressed as 0.85-0.85*(ratio-0.6). According to this calculation method, when the life consumption ratio is 0.5, the corresponding first safety index is 0.9. This safety index calculation method based on the life consumption ratio can accurately reflect the impact of the aging degree of the equipment on its safety, provide a basic reference value for subsequent fault risk assessment, and help to timely discover potential safety hazards in equipment operation.

[0039] S22, obtaining the number of failures of the target power equipment from the historical failure data, and comparing the number of failures with a preset reference number of failures.

[0040] S23, when the number of failures is greater than the reference number of failures, the first safety index is decreased according to a preset attenuation coefficient to determine the safety index of the target power equipment; when the number of failures is not greater than the reference number of failures, the first safety index is set as the safety index of the target power equipment.

[0041] Historical fault data reflects the operating stability and reliability level of power equipment. The number of faults directly affects the safe operating status of the equipment. Therefore, fault statistical data needs to be used as an important basis for adjusting the safety index.

[0042] In the specific implementation, firstly, the number of failures of the target power equipment within a specific statistical period (such as one year) is extracted from the historical failure record database of the power equipment management system. At the same time, based on industry statistics and equipment operation experience, the failure benchmark number of the corresponding type of equipment is pre-set, which reflects the average failure level of the same type of equipment.

[0043] For example, for a certain type of transformer, the baseline number of failures is set to 2 times / year, and statistics from historical data show that the target transformer has experienced 3 failures in the past year. When it is found that the actual number of failures of the target device exceeds the baseline number, it means that the reliability of the device is lower than the industry average, and the first safety index needs to be corrected by the preset attenuation coefficient. The attenuation coefficient can be determined in a progressive manner: when the number of failures exceeds the baseline number by 1, the attenuation coefficient is 0.9; when it exceeds 2 times, the attenuation coefficient is 0.8; when it exceeds 3 times or more, the attenuation coefficient is 0.7. Multiply the first safety index by the corresponding attenuation coefficient to obtain the final safety index. For example, the first safety index of the aforementioned transformer is 0.9. Since its number of failures exceeds the baseline number by 1, an attenuation coefficient of 0.9 is used for adjustment, and the final safety index is 0.81.

[0044] If the number of failures of the target device does not exceed the benchmark number, the first safety index is directly used as the final safety index. This safety index adjustment method based on historical failure statistics can effectively reflect the actual operating reliability of the equipment, making the final safety index more valuable for reference and providing more accurate basic data for subsequent abnormal status assessment.

[0045] S103: Determine a first abnormality index of the target power equipment according to the first operating data.

[0046] The real-time operating status of the electric power equipment directly reflects its current health level, so it is necessary to analyze the first operating data to determine the first abnormality index of the target electric power equipment.

[0047] In the specific implementation process, the first operating data of the target power equipment is first obtained, including three key parameters: voltage, current and temperature. The deviations between these parameters and their standard values ​​are calculated by the data processing unit: the difference between the measured voltage value and the standard voltage value is recorded as the first difference, the difference between the measured current value and the standard current value is recorded as the second difference, and the difference between the measured temperature value and the standard temperature value is recorded as the third difference.

[0048] Considering that different parameters have different degrees of influence on the abnormal state of the equipment, it is necessary to introduce corresponding weight coefficients for weighted calculation: according to historical statistical data and expert experience, set the first weight coefficient corresponding to voltage (such as 0.4), the second weight coefficient corresponding to current (such as 0.35), and the third weight coefficient corresponding to temperature (such as 0.25). Multiply the first difference by the first weight coefficient to obtain the first index, multiply the second difference by the second weight coefficient to obtain the second index, and multiply the third difference by the third weight coefficient to obtain the third index. Finally, perform arithmetic addition on these three weighted indices to obtain the first abnormal index of the target power equipment.

[0049] For example, the voltage deviation of a transformer is 5%, the current deviation is 3%, and the temperature deviation is 8%. After weighted calculation, the first abnormality index is 5.15, which comprehensively reflects the abnormality of the current operating state of the equipment. This multi-parameter weighted calculation method not only takes into account the actual deviation of each operating parameter, but also reflects the importance of different parameters through weight coefficients, so that the final first abnormality index can more accurately characterize the operating state of the equipment and provide reliable data support for subsequent fault risk assessment.

[0050] Based on the above embodiment, as an optional implementation, in S103, the first operation data includes voltage, current and temperature, and determining the first abnormality index of the target power equipment according to the first operation data specifically includes S31-S34: S31, calculating a first difference between the voltage and the standard voltage, a second difference between the current and the standard current, and a third difference between the temperature and the standard temperature.

[0051] S32, obtaining a first weight coefficient corresponding to the voltage, a second weight coefficient corresponding to the current, and a third weight coefficient corresponding to the temperature.

[0052] S33, arithmetically multiplying the first difference by the first weight coefficient to generate a first index; arithmetically multiplying the second difference by the second weight coefficient to generate a second index; and arithmetically multiplying the third difference by the third weight coefficient to generate a third index.

[0053] S34, arithmetically adding the first index, the second index and the third index to generate a first abnormality index of the target power equipment.

[0054] The operating status of power equipment is mainly characterized by three key parameters: voltage, current and temperature. Abnormal fluctuations in these parameters often indicate potential failure risks. Therefore, a comprehensive analysis of these operating parameters is required to evaluate the abnormal status of the equipment.

[0055] In the specific implementation, the first operation data, including the measured voltage, current and temperature values, are collected in real time through the sensors on the equipment, and the standard operation parameter values ​​are obtained from the equipment technical specifications. The voltage deviation (first difference), current deviation (second difference) and temperature deviation (third difference) are obtained by calculating the difference between the measured value and the standard value.

[0056] For example, if the standard operating voltage of a transformer is 10kV and the measured value is 10.5kV, the first difference is 5%; if the standard current is 100A and the measured value is 105A, the second difference is 5%; if the standard temperature is 60℃ and the measured value is 66℃, the third difference is 10%. Considering the different degrees of influence of different parameters on the abnormal state of the equipment, it is necessary to introduce corresponding weight coefficients for weighted calculation.

[0057] Based on historical fault data analysis and expert experience, the first weight coefficient of the voltage parameter is set to 0.4, the second weight coefficient of the current parameter is set to 0.35, and the third weight coefficient of the temperature parameter is set to 0.25. The sum of the three weight coefficients is 1. The difference of each parameter is multiplied by the corresponding weight coefficient to obtain a weighted index reflecting the abnormality of each parameter: the first difference of 5% is multiplied by the weight of 0.4 to obtain the first index of 2.0, the second difference of 5% is multiplied by the weight of 0.35 to obtain the second index of 1.75, and the third difference of 10% is multiplied by the weight of 0.25 to obtain the third index of 2.5. Finally, the three weighted indices are arithmetically added to obtain the final first abnormality index of 6.25.

[0058] S104, obtaining second operating data of an associated device of the target power device, and adjusting the first abnormality index according to the second operating data to generate a second abnormality index.

[0059] Power equipment usually does not operate independently, but is physically and electrically associated with other equipment. The operating status of the associated equipment will have a direct or indirect impact on the target equipment. Therefore, it is necessary to consider the operating status of the associated equipment to adjust the abnormality index of the target equipment.

[0060] In specific implementation, firstly, the power equipment management system identifies the equipment associated with the target power equipment, such as the circuit breaker and busbar connected to a transformer, and obtains the second operation data of these associated equipment. Based on the second operation data, it is judged whether the associated equipment is in an abnormal state. The judgment method can adopt a parameter deviation analysis method similar to that of the target equipment. When the operating parameters of the associated equipment exceed the preset range, it is determined that it is in an abnormal state.

[0061] When an associated device is found to be in an abnormal state, it is necessary to obtain the coupling coefficient between the associated device and the target power device, which reflects the degree of influence of the abnormal state of the associated device on the target device. For example, the coupling coefficient between a circuit breaker and a transformer is 0.85. The first abnormal index is multiplied by the coupling coefficient to generate a second abnormal index reflecting the impact of the association; if the associated device is in a normal operating state, the first abnormal index is directly used as the second abnormal index.

[0062] For example, when the first abnormality index of the target transformer is 5.15, its associated circuit breaker is in an abnormal state and the coupling coefficient is 0.85, the calculated second abnormality index is 4.38. This adjustment method that takes into account the impact of associated equipment can more comprehensively evaluate the abnormal state of the target equipment, avoid the one-sidedness of only focusing on the equipment's own state and ignoring external influences, and improve the accuracy and reliability of fault warning.

[0063] Based on the above embodiment, as an optional implementation, in S104, adjusting the first abnormality index according to the second operation data to generate the second abnormality index specifically includes S41-S43: S41, judging whether the associated device is in an abnormal state according to the second operation data.

[0064] S42, if the associated device is in an abnormal state, obtain a coupling coefficient between the associated device and the target power device, and arithmetically multiply the first abnormal index by the coupling coefficient to generate a second abnormal index.

[0065] S43: If the associated device is in a normal state, the first abnormality index is set as the second abnormality index.

[0066] There are complex electrical and physical relationships between power equipment. The operating status of associated equipment will have a direct impact on the target equipment. Therefore, it is necessary to consider the status of associated equipment to correct the abnormal index of the target equipment.

[0067] In the specific implementation process, the second operating data of the equipment that is associated with the target equipment (such as the circuit breaker and busbar connected to the transformer) is first obtained through the equipment management system, and the same parameter analysis method as the target equipment is used to determine whether the associated equipment is in an abnormal state.

[0068] For example, the operating parameters of the associated circuit breaker of a transformer include contact temperature, operation time, and contact resistance. When any of these parameters exceeds the preset threshold, the circuit breaker is determined to be in an abnormal state. When the system detects that an associated device is in an abnormal state, it is necessary to obtain the coupling coefficient between the associated device and the target power device. This coefficient reflects the degree of association and the intensity of mutual influence between the two devices. The determination of the coupling coefficient requires comprehensive consideration of factors such as the physical distance and electrical connection relationship between the devices. For example, the coupling coefficient between a directly connected circuit breaker and a transformer may be 0.85, while the coupling coefficient between devices indirectly connected through a busbar may be 0.6. The first abnormal index of the target device is arithmetically multiplied by the coupling coefficient of the associated device to obtain a second abnormal index after considering the association effect.

[0069] For example, if the first abnormal index of a transformer is 6.25, and the coupling coefficient of the associated circuit breaker in its abnormal state is 0.85, the calculated second abnormal index is 5.31. If the detection finds that the associated equipment is in normal operation, it means that the associated equipment has no adverse effect on the target equipment. In this case, the first abnormal index is directly used as the second abnormal index. This abnormal index adjustment method that takes into account the influence of associated equipment can more accurately reflect the actual operating status of the equipment in the power system, avoids the one-sidedness that may be caused by isolated evaluation, and improves the accuracy and reliability of fault warning.

[0070] Obtaining the coupling coefficient between the associated device and the target power device specifically includes: Obtain the physical distance and electrical connection relationship between the associated device and the target power device; determine a first coupling coefficient between the associated device and the target power device based on the physical distance, the first coupling coefficient being in inverse proportion to the physical distance; determine a second coupling coefficient between the associated device and the target power device based on the electrical connection relationship, the second coupling coefficient decreasing as the number of transfer points in the electrical connection path increases; perform weighted summation of the first coupling coefficient and the second coupling coefficient according to a preset ratio to obtain a coupling coefficient between the associated device and the target power device.

[0071] In one example, the coupling impact between power equipment is mainly reflected in two dimensions: spatial distance and electrical connection. Accurately quantifying the degree of coupling in these two dimensions is crucial to accurately assess the mutual impact between equipment.

[0072] In the specific implementation, firstly, the physical distance data of the associated equipment and the target power equipment is extracted from the equipment layout diagram of the power equipment management system, and the electrical connection relationship information between them is obtained from the system wiring diagram. Taking a transformer and its associated circuit breaker as an example, the physical distance between the two is 2 meters through measurement, and the electrical connection path contains a busbar as a transfer point. When determining the first coupling coefficient, the inverse proportional function k / (d+1) is used for calculation, where k is the distance influence factor (value 2.0) and d is the measured physical distance. This calculation method can reflect the physical property that the coupling strength attenuates with the increase of distance. For example, when the distance is 2 meters, the calculated first coupling coefficient is 0.67. For the determination of the second coupling coefficient, considering that the electrical signal will be affected by the transfer point during the transmission process, the decreasing function m*0.8^n is used for calculation, where m is the reference coefficient (value 1.0) and n is the number of transfer points. This method can reflect the attenuation effect of the transfer point on the coupling strength. For example, when there is a transfer point, the calculated second coupling coefficient is 0.8.

[0073] Finally, considering the different weights of physical distance and electrical connection on device coupling, the two coupling coefficients are weighted and summed using a preset weight ratio (such as physical distance weight 0.6, electrical connection weight 0.4), and the final coupling coefficient is 0.722. This coupling coefficient calculation method that takes into account dual influencing factors not only reflects the physical nature of the coupling relationship between devices, but also reflects the influence of electrical characteristics, making the final coupling coefficient more objective and accurate, and providing reliable basic data for subsequent abnormal state assessment.

[0074] S105, adjusting the second abnormality index by the safety index to generate a target abnormality index. When the target abnormality index is greater than a preset threshold, it is determined that the target power equipment has a failure risk and a failure warning is triggered.

[0075] The safety index of power equipment reflects its overall reliability level. This index will affect the equipment's ability to withstand abnormal conditions. Therefore, the safety index needs to be used as an adjustment factor to correct the second abnormal index in order to obtain a more accurate fault risk assessment result.

[0076] In the specific implementation, the corresponding adjustment coefficient is first determined based on the safety index calculated in the previous stage. The two are in a positive proportional relationship. For example, when the safety index of a transformer is 0.72, its adjustment coefficient can be calculated as 1.2 through a linear function. The second abnormal index is multiplied by the adjustment coefficient to generate the final target abnormal index, which comprehensively reflects the equipment's service life, historical faults, current operating status, and the impact of related equipment.

[0077] For example, when the second abnormality index is 4.38 and the adjustment coefficient is 1.2, the calculated target abnormality index is 5.26. Subsequently, the target abnormality index is compared with the system preset risk warning threshold (such as 5.0). When the target abnormality index exceeds the threshold, the system determines that the target power equipment has a failure risk and automatically triggers the failure warning process. The warning information may include equipment number, abnormality type, risk level, etc., which is pushed to the terminal equipment of relevant maintenance personnel through the equipment management system. This fault warning mechanism based on multi-level indicator adjustment not only takes into account the current abnormal state of the equipment, but also combines the historical reliability of the equipment for comprehensive analysis. It can more accurately identify potential failure risks, effectively reduce the false alarm rate, improve the accuracy and credibility of the warning, and provide a reliable basis for equipment maintenance decisions.

[0078] Based on the above embodiment, as an optional implementation, in S105, adjusting the second abnormality index by the safety index to generate the target abnormality index specifically includes S51-S52: S51, obtaining an adjustment coefficient corresponding to the safety index, where the adjustment coefficient is in direct proportion to the safety index.

[0079] S52: Perform arithmetic multiplication of the second abnormality index and the adjustment coefficient to generate a target abnormality index.

[0080] The overall safety level of power equipment will affect its sensitivity and tolerance to abnormal conditions. Therefore, the safety index needs to be used as an adjustment factor to correct the abnormal index in order to obtain a more accurate fault risk assessment result.

[0081] In the specific implementation, the corresponding adjustment coefficient is first determined based on the safety index calculated in the previous stage, and the two are mapped in a positive proportional relationship, which can be expressed as the adjustment coefficient equals the reference coefficient (such as 1.5) multiplied by the safety index. For example, if the safety index of a transformer is 0.8, the corresponding adjustment coefficient is 1.2. This positive proportional mapping relationship reflects the characteristic that the higher the safety of the equipment, the stronger its tolerance to abnormal conditions. Subsequently, the second abnormal index is arithmetically multiplied by the determined adjustment coefficient to obtain the final target abnormal index. Taking a transformer as an example, when its second abnormal index is 5.31 and the adjustment coefficient is 1.2, the calculated target abnormal index is 6.37. This abnormal state adjustment method based on the safety index can organically combine the historical reliability of the equipment with the current operating state, so that the final abnormal index is more in line with the actual operating characteristics of the equipment. When the safety of the equipment is low, the influence of the abnormal state is amplified by a larger adjustment coefficient to improve the sensitivity of the fault warning; when the safety of the equipment is high, the influence of the abnormal state is reduced by a smaller adjustment coefficient to reduce the possibility of false alarms, thereby improving the accuracy and reliability of the fault warning.

[0082] The regulation coefficient of power equipment needs to be reasonably mapped according to the safety index, and this mapping relationship should reflect the relationship between equipment safety and sensitivity to abnormal conditions.

[0083] In the specific implementation, the adjustment coefficient is determined by the piecewise linear mapping method, and different adjustment benchmarks are set according to different intervals of the safety index. When the safety index is in the interval [0.8,1.0], it indicates that the equipment is in a relatively good safety state. At this time, the adjustment coefficient adopts a smaller value, which can be expressed as 1.0+0.5*(1-safety index); when the safety index is in the interval [0.6,0.8), it indicates that the safety of the equipment is at a medium level, and the adjustment coefficient increases accordingly, which can be expressed as 1.1+0.75*(0.8-safety index); when the safety index is in the interval [0,0.6), it indicates that the safety of the equipment is poor, and the sensitivity of the abnormal state needs to be improved. The adjustment coefficient is further increased, which can be expressed as 1.25+1.25*(0.6-safety index). For example, the safety index of a transformer is 0.75, which belongs to the medium safety level interval. According to the corresponding calculation formula, the adjustment coefficient is 1.1375. This segmented mapping method can more accurately reflect the sensitivity of equipment to abnormal conditions under different safety levels. When the safety index is low, a larger adjustment coefficient is used to promptly detect potential fault risks. When the safety index is high, a smaller adjustment coefficient is used to avoid excessive warnings, thereby achieving precise and differentiated fault warnings.

[0084] After the fault warning is triggered, it also includes: Obtain backup plan information and maintenance records of the target power equipment; determine the backup switching strategy of the target power equipment based on the backup plan information; generate maintenance suggestions for the target power equipment based on the maintenance records; send the backup switching strategy and maintenance suggestions to the terminal device of the maintenance personnel so that the maintenance personnel can handle the fault.

[0085] When there is a risk of failure in power equipment, taking effective countermeasures in a timely manner is crucial to ensuring power supply reliability and reducing failure losses.

[0086] In the specific implementation, first retrieve the backup plan information of the target power equipment from the equipment management system, including the type of backup equipment, capacity parameters, switching time requirements, etc., and obtain the historical maintenance records of the equipment, including detailed information such as fault type, maintenance method, and maintenance effect.

[0087] Taking a transformer as an example, when the system triggers a fault warning, it retrieves from the database that the transformer is equipped with a spare transformer of the same model, the switching time requirement is within 30 minutes, and there is a recent insulation aging fault maintenance record. Based on the backup plan information, the system automatically generates a backup switching strategy including switching steps, precautions and time nodes, such as the need to transfer loads first, disconnect the faulty equipment, and put the backup equipment into use. At the same time, combined with the experience data in the historical maintenance records, the system generates targeted maintenance suggestions through data analysis, including key inspection items, possible causes of failure, and recommended maintenance methods. For example, if the transformer has experienced insulation aging failures many times, the inspection and maintenance meeting will focus on checking key parts such as the quality of insulating oil and the status of the bushing. Finally, the system pushes the generated backup switching strategy and maintenance suggestions to relevant maintenance personnel through mobile terminal devices, and the maintenance personnel can quickly carry out fault handling work based on the received information. This intelligent fault response mechanism can not only help maintenance personnel quickly formulate treatment plans, but also use historical experience to provide more targeted maintenance guidance, improve the efficiency and accuracy of fault handling, and minimize the impact of faults.

[0088] Based on the above method, the present application also discloses an intelligent maintenance system for power equipment, such as Figure 2 As shown, Figure 2 : is a structural diagram of an intelligent maintenance system for electric power equipment provided by an embodiment of the present application, the system includes: an acquisition module, a combination module, a determination module, a generation module and an adjustment module; wherein, An acquisition module is used to acquire the usage time and historical fault data of the target power equipment, as well as the first operating data of the target power equipment within a preset time; a combination module is used to determine the safety index of the target power equipment by combining the usage time and historical fault data; a determination module is used to determine the first abnormality index of the target power equipment based on the first operating data; a generation module is used to acquire the second operating data of the associated equipment of the target power equipment, and adjust the first abnormality index according to the second operating data to generate a second abnormality index; an adjustment module is used to adjust the second abnormality index through the safety index to generate a target abnormality index. When the target abnormality index is greater than a preset threshold, it is determined that the target power equipment has a failure risk and a failure warning is triggered.

[0089] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, 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 embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0090] See also Figure 3 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 As 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 .

[0091] The communication bus 1002 is used to realize the connection and communication between these components.

[0092] The user interface 1003 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0093] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0094] Among them, the processor 1001 may include one or more processing cores. The processor 1001 uses various interfaces and lines to connect various parts in the entire server, 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 calling data stored in the memory 1005. Optionally, the processor 1001 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 1001 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 1001, and it can be implemented separately through a chip.

[0095] Among them, the memory 1005 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (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, codes, code sets or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1005 may optionally also be at least one storage device located away from the aforementioned processor 1001. As Figure 3 As 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 of an intelligent maintenance method for electric power equipment.

[0096] exist Figure 3In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 1001 can be used to call an application program stored in the memory 1005 for an intelligent maintenance method for power equipment. When executed by one or more processors, the electronic device executes one or more methods described in the above embodiments.

[0097] An electronic device readable storage medium stores instructions, which, when executed by one or more processors, enable the electronic device to execute one or more of the methods described in the above embodiments.

[0098] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0099] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0100] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0101] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0102] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0103] 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, 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, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0104] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for intelligent maintenance of electric power equipment, characterized in that: The method comprises: Acquire the usage time and historical fault data of the target electric equipment, and the first operation data of the target electric equipment within a preset time; Determine the safety index of the target power equipment by combining the usage time and the historical fault data; determining a first abnormality index of the target power equipment according to the first operating data; Acquire second operating data of an associated device of the target power device, and adjust the first abnormality index according to the second operating data to generate a second abnormality index; The second abnormality index is adjusted by the safety index to generate a target abnormality index. When the target abnormality index is greater than a preset threshold, it is determined that the target power equipment has a failure risk and a failure warning is triggered.

2. The intelligent maintenance method for electric power equipment according to claim 1, characterized in that: The determining the safety index of the target power equipment by combining the usage time and the historical fault data includes: Calculating a ratio of the usage time to the full life cycle of the target power equipment, and determining a first safety index of the target power equipment according to the size of the ratio; Acquire the number of failures of the target power equipment from the historical failure data, and compare the number of failures with a preset reference number of failures; When the number of failures is greater than the failure reference number, the first safety index is reduced according to a preset attenuation coefficient to determine the safety index of the target power equipment; when the number of failures is not greater than the failure reference number, the first safety index is set as the safety index of the target power equipment.

3. The intelligent maintenance method for electric power equipment according to claim 1, characterized in that: The first operating data includes voltage, current and temperature, and determining the first abnormality index of the target power equipment according to the first operating data includes: Calculating a first difference between the voltage and a standard voltage, a second difference between the current and a standard current, and a third difference between the temperature and a standard temperature; Obtaining a first weight coefficient corresponding to the voltage, a second weight coefficient corresponding to the current, and a third weight coefficient corresponding to the temperature; arithmetically multiplying the first difference value by the first weight coefficient to generate a first index; arithmetically multiplying the second difference value by the second weight coefficient to generate a second index; arithmetically multiplying the third difference value by the third weight coefficient to generate a third index; The first index, the second index, and the third index are arithmetically added to generate a first abnormality index of the target power device.

4. The intelligent maintenance method for electric power equipment according to claim 1, characterized in that: The step of adjusting the first abnormality index according to the second operating data to generate a second abnormality index includes: determining, according to the second operating data, whether the associated device is in an abnormal state; If the associated device is in an abnormal state, obtaining a coupling coefficient between the associated device and the target power device, and arithmetically multiplying the first abnormal index by the coupling coefficient to generate a second abnormal index; If the associated device is in a normal state, the first abnormality index is set to the second abnormality index.

5. The intelligent maintenance method for electric power equipment according to claim 4, characterized in that: The obtaining of the coupling coefficient between the associated device and the target power device includes: Acquire the physical distance and electrical connection relationship between the associated device and the target electrical device; Determining a first coupling coefficient between the associated device and the target power device according to the physical distance, wherein the first coupling coefficient is in inverse proportion to the physical distance; Determining a second coupling coefficient between the associated device and the target electrical device according to the electrical connection relationship, wherein the second coupling coefficient decreases as the number of transfer points in the electrical connection path increases; The first coupling coefficient and the second coupling coefficient are weighted and summed according to a preset ratio to obtain a coupling coefficient between the associated device and the target power device.

6. The intelligent maintenance method for electric power equipment according to claim 1, characterized in that: The step of adjusting the second abnormality index by using the safety index to generate a target abnormality index includes: Obtaining an adjustment coefficient corresponding to the safety index, wherein the adjustment coefficient is in direct proportion to the safety index; The second abnormality index is arithmetically multiplied by the adjustment coefficient to generate a target abnormality index.

7. The intelligent maintenance method for electric power equipment according to claim 1, characterized in that: After the fault warning is triggered, the method further includes: Obtaining backup plan information and maintenance records of the target power equipment; Determining a backup switching strategy for the target power equipment according to the backup plan information; Generating maintenance suggestions for the target power equipment according to the maintenance record; The standby switching strategy and the maintenance suggestion are sent to a terminal device of a maintenance person so that the maintenance person can perform fault handling.

8. An intelligent maintenance system for electric power equipment, characterized in that: The system includes: an acquisition module, a combination module, a determination module, a generation module and an adjustment module; wherein, The acquisition module is used to acquire the usage time and historical fault data of the target power equipment, and the first operation data of the target power equipment within a preset time; The combining module is used to combine the usage time and the historical fault data to determine the safety index of the target power equipment; The determining module is used to determine a first abnormality index of the target power equipment according to the first operating data; The generating module is configured to obtain second operating data of an associated device of the target power device, and adjust the first abnormality index according to the second operating data to generate a second abnormality index; The adjustment module is used to adjust the second abnormality index through the safety index to generate a target abnormality index. When the target abnormality index is greater than a preset threshold, it is determined that the target power equipment has a failure risk and a failure warning is triggered.

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, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-7.

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

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