Power equipment maintenance method, system and equipment and storage medium

Abstract

The invention provides a power equipment maintenance method, system and device and a storage medium, and relates to the technical field of fault monitoring, and the method comprises the steps: determining the stability coefficient of target power equipment according to the operation parameters of the target power equipment; predicting the load change rate of the power grid within a second preset time length by combining the historical operation data of the power grid and the first load change rate; on the basis of the stability coefficient, according to the load change rate, calculating a first stability coefficient of the target power equipment within a second preset duration; obtaining a second stability coefficient of associated equipment of the target power equipment in a second preset time length, adjusting the first stability coefficient according to the second stability coefficient, and generating a target stability coefficient of the target power equipment in the second preset time length; and if the target stability coefficient is lower than a set threshold value, generating a maintenance plan of the target power equipment. The technical effect of the invention is that the rationality of maintenance resource configuration is improved.

Description

Technical Field

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

[0002] With the continuous expansion of the power grid and the continuous growth of load demand, the safe and stable operation of power equipment faces increasing challenges. In order to ensure the reliable power supply of the power grid, it is necessary to timely discover the potential operating risks of equipment and formulate a reasonable maintenance plan.

[0003] At present, the existing power equipment maintenance method mainly executes the maintenance plan based on a preset fixed maintenance duration. However, this maintenance method has the following technical problems in practical application: due to the dynamic change characteristics of the power grid load, maintenance based only on a fixed duration cannot accurately reflect the actual operating status of the equipment, resulting in unreasonable allocation of maintenance resources. Summary of the invention

[0004] The present application provides a power equipment maintenance method, system, equipment and storage medium for improving the rationality of maintenance resource allocation.

[0005] In a first aspect, the present application provides a method for maintenance of electric power equipment, the method comprising: obtaining operating parameters of a target electric power equipment of a power grid at a current moment, and determining a stability coefficient of the target electric power equipment based on the operating parameters; obtaining historical operating data of the power grid, and a first load change rate of the power grid within a first preset time period, and predicting the load change rate of the power grid within a second preset time period in combination with the historical operating data and the first load change rate, wherein the first preset time period is before the second preset time period; on the basis of the stability coefficient, calculating the first stability coefficient of the target electric power equipment within the second preset time period based on the load change rate; obtaining the second stability coefficient of an associated device of the target electric power equipment within the second preset time period, adjusting the first stability coefficient based on the second stability coefficient, and generating a target stability coefficient of the target electric power equipment within the second preset time period; if the target stability coefficient is lower than a set threshold, generating a maintenance plan for the target electric power equipment.

[0006] By adopting the above technical scheme, the stability coefficient is determined by obtaining the current operating parameters of the target power equipment, and the load change rate within the second preset time period is predicted by combining the historical operating data and the first load change rate within the first preset time period, and then the first stability coefficient is calculated. Then, the first stability coefficient is adjusted according to the second stability coefficient of the associated equipment to obtain the target stability coefficient, so that a maintenance plan is generated in time when the target stability coefficient is lower than the set threshold, thereby realizing dynamic monitoring and predictive maintenance of the operating status of the power equipment and improving the rationality of maintenance resource allocation.

[0007] Optionally, the operating parameters include voltage, current and temperature, and determining the stability coefficient of the target power equipment based on the operating parameters includes: calculating a first difference between the voltage and a voltage standard value, a second difference between the current and a current standard value, and a third difference between the temperature and a temperature standard value; arithmetically multiplying the first difference by a first weight coefficient to obtain a first coefficient; arithmetically multiplying the second difference by a second weight coefficient to obtain a second coefficient; arithmetically multiplying the third difference by a third weight coefficient to obtain a third coefficient; arithmetically adding the first coefficient, the second coefficient and the third coefficient to obtain a comprehensive deviation value; and determining the stability coefficient of the target power equipment based on the correspondence between the comprehensive deviation value and a preset deviation threshold.

[0008] By adopting the above technical scheme, the differences between voltage, current and temperature and their standard values ​​are calculated, and multiplied with corresponding weight coefficients to obtain the first coefficient, the second coefficient and the third coefficient respectively, and then these coefficients are added to obtain the comprehensive deviation value. Finally, the stability coefficient of the target power equipment is determined according to the corresponding relationship between the comprehensive deviation value and the preset deviation threshold, thereby realizing a multi-parameter comprehensive evaluation of the equipment operating status and improving the calculation accuracy of the stability coefficient.

[0009] Optionally, combining the historical operating data and the first load change rate to predict the load change rate of the power grid within a second preset time period includes: determining the historical load change rate of the power grid in each time period based on the historical operating data; determining the second load change rate of the power grid within the second preset time period based on the historical load change rate of the power grid in each time period; and predicting the load change rate of the power grid within the second preset time period by combining the first load change rate and the second load change rate.

[0010] By adopting the above technical solution, the historical load change rate of each time period is obtained by analyzing the historical operation data, and then the second load change rate within the second preset time period is determined, and it is combined with the current first load change rate to predict the load change rate of the power grid within the second preset time period, thereby achieving accurate prediction of power grid load changes and improving the accuracy of assessment of future load change trends.

[0011] Optionally, the combination of the first load change rate and the second load change rate to predict the load change rate of the power grid within the second preset time period includes: predicting a third load change rate of the power grid within the second preset time period based on the first load change rate; determining whether there is an overlapping interval between the fluctuation range of the third load change rate and the fluctuation range of the second load change rate; if there is an overlapping interval, taking the mean of the overlapping interval as the load change rate of the power grid within the second preset time period; if there is no overlapping interval, taking the arithmetic mean of the third load change rate and the second load change rate as the load change rate of the power grid within the second preset time period.

[0012] By adopting the above technical scheme, the third load change rate is obtained based on the prediction of the first load change rate. By judging whether there is an overlapping interval between the third load change rate and the fluctuation range of the second load change rate, the mean of the overlapping interval is adopted when there is an overlapping interval, and the arithmetic mean of the two is adopted when there is no overlapping interval. This is used as the load change rate of the power grid within the second preset time period, thereby realizing the reasonable integration of historical data and current data and improving the reliability of the load change rate prediction.

[0013] Optionally, based on the stability coefficient, according to the load change rate, the first stability coefficient of the target power equipment within the preset time period is calculated, including: obtaining the fluctuation direction and fluctuation amplitude of the load change rate; determining a correction coefficient according to the fluctuation direction, if the fluctuation direction is rising, the correction coefficient is a positive value, and the correction coefficient is proportional to the fluctuation direction; if the fluctuation direction is falling, the correction coefficient is a negative value, and the correction coefficient is inversely proportional to the fluctuation direction; converting the fluctuation amplitude into a standardized coefficient, the standardized coefficient is the ratio of the fluctuation amplitude to a benchmark load; multiplying the standardized coefficient by the correction coefficient to obtain a load influence factor; performing arithmetic operations on the stability coefficient and the load influence factor to obtain the first stability coefficient of the target power equipment within the second preset time period.

[0014] By adopting the above technical scheme, by obtaining the fluctuation direction and fluctuation amplitude of the load change rate, the positive and negative correction coefficients are determined according to the fluctuation direction and the fluctuation amplitude is converted into a standardized coefficient. The two are multiplied to obtain the load influence factor, and then an arithmetic operation is performed with the stability coefficient to obtain the first stability coefficient. This realizes a quantitative evaluation of the impact of load changes on equipment stability, and improves the scientificity and accuracy of the stability coefficient calculation.

[0015] Optionally, the first stability coefficient is adjusted according to the second stability coefficient to generate a target stability coefficient of the target power equipment within the second preset time period, including: judging whether the associated equipment is abnormal according to the second stability coefficient, and if the associated equipment is abnormal, obtaining the degree of association between the target power equipment and the associated equipment; attenuating the first stability coefficient according to the degree of association to obtain the target stability coefficient; if the associated equipment is normal, using the first stability coefficient as the target stability coefficient.

[0016] By adopting the above technical scheme, by judging whether the associated equipment is abnormal, when the associated equipment is abnormal, the first stability coefficient is attenuated according to the degree of association between the associated equipment and the target power equipment to obtain the target stability coefficient. When the associated equipment is normal, the first stability coefficient is directly used as the target stability coefficient, thereby achieving an accurate assessment of the impact on the operating status of the associated equipment and improving the assessment accuracy of the target stability coefficient.

[0017] Optionally, obtaining the degree of association between the target power device and the associated device includes: obtaining the electrical connection relationship between the target power device and the associated device, and the physical distance between the target power device and the associated device; determining a first association factor based on the electrical connection relationship, the first association factor being proportional to the closeness of the electrical connection relationship; determining a second association factor based on the physical distance, the second association factor being inversely proportional to the physical distance; multiplying the first association factor by a first influence weight to obtain a first weighted value; multiplying the second association factor by a second influence weight to obtain a second weighted value; and adding the first weighted value to the second weighted value to obtain the degree of association between the target power device and the associated device.

[0018] By adopting the above technical scheme, by obtaining the electrical connection relationship and physical distance between the target power equipment and the associated equipment, a first correlation factor proportional to the tightness of the electrical connection and a second correlation factor inversely proportional to the physical distance are determined respectively, and a first weighted value and a second weighted value are obtained by weighted calculation through influencing weights, and finally the degree of correlation is obtained by adding them up, thereby realizing a multi-dimensional quantitative evaluation of the correlation relationship between devices and improving the rationality and comprehensiveness of the calculation of the degree of correlation.

[0019] In a second aspect, the present application provides an electric power equipment maintenance system, the system comprising: an acquisition module, a prediction module, a calculation module, an adjustment module and a generation module; wherein the acquisition module is used to acquire the usage time and historical fault data of the target electric power equipment, as well as the first operation data of the target electric power equipment within a preset time; the prediction module is used to acquire the historical operation data of the power grid, and the first load change rate of the power grid within the first preset time, and predict the load change rate of the power grid within a second preset time in combination with the historical operation data and the first load change rate, wherein the first preset time is before the second preset time; the calculation module is used to calculate the first stability coefficient of the target electric power equipment within the second preset time according to the load change rate based on the stability coefficient; the adjustment module is used to acquire the second stability coefficient of the associated equipment of the target electric power equipment within the second preset time, adjust the first stability coefficient according to the second stability coefficient, and generate the target stability coefficient of the target electric power equipment within the second preset time; the generation module is used to generate a maintenance plan for the target electric power equipment if the target stability coefficient is lower than a set threshold.

[0020] In a third aspect, the present application provides an electronic device that adopts the following technical solution: 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 a computer program such as any one of the above-mentioned power equipment maintenance methods.

[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 power equipment maintenance methods.

[0022] In summary, the present application includes at least one of the following beneficial technical effects: The stability coefficient is determined by acquiring the current operating parameters of the target power equipment, and the load change rate within the second preset time period is predicted by combining the historical operating data and the first load change rate within the first preset time period, and then the first stability coefficient is calculated. The first stability coefficient is adjusted according to the second stability coefficient of the associated equipment to obtain the target stability coefficient, so that a maintenance plan is generated in time when the target stability coefficient is lower than the set threshold, thereby realizing dynamic monitoring and predictive maintenance of the operating status of the power equipment and improving the rationality of maintenance resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flowchart of a method for repairing electric power equipment provided in an embodiment of the present application; Figure 2 It is a structural schematic diagram of a power equipment maintenance system 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 1 is a flow chart of a method for repairing electric power equipment provided in an embodiment of the present application. Figure 1 As shown, the method includes S101-S105: S101, obtaining operating parameters of a target power device in a power grid at a current moment, and determining a stability coefficient of the target power device according to the operating parameters.

[0028] In this embodiment, the operating parameters of the target power equipment of the power grid at the current moment are first obtained, and the operating parameters include voltage, current and temperature. In actual operation, the power equipment may deviate from its standard working state due to various factors, and such deviation may cause the performance of the equipment to deteriorate or fail. Therefore, by monitoring the operating parameters of the equipment in real time and calculating its stability coefficient, potential problems can be discovered in time.

[0029] Specifically, the voltage, current and temperature data at the current moment are collected by sensors installed on the target power equipment. In order to accurately evaluate the operating status of the equipment, it is necessary to compare these parameters with their standard values. First, the first difference between the voltage and the voltage standard value, the second difference between the current and the current standard value, and the third difference between the temperature and the temperature standard value are calculated. Taking into account the different degrees of influence of different parameters on the stability of the equipment, weight coefficients are introduced for weighted calculation: the first difference is multiplied by the first weight coefficient to obtain the first coefficient, the second difference is multiplied by the second weight coefficient to obtain the second coefficient, and the third difference is multiplied by the third weight coefficient to obtain the third coefficient.

[0030] Next, the first coefficient, the second coefficient, and the third coefficient are arithmetically added to obtain a comprehensive deviation value. The comprehensive deviation value can fully reflect the degree of deviation between the current operating state of the equipment and the ideal state. According to the pre-established correspondence between the comprehensive deviation value and the preset deviation threshold, the stability coefficient of the target power equipment is determined. For example, when the comprehensive deviation value is small, it corresponds to a higher stability coefficient; when the comprehensive deviation value is large, it corresponds to a lower stability coefficient.

[0031] Based on the above embodiment, as an optional implementation, in S101, the operating parameters include voltage, current and temperature, and determining the stability coefficient of the target power equipment according to the operating parameters specifically includes S11-S14: S11, calculating a first difference between a voltage and a voltage standard value, a second difference between a current and a current standard value, and a third difference between a temperature and a temperature standard value.

[0032] S12, arithmetically multiplying the first difference by the first weight coefficient to obtain the first coefficient; arithmetically multiplying the second difference by the second weight coefficient to obtain the second coefficient; arithmetically multiplying the third difference by the third weight coefficient to obtain the third coefficient.

[0033] S13, arithmetically adding the first coefficient, the second coefficient and the third coefficient to obtain a comprehensive deviation value.

[0034] S14, determining the stability coefficient of the target power equipment according to the corresponding relationship between the comprehensive deviation value and the preset deviation threshold.

[0035] In this embodiment, in order to more accurately evaluate the operating status of the target power equipment, it is necessary to quantify and analyze the key operating parameters of the equipment. Voltage, current and temperature are the most basic and important parameters that reflect the operating status of the equipment. Abnormalities in these parameters often indicate potential faults of the equipment. Through comprehensive analysis of these parameters, the stability of the equipment can be accurately evaluated.

[0036] Specifically, the voltage, current and temperature data of the target power equipment are first collected in real time through intelligent sensors. In order to evaluate the degree of deviation of these parameters, it is necessary to compare the actual values ​​collected with the standard operating parameters of the equipment. The first difference between the voltage and the voltage standard value is calculated, which reflects the degree of deviation of the voltage; the second difference between the current and the current standard value is calculated to characterize the fluctuation of the current; the third difference between the temperature and the temperature standard value is calculated to reflect the heating condition of the equipment. These differences can be absolute differences or relative differences, and the specific choice should be determined according to the actual application scenario.

[0037] Considering the different degrees of influence of different parameters on the stability of the equipment, weight coefficients are introduced for differentiated processing. The first difference is multiplied by the first weight coefficient to obtain the first coefficient, which reflects the actual influence of voltage deviation on the stability of the equipment; the second difference is multiplied by the second weight coefficient to obtain the second coefficient, which reflects the influence of current deviation; the third difference is multiplied by the third weight coefficient to obtain the third coefficient, which represents the influence of temperature deviation. These weight coefficients can be determined through historical data analysis or expert experience, and can be dynamically adjusted according to the equipment type and operating environment.

[0038] The calculated first coefficient, second coefficient and third coefficient are arithmetically added to obtain a comprehensive deviation value. This weighted summation method can combine the influence of multiple parameters to obtain a unified evaluation index. The larger the comprehensive deviation value, the more serious the overall deviation of the equipment from the normal working state. According to the correspondence between the pre-established comprehensive deviation value and the preset deviation threshold, the stability coefficient of the target power equipment is determined. For example, the comprehensive deviation value can be divided into multiple intervals, each interval corresponds to a stability coefficient value, and the larger the deviation, the lower the corresponding stability coefficient.

[0039] S102, obtaining historical operation data of the power grid and a first load change rate of the power grid within a first preset time period, combining the historical operation data and the first load change rate, and predicting the load change rate of the power grid within a second preset time period, the first preset time period being before the second preset time period.

[0040] In this embodiment, in order to accurately predict the future operating status of the power equipment, it is necessary to analyze the load change trend of the power grid. The change of the power grid load directly affects the operating pressure of the equipment, and the load change often has a certain regularity and continuity, so the future load change can be predicted through historical data and recent change trends.

[0041] Specifically, firstly, the historical operation data of the power grid is obtained from the power grid management system database. These data contain the load conditions of the power grid in different time periods, which can reflect the periodic characteristics and change laws of load changes. By analyzing the historical operation data, the historical load change rate of the power grid in each time period is determined, and then the second load change rate within the second preset time period is obtained. For example, if the historical data shows that similar load change trends are shown in the same period and time period of each year, this change trend can be used as an important reference for prediction.

[0042] At the same time, the first load change rate of the power grid within the first preset time (i.e., a period of time before the second preset time) is obtained. The first load change rate reflects the latest change trend of the power grid load, and this trend has a strong continuity. Based on the first load change rate, the third load change rate of the power grid within the second preset time is predicted by the trend extrapolation method.

[0043] In order to improve the accuracy of the prediction, it is necessary to conduct a comprehensive analysis of the second load change rate based on historical data and the third load change rate based on recent trend prediction. Specifically, it is to determine whether there is an overlapping interval between the fluctuation range of the third load change rate and the fluctuation range of the second load change rate. If there is an overlapping interval, the mean of the overlapping interval is used as the load change rate of the power grid within the second preset time length; if there is no overlapping interval, the arithmetic mean of the third load change rate and the second load change rate is used as the prediction result.

[0044] Based on the above embodiment, as an optional implementation, in S102, combining the historical operation data and the first load change rate, predicting the load change rate of the power grid within the second preset time period specifically includes S21-S23: S21, determining the historical load change rate of the power grid in each time period based on the historical operation data.

[0045] S22, determining a second load change rate of the power grid within a second preset time period according to the historical load change rate of the power grid in each time period.

[0046] S23, combining the first load change rate and the second load change rate, predicting the load change rate of the power grid within a second preset time period.

[0047] In this embodiment, accurately predicting the load change of the power grid is crucial to evaluating the stability of the equipment. The load change of the power grid has certain regularities, which can be discovered by analyzing historical data. At the same time, combined with the recent load change trend, the future load change can be predicted more accurately. This prediction method takes into account both historical laws and current actual conditions, and can provide more reliable prediction results.

[0048] Specifically, first extract historical operation data from the database of the power grid monitoring system. These data need to be classified and sorted according to the time dimension, including load data of different time scales such as annual, seasonal, monthly, weekly and daily. Through data analysis, determine the historical load change rate of the power grid in each time period. For example, analyze the power load curve of a certain period, calculate the load change amplitude between adjacent time points, and obtain the historical load change rate of the period. This analysis should pay attention to both the regular change rules and the load change characteristics of special periods (such as holidays, extreme weather, etc.).

[0049] On the basis of obtaining the historical load change rate of each time period, focus on analyzing the historical time period data with similar characteristics to the second preset time period. For example, if you need to predict the load change rate of a certain time period next week, you should focus on analyzing the load changes of the same period and the same type of day in history. By performing statistical analysis on these similar historical data, such as calculating the average value, identifying the change trend, etc., the second load change rate of the power grid within the second preset time period is determined. This second load change rate reflects the load change trend predicted based on historical laws.

[0050] In order to make the prediction results more accurate, it is necessary to combine and analyze the second load change rate obtained based on historical data with the first load change rate (i.e., the actual load change rate before the second preset time). The first load change rate reflects the latest load change trend, which often has a strong continuity, especially in short-term prediction. It has important reference value. By designing a suitable mathematical model, the two change rates are weighted and fused, and finally the load change rate forecast value of the power grid within the second preset time is obtained.

[0051] Based on the above embodiment, as an optional implementation, in S23, combining the first load change rate and the second load change rate, predicting the load change rate of the power grid within the second preset time period specifically includes S231-S234: S231, predicting a third load change rate of the power grid within a second preset time period according to the first load change rate.

[0052] In this embodiment, the load change of the power grid has the characteristics of short-term continuity, that is, the recent load change trend tends to continue for a certain period of time. Therefore, by analyzing the first load change rate (that is, the actual load change situation before the second preset time), the third load change rate of the power grid within the second preset time can be predicted. This prediction based on recent data is of great significance for grasping the latest trend of load changes.

[0053] Specifically, the first load change rate within the first preset time period is first analyzed. This includes analyzing the characteristics of the load change direction, amplitude and rate, and focusing on the acceleration characteristics of the load change, that is, the change trend of the load change rate itself. The first load change rate data is processed through time series analysis methods, such as moving average, exponential smoothing and other technologies, to eliminate the influence of random fluctuations and extract the real change trend.

[0054] Based on the trend extraction, an appropriate mathematical model is used for extrapolation prediction. Different prediction models can be selected according to the characteristics of the first load change rate. For example, when the load change shows an obvious linear trend, a linear regression model can be used; when the change trend is more complex, a nonlinear regression or time series prediction model can be used. The prediction model needs to consider the influence of the time span, that is, the relationship between the second preset time length and the first preset time length. The longer the time span, the greater the uncertainty of the prediction, and the model needs to adjust the prediction parameters appropriately.

[0055] In order to improve the reliability of the forecast, it is also necessary to consider external factors that affect load changes, such as weather changes, production arrangements, etc. These factors may cause a turn in the load change trend, so when forecasting, the forecast results should be corrected in combination with the changes in these factors. For example, if the weather will change significantly during the forecast period, the forecast results need to be adjusted based on the load response characteristics of similar historical situations.

[0056] S232, determining whether there is an overlapping interval between the fluctuation range of the third load change rate and the fluctuation range of the second load change rate.

[0057] S233: If there is an overlapping interval, the average value of the overlapping interval is used as the load change rate of the power grid within the second preset time period.

[0058] S234: If there is no overlapping interval, the arithmetic mean of the third load change rate and the second load change rate is used as the load change rate of the power grid within the second preset time period.

[0059] In this embodiment, in order to obtain a more accurate load change rate prediction value, it is necessary to integrate and analyze the second load change rate obtained based on historical data and the third load change rate based on recent trend prediction. Since these two prediction methods each have their own applicable scenarios and advantages, by analyzing the overlap of their fluctuation ranges, the final prediction value can be determined more scientifically.

[0060] Specifically, it is necessary to first determine the fluctuation range of the second load change rate and the third load change rate. For the second load change rate, its fluctuation range is obtained based on the statistical analysis of historical data of the same period, and the upper and lower limits can be determined by adding and subtracting the standard deviation of the mean; for the third load change rate, its fluctuation range is determined based on the confidence interval of the prediction model, reflecting the uncertainty range of the prediction results. By comparing these two fluctuation ranges, it is determined whether they have overlapping intervals.

[0061] When there is an overlapping interval between two fluctuation ranges, it means that the results obtained by the two prediction methods have a certain consistency, and this consistency increases the credibility of the prediction results. At this time, it is more reasonable to select the mean of the overlapping interval as the final load change rate prediction value. Because the overlapping interval represents the range of changes that both prediction methods believe are more likely to occur, taking its mean can balance the impact of the two prediction methods. For example, if the fluctuation range of the second load change rate is 5%~8%, and the fluctuation range of the third load change rate is 7%~10%, then the overlapping interval is 7%~8%, and the mean of 7.5% is taken as the final prediction value.

[0062] When there is no overlap between the two fluctuation ranges, it means that there is a large difference between the results obtained by the two prediction methods. This difference may be due to the abnormality of load changes or the limitations of the prediction methods. In this case, in order to balance the two prediction results, the arithmetic mean of the second load change rate and the third load change rate is used as the final prediction value. This processing method takes into account the influence of historical laws and recent change trends, and can reduce the deviation that may be caused by a single prediction method to a certain extent.

[0063] S103, calculating a first stability coefficient of the target power equipment within a second preset time period based on the stability coefficient and according to the load change rate.

[0064] In this embodiment, the stability of the power equipment depends not only on its current state, but also on future load changes. Load changes will have a significant impact on the operating state of the equipment, so it is necessary to incorporate the predicted load changes into the stability evaluation system to more accurately predict the stability of the equipment within the second preset time period.

[0065] Specifically, the characteristics of the predicted load change rate are first analyzed to obtain its fluctuation direction and fluctuation amplitude. The fluctuation direction indicates whether the load is in an upward or downward trend, and the fluctuation amplitude reflects the severity of the load change. These two characteristics have different impact mechanisms on the stability of the equipment. The correction coefficient is determined according to the fluctuation direction. When the fluctuation direction is upward, the increase in load will cause greater pressure on the equipment, so the correction coefficient is set to a positive value, and the size of the correction coefficient is proportional to the fluctuation direction; when the fluctuation direction is downward, the load reduction is conducive to the stable operation of the equipment, so the correction coefficient is set to a negative value, and the size of the correction coefficient is inversely proportional to the fluctuation direction.

[0066] In order to make the fluctuation amplitude comparable, it needs to be converted into a standardized coefficient. Specifically, the ratio of the fluctuation amplitude to the benchmark load is used as the standardized coefficient. This processing method can eliminate the dimensional difference of the fluctuation amplitude under different load levels. Then, the standardized coefficient is multiplied by the correction coefficient to obtain the load influence factor. This factor comprehensively considers the direction and amplitude of the load change, and can fully reflect the impact of the load change on the stability of the equipment.

[0067] Finally, the stability coefficient obtained above and the load impact factor are subjected to arithmetic operation to obtain the first stability coefficient of the target power equipment within the second preset time. This calculation method enables the first stability coefficient to include information about the current state of the equipment and reflect the possible impact of future load changes.

[0068] Based on the above embodiment, as an optional implementation, in S103, based on the stability coefficient, according to the load change rate, calculating the first stability coefficient of the target power equipment within a preset time period specifically includes S31-S35: S31, obtaining the fluctuation direction and fluctuation amplitude of the load change rate.

[0069] S32, determining a correction coefficient according to the fluctuation direction. If the fluctuation direction is rising, the correction coefficient is a positive value, and the correction coefficient is proportional to the fluctuation direction; if the fluctuation direction is falling, the correction coefficient is a negative value, and the correction coefficient is inversely proportional to the fluctuation direction.

[0070] In this embodiment, power load fluctuation has an important impact on the operating state of the equipment. The upward trend of load will increase the operating pressure of the equipment, while the downward trend of load will reduce the burden on the equipment. Therefore, it is necessary to set the corresponding correction coefficient according to the fluctuation direction to accurately reflect the impact of load changes on the stability of the equipment.

[0071] Specifically, the fluctuation direction is determined by calculating the change trend of the load change rate. When the load change rate is greater than zero, it is determined to be an upward trend; when the load change rate is less than zero, it is determined to be a downward trend. For the upward trend, a positive correction coefficient is set, and the size of the correction coefficient is positively correlated with the fluctuation trend. For example, when the load change rate is 2%, the correction coefficient can be set to 0.2; when the load change rate is 5%, the correction coefficient can be set to 0.5, reflecting the adverse effect of load increase on equipment stability.

[0072] For the downward trend, a negative correction coefficient is set, and the absolute value of the correction coefficient is inversely proportional to the fluctuation trend. For example, when the load change rate is -2%, the correction coefficient can be set to -0.3; when the load change rate is -5%, the correction coefficient can be set to -0.2, reflecting the gradual weakening of the favorable effect of load reduction on equipment stability.

[0073] S33, converting the fluctuation amplitude into a standardization coefficient, where the standardization coefficient is a ratio of the fluctuation amplitude to the reference load.

[0074] In this embodiment, in order to accurately evaluate the impact of load fluctuations on equipment stability, it is necessary to convert the fluctuation amplitudes of different magnitudes and units into comparable standardized forms. Since the rated load levels of different power equipment may vary greatly, directly using the absolute value of the fluctuation amplitude cannot accurately reflect the actual impact of load changes on the equipment. By converting the fluctuation amplitude into a standardized coefficient, the dimensional effect can be eliminated, and a unified evaluation of the impact of load changes in different situations can be achieved.

[0075] Specifically, we first need to determine the fluctuation amplitude and the benchmark load value. The fluctuation amplitude refers to the difference between the maximum and minimum values ​​of the load change, which can be obtained by analyzing the load change curve during the forecast period. The benchmark load usually selects the rated load of the equipment or the average load value of a specific period. This selection needs to consider the actual operating characteristics of the equipment. For example, for a transformer, its rated capacity can be selected as the benchmark load; for a transmission line, its rated transmission power can be selected as the benchmark load.

[0076] During standardization, the fluctuation amplitude is divided by the base load to obtain the standardization coefficient. This ratio reflects the relative degree of load change and eliminates the size differences between different equipment. For example, if the fluctuation amplitude of a certain equipment is 2MW and the base load is 10MW, the standardization coefficient is 0.2, indicating that the load change amplitude accounts for 20% of the base load. This standardization process allows the load changes of equipment of different capacity levels to be directly compared.

[0077] In order to improve the accuracy of the standardization process, some special cases need to be considered during the calculation process. For example, when the actual load is much lower than the benchmark load, the benchmark load may need to be adjusted appropriately to avoid underestimating the impact of load changes by making the standardization coefficient too small. At the same time, attention should also be paid to the handling of sudden load changes. The upper limit of the standardization coefficient can be set to prevent calculation errors in extreme cases.

[0078] S34, multiplying the normalization coefficient by the correction coefficient to obtain the load influence factor.

[0079] S35, performing an arithmetic operation on the stability coefficient and the load influence factor to obtain a first stability coefficient of the target power equipment within a second preset time period.

[0080] In this embodiment, in order to comprehensively evaluate the impact of load changes on equipment stability, it is necessary to comprehensively consider the standardized load fluctuation and directional correction. The load impact factor is obtained by multiplying the standardized coefficient and the correction coefficient. This factor contains both the relative amplitude information of the load change and the impact characteristics of the change direction. The load impact factor is further combined with the current stability coefficient to obtain the first stability coefficient after considering the impact of load changes.

[0081] Specifically, first multiply the above-mentioned standardization coefficient and correction coefficient. Since the correction coefficient already contains the positive and negative sign information, this multiplication operation can simultaneously reflect the magnitude and direction of the load change. For example, when the standardization coefficient is 0.2 (indicating that the load change is 20% of the baseline load) and the correction coefficient is 0.5 (indicating a moderate impact of an upward trend), the load impact factor obtained by multiplication is 0.1, indicating that the load change has a 10% adverse effect on the stability of the equipment. On the contrary, if the correction coefficient is -0.5 (indicating a moderate impact of a downward trend), the load impact factor obtained is -0.1, indicating that the load change has a 10% favorable effect on the stability of the equipment.

[0082] After obtaining the load influence factor, it is necessary to perform an arithmetic operation on it and the stability coefficient to obtain the first stability coefficient. Considering that the load influence factor may be positive or negative, a subtraction operation is used here: subtract the load influence factor from the stability coefficient to obtain the first stability coefficient. This operation method conforms to the actual situation: when the load influence factor is positive (load increases), the subtraction operation will reduce the stability coefficient; when the load influence factor is negative (load decreases), the subtraction operation will increase the stability coefficient. For example, if the current stability coefficient is 0.8 and the load influence factor is 0.1, the first stability coefficient is 0.7, reflecting the decrease in stability caused by the increase in load; if the load influence factor is -0.1, the first stability coefficient is 0.9, reflecting the increase in stability caused by the decrease in load.

[0083] S104, obtaining a second stability coefficient of an associated device of the target power device within a second preset time period, adjusting the first stability coefficient according to the second stability coefficient, and generating a target stability coefficient of the target power device within the second preset time period.

[0084] In this embodiment, there are complex electrical connections and mutual influence relationships between power devices, and the operating status of a single device is often affected by the status of its associated devices. Therefore, when evaluating the stability of the target power device, the operating status of the associated devices needs to be considered to achieve a more accurate stability prediction.

[0085] Specifically, first obtain the second stability coefficient of the associated device that has an associated relationship with the target power device within the second preset time length. By analyzing the second stability coefficient, it is determined whether the associated device is in an abnormal state. When the second stability coefficient is lower than the preset normal operation threshold, the associated device is considered abnormal. If the associated device is found to be abnormal, it is necessary to evaluate the impact of this abnormality on the target power device. To this end, the electrical connection relationship between the target power device and the associated device and the physical distance between them are obtained. These two factors directly determine the impact between the devices.

[0086] The first correlation factor is determined according to the electrical connection relationship. The tighter the electrical connection, the larger the first correlation factor is, which reflects the degree of influence of the electrical correlation. At the same time, the second correlation factor is determined according to the physical distance. The closer the physical distance is, the larger the second correlation factor is, which reflects the influence of the spatial position. Considering that the influence weights of the electrical connection relationship and the physical distance on the stability of the equipment may be different, the first influence weight and the second influence weight are introduced for weighted calculation. The first correlation factor is multiplied by the first influence weight to obtain the first weighted value, and the second correlation factor is multiplied by the second influence weight to obtain the second weighted value, and then the two weighted values ​​are added to obtain the degree of association between the target power equipment and the associated equipment.

[0087] When it is determined that the associated device is abnormal, the first stability coefficient is attenuated according to the calculated degree of association to generate a target stability coefficient. The degree of attenuation is proportional to the degree of association. The higher the degree of association, the more obvious the attenuation effect. If the associated device is in a normal state, the first stability coefficient is directly used as the target stability coefficient without adjustment.

[0088] Based on the above embodiment, as an optional implementation, in S104, adjusting the first stability coefficient according to the second stability coefficient to generate the target stability coefficient of the target power equipment within the second preset time period specifically includes S41-S43: S41, judging whether the associated device is abnormal according to the second stability coefficient, and if the associated device is abnormal, obtaining the degree of association between the target power device and the associated device.

[0089] In this embodiment, there are complex associations between power devices, and the abnormal operating state of one device often affects other devices related to it. Therefore, when evaluating the stability of the target power device, it is necessary to consider the operating state of the associated device and its degree of influence. By analyzing the second stability coefficient of the associated device and determining the degree of association, the impact of the abnormality of the associated device on the stability of the target device can be scientifically evaluated.

[0090] Specifically, it is first necessary to determine whether the associated device is in an abnormal state according to a preset threshold. Obtain the second stability coefficient of the associated device within the second preset time length, and compare it with the preset normal operation threshold. For example, 0.7 can be set as the critical value of stable operation. When the second stability coefficient is lower than 0.7, the associated device is determined to be in an abnormal state. The setting of this threshold needs to consider multiple factors such as device type and operating environment, and can be determined based on historical operating experience and expert knowledge.

[0091] When it is determined that the associated equipment is in an abnormal state, it is necessary to further obtain the degree of association between the target power equipment and the associated equipment. The degree of association is evaluated mainly from two dimensions: electrical connection relationship and physical distance. The electrical connection relationship reflects the functional association between the equipment, including direct connection, indirect connection and other forms; the physical distance reflects the spatial association between the equipment, including the actual distance between the equipment, the similarity of the environment and other factors.

[0092] In order to accurately evaluate the degree of association, a scientific evaluation system needs to be established. For electrical connection relationships, the first association factor can be determined based on factors such as the directness of the connection and the circuit topology; for physical distance, the second association factor can be calculated using an inverse distance function. These two association factors need to be weighted based on actual operating experience to ultimately obtain a comprehensive degree of association. For example, a transformer has a high electrical association with the circuit breaker it is directly connected to, and because of their close physical locations, the degree of association ultimately determined is also high.

[0093] Based on the above embodiment, as an optional implementation, in S41, obtaining the degree of association between the target power device and the associated device specifically includes S411-S414: S411, acquiring the electrical connection relationship between the target power device and the associated devices, and the physical distance between the target power device and the associated devices.

[0094] S412, determining a first correlation factor according to the electrical connection relationship, the first correlation factor being proportional to the tightness of the electrical connection relationship; determining a second correlation factor according to the physical distance, the second correlation factor being inversely proportional to the physical distance.

[0095] S413: multiply the first correlation factor by the first influence weight to obtain a first weighted value; and multiply the second correlation factor by the second influence weight to obtain a second weighted value.

[0096] S414: Add the first weighted value and the second weighted value to obtain the degree of association between the target power device and the associated devices.

[0097] In this embodiment, in order to accurately evaluate the degree of association between the target power equipment and the associated equipment, it is necessary to comprehensively consider two key factors: electrical connection relationship and physical distance. These two factors reflect the mutual influence characteristics between the equipment from different dimensions. Through scientific quantification methods and weight allocation, a more accurate evaluation result of the degree of association can be obtained.

[0098] Specifically, the electrical connection relationship between the target power equipment and the associated equipment is first obtained from the power grid topology database, and the physical distance between them is obtained from the equipment layout diagram. The electrical connection relationship includes direct connection, primary indirect connection, multiple indirect connection and other forms. The tightness of the connection relationship needs to be determined according to the different connection methods. For example, the tightness of the direct connection can be set to 1.0, the primary indirect connection to 0.7, the secondary indirect connection to 0.4, and so on. The physical distance needs to consider the actual spatial distance between the devices, as well as environmental factors such as whether there are shielding objects.

[0099] The first correlation factor is determined based on the acquired electrical connection relationship, in the form of a piecewise function: when it is a direct connection, the first correlation factor takes a value of 0.80.8; when it is a secondary indirect connection, it takes a value of 0.3~0.5. At the same time, the second correlation factor is determined based on the physical distance, and an inverse function can be used: the second correlation factor = K / (d+d0), where K is the proportional coefficient, d is the actual distance, and d0 is the corrected distance (used to avoid singularity when the distance is zero). This setting ensures the consistency of the correlation factor and the actual degree of influence.

[0100] Considering that the influence weights of electrical connection relationship and physical distance on equipment stability may be different, a weight coefficient is introduced for weighted processing. The first correlation factor is multiplied by the first influence weight (reflecting the importance of electrical connection) to obtain the first weighted value; the second correlation factor is multiplied by the second influence weight (reflecting the importance of physical distance) to obtain the second weighted value. The setting of weights needs to be based on historical operating experience and expert knowledge. For example, the first influence weight can be set to 0.7 and the second influence weight can be set to 0.3 to reflect the dominant role of the electrical connection relationship.

[0101] Finally, the first weighted value is added to the second weighted value to obtain the comprehensive degree of association between the target power device and the associated device. This weighted summation method can balance the influence of the two dimensions and obtain a more reasonable evaluation result. For example, if the first weighted value is 0.6 (reflecting a stronger electrical association) and the second weighted value is 0.2 (reflecting a greater physical distance), the final degree of association is 0.8, indicating that there is a strong association between the two devices.

[0102] S42, performing attenuation processing on the first stability coefficient according to the correlation degree to obtain a target stability coefficient.

[0103] In this embodiment, when an abnormality occurs in an associated device, its impact will be transmitted to the target power device through the association relationship of the electrical connection and the physical space, resulting in reduced stability of the target device. By performing attenuation processing on the first stability coefficient based on the degree of association, this chain effect can be quantified to obtain a more accurate target stability coefficient. This attenuation processing should not only reflect the severity of the impact, but also ensure the rationality of the processing result.

[0104] Specifically, we first need to design a scientific attenuation processing model. Considering that the impact of abnormalities in associated devices on target devices has nonlinear characteristics, it is more practical to use an exponential attenuation function for processing. The attenuation function can be expressed as: target stability coefficient = first stability coefficient × (1-α × e^(-β / R)), where α is the attenuation coefficient (range 0~1), β is the adjustment factor, and R is the degree of association. This function form ensures that the attenuation effect increases with the increase in the degree of association, but does not cause the stability coefficient to drop to zero.

[0105] In actual calculations, the selection of the attenuation coefficient α needs to consider the severity of the abnormality of the associated equipment. For example, when the second stability coefficient of the associated equipment is seriously lower than the normal operation threshold, a larger attenuation coefficient can be selected; when the second stability coefficient is close to the threshold, a smaller attenuation coefficient is selected. The adjustment factor β is used to control the shape of the attenuation curve and can be adjusted according to actual operating experience. For example, when the first stability coefficient is 0.8 and the degree of association is 0.6, when α=0.3 and β=1 are selected, the calculated target stability coefficient may drop to about 0.65.

[0106] In order to ensure the rationality of the attenuation processing, it is necessary to set constraints on the processing results. First, the target stability coefficient should not be lower than the preset minimum safety value to prevent excessive attenuation; second, when the degree of correlation is small, the attenuation effect should be limited to avoid weak correlation leading to significant stability reduction; finally, the superposition effect when multiple associated devices are abnormal at the same time should be considered to ensure that the final target stability coefficient still has actual physical meaning.

[0107] If the associated device is normal, the first stability coefficient is used as the target stability coefficient in S41.

[0108] S105: If the target stability coefficient is lower than a set threshold, a maintenance plan for the target power equipment is generated.

[0109] In this embodiment, the target stability coefficient is a comprehensive evaluation result of the future operating status of the power equipment, which includes both the equipment status information and the impact of load changes and associated equipment. When the target stability coefficient is lower than the set threshold, it indicates that the equipment may have safety hazards or performance degradation risks, and timely inspection and maintenance are required to ensure the safe and stable operation of the power grid.

[0110] Specifically, the system first compares the calculated target stability coefficient with a preset threshold. The threshold is a critical value determined based on the historical operating experience of the equipment and safety management requirements. A value lower than this value means that the equipment operation risk exceeds the acceptable range. When it is detected that the target stability coefficient is lower than the set threshold, the system will automatically trigger the maintenance plan generation process. The generation of the maintenance plan requires comprehensive consideration of multiple factors, including the importance of the equipment, load conditions, maintenance resources, and coordination with other equipment maintenance work.

[0111] When generating a maintenance plan, the maintenance priority is first determined based on the degree to which the target stability coefficient deviates from the set threshold. The greater the deviation, the more critical the equipment status is and the higher the maintenance priority is. At the same time, combined with the aforementioned predicted load change rate, a suitable maintenance time window is selected to avoid the peak load period as much as possible to reduce the impact of maintenance work on power grid operation. In addition, the operating status of related equipment needs to be considered, and the maintenance sequence needs to be arranged reasonably to avoid a chain reaction caused by maintenance work.

[0112] The contents of the maintenance plan include specific maintenance items, required resource allocation, maintenance schedule, and emergency plans. The determination of maintenance items is mainly based on the specific reasons that lead to the reduction of the target stability factor. For example, if the stability is reduced due to voltage fluctuations, the focus is on repairing components related to voltage regulation. Resource allocation includes the required maintenance personnel, spare parts, special tools, etc., and the scheduling plan for various resources is clarified. The time arrangement needs to be refined to the specific maintenance process to ensure that the maintenance work is carried out in an orderly manner.

[0113] Based on the above method, the present application also discloses a power equipment maintenance system, such as Figure 2 As shown, Figure 2 : is a structural diagram of a power equipment maintenance system provided by an embodiment of the present application, the system includes: an acquisition module, a prediction module, a calculation module, a regulation module and a generation 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 operation data of the target power equipment within a preset time; a prediction module is used to acquire the historical operation data of the power grid, as well as the first load change rate of the power grid within the first preset time, and predict the load change rate of the power grid within a second preset time in combination with the historical operation data and the first load change rate, the first preset time being before the second preset time; a calculation module is used to calculate the first stability coefficient of the target power equipment within the second preset time based on the stability coefficient and according to the load change rate; an adjustment module is used to acquire the second stability coefficient of the associated equipment of the target power equipment within the second preset time, adjust the first stability coefficient according to the second stability coefficient, and generate a target stability coefficient of the target power equipment within the second preset time; a generation module is used to generate a maintenance plan for the target power equipment if the target stability coefficient is lower than a set threshold.

[0114] 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.

[0115] 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 .

[0116] The communication bus 1002 is used to realize the connection and communication between these components.

[0117] 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.

[0118] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0119] 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.

[0120] 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 a method for repairing electric power equipment.

[0121] 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 storing a method for repairing electric equipment in the memory 1005. When executed by one or more processors, the electronic device executes one or more methods described in the above embodiments.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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 repairing electric power equipment, characterized in that: The method comprises: Acquire the operating parameters of the target power equipment of the power grid at the current moment, and determine the stability coefficient of the target power equipment according to the operating parameters; Acquire historical operation data of the power grid and a first load change rate of the power grid within a first preset time period, and predict the load change rate of the power grid within a second preset time period by combining the historical operation data and the first load change rate, where the first preset time period is before the second preset time period; On the basis of the stability coefficient, according to the load change rate, calculating a first stability coefficient of the target power equipment within the second preset time period; Acquire a second stability coefficient of an associated device of the target power device within the second preset time period, adjust the first stability coefficient according to the second stability coefficient, and generate a target stability coefficient of the target power device within the second preset time period; If the target stability coefficient is lower than a set threshold, a maintenance plan for the target power equipment is generated.

2. The power equipment maintenance method according to claim 1, characterized in that: The operating parameters include voltage, current and temperature, and determining the stability coefficient of the target power equipment according to the operating parameters includes: Calculating a first difference between the voltage and a voltage standard value, a second difference between the current and a current standard value, and a third difference between the temperature and a temperature standard value; Arithmetically multiplying the first difference by the first weight coefficient to obtain a first coefficient; Arithmetically multiplying the second difference by the second weight coefficient to obtain a second coefficient; Arithmetically multiplying the third difference by the third weight coefficient to obtain a third coefficient; arithmetically adding the first coefficient, the second coefficient and the third coefficient to obtain a comprehensive deviation value; The stability coefficient of the target electric power equipment is determined according to the corresponding relationship between the comprehensive deviation value and the preset deviation threshold.

3. The power equipment maintenance method according to claim 1, characterized in that: The step of combining the historical operation data and the first load change rate to predict the load change rate of the power grid within a second preset time period includes: Determining the historical load change rate of the power grid in each time period according to the historical operation data; Determining a second load change rate of the power grid within a second preset time period according to the historical load change rate of the power grid in each time period; The load change rate of the power grid within a second preset time period is predicted by combining the first load change rate and the second load change rate.

4. The method for repairing electric power equipment according to claim 3, characterized in that: The step of combining the first load change rate and the second load change rate to predict the load change rate of the power grid within a second preset time period includes: Predicting a third load change rate of the power grid within a second preset time period according to the first load change rate; Determining whether there is an overlapping interval between the fluctuation range of the third load change rate and the fluctuation range of the second load change rate; If the overlapping interval exists, taking the average value of the overlapping interval as the load change rate of the power grid within the second preset time period; If the overlapping interval does not exist, the arithmetic mean of the third load change rate and the second load change rate is used as the load change rate of the power grid within the second preset time period.

5. The power equipment maintenance method according to claim 1, characterized in that: The step of calculating the first stability coefficient of the target power equipment within the preset time period based on the stability coefficient and according to the load change rate includes: Obtaining the fluctuation direction and fluctuation amplitude of the load change rate; Determine a correction coefficient according to the fluctuation direction. If the fluctuation direction is rising, the correction coefficient is a positive value and is proportional to the fluctuation direction; if the fluctuation direction is falling, the correction coefficient is a negative value and is inversely proportional to the fluctuation direction. Converting the fluctuation amplitude into a normalized coefficient, wherein the normalized coefficient is a ratio of the fluctuation amplitude to a reference load; Multiplying the standardization coefficient by the correction coefficient to obtain a load influence factor; An arithmetic operation is performed on the stability coefficient and the load influence factor to obtain a first stability coefficient of the target power equipment within the second preset time period.

6. The method for repairing electric power equipment according to claim 1, characterized in that: The step of adjusting the first stability coefficient according to the second stability coefficient to generate a target stability coefficient of the target power equipment within the second preset time period includes: determining whether the associated device is abnormal according to the second stability coefficient, and if the associated device is abnormal, obtaining the degree of association between the target power device and the associated device; Performing attenuation processing on the first stability coefficient according to the correlation degree to obtain the target stability coefficient; If the associated device is normal, the first stability coefficient is used as the target stability coefficient.

7. The power equipment maintenance method according to claim 6, characterized in that: The obtaining the degree of association between the target power device and the associated device includes: Acquire the electrical connection relationship between the target power device and the associated device, and the physical distance between the target power device and the associated device; Determine a first correlation factor according to the electrical connection relationship, the first correlation factor being proportional to the tightness of the electrical connection relationship; determine a second correlation factor according to the physical distance, the second correlation factor being inversely proportional to the physical distance; Multiplying the first correlation factor by the first influence weight to obtain a first weighted value; multiplying the second correlation factor by the second influence weight to obtain a second weighted value; The first weighted value and the second weighted value are added to obtain the degree of association between the target power device and the associated device.

8. An electric power equipment maintenance system, characterized in that: The system includes: an acquisition module, a prediction module, a calculation module, an adjustment module and a generation 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 prediction module is used to obtain historical operation data of the power grid and a first load change rate of the power grid within a first preset time period, and predict the load change rate of the power grid within a second preset time period by combining the historical operation data and the first load change rate, wherein the first preset time period is before the second preset time period; The calculation module is used to calculate the first stability coefficient of the target power equipment within the second preset time period based on the stability coefficient and according to the load change rate; The adjustment module is used to obtain a second stability coefficient of an associated device of the target power device within the second preset time period, adjust the first stability coefficient according to the second stability coefficient, and generate a target stability coefficient of the target power device within the second preset time period; The generation module is used to generate a maintenance plan for the target power equipment if the target stability coefficient is lower than a set 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, 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.

Images