Power Equipment Maintenance Method, System, Device and Medium Based on Digital Twin

Abstract

The present application relates to the field of power equipment technology, and in particular to a method, system, device, and medium for power equipment maintenance based on digital twins. The method comprises obtaining operating status data of the power equipment; performing anomaly detection and analysis on the operating status data to obtain anomaly detection results; determining a health index of the power equipment based on the anomaly detection results, and calculating a maintenance priority index of the power equipment based on the health index; determining a maintenance management strategy based on the maintenance priority index, and executing maintenance management measures on the power equipment based on the maintenance management strategy. The present application can improve the maintenance accuracy of power equipment while reducing operation and maintenance costs.

Description

Technical Field

[0001] The present application relates to the technical field of power equipment, and in particular to a power equipment maintenance method, system, equipment and medium based on digital twins. Background Art

[0002] Currently, the operation and maintenance of power equipment is crucial to the stability and security of the power grid. As power grid systems become increasingly complex and intelligent, traditional power equipment maintenance methods are no longer able to meet the needs of modern power systems. Intelligent operation and maintenance of power equipment has become a trend in the industry.

[0003] In order to solve the maintenance problem of power equipment in related technologies, regular inspections are usually adopted to maintain power equipment. Potential faults are discovered by manually checking the appearance and operating parameters of the equipment. However, this method fails to fully consider the actual operating status of the equipment, which can easily lead to excessive or insufficient maintenance, thereby increasing operation and maintenance costs or causing equipment damage due to failure to promptly handle potential faults. Therefore, there is room for improvement. Summary of the Invention

[0004] The present application provides a method, system, equipment and medium for maintaining power equipment based on digital twins, which can improve the maintenance accuracy of power equipment and reduce operation and maintenance costs.

[0005] The above-mentioned invention objective of this application is achieved through the following technical solutions:

[0006] A digital twin-based power equipment maintenance method, the digital twin-based power equipment maintenance method comprising:

[0007] Obtain operating status data of power equipment;

[0008] Performing anomaly detection analysis on the operating status data to obtain an anomaly detection result;

[0009] Determining a health index of the power equipment based on the abnormality detection result, and calculating a maintenance priority index of the power equipment according to the health index;

[0010] A maintenance management strategy is determined according to the maintenance priority index, and maintenance management measures are performed on the power equipment based on the maintenance management strategy.

[0011] By adopting the above technical solution, by obtaining the operating status data of the power equipment, the integrity and accuracy of the equipment operating data can be ensured, thereby providing a reliable basis for subsequent anomaly detection, health assessment and maintenance decisions, avoiding misjudgments due to data missing or errors, and by performing anomaly detection and analysis on the operating status data, abnormal conditions in the equipment operation can be effectively identified, thereby avoiding the expansion of equipment failures due to the failure to discover the anomaly in time, improving the operating safety and stability of the system, and by calculating the health index and maintenance priority index of the power equipment based on the anomaly detection results, the health status and maintenance urgency of the equipment can be quantified, thereby helping maintenance personnel give priority to high-risk equipment, optimize maintenance resource allocation, and improve overall maintenance efficiency, and by formulating maintenance management strategies and executing maintenance management measures according to the maintenance priority index, it is possible to intelligently select maintenance methods according to the actual status of the equipment, thereby reducing unnecessary maintenance costs, while reducing the sudden failure rate and improving the availability and operating efficiency of the equipment.

[0012] In a preferred example, the present application may be further configured as follows: performing anomaly detection analysis on the operating status data to obtain an anomaly detection result specifically includes:

[0013] Based on data processing technology, the operating status data is subjected to data normalization processing to remove noise and smooth the data to obtain pre-processed operating status data;

[0014] According to the operating status data, the abnormal condition of the electric power equipment is identified to obtain the abnormality detection result.

[0015] By adopting the above technical solution, by performing data normalization processing on the operating status data, the noise in the data can be removed and the data can be smoothed, thereby improving the accuracy of anomaly detection, avoiding false detection or missed detection due to abnormal data fluctuations, and accurately detecting abnormal equipment conditions by identifying equipment abnormal conditions based on the processed operating status data.

[0016] In a preferred example, the present application may be further configured as follows: identifying the abnormal condition of the power equipment according to the operating status data to obtain the abnormality detection result specifically includes:

[0017] Obtaining historical operating status data and anomaly detection benchmark thresholds of the power equipment;

[0018] Based on an abnormality scoring formula, the abnormal state of the power equipment is evaluated according to the historical operating state data, the abnormality detection reference threshold and the operating state data to obtain an abnormality scoring value;

[0019] Determining, based on the anomaly score value, whether the anomaly score value is greater than the anomaly detection reference threshold;

[0020] If the abnormality score value is greater than the abnormality detection reference threshold, an abnormality detection result of the device abnormality is generated.

[0021] By adopting the above technical solution, by obtaining the historical operating status data of the equipment and the anomaly detection benchmark threshold, it is possible to provide an analysis benchmark for the long-term operating trend of the equipment, thereby improving the accuracy of anomaly detection and avoiding misjudgment caused by short-term fluctuations. By calculating the anomaly score value based on the anomaly scoring formula, the degree of anomaly of the equipment can be quantified, providing an intuitive basis for judgment, and improving the efficiency of maintenance work. By judging whether the equipment is abnormal based on the anomaly score value, a reasonable anomaly detection threshold can be set, thereby reducing false alarms and missed alarms and improving the accuracy of anomaly detection. By generating an anomaly detection result when the anomaly score exceeds the threshold, the health index can be calculated in time when the equipment anomaly occurs, and the health status and maintenance urgency of the equipment can be quantified, thereby ensuring that equipment anomalies can be responded to quickly and improving the accuracy of fault detection.

[0022] In a preferred example, the present application may be further configured as follows: determining the health index of the power equipment based on the abnormality detection result, and calculating the maintenance priority index of the power equipment according to the health index, specifically including:

[0023] If the abnormality detection result is that the equipment is abnormal, the operating data is input into a preset digital twin model, and a digital twin simulation result is obtained from the digital twin model;

[0024] Calculate the health of the device based on the digital twin simulation results and the health calculation formula to obtain the health index;

[0025] A maintenance priority index of the power equipment is determined according to the health index, the abnormality score value and the operating status data.

[0026] By adopting the above technical solution, by using the digital twin model for simulation analysis when an abnormality occurs in the equipment, the operating status of the equipment can be reproduced based on the virtual environment, thereby assisting fault diagnosis and providing more accurate abnormality cause analysis. By calculating the equipment health index based on the digital twin simulation results, accurate health assessment can be performed in combination with the operating characteristics of the equipment, thereby improving the accuracy of health calculation and providing more reliable data support for maintenance priority assessment. By calculating the maintenance priority index based on the health index and abnormality score, the maintenance urgency of the equipment can be quantified, thereby helping maintenance personnel to reasonably allocate maintenance resources, improve operation and maintenance efficiency, and reduce operation and maintenance costs.

[0027] In a preferred example, the present application may be further configured as follows: determining the maintenance priority index of the power equipment according to the health index, the abnormality score value, and the operating status data, specifically including:

[0028] Obtaining equipment importance parameters of the power equipment;

[0029] Based on a maintenance priority index calculation formula, the maintenance priority index of the power equipment is determined according to the health index, the abnormality score value, the equipment importance parameter and the operating status data.

[0030] By adopting the above technical solution and obtaining the importance parameters of the equipment, it is possible to comprehensively evaluate the importance of the equipment in combination with its criticality in the system, thereby ensuring that maintenance decisions give priority to equipment that has a greater impact on system operation and improve the overall stability of the power system. By calculating the maintenance priority index based on the maintenance priority index calculation formula, it is possible to comprehensively evaluate the maintenance needs of the equipment in combination with multiple factors such as health, anomaly score and equipment importance, thereby improving the scientificity and rationality of maintenance measures and optimizing the allocation of maintenance resources.

[0031] In a preferred example, the present application may be further configured as follows: determining a maintenance management strategy according to the maintenance priority index, and executing maintenance management measures on the power equipment based on the maintenance management strategy, specifically including:

[0032] If the maintenance priority index is greater than a first set threshold, emergency maintenance measures are taken to immediately repair or replace the equipment;

[0033] If the maintenance priority index is between the first set threshold and the second set threshold, predictive maintenance measures are taken, and an optimal maintenance time window for the power equipment is formulated in combination with the digital twin simulation results to reduce unplanned downtime;

[0034] If the maintenance priority index is less than the second set threshold, preventive maintenance measures are taken and included in the regular inspection plan to reduce the probability of future equipment failure.

[0035] By adopting the above technical solution, by performing emergency maintenance when the maintenance priority index exceeds the first set threshold, it is possible to ensure that high-risk equipment is handled in a timely manner, thereby reducing the occurrence rate of serious failures and avoiding equipment damage or shutdown due to sudden failures. By performing predictive maintenance when the maintenance priority index is between the first set threshold and the second set threshold, maintenance can be scheduled in advance while the equipment can still operate normally, thereby reducing unplanned downtime and improving equipment utilization. By performing preventive maintenance when the maintenance priority index is lower than the second set threshold, the equipment can be included in the regular inspection plan, thereby reducing the potential risk of aging or failure during long-term operation and improving the long-term operation reliability of the equipment.

[0036] In a preferred example, the present application can be further configured as follows: after executing the maintenance management measures, obtaining maintenance results, and using the maintenance results to optimize the digital twin model and adjust equipment parameters to optimize the false alarm rate of abnormality detection.

[0037] By adopting the above technical solution and obtaining the maintenance results after the execution of maintenance management measures, the maintenance effect can be accurately evaluated, thereby providing data support for subsequent maintenance optimization, improving the pertinence and effectiveness of maintenance, and optimizing the digital twin model based on the maintenance results and adjusting the equipment parameters. The simulation model parameters can be adjusted based on actual maintenance feedback, thereby improving the accuracy of the digital twin model, making it more consistent with the actual operating status of the equipment, improving the reliability of anomaly detection, and reducing operation and maintenance costs and equipment losses caused by misjudgment.

[0038] The second object of the present invention is achieved through the following technical solutions:

[0039] A digital twin-based power equipment maintenance system, comprising:

[0040] Data acquisition module, used to obtain operating status data of power equipment;

[0041] An anomaly detection module, used to perform an anomaly detection analysis on the operating status data to obtain an anomaly detection result;

[0042] a health assessment module, configured to determine a health index of the power equipment based on the abnormality detection result, and calculate a maintenance priority index of the power equipment according to the health index;

[0043] A maintenance decision module is used to determine a maintenance management strategy for the power equipment according to the maintenance priority index, and to execute maintenance management measures on the power equipment based on the maintenance management strategy.

[0044] By adopting the above technical solution, by obtaining the operating status data of the power equipment, the integrity and accuracy of the equipment operating data can be ensured, thereby providing a reliable basis for subsequent anomaly detection, health assessment and maintenance decisions, avoiding misjudgments due to data missing or errors, and by performing anomaly detection and analysis on the operating status data, abnormal conditions in the equipment operation can be effectively identified, thereby avoiding the expansion of equipment failures due to the failure to discover the anomaly in time, improving the operating safety and stability of the system, and by calculating the health index and maintenance priority index of the power equipment based on the anomaly detection results, the health status and maintenance urgency of the equipment can be quantified, thereby helping maintenance personnel give priority to high-risk equipment, optimize maintenance resource allocation, and improve overall maintenance efficiency, and by formulating maintenance management strategies and executing maintenance management measures according to the maintenance priority index, it is possible to intelligently select maintenance methods according to the actual status of the equipment, thereby reducing unnecessary maintenance costs, while reducing the sudden failure rate and improving the availability and operating efficiency of the equipment.

[0045] The third objective of this application is achieved through the following technical solutions:

[0046] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned power equipment management method when executing the computer program.

[0047] The fourth objective of this application is achieved through the following technical solutions:

[0048] A computer-readable storage medium stores a computer program, which implements the steps of the above-mentioned power equipment management method when executed by a processor.

[0049] In summary, this application includes at least one of the following beneficial technical effects:

[0050] 1. By acquiring the operating status data of power equipment, the integrity and accuracy of the equipment operating data can be ensured, thereby providing a reliable basis for subsequent anomaly detection, health assessment and maintenance decision-making, avoiding misjudgments caused by data loss or errors. By performing anomaly detection and analysis on the operating status data, abnormal conditions in equipment operation can be effectively identified, thereby avoiding the expansion of equipment failures due to untimely detection of anomalies, improving the operational safety and stability of the system. By calculating the health index and maintenance priority index of power equipment based on the anomaly detection results, the health status and maintenance urgency of the equipment can be quantified, thereby helping maintenance personnel prioritize high-risk equipment, optimize maintenance resource allocation, and improve overall maintenance efficiency. By formulating maintenance management strategies and implementing maintenance management measures based on the maintenance priority index, maintenance methods can be intelligently selected according to the actual status of the equipment, thereby reducing unnecessary maintenance costs, while reducing sudden failure rates and improving equipment availability and operating efficiency.

[0051] 2. By using the digital twin model for simulation analysis when an equipment anomaly occurs, the operating status of the equipment can be reproduced in a virtual environment, thereby assisting in fault diagnosis and providing more accurate analysis of the cause of the anomaly. By calculating the equipment health index based on the digital twin simulation results, accurate health assessment can be performed based on the equipment's operating characteristics, thereby improving the accuracy of health calculations and providing more reliable data support for maintenance priority assessments. By calculating the maintenance priority index based on the health index and anomaly score, the maintenance urgency of the equipment can be quantified, thereby helping maintenance personnel to rationally allocate maintenance resources, improve operation and maintenance efficiency, and reduce operation and maintenance costs.

[0052] 3. By obtaining the maintenance results after the execution of maintenance management measures, the maintenance effect can be accurately evaluated, thereby providing data support for subsequent maintenance optimization, improving the pertinence and effectiveness of maintenance, and optimizing the digital twin model based on the maintenance results and adjusting the equipment parameters. The simulation model parameters can be adjusted based on actual maintenance feedback, thereby improving the accuracy of the digital twin model, making it more consistent with the actual operating status of the equipment, improving the reliability of anomaly detection, and reducing operation and maintenance costs and equipment losses caused by misjudgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flowchart of an implementation method for power equipment maintenance based on digital twins in one embodiment of the present application;

[0054] Figure 2 This is a flowchart for implementing step S20 in the power equipment maintenance method based on digital twins in one embodiment of the present application;

[0055] Figure 3 This is a flowchart for implementing step S22 in the power equipment maintenance method based on digital twins in one embodiment of the present application;

[0056] Figure 4 This is a flowchart for implementing step S30 in the power equipment maintenance method based on digital twins in one embodiment of the present application;

[0057] Figure 5 This is a flowchart for implementing step S33 in the power equipment maintenance method based on digital twins in one embodiment of the present application;

[0058] Figure 6 This is a flowchart for implementing step S40 in the power equipment maintenance method based on digital twins in one embodiment of the present application;

[0059] Figure 7 This is a principle block diagram of a power equipment maintenance system based on digital twins in one embodiment of the present application;

[0060] Figure 8 It is a schematic diagram of the internal structure of a computer device in one embodiment of the present application. DETAILED DESCRIPTION

[0061] The following examples will help those skilled in the art further understand the purpose of this application, but are not intended to limit this application in any form. It should be noted that those skilled in the art may make several modifications and improvements without departing from the scope of this application. These modifications and improvements are all within the scope of this application.

[0062] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, systems, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0063] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0064] The present application is further described in detail below with reference to the accompanying drawings.

[0065] In one embodiment, if Figure 1 As shown, the present application discloses a method for maintaining power equipment based on digital twins, which specifically includes the following steps:

[0066] S10: Obtaining operating status data of the power equipment.

[0067] Specifically, operating status data is collected in real time by a variety of sensors on the power equipment. The types of sensors include voltage sensors for measuring voltage, current sensors for measuring current, temperature sensors for measuring temperature, and acceleration sensors for measuring vibration. These sensors collect data according to a fixed sampling period, where the sampling period can be 1s, 10s or 60s, and transmit the data to the data processing module through the industrial bus protocol. A verification mechanism, such as cyclic redundancy check, is used during the data collection process to ensure the integrity and accuracy of data transmission. If the sensor data is lost or abnormal, the data compensation mechanism is triggered. The compensation methods include historical data interpolation, comparison of adjacent equipment data and adaptive estimation to ensure data integrity.

[0068] S20: Perform anomaly detection analysis on the operating status data to obtain an anomaly detection result.

[0069] Specifically, anomaly detection analysis adopts a method based on statistical analysis and time series analysis. First, the mean and standard deviation of each operating parameter are calculated, and preliminary screening is performed in combination with the set threshold. Then, the exponentially weighted moving average (EWMA) method is used to analyze short-term and long-term trend changes to detect sudden anomalies and trend anomalies, thereby obtaining anomaly detection results.

[0070] S30: Determine a health index of the power equipment based on the abnormality detection result, and calculate a maintenance priority index of the power equipment according to the health index.

[0071] Specifically, when the abnormality detection result indicates that the equipment is abnormal, the health of the power equipment is calculated based on the abnormality detection result, and the health status of the equipment is expressed in a quantitative manner. The higher the value, the better the health status of the equipment. Therefore, when calculating the maintenance priority index based on the health index, the health index, abnormality score value, and equipment importance parameters are comprehensively considered, and the final maintenance priority index is obtained through a weighted calculation method. The higher the maintenance priority index, the more priority maintenance the equipment requires.

[0072] S40: Determine a maintenance management strategy according to the maintenance priority index, and execute maintenance management measures on the power equipment based on the maintenance management strategy.

[0073] Specifically, after the maintenance priority index is calculated, the preset maintenance priority index is compared with the preset maintenance level threshold to determine the maintenance strategy. For example, if MPI>MPI 紧急 , then perform emergency maintenance, if MPI 预测 ≤MPI≤MPI 紧急 , then perform predictive maintenance, if MPI <MPI 预测 , perform preventive maintenance.

[0074] In one embodiment, if Figure 2 As shown, in step S20, anomaly detection analysis is performed on the operating status data to obtain anomaly detection results, which specifically include:

[0075] S21: Based on data processing technology, the operating status data is normalized to remove noise and smooth the data to obtain preprocessed operating status data.

[0076] Specifically, based on data processing technology, the operating status data is normalized. For example, the Z-score normalization method is used to standardize data of different units and magnitudes for subsequent analysis. At the same time, noise removal can use Kalman filtering or wavelet transform methods to eliminate high-frequency noise introduced by sensor errors. Data smoothing can use sliding average or exponential smoothing methods to reduce the impact of short-term fluctuations on anomaly detection, thereby ultimately obtaining stable operating status data.

[0077] S22: Identify abnormal conditions of the power equipment based on the operating status data and obtain abnormality detection results.

[0078] Specifically, after completing data normalization and noise removal based on the operating status data, the anomaly detection and analysis phase begins. Anomaly detection and analysis are performed on the normalized data. If the equipment parameters exceed the set safe operating range, they are marked as abnormal. All abnormal detection results are recorded in the abnormality log, and the next step of health assessment is entered.

[0079] In one embodiment, if Figure 3 As shown, in step S22, the abnormal condition of the power equipment is identified based on the operating status data to obtain the abnormality detection result, which specifically includes:

[0080] S221: Acquire historical operating status data and abnormality detection benchmark thresholds of power equipment.

[0081] Specifically, historical operating status data is extracted from the database, including equipment operating parameters, maintenance records and fault data within the past set period. The anomaly detection benchmark threshold is calculated based on historical statistical analysis. First, the historical data is clustered and analyzed to divide different operating modes. The distribution range of operating parameters under each mode is calculated, the upper and lower limits of the normal range are set, and the anomaly detection threshold is adjusted based on expert experience. If the equipment is in different operating modes, the threshold is dynamically adjusted to adapt to different operating conditions. For example, for a transformer, its normal temperature range may vary due to different load rates, so the threshold should be adaptively adjusted according to the load conditions. All historical data and threshold information are stored in the cache for subsequent use.

[0082] S222: Based on the abnormality scoring formula, the abnormal state of the power equipment is evaluated according to the historical operating state data, the abnormality detection benchmark threshold and the operating state data to obtain an abnormality score value.

[0083] Specifically, the anomaly scoring formula uses a weighted calculation formula to combine multiple anomaly factors into a single score. The score range is usually 0 to 100. The higher the score, the more serious the anomaly. The calculation process takes into account the current status of the equipment, historical anomaly records, and the rate of change of anomalies. If the score is higher than the set threshold, the maintenance priority calculation phase begins. The anomaly scoring formula is as follows:

[0084]

[0085] Where X is the current equipment monitoring value, such as current, voltage, temperature, etc.; μ is the mean of the equipment's historical normal operation data; σ is the standard deviation of the equipment's historical operation data; α is the trend influencing factor, which is used to control the balance between short-term fluctuations and long-term trends; EWMA t It is an exponentially weighted moving average, which represents the long-term operating trend of the equipment.

[0086] For example, the calculation formula of EWMA at time t is as follows:

[0087]

[0088] X t is the equipment operation data at the current time t, such as current, voltage, temperature, vibration, etc.; λ is the smoothing factor, which determines the sensitivity of EWMA to new data, 0<λ≤1; EWMA t-1 The EWMA value calculated at the previous time t−1; if you want EWMA to mainly consider the most recent 10 data points, then If you want EWMA to pay more attention to the latest data points, you can directly set a larger λ, such as 0.8. If α is small, the system pays more attention to fluctuations in a short period of time, which is suitable for detecting transient anomalies, such as sudden current increases and voltage fluctuations. If α is large, EWMA contributes more to the anomaly score AS, which is suitable for detecting trend anomalies, such as slowly rising temperatures and cable aging. Among them, key equipment such as main transformers and GIS equipment are greatly affected by the aging trend of equipment, so a higher α value is set, between 0.5 and 0.8, to improve the ability to detect trend anomalies. Non-critical equipment such as secondary equipment and sensors usually have more sudden failures, so a lower α value is set, between 0.1 and 0.3, to avoid excessive focus on trend changes.

[0089] S223: Determine whether the abnormality score value is greater than the abnormality detection reference threshold according to the abnormality score value.

[0090] Specifically, the anomaly score value is compared with the set anomaly detection benchmark threshold. If the anomaly score is higher than the anomaly detection benchmark threshold, the device is determined to have an anomaly; otherwise, the device is determined to be operating normally or with a slight anomaly.

[0091] S224: If the anomaly score value is greater than the anomaly detection reference threshold, an anomaly detection result of the device anomaly is generated.

[0092] Specifically, when the anomaly score is higher than the anomaly detection reference threshold, it is determined that the device is abnormal, and thus an anomaly detection result indicating device abnormality is generated.

[0093] In one embodiment, if Figure 4 As shown, in step S30, based on the abnormality detection result, the health index of the power equipment is determined, and the maintenance priority index of the power equipment is calculated according to the health index, which specifically includes:

[0094] S31: If the abnormality detection result is that the equipment is abnormal, the operating data is input into the preset digital twin model, and the digital twin simulation result is obtained from the digital twin model.

[0095] Specifically, when the anomaly detection result is an equipment anomaly, after the operating data is input into the digital twin model, data mapping is first performed to ensure that the input data of the digital twin model is consistent with the actual operating data of the equipment. Then, the digital twin simulation is run using the simulation engine to simulate the operating status of the equipment under different working conditions and predict the operating trend within a certain period of time in the future. The simulation results include health trends, possible failure points and recommended maintenance measures. The digital twin simulation results are used to optimize health calculations and provide data support for maintenance strategies.

[0096] S32: Based on the digital twin simulation results and the health calculation formula, the equipment health is calculated to obtain the health index.

[0097] Specifically, when the simulation error of the digital twin simulation result obtained by the digital twin model simulation exceeds the set threshold, the health calculation step of the power equipment is entered, and the health index is calculated using the health calculation formula. After the calculation is completed, the health index is stored in the database and used for maintenance priority calculation. The health calculation formula is as follows:

[0098]

[0099] Among them, T dev , I dev 、V dev 、A dev Respectively represent the deviation of temperature, current, voltage and vibration; T nom , I nom 、Vnom 、A nom is the benchmark value for normal operation of the equipment; W1, W2, W3, and W4 are the weights of the influencing factors, which are determined by historical data analysis. Based on historical operating data, the influence of various influencing factors such as temperature, current, voltage, and vibration on the health of the equipment is calculated, and their standardized weights are calculated. x is the standard deviation of the parameter, μ x is the mean value of the parameter, which is calculated as follows:

[0100]

[0101]

[0102] For example, if the current data has a temperature of 90°C, a current of 55A, a voltage of 230V, and a vibration parameter of 0.8m / s², and the normal reference values have a temperature of 85°C, a current of 50A, a voltage of 220V, and a vibration parameter of 0.5m / s², the deviation values T are calculated. dev 5, I dev 5, V dev is 10, A dev is 0.3, the influencing factors W1 is 0.3, W2 is 0.25, W3 is 0.25, and W4 is 0.2, resulting in a health index of 82.6, indicating that the equipment is still in good condition, but some parameters such as temperature, current, and vibration have large deviations and need to be continuously monitored.

[0103] S33: Determine a maintenance priority index for the power equipment based on the health index, the abnormality score value, and the operating status data.

[0104] Specifically, the calculation of the maintenance priority index integrates the health index, anomaly score value, and equipment importance parameters to ensure that maintenance resources are allocated to high-risk equipment first. The calculation process first obtains the health index HI and normalizes it to limit its range to between 0 and 100. At the same time, the anomaly score is obtained and the weight is adjusted in combination with the equipment's historical data to reflect the impact of short-term and long-term anomalies. Then, the equipment's importance parameters are obtained, and the maintenance priority index is calculated based on the health index and anomaly score.

[0105] In one embodiment, if Figure 5 As shown, in step S33, the maintenance priority index of the power equipment is determined based on the health index, the abnormality score value and the operating status data, specifically including:

[0106] S331: Obtain equipment importance parameters of the power equipment.

[0107] Specifically, the equipment importance parameter is assigned based on the criticality of the power equipment in the entire power grid, including the impact of the equipment on load stability, historical failure rate, and degree of dependence on upstream or downstream equipment. For example, the main transformer of the substation occupies a key position in the system and its importance parameter is relatively high, while the importance parameters of some secondary load equipment are relatively low. All calculated equipment importance parameters are normalized to between 0 and 1 to reasonably allocate weights in the maintenance priority calculation.

[0108] S332: Based on the maintenance priority index calculation formula, the maintenance priority index of the power equipment is determined according to the health index, the abnormality score value, the equipment importance parameter and the operating status data.

[0109] Specifically, the maintenance priority index is calculated based on the equipment health index HI and its normalized processing results, and is comprehensively calculated in combination with the anomaly score, equipment importance, and operating status data. The calculation formula is as follows:

[0110]

[0111] Among them, MPI is the maintenance priority index, and a larger value indicates a more urgent equipment maintenance need. AS is the anomaly score, which is used to measure the current anomaly level of the equipment. HI is the health index, which reflects the current health status of the equipment. ω is the equipment importance parameter, which indicates the criticality of the equipment in the system. It is determined by factors such as the grid topology and the impact of historical faults. The value range is usually 0.1-2.0. Critical equipment such as main transformers have an ω value between 1.5 and 2.0, and may be given priority maintenance even if their health is high. Non-critical equipment such as sensors have an ω value between 0.1 and 0.5, and may still have a low maintenance priority even if their health is low.

[0112] In one embodiment, if Figure 6 As shown, in step S40, a maintenance management strategy is determined according to the maintenance priority index, and maintenance management measures are executed on the power equipment based on the maintenance management strategy, specifically including:

[0113] S41: If the maintenance priority index is greater than the first set threshold, emergency maintenance measures are taken to immediately repair or replace the equipment.

[0114] Specifically, if the maintenance priority index is greater than the first set threshold, that is, MPI>MPI 紧急, emergency maintenance is immediately triggered. Emergency maintenance includes equipment shutdown inspection, replacement of faulty components, and adjustment of operation strategies. First, the latest operation data and anomaly detection records of the equipment are obtained, and the digital twin simulation results are retrieved to analyze the cause of the failure. If the key components of the equipment are damaged or the operating parameters exceed the safe range, the equipment is replaced or repaired. After the emergency maintenance is completed, the maintenance results are stored in the database and fed back to the health calculation module to optimize the anomaly detection threshold.

[0115] S42: If the maintenance priority index is between the first set threshold and the second set threshold, predictive maintenance measures are taken, and the optimal maintenance time window of the power equipment is formulated in combination with the digital twin simulation results to reduce unplanned downtime.

[0116] Specifically, if the maintenance priority index is between the first set threshold and the second set threshold, that is, MPI 预测 ≤MPI≤MPI 紧急 , a predictive maintenance strategy is adopted. The goal of predictive maintenance is to determine the optimal maintenance time while the equipment is still operational to reduce unplanned downtime. During the calculation process, the remaining life of the equipment is first analyzed in combination with the digital twin simulation data. If the remaining life of the equipment is shorter than the set maintenance lead time, a maintenance work order is automatically generated. Subsequently, the equipment operation schedule is analyzed, and the period with lower load is selected as the optimal maintenance time window, and the maintenance personnel are notified. After the predictive maintenance is completed, the relevant data is stored in the database and fed back to the digital twin model for optimization and adjustment.

[0117] S43: If the maintenance priority index is less than the second set threshold, preventive maintenance measures are taken and incorporated into the regular inspection plan to reduce the probability of future equipment failures.

[0118] Specifically, if the maintenance priority index is less than the second set threshold, that is, MPI <MPI 预测 , then the equipment does not require emergency or predictive maintenance, but is included in the regular inspection plan. The inspection plan is formulated based on the equipment category and historical maintenance cycle to ensure the rational allocation of maintenance resources. Preventive maintenance measures include operating status inspection, equipment parameter adjustment and necessary component replacement. During the inspection process, if any abnormality is found, the equipment maintenance priority is recalculated and the maintenance plan is adjusted according to the calculation results. After the preventive maintenance is completed, the maintenance records are stored in the database for subsequent abnormality detection and health assessment optimization.

[0119] In one embodiment, after step S40, after executing the maintenance management measures, the maintenance results are obtained, and the maintenance results are used to optimize the digital twin model and adjust the equipment parameters to optimize the anomaly detection false alarm rate.

[0120] Specifically, after the equipment performs maintenance management measures, the maintenance results are obtained, including data such as the equipment fault repair status, the change trend of key parameters before and after maintenance, and the equipment operation stability assessment. The maintenance results can be monitored by sensors to monitor the equipment status changes, and the data is recorded in combination with the feedback from the operation and maintenance personnel. The data sources of the maintenance results include: equipment repair logs; equipment operation status data after maintenance (voltage, current, temperature, vibration, etc. change trends); comparison of abnormality scores before and after maintenance to assess whether the abnormal conditions have been significantly reduced.

[0121] More specifically, based on the operating data of the equipment after maintenance, the parameters of the digital twin model are adjusted to make the simulation data closer to the actual equipment status. Data fitting techniques such as least squares or gradient descent are used to optimize the model parameters, and the LSTM prediction model is used to train the digital twin model. This gives the digital twin model adaptive capabilities and improves the accuracy of future anomaly detection.

[0122] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0123] In one embodiment, a power equipment maintenance system based on digital twin is provided, and the power equipment maintenance system based on digital twin corresponds one-to-one with the power equipment maintenance method based on digital twin in the above embodiment. Figure 7 As shown in Figure 1, the power equipment maintenance system based on digital twins includes a data acquisition module, an anomaly detection module, a health assessment module, and a maintenance decision module. The functional modules are described in detail as follows:

[0124] Data acquisition module, used to obtain operating status data of power equipment;

[0125] Anomaly detection module, used to perform anomaly detection analysis on the running status data and obtain anomaly detection results;

[0126] A health assessment module is used to determine the health index of the power equipment based on the abnormality detection results, and calculate the maintenance priority index of the power equipment according to the health index;

[0127] The maintenance decision module is used to determine the maintenance management strategy according to the maintenance priority index and perform maintenance management measures on the power equipment based on the maintenance management strategy.

[0128] Optionally, the anomaly detection module specifically includes:

[0129] The data preprocessing submodule is used to perform data normalization processing on the operating status data based on data processing technology to remove noise and smooth the data to obtain preprocessed operating status data;

[0130] The abnormality identification submodule is used to identify the abnormal conditions of the power equipment based on the operating status data and obtain the abnormality detection results.

[0131] Optionally, the anomaly identification submodule specifically includes:

[0132] A historical data acquisition unit, used to acquire historical operating status data and abnormality detection benchmark thresholds of power equipment;

[0133] An anomaly scoring calculation unit is used to evaluate the abnormal state of the power equipment based on the anomaly scoring formula, according to the historical operating state data, the anomaly detection benchmark threshold and the operating state data, and obtain an anomaly scoring value;

[0134] An abnormality threshold judgment unit, used to determine whether the abnormality score value is greater than the abnormality detection reference threshold according to the abnormality score value;

[0135] The abnormality alarm unit is used to generate an abnormality detection result of the device abnormality if the abnormality score value is greater than the abnormality detection reference threshold.

[0136] Optionally, the health assessment module specifically includes:

[0137] The digital twin simulation submodule is used to input the operating data into the preset digital twin model if the abnormality detection result is an equipment abnormality, and obtain the digital twin simulation result from the digital twin model;

[0138] The health calculation submodule is used to calculate the health of the equipment based on the digital twin simulation results and the health calculation formula to obtain the health index;

[0139] The maintenance priority calculation submodule is used to determine the maintenance priority index of the power equipment based on the health index, abnormality score value and operating status data.

[0140] Optionally, the maintenance priority calculation submodule specifically includes:

[0141] An equipment importance analysis unit, used to obtain equipment importance parameters of power equipment;

[0142] The maintenance priority calculation unit is used to determine the maintenance priority index of the power equipment based on the maintenance priority index calculation formula, the health index, the abnormality score value, the equipment importance parameter and the operating status data.

[0143] Optionally, the maintenance decision module specifically includes:

[0144] An emergency maintenance processing submodule is used to take emergency maintenance measures and immediately repair or replace the equipment if the maintenance priority index is greater than a first set threshold;

[0145] A predictive maintenance submodule is used to take predictive maintenance measures if the maintenance priority index is between a first set threshold and a second set threshold, and to formulate an optimal maintenance time window for the power equipment based on the digital twin simulation results to reduce unplanned downtime;

[0146] The preventive maintenance submodule is used to take preventive maintenance measures if the maintenance priority index is less than a second set threshold value and include them in the regular inspection plan to reduce the probability of future equipment failure.

[0147] Optional, digital twin-based power equipment maintenance system also includes:

[0148] The maintenance result analysis module is used to obtain maintenance results after executing maintenance management measures, and use the maintenance results to optimize the digital twin model and adjust equipment parameters to optimize the false alarm rate of anomaly detection.

[0149] For the specific definition of the power equipment maintenance system based on digital twins, please refer to the definition of the power equipment maintenance method based on digital twins above, which will not be repeated here. The various modules in the above-mentioned power equipment maintenance system based on digital twins can be implemented in whole or in part through software, hardware, and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0150] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data information such as operating status data, anomaly detection results, and digital twin models. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for maintaining power equipment based on digital twins is implemented.

[0151] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are performed:

[0152] Obtain operating status data of power equipment;

[0153] Perform anomaly detection analysis on the operating status data to obtain anomaly detection results;

[0154] Based on the abnormality detection results, determine the health index of the power equipment, and calculate the maintenance priority index of the power equipment according to the health index;

[0155] According to the maintenance priority index, a maintenance management strategy is determined, and maintenance management measures are implemented on the power equipment based on the maintenance management strategy.

[0156] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0157] Obtain operating status data of power equipment;

[0158] Perform anomaly detection analysis on the operating status data to obtain anomaly detection results;

[0159] Based on the abnormality detection results, determine the health index of the power equipment, and calculate the maintenance priority index of the power equipment according to the health index;

[0160] According to the maintenance priority index, a maintenance management strategy is determined, and maintenance management measures are implemented on the power equipment based on the maintenance management strategy.

[0161] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0162] Those skilled in the art will clearly understand that for the sake of convenience and brevity in description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0163] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for maintenance and management of power equipment based on digital twins, characterized in that: The digital twin-based power equipment maintenance and management method includes: Obtain operating status data of power equipment; Performing anomaly detection analysis on the operating status data to determine whether the power equipment has an abnormality and obtain an anomaly detection result; Determining a health index of the power equipment based on the abnormality detection result, and calculating a maintenance priority index of the power equipment according to the health index and the abnormality detection result; Determining a maintenance management strategy for the power equipment according to the maintenance priority index, and performing maintenance management measures on the power equipment based on the maintenance management strategy; The determining of the health index of the power equipment based on the abnormality detection result and calculating the maintenance priority index of the power equipment according to the health index specifically includes: If the abnormality detection result is that the equipment is abnormal, the operating data is input into a preset digital twin model, and a digital twin simulation result is obtained from the digital twin model; Calculate the health of the device based on the digital twin simulation results and the health calculation formula to obtain the health index; Determining a maintenance priority index of the power equipment according to the health index, the abnormality score value, and the operating status data; Determining the maintenance priority index of the power equipment according to the health index, the abnormality score value, and the operating status data specifically includes: Obtaining equipment importance parameters of the power equipment; Based on a maintenance priority index calculation formula, the maintenance priority index of the power equipment is determined according to the health index, the abnormality score value, the equipment importance parameter and the operating status data.

2. The power equipment maintenance and management method based on digital twin according to claim 1 is characterized in that: The performing anomaly detection analysis on the operating status data to obtain an anomaly detection result specifically includes: Based on data processing technology, the operating status data is subjected to data normalization processing to remove noise and smooth the data to obtain pre-processed operating status data; According to the operating status data, the abnormal condition of the electric power equipment is identified to obtain the abnormality detection result.

3. The power equipment maintenance and management method based on digital twin according to claim 2 is characterized in that: The identifying, based on the operating status data, an abnormal condition of the electric power equipment and obtaining the abnormality detection result specifically includes: Obtaining historical operating status data and anomaly detection benchmark thresholds of the power equipment; Based on an abnormality scoring formula, the abnormal state of the power equipment is evaluated according to the historical operating state data, the abnormality detection reference threshold and the operating state data to obtain an abnormality scoring value; Determining, based on the anomaly score value, whether the anomaly score value is greater than the anomaly detection reference threshold; If the abnormality score value is greater than the abnormality detection reference threshold, an abnormality detection result of the device abnormality is generated.

4. The power equipment maintenance and management method based on digital twin according to claim 1 is characterized in that: Determining a maintenance management strategy for the electric power equipment according to the maintenance priority index, and executing maintenance management measures on the electric power equipment based on the maintenance management strategy, specifically includes: If the maintenance priority index is greater than a first set threshold, emergency maintenance measures are taken to immediately repair or replace the equipment; If the maintenance priority index is between the first set threshold and the second set threshold, predictive maintenance measures are taken, and an optimal maintenance time window for the power equipment is formulated in combination with the digital twin simulation results to reduce unplanned downtime; If the maintenance priority index is less than the second set threshold, preventive maintenance measures are taken and included in the regular inspection plan to reduce the probability of future equipment failure.

5. The power equipment maintenance and management method based on digital twin according to any one of claims 1 to 4, characterized in that: After executing the maintenance management measures, maintenance results are obtained, and the digital twin model is optimized using the maintenance results to adjust equipment parameters to optimize the anomaly detection false alarm rate.

6. A power equipment maintenance management system based on digital twins, characterized in that: The digital twin-based power equipment maintenance management system includes: Data acquisition module, used to obtain operating status data of power equipment; an abnormality detection module, configured to perform abnormality detection and analysis on the operating status data to determine whether the power equipment has an abnormality and obtain an abnormality detection result; a health assessment module, configured to determine a health index of the power equipment based on the abnormality detection result, and calculate a maintenance priority index of the power equipment according to the health index and the abnormality detection result; a maintenance decision module, configured to determine a maintenance management strategy for the power equipment according to the maintenance priority index, and execute maintenance management measures on the power equipment based on the maintenance management strategy; The health assessment module specifically includes: The digital twin simulation submodule is used to input the operating data into the preset digital twin model if the abnormality detection result is an equipment abnormality, and obtain the digital twin simulation result from the digital twin model; The health calculation submodule is used to calculate the health of the equipment based on the digital twin simulation results and the health calculation formula to obtain the health index; The maintenance priority calculation submodule is used to determine the maintenance priority index of the power equipment based on the health index, abnormality score value and operating status data; The maintenance priority calculation submodule specifically includes: An equipment importance analysis unit, used to obtain equipment importance parameters of power equipment; The maintenance priority calculation unit is used to determine the maintenance priority index of the power equipment based on the maintenance priority index calculation formula, the health index, the abnormality score value, the equipment importance parameter and the operating status data.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the power equipment maintenance and management method based on digital twins as described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the power equipment maintenance and management method based on digital twins as described in any one of claims 1 to 5 are implemented.

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