Power equipment fault monitoring method, system and device based on edge calculation and storage medium

Abstract

The invention discloses a power equipment fault monitoring method based on edge calculation. The method comprises the following steps: acquiring a first operation data set of power equipment; the first operation data set is a set of operation data of the current sampling moment and each sampling moment before the current sampling moment; according to the first operation data set, judging whether the power equipment has a power fault or not; and if yes, determining the fault type of the power equipment based on the first operation data set. According to the method, early warning, accurate judgment and efficient response of the power equipment fault can be realized, the methods can perform timely pre-judgment and adjustment before the equipment has the fault, the stability and safety of the power system are guaranteed, and the method can be widely applied to the technical field of power equipment monitoring.

Description

Technical Field

[0001] The present invention relates to the field of power equipment monitoring technology, and in particular to a power equipment fault monitoring method, system, device and storage medium based on edge computing. Background Art

[0002] As power systems continue to expand, the operational state of power equipment becomes increasingly complex, and the types and frequency of faults are increasing. Power equipment is prone to various faults, such as current and voltage fluctuations, and temperature anomalies, when operating under long-term, high-load conditions. If these faults are not detected promptly and effective measures are not taken, they will pose a serious threat to the safe and stable operation of the power system.

[0003] Traditional methods for monitoring power equipment faults rely primarily on centralized data acquisition and analysis systems. These methods typically use sensors to monitor the real-time operating status of equipment and send the collected data to a remote central control system for processing. However, this monitoring approach, which only considers real-time operating status, can easily compromise fault detection accuracy, leading to decreased fault detection accuracy. To address this issue, we propose a method for monitoring power equipment faults based on edge computing. Summary of the Invention

[0004] The purpose of this application is to solve one of the technical problems existing in the prior art to at least a certain extent.

[0005] To this end, one purpose of an embodiment of the present application is to provide a method, system, device and storage medium for power equipment fault monitoring based on edge computing, which can improve user experience and improve data analysis efficiency.

[0006] To achieve the above technical objectives, the technical solution adopted by the embodiments of the present application includes: a method for monitoring power equipment faults based on edge computing, comprising: obtaining a first operating data set of the power equipment; the first operating data set is a collection of operating data at a current sampling time and each sampling time before the current sampling time;

[0007] determining, based on the first operating data set, whether the power equipment has a power fault;

[0008] If so, the fault type of the electric equipment is determined based on the first operating data set.

[0009] When monitoring faults, the present application fully considers the operating data of the power equipment at the current sampling moment and the operating data at the historical sampling moments, and determines whether there is a fault in the power equipment through the operating data at the current sampling moment and the operating data at the historical sampling moments. After determining that there is a fault, the present application can improve the accuracy of fault testing through the operating data at the current sampling moment and the operating data at the historical sampling moments.

[0010] In addition, the power equipment fault monitoring method based on edge computing according to the above embodiment of the present invention may also have the following additional technical features:

[0011] Furthermore, in the embodiment of the present application, determining whether the power equipment has a power fault based on the first operating data set includes:

[0012] determining a relative change rate of a fault characteristic of the electric power equipment based on the first operating data set;

[0013] It is determined whether the power equipment has a power fault according to the relative change rate of the fault characteristic.

[0014] Furthermore, in an embodiment of the present application, determining the relative change rate of the fault characteristics of the power equipment at the current sampling moment based on the first operating data set includes:

[0015] Extracting the operation data at each sampling moment from the first operation data set;

[0016] For any one of the operating data, calculating a first difference between the operating data and a preset standard value;

[0017] Summing up the absolute values ​​of all the first differences to obtain a sum of relative changes in fault characteristics;

[0018] The relative change rate of the fault feature is obtained by dividing the sum of the relative changes of the fault feature by the number of the sampling moments.

[0019] Furthermore, in an embodiment of the present application, judging whether the power equipment has a power fault based on the relative change rate of the fault characteristic includes:

[0020] If the relative change rate of the fault characteristic is greater than a preset threshold, determining that the power equipment has a power fault;

[0021] If the relative change rate of the fault characteristic is less than or equal to the preset threshold, it is determined that there is no power fault in the power equipment.

[0022] Furthermore, in an embodiment of the present application, determining the fault type of the power equipment based on the first operating data set includes:

[0023] determining, based on the first operating data set, a power direction of the electric power device at a current sampling moment;

[0024] A fault type of the electric power equipment is determined according to the power direction.

[0025] Furthermore, in an embodiment of the present application, the operating data includes zero-sequence voltage and zero-sequence current, and determining the power direction of the power equipment at the current sampling moment based on the first operating data set includes:

[0026] Calculating the power of the electric equipment between the zero-sequence voltage and the zero-sequence current at the same sampling moment in the first operating data set;

[0027] The power of the electric equipment at all sampling moments is summed to obtain a total power; and the direction of the total power is used as the power direction.

[0028] Furthermore, in the embodiment of the present application, determining the fault type of the power equipment according to the power direction includes:

[0029] If the power direction is negative, it is determined that the fault type is a fault occurring inside the device;

[0030] If the power direction is positive, it is determined that the fault type is that the fault occurs outside the device.

[0031] On the other hand, an embodiment of the present application further provides an edge computing-based power equipment fault monitoring system, comprising:

[0032] A first processing unit is configured to obtain a first operating data set of the power equipment; the first operating data set is a collection of operating data at a current sampling moment and each sampling moment before the current sampling moment;

[0033] a second processing unit, configured to determine whether the power equipment has a power fault based on the first operating data set;

[0034] The third processing unit is configured to determine a fault type of the power equipment based on the first operating data set, if any.

[0035] On the other hand, the present application also provides an edge computing-based power equipment fault monitoring device, comprising:

[0036] at least one processor;

[0037] at least one memory for storing at least one program;

[0038] When the at least one program is executed by the at least one processor, the at least one processor implements an edge computing-based power equipment fault monitoring method as described in any one of the invention contents.

[0039] In addition, the present application also provides a computer-readable storage medium, which stores processor-executable instructions. When the processor executes the instructions, the processor-executable instructions are used to execute an edge computing-based power equipment fault monitoring method as described in any of the above items.

[0040] The advantages and benefits of this application will be partially given in the following description, and partially become apparent from the following description, or learned through practice of this application:

[0041] When monitoring faults, the present application fully considers the operating data of the power equipment at the current sampling moment and the operating data at the historical sampling moments, and determines whether there is a fault in the power equipment through the operating data at the current sampling moment and the operating data at the historical sampling moments. After determining that there is a fault, the present application can improve the accuracy of fault testing through the operating data at the current sampling moment and the operating data at the historical sampling moments. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a schematic diagram of the steps of a method for monitoring power equipment faults based on edge computing in a specific embodiment of the present invention;

[0043] Figure 2 Schematic diagram of a flow chart of a method for monitoring power equipment faults based on edge computing in a specific embodiment of the present invention;

[0044] Figure 3 This is a structural diagram of a power equipment fault monitoring system based on edge computing in a specific embodiment of the present invention;

[0045] Figure 4 This is a structural diagram of an edge computing-based power equipment fault monitoring device in a specific embodiment of the present invention. DETAILED DESCRIPTION

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0047] The present invention provides Figure 1A power equipment fault monitoring method based on edge computing is shown, including steps S101 to S103.

[0048] S101. Acquire a first operating data set of an electric power device; the first operating data set is a collection of operating data at a current sampling moment and each sampling moment before the current sampling moment.

[0049] S102: Determine whether there is a power fault in the power equipment based on the first operating data set.

[0050] S103: If yes, determine the fault type of the power equipment based on the first operating data set.

[0051] This embodiment fully considers the operating data of the power equipment at the current sampling time and the operating data at the historical sampling time when monitoring faults, and judges whether the power equipment has a fault through the operating data at the current sampling time and the operating data at the historical sampling time. After determining that there is a fault, the application can improve the accuracy of fault testing through the operating data at the current sampling time and the operating data at the historical sampling time.

[0052] Furthermore, in the embodiment of the present application, determining whether there is a power fault in the power equipment based on the first operating data set includes:

[0053] Based on the first operating data set, a relative change rate of a fault characteristic of the electric power device is determined.

[0054] Determine whether there is a power fault in the power equipment based on the relative change rate of the fault characteristics.

[0055] Furthermore, in an embodiment of the present application, determining the relative change rate of the fault characteristics of the power equipment at the current sampling moment based on the first operating data set includes:

[0056] The operation data at each sampling moment in the first operation data set is extracted.

[0057] For any operating data, a first difference between the operating data and a preset standard value is calculated.

[0058] The absolute values ​​of all first differences are summed to obtain the total relative change of the fault characteristics.

[0059] The relative change rate of the fault characteristics is obtained by dividing the sum of the relative changes of the fault characteristics by the number of sampling moments.

[0060] Specifically, the relative change rate of the fault characteristics in this embodiment can be calculated using the following formula:

[0061]

[0062] In the above formula (1), Xi (t) is the collected value at the i-th sampling moment, X norm is the standard value under normal operating conditions, N is the number of sampling points at the sampling moment, and △X is the relative rate of change of the fault signature. When △X exceeds the set threshold, a fault state is determined. By calculating the relative rate of change of the fault signature, the degree of change of each parameter during the fault process can be quantified. The relative rate of change can better reflect the changing trend of the signal rather than a single instantaneous data point, thus avoiding misjudgment caused by data fluctuations.

[0063] Furthermore, in the embodiment of the present application, judging whether there is a power fault in the power equipment based on the relative change rate of the fault characteristic includes:

[0064] If the relative change rate of the fault characteristics is greater than a preset threshold, it is determined that there is a power fault in the power equipment.

[0065] If the relative change rate of the fault characteristics is less than or equal to the preset threshold, it is determined that there is no power fault in the power equipment.

[0066] Furthermore, in the embodiment of the present application, determining the fault type of the power equipment based on the first operating data set includes:

[0067] The power direction of the electric power equipment at the current sampling moment is determined based on the first operating data set.

[0068] Determine the fault type of the power equipment based on the power direction.

[0069] Furthermore, in an embodiment of the present application, the operating data includes zero-sequence voltage and zero-sequence current. Determining the power direction of the power equipment at the current sampling moment based on the first operating data set includes:

[0070] The power of the electric equipment between the zero-sequence voltage and the zero-sequence current at the same sampling moment in the first operating data set is calculated.

[0071] The power of the electric equipment at all sampling moments is summed to obtain the total power; the direction of the total power is used as the power direction.

[0072] Specifically, in this embodiment, the power direction of the electric equipment at the current sampling moment can be determined by the following formula:

[0073]

[0074] In the above formula (2), V zero (t j ) is the sampling value of the zero-sequence voltage at sampling time j, I zero (t j ) is the sampling value of the zero-sequence current at sampling time j, M is the number of sampling points at sampling time, when Pzero When it is negative, it is considered that the fault occurs inside the device. zero When it is positive, it is considered that the fault occurs outside the equipment; the power direction calculation of the zero-sequence voltage and current can further confirm the nature of the fault. By calculating the power direction of the zero-sequence voltage and current, it can be confirmed whether the equipment has an internal fault or an external fault. When the power direction P zero When negative, it indicates that the fault is caused by a problem within the device.

[0075] Furthermore, in the embodiment of the present application, determining the fault type of the power equipment according to the power direction includes:

[0076] If the power direction is negative, it is determined that the fault type is internal to the device.

[0077] If the power direction is positive, the fault type is determined to be outside the device.

[0078] The following is combined with Figure 2 Explain the specific principles of this application.

[0079] exist Figure 2 In the present application, the power equipment fault monitoring method based on edge computing may include steps S1 to S6.

[0080] Step S1: Collect the operating data of the power equipment in real time through the edge computing device.

[0081] In this embodiment, the specific steps of step S1 include:

[0082] Step 1.1: Collect the current, voltage, frequency, and temperature operating parameters of power equipment in real time to form a multidimensional dataset. By collecting multiple operating parameters such as current, voltage, frequency, and temperature in real time, a comprehensive picture of the equipment status can be obtained. This multidimensional dataset can provide richer and more comprehensive information for subsequent analysis. The status of power equipment can be continuously monitored to promptly determine whether the equipment is within the normal operating range. Real-time data collection provides the necessary foundation for subsequent fault detection and early warning. Multi-dimensional collection enables the system to capture various status changes during equipment operation, improving the accuracy of fault diagnosis.

[0083] Step 1.2: De-noise the collected data by using a low-pass filter to remove high-frequency noise. The collected data from power equipment may be affected by factors such as environmental noise and electromagnetic interference, resulting in unstable or inaccurate signals. Using a low-pass filter to filter out high-frequency noise can effectively eliminate this irrelevant interference information, thereby improving the signal-to-noise ratio of the data. The filtered data is smoother, allowing subsequent analysis algorithms to perform fault diagnosis based on more reliable signals. After removing noise, it can ensure that the fault analysis algorithm is not misled, especially when the high-frequency signal fluctuates greatly. This is particularly important.

[0084] Step 1.3: Determine abnormalities in the collected data based on the set standard values. If the data deviation exceeds a predetermined threshold, it is marked as abnormal data. By comparing it with the standard values ​​of normal equipment operation, abnormal fluctuations in the collected data can be identified. For example, if parameters such as voltage, current, and frequency deviate from normal values, it may mean that the equipment has a malfunction. When the data deviation exceeds the predetermined threshold, it is immediately marked as abnormal data, providing an early warning for subsequent fault type analysis. This step can provide early fault indications for the equipment and promptly notify maintenance personnel to conduct inspections. By comparing with standard values, data quality can be ensured, abnormal data caused by equipment problems or sensor errors can be eliminated, and subsequent analysis can rely on valid data.

[0085] Step S2: pre-process the collected data.

[0086] In this embodiment, the specific steps of step 2 include:

[0087] Step 2.1. Perform a Fourier transform on the processed voltage and current data to extract the fundamental RMS value of each cycle. The Fourier transform converts time-domain signals into frequency-domain signals, revealing the frequency components of voltage and current signals. This is an important tool for identifying potential faults in power systems. The fundamental RMS value represents the dominant frequency component of the signal (usually the fundamental frequency of the power system), reflecting the normal operating status of the equipment. When the equipment is operating normally, the fundamental RMS value should remain stable. Therefore, extracting the fundamental RMS value helps identify abnormal fluctuations in the equipment. By extracting the fundamental RMS value, the system can reduce computational complexity and focus analysis on the most important frequency components, thereby improving subsequent processing efficiency.

[0088] Step 2.2: Calculate the difference between the fundamental waves of adjacent cycles and compare it with a preset threshold. If the difference exceeds the set standard, it is considered that abnormal fluctuations have occurred in the data. By calculating the difference between the fundamental waves of adjacent cycles, the fluctuations of the current and voltage signals in consecutive cycles can be detected. If the fluctuation exceeds the set standard, it may mean that the system is experiencing abnormal load fluctuations or other abnormal phenomena. This step can help identify sudden electrical fluctuations, such as sudden changes in voltage and current within a short period of time, which are usually caused by equipment failures, sudden load changes, etc. By setting a threshold, the accuracy of fault diagnosis can be further improved. For example, the system can exclude some small, irrelevant fluctuations and only focus on those that exceed the set standard, thereby reducing false alarms.

[0089] Step 2.3: If the maximum value of the difference exceeds 500V, the time point is determined to be a possible fault time point, and the voltage and current data at that moment are recorded to provide a basis for fault type identification. By setting a specific threshold (such as 500V), small fluctuations in normal operation can be filtered out, and the focus can be placed on abnormal fluctuations that may affect the normal operation of the equipment. This threshold can be adjusted according to the characteristics of the equipment to ensure a balance between system sensitivity and reliability. When the fundamental wave difference exceeds 500V, it is determined to be a possible fault time point, which provides a clear time point for subsequent fault diagnosis, allowing the fault type identification system to perform more accurate analysis of the data at that moment. Recording the voltage and current data at that moment is very critical, as it provides basic data support for subsequent fault type analysis. By analyzing these data, the specific nature of the fault (such as short circuit, overload, etc.) can be further determined.

[0090] Step S3: Calculate various fault characteristics based on the processed data, and use a standard value comparison method to determine whether a fault exists.

[0091] In this embodiment, the specific steps of step 3 include:

[0092] Step 3.1. Calculate the difference between data deviation and normal operating values ​​using fault characteristics such as zero-sequence current and zero-sequence voltage. Zero-sequence current and zero-sequence voltage are common fault characteristics in power systems, especially in the event of an asymmetric fault (such as a single-phase grounding fault). These fault characteristics can effectively reflect the abnormal state of power equipment and are therefore an important basis for fault diagnosis. By calculating the difference between the zero-sequence current and zero-sequence voltage and normal operating values, it is possible to clearly identify whether the equipment has deviated from its normal state during operation. This deviation is an early sign of a fault and helps to identify potential equipment problems. Using characteristics such as zero-sequence current and voltage, it is possible to accurately identify asymmetric faults in equipment operation, laying the foundation for subsequent more specific fault type determination.

[0093] Step 3.2: Set the standard value as the normal operating range of the equipment. Calculate the deviation between parameters such as current and voltage for each cycle and the standard value. If the deviation exceeds a predetermined threshold, a fault is determined to be possible. Set a standard value (such as the normal operating range of current and voltage) for each electrical device as a reference for the normal state of the equipment. This standard value provides a clear "benchmark." When the equipment operates within this range, it is considered normal. By calculating the deviation between parameters such as current and voltage for each cycle and the standard value, any abnormalities in the equipment's operation can be detected. If the deviation exceeds a predetermined threshold, it indicates that the equipment may have a fault. This process helps to quickly identify potential fault risks and conduct timely troubleshooting. By calculating this deviation, an early warning of faults can be provided to the equipment. When the possibility of a fault is high, an immediate response can be taken to avoid further deterioration of the fault or damage to the equipment.

[0094] The specific steps of step S3 further include:

[0095] Step 3.3: Use the relative change rate of the fault characteristics to make a judgment. The calculation formula of the relative change rate is:

[0096]

[0097] Where Xi(t) is the value collected at the i-th time point, Xnorm is the standard value under normal operating conditions, and N is the number of sampling points. When △X exceeds the set threshold, a fault state is determined. By calculating the relative rate of change of the fault signature, the degree of change of each parameter during the fault process can be quantified. The relative rate of change better reflects the changing trend of the signal rather than a single instantaneous data, avoiding misjudgments caused by data fluctuations. When the relative rate of change △X exceeds the set threshold, it means that the operating state of the device has changed significantly, possibly entering a fault state. This calculation formula provides a precise quantitative standard for fault diagnosis, which can effectively determine whether the device is in a fault state. The relative rate of change can comprehensively evaluate the changes in fault signatures from multiple sampling points, avoiding the judgment of whether the device has failed based solely on single-point data. Therefore, this calculation method improves the sensitivity and accuracy of fault diagnosis.

[0098] Step S4: Analyze the fault type using a logic jump method to determine the fault location and fault level.

[0099] In this embodiment, the specific steps of step S4 include:

[0100] Step 4.1. Determine the fault location based on fault feature analysis. Use current mutation and voltage fluctuation information to determine whether the fault occurs inside or outside the equipment. Current mutation and voltage fluctuation are common signs of power system faults. When a device fails, the current or voltage fluctuates dramatically. By analyzing these mutations and fluctuations, the nature of the fault can be quickly identified, further determining whether the fault is caused by an internal problem or external factors. Internal faults typically cause dramatic fluctuations in the device current, while external faults can cause large voltage fluctuations. This information can be used to preliminarily distinguish the source of the fault and provide guidance for subsequent fault analysis. This step uses specific electrical features (current mutation and voltage fluctuation) to determine the fault location, thereby improving the accuracy of fault location and reducing the possibility of misjudgment.

[0101] Step 4.2: Through logical judgment, if the rate of change of the zero-sequence current is greater than a certain threshold, it is considered that the fault occurs inside the equipment; otherwise, it is determined to be an external fault; the zero-sequence current change rate is an important fault characteristic. When an asymmetric fault such as a single-phase grounding fault occurs, the zero-sequence current change will increase significantly. By setting a threshold, if the rate of change of the zero-sequence current exceeds the threshold, it indicates that the fault occurs inside the equipment; otherwise, the fault is determined to occur externally. According to the operating status and specific characteristics of the equipment, the threshold value of the zero-sequence current change rate can be flexibly adjusted, so that the system can adapt to the fault diagnosis needs of different equipment and different working environments. By introducing the zero-sequence current change rate, the system can more accurately determine the fault location, avoiding the errors that may be caused by judging only by voltage fluctuations and current mutations.

[0102] Wherein, step S4 further includes:

[0103] Step 4.3: Determine the fault type by calculating the power direction of the zero-sequence voltage and current. The calculation formula for the power direction of the zero-sequence voltage and current is:

[0104]

[0105] Among them, V zero (t j ) is the sampling value of the zero-sequence voltage at sampling time j, Izero(t j ) is the sampling value of the zero-sequence current at sampling time j, M is the number of sampling points at sampling time, when P zero When it is negative, it is considered that the fault occurs inside the device. zero When it is positive, it is considered that the fault occurs outside the equipment; the power direction calculation of the zero-sequence voltage and current can further confirm the nature of the fault. By calculating the power direction of the zero-sequence voltage and current, it can be confirmed whether the equipment has an internal fault or an external fault. When the power direction P zeroWhen it is negative, it indicates that the fault is caused by an internal problem of the equipment. zero When it is positive, it indicates that the fault originates from the external power system. This determination method is very valuable in power system fault analysis and can help maintenance personnel accurately identify the fault type. By calculating the power direction, further confirmation of the fault location can be obtained. This is especially true in an environment with complex systems and multiple power equipment operating simultaneously. It can effectively improve the accuracy and reliability of fault diagnosis.

[0106] Step S5: Automatically adjust the operation strategy of the power equipment according to the fault analysis result and issue an alarm notification.

[0107] In this embodiment, the specific steps of step S5 include:

[0108] Step 5.1: If a fault is identified, automatically upload the fault information to the equipment management system and generate an alarm notification. When the system determines that a fault has occurred, it can quickly and automatically upload the fault information to the equipment management system, achieving rapid information flow. This provides maintenance personnel with real-time fault information, facilitating rapid response and action. Generating an alarm notification can promptly alert equipment operators or maintenance personnel to equipment anomalies. By sending an alarm to relevant personnel, the potential risks brought by equipment failures can be reduced, and further escalation of accidents can be avoided. Automatically uploading fault information and sending alarm notifications greatly improves the speed and efficiency of fault response and reduces the possibility of human error and response delays.

[0109] Step 5.2: Trigger the device's automatic repair program and shutdown process based on the type and severity of the device fault. Based on the type and severity of the device fault, the system can determine the appropriate repair strategy. For example, a minor fault may only require adjustment or repair, while a severe fault may require immediate shutdown. This judgment ensures that the device receives the most appropriate response measures after the fault occurs. The device's automatic repair program can automatically select the appropriate repair method based on the fault type, such as self-repair, load adjustment, and self-test, reducing manual intervention and improving fault recovery speed. For severe faults, the system can immediately trigger shutdown processing to prevent further damage to the device or the spread of the fault. For example, in the event of high-risk faults such as overload and short circuit, automatic shutdown can effectively avoid larger-scale system failures or equipment damage. Automatic repair and shutdown processing can quickly recover the device from the fault state, minimize losses, and prevent the fault from spreading or affecting other devices.

[0110] Step 5.3. Adjust the load distribution strategy of the power equipment according to the operating status of the equipment. When a device fails, the load distribution strategy of the power system needs to be dynamically adjusted according to the fault information. For example, if a device fails, its load can be transferred to other healthy devices to ensure the continuous operation of the system. This helps prevent the entire system from shutting down and improves the stability of the system. By adjusting the load distribution strategy in real time, the power system can allocate resources more reasonably, avoid excessive load or waste of resources, and maintain the optimal operating status of the equipment. When adjusting the load, the system will ensure that other equipment is not overloaded according to the working status and load capacity of the equipment, avoiding more equipment failures due to excessive load. Through real-time load adjustment, the system can still maintain operation in the face of partial equipment failures, thereby improving the robustness and reliability of the entire power system.

[0111] Step S6: Upload fault data to the equipment management system in real time for automated fault response and maintenance.

[0112] In this embodiment, the specific steps of step S6 include:

[0113] Step 6.1. Upload fault analysis results to the equipment management system in real time and adjust equipment operation strategies based on the data analysis results. Uploading fault analysis results to the equipment management system in real time allows system administrators to immediately receive detailed information about equipment failures, reducing response time. This helps speed up fault diagnosis and response. Based on the data analysis results, the equipment management system can automatically adjust equipment operation strategies. For example, in the event of a failure, it may be necessary to transfer the load to other equipment or adjust the equipment's operating mode. The system can make dynamic decisions based on changes in real-time data to ensure efficient and safe operation of power equipment. By uploading fault analysis results in real time, the equipment management system can obtain immediate feedback, helping managers make more reasonable decisions. This real-time information flow greatly enhances the system's intelligent management capabilities.

[0114] Step 6.2: Monitor equipment with abnormal operating conditions in real time and optimize based on historical data. Real-time monitoring of equipment with abnormal operating conditions continuously tracks the equipment's operating status, ensuring immediate detection and intervention when equipment problems arise. This facilitates rapid response and the implementation of appropriate maintenance measures to prevent further deterioration of the problem. By combining historical data, the system can understand the long-term operating trends of the equipment and provide optimized solutions when equipment anomalies occur. For example, historical data can help identify which failure modes have occurred in the past and provide possible repair solutions or adjustment suggestions. By combining historical data with real-time monitoring, the system can better predict potential equipment failures and take proactive measures to reduce the risk of failure. For example, if certain equipment components may experience aging problems, historical data can help identify such trends and provide maintenance recommendations.

[0115] Step 6.3: Use edge computing devices to implement real-time fault prediction and dynamic adjustment of power equipment. Edge computing devices can analyze and process equipment operating data in real time without waiting for the data to be transmitted to the central system before responding. In this way, the system can predict and make adjustments before equipment failure occurs, avoiding complete damage to the equipment or the spread of the fault. Through the real-time computing capabilities of edge computing, the system can dynamically adjust the operating status of the equipment and make immediate responses. For example, when it detects that a certain device is about to fail, the edge computing device can adjust the device's workload or optimize its operating mode to delay the occurrence of the failure or put the device into safe mode. Because edge computing can process data locally on the device, it reduces the computing pressure on the central management system. The equipment management system only needs to receive processing results and decisions without having to process large amounts of real-time data, thereby improving the operating efficiency of the overall system. Edge computing can speed up fault response time, reduce communication delays between the device and the central system, improve the real-time and accuracy of fault processing, and enhance system reliability.

[0116] In addition, with Figure 1 Method corresponding to, refer to Figure 3 , an embodiment of the present application also provides an edge computing-based power equipment fault monitoring system, which may include a first processing unit 1001, a second processing unit 1002 and a third processing unit 1003.

[0117] The first processing unit 1001 is configured to obtain a first operating data set of the power equipment; the first operating data set is a collection of operating data at a current sampling moment and each sampling moment before the current sampling moment.

[0118] The second processing unit 1002 is configured to determine whether the power equipment has a power fault according to the first operating data set.

[0119] The third processing unit 1003 is configured to determine the fault type of the power equipment based on the first operating data set, if any.

[0120] It should be noted that the first processing unit and the second processing unit may also be any integrated circuit module or microprocessor module obtained by integrating a processing chip and its peripheral circuits using existing integration technology. The first processing unit and the second processing unit may also include one or more memories. The one or more memories may be used to store the specific algorithms in this application.

[0121] In some embodiments of the present application, the first processing unit 1001 and the second processing unit 1002 may be disposed in the same gateway or device having a processor. The specific device connection method and device configuration of the first processing unit 1001 and the second processing unit 1002, and the second processing unit 1002 and the third processing unit 1003 are not limited.

[0122] It should be noted that the contents of the above-mentioned embodiment of the power equipment fault monitoring method based on edge computing are applicable to the embodiment of this test data analysis system. The functions specifically implemented by this test data analysis system embodiment are the same as those of the above-mentioned embodiment of the power equipment fault monitoring method based on edge computing, and the beneficial effects achieved are also the same as the beneficial effects achieved by the above-mentioned embodiment of the power equipment fault monitoring method based on edge computing.

[0123] Corresponding to the method, the embodiment of the present application also provides a power equipment fault monitoring device based on edge computing, the specific structure of which can be referred to Figure 4 ,include:

[0124] At least one processor S1011.

[0125] At least one memory S1012, used to store at least one program.

[0126] When the at least one program is executed by the at least one processor, the at least one processor implements the edge computing-based power equipment fault monitoring method.

[0127] The contents of the above method embodiments are all applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0128] and Figure 1Corresponding to the method, an embodiment of the present application further provides a computer-readable storage medium, which stores processor-executable instructions, and the processor-executable instructions are used to execute the edge computing-based power equipment fault monitoring method when executed by the processor.

[0129] The contents of the above-mentioned embodiment of the power equipment fault monitoring method based on edge computing are all applicable to the embodiment of this storage medium. The functions specifically implemented by this storage medium embodiment are the same as those of the above-mentioned embodiment of the power equipment fault monitoring method based on edge computing, and the beneficial effects achieved are also the same as the beneficial effects achieved by the above-mentioned embodiment of the power equipment fault monitoring method based on edge computing.

[0130] In some optional embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiments presented and described in the flow chart of the present application are provided in an exemplary manner for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operations and logical flows presented herein. Optional embodiments are contemplated in which the order of the various operations can be changed and the sub-operations described as a part of a larger operation can be performed independently.

[0131] In addition, although the present application is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present application. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the module will be understood within the routine skills of an engineer. Therefore, a person skilled in the art can implement the present application as set forth in the claims using ordinary techniques without undue experimentation. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present application, which is determined by the full scope of the appended claims and their equivalents.

Claims

1. A method for monitoring power equipment faults based on edge computing, characterized in that: The following steps are involved: Acquiring a first operating data set of the electric power equipment; The first operating data set is a set of operating data at the current sampling moment and each sampling moment before the current sampling moment; determining, based on the first operating data set, whether the power equipment has a power fault; If so, the fault type of the electric equipment is determined based on the first operating data set.

2. The method for monitoring power equipment faults based on edge computing according to claim 1, characterized in that: The determining, based on the first operating data set, whether the power equipment has a power fault includes: determining a relative change rate of a fault characteristic of the electric power equipment based on the first operating data set; It is determined whether the power equipment has a power fault according to the relative change rate of the fault characteristic.

3. The method for monitoring power equipment faults based on edge computing according to claim 2, characterized in that: The determining, based on the first operating data set, a relative change rate of the fault characteristics of the power equipment at a current sampling moment includes: Extracting the operation data at each sampling moment from the first operation data set; For any one of the operating data, calculating a first difference between the operating data and a preset standard value; Summing up the absolute values ​​of all the first differences to obtain a sum of relative changes in fault characteristics; The relative change rate of the fault feature is obtained by dividing the sum of the relative changes of the fault feature by the number of the sampling moments.

4. The method for monitoring power equipment faults based on edge computing according to claim 2, characterized in that: The determining, based on the relative change rate of the fault characteristic, whether the power equipment has a power fault includes: If the relative change rate of the fault characteristic is greater than a preset threshold, determining that the power equipment has a power fault; If the relative change rate of the fault characteristic is less than or equal to the preset threshold, it is determined that there is no power fault in the power equipment.

5. The method for monitoring power equipment faults based on edge computing according to claim 1, characterized in that: The determining, based on the first operating data set, a fault type of the power equipment includes: determining, based on the first operating data set, a power direction of the electric power device at a current sampling moment; A fault type of the electric power equipment is determined according to the power direction.

6. The method for monitoring power equipment faults based on edge computing according to claim 5, characterized in that: The operating data includes zero-sequence voltage and zero-sequence current, and determining the power direction of the electric power equipment at a current sampling moment based on the first operating data set includes: Calculating the power of the electric equipment between the zero-sequence voltage and the zero-sequence current at the same sampling moment in the first operating data set; The power of the electric equipment at all sampling moments is summed to obtain a total power; and the direction of the total power is used as the power direction.

7. The method for monitoring power equipment faults based on edge computing according to claim 5, characterized in that: Determining the fault type of the power equipment according to the power direction includes: If the power direction is negative, it is determined that the fault type is a fault occurring inside the device; If the power direction is positive, it is determined that the fault type is that the fault occurs outside the device.

8. A power equipment fault monitoring system based on edge computing, characterized in that: include: A first processing unit is configured to obtain a first operating data set of the power equipment; The first operating data set is a set of operating data at the current sampling moment and each sampling moment before the current sampling moment; a second processing unit, configured to determine whether the power equipment has a power fault based on the first operating data set; The third processing unit is configured to determine a fault type of the power equipment based on the first operating data set, if any.

9. A power equipment fault monitoring device based on edge computing, characterized in that include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the power equipment fault monitoring method based on edge computing as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing instructions executable by a processor, characterized in that: The processor-executable instructions, when executed by the processor, are used to execute a power equipment fault monitoring method based on edge computing as described in any one of claims 1-7.

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