Real-time power equipment operation state analysis system and method

Abstract

The invention provides a real-time power equipment operation state analysis system and method, and belongs to the technical field of power monitoring. Comprising a data collection module, a data analysis module, a state evaluation module, a predictive opinion proposing module, an enhanced operation technology module, a fault diagnosis module and a prevention module, the data of the operation state of the power equipment is collected in real time, the collected data is analyzed and processed, and based on an analysis result, evaluation is performed and predictive opinions are provided. According to predictive suggestions, the operation state of the power equipment is improved, when the equipment is in a warning or emergency state, fault diagnosis is carried out, and corresponding prevention measures are taken, so that real-time monitoring and state evaluation of the power equipment can be realized, potential faults can be found in time, and corresponding prevention measures are taken. Therefore, the reliability and the safety of the equipment are improved, and the maintenance cost and the personnel risk are reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power monitoring, and in particular relates to a real-time electric power equipment operating status analysis system and method. Background Art

[0002] The analysis of the operating status of power equipment not only improves the safety, reliability and efficiency of the power system through real-time monitoring, data analysis and preventive maintenance strategies, but also reduces operating costs. It is of great significance to promote the sustainable and healthy development of the power industry. Through the analysis of the operating status of power equipment, hidden problems in the operation of equipment can be discovered in time, and minor problems can be nipped in the bud, effectively reducing the situation where equipment cannot be used normally due to faults, laying a good foundation for improving the quality of power supply.

[0003] Patent CN103278715B proposes a method for testing power equipment, including: establishing a test data model for various types of power equipment based on the test information of the preventive test procedures for power equipment; obtaining the test data generated by each test of each power equipment based on the test data model, saving the test data in the power production system, and associating it with the equipment ledger; reading the equipment ledger information of the tested power equipment from the equipment ledger; obtaining the corresponding test data from the power production system based on the test data model and the equipment ledger information of the tested power equipment, and judging whether there are hidden dangers of faults in the test data. The above-mentioned power equipment testing method provides a basis for the application of test data, obtains the corresponding test data according to the test data model to analyze the operating status of the power equipment, reduces the workload of test data processing, improves efficiency and accuracy, and can realize automated analysis of test data to detect the status of power equipment, meeting the current development requirements of power system automation.

[0004] However, the power equipment management system of the above scheme may not be perfect, and there are some blind spots in management, resulting in incomplete and inaccurate analysis of the equipment operating status, which may not be able to adapt to the complexity and diversity of modern power equipment, resulting in low maintenance efficiency, and failure to promptly discover and deal with problems in equipment operation. Excessive heating of electrical equipment may cause abnormal operation, and in severe cases may cause damage to the electrical module, affecting the stable operation of the equipment. The maintenance quality of substation equipment has not been improved accordingly, resulting in the equipment being in an unstable state and prone to frequent failures. Summary of the invention

[0005] The purpose of the present invention is to overcome the low efficiency of existing power equipment maintenance and the inability to timely discover and deal with problems in equipment operation, and to provide a real-time power equipment operation status analysis system and method, which can realize real-time monitoring and status evaluation of power equipment, timely discover potential faults and take corresponding preventive measures. This not only improves the reliability and safety of the equipment, but also reduces maintenance costs and personnel risks.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides a real-time power equipment operating status analysis method, comprising the following steps: Real-time monitoring and collection of operating data of power equipment; Process and analyze the collected operation data to generate data analysis results; Based on the results of the data analysis, and comparing with preset evaluation standards or thresholds, determine whether the power equipment is in a normal, alert or emergency state, and obtain an evaluation result of the power equipment operation state; Based on the current evaluation results of the power equipment operation status, the prediction model is used to predict the possible future status of the equipment and generate corresponding predictive opinions; Based on the predictive opinions, targeted improvement measures are implemented to continuously improve the operating status of power equipment; When the power equipment is in a warning or emergency state, perform fault diagnosis and generate fault diagnosis results; Take appropriate preventive measures based on the fault diagnosis results.

[0007] The operating data of the electric power equipment includes voltage, current, power factor and temperature parameters.

[0008] The processing and analysis of the collected data includes the following steps: Conduct power flow analysis on the collected operating data to determine the voltage, current and power parameters of each component in the power system; Conduct stability analysis based on the determined operating data parameters to evaluate the power system's ability to recover after a disturbance; Evaluate the stability of the power system by performing eigenvalue analysis and time domain analysis based on the power system's recovery capability; Predict future load demand based on historical power system operation data and generate analysis results.

[0009] Generating predictive opinions involves the following steps: Determine the forecast purpose and develop a forecast plan, clarifying the scope and time frame of the forecast; According to the forecast plan, use dynamic line graphs or scatter graph tools to observe the trajectory of image changes, pay special attention to outlier values ​​and turning points, and obtain analysis results; Based on the analysis results, a load forecasting model is established through a linear regression model; Continuously manage and monitor the predictive model to generate forecast results.

[0010] In a second aspect, the present invention provides a real-time power equipment operation status analysis system, comprising: Data collection module, used to collect the operation data of power equipment in real time; Data analysis module, used to process operation data and generate data analysis results; The status assessment module is used to compare the data analysis results with the system preset thresholds to obtain the current operating status assessment results of the power equipment and determine whether the equipment is in a normal, alert or emergency state; The predictive opinion making module is used to make predictive opinions using the prediction model based on the current power equipment operation status evaluation results; Strengthen the operation technology module to implement targeted measures to improve the operation status of power equipment based on predictive opinions; Fault diagnosis module: It is activated when the status assessment module determines that the power equipment is in a warning or emergency state, diagnoses the specific fault type and possible causes, and generates fault diagnosis results; Prevention module: Take corresponding preventive measures according to the results of the fault diagnosis module.

[0011] The data collection module collects the operation data of the power equipment in real time through sensors.

[0012] The data analysis module includes an anomaly detection module and a data generation module. The anomaly detection module is used to detect abnormal fluctuations in operating data, and the data generation module is used to generate data analysis results.

[0013] The prediction model of the predictive opinion making module is a linear regression model. The linear regression model predicts possible future power equipment failures based on the current system operation status evaluation results and historical operation data, and makes corresponding predictive opinions.

[0014] In a third aspect, the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of a real-time power equipment operating status analysis method when executing the computer program.

[0015] In a fourth aspect, the present invention provides a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a real-time power equipment operating status analysis method.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a real-time power equipment operation status analysis system and method, including a data collection module, a data analysis module, a status evaluation module, a predictive opinion proposing module, an enhanced operation technology module, a fault diagnosis module and a prevention module. By collecting data on the operation status of the power equipment, analyzing and processing the collected data, and based on the analysis results, evaluating and proposing predictive opinions, the operation status of the power equipment is improved according to the predictive opinions. When the equipment is in an alert or emergency state, fault diagnosis is performed and corresponding preventive measures are taken. Real-time monitoring and status evaluation of the power equipment can be achieved, potential faults are discovered in time and corresponding preventive measures are taken. This not only improves the reliability and safety of the equipment, but also reduces maintenance costs and personnel risks. At the same time, by continuously strengthening the research and summary of substation operation technology, the continuous progress and development of power equipment maintenance technology can be promoted.

[0017] Furthermore, the data analysis module includes an anomaly detection module and a data generation module. The anomaly detection module can identify and process abnormal values ​​or outliers in the data set, which may be caused by errors, interference or special circumstances. By removing or correcting these abnormal values, the accuracy and reliability of the data set can be improved, providing more valuable information for subsequent data analysis. The data generation module can generate new data sets according to specific requirements or rules. This helps to enrich the existing data sets and provide more samples and cases for data analysis, thereby improving the accuracy and reliability of the analysis. Together, they can improve the accuracy and efficiency of data analysis.

[0018] Furthermore, the linear regression model is used as a prediction model, which can quickly process large-scale data sets and give prediction results. This makes linear regression have certain advantages in prediction scenarios that require rapid response. The coefficients of the linear regression model have clear interpretability, which helps to understand the degree of influence of the independent variable on the dependent variable and assists in the interpretation of the prediction results to a certain extent. This interpretability makes linear regression more advantageous in scenarios where the prediction results need to be explained. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 It is a flow chart of the present invention.

[0021] Figure 2 This is a system diagram of the present invention.

[0022] Figure 3 This is the system diagram of Example 3. DETAILED DESCRIPTION

[0023] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0024] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0025] Embodiment 1: like Figure 1 As shown, a real-time power equipment operating status analysis method includes the following steps: S1: Real-time monitoring and collection of operating data of power equipment; S2: Process and analyze the collected operation data to generate data analysis results; S3: Based on the result of the data analysis, and in comparison with a preset evaluation standard or threshold, determine whether the power equipment is in a normal, alert or emergency state, and obtain an evaluation result of the power equipment operation state; S4: Based on the current evaluation results of the power equipment operation status, use the prediction model to predict the possible future status of the equipment and generate corresponding predictive opinions; S5: Based on the predictive opinions, implement targeted improvement measures to continuously improve the operating status of power equipment; S6: When the power equipment is in a warning or emergency state, perform fault diagnosis and generate fault diagnosis results; S7: Take appropriate preventive measures based on the fault diagnosis results.

[0026] Specifically, the operating data of the electric power equipment in S1 includes voltage, current, power factor and temperature parameters.

[0027] Specifically, the data analysis in S2 includes the following steps: S21: Power flow analysis: Determine the voltage, current and power parameters of each component in the power system, and understand key information such as power distribution, voltage level, current size and power loss of the power system; S22: Stability analysis: Based on the anomaly detection algorithm, monitor the abnormal fluctuations of operating data in real time; based on the state recognition algorithm, identify the operating state of the power system, discover and handle abnormal situations in a timely manner, and evaluate the recovery ability of the power system after disturbance; S23: Eigenvalue analysis and time domain analysis: Eigenvalue analysis determines the stability of the system by calculating the eigenvalue of the linearized model of the power system. If the real part of the eigenvalue is greater than zero, the system is considered to be unstable. Time domain analysis evaluates the stability of the system by simulating the dynamic behavior of the power system in the time domain. It can capture the behavior of the power system in transient and dynamic processes, including changes in parameters such as voltage, current and power.

[0028] S24: Load forecasting: Predict future load demand based on historical data.

[0029] Specifically, generating predictive opinions in S4 includes the following steps: S41: Determine forecasting objectives and develop forecasting plans: First, it is necessary to clarify the forecasting goals and objectives, as well as the forecasting scope and time frame; S42: Preliminary analysis: Use tools such as dynamic line graphs or scatter plots to observe the trajectory of image changes, paying special attention to outlier values ​​and turning points; S43: Establish a prediction model: Based on the analysis results, a load prediction model is established through a linear regression model. This step is the key to the formulation of predictive opinions. The mathematical model is used to predict future load conditions and obtain prediction results. The process of establishing a composite prediction model using the power system linear regression model is as follows: The linear regression model is trained using the real-time operating status data of the power equipment. The model parameters are obtained by solving the least squares method. The cross-validation method is used to evaluate the performance of the model, and the model parameters are continuously adjusted to improve the prediction accuracy.

[0030] Apply the trained linear regression model to actual load forecasting, and predict future load values ​​based on the input independent variables (such as historical load data). Compare the predicted results with the actual load, analyze the source of error, and continuously improve the model.

[0031] S44: Load forecast management: Continuous management and monitoring of forecast results, regular updating and maintenance of forecast models, and continuous evaluation and adjustment of forecast results.

[0032] Specifically, the process of improving the operating state of the power equipment in S5 is: S51: Improve traditional maintenance methods: introduce advanced maintenance technology and equipment to improve maintenance efficiency and accuracy, and ensure that problems in equipment operation can be discovered and handled in a timely manner; S52: Strengthen the construction of equipment management system: Establish a complete equipment management system, including the management of equipment procurement, use, maintenance, scrapping and other aspects; S53: Optimize the operating environment of electrical equipment: by improving the operating environment of electrical equipment, such as lowering the operating temperature of equipment and reducing the impact of the external environment on the equipment; S54: Enhance employees’ safety awareness and skills: Through training and education, improve employees’ attention to equipment safety operation and operating skills; S55: Introduce advanced technologies and management systems: Use Internet technology, big data analysis and other advanced technologies to monitor and analyze the operating status of power equipment in real time.

[0033] Specifically, the specific steps of performing fault diagnosis in S6 and generating fault diagnosis results are as follows: S61: Condition monitoring: Use various detection, measurement, monitoring, analysis and identification methods, combine the history and current status of the system, and consider environmental factors to evaluate the operating status of the equipment; S62: Fault diagnosis: Based on the information obtained from condition monitoring, combined with the structural characteristics, parameters and environmental conditions of the equipment, predict, analyze and judge the possible or existing faults of the equipment; S63: Troubleshooting: According to the cause of the fault, formulate corresponding troubleshooting plan, including repair, replacement, adjustment and other methods.

[0034] Perform a visual inspection of the equipment, check whether the equipment's connection lines, power supply, etc. are normal, observe the equipment's indicator lights and display screens, and use diagnostic tools for more detailed inspections, which may include using test tools or instruments to test the equipment, such as measuring voltage, current, signals, etc., to determine whether the equipment is faulty. Based on the test results, analyze the cause of the equipment failure, find the most likely fault point, repair the equipment according to the established plan, replace damaged parts or perform maintenance to ensure that the equipment can operate normally.

[0035] Specifically, in S7, according to the result of the fault diagnosis module, the specific steps of taking corresponding preventive measures are as follows: S71: Develop a test plan: Based on the use and importance of the equipment and the manufacturer's recommendations, develop a detailed test plan, including the scope, schedule, required personnel and resources, etc. S72: Determine the test standards and requirements: Determine the specific standards and requirements for this test based on the relevant national or industry standards and the actual conditions of the equipment; S73: Perform standardized tests: including insulation resistance test, voltage withstand test, ground resistance test, etc., to fully detect the electrical performance and safety of the equipment; S74: Data analysis and processing: The collected test data needs to be analyzed in detail and compared with historical data to see if there is a trend of performance degradation.

[0036] Embodiment 2: like Figure 2 As shown, a real-time power equipment operation status analysis system comprises: Data collection module, used to collect the operation data of power equipment in real time; Data analysis module, used to process operation data and generate data analysis results; The status assessment module is used to compare the data analysis results with the system preset thresholds to obtain the current operating status assessment results of the power equipment and determine whether the equipment is in a normal, alert or emergency state; The predictive opinion making module is used to make predictive opinions using the prediction model based on the current power equipment operation status evaluation results; Strengthen the operation technology module to implement targeted measures to improve the operation status of power equipment based on predictive opinions; Fault diagnosis module: It is activated when the status assessment module determines that the power equipment is in a warning or emergency state, diagnoses the specific fault type and possible causes, and generates fault diagnosis results; Prevention module: Take corresponding preventive measures according to the results of the fault diagnosis module.

[0037] In this embodiment, data on the operating status of the power equipment is collected by sensors, and the collected data is analyzed based on the anomaly detection module and the status recognition module to obtain analysis results. The operating status of the power equipment is evaluated based on the linear regression model and predictive opinions are put forward. According to the predictive opinions, the operating status of the power equipment is improved. When the equipment is in an alert or emergency state, fault diagnosis is performed and corresponding preventive measures are taken. Real-time monitoring and status evaluation of the power equipment can be achieved, potential faults can be discovered in time, and corresponding preventive measures can be taken.

[0038] Embodiment 3: like Figure 3 As shown, the present invention also provides an electronic device 100 for a real-time power equipment operating status analysis method; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0039] The memory 101 can be used to store the computer program 103, and the processor 102 implements the steps of the real-time power equipment operation status analysis method described in Example 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 can include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0040] The at least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.

[0041] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a real-time power equipment operation status analysis method, and the processor 102 can execute the plurality of instructions to implement: Real-time monitoring and collection of operating data of power equipment; Process and analyze the collected operation data to generate data analysis results; Based on the results of the data analysis, and comparing with preset evaluation standards or thresholds, determine whether the power equipment is in a normal, alert or emergency state, and obtain an evaluation result of the power equipment operation state; Based on the current evaluation results of the power equipment operation status, the prediction model is used to predict the possible future status of the equipment and generate corresponding predictive opinions; Based on the predictive opinions, targeted improvement measures are implemented to continuously improve the operating status of power equipment; When the power equipment is in a warning or emergency state, perform fault diagnosis and generate fault diagnosis results; Take appropriate preventive measures based on the fault diagnosis results.

[0042] Embodiment 4: If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0043] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0044] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0045] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0046] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0047] Finally, it should be noted that the above embodiments only describe the basic principles and main features of the present invention and the advantages of the present invention. For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

[0048] In addition, it should be understood that although this specification is described in accordance with the implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation modes that can be understood by those skilled in the art. The above content is only to illustrate the technical idea of ​​the present invention, and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution according to the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A real-time power equipment operating status analysis method, characterized in that: The steps include: Real-time monitoring and collection of operating data of power equipment; Process and analyze the collected operation data to generate data analysis results; Based on the results of the data analysis, and comparing with preset evaluation standards or thresholds, determine whether the power equipment is in a normal, alert or emergency state, and obtain an evaluation result of the power equipment operation state; Based on the current evaluation results of the power equipment operation status, the prediction model is used to predict the possible future status of the equipment and generate corresponding predictive opinions; Based on the predictive opinions, targeted improvement measures are implemented to continuously improve the operating status of power equipment; When the power equipment is in a warning or emergency state, perform fault diagnosis and generate fault diagnosis results; Take appropriate preventive measures based on the fault diagnosis results.

2. A real-time power equipment operating status analysis method according to claim 1, characterized in that: The operating data of the electric power equipment includes voltage, current, power factor and temperature parameters.

3. A real-time power equipment operating status analysis method according to claim 1, characterized in that: The processing and analysis of the collected data includes the following steps: Conduct power flow analysis on the collected operating data to determine the voltage, current and power parameters of each component in the power system; Conduct stability analysis based on the determined operating data parameters to evaluate the power system's ability to recover after a disturbance; Evaluate the stability of the power system by performing eigenvalue analysis and time domain analysis based on the power system's recovery capability; Predict future load demand based on historical power system operation data and generate analysis results.

4. A real-time power equipment operating status analysis method according to claim 1, characterized in that: Generating predictive opinions involves the following steps: Determine the purpose of the forecast and develop a forecast plan, clarifying the scope and time frame of the forecast; According to the forecast plan, use dynamic line graphs or scatter plot tools to observe the trajectory of image changes, pay special attention to outlier values ​​and turning points, and obtain analysis results; Based on the analysis results, a load forecasting model is established through a linear regression model; Continuously manage and monitor the predictive model to generate forecast results.

5. A real-time power equipment operation status analysis system, characterized in that: include: Data collection module, used to collect the operation data of power equipment in real time; Data analysis module, used to process operation data and generate data analysis results; The status assessment module is used to compare the data analysis results with the system preset thresholds to obtain the current operating status assessment results of the power equipment and determine whether the equipment is in a normal, alert or emergency state; The predictive opinion making module is used to make predictive opinions using the prediction model based on the current power equipment operation status evaluation results; Strengthen the operation technology module to implement targeted measures to improve the operation status of power equipment based on predictive opinions; Fault diagnosis module: It is activated when the status assessment module determines that the power equipment is in a warning or emergency state, diagnoses the specific fault type and possible causes, and generates fault diagnosis results; Prevention module: Take corresponding preventive measures according to the results of the fault diagnosis module.

6. A real-time power equipment operating status analysis system according to claim 5, characterized in that: The data collection module collects the operation data of the power equipment in real time through sensors.

7. A real-time power equipment operation status analysis system according to claim 5, characterized in that: The data analysis module includes an anomaly detection module and a data generation module. The anomaly detection module is used to detect abnormal fluctuations in operating data, and the data generation module is used to generate data analysis results.

8. A real-time power equipment operating status analysis system according to claim 5, characterized in that: The prediction model of the predictive opinion making module is a linear regression model. The linear regression model predicts possible future power equipment failures based on the current system operation status evaluation results and historical operation data, and makes corresponding predictive opinions.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a real-time power equipment operating status analysis method described in any one of claims 1 to 4 are implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a real-time power equipment operating status analysis method according to any one of claims 1 to 4 are implemented.

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