Electric power data storage system and method

Abstract

The invention relates to the technical field of power storage, in particular to a power data storage system and method. The method comprises the following steps: acquiring power equipment data, acquiring rated parameters of the equipment, and performing operation state simulation based on the rated parameters to generate power equipment operation simulation data; the dynamic load response of the equipment is detected by analyzing the simulation data, the transient current disturbance of the equipment is evaluated, and then thermal overload analysis of the equipment is carried out to obtain thermal overload data of the equipment; in combination with the transient current disturbance data of the equipment, estimating an equipment load imbalance state, and further performing equipment fault trend analysis to generate fault trend data of the power equipment; acquiring operation data of the power equipment according to the equipment fault trend data, storing the operation data in a database, and analyzing and optimizing the stored data; according to the invention, through optimization of the electric power data, more accurate fault identification of the electric power equipment is realized.

Description

Technical Field

[0001] The present invention relates to the field of power storage technology, and in particular to a power data storage system and method. Background Art

[0002] Power data includes a variety of key parameters, including voltage, current, power, frequency, phase, and temperature. The storage of this data must not only meet high real-time and high reliability requirements, but also ensure data integrity and security. Traditional power data storage methods face numerous technical bottlenecks in data acquisition, transmission, storage, and processing. Power data storage also faces security issues, with malicious attacks, electromagnetic interference, and equipment failures leading to data loss or tampering. The multi-source, heterogeneous nature of power data is becoming increasingly evident. For example, data formats from different devices, different manufacturers, and different communication protocols are not uniform, increasing the complexity of data fusion and storage. Furthermore, the storage of large-scale power data requires efficient data compression, index optimization, and intelligent retrieval technologies to improve storage efficiency and data analysis capabilities. However, traditional power data storage suffers from inaccurate analysis of power equipment failure factors and inaccurate analysis of power equipment demand. Summary of the Invention

[0003] Based on this, it is necessary to provide a power data storage system and method to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for storing power data includes the following steps:

[0005] Step S1: acquiring power equipment data; collecting equipment rated parameters based on the power equipment data, and simulating the operating state based on the equipment rated parameters to obtain power equipment operation simulation data;

[0006] Step S2: Performing equipment dynamic load response detection based on the power equipment operation simulation data, and performing equipment transient current disturbance assessment based on the equipment dynamic load response, performing equipment thermal overload analysis on the equipment transient current disturbance, and obtaining equipment thermal overload data;

[0007] Step S3: Estimating device load imbalance based on device thermal overload data and device transient current disturbance, and performing device failure trend analysis based on the device load imbalance to generate device failure trend data;

[0008] Step S4: collecting equipment power data based on the power equipment fault trend data, storing power data based on the equipment power data to obtain equipment power storage data, and optimizing the power equipment data using the equipment power storage data to obtain power equipment optimization data.

[0009] In the operation and management of power equipment, the present invention can improve the reliability and stability of the equipment through efficient data acquisition, analysis and optimization. By acquiring power equipment data and collecting equipment rated parameters, it can ensure that the operating status of the equipment meets the design standards and provide accurate basic data for subsequent simulation analysis. Based on the operation status simulation of the equipment rated parameters, the operating performance of the equipment is predicted in the absence of actual load, potential abnormal conditions are discovered in advance, the fault warning capability is improved, and the occurrence of sudden accidents is reduced. The dynamic load response detection of the equipment can effectively monitor the load changes of the equipment under different working conditions, identify the current shock phenomenon that occurs during operation, and ensure the stability of the equipment when the load fluctuates. The transient current disturbance assessment of the equipment can identify equipment losses and power quality problems caused by sudden current fluctuations, and provide data support for the safe operation of the equipment. The thermal overload analysis of the equipment can assess whether the equipment is damaged by overheating due to continuous overload by quantifying the thermal effect caused by the current shock, ensure the thermal stability of the equipment in long-term operation, and reduce the risk of component aging due to thermal runaway. Based on the equipment thermal overload data and equipment transient current disturbance, load imbalance estimation is performed to effectively identify problems such as grid current distortion and voltage fluctuation caused by uneven distribution of power load, providing a reference for balanced load distribution. Equipment failure trend analysis is based on historical operating data and real-time monitoring data, which can predict the failure mode of the equipment, improve the fault warning capability, and reduce the risk of downtime due to equipment failure. Therefore, the present invention is an optimization process for the traditional power storage system, which solves the problem of inaccurate analysis of power equipment failure factors and inaccurate analysis of power equipment demand in the traditional power storage system, and improves the accuracy of power equipment failure factor analysis and power equipment demand analysis.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Acquire power equipment data;

[0012] Step S12: collecting equipment rated parameters based on the power equipment data, thereby generating equipment rated parameters;

[0013] Step S13: Analyze the working principle of the power equipment according to the equipment rated parameters and the power equipment data, thereby obtaining the working principle of the power equipment;

[0014] Step S14: performing operation state simulation according to the rated parameters of the equipment and the working principle of the power equipment to obtain power equipment operation simulation data.

[0015] In the process of power equipment operation management and optimization, the present invention can improve the reliability and operating efficiency of the equipment through efficient data acquisition, analysis and simulation. By acquiring power equipment data, the operating parameters of the equipment under different working conditions can be fully understood, providing detailed basic data for subsequent analysis and optimization. The acquisition of equipment rated parameters ensures that all analyses are based on the design standards and operating requirements of the equipment, avoiding misjudgments due to parameter deviations and improving the accuracy of the data. Based on the equipment rated parameters and power equipment data, the working principle analysis can deeply understand the operating mechanism of the equipment, identify key operating links, and ensure that the analysis and optimization measures are in line with the actual working characteristics of the equipment. The operating status simulation combines the equipment rated parameters and working principles, and by predicting and analyzing the performance of the power equipment under different working conditions, it can identify potential operating anomalies in advance, providing a scientific basis for equipment maintenance and optimization. Through simulation analysis, the operating status of the equipment under different loads and different external environmental influences is evaluated, providing data support for the formulation of scientific and reasonable operating strategies and scheduling plans, reducing sudden failures, and improving the stability and safety of equipment operation.

[0016] Preferably, step S13 includes the following steps:

[0017] Step S131: Identify the function type of the power equipment according to the power equipment data, thereby obtaining the power equipment function type data;

[0018] Step S132: performing equipment geometric structure analysis based on the power equipment function type data, thereby obtaining power equipment geometric structure data;

[0019] Step S133: collecting the spatial layout of the power equipment according to the power equipment data, thereby obtaining the spatial layout data of the power equipment;

[0020] Step S134: performing power equipment load distribution calculation based on the power equipment spatial layout and the power equipment geometric structure data to obtain power equipment load distribution data;

[0021] Step S135: performing equipment coordination analysis based on the power equipment spatial layout data and the power equipment geometric structure data to obtain power equipment coordination data;

[0022] Step S136: Calculating the power equipment operating efficiency based on the power equipment coordination data, the equipment rated parameters, and the power equipment load distribution data to obtain power equipment operating efficiency data;

[0023] Step S137: Analyze the working principle of the power equipment based on the power equipment operation efficiency data and the power equipment coordination data, so as to obtain the working principle of the power equipment.

[0024] During the operation management and optimization of power equipment, this invention utilizes precise function identification, geometric structure analysis, spatial layout collection, and load distribution calculation to improve equipment efficiency and reliability. By identifying the functional type of power equipment, the functional characteristics of different devices can be classified, enabling subsequent analysis to optimize the specific operational requirements of each device type. Device geometric structure analysis based on function type data helps determine the device's external dimensions, installation method, and internal structural characteristics, providing geometric parameter support for load distribution and collaborative analysis. Power equipment spatial layout collection records the device's actual installation location within the power system, the spatial relationship between adjacent devices, and their impact on the overall system, providing data for optimizing device operation. Load distribution calculation based on spatial layout and geometric structure data accurately analyzes the load tolerance of each device, ensuring load balance and reducing equipment losses and failure risks caused by local overloads. Furthermore, device collaboration analysis based on spatial layout and geometric structure data assesses the mutual impact between adjacent devices, optimizes collaborative working mechanisms, and improves overall system efficiency. Calculating device operational efficiency based on device collaboration and load distribution data quantifies the energy conversion efficiency and load adaptation of each device, providing guidance for energy efficiency optimization and scheduling.

[0025] Preferably, the equipment dynamic load response detection in step S2 includes:

[0026] Use 1-minute intervals to collect power equipment usage demand;

[0027] Plotting equipment demand fluctuations in power equipment usage demand;

[0028] Calculate the equipment demand fluctuation slope for every 1 minute within 168 hours in the equipment demand fluctuation graph;

[0029] Collect statistics on sudden equipment demands with a fluctuation slope exceeding 1.2KW / min;

[0030] Use the equipment demand fluctuation slope and equipment sudden demand exceeding 1.5kW / min to estimate dynamic step-by-step demand;

[0031] Calculate the duration of load exceeding 80% of rated power based on dynamic step-by-step demand increase and sudden fluctuations in equipment demand to obtain equipment continuous excess load data;

[0032] The dynamic load response of the equipment is detected by testing the operation simulation data of the power equipment according to the continuous excess load data of the equipment to obtain the dynamic load response of the equipment.

[0033] This invention accurately assesses the dynamic load response of power equipment during operation, which is crucial for optimizing power dispatch and extending equipment life. By collecting power equipment demand at 1-minute intervals, it accurately records real-time changes in equipment power consumption, providing high-precision data support for subsequent demand analysis. Plotting equipment demand fluctuations helps visualize the changing trends of equipment loads and directly presents key characteristics of load fluctuations. Calculating the slope of equipment demand fluctuations over 168 hours quantifies the rate of load change and identifies long-term trends in load growth. Counting sudden demand for equipment with a demand fluctuation slope exceeding 1.2 kW / min accurately captures dramatic load changes in a short period of time, providing basic data for analyzing sudden power consumption characteristics. Using the demand fluctuation slope and sudden demand to estimate dynamic step-by-step demand increases effectively assesses the impact of equipment on the power system during gradual load increases and analyzes load growth patterns. Calculating the duration of loads exceeding 80% of rated power based on dynamic step-by-step demand increases and sudden demand fluctuations accurately assesses equipment overload conditions, providing data support for overload protection and operational optimization.

[0034] Preferably, the equipment transient current disturbance assessment in step S2 comprises the following steps:

[0035] Evaluate the equipment's dynamic load response to intensified equipment losses;

[0036] Predict grid stability degradation data as equipment losses intensify;

[0037] Analyze grid voltage instability status from grid stability decay data;

[0038] Use the unstable state of grid voltage to evaluate the voltage fluctuation and flicker of power equipment;

[0039] Detect equipment power quality anomalies based on power equipment voltage fluctuations and flicker and grid voltage instability, and obtain equipment power quality anomaly data;

[0040] Calculate the current waveform distortion status of the equipment power quality abnormality data, and evaluate the equipment current overload data based on the current waveform distortion status;

[0041] The equipment transient current disturbance is evaluated based on the equipment current overload data and the current waveform distortion status to obtain the equipment transient current disturbance.

[0042] This invention evaluates equipment loss exacerbation based on dynamic load response data, identifying additional energy losses caused by load fluctuations, and providing data support for subsequent grid stability analysis. Predicting grid stability degradation data associated with exacerbated equipment loss quantifies the impact of equipment operating conditions on grid stability and provides a data foundation for analyzing grid voltage fluctuations. Analyzing grid voltage instability from grid stability degradation data helps identify abnormal grid voltage fluctuations caused by equipment operation, providing a reference for voltage regulation and power quality management. Using grid voltage instability to assess voltage fluctuations and flicker in power equipment accurately measures the stability of power equipment under voltage fluctuations and quantifies the impact of voltage flicker on equipment. Power quality anomalies are detected based on power equipment voltage fluctuations and flicker and grid voltage instability, precisely locating the specific source of power quality anomalies and providing data support for power system optimization. Calculating the current waveform distortion of equipment power quality anomaly data and evaluating equipment current overload data based on the current waveform distortion quantifies the impact of current distortion on equipment, providing accurate data support for equipment overload assessment.

[0043] Preferably, the equipment thermal overload analysis in step S2 includes:

[0044] Evaluate the high-frequency electromagnetic pulse phenomenon of equipment when the transient current disturbance of the equipment is 3000A;

[0045] The device insulation breakdown data is obtained by using the high-frequency electromagnetic pulse phenomenon of the device when the electromagnetic pulse exceeds the safety threshold of 85dBμV / m and the current impact effect of the device;

[0046] Predict damage to electronic components based on equipment insulation breakdown data and high-frequency electromagnetic pulse phenomena;

[0047] Based on the transient current disturbance of the equipment, the cumulative thermal effect statistics of the damage to the electronic components of the equipment within 48 hours are calculated to obtain the cumulative thermal effect data of the equipment;

[0048] Estimate the equipment thermal positive feedback effect based on the accumulated data of the equipment thermal effect;

[0049] The equipment thermal overload analysis is performed on the equipment thermal positive feedback effect and the equipment thermal effect cumulative data exceeding 144°C to obtain the equipment thermal overload data.

[0050] According to the present invention, during the operation of high-power electrical equipment, transient current disturbances will trigger high-frequency electromagnetic pulse phenomena, which will affect the insulation performance of the equipment and the stability of the electronic components. Therefore, by evaluating the high-frequency electromagnetic pulse phenomenon of the equipment with transient current disturbances of 3000A equipment, the degree of interference of the electromagnetic pulse on the internal structure of the equipment and the surrounding environment can be identified, providing data support for subsequent equipment safety assessments. The use of the high-frequency electromagnetic pulse phenomenon of the equipment with electromagnetic pulses exceeding the safety threshold of 85dBμV / m and the equipment current impact effect to predict the insulation breakdown of the equipment can effectively identify the damage to the insulation layer of the equipment in a high electric field environment and provide a basis for the insulation life assessment of the power equipment. Based on the equipment insulation breakdown data and the high-frequency electromagnetic pulse phenomenon of the equipment to predict the damage phenomenon of the electronic components of the equipment, the degree of damage to the electronic components caused by transient electromagnetic interference and local overload can be accurately assessed, reducing the risk of failure of electronic devices due to electrical stress.

[0051] Preferably, step S3 includes the following steps:

[0052] Step S31: Estimating device load imbalance based on device thermal overload data and device transient current disturbance to obtain device load imbalance;

[0053] Step S32: Calculating the mechanical stress growth of the equipment based on the equipment load imbalance to obtain equipment mechanical stress growth data;

[0054] Step S33: Estimating the fatigue state of the power equipment based on the equipment mechanical stress growth data to obtain fatigue state data of the power equipment;

[0055] Step S34: performing equipment failure trend analysis based on the power equipment fatigue state data and the equipment mechanical stress growth data to generate power equipment failure trend data.

[0056] During the long-term operation of the power equipment of the present invention, load imbalance will lead to local overload, abnormal local temperature rise and stress changes in the mechanical structure. Therefore, the equipment load imbalance is estimated based on the equipment thermal overload data and the transient current disturbance of the equipment, which can accurately identify the distribution of the current of each phase of the equipment and provide a basis for the balanced load adjustment of the equipment. The mechanical stress growth of the equipment is calculated based on the equipment load imbalance, which can quantify the cumulative effect of mechanical fatigue caused by long-term uneven stress on the equipment, and provide data support for the mechanical structure optimization and strength assessment of the equipment. The fatigue state of the power equipment is estimated based on the mechanical stress growth data of the equipment, and the fatigue life trend of the key components of the equipment can be analyzed, and the potential damage sites caused by stress accumulation can be identified to improve the targeted maintenance of the equipment. The equipment failure trend analysis is performed based on the fatigue state data of the power equipment and the mechanical stress growth data of the equipment. The historical operation data and the current state data can be combined to predict the development direction of the equipment failure, and provide a decision-making basis for the preventive maintenance and optimized operation strategy of the equipment.

[0057] Preferably, step S34 includes the following steps:

[0058] Step S341: performing power equipment vibration aggravation detection based on power equipment fatigue state data to obtain power equipment vibration aggravation data;

[0059] Step S342: Predicting looseness of connection components of the power equipment based on the power equipment vibration intensification data to obtain looseness data of connection components of the power equipment;

[0060] Step S343: performing mechanical metal structure degradation detection based on the loosening data of the power equipment connection components and the equipment mechanical stress growth data to obtain mechanical metal structure degradation data;

[0061] Step S344: performing equipment failure trend analysis based on the mechanical metal structure degradation data and the loosening data of the power equipment connection components to generate power equipment failure trend data.

[0062] During the long-term operation of the power equipment of the present invention, the vibration state of the mechanical structure directly affects the stability of the equipment. Therefore, by performing vibration aggravation detection on the power equipment based on the fatigue state data of the power equipment, the vibration amplitude change of the equipment due to fatigue accumulation can be identified, and the degree of vibration abnormality of the equipment can be accurately judged. The loosening of the connection parts of the power equipment is estimated based on the vibration aggravation data of the power equipment, and the influence of vibration on the connection parts can be further analyzed, and the loosening trend of the bolts of the equipment under the vibration environment can be quantified, providing data support for the evaluation of the stability of the equipment structure. The degradation detection of the mechanical metal structure is performed based on the loosening data of the connection parts of the power equipment and the mechanical stress growth data of the equipment. The stress changes caused by the loosening of the parts can be combined to evaluate the fatigue degradation degree of the metal structure of the equipment, identify hidden dangers such as material aging and crack extension in advance, and improve the safety of equipment operation. The equipment failure trend analysis is performed based on the mechanical metal structure degradation data and the loosening data of the connection parts of the power equipment. The vibration state of the equipment, the loosening of the parts and the degradation of the metal structure can be integrated to construct an equipment health trend evaluation model, providing data support for the status prediction, preventive maintenance and optimized operation of the equipment.

[0063] Preferably, step S4 includes the following steps:

[0064] Step S41: Analyze the performance degradation of the power equipment according to the failure trend of the power equipment to obtain the performance degradation data of the power equipment;

[0065] Step S42: collecting equipment power data based on the power equipment performance degradation data to obtain equipment power data;

[0066] Step S43: storing the device power data in a database to obtain device power storage data;

[0067] Step S44: predicting the performance bottleneck of the power equipment based on the power storage data of the equipment and the performance degradation data of the power equipment to obtain the performance bottleneck of the power equipment;

[0068] Step S45: Optimizing the power equipment data according to the power equipment performance bottleneck to obtain power equipment optimization data.

[0069] The long-term operating status of the power equipment of the present invention is affected by multiple factors, including load changes, mechanical stress growth, and fatigue accumulation. Therefore, by analyzing power equipment performance degradation based on power equipment failure trends, the performance degradation process of the equipment can be accurately quantified, the changing trends of key parameters can be identified, and the health status of the equipment can be understood. Equipment power data collection based on power equipment performance degradation data can accurately extract key data such as current, voltage, and power factor during operation based on the performance degradation characteristics of different types of equipment, forming complete equipment operation information. Storing the collected equipment power data in a database ensures the integrity of the equipment operation data and provides reliable data support for subsequent performance trend analysis and optimization. Power equipment performance bottleneck prediction based on equipment power storage data and power equipment performance degradation data can identify key factors affecting the stable operation of the equipment, such as insufficient overload capacity and reduced heat dissipation performance, and analyze the equipment's maximum operating capacity under various operating conditions. Equipment optimization based on power equipment performance bottlenecks can be combined with operation data and historical fault information to adjust the equipment's operation strategy, load distribution plan, or maintenance plan, thereby improving the long-term stability and safety of the equipment.

[0070] The present invention further provides a power data storage system for executing the power data storage method described above, the power data storage system comprising:

[0071] The operation status simulation module is used to obtain power equipment data; collect equipment rated parameters based on the power equipment data, and simulate the operation status based on the equipment rated parameters to obtain power equipment operation simulation data;

[0072] The equipment thermal overload analysis module is used to detect the dynamic load response of the equipment based on the power equipment operation simulation data, and to evaluate the transient current disturbance of the equipment based on the dynamic load response of the equipment. The transient current disturbance of the equipment is then analyzed for thermal overload to obtain the equipment thermal overload data;

[0073] The equipment failure trend analysis module is used to estimate equipment load imbalance based on equipment thermal overload data and equipment transient current disturbances, and to analyze equipment failure trends based on equipment load imbalance to generate equipment failure trend data;

[0074] The equipment optimization module is used to collect equipment power data based on power equipment fault trend data, store power data based on the equipment power data, obtain equipment power storage data, and use the equipment power storage data to optimize the power equipment data to obtain power equipment optimization data.

[0075] The present invention is that in the operation and management of power equipment, efficient data acquisition, analysis and optimization can improve the reliability and stability of the equipment. By acquiring power equipment data and collecting equipment rated parameters, it is possible to ensure that the operating status of the equipment meets the design standards and provide accurate basic data for subsequent simulation analysis. Based on the operation status simulation of the equipment rated parameters, the operating performance of the equipment is predicted in the absence of actual load, potential abnormal conditions are discovered in advance, the fault warning capability is improved, and the occurrence of sudden accidents is reduced. The dynamic load response detection of the equipment can effectively monitor the load changes of the equipment under different working conditions, identify the current shock phenomenon that occurs during operation, and ensure the stability of the equipment when the load fluctuates. The transient current disturbance assessment of the equipment can identify equipment losses and power quality problems caused by sudden current fluctuations, and provide data support for the safe operation of the equipment. The thermal overload analysis of the equipment can assess whether the equipment is damaged by overheating due to continuous overload by quantifying the thermal effect caused by the current shock, ensuring the thermal stability of the equipment in long-term operation and reducing the risk of component aging due to thermal runaway. Based on the equipment thermal overload data and equipment transient current disturbance, load imbalance estimation is performed to effectively identify problems such as grid current distortion and voltage fluctuation caused by uneven distribution of power load, providing a reference for balanced load distribution. Equipment failure trend analysis is based on historical operating data and real-time monitoring data, which can predict the failure mode of the equipment, improve the fault warning capability, and reduce the risk of downtime due to equipment failure. Therefore, the present invention is an optimization process for the traditional power storage system, which solves the problem of inaccurate analysis of power equipment failure factors and inaccurate analysis of power equipment demand in the traditional power storage system, and improves the accuracy of power equipment failure factor analysis and power equipment demand analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 A schematic flow chart of the steps of a method for storing power data;

[0077] Figure 2 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0078] Figure 3 for Figure 2 Detailed implementation steps of step S34 are shown in the flowchart;

[0079] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0080] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0081] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0082] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0083] To achieve this, please refer to Figures 1 to 3 , a method for storing power data, comprising the following steps:

[0084] Step S1: acquiring power equipment data; collecting equipment rated parameters based on the power equipment data, and simulating the operating state based on the equipment rated parameters to obtain power equipment operation simulation data;

[0085] Step S2: Performing equipment dynamic load response detection based on the power equipment operation simulation data, and performing equipment transient current disturbance assessment based on the equipment dynamic load response, performing equipment thermal overload analysis on the equipment transient current disturbance, and obtaining equipment thermal overload data;

[0086] Step S3: Estimating device load imbalance based on device thermal overload data and device transient current disturbance, and performing device failure trend analysis based on the device load imbalance to generate device failure trend data;

[0087] Step S4: collecting equipment power data based on the power equipment fault trend data, storing power data based on the equipment power data to obtain equipment power storage data, and optimizing the power equipment data using the equipment power storage data to obtain power equipment optimization data.

[0088] In the embodiment of the present invention, reference Figure 1 As shown, in this example, the power data storage method includes the following steps:

[0089] Step S1: acquiring power equipment data; collecting equipment rated parameters based on the power equipment data, and simulating the operating state based on the equipment rated parameters to obtain power equipment operation simulation data;

[0090] In an embodiment of the present invention, a high-precision data acquisition system (such as an NI data acquisition card or a Schneider PM8000 series power monitoring device) is used to collect real-time data on the operating status of power equipment. The collected parameters include key power indicators such as voltage (V), current (A), power (W), power factor, harmonic content, equipment operating temperature (°C), and load factor (%). The data acquisition frequency is set to 1kHz to ensure high temporal resolution. After data acquisition, an industrial-grade edge computing server (such as Advantech Edge Gateway) is used for data preprocessing, including data denoising, outlier removal, and signal filtering. Wavelet transform is used to remove high-frequency noise signals. Based on the collected power equipment data, the rated parameters of the equipment are extracted. Rated parameters include rated voltage (e.g., 10kV), rated power (e.g., 500kW), and rated current (e.g., 50A). These parameters can be obtained from the equipment nameplate information, the manufacturer's technical manual, or the equipment archive database in the SCADA system. After extracting the parameters, the operating status is simulated based on the physical model of the equipment. For example, for a three-phase motor, electromagnetic transient simulation software (such as PSCAD or MATLAB / Simulink) is used to construct a mathematical model of the asynchronous motor based on dq-axis coordinate transformation. By setting different load conditions, the current, voltage waveform, temperature rise change, and power fluctuation of the equipment under different load conditions are simulated, thereby obtaining the power equipment operation simulation data.

[0091] Step S2: Performing equipment dynamic load response detection based on the power equipment operation simulation data, and performing equipment transient current disturbance assessment based on the equipment dynamic load response, performing equipment thermal overload analysis on the equipment transient current disturbance, and obtaining equipment thermal overload data;

[0092] In this embodiment of the present invention, the dynamic load response of power equipment is monitored, using simulated data from equipment operation and load curve analysis. For example, by analyzing the rate of change of the load curve, rapid and sudden changes in load are identified. If the rate of change in the equipment load exceeds a set threshold, the system detects a sudden load change. The current changes and fluctuations following the sudden load change are further analyzed, the sudden change point is recorded, and key information such as the magnitude and duration of the load change is calculated. To assess transient current disturbances, the system collects current waveform data and uses time-domain analysis to detect sudden changes in the current waveform. Specifically, during sudden load changes, the current surge and its rate of change are recorded and evaluated. If the current waveform is severely distorted and the current fluctuation amplitude exceeds the rated value of the equipment, the system determines that a current disturbance exists and enters the thermal overload analysis phase. Based on the transient current disturbance, the system assesses the impact of the current on the equipment's thermal load. Through thermal-electric coupling analysis, it is determined that changes in the equipment's current will cause an increase in the internal temperature of the equipment. The temperature rise of the equipment is combined with characteristics such as thermal resistance, mass, and specific heat capacity to derive the equipment's temperature rise data. If the temperature rise of the equipment exceeds the design limit of the equipment, the system will determine that the equipment is in a thermal overload state and record the corresponding thermal overload data.

[0093] Step S3: Estimating device load imbalance based on device thermal overload data and device transient current disturbance, and performing device failure trend analysis based on the device load imbalance to generate device failure trend data;

[0094] In an embodiment of the present invention, the load imbalance of the equipment is estimated based on the thermal overload data and transient current disturbance data. By measuring and analyzing the three-phase current of the equipment, the difference in the current of each phase is calculated to determine whether there is a load imbalance phenomenon. If the current imbalance exceeds the set safety standard, the system will mark the risk of load imbalance in the equipment. Based on the load imbalance data, the failure trend analysis of the equipment is performed. This process uses historical data to analyze the impact of equipment load imbalance on the life of the equipment, and combines the mechanical stress, temperature rise and other factors of the equipment to deduce the probability of equipment failure and generate equipment failure trend data. The system monitors the operating status of the equipment over a long period of time and promptly warns of potential failure trends of the equipment.

[0095] Step S4: collecting equipment power data based on the power equipment fault trend data, storing power data based on the equipment power data to obtain equipment power storage data, and optimizing the power equipment data using the equipment power storage data to obtain power equipment optimization data.

[0096] In an embodiment of the present invention, the power data of the equipment is collected and monitored in real time. The collected data includes key information such as voltage, current, power, frequency, etc., and the collection frequency is once per second. The collected data is transmitted to the data center through the industrial gateway and stored in a time series database. The time series database (such as InfluxDB or TimescaleDB) is used to store these power data. After the data storage is completed, the system will optimize the stored data. For example, high-frequency noise is removed by a filtering algorithm, or a data compression algorithm is used to reduce the storage space occupied. At the same time, based on historical operating data and current equipment status, the system will analyze the operating status of the equipment through data mining technology, identify potential failure modes, and predict the future operating trends of the equipment based on these modes. By analyzing the optimization data of the equipment, an optimization report of the power equipment is output.

[0097] Preferably, step S1 includes the following steps:

[0098] Step S11: Acquire power equipment data;

[0099] Step S12: collecting equipment rated parameters based on the power equipment data, thereby generating equipment rated parameters;

[0100] Step S13: Analyze the working principle of the power equipment according to the equipment rated parameters and the power equipment data, thereby obtaining the working principle of the power equipment;

[0101] Step S14: performing operation state simulation according to the rated parameters of the equipment and the working principle of the power equipment to obtain power equipment operation simulation data.

[0102] In an embodiment of the present invention, data is collected using an electric power equipment monitoring system. Taking a distribution transformer as an example, its real-time operating parameters include voltage, current, load factor, power, and frequency. This data is collected by power monitoring equipment installed on the transformer. For example, Schneider Electric's PM8000 series smart meters monitor multiple power indicators, such as voltage, current, and power. These smart meters support high-frequency data collection (once per second). The equipment transmits data to a data acquisition system (such as a PLC controller or SCADA system) via standard Modbus or industrial communication protocols. The data acquisition system must be configured to collect and store data at regular intervals and transmit it to a central database system for storage. During the data collection process, all key information within the equipment (such as the current, voltage, and power of the equipment during operation) is recorded, and each set of data is timestamped. To ensure data accuracy, a high-speed data acquisition card with a sampling frequency of 1000 times per second is used during the collection process. Rated parameters are collected based on the power equipment data. Rated parameters refer to the standard operating parameters specified during the equipment design and manufacturing process. For example, for a power transformer, the rated voltage refers to the nominal voltage during normal operation, the rated current refers to the maximum current required for normal operation, and the rated power refers to the transformer's maximum output power under standard operating conditions. Relevant rated value data can be directly extracted based on the manufacturer's technical specifications, nameplate data, equipment design manual, or information in the system's archive database. For example, a distribution transformer has a rated voltage of 220V and a rated power of 500kVA. Then, combined with data collected from the power equipment (such as real-time operating voltage and current), the deviation range between the collected real-time data and the equipment's rated parameters is determined. By comparing the rated parameters with the real-time data during operation, the system can determine whether the equipment is operating normally. Based on the collected power equipment data and rated parameters, the system analyzes the equipment's operating principle. For example, the operating principle of a power transformer is primarily based on electromagnetic induction, with electrical energy conversion achieved through the transmission of current between the primary and secondary windings. The system then uses the equipment's rated voltage and rated current parameters to calculate the equipment's rated power and its operating status under different load conditions. Based on the actual operating data of the equipment (such as current and voltage waveforms and frequency), calculate the power factor of the equipment during operation and analyze its impact on the equipment load. A low power factor means that the equipment has a large amount of reactive power during operation, resulting in reduced transformer efficiency, which in turn affects the equipment's operating principle and output. Based on the equipment's rated parameters and operating principle, use power system simulation software to simulate the equipment's operating status. For example, use MATLAB / Simulink or PSCAD to create a mathematical model of the power equipment (such as a distribution transformer).The simulation model should include the basic parameters of the equipment, such as rated voltage, rated power, load curve, temperature limit, etc. After the simulation starts, the system will input the real-time operating data of the power equipment and simulate the performance of the equipment under different working conditions. The specific operations in the operating status simulation process include: inputting the rated parameters of the equipment (such as voltage, power factor, rated current), and then performing simulation based on the real-time collected equipment data (such as load changes, current fluctuations, frequency changes), and analyzing the response of the equipment under these changing conditions. The simulation results can give the actual operating status of the equipment, such as whether there is an overload, power factor fluctuations, and equipment temperature rise. In addition, through simulation, the system can predict the operating conditions of the equipment in the future, such as the temperature changes of the equipment under high-load conditions, the probability of failure, etc.

[0103] Preferably, step S13 includes the following steps:

[0104] Step S131: Identify the function type of the power equipment according to the power equipment data, thereby obtaining the power equipment function type data;

[0105] Step S132: performing equipment geometric structure analysis based on the power equipment function type data, thereby obtaining power equipment geometric structure data;

[0106] Step S133: collecting the spatial layout of the power equipment according to the power equipment data, thereby obtaining the spatial layout data of the power equipment;

[0107] Step S134: performing power equipment load distribution calculation based on the power equipment spatial layout and the power equipment geometric structure data to obtain power equipment load distribution data;

[0108] Step S135: performing equipment coordination analysis based on the power equipment spatial layout data and the power equipment geometric structure data to obtain power equipment coordination data;

[0109] Step S136: Calculating the power equipment operating efficiency based on the power equipment coordination data, the equipment rated parameters, and the power equipment load distribution data to obtain power equipment operating efficiency data;

[0110] Step S137: Analyze the working principle of the power equipment based on the power equipment operation efficiency data and the power equipment coordination data, so as to obtain the working principle of the power equipment.

[0111] In an embodiment of the present invention, the functional category of a device is determined by analyzing its operating data. The device collects basic operating data, including parameters such as voltage, current, frequency, and power factor, through a power monitoring system. The functional characteristics described in the device's manufacturer documentation and technical specifications are combined to perform a preliminary identification of the device's functional type. For example, for a distribution transformer, its rated power, rated voltage, and rated current parameters can be used to infer whether it is a main transformer in a substation, a low-voltage distribution transformer in a distribution substation, or the like. Using data analysis tools (such as Python's Pandas and Numpy libraries for data cleaning and processing), the real-time data collected by the device is compared with the functional identification in the device manual to determine the functional type of the power device. For certain complex devices, in-depth analysis is performed based on the device's functional label, operating status, and load curve to further subdivide the device's functional type. Based on the identified functional type of the power device, the device's geometric structure is analyzed. During this process, three-dimensional geometric data of the power device is obtained, and a spatial layout drawing of the device is obtained using a CAD (computer-aided design) tool (such as AutoCAD or SolidWorks). By analyzing the device's geometric structure, it is understood how the device's spatial structure affects its operating efficiency and load distribution. The specific operation involves extracting the dimensions, installation location, and relative spatial layout of each component from the design drawings. Combining this structural information, the impact of the physical connections and spatial positions of each component on heat accumulation and mechanical stress distribution during equipment operation is analyzed. Geometric data is typically converted from digital drawings into a 3D model or structural parameters are generated using the equipment's structural diagram. The key to this step is collecting spatial layout data using a sensor system installed within the power equipment. Spatial layout collection encompasses not only the physical location of the equipment itself but also its overall positional relationship within the power system. For example, in power transformer applications, a GPS positioning system or device positioning system records the equipment's installation location, surrounding environment, and spatial distribution. A PLC (programmable logic controller) interfaces with the sensors for data transmission, acquiring real-time spatial location information of the equipment within the power network. By carefully recording the surrounding environment of the power equipment, the relative position of the equipment to key equipment (such as circuit breakers, switches, and busbars) is determined. Load distribution calculations are performed based on the collected spatial layout and geometric data of the power equipment. Load distribution calculations determine how power equipment shares the load in the power network based on the geometric structure and spatial layout of the equipment. Calculations are performed using specialized load distribution calculation tools (such as SimPowerSystems or DIgSILENT PowerFactory in MATLAB) based on the geometric structure data and spatial layout data of the power equipment.By building mathematical models of power equipment, the operating states of different devices in the power system are simulated, and the load demand and response capacity of each device are calculated. For transformers, the load distribution ratio is calculated based on the rated power, actual load, and spatial layout of the devices. Equipment collaboration analysis evaluates how power equipment can work together to optimize overall system efficiency based on their interactions. The synergy between devices is analyzed using spatial layout data and geometric structure data. For example, in the collaborative analysis of transformers and distribution networks, the system considers load distribution, energy transmission paths, temperature distribution, and the mutual influence of devices. Modeling software (such as ANSYS or COMSOL) is used to jointly analyze the thermodynamic, mechanical stress, and electrical properties of power equipment to assess the efficiency and impact of collaborative operation. During the analysis, the system uses optimization algorithms (such as genetic algorithms or particle swarm optimization) to adjust the coordination. Based on the power equipment collaboration analysis data and load distribution calculation results, the operating efficiency of the power equipment is calculated. This step involves evaluating the efficiency of power equipment under actual load and collaborative operation conditions, as well as the rated parameters of the equipment. By inputting load distribution data and equipment coordination data, the efficiency calculation tool (EnergyPlus) performs detailed calculations of equipment rated parameters, energy consumption, heat generation, and losses. The operating principles of power equipment are analyzed by combining equipment operating efficiency data and coordination data. A comprehensive assessment of the operating principles of power equipment is conducted by combining mathematical models with actual operating data. For example, when analyzing the operating principle of a transformer, the electrical operating process (electromagnetic induction when current passes through the windings) is considered. Then, combined with factors such as the equipment's operating efficiency data, load distribution, and equipment coordination status, the equipment's operating performance is comprehensively assessed using data such as input and output power, load response, and temperature distribution, resulting in a precise understanding of the equipment's operating principle.

[0112] Preferably, the equipment dynamic load response detection in step S2 includes:

[0113] Use 1-minute intervals to collect power equipment usage demand;

[0114] Plotting equipment demand fluctuations in power equipment usage demand;

[0115] Calculate the equipment demand fluctuation slope for every 1 minute within 168 hours in the equipment demand fluctuation graph;

[0116] Collect statistics on sudden equipment demands with a fluctuation slope exceeding 1.2KW / min;

[0117] Use the equipment demand fluctuation slope and equipment sudden demand exceeding 1.5kW / min to estimate dynamic step-by-step demand;

[0118] Calculate the duration of load exceeding 80% of rated power based on dynamic step-by-step demand increase and sudden fluctuations in equipment demand to obtain equipment continuous excess load data;

[0119] The dynamic load response of the equipment is detected by testing the operation simulation data of the power equipment according to the continuous excess load data of the equipment to obtain the dynamic load response of the equipment.

[0120] In an embodiment of the present invention, intelligent monitoring equipment (such as smart meters or power quality analyzers) on power equipment collects real-time usage demand data on the equipment at one-minute intervals. This data includes operating parameters such as voltage, current, power, power factor, and frequency. The intelligent monitoring equipment regularly collects data and transmits it to a data acquisition server or cloud platform for storage, ensuring data accuracy and real-time performance. During data collection, the acquisition equipment must maintain a certain level of accuracy and sampling frequency. Typically, voltage and current accuracy must reach 0.5, and the sampling frequency must be once per minute. During this process, changes in equipment usage demand are monitored in real time and recorded as time series data, with each data item corresponding to the equipment's operating status at a specific moment. The data collected in step 1 is used to create a device demand fluctuation chart using a chart generation tool (such as Matlab or Python's Matplotlib library). The horizontal axis of the fluctuation chart represents time (in minutes), and the vertical axis represents the equipment's power demand (in kilowatts). To create the fluctuation chart, the equipment demand data points for each minute are plotted on the chart, and these data points are connected to generate a curve chart of the equipment demand fluctuations. The fluctuation chart in the figure shows the fluctuations in device demand over different time periods, including peak and valley periods. To perform this operation, the data should be divided by date and time period based on the amount of collected data. This ensures that the chart accurately displays the full picture of device demand and identifies sudden changes or trends in power load. After plotting the device demand fluctuation chart, the next step is to calculate the slope of the device demand fluctuation. Select 168 consecutive hours of data from the fluctuation chart and calculate the slope of the device demand change over that 168-hour period. The slope is calculated by taking the difference between the maximum and minimum device demand values during that period and dividing it by the total time (in minutes) for that period. Specifically, the demand change at each time point within the 168-hour period is first calculated, and then the slope is calculated based on the demand difference between each time point. For example, if the device demand increases from 10 kW to 30 kW within a 10-minute period, the slope is (30 - 10) / 10 = 2 kW / min. This operation will mark the slope value on the equipment demand fluctuation graph, and evaluate the volatility of equipment demand changes by counting the slopes of each 168 hours. The 168-hour equipment demand fluctuation slope data obtained will then be filtered for the parts where the equipment demand fluctuation slope exceeds 1.2KW / min. All periods where the fluctuation slope exceeds 1.2KW / min are considered sudden equipment demands. In this process, statistical analysis tools (such as Excel or Pandas in Python) are used to filter the slope data, extracting all time periods that exceed the standard and their corresponding equipment demand values. In specific operations, for each period exceeding 1.5KW / min, the start and end time of the period and the corresponding equipment demand value are recorded.The statistical results will clearly indicate when and at what rate sudden changes in equipment demand occur. The statistically obtained equipment sudden demand data and equipment demand fluctuation slope data will be used to estimate the dynamic step-by-step incremental demand. In this step, the specific time period of each sudden demand is identified, and then the step-by-step incremental demand pattern is set according to the fluctuation slope of the time period. Specifically, for the demand changes of equipment within a certain time period, when the equipment demand fluctuation slope is large, it is assumed that the equipment demand within the time period presents a step-by-step increase. At this time, the demand will be divided into multiple increasing stages, and the demand of each stage is set as a step according to the fluctuation slope. When setting these steps, it is necessary to reverse the actual data to ensure that the step increase matches the rate of sudden demand. Using a computational model (such as linear regression or time series analysis), by combining the sudden demand fluctuation slope with the actual equipment demand data, the increasing trend of future demand can be gradually calculated. The calculated dynamic step-by-step demand is combined with the rated power of the equipment to further calculate the duration of time when the load exceeds 80% of the rated power. Since the rated power of the equipment is known, a threshold of 80% is set. By gradually comparing the dynamic step-by-step demand values with this threshold, the time periods during which the equipment demand exceeds 80% of the rated power are identified. Specifically, each step-by-step demand data is evaluated. When the demand value exceeds 80% of the rated power of the equipment, the duration of the period is recorded. During this process, the equipment demand fluctuation data is combined with timestamps to calculate the duration of the equipment overload. Each period is analyzed using tools such as Matlab or Python's NumPy library to ensure accuracy. Using this continuous overload data, dynamic load response monitoring is performed on the operating status of the power equipment. Based on the equipment's response to overload, load monitoring and analysis are performed to obtain real-time simulated operational data of the power equipment. This data is obtained through the power system's monitoring system (such as the SCADA system). Based on the actual load conditions and simulation data of the equipment, the current, voltage changes, power factor and other data when the load exceeds 80% of the rated power are analyzed. By calculating the relationship between load changes and equipment response, the response characteristics of the equipment under dynamic load conditions are obtained.

[0121] Preferably, the equipment transient current disturbance assessment in step S2 comprises the following steps:

[0122] Evaluate the equipment's dynamic load response to intensified equipment losses;

[0123] Predict grid stability degradation data as equipment losses intensify;

[0124] Analyze grid voltage instability status from grid stability decay data;

[0125] Use the unstable state of grid voltage to evaluate the voltage fluctuation and flicker of power equipment;

[0126] Detect equipment power quality anomalies based on power equipment voltage fluctuations and flicker and grid voltage instability, and obtain equipment power quality anomaly data;

[0127] Calculate the current waveform distortion status of the equipment power quality abnormality data, and evaluate the equipment current overload data based on the current waveform distortion status;

[0128] The equipment transient current disturbance is evaluated based on the equipment current overload data and the current waveform distortion status to obtain the equipment transient current disturbance.

[0129] In an embodiment of the present invention, dynamic load response data of equipment is collected via a real-time monitoring system (such as a power quality monitoring instrument) on the power equipment. This data includes parameters such as current, voltage, and power factor under different loads. When assessing equipment loss exacerbation, the loss increase due to load changes during operation is calculated based on the operating conditions and operational status of the power equipment. The loss increase is typically correlated with current fluctuations, power factor changes, and the equipment load. The dynamic load response of the equipment is simulated using power system analysis software (such as ETAP and PowerWorld). By simulating the changes in equipment losses under different load conditions, the extent of equipment loss exacerbation under different operating conditions can be determined. At this point, long-term equipment operating data is collected and analyzed. Changes in the dynamic load response can effectively reflect the exacerbation of equipment losses, especially during high-load periods and severe load fluctuations. Based on this equipment loss exacerbation data, the power system stability analysis model is further used to predict the stability degradation of the power grid. This prediction process requires analyzing the interactive relationship between the equipment's operating status and the grid load. It is assumed that load fluctuations of power equipment lead to fluctuations in the grid load, which in turn affects grid stability. By collecting grid operational data (such as voltage, current, and frequency), grid stability assessment models (such as dynamic stability analysis based on time-domain analysis) are used to predict grid stability degradation caused by increased equipment losses. Based on real-time grid data, the grid's degradation after increased equipment losses is analyzed. This degradation data is typically obtained by combining power flow analysis with dynamic simulation. This grid stability degradation data is then used to further analyze grid voltage instability. Grid voltage instability typically manifests as voltage deviations from the normal range, such as voltage fluctuations and frequent voltage jumps. Voltage instability analysis compares normal grid voltage data with voltage data affected by increased equipment losses, using voltage fluctuation indicators to determine grid voltage stability. By setting voltage instability thresholds and combining them with the grid's voltage curve, periods of voltage instability can be accurately identified. Tools used in this process include a grid monitoring system and a real-time voltage fluctuation analysis tool. Voltage fluctuation calculations are performed by analyzing time series voltage data from different monitoring points on the grid to assess changes in grid voltage stability. Grid voltage instability can directly affect the operating status of power equipment, leading to voltage fluctuations and flicker. Voltage fluctuations and flicker in power equipment refer to the frequency and amplitude of voltage fluctuations experienced by the equipment. These fluctuations typically occur when the equipment's load fluctuates or when the grid voltage is unstable. By further processing the analysis data of grid voltage instability and combining it with the voltage monitoring data of the power equipment, we assess the voltage fluctuations and flicker experienced by power equipment under unstable grid voltage conditions. Using a power quality analyzer, we collect the voltage fluctuations of the power equipment, obtain the amplitude and frequency of the voltage fluctuations, and correlate these fluctuations with grid voltage fluctuations. If the voltage fluctuation amplitude of a device exceeds the set normal fluctuation range, the device is considered to have experienced voltage flicker.Voltage flicker assessment is based on a comparison of the device's operating voltage fluctuation data with grid voltage fluctuations. Voltage fluctuations and flicker are closely related to grid voltage instability. Combining these two factors allows for further detection of power quality anomalies in the device. Power quality anomalies are primarily manifested as current and voltage fluctuations, frequency instability, and current waveform distortion. When testing device power quality, power quality monitoring devices (such as power quality analyzers) collect parameters such as the current waveform, frequency, and voltage, and perform time series analysis on these parameters. Current waveform distortion and power factor fluctuations can be considered power quality anomalies. During testing, focus is placed on power quality data during periods of significant voltage flicker. The device's current waveform is compared with the waveform under normal operating conditions to identify abnormal waveform data. Based on the current waveform in the abnormal power quality data, the distortion of the current waveform is further calculated. Current waveform distortion is primarily manifested by non-sinusoidal current waveforms, increased harmonic content, and excessive peak values. To this end, Fourier transform is used to perform frequency domain analysis of the current waveform to extract the harmonic content. Waveform distortion can cause device current overload, necessitating further assessment of the degree of current overload. The ratio of the device current to the rated current is measured to determine whether the device is overloaded. Specifically, when the ratio exceeds a certain threshold, the device is considered overloaded. Based on the waveform distortion and current overload analysis results, the device's load-bearing capacity and potential safety hazards are further assessed. By combining the device's current overload data and the current waveform distortion, the device's transient current disturbance is assessed. Transient current disturbances typically occur when the device load undergoes drastic changes, resulting in significant current fluctuations. Based on the current waveform distortion, the transient characteristics of the current fluctuations are assessed. Using the instantaneous current waveform data, analysis methods such as fast Fourier transform (FFT) are used to determine the transient characteristics of the current disturbance. Specifically, the current waveform data is paired with the time axis, and the amplitude of the transient current fluctuation and the degree of waveform abnormality are calculated to determine the degree of the device's transient current disturbance. Combining all the analysis data, the device's transient current disturbance is determined.

[0130] Preferably, the equipment thermal overload analysis in step S2 includes:

[0131] Evaluate the high-frequency electromagnetic pulse phenomenon of equipment when the transient current disturbance of the equipment is 3000A;

[0132] The device insulation breakdown data is obtained by using the high-frequency electromagnetic pulse phenomenon of the device when the electromagnetic pulse exceeds the safety threshold of 85dBμV / m and the current impact effect of the device;

[0133] Predict damage to electronic components based on equipment insulation breakdown data and high-frequency electromagnetic pulse phenomena;

[0134] Based on the transient current disturbance of the equipment, the cumulative thermal effect statistics of the damage to the electronic components of the equipment within 48 hours are calculated to obtain the cumulative thermal effect data of the equipment;

[0135] Estimate the equipment thermal positive feedback effect based on the accumulated data of the equipment thermal effect;

[0136] The equipment thermal overload analysis is performed on the equipment thermal positive feedback effect and the equipment thermal effect cumulative data exceeding 144°C to obtain the equipment thermal overload data.

[0137] In this embodiment of the present invention, transient current disturbances in equipment can be detected by real-time monitoring of the equipment's current waveform, particularly during periods of high-load operation, to determine whether the equipment is experiencing current disturbances. For 3000A equipment, transient current disturbances can cause significant electromagnetic radiation, manifesting as high-frequency electromagnetic pulses. To assess the electromagnetic pulse effects of the equipment, a high-frequency electromagnetic field analyzer (such as a spectrum analyzer) is used to collect electromagnetic waves around the equipment. During the collection process, special attention should be paid to the high-frequency components generated by transient current fluctuations, with emphasis on recording the intensity and frequency characteristics of the electromagnetic pulses. The intensity of the electromagnetic pulses is determined by measuring their electric field strength, typically in dBμV / m, and recording changes in the electromagnetic pulses during equipment operation. Assuming that the electromagnetic pulse exceeds a certain safety threshold (85dBμV / m) during equipment operation, this indicates significant electromagnetic interference. If the high-frequency electromagnetic pulse intensity of the equipment exceeds 85dBμV / m, further analysis is performed to determine the impact of the electromagnetic pulses on the equipment's insulation performance. The intensity and frequency of electromagnetic pulses, especially at higher intensities, can have a disruptive effect on the electrical insulation within the device. In particular, during transient current surges, the insulation material cannot withstand the rapid changes in current, leading to insulation breakdown. Predicting insulation breakdown requires obtaining the electrical properties of the device's insulation material, including its withstand voltage and resistance to high-frequency electromagnetic pulses. Combining this electromagnetic pulse data, the impact of electromagnetic waves on the device's insulation and the electric field strength generated by the transient current are calculated to determine whether the insulation breakdown threshold has been reached or exceeded. This approach predicts whether the device's insulation is at risk of breakdown during future operation. This prediction process utilizes the performance curves of the electrical insulation material and combines the intensity of the transient current surge with numerical calculations and assessments. The occurrence of insulation breakdown in the device can cause serious damage to the device's internal electronic components. Electromagnetic pulses exacerbate this risk. High-frequency electromagnetic pulses not only affect the insulation material but also critical electronic components within the device, such as semiconductors and switches. Using the device insulation breakdown data from step S2, the risk of electronic component damage can be further assessed, and the operating voltage and current characteristics of key electronic components within the device can be analyzed, particularly their response to electromagnetic pulse interference. By monitoring the device's transient current waveform and voltage changes, combined with insulation breakdown data, it is determined whether the electromagnetic pulse affects the device's internal electronic components through the breakdown point. For each component (such as resistors, capacitors, transistors, etc.), its tolerance limit under the action of electromagnetic pulses is set. By comparing with actual test data, the damage to the device's components in different electromagnetic environments is predicted. Transient current disturbances not only cause electrical failure of the device, but also produce thermal effects, especially on electronic components. Thermal effects cause components to overheat, causing damage or performance degradation. Accurately predict this thermal effect and use thermal imagers and temperature sensors to monitor the temperature of the electronic components inside the device. Record the temperature impact of transient current disturbances on the device's electronic components.Based on 48 hours of cumulative temperature data, the accumulated thermal effects of electronic components during this period are calculated. Temperature increases are closely related to the operating frequency, current fluctuations, and voltage fluctuations of the electronic components. By monitoring the real-time temperature of the electronic components and combining it with the intensity of transient current disturbances, the accumulated thermal effect data over the 48-hour period is calculated. This process requires comprehensive calculation of the temperature rise and thermal failure risk of the electronic components, taking into account the device's heat dissipation design, operating environment, and device load. The accumulated thermal effect data of the device provides a basis for evaluating positive thermal feedback effects. Positive thermal feedback occurs when the device's operating temperature continues to rise due to increasing thermal effects, further exacerbating device losses and forming a vicious cycle. Based on this accumulated thermal effect data, the device's positive thermal feedback effects are predicted using a thermodynamic model. The device's thermal balance equation is established based on the thermal effects of the electronic components and the device's heat conduction characteristics. By simulating the temperature changes of the device under continuous operation, the presence of a thermal feedback loop is determined. Changes in component characteristics (such as increased resistance) caused by temperature fluctuations further exacerbate the thermal effects, forming a positive feedback effect. By analyzing this data, the thermal positive feedback effects of the equipment during future operation can be estimated. By combining the equipment's thermal positive feedback effects with the accumulated thermal effect data, a thermal overload analysis of the equipment can be conducted. The core of thermal overload analysis is to assess whether the equipment's thermal capacity limit (144°C) will be exceeded due to the continuous accumulation of thermal effects during long-term operation, resulting in thermal overload. Thermal load analysis tools (such as ANSYS, Flotherm, and other thermal simulation software) are used to perform thermal flow simulations to predict changes in the equipment's thermal effects under different loads. By comparing with the equipment's rated temperature range, it is determined whether the equipment is at risk of overload. The equipment's thermal overload data includes the temperature rise of each component within the equipment under different load conditions and its deviation from the rated temperature. This data helps further understand the equipment's thermal stability under extreme operating conditions and the risk of failure. Through thermal overload analysis, the equipment's thermal overload data is generated.

[0138] Preferably, step S3 includes the following steps:

[0139] Step S31: Estimating device load imbalance based on device thermal overload data and device transient current disturbance to obtain device load imbalance;

[0140] Step S32: Calculating the mechanical stress growth of the equipment based on the equipment load imbalance to obtain equipment mechanical stress growth data;

[0141] Step S33: Estimating the fatigue state of the power equipment based on the equipment mechanical stress growth data to obtain fatigue state data of the power equipment;

[0142] Step S34: performing equipment failure trend analysis based on the power equipment fatigue state data and the equipment mechanical stress growth data to generate power equipment failure trend data.

[0143] As an example of the present invention, refer to Figure 2 As shown, in this example, step S3 includes:

[0144] Step S31: Estimating device load imbalance based on device thermal overload data and device transient current disturbance to obtain device load imbalance;

[0145] In an embodiment of the present invention, load imbalance of a device refers to the uneven distribution of loads between various components of a device or between multiple devices, which causes the device to be overloaded, damaged, or have reduced efficiency. The device thermal overload data and transient current disturbance data obtained in step S2 provide a basis for evaluating load imbalance. Based on the thermal load data of each component of the device, the difference between the actual load and the rated load of each component is calculated. This process determines the load state of each component by monitoring the changes in the current, power consumption, and thermal effect of each component. For transient current disturbances, the current waveform is captured in real time to determine whether the current distribution of different components is uniform, and the frequency components in the transient current signal are analyzed using methods such as fast Fourier transform (FFT). Combined with the theoretical model of device load distribution, the degree of load imbalance is calculated, and the abnormal fluctuation part is identified, thereby obtaining the device load imbalance data.

[0146] Step S32: Calculating the mechanical stress growth of the equipment based on the equipment load imbalance to obtain equipment mechanical stress growth data;

[0147] In an embodiment of the present invention, an unbalanced load on the equipment can cause the mechanical components to be subjected to uneven pressure and stress, thereby affecting the long-term operational stability of the equipment. To evaluate the growth of the mechanical stress of the equipment, it is necessary to understand the structural characteristics of each component and its ability to withstand loads. Based on the load imbalance data obtained in step S31, the stress of the mechanical components is estimated using a stress-strain model. Taking the core components of the equipment (such as the motor rotor, gears, support frame, etc.) as an example, the initial stress state of each component under the load is calculated using mechanical formulas. Then, using the load fluctuation data of the equipment, combined with the material properties such as the fatigue limit and yield strength of the mechanical components, the stress growth under the unbalanced load is further calculated. This process utilizes advanced calculation methods such as finite element analysis (FEA) to model and simulate the structure of each component of the equipment to obtain a stress distribution diagram for each component. In the case of unbalanced load, the stress change trend of each component is calculated, the components most prone to damage or failure are identified, and the stress growth data are summarized to form the mechanical stress growth data of the equipment.

[0148] Step S33: Estimating the fatigue state of the power equipment based on the equipment mechanical stress growth data to obtain fatigue state data of the power equipment;

[0149] In an embodiment of the present invention, the mechanical stress growth of the equipment leads to fatigue damage, especially when it is subjected to uneven loads for a long time, the fatigue damage will gradually accumulate and cause the equipment to fail. According to the mechanical stress growth data obtained in step S32, the fatigue state of the equipment is estimated using material fatigue theory and SN curve methods, and the maximum stress and cyclic stress of each component in different time periods are calculated based on the mechanical stress growth data. These stress values will serve as basic data for fatigue damage. Using the SN curve and Miner rule, the durability of each component at different stress levels is analyzed, and the remaining service life of the equipment is calculated based on the actual load history data. The fatigue damage accumulation model is used to further predict the fatigue condition of the equipment in the future. This process simulates the stress-fatigue relationship in long-term operation, combines the stress growth data, and calculates the gradual aggravation process of fatigue damage to obtain fatigue state data of the power equipment.

[0150] Step S34: performing equipment failure trend analysis based on the power equipment fatigue state data and the equipment mechanical stress growth data to generate power equipment failure trend data.

[0151] In an embodiment of the present invention, the failure trend analysis of the equipment is to predict the risk of future failure of the equipment by combining the fatigue state data and mechanical stress growth data of the equipment, and to determine the failure risk of the equipment by using the fatigue state data in step S33 and combining the mechanical stress growth of the equipment during actual operation. This process identifies the key components of the equipment and monitors these components over a long period of time to obtain data on fatigue damage and stress growth. By analyzing the failure mode of the equipment and combining historical failure data with existing stress and fatigue status, a failure trend prediction model is established. The model calculates the probability of failure of the equipment at different time nodes based on known equipment material properties, load conditions, and actual stress growth conditions. Through trend analysis, the equipment failure modes are divided into different levels, ranging from initial fatigue crack expansion to more serious equipment failures.

[0152] Preferably, step S34 includes the following steps:

[0153] Step S341: performing power equipment vibration aggravation detection based on power equipment fatigue state data to obtain power equipment vibration aggravation data;

[0154] Step S342: Predicting looseness of connection components of the power equipment based on the power equipment vibration intensification data to obtain looseness data of connection components of the power equipment;

[0155] Step S343: performing mechanical metal structure degradation detection based on the loosening data of the power equipment connection components and the equipment mechanical stress growth data to obtain mechanical metal structure degradation data;

[0156] Step S344: performing equipment failure trend analysis based on the mechanical metal structure degradation data and the loosening data of the power equipment connection components to generate power equipment failure trend data.

[0157] As an example of the present invention, refer to Figure 3 As shown, in this example, step S34 includes:

[0158] Step S341: performing power equipment vibration aggravation detection based on power equipment fatigue state data to obtain power equipment vibration aggravation data;

[0159] In an embodiment of the present invention, during the operation of the equipment, the accumulation of fatigue conditions will cause changes in the vibration characteristics of the equipment, especially when it is subjected to uneven loads or frequent starts and stops. Based on the fatigue status data of the equipment, the vibration signal of the equipment is collected by a vibration sensor. The changes in the vibration data in the frequency domain and time domain reflect the health status of the equipment. The vibration amplitude, frequency and waveform of the equipment are monitored in real time by an accelerometer or a vibration sensor to obtain vibration data. The vibration signal is spectrally analyzed using Fourier transform to identify the vibration intensity of different frequency bands. When the fatigue state of the equipment is more serious, the spectral characteristics of the vibration signal will usually change, and abnormal high-frequency vibrations or an increase in low-frequency vibrations will occur. According to the analysis results, the phenomenon of vibration aggravation can be identified, which is specifically manifested in the increase of vibration amplitude or the change of vibration mode. By setting a threshold, the situation of vibration aggravation is judged and vibration aggravation data is generated.

[0160] Step S342: Predicting looseness of connection components of the power equipment based on the power equipment vibration intensification data to obtain looseness data of connection components of the power equipment;

[0161] In an embodiment of the present invention, the loosening of the equipment connection parts is usually manifested as increased vibration and noise during the operation of the equipment. The vibration intensification data obtained in step S341 is used to estimate the looseness of the connection parts, and the various connection parts of the equipment are monitored, especially those directly connected to the moving parts (such as bearings, gears, connecting bolts, etc.). The vibration data obtained by the sensor is compared with the standard vibration model to analyze whether there are irregular vibration patterns or abnormal vibration frequencies. The loosening of the equipment connection parts usually causes a sudden change in the frequency or amplitude of the vibration, and the range of change gradually increases. The loose position is identified by comparing the vibration data of the equipment in normal operation and loose conditions. Using regression analysis or machine learning models, combined with the amplitude and frequency change trends of the vibration intensification, the specific degree of looseness of the connection parts is inferred. This process combines vibration signal analysis, spectrum analysis and fault diagnosis methods to generate loose data of equipment connection parts.

[0162] Step S343: performing mechanical metal structure degradation detection based on the loosening data of the power equipment connection components and the equipment mechanical stress growth data to obtain mechanical metal structure degradation data;

[0163] In an embodiment of the present invention, loose connections and uneven loads of mechanical components may cause degradation of the metal structure, especially in high stress and high vibration environments. The loose connection data of the connection components obtained in step S342 and the equipment mechanical stress growth data obtained in step S32 provide an important basis for the detection of metal structure degradation. By combining the loose data and stress data, the stress conditions of the key components of the equipment (such as the frame, support frame, connection components, etc.) are analyzed. During the operation of the equipment, due to the loose connection components and unbalanced loads, the metal structure will be subjected to repeated mechanical shocks and pressures, which will lead to metal fatigue and the formation of cracks. By regularly performing non-destructive detection methods such as ultrasonic testing and X-ray testing on the equipment structure, combined with the stress data of the equipment, the degradation of the metal components is monitored in real time. In this process, tools such as stress-strain curves and material fatigue life models are used to estimate the degree of degradation of the equipment metal structure and predict the expansion of cracks. By detecting the degradation of the metal structure, the degradation data of the mechanical metal structure is obtained.

[0164] Step S344: performing equipment failure trend analysis based on the mechanical metal structure degradation data and the loosening data of the power equipment connection components to generate power equipment failure trend data.

[0165] In the embodiment of the present invention, the degradation of the mechanical metal structure of the equipment and the loosening of the connecting parts will affect the overall performance and stability of the equipment, leading to equipment failure. Therefore, by combining the mechanical metal structure degradation data obtained in step S343 with the loosening data of the connecting parts obtained in step S342, an equipment failure trend analysis is performed. Based on the historical operation data of the equipment and the fatigue state of the internal components of the equipment, time series analysis or trend analysis methods are used, combined with the current degradation data and loosening data, to predict the failure mode of the equipment. Using regression analysis, neural network and other technologies, an equipment failure prediction model is established, which integrates factors such as the degradation of the equipment's metal structure, loosening of connecting parts, and mechanical stress growth to derive the probability and trend of equipment failure.

[0166] Preferably, step S4 includes the following steps:

[0167] Step S41: Analyze the performance degradation of the power equipment according to the failure trend of the power equipment to obtain the performance degradation data of the power equipment;

[0168] Step S42: collecting equipment power data based on the power equipment performance degradation data to obtain equipment power data;

[0169] Step S43: storing the device power data in a database to obtain device power storage data;

[0170] Step S44: predicting the performance bottleneck of the power equipment based on the power storage data of the equipment and the performance degradation data of the power equipment to obtain the performance bottleneck of the power equipment;

[0171] Step S45: Optimizing the power equipment data according to the power equipment performance bottleneck to obtain power equipment optimization data.

[0172] In embodiments of the present invention, the performance of power equipment gradually declines over time. In particular, during long-term operation, equipment performance gradually deteriorates due to factors such as power load fluctuations, changing environmental conditions, and equipment aging. Based on the results of the power equipment failure trend analysis, a detailed analysis of the equipment's performance degradation is further performed. In this step, a trend analysis of various performance indicators is conducted, combining the equipment's historical operating data and fault diagnosis data. These indicators primarily include key indicators such as the equipment's power output, load operation, electrical insulation strength, equipment temperature, and vibration levels. These indicators change as the equipment's failure trends change. Therefore, analyzing the failure trend data can clearly identify which equipment performance indicators are experiencing a gradual decline. Specifically, by setting thresholds and comparing the current values of various performance indicators with the equipment's initial design parameters, the degree of performance degradation is calculated. Following the power equipment performance degradation analysis, actual power data from the equipment must be collected to more accurately monitor the equipment's actual operating status. This equipment power data collection relies on high-precision power measurement instruments, such as power meters, frequency meters, and power factor meters. These instruments can monitor key power parameters such as the equipment's input power, output power, load factor, current, voltage, and power factor in real time. The collection interval is set to 1 minute to ensure a comprehensive understanding of power data during device operation. By comparing the collected power data with device performance degradation data, a more accurate assessment of device operating efficiency and whether it is affected by performance degradation can be made. The collected device power data not only includes real-time power consumption under different loads, but also includes power fluctuations during peak and low loads. The collected device power data needs to be stored for subsequent analysis, query, and forecasting. The storage database must have efficient storage and retrieval capabilities. Therefore, when selecting a database system, consider using a relational database (such as MySQL, PostgreSQL) or a time series database (such as InfluxDB). These databases can efficiently store and query device power data based on timestamps. The storage process includes preprocessing the collected power data to remove outliers and categorizing the data into different tables based on information such as device model, operating time, and load conditions. Each data item is timestamped to facilitate subsequent time series analysis and trend forecasting. Furthermore, data integrity and security must be ensured, with encrypted storage measures implemented to prevent data loss or leakage. A device performance bottleneck typically refers to a capacity limitation encountered under a certain load, caused by factors such as hardware aging and electrical component fatigue. Based on the device power data collected in step S42 and the device performance degradation data obtained in step S41, statistical analysis methods are used to predict the device's performance bottleneck. Based on the device's historical operating data, the device's power consumption patterns and power output under different loads and operating conditions are calculated.Through regression or trend analysis of this data, potential locations of equipment performance bottlenecks can be identified. For example, when a device reaches a certain load point, power output no longer increases linearly and the power factor decreases, indicating that the device is approaching its performance bottleneck. Furthermore, by analyzing the volatility of power data, early warning signs of equipment performance bottlenecks, such as excessive power fluctuations or increased current fluctuations, can be detected. Based on this information, the location of the equipment bottleneck is clearly identified. Once the performance bottleneck of the power equipment is predicted, the next step is to optimize the equipment data. The key to this step is to use the device power data and performance bottleneck prediction results to perform targeted optimization adjustments. The device's operating strategy, including load distribution strategy, device start-stop mode, and power consumption control strategy, is analyzed to identify factors affecting device performance. Based on the device power storage data and performance bottleneck prediction results, the device's operating efficiency is optimized by adjusting load balancing, optimizing load distribution, and reducing peak load. For example, if a device is overloaded, the load distribution or start-stop strategy can be adjusted to reduce the overload and slow down the device's performance degradation. This process combines intelligent algorithms (such as genetic algorithms and particle swarm optimization) for optimization calculations to automatically find the optimal device operating parameters.

[0173] The present invention further provides a power data storage system for executing the power data storage method described above, the power data storage system comprising:

[0174] The operation status simulation module is used to obtain power equipment data; collect equipment rated parameters based on the power equipment data, and simulate the operation status based on the equipment rated parameters to obtain power equipment operation simulation data;

[0175] The equipment thermal overload analysis module is used to detect the dynamic load response of the equipment based on the power equipment operation simulation data, and to evaluate the transient current disturbance of the equipment based on the dynamic load response of the equipment. The transient current disturbance of the equipment is then analyzed for thermal overload to obtain the equipment thermal overload data;

[0176] The equipment failure trend analysis module is used to estimate equipment load imbalance based on equipment thermal overload data and equipment transient current disturbances, and to analyze equipment failure trends based on equipment load imbalance to generate equipment failure trend data;

[0177] The equipment optimization module is used to collect equipment power data based on power equipment fault trend data, store power data based on the equipment power data, obtain equipment power storage data, and use the equipment power storage data to optimize the power equipment data to obtain power equipment optimization data.

[0178] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for storing power data, characterized in that: The following steps are involved: Step S1: Acquire power equipment data; Collect equipment rated parameters based on power equipment data, and simulate the operating status based on the equipment rated parameters to obtain power equipment operation simulation data; Step S2: Performing equipment dynamic load response detection based on the power equipment operation simulation data, and performing equipment transient current disturbance assessment based on the equipment dynamic load response, performing equipment thermal overload analysis on the equipment transient current disturbance, and obtaining equipment thermal overload data; Step S3: Estimating device load imbalance based on device thermal overload data and device transient current disturbance, and performing device failure trend analysis based on the device load imbalance to generate device failure trend data; Step S4: collecting equipment power data based on the power equipment fault trend data, storing power data based on the equipment power data to obtain equipment power storage data, and optimizing the power equipment data using the equipment power storage data to obtain power equipment optimization data.

2. The power data storage method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire power equipment data; Step S12: collecting equipment rated parameters based on the power equipment data, thereby generating equipment rated parameters; Step S13: Analyze the working principle of the power equipment according to the equipment rated parameters and the power equipment data, thereby obtaining the working principle of the power equipment; Step S14: performing operation state simulation according to the rated parameters of the equipment and the working principle of the power equipment to obtain power equipment operation simulation data.

3. The power data storage method according to claim 2, characterized in that: Step S13 includes the following steps: Step S131: Identify the function type of the power equipment according to the power equipment data, thereby obtaining the power equipment function type data; Step S132: performing equipment geometric structure analysis based on the power equipment function type data, thereby obtaining power equipment geometric structure data; Step S133: collecting the spatial layout of the power equipment according to the power equipment data, thereby obtaining the spatial layout data of the power equipment; Step S134: performing power equipment load distribution calculation based on the power equipment spatial layout and the power equipment geometric structure data to obtain power equipment load distribution data; Step S135: performing equipment coordination analysis based on the power equipment spatial layout data and the power equipment geometric structure data to obtain power equipment coordination data; Step S136: Calculating the power equipment operating efficiency based on the power equipment coordination data, the equipment rated parameters, and the power equipment load distribution data to obtain power equipment operating efficiency data; Step S137: Analyze the working principle of the power equipment based on the power equipment operation efficiency data and the power equipment coordination data, so as to obtain the working principle of the power equipment.

4. The power data storage method according to claim 1, characterized in that: The equipment dynamic load response detection in step S2 includes: Use 1-minute intervals to collect power equipment usage demand; Plotting equipment demand fluctuations in power equipment usage demand; Calculate the equipment demand fluctuation slope for every 1 minute within 168 hours in the equipment demand fluctuation graph; Collect statistics on sudden equipment demands with a fluctuation slope exceeding 1.2KW / min; Use the equipment demand fluctuation slope and equipment sudden demand exceeding 1.5kW / min to estimate dynamic step-by-step demand; Calculate the duration of load exceeding 80% of rated power based on dynamic step-by-step demand increase and sudden fluctuations in equipment demand to obtain equipment continuous excess load data; The dynamic load response of the equipment is detected by testing the operation simulation data of the power equipment according to the continuous excess load data of the equipment to obtain the dynamic load response of the equipment.

5. The power data storage method according to claim 1, characterized in that: The equipment transient current disturbance assessment described in step S2 includes the following steps: Evaluate the equipment's dynamic load response to intensified equipment losses; Predict grid stability degradation data as equipment losses intensify; Analyze grid voltage instability status from grid stability decay data; Use the unstable state of grid voltage to evaluate the voltage fluctuation and flicker of power equipment; Detect equipment power quality anomalies based on power equipment voltage fluctuations and flicker and grid voltage instability, and obtain equipment power quality anomaly data; Calculate the current waveform distortion status of the equipment power quality abnormality data, and evaluate the equipment current overload data based on the current waveform distortion status; The equipment transient current disturbance is evaluated based on the equipment current overload data and the current waveform distortion status to obtain the equipment transient current disturbance.

6. The power data storage method according to claim 4, characterized in that: The equipment thermal overload analysis described in step S2 includes: Evaluate the high-frequency electromagnetic pulse phenomenon of equipment when the transient current disturbance of the equipment is 3000A; The device insulation breakdown data is obtained by using the high-frequency electromagnetic pulse phenomenon of the device when the electromagnetic pulse exceeds the safety threshold of 85dBμV / m and the current impact effect of the device; Predict damage to electronic components based on equipment insulation breakdown data and high-frequency electromagnetic pulse phenomena; Based on the transient current disturbance of the equipment, the cumulative thermal effect statistics of the damage to the electronic components of the equipment within 48 hours are calculated to obtain the cumulative thermal effect data of the equipment; Estimate the equipment thermal positive feedback effect based on the accumulated data of the equipment thermal effect; The equipment thermal overload analysis is performed on the equipment thermal positive feedback effect and the equipment thermal effect cumulative data exceeding 144°C to obtain the equipment thermal overload data.

7. The power data storage method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Estimating device load imbalance based on device thermal overload data and device transient current disturbance to obtain device load imbalance; Step S32: Calculating the mechanical stress growth of the equipment based on the equipment load imbalance to obtain equipment mechanical stress growth data; Step S33: Estimating the fatigue state of the power equipment based on the equipment mechanical stress growth data to obtain fatigue state data of the power equipment; Step S34: performing equipment failure trend analysis based on the power equipment fatigue state data and the equipment mechanical stress growth data to generate power equipment failure trend data.

8. The power data storage method according to claim 7, characterized in that: Step S34 includes the following steps: Step S341: performing power equipment vibration aggravation detection based on power equipment fatigue state data to obtain power equipment vibration aggravation data; Step S342: Predicting looseness of connection components of the power equipment based on the power equipment vibration intensification data to obtain looseness data of connection components of the power equipment; Step S343: performing mechanical metal structure degradation detection based on the loosening data of the power equipment connection components and the equipment mechanical stress growth data to obtain mechanical metal structure degradation data; Step S344: performing equipment failure trend analysis based on the mechanical metal structure degradation data and the loosening data of the power equipment connection components to generate power equipment failure trend data.

9. The power data storage method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: Analyze the performance degradation of the power equipment according to the failure trend of the power equipment to obtain the performance degradation data of the power equipment; Step S42: collecting equipment power data based on the power equipment performance degradation data to obtain equipment power data; Step S43: storing the device power data in a database to obtain device power storage data; Step S44: predicting the performance bottleneck of the power equipment based on the power storage data of the equipment and the performance degradation data of the power equipment to obtain the performance bottleneck of the power equipment; Step S45: Optimizing the power equipment data according to the power equipment performance bottleneck to obtain power equipment optimization data.

10. An electric power data storage system, characterized in that: For executing the power data storage method according to claim 1, the power data storage system comprises: The operation status simulation module is used to obtain power equipment data; collect equipment rated parameters based on the power equipment data, and simulate the operation status based on the equipment rated parameters to obtain power equipment operation simulation data; The equipment thermal overload analysis module is used to detect the dynamic load response of the equipment based on the power equipment operation simulation data, and to evaluate the transient current disturbance of the equipment based on the dynamic load response of the equipment. The transient current disturbance of the equipment is then analyzed for thermal overload to obtain the equipment thermal overload data; The equipment failure trend analysis module is used to estimate equipment load imbalance based on equipment thermal overload data and equipment transient current disturbances, and to analyze equipment failure trends based on equipment load imbalance to generate equipment failure trend data; The equipment optimization module is used to collect equipment power data based on power equipment fault trend data, store power data based on the equipment power data, obtain equipment power storage data, and use the equipment power storage data to optimize the power equipment data to obtain power equipment optimization data.

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