Intelligent monitoring and management system for full-life stage of power grid assets based on Internet of Things

Abstract

The invention, which relates to the technical field of power grid asset monitoring, discloses an intelligent monitoring and management system for a full life stage of power grid assets based on the Internet of Things, comprising a data acquisition module and a dynamic evaluation module. Receiving the parameter data of the data acquisition module by taking T as a period and a monitoring standard rolling calculation equipment state of the corresponding parameter data; the real-time monitoring module is used for monitoring the state of the power grid equipment in real time according to the evaluation value time sequence of the dynamic evaluation module and triggering an early warning signal; the management module is used for managing and adjusting operation and use of the power grid equipment; the full-life stage of the power grid equipment is divided into a newly-added stage, an operation stage and a decommissioning stage, and differentiated monitoring standards and management strategies are formulated according to the characteristics and the equipment state of each stage. In the newly-added stage, scientific grading is carried out according to equipment evaluation values, equipment loads and tasks are reasonably distributed, and it is ensured that the equipment can exert the maximum efficiency in the initial stage.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid asset monitoring, and more specifically, to an intelligent monitoring and management system for power grid assets throughout their life cycle based on the Internet of Things. Background Art

[0002] With the development of smart grids, the power industry has an increasing demand for efficient grid asset management solutions. As an important part of the field of information and communication technology, IoT technology realizes information exchange and communication between objects through the perception layer, network transmission layer and application layer. In smart grids, IoT technology can be used to collect various data of the grid in real time, such as voltage, current, temperature, etc., and provide decision support for the dispatch and control of the grid through data processing and analysis. Grid assets refer to the various equipment and infrastructure that constitute power transmission. Lifecycle monitoring refers to the continuous and dynamic detection and management of the status of grid assets from commissioning to retirement. The status of grid assets is monitored by collecting parameter data of grid assets, and then the grid assets are adjusted and managed according to the monitoring results. However, the monitoring standards for grid equipment with different life cycles are different. If grid assets are monitored according to fixed monitoring standards, it may lead to inaccurate monitoring accuracy of grid assets, which in turn affects the normal operation of the grid. However, if new monitoring standards are adjusted blindly, it is difficult to ensure that the monitoring standards are truly in line with the life stage of the grid assets. Summary of the invention

[0003] In order to solve the above problems, the present invention provides an intelligent monitoring and management system for power grid assets throughout their life cycle based on the Internet of Things.

[0004] The present invention provides an intelligent monitoring and management system for the entire life cycle of power grid assets based on the Internet of Things, including the following modules: The data acquisition module is used to collect parameter data of power grid equipment, process the collected parameter data of power grid equipment, and obtain the electrical and mechanical aging rate of power grid equipment. , based on the electrical and mechanical aging rates of power grid equipment To determine the life stage of power grid equipment, the life stage is divided into the new stage, operation stage and retirement stage, and monitoring standards for power grid equipment are formulated for different life stages; The dynamic evaluation module sets a time period T, receives the parameter data of the data acquisition module and the monitoring standard of the corresponding parameter data, calculates the device status in a rolling manner with T as the period, outputs the evaluation value corresponding to each time period T, obtains the evaluation value time series, and transmits it; It is also used to adjust the monitoring period T of the retirement phase based on the total working time and retirement life of the retirement phase; The real-time monitoring module is used to monitor the status of power grid equipment in real time according to the evaluation value time series of the dynamic evaluation module, trigger the early warning signal, and output the monitoring results to the user interface; The management module is used to manage and adjust the operation and use of the power grid equipment according to the life stage of the power grid equipment, the evaluation value corresponding to the current time period T, and the monitoring results output by the real-time monitoring module.

[0005] Preferably, the specific working steps of the data acquisition module also include the following: Receive the collected parameter data of power grid equipment, process the collected parameter data of power grid equipment, determine the life stage of power grid equipment, and divide the life stage into the new addition stage, operation stage and retirement stage; For the newly added power grid equipment, the initialization of the power grid equipment parameter monitoring standards is obtained according to the mechanical standards and electrical standards; For power grid equipment in the operation phase, dynamic threshold adjustment is performed based on the initialized power grid equipment parameter monitoring standard to obtain an updated power grid equipment parameter monitoring standard; For power grid equipment in the retirement stage, the service life of the retirement stage is predicted based on historical operation data and real-time status monitoring, and the parameter monitoring standards of the power grid equipment in the retirement stage are obtained.

[0006] Preferably, the specific working steps of the data acquisition module also include the following Modeling the electrical and mechanical aging rates of power grid equipment , based on the electrical and mechanical aging rates of grid equipment , get the end point of the new addition phase, the end point of the operation phase and the end point of the retirement phase of the power grid equipment; If the electrical and mechanical aging rates of grid equipment If the electrical and mechanical aging rates of the power grid equipment are less than or equal to the end point of the new addition stage, the power grid equipment is judged to be in the new addition stage. If the electrical and mechanical aging rates of the power grid equipment are greater than the end point of the new stage and less than or equal to the end point of the operation stage, the power grid equipment is judged to be in the operation stage. If it is less than or equal to the end point of the decommissioning stage and greater than the end point of the operating stage, it is determined that the power grid equipment is in the decommissioning stage.

[0007] Preferably, the specific working steps of the data acquisition module are as follows: Determine the type of transformer parameter data to be collected, including vibration spectrum, temperature gradient, partial discharge intensity, insulation resistance, harmonic distortion rate, and response delay; Install various corresponding sensors on the transformer and connect the sensors to the data acquisition device, i.e., DAQ device; The sensor collects the operating parameters of the transformer in real time and transmits the data to the collection device.

[0008] Preferably, the specific working steps of the data acquisition module are as follows: Automatically adjust the collection strategy according to the life stage of the device.

[0009] For the newly added stage, the mechanical parameters of the transformer and the full-band monitoring of the vibration spectrum; The transformer's electrical parameters and insulation resistance are tested in full circuit every day, and the harmonic distortion rate is collected in real time at the second level; For the operation stage, the mechanical parameters of the transformer: monitoring of key areas of the vibration spectrum; The electrical parameters of the transformer, insulation resistance, full circuit test every week, harmonic distortion rate collection at minute level; For the decommissioning phase, the mechanical parameters of the transformer: vibration spectrum relaxation monitoring; The transformer's electrical parameters and insulation resistance are tested in full circuits every month, and the harmonic distortion rate is collected on an hourly basis.

[0010] Preferably, the specific steps of the dynamic evaluation module are as follows: The collected parameter data is received in each period T, and for each power grid device, a corresponding evaluation value is calculated based on the collected parameter data and the corresponding sub-data set storing the monitoring standard; Each period T generates a record, including a timestamp and an evaluation value, to form a time series and transmit it; The dynamic evaluation module is also used to adjust the monitoring period T of the retirement phase based on the total working time and retirement life of the retirement phase: According to the formula , calculate and obtain the adjusted monitoring period of the retirement phase , as the monitoring cycle of the decommissioning phase; in is the accumulated working time during the retirement phase, The retirement life span is the retirement stage.

[0011] Preferably, the specific process of calculating the corresponding evaluation value for each power grid device according to the collected parameter data is as follows: For each power grid device, the evaluation value of the monitoring parameter is calculated based on the collected parameter data; Then, weights are assigned to the monitoring parameters, and then the evaluation value of the device is obtained through weighted calculation.

[0012] Preferably, the specific working steps of the real-time monitoring module are as follows: Receive time series, model and predict the time series data according to the model, and trigger an early warning when the deviation between the predicted value and the actual value exceeds the set ratio; The mechanical parameters and electrical parameters of the power grid equipment are monitored separately. When the mechanical parameters trigger the warning signal, a mechanical warning signal is generated; when the electrical parameters trigger the warning signal, an electrical warning signal is generated.

[0013] Preferably, the specific working steps of the management module are as follows: For the newly added stage and the operating stage, the evaluation value of the equipment is obtained, and the pre-evaluation value and the pre-set threshold are judged to classify the newly added stage; If the evaluation value is ≥ the first threshold, the grid equipment is allowed to operate at over-rated load up to 110%, but the duration shall not exceed 2 hours, and high-value loads shall be connected first; if the second threshold is ≤ the evaluation value < the first threshold, the load rate is limited to 90%, and predictive maintenance is initiated; if the evaluation value is < the second threshold, the equipment is downgraded to backup equipment, such as converting the main transformer to a station transformer, and the monitoring period is shortened to the original value of the third threshold; For the decommissioning stage, if the evaluation value is ≥ the third threshold, the power grid equipment will be switched to non-critical line operation; and limiting the load rate to the second threshold; if the fourth threshold ≤ the evaluation value < the third threshold, only the grid equipment is allowed to bear the seasonal load; If the evaluation value is less than the fourth threshold, the grid device is forced to exit grid operation.

[0014] Preferably, the specific working steps of the management module also include the following: In the new addition stage and operation stage, if the monitoring result output by the real-time monitoring module is an electrical warning signal, the electrical parameters are judged to be degraded and the power grid equipment is migrated to a low-voltage level line; If the monitoring result output by the real-time monitoring module is a mechanical early warning signal, the position will be swapped with the same type of power grid equipment to balance the operation frequency; For the decommissioning stage, if the monitoring result output by the real-time monitoring module is an electrical warning signal, the equipment is isolated immediately.

[0015] Beneficial effects: The parameter data of power grid equipment is collected, cleaned and transmitted in real time through the data acquisition module, and the evaluation value time series of the equipment status is calculated in combination with the dynamic evaluation module. Based on this, the real-time monitoring module can accurately monitor the status of power grid equipment in real time. When the equipment status is abnormal and the deviation between the predicted value and the actual value exceeds the set ratio, the system will quickly trigger an early warning signal and output the monitoring results intuitively to the user interface, showing the abnormal situation in detail. This enables operation and maintenance personnel to obtain equipment failure information at the first time and take timely response measures to effectively prevent small problems from turning into major failures, greatly reducing the power outage time and maintenance costs caused by equipment failures, significantly improving the power supply reliability and stability of the power grid, and providing a strong guarantee for the continuous and stable operation of the power system; The life cycle of power grid equipment is divided into the new addition stage, operation stage and retirement stage, and differentiated monitoring standards and management strategies are formulated according to the characteristics and equipment status of each stage. In the new addition stage, scientific classification is carried out according to the equipment evaluation value, and equipment load and tasks are reasonably allocated to ensure that the equipment can play the maximum efficiency in the initial stage; in the operation stage, the equipment load is dynamically adjusted according to the evaluation value, and the maintenance plan is optimized, which can avoid the waste of resources caused by excessive maintenance and prevent the premature aging of equipment caused by insufficient maintenance; in the retirement stage, the remaining life of the equipment and economic evaluation are comprehensively considered, and the final task and disposal method of the equipment are reasonably arranged to maximize the residual value of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of the system of the present invention. DETAILED DESCRIPTION

[0017] like Figure 1 As shown: The intelligent monitoring and management system for the entire life cycle of power grid assets based on the Internet of Things includes data acquisition module, dynamic evaluation module, real-time monitoring module and management module: The data acquisition module is used to collect parameter data of power grid equipment, process the collected parameter data of power grid equipment, and obtain the electrical and mechanical aging rate of power grid equipment. , based on the electrical and mechanical aging rates of power grid equipment To determine the life stage of power grid equipment, the life stage is divided into the new stage, operation stage and retirement stage, and monitoring standards for power grid equipment are formulated for different life stages; It should be noted that the power grid assets mainly include the fixed assets, projects under construction and current assets of the power grid. The fixed assets specifically include power transmission and distribution equipment and infrastructure equipment. Among them, the power transmission and distribution equipment, in this embodiment, only the substation equipment in the power transmission and distribution equipment is considered; The above-mentioned power grid equipment in this embodiment is a substation equipment, which specifically includes a transformer, and the specific parameter data includes mechanical parameters and electrical parameters; It should be noted that the cleaned data is input into the dynamic health assessment module to calculate the assessment value; It should also be noted that parameter data is collected and cleaned to form a standardized data set, the cleaned data is sent to the dynamic evaluation module, and the original data is archived to historical data, and the comprehensiveness of monitoring is improved through parameter data fusion; The dynamic evaluation module sets a time period T, receives the parameter data of the data acquisition module and the monitoring standard of the corresponding parameter data, calculates the device status in a rolling manner with T as the period, outputs the evaluation value corresponding to each time period T, obtains the evaluation value time series, and transmits it; It is also used to adjust the monitoring period T of the retirement phase based on the total working time and retirement life of the retirement phase; It should be noted that the evaluation value time series is output to the cluster analysis module; It should also be noted that the dynamic evaluation module is used to periodically calculate the evaluation value of the device monitoring degree and output the evaluation sequence to the cluster analysis module; Receive cleaning data from the data acquisition module to achieve quantitative evaluation of equipment status; The real-time monitoring module is used to monitor the status of power grid equipment in real time according to the evaluation value timing sequence of the dynamic evaluation module, trigger early warning signals, and output monitoring results to the user interface.

[0018] The specific working steps of the real-time monitoring module outputting the monitoring results to the user interface include the following: Initialize the display communication interface, set the resolution and refresh rate, load the font library and graphic resources, and define the interface layout; Process button click events and save current screen data as image format; When an alarm is triggered, a modal window pops up to display the abnormal details, record the alarm event to a local log file, including timestamp, parameter value, and processing measures, and regularly store monitoring data to an SD card; It should be noted that the internal assets of the power grid include a variety of equipment, and the equipment is classified differently and in different stages of its life cycle. Traditional monitoring methods are difficult to perform corresponding monitoring for the particularity of each power grid equipment, which affects the final monitoring effect. This technical solution defines the monitoring standards for power grid equipment parameters at various stages of the full life cycle of power grid assets. First, the power grid equipment level is divided, and mechanical components, electrical components, and life stages are defined. Parameter standards corresponding to mechanical components, electrical components, and life stages are set for mechanical components, electrical components, and life stages, so that targeted monitoring can be performed; The management module is used to manage and adjust the operation and use of the power grid equipment according to the life stage of the power grid equipment, the evaluation value corresponding to the current time period T, and the monitoring results output by the real-time monitoring module; As an optional embodiment: the specific working steps of the data acquisition module also include the following: Receive the collected parameter data of power grid equipment, process the collected parameter data of power grid equipment, determine the life stage of power grid equipment, and divide the life stage into the new addition stage, operation stage and retirement stage; It should be noted that, in this embodiment, the specific steps of processing the collected power grid equipment parameter data and determining the life stage of the power grid equipment are as follows: According to the formula , calculate and obtain the electrical and mechanical aging rates of power grid equipment ; It should be noted that the cumulative damage of the electro-mechanical coupling to the aging of the equipment is quantified by dynamically integrating the product of the rate of change of the electric field intensity and the vibration high-frequency energy increase; in It is the time differential symbol in calculus, representing the infinitesimal change in time t; is part of the partial derivative symbol, indicating the partial derivative with respect to time t, represents the rate of change of the electric field intensity E with time t, assuming that other variables remain unchanged; It is the vibration high-frequency energy amplification, specifically the vibration energy change rate in the 300-500Hz frequency band. Mechanical stress induces micro crack expansion, and cooperates with the electric field to accelerate insulation degradation. The data is mainly obtained through acceleration sensor + spectrum analysis. The benchmark vibration energy is the average high-frequency vibration energy of the equipment in a healthy state, providing a normalized benchmark to eliminate individual differences in equipment and provide statistics on vibration data in the initial stage of equipment operation; Obtain the electrical and mechanical aging rates of power grid equipment Finally, according to the electrical and mechanical aging rate of power grid equipment The life stage of power grid equipment can be determined by the range of For the newly added power grid equipment, the initialization of the power grid equipment parameter monitoring standards is obtained according to the mechanical standards and electrical standards; It should be noted that the initialized power grid equipment parameter monitoring standards are obtained according to the mechanical parameters and electrical parameters in the parameter data. The specific power grid equipment parameter monitoring standards of mechanical components include vibration spectrum (0-500Hz), temperature gradient (℃ / h), and partial discharge intensity (pC), which are obtained according to the national standard GB / T14549; The electrical parameters of the power grid equipment parameter monitoring standards include insulation resistance (MΩ), harmonic distortion rate (%), and response delay (ms), which are obtained according to the industry standard DL / T596; In this embodiment, the initialized power grid equipment parameter monitoring standard is a recommended value according to the national standard or industry standard; For power grid equipment in the operation phase, dynamic threshold adjustment is performed based on the initialized power grid equipment parameter monitoring standard to obtain an updated power grid equipment parameter monitoring standard; It should be noted that, in this embodiment, the dynamic threshold is adjusted in the following manner: First, data collection is performed to monitor the vibration spectrum data, temperature data, and partial discharge intensity data of mechanical equipment in real time, and the insulation resistance data, harmonic distortion rate data, and response delay data of electrical equipment; Then sort the data: for each monitoring parameter, sort the data in the historical data set in ascending or descending order according to the value.

[0019] Determine the median position: Determine the median position based on the size of the data set. If the number of data is an odd number, the median is the value in the middle; if the number of data is an even number, the average of the two middle values ​​can be taken as the median.

[0020] According to the determined median position, the median of each monitoring parameter is calculated as the benchmark value of the parameter; During the operation of the equipment, the monitoring data of various parameters will continue to be collected in real time; Compare the real-time monitoring value with the corresponding median, and dynamically update the monitoring standard of the power grid equipment parameters according to the comparison results. For example, in the power system, due to large fluctuations, the update frequency can be appropriately reduced, and more historical data can be relied on to smooth noise; while in the mechanical system, in order to quickly respond to recent changes to capture sudden failures, the update frequency can be increased to timely reflect changes in equipment status; By dynamically updating parameters, since the operating stage and the new stage are different, the parameters themselves will be affected during the long-term operation process. If the fixed parameter standards are used to measure the accuracy, the accuracy will be insufficient. Therefore, the updated power grid equipment parameter monitoring standards can be obtained by integrating the real-time monitoring data of the operating stage with the initialized power grid equipment parameter monitoring standards, which can better meet the monitoring needs of the operating stage. For power grid equipment in the decommissioning stage, the service life of the decommissioning stage is predicted based on historical operation data and real-time status monitoring. Combined with the service life in the decommissioning stage and economic evaluation, the parameter monitoring standard of power grid equipment in the decommissioning stage is obtained.

[0021] Compared with the new addition stage and the operation stage, the monitoring standards in the decommissioning stage are more complicated. Therefore, it is necessary to predict the equipment life through historical operation data and real-time status monitoring, and formulate power grid equipment parameter monitoring standards in combination with economic evaluation, so as to ensure that the equipment can operate in the final period while avoiding the cost of maintenance and monitoring exceeding expectations.

[0022] As an optional embodiment: the specific working steps of the data acquisition module also include the following; Receive the historical operation time series and corresponding retirement time of the transformer of the power grid equipment, and establish a model to solve the electrical and mechanical aging rate of the power grid equipment , based on the electrical and mechanical aging rates of grid equipment , get the end point of the new addition phase, the end point of the operation phase and the end point of the retirement phase of the power grid equipment; If the electrical and mechanical aging rates of grid equipment If the electrical and mechanical aging rates of the power grid equipment are less than or equal to the end point of the new addition stage, the power grid equipment is judged to be in the new addition stage. If the electrical and mechanical aging rates of the power grid equipment are greater than the end point of the new stage and less than or equal to the end point of the operation stage, the power grid equipment is judged to be in the operation stage. If it is less than or equal to the end point of the decommissioning stage and greater than the end point of the operating stage, it is determined that the power grid equipment is in the decommissioning stage.

[0023] It should be noted that traditional models or judgments of the life stage of power grid equipment all rely on historical data to obtain values, which cannot reflect the real-time operating environment (such as sudden changes in temperature and humidity, extreme weather). Temperature fluctuations can cause the aging rate of equipment to deviate; Modeling the electrical and mechanical aging rates of power grid equipment Determine the boundary of the life stage of power grid equipment; The model specifically includes the following, establishing the formula:

[0024] Get the life threshold under failure probability P , where P is the failure probability. The life stage is divided into the new addition stage, the operation stage and the retirement stage. The failure probability has different values. A fixed value is selected according to the different stages. In this embodiment, the order is 0.1, 0.5 and 0.9. When P is 0.5, the end point of the new addition stage of the power grid equipment is calculated. When P is 0.1, the end point of the operation stage of the power grid equipment is calculated. When P is 0.9, the end point of the retirement stage of the power grid equipment is calculated. According to the value of adjustment P, the end point of the new addition stage, the end point of the operation stage and the end point of the retirement stage are obtained; , and are Weibull parameters, which are shape parameter, scale parameter and location parameter in order. Initially, , and The benchmark parameters are 2.1, 35 and 3; Combined with real-time temperature, humidity and wind speed, , and Make adjustments: According to the formula , calculated according to the current temperature, humidity and wind speed adjusted , and ; Among them, the above t represents the meaning of the parameters related to the current time point, for example Indicates the current real-time parameters after adjustment ; in , and for , and Benchmark parameters of in is the environmental stress factor for the current time period, according to the formula:

[0025] Get, where T is the real-time temperature, is the real-time humidity, V is the real-time wind speed, and the corresponding is the reference value of temperature, which is obtained based on the average value of the power grid equipment throughout the year. is the reference value of humidity, which is obtained based on the average value of power grid equipment throughout the year. is the reference value of wind speed, which is obtained based on the average value of power grid equipment throughout the year; , and is the environmental coupling coefficient, which is taken as 0.32, 0.12 and 0.07 in this implementation; Substituting the above formula again to calculate, we can get the end point of the new addition phase, the end point of the operation phase and the end point of the retirement phase of the power grid equipment; It should be noted that the above scheme combines the aging of historical power grid equipment and environmental impact to determine the life stage of existing power grid equipment. Compared with the existing scheme, it can more accurately determine the life stage of power grid equipment, and improve the accuracy of subsequent monitoring of different life stages. The specific working steps of receiving the collected power grid equipment parameter data, processing the collected power grid equipment parameter data, determining the life stage of the power grid equipment, and dividing the life stage into the new addition stage, the operation stage, and the retirement stage also include the following: Receive real-time meteorological data, and when extreme weather occurs, adjust the above-established model, thereby adjusting the end point of the new addition phase, the end point of the operation phase, and the end point of the retirement phase of the power grid equipment.

[0026] It should be noted that meteorological data is obtained through the local meteorological center, and red and above warnings are marked as extreme weather; Temporary correction of shape parameters during extreme weather (e.g. thunderstorms, snow and ice) using short-term accelerated aging models and positional parameters , the modified shape parameters after adjustment and positional parameters It is obtained by multiplying the original base value by 1.25 and then multiplying it by the change ratio of wind speed; The environmental stress factor is based on the original value and adds the extreme event impact term, which is obtained by multiplying the intensity of the extreme event by the duration; The adjusted corrected shape parameters and positional parameters Substituting the environmental stress factor into the above formula to calculate the end point of the new addition phase, the end point of the operation phase and the end point of the retirement phase of the power grid equipment; This impact term is added to the calculation of the environmental stress factor to comprehensively consider the superposition of multiple extreme weather events; the new environmental stress factor may be higher, thereby further adjusting the parameters of the Weibull distribution to more accurately reflect the failure risk under extreme weather conditions. The parameters of the Weibull distribution can be dynamically adjusted according to short-term extreme weather conditions to evaluate the reliability of equipment or materials under these special conditions. It should be noted that in actual use, because power grid equipment is installed outdoors, it is also necessary to consider the impact of extreme weather on the life of power grid assets; In this embodiment, the Weibull parameter extreme weather is dynamically adjusted by considering the extreme weather, and the transient impact is reflected in the calculation of the environmental stress factor, and then the sudden change of the mechanical aging rate is considered, so that the obtained life stage classification is more in line with the actual value. As an optional embodiment, the specific working steps of the data acquisition module are as follows: Determine the type of transformer parameter data to be collected, including vibration spectrum, temperature gradient, partial discharge intensity, insulation resistance, harmonic distortion rate, and response delay; Install various corresponding sensors on the transformer and connect the sensors to the data acquisition device, i.e., DAQ device. It should be noted that the vibration spectrum is collected Sensor selection: Use piezoelectric accelerometers and install the sensors at key locations of the transformer, such as different directions of the housing, core grounding locations, and windings, to fully monitor the vibration of the equipment. To ensure measurement accuracy, ensure that the sensor is in close contact with the surface being measured, and use appropriate coupling agents or adhesives.

[0027] Calibrate and debug the sensor to ensure it works properly.

[0028] Set the parameters of the acquisition device, such as sampling frequency, number of sampling points, etc. Generally, the sampling frequency can be determined according to the highest operating frequency of the device; Start the acquisition device to start collecting vibration signals and transmit the collected data to the computer for storage and analysis; Collect temperature gradients and use platinum resistance temperature sensors. Install temperature sensors at different locations of the transformer, such as the oil tank, windings, and core, to accurately measure the temperature distribution inside the transformer. Calibrate and debug the temperature sensor to ensure its measurement accuracy, connect the sensor to the data acquisition device, set the acquisition parameters, such as sampling interval, etc., which can generally be determined according to the operating conditions and monitoring requirements of the transformer, start the acquisition device, collect temperature data in real time, and transmit the data to the computer for processing and analysis; To collect the intensity of partial discharge, partial discharge sensors are used, such as high-frequency pulse current sensors, ultrasonic sensors and ultra-high frequency sensors. These sensors can sense the partial discharge signals generated inside the transformer and convert them into electrical signals for output. The partial discharge sensors are installed at appropriate locations of the transformer, such as the oil tank casing, bushing, etc., so as to effectively capture the internal partial discharge signals.

[0029] Calibrate and debug the partial discharge sensor to ensure its normal operation, connect the sensor to the signal processing equipment, set appropriate parameters such as sampling frequency, gain, etc. to improve the signal-to-noise ratio and resolution of the signal, start the acquisition equipment, start collecting partial discharge signals, and transmit the signals to the computer for analysis and processing; Collect insulation resistance. Use an insulation resistance tester to measure the insulation resistance of the transformer windings, core and other parts to evaluate the insulation performance of the transformer. Connect the test line of the insulation resistance tester to the corresponding parts of the transformer to ensure that the connection is firm and reliable. Set the parameters of the tester, such as test voltage, etc. Generally, select a suitable test voltage according to the rated voltage and insulation level of the transformer, start measuring the insulation resistance, and record the measurement results; Collect harmonic distortion rate, use current transformer (CT) and voltage transformer (PT) to convert the current and voltage signals of the transformer into signals suitable for measurement, and then measure the harmonic distortion rate through equipment such as power analyzer or harmonic analyzer; Install current transformers and voltage transformers at the input and output ends of the transformer respectively to measure the input and output current and voltage; Calibrate and debug current transformers and voltage transformers to ensure their measurement accuracy; Connect the transformer to the harmonic analyzer and set the analyzer parameters, such as analysis frequency range, sampling frequency, etc. Collecting response delays. The collection of response delays usually requires the combination of multiple sensors and devices, such as installing voltage sensors and current sensors in the control circuit of the transformer, and using devices such as oscilloscopes or logic analyzers to measure the signal delay time; installing corresponding sensors at the control signal input and output ends of the transformer to monitor the transmission process of the control signal; Calibrate and debug the sensor and measuring equipment to ensure their normal operation, connect the sensor to the measuring equipment, set appropriate parameters such as sampling frequency, trigger mode, etc., start the measuring equipment, send control signals, and record the time difference between the input and output ends of the signal, which is the response delay time. The sensor collects the operating parameters of the transformer in real time and transmits the data to the collection device; The specific steps of combining the service life and economic evaluation in the retirement stage to obtain the monitoring standards of power grid equipment parameters in the retirement stage are as follows: First, the initial investment cost, annual operating cost and maintenance cost of the decommissioning stage are collected, and the decommissioning life of the decommissioning stage is calculated based on the initial investment cost, annual operating cost and maintenance cost; The remaining life percentage for each retirement stage is obtained by dividing the current working years at the retirement stage by the retirement life span; Then, the adjustment coefficient of the power grid equipment parameter monitoring standard for each retirement stage is calculated based on the remaining life percentage of each retirement stage. ; It should be noted that in this embodiment, the adjustment coefficient of the power grid equipment parameter monitoring standard at each retirement stage is The specific way to obtain is as follows: According to the formula ; Calculate and obtain the first Adjustment coefficients for monitoring standards of power grid equipment parameters in the decommissioning phase ; in For the The percentage of remaining life in the retirement phase; Adjustment coefficients for grid equipment parameter monitoring standards based on each decommissioning stage The updated average grid equipment parameter monitoring standard for all operation stages is used to calculate the grid equipment parameter monitoring standard for each decommissioning stage.

[0030] It should be noted that, in this embodiment, the calculation steps are: According to the formula Calculate the monitoring standards of power grid equipment parameters at each decommissioning stage ;in The average power grid equipment parameter monitoring standard after all operation stages is updated, which is obtained by adding the updated power grid equipment parameter monitoring standard for each operation stage and dividing it by the number of operation stages. is the design life of this type of equipment, i.e. , For the The predicted remaining life of each decommissioning phase; It should also be noted that by calculating the remaining life percentage of each retirement stage and determining the adjustment coefficient of the power grid equipment parameter monitoring standard accordingly, the power grid equipment parameter monitoring standard can be closely aligned with the actual use of the equipment. The lower the remaining life percentage, the closer the equipment is to retirement life, and the adjustment coefficient of its power grid equipment parameter monitoring standard increases accordingly, thereby achieving dynamic adjustment of the power grid equipment parameter monitoring standard and ensuring stricter monitoring during the accelerated stage of equipment performance degradation; Based on the updated average power grid equipment parameter monitoring standards of all operation stages, the monitoring level of group equipment is comprehensively considered. This integration method avoids the one-sidedness of the power grid equipment parameter monitoring standards of a single device, so that the power grid equipment parameter monitoring standards in the decommissioning stage incorporate the common characteristics of group equipment on the basis of individual differences, and improve the scientificity and rationality of the power grid equipment parameter monitoring standards; As the equipment operates and ages, its remaining life percentage changes continuously, and the monitoring standards for power grid equipment parameters are also adjusted dynamically. Dynamic adaptability can timely capture the changing trend of equipment performance, ensure the timeliness and effectiveness of monitoring work, and avoid insufficient or excessive monitoring caused by fixed monitoring standards for power grid equipment parameters. The specific steps of calculating the retirement life of the retirement phase based on the initial investment cost, annual operating cost and maintenance cost are as follows: First, the initial investment cost, annual operating cost and maintenance cost of the decommissioning phase are collected. Then add up and calculate the total annual costs; Then calculate the cumulative cost from year 1 to year 2 The cumulative cost of the years is divided by the useful life , and the average annual cost ; It should be noted that in this embodiment Year is the design service life of the equipment. For the total accumulated cost in the future, the operation and maintenance cost of a certain year is obtained according to the exponential acceleration model, which is equal to the operation and maintenance cost of the previous year multiplied by (1 plus the base annual average growth rate, plus the equipment degradation sensitivity coefficient multiplied by the equipment degradation rate function). The base annual average growth rate reflects the average growth trend of the operation and maintenance cost; the equipment degradation sensitivity coefficient is a value between 0.05 and 0.2, and in this embodiment, the value is 0.1, which indicates the amplification effect of equipment performance degradation on cost; the equipment degradation rate function is related to time or operating parameters, and specifically is the annual growth rate of vibration amplitude; In the calculation process, the operation and maintenance cost is predicted year by year based on the initial operation and maintenance cost and related parameters of the equipment. For example, for a certain equipment, the average annual growth rate of its operation and maintenance cost in the previous few years and the equipment degradation rate function, as well as the equipment degradation sensitivity coefficient, are known, and the operation and maintenance cost of the next year can be predicted; It can directly quantify equipment performance degradation as a cost acceleration factor, making cost prediction closer to the actual aging law. In this way, the model can more accurately reflect the impact of equipment aging on operation and maintenance costs, providing a more reliable decision-making basis for equipment management and maintenance; By service life The horizontal axis is the average annual cost As the vertical axis, draw a curve. When the curve changes from falling to rising, the retirement life in the retirement stage is obtained; As an optional embodiment: the specific working steps of the data acquisition module are as follows: Automatically adjust the collection strategy according to the life stage of the device.

[0031] For the newly added stage, the mechanical parameters of the transformer and the full-band monitoring of the vibration spectrum; The electrical parameters of the transformer, the insulation resistance is tested in full loop every day, and the harmonic distortion rate is collected in real time at the second level; in this embodiment, the full loop test is (0-500Hz) with a collection frequency of 5 minutes / time; For the operation stage, the mechanical parameters of the transformer: monitoring of key areas of the vibration spectrum; The electrical parameters and insulation resistance of the transformer are tested in full circuit every week, and the harmonic distortion rate is collected on a minute-by-minute basis. In this embodiment, the key area of ​​the vibration spectrum (200-400Hz) is monitored, and the collection frequency is 15 minutes / time. For the decommissioning phase, the mechanical parameters of the transformer: vibration spectrum relaxation monitoring; The electrical parameters and insulation resistance of the transformer are tested in full circuit every month, and the harmonic distortion rate is collected at hourly level. In this embodiment, the vibration spectrum is relaxed (0-first threshold 0Hz) and the collection frequency is: fourth threshold minute / time; It should be noted that it is possible to automatically adjust the collection strategy according to the life cycle stage of the equipment to ensure the comprehensiveness, accuracy and efficiency of data collection, and provide strong support for equipment health assessment and management.

[0032] As an optional embodiment: the specific steps of the dynamic evaluation module are as follows: The collected parameter data is received in each period T, and for each power grid device, a corresponding evaluation value is calculated based on the collected parameter data and the corresponding sub-data set storing the monitoring standard; It should be noted that through this process, the evaluation value can quantify the health status of the equipment and provide an intuitive basis for operation and maintenance decisions; A record is generated in each cycle T, including a timestamp and an evaluation value, forming a time series sequence and transmitting it. It should be noted that the evaluation value time series sequence reflects the correlation between the real-time status of the device and historical data, and can dynamically adjust the warning threshold to avoid misjudgment caused by data drift of the traditional fixed threshold. The dynamic evaluation module is also used to adjust the monitoring period T of the retirement stage based on the total working time and retirement life of the retirement stage: it should be noted that the monitoring period of the new addition stage and the operation stage are fixed and can be a longer time because their working performance is relatively stable, but for the retirement stage, their working performance decreases, so it is necessary to adjust the monitoring frequency in a targeted manner; According to the formula , calculate and obtain the adjusted monitoring period of the retirement phase , as the monitoring cycle of the decommissioning phase; in is the accumulated working time during the retirement phase, It is the retirement life of the equipment during the retirement phase. It should be noted that the remaining life ratio method can dynamically adjust the monitoring cycle of the equipment to ensure that the monitoring frequency is increased when the equipment is close to its economic life, so as to detect potential problems in a timely manner and improve the efficiency and safety of equipment management.

[0033] As an optional embodiment: the specific process of calculating the corresponding evaluation value for each power grid device according to the collected parameter data is as follows: For each power grid device, the evaluation value of the monitoring parameter is calculated based on the collected parameter data; It should be noted that, in this embodiment, the specific calculation steps are: According to the formula , calculate the index value of each monitoring parameter in the device; For the Device The actual value of the monitoring parameter; For the Device The monitoring standard value of the power grid equipment parameter of each monitoring parameter is obtained through the data acquisition module; Then, weights are assigned to the monitoring parameters, and then the evaluation value of the device is obtained through weighted calculation.

[0034] It should be noted that, in this embodiment, the weight of the mechanical parameters is 0.6, the weight of the electrical parameters is 0.4, and further, the weights of the vibration spectrum, temperature gradient and partial discharge intensity in the mechanical parameters are all 0.2, the weight of the insulation resistance in the electrical parameters is 0.15, the weight of the harmonic distortion rate is 0.15, and the weight of the response delay is 0.1; It should be noted that the comprehensive evaluation value quantifies the multi-dimensional status of the equipment into a single indicator, providing a quantitative basis for the operation and maintenance strategy.

[0035] As an optional embodiment: the specific working steps of the real-time monitoring module are as follows: Receive time series, model and predict the time series data according to the model, and trigger an early warning when the deviation between the predicted value and the actual value exceeds the set ratio; It should be noted that the specific steps are: determine the autoregressive order p by drawing a partial autocorrelation graph, and the significant truncation position in the PACF graph is the value of p; The moving average order q is determined by drawing an autocorrelation diagram (ACF). The significant truncation position in the ACF diagram is the value of q. The minimum number of differences d is determined through the ADF test to make the sequence reach a stable state; Use AIC to traverse different parameter combinations and select the model with the smallest information loss as the final ARIMA model; Adopting the rolling window mechanism, the model parameters are updated every time new data is received; Calculate the residual between the actual value and the model predicted value to evaluate the prediction accuracy of the model. The smaller the residual, the better the prediction effect of the model. Use the Ljung-Box test to determine whether the residual is white noise. If the p-value of the test is greater than 0.05, it means that the residual has no autocorrelation and the model fits well. When three consecutive residuals exceed three times the standard deviation (3σ principle) or the prediction deviation rate exceeds the set threshold (such as 20%), a warning signal is triggered; The mechanical parameters and electrical parameters of the power grid equipment are monitored separately. When the mechanical parameters trigger the warning signal, a mechanical warning signal is generated; when the electrical parameters trigger the warning signal, an electrical warning signal is generated.

[0036] It should also be noted that the real-time monitoring module can receive and analyze equipment operation data in real time, helping operation and maintenance personnel to understand the health status of the equipment at any time; In the early stage of equipment use, quickly identify the equipment's running-in status, discover potential problems in time, and prevent small problems from turning into major failures; During the normal operation of the equipment, accurate prediction of equipment status can help operation and maintenance personnel arrange maintenance plans reasonably to avoid over-maintenance or under-maintenance. Traditional monitoring methods often rely on regular inspections and manual data analysis, which can lead to monitoring delays. The real-time monitoring module achieves real-time monitoring of equipment status through automated data processing and analysis, greatly shortening the time to discover problems. Traditional methods are sensitive to data noise and outliers, which can easily lead to false positives or false negatives. The real-time monitoring module effectively reduces the impact of data noise and outliers through data preprocessing and model optimization, and improves the accuracy of monitoring; Traditional models often need to be retrained when faced with changes in data characteristics, and their adaptability is poor. The real-time monitoring module uses a rolling window mechanism and dynamic parameter updates to enable the model to adapt to data changes in real time and maintain predictive performance.

[0037] As an optional embodiment: the specific working steps of the management module are as follows: For the newly added stage and the operating stage, the evaluation value of the equipment is obtained, and the pre-evaluation value and the pre-set threshold are judged to classify the newly added stage; If the evaluation value is ≥ the first threshold, the grid equipment is allowed to operate at over-rated load, up to 110%, but the duration shall not exceed 2 hours, and high-value loads shall be connected first; if the second threshold is ≤ the evaluation value < the first threshold, the load rate is limited to 90%, and predictive maintenance is initiated at the same time; if the third threshold is ≤ the evaluation value < the second threshold, the equipment is downgraded to standby equipment, such as converting the main transformer to a station transformer; And limit the load rate to the second threshold; if the fourth threshold ≤ the evaluation value < the third threshold, only allow the grid equipment to bear seasonal loads; for example, short-term activation during the winter heating period; If the evaluation value is less than the fourth threshold, the grid equipment is forced to withdraw from grid operation, and the equipment is disassembled and recycled to recover usable parts, such as transformer winding copper; In this embodiment, the first threshold is greater than the second threshold, greater than the third threshold, and greater than the fourth threshold. The specific values ​​are set according to the power grid standard. It should also be noted that by setting up precise management logic at different stages of power grid equipment, the utilization rate and operating efficiency of the equipment are effectively improved. In the new addition stage, the equipment is graded and reasonably allocated according to the evaluation value. For example, a 500kV transformer with an evaluation value of 85 after commissioning can be allocated to a regional hub station due to its excellent performance, and the load rate is increased to 105%, realizing the maximum efficiency of the equipment in the initial stage; in the operation stage, the load is dynamically adjusted according to the evaluation value and corresponding maintenance measures are taken. Taking the 500kV transformer as an example, when the evaluation value dropped to 65 in the 8th year, it was migrated to the 220kV line, reducing the annual maintenance cost by 40%, extending the service life of the equipment and optimizing cost expenditure; in the decommissioning stage, the equipment tasks are reasonably arranged according to the evaluation value. For example, when the remaining life is 1 year, the transformer has an evaluation value of 40, and it is converted into a special transformer for winter heating, extending the asset income cycle by 2 years, realizing the maximum utilization of the residual value of the equipment, thereby improving the operating efficiency and economic benefits of the overall power grid assets.

[0038] As an optional embodiment: the specific working steps of the management module also include the following: In the new addition stage and operation stage, if the monitoring result output by the real-time monitoring module is an electrical warning signal, the electrical parameters are judged to be degraded and the power grid equipment is migrated to a low-voltage level line; If the monitoring result output by the real-time monitoring module is a mechanical early warning signal, the position will be swapped with the same type of power grid equipment to balance the operation frequency; If the wear of the circuit breaker contacts exceeds the third threshold %, the positions will be swapped with the same type of equipment to balance the operation frequency; For the decommissioning stage, if the monitoring result output by the real-time monitoring module is an electrical warning signal, the equipment is isolated immediately.

[0039] Take the 500kV transformer as an example: New stage: After commissioning, the evaluation value = 85. With its excellent performance, it was assigned to the regional hub station, and the load rate was increased to 105%. By continuously monitoring the oil chromatography data, the equipment operation status is ensured to be controllable; Operation phase: In the eighth year, it dropped to 65. According to the plan, it was moved to a 220 kV line, which reduced the annual maintenance cost by 40%, extending the service life of the equipment and optimizing the cost expenditure. Retirement stage: Remaining life 1 year = 40, converted into a dedicated transformer for winter heating, successfully extending the asset income cycle by 2 years and maximizing the utilization of the residual value of the equipment.

[0040] It should be noted that for equipment in the new and operating stages, if electrical warning signals are monitored in real time, the electrical parameter degradation can be judged in time and the equipment can be migrated to low-voltage level lines; in the face of mechanical warning signals, the operation frequency can be balanced by exchanging positions with equipment of the same model, such as exchanging positions when the wear of the circuit breaker contacts exceeds the third threshold%. In the decommissioning stage, if an electrical warning signal is encountered, the equipment can be immediately isolated and the load transfer of adjacent sites can be started to ensure the continuity of power supply to the power grid; if a mechanical warning signal appears, it will be marked as a red warning for key attention and timely processing; this real-time monitoring and rapid response mechanism effectively avoids the risk of sudden equipment failure, ensures the stable operation of the power grid, improves the quality and reliability of power supply services, and reduces power outage losses and maintenance costs caused by equipment failures.

[0041] The above are only preferred implementations of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technical staff in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of this template.

Claims

1. An intelligent monitoring and management system for the entire life cycle of power grid assets based on the Internet of Things, characterized by: Includes the following modules: The data acquisition module is used to collect parameter data of power grid equipment, process the collected parameter data of power grid equipment, and obtain the electrical and mechanical aging rate of power grid equipment. , based on the electrical and mechanical aging rates of power grid equipment To determine the life stage of power grid equipment, the life stage is divided into the new stage, operation stage and retirement stage, and monitoring standards for power grid equipment are formulated for different life stages; The dynamic evaluation module sets a time period T, receives the parameter data of the data acquisition module and the monitoring standard of the corresponding parameter data, calculates the device status in a rolling manner with T as the period, outputs the evaluation value corresponding to each time period T, obtains the evaluation value time series, and transmits it; It is also used to adjust the monitoring period T of the retirement phase based on the total working time and retirement life of the retirement phase; The real-time monitoring module is used to monitor the status of power grid equipment in real time according to the evaluation value time series of the dynamic evaluation module, trigger the early warning signal, and output the monitoring results to the user interface; The management module is used to manage and adjust the operation and use of the power grid equipment according to the life stage of the power grid equipment, the evaluation value corresponding to the current time period T, and the monitoring results output by the real-time monitoring module.

2. According to the Internet of Things-based intelligent monitoring and management system for power grid assets throughout their life cycle, the system is characterized in that: The specific working steps of the data acquisition module also include the following: Receive the collected power grid equipment parameter data, process it according to the collected power grid equipment parameter data, and obtain the electrical and mechanical aging rate of the power grid equipment , based on the electrical and mechanical aging rates of power grid equipment To determine the life stage of power grid equipment, the life stage is divided into the new stage, the operation stage and the retirement stage; For the newly added power grid equipment, the initialization of the power grid equipment parameter monitoring standards is obtained according to the mechanical standards and electrical standards; For power grid equipment in the operation phase, dynamic threshold adjustment is performed based on the initialized power grid equipment parameter monitoring standard to obtain an updated power grid equipment parameter monitoring standard; For power grid equipment in the retirement stage, the service life of the retirement stage is predicted based on historical operation data and real-time status monitoring, and the parameter monitoring standards of the power grid equipment in the retirement stage are obtained.

3. The intelligent monitoring and management system for the entire life cycle of power grid assets based on the Internet of Things according to claim 2 is characterized in that: The specific working steps of the data acquisition module also include the following: Modeling the electrical and mechanical aging rates of power grid equipment , based on the electrical and mechanical aging rates of grid equipment , get the end point of the new addition phase, the end point of the operation phase and the end point of the retirement phase of the power grid equipment; If the electrical and mechanical aging rates of grid equipment If the electrical and mechanical aging rates of the power grid equipment are less than or equal to the end point of the new addition stage, the power grid equipment is judged to be in the new addition stage. If the electrical and mechanical aging rates of the power grid equipment are greater than the end point of the new stage and less than or equal to the end point of the operation stage, the power grid equipment is judged to be in the operation stage. If it is less than or equal to the end point of the decommissioning stage and greater than the end point of the operating stage, it is determined that the power grid equipment is in the decommissioning stage.

4. The intelligent monitoring and management system for the entire life cycle of power grid assets based on the Internet of Things according to claim 3 is characterized in that: The specific working steps of the data acquisition module are as follows: Determine the type of transformer parameter data to be collected, including vibration spectrum, temperature gradient, partial discharge intensity, insulation resistance, harmonic distortion rate, and response delay; Install various corresponding sensors on the transformer and connect the sensors to the data acquisition device, i.e., DAQ device; The sensor collects the operating parameters of the transformer in real time and transmits the data to the collection device.

5. The intelligent monitoring and management system for the entire life cycle of power grid assets based on the Internet of Things according to claim 4 is characterized in that: The data acquisition module is also used to automatically adjust the acquisition strategy according to the life stage of the equipment according to the feedback. The specific working steps are as follows: For the newly added stage, the mechanical parameters of the transformer and the full-band monitoring of the vibration spectrum; The transformer's electrical parameters and insulation resistance are tested in full circuit every day, and the harmonic distortion rate is collected in real time at the second level; For the operation stage, the mechanical parameters of the transformer: monitoring of key areas of the vibration spectrum; The electrical parameters of the transformer, insulation resistance, full circuit test every week, harmonic distortion rate collection at minute level; For the decommissioning phase, the mechanical parameters of the transformer: vibration spectrum relaxation monitoring; The transformer's electrical parameters and insulation resistance are tested in full circuits every month, and the harmonic distortion rate is collected on an hourly basis.

6. The intelligent monitoring and management system for the entire life cycle of power grid assets based on the Internet of Things according to claim 3 is characterized in that: The specific steps of the dynamic evaluation module are as follows: The collected parameter data is received in each period T, and for each power grid device, a corresponding evaluation value is calculated based on the collected parameter data and the corresponding sub-data set storing the monitoring standard; Each period T generates a record, including a timestamp and an evaluation value, to form a time series and transmit it; The dynamic evaluation module is also used to adjust the monitoring period T of the retirement phase based on the total working time and retirement life of the retirement phase: According to the formula , calculate and obtain the adjusted monitoring period of the retirement phase , as the monitoring cycle of the decommissioning phase; in is the accumulated working time during the retirement phase, The retirement life span is the retirement stage.

7. The intelligent monitoring and management system for the entire life cycle of power grid assets based on the Internet of Things according to claim 6 is characterized in that: The specific process of calculating the corresponding evaluation value for each power grid device based on the collected parameter data is as follows: For each power grid device, the evaluation value of the monitoring parameter is calculated based on the collected parameter data; Then, weights are assigned to the monitoring parameters, and then the evaluation value of the device is obtained through weighted calculation.

8. The intelligent monitoring and management system for the entire life cycle of power grid assets based on the Internet of Things according to claim 7 is characterized in that: The specific working steps of the real-time monitoring module are as follows: Receive time series, model and predict the time series data according to the model, and trigger an early warning when the deviation between the predicted value and the actual value exceeds the set ratio; The mechanical parameters and electrical parameters of the power grid equipment are monitored separately. When the mechanical parameters trigger the warning signal, a mechanical warning signal is generated; when the electrical parameters trigger the warning signal, an electrical warning signal is generated.

9. The intelligent monitoring and management system for the entire life cycle of power grid assets based on the Internet of Things according to claim 8 is characterized in that: The specific working steps of the management module are as follows: For the newly added stage and the operating stage, the evaluation value of the equipment is obtained, and the pre-evaluation value and the pre-set threshold are judged to classify the newly added stage; If the evaluation value is ≥ the first threshold, the grid equipment is allowed to operate over the rated load, and high-value loads are connected first; if the second threshold is ≤ the evaluation value < the first threshold; if the evaluation value is < the second threshold, the equipment is downgraded to a standby device, such as converting the main transformer to a station transformer; For the decommissioning stage, if the evaluation value is ≥ the third threshold, the power grid equipment will be switched to non-critical line operation; and limiting the load rate to the second threshold; if the fourth threshold ≤ the evaluation value < the third threshold, only the grid equipment is allowed to bear the seasonal load; If the evaluation value is less than the fourth threshold, the grid device is forced to exit grid operation.

10. The intelligent monitoring and management system for the entire life cycle of power grid assets based on the Internet of Things according to claim 9 is characterized in that: The specific working steps of the management module also include the following: In the new addition stage and operation stage, if the monitoring result output by the real-time monitoring module is an electrical warning signal, the electrical parameters are judged to be degraded and the power grid equipment is migrated to a low-voltage level line; If the monitoring result output by the real-time monitoring module is a mechanical warning signal, the position will be swapped with the same type of power grid equipment to balance the operation frequency; For the decommissioning stage, if the monitoring result output by the real-time monitoring module is an electrical warning signal, the equipment is isolated immediately.

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