Intelligent monitoring and management system for the entire life cycle of power grid assets based on the Internet of Things

Abstract

The present invention discloses an intelligent monitoring and management system for the entire life cycle of power grid assets based on the Internet of Things, which relates to the technical field of power grid asset monitoring. The system includes a data acquisition module and a dynamic evaluation module. The system sets a time period T and receives parameter data from the data acquisition module and the corresponding monitoring standard of the parameter data in a rolling calculation of the device status. The system also includes a real-time monitoring module for monitoring the status of power grid equipment in real time and triggering early warning signals based on the time series of the evaluation values ​​of the dynamic evaluation module. The system also includes a management module for managing and regulating the operation and use of power grid equipment. The system divides the entire life cycle of power grid equipment into a new addition stage, an operation stage, and a decommissioning stage, and develops differentiated monitoring standards and management strategies based on the characteristics and device status of each stage. In the new addition stage, equipment is scientifically graded based on its evaluation value, and equipment loads and tasks are reasonably allocated to ensure that the equipment can achieve 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 is increasingly demanding efficient grid asset management solutions. As an important component of information and communication technology, IoT technology enables 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 grid data in real time, such as voltage, current, and temperature. Through data processing and analysis, it provides decision support for grid scheduling and control.

[0003] Grid assets refer to the various equipment and infrastructure that make up power transmission. Lifecycle monitoring refers to the continuous and dynamic detection and management of the status of grid assets from commissioning to retirement. By collecting parameter data of grid assets, the status of grid assets is monitored, and then adjustments and management of grid assets are made based on the monitoring results.

[0004] However, the monitoring standards for grid equipment with different life cycles are different. If grid assets are monitored according to fixed monitoring standards, the monitoring accuracy of grid assets may be inaccurate, which in turn affects the normal operation of the grid. However, if new monitoring standards are blindly adjusted, 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

[0005] 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.

[0006] 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:

[0007] 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, the operation stage and the retirement stage, and monitoring standards for power grid equipment are formulated for different life stages;

[0008] The dynamic evaluation module sets a time period T, receives the parameter data of the data acquisition module and the corresponding monitoring standard of the parameter data, calculates the device status in a rolling manner, outputs the evaluation value corresponding to each time period T, obtains the evaluation value time series, and transmits it;

[0009] 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;

[0010] 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 early warning signals, and output monitoring results to the user interface;

[0011] The management module is used to manage and adjust the operation 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.

[0012] Preferably, the specific working steps of the data acquisition module also include the following:

[0013] Receive the collected parameter data of power grid equipment, process it according to the collected parameter data, determine the life stage of the power grid equipment, and divide the life stage into the new addition stage, operation stage and retirement stage;

[0014] For newly added grid equipment, initialized grid equipment parameter monitoring standards are obtained according to mechanical and electrical standards;

[0015] 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;

[0016] 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.

[0017] Preferably, the specific working steps of the data acquisition module also include the following

[0018] 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 decommissioning phase of the power grid equipment;

[0019] 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 stage, the power grid equipment is judged to be in the new stage. If the aging rate of the power grid equipment is 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 the electrical and mechanical aging rate of the power grid equipment is 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, the power grid equipment is judged to be in the decommissioning stage.

[0020] Preferably, the specific working steps of the data acquisition module are as follows:

[0021] 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;

[0022] Install various corresponding sensors on the transformer and connect the sensors to the data acquisition device, i.e. DAQ device;

[0023] The sensor collects the operating parameters of the transformer in real time and transmits the data to the collection device.

[0024] Preferably, the specific working steps of the data acquisition module are as follows:

[0025] Automatically adjust the collection strategy according to the life cycle stage of the device.

[0026] For the newly added stage, the transformer’s mechanical parameters and vibration spectrum are monitored in full frequency bands;

[0027] The transformer's electrical parameters and insulation resistance are tested daily throughout the entire circuit, and the harmonic distortion rate is collected in real time at the second level.

[0028] For the operation phase, the mechanical parameters of the transformer: monitoring of key areas of the vibration spectrum;

[0029] The transformer's electrical parameters and insulation resistance are tested in full circuit every week, and harmonic distortion rate is collected at the minute level;

[0030] For the decommissioning phase, the transformer’s mechanical parameters: vibration spectrum relaxation monitoring;

[0031] The transformer's electrical parameters and insulation resistance are tested in full circuits every month, and harmonic distortion rate is collected on an hourly basis.

[0032] Preferably, the specific steps of the dynamic evaluation module are as follows:

[0033] The collected parameter data is received in each period T, and the corresponding evaluation value is calculated for each power grid device based on the collected parameter data and the corresponding sub-data set storing the monitoring standard;

[0034] Each period T generates a record, including a timestamp and evaluation value, forming a time series and transmitting it;

[0035] The dynamic evaluation module is further configured to adjust the monitoring period T of the retirement phase based on the total working time and retirement life of the retirement phase:

[0036] According to the formula , calculate and obtain the adjusted monitoring period of the retirement phase , as a monitoring cycle during the decommissioning phase;

[0037] in is the accumulated working time during the retirement phase, The retirement life span is the retirement stage.

[0038] Preferably, the specific process of calculating the corresponding evaluation value for each power grid device based on the collected parameter data is as follows:

[0039] For each power grid device, the evaluation value of the monitoring parameter is calculated based on the collected parameter data;

[0040] Then, weights are assigned to the monitoring parameters, and the evaluation value of the device is obtained by weighted calculation.

[0041] Preferably, the specific working steps of the real-time monitoring module are as follows:

[0042] Receive time series data, model and predict the time series data according to the model, and trigger an alert when the deviation between the predicted value and the actual value exceeds the set ratio;

[0043] 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.

[0044] Preferably, the specific working steps of the management module are as follows:

[0045] 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;

[0046] If the assessment 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 assessment value < the first threshold, the load rate is limited to 90%, and predictive maintenance is initiated at the same time; if the assessment 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;

[0047] In the decommissioning phase, if the evaluation value is ≥ the third threshold, the grid equipment will be switched to non-critical line operation;

[0048] The load rate is limited 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;

[0049] If the evaluation value is less than the fourth threshold, the grid device is forced to exit grid operation.

[0050] Preferably, the specific working steps of the management module also include the following:

[0051] During the new addition and operation phases, if the real-time monitoring module outputs an electrical warning signal, the electrical parameters are judged to be degraded and the grid equipment is migrated to a low-voltage level line.

[0052] If the monitoring result output by the real-time monitoring module is a mechanical early warning signal, the position of the equipment will be swapped with the same type of power grid equipment to balance the operation frequency;

[0053] During the decommissioning phase, if the monitoring result output by the real-time monitoring module is an electrical warning signal, the equipment is isolated immediately.

[0054] Beneficial effects: The data acquisition module collects, cleans, and transmits parameter data of power grid equipment in real time, and combines it with the dynamic evaluation module to calculate the time series of equipment status evaluation values. 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, effectively preventing small problems from turning into major failures, greatly reducing power outages 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.

[0055] The lifecycle of power grid equipment is divided into the new installation phase, the operating phase, and the retirement phase. Differentiated monitoring standards and management strategies are developed based on the characteristics and equipment status of each phase. During the new installation phase, equipment is scientifically graded based on its assessed value, and loads and tasks are rationally allocated to ensure maximum performance from the outset. During the operation phase, equipment loads are dynamically adjusted based on assessed values, and maintenance plans are optimized to avoid resource waste caused by excessive maintenance and premature equipment aging caused by insufficient maintenance. During the retirement phase, the remaining life of the equipment and economic assessment are comprehensively considered to rationally arrange the equipment's final task and disposal method, maximizing its residual value. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a flow chart of the system of the present invention. DETAILED DESCRIPTION

[0057] 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 a data acquisition module, a dynamic evaluation module, a real-time monitoring module, and a management module:

[0058] 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, the operation stage and the retirement stage, and monitoring standards for power grid equipment are formulated for different life stages;

[0059] It should be noted that power grid assets mainly include fixed assets, projects under construction, and current assets of the power grid. Fixed assets specifically include power transmission and distribution equipment and infrastructure. Among these, in this embodiment, only substation equipment in the power transmission and distribution equipment is considered.

[0060] In this embodiment, the power grid equipment is a transformer, which specifically includes a transformer. The specific parameter data includes mechanical parameters and electrical parameters.

[0061] It should be noted that the cleaned data is input into the dynamic health assessment module to calculate the assessment value;

[0062] 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, so that the comprehensiveness of monitoring can be improved through parameter data fusion;

[0063] The dynamic evaluation module sets a time period T, receives the parameter data of the data acquisition module and the corresponding monitoring standard of the parameter data, calculates the device status in a rolling manner, outputs the evaluation value corresponding to each time period T, obtains the evaluation value time series, and transmits it;

[0064] 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;

[0065] It should be noted that the evaluation value time series is output to the cluster analysis module;

[0066] 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;

[0067] Receive cleaning data from the data acquisition module to achieve quantitative evaluation of equipment status;

[0068] 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 early warning signals, and output monitoring results to the user interface.

[0069] The specific steps of outputting the monitoring results to the user interface of the real-time monitoring module include the following:

[0070] Initialize the display communication interface, set the resolution and refresh rate, load the font library and graphic resources, and define the interface layout;

[0071] Process button click events and save current screen data as image format;

[0072] 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 save the monitoring data to the SD card;

[0073] It should be noted that the assets within the power grid include a variety of equipment. Different equipment types are classified differently and are at different stages within their lifecycles. Traditional monitoring methods are unable to perform corresponding monitoring based on the particularities of each power grid equipment, which affects the final monitoring effect. This technical solution defines the monitoring standards for power grid equipment parameters at each stage of the power grid asset's lifecycle. First, the power grid equipment is divided into hierarchies, defining mechanical components, electrical components, and lifecycle stages. Parameter standards corresponding to these mechanical components, electrical components, and lifecycle stages are then set, enabling targeted monitoring.

[0074] The management module is used to manage and adjust the operation of the power grid equipment based on 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;

[0075] As an optional embodiment: the specific working steps of the data acquisition module also include the following:

[0076] Receive the collected parameter data of power grid equipment, process it according to the collected parameter data, determine the life stage of the power grid equipment, and divide the life stage into the new addition stage, operation stage and retirement stage;

[0077] 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:

[0078] According to the formula , calculate and obtain the electrical and mechanical aging rates of power grid equipment ;

[0079] It should be noted that the cumulative damage caused by electro-mechanical coupling to equipment aging is quantified by dynamically integrating the product of the rate of change of electric field intensity and the vibration high-frequency energy amplification;

[0080] in It is the time differential symbol in calculus, representing the infinitesimal change in time t;

[0081] 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;

[0082] It is the amplification of high-frequency vibration energy, specifically the rate of change of vibration energy in the 300-500Hz frequency band. Mechanical stress triggers micro crack expansion, which synergizes with the electric field to accelerate insulation degradation. The data is mainly obtained through acceleration sensors and spectrum analysis.

[0083] The benchmark vibration energy and the average high-frequency vibration energy of the equipment in a healthy state provide a normalized benchmark to eliminate individual differences in equipment and generate vibration data statistics in the initial stage of equipment operation;

[0084] 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 judged by the range of

[0085] For newly added grid equipment, initialized grid equipment parameter monitoring standards are obtained according to mechanical and electrical standards;

[0086] It should be noted that the initialized grid equipment parameter monitoring standards are obtained according to the mechanical and electrical parameters in the parameter data. The specific grid equipment parameter monitoring standards for mechanical components include vibration spectrum (0-500Hz), temperature gradient (°C / h), and partial discharge intensity (pC), which are obtained according to the national standard GB / T14549.

[0087] 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;

[0088] In this embodiment, the initialized grid equipment parameter monitoring standard is the recommended value according to the national standard or industry standard;

[0089] 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;

[0090] It should be noted that, in this embodiment, the dynamic threshold is adjusted as follows:

[0091] First, data collection is performed to monitor the vibration spectrum data, temperature data, and partial discharge intensity data of mechanical equipment in real time, as well as the insulation resistance data, harmonic distortion rate data, and response delay data of electrical equipment.

[0092] 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.

[0093] Determine the median: Based on the size of the data set, determine the median. If the number of data points is odd, the median is the value in the middle. If the number of data points is even, the median is the average of the two middle values.

[0094] According to the determined median position, the median of each monitoring parameter is calculated as the benchmark value of the parameter;

[0095] During the operation of the equipment, the monitoring data of various parameters will continue to be collected in real time;

[0096] Compare the real-time monitoring value with the corresponding median, and dynamically update the grid equipment parameter monitoring standard based on the comparison results. For example, in power systems, due to large fluctuations, the update frequency can be appropriately reduced, relying more on historical data to smooth out noise; while in mechanical systems, in order to quickly respond to recent changes and catch sudden failures, the update frequency can be increased to promptly reflect changes in equipment status;

[0097] By dynamically updating parameters, the parameters of the equipment will be affected during long-term operation because the operating phase and the newly added phase are different. If the parameters are measured using fixed parameter standards, the accuracy will be insufficient. Therefore, the updated monitoring standards for power grid equipment parameters are obtained by integrating the initial monitoring standards for power grid equipment with the real-time monitoring data during the operating phase, which can better meet the monitoring needs during the operating phase.

[0098] For power grid equipment in the decommissioning stage, the decommissioning life is predicted based on historical operation data and real-time status monitoring. Combined with the decommissioning life and economic evaluation, the parameter monitoring standards for power grid equipment in the decommissioning stage are obtained.

[0099] 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.

[0100] As an optional embodiment: the specific working steps of the data acquisition module also include the following;

[0101] Receive the historical operation time series and corresponding retirement time of power grid equipment transformers, and establish a model to solve the electrical and mechanical aging rate 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 decommissioning phase of the power grid equipment;

[0102] 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 stage, the power grid equipment is judged to be in the new stage. If the aging rate of the power grid equipment is 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 the electrical and mechanical aging rate of the power grid equipment is 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, the power grid equipment is judged to be in the decommissioning stage.

[0103] It should be noted that traditional models or judgments about the life stage of power grid equipment rely on historical data to determine 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;

[0104] Modeling the electrical and mechanical aging rates of power grid equipment Determine the life cycle of power grid equipment;

[0105] The model specifically includes the following, and the formula is established:

[0106]

[0107] 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. Fixed values ​​are selected according to different stages. In this embodiment, they are 0.1, 0.5, and 0.9 in order. 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.

[0108] 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;

[0109] 、 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 respectively;

[0110] Combined with real-time temperature, humidity and wind speed, 、 and Make adjustments:

[0111] According to the formula , calculated based on the current temperature, humidity and wind speed 、 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 ;

[0112] in 、 and for 、 and Benchmark parameters;

[0113] in is the environmental stress factor for the current time period, according to the formula:

[0114]

[0115] 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 the power grid equipment throughout the year. is the baseline value of wind speed, which is obtained based on the average value of power grid equipment throughout the year;

[0116] 、 and is the environmental coupling coefficient, which is taken as 0.32, 0.12 and 0.07 in this implementation;

[0117] Substituting the above formula again to calculate the end point of the new addition phase, the end point of the operation phase, and the end point of the decommissioning phase of the power grid equipment;

[0118] It should be noted that the above solution combines the historical aging of power grid equipment and environmental impacts to determine the life stage of existing power grid equipment. Compared with existing solutions, it can more accurately determine the life stage of power grid equipment, thereby improving the accuracy of subsequent monitoring based on different life stages.

[0119] The specific steps of receiving the collected parameter data of the power grid equipment, processing the collected parameter data of the power grid equipment, 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:

[0120] Receive real-time meteorological data and adjust the above-established model when extreme weather occurs, thereby adjusting the end points of the new addition phase, operation phase, and retirement phase of the power grid equipment.

[0121] It should be noted that meteorological data is obtained through the local meteorological center, and red and above warnings are marked as extreme weather;

[0122] Temporary correction of shape parameters during extreme weather (e.g. thunderstorms, snow and ice) using short-term accelerated aging models and positional parameters , the adjusted modified shape parameters 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;

[0123] 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 its duration;

[0124] 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;

[0125] This impact term is added to the calculation of the environmental stress factor to comprehensively consider the superimposed impact 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.

[0126] It should be noted that in actual use, since power grid equipment is installed outdoors, the impact of extreme weather on the life of power grid assets also needs to be considered;

[0127] In this embodiment, the Weibull parameter is dynamically adjusted by considering extreme weather, and the transient impact of extreme weather is reflected in the calculation of the environmental stress factor, and then the sudden change in the mechanical aging rate is considered, so that the obtained life stage classification is more in line with the actual value.

[0128] As an optional embodiment, the specific working steps of the data acquisition module are as follows:

[0129] 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;

[0130] Install various sensors on the transformer and connect them to the data acquisition device, i.e. DAQ device. It should be noted that the vibration spectrum is collected.

[0131] Sensor Selection: Use piezoelectric accelerometers and install them in key locations on the transformer, such as at different angles on the housing, at the core grounding point, and on the windings, to comprehensively monitor the equipment's vibration. To ensure measurement accuracy, ensure close contact between the sensor and the surface being measured. Use an appropriate coupling agent or adhesive.

[0132] Calibrate and debug the sensor to ensure it works properly.

[0133] 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 maximum operating frequency of the device;

[0134] Start the acquisition equipment, start collecting vibration signals, and transmit the collected data to the computer for storage and analysis;

[0135] 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.

[0136] Calibrate and debug the temperature sensor to ensure its measurement accuracy, connect the sensor to the data acquisition equipment, set the acquisition parameters, such as sampling interval, etc., which can generally be determined according to the operating conditions of the transformer and monitoring requirements, start the acquisition equipment, collect temperature data in real time, and transmit the data to the computer for processing and analysis;

[0137] 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.

[0138] Calibrate and debug the partial discharge sensor to ensure it is working properly, connect the sensor to the signal processing equipment, set appropriate parameters such as sampling frequency and gain to improve the signal-to-noise ratio and resolution, start the acquisition equipment, start collecting partial discharge signals, and transmit the signals to the computer for analysis and processing;

[0139] 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 wires of the insulation resistance tester to the corresponding parts of the transformer to ensure that the connection is firm and reliable.

[0140] Set the tester parameters, such as test voltage. Generally, select an appropriate test voltage based on the rated voltage and insulation grade of the transformer, start measuring insulation resistance, and record the measurement results.

[0141] To collect harmonic distortion rate, current transformers (CTs) and voltage transformers (PTs) are used to convert the transformer's current and voltage signals into signals suitable for measurement. The harmonic distortion rate is then measured using a power analyzer or harmonic analyzer.

[0142] 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;

[0143] Calibrate and debug current transformers and voltage transformers to ensure their measurement accuracy;

[0144] Connect the mutual inductor to the harmonic analyzer and set the analyzer parameters, such as analysis frequency range, sampling frequency, etc.

[0145] Acquiring response delay usually requires combining multiple sensors and devices. For example, installing voltage and current sensors in the transformer's control circuit and using an oscilloscope or logic analyzer to measure the signal delay time are also important. Sensors are also installed at the transformer's control signal input and output terminals to monitor the transmission process of the control signal.

[0146] Calibrate and debug the sensors and measuring equipment to ensure they are working properly. Connect the sensors to the measuring equipment and set appropriate parameters, such as sampling frequency and trigger mode. Start the measuring equipment, send control signals, and record the time difference between the input and output of the signals, which is the response delay time. The sensors collect the operating parameters of the transformer in real time and transmit the data to the collection equipment.

[0147] The specific steps for obtaining the monitoring standards for power grid equipment parameters in the retirement stage by combining the service life and economic evaluation in the retirement stage are as follows:

[0148] First, the initial investment cost, annual operating cost, and maintenance cost of the decommissioning phase are collected, and the decommissioning life of the decommissioning phase is calculated based on the initial investment cost, annual operating cost, and maintenance cost.

[0149] The remaining life percentage for each retirement stage is obtained by dividing the current working years of the retirement stage by the retirement life span;

[0150] Then, the adjustment coefficient of the grid equipment parameter monitoring standard for each retirement stage is calculated based on the remaining life percentage of each retirement stage. ;

[0151] It should be noted that in this embodiment, the adjustment coefficient of the grid equipment parameter monitoring standard at each decommissioning stage is The specific way to obtain it is as follows:

[0152] According to the formula ; Calculate and obtain the first Adjustment coefficients for grid equipment parameter monitoring standards in the decommissioning phase ;

[0153] in For the The percentage of remaining life in each decommissioning phase;

[0154] Adjustment coefficients based on grid equipment parameter monitoring standards at each decommissioning stage The updated average grid equipment parameter monitoring standards of all operation stages are used to calculate the grid equipment parameter monitoring standards of each decommissioning stage.

[0155] It should be noted that, in this embodiment, the calculation steps are:

[0156] According to the formula Calculate the monitoring standards of power grid equipment parameters at each decommissioning stage ;in is the average grid equipment parameter monitoring standard after all operation stages are updated, which is obtained by adding the updated 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;

[0157] It's also important to note that by calculating the remaining life percentage at each retirement stage and determining the adjustment coefficient for the power grid equipment parameter monitoring standards accordingly, the standards can be closely aligned with the actual usage of the equipment. The lower the remaining life percentage, the closer the equipment is to retirement, and the larger the adjustment coefficient for the power grid equipment parameter monitoring standards. This allows for dynamic adjustment of the power grid equipment parameter monitoring standards, ensuring stricter monitoring during periods of accelerated equipment degradation.

[0158] Based on the updated average grid equipment parameter monitoring standards for all operating stages, the monitoring level of the entire group of equipment is comprehensively considered. This integrated approach avoids the one-sidedness of the grid equipment parameter monitoring standards for a single device. The grid equipment parameter monitoring standards for the decommissioning stage incorporate the common characteristics of the entire group of equipment on the basis of individual differences, thus improving the scientific nature and rationality of the grid equipment parameter monitoring standards.

[0159] As equipment operates and ages, its remaining life percentage changes continuously, and the grid equipment parameter monitoring standards are also dynamically adjusted accordingly. Dynamic adaptability can promptly capture the changing trends of equipment performance, ensuring the timeliness and effectiveness of monitoring work, and avoiding insufficient or excessive monitoring caused by fixed grid equipment parameter monitoring standards;

[0160] The specific steps for calculating the retirement life in the retirement stage based on the initial investment cost, annual operating cost and maintenance cost are as follows:

[0161] First, the initial investment cost, annual operating cost and maintenance cost of the decommissioning phase are collected.

[0162] Then add up and calculate the total annual cost;

[0163] 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 ;

[0164] It should be noted that in this embodiment Years are the designed service life of the equipment. For the total cumulative cost in the future, the exponential acceleration model is used to obtain the operation and maintenance cost of a particular year equal to the operation and maintenance cost of the previous year multiplied by (1 plus the baseline average annual growth rate, plus the equipment degradation sensitivity coefficient multiplied by the equipment degradation rate function). The baseline average annual 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 is 0.1, indicating the amplifying effect of equipment performance degradation on cost. The equipment degradation rate function is related to time or operating parameters, specifically the annual growth rate of vibration amplitude.

[0165] During the calculation process, the operation and maintenance costs are predicted year by year based on the initial operation and maintenance costs and related parameters of the equipment. For example, for a certain equipment, if the average annual growth rate of its operation and maintenance costs in the previous few years and the equipment degradation rate function, as well as the equipment degradation sensitivity coefficient, are known, the operation and maintenance costs for the next year can be predicted.

[0166] It can directly quantify equipment performance degradation as a cost acceleration factor, making cost predictions more closely aligned with actual aging patterns. In this way, the model can more accurately reflect the impact of equipment aging on operation and maintenance costs, providing a more reliable basis for decision-making in equipment management and maintenance.

[0167] By service life The horizontal axis is the average annual cost As the vertical axis, draw a curve. When the curve changes from descending to ascending, the retirement life in the retirement stage is obtained;

[0168] As an optional embodiment: the specific working steps of the data acquisition module are as follows:

[0169] Automatically adjust the collection strategy according to the life cycle stage of the device.

[0170] For the newly added stage, the transformer’s mechanical parameters and vibration spectrum are monitored in full frequency bands;

[0171] The transformer's electrical parameters and insulation resistance are tested daily in the full circuit, and the harmonic distortion rate is collected in real time at the second level. In this embodiment, the full circuit test is (0-500Hz) with a collection frequency of 5 minutes / time.

[0172] For the operation phase, the mechanical parameters of the transformer: monitoring of key areas of the vibration spectrum;

[0173] The transformer's electrical parameters and insulation resistance are tested weekly in full circuits, and harmonic distortion rates are collected on a minute-by-minute basis. In this embodiment, the key areas of the vibration spectrum (200-400 Hz) are monitored, with a collection frequency of 15 minutes per time.

[0174] For the decommissioning phase, the transformer’s mechanical parameters: vibration spectrum relaxation monitoring;

[0175] The transformer's electrical parameters and insulation resistance are tested monthly in full circuits, and harmonic distortion rates are collected hourly. In this embodiment, the vibration spectrum is relaxed (0-first threshold 0Hz) and the collection frequency is: fourth threshold minute / time;

[0176] It should be noted that the collection strategy can be automatically adjusted 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.

[0177] As an optional embodiment: the specific steps of the dynamic evaluation module are as follows:

[0178] The collected parameter data is received in each period T, and the corresponding evaluation value is calculated for each power grid device based on the collected parameter data and the corresponding sub-data set storing the monitoring standard;

[0179] 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;

[0180] Each cycle T generates a record, including a timestamp and 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 misjudgments caused by data drift caused by traditional fixed thresholds.

[0181] The dynamic assessment module is further configured 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 monitoring periods of the newly added phase and the operating phase are fixed and can be longer because their working performance is relatively stable. However, in the retirement phase, their working performance decreases, so the monitoring frequency needs to be adjusted accordingly.

[0182] According to the formula , calculate and obtain the adjusted monitoring period of the retirement phase , as a monitoring cycle during the decommissioning phase;

[0183] in is the accumulated working time during the retirement phase, The remaining lifespan ratio (RLR) is the lifespan of a device during the retirement phase. It should be noted that the remaining lifespan ratio method allows for dynamic adjustment of the equipment monitoring cycle, ensuring that monitoring frequency is increased as the device nears its economic lifespan, enabling timely detection of potential problems and improving the efficiency and safety of equipment management.

[0184] As an optional embodiment, the specific process of calculating the corresponding evaluation value for each power grid device based on the collected parameter data is as follows:

[0185] For each power grid device, the evaluation value of the monitoring parameter is calculated based on the collected parameter data;

[0186] It should be noted that, in this embodiment, the specific calculation steps are:

[0187] According to the formula , calculate the index value of each monitoring parameter in the device; For the The first device The actual value of each monitoring parameter;

[0188] For the The first device The monitoring standard value of the power grid equipment parameter of each monitoring parameter is obtained through the data acquisition module;

[0189] Then, weights are assigned to the monitoring parameters, and the evaluation value of the device is obtained by weighted calculation.

[0190] 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 weight 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;

[0191] 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.

[0192] As an optional embodiment: the specific working steps of the real-time monitoring module are as follows:

[0193] Receive time series data, model and predict the time series data according to the model, and trigger an alert when the deviation between the predicted value and the actual value exceeds the set ratio;

[0194] 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;

[0195] The moving average order q is determined by drawing the autocorrelation diagram (ACF). The significant truncation position in the ACF diagram is the value of q.

[0196] The minimum number of differences d is determined by the ADF test to make the series reach a stable state;

[0197] Use AIC to traverse different parameter combinations and select the model with the smallest information loss as the final ARIMA model;

[0198] Adopting a rolling window mechanism, the model parameters are updated every time new data is received;

[0199] 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.

[0200] Use the Ljung-Box test to determine whether the residuals are white noise. If the p-value of the test is greater than 0.05, it means that the residuals have no autocorrelation and the model fit is good;

[0201] When three consecutive residuals exceed three times the standard deviation (3σ principle) or the prediction deviation rate exceeds the set threshold (such as 20%), an early warning signal is triggered;

[0202] 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.

[0203] 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;

[0204] In the early stages of equipment use, quickly identify the equipment's running-in status and promptly discover potential problems to prevent minor problems from turning into major failures;

[0205] During the normal operation of the equipment, accurate prediction of equipment status helps operation and maintenance personnel arrange maintenance plans reasonably to avoid over-maintenance or under-maintenance.

[0206] Traditional monitoring methods often rely on regular inspections and manual data analysis, which can lead to monitoring delays. The real-time monitoring module enables real-time monitoring of equipment status through automated data processing and analysis, significantly reducing the time to problem discovery.

[0207] Traditional methods are sensitive to data noise and outliers, which can easily lead to false positives or missed reports. The real-time monitoring module effectively reduces the impact of data noise and outliers through data preprocessing and model optimization, thereby improving monitoring accuracy.

[0208] Traditional models often require retraining when faced with changes in data characteristics, resulting in poor adaptability. The real-time monitoring module uses a rolling window mechanism and dynamic parameter updates, enabling the model to adapt to data changes in real time and maintain predictive performance.

[0209] As an optional embodiment: the specific working steps of the management module are as follows:

[0210] 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;

[0211] If the assessment value is ≥ the first threshold, grid equipment is allowed to operate at over-rated load, up to 110%, but for a duration not exceeding 2 hours, and high-value loads are prioritized. If the second threshold is ≤ the assessment value < the first threshold, the load factor is limited to 90%, and predictive maintenance is initiated. If the third threshold is ≤ the assessment value < the second threshold, the equipment is downgraded to backup equipment, such as converting the main transformer to a station transformer.

[0212] The load rate is limited to the second threshold. If the fourth threshold is less than or equal to the evaluation value and less than the third threshold, only seasonal loads are allowed to be borne by the grid equipment; for example, it is temporarily enabled during the winter heating period.

[0213] If the evaluation value is less than the fourth threshold, the grid equipment is forced to exit grid operation and be disassembled and recycled to recover usable parts, such as transformer winding copper;

[0214] 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.

[0215] 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 have been 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 its evaluation value dropped to 65 in the eighth year, it was migrated to the 220kV line, reducing the annual maintenance cost by 40%, extending the equipment service life and optimizing cost expenditure; in the decommissioning stage, the equipment tasks are reasonably arranged according to the evaluation value. For example, if the transformer has an evaluation value of 40 with a remaining life of 1 year, 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.

[0216] As an optional embodiment, the specific working steps of the management module further include the following:

[0217] During the new addition and operation phases, if the real-time monitoring module outputs an electrical warning signal, the electrical parameters are judged to be degraded and the grid equipment is migrated to a low-voltage level line.

[0218] If the monitoring result output by the real-time monitoring module is a mechanical early warning signal, the position of the equipment will be swapped with the same type of power grid equipment to balance the operation frequency;

[0219] If the wear of the circuit breaker contacts exceeds the third threshold %, they will be swapped with devices of the same model to balance the operating frequency;

[0220] During the decommissioning phase, if the monitoring result output by the real-time monitoring module is an electrical warning signal, the equipment is isolated immediately.

[0221] Take the 500kV transformer as an example: New stage: After commissioning, the evaluation value is 85. Due to its excellent performance, it was assigned to the regional hub station, and the load factor was increased to 105%. Continuous monitoring of oil chromatography data ensures that the equipment's operating status is controllable.

[0222] Operational phase: In the eighth year, the voltage dropped to 65. According to the plan, the system was moved to a 220 kV line, which reduced annual maintenance costs by 40%, extending the service life of the equipment while optimizing costs.

[0223] Decommissioning stage: Remaining life 1 year = 40, converted into a dedicated transformer for winter heating, successfully extending the asset return cycle by 2 years and maximizing the utilization of the equipment's residual value.

[0224] It should be noted that for equipment in the newly added and operational stages, if electrical warning signals are detected in real time, electrical parameter degradation can be promptly determined and the equipment can be relocated to lower-voltage lines. In the event of mechanical warning signals, the frequency of operation can be balanced by swapping positions with similarly modeled equipment. For example, if circuit breaker contact wear exceeds the third threshold, position swapping can be performed. During the decommissioning phase, if an electrical warning signal is encountered, the equipment can be immediately isolated and load transfer to adjacent sites initiated to ensure grid power continuity. Mechanical warning signals are marked as red alerts for focused attention and timely processing. This real-time monitoring and rapid response mechanism effectively mitigates the risk of sudden equipment failures, ensures stable grid operation, improves the quality and reliability of power supply services, and reduces power outage losses and repair costs caused by equipment failures.

[0225] The above are only preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions that fall within the scope of protection of the present invention are within the scope of protection of the present invention. It should be pointed out that for ordinary technical personnel in this technical field, certain improvements and modifications that do not depart from the principles of the present invention should also be considered as the scope of protection 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, the operation stage and the 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 corresponding monitoring standard of the parameter data, calculates the device status in a rolling manner, 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 early warning signals, and output monitoring results to the user interface; The management module is used to manage and adjust the operation of the power grid equipment based on 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; The specific working steps of the data acquisition module also include the following: Receive the collected parameter data of power grid equipment, process it according to the collected 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, operation stage and retirement stage; For newly added grid equipment, initialized grid equipment parameter monitoring standards are obtained according to mechanical 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 decommissioning stage, the service life of the decommissioning 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 decommissioning stage are obtained; 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 decommissioning 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 stage, the power grid equipment is judged to be in the new stage. If the aging rate of the power grid equipment is 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 the electrical and mechanical aging rate of the power grid equipment is 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, the power grid equipment is judged to be in the decommissioning stage; The specific steps of the dynamic evaluation module are as follows: The collected parameter data is received in each period T, and the corresponding evaluation value is calculated for each power grid device 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 evaluation value, forming a time series and transmitting it; The dynamic evaluation module is further configured 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 a monitoring cycle during the decommissioning phase; in is the accumulated working time during the retirement phase, The retirement life span is the retirement phase; 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 the evaluation value of the device is obtained by weighted calculation.

2. The intelligent monitoring and management system for power grid assets throughout their life cycle based on the Internet of Things according to claim 1 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.

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 data acquisition module is also used to automatically adjust the acquisition strategy according to the life stage of the device according to the feedback obtained. The specific working steps are as follows: For the newly added stage, the transformer’s mechanical parameters and vibration spectrum are monitored in full frequency bands; The transformer's electrical parameters and insulation resistance are tested daily throughout the entire circuit, and the harmonic distortion rate is collected in real time at the second level. For the operation phase, the mechanical parameters of the transformer: monitoring of key areas of the vibration spectrum; The transformer's electrical parameters and insulation resistance are tested in full circuit every week, and harmonic distortion rate is collected at the minute level; For the decommissioning phase, the transformer’s mechanical parameters: vibration spectrum relaxation monitoring; The transformer's electrical parameters and insulation resistance are tested in full circuits every month, and harmonic distortion rate is collected on an hourly basis.

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 1 is characterized in that: The specific working steps of the real-time monitoring module are as follows: Receive time series data, model and predict the time series data according to the model, and trigger an alert 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.

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 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, 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 backup device, such as converting the main transformer to a station transformer; In the decommissioning phase, if the evaluation value is ≥ the third threshold, the grid equipment will be switched to non-critical line operation; The load rate is limited 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.

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 5 is characterized in that: The specific working steps of the management module also include the following: During the new addition and operation phases, if the real-time monitoring module outputs an electrical warning signal, the electrical parameters are judged to be degraded and the 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 of the equipment will be swapped with the same type of power grid equipment to balance the operation frequency; During the decommissioning phase, 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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