A communication control system and method for intelligent power transmission and distribution equipment

Abstract

The present invention relates to the technical field of power transmission and distribution equipment, and particularly to a communication control system and method for intelligent power transmission and distribution equipment. The method includes the following steps: obtaining data of intelligent power transmission and distribution equipment, estimating initial performance parameters of the equipment and detecting the operating state; identifying dynamic abnormal load conditions of the equipment based on the operating data, analyzing the gradient decay of electrical stability and predicting the trend of electrical interference compound instability; further evaluating the accumulation of thermal effects based on the electrical instability conditions, estimating the trend of load collapse and the degradation trend of the communication link; identifying abnormal conditions in combination with the deteriorated performance of the equipment operation, conducting a fault risk assessment, generating risk data and uploading it to the cloud platform to achieve early warning control; the present invention realizes more stable operation of intelligent power transmission and distribution equipment through early warning of abnormal conditions of power transmission and distribution equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission and distribution equipment, and particularly to a communication control system and method for intelligent power transmission and distribution equipment. Background Art

[0002] The power system is gradually evolving towards intelligence, distribution, and high interconnectivity. New types of power transmission and distribution equipment such as intelligent circuit breakers, intelligent transformers, switch cabinet monitoring units, and intelligent protection devices have been widely deployed in the power transmission and distribution links. These devices generally have functions of data acquisition, communication interaction, adaptive control, and remote linkage, forming a hierarchical operation architecture in which edge sensing nodes cooperate with a centralized management platform. On this basis, in order to achieve the efficient operation of the power grid and accurate fault early warning, it is urgent to construct a comprehensive control method that can integrate equipment operation data, electrical state parameters, and the health status of communication links to ensure the stability and reliability of the power transmission and distribution system in a complex environment. There is a lack of in-depth detection means for the stability, response ability, and redundancy status of communication links, making it difficult to identify and dynamically intervene in the early stage of communication degradation trends. However, traditional intelligent power transmission and distribution equipment has problems of inaccurate analysis of electrical abnormal attenuation of power transmission and distribution equipment and inaccurate analysis of communication link degradation of power transmission and distribution equipment. Summary of the Invention

[0003] Based on this, it is necessary to provide a communication control system and method for intelligent power transmission and distribution equipment to solve at least one of the above technical problems.

[0004] To achieve the above object, a communication control method for intelligent power transmission and distribution equipment includes the following steps:

[0005] Step S1: Obtain intelligent power transmission and distribution equipment data; estimate the initial performance parameters of the intelligent power transmission and distribution equipment according to the intelligent power transmission and distribution equipment data; detect the operating state of the intelligent power transmission and distribution equipment according to the initial performance parameters of the intelligent power transmission and distribution equipment;

[0006] Step S2: Estimate the dynamic abnormal load condition of the equipment according to the intelligent power transmission and distribution equipment data; detect the electrical stability gradient attenuation condition of the intelligent power transmission and distribution equipment according to the dynamic abnormal load condition of the equipment; predict the electrical interference composite instability condition based on the electrical stability gradient attenuation condition of the equipment;

[0007] Step S3: Estimate the cumulative heat effect of the power transmission and distribution equipment according to the electrical interference composite instability condition; estimate the load collapse trend of the power transmission and distribution equipment based on the cumulative heat effect of the power transmission and distribution equipment; estimate the communication link degradation trend of the equipment based on the load collapse trend of the power transmission and distribution equipment;

[0008] Step S4: Detect the abnormal conditions of the power transmission and distribution equipment according to the deterioration trend of the equipment communication link and the load collapse trend of the power transmission and distribution equipment; conduct a fault risk assessment of the power transmission and distribution equipment based on the abnormal conditions of the power transmission and distribution equipment to obtain the fault risk data of the power transmission and distribution equipment, and upload it to the cloud platform for early warning.

[0009] Through the data acquisition and analysis of intelligent power transmission and distribution equipment, the present invention realizes the accurate assessment of equipment performance and operating status, can monitor the abnormal load conditions and the gradient decay of electrical stability of the equipment in real time, provides data support for predicting electrical interference and complex instability conditions, and further promotes the improvement of equipment safety. By predicting the thermal effect and load collapse trend of the equipment, potential equipment fault risks and communication link deterioration problems can be identified in advance, effectively avoiding the occurrence of equipment failures and extending the service life of the equipment. Based on these early warning information, early warning and risk control of the equipment can be realized, thereby improving the stability and reliability of the entire power system, avoiding large-scale system collapses caused by equipment failures, and ensuring the continuity and stability of power supply. Through the data upload and early warning mechanism of the cloud platform, intelligent monitoring across regions and equipment can be realized, management efficiency can be improved, and remote fault diagnosis and maintenance can be achieved to ensure that the equipment is always in the best operating state. Therefore, the present invention is an optimized treatment for the traditional communication control method for intelligent power transmission and distribution equipment, solving the problems existing in the traditional communication control method for intelligent power transmission and distribution equipment, such as inaccurate analysis of electrical abnormal attenuation of power transmission and distribution equipment and inaccurate analysis of communication link deterioration of power transmission and distribution equipment. It improves the accuracy of electrical abnormal attenuation analysis of power transmission and distribution equipment and the accuracy of electrical abnormal attenuation of power transmission and distribution equipment.

[0010] The present invention also provides a communication control system for intelligent power transmission and distribution equipment, which is used to execute the communication control method for intelligent power transmission and distribution equipment as described above. The communication control system for intelligent power transmission and distribution equipment includes:

[0011] An operating state detection module, which is used to obtain the data of intelligent power transmission and distribution equipment; estimate the initial performance parameters of intelligent power transmission and distribution equipment according to the data of intelligent power transmission and distribution equipment; detect the operating state of intelligent power transmission and distribution equipment according to the initial performance parameters of intelligent power transmission and distribution equipment;

[0012] An electrical interference and complex instability prediction module, which is used to estimate the dynamic abnormal load conditions of the equipment according to the data of intelligent power transmission and distribution equipment; detect the gradient decay condition of the electrical stability of the intelligent power transmission and distribution equipment operating state detection equipment according to the dynamic abnormal load conditions of the equipment; predict the electrical interference and complex instability conditions based on the gradient decay condition of the equipment's electrical stability;

[0013] A communication link degradation trend prediction module is used to predict the cumulative thermal effect of power transmission and distribution equipment according to the electrical interference composite instability condition; predict the load collapse trend of power transmission and distribution equipment based on the cumulative thermal effect of power transmission and distribution equipment; predict the degradation trend of the equipment communication link based on the load collapse trend of power transmission and distribution equipment;

[0014] A risk warning module is used to detect the abnormal condition of power transmission and distribution equipment according to the degradation trend of the equipment communication link and the load collapse trend of power transmission and distribution equipment; conduct a fault risk assessment of power transmission and distribution equipment according to the abnormal condition of power transmission and distribution equipment to obtain the fault risk data of power transmission and distribution equipment, and upload it to the cloud platform for warning.

[0015] The present invention is used for an intelligent power transmission and distribution equipment communication control system, which can implement any one of the intelligent power transmission and distribution equipment communication control methods of the present invention, and is used as a medium for combining the operations and signal transmissions between each module to complete the intelligent power transmission and distribution equipment communication control method. The internal modules of the system cooperate with each other, identify equipment fault risks in advance, provide accurate warnings, thereby improving the operation stability of the equipment and the reliability of the power system. Brief Description of the Drawings

[0016] Figure 1 It is a schematic diagram of the step flow of a method for intelligent power transmission and distribution equipment communication control;

[0017] Figure 2 For Figure 1 It is a schematic diagram of the detailed implementation step flow of step S3 in

[0018] Figure 3 For Figure 1 It is a schematic diagram of the detailed implementation step flow of step S4 in

[0019] The realization, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiment

[0020] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0021] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0022] It should be understood that although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed related items.

[0023] To achieve the above object, please refer to Figures 1 to 3 , a communication control method for intelligent power transmission and distribution equipment, comprising the following steps:

[0024] Step S1: Obtain the data of the intelligent power transmission and distribution equipment; estimate the initial performance parameters of the intelligent power transmission and distribution equipment according to the data of the intelligent power transmission and distribution equipment; detect the operating state of the intelligent power transmission and distribution equipment according to the initial performance parameters of the intelligent power transmission and distribution equipment;

[0025] In the embodiments of the present invention, real-time operation data of intelligent power transmission and distribution equipment is obtained through sensor devices. These data mainly include electrical parameters such as current, voltage, frequency, power, and temperature, and geographical location information of the equipment, ambient temperature, humidity and other external environment data should be collected according to the specific configuration of the equipment. The data acquisition tool uses a multi-channel sensor with a high sampling frequency to ensure the accuracy of real-time data. The collected data is transmitted to the central data processing unit through a wireless communication module. By preprocessing the collected raw data, including denoising, data normalization and time series correction, preliminary performance parameters of the equipment under the current operating conditions are obtained, such as equipment load capacity, communication link stability, thermal effect, etc. The estimation methods include regression analysis based on historical data, neural network models or machine learning algorithms, and these methods estimate parameters according to historical operation records and equipment status. Specifically, statistical analysis is carried out based on the past operation data of the equipment to identify the change trend of equipment performance, so as to calculate the initial performance parameters of the equipment. These initial performance parameters mainly cover basic indicators such as the maximum power output, electrical efficiency, communication bandwidth and reliability of the equipment. By analyzing these performance parameters, the current operating state of the equipment is further detected. The goal of this step is to confirm whether the equipment is within the normal operating range and check whether there are abnormal conditions where the performance parameters deviate from the expected values. If parameters such as the voltage, current or temperature of the equipment exceed the normal threshold, an alarm will be immediately triggered to indicate that there are potential problems with the equipment. This process uses precise real-time monitoring technology, combined with the health status assessment algorithm of the equipment, to dynamically feedback the operating state of the equipment and obtain the operating state of the intelligent power transmission and distribution equipment.

[0026] Step S2: Estimate the dynamic abnormal load condition of the equipment according to the data of the intelligent power transmission and distribution equipment; Detect the gradient attenuation condition of the electrical stability of the intelligent power transmission and distribution equipment according to the dynamic abnormal load condition of the equipment; Predict the electrical interference composite instability condition based on the gradient attenuation condition of the equipment electrical stability;

[0027] In the embodiments of the present invention, dynamic load analysis is performed based on the operation data of intelligent power transmission and distribution equipment, especially load data such as current and voltage. By analyzing the detailed operation logs of the equipment and combining with the equipment operation time series analysis technology, the fluctuation of the equipment load demand is obtained. The time series analysis method (such as: sliding window method or Fourier transform) is used to identify the periodic changes of the load demand, and further analyze the periods of sudden increase in demand. For example, when the load fluctuation amplitude of the equipment exceeds a preset threshold (such as ±10 kW), fault prediction will be automatically performed, and the characteristics of sudden load increase will be recorded. According to these load fluctuation data, the analysis of the gradient decay of electrical stability will be carried out. In this process, the system will evaluate the stability of the electrical system according to the equipment load condition and the equipment operation mode, including the fluctuation range of current and voltage and the stable state of each module inside the equipment. If the load demand of the equipment increases too fast, the system will take corresponding warning measures before the electrical stability decay occurs. By predicting the gradient decay of the electrical stability of the equipment, the system will further predict the compound instability of electrical interference. This process takes into account the external environment (such as the vibration and temperature change of electrical equipment) and the complex electrical interaction inside the equipment, and predicts whether compound instability will occur on the basis of stability decay. Compound instability will lead to more serious electrical faults.

[0028] Step S3: Estimate the cumulative heat effect of the power transmission and distribution equipment according to the compound instability of electrical interference; estimate the load collapse trend of the power transmission and distribution equipment based on the cumulative heat effect of the power transmission and distribution equipment; estimate the deterioration trend of the equipment communication link based on the load collapse trend of the power transmission and distribution equipment;

[0029] In the embodiments of the present invention, a detailed analysis is carried out on the thermal effects brought about by the combined instability of electrical interference. According to the operating state of electrical equipment, the load conditions, and the power loss generated inside the equipment, the system can monitor the thermal effects of the equipment in real time through temperature sensors and thermal imaging devices. By calculating the cumulative situation of the equipment's thermal effects, real-time change data of the heat accumulation amount is obtained. Specifically, based on the heat capacity, material characteristics, and heat dissipation capacity of different components, the system uses a heat conduction model (such as the heat conduction equation or finite element analysis) to predict the thermal damage area of the equipment and evaluate the impact of the long-term operation of the equipment on temperature accumulation. Based on the cumulative situation of the thermal effects, the system further evaluates the load collapse trend of the equipment. In this process, the system combines the thermal effects of the equipment and the load change trend to analyze the operating state of the equipment at high temperatures and predict whether the equipment will cause a load collapse due to overload or insufficient heat dissipation. Load collapse usually causes the overload of the equipment system and leads to the shutdown of the system. Therefore, this prediction can be an important link in protecting the equipment. Based on the load collapse trend, the system then evaluates the degradation trend of the equipment communication link. When the equipment load is too large or a load collapse occurs, the communication link is severely affected. The system comprehensively judges the stability of the link by real-time detecting the bandwidth, delay, and packet loss rate of the communication link and predicts whether the communication performance of the link will decline due to overload or failure. The link degradation trend is realized through a communication quality analysis algorithm (such as channel estimation and noise analysis) to identify potential communication problems in advance and ensure the security and reliability of data transmission.

[0030] Step S4: Detect the abnormal conditions of the power transmission and distribution equipment according to the degradation trend of the equipment communication link and the load collapse trend of the power transmission and distribution equipment; conduct a fault risk assessment on the power transmission and distribution equipment according to the abnormal conditions of the power transmission and distribution equipment to obtain the fault risk data of the power transmission and distribution equipment, and upload it to the cloud platform for early warning.

[0031] In the embodiments of the present invention, the key task in step S4 is to detect whether there are abnormal conditions in the equipment and conduct a fault risk assessment. Based on the degradation trend of the equipment communication link and the load collapse trend obtained in the previous step, the system deeply analyzes the operating state of the equipment. Specifically, the system comprehensively evaluates the safety of the equipment operation through various indicators (such as temperature, pressure, current, voltage, etc.) and analyzes whether the equipment has abnormal conditions such as overload, overheating, and short circuit. Through this process, potential faults of the equipment can be monitored in real time and judged whether they reach the fault warning line. If an equipment abnormality is detected, the system will immediately start the fault risk assessment module and use a risk assessment model based on historical data and machine learning algorithms to predict the probability of the equipment failure and its impact degree. The system uploads the fault risk data to the cloud platform for early warning so as to take maintenance, scheduling, or emergency measures in advance to avoid accidents.

[0032] Preferably, step S1 includes the following steps:

[0033] Step S11: Obtain the data of intelligent power transmission and distribution equipment;

[0034] In the embodiments of the present invention, various operation data of intelligent power transmission and distribution equipment are obtained through a sensor array and a collection system. The data sources include, but are not limited to, physical quantities such as current, voltage, frequency, temperature, and humidity. In addition, external environmental data, such as the temperature, humidity, and air quality around the equipment, are collected through environmental monitoring equipment. These sensors can perform high-frequency data collection at predetermined time intervals to ensure an accurate reflection of the equipment operation status. All the collected data will be transmitted to the central processing unit through a wireless communication module (such as LoRa, Zigbee, or Wi-Fi module). During the collection process, noise filtering and denoising processing are performed on the signals. Filtering techniques (such as Kalman filtering) are used to smooth the measurement data, eliminate the errors caused by external interference, and maintain the accuracy and real-time nature of the data. At the same time, to avoid overloading the equipment, the collection frequency is dynamically adjusted according to the working load of the equipment to ensure a balance between data collection and processing. All the collected data will be stored in the database and undergo time synchronization processing before further processing to obtain the data of intelligent power transmission and distribution equipment.

[0035] Step S12: Extract the communication link structure of the intelligent power transmission and distribution equipment according to the data of the intelligent power transmission and distribution equipment;

[0036] In the embodiments of the present invention, through further analysis of the data obtained in step S11, the communication link structure of the equipment is extracted. By analyzing network communication metrics such as data transmission delay, bandwidth, and packet loss rate, the data exchange method between each module of the equipment is analyzed to identify the internal communication structure of the equipment. The system will use network topology analysis algorithms (such as the shortest path algorithm, Dijkstra algorithm) to construct the communication link topology diagram inside the equipment. This topology diagram is based on the transmission capacity and communication quality of each equipment module and draws the direct connection relationship between each module of the equipment. For example, there are multiple communication nodes inside the equipment, such as sensor modules, control modules, power supply modules, etc., and each node is connected through different types of communication protocols (for example: Ethernet, wireless communication, CAN bus, etc.). When extracting the communication link structure, consider the communication method and stability between the equipment and external systems. By analyzing the interaction data between the equipment and external communication systems such as cloud platforms and operation and maintenance servers, the overall communication structure of the equipment can be further determined, and potential bottlenecks and fault points can be identified. Parameters such as the delay, bandwidth, and reliability of all communication links will be used as metrics for link quality.

[0037] Step S13: Estimate the initial performance parameters of the intelligent power transmission and distribution equipment according to the data of the intelligent power transmission and distribution equipment;

[0038] In the embodiments of the present invention, the initial performance parameters of intelligent power transmission and distribution equipment are deduced through data analysis methods. In this step, the historical operation data of the equipment and the real-time data collected currently are compared and analyzed. These data mainly include parameters such as voltage, current, temperature, and load. The historical data of the equipment is modeled through time series analysis techniques (such as the autoregressive integrated moving average model ARIMA or the long short-term memory neural network LSTM) to estimate the performance change trend of the equipment under different working conditions. For example, by analyzing the change rules of current and voltage, the power output ability of the equipment under different loads is estimated; and through temperature data, the heat dissipation ability of the equipment and the overheating risk that occurs are deduced. According to these prediction results, the initial performance parameters of the equipment are calculated, such as the maximum output power, load-bearing capacity, communication bandwidth, etc. Combining the design parameters of the equipment with the performance indicators given by the manufacturer, the rationality of the estimated value is verified. If it is found that the estimated result deviates too much from the expectation, it indicates that there is a risk of failure or damage to the equipment, and the system will issue an alarm. A calibration mechanism is introduced. By comparing with the operating state of the actual equipment and inversely calculating and adjusting the performance parameters, the initial performance parameters of the intelligent power transmission and distribution equipment are obtained.

[0039] Step S14: Detect the operating state of the intelligent power transmission and distribution equipment according to the initial performance parameters of the intelligent power transmission and distribution equipment and the communication link structure of the intelligent power transmission and distribution equipment.

[0040] In the embodiments of the present invention, the operating state of the equipment is detected in real time by combining the initial performance parameters of the equipment with the communication link structure. In this step, the system evaluates the current working state of the equipment according to the initial performance parameters (such as current, voltage, load, etc.) obtained in step S13. For example, by comparing the maximum power output of the equipment with the current load situation, it is judged whether the equipment is within the normal load range and whether there is an overloaded or underloaded operation situation. According to the communication link structure extracted in step S12, the communication stability inside and outside the equipment is analyzed. If there are high delays, packet loss rates or bandwidth bottlenecks in the communication link of the equipment, it will affect the real-time data transmission and the execution of control instructions of the equipment, resulting in abnormal operation of the equipment. Therefore, the system analyzes the health status of the link by monitoring the performance of the link in real time and combining historical data, and timely discovers potential communication faults. Combining the performance parameters and the state of the communication link, algorithms based on rules or machine learning are used to evaluate the overall operating state of the equipment. For example, if abnormal fluctuations in parameters such as voltage and current are found during the operation of the equipment, and at the same time the stability of the communication link decreases, the system will infer that there is a risk of failure or impending failure of the equipment and issue a warning signal. Through a comprehensive evaluation of the initial performance parameters of the equipment and the communication link structure, the system can accurately monitor the state of the equipment, predict the fault risk of the equipment in advance, and take corresponding protection measures.

[0041] Preferably, step S13 includes the following steps:

[0042] Step S131: Acquire the geometric structure parameters of the intelligent power transmission and distribution equipment according to the intelligent power transmission and distribution equipment data.

[0043] In the embodiment of the present invention, the geometric structure parameters of the intelligent power transmission and distribution equipment are obtained through a high-precision sensor array. The sensors used include three-dimensional laser scanners, infrared rangefinders, image sensors, etc. These sensors can accurately measure the external dimensions, clearances, curvatures, and key geometric characteristics of the equipment. The three-dimensional point cloud data of each dimension of the equipment is obtained through the laser scanner, and further combined with image processing algorithms (such as edge detection, feature matching, etc.) to process the point cloud data and extract the accurate geometric dimension information of the equipment. During the data acquisition process, the sensors will scan the equipment in all directions multiple times to ensure that the data at each angle is completely covered. By comparing the data at different time points, changes in the geometric structure of the equipment can be detected in a timely manner, further providing accurate geometric parameters for the subsequent steps. All the collected geometric structure data will be transmitted to the central processing system in real time through the wireless network to obtain the geometric structure parameters of the intelligent power transmission and distribution equipment.

[0044] Step S132: Identify the physical layout of the intelligent power transmission and distribution equipment based on the geometric structure parameters of the intelligent power transmission and distribution equipment.

[0045] In the embodiment of the present invention, using the geometric structure parameters collected in step S131, the physical layout of the intelligent power transmission and distribution equipment is identified. According to the geometric parameters of the equipment, spatial geometric analysis algorithms (such as volume calculation, surface fitting, etc.) are used to hierarchically segment each component of the equipment to identify the layout structure of each part inside the equipment. Through these parameters, the relative position relationship between each module inside the equipment is further deduced to understand the spatial layout of the equipment. Based on the identification of the physical layout, the system will classify according to the use of the equipment, the deployment of functional modules, and the position of external interfaces. For example, if the equipment has multiple input ports or output ports, the layout relationship of these ports will affect the input and output capabilities and working efficiency of the equipment. During this process, computer-aided design (CAD) data is combined with geometric analysis technology to generate a three-dimensional model of the equipment, and the working principle of the equipment is deduced based on information such as the distance and connection method between modules.

[0046] Step S133: Identify the space compatibility status of the power transmission and distribution equipment for detection according to the density of the physical layout of the intelligent power transmission and distribution equipment exceeding 0.7 and the geometric structure parameters of the intelligent power transmission and distribution equipment.

[0047] In the embodiments of the present invention, when the density of the physical layout of the intelligent power transmission and distribution equipment exceeds 0.7, in combination with the geometric structure parameters of the equipment, the spatial compatibility detection of the power transmission and distribution equipment is carried out. By calculating the space occupied by each module inside the equipment and the gap ratio between the modules, the space density of the equipment is obtained. The space density refers to the ratio of the space occupied by all modules inside the equipment to the total space of the equipment. When the density value is relatively high, it means that the internal space of the equipment is used relatively tightly, and there are problems such as mutual interference between modules or poor heat dissipation. When the space density exceeds 0.7, the system will perform spatial compatibility detection. At this time, the system will adopt simulation technology. By establishing a virtual three-dimensional model of the equipment, the relative positions and working states of the modules inside the equipment are simulated, and whether there are conflicts or incompatibilities in space is analyzed. An overly compact layout will cause problems such as cable harnesses and air circulation, affecting the stability of the equipment. Combining operating parameters such as temperature, humidity, and power, a thermodynamic analysis is carried out on the modules inside the equipment. If the layout of some modules causes heat concentration and the temperature of a local area is too high, it will affect the normal operation of the equipment. Therefore, the spatial compatibility detection not only considers the geometric structure but also needs to be comprehensively analyzed in combination with the thermal effect of the equipment.

[0048] Step S134: Evaluate the heat dissipation capacity data of the power transmission and distribution equipment according to the spatial compatibility condition of the power transmission and distribution equipment being greater than 150 mm / m²;

[0049] In the embodiments of the present invention, based on the spatial compatibility condition of the power transmission and distribution equipment being greater than 150 mm / m², the heat dissipation capacity of the equipment is evaluated. Through the analysis of the spatial compatibility of the equipment, the heat source areas and the heat aggregation situation inside the equipment are determined. If the spatial compatibility condition exceeds the set value (150 mm / m²), the system will evaluate the heat dissipation capacity for these high-density areas. By using a heat conduction analysis model, the heat distribution in each area inside the equipment is simulated, and in combination with the layout and design parameters of the heat dissipation system such as heat sinks, fans, and heat exchangers, the heat dissipation capacity of the equipment is evaluated. The evaluation process uses numerical simulation technologies (such as the finite element method, heat flux simulation, etc.) to calculate the temperature distribution and heat dissipation efficiency of the equipment under different working loads. For the emerging high-temperature areas, the system will monitor the temperature change of the equipment in real time. If it is found that the temperature exceeds the set safety threshold, an alarm will be issued to prompt for maintenance or optimization of the heat dissipation design.

[0050] Step S135: Evaluate the environmental adaptability parameters of the power transmission and distribution equipment based on the heat dissipation capacity data of the power transmission and distribution equipment;

[0051] In the embodiments of the present invention, based on the equipment heat dissipation capacity data obtained in step S134, the environmental adaptability of the power transmission and distribution equipment is further evaluated. At this time, the environmental adaptability of the equipment not only includes the heat dissipation capacity, but also involves the operation stability of the equipment under different external environmental conditions. For example, factors such as the environmental temperature, humidity, and dust concentration where the equipment is located will all affect its operation performance. By simulating the operation of the equipment under different environmental conditions, the system will calculate the working ability of the equipment in environments such as high temperature, high humidity, and low temperature. For example, in an extremely high temperature environment, if the heat dissipation capacity of the equipment is insufficient, it will cause components to overheat and malfunction. The system will evaluate the environmental adaptability of the equipment, including its reliability and stability under different climate conditions, based on the design parameters and environmental data of the equipment, combined with the heat dissipation simulation results.

[0052] Step S136: Evaluate the electrical performance data of the intelligent power transmission and distribution equipment according to the intelligent power transmission and distribution equipment data;

[0053] In the embodiments of the present invention, the electrical performance of the intelligent power transmission and distribution equipment is evaluated using the real-time data collected in step S11. The system will analyze electrical parameters such as the voltage, current, and frequency of the equipment to determine whether they meet the design specifications and operation requirements of the equipment. By real-time monitoring the electrical performance data and combining the historical data and working status of the equipment, the system can identify whether there are trends of electrical faults or performance degradation in the equipment. For example, if the current exceeds the rated value of the equipment, or the voltage fluctuates, it will cause equipment damage or reduced efficiency. Through the real-time monitoring and evaluation of these data, the system can promptly detect abnormal electrical performance, give a fault warning, and prevent the equipment from overloading or electrical accidents.

[0054] Step S137: Estimate the initial performance parameters of the intelligent power transmission and distribution equipment based on the electrical performance data of the intelligent power transmission and distribution equipment and the environmental adaptability parameters of the power transmission and distribution equipment.

[0055] In the embodiments of the present invention, combining the electrical performance data in step S136 with the environmental adaptability parameters in step S135, the initial performance parameters of the intelligent power transmission and distribution equipment are estimated. By comprehensively analyzing the electrical performance and environmental adaptability, the system can more accurately estimate the initial performance parameters of the equipment, such as the maximum load, power output, and working efficiency. This process uses multi-dimensional weighted analysis technology to weight and integrate the electrical performance and environmental adaptability data to form a comprehensive performance evaluation of the equipment. By comparing with the design specifications and historical operation data of the equipment, the system can determine whether the actual performance of the equipment meets the expectations. If not, it will prompt the equipment to be further debugged or optimized, and based on these estimated data, generate the initial performance parameters of the intelligent power transmission and distribution equipment.

[0056] Preferably, step S14 includes the following steps:

[0057] Step S141: Detect the redundancy status of the power transmission and distribution equipment communication link based on the intelligent power transmission and distribution equipment communication link.

[0058] In the embodiment of the present invention, the communication link of the intelligent power transmission and distribution equipment is comprehensively detected to determine its redundancy status. The purpose of the redundant link design is to ensure the stability of communication when the equipment fails. Therefore, in this step, the system will identify and analyze all communication links of the equipment. Through the data flow monitoring sensors installed on the equipment, the load conditions and connection status of each link are monitored in real time. The sensors judge whether there is redundancy in each link by obtaining data such as the transmission bandwidth, delay, and packet loss rate of each link and combining the known network topology. Once a link redundancy problem is detected, the system will evaluate the quality and effectiveness of the redundant link according to the communication requirements and link status of the equipment. For example, if a certain link is disconnected, the system will check whether the backup link can carry the communication load of the original link to ensure that the system operation is not affected. Through this method, the system can clearly display the redundancy status of the equipment communication link and feedback it to the monitoring system in real time for subsequent decision-making and adjustment.

[0059] Step S142: Predict the stability of the intelligent power transmission and distribution equipment communication link based on the redundancy status of the power transmission and distribution equipment communication link.

[0060] In the embodiment of the present invention, according to the communication link redundancy status detected in step S141, the system predicts the stability of the equipment communication link. The system will evaluate factors such as the load conditions, transmission rates, and signal strengths of all links and analyze whether there are potential stability problems in the links. If the number of redundant links is insufficient or the quality of the redundant links is poor, the system will predict the link stability problems based on factors such as the current load, signal quality, and network environment of the link. To accurately predict the link stability, the system will also analyze in combination with historical data. By archiving the past link failures and outages and comparing them with the current link conditions, the system can identify the potential causes affecting the link stability and speculate on the future performance degradation or interruption of the link. This step combines the sensor monitoring data, link load monitoring data, and historical fault records and uses data analysis techniques (such as regression analysis, time series prediction, etc.) to accurately predict the stability of each link.

[0061] Step S143: Estimate the link response speed of the power transmission and distribution equipment according to the stability of the intelligent power transmission and distribution equipment communication link and the redundancy status of the power transmission and distribution equipment communication link.

[0062] In the embodiments of the present invention, the system further estimates the link response speed of the device by combining the communication link stability data obtained in step S142 and the redundant link status in step S141. The link response speed refers to the time it takes for the system to re-stabilize and resume normal operation when the device faces load changes or link switches. In this step, the system will evaluate the link response speed of the device by simulating different load and link switch scenarios. The system will simulate the communication link of the device under different loads and record parameters such as latency, data packet loss, and bandwidth changes during the link stability and recovery processes. If the redundant link can effectively take over the disconnected primary link, the response speed will be faster. Conversely, if the quality of the redundant link is poor or the quantity is insufficient, the response time will be extended. The system calculates the response time of each link under different conditions and combines it with the communication load requirements of the device to give a prediction of the link response speed of the device.

[0063] Step S144: Evaluate the communication interaction ability of the power transmission and distribution equipment according to the link response speed of the power transmission and distribution equipment and the communication link stability of the intelligent power transmission and distribution equipment;

[0064] In the embodiments of the present invention, based on the link response speed estimated in step S143 and the link stability obtained in step S142, the communication interaction ability of the device is evaluated. The communication interaction ability refers to the ability of the device to process and respond to external requests under different network states. At this time, factors such as the response speed, stability of the link, and the processing ability of the device are comprehensively considered. According to the link response speed of the device and combining the communication tasks carried by the device (such as data transmission, control instruction issuance, etc.), the average time required for the device to complete the communication task is calculated. On this basis, the system will also analyze the performance of the device under different stability states. For example, when the link stability is poor, the communication interaction ability of the device will be affected, resulting in an increase in data transmission latency or slow instruction response. Therefore, the system will evaluate the transmission stability of each link, combine the response speed and the processing ability of the device to obtain the evaluation result of the communication interaction ability of the device.

[0065] Step S145: Evaluate the operation safety parameters of the power transmission and distribution equipment according to the initial performance parameters of the intelligent power transmission and distribution equipment;

[0066] In the embodiment of the present invention, based on the initial performance parameters of the intelligent power transmission and distribution equipment obtained in step S137, the operation safety parameters of the power transmission and distribution equipment are evaluated. The system evaluates the safety of the equipment under the working load by analyzing in detail the initial performance parameters of the equipment (such as load, power, current, etc.). For example, if the power of the equipment under a specific load exceeds the rated value, an overload fault will occur. By real-time monitoring of these parameters, the system can predict whether there are potential safety hazards in the equipment. During this process, factors such as the environmental adaptability and heat dissipation ability of the equipment are combined to comprehensively evaluate the operation status of the equipment. For example, in a high-temperature environment, the equipment may have a high-temperature overload situation due to insufficient heat dissipation. By integrating these factors, the system evaluates the safe operation parameters of the equipment.

[0067] Step S146: Detect the operation status of the intelligent power transmission and distribution equipment according to the operation safety parameters of the power transmission and distribution equipment and the communication interaction ability of the power transmission and distribution equipment.

[0068] In the embodiment of the present invention, based on the operation safety parameters evaluated in step S145 and the communication interaction ability evaluated in step S144, the system detects the operation status of the intelligent power transmission and distribution equipment. At this time, the system will comprehensively detect the overall operation status of the equipment, comprehensively analyze parameters such as the safety, communication ability, and load status of the equipment, and judge whether the equipment is in a normal operation state. By real-time monitoring of the operation safety parameters of the equipment, it is judged whether the equipment is within the safe operation range. If the operation safety parameters of the equipment exceed the preset safety range, the system immediately issues a warning signal. At the same time, the system will also combine the evaluation results of the communication interaction ability to check the communication status of the equipment to ensure that the equipment can efficiently and stably perform data interaction with the equipment. If the communication link of the equipment is unstable or the interaction ability is insufficient, the system will give an alarm and start a fault handling mechanism. By comprehensively considering all safety parameters and communication interaction abilities, the system can accurately judge the operation status of the equipment and timely discover potential fault risks.

[0069] Preferably, the prediction of the dynamic abnormal load condition of the equipment in step S2 includes:

[0070] Collect the operation log of the power transmission and distribution equipment according to the intelligent power transmission and distribution equipment data; perform time series analysis on the operation of the power transmission and distribution equipment based on the operation log of the power transmission and distribution equipment to obtain the time series data of the equipment operation;

[0071] In the embodiments of the present invention, the monitoring sensors deployed in intelligent power transmission and distribution equipment collect the operation log data of the equipment in real time. The operation logs include information such as the power consumption, load changes, operation duration, and equipment status of the equipment. The data acquisition system periodically extracts real-time logs from the equipment memory or controller according to the working mode and operating environment of the equipment to ensure the continuity and accuracy of the data. These operation logs can reflect the real-time working status, load fluctuation conditions, and historical operation characteristics of the equipment, providing necessary data support for the subsequent steps. Specifically, the system will collect the power change data per second and per minute through data interaction with the internal communication interface of the equipment and record it in the data storage system for further analysis and processing. Perform time series analysis on the data extracted from the equipment operation logs. The purpose of time series analysis is to understand the law of the equipment load changing over time. The system will sort the power data in the equipment operation logs in chronological order and perform smoothing processing to eliminate the influence of abnormal fluctuations on data analysis. By performing time series modeling on these data, the system can accurately identify the fluctuation patterns of the equipment operation, including characteristics such as the load distribution of the equipment in different time periods, the load change frequency, and the periodic fluctuations. Apply traditional time series analysis techniques such as autoregressive (AR) and moving average (MA) models, or use more complex trend prediction methods (such as exponential smoothing method) to identify potential load fluctuation trends. Based on these models, generate a time series data set to clarify the power consumption of the equipment at each time point and mark the periodic fluctuations and long-term trends of the equipment load.

[0072] Monitor the fluctuation of the equipment operation demand based on the equipment operation time series data;

[0073] In the embodiments of the present invention, based on the obtained equipment operation time series data, analyze and monitor the load fluctuation of the equipment. The specific operations include calculating and analyzing the changes in the power demand of the equipment in different time periods. The system calculates the fluctuation range of the equipment demand by comparing the historical load data with the current load. If the load change of the equipment in certain time periods exceeds the predetermined fluctuation range, then mark that time period as a time period with large load fluctuations. Further judge the load fluctuation of the equipment by calculating the equipment load change rate (that is, the speed of power increase or decrease). If the load change exceeds the preset standard value in a certain time period, the system will identify that time period as the fluctuation time period of the equipment load for subsequent processing. This data helps to evaluate the load volatility of the equipment and provides a basis for subsequent load prediction and abnormal situation analysis.

[0074] Collect the periodic changes in the equipment operation demand when the fluctuation of the equipment operation demand is ±10 kW;

[0075] In the embodiments of the present invention, when the load fluctuation range of the device exceeds ±10 kW, the system will further collect the periodic change situation of the device's operation requirements. At this time, the system will analyze the load change law of the device within the fluctuation range, and pay attention to the peak and trough periods of the power demand of the device during the periodic fluctuation process. The system records the peak and trough values of each load fluctuation at all times, and conducts a comparative analysis in combination with historical data to extract the characteristics of the periodic fluctuation. Based on this analysis, the system can clarify the load fluctuation period of the device and the time law of the appearance of peaks and troughs. Perform frequency analysis on the load fluctuation of the device, and identify the periodic pattern of the fluctuation through methods such as Fourier transform. If it is found that the device load fluctuation occurs repeatedly within certain specific frequency ranges, the system will confirm these frequencies as the periodic change characteristics of the device's operation requirements.

[0076] Estimate and statistically determine the time period of sudden growth in demand based on the periodic change situation of the device's operation and the fluctuation of the device's operation requirements;

[0077] In the embodiments of the present invention, in combination with the obtained periodic change data of the device's operation and the fluctuation data of the device's operation requirements, estimate the time period of sudden growth in demand that occurs in the device. The time period of sudden growth in demand refers to the situation where the load demand of the device rapidly and significantly increases during certain time periods, exceeding the device's carrying capacity. The system will predict whether a similar sudden increase in demand will occur in the device during certain future time periods through regression analysis of the device's historical load data and in combination with the previous patterns of sudden load growth. By setting a threshold, if the load growth rate exceeds a predetermined value within a certain time period, then this time period will be regarded as a time period of sudden growth. At the same time, the system will also analyze the load fluctuation situation in the surrounding time periods to identify potential triggering factors related to sudden growth. For example, if the load fluctuation range of the device reaches a certain level during certain time periods, the system will mark these time periods as high-probability time periods of sudden growth in demand and use them as risk warning data.

[0078] Calculate the abnormal growth trend of the demand fluctuation slope according to the fluctuation situation of the device's operation requirements;

[0079] In the embodiments of the present invention, the fluctuations in the operating requirements of the device are further analyzed to calculate the abnormal growth trend of the demand fluctuation slope. The demand fluctuation slope represents the rate of change of the device load per unit time. The system calculates the slope change for each time period based on the load data and compares it with the historical data. If the fluctuation slope in a certain period is greater than the normal range, it is regarded as an abnormal growth trend, and the system will mark this period as a potential risk period of abnormal load growth. Mathematical algorithms, such as the difference method or the weighted moving average method, will be used to smooth the demand fluctuation slope and compare it with the past trend data to identify trends that exceed the normal fluctuation range. By comparing the fluctuation slopes of the device at different time periods, the system can accurately determine whether the rate of load growth of the device is abnormal during certain periods, and then estimate the future load change trend.

[0080] According to the abnormal growth trend of the demand fluctuation slope greater than 30 kW / min and the fluctuations in the operating requirements of the device, the overload situation of the device operation is statistically analyzed.

[0081] In the embodiments of the present invention, in combination with the obtained data on the abnormal growth trend of the demand fluctuation slope, if the slope exceeds 30 kW / min, the system will further statistically analyze the overload situation of the device. The overload situation refers to the actual load of the device exceeding the rated load range, resulting in the risk of device damage or system failure. In this step, the system will calculate the difference between the actual load and the maximum load capacity of the device by real-time monitoring the power consumption of the device. If the load of the device exceeds the rated value and persists for a period of time, the system will determine that the device has entered the overload state, analyze the overload behavior of the device under different loads based on historical data and device characteristics, and determine whether the device can withstand a sudden increase in load. In this way, the system can accurately predict the overload state of the device.

[0082] Estimate the dynamic abnormal load condition of the device by using the period of sudden demand growth and the overload situation of the device operation.

[0083] In the embodiments of the present invention, by using the period of sudden demand growth and the overload situation of the device operation, the dynamic abnormal load condition of the device is estimated. Through comprehensive analysis of various operating parameters of the device, the system can predict the working state of the device under different load conditions and accurately identify the abnormal load states that occur to the device. By comprehensively considering the historical operation data of the device, the current load fluctuations, the sudden demand growth, and the overload state, real-time monitoring and prediction are carried out to ensure that when the device has an abnormal load, emergency treatment measures can be taken in a timely manner to avoid device failures and generate the dynamic abnormal load state of the device.

[0084] Preferably, the detection of the electrical stability gradient decay condition in step S2 includes:

[0085] Detect the load accumulation status of intelligent power transmission and distribution equipment according to the dynamic abnormal load status of the equipment, and obtain the load accumulation status data of the power transmission and distribution equipment;

[0086] In the embodiment of the present invention, according to the equipment operation data obtained from the previous steps (such as the prediction of the dynamic abnormal load status of the equipment), the load of the equipment is cumulatively detected. The load accumulation status of the equipment refers to the continuous change of the load of the equipment over a period of time and its cumulative effect. The system collects real-time power data from the equipment monitoring sensors, compares these data with the rated load of the equipment, and calculates the current load level of the equipment. Specifically, the system integrates the load fluctuation data of the equipment within a certain time range to obtain the cumulative effect of the load. According to the working load of the equipment, data collection is carried out multiple times at certain time intervals, and then through the weighted average of each load change, a long-term load change trend is obtained. These data will reflect the load status of the equipment in different time periods and whether the load is within the normal range. The load accumulation status data will provide a basis for subsequent electrical stability analysis.

[0087] Estimate the drift of the equipment load spatial distribution according to the load accumulation status data of the power transmission and distribution equipment;

[0088] In the embodiment of the present invention, according to the load accumulation status data of the equipment, the spatial distribution of the equipment load is analyzed. The load spatial distribution drift refers to the change in the load distribution of the equipment between different regions or different lines, which is caused by the increase in load demand or the change in system configuration. To analyze this drift, the system will perform spatio-temporal analysis on the load distribution of the equipment. By monitoring the load data of each sub-region of the equipment, the regions with large load fluctuations are identified, and the distribution change of the load in different time periods is calculated. The system will use a distributed sensor network to collect real-time load data at different locations and input these data into a spatial analysis algorithm, such as calculating the load distribution of each region through the region division method. Through these data, it is judged whether there is a load drift phenomenon in the equipment, and the direction and intensity of the drift are predicted.

[0089] Estimate the local load gradient growth of the equipment based on the drift of the equipment load spatial distribution;

[0090] In the embodiments of the present invention, based on the predicted drift of the spatial distribution of the equipment load, the local load gradient growth of the equipment is further analyzed. The local load gradient refers to the rate of change of the load over time or space in a specific area or a subsystem. The growth of the load gradient represents that the load of the equipment changes relatively fast in some areas, which may lead to overloading or imbalance of the equipment load. To calculate the local load gradient of the equipment, the system performs refined processing on the load data of the equipment. The system will perform differential processing on the numerical values of the load spatial distribution to obtain the rate of change of the load between regions. Specifically, the system will compare the loads in different regions at each time point, calculate the change in the load gradient, and determine its growth trend. If the local load gradient shows a relatively fast growth in a certain area, the system will mark this area as a high-risk area and further estimate the probability of load overloading.

[0091] Detect the attenuation trend of the power transmission quality of the equipment according to the growth of the local load gradient of the equipment and the cumulative load condition of the power transmission and distribution equipment;

[0092] In the embodiments of the present invention, according to the local load gradient growth data and the load cumulative condition data of the equipment, the attenuation trend of the power transmission quality of the equipment is analyzed. The attenuation of power quality refers to the phenomena of voltage fluctuation, frequency fluctuation, etc. that occur during the power transmission process of the equipment as the equipment load continuously increases or the local load gradient changes. By monitoring data such as the voltage, current, and frequency of the equipment, combined with the load changes of the equipment, the change trend of the power quality is analyzed. If the growth of the local load gradient is relatively significant, it may lead to situations such as voltage instability or frequency deviation during the power transmission process. The system will apply power quality analysis algorithms, such as spectrum analysis based on Fourier transform, to detect the attenuation of power quality in real time and combine it with the load cumulative data to determine the attenuation trend. Detect the current waveform in the power system and use digital signal processing technology to analyze the degree of attenuation of the power quality. If the system detects an obvious attenuation trend of the power quality, the system will output the attenuation trend data and provide support for subsequent prediction of power voltage collapse.

[0093] Predict the voltage drop condition of the equipment power transmission based on the attenuation trend of the equipment transmission power quality;

[0094] In the embodiments of the present invention, based on the power quality attenuation trend, the power transmission voltage collapse situation of the device is further predicted. Voltage collapse is usually a phenomenon of a significant voltage drop caused by load overload, poor power quality or system faults, which can lead to unstable operation or shutdown of the device. By real-time monitoring the voltage situation of the power device and combining with the power quality attenuation trend, the occurrence probability of voltage collapse is predicted. Specifically, the system will use an early warning model based on time series analysis to predict the voltage change during power transmission under a certain load level and attenuation trend. If the voltage drop rate reaches a certain threshold, the system will judge the occurrence time of voltage collapse and issue an alarm in a timely manner. In addition, the system will also perform voltage prediction based on the real-time data of voltage and current and adjust the working state of the device in real time.

[0095] Predict the risk status of current backflow of the device according to the voltage drop situation of the device's power transmission and the gradient growth of the device's local load;

[0096] In the embodiments of the present invention, combining the obtained power transmission voltage collapse data and the gradient growth of the device's local load, the risk of current backflow of the device is predicted. Current backflow refers to the phenomenon that the current direction is opposite to the expected transmission direction, which is caused by voltage instability, improper connection of the power system or faults, resulting in reverse current flow. The backflow can cause device damage or affect system stability. By monitoring the current data and voltage changes of the device, the probability of current backflow is analyzed. The system will apply an instantaneous current reverse judgment algorithm to detect the current flow direction in real time. If the current flow direction shows abnormal changes and is related to the voltage change trend of the device, the system will mark this period as a high-risk period of current backflow.

[0097] Detect the gradient attenuation status of the device's electrical stability according to the risk status of the device's current backflow and the voltage drop situation of the device's power transmission.

[0098] In the embodiments of the present invention, combining the current backflow risk data and the power transmission voltage collapse data of the device, the gradient attenuation status of the device's electrical stability is detected. The gradient attenuation of electrical stability means that during the operation of the device, due to factors such as load imbalance, voltage fluctuation and current backflow, the stability of the device's electrical system gradually decreases. By multi-dimensional monitoring and analysis of data such as voltage, current, load, frequency, etc., and combining with the electrical characteristics of the device, the change of the electrical stability gradient is calculated. If the system detects a decrease in electrical stability, the system will output a report on the gradient attenuation of electrical stability and mark potential risk areas. According to the trend of electrical stability attenuation, the system can take corresponding control measures, such as adjusting the device load, optimizing the power transmission method, etc., to ensure the safe and stable operation of the device.

[0099] Preferably, the prediction of the electrical interference composite instability status in step S2 includes:

[0100] Estimate the instantaneous high - voltage impact on the device based on the gradient decay of the device's electrical stability;

[0101] In the embodiments of the present invention, based on the data of the gradient decay of the device's electrical stability, analyze the instantaneous high - voltage impact that the device will encounter during operation. The gradient decay of electrical stability refers to the gradual decrease in the stability of the device's electrical system due to factors such as uneven load and equipment aging. The decay of the electrical stability gradient will trigger an instantaneous voltage impact, especially when the load changes drastically or the equipment fails, and the voltage impact phenomenon is particularly significant. By monitoring data such as the voltage change, load fluctuation, and reverse flow of current of the device, and using time - frequency analysis methods such as the Fast Fourier Transform (FFT) or wavelet transform, analyze the instantaneous high - voltage impact in the power system. The system will also capture the voltage spike phenomenon in real - time and calculate the frequency and amplitude of the high - voltage impact. According to the decay of the electrical stability gradient and combined with the rapid response of voltage fluctuations, the system can identify the occurrence period of the instantaneous voltage impact and output high - voltage impact warning data.

[0102] Estimate the damage degree of the device transformer based on the instantaneous high - voltage impact on the device;

[0103] In the embodiments of the present invention, based on the obtained data of the instantaneous high - voltage impact on the device, the system will further evaluate the damage degree of the device transformer. The transformer plays a key role in voltage transformation in the power system. When an instantaneous high - voltage impact occurs, the insulation and core components of the transformer will be severely damaged, resulting in unstable operation of the device. By collecting the voltage and current data of the transformer and combining the characteristics of the instantaneous high - voltage impact, conduct a damage assessment. In specific implementation, the system uses the current integration method to detect the correlation between the current peak and voltage fluctuation and evaluate the change of the current waveform inside the transformer. If the current waveform shows frequent mutations or obvious voltage fluctuations, it is judged that the transformer has been overloaded or damaged. Based on the analysis of the overload period and the amplitude of the high - voltage impact, the system will calculate the damage degree of the transformer and output a damage assessment report.

[0104] Analyze the instability condition of the device power system based on the damage degree of the device transformer and the instantaneous high - voltage impact on the device;

[0105] In the embodiments of the present invention, based on the obtained degree of transformer damage and instantaneous high - voltage impact conditions, further analyze whether the power system of the device is in an unstable state. The instability of the power system is caused by reasons such as transformer damage and excessive voltage fluctuations, which may lead to the collapse of the entire power system in severe cases. By real - time monitoring the voltage, current, and frequency data of the power system, and using state estimation and instability analysis methods, evaluate the stability of the power system. For example, use robust control methods to evaluate the electrical characteristics of the device and determine whether the transformer damage has affected the normal operation of the power system. Utilize power system transient analysis, by calculating the time - domain response of the power system, further analyze the impact of instantaneous voltage shocks, and obtain the instability probability of the power system. If the instability risk is relatively high, the system will output a power system instability warning message.

[0106] Use the device to detect the abnormal electromagnetic generation condition based on the instability condition of the power system;

[0107] In the embodiments of the present invention, based on the instability condition of the power system, further detect whether the device generates abnormal electromagnetic interference. The instability of the power system usually causes the operation of electrical equipment to be unstable, leading to abnormal electromagnetic radiation, especially for high - voltage electrical equipment such as transformers and switchgear. The generation of abnormal electromagnetic fields will affect surrounding equipment and systems, and may even cause equipment damage or communication interference. By monitoring the electromagnetic radiation intensity of the device, combined with the power system instability data, use electromagnetic field monitoring sensors to real - time detect the electromagnetic field distribution of the device. The system will perform spectral analysis on the electromagnetic radiation data to identify the electromagnetic interference source. If the electromagnetic radiation intensity increases abnormally and is related to the power system instability phenomenon, the system will mark the time period of abnormal electromagnetic interference generation and calculate the generation intensity of abnormal electromagnetic fields. The system will also further predict the influence range of electromagnetic wave propagation based on the electromagnetic interference data.

[0108] Evaluate the growth trend of the device's electromagnetic interference based on the abnormal electromagnetic generation condition of the device;

[0109] In the embodiments of the present invention, based on the obtained abnormal electromagnetic generation data of the device, the system will measure the growth trend of the device's electromagnetic interference. The growth trend of electromagnetic interference is identified through spectral analysis methods, which decompose electromagnetic signals into multiple frequency bands and analyze the interference degree and its changes in each frequency band. By long - term monitoring of the electromagnetic field data of the device, combined with the operating state of the device, use time - series analysis methods to model the electromagnetic interference and calculate the intensity growth rate of electromagnetic interference. The system will identify the key nodes of interference growth according to the amplitude increase of the electromagnetic interference signal and estimate the further expansion trend of the interference. The system will also combine the electromagnetic interference intensity with the stability data of the device to determine whether the device will be severely affected and further affect the power transmission quality.

[0110] Predict the distortion degree of the device's transmitted current based on the growth trend of the device's electromagnetic interference;

[0111] In an embodiment of the present invention, the obtained electromagnetic interference growth trend is utilized to further predict the degree of current transmission distortion of the device. Electromagnetic interference will cause the current signal inside the device to be distorted, manifested as phenomena such as distortion of the current waveform and frequency deviation. Current distortion will affect the normal operation of the device, resulting in power loss and device damage. By analyzing the spectral data of electromagnetic interference and combining with the change of the current waveform, a current waveform analysis tool is used to predict the degree of current distortion. The system measures the degree of waveform distortion of the current and calculates the distortion index. If the current distortion reaches a certain threshold, the system will output a current distortion warning and analyze the impact of current distortion on device transmission.

[0112] Identify the breakdown of the device insulation layer according to the instantaneous high-voltage impact situation of the device;

[0113] In an embodiment of the present invention, based on the instantaneous high-voltage impact situation of the device, it is identified whether the insulation layer of the device has broken down. When the insulation layer of the device withstands a high-voltage impact, breakdown will occur, resulting in faults such as short circuits and damage to the device. The system detects data such as the voltage, temperature, and vibration of the device to identify whether the insulation layer has been impacted. Using high-frequency voltage measurement and insulation performance analysis tools, combined with the amplitude and duration of the voltage impact, the damage situation of the device insulation layer is predicted. The system will also monitor the current change of the device in real time, detect abnormal increase of the current or change of the current path to determine whether the breakdown phenomenon of the insulation layer occurs.

[0114] Predict the electrical interference composite instability condition based on the breakdown situation of the device insulation layer and the degree of current transmission distortion of the device.

[0115] In an embodiment of the present invention, combining the breakdown situation of the device insulation layer and the degree of current transmission distortion of the device, the electrical interference composite instability condition is predicted. Electrical interference composite instability refers to the situation where multiple factors such as device insulation layer damage, current distortion, and electromagnetic interference act together, resulting in the instability of the device electrical system and even complete failure. Combining all the collected data, a multi-dimensional analysis method is used to comprehensively evaluate the risk of electrical interference composite instability. The system inputs the data such as the breakdown of the device insulation layer, transmission current distortion, and electromagnetic interference monitored into the instability prediction algorithm to calculate the probability and severity of electrical interference composite instability. The system will comprehensively evaluate the electrical performance of the device and output a warning message before the occurrence of the instability event.

[0116] Preferably, step S3 includes the following steps:

[0117] Step S31: Estimate the cumulative heat effect of the power transmission and distribution equipment according to the electrical interference composite instability condition;

[0118] In the embodiments of the present invention, the voltage breakdown records, current distortion coefficients, and electromagnetic interference spectrum information recorded by the device in multiple working cycles are sorted in time sequence. Curve fitting is performed on the above indicators through a high-frequency data analysis tool for continuous acquisition, especially for the time periods when the voltage suddenly rises by more than 15% of the rated value each time, and separate markings are made. The electrical disturbance density is extracted based on the current fluctuation amplitude within the corresponding time period. Then, based on the abnormally enhanced region of the electromagnetic spectrum, the local heating tendency corresponding to the device heat dissipation channel is determined using the Fourier frequency band division result, and a thermal stress accumulation statistical map is constructed in combination with the historical working temperature rise data. Subsequently, the temperature integration result collected by the thermal sensor over time is aligned with the above thermal stress statistical map, and the thermal effect values of each unit structural member are accumulated through a superposition algorithm to obtain the thermal effect accumulation of each module level. For example, in a set of high-voltage switchgear at a substation terminal, the thermal stress at the bus branch node shows a daily increasing trend within 30 operating days, and the average terminal temperature rise increases from 42°C to 67°C. Based on this, it is judged that the thermal effect accumulation value exceeds the set warning threshold.

[0119] Step S32: Predict the structural aging trend of the power transmission and distribution equipment based on the thermal effect accumulation of the power transmission and distribution equipment;

[0120] In the embodiments of the present invention, after the extraction of the thermal effect accumulation data is completed, a high-precision image of the surface of the device and its connection parts is collected using an infrared thermal imaging device. An image processing algorithm is used to identify the hot spot expansion trend of the metal components, insulating parts, and the connection parts of the housing. The correlation analysis is performed between the hot spot area expansion rate and the previously calculated thermal effect accumulation value, and the initial occurrence positions of thermal aging and the crack occurrence frequencies of each part are counted. At the same time, by comparing the infrared brightness change trends of the key structural connection bolts, insulating oil seals, and high-voltage connection sub-assemblies, the growth rate of the thermal fatigue parameters of each unit structural member is calculated, and the structural stress relaxation speed and aging expansion period are obtained. For example, through continuous 24-hour tracking of the infrared brightness around the sealing gasket of the connection flange of a group of high-voltage transformers, it is found that the consistency between the abnormally increasing brightness range and the bolt loosening frequency exceeds 92%. Combining its thermal effect index, the predicted period for the structural aging trend of this part to enter the micro-crack stage is within 7 days, and it enters the edge state of structural failure after 15 days.

[0121] Step S33: Estimate the load collapse trend of the power transmission and distribution equipment based on the thermal effect accumulation of the power transmission and distribution equipment and the structural aging trend of the power transmission and distribution equipment;

[0122] In the embodiment of the present invention, by performing spatial mapping and pairing on the thermal effect cumulative intensity distribution map obtained in step S31 and the structural aging progress map generated in step S32, the structural stability attenuation rate is marked on each module node. Subsequently, a load stability test system is used to sample the bearing response data of each node under different load densities, and analyze whether the micro-deformation caused by thermal expansion leads to current path deviation or resistance mutation. The temperature response and mechanical response curves of the device under the peak load state within multiple observation periods are superimposed to form a multi-axis thermal analysis diagram, and the response delay change curve before the critical load is extracted therefrom, and the trend line formed by the delay change points is traced. Based on this, the inflection point position at which the device enters the non-linear load response state is calculated and superimposed with the structural aging nodes to form an overall load collapse trend diagram of the device. For example, in a set of busbar distributed switch networks, when the thermal accumulation value exceeds 680 J / cm², a resistance increase region matching the concentrated position of the micro-cracks at the support points appears simultaneously. Through continuous recording, it can be obtained that the load response lag increases from 180 ms to 500 ms, predicting that there is a precursor to load collapse at this node.

[0123] Step S34: Estimate the deterioration trend of the device communication link based on the load collapse trend of the power transmission and distribution equipment.

[0124] In the embodiment of the present invention, for the positions where the load collapse trend appears, a high-frequency communication reflection monitoring module and a link interruption time recording device are respectively arranged to perform real-time observation on the transmission stability of the communication link during the operation cycle of the device. Based on the structural micro-deformation region determined in the foregoing steps, the transmission signal stability data of the communication link in this region is collected, and special attention is paid to the time periods with increased signal reflection, sudden delay change, and increased packet loss rate. By analyzing the correlation between the instantaneous change of the link impedance and the number of consecutive transmission failures of data packets, a link deterioration sensitivity curve is constructed. Further, by introducing the communication frequency stability monitoring data, the link power loss curve of each node is extracted and cross-analyzed with the structural fatigue index to identify the coupling strength between the frequently unstable regions and the structural deformation centers. In this way, the degree of performance deterioration of the communication link caused by load collapse within the next 10 days is evaluated. For example, in a feeder protection circuit of a certain substation, the optical signal reflection echo ratio of a section of optical fiber communication path increases from 1.2 to 4.8 due to load deformation, and the number of communication interruptions increases to 15 times within 72 hours. It is confirmed on the analysis diagram that the deterioration trend of its communication link rises strongly, and it is judged that this path will reach the interruption risk boundary within 96 hours.

[0125] Particularly importantly, step S32 includes the following steps:

[0126] Step S321: Detect the structural thermal expansion degree of the power transmission and distribution equipment according to the thermal effect accumulation of the power transmission and distribution equipment;

[0127] In the embodiments of the present invention, the thermal expansion of the equipment is monitored in real time through the temperature change rate and the thermal strain calculation model. A strain sensor or a displacement sensor is used to directly measure the degree of expansion or deformation of the structure. The thermal expansion of the equipment usually manifests as a change in volume or length of the material after heating. According to the thermal expansion coefficients of different materials, the degree of expansion is calculated through the real-time temperature data of the equipment. For example, if the sensor detects a temperature change and a physical quantity (such as length or angle) changes, the degree of expansion of that part is obtained.

[0128] Step S322: Detect the deformation trend of the power transmission and distribution equipment structure based on the degree of thermal expansion of the power transmission and distribution equipment structure;

[0129] In the embodiments of the present invention, after detecting the degree of thermal expansion of the power transmission and distribution equipment, the next step is to predict the deformation trend of the equipment structure based on the thermal expansion situation. The thermal expansion data obtained through the foregoing steps is input into the structure analysis system for more in-depth physical calculations. The structure analysis system uses finite element analysis (FEA) technology. By establishing a structure model of the equipment, the deformation of the whole or local area of the equipment under different degrees of thermal expansion is simulated. To obtain a highly accurate prediction of the deformation trend, the displacement data of each thermal expansion point must be matched and analyzed with the mechanical properties of the equipment material. Specifically, different materials (such as steel, aluminum, copper, etc.) have different expansion coefficients and different responses to deformation. Through the cooperation of these data, the structure deformation trend caused by thermal expansion can be calculated in real time in the system. For example, in a high-temperature environment, some metal parts of the equipment will produce slight deformation due to thermal expansion, resulting in a change in the working structure of the equipment. By monitoring the deformation degree of different parts of the equipment, the deformation trend of the overall structure of the equipment under the current environmental conditions can be obtained.

[0130] Step S323: Estimate the growth of the thermal stress of the power transmission and distribution equipment structure according to the deformation trend of the power transmission and distribution equipment structure and the degree of thermal expansion of the power transmission and distribution equipment structure;

[0131] In the embodiments of the present invention, based on the analysis results of the previous two steps (i.e., the thermal expansion degree of the device and the structural deformation trend), the growth of the structural thermal stress of the device is predicted. Structural stress is the internal force generated due to the deformation of materials, under high load or high temperature conditions. The thermal effect will cause the structure to deform, and then generate internal stress. By using the deformation data obtained previously, stress calculation is carried out on each structural component of the device by using stress analysis models (such as Von Mises stress analysis, principal stress analysis, etc.). Specifically, by using the displacement data and temperature change information feedback by sensors, combined with the geometric parameters and material properties of the device, the magnitude of the thermal stress and its distribution in the device structure are calculated. Stress prediction is not only a static calculation, but also needs to consider the dynamic thermal stress accumulation during the long-term operation of the device. The thermal stress gradually increases over time, especially when operating under high load and high temperature for a long time, and the stress growth will show a non-linear upward trend. Based on these data, the growth of the thermal stress of the structural components can be predicted.

[0132] Step S324: Identify the expansion trend of microcracks in the device material according to the growth of the structural thermal stress of the power transmission and distribution equipment;

[0133] In the embodiments of the present invention, according to the structural thermal stress data obtained in the previous steps, the next step is to identify the expansion trend of microcracks in the device material. When the structure of the device is under the action of long-term thermal stress, especially in a high-temperature and high-load environment, microcracks will gradually appear on the surface of the material. In particular, metal components are more likely to experience crack expansion under tensile or compressive conditions. Strain sensors or crack detection sensors (such as ultrasonic sensors or X-ray imaging technology) are installed to continuously monitor the device. When the thermal stress of the device structure reaches a certain threshold, it will trigger the expansion of microcracks, and the sensor judges whether the microcracks have started to expand by detecting the change characteristics of the cracks. Using the sensor monitoring data, combined with crack expansion prediction models (such as Paris law or mineralogical damage model), the expansion trend of microcracks is predicted.

[0134] Step S325: Estimate the thermal fatigue degree of the device based on the expansion trend of microcracks in the device material and the growth of the structural thermal stress of the power transmission and distribution equipment;

[0135] In the embodiments of the present invention, combining the microcrack expansion situation and the thermal stress growth data, the thermal fatigue degree of the device is estimated. Thermal fatigue is the gradual deterioration of material properties due to long-term thermal cycling. Especially during the thermal expansion and contraction process, the microcracks in the material will gradually expand, resulting in fatigue failure of the structure. To evaluate the thermal fatigue degree, combined with the operation history data of the device, such as factors like working temperature fluctuations and load changes, fatigue life analysis is carried out. At this time, using the S-N curve (stress-life curve) and stress-strain cycle analysis, the degree of fatigue damage accumulation borne by the material under the action of thermal stress is calculated.

[0136] Step S326: Predict the structural aging trend of the power transmission and distribution equipment based on the degree of thermal fatigue of the equipment and the trend of microcrack propagation in the equipment material.

[0137] In the embodiment of the present invention, based on the degree of thermal fatigue of the equipment and the trend of microcrack propagation, the structural aging trend is predicted. The structural aging of the equipment is a long-term and gradually accumulating process, and thermal fatigue and crack propagation are important factors leading to aging. By combining the fatigue analysis results with the crack propagation data, the structural aging trend of the equipment is predicted. Combining data mining technology and an aging prediction model, an aging trend graph of the equipment is generated according to factors such as historical data, the durability of the equipment material, and the accumulation of thermal stress.

[0138] Particularly importantly, step S33 includes the following steps:

[0139] Step S331: Estimate the structural displacement of the equipment according to the cumulative situation of the thermal effect of the power transmission and distribution equipment and the structural aging trend of the power transmission and distribution equipment;

[0140] In the embodiment of the present invention, according to the cumulative data of the thermal effect of the equipment and the structural aging trend, and the thermal expansion and deformation trend data obtained through the foregoing steps, combined with the long-term operation history of the equipment, the structural displacement is estimated. During long-term use, the equipment will be affected by factors such as thermal load and load changes, and displacement will gradually occur in the structure. Especially during the process of equipment thermal expansion or aging, excessive deformation or displacement will occur in local areas. The equipment is monitored in real time through high-precision displacement sensors (such as fiber optic displacement sensors or laser rangefinder sensors) to obtain the displacement data of each key part. Subsequently, combined with finite element analysis (FEA) technology, the thermal stress and load conditions in different working environments are simulated in the equipment model to analyze the range and trend of displacement. At this time, the structural aging trend of the equipment (such as microcrack propagation and fatigue damage of the material) is also considered because aging will cause the hardness and toughness of the material to decrease, making the structure more prone to deformation. Through these data, the displacement situation and change trend of the structure of the equipment under specific operating conditions can be estimated, especially the displacement changes of key structural parts.

[0141] Step S332: Detect the poor contact situation of the equipment transmission line according to the structural displacement of the equipment;

[0142] In an embodiment of the present invention, the structural displacement data obtained by step S331 is further used to detect the poor contact of the transmission line of the device. Structural displacement will affect the connection points and contact parts inside the device, especially the contact surface of the transmission line will cause poor contact or looseness due to displacement. Contact detection sensors (such as contact resistance sensors, current sensors, etc.) are installed at the key contact points of the transmission line, and these sensors monitor the resistance changes of the line contact in real time. If the contact point of the line is loose due to displacement, the contact resistance will increase and the transmission current will be affected. By monitoring the resistance changes of the contact points in real time, the poor contact is detected and the location of the problem is located in time. In addition to the detection of the sensor, the vibration sensor is used to monitor whether the device generates abnormal vibration during operation, and the vibration is caused by poor contact. In the case of poor contact, the device usually generates abnormal heat, which can also be detected by the temperature sensor. Therefore, the linkage of the temperature sensor, the vibration sensor and the resistance sensor comprehensively judges whether the transmission line of the device has poor contact.

[0143] Step S333: estimating the restricted current flow path of the device based on the poor structure of the device transmission line;

[0144] In an embodiment of the present invention, after poor contact is detected in the transmission line, the next step is to evaluate whether the current flow path will be restricted as a result. Poor contact of the transmission line will cause poor current flow, especially at the connection point of the device, the current flow path will change, thereby affecting the efficiency of the entire current distribution. Through the aforementioned detected poor contact data, combined with the real-time current value obtained by the current sensor, the change in the current flow path caused by poor contact is further calculated. The current flow path is accurately evaluated, and the current path of the device is modeled and analyzed with the help of electrical network analysis tools. Through these analyses, it is possible to find out whether there is an obstruction or offset in the current flow process, and evaluate the current restricted area caused by poor contact of the transmission line. The thermal imaging data of the device is used to determine the temperature rise at the current restricted position. Excessive temperature is usually related to the current restricted area, so the current flow path restriction is further verified by combining the temperature monitoring data.

[0145] Step S334: estimating the local concentration of the device transmission current according to the device current flow path restriction;

[0146] In the embodiments of the present invention, when the current flow path is restricted, the distribution of current in the device will change, resulting in local current concentration in some areas. This local current concentration will cause some parts of the device to bear a greater load, thereby increasing the risk of failure. Accurately estimate the local concentration of the device current and conduct a detailed analysis of the current flow path. Based on the current flow path restriction data in step S333, combined with the current density model of the device, calculate the distribution of current on different paths in detail. The current density model can help analyze the flow of current in different components and identify the areas where the current density is too high. By installing current sensors and monitoring the current changes at each key node in real time, obtain the real-time data of the current density. For the areas where the flow path is restricted, judge whether there is local current concentration by analyzing the change of current distribution.

[0147] Step S335: Predict the deterioration of the electrical circulation capacity of the device according to the local concentration of the current transmitted by the device;

[0148] In the embodiments of the present invention, when there is local current concentration inside the device, it will cause the deterioration of the electrical circulation capacity in this area, manifested as phenomena such as the decrease in the current-carrying capacity of the device, too high device temperature, and increased electrical loss. Based on the local current concentration data obtained in step S334, the next step is to predict the deterioration of the electrical circulation capacity of the device. By analyzing the local current concentration, use the current loss model to calculate the deterioration of electrical performance caused by the local current concentration. These losses will directly affect the electrical circulation capacity of the device. Especially in the high-load state of the device, the current concentration will further exacerbate the temperature rise of the device and cause damage to electrical components.

[0149] Step S336: Estimate the load collapse trend of the power transmission and distribution equipment based on the deterioration of the electrical circulation capacity of the device and the local concentration of the current transmitted by the device.

[0150] In the embodiments of the present invention, according to the electrical circulation capacity deterioration data obtained in step S335 and the analysis results of the local current concentration, estimate the load collapse trend of the device. Load collapse means that when the device is under high load or current overload, due to the decrease in the electrical circulation capacity, the electrical system of the device cannot continue to work stably, resulting in collapse. Utilize the aforementioned electrical circulation capacity deterioration data, combined with the load distribution model of the device, to analyze the electrical load-bearing capacity of the device under different load states. When the electrical circulation capacity decreases, the stability of the device under high load will decrease, resulting in system collapse or failure. By monitoring the load situation and current changes of the device in real time, detect the precursors of load collapse in time.

[0151] Preferably, step S4 includes the following steps:

[0152] Step S41: Estimate the abnormal communication condition of the power transmission and distribution equipment circuit according to the degradation trend of the device communication link;

[0153] In the embodiment of the present invention, the communication link quality detection module is called to archive and analyze the data transmission stability within the historical time window, specifically including the signal-to-noise ratio fluctuation curve, the bit error rate change, the cumulative packet loss rate, and the link jitter frequency. The above data is read through the physical layer interface in the underlying communication chip and summarized and processed by the edge computing unit. Subsequently, based on the clear degradation trend of the communication link, the time consistency feature of the increase in the link packet loss rate and the decrease in the signal-to-noise ratio is extracted, an abnormal peak frequency table within the time series is constructed, and the retransmission ratio and delay drift in adjacent communication cycles are incrementally calculated. By analyzing the growth curve of the abnormal communication data ratio, when the communication link state deteriorates continuously for more than three cycles, it is determined that there is an abnormal communication fluctuation condition in the current device circuit, and it is marked as the communication abnormal state levels 1 to 3. The output circuit communication abnormal condition data includes: communication delay drift coefficient, retransmission surge amplitude, abnormal fluctuation intensity level.

[0154] Step S42: Estimate the response imbalance situation of the device communication unit according to the abnormal communication condition of the power transmission and distribution equipment circuit;

[0155] In the embodiment of the present invention, based on the identified circuit communication abnormal level data, the communication response control record module is called to obtain information such as the interruption response log, scheduling delay record, and queue blocking frequency under the main control communication path of the device. The control instruction execution recorder is used to monitor the round-trip response cycle between the main control processor and the communication response unit, and extract indicators such as the average response interval, peak response delay, and asymmetric scheduling deviation amount. If there is a response delay deviation from the reference value by more than twenty milliseconds in more than three consecutive cycles, or the main control processing logic triggers the backup path wake-up mechanism multiple times, it is determined that the device communication module has entered the response imbalance state. The response imbalance state is further classified according to the response window offset range, instruction stack depth, and scheduling inconsistency rate to form the response imbalance level evaluation data.

[0156] Step S43: Detect the increasing trend of the device communication control difficulty based on the abnormal communication condition of the power transmission and distribution equipment circuit and the response imbalance situation of the device communication unit;

[0157] In the embodiment of the present invention, after confirming the communication anomaly level and response imbalance level, a control complexity calculation tool is used to analyze the operation iteration load of the current communication control path under continuous anomalies. The method includes calculating the ratio of the total number of communication control instructions to the number of retries within a unit control period, retrieving the task blocking list and cache overflow log in the scheduling and regulation unit, and detecting the execution path reorganization behavior caused by link anomalies. If the number of effective completion paths of the communication control task exceeds twice the initial configured path number, and the increase in the reconstruction path delay exceeds 50 milliseconds, it is marked as a state of increasing communication control difficulty. Further, the average interval time of scheduling failures and the number of task reorderings are statistically analyzed, and by deriving the growth curve of the control resource utilization rate, it is judged whether the system enters a stage of rapid increase in control pressure to form the level data of the increasing trend of communication control difficulty.

[0158] Step S44: Detect the abnormal conditions of the power transmission and distribution equipment based on the increasing trend of the communication control difficulty of the equipment and the load collapse trend of the power transmission and distribution equipment;

[0159] In the embodiment of the present invention, the level data of the increasing trend of communication control difficulty and the level data of the load collapse trend output in the previous steps are called for two-factor coupling analysis. By constructing an operation anomaly resonance matching graph, the time-sequence position relationship between the control pressure peak value and the high-risk section of load anomaly is compared frame by frame. If the synchronous coincidence area of the resonance amplitudes of the two exceeds 60% of the current sliding time window, and a trend synchronous intersection point is formed between the control resource utilization rate and the load anomaly amplitude, it is determined that the current equipment operation enters an unstable state. Further, the operation monitoring module is called to read redundant operation state indicators such as temperature sensors, internal power supply fluctuation amplitude, circuit recovery times, and control logic repeated initialization behavior, etc., to assist in constructing the abnormal condition quantification graph. The abnormal condition data of the power transmission and distribution equipment output includes abnormal coupling strength, control-load resonance duration, and synchronous peak delay indicators.

[0160] Step S45: Conduct a fault risk assessment of the power transmission and distribution equipment according to the abnormal conditions of the power transmission and distribution equipment, obtain the fault risk data of the power transmission and distribution equipment, and upload it to the cloud platform for early warning.

[0161] In an embodiment of the present invention, when the abnormal condition level identified in step S44 reaches a preset trigger standard, the system enters a fault risk assessment process. In this process, the system extracts highly sensitive fields from the load operation trajectory, communication link status, response behavior pattern, electrical interference log, structural fatigue record, and historical warning data. Through a causal logic analysis method in the assessment process, the triggering probabilities of each fault impact factor under multiple paths are deduced multiple times to determine the current fault risk level. Specific fields include the fault occurrence probability index, the cause source path number, the earliest failed component number, the expected failure time period, etc., forming complete fault risk data for power transmission and distribution equipment. Through the communication protocol stack built into the edge processing terminal and using the MQTT publishing mechanism, the assessment results are structured and uploaded to the corresponding node of the cloud platform. After receiving the data, the cloud platform generates a risk warning signal through preset warning rules and sends it to the control room display terminal and the remote operation and maintenance server to ensure the closed-loop execution of the warning response chain of the equipment remote monitoring system.

[0162] The present invention also provides a communication control system for intelligent power transmission and distribution equipment, which is used to execute the communication control method for intelligent power transmission and distribution equipment as described above. The communication control system for intelligent power transmission and distribution equipment includes:

[0163] An operating state detection module, which is used to obtain data of intelligent power transmission and distribution equipment; estimate the initial performance parameters of intelligent power transmission and distribution equipment according to the data of intelligent power transmission and distribution equipment; detect the operating state of intelligent power transmission and distribution equipment according to the initial performance parameters of intelligent power transmission and distribution equipment;

[0164] An electrical interference composite instability prediction module, which is used to estimate the dynamic abnormal load condition of the equipment according to the data of intelligent power transmission and distribution equipment; detect the electrical stability gradient decay condition of the operating state detection equipment of intelligent power transmission and distribution equipment according to the dynamic abnormal load condition of the equipment; predict the electrical interference composite instability condition based on the electrical stability gradient decay condition of the equipment;

[0165] A communication link degradation trend prediction module, which is used to estimate the heat effect accumulation of power transmission and distribution equipment according to the electrical interference composite instability condition; estimate the load collapse trend of power transmission and distribution equipment based on the heat effect accumulation of power transmission and distribution equipment; estimate the communication link degradation trend of the equipment based on the load collapse trend of power transmission and distribution equipment;

[0166] A risk warning module, which is used to detect the abnormal condition of power transmission and distribution equipment according to the communication link degradation trend of the equipment and the load collapse trend of power transmission and distribution equipment; conduct a fault risk assessment of power transmission and distribution equipment according to the abnormal condition of power transmission and distribution equipment to obtain fault risk data of power transmission and distribution equipment, and upload it to the cloud platform for warning.

[0167] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A communication control method for intelligent power transmission and distribution equipment, characterized in that, It includes the following steps: Step S1: Obtain the data of intelligent power transmission and distribution equipment; estimate the initial performance parameters of the intelligent power transmission and distribution equipment according to the data of the intelligent power transmission and distribution equipment; Detect the operating state of the intelligent power transmission and distribution equipment according to the initial performance parameters of the intelligent power transmission and distribution equipment; Step S2: Estimate the dynamic abnormal load condition of the equipment according to the data of the intelligent power transmission and distribution equipment; Detect the electrical stability gradient attenuation condition of the operating state detection equipment of the intelligent power transmission and distribution equipment according to the dynamic abnormal load condition of the equipment. Among them, the detection of the electrical stability gradient attenuation condition in step S2 includes: Detect the load accumulation condition of the power transmission and distribution equipment for the operating state of the intelligent power transmission and distribution equipment according to the dynamic abnormal load condition of the equipment, and obtain the load accumulation condition data of the power transmission and distribution equipment; Estimate the drift of the equipment load spatial distribution according to the load accumulation condition data of the power transmission and distribution equipment; Estimate the local load gradient growth condition of the equipment based on the drift of the equipment load spatial distribution; Detect the attenuation trend of the transmitted power quality of the equipment according to the local load gradient growth condition of the equipment and the load accumulation condition detection of the power transmission and distribution equipment; Predict the voltage drop condition of the equipment power transmission based on the attenuation trend of the equipment transmitted power quality; Predict the risk condition of reverse current of the equipment according to the voltage drop condition of the equipment power transmission and the local load gradient growth condition of the equipment; Detect the electrical stability gradient attenuation condition of the equipment according to the risk condition of reverse current of the equipment and the voltage drop condition of the equipment power transmission; Predict the complex instability condition of electrical interference based on the electrical stability gradient attenuation condition of the equipment. Among them, the prediction of the complex instability condition of electrical interference in step S2 includes: Estimate the instantaneous high voltage impact condition of the equipment based on the electrical stability gradient attenuation condition of the equipment; Estimate the damage degree of the equipment transformer using the instantaneous high voltage impact condition of the equipment; Analyze the instability condition of the equipment power system based on the damage degree of the equipment transformer and the instantaneous high voltage impact condition of the equipment; Detect the abnormal electromagnetic generation condition of the equipment using the instability condition of the equipment power system; Evaluate the growth trend of electromagnetic interference of the equipment according to the abnormal electromagnetic generation condition of the equipment; Predict the distortion degree of the transmitted current of the equipment according to the growth trend of electromagnetic interference of the equipment; Identify the breakdown condition of the equipment insulation layer according to the instantaneous high voltage impact condition of the equipment; Predict the complex instability condition of electrical interference based on the breakdown condition of the equipment insulation layer and the distortion degree of the equipment transmitted current; Step S3: Estimate the cumulative heat effect of the power transmission and distribution equipment according to the complex instability condition of electrical interference; estimate the load collapse trend of the power transmission and distribution equipment based on the cumulative heat effect of the power transmission and distribution equipment; estimate the degradation trend of the equipment communication link based on the load collapse trend of the power transmission and distribution equipment; Step S4: Detect the abnormal condition of the power transmission and distribution equipment according to the degradation trend of the equipment communication link and the load collapse trend of the power transmission and distribution equipment; conduct a fault risk assessment on the power transmission and distribution equipment according to the abnormal condition of the power transmission and distribution equipment to obtain the fault risk data of the power transmission and distribution equipment, and upload it to the cloud platform for early warning.

2. The communication control method for intelligent power transmission and distribution equipment according to claim 1, wherein Step S1 includes the following steps: Step S11: Obtain the data of intelligent power transmission and distribution equipment; Step S12: Extract the communication link structure of the intelligent power transmission and distribution equipment according to the data of the intelligent power transmission and distribution equipment; Step S13: Estimate the initial performance parameters of the intelligent power transmission and distribution equipment according to the data of the intelligent power transmission and distribution equipment; Step S14: Detect the operating state of the intelligent power transmission and distribution equipment based on the initial performance parameters of the intelligent power transmission and distribution equipment and the communication link structure of the intelligent power transmission and distribution equipment.

3. The communication control method for intelligent power transmission and distribution equipment according to claim 2, wherein Step S13 includes the following steps: Step S131: Collect the geometric structure parameters of the intelligent power transmission and distribution equipment based on the data of the intelligent power transmission and distribution equipment; Step S132: Identify the physical layout of the intelligent power transmission and distribution equipment based on the geometric structure parameters of the intelligent power transmission and distribution equipment; Step S133: When the density of the physical layout of the intelligent power transmission and distribution equipment exceeds 0.7 and based on the geometric structure parameters of the intelligent power transmission and distribution equipment, identify and detect the spatial compatibility status of the power transmission and distribution equipment; Step S134: Evaluate the heat dissipation capacity data of the power transmission and distribution equipment when the spatial compatibility status of the power transmission and distribution equipment is greater than 150 mm / m²; Step S135: Evaluate the environmental adaptability parameters of the power transmission and distribution equipment based on the heat dissipation capacity data of the power transmission and distribution equipment; Step S136: Evaluate the electrical performance data of the intelligent power transmission and distribution equipment based on the data of the intelligent power transmission and distribution equipment; Step S137: Estimate the initial performance parameters of the intelligent power transmission and distribution equipment based on the electrical performance data of the intelligent power transmission and distribution equipment and the environmental adaptability parameters of the power transmission and distribution equipment.

4. The communication control method for intelligent power transmission and distribution equipment according to claim 2, wherein Step S14 includes the following steps: Step S141: Detect the redundancy status of the communication link of the power transmission and distribution equipment based on the communication link of the intelligent power transmission and distribution equipment; Step S142: Predict the stability of the communication link of the intelligent power transmission and distribution equipment based on the redundancy status of the communication link of the power transmission and distribution equipment; Step S143: Estimate the link response speed of the power transmission and distribution equipment based on the stability of the communication link of the intelligent power transmission and distribution equipment and the redundancy status of the communication link of the power transmission and distribution equipment; Step S144: Evaluate the communication interaction ability of the power transmission and distribution equipment based on the link response speed of the power transmission and distribution equipment and the stability of the communication link of the intelligent power transmission and distribution equipment; Step S145: Evaluate the operation safety parameters of the power transmission and distribution equipment based on the initial performance parameters of the intelligent power transmission and distribution equipment; Step S146: Detect the operating state of the intelligent power transmission and distribution equipment based on the operation safety parameters of the power transmission and distribution equipment and the communication interaction ability of the power transmission and distribution equipment.

5. The communication control method for intelligent power transmission and distribution equipment according to claim 1, characterized in that The estimation of the dynamic abnormal load condition of the equipment in Step S2 includes: Collect the operation log of the power transmission and distribution equipment based on the data of the intelligent power transmission and distribution equipment; conduct time series analysis of the operation of the power transmission and distribution equipment based on the operation log of the power transmission and distribution equipment to obtain the time series data of the equipment operation; Monitor the fluctuation of the equipment operation demand based on the time series data of the equipment operation; Collect the periodic change of the equipment operation demand when the fluctuation of the equipment operation demand is ±10 kW; Estimate the statistical demand sudden growth period based on the periodic change of the equipment operation and the fluctuation of the equipment operation demand; Calculate the abnormal growth trend of the demand fluctuation slope based on the fluctuation of the equipment operation demand; Statistically count the overload condition of the equipment operation demand when the abnormal growth trend of the demand fluctuation slope is greater than 30 kW / min and based on the fluctuation of the equipment operation demand; Estimate the dynamic abnormal load condition of the equipment by using the demand sudden growth period and the overload condition of the equipment operation demand.

6. The communication control method for intelligent power transmission and distribution equipment according to claim 1, characterized in that Step S3 includes the following steps: Step S31: Estimate the cumulative heat effect of the power transmission and distribution equipment according to the electrical interference complex instability condition; Step S32: Predict the structural aging trend of the power transmission and distribution equipment based on the cumulative thermal effect of the power transmission and distribution equipment; Step S33: Estimate the load collapse trend of the power transmission and distribution equipment based on the cumulative thermal effect of the power transmission and distribution equipment and the structural aging trend of the power transmission and distribution equipment; Step S34: Estimate the deterioration trend of the equipment communication link based on the load collapse trend of the power transmission and distribution equipment.

7. The communication control method for intelligent power transmission and distribution equipment according to claim 1, characterized in that Step S4 includes the following steps: Step S41: Estimate the abnormal condition of the circuit communication of the power transmission and distribution equipment according to the deterioration trend of the equipment communication link; Step S42: Estimate the imbalance of the response of the equipment communication unit according to the abnormal condition of the circuit communication of the power transmission and distribution equipment; Step S43: Detect the increasing trend of the communication control difficulty of the equipment based on the abnormal condition of the circuit communication of the power transmission and distribution equipment and the imbalance of the response of the equipment communication unit; Step S44: Detect the abnormal condition of the power transmission and distribution equipment based on the increasing trend of the communication control difficulty of the equipment and the load collapse trend of the power transmission and distribution equipment; Step S45: Conduct a fault risk assessment on the power transmission and distribution equipment according to the abnormal condition of the power transmission and distribution equipment, obtain the fault risk data of the power transmission and distribution equipment, and upload it to the cloud platform for early warning.

8. A communication control system for intelligent power transmission and distribution equipment, characterized in that, For implementing the intelligent power transmission and distribution equipment communication control method as described in claim 1, the intelligent power transmission and distribution equipment communication control system includes: An operating state detection module, configured to obtain the data of the intelligent power transmission and distribution equipment; estimate the initial performance parameters of the intelligent power transmission and distribution equipment according to the data of the intelligent power transmission and distribution equipment; detect the operating state of the intelligent power transmission and distribution equipment according to the initial performance parameters of the intelligent power transmission and distribution equipment; An electrical interference composite instability prediction module, configured to estimate the dynamic abnormal load condition of the equipment according to the data of the intelligent power transmission and distribution equipment; detect the electrical stability gradient attenuation condition of the operating state of the intelligent power transmission and distribution equipment according to the dynamic abnormal load condition of the equipment; predict the electrical interference composite instability condition based on the electrical stability gradient attenuation condition of the equipment; A communication link deterioration trend estimation module, configured to estimate the cumulative thermal effect of the power transmission and distribution equipment according to the electrical interference composite instability condition; estimate the load collapse trend of the power transmission and distribution equipment based on the cumulative thermal effect of the power transmission and distribution equipment; estimate the deterioration trend of the equipment communication link based on the load collapse trend of the power transmission and distribution equipment; A risk early warning module, configured to detect the abnormal condition of the power transmission and distribution equipment according to the deterioration trend of the equipment communication link and the load collapse trend of the power transmission and distribution equipment; conduct a fault risk assessment on the power transmission and distribution equipment according to the abnormal condition of the power transmission and distribution equipment, obtain the fault risk data of the power transmission and distribution equipment, and upload it to the cloud platform for early warning.

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