Communication control system and method for intelligent power transmission and distribution equipment

Abstract

The invention relates to the technical field of power transmission and distribution equipment, in particular to a communication control system and method for intelligent power transmission and distribution equipment. The method comprises the following steps: acquiring data of intelligent power transmission and distribution equipment, estimating initial performance parameters of the equipment, and detecting an operation state; identifying a dynamic abnormal load condition of the equipment based on the operation data, analyzing electrical stability gradient attenuation and predicting an electrical interference composite instability trend; further evaluating a thermal effect accumulation condition based on an electrical instability condition, and estimating a load collapse trend and a communication link degradation trend; abnormal conditions are identified in combination with equipment operation degradation performance, fault risk assessment is carried out, and risk data are generated and uploaded to the cloud platform to realize early warning control; according to the invention, through early warning of abnormity of the power transmission and distribution equipment, more stable operation of the intelligent power transmission and distribution equipment is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission and distribution equipment, and in particular 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 interconnection. New transmission and distribution equipment such as intelligent circuit breakers, intelligent transformers, switch cabinet monitoring units, and intelligent protection devices are widely deployed in the transmission and distribution links. These devices generally have data collection, communication interaction, adaptive control and remote linkage functions, forming a hierarchical operation architecture that coordinates edge sensing nodes with centralized management platforms. On this basis, in order to achieve efficient operation of the power grid and accurate fault warning, it is urgent to build a comprehensive control method that can integrate equipment operation data, electrical state parameters and communication link health status to ensure the stability and reliability of the transmission and distribution system in a complex environment. The lack of in-depth detection methods for the stability, response capability and redundancy of the communication link makes it difficult to identify and dynamically intervene in the communication degradation trend at an early stage. However, traditional intelligent transmission and distribution equipment has the problem of inaccurate analysis of abnormal electrical attenuation of transmission and distribution equipment, as well as inaccurate analysis of communication link degradation of 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 comprises the following steps: Step S1: Acquire data of the intelligent power transmission and distribution equipment; estimate 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 status of the intelligent power transmission and distribution equipment according to the initial performance parameters of the intelligent power transmission and distribution equipment; Step S2: estimating the dynamic abnormal load condition of the equipment according to the data of the intelligent power transmission and distribution equipment; detecting the electrical stability gradient attenuation condition of the intelligent power transmission and distribution equipment according to the dynamic abnormal load condition of the equipment; predicting the electrical interference composite instability condition based on the electrical stability gradient attenuation condition of the equipment; Step S3: estimating the cumulative thermal effect of the power transmission and distribution equipment based on the composite instability of electrical interference; estimating the load collapse trend of the power transmission and distribution equipment based on the cumulative thermal effect of the power transmission and distribution equipment; estimating the equipment communication link degradation trend based on the load collapse trend of the power transmission and distribution equipment; Step S4: Detect abnormal conditions of power transmission and distribution equipment according to the degradation trend of equipment communication link and the collapse trend of power transmission and distribution equipment load; conduct power transmission and distribution equipment failure risk assessment according to the abnormal conditions of power transmission and distribution equipment, obtain power transmission and distribution equipment failure risk data, and upload it to the cloud platform for early warning.

[0005] The present invention realizes accurate evaluation of equipment performance and operating status through data acquisition and analysis of intelligent power transmission and distribution equipment, can monitor abnormal load conditions and electrical stability gradient attenuation of equipment in real time, provides data support for predicting electrical interference and compound instability conditions, and further promotes the safety improvement of equipment. By estimating the thermal effect and load collapse trend of the equipment, it is possible to identify potential equipment failure risks and communication link degradation problems in advance, effectively avoid the occurrence of equipment failure, and extend the service life of the equipment. Based on these early warning information, early warning and risk control of equipment can be achieved, thereby improving the stability and reliability of the entire power system, avoiding large-scale system collapse caused by equipment failure, and ensuring the continuity and stability of power supply. Through the data upload and early warning mechanism of the cloud platform, cross-regional and cross-device intelligent monitoring is realized, management efficiency is improved, and remote fault diagnosis and maintenance are realized to ensure that the equipment is always in the best operating state. Therefore, the present invention is an optimization process made for the traditional communication control method for intelligent power transmission and distribution equipment, which solves the problem that the traditional communication control method for intelligent power transmission and distribution equipment has 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. The accuracy of the analysis of abnormal electrical attenuation of power transmission and distribution equipment and the accuracy of abnormal electrical attenuation of power transmission and distribution equipment are improved.

[0006] 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: An operation status detection module is used to obtain data of intelligent power transmission and distribution equipment; estimate initial performance parameters of the intelligent power transmission and distribution equipment based on the data of the intelligent power transmission and distribution equipment; and detect the operation status of the intelligent power transmission and distribution equipment based on the initial performance parameters of the intelligent power transmission and distribution equipment; The electrical interference composite instability prediction module is used to estimate the dynamic abnormal load condition of the equipment based on the data of the intelligent power transmission and distribution equipment; 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; and predict the electrical interference composite instability condition based on the electrical stability gradient attenuation condition of the equipment; The communication link degradation trend prediction module is used to predict the cumulative thermal effect of the power transmission and distribution equipment based on the composite instability of electrical interference; to predict 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 to predict the equipment communication link degradation trend based on the load collapse trend of the power transmission and distribution equipment. The risk warning module is used to detect abnormal conditions of power transmission and distribution equipment based on the degradation trend of equipment communication links and the collapse trend of power transmission and distribution equipment loads; conduct power transmission and distribution equipment failure risk assessment based on the abnormal conditions of power transmission and distribution equipment, obtain power transmission and distribution equipment failure risk data, and upload it to the cloud platform for early warning.

[0007] The present invention is used for a communication control system for intelligent power transmission and distribution equipment. The system can implement any one of the communication control methods for intelligent power transmission and distribution equipment of the present invention, and is used to combine the medium for operation and signal transmission between various modules to complete the communication control method for intelligent power transmission and distribution equipment. The internal modules of the system cooperate with each other to identify the risk of equipment failure in advance and provide accurate early warning, thereby improving the stability of equipment operation and the reliability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A schematic diagram of a process flow of a communication control method for intelligent power transmission and distribution equipment; Figure 2 for Figure 1 Detailed implementation steps of step S3 in FIG. Figure 3 for Figure 1 Detailed implementation steps of step S4 in FIG. The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0009] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0010] 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 figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0011] 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 used only to distinguish one unit from another unit. 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 associated items.

[0012] To achieve this, please refer to Figures 1 to 3 , a communication control method for intelligent power transmission and distribution equipment, comprising the following steps: Step S1: Acquire data of the intelligent power transmission and distribution equipment; estimate 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 status of the intelligent power transmission and distribution equipment according to the initial performance parameters of the intelligent power transmission and distribution equipment; In an embodiment of the present invention, real-time operation data of intelligent power transmission and distribution equipment is obtained through sensor equipment. These data mainly include electrical parameters such as current, voltage, frequency, power, temperature, etc., and the geographical location information, ambient temperature, humidity and other external environmental data of the equipment should be collected according to the specific configuration of the equipment. The data acquisition tool adopts 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, the 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 method includes regression analysis based on historical data, neural network model or machine learning algorithm, which estimates parameters based on historical operation records and equipment status. Specifically, statistical analysis is performed based on the past operation data of the equipment to identify the performance change trend of the equipment, so as to infer 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 status of the equipment is further detected. The goal of this step is to confirm whether the equipment is within the normal working range and check whether various performance parameters deviate from the expected abnormal situation. If the parameters such as voltage, current or temperature of the equipment exceed the normal threshold, an alarm will be triggered immediately to indicate that there is a potential problem with the equipment. This process uses precise real-time monitoring technology, combined with the equipment health status assessment algorithm, to dynamically feedback the operating status of the equipment and obtain the operating status of the intelligent power transmission and distribution equipment.

[0013] Step S2: estimating the dynamic abnormal load condition of the equipment according to the data of the intelligent power transmission and distribution equipment; detecting the electrical stability gradient attenuation condition of the intelligent power transmission and distribution equipment according to the dynamic abnormal load condition of the equipment; predicting the electrical interference composite instability condition based on the electrical stability gradient attenuation condition of the equipment; In an embodiment of the present invention, dynamic load analysis is performed based on the operation data of the intelligent power transmission and distribution equipment, especially the load data such as current and voltage. By analyzing the equipment operation log in detail and combining the equipment operation time series analysis technology, the fluctuation of the equipment load demand is obtained. A time series analysis method (for example, a sliding window method or a Fourier transform) is used to identify the periodic changes in the load demand, and further analyze the period of sudden demand growth. For example, when the fluctuation amplitude of the equipment load exceeds a preset threshold (such as ±10kW), a fault prediction is automatically performed and the characteristics of the sudden load growth are recorded. Based on these load fluctuation data, an analysis of the electrical stability gradient attenuation is performed. 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 early warning measures before the electrical stability attenuation occurs. By predicting the electrical stability gradient attenuation of the equipment, the system will further predict the composite instability of electrical interference. This process takes into account the external environment (such as vibration and temperature changes of electrical equipment) and the complex electrical interactions inside the equipment, and predicts whether composite instability will occur on the basis of stability attenuation. Compound instability can lead to more serious electrical failures.

[0014] Step S3: estimating the cumulative thermal effect of the power transmission and distribution equipment based on the composite instability of electrical interference; estimating the load collapse trend of the power transmission and distribution equipment based on the cumulative thermal effect of the power transmission and distribution equipment; estimating the equipment communication link degradation trend based on the load collapse trend of the power transmission and distribution equipment; In an embodiment of the present invention, the thermal effect caused by the composite instability of electrical interference is analyzed in detail. According to the operating state, load condition and power loss generated inside the electrical equipment, the system can monitor the thermal effect of the equipment in real time through temperature sensors and thermal imaging equipment. By calculating the accumulation of the thermal effect of the equipment, real-time change data of the heat accumulation amount is obtained. Specifically, the system will use a heat conduction model (for example, a heat conduction equation or a 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 the temperature accumulation based on the thermal capacitance, material properties and heat dissipation capacity of different components. Based on the accumulation of thermal effects, the system further evaluates the load collapse trend of the equipment. In this process, the system will analyze the working state of the equipment at high temperature in combination with the thermal effect and load change trend of the equipment, and predict whether the equipment will cause load collapse due to overload or insufficient heat dissipation. Load collapse usually causes overload of the equipment system and causes system shutdown, so this prediction can be used as an important link to protect the equipment. Based on the load collapse trend, the system then evaluates the degradation trend of the equipment communication link. In the case of excessive load or load collapse of the equipment, the communication link is seriously affected. The system detects the bandwidth, delay and packet loss rate of the communication link in real time, comprehensively judges the stability of the link, and predicts whether the link will experience communication performance degradation due to overload or failure. The link degradation trend is realized through communication quality analysis algorithms (such as channel estimation and noise analysis), identifying potential communication problems in advance and ensuring the security and reliability of data transmission.

[0015] Step S4: Detect abnormal conditions of power transmission and distribution equipment according to the degradation trend of equipment communication link and the collapse trend of power transmission and distribution equipment load; conduct power transmission and distribution equipment failure risk assessment according to the abnormal conditions of power transmission and distribution equipment, obtain power transmission and distribution equipment failure risk data, and upload it to the cloud platform for early warning.

[0016] In the embodiment of the present invention, the key task in step S4 is to detect whether the equipment has abnormal conditions and perform fault risk assessment. Based on the equipment communication link degradation trend and load collapse trend obtained in the previous step, the system conducts an in-depth analysis of the equipment's operating status. Specifically, the system comprehensively evaluates the safety of equipment operation through a variety of indicators (such as temperature, pressure, current, voltage, etc.), and analyzes whether the equipment has abnormal conditions such as overload, overheating, short circuit, etc. Through this process, the potential failure of the equipment can be monitored in real time, and it is judged whether the fault warning line is reached. If an abnormality is detected in the equipment, 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 equipment failure and its impact. The system uploads the fault risk data to the cloud platform for early warning, so that maintenance, scheduling or emergency measures can be taken in advance to avoid accidents.

[0017] Preferably, step S1 comprises the following steps: Step S11: Acquire data of intelligent power transmission and distribution equipment; In an embodiment of the present invention, various operating data of intelligent power transmission and distribution equipment are obtained through a sensor array and an acquisition system. 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 temperature, humidity, and air quality around the equipment are collected through environmental monitoring equipment. These sensors can perform high-frequency data acquisition within a predetermined time interval to ensure accurate reflection of the operating status of the equipment. All 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 acquisition process, the signal will be subjected to noise filtering and denoising. Filter technology (such as Kalman filtering) is used to smooth the measured data, eliminate errors caused by external interference, and maintain the accuracy and real-time nature of the data. At the same time, to avoid excessive equipment load, the acquisition frequency is dynamically adjusted according to the workload of the equipment to ensure a balance between data acquisition and processing. All collected data will be stored in a database and time-synchronized before further processing to obtain intelligent power transmission and distribution equipment data.

[0018] Step S12: extracting the communication link structure of the intelligent power transmission and distribution equipment according to the data of the intelligent power transmission and distribution equipment; In an embodiment of the present invention, by further analyzing the data obtained in step S11, the communication link structure of the device is extracted, and the data exchange mode between the modules of the device is analyzed through network communication indicators such as data transmission delay, bandwidth, and packet loss rate, and the communication structure inside the device is identified. The system will use a network topology analysis algorithm (such as the shortest path algorithm and the Dijkstra algorithm) to construct a communication link topology map inside the device. This topology map draws the direct connection relationship between the modules of the device based on the transmission capacity and communication quality of each device module. For example, there are multiple communication nodes inside the device, such as a sensor module, a control module, a power module, 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, the communication mode and stability between the device and the external system are considered. By analyzing the interaction data between the device and external communication systems such as cloud platforms and operation and maintenance servers, the overall communication structure of the device can be further determined, and potential bottlenecks and failure points can be identified. Parameters such as delay, bandwidth, and reliability of all communication links will be used as link quality metrics.

[0019] Step S13: estimating initial performance parameters of the intelligent power transmission and distribution equipment according to the data of the intelligent power transmission and distribution equipment; In an embodiment of the present invention, the initial performance parameters of the intelligent power transmission and distribution equipment are calculated by a data analysis method. In this step, the historical operation data of the equipment and the real-time data currently collected are compared and analyzed. These data mainly include parameters such as voltage, current, temperature, and load. The historical data of the equipment is modeled by time series analysis technology (such as autoregressive integral moving average model ARIMA or 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 law of current and voltage, the power output capacity of the equipment under different loads is estimated; and through temperature data, the heat dissipation capacity of the equipment and the risk of overheating are calculated. Based on these prediction results, the initial performance parameters of the equipment, such as maximum output power, load carrying capacity, communication bandwidth, etc., are calculated. Combine the design parameters of the equipment with the performance indicators given by the manufacturer to verify the rationality of the estimated value. If it is found that the estimated result deviates too much from the expectation, it means that the equipment is at risk of failure or damage, and the system will issue an alarm. Introduce a calibration mechanism. By comparing with the actual operating status of the equipment, and reversely calculating and adjusting the performance parameters, the initial performance parameters of the intelligent power transmission and distribution equipment are obtained.

[0020] Step S14: detecting the operating status 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.

[0021] In the embodiment of the present invention, the operating state of the device is detected in real time by combining the initial performance parameters of the device with the communication link structure. In this step, the system evaluates the current working state of the device 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 device with the current load condition, it is determined whether the device is within the normal load range and whether there is an overload or underload operation. According to the communication link structure extracted in step S12, the communication stability inside and outside the device is analyzed. If the communication link of the device has a high delay, packet loss rate or bandwidth bottleneck, it will affect the real-time data transmission and control instruction execution of the device, thereby causing abnormal operation of the device. To this end, the system monitors the performance of the link in real time, combines historical data, analyzes the health of the link, and promptly discovers potential communication failures. Combined with the performance parameters and the state of the communication link, a rule-based or machine learning algorithm is used to evaluate the overall operating state of the device. For example, if the device finds abnormal fluctuations in parameters such as voltage and current during operation, and the stability of the communication link decreases at the same time, the system will infer that the device is at risk of failure or impending failure, and issue an early warning signal. Through a comprehensive evaluation of the equipment's initial performance parameters and communication link structure, the system can accurately monitor the equipment's status, predict equipment failure risks in advance, and take corresponding protective measures.

[0022] Preferably, step S13 comprises the following steps: Step S131: Collecting geometric structure parameters of the intelligent power transmission and distribution equipment according to the data of the intelligent power transmission and distribution equipment; In an embodiment of the present invention, the geometric structure parameters of the intelligent power transmission and distribution equipment are obtained by 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, gaps, curvatures and key geometric characteristics of the equipment. The three-dimensional point cloud data of each dimension of the equipment is obtained by a laser scanner, and the point cloud data is further processed in combination with image processing algorithms (such as edge detection, feature matching, etc.) to extract the precise geometric dimension information of the equipment. During the data acquisition process, the sensor will perform a full-range scan of the equipment multiple times to ensure that the data at each angle is fully covered. By comparing the data at different time points, the changes in the geometric structure of the equipment can be discovered in time, and accurate geometric parameters can be further provided for subsequent steps. All the collected geometric structure data will be transmitted to the central processing system in real time via a wireless network to obtain the geometric structure parameters of the intelligent power transmission and distribution equipment.

[0023] Step S132: Identifying 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; In an embodiment of the present invention, the geometric structure parameters collected in step S131 are used to identify the physical layout of the intelligent power transmission and distribution equipment. According to the geometric parameters of the equipment, the spatial geometric analysis algorithm (such as volume calculation, surface fitting, etc.) is used to hierarchically segment the various components of the equipment to identify the layout structure of each part of the equipment. Through these parameters, the relative position relationship between the modules inside the equipment is further calculated to understand the spatial layout of the equipment. Based on the identification of the physical layout, the system will classify the equipment according to its purpose, the deployment of functional modules and the location 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 work efficiency of the equipment. In 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 derived based on information such as the distance between modules and the connection method.

[0024] Step S133: when the density of the physical layout of the intelligent power transmission and distribution equipment exceeds 0.7 and the geometric structure parameters of the intelligent power transmission and distribution equipment are used to identify the power transmission and distribution equipment and detect the spatial compatibility of the power transmission and distribution equipment; In the embodiment of the present invention, when the density of the physical layout of the intelligent power transmission and distribution equipment exceeds 0.7, the spatial compatibility detection of the power transmission and distribution equipment is performed in combination with the geometric structure parameters of the equipment, and the spatial density of the equipment is obtained by calculating the space occupied by each module inside the equipment and the gap ratio between the modules. Spatial 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 high, it means that the internal space of the equipment is used more tightly, and there are problems of mutual interference between modules or poor heat dissipation. When the spatial density exceeds 0.7, the system will perform spatial compatibility detection. At this time, the system will use simulation technology to simulate the relative position and working state between the modules inside the equipment by establishing a virtual three-dimensional model of the equipment, and analyze whether there is conflict or incompatibility in space. An overly compact layout will cause problems such as cable harnesses and air circulation, affecting the stability of the equipment. Combined with operating parameters such as temperature, humidity, and power, the modules inside the equipment are thermodynamically analyzed. If the layout of some modules causes heat concentration, resulting in excessively high temperatures in local areas, it will affect the normal operation of the equipment. Therefore, spatial compatibility detection not only considers the geometric structure, but also needs to be combined with the thermal effect of the equipment for comprehensive analysis.

[0025] Step S134: evaluating the heat dissipation capacity data of the power transmission and distribution equipment when the space compatibility of the power transmission and distribution equipment is greater than 150 mm / m²; In an embodiment of the present invention, when the spatial compatibility of the power transmission and distribution equipment is greater than 150mm / m², the heat dissipation capacity of the equipment is evaluated, and the heat source area and heat accumulation inside the equipment are determined through the equipment space compatibility analysis. If the spatial compatibility condition exceeds the set value (150mm / m²), the system will evaluate the heat dissipation capacity for these high-density areas. By using a heat conduction analysis model to simulate the heat distribution of each area inside the equipment, and combining 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 technology (such as finite element method, heat flow simulation, etc.) to calculate the temperature distribution and heat dissipation efficiency of the equipment under different workloads. For high-temperature areas, the system will monitor the temperature changes of the equipment in real time. If the temperature exceeds the set safety threshold, an alarm will be issued to prompt maintenance or optimization of the heat dissipation design.

[0026] Step S135: evaluating 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; In an embodiment of the present invention, the environmental adaptability of the power transmission and distribution equipment is further evaluated based on the equipment heat dissipation capacity data obtained in step S134. At this time, the environmental adaptability of the equipment not only includes the heat dissipation capacity, but also involves the operating stability of the equipment under different external environmental conditions. For example, factors such as the ambient temperature, humidity, and dust concentration of the equipment will have an impact on its operating performance. By simulating the operation of the equipment under different environmental conditions, the system will calculate the working ability of the equipment in high temperature, high humidity, low temperature and other environments. For example, in an extremely high temperature environment, if the heat dissipation capacity of the equipment is insufficient, the components will overheat and fail. The system will evaluate the environmental adaptability of the equipment, including its reliability and stability under different climatic conditions, based on the design parameters and environmental data of the equipment, combined with the heat dissipation simulation results.

[0027] Step S136: Evaluating the electrical performance data of the intelligent power transmission and distribution equipment according to the data of the intelligent power transmission and distribution equipment; In an embodiment 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 analyzes the electrical parameters of the equipment, such as voltage, current, and frequency, to determine whether they meet the design specifications and operating requirements of the equipment. By real-time monitoring of electrical performance data, combined with the historical data and working status of the equipment, the system can identify whether the equipment has an electrical failure or a trend of performance degradation. For example, if the current exceeds the rated value of the equipment, or the voltage fluctuates, it will cause damage to the equipment or reduce efficiency. Through real-time monitoring and evaluation of these data, the system can promptly detect electrical performance anomalies, issue fault warnings, and prevent equipment overloads or electrical accidents.

[0028] Step S137: estimating 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 intelligent power transmission and distribution equipment.

[0029] In an embodiment of the present invention, the initial performance parameters of the intelligent power transmission and distribution equipment are estimated by combining the electrical performance data in step S136 with the environmental adaptability parameters in step S135. By comprehensively analyzing the electrical performance and environmental adaptability, the system can more accurately estimate the initial performance parameters of the equipment, such as maximum load, power output, work efficiency, etc. This process uses multi-dimensional weighted analysis technology to weightedly 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 operating data of the equipment, the system can determine whether the actual performance of the equipment meets expectations. If it does not meet the requirements, the equipment is prompted to be further debugged or optimized, and the initial performance parameters of the intelligent power transmission and distribution equipment are generated based on these estimated data.

[0030] Preferably, step S14 comprises the following steps: Step S141: Detecting 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; In an embodiment of the present invention, the communication link of the intelligent power transmission and distribution equipment is comprehensively tested to determine its redundancy status. The design purpose of the redundant link is to ensure that the equipment can maintain the stability of communication when a failure occurs. Therefore, in this step, the system will identify and analyze all communication links of the equipment. The load and connection status of each link are monitored in real time through the data flow monitoring sensor installed on the equipment. The sensor obtains the transmission bandwidth, delay, packet loss rate and other data of each link, and combines the known network topology to determine whether each link has redundancy. Once a link redundancy problem is detected, the system will evaluate the quality and effectiveness of the redundant link based on the communication requirements and link status of the device. For example, if a 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. In this way, the system can clearly display the redundancy status of the equipment communication link and provide real-time feedback to the monitoring system for subsequent decision-making and adjustment.

[0031] Step S142: predicting 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; In an embodiment of the present invention, according to the redundancy status of the communication link detected in step S141, the system predicts the stability of the device communication link. The system will evaluate the load conditions, transmission rates, signal strengths and other factors 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 redundant links is poor, the system will predict the link stability problems that will occur based on factors such as the current load, signal quality and network environment of the links. To accurately predict link stability, the system will also analyze historical data. By archiving link failures and failures that occurred in the past and comparing them with the current link status, the system can identify potential causes of link stability problems and infer future performance degradation or interruptions of the links. This step combines 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.

[0032] Step S143: estimating the link response speed of the power transmission and distribution equipment according to 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; In an embodiment of the present invention, the system combines the communication link stability data obtained in step S142 and the redundant link status in step S141 to further estimate the link response speed of the device. The link response speed refers to the time it takes for the system to stabilize again and resume normal operation when the device faces a load change or link switching. In this step, the system will evaluate the link response speed of the device by simulating different load and link switching scenarios. The system will simulate the communication link of the device under different loads and record parameters such as delay, data packet loss, and bandwidth changes during link stabilization and recovery. If the redundant link can effectively take over the disconnected main link, the response speed is faster. On the contrary, if the redundant link quality is poor or the number is insufficient, the response time will be extended. The system calculates the response time of each link under different conditions, combined with the communication load requirements of the device, to give a response speed prediction of the device link.

[0033] Step S144: evaluating the communication interaction capability of the power transmission and distribution equipment according to the power transmission and distribution equipment link response speed and the communication link stability of the intelligent power transmission and distribution equipment; In an embodiment of the present invention, the communication interaction capability of the device is evaluated based on the link response speed estimated in step S143 and the link stability obtained in step S142. The communication interaction capability 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 and stability of the link and the processing capability of the device are comprehensively considered. According to the link response speed of the device, combined with the communication tasks carried by the device (such as data transmission, control command 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 capability of the device will be affected, resulting in increased data transmission delay or slow command response. Therefore, the system will evaluate the transmission stability of each link, combined with the response speed and the processing capability of the device, to obtain the communication interaction capability evaluation result of the device.

[0034] Step S145: evaluating 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; In an embodiment of the present invention, based on the initial performance parameters of the intelligent power transmission and distribution equipment obtained in step S137, the operating safety parameters of the power transmission and distribution equipment are evaluated. The system evaluates the safety of the equipment under the working load by performing a detailed analysis of 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 failure will occur. By real-time monitoring of these parameters, the system can predict whether the equipment has potential safety hazards. In this process, the operating status of the equipment is comprehensively evaluated in combination with factors such as the environmental adaptability and heat dissipation capacity of the equipment. For example, in a high temperature environment, the equipment is in a high temperature overload situation caused by insufficient heat dissipation. By combining these factors, the system evaluates the safe operating parameters of the equipment.

[0035] Step S146: Detect the operating status of the intelligent power transmission and distribution equipment according to the power transmission and distribution equipment operating safety parameters and the power transmission and distribution equipment communication and interaction capabilities.

[0036] In an embodiment of the present invention, based on the operational safety parameters evaluated in step S145 and the communication interaction capabilities evaluated in step S144, the system detects the operational status of the intelligent power transmission and distribution equipment. At this point, the system will conduct a comprehensive detection of the overall operational status of the equipment, comprehensively analyze the equipment's safety, communication capabilities, load status and other parameters, and determine whether the equipment is in a normal operating state. By real-time monitoring of the equipment's operational safety parameters, it is determined whether the equipment is within a safe operating range. If the equipment's operational safety parameters exceed the preset safety range, the system immediately issues an early warning signal. At the same time, the system will also check the equipment's communication status in combination with the evaluation results of the communication interaction capabilities to ensure that the equipment can efficiently and stably interact with the equipment for data. If the equipment's communication link is unstable or the interaction capabilities are insufficient, the system will sound an alarm and start a fault handling mechanism. By comprehensively considering all safety parameters and communication interaction capabilities, the system can accurately determine the equipment's operational status and promptly discover potential fault risks.

[0037] Preferably, 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 according to the data of the intelligent power transmission and distribution equipment; perform the operation time series analysis of the power transmission and distribution equipment based on the operation log of the power transmission and distribution equipment to obtain the equipment operation time series data; In an embodiment of the present invention, the operation log data of the equipment is collected in real time by monitoring sensors deployed in the intelligent power transmission and distribution equipment. The operation log includes information such as the power consumption, load change, operation time, 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 and historical operation characteristics of the equipment, and provide necessary data support for subsequent steps. Specifically, the system will collect power change data every second and every 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. Time series analysis is performed on the data extracted from the equipment operation log. The purpose of time series analysis is to understand the law of equipment load change over time. The system will sort the power data in the equipment operation log in chronological order and perform smoothing to eliminate the impact of abnormal fluctuations on data analysis. By performing time series modeling on these data, the system can accurately identify the fluctuation mode of equipment operation, including the characteristics of load distribution, load change frequency, and periodic fluctuation of the equipment in different time periods. Apply traditional time series analysis techniques such as autoregression (AR) and moving average (MA) models, or use more complex trend prediction methods such as exponential smoothing to identify potential load fluctuation trends. Based on these models, a time series data set is generated to clearly identify the power consumption of the equipment at each time point, and mark the periodic fluctuations and long-term trends of the equipment load.

[0038] Monitor fluctuations in equipment operation demand based on equipment operation time series data; In an embodiment of the present invention, the load fluctuation of the equipment is analyzed and monitored based on the obtained equipment operation time series data. The specific operation includes 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, the time period will be marked as a time period with large load fluctuation. The fluctuation of the equipment load is further judged by calculating the load change rate of the equipment (i.e., the speed of power increase and decrease). If the load change exceeds the preset standard value in a certain time period, the system will identify the time period as a fluctuation 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 estimation and abnormal situation analysis.

[0039] Collect the periodic changes in equipment operation demand when the fluctuation of equipment operation demand is ±10kW; In an embodiment of the present invention, when the load fluctuation amplitude of the equipment exceeds ±10kW, the system will further collect the periodic changes in the operating requirements of the equipment. At this time, the system will analyze the load change law of the equipment within the fluctuation range, and pay attention to the peak and trough periods of the power demand of the equipment during the periodic fluctuation process. The system records the peak and valley values ​​of each load fluctuation at all times, combines historical data for comparative analysis, and extracts the characteristics of periodic fluctuations. Based on this analysis, the system can clearly define the load fluctuation cycle of the equipment and the time law of the peak and valley occurrence. Perform frequency analysis on the load fluctuation of the equipment, and identify the periodic pattern of the fluctuation through methods such as Fourier transform. If it is found that the load fluctuation of the equipment occurs repeatedly within certain specific frequency ranges, the system will confirm these frequencies as the periodic change characteristics of the equipment's operating requirements.

[0040] Estimate and count periods of sudden demand growth based on periodic changes in equipment operation and fluctuations in equipment operation demand; In an embodiment of the present invention, the obtained equipment operation periodic change data and equipment operation demand fluctuation data are combined to estimate the period of sudden demand growth of the equipment. The period of sudden demand growth refers to the situation that the load demand of the equipment increases rapidly and significantly in certain time periods, exceeding the carrying capacity of the equipment. The system will predict whether similar sudden demand growth will occur in the equipment in certain time periods in the future through regression analysis of the historical load data of the equipment and previous patterns of sudden load growth. By setting a threshold, if the load growth rate exceeds a predetermined value in a certain time period, the time period will be regarded as a sudden growth period. At the same time, the system will also analyze the load fluctuations in the surrounding time periods and identify potential triggering factors related to sudden growth. For example, if the fluctuation amplitude of the equipment load reaches a certain level in certain time periods, the system will mark these time periods as high-probability periods of sudden demand growth and use them as risk warning data.

[0041] Calculate the abnormal growth trend of demand fluctuation slope based on the fluctuation of equipment operation demand; In an embodiment of the present invention, the fluctuation of the operating demand of the equipment is further analyzed, and the abnormal growth trend of the slope of the demand fluctuation is calculated. The demand fluctuation slope represents the rate of change of the equipment load per unit time. The system calculates the slope change of each time period based on the load data and compares it with the historical data. If the fluctuation slope of a certain time period is greater than the normal range, it is regarded as an abnormal growth trend, and the system will mark the time period as a potential risk period for 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 compared with past trend data to identify trends that exceed the normal fluctuation range. By comparing the fluctuation slopes of the equipment in different time periods, the system can accurately determine whether the rate of load growth of the equipment in certain time periods is abnormal, and then estimate future load change trends.

[0042] According to the abnormal growth trend of demand fluctuation slope greater than 30kW / min and the fluctuation of equipment operation demand, the equipment operation demand overload situation is counted; In an embodiment of the present invention, combined with the obtained demand fluctuation slope abnormal growth trend data, if the slope exceeds 30kW / min, the system will further count the overload situation of the equipment. The overload situation refers to the risk of equipment damage or system failure caused by the actual load of the equipment exceeding the rated load range. In this step, the system will calculate the difference between the actual load and the maximum load-bearing load of the equipment by monitoring the power consumption of the equipment in real time. If the load of the equipment exceeds the rated value and maintains for a period of time, the system will determine that the equipment has entered an overload state, and will analyze the overload behavior of the equipment under different loads based on historical data and equipment characteristics, and determine whether the equipment can withstand the sudden load increase. In this way, the system can accurately predict the overload state of the equipment.

[0043] Use periods of sudden demand growth and equipment operating demand overload to estimate the dynamic abnormal load conditions of equipment.

[0044] In the embodiment of the present invention, the dynamic abnormal load condition of the equipment is estimated by using the period of sudden increase in demand and the overload condition of the equipment operation demand. Through the comprehensive analysis of various operating parameters of the equipment, the system can predict the working state of the equipment under different load conditions and accurately identify the abnormal load state of the equipment. Comprehensively considering the historical operating data of the equipment, the current load fluctuation, the sudden increase in demand and the overload state, real-time monitoring and prediction are carried out to ensure that when the equipment has an abnormal load, emergency measures can be taken in time to avoid equipment failure and generate a dynamic abnormal load state of the equipment.

[0045] Preferably, the electrical stability gradient attenuation condition detection in step S2 includes: According to the dynamic abnormal load condition of the equipment, the load accumulation condition of the intelligent power transmission and distribution equipment is detected for the operation state of the intelligent power transmission and distribution equipment to obtain the load accumulation condition data of the power transmission and distribution equipment; In an embodiment of the present invention, the load of the equipment is cumulatively detected based on the equipment operation data obtained in the previous steps (such as the estimation of the dynamic abnormal load condition of the equipment). The cumulative load condition 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, multiple data collections are performed within a certain time interval, and then a long-term load change trend is obtained by weighted averaging each load change. These data will reflect the load condition of the equipment in different time periods, and whether the load is within the normal range. The cumulative load condition data will provide a basis for subsequent electrical stability analysis.

[0046] Estimate the spatial distribution drift of equipment load based on the cumulative load status data of power transmission and distribution equipment; In an embodiment of the present invention, the spatial distribution of the equipment load is analyzed based on the accumulated load status data of the equipment. Load spatial distribution drift refers to a change in the distribution of the equipment load between different areas or different lines, which is caused by an increase in load demand or a change in system configuration. To analyze this drift, the system will perform a spatiotemporal analysis of the load distribution of the equipment. By monitoring the load data of each sub-area of ​​the equipment, areas with large load fluctuations are identified, and changes in load distribution in different time periods are calculated. The system uses a distributed sensor network to collect real-time load data at different locations, and inputs this data into a spatial analysis algorithm, such as calculating the load distribution of each area through a regional division method. Based on these data, it is determined whether the equipment has a load drift phenomenon, and the direction and intensity of the drift are predicted.

[0047] Estimate the local load gradient growth of equipment based on the spatial distribution drift of equipment load; In an embodiment of the present invention, based on the estimated drift of the spatial distribution of the equipment load, the growth of the local load gradient of the equipment is further analyzed. The local load gradient refers to the rate at which the load changes over time or space in a specific area or a subsystem. The growth of the load gradient represents that the load of the equipment in certain areas changes rapidly, which may cause the load of the equipment to be overloaded or unbalanced. The local load gradient of the equipment is calculated, and the system performs detailed processing on the load data of the equipment. The system will perform differential processing on the numerical values ​​of the spatial distribution of the load to obtain the rate of change of the load between each area. Specifically, the system will compare the loads in different areas at each time point, calculate the change in the load gradient, and determine its growth trend. If the local load gradient shows a faster growth in a certain area, the system will mark the area as a high-risk area and further estimate the probability of load overload.

[0048] Detect the attenuation trend of the power quality transmitted by the equipment based on the local load gradient growth of the equipment and the cumulative load status of the power transmission and distribution equipment; In an embodiment of the present invention, the power transmission quality attenuation trend of the equipment is analyzed based on the local load gradient growth data and the load accumulation status data of the equipment. Power quality attenuation refers to the quality attenuation phenomenon such as voltage fluctuation and frequency fluctuation that occurs in the equipment during power transmission as the load of the equipment continues to increase or the local load gradient changes. By monitoring the voltage, current, frequency and other data of the equipment, combined with the load change of the equipment, the change trend of power quality is analyzed. If the growth of the local load gradient is more significant, it will cause voltage instability or frequency deviation during power transmission. 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 accumulation data to determine the attenuation trend. The current waveform in the power system is detected, and the degree of attenuation of power quality is analyzed using digital signal processing technology. If the system detects that the power quality has a trend of obvious attenuation, the system will output attenuation trend data and provide support for subsequent power voltage collapse prediction.

[0049] Predict the voltage drop condition of equipment power transmission based on the power quality attenuation trend of equipment transmission; In an embodiment of the present invention, based on the power quality attenuation trend, the power transmission voltage collapse of the equipment is further predicted. Voltage collapse is usually a phenomenon of sharp voltage drop caused by load overload, poor power quality or system failure, which will cause unstable operation or shutdown of the equipment. By real-time monitoring of the voltage of the power equipment and combining the power quality attenuation trend, the probability of voltage collapse can be predicted. Specifically, the system uses an early warning model based on time series analysis to predict the change in voltage during power transmission under a certain load level and attenuation trend. If the voltage drop rate reaches a certain threshold, the system will determine the time when the voltage collapse occurs and issue an alarm in time. In addition, the system will also predict the voltage and adjust the working status of the equipment in real time based on the real-time data of voltage and current.

[0050] Predict the risk of equipment current reverse flow based on the equipment power transmission voltage drop and the equipment local load gradient growth; In an embodiment of the present invention, the current reverse flow risk of the equipment is predicted by combining the obtained power transmission voltage collapse data and the local load gradient growth of the equipment. Current reverse flow refers to the current direction being opposite to the expected transmission direction. It is a phenomenon of current reverse flow caused by voltage instability, improper connection of the power system or failure. Reverse flow causes damage to the equipment or affects the stability of the system. The probability of current reverse flow is analyzed by monitoring the current data and voltage changes of the equipment. The system will apply an instantaneous current reversal judgment algorithm to detect the direction of current flow in real time. If the direction of current flow changes abnormally and is related to the voltage change trend of the equipment, the system will mark the period as a high-risk period for current reverse flow.

[0051] The electrical stability gradient attenuation condition of the equipment is detected based on the equipment current reverse flow risk condition and the equipment power transmission voltage drop condition.

[0052] In an embodiment of the present invention, the electrical stability gradient attenuation condition of the equipment is detected in combination with the current reverse flow risk data of the equipment and the power transmission voltage collapse data. Electrical stability gradient attenuation refers to the gradual decrease in the stability of the equipment's electrical system due to factors such as load imbalance, voltage fluctuation and current reverse flow during the operation of the equipment. By monitoring and analyzing data such as voltage, current, load, frequency and other data in multiple dimensions, combined with the electrical characteristics of the equipment, the change in the electrical stability gradient is calculated. If the system detects a decrease in electrical stability, the system will output an electrical stability gradient attenuation report and mark potential risk areas. According to the trend of electrical stability attenuation, the system can take corresponding control measures, such as adjusting the equipment load, optimizing the power transmission method, etc., to ensure the safe and stable operation of the equipment.

[0053] Preferably, the prediction of the electrical interference composite instability condition in step S2 includes: Estimate the instantaneous high voltage impact of the equipment based on the gradient attenuation of the equipment's electrical stability; In an embodiment of the present invention, based on the electrical stability gradient attenuation status data of the equipment, the instantaneous high voltage shock that the equipment may encounter during operation is analyzed. Electrical stability gradient attenuation refers to the gradual decrease in the stability of the electrical system of the equipment due to factors such as uneven load and equipment aging. The attenuation of the electrical stability gradient will trigger an instantaneous voltage shock, especially when the load changes drastically or the equipment fails, the voltage shock phenomenon is particularly significant. By monitoring the voltage changes, load fluctuations and reverse current flow of the equipment, the instantaneous high voltage shock of the power system is analyzed using time-frequency analysis methods such as fast Fourier transform (FFT) or wavelet transform. The system will also capture the voltage spike phenomenon in real time and calculate the frequency and amplitude of the high voltage shock. According to the attenuation of the electrical stability gradient, combined with the rapid response of the voltage fluctuation, the system can identify the period of occurrence of the instantaneous voltage shock and output the high voltage shock warning data.

[0054] Use the instantaneous high voltage impact of the equipment to estimate the damage degree of the equipment transformer; In an embodiment of the present invention, based on the obtained data of instantaneous high voltage shock of the equipment, the system will further evaluate the degree of damage of the equipment transformer. The transformer plays a key role in voltage conversion in the power system. When an instantaneous high voltage shock occurs, the insulation and core components of the transformer will be seriously damaged, resulting in unstable operation of the equipment. Damage assessment is performed by collecting the voltage and current data of the transformer and combining the characteristics of the instantaneous high voltage shock. In a specific implementation, the system uses the current integration method to detect the correlation between the current peak and the voltage fluctuation, and evaluates the current waveform changes inside the transformer. If the current waveform shows frequent mutations or the voltage fluctuates significantly, 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 shock, the system will calculate the degree of damage to the transformer and output a damage assessment report.

[0055] Analyze the instability of the equipment power system based on the damage degree of the equipment transformer and the instantaneous high voltage impact of the equipment; In an embodiment of the present invention, based on the obtained degree of transformer damage and instantaneous high voltage shock, it is further analyzed whether the power system of the equipment is in an unstable state. The instability of the power system is caused by transformer damage, excessive voltage fluctuations, etc., which may lead to the collapse of the entire power system in severe cases. By real-time monitoring of the voltage, current and frequency data of the power system, the stability of the power system is evaluated using state estimation and instability analysis methods. For example, the electrical characteristics of the equipment are evaluated using a robust control method to determine whether the damage to the transformer has affected the normal operation of the power system. Using power system transient analysis, by calculating the time domain response of the power system, the impact of the instantaneous voltage shock is further analyzed to obtain the instability probability of the power system. If the risk of instability is high, the system will output power system instability warning information.

[0056] Utilize the instability of the equipment power system to detect abnormal electromagnetic generation of the equipment; In an embodiment of the present invention, based on the instability of the power system, it is further detected whether the equipment generates abnormal electromagnetic interference. The instability of the power system usually leads to unstable operation of electrical equipment and causes abnormal electromagnetic radiation, especially high-voltage electrical equipment such as transformers and switchgear. Abnormal electromagnetic generation will have an impact on surrounding equipment and systems, and even cause equipment damage or communication interference. By monitoring the electromagnetic radiation intensity of the equipment and combining the power system instability data, an electromagnetic field monitoring sensor is used to detect the electromagnetic field distribution of the equipment in real time. The system will perform spectrum analysis on the electromagnetic radiation data to identify the source of electromagnetic interference. If the electromagnetic radiation intensity increases abnormally and is related to the instability of the power system, the system will mark it as a period of abnormal electromagnetic interference generation and calculate the intensity of abnormal electromagnetic generation. The system will also further predict the impact range of electromagnetic wave propagation based on the electromagnetic interference data.

[0057] Evaluate the growth trend of equipment electromagnetic interference based on abnormal electromagnetic generation conditions of the equipment; In an embodiment of the present invention, based on the abnormal electromagnetic generation data of the equipment, the system will measure the growth trend of the electromagnetic interference of the equipment. The growth trend of electromagnetic interference is identified by spectrum analysis, which decomposes the electromagnetic signal into multiple frequency bands and analyzes the interference degree and its changes in each frequency band. By monitoring the electromagnetic field data of the equipment for a long time and combining it with the operating status of the equipment, the electromagnetic interference is modeled using a timing analysis method to calculate the intensity growth rate of the electromagnetic interference. The system will identify the key nodes of interference growth based on the increase in the electromagnetic interference signal and estimate the trend of further expansion of the interference. The system will also combine the electromagnetic interference intensity with the stability data of the equipment to determine whether the equipment will be seriously affected, further affecting the quality of power transmission.

[0058] Predict the degree of current distortion in device transmission based on the growth trend of electromagnetic interference in the device; In an embodiment of the present invention, the obtained electromagnetic interference growth trend is used to further predict the degree of current transmission distortion of the device. Electromagnetic interference can cause distortion of the current signal inside the device, which is manifested as distortion of the current waveform, frequency deviation and other phenomena. Current distortion can affect the normal operation of the device, resulting in power loss and equipment damage. By analyzing the spectrum data of electromagnetic interference and combining the changes in the current waveform, a current waveform analysis tool is used to predict the degree of current distortion. The system calculates the distortion index by measuring the degree of waveform distortion of the current. 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.

[0059] Identify the breakdown of the equipment insulation layer based on the instantaneous high voltage impact of the equipment; In the embodiment of the present invention, based on the instantaneous high voltage impact of the device, it is identified whether the insulation layer of the device is broken down. When the insulation layer of the device is subjected to a high voltage impact, it will break down, resulting in faults such as short circuit and damage to the device. The system identifies whether the insulation layer is impacted by detecting the voltage, temperature, vibration and other data of the device. High-frequency voltage measurement and insulation performance analysis tools are used to predict the damage to the insulation layer of the device in combination with the amplitude and duration of the voltage impact. The system will also monitor the current changes of the device in real time, detect abnormal increase in current or change in current path, to determine whether the insulation layer is broken down.

[0060] The composite instability condition of electrical interference is predicted based on the breakdown of the equipment insulation layer and the degree of distortion of the equipment transmission current.

[0061] In an embodiment of the present invention, the electrical interference composite instability condition is predicted by combining the insulation breakdown of the equipment and the degree of current distortion of the equipment transmission. Electrical interference composite instability refers to the instability of the equipment electrical system or even complete failure caused by the combined effects of multiple factors such as equipment insulation damage, current distortion, and electromagnetic interference. Combining all collected data, a multi-dimensional analysis method is used to comprehensively evaluate the risk of electrical interference composite instability. The system inputs the monitored data such as equipment insulation breakdown, transmission current distortion, and electromagnetic interference into the instability prediction algorithm to calculate the probability and severity of electrical interference composite instability. The system will conduct a comprehensive assessment of the electrical performance of the equipment and output early warning information before an instability event occurs.

[0062] Preferably, step S3 comprises the following steps: Step S31: estimating the cumulative thermal effect of power transmission and distribution equipment according to the composite instability condition of electrical interference; In an embodiment 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 time-series sorted. The above indicators are curve-fitted by a continuously collected high-frequency data analysis tool, especially the period when each voltage surge exceeds the rated value by more than 15% is marked separately, and the electrical disturbance density is extracted according to the current fluctuation amplitude in the corresponding time period. Based on the abnormal enhancement area of ​​the electromagnetic spectrum, the local heating tendency of the position corresponding to the equipment heat dissipation channel is determined by using the Fourier frequency band division result, and the cumulative statistical map of thermal stress is constructed in combination with the historical working temperature rise data. Subsequently, the temperature integration result collected by the thermistor over time is aligned with the above-mentioned thermal stress statistical map, and the thermal effect value of the unit structural component is accumulated by the superposition algorithm, so as to obtain the cumulative thermal effect of each module level. For example, in a set of high-voltage switchgear at the substation end, the thermal stress of the busbar 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 cumulative value of its thermal effect exceeds the set warning threshold.

[0063] Step S32: predicting the structural aging trend of the power transmission and distribution equipment based on the cumulative thermal effect of the power transmission and distribution equipment; In an embodiment of the present invention, after completing the extraction of the cumulative data of thermal effects, an infrared thermal imaging device is used to collect high-precision images of the equipment surface and its connection parts. The image processing algorithm is used to identify the expansion trend of hot spots in metal components, insulating components and shell connection parts. The hot spot area expansion rate is correlated with the previously calculated cumulative value of thermal effects, and the initial location of thermal aging and the frequency of cracks in each part are statistically analyzed. At the same time, by comparing the infrared brightness change trends of key structural connecting bolts, insulating oil seals and high-voltage connecting sub-assemblies, the growth rate of thermal fatigue parameters of unit structural parts is calculated, and the structural stress relaxation rate and aging expansion cycle are obtained. For example, by continuously tracking the thermal image brightness around a group of high-voltage transformer connection flange sealing gaskets for 24 hours, it is found that the consistency between the abnormal brightness growth range and the bolt loosening frequency exceeds 92%. Combined with its thermal effect index, it is concluded that the structural aging trend prediction period of this part is to enter the microcrack stage within 7 days and enter the structural failure edge state after 15 days.

[0064] Step S33: estimating the load collapse trend of the power transmission and distribution equipment based on the accumulated thermal effect of the power transmission and distribution equipment and the structural aging trend of the power transmission and distribution equipment; In an embodiment of the present invention, the thermal effect cumulative intensity distribution diagram obtained in step S31 is spatially mapped and paired with the structural aging progress diagram generated in step S32, and the structural stability decay rate is marked on each module node. Subsequently, the load stability test system is used to sample the load response data of each node under different load densities, and analyze whether the micro-deformation caused by thermal expansion causes the current path offset or resistance mutation. The temperature response of the device under the peak load state in multiple observation cycles is superimposed with the mechanical response curve to form a multi-axis thermal analysis diagram, and the response delay change curve before the critical load is extracted, and the trend line formed by the delay change point is tracked. In this way, the inflection point position of the device entering the nonlinear load response state is calculated and superimposed with the structural aging node to form the overall load collapse trend diagram of the device. For example, in a group of busbar distributed switch networks, when the heat accumulation value exceeds 680J / cm², the resistance increase area matching the concentrated position of the support point microcrack distribution appears at the same time. Through continuous recording, it can be obtained that the load response lag increases from 180ms to 500ms, and it is predicted that there is a precursor to load collapse at this node.

[0065] Step S34: estimating the equipment communication link degradation trend based on the load collapse trend of the power transmission and distribution equipment.

[0066] In the embodiment of the present invention, a high-frequency communication reflection monitoring module and a link interruption time recording device are respectively arranged for the position where the load collapse trend appears, and the transmission stability of the communication link in the equipment operation cycle is observed in real time. Based on the structural micro-deformation area determined in the above steps, the transmission signal stability data of the communication link in the area is collected, and special attention is paid to the time period when the signal reflection increases, the delay changes suddenly, and the packet loss rate increases. By analyzing the correlation between the instantaneous change of the link impedance and the number of consecutive data packet transmission failures, a link degradation sensitivity curve is constructed. Further, the communication frequency stability monitoring data is introduced, 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 frequent instability area and the structural deformation center. In this way, the performance degradation degree of the communication link caused by load collapse in the next 10 days is evaluated. For example, in a feeder protection circuit of a substation, a section of the optical fiber communication path is affected by the load deformation, and the optical signal reflection echo ratio increases from 1.2 to 4.8, and the number of communication interruptions increases to 15 times within 72 hours. The analysis chart confirms that the degradation trend of the communication link is strongly rising, and it is judged that the path will reach the interruption risk boundary within 96 hours.

[0067] It is particularly important that step S32 includes the following steps: Step S321: detecting the degree of thermal expansion of the structure of the power transmission and distribution equipment according to the accumulation of thermal effects of the power transmission and distribution equipment; In an embodiment of the present invention, the thermal expansion of the device is monitored in real time by using a temperature change rate and a thermal strain calculation model. A strain sensor or a displacement sensor is used to directly measure the expansion or deformation degree of the structure. The thermal expansion of the device is usually manifested as a change in volume or length of the material after heating. According to the thermal expansion coefficient of different materials, the expansion degree is calculated through the real-time temperature data of the device. For example, if the sensor detects a temperature change and a physical quantity (such as length or angle) changes, the expansion degree of the part is obtained.

[0068] Step S322: detecting the deformation trend of the power transmission and distribution equipment structure based on the thermal expansion degree of the power transmission and distribution equipment structure; In the embodiment of the present invention, after the degree of thermal expansion of the power transmission and distribution equipment is detected, the next step is to predict the structural deformation trend of the equipment based on the thermal expansion situation. The thermal expansion data obtained through the above steps is input into the structural analysis system for more in-depth physical calculations. The structural analysis system uses finite element analysis (FEA) technology to simulate the deformation of the entire or local area of ​​the equipment under different degrees of thermal expansion by establishing a structural model of the equipment. To obtain a high-precision 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 coordination of these data, the structural 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 deformations due to thermal expansion, resulting in changes in the working structure of the equipment. By monitoring the degree of deformation of different parts of the equipment, the deformation trend of the overall structure of the equipment under the current environmental conditions is obtained.

[0069] Step S323: estimating the growth of 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; In an embodiment of the present invention, the growth of the structural thermal stress of the equipment is predicted in combination with the analysis results of the first two steps (i.e., the degree of thermal expansion of the equipment and the structural deformation trend). Structural stress is an internal force generated by the deformation of the material under high load or high temperature conditions. The thermal effect will cause the structure to deform, thereby generating internal stress. Through the deformation data obtained previously, a stress analysis model (such as Von Mises stress analysis, principal stress analysis, etc.) is used to calculate the stress of each structural component of the equipment. Specifically, the displacement data and temperature change information fed back by the sensor are combined with the geometric parameters and material properties of the equipment to calculate the magnitude of the thermal stress and its distribution in the equipment structure. Stress prediction is not only a static calculation, but also needs to take into account the dynamic thermal stress accumulation of the equipment during long-term operation. Thermal stress gradually increases over time, especially when it is operated under long-term load and high temperature, the stress growth will show a nonlinear upward trend. Based on these data, the growth of thermal stress of structural components can be predicted.

[0070] Step S324: Identify the growth trend of micro-cracks in equipment materials according to the growth of thermal stress in the structure of the power transmission and distribution equipment; In an embodiment of the present invention, based on the structural thermal stress data obtained in the above steps, the next step is to identify the expansion trend of microcracks in the equipment material. When the structure of the equipment is under long-term thermal stress, especially in a high temperature and high load environment, microcracks will gradually appear on the surface of the material, especially metal parts are more prone to crack expansion under tension or compression. Install strain sensors or crack detection sensors (such as ultrasonic sensors or X-ray imaging technology) to continuously monitor the equipment. When the thermal stress of the equipment structure reaches a certain threshold, it triggers the expansion of microcracks. The sensor determines whether the microcracks have begun to expand by detecting the changing characteristics of the cracks. The sensor monitoring data is combined with a crack expansion prediction model (such as the Paris law or the mineralogical damage model) to predict the expansion trend of microcracks.

[0071] Step S325: estimating the degree of thermal fatigue of the equipment based on the microcrack growth trend of the equipment material and the thermal stress growth of the power transmission and distribution equipment structure; In an embodiment of the present invention, the thermal fatigue degree of the equipment is estimated by combining the microcrack expansion and thermal stress growth data. Thermal fatigue is the gradual degradation of material properties due to long-term thermal cycles. In particular, during thermal expansion and contraction, the microcracks of the material will gradually expand, leading to fatigue failure of the structure. To evaluate the degree of thermal fatigue, fatigue life analysis is performed in combination with the equipment's operating history data, such as operating temperature fluctuations, load changes and other factors. At this time, the SN curve (stress-life curve) and stress-strain cycle analysis are used to calculate the degree of fatigue damage accumulation suffered by the material under the action of thermal stress.

[0072] Step S326: predicting the structural aging trend of the power transmission and distribution equipment based on the thermal fatigue degree of the equipment and the microcrack growth trend of the equipment material.

[0073] In the embodiment of the present invention, the structural aging trend is predicted based on the thermal fatigue degree and microcrack extension trend of the equipment. The structural aging of the equipment is a long-term, gradual accumulation process, and thermal fatigue and crack extension are important factors leading to aging. The structural aging trend of the equipment is predicted by combining the fatigue analysis results with the crack extension data. Combining data mining technology and aging prediction models, an aging trend graph of the equipment is generated based on historical data, durability of equipment materials, thermal stress accumulation and other factors.

[0074] It is particularly important that step S33 includes the following steps: Step S331: estimating the displacement of the equipment structure according to the accumulated thermal effect of the power transmission and distribution equipment and the aging trend of the power transmission and distribution equipment structure; In an embodiment of the present invention, according to the accumulated data of thermal effects and structural aging trends of the equipment, the thermal expansion and deformation trend data obtained through the above 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 heat load and load change, and the structure will gradually shift, especially in the process of thermal expansion or aging of the equipment, excessive deformation or displacement will occur in the local area. The equipment is monitored in real time by a high-precision displacement sensor (such as an optical fiber displacement sensor or a laser ranging sensor) to obtain the displacement data of each key part. Subsequently, combined with the finite element analysis (FEA) technology, the thermal stress and load conditions under 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 extension of the material, fatigue damage, etc.) is also considered, because aging will cause the hardness and toughness of the material to decrease, making the structure more deformable. Through these data, the displacement and change trend of the structure of the equipment under specific operating conditions, especially the displacement change of key structural parts, can be estimated.

[0075] Step S332: Detecting poor contact of the device transmission line according to the device structure displacement; 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.

[0076] Step S333: estimating the restricted current flow path of the device based on the poor structure of the device transmission line; 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.

[0077] Step S334: estimating the local concentration of the device transmission current according to the device current flow path restriction; In an embodiment 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 certain areas. This local current concentration will cause certain parts of the device to bear a greater load, thereby increasing the risk of failure. Accurately estimate the local concentration of equipment current and perform 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, the distribution of current on different paths is calculated in detail. The current density model can help analyze the flow of current in different components and identify areas where the current density is too high. By installing current sensors and monitoring the current changes of each key node in real time, real-time data on current density is obtained. For areas with restricted flow paths, by analyzing the changes in current distribution, it is determined whether there is local current concentration.

[0078] Step S335: predicting the deterioration of the electrical flow capacity of the equipment according to the local concentration of the equipment transmission current; In an embodiment of the present invention, when the current inside the device is locally concentrated, the electrical flow capacity of the area will be degraded, which is manifested as a decrease in the current carrying capacity of the device, excessive temperature of the device, increased electrical losses, and the like. Based on the local current concentration data obtained in step S334, the next step is to predict the degradation of the electrical flow capacity of the device. By analyzing the local current concentration, the current loss model is used to calculate the electrical performance degradation caused by the local current concentration. These losses will directly affect the electrical flow capacity of the device, especially when the device is under high load, the current concentration will further aggravate the temperature increase of the device and cause damage to electrical components.

[0079] Step S336: estimating the load collapse trend of the power transmission and distribution equipment based on the degradation of the equipment's electrical flow capacity and the local concentration of the equipment's transmission current.

[0080] In an embodiment of the present invention, the load collapse trend of the equipment is estimated based on the electrical flow capacity degradation data and the analysis results of the local current concentration obtained in step S335. Load collapse refers to the situation where the electrical system of the equipment cannot continue to work stably due to the decrease in electrical flow capacity when the equipment is under high load or current overload, resulting in collapse. The aforementioned electrical flow capacity degradation data is combined with the load distribution model of the equipment to analyze the electrical carrying capacity of the equipment under different load conditions. When the electrical flow capacity decreases, the stability of the equipment under high load will decrease, causing the system to collapse or fail. By real-time monitoring of the load conditions and current changes of the equipment, the precursors of load collapse can be discovered in time.

[0081] Preferably, step S4 comprises the following steps: Step S41: estimating abnormal circuit communication status of power transmission and distribution equipment according to the degradation trend of equipment communication link; In an embodiment of the present invention, the communication link quality detection module is called to archive and analyze the stability of data transmission within the historical time window, specifically including the signal-to-noise ratio fluctuation curve, bit error rate change, packet loss rate cumulative value and 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, on the basis of the clear trend of communication link degradation, the time consistency characteristics of the increase in link packet loss rate and the decrease in signal-to-noise ratio are extracted, the abnormal peak frequency table in 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 proportion of abnormal communication data, when the communication link state deteriorates continuously for more than 3 cycles, it is determined that the current device circuit has a communication abnormal fluctuation condition, and it is marked as a communication abnormal state level 1 to 3. The output circuit communication abnormal state data includes: communication delay drift coefficient, retransmission surge amplitude, and abnormal fluctuation intensity level.

[0082] Step S42: estimating the imbalance of the response of the communication unit of the equipment according to the abnormal communication status of the circuit of the power transmission and distribution equipment; In an embodiment of the present invention, based on the identified circuit communication abnormality level data, the communication response control recording module is called to obtain information such as the interrupt response log, scheduling delay record, queue blocking frequency, etc. under the device master communication path. 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. If there are more than three consecutive cycles in which the response delay deviates from the reference value by more than 20 milliseconds, 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 a response imbalance state. The response imbalance state is further graded according to the response window offset range, instruction accumulation depth, and scheduling inconsistency rate to form response imbalance level evaluation data.

[0083] Step S43: Detecting the increasing trend of equipment communication control difficulty based on the abnormal communication status of the power transmission and distribution equipment circuit and the imbalanced response status of the equipment communication unit; In an embodiment of the present invention, after confirming the communication anomaly level and the response imbalance level, a control complexity calculation tool is used to analyze the operation iteration load of the current communication control path under continuous abnormal conditions. The method includes calculating the ratio of the total number of communication control instructions to the number of retries within a unit control cycle, retrieving the task blocking list and cache overflow log in the scheduling and control unit, and detecting the execution path reorganization behavior caused by the link anomaly. If the number of valid completion paths of the communication control task exceeds the number of initial configuration paths by more than one times, and the increase in the delay of the reconstructed path exceeds fifty milliseconds, it is marked as a communication control difficulty growth state. Further statistics are made on the average interval time of scheduling failures and the number of task reorderings, and the control resource utilization growth curve is derived to determine whether the system has entered a stage of rapid increase in control pressure to form communication control difficulty growth trend level data.

[0084] Step S44: detecting abnormal conditions of the power transmission and distribution equipment based on the increasing trend of equipment communication control difficulty and the load collapse trend of the power transmission and distribution equipment; In an embodiment of the present invention, the communication control difficulty growth trend level data and the load collapse trend level data output in the previous step are called to perform a two-factor coupling analysis. By constructing an operation abnormality resonance matching diagram, the temporal position relationship between the control pressure peak and the load abnormality high risk segment is compared frame by frame. If the synchronous overlap area of ​​the resonance amplitudes of the two exceeds sixty percentage points of the current sliding time window, and a trend synchronization intersection is formed between the control resource utilization rate and the load abnormality amplitude, it is determined that the current equipment operation has entered an unstable state. The operation monitoring module is further called to read redundant operation status indicators such as temperature sensors, internal power supply fluctuation amplitudes, circuit recovery times, and control logic repeated initialization behaviors, to assist in constructing the abnormal condition quantification map output of the power transmission and distribution equipment abnormal condition data including abnormal coupling strength, control-load resonance duration, and synchronization peak delay indicators.

[0085] Step S45: Perform a power transmission and distribution equipment failure risk assessment based on the abnormal conditions of the power transmission and distribution equipment, obtain power transmission and distribution equipment failure risk data, and upload it to the cloud platform for early warning.

[0086] In an embodiment of the present invention, when the abnormal condition level identified in step S44 reaches the preset trigger standard, the system enters the fault risk assessment process. In the 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. The assessment process uses a causal logic analysis method to multiple deduces the probability of each fault influencing factor under multiple paths to determine the current fault risk level. Specific fields include fault occurrence probability indicators, cause source path numbers, earliest failure component numbers, expected failure time periods, etc. to form complete power transmission and distribution equipment failure risk data, and the evaluation results are structured and uploaded to the corresponding node of the cloud platform through the built-in communication protocol stack of the edge processing terminal and the MQTT publishing mechanism. After receiving the data, the cloud platform generates a risk warning signal through the 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.

[0087] 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: An operation status detection module is used to obtain data of intelligent power transmission and distribution equipment; estimate initial performance parameters of the intelligent power transmission and distribution equipment based on the data of the intelligent power transmission and distribution equipment; and detect the operation status of the intelligent power transmission and distribution equipment based on the initial performance parameters of the intelligent power transmission and distribution equipment; The electrical interference composite instability prediction module is used to estimate the dynamic abnormal load condition of the equipment based on the data of the intelligent power transmission and distribution equipment; 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; and predict the electrical interference composite instability condition based on the electrical stability gradient attenuation condition of the equipment; The communication link degradation trend prediction module is used to predict the cumulative thermal effect of the power transmission and distribution equipment based on the composite instability of electrical interference; to predict 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 to predict the equipment communication link degradation trend based on the load collapse trend of the power transmission and distribution equipment. The risk warning module is used to detect abnormal conditions of power transmission and distribution equipment based on the degradation trend of equipment communication links and the collapse trend of power transmission and distribution equipment loads; conduct power transmission and distribution equipment failure risk assessment based on the abnormal conditions of power transmission and distribution equipment, obtain power transmission and distribution equipment failure risk data, and upload it to the cloud platform for early warning.

[0088] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest 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: The following steps are involved: Step S1: Acquire data of the intelligent power transmission and distribution equipment; estimate 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 status of the intelligent power transmission and distribution equipment according to the initial performance parameters of the intelligent power transmission and distribution equipment; Step S2: estimating 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 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 of the equipment; Step S3: estimating the cumulative thermal effect of the power transmission and distribution equipment according to the composite instability condition of electrical interference; estimating the load collapse trend of the power transmission and distribution equipment based on the cumulative thermal effect of the power transmission and distribution equipment; Predict equipment communication link degradation trends based on load collapse trends of transmission and distribution equipment; Step S4: Detecting abnormal conditions of the power transmission and distribution equipment according to the degradation trend of the equipment communication link and the collapse trend of the power transmission and distribution equipment load; Conduct a risk assessment of power transmission and distribution equipment failure based on the abnormal conditions of the power transmission and distribution equipment, obtain the power transmission and distribution equipment failure risk data, 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, characterized in that: Step S1 includes the following steps: Step S11: Acquire data of intelligent power transmission and distribution equipment; Step S12: extracting 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: estimating 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: detecting the operating status 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.

3. The communication control method for intelligent power transmission and distribution equipment according to claim 2, characterized in that: Step S13 includes the following steps: Step S131: Collecting geometric structure parameters of the intelligent power transmission and distribution equipment according to the data of the intelligent power transmission and distribution equipment; Step S132: identifying 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 the geometric structure parameters of the intelligent power transmission and distribution equipment are used to identify the power transmission and distribution equipment and detect the spatial compatibility of the power transmission and distribution equipment; Step S134: evaluating the heat dissipation capacity data of the power transmission and distribution equipment when the space compatibility of the power transmission and distribution equipment is greater than 150 mm / m²; Step S135: evaluating 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: Evaluating the electrical performance data of the intelligent power transmission and distribution equipment according to the data of the intelligent power transmission and distribution equipment; Step S137: estimating 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 intelligent power transmission and distribution equipment.

4. The communication control method for intelligent power transmission and distribution equipment according to claim 2, characterized in that: Step S14 includes the following steps: Step S141: Detecting 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: predicting 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: estimating the link response speed of the power transmission and distribution equipment according to 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: evaluating the communication interaction capability of the power transmission and distribution equipment according to 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: evaluating 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; Step S146: Detect the operating status of the intelligent power transmission and distribution equipment according to the power transmission and distribution equipment operating safety parameters and the power transmission and distribution equipment communication and interaction capabilities.

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 according to the data of the intelligent power transmission and distribution equipment; perform the operation time series analysis of the power transmission and distribution equipment based on the operation log of the power transmission and distribution equipment to obtain the equipment operation time series data; Monitor fluctuations in equipment operation demand based on equipment operation time series data; Collect the periodic changes in equipment operation demand when the fluctuation of equipment operation demand is ±10kW; Estimate and count periods of sudden demand growth based on periodic changes in equipment operation and fluctuations in equipment operation demand; Calculate the abnormal growth trend of demand fluctuation slope based on the fluctuation of equipment operation demand; According to the abnormal growth trend of demand fluctuation slope greater than 30kW / min and the fluctuation of equipment operation demand, the equipment operation demand overload situation is counted; Use periods of sudden demand growth and equipment operating demand overload to estimate the dynamic abnormal load conditions of equipment.

6. The communication control method for intelligent power transmission and distribution equipment according to claim 1, characterized in that: The electrical stability gradient decay condition detection in step S2 includes: According to the dynamic abnormal load condition of the equipment, the load accumulation condition of the intelligent power transmission and distribution equipment is detected for the operation state of the intelligent power transmission and distribution equipment to obtain the load accumulation condition data of the power transmission and distribution equipment; Estimate the spatial distribution drift of equipment load based on the cumulative load status data of power transmission and distribution equipment; Estimate the local load gradient growth of equipment based on the spatial distribution drift of equipment load; Detect the attenuation trend of the power quality transmitted by the equipment based on the local load gradient growth of the equipment and the cumulative load status of the power transmission and distribution equipment; Predict the voltage drop condition of equipment power transmission based on the power quality attenuation trend of equipment transmission; Predict the risk of equipment current reverse flow based on the equipment power transmission voltage drop and the equipment local load gradient growth; The electrical stability gradient attenuation condition of the equipment is detected based on the equipment current reverse flow risk condition and the equipment power transmission voltage drop condition.

7. The communication control method for intelligent power transmission and distribution equipment according to claim 1, characterized in that: The prediction of the electrical interference compound instability condition in step S2 includes: Estimate the instantaneous high voltage impact of the equipment based on the gradient attenuation of the equipment's electrical stability; Use the instantaneous high voltage impact of the equipment to estimate the damage degree of the equipment transformer; Analyze the instability of the equipment power system based on the damage degree of the equipment transformer and the instantaneous high voltage impact of the equipment; Utilize the instability of the equipment power system to detect abnormal electromagnetic generation of the equipment; Evaluate the growth trend of equipment electromagnetic interference based on abnormal electromagnetic generation conditions of the equipment; Predict the degree of current distortion in device transmission based on the growth trend of electromagnetic interference in the device; Identify the breakdown of the equipment insulation layer based on the instantaneous high voltage impact of the equipment; The composite instability condition of electrical interference is predicted based on the breakdown of the equipment insulation layer and the degree of distortion of the equipment transmission current.

8. 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: estimating the cumulative thermal effect of power transmission and distribution equipment according to the composite instability condition of electrical interference; Step S32: predicting 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: estimating the load collapse trend of the power transmission and distribution equipment based on the accumulated thermal effect of the power transmission and distribution equipment and the structural aging trend of the power transmission and distribution equipment; Step S34: estimating the equipment communication link degradation trend based on the load collapse trend of the power transmission and distribution equipment.

9. 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: estimating abnormal circuit communication status of power transmission and distribution equipment according to the degradation trend of equipment communication link; Step S42: estimating the imbalance of the response of the communication unit of the equipment according to the abnormal communication status of the circuit of the power transmission and distribution equipment; Step S43: Detecting the increasing trend of equipment communication control difficulty based on the abnormal communication status of the power transmission and distribution equipment circuit and the imbalanced response status of the equipment communication unit; Step S44: detecting abnormal conditions of the power transmission and distribution equipment based on the increasing trend of equipment communication control difficulty and the load collapse trend of the power transmission and distribution equipment; Step S45: Perform a power transmission and distribution equipment failure risk assessment based on the abnormal conditions of the power transmission and distribution equipment, obtain power transmission and distribution equipment failure risk data, and upload it to the cloud platform for early warning.

10. A communication control system for intelligent power transmission and distribution equipment, characterized in that: Used to execute the communication control method for intelligent power transmission and distribution equipment as claimed in claim 1, the communication control system for intelligent power transmission and distribution equipment comprises: An operation status detection module is used to obtain data of intelligent power transmission and distribution equipment; estimate initial performance parameters of the intelligent power transmission and distribution equipment based on the data of the intelligent power transmission and distribution equipment; and detect the operation status of the intelligent power transmission and distribution equipment based on the initial performance parameters of the intelligent power transmission and distribution equipment; The electrical interference composite instability prediction module is used to estimate the dynamic abnormal load condition of the equipment based on the data of the intelligent power transmission and distribution equipment; 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; and predict the electrical interference composite instability condition based on the electrical stability gradient attenuation condition of the equipment; The communication link degradation trend prediction module is used to predict the cumulative thermal effect of the power transmission and distribution equipment based on the composite instability of electrical interference; to predict 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 to predict the equipment communication link degradation trend based on the load collapse trend of the power transmission and distribution equipment. The risk warning module is used to detect abnormal conditions of power transmission and distribution equipment based on the degradation trend of equipment communication links and the collapse trend of power transmission and distribution equipment loads; conduct power transmission and distribution equipment failure risk assessment based on the abnormal conditions of power transmission and distribution equipment, obtain power transmission and distribution equipment failure risk data, and upload it to the cloud platform for early warning.

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