Adaptive fault diagnosis method in electrical automation control system

Abstract

The invention relates to the technical field of data management, and particularly discloses a self-adaptive fault diagnosis method in an electrical automation control system, which can comprehensively analyze and identify potential abnormal early warning factors by accurately acquiring power demand prediction parameters and electrical operation parameters of a target area, and can improve the fault diagnosis accuracy. In the process, early-warning control process parameters of the target area are received and processed in real time, the response efficiency factor of the target area is scientifically evaluated in combination with the abnormal early-warning factor, and the fault diagnosis and early-warning control are automatically executed based on the evaluation result. According to the method, the electrical automation control system can intelligently carry out self-adaptive optimization and updating on the fault diagnosis and early warning control strategy, and it is ensured that the operation state of the power grid can be accurately judged under various complex conditions, so that the accuracy of fault diagnosis is improved; and the fault response speed and the self-optimization capability of the electrical automation control system are obviously enhanced.

Description

Technical Field

[0001] The invention relates to the technical field of data management, in particular to a self-adaptive fault diagnosis method in an electric automation control system. Background Art

[0002] In electrical automation control systems, intelligent fault diagnosis technology has been widely used in many fields such as power grids, industrial production lines, and building electrical systems. In power grids, intelligent fault diagnosis systems can monitor the operating status of the power grid in real time, quickly identify and locate fault points, and effectively prevent the occurrence of power grid accidents.

[0003] For example, the invention patent with publication number CN115130748A discloses a method for fault diagnosis of building electrical systems, including the following steps: S1: fault data collection; S2: constructing a variational mode decomposition and mutual dimensionless indicator model; S3: training a variational mode decomposition and mutual dimensionless indicator model; S4: recognition and classification using a support vector machine optimized by a quantum genetic algorithm.

[0004] For example, the invention patent with publication number CN114239972A discloses a campus energy efficiency and electrical safety management method and system based on artificial intelligence technology. The method includes the following steps: S1: Collecting front-end real-time energy consumption data and environmental information; S2: Classifying and storing the front-end real-time energy consumption data and environmental information collected in step S1 and historical energy consumption data; S3: Based on the real-time energy consumption data, establishing a BP neural network model, and using the particle swarm algorithm to optimize the BP neural network model to predict campus energy consumption, and according to the prediction of campus energy consumption, obtaining the location and cause of energy waste; S4: Finding the corresponding energy-consuming equipment according to the energy waste location, and automatically controlling the working status of the energy-consuming equipment.

[0005] However, in the process of implementing the embodiments of the present application, it was found that the above-mentioned technology has at least the following technical problems: when the existing technology diagnoses faults in the power system, it is usually judged only based on preset fixed conditions and thresholds. This leads to that in actual applications, the fault diagnosis results are often unable to accurately reflect the actual situation of the fault, and may even lead to false alarms or missed alarms due to unreasonable prediction conditions, thereby affecting the timeliness and effectiveness of the fault response. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides an adaptive fault diagnosis method in an electrical automation control system, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: an adaptive fault diagnosis method in an electrical automation control system, comprising: step one, based on the operation of the electrical automation control system in the target area, obtaining the power demand forecast parameters of the target area and the electrical operation parameters of the target area, and comprehensively analyzing the abnormal warning factors of the target area; step two, according to the abnormal warning factors of the target area, the electrical automation control system automatically performs fault diagnosis and early warning control; step three, based on the electrical automation control system, automatically performs fault diagnosis and early warning control, thereby obtaining the early warning control process parameters of the target area, and at the same time comprehensively evaluates the response efficiency factor of the target area by the abnormal warning factors of the target area, and adaptively optimizes and updates the fault diagnosis and early warning control of the electrical automation control system according to the response efficiency factors of the target area.

[0008] As a further method, the electrical automation control system automatically performs fault diagnosis and early warning control. The specific fault diagnosis process is: the electrical automation control system compares the abnormal warning factor of the target area with the first warning reference value and the second warning reference value stored in the electrical database. If the abnormal warning factor of the target area is less than or equal to the first warning reference value, the fault level of the target area is marked as the first level. If the abnormal warning factor of the target area is greater than the first warning reference value and the abnormal warning factor of the target area is less than or equal to the second warning reference value, the fault level of the target area is marked as the second level. If the abnormal warning factor of the target area is greater than the second warning reference value, the fault level of the target area is marked as the third level. The electrical automation control system thus diagnoses the fault level of the target area.

[0009] As a further method, the electrical automation control system automatically performs fault diagnosis and early warning control, and the specific early warning control process is:

[0010] The electrical automation control system matches the fault level of the target area with the early warning control set corresponding to each fault level in the electrical database. If the fault level of the target area is the first level, the first early warning control set of the target area is matched from the electrical database. If the fault level of the target area is the second level, the second early warning control set of the target area is matched from the electrical database. If the fault level of the target area is the third level, the third early warning control set of the target area is matched from the electrical database. The electrical automation control system automatically performs early warning control.

[0011] As a further method, the fault diagnosis and early warning control of the electrical automation control system are adaptively optimized and updated, and the specific fault diagnosis optimization and updating process is: if the response efficiency factor of the target area is less than or equal to the response efficiency threshold, the response efficiency threshold and the response efficiency factor of the target area are subjected to difference processing, the difference processing result and the response efficiency threshold are subjected to ratio processing, and the ratio processing result is marked as the response efficiency lag ratio of the target area; the first early warning reference value and the second early warning reference value stored in the electrical database are reduced according to the response efficiency lag ratio of the target area, thereby updating the corresponding fault level and storing it in the electrical database; if the response efficiency factor of the target area is greater than the response efficiency threshold, the response efficiency factor of the target area and the response efficiency threshold are subjected to difference processing, the difference processing result and the response efficiency threshold are subjected to ratio processing, and the ratio processing result is marked as the response efficiency excess ratio of the target area; the first early warning reference value and the second early warning reference value stored in the electrical database are expanded according to the response efficiency excess ratio of the target area, thereby updating the corresponding fault level and storing it in the electrical database.

[0012] As a further method, the fault diagnosis and early warning control of the electrical automation control system are adaptively optimized and updated, and the specific early warning control optimization and updating process is: if the response efficiency factor of the target area is less than or equal to the response efficiency threshold, the control parameters in the early warning control set corresponding to each fault level are processed according to the response efficiency lag ratio of the target area, updated and stored in the electrical database; if the response efficiency factor of the target area is greater than the response efficiency threshold, the early warning control set is not optimized and updated.

[0013] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0014] (1) The present invention provides an adaptive fault diagnosis method in an electrical automation control system. By accurately acquiring the power demand forecast parameters and electrical operation parameters of the target area, the method can comprehensively analyze and identify potential abnormal warning factors, and then automatically perform fault diagnosis and warning control. In this process, the warning control process parameters of the target area are received and processed in real time, and the response efficiency factor of the target area is scientifically evaluated in combination with the abnormal warning factors. Based on this evaluation result, the electrical automation control system can intelligently and adaptively optimize and update the fault diagnosis and warning control strategies to ensure that the operating status of the power grid can be accurately determined in the face of various complex situations. This method not only improves the accuracy of fault diagnosis, but also significantly enhances the fault response speed and the self-optimization capability of the electrical automation control system.

[0015] (2) The present invention uses the electrical automation control system to automatically perform fault diagnosis and early warning control according to the abnormal early warning factors of the target area, which not only significantly improves the scientificity and accuracy of fault diagnosis, but also can effectively guide the electrical automation control system to perform timely control early warning based on the scientific abnormal early warning factors. It not only speeds up the fault response speed and ensures the stable operation and efficient maintenance of the electrical automation control system, but also greatly reduces the potential losses caused by faults and improves the efficiency and safety of the electrical automation control system.

[0016] (3) The present invention can intelligently evaluate the response efficiency factor by comprehensively analyzing the abnormal warning factors of the target area and combining them with the warning control process parameters, thereby guiding the optimization of fault diagnosis and warning control strategies. Driven by this adaptive mechanism, the electrical automation control system shows adaptability and flexibility. Faced with the complex and changeable power grid operating environment, it can quickly adjust the fault diagnosis and warning control strategies to ensure that the power grid can maintain the best operating state under any circumstances. This capability not only significantly improves the accuracy of power grid fault diagnosis in the target area, but also ensures the timeliness and effectiveness of warning control, thereby effectively reducing the potential risks and losses caused by faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0018] Figure 1 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0020] Reference Figure 1 As shown, the present invention provides an adaptive fault diagnosis method in an electrical automation control system, including: step one, based on the operation of the electrical automation control system in the target area, obtaining the power demand prediction parameters of the target area and the electrical operation parameters of the target area, and comprehensively analyzing the abnormal warning factors of the target area.

[0021] It should be explained that this embodiment is an adaptive fault diagnosis method for the electrical automation control system in the school area when facing an abnormal increase in power consumption, which aims to solve the problem of unstable operation of the electrical automation control system that may be caused by the abnormal increase in power consumption, wherein the target area is the school area.

[0022] The above-mentioned electrical automation control system is a system that integrates components such as sensors, actuators, controllers and human-machine interfaces. It can automatically monitor, control and adjust the operating status of electrical equipment without direct human or human participation. In the school environment, this system accurately senses environmental parameters (such as temperature and pressure, etc.) and, based on preset control logic, efficiently and automatically controls electrical equipment in areas such as teaching buildings, libraries, dormitories and entertainment facilities, such as adjusting power distribution, ensuring power demand in important areas and limiting power load in non-important areas. At the same time, the electrical automation control system can also monitor the working status of the equipment in real time, promptly detect and warn of potential faults, and ensure the safe operation of school facilities.

[0023] In a specific embodiment, the present invention automatically performs fault diagnosis and warning control according to the abnormal warning factors of the target area through the electrical automation control system, which not only significantly improves the scientificity and accuracy of fault diagnosis, but also can effectively guide the electrical automation control system to perform timely control warning based on scientific abnormal warning factors, which not only speeds up the fault response speed and ensures the stable operation and efficient maintenance of the electrical automation control system, but also greatly reduces the potential losses caused by faults and improves the efficiency and safety of the electrical automation control system.

[0024] Specifically, the comprehensive analysis of abnormal warning factors of the target area specifically refers to analyzing the power demand forecast parameters of the target area to obtain the power demand deviation index of the target area, analyzing the electrical operation parameters of the target area to obtain the power operation robustness index of the target area; according to the power demand deviation index of the target area, matching the power operation robustness threshold of the target area from the electrical database; wherein the specific matching rule is: the power operation robustness threshold corresponding to each power demand deviation index interval is stored in the electrical database, and the power demand deviation index interval stored in the electrical database to which the power demand deviation index of the target area belongs is queried, and the power operation robustness threshold corresponding to the power demand deviation index interval is the power operation robustness threshold of the target area, and the power operation robustness threshold of the target area represents the minimum value of the reasonable range of the power operation robustness index of the target area.

[0025] The power demand deviation index of the target area is a numerical value indicating the degree of deviation between the current power demand and the power demand in the corresponding historical time period; the power operation robustness index of the target area is a numerical value indicating the stability performance of the power system in the target area when facing changes in demand.

[0026] The power demand deviation index of the target area, the power operation robustness threshold of the target area, and the power operation robustness index of the target area are combined to obtain the abnormal warning factor of the target area. In this embodiment, the abnormal warning factor of the target area is used to represent the numerical value of the abnormal degree of operation of the electrical automation control system of the target area. The specific analysis method is as follows: ;

[0027] In the formula, is the abnormal warning factor of the target area, is the power demand deviation index of the target area, is the weight factor corresponding to the power demand deviation index preset in the electrical database, is the power operation robustness index of the target area, Robust thresholds for power operation in target areas.

[0028] The weight factor corresponding to the above-mentioned power demand deviation index represents the proportion of the power demand deviation index of the target area to the abnormal warning factor of the target area. The electrical database stores the correspondence between the power demand deviation index and its corresponding weight factor. For example, the power demand deviation index of the target area is input into the electrical database, and the electrical database can match the weight factor corresponding to the power demand deviation index.

[0029] It should be explained that the present embodiment aims to elaborate on an early warning control mechanism for the electrical automation control system in the school area. The power supply mode in the school area usually presents a relatively regular weekly cycle characteristic. However, when the power demand on a specific date (such as Monday) increases abnormally and there is a significant deviation compared with the power demand on the corresponding date last week (Monday of last week), it is regarded as abnormally large power demand. In this situation, if the power operation in the target area is not robust enough to cope with such demand changes, it will directly lead to a higher degree of abnormal operation of the electrical automation control system.

[0030] The electrical database is a database used to store parameters involved in the adaptive fault diagnosis method in the electrical automation control system.

[0031] In this exemplary embodiment, the abnormal warning factors of the target area and the change table of their corresponding parameters are shown in Table 1:

[0032] Table 1. Changes of abnormal warning factors and their corresponding parameters in the target area

[0033] In this example embodiment, the power operation robustness threshold of the target area is set to 350%, and the value of the weight factor corresponding to the power demand deviation index is set to 0.78. As can be seen from Table 1, when the power demand deviation index reaches a relatively high level, specifically 612%, if the power operation robustness index is maintained at a relatively low level, specifically 425%, then this situation will not cause the abnormal warning factor to rise to an abnormally high level, and its actual value is 132.7%. In other words, although there is a significant deviation in power demand, the abnormal warning has not been significantly aggravated because the power operation remains relatively robust.

[0034] Specifically, the power operation robustness index of the target area is analyzed in the following steps:

[0035] The electrical operation parameters of the target area include the real-time load impedance modulus of each detection load node in the target area during the power monitoring period, the real-time power factor of each detection load node in the target area during the power monitoring period, and the average temperature of each sampled cable segment in the target area during the power monitoring period; the above-mentioned power monitoring period refers to the time period for detecting and analyzing the power operation parameters of the target area, and the specific duration is determined by the power operation and maintenance specialist; the above-mentioned real-time load impedance modulus refers to the absolute value of the real-time impedance of each detection load node in the target area during the power monitoring period. Impedance is a core concept in circuit theory. It reflects the resistance of the circuit to the flow of AC signals. The modulus of impedance indicates the magnitude of the resistance of the circuit to AC signals. The larger the modulus, the stronger the resistance of the circuit to AC; the above-mentioned real-time power factor refers to the absolute value of the real-time impedance of each detection load node in the target area during the power monitoring period. Impedance is a core concept in circuit theory. It reflects the resistance of the circuit to the flow of AC signals. The modulus of impedance indicates the magnitude of the resistance of the circuit to AC signals. The larger the modulus, the stronger the resistance of the circuit to AC. During the power monitoring cycle, the ratio of active power to apparent power, real-time load impedance modulus and real-time power factor of each detection load node in the target area can be monitored and obtained through power monitoring equipment (such as smart meters). Among them, the load node is the access point of the power load and the interface between the school power system and the user. Each detection load node is a detection point randomly selected by the power operation and maintenance specialist from the school load nodes; the above-mentioned average temperature refers to the average temperature of each sampled cable segment in the target area during the power monitoring cycle, which can be obtained by measuring and analyzing with a temperature sensor. Each sampled cable segment is a part of representative cable segments selected for temperature monitoring in order to fully understand and grasp the temperature distribution of cables in the school area. The sampled cable segments should cover the main cable paths and key nodes in the school area to ensure the comprehensiveness and accuracy of the monitoring results.

[0036] The real-time load impedance modulus values ​​of each detection load node belonging to the target area within the power monitoring cycle are averaged to obtain the average load impedance modulus value of the target area within the power monitoring cycle. Specifically, the real-time load impedance modulus values ​​of each detection load node belonging to the target area within the power monitoring cycle are averaged to obtain the average load impedance modulus value of each detection load node belonging to the target area within the power monitoring cycle, and then averaged to obtain the average load impedance modulus value of the target area within the power monitoring cycle. The above-mentioned average load impedance modulus value of the target area within the power monitoring cycle represents the comprehensive average level of the real-time load impedance modulus values ​​of each detection load node belonging to the school area within the power monitoring cycle.

[0037] The maximum power factor and the minimum power factor are extracted from the real-time power factors of each detection load node belonging to the target area within the power monitoring cycle, and difference processing is performed. The processing results are marked as the power factor fluctuation values ​​of each detection load node belonging to the target area within the power monitoring cycle. The average power factor fluctuation value of the target area within the power monitoring cycle is obtained by mean processing. The above power factor fluctuation value indicates the power factor change level of each detection load node in the target area within the monitoring cycle; the above average power factor fluctuation value indicates the average level of power factor change of each detection load node in the target area within the monitoring cycle.

[0038] The average load impedance modulus value of the target area during the power monitoring period, the average power factor fluctuation value of the target area during the power monitoring period, and the average temperature of each sampled cable section of the target area during the power monitoring period are comprehensively analyzed to obtain the power operation robustness index of the target area. The specific analysis method of the power operation robustness index of the target area is as follows: ;

[0039] In the formula, is the power operation robustness index of the target area, is the average load impedance modulus of the target area during the power monitoring period, is the average power factor fluctuation value of the target area during the power monitoring period, is the average temperature of the dth sampling cable section in the target area during the power monitoring period, It is the average load impedance modulus reference value preset in the electrical database. It is the power operation robustness index weight factor corresponding to the average load impedance modulus unit value preset in the electrical database. The power operation robustness index weight factor corresponding to the unit value of the average power factor fluctuation value preset in the electrical database, is the average temperature limit value of the dth sampled cable segment preset in the electrical database, e is a natural constant, d is the number of each sampled cable segment, , y is the total number of sampled cable segments.

[0040] The above-mentioned average load impedance modulus reference value represents the reference value of the average load impedance modulus of the target area within the power monitoring period; the power operation robustness index weight factor corresponding to the above-mentioned average load impedance modulus unit value represents the numerical value of the influence of the average load impedance modulus unit value on the power operation robustness index of the target area, and the electrical database stores the corresponding relationship between the average load impedance modulus and its corresponding power operation robustness index weight factor. For example, the average load impedance modulus is input into the electrical database, and the electrical database can match the power operation robustness index weight factor corresponding to the average load impedance modulus unit value; the power operation robustness index weight factor corresponding to the above-mentioned average power factor fluctuation value unit value represents the numerical value of the influence of the average power factor fluctuation value unit value on the power operation robustness index of the target area, and the electrical database stores the corresponding relationship between the average power factor fluctuation value and its corresponding power operation robustness index weight factor. For example, the average power factor fluctuation value is input into the electrical database, and the electrical database can match the power operation robustness index weight factor corresponding to the average power factor fluctuation value unit value; the average temperature limit value of the above-mentioned d-th sampling cable segment represents the maximum allowable average temperature of the d-th sampling cable segment belonging to the target area within the power monitoring period.

[0041] It needs to be explained that in the electrical automation control system in the school area, when faced with an abnormal increase in power consumption, if the electrical automation control system fails to implement effective power dispatching measures in a timely manner, the load impedance modulus of the load nodes in the school area will significantly deviate from its preset reference value. Specifically, if the load impedance modulus is significantly larger than the corresponding reference value, this will directly lead to a drop in the voltage level in the school power grid, thereby seriously affecting the power supply quality and causing inconvenience to the normal power consumption of the school. If the load impedance modulus is significantly smaller than the corresponding reference value, it will cause an abnormal increase in current in the school power grid. The significant deviation of the load impedance modulus of the load nodes in the school area from their preset reference value may not only cause damage to the power grid equipment due to overheating, but may also further cause a significant drop in the power factor of the power grid and generate a large amount of reactive power. The accumulation of such reactive power will cause the cables to The temperature of the cable segment rises abnormally and aggravates the load pressure of the power grid. The reason is that the flow of reactive power in the power grid will increase the loss of the power grid, especially when the reactive power is too large, the loss of electrical equipment such as transformers and transmission lines in the power grid will increase significantly, and these losses will eventually be converted into heat energy, causing the temperature of the cable segment and its surrounding environment to rise abnormally. At the same time, the accumulation of reactive power will also affect the power factor of the power grid, causing the power factor of the power grid to decrease. The reduction in power factor means that more reactive power is flowing in the power grid, and this part of reactive power does not actually do work, but occupies the capacity of the power grid, resulting in a reduction in the effective power output of the power grid. In order to meet the school's electricity demand, the power grid has to increase the output of current, aggravating the load pressure of the power grid, making the power supply of the power grid in the school area extremely unstable, thereby causing power supply failures in the school.

[0042] Furthermore, the power demand deviation index of the target area is specifically analyzed as follows: the power demand prediction parameters of the target area include the average light intensity of the target area during the power monitoring period, the average ambient temperature of the target area during the power monitoring period, the single room occupancy rate of the target area during the power monitoring period, the average light intensity of the target area during the historical corresponding power monitoring period, the average ambient temperature of the target area during the historical corresponding power monitoring period, and the single room occupancy rate of the target area during the historical corresponding power monitoring period; the above-mentioned average light intensity refers to the average level of light intensity in the target area, which can be measured by a photometer Obtained through measurement and analysis; the above-mentioned average ambient temperature refers to the average level of ambient temperature in the target area, which can be obtained through measurement and analysis of temperature sensors; the above-mentioned single room occupancy rate refers to the proportion of each independent single room (such as classroom) in the target area that is occupied, which can be extracted from the school's academic affairs system; the above-mentioned historical corresponding power monitoring cycle, its specific duration is equal to the duration of the power monitoring cycle, and the two are separated by a fixed duration on the time axis. It is necessary to ensure that the historical corresponding power monitoring cycle is comparable in time with the current power monitoring cycle. This fixed duration can be set according to the school's teaching rules, for example, a fixed duration of seven days or fourteen days.

[0043] The average light intensity of the target area during the historical corresponding power monitoring period is differenced with the average light intensity of the target area during the power monitoring period, and the processing result is marked as the light intensity deviation value of the target area during the power monitoring period, which indicates the change in the average light intensity of the target area during the power monitoring period relative to the average light intensity of the target area during the historical corresponding power monitoring period. A large light intensity deviation value indicates that the light in the school area during the power monitoring period is weak. In order to ensure the teaching quality, the electricity demand will increase.

[0044] The difference between the ambient temperature of the target area in the historical corresponding power monitoring period and the ambient temperature of the target area in the power monitoring period is processed, and the difference is processed as an absolute value. The final result is marked as the ambient temperature deviation value of the target area in the power monitoring period, which indicates the deviation of the ambient temperature of the target area in the historical corresponding power monitoring period relative to the ambient temperature of the target area in the power monitoring period. If the ambient temperature deviation value is large, it indicates that the temperature in the school area during the power monitoring period is too cold or too hot. In order to ensure the quality of teaching and teaching equipment, the electricity demand will increase.

[0045] The difference between the single-room occupancy rate of the target area during the power monitoring period and the single-room occupancy rate of the target area during the corresponding historical power monitoring period is processed, and the processing result is marked as the single-room occupancy rate deviation value of the target area during the power monitoring period, which indicates the change in the single-room occupancy rate of the target area during the power monitoring period relative to the single-room occupancy rate of the target area during the corresponding historical power monitoring period. If the single-room occupancy rate deviation value is large, it indicates that the classrooms in the school area are used more frequently during the power monitoring period. In order to ensure the teaching quality, the electricity demand will increase.

[0046] The power demand deviation index of the target area is obtained by comprehensively considering the light intensity deviation value of the target area during the power monitoring period, the ambient temperature deviation value of the target area during the power monitoring period, and the single room occupancy rate deviation value of the target area during the power monitoring period. The specific analysis method of the power demand deviation index of the target area is as follows: ;

[0047] In the formula, is the power demand deviation index of the target area, is the light intensity deviation value of the target area during the power monitoring cycle, is the ambient temperature deviation value of the target area during the power monitoring cycle, is the single room occupancy rate deviation value of the target area during the power monitoring period, The power demand deviation index weight factor corresponding to the unit value of the light intensity deviation value preset in the electrical database, The power demand deviation index weight factor corresponding to the unit value of the ambient temperature deviation value preset in the electrical database, It is the power demand deviation index weight factor corresponding to the unit value of the single room occupancy rate deviation value preset in the electrical database, and e is a natural constant.

[0048] The power demand deviation index weight factor corresponding to the above-mentioned light intensity deviation value unit value represents the numerical value of the influence of the light intensity deviation value unit value on the power demand deviation index of the target area. The electrical database stores the corresponding relationship between the light intensity deviation value and its corresponding power demand deviation index weight factor. For example, the light intensity deviation value is input into the electrical database, and the electrical database can match the power demand deviation index weight factor corresponding to the light intensity deviation value unit value; the power demand deviation index weight factor corresponding to the above-mentioned ambient temperature deviation value unit value represents the numerical value of the influence of the ambient temperature deviation value unit value on the power demand deviation index of the target area. The electrical database stores the ambient temperature deviation value and the corresponding power demand deviation index weight factor. The corresponding relationship between the power demand deviation index weight factor, for example, the ambient temperature deviation value is input into the electrical database, and the electrical database can match the power demand deviation index weight factor corresponding to the unit value of the ambient temperature deviation value; the power demand deviation index weight factor corresponding to the unit value of the single room occupancy deviation value above represents the degree of influence of the unit value of the single room occupancy deviation value on the power demand deviation index of the target area. The electrical database stores the corresponding relationship between the single room occupancy deviation value and the corresponding power demand deviation index weight factor. For example, the single room occupancy deviation value is input into the electrical database, and the electrical database can match the power demand deviation index weight factor corresponding to the unit value of the single room occupancy deviation value.

[0049] It needs to be explained that when the deviation value of the occupancy rate of a single room in the school area is large, it means that the occupancy rate of the classroom has increased significantly compared with the historical state (that is, the occupancy rate of a single room in the corresponding historical power monitoring period). This is usually due to the adjustment of the curriculum, which has led to more classes being scheduled in the current time period. In this case, if the light intensity deviation value is large, that is, the natural light in the classroom is insufficient, in order to meet the lighting needs, more lamps need to be turned on to supplement the light, thereby increasing power consumption. On the other hand, if the ambient temperature deviation value is also large, whether it is overheated or overcooled, it is necessary to adjust the indoor temperature by turning on temperature control equipment such as air conditioners, which will also increase power consumption. The combined effect of the above three parameters directly reflects the degree of deviation in the power demand of the target area. When these three parameters have large deviations at the same time, it means that the electricity demand in the school area has increased significantly and the degree of deviation in the power demand is large, which puts higher requirements on the electrical automation control system to control the supply and demand balance.

[0050] Step 2: According to the abnormal warning factors of the target area, the electrical automation control system automatically performs fault diagnosis and warning control.

[0051] Specifically, the electrical automation control system automatically performs fault diagnosis and early warning control. The specific early warning control process is: the electrical automation control system matches the fault level of the target area with the early warning control set corresponding to each fault level in the electrical database. If the fault level of the target area is the first level, the first early warning control set of the target area is matched from the electrical database. If the fault level of the target area is the second level, the second early warning control set of the target area is matched from the electrical database. If the fault level of the target area is the third level, the third early warning control set of the target area is matched from the electrical database. Thus, the electrical automation control system automatically performs early warning control. In an example embodiment, assuming that the fault level of the target area is the second level, the matched second early warning control set of the target area includes: Early warning: The electrical automation control system sends an early warning signal to school administrators or power operation and maintenance personnel through sound and light alarms, text messages or email notifications, prompting the school's electricity consumption is close to or has reached an overload state. The electrical automation control system automatically adjusts the power distribution according to the power demand and load conditions of each area. Distribution, control parameters: give priority to guaranteeing the power demand of important areas (such as teaching areas and libraries, etc.), the maximum power load of the teaching area is 1000kW, the electrical automation control system increases the power supply of the teaching area during power distribution, so that it obtains 80% of the power supply, that is, 800kW, the maximum power load of the library is 500kW, the electrical automation control system increases the power supply of the library, so that it obtains 60% of the power supply, that is, 300kW, and at the same time limits or reduces the power load of non-important areas (such as dormitories and entertainment facilities, etc.), the maximum power load of the dormitory area is 2000kW, the electrical automation control system reduces the power supply of the dormitory area to 50%, that is, 1000kW, the maximum power load of the entertainment facilities is 800kW, the electrical automation control system reduces its power supply to 20%, that is, 160kW, and automatically calls the school battery pack to supplement the power supply. The battery pack is used for 2 hours and the discharge depth is 80% (that is, 800kWh). Therefore, the electrical automation control system automatically performs early warning control by executing the second early warning control set of the target area.

[0052] Furthermore, the electrical automation control system automatically performs fault diagnosis and early warning control, and the specific fault diagnosis process is: the electrical automation control system compares the abnormal warning factor of the target area with the first warning reference value and the second warning reference value stored in the electrical database; if the abnormal warning factor of the target area is less than or equal to the first warning reference value, the fault level of the target area is marked as the first level; if the abnormal warning factor of the target area is greater than the first warning reference value and the abnormal warning factor of the target area is less than or equal to the second warning reference value, the fault level of the target area is marked as the second level; if the abnormal warning factor of the target area is greater than the second warning reference value, the fault level of the target area is marked as the third level, thereby the electrical automation control system diagnoses the fault level of the target area; the above-mentioned first warning reference value is a parameter used to define the first level and the second level of the electrical fault state of the target area; the above-mentioned second warning reference value is a parameter used to define the second level and the third level of the electrical fault state of the target area.

[0053] Step 3: Automatically perform fault diagnosis and early warning control based on the electrical automation control system, thereby obtaining the early warning control process parameters of the target area, and at the same time comprehensively evaluate the response efficiency factor of the target area based on the abnormal early warning factor of the target area, and adaptively optimize and update the fault diagnosis and early warning control of the electrical automation control system based on the response efficiency factor of the target area.

[0054] In a specific embodiment, the present invention can intelligently evaluate the response efficiency factor by comprehensively analyzing the abnormal warning factors of the target area and combining them with the warning control process parameters, thereby guiding the optimization of fault diagnosis and warning control strategies. Driven by this adaptive mechanism, the electrical automation control system exhibits adaptability and flexibility. Faced with a complex and changeable power grid operating environment, it can quickly adjust the fault diagnosis and warning control strategies to ensure that the power grid can maintain the best operating state under any circumstances. This capability not only significantly improves the accuracy of power grid fault diagnosis in the target area, but also ensures the timeliness and effectiveness of warning control, thereby effectively reducing the potential risks and losses caused by faults.

[0055] Specifically, the response efficiency factor of the target area is evaluated by: ;

[0056] is the response efficiency factor of the target area, is the abnormal warning factor of the target area, is the weight factor corresponding to the abnormal warning factor preset in the electrical database, is the total warning response time of the target area, is the power grid frequency dispersion of the target area at the end of the early warning response sub-cycle, is the power grid load recovery rate of the target area in the sub-cycle at the end of the early warning response, It is the total time limit of the early warning response preset in the electrical database. is the response efficiency weight factor corresponding to the unit value of the power grid frequency dispersion preset in the electrical database, It is the response efficiency weight factor corresponding to the unit value of the power grid load recovery rate preset in the electrical database, and e is a natural constant.

[0057] The above-mentioned total warning response time limit value indicates the maximum allowable total warning response time of the target area; the weight factor corresponding to the above-mentioned abnormal warning factor indicates the proportion of the abnormal warning factor of the target area to the response efficiency factor of the target area, and the electrical database stores the corresponding relationship between the abnormal warning factor and its corresponding weight factor. For example, the abnormal warning factor of the target area is input into the electrical database, and the electrical database can match the weight factor corresponding to the abnormal warning factor; the response efficiency weight factor corresponding to the above-mentioned power grid frequency dispersion unit value indicates the numerical value of the influence of the power grid frequency dispersion unit value on the response efficiency factor of the target area, and the electrical database stores the corresponding relationship between the power grid frequency dispersion and its corresponding response efficiency weight factor. For example, the power grid frequency dispersion is input into the electrical database, and the electrical database can match the response efficiency weight factor corresponding to the power grid frequency dispersion unit value; the response efficiency weight factor corresponding to the above-mentioned power grid load recovery rate unit value indicates the numerical value of the influence of the power grid load recovery rate unit value on the response efficiency factor of the target area, and the electrical database stores the corresponding relationship between the power grid load recovery rate and its corresponding response efficiency weight factor. For example, the power grid load recovery rate is input into the electrical database, and the electrical database can match the response efficiency weight factor corresponding to the power grid load recovery rate unit value.

[0058] It needs to be explained that when the abnormal warning factor of the school area is large, it usually means that the ability of the electrical automation control system to automatically adapt and adjust when facing the abnormal increase in power consumption is limited, and it cannot effectively balance the supply and demand relationship, further affecting the response performance of the electrical automation control system in the school area. At this time, if the power grid load recovery rate in the school area performs poorly within the predetermined warning response cycle, that is, the load recovery rate is small, then this further shows that the power grid is not able to adapt to the current power demand. In this case, the power grid may not have fully adapted to the current load changes, resulting in the stability of the power grid frequency being affected. The instability of the power grid frequency will not only affect the normal operation of the power equipment, but may also trigger a series of chain reactions, such as voltage fluctuations and current shocks, thereby further increasing the dispersion of the power grid frequency, that is, the dispersion of the power grid frequency increases. At the same time, the total warning response time is also an important indicator for evaluating the adaptability of the power grid. If the total warning response time is too long, it means that the time span from the warning trigger to the stable recovery of the power grid load is large, which may cause the power grid to be in an unstable state for a long time, further aggravating the problem of power grid frequency dispersion. In summary, the mutual influence of the above parameters indicates that the adaptive warning control of the electrical automation control system needs to be further adjusted.

[0059] Specifically, the response effectiveness factor of the target area is evaluated, and the specific evaluation process is as follows: the early warning control process parameters of the target area include the total warning response time of the target area, the detected power grid frequencies of the target area in the sub-period at the end of the early warning response, and the average power grid load of the target area in the sub-period at the end of the early warning response; the total warning response time of the above-mentioned target area refers to the total time from the time when the electrical automation control system identifies the fault level of the target area and matches the corresponding early warning control set to the time when the electrical automation control system completely executes all instructions and measures in the early warning control set, which can be extracted from the timestamp log of the electrical automation control system. It should be explained that the response effectiveness factor of the target area can be evaluated specifically as the response effectiveness factor of the target area in the early warning response cycle. If the electrical automation control system has not completed the execution of the early warning control set at the end time of the early warning response cycle, then the total warning response time of the target area That is, the duration corresponding to the early warning response cycle. The early warning response cycle is formulated by the power test engineer to set a reasonable time range for evaluating the efficiency and ability of the electrical automation control system in identifying faults, matching early warning control sets, and executing corresponding early warning control measures. If the electrical automation control system can complete the execution of all early warning control instructions and measures within the early warning response cycle, it indicates that the response capability of the electrical automation control system is in line with expectations. Generally speaking, most electrical automation control systems can complete the necessary early warning control measures within the early warning response cycle. On the contrary, if the electrical automation control system cannot complete the execution of all early warning control instructions and measures within the early warning response cycle, this may indicate that the electrical automation control system has problems with response speed or execution capability. In this case, the power test engineer conducts further inspection and analysis of the electrical automation control system to determine the root cause of the problem.

[0060] The detected grid frequencies of the above-mentioned target area in the last sub-cycle of the early warning response refer to the results of detecting the grid frequencies of the target area according to the preset sampling frequency in the last sub-cycle of the early warning response, which can be obtained through detection by a frequency meter; the grid load represents the sum of the power consumed by all electrical equipment in the school power system at the same time, and the average grid load of the above-mentioned target area in the last sub-cycle of the early warning response represents the average level of real-time grid load in the target area in the last sub-cycle of the early warning response, which can be obtained through detection by a power load meter.

[0061] The above-mentioned warning response end sub-cycle represents the end time period of the warning response cycle. The specific start and end time points are determined by the power test engineer. The warning response cycle is a time period set by the power test engineer to evaluate the performance of the electrical automation control system in fault warning and response. During this cycle, it is necessary to identify the fault level, match the warning control set, and execute the corresponding warning control measures. The warning response end sub-cycle is the last stage of this cycle, which indicates that the electrical automation control system is about to complete the warning response task and enter a stable state.

[0062] The standard deviation of each detected power grid frequency in the target area in the sub-cycle at the end of the early warning response is processed, and the processing result is marked as the power grid frequency dispersion of the target area in the sub-cycle at the end of the early warning response, which represents the numerical value of the fluctuation degree of the power grid frequency in the target area in the sub-cycle at the end of the early warning response.

[0063] The average grid load of the target area in the corresponding historical power monitoring period is obtained. According to the power demand deviation index of the target area, the grid average load correction value is matched from the electrical database, and the grid average load correction value is accumulated with the grid average load of the target area in the corresponding historical power monitoring period. The accumulated result is marked as the reference grid average load of the target area in the sub-period at the end of the early warning response. The grid average load of the target area in the sub-period at the end of the early warning response is ratio-processed with the reference grid average load of the target area in the sub-period at the end of the early warning response. The processing result is marked as the grid load recovery rate of the target area in the sub-period at the end of the early warning response. The average load represents the average value of the grid load in the target area during the historical corresponding power monitoring period, which is extracted from the electrical database; the above-mentioned grid average load correction value is used to adjust the value of the grid average load in the target area during the historical corresponding power monitoring period, and after adjustment, the reference grid average load of the target area in the sub-period at the end of the early warning response can be obtained; the reference grid average load of the above-mentioned target area in the sub-period at the end of the early warning response represents the reference value of the grid average load of the target area in the sub-period at the end of the early warning response; the grid load recovery rate of the above-mentioned target area in the sub-period at the end of the early warning response represents the recovery status of the grid load in the target area during the early warning response process and the value of the recovery efficiency.

[0064] The specific matching process of the above-mentioned grid average load correction value is as follows: the grid average load correction value corresponding to each power demand deviation index interval is stored in the electrical database, and the power demand deviation index interval in the electrical database to which the power demand deviation index of the target area belongs is queried. The grid average load correction value corresponding to the interval is the grid average load correction value matched by the power demand deviation index of the target area.

[0065] The abnormal warning factor of the target area, the total warning response time of the target area, the grid frequency dispersion of the target area in the sub-cycle at the end of the warning response, and the grid load recovery rate of the target area in the sub-cycle at the end of the warning response are comprehensively evaluated to obtain the response efficiency factor of the target area. In this embodiment, the response efficiency factor of the target area is a numerical value indicating the efficiency and ability of the electrical automation control system to handle faults in the target area during the warning response process.

[0066] Specifically, the fault diagnosis and early warning control of the electrical automation control system are adaptively optimized and updated, and the specific fault diagnosis optimization and updating process is: if the response efficiency factor of the target area is less than or equal to the response efficiency threshold, the response efficiency threshold and the response efficiency factor of the target area are difference processed, the difference processing result and the response efficiency threshold are ratio processed, and the ratio processing result is marked as the response efficiency lag ratio of the target area, which indicates the degree of lag of the response efficiency of the target area relative to the response efficiency threshold; the above-mentioned response efficiency threshold represents the minimum value of the reasonable range of the response efficiency factor of the target area, which is extracted from the electrical database.

[0067] The first warning reference value and the second warning reference value stored in the electrical database are reduced according to the response efficiency hysteresis ratio of the target area, thereby updating the corresponding fault level and storing them in the electrical database; wherein the first warning reference value is less than the second warning reference value. In an example embodiment, assuming that the response efficiency hysteresis ratio of the target area is 25%, assuming that the first warning reference value is X, assuming that the second warning reference value is Y, and both are reduced by 25%, then the updated first warning reference value is 75%X, and the updated second warning reference value is 75%Y. Therefore, if the abnormal warning factor of the target area is less than or equal to 75%X, the fault level of the target area is marked as the first level. If the abnormal warning factor of the target area is greater than 75%X and the abnormal warning factor of the target area is less than or equal to 75%Y, the fault level of the target area is marked as the second level. If the abnormal warning factor of the target area is greater than 75%X and the abnormal warning factor of the target area is less than or equal to 75%Y, the fault level of the target area is marked as the second level. If the abnormal warning factor of the target area is greater than 75%Y, the fault level of the target area is marked as the third level.

[0068] If the response efficiency factor of the target area is greater than the response efficiency threshold, the response efficiency factor of the target area is subjected to difference processing with the response efficiency threshold, and the difference processing result is subjected to ratio processing with the response efficiency threshold. The ratio processing result is marked as the response efficiency excess ratio of the target area, indicating the degree to which the response efficiency of the target area exceeds the response efficiency threshold.

[0069] The first warning reference value and the second warning reference value stored in the electrical database are expanded according to the response efficiency excess ratio of the target area, thereby updating the corresponding fault level and storing it in the electrical database. In an example embodiment, assuming that the response efficiency excess ratio of the target area is 30%, assuming that the first warning reference value is X, assuming that the second warning reference value is Y, and both are expanded by 30%, then the updated first warning reference value is 130%X, and the updated second warning reference value is 130%Y. Therefore, if the abnormal warning factor of the target area is less than or equal to 130%X, the fault level of the target area is marked as the first level. If the abnormal warning factor of the target area is greater than 130%X and the abnormal warning factor of the target area is less than or equal to 130%Y, the fault level of the target area is marked as the second level. If the abnormal warning factor of the target area is greater than 130%X and the abnormal warning factor of the target area is less than or equal to 130%Y, the fault level of the target area is marked as the second level. If the abnormal warning factor of the target area is greater than 130%Y, the fault level of the target area is marked as the third level.

[0070] Furthermore, the fault diagnosis and early warning control of the electrical automation control system are adaptively optimized and updated. The specific early warning control optimization and updating process is: if the response efficiency factor of the target area is less than or equal to the response efficiency threshold, the control parameters in the early warning control set corresponding to each fault level are processed according to the response efficiency hysteresis ratio of the target area, updated and stored in the electrical database; if the response efficiency factor of the target area is greater than the response efficiency threshold, the early warning control set is not optimized and updated. Assuming that the response efficiency hysteresis ratio of the target area is 25%, the control parameters in the early warning control set corresponding to each fault level are expanded and adjusted by 25%. For example, the control parameters of the second early warning control set of the target area include: giving priority to ensuring the electricity demand of important areas (such as teaching areas and libraries, etc.), and the maximum power load of the teaching area is 1000kW, When distributing electricity, the electrical automation control system increases the power supply to the teaching area so that it obtains 80% of the power supply, that is, 800kW. The maximum power load of the library is 500kW. The electrical automation control system increases the power supply to the library so that it obtains 60% of the power supply, that is, 300kW. At the same time, it limits or reduces the power load of non-important areas (such as dormitories and entertainment facilities). The maximum power load of the dormitory area is 2000kW. The electrical automation control system reduces the power supply to the dormitory area to 50%, that is, 1000kW. The maximum power load of the entertainment facilities is 800kW. The electrical automation control system reduces its power supply to 20%, that is, 160kW, and automatically calls the school battery pack to supplement the power supply. The battery pack is used for 2 hours and the discharge depth is 80% (that is, 800kWh). The second early warning control set of the optimized and expanded target area includes: giving priority to guaranteeing the power demand of important areas (such as teaching areas and libraries, etc.), the maximum power load of the teaching area is 1000kW, the electrical automation control system increases the power supply of the teaching area during power distribution, so that it obtains 100% of the power supply, that is, 1000kW, the maximum power load of the library is 500kW, the electrical automation control system increases the power supply of the library, so that it obtains 75% of the power supply, that is, 375kW, and at the same time limits or reduces the power load of non-important areas (such as dormitories and entertainment facilities, etc.), the maximum power load of the dormitory area is 2000kW, the electrical automation control system reduces the power supply of the dormitory area to 37.5%, that is, 750kW, the maximum power load of the entertainment facilities is 800kW, the electrical automation control system reduces its power supply to 15%, that is, 120kW, and automatically calls the school battery pack to supplement the power supply. The battery pack is used for 2.5 hours and the discharge depth is 100% (that is, 100kWh).

[0071] The above-mentioned processing of the control parameters in the early warning control set corresponding to each fault level based on the response efficiency hysteresis ratio of the target area refers to enlarging the increased parameters in the early warning control set by the multiple corresponding to the response efficiency hysteresis ratio (a response efficiency hysteresis ratio of 25% means an increase of 25% times), and reducing the reduced parameters in the early warning control set by the multiple corresponding to the response efficiency hysteresis ratio.

[0072] In a specific embodiment, the present invention provides an adaptive fault diagnosis method in an electrical automation control system. By accurately acquiring the power demand forecast parameters and electrical operation parameters of the target area, it can comprehensively analyze and identify potential abnormal warning factors, and then automatically perform fault diagnosis and warning control. In this process, the warning control process parameters of the target area are received and processed in real time, and combined with the abnormal warning factors, the response efficiency factor of the target area is scientifically evaluated. Based on this evaluation result, the electrical automation control system can intelligently and adaptively optimize and update the fault diagnosis and warning control strategies to ensure that the operating status of the power grid can be accurately determined when facing various complex situations. This method not only improves the accuracy of fault diagnosis, but also significantly enhances the fault response speed and the self-optimization capability of the electrical automation control system.

[0073] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. An adaptive fault diagnosis method in an electrical automation control system, characterized in that: include: Step 1: Based on the operation of the electrical automation control system in the target area, the power demand forecast parameters and electrical operation parameters of the target area are obtained, and the abnormal warning factors of the target area are comprehensively analyzed; Step 2: According to the abnormal warning factors of the target area, the electrical automation control system automatically performs fault diagnosis and early warning control; Step 3: Automatically perform fault diagnosis and early warning control based on the electrical automation control system, thereby obtaining the early warning control process parameters of the target area, and at the same time comprehensively evaluate the response efficiency factor of the target area based on the abnormal early warning factor of the target area, and adaptively optimize and update the fault diagnosis and early warning control of the electrical automation control system based on the response efficiency factor of the target area.

2. The adaptive fault diagnosis method in an electrical automation control system according to claim 1, characterized in that: The comprehensive analysis of the abnormal warning factors of the target area specifically refers to analyzing the power demand forecast parameters of the target area to obtain the power demand deviation index of the target area, and analyzing the electrical operation parameters of the target area to obtain the power operation robustness index of the target area; According to the power demand deviation index of the target area, the power operation robustness threshold of the target area is matched from the electrical database; The abnormal warning factor of the target area is obtained by comprehensively considering the power demand deviation index of the target area, the power operation robustness threshold of the target area and the power operation robustness index of the target area.

3. The adaptive fault diagnosis method in an electrical automation control system according to claim 2, characterized in that: The specific analysis process of the power demand deviation index of the target area is as follows: The power demand forecast parameters of the target area include the average light intensity of the target area during the power monitoring period, the average ambient temperature of the target area during the power monitoring period, the single room occupancy rate of the target area during the power monitoring period, the average light intensity of the target area during the historical corresponding power monitoring period, the average ambient temperature of the target area during the historical corresponding power monitoring period, and the single room occupancy rate of the target area during the historical corresponding power monitoring period; Perform difference processing on the average light intensity of the target area in the corresponding historical power monitoring period and the average light intensity of the target area in the power monitoring period, and mark the processing result as the light intensity deviation value of the target area in the power monitoring period; Perform difference processing on the average ambient temperature of the target area in the corresponding historical power monitoring period and the average ambient temperature of the target area in the power monitoring period, and perform absolute value processing on the difference, and the final result is marked as the ambient temperature deviation value of the target area in the power monitoring period; Perform difference processing on the single room occupancy rate of the target area in the power monitoring cycle and the single room occupancy rate of the target area in the corresponding historical power monitoring cycle, and mark the processing result as the single room occupancy rate deviation value of the target area in the power monitoring cycle; The power demand deviation index of the target area is obtained by comprehensively considering the light intensity deviation value of the target area during the power monitoring period, the ambient temperature deviation value of the target area during the power monitoring period, and the single room occupancy rate deviation value of the target area during the power monitoring period.

4. The adaptive fault diagnosis method in an electrical automation control system according to claim 2, characterized in that: The specific analysis process of the power operation robustness index of the target area is as follows: The electrical operation parameters of the target area include the real-time load impedance modulus of each detection load node belonging to the target area during the power monitoring period, the real-time power factor of each detection load node belonging to the target area during the power monitoring period, and the average temperature of each sampled cable segment belonging to the target area during the power monitoring period; Performing mean processing on the real-time load impedance modulus values ​​of each detection load node belonging to the target area within the power monitoring period to obtain the average load impedance modulus value of the target area within the power monitoring period; Extracting the maximum power factor and the minimum power factor from the real-time power factors of each detection load node belonging to the target area within the power monitoring cycle, and performing difference processing, the processing result is marked as the power factor fluctuation value of each detection load node belonging to the target area within the power monitoring cycle, and the average power factor fluctuation value of the target area within the power monitoring cycle is obtained by mean processing; The power operation robustness index of the target area is obtained by comprehensively analyzing the average load impedance modulus value of the target area during the power monitoring period, the average power factor fluctuation value of the target area during the power monitoring period, and the average temperature of each sampled cable section in the target area during the power monitoring period.

5. The adaptive fault diagnosis method in an electrical automation control system according to claim 1, characterized in that: The electrical automation control system automatically performs fault diagnosis and early warning control, and the specific fault diagnosis process is as follows: The electrical automation control system compares the abnormal warning factor of the target area with the first warning reference value and the second warning reference value stored in the electrical database. If the abnormal warning factor of the target area is less than or equal to the first warning reference value, the fault level of the target area is marked as the first level. If the abnormal warning factor of the target area is greater than the first warning reference value and the abnormal warning factor of the target area is less than or equal to the second warning reference value, the fault level of the target area is marked as the second level. If the abnormal warning factor of the target area is greater than the second warning reference value, the fault level of the target area is marked as the third level. The electrical automation control system thus diagnoses the fault level of the target area.

6. The adaptive fault diagnosis method in an electrical automation control system according to claim 5, characterized in that: The electrical automation control system automatically performs fault diagnosis and early warning control, and the specific early warning control process is: The electrical automation control system matches the fault level of the target area with the early warning control set corresponding to each fault level in the electrical database. If the fault level of the target area is the first level, the first early warning control set of the target area is matched from the electrical database. If the fault level of the target area is the second level, the second early warning control set of the target area is matched from the electrical database. If the fault level of the target area is the third level, the third early warning control set of the target area is matched from the electrical database. The electrical automation control system automatically performs early warning control.

7. The adaptive fault diagnosis method in an electrical automation control system according to claim 1, characterized in that: The response efficiency factor of the target area is evaluated, and the specific evaluation process is as follows: The early warning control process parameters of the target area include the total warning response time of the target area, the detected power grid frequencies of the target area in the late sub-period of the early warning response, and the average power grid load of the target area in the late sub-period of the early warning response; Performing standard deviation processing on each detected power grid frequency of the target area in the sub-period at the end of the early warning response, and marking the processing result as the power grid frequency dispersion of the target area in the sub-period at the end of the early warning response; Obtain the average grid load of the target area in the corresponding historical power monitoring cycle, match the average grid load correction value from the electrical database according to the power demand deviation index of the target area, and add it with the average grid load of the target area in the corresponding historical power monitoring cycle. The accumulated result is marked as the reference grid average load of the target area in the sub-cycle at the end of the early warning response. The average grid load of the target area in the sub-cycle at the end of the early warning response is ratio-processed with the reference grid average load of the target area in the sub-cycle at the end of the early warning response. The processing result is marked as the grid load recovery rate of the target area in the sub-cycle at the end of the early warning response. The abnormal warning factor of the target area, the total warning response time of the target area, the grid frequency dispersion of the target area in the sub-cycle at the end of the warning response, and the grid load recovery rate of the target area in the sub-cycle at the end of the warning response are comprehensively evaluated to obtain the response efficiency factor of the target area.

8. The adaptive fault diagnosis method in an electrical automation control system according to claim 7, characterized in that: The response efficiency factor of the target area is specifically evaluated by: ； is the response efficiency factor of the target area, is the abnormal warning factor of the target area, is the weight factor corresponding to the abnormal warning factor preset in the electrical database, is the total warning response time of the target area, is the power grid frequency dispersion of the target area in the sub-cycle at the end of the early warning response, is the power grid load recovery rate of the target area in the sub-cycle at the end of the early warning response, It is the total time limit of the early warning response preset in the electrical database. is the response efficiency weight factor corresponding to the unit value of the power grid frequency dispersion preset in the electrical database, It is the response efficiency weight factor corresponding to the unit value of the power grid load recovery rate preset in the electrical database, and e is a natural constant.

9. The adaptive fault diagnosis method in an electrical automation control system according to claim 1, characterized in that: The fault diagnosis and early warning control of the electrical automation control system is adaptively optimized and updated, and the specific fault diagnosis optimization and updating process is as follows: If the response efficiency factor of the target area is less than or equal to the response efficiency threshold, the response efficiency threshold and the response efficiency factor of the target area are subjected to difference processing, the difference processing result is subjected to ratio processing with the response efficiency threshold, and the ratio processing result is marked as the response efficiency lag ratio of the target area; The first warning reference value and the second warning reference value stored in the electrical database are reduced according to the response efficiency hysteresis ratio of the target area, thereby updating the corresponding fault level and storing it in the electrical database; If the response efficiency factor of the target area is greater than the response efficiency threshold, the response efficiency factor of the target area is subjected to difference processing with the response efficiency threshold, the difference processing result is subjected to ratio processing with the response efficiency threshold, and the ratio processing result is marked as the response efficiency excess ratio of the target area; The first warning reference value and the second warning reference value stored in the electrical database are expanded according to the response efficiency excess ratio of the target area, thereby updating the corresponding fault level and storing it in the electrical database.

10. The adaptive fault diagnosis method in an electrical automation control system according to claim 9, characterized in that: The fault diagnosis and early warning control of the electrical automation control system are adaptively optimized and updated. The specific early warning control optimization and update process is as follows: If the response efficiency factor of the target area is less than or equal to the response efficiency threshold, the control parameters in the early warning control set corresponding to each fault level are processed according to the response efficiency hysteresis ratio of the target area, updated and stored in the electrical database; If the response efficiency factor of the target area is greater than the response efficiency threshold, the optimization update of the early warning control set will not be performed.

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