Power grid load distribution system and method based on intelligent control

Abstract

The invention relates to the technical field of intelligent distribution, in particular to a power grid load distribution system and method based on intelligent control, and the system comprises a data collection and processing module which carries out the real-time collection and data processing of all node data and meteorological data of a power grid, and obtains the actual power grid load data; the communication network module is used for transmitting the actual power grid load data; the intelligent regulation and control module is used for constructing a rural power grid load model to obtain a load prediction value; the monitoring alarm module is used for monitoring the actual rural electricity consumption condition in real time according to the load prediction value; the system verification module is used for verifying the stability of the power grid after regulation and control configuration, verifying the resource utilization efficiency of the power grid according to the load rate of the transformer, and adjusting the regulation and control configuration; and the execution feedback module is used for performing control strategy command execution on the power grid equipment. The power supply and demand balance of the rural power grid is realized, and the utilization efficiency of power grid resources is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent distribution, and particularly to a power grid load distribution system and method based on intelligent control. Background Art

[0002] With the rapid development of the global economy and the continuous growth of the population, the energy demand continues to increase, and the power supply faces huge challenges. The traditional power system has problems such as unbalanced power load, unstable power supply, energy waste, relatively weak power load forecasting and regulation capabilities, inability to meet the increasing power demand and the safety and stability requirements of power supply, limited data collection and processing capabilities, inability to obtain and analyze large-scale power system operation data in real time, and challenges to the safety and reliability of the power grid load.

[0003] Chinese Patent Publication No.: CN110728403A discloses a method for predicting the medium- and long-term power grid load in rural areas, including obtaining the types of rural electricity loads; classifying the plots within the power supply range; calculating the historical load density; using the GRU algorithm and the CA algorithm to predict the change results of the load density and the change results of the plot functions; performing plot and load matching to obtain the final medium- and long-term power grid load prediction results in rural areas. It can be seen that the method for predicting the medium- and long-term power grid load in rural areas has the following problems: it is impossible to predict the load conditions of each region and each time period of the rural power grid, thus unable to realize the intelligent distribution scheduling and control of the real-time power grid load, resulting in the imbalance between power supply and demand in the rural power grid and the low utilization efficiency of power grid resources. Summary of the Invention

[0004] Therefore, the present invention provides a power grid load distribution system and method based on intelligent control to overcome the problems in the prior art that it is impossible to predict the load conditions of each region and each time period of the rural power grid, thus unable to realize the intelligent distribution scheduling and control of the real-time power grid load, resulting in the imbalance between power supply and demand in the rural power grid and the low utilization efficiency of power grid resources.

[0005] To achieve the above object, on the one hand, the present invention provides a power grid load distribution system based on intelligent control, and the system includes: A data acquisition and processing module for real-time acquisition of data of each node of the power grid and meteorological data, and also for data processing of the data of each node of the power grid and meteorological data to obtain actual power grid load data; A communication network module for transmitting the actual power grid load data; An intelligent regulation and control module for constructing a rural power grid load model according to the actual power grid load data to obtain a load prediction value, and for regulating and configuring the operation state of the power grid according to the load prediction value to obtain the equipment impedance change value, power factor and transformer load rate; The monitoring and alarming module is used to monitor the actual electricity consumption situation in rural areas in real time according to the load prediction value, obtain real-time monitoring data, judge the operating temperature status of the equipment according to the real-time monitoring data, alarm when the operating temperature status of the equipment is abnormal, output the first fault reason, and is also used to calculate the power factor of the equipment according to the real-time monitoring data, adjust the operating temperature of the equipment according to the power factor of the equipment, and is also used to calculate the random factor value according to the real-time monitoring data, optimize the power factor of the equipment according to the random factor value, and is also used to judge the rural power grid load status according to the real-time power grid load data in the real-time monitoring data, and alarm when the rural power grid load status is abnormal, output the second fault reason; The system verification module is used to verify the data acquisition and processing ability according to the device current in the real-time monitoring data, adjust the data acquisition and processing process, and is also used to verify the communication network layer according to the device impedance change value, and is also used to verify the power grid stability after regulation and configuration according to the power factor, and is also used to verify the power grid resource utilization efficiency according to the transformer load rate, and adjust the regulation and configuration; The execution feedback module is used to obtain the first fault reason and the second fault reason for feedback, obtain a feedback result, and execute a control strategy command on the power grid equipment according to the feedback result.

[0006] Further, the intelligent regulation module inputs the actual power grid load data into the rural power grid load model to obtain a load prediction value, constructs a rural power grid load model. Among them, the actual power grid load data is divided into a 70% load training set, a 20% load verification set, and a 10% load test set. The load training set is input into the decision tree structure model to train the decision tree structure model, and the load verification set is input into the trained decision tree structure model to perform hyperparameter iterative optimization on the trained decision tree structure model. The load test set is input into the iteratively optimized decision tree structure model to perform a load test on the iteratively optimized decision tree structure model to obtain a load test result. Set the total number of samples in the load test set as k0, the number of correct load test samples as k, and the load test accuracy as K. Set K = k / k0. Compare the load test accuracy K with the preset load test accuracy K0, judge the training compliance of the iteratively optimized decision tree structure model according to the comparison result, and output according to the judgment result, where: When K≥K0, it is determined that the training of the iteratively optimized decision tree structure model is qualified, and the iteratively optimized decision tree structure model is output as the rural power grid load model; When K < K0, it is determined that the training of the decision tree structure model after iterative optimization is not up to standard. Update the actual power grid load database to obtain the updated actual power grid load database, and train, perform hyperparameter iterative optimization, and conduct load testing on the decision tree structure model according to the updated actual power grid load database until the training of the decision tree structure model reaches the standard, and output the load prediction value Fy; The intelligent control module adjusts and configures the operation state of the power grid according to the load prediction value Fy, obtains the device impedance change value Ω, power factor ℇ, and transformer load rate β, compares the load prediction value Fy with the preset load value Fy0, judges the operation state of the power grid according to the comparison result, and performs adjustment and configuration according to the judgment result, where: When Fy < Fy0, it is determined that the operation state of the power grid is normal. At this time, the future power grid load level is low, and the distributed power generation is stored in the distributed energy storage system; When Fy ≥ Fy0, it is determined that the operation state of the power grid is abnormal. At this time, the future power grid load level is high. Adjust the transformer tap in the load growth area and distribute part of the load to the adjacent substation.

[0007] Furthermore, the monitoring and alarm module monitors the actual electricity consumption situation in rural areas in real time according to the load prediction value Fy to obtain real-time monitoring data. The real-time monitoring data includes real-time device temperature C, real-time load data F, active power P, reactive power Q, device impedance change value Ω, device power factor change frequency f, device current I, and real-time device voltage U. Compare the real-time device temperature C in the real-time monitoring data with the preset device temperature C0, judge the operation temperature state of the device according to the comparison result, and give an alarm according to the judgment result, where: When C ≤ C0, it is determined that the operation temperature state of the device is normal, and no abnormal alarm is given at this time; When C > C0, it is determined that the operation temperature state of the device is abnormal. At this time, trigger the buzzer for abnormal alarm and output the first cause of the fault; The monitoring and alarm module calculates the device power factor PF through the active power P and the reactive power Q, and sets , compare the device power factor PF with the preset device power factor PF0, judge the reactive power compensation capacity of the power grid according to the comparison result, and adjust the operation temperature C of the device according to the judgment result, where: When PF ∈ PF0, it is determined that the device power factor PF is normal and the reactive power compensation capacity of the power grid is stable; When PF When PF = 0, it is determined that the power factor PF of the device is abnormal and the reactive power compensation capacity of the power grid is unstable. At this time, the operating temperature C of the device is adjusted, and the adjusted operating temperature of the device is C1. It is set that C1 = PF × C.

[0008] Further, the monitoring and warning module compares the change frequency f of the device power factor with a preset power factor change frequency f0, judges the cause of the fault according to the comparison result, and outputs according to the judgment result, where: When f ≤ f0, it is determined that the change frequency f of the device power factor is normal, and at this time, the reactive power compensation device of the power grid has no fault; When f > f0, it is determined that the change frequency f of the device power factor is abnormal. At this time, the reactive power compensation device of the power grid has a fault, and this fault is output; The monitoring and warning module calculates the random factor value S according to the real-time monitoring data. It is set that S = 0.5 × h + 0.5 × z, where h is the electricity demand coefficient and z is the weather influence coefficient. The random factor value S is compared with a preset random factor value S0, and the influence on the change of the device power factor is judged according to the comparison result, and the change rate f of the device power factor is adjusted according to the judgment result, where: When S ≤ S0, it is determined that the random factor value has no influence on the change of the device power factor, and at this time, the change rate f of the device power factor is not adjusted; When S > S0, it is determined that the random factor value has an influence on the change of the device power factor. At this time, the change rate f of the device power factor is adjusted, and the adjusted change rate of the device power factor is f1. It is set that f1 = 2.55 × e (-2S) + 0.5 × f, where e is the base of the natural logarithm.

[0009] Further, the monitoring and warning module compares the real-time power grid load data F with a preset power grid load data F0, judges the rural power grid load density according to the comparison result, and gives an alarm when the judgment result is abnormal, where: When F > F0, it is determined that the rural power grid load density is normal, and at this time, no abnormal alarm is given; When F ≤ F0, it is determined that the rural power grid load density is abnormal. At this time, the buzzer is triggered for abnormal alarm, and the second cause of the fault is output. At this time, the real-time power grid load data F is adjusted, and the adjusted real-time power grid load data is F1. It is set that F1 = 1.28 × F.

[0010] Further, the system verification module verifies the data acquisition and processing capabilities based on the device current I, compares the device current I with the device current standard value I0, verifies the data acquisition and processing capabilities according to the comparison result, and adjusts the data acquisition and processing process according to the verification result, where: When the device power data acquisition and processing capabilities are verified to be normal, and at this time, the data acquisition and processing process is not adjusted; When the device power data acquisition and processing capabilities are verified to be abnormal, and at this time, the data acquisition and processing process is adjusted, the data collected 3 hours before this collection point is collected, and the outlier is reset using the 3σ principle until normal grid load data is obtained.

[0011] Further, the system verification module verifies the communication network layer based on the device impedance change value Ω, compares the device impedance change value Ω with the preset device impedance change value Ω0, judges the device impedance change according to the comparison result, and adjusts the communication network layer according to the judgment result, where: When Ω ≤ Ω0, it is determined that the device impedance change is normal, and at this time, the communication network layer is not adjusted; When Ω > Ω0, it is determined that the device impedance change is abnormal, and at this time, the communication network layer is adjusted according to the communication adjustment method, and the communication adjustment method includes:; Step S20, troubleshoot the access layer terminal device, adjust the data network channel, and perform maintenance and replacement; Step S21, check the operating status of the aggregation layer device, upgrade the hardware and software of the aggregation device, and evenly distribute the data traffic to the backup link using the load balancing function of the aggregation device; Step S22, use an optical time domain reflectometer device to locate the fault of the backbone optical fiber link, and replace and repair it in time, and switch to the backup link at the same time.

[0012] Further, the system verification module verifies the stability of the power grid after regulation and configuration based on the power factor ℇ, compares the power factor ℇ with the power factor ℇ0 before regulation and configuration, judges the stability of the power grid after regulation and configuration according to the comparison result, and adjusts the power grid distribution of the regulation and configuration according to the judgment result, where: When the stability of the power grid after regulation and configuration is determined to increase; When the stability of the power grid after regulation and configuration is determined to decrease, and at this time, the power grid distribution of the regulation and configuration is adjusted, and the output power of the distributed power source is controlled according to the bearing capacity and load demand of the power grid, and the power of the distributed power source is restricted; The system verification module verifies the utilization efficiency of grid resources according to the transformer load rate β, compares the transformer load rate β with the transformer load rate β0 before implementation, judges the grid utilization rate after regulation and configuration according to the comparison result, and adjusts the grid load distribution situation after regulation and configuration according to the judgment result, where: When β ∈ β0, it is determined that the grid utilization rate after this regulation and configuration is high, and at this time, the grid load distribution situation after regulation and configuration is not adjusted; When β β0, it is determined that the grid utilization rate after this regulation and configuration is low, and at this time, the grid load distribution situation after regulation and configuration is adjusted, and the grid load is transferred from the high-load-rate transformer to the low-load-rate transformer by adjusting the substation tie switch.

[0013] Furthermore, the execution feedback module obtains the first fault cause and the second fault cause according to the monitoring and warning system for feedback, obtains a feedback result, and executes a control strategy command on the grid equipment according to the feedback result, where: When the first fault cause is determined that the device cannot execute the command, the standby device is started at this time; When the second fault cause is determined that the rural grid load density is abnormal, the redundant system is switched to at this time.

[0014] On the other hand, the present invention provides a method for grid load distribution based on intelligent control, and the method includes: Step S1, collecting the data of each node of the grid and meteorological data in real time, processing the data of each node of the grid and meteorological data, and obtaining the actual grid load data; Step S2, transmitting the actual grid load data; Step S3, constructing a rural grid load model according to the actual grid load data, obtaining a load prediction value, and regulating and configuring the grid operation state according to the load prediction value, and obtaining the device impedance change value, power factor and transformer load rate; Step S4, monitoring the actual electricity consumption situation in rural areas in real time according to the load prediction value, obtaining real-time monitoring data, judging the operating temperature state of the device according to the real-time monitoring data, and alarming when the operating temperature state of the device is abnormal, and outputting the first fault cause; Step S5, calculating the power factor of the device according to the real-time monitoring data, and adjusting the operating temperature of the device according to the power factor of the device; Step S6, calculating the random factor value according to the real-time monitoring data, and optimizing the power factor of the device according to the random factor value; Step S7: Judge the load status of the rural power grid according to the real-time grid load data in the real-time monitoring data, give an alarm when the load status of the rural power grid is abnormal, and output the second cause of the fault. Step S8: Verify the data acquisition and processing capabilities according to the device current in the real-time monitoring data, adjust the data acquisition and processing process, and verify the communication network layer according to the device impedance change value. Step S9: Verify the stability of the power grid after regulation and configuration according to the power factor, verify the power grid resource utilization efficiency according to the transformer load rate, and adjust the regulation and configuration. Step S10: Obtain the first cause of the fault and the second cause of the fault for feedback to get a feedback result, and execute a control strategy command on the power grid equipment according to the feedback result.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows. The system collects and processes the data of each node of the power grid and meteorological data in real time through the data acquisition and processing module, providing basic data support for subsequent analysis and decision-making. The system transmits the actual power grid load data through the communication network module, thereby ensuring the circulation of data within the system and promoting the stable operation of the power grid management system. The system adjusts the operation state of the power grid through the intelligent control module to realize the intelligent distribution of the power grid load, thereby improving the power supply stability and efficiency of the power grid. The system monitors the actual electricity consumption situation in rural areas in real time through the monitoring and alarm module, gives an alarm for abnormal situations and outputs the cause of the fault, ensuring the timely discovery and handling of potential problems in the operation of the power grid, thereby ensuring the reliable power supply of the rural power grid. The system verifies and optimizes the effectiveness and rationality of each key link of the system through the system verification module to realize the balance between power supply and demand of the rural power grid, thereby improving the power grid resource utilization efficiency and power supply stability. The system executes the control strategy command on the power grid equipment through the execution feedback module, responds to the problems in the operation of the power grid in a timely manner, and ensures that the power grid equipment can operate according to the predetermined strategy, further improving the stability and safety of the power grid.

[0016] In particular, in the data acquisition and processing module, through the effective combination of intelligent sensors, the 3-sigma principle and the Z-score method, the accurate acquisition and efficient processing of power grid data are realized, and the accuracy and availability of power grid load data are improved.

[0017] In particular, in the communication network module, the actual power grid load data is transmitted by adopting a hierarchical architecture to realize the summary analysis and transmission control of data, and improve the data transmission efficiency.

[0018] In particular, in the intelligent regulation module, by constructing a rural power grid load model and intelligent regulation operations, accurate prediction and intelligent regulation of the power grid load are achieved, improving the power grid regulation efficiency and power supply stability, thereby ensuring the stable operation of the power grid.

[0019] In particular, in the monitoring and warning module, through the real-time monitoring and application of the intelligent warning system, comprehensive monitoring and timely response to the operating status of rural power grid equipment are realized, improving the operating stability and security of the power grid.

[0020] In particular, in the system verification module, through real-time monitoring and intelligent verification, optimization of the data acquisition and processing capabilities, communication network layer, equipment at all levels of the power grid, and power grid regulation configuration is achieved, improving the reliability, stability, and resource utilization efficiency of the power grid.

[0021] In particular, in the execution feedback module, through the monitoring and warning system and accurate feedback and response to the causes of power grid equipment failures, comprehensive monitoring and intelligent management of the power grid operation are realized, improving the stability and security of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic structural diagram of the power grid load distribution system based on intelligent control in this embodiment; Figure 2 It is a schematic flow diagram of the power grid load distribution method based on intelligent control in this embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] In order to make the objectives and advantages of the present invention clearer and more understandable, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only for explaining the present invention and are not used to limit the present invention.

[0024] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0025] It should be noted that in the description of the present invention, the terms indicating directions or positional relationships such as "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings. This is only for convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be construed as a limitation of the present invention.

[0026] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and defined, the terms "installation", "connection", and "coupling" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be a direct connection or an indirect connection through an intermediate medium, and it may be the communication inside two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0027] Please refer to Figure 1 as shown, which is a schematic structural diagram of the power grid load distribution system based on intelligent control in this embodiment. The system includes: A data acquisition and processing module for real-time acquisition of power grid node data and meteorological data, and also for data processing of power grid node data and meteorological data to obtain actual power grid load data; A communication network module for transmitting the actual power grid load data. The communication network module is connected to the data acquisition and processing module; An intelligent regulation module for constructing a rural power grid load model based on the actual power grid load data to obtain a load prediction value, and for regulating and configuring the power grid operation state according to the load prediction value to obtain the device impedance change value, power factor, and transformer load rate. The intelligent regulation module is connected to the communication network module; A monitoring and warning module for real-time monitoring of the actual electricity consumption situation in rural areas based on the load prediction value to obtain real-time monitoring data, for judging the device operation temperature state according to the real-time monitoring data, and for giving an alarm when the device operation temperature state is abnormal and outputting the first fault reason. It is also used for calculating the device power factor according to the real-time monitoring data, for adjusting the device operation temperature according to the device power factor, for calculating the random factor value according to the real-time monitoring data, for optimizing the device power factor according to the random factor value, for judging the rural power grid load state according to the real-time power grid load data in the real-time monitoring data, and for giving an alarm when the rural power grid load state is abnormal and outputting the second fault reason. The monitoring and warning module is connected to the intelligent regulation module; A system verification module for verifying the data acquisition and processing ability according to the device current in the real-time monitoring data and for adjusting the data acquisition and processing process, for verifying the communication network layer according to the device impedance change value, for verifying the power grid stability after regulation and configuration according to the power factor, for verifying the power grid resource utilization efficiency according to the transformer load rate, and for adjusting the regulation and configuration. The system verification module is connected to the monitoring and warning module, the intelligent regulation module, the communication network module, and the data acquisition and processing module; An execution feedback module is used to obtain the first fault cause and the second fault cause for feedback, obtain a feedback result, and execute a control strategy command on the power grid equipment according to the feedback result.

[0028] Specifically, the system is set in a power grid load distribution terminal based on intelligent control. By constructing a rural power grid load model through actual power grid load data, the prediction of the power grid load value is realized, and the regulation and configuration of the power grid operation state are realized, so as to improve the intelligent distribution of the power grid load. By real-time monitoring the power grid operation state, abnormal equipment temperature and abnormal power grid load conditions are timely detected and alarmed, so as to realize the stable power supply of the rural power grid, further improve the power grid resource utilization efficiency, and ensure the stable operation of the power grid. Among them, the system real-time collects and processes the data of each node of the power grid and meteorological data through a data acquisition and processing module, providing basic data support for subsequent analysis and decision-making. The system transmits the actual power grid load data through a communication network module, thus ensuring the circulation of data within the system and promoting the stable operation of the power grid management system. The system regulates and configures the power grid operation state through an intelligent control module to realize the intelligent distribution of the power grid load, so as to improve the power supply stability and efficiency of the power grid. The system real-time monitors the actual electricity consumption situation in rural areas through a monitoring and alarm module, alarms abnormal situations and outputs the fault causes, ensuring the timely discovery and handling of potential problems in the power grid operation, thus guaranteeing the reliable power supply of the rural power grid. The system verifies and optimizes the effectiveness and rationality of each key link of the system through a system verification module to realize the balance between power supply and demand of the rural power grid, thus improving the power grid resource utilization efficiency and power supply stability. The system executes a control strategy command on the power grid equipment through an execution feedback module, timely responds to the problems in the power grid operation, and ensures that the power grid equipment can operate according to the predetermined strategy, further enhancing the stability and security of the power grid.

[0029] Specifically, the data acquisition and processing module real-time collects the data of each node of the power grid and meteorological data through intelligent sensors; The data acquisition and processing module identifies and processes outliers in the data of each node of the power grid and meteorological data through the 3-sigma principle, obtains normal power grid data and normal meteorological data, and normalizes the normal power grid data and normal meteorological data by using the Z-score method to obtain actual power grid load data.

[0030] Specifically, the intelligent sensor refers to a sensor used to collect data of each node of the power grid and meteorological data in real time. The type of the intelligent sensor is not limited in this embodiment, and those skilled in the relevant art can freely set it according to the actual situation, as long as it meets the requirement of collecting data of each node of the power grid and meteorological data in real time. For example, the type of the intelligent sensor can be set as an electric quantity sensor. The data of each node of the power grid refers to the electrical parameters at each key position in the power grid, including equipment current, equipment voltage, equipment power factor, active power, reactive power, equipment temperature, and equipment impedance. The equipment current refers to the magnitude of the current passing through the electrical equipment. The equipment voltage refers to the voltage difference across the electrical equipment. The equipment power factor refers to the situation of effectively using electric energy by the electrical equipment in the AC circuit. The active power refers to the amount of alternating current energy actually generated per unit time. The reactive power refers to the electric power that does not do work in the AC circuit. The equipment temperature refers to the temperature of the electrical equipment during operation. The equipment impedance refers to the resistance of the electrical equipment to alternating current. The meteorological data refers to the weather conditions for the operation of the power grid, such as temperature, humidity, wind speed, and rainfall. The 3-sigma principle refers to a method for identifying outliers based on statistics. The normal power grid data refers to the data of each node of the power grid processed by the 3-sigma principle, and its full name is the three-sigma principle. The normal meteorological data refers to the meteorological data after screening and processing. The Z-score method refers to a data standardization processing technology, named standard score, and its full name is standard score. The actual power grid load data refers to the power grid load data obtained through data processing.

[0031] Specifically, in the data acquisition and processing module, through the effective combination of the intelligent sensor, the 3-sigma principle, and the Z-score method, the accurate acquisition and efficient processing of power grid data are realized, and the accuracy and availability of the power grid load data are improved.

[0032] Specifically, the communication network module transmits the actual power grid load data through a hierarchical architecture. The hierarchical architecture includes an access layer, a backbone layer, and an aggregation layer. The access layer transmits the actual power grid load data to the aggregation layer. The aggregation layer uses a switch to summarize and analyze the actual power grid load data. The backbone layer receives the data from the aggregation layer and simultaneously issues node control instructions. The aggregation layer receives the control instructions from the backbone layer and forwards them to the access layer devices through the OSPF protocol. The access layer executes the control instructions.

[0033] Specifically, the hierarchical architecture refers to a structure used to divide a communication network into three logical layers responsible for data analysis and transmission. The access layer refers to the interface between users and the network. The backbone layer refers to the high-speed switching backbone in the communication network. The aggregation layer refers to the network devices connecting the access layer and the backbone layer. The switch refers to a network device used to aggregate data. The node control instruction refers to the operation instruction used to manage and control network nodes. The control instruction refers to the operation command used to configure, manage, and monitor the OSPF protocol. The OSPF protocol refers to the Open Shortest Path First protocol, whose full name is Open Shortest Path First.

[0034] Specifically, in the communication network module, the actual power grid load data is transmitted by adopting a hierarchical architecture to achieve the summary analysis and transmission control of data and improve the data transmission efficiency.

[0035] Specifically, the intelligent regulation module inputs the actual power grid load data into the rural power grid load model to obtain the load prediction value and constructs the rural power grid load model. Among them, the actual power grid load data is divided into a 70% load training set, a 20% load validation set, and a 10% load test set. The load training set is input into the decision tree structure model to train the decision tree structure model, and the load validation set is input into the trained decision tree structure model to perform hyperparameter iterative optimization on the trained decision tree structure model. Then, the load test set is input into the iteratively optimized decision tree structure model to perform a load test on the iteratively optimized decision tree structure model to obtain the load test result. Set the total number of samples in the load test set as k0, the number of correct load test samples as k, and the load test accuracy rate as K. Set K = k / k0. Compare the load test accuracy rate K with the preset load test accuracy rate K0, judge the training compliance of the iteratively optimized decision tree structure model according to the comparison result, and output according to the judgment result. Among them: When K ≥ K0, it is determined that the training of the iteratively optimized decision tree structure model is qualified, and the iteratively optimized decision tree structure model is output as the rural power grid load model; When K < K0, it is determined that the training of the iteratively optimized decision tree structure model is unqualified. Update the actual power grid load database to obtain the updated actual power grid load database, and train, perform hyperparameter iterative optimization, and conduct a load test on the decision tree structure model according to the updated actual power grid load database until the training of the decision tree structure model is qualified and output the load prediction value Fy; The intelligent regulation module regulates and configures the operation state of the power grid according to the load prediction value Fy, obtains the device impedance change value Ω, power factor ℇ, and transformer load rate β, compares the load prediction value Fy with the preset load value Fy0, judges the operation state of the power grid according to the comparison result, and performs regulation and configuration according to the judgment result, where: When Fy < Fy0, it is determined that the operation state of the power grid is normal. At this time, the future grid load level is low, and the distributed power generation is stored in the distributed energy storage system; When Fy ≥ Fy0, it is determined that the operation state of the power grid is abnormal. At this time, the future grid load level is high. Adjust the transformer tap in the load growth area and distribute part of the load to the adjacent substation.

[0036] Specifically, the rural power grid load model refers to a decision tree structure model constructed based on the characteristics of the rural power grid and historical load data. The load prediction value refers to the prediction result of the power grid load in a future period through the rural power grid load model. The load training set refers to the data set used to train the decision tree structure model. The load validation set refers to the data set used to verify the performance of the decision tree structure model. The load test set refers to the data set used to finally test the performance of the decision tree structure model. The hyperparameter iterative optimization refers to continuously adjusting the hyperparameters of the decision tree structure model. The load test result refers to the prediction result obtained after inputting the load test set into the iteratively optimized decision tree structure model, including the power grid load in the future period and the load test accuracy rate K. The power grid load in the future period refers to the predicted value of the total electric power consumed by various electrical equipment borne by the power grid in a specific future time period. The load test accuracy rate K refers to the ratio of the number of samples correctly predicted by the model in the load test to the total number of samples. The total number of samples k0 in the load test set refers to the total number of samples included in the load test set. The number of correct load test samples k refers to the number of samples correctly predicted by the model. The preset load test accuracy rate K0 refers to the threshold of the load test accuracy rate set in advance. In this embodiment, the range of the preset load test accuracy rate K0 is not specifically limited, and relevant technical personnel in the field can freely set it according to the actual situation, as long as it meets the requirement of comparing with the load test accuracy rate K. For example, the range of the preset load test accuracy rate K0 can be set to 85% - 90%. The update refers to updating the actual power grid load database when the training of the decision tree structure model does not meet the standard. In this embodiment, the update method is not limited, and relevant technical personnel in the field can freely set it according to the actual situation, as long as it meets the requirement of updating the actual power grid load data. For example, the update method can be set to big data update. The updated actual power grid load database refers to the database of the latest load data. The power grid operation state refers to the working condition of the power grid during the operation period. The equipment impedance change value Ω refers to the change in the impedance value of the equipment in the power grid with the change of the load. The power factor ℇ refers to the physical quantity of the relationship between the active power and the apparent power in the power grid. The transformer load rate β refers to the ratio of the actual load borne by the transformer to its rated capacity. The preset load value Fy0 refers to the load threshold used to judge whether the power grid operation state is normal. In this embodiment, the range of the preset load value Fy0 is not limited. For example, it can be set to 500 kW - 5 MW. The distributed power generation electricity storage refers to storing the electric energy generated by the distributed power generation. The distributed energy storage system refers to the system used to store the electric energy generated by the distributed power generation. The transformer tap refers to the component used to adjust the voltage. The adjacent substations refer to the substations with close geographical locations and electrically connected to each other.

[0037] Specifically, in the intelligent regulation module, by constructing a rural power grid load model and intelligent regulation operations, accurate prediction and intelligent regulation of the power grid load are achieved, improving the power grid regulation efficiency and power supply stability, thereby ensuring the stable operation of the power grid.

[0038] Specifically, the monitoring and warning module monitors the actual electricity consumption situation in rural areas in real time according to the load prediction value Fy, obtains real-time monitoring data, and the real-time monitoring data includes real-time device temperature C, real-time load data F, active power P, reactive power Q, device impedance change value Ω, device power factor change frequency f, device current I, and real-time device voltage U. Compare the real-time device temperature C in the real-time monitoring data with the preset device temperature C0, judge the operating temperature state of the device according to the comparison result, and give an alarm according to the judgment result, where: When C ≤ C0, it is determined that the operating temperature state of the device is normal, and no abnormal alarm is given at this time; When C > C0, it is determined that the operating temperature state of the device is abnormal, and at this time, the buzzer is triggered for abnormal alarm, and the first fault cause is output; The monitoring and warning module calculates the device power factor PF through the active power P and the reactive power Q, and sets , compare the device power factor PF with the preset device power factor PF0, judge the reactive power compensation capacity of the power grid according to the comparison result, and adjust the operating temperature C of the device according to the judgment result, where: When PF ∈ PF0, it is determined that the device power factor PF is normal, and the reactive power compensation capacity of the power grid is stable; When PF PF0, it is determined that the device power factor PF is abnormal, and the reactive power compensation capacity of the power grid is unstable. At this time, the operating temperature C of the device is adjusted, and the adjusted operating temperature of the device is C1, and it is set that C1 = PF × C; The monitoring and warning module compares the device power factor change frequency f with the preset power factor change frequency f0, judges the fault cause according to the comparison result, and outputs according to the judgment result, where: When f ≤ f0, it is determined that the device power factor change frequency f is normal, and at this time, the reactive power compensation device of the power grid has no fault; When f > f0, it is determined that the device power factor change frequency f is abnormal, and at this time, the reactive power compensation device of the power grid has a fault, and the fault is output; The monitoring and warning module calculates the random factor value S based on the real-time monitoring data, and sets S = 0.5×h + 0.5×z, where h is the electricity demand coefficient and z is the weather influence coefficient. The random factor value S is compared with the preset random factor value S0, and the influence on the change of the device power factor is judged according to the comparison result. The change rate f of the device power factor is adjusted according to the judgment result, where: When S ≤ S0, it is determined that the random factor value has no influence on the change of the device power factor, and at this time, the change rate f of the device power factor is not adjusted; When S > S0, it is determined that the random factor value has an influence on the change of the device power factor, and at this time, the change rate f of the device power factor is adjusted. After adjustment, the change rate of the device power factor is f1, and it is set that f1 = 2.55×e (-2S) + 0.5×f, where e is the base of the natural logarithm; The monitoring and warning module compares the real-time power grid load data F with the preset power grid load data F0, judges the rural power grid load density according to the comparison result, and issues an alarm when the judgment result is abnormal, where: When F > F0, it is determined that the rural power grid load density is normal, and at this time, no abnormal alarm is issued; When F ≤ F0, it is determined that the rural power grid load density is abnormal, and at this time, the buzzer is triggered for abnormal alarm, and the second fault cause is output. At this time, the real-time power grid load data F is adjusted. After adjustment, the real-time power grid load data is F1, and it is set that F1 = 1.28×F.

[0039] Specifically, the actual electricity consumption situation in rural areas refers to the actual power usage in rural regions. The real-time monitoring refers to the process of continuously and uninterruptedly observing and recording the actual electricity consumption situation in rural areas through technical means. The real-time device temperature C refers to the temperature value of the power device at the moment of real-time monitoring. The real-time load data F refers to the power load situation in rural areas at the moment of real-time monitoring. The active power P refers to the power actually consumed in the power system. The reactive power Q refers to the part of the power used to establish and maintain the electric and magnetic fields. The device power factor change frequency f refers to the frequency at which the power factor of the power device changes within a certain period. The device current I refers to the current value of the power device at the moment of real-time monitoring. The real-time device voltage U refers to the voltage value of the power device at the moment of real-time monitoring. The preset device temperature C0 refers to the normal temperature threshold set for the power device. In this embodiment, the range of the preset device temperature C0 is not limited. For example, it can be set to 65°C - 80°C. The device operating temperature state refers to the current temperature state of the power device. The buzzer refers to a sound alarm. In this embodiment, the type of the buzzer is not limited. Those skilled in the relevant art can freely set it according to the actual situation as long as it meets the requirement of warning about abnormal situations. For example, it can be set as a passive buzzer. The first fault cause refers to the fault cause determined by the system when the device temperature is abnormal. The preset device power factor PF0 refers to the normal power factor threshold set for the power device. In this embodiment, the range of the preset device power factor PF0 is not limited. For example, it can be set that 0.9 ≤ PF0 < 1. The grid reactive power compensation capacity refers to the total capacity of the devices used to compensate reactive power in the power system. The preset power factor change frequency f0 refers to the normal power factor change frequency threshold set for the power device. In this embodiment, the normal power factor change frequency threshold is not limited. For example, it can be set to 3 times / day ≤ f0 ≤ 5 times / day. The fault cause refers to the specific reason that causes abnormalities in the power device and the power grid. The grid reactive power compensation device refers to the device used to compensate reactive power in the grid. The electricity demand coefficient h refers to the coefficient reflecting the electricity demand in rural areas. The weather influence coefficient z refers to the coefficient reflecting the influence of weather conditions on electricity demand and usage. The preset random factor value S0 refers to the threshold set for judging the influence of random factors on the change of the device power factor. In this embodiment, the threshold set for the influence of the device power factor change is not limited. For example, it can be set to 0.05 - 0.1. The influence on the power factor change of the device refers to the degree of influence of random factors on the power factor change of the device. The preset power grid load data F0 refers to the threshold set for judging whether the load density of the rural power grid is abnormal. The scope of the preset power grid load data F0 is not limited in this embodiment and can be set to 50kW - 300kW, for example. The rural power grid load density refers to the amount of power load per unit population of the power grid in rural areas. The second cause of the fault refers to the specific cause of the fault determined by the system when the load density of the power grid is abnormal.

[0040] Specifically, in the monitoring and warning module, through the application of real-time monitoring and intelligent warning system, the comprehensive monitoring and timely response to the operation status of rural power grid equipment are realized, and the operation stability and security of the power grid are improved.

[0041] Specifically, the system verification module verifies the data acquisition and processing ability according to the device current I, compares the device current I with the device current standard value I0, verifies the data acquisition and processing ability according to the comparison result, and adjusts the data acquisition and processing process according to the verification result, where: When the power data acquisition and processing ability of the device is verified to be normal, and the data acquisition and processing process is not adjusted at this time When the power data acquisition and processing ability of the device is verified to be abnormal, and the data acquisition and processing process is adjusted at this time. The data collected in the previous 3 hours before this collection point is collected, and the abnormal value is reset using the 3σ principle until normal power grid load data is obtained; The system verification module verifies the communication network layer according to the device impedance change value Ω, compares the device impedance change value Ω with the preset device impedance change value Ω0, judges the device impedance change according to the comparison result, and adjusts the communication network layer according to the judgment result, where: When Ω ≤ Ω0, it is determined that the device impedance change is normal, and the communication network layer is not adjusted at this time; When Ω > Ω0, it is determined that the device impedance change is abnormal, and the communication network layer is adjusted according to the communication adjustment method at this time. The communication adjustment method includes:; Step S20, perform fault troubleshooting on the access layer terminal device, adjust the data network channel, and perform repair and replacement; Step S21, check the operation status of the aggregation layer device, upgrade the hardware and software of the aggregation device, and evenly distribute the data traffic to the backup link using the load balancing function of the aggregation device; Step S22, use an optical time domain reflectometer device to locate the fault of the backbone optical fiber link, and replace and repair it in time, and switch to the backup link at the same time The system verification module verifies the stability of the power grid after regulation and configuration according to the power factor ℇ, compares the power factor ℇ with the power factor ℇ0 before regulation and configuration, judges the stability of the power grid after regulation and configuration according to the comparison result, and adjusts the power grid distribution after regulation and configuration according to the judgment result, where: When , it is determined that the stability of the power grid after this regulation and configuration increases; When , it is determined that the stability of the power grid after this regulation and configuration decreases. At this time, the power grid distribution after regulation and configuration is adjusted, the output power of the distributed power source is controlled according to the load capacity and load demand of the power grid, and the power of the distributed power source is restricted; The system verification module verifies the utilization efficiency of power grid resources according to the transformer load rate β, compares the transformer load rate β with the transformer load rate β0 before implementation, judges the utilization rate of the power grid after regulation and configuration according to the comparison result, and adjusts the power grid load distribution situation after regulation and configuration according to the judgment result, where: When β ∈ β0, it is determined that the utilization rate of the power grid after this regulation and configuration is high, and at this time, the power grid load distribution situation after regulation and configuration is not adjusted; When β β0, it is determined that the utilization rate of the power grid after this regulation and configuration is low. At this time, the power grid load distribution situation after regulation and configuration is adjusted, and the power grid load is transferred from the high-load-rate transformer to the low-load-rate transformer by adjusting the substation tie switch.

[0042] Specifically, the data acquisition and processing capability refers to the ability of the device to collect and process real-time data. The standard value I0 of the device current refers to a preset standard value that the current of the device should reach under normal operating conditions. The data acquisition and processing process refers to the process of collecting and processing data. The 3σ principle is used to identify and process outliers in the data. The normal grid load data refers to the data that fluctuates within a certain range and does not exceed the grid's carrying capacity. The preset device impedance change value Ω0 refers to the preset threshold for the change in device impedance. In this embodiment, the range of the preset device impedance change value Ω0 is not limited. For example, it can be set to 0.1 - 0.3. The device impedance change refers to the change in the impedance value over time. The communication adjustment method refers to the technical means of adjusting communication parameters, signal formats, and system configurations in a communication system, including fault troubleshooting, load balancing function of aggregation devices, and fault location. Fault troubleshooting refers to diagnosing the problems that occur in the device to find out the cause and location of the fault. The load balancing function of aggregation devices refers to distributing data traffic to available links. Fault location refers to determining the specific location where the fault occurs in the network. The access layer terminal device refers to the lowest-level device in the network architecture. The data network channel refers to the link through which data is transmitted in the network. Repair and replacement refer to repairing and replacing the faulty device. Hardware refers to the physical components of an electronic device. Software refers to the set of instructions that control the operation of the hardware. Upgrade refers to updating the hardware and software. In this embodiment, the method of upgrade is not limited. Those skilled in the relevant art can freely set it according to the actual situation, as long as the requirement of updating the hardware and software is met. For example, the method of upgrade can be set to automatic upgrade. The data traffic refers to the amount of data transmitted on the network. The backup link refers to the standby link used to replace the main link for data transmission. The optical time domain reflectometer device refers to an instrument used to measure the length, loss, and fault location of an optical fiber link. The backbone optical fiber link refers to the main optical fiber transmission path in the network. The stability of the power grid after regulation and configuration refers to the stability and reliability of the power grid during operation. The power factor ℇ0 before regulation and configuration refers to the power factor of the power grid before implementing regulation and configuration. In this embodiment, the threshold of the power factor ℇ0 before regulation and configuration is not limited. For example, it can be set to 0.6 - 0.8. The power grid distribution refers to the process of allocating and dispatching electric power resources. The carrying capacity of the power grid refers to the maximum load that the power grid can withstand under normal operating conditions. The load demand refers to the power demand of users. The output power of the distributed power source refers to the electric power generated by the distributed generation equipment. The power generation of the distributed power source refers to the total power generated by the distributed generation equipment within a certain period. The utilization efficiency of power grid resources refers to the effective utilization degree of electric power resources. The load rate β0 of the transformer before implementation refers to the load rate of the transformer before implementing the regulation and configuration. In this embodiment, the threshold of the load rate β0 of the transformer before implementation is not limited. For example, it can be set as 40% ≤ β0 ≤ 70%. The utilization efficiency of the power grid after regulation and configuration refers to the utilization efficiency of power grid resources after regulating and configuring the power grid. The load distribution of the power grid after regulation and configuration refers to the load distribution of each part of the power grid. The substation tie switch refers to the switching equipment used to connect different substations. The high-load-rate transformer refers to the transformer whose load rate exceeds the preset threshold. The low-load-rate transformer refers to the transformer whose load rate is lower than the preset threshold.

[0043] Specifically, in the system verification module, through real-time monitoring and intelligent verification, the optimization of data acquisition and processing capabilities, the communication network layer, the optimal operation of equipment at all levels of the power grid, and the optimization of power grid regulation and configuration are realized, improving the reliability, stability, and resource utilization efficiency of the power grid.

[0044] Specifically, the execution feedback module obtains the first failure cause and the second failure cause according to the monitoring and warning system for feedback, obtains the feedback result, and executes the control strategy command on the power grid equipment according to the feedback result, where: When the first failure cause is determined that the device cannot execute the command, the standby device is started at this time; When the second failure cause is determined that the load density of the rural power grid is abnormal, the redundant system is switched to at this time.

[0045] Specifically, the monitoring and warning system refers to the system used to monitor the operating state of power grid equipment in real time and issue warning signals. The feedback result refers to the conclusion obtained after analyzing the information provided by the monitoring and warning system. The power grid equipment refers to various equipment that constitutes the power grid, such as transformers, switches, lines, and capacitors. The execution of the control strategy command refers to executing the corresponding control command on the power grid equipment according to the feedback result. The device cannot execute the command refers to the control command that cannot be responded to when the power grid equipment fails. The standby device refers to the alternative device prepared to cope with the failure of the power grid equipment. The redundant system refers to the set standby system.

[0046] Specifically, in the execution feedback module, through monitoring the alarm system and precisely feedbacking and responding to the causes of power grid equipment failures, comprehensive monitoring and intelligent management of the power grid operation are realized, improving the stability and security of the power grid.

[0047] Please refer to Figure 2 as shown, which is a schematic flow diagram of the power grid load distribution method based on intelligent control in this embodiment. The method includes: Step S1, collect the data of each node of the power grid and meteorological data in real time, and process the data of each node of the power grid and meteorological data to obtain the actual power grid load data; Step S2, transmit the actual power grid load data; Step S3, construct a rural power grid load model according to the actual power grid load data to obtain a load prediction value, and regulate and configure the power grid operation state according to the load prediction value to obtain the equipment impedance change value, power factor, and transformer load rate; Step S4, monitor the actual electricity consumption situation in rural areas in real time according to the load prediction value to obtain real-time monitoring data, judge the equipment operation temperature state according to the real-time monitoring data, and give an alarm when the equipment operation temperature state is abnormal, and output the first fault cause; Step S5, calculate the power factor of the equipment according to the real-time monitoring data, and adjust the equipment operation temperature according to the power factor of the equipment; Step S6, calculate the random factor value according to the real-time monitoring data, and optimize the power factor of the equipment according to the random factor value; Step S7, judge the load state of the rural power grid according to the real-time power grid load data in the real-time monitoring data, and give an alarm when the load state of the rural power grid is abnormal, and output the second fault cause; Step S8, verify the data acquisition and processing ability according to the equipment current in the real-time monitoring data, adjust the data acquisition and processing process, and verify the communication network layer according to the equipment impedance change value; Step S9, verify the stability of the power grid after regulation and configuration according to the power factor, verify the power grid resource utilization efficiency according to the transformer load rate, and adjust the regulation and configuration; Step S10, obtain the first fault cause and the second fault cause for feedback to get a feedback result, and execute the control strategy command on the power grid equipment according to the feedback result.

[0048] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of the present invention.

Claims

1. A power grid load distribution system based on intelligent control, characterized in that: The system comprises: The data acquisition and processing module is used to collect data from each node of the power grid and meteorological data in real time, and is also used to process data from each node of the power grid and meteorological data to obtain actual power grid load data; A communication network module, used for transmitting the actual power grid load data; An intelligent control module is used to construct a rural power grid load model according to the actual power grid load data, obtain a load prediction value, and control and configure the power grid operation state according to the load prediction value to obtain a device impedance change value, a power factor, and a transformer load rate; A monitoring and alarm module, used to monitor the actual rural electricity consumption in real time according to the load forecast value, obtain real-time monitoring data, judge the equipment operating temperature state according to the real-time monitoring data, and issue an alarm when the equipment operating temperature state is abnormal, and output a first fault cause, and also used to calculate the equipment power factor according to the real-time monitoring data, and adjust the equipment operating temperature according to the equipment power factor, and also used to calculate the random factor value according to the real-time monitoring data, and optimize the equipment power factor according to the random factor value, and also used to judge the rural power grid load state according to the real-time power grid load data in the real-time monitoring data, and issue an alarm when the rural power grid load state is abnormal, and output a second fault cause; A system verification module, for verifying the data acquisition and processing capabilities according to the device current in the real-time monitoring data, and adjusting the data acquisition and processing process, and for verifying the communication network layer according to the device impedance change value, and for verifying the stability of the power grid after the control configuration according to the power factor, and for verifying the power grid resource utilization efficiency according to the transformer load rate, and adjusting the control configuration; The execution feedback module is used to obtain the first fault cause and the second fault cause for feedback, obtain feedback results, and execute control strategy commands on the power grid equipment according to the feedback results.

2. The power grid load distribution system based on intelligent control according to claim 1 is characterized in that: The intelligent control module inputs the actual power grid load data into the rural power grid load model to obtain the load prediction value and construct the rural power grid load model, wherein the actual power grid load data is divided into 70% of the load training set, 20% of the load verification set and 10% of the load test set, the load training set is input into the decision tree structure model to train the decision tree structure model, and the load verification set is input into the trained decision tree structure model, the hyperparameters of the trained decision tree structure model are iteratively optimized, and the load test set is input into the iteratively optimized decision tree structure model to perform load testing on the iteratively optimized decision tree structure model to obtain the load test result, the total number of samples in the load test set is set to k0, the number of correct load test samples is set to k, the load test accuracy is set to K, K=k / k0, the load test accuracy K is compared with the preset load test accuracy K0, the training compliance of the iteratively optimized decision tree structure model is judged according to the comparison result, and the judgment result is output, wherein: When K≥K0, it is determined that the iteratively optimized decision tree structure model training has reached the standard, and the iteratively optimized decision tree structure model is output as a rural power grid load model; When K<K0, it is determined that the iteratively optimized decision tree structure model training does not meet the standard, the actual power grid load database is updated to obtain an updated actual power grid load database, and the decision tree structure model is trained, hyperparameters are iteratively optimized and load tested according to the updated actual power grid load database until the decision tree structure model training meets the standard and outputs the load prediction value Fy; The intelligent control module controls and configures the grid operation state according to the load prediction value Fy, obtains the equipment impedance change value Ω, the power factor ℇ and the transformer load rate β, compares the load prediction value Fy with the preset load value Fy0, judges the grid operation state according to the comparison result, and performs control and configuration according to the judgment result, wherein: When Fy<Fy0, the grid is judged to be in normal operation state. At this time, the load level of the future grid is low, and the power of the distributed power generation is stored in the distributed energy storage system; When Fy≥Fy0, the grid operation status is determined to be abnormal. At this time, the load level of the future section grid is high. The transformer tap is adjusted in the load growth area to distribute part of the load to the adjacent substation.

3. The power grid load distribution system based on intelligent control according to claim 1 is characterized in that: The monitoring and alarm module monitors the actual rural electricity consumption in real time according to the load prediction value Fy to obtain real-time monitoring data, wherein the real-time monitoring data includes real-time equipment temperature C, real-time load data F, active power P, reactive power Q, equipment impedance change value Ω, equipment power factor change frequency f, equipment current I and real-time equipment voltage U. The real-time equipment temperature C in the real-time monitoring data is compared with the preset equipment temperature C0, and the equipment operating temperature state is judged according to the comparison result, and an alarm is issued according to the judgment result, wherein: When C≤C0, the operating temperature of the device is determined to be normal, and no abnormal alarm is issued; When C>C0, the operating temperature of the equipment is determined to be abnormal, and the buzzer is triggered to give an abnormal alarm and output the first fault cause; The monitoring alarm module calculates the equipment power factor PF through the active power P and the reactive power Q, and sets , compare the equipment power factor PF with the preset equipment power factor PF0, and judge the reactive power compensation capacity of the power grid according to the comparison result, and adjust the equipment operating temperature C according to the judgment result, where: When PF∈PF0, the power factor PF of the equipment is judged to be normal and the reactive power compensation capacity of the power grid is stable; When PF When PF0, it is determined that the power factor PF of the equipment is abnormal and the reactive power compensation capacity of the power grid is unstable. At this time, the equipment operating temperature C is adjusted. The adjusted equipment operating temperature is C1, and C1 is set to be PF×C.

4. The power grid load distribution system based on intelligent control according to claim 3 is characterized in that: The monitoring alarm module compares the power factor change frequency f of the device with the preset power factor change frequency f0, and judges the cause of the fault according to the comparison result, and outputs according to the judgment result, wherein: When f≤f0, it is determined that the power factor change frequency f of the equipment is normal, and the reactive power compensation device of the power grid has no fault; When f>f0, the power factor change frequency f of the equipment is determined to be abnormal. At this time, the reactive power compensation device of the power grid fails and the fault is output; The monitoring alarm module calculates the random factor value S according to the real-time monitoring data, sets S=0.5×h+0.5×z, where h is the power demand coefficient and z is the weather influence coefficient, compares the random factor value S with the preset random factor value S0, and judges the impact of the equipment power factor change according to the comparison result, and adjusts the equipment power factor change rate f according to the judgment result, where: When S≤S0, it is determined that the random factor value has no effect on the change of the equipment power factor, and the equipment power factor change rate f is not adjusted at this time; When S>S0, it is determined that the random factor value has an impact on the change of the equipment power factor. At this time, the equipment power factor change rate f is adjusted. After adjustment, the equipment power factor change rate is f1, and f1=2.55×e (-2S) +0.5×f, where e is the natural number base.

5. The power grid load distribution system based on intelligent control according to claim 4 is characterized in that: The monitoring and alarm module compares the real-time power grid load data F with the preset power grid load data F0, and judges the rural power grid load density according to the comparison result, and issues an alarm when the judgment result is abnormal, wherein: When F>F0, the rural power grid load density is judged to be normal, and no abnormal alarm is issued; When F≤F0, the rural power grid load density is determined to be abnormal. At this time, the buzzer is triggered to issue an abnormal alarm and output the second fault cause. At this time, the real-time power grid load data F is adjusted. After adjustment, the real-time power grid load data is F1, and F1 is set to 1.28×F.

6. The power grid load distribution system based on intelligent control according to claim 1, characterized in that: The system verification module verifies the data acquisition and processing capability according to the device current I, compares the device current I with the device current standard value I0, verifies the data acquisition and processing capability according to the comparison result, and adjusts the data acquisition and processing process according to the verification result, wherein: when When the equipment power data collection and processing capability is verified to be normal, no adjustment is made to the data collection and processing process; when When the equipment power data collection and processing capability is verified to be abnormal, the data collection and processing process is adjusted, and the data collected 3 hours before the collection point is collected. The abnormal value is reset using the 3σ principle until normal power grid load data is obtained.

7. The power grid load distribution system based on intelligent control according to claim 6, characterized in that: The system verification module verifies the communication network layer according to the device impedance change value Ω, compares the device impedance change value Ω with the preset device impedance change value Ω0, and judges the device impedance change according to the comparison result, and adjusts the communication network layer according to the judgment result, wherein: When Ω≤Ω0, the impedance change of the device is determined to be normal, and the communication network layer is not adjusted at this time; When Ω>Ω0, it is determined that the impedance change of the device is abnormal. At this time, the communication network layer is adjusted according to the communication adjustment method, and the communication adjustment method includes: Step S20, troubleshoot the access layer terminal equipment, adjust the data network channel, repair and replace; Step S21, check the running status of the aggregation layer equipment, upgrade the hardware and software of the aggregation equipment, and use the load balancing function of the aggregation equipment to evenly distribute the data traffic to the backup link; Step S22: Use an optical time domain reflectometer to locate the fault of the backbone optical fiber link, replace and repair it in time, and switch to the backup link at the same time.

8. The power grid load distribution system based on intelligent control according to claim 7, characterized in that: The system verification module verifies the stability of the power grid after the control configuration according to the power factor ℇ, compares the power factor ℇ with the power factor ℇ0 before the control configuration, and judges the stability of the power grid after the control configuration according to the comparison result, and adjusts the power grid allocation of the control configuration according to the judgment result, wherein: when When , it is determined that the grid stability is improved after the control configuration; when When the grid stability is determined to be reduced after the control configuration, the grid allocation of the control configuration is adjusted, and the output power of the distributed power source is controlled according to the carrying capacity and load demand of the grid to limit the power of the distributed power source; The system verification module verifies the utilization efficiency of power grid resources according to the transformer load rate β, compares the transformer load rate β with the transformer load rate β0 before implementation, and judges the utilization rate of the power grid after the regulation configuration according to the comparison result, and adjusts the load distribution of the power grid after the regulation configuration according to the judgment result, wherein: When β∈β0, it is determined that the power grid utilization rate after the control configuration is high, and the load distribution of the power grid after the control configuration is not adjusted; When β When β0, it is determined that the utilization rate of the power grid after the control configuration is low. At this time, the load distribution of the power grid after the control configuration is adjusted, and the power grid load is transferred from the high-load transformer to the low-load transformer by adjusting the substation contact switch.

9. The power grid load distribution system based on intelligent control according to claim 1, characterized in that: The execution feedback module obtains the first fault cause and the second fault cause according to the monitoring alarm system, and provides feedback to obtain a feedback result, and executes a control strategy command on the power grid device according to the feedback result, wherein: When the first fault cause is determined to be that the device cannot execute the command, the backup device is started; When the second fault cause is determined to be abnormal load density of the rural power grid, the system switches to the redundant system.

10. A method applied to a system for power grid load distribution based on intelligent control as claimed in any one of claims 1 to 9, characterized in that: The method comprises: Step S1, real-time collection of data from each node of the power grid and meteorological data, data processing of the data from each node of the power grid and meteorological data, and obtaining actual power grid load data; Step S2, transmitting the actual power grid load data; Step S3, constructing a rural power grid load model according to the actual power grid load data, obtaining a load prediction value, and regulating and configuring the power grid operation state according to the load prediction value, and obtaining a device impedance change value, a power factor, and a transformer load rate; Step S4, real-time monitoring of the actual power consumption in rural areas is performed according to the load forecast value to obtain real-time monitoring data, and the operating temperature state of the equipment is judged according to the real-time monitoring data, and an alarm is issued when the operating temperature state of the equipment is abnormal, and the first fault cause is output; Step S5, calculating the equipment power factor according to the real-time monitoring data, and adjusting the equipment operating temperature according to the equipment power factor; Step S6, calculating the random factor value according to the real-time monitoring data, and optimizing the equipment power factor according to the random factor value; Step S7, judging the rural power grid load state according to the real-time power grid load data in the real-time monitoring data, and issuing an alarm when the rural power grid load state is abnormal, and outputting the second fault cause; Step S8, verifying the data collection and processing capabilities according to the device current in the real-time monitoring data, adjusting the data collection and processing process, and verifying the communication network layer according to the device impedance change value; Step S9, verifying the stability of the power grid after the control configuration according to the power factor, verifying the utilization efficiency of the power grid resources according to the transformer load rate, and adjusting the control configuration; Step S10, obtaining the first fault cause and the second fault cause for feedback, obtaining a feedback result, and executing a control strategy command on the power grid device according to the feedback result.

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