Intelligent identification and self-adaptive scheduling method and system for abnormal power supply guarantee

Abstract

The invention relates to the technical field of power supply abnormity identification, in particular to a power supply abnormity intelligent identification and adaptive scheduling method and system, and the method comprises the steps: obtaining the position information, real-time power consumption data and historical power consumption data of all intelligent ammeters in a target region, and a line topological graph of all intelligent ammeters and a power supply; setting a first corresponding relation between each intelligent electric meter and the corresponding branch line and a second corresponding relation between each branch line and the main line according to the line topological graph; sub-region division is carried out on the target region, and a corresponding power consumption range is set; whether the power consumption of the sub-region is within the corresponding power consumption range is detected, and the sub-region is marked as a risk region; according to the real-time power consumption data of the risk region, the power consumption change rate and the standard deviation are calculated; scheduling the risk area based on the electricity consumption change rate and the electricity consumption standard deviation through a scheduling control strategy; rapid identification and self-adaptive scheduling control of power supply abnormity are realized, and the stability and safety of a power grid are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power supply anomaly recognition, and particularly to an intelligent recognition and adaptive scheduling method and system for power supply guarantee anomalies. Background Art

[0002] In the field of power supply management, ensuring the stable operation and efficient power supply of the power grid is of crucial importance. Traditional power supply guarantee management mainly relies on manual monitoring and manual scheduling. This method is inefficient in processing large-scale power grid data and is difficult to identify and process power supply anomalies in a timely and accurate manner. With the development of smart grid technology, the wide application of devices such as smart meters and sensors has made it possible to collect and analyze power grid data in real time. However, there are still some deficiencies in the existing technologies in practical applications. Specifically, although the existing technologies can collect the location information, real-time power consumption data, and historical power consumption data of smart meters in the target area, there are still deficiencies in processing these data to identify power supply anomalies and perform adaptive scheduling control. For example, existing systems often lack effective methods to accurately correspond complex line topologies with smart meters and branch lines, making it difficult to quickly locate the problem area when a power supply anomaly occurs. In addition, the existing technologies usually rely on empirical judgments when setting the power consumption range of sub-areas and lack scientific analysis based on historical data, which may lead to inaccurate setting of the power consumption range and thus affect the accuracy of anomaly recognition.

[0003] In terms of anomaly detection, existing technologies usually can only simply judge whether the power consumption exceeds the preset range and cannot deeply analyze the trends and characteristics of power consumption changes. This simple detection method often fails to detect potential power supply risks in a timely manner, such as abnormal situations where the power consumption suddenly increases or decreases. At the same time, in terms of scheduling control strategies, existing technologies often lack flexibility and pertinence and cannot take corresponding scheduling measures according to specific types of power consumption anomalies (such as a sudden increase or decrease in power consumption). For example, when the power consumption suddenly increases, the existing system may not be able to increase the power supply capacity in a timely manner and start emergency power generation equipment, resulting in insufficient power supply; when the power consumption suddenly decreases, the existing system may not be able to accurately identify the faulty equipment and isolate it from the power grid in a timely manner to prevent the spread of the fault. Summary of the Invention

[0004] In view of the problems existing in the above-mentioned prior art, the present invention is proposed.

[0005] Therefore, the technical problem to be solved by the present invention is to address the issues of low efficiency, inaccurate positioning, and untimely response existing in the existing power supply anomaly identification and dispatching control technologies. By obtaining the location information of smart meters, real-time power consumption data, historical power consumption data, and line topology diagrams, establishing the corresponding relationships between smart meters and branch lines, and between branch lines and main lines, dividing sub-regions and setting power consumption ranges, monitoring the power consumption situation in real time, identifying risk areas, and performing adaptive dispatching control based on the power consumption change rate and standard deviation, the stability and security of the power grid can be improved, and the rapid identification and precise dispatching of power supply anomalies can be achieved.

[0006] To solve the above technical problems, the present invention provides the following technical solution, a method for intelligent identification and adaptive dispatching of power supply anomalies, including: obtaining smart meter information and line topology diagrams of smart meters and power supply sources in the target area; setting the first corresponding relationship between smart meters and corresponding branch lines, and the second corresponding relationship between each branch line and the main line according to the line topology diagram; dividing the target area into sub-regions according to the positions of smart meters according to the first corresponding relationship, and setting power consumption ranges for the power consumption time of the sub-regions according to the historical power consumption data of the sub-regions; detecting the power consumption situation and power consumption range of the sub-regions according to the real-time power consumption data, and marking the risk areas; calculating the power consumption change rate and power consumption standard deviation according to the real-time power consumption data of the risk areas, and performing dispatching on the risk areas through dispatching control policies.

[0007] As a preferred embodiment of the method for intelligent identification and adaptive dispatching of power supply anomalies according to the present invention, wherein: the smart meter information includes smart meter location information, real-time power consumption data, and historical power consumption data.

[0008] As a preferred embodiment of the method for intelligent identification and adaptive dispatching of power supply anomalies according to the present invention, wherein: the detecting the power consumption situation of the sub-regions according to the real-time power consumption data includes cleaning the collected real-time power consumption data to remove extreme values and missing values that deviate from the historical data range; using a sliding window to calculate the average power consumption and change rate of the sub-region within a preset time period, and comparing with the historical data of the same period to identify abnormal power consumption trends.

[0009] As a preferred embodiment of the method for intelligent identification and adaptive dispatching of power supply anomalies according to the present invention, wherein: the dispatching control policy includes that if the power consumption change rate is positive and exceeds the set first threshold, it is determined that the power consumption increases; locking the first power supply line of the risk area according to the first corresponding relationship and the second corresponding relationship, increasing the power supply capacity of the first power supply line, and starting the emergency power generation equipment;

[0010] If the power consumption change rate is negative and exceeds the set first value, it is determined that the power consumption decreases; obtain the power consumption change rate of the smart meters in the risk area. If the power consumption change rates of the smart meters within the preset number are detected to be negative in the risk area, find the branches corresponding to the smart meters with negative power consumption change rates according to the first correspondence relationship: If the power consumption change rates of the smart meters in the risk area are all negative, lock the second power supply line of the risk area according to the first correspondence relationship and the second correspondence relationship, and use a traversal method to detect the risk devices whose detection values of the sensors on the second power supply line are outside the standard range; start the fault isolation program and isolate the risk devices by adjusting the status of the corresponding circuit breakers.

[0011] As a preferred solution of the power supply guarantee abnormal intelligent identification and adaptive scheduling method described in the present invention, wherein: the starting the fault isolation program includes if the risk device is a critical load device, enabling the standby power supply and the fast mobile power generation device to provide temporary power supply for the critical load device; collecting the load conditions, power parameters and device operation status information of each line in the smart grid system in real time through the sensors deployed in the smart grid system; updating the first correspondence relationship and the second correspondence relationship in real time according to the line topology diagram and the power grid structure after fault isolation; using the path search algorithm to search for the target path that meets the power consumption conditions from the critical load device to the available stable power supply line for the updated first correspondence relationship and the second correspondence relationship.

[0012] As a preferred solution of the power supply guarantee abnormal intelligent identification and adaptive scheduling method described in the present invention, wherein: the enabling the standby power supply and the fast mobile power generation device to provide temporary power supply for the critical load device includes sorting the critical load devices affected after fault isolation according to the preset critical load device list, real-time power consumption and device importance; during the temporary power supply period, monitoring the power supply quality and device operation status in real time through the sensors deployed on the critical load devices and the temporary power supply devices. When an abnormal state is detected, a mobile terminal is used to prompt that the temporary power supply is abnormal; when the fault is repaired, switch back to the main grid power supply in the order of priority.

[0013] As a preferred solution of the power supply guarantee abnormal intelligent identification and adaptive scheduling method described in the present invention, wherein: the real-time monitoring of the power supply quality and device operation status includes installing sensors on the critical load devices and the temporary power supply devices to collect power parameters, device temperature and vibration in real time; preprocessing the collected data, including data cleaning, format conversion and outlier detection;

[0014] The abnormal state includes setting corresponding thresholds for the power parameters and operation status information; when the collected data exceeds the corresponding thresholds, it is regarded as an abnormal state.

[0015] Another object of the present invention is to provide an intelligent identification and adaptive scheduling system for power supply guarantee anomalies. By isolating faulty devices in real time, transferring loads to standby lines, and enabling temporary power supply devices to ensure the operation of critical loads, and at the same time, notifying users of the power supply status in real time through an intelligent electricity meter system or a mobile terminal, the impact of power supply interruption on users is minimized to the greatest extent and the emergency response efficiency is improved on the basis of ensuring the stability of the power grid.

[0016] To solve the above technical problems, the present invention provides the following technical solutions: An intelligent identification and adaptive scheduling system for power supply guarantee anomalies, including: a data acquisition module, a correspondence establishment module, a sub-region division module, a risk region determination module, and a scheduling module;

[0017] The data acquisition module is used to acquire the location information, real-time power consumption data, historical power consumption data of all intelligent electricity meters in the target area, and the line topology diagram of all intelligent electricity meters and power sources;

[0018] The correspondence establishment module is used to set the first correspondence between each intelligent electricity meter and the corresponding branch line, and the second correspondence between each branch line and the main line according to the line topology diagram;

[0019] The sub-region division module is used to divide the target area into sub-regions according to the location of the intelligent electricity meters according to the first correspondence, and set corresponding power consumption ranges for different power consumption times of each sub-region according to the historical power consumption data of each sub-region;

[0020] The risk region determination module is used to detect the power consumption situation of each sub-region according to the real-time power consumption data, detect whether the power consumption of each sub-region is within the corresponding power consumption range, and mark the region that is continuously not within the corresponding power consumption range as a risk region;

[0021] The scheduling module is used to calculate the power consumption change rate and the power consumption standard deviation according to the real-time power consumption data of the risk region; and perform scheduling on the risk region based on the power consumption change rate and the power consumption standard deviation through a scheduling control strategy.

[0022] A computer device includes a memory and a processor. The memory stores a computer program. The processor, when executing the computer program, implements the steps of the above-mentioned intelligent identification and adaptive scheduling method for power supply guarantee anomalies.

[0023] A computer-readable storage medium stores a computer program. The computer program, when executed by a processor, implements the steps of the above-mentioned intelligent identification and adaptive scheduling method for power supply guarantee anomalies.

[0024] Advantages of the present invention: By collecting the positions, real-time and historical power consumption data of smart meters in the target area, and combining with the line topology diagram, the present invention first divides the sub-areas and sets the power consumption ranges of each sub-area at different times. Subsequently, it monitors the power consumption situation in real time, identifies the areas deviating from the preset range as risk areas, and calculates the power consumption change rate and standard deviation of these areas. For the risk areas, if the power consumption increases sharply, the power supply is enhanced and emergency power generation is started; if the power consumption decreases sharply, it checks the power consumption changes of the smart meters in the risk areas, locks the abnormal branches or power supply lines, and isolates the faulty equipment by detecting the sensor values to prevent the spread of the fault. It effectively realizes the rapid identification of abnormal power supply guarantee and adaptive scheduling control, and improves the stability and security of the power grid. Description of the Drawings

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0026] Figure 1 It is a flowchart of the intelligent identification and adaptive scheduling method for abnormal power supply guarantee provided by an embodiment of the present invention.

[0027] Figure 2 It is a schematic diagram of the system structure of the intelligent identification and adaptive scheduling method for abnormal power supply guarantee provided by an embodiment of the present invention.

[0028] Figure 3 It is a schematic diagram of the electronic device structure of the intelligent identification and adaptive scheduling method for abnormal power supply guarantee provided by an embodiment of the present invention. Detailed Embodiments

[0029] In order to make the above objects, features and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0030] Example 1, referring to Figure 1 , which is an embodiment of the present invention. This embodiment provides an intelligent identification and adaptive scheduling method for abnormal power supply guarantee, including:

[0031] S1: Obtain the information of smart meters in the target area and the line topology diagram of the smart meters and the power supply source.

[0032] It should be noted that, as Figure 1 shown in S101, obtain the location information, real-time power consumption data, historical power consumption data of all smart meters within the target area, and the line topology diagram of all smart meters and the power supply source.

[0033] Furthermore, obtaining the location information of all smart meters within the target area helps the system understand the geographical distribution of smart meters, provides basic data for subsequent sub-region division, ensures that the dispatching control strategy can accurately locate specific geographical areas, and improves the response speed and efficiency; real-time data reflects the current power consumption status, while historical data provides power consumption trends and patterns. The combination of the two can more accurately predict power consumption demand and identify abnormal power consumption situations; historical data is also used to set the power consumption range of each sub-region at different power consumption times.

[0034] Even further, the line topology diagram details the connection relationships among smart meters, branch lines, and main lines; by understanding this complex line connection, the risk area can be quickly located, and appropriate dispatching measures can be formulated based on the layout and capacity of the lines. For example, increasing the power supply capacity or isolating faulty equipment.

[0035] S2: Set the first corresponding relationship between the smart meters and the corresponding branch lines, and the second corresponding relationship between each branch line and the main line according to the line topology diagram.

[0036] It should be noted that, as Figure 1 shown in S102, set the first corresponding relationship between each smart meter and the corresponding branch line, and the second corresponding relationship between each branch line and the main line according to the line topology diagram.

[0037] Furthermore, the system sets the first corresponding relationship between each smart meter and its corresponding branch line according to the line topology diagram, that is, the complex connection diagram among smart meters, branch lines, and main lines; this corresponding relationship clarifies the branch line to which each smart meter belongs, enabling the system to accurately trace the power supply source of each meter.

[0038] Even further, the system further sets the second corresponding relationship between each branch line and the main line; enabling the system to understand how the branch lines converge into the main line and the power supply areas responsible for each main line; through the setting of the second corresponding relationship, a refined analysis of the power supply network structure is achieved, laying a solid foundation for subsequent sub-region division, power consumption range setting, risk area detection, and the implementation of the dispatching control strategy; improving the accuracy and response speed of dispatching control, and ensuring that the system can quickly locate the risk points in case of abnormal power supply.

[0039] S3: Divide the target area into sub - areas according to the positions of smart meters based on the first correspondence relationship, and set the power consumption range for the power consumption time of each sub - area according to the historical power consumption data of the sub - area.

[0040] It should be noted that, as Figure 1 shown in S103, divide the target area into sub - areas according to the positions of smart meters based on the first correspondence relationship, and set the corresponding power consumption ranges for different power consumption times of each sub - area according to the historical power consumption data of each sub - area.

[0041] Furthermore, first, use the collected smart meter position information, combined with the geographical characteristics and power consumption demand distribution of the target area, to divide the entire area into several sub - areas; considering the geographical distribution of smart meters implies considerations of power load density and similarity of power consumption patterns, making each sub - area have a certain degree of homogeneity and predictability in power consumption behavior.

[0042] Even further, according to the historical power consumption data of each sub - area, analyze the power consumption trends and patterns in different time periods, and set corresponding power consumption ranges for different power consumption times of each sub - area; the key is that historical data can provide power consumption behavior information to help the system identify the peak and valley power consumption periods of each sub - area, as well as the corresponding power consumption fluctuation ranges; by setting reasonable power consumption ranges, the system can more accurately monitor and evaluate the power consumption of each sub - area.

[0043] S4: Detect the power consumption situation and power consumption range of the sub - area according to the real - time power consumption data, and mark the risk areas.

[0044] It should be noted that, as Figure 1 shown in S104, detect the power consumption situation of each sub - area according to the real - time power consumption data, detect whether the power consumption of each sub - area is within the corresponding power consumption range, and mark the area that continuously stays outside the corresponding power consumption range as a risk area.

[0045] Furthermore, after completing the division of the target area into sub - areas and setting the power consumption ranges of each sub - area, the system enters the stage of monitoring the real - time power consumption situation; mainly relying on the real - time power consumption data to detect the power consumption situation of each sub - area; that is, the system will continuously collect the real - time power consumption data of each sub - area and compare these data with the previously set power consumption ranges; to judge whether the current power consumption of each sub - area is within the preset reasonable range.

[0046] Even further, if the power consumption of a certain sub - area continuously stays outside the preset range, whether it is too high or too low, the system will immediately mark it as a risk area; helping the system quickly identify the areas where there may be abnormal power supply or abnormal power consumption.

[0047] S5: Based on the real-time electricity consumption data of the risk area, calculate the electricity consumption change rate and electricity consumption standard deviation, and dispatch the risk area through the dispatch control policy.

[0048] It should be noted that if Figure 1 As shown in S105, based on the real-time electricity consumption data of the risk area, the electricity consumption change rate and the electricity consumption standard deviation are calculated; the risk area is dispatched based on the electricity consumption change rate and the electricity consumption standard deviation through a scheduling control strategy; wherein the scheduling control strategy includes: if the electricity consumption change rate is positive and exceeds a set first threshold, it is determined that the electricity consumption has increased sharply; based on the first corresponding relationship and the second corresponding relationship, the first power supply line in the risk area is locked, the power supply capacity of the first power supply line is increased, and the emergency power generation equipment is started;

[0049] If the power consumption change rate is negative and exceeds a set first threshold, it is determined that the power consumption has decreased sharply; the power consumption change rate of all smart meters in the risk area is obtained. If the power consumption change rates of a preset number of smart meters in the risk area are all negative, the branch corresponding to the smart meters with negative power consumption change rates is found according to the first corresponding relationship;

[0050] If the electricity consumption change rates of all smart meters in the risk area are negative, the second power supply line in the risk area is locked according to the first and second correspondences, and a traversal method is used to detect risk devices whose detection values of all sensors on the second power supply line are not within the standard range; the fault isolation program is started, and the risk devices are isolated from the power grid by adjusting the status of the corresponding circuit breakers to prevent the fault from spreading;

[0051] Specifically, after identifying the risk area, the system immediately enters the in-depth analysis and dispatching control stage for the risk area; pays attention to the real-time power consumption data of the risk area, deeply explores the changing trends and volatility behind these data, and uses this as an important basis for formulating dispatching control strategies; the system calculates the power consumption change rate and the standard deviation of power consumption based on the real-time power consumption data of the risk area; these two indicators respectively reflect the change speed of power consumption over time and the degree of dispersion of power consumption, and are important indicators for evaluating the stability and abnormality of the power consumption state; according to the values of these two indicators and in combination with the preset dispatching control strategy, targeted dispatching is carried out on the risk area; if the power consumption change rate is positive and exceeds the set first threshold, it is determined that the power consumption has increased sharply, which is caused by the newly added power load or equipment failure; at this time, according to the first correspondence relationship between the smart meter and the power supply line, and the second correspondence relationship between the branch line and the main line, the first power supply line of the risk area is locked; in order to cope with the rapidly increasing power demand, the power supply capacity of this power supply line is increased, and the emergency power generation equipment is started to ensure the stability and reliability of the power supply; on the contrary, if the power consumption change rate is negative and exceeds the set first threshold, the system determines that the power consumption has decreased sharply, which is caused by the failure of some electrical equipment or the user's initiative to reduce power consumption; at this time, further analyze the power consumption change rate of all smart meters in the risk area. If it is detected that the power consumption change rates of the preset number of smart meters are all negative, the system will lock the branch lines corresponding to these smart meters; if the power consumption change rates of all smart meters in the risk area are all negative, the system will lock the second power supply line of the risk area and use a traversal method to detect the detection values of all sensors on this line to find out the risk equipment that is not within the standard range; once the risk equipment is identified, the system immediately starts the fault isolation program and isolates the equipment from the power grid by adjusting the state of the corresponding circuit breaker to prevent the further spread of the fault and ensure the safe and stable operation of the entire power supply network; through this series of adaptive dispatching controls, rapid response and effective control of the risk area are achieved, and the stability and reliability of the entire power supply network are also improved.

[0052] Furthermore, after starting the fault isolation program, to ensure the continuity and stability of power supply, the system needs to act quickly to transfer some of the loads originally powered by the faulty device to other normally operating lines or devices; for load transfer, first analyze the power supply lines connected to the faulty device and their load distribution, and then evaluate the remaining capacity and power supply capabilities of other lines or devices; on the premise of ensuring safety, formulate an optimal load transfer plan to smoothly transfer some or all of the loads on the faulty device to other lines or devices; after identifying the risk area and successfully locating the specific risk device, immediately start the fault isolation program and quickly cut off the fault source; the fault isolation program isolates the risk device from the power grid by adjusting the status of the corresponding circuit breaker; after starting the fault isolation program, it is also necessary to notify the affected users of the power outage situation through the smart meter system or by text message or APP push; if the risk device is a critical load device, enable the backup power supply or quickly move the power generation device to provide temporary power supply for the critical load device; after starting the fault isolation program, the system first determines whether the isolated risk device is a critical load device; critical load devices include those with extremely high requirements for power supply continuity and stability, and will cause serious consequences once power is cut off, such as the life support system in a hospital and the communication equipment in an emergency command center; if the risk device is confirmed to be a critical load device, immediately take emergency measures to enable the backup power supply or quickly dispatch the mobile power generation device to the scene to ensure that these critical load devices can obtain uninterrupted temporary power supply during fault isolation;

[0053] Through the sensors deployed in the smart grid system, continuously collect the load conditions, power parameters of each line in the smart grid system, and the operating status information of each device; specifically, through various sensors deployed in the smart grid system, continuously collect the load conditions, power parameters (such as voltage, current, frequency, etc.) of each line in the grid and the operating status information of each device, providing a comprehensive view of the grid operation for the system; according to the latest line topology diagram and the grid structure after fault isolation, update the first correspondence and the second correspondence in real time; according to the latest line topology diagram and the grid structure after fault isolation, update the first correspondence between the smart meter and the branch line, and the second correspondence between the branch line and the main line in real time; using the path search algorithm, search for the target path that meets the power consumption conditions from the critical load device to the available stable power supply line based on the updated first correspondence and the second correspondence; specifically, using the path search algorithm, search for the target path that meets the power consumption conditions from the critical load device to the available stable power supply line based on the updated first correspondence and the second correspondence;

[0054] Use a backup power supply or a fast-moving power generation device to provide temporary power supply for critical load devices. Based on a preset list of critical load devices, as well as real-time power consumption and device importance, prioritize the critical load devices affected after fault isolation; for example, the priorities from high to low are: hospital operating room equipment, data center servers, office area lighting and ventilation systems, household electricity; during the temporary power supply period, through sensors deployed on critical load devices and temporary power supply devices, monitor the power supply quality and device operation status in real time. When an abnormal state is detected, prompt the relevant personnel through a mobile terminal that there is an abnormality in the temporary power supply; when the fault is repaired, gradually switch back to the main grid power supply in the order of priority.

[0055] Specifically, based on a preset list of critical load devices, combine real-time power consumption and device importance to prioritize the critical load devices affected by fault isolation; the priority ranking considers factors such as the urgency of the device, the impact on social economy, and the difficulty of power supply restoration, etc.; according to the ranking results, enable a backup power supply or a fast-moving power generation device to provide temporary power supply for these critical load devices; the backup power supply includes emergency generators, uninterruptible power supplies (UPS), etc., and the fast-moving power generation devices include mobile devices that can be quickly deployed to the site to provide power, such as mobile generators; during the temporary power supply period, deploy sensors on critical load devices and temporary power supply devices to monitor the power supply quality and device operation status in real time; collect key parameters such as voltage, current, and frequency in real time, and transmit the data back to the system for analysis; if an abnormal state is detected, such as excessive voltage fluctuation, current overload, etc., then send a prompt to relevant personnel through a mobile terminal to remind them to take measures in time for handling; when the fault is repaired and the main grid resumes normal power supply, switch the critical load devices back to the main grid power supply in the set order of priority.

[0056] Furthermore, install sensors on critical load devices and temporary power supply devices to collect power parameters and operation status information such as device temperature and vibration in real time; preprocess the collected data, including data cleaning, format conversion, and outlier detection; based on technical specifications and operation experience, set corresponding thresholds for each power parameter and operation status information; when the collected data exceeds the corresponding threshold, it is regarded as an abnormal state; for example, too high device temperature may mean poor heat dissipation or overload operation, and abnormal vibration may indicate wear or looseness of mechanical components; once an abnormal state is detected, the system will take corresponding measures according to the preset response mechanism, including sending an alarm to notify the operation and maintenance personnel, starting a fault diagnosis program to locate the root cause of the problem; automatically adjust the device operation parameters according to the abnormal type to reduce the impact; for critical load devices, trigger the backup power supply switch or start the emergency power generation device.

[0057] Clean the collected real-time power consumption data to remove extreme values and missing values that deviate from the historical data range; use a sliding window to calculate the average power consumption and change rate of each sub-region within a recent preset time period, and compare with the historical data of the same period to identify trends of abnormal increase or decrease in power consumption; specifically, preprocess the collected real-time power consumption data, that is, data cleaning; during the data collection process, due to various reasons (such as sensor failures, data transmission errors, etc.), extreme values and missing values that deviate from the historical data range will be generated; it is necessary to first clean the real-time power consumption data to remove abnormal values; use the sliding window technique to calculate the average power consumption and change rate of each sub-region within a recent preset time period, and compare with the historical data of the same period; specifically, the sliding window technique is a time series analysis method that reveals the local change trend of the time series by sliding a fixed-size window on the time series and calculating the statistical characteristics (such as average value, change rate, etc.) of the data within the window; by setting a reasonable window size (i.e., preset time period), calculate the average power consumption and change rate of each sub-region within this time period to reflect the recent power consumption behavior characteristics of the sub-region; compare with the historical data of the same period to identify trends of abnormal increase or decrease in power consumption.

[0058] The above is a schematic solution of an intelligent identification and adaptive scheduling method for power supply guarantee anomalies in this embodiment. It should be noted that the technical solution of the system of the intelligent identification and adaptive scheduling method for power supply guarantee anomalies belongs to the same concept as the above-mentioned technical solution of the intelligent identification and adaptive scheduling method for power supply guarantee anomalies. For the details not described in detail in the technical solution of the intelligent identification and adaptive scheduling system for power supply guarantee anomalies in this embodiment, reference can be made to the description of the technical solution of the intelligent identification and adaptive scheduling method for power supply guarantee anomalies above.

[0059] Embodiment 2, refer to Figures 2 - 3 , which is an embodiment of the present invention. This embodiment provides an intelligent identification and adaptive scheduling system for power supply guarantee anomalies, including:

[0060] As Figure 2 shown, a data acquisition module 201, a correspondence establishment module 202, a sub-region division module 203, a risk region determination module 204, and a scheduling module 205;

[0061] The data acquisition module 201 is used to acquire the position information, real-time power consumption data, historical power consumption data of all smart meters in the target area, and the line topology diagram of all smart meters and power sources;

[0062] The correspondence establishment module 202 is used to set the first correspondence between each smart meter and the corresponding branch line, and the second correspondence between each branch line and the main line according to the line topology diagram;

[0063] The sub-region division module 203 is configured to divide the target region into sub-regions according to the positions of the smart electricity meters based on the first correspondence relationship, and set corresponding power consumption ranges for different power consumption times of each sub-region according to the historical power consumption data of each sub-region;

[0064] The risk region determination module 204 is configured to detect the power consumption conditions of each sub-region according to the real-time power consumption data, detect whether the power consumption of each sub-region is within the corresponding power consumption range, and mark the regions that are continuously not within the corresponding power consumption range as risk regions;

[0065] The scheduling module 205 is configured to calculate the power consumption change rate and the power consumption standard deviation according to the real-time power consumption data of the risk regions; and perform scheduling on the risk regions based on the power consumption change rate and the power consumption standard deviation through a scheduling control strategy.

[0066] This embodiment also provides a computing device applicable to the situation of intelligent identification and adaptive scheduling method for abnormal power supply guarantee, including:

[0067] As Figure 3 shown, a memory 302 and a processor 301; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the intelligent identification and adaptive scheduling method for abnormal power supply guarantee proposed in the above embodiment.

[0068] This embodiment also provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the intelligent identification and adaptive scheduling method for abnormal power supply guarantee proposed in the above embodiment.

[0069] The storage medium proposed in this embodiment and the intelligent identification and adaptive scheduling method for abnormal power supply guarantee proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0070] When the above-mentioned functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0071] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0072] It should be understood that each part of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following well-known technologies in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGA), field-programmable gate arrays (FPGA), etc.

[0073] It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solution of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solution of the present invention, and all of them should be covered by the scope of the claims of the present invention.

Claims

1. A method for intelligent identification and adaptive scheduling of power supply anomalies, characterized by: Including: Obtain the information of smart meters within the target area and the line topology diagram of the smart meters and the power supply source; Set the first corresponding relationship between the smart meters and the corresponding branch lines, and the second corresponding relationship between each branch line and the main line according to the line topology diagram; Divide the target area into sub-areas according to the positions of the smart meters according to the first corresponding relationship, and set the power consumption range for the power consumption time of the sub-areas according to the historical power consumption data of the sub-areas; Detect the power consumption situation and power consumption range of the sub-areas according to the real-time power consumption data, and mark the risk areas; Calculate the power consumption change rate and the standard deviation of power consumption according to the real-time power consumption data of the risk areas, and perform dispatching on the risk areas through the dispatching control policy.

2. The intelligent identification and adaptive scheduling method for abnormal power supply guarantee according to claim 1, characterized in that: The smart meter information includes the smart meter position information, the real-time power consumption data, and the historical power consumption data.

3. The intelligent identification and adaptive scheduling method for abnormal power supply protection according to claim 2, wherein: The detecting the power consumption situation of the sub-areas according to the real-time power consumption data includes cleaning the collected real-time power consumption data to remove extreme values and missing values that deviate from the historical data range; using a sliding window to calculate the average power consumption and change rate of the sub-areas within a preset time period, and comparing with the historical data of the same period to identify abnormal power consumption trends.

4. The intelligent identification and adaptive scheduling method for abnormal power supply guarantee according to claim 3, characterized in that: The dispatching control policy includes: if the power consumption change rate is positive and exceeds the set first threshold, it is determined that the power consumption increases; lock the first power supply line of the risk area according to the first corresponding relationship and the second corresponding relationship, increase the power supply capacity of the first power supply line, and start the emergency power generation equipment; If the power consumption change rate is negative and exceeds the set first value, it is determined that the power consumption decreases; obtain the power consumption change rate of the smart meters within the risk area. If the power consumption change rates of the smart meters within the preset number are all negative within the risk area, then find the branch lines corresponding to the smart meters with negative power consumption change rates according to the first corresponding relationship: if the power consumption change rates of the smart meters within the risk area are all negative, then lock the second power supply line of the risk area according to the first corresponding relationship and the second corresponding relationship, and use a traversal method to detect the risk equipment whose detection values of the sensors on the second power supply line are outside the standard range; Start the fault isolation program, and isolate the risk equipment by adjusting the status of the corresponding circuit breakers.

5. The intelligent identification and adaptive scheduling method for abnormal power supply protection according to claim 4, characterized in that: The starting the fault isolation program includes: if the risk equipment is a critical load equipment, enable the standby power supply and the fast-moving power generation equipment to provide temporary power supply for the critical load equipment; collect the load conditions, power parameters, and equipment operation status information of each line in the smart grid system in real time through the sensors deployed in the smart grid system; update the first corresponding relationship and the second corresponding relationship in real time according to the line topology diagram and the power grid structure after fault isolation; use the path search algorithm to search for the target path that meets the power consumption conditions from the critical load equipment to the available stable power supply line for the updated first corresponding relationship and the second corresponding relationship.

6. The intelligent identification and adaptive scheduling method for abnormal power supply guarantee according to claim 5, characterized in that: The enabling the standby power supply and the fast-moving power generation equipment to provide temporary power supply for the critical load equipment includes sorting the critical load equipment affected after fault isolation according to the preset list of critical load equipment, the real-time power consumption, and the importance of the equipment; During temporary power supply, sensors deployed on critical load equipment and temporary power supply equipment are used to monitor the power supply quality and the operating status of the equipment in real time. When an abnormal status is detected, a mobile terminal is used to prompt that there is an abnormality in the temporary power supply; After the fault is repaired, the power supply is switched back to the main power grid according to the priority order.

7. The intelligent identification and adaptive scheduling method for abnormal power supply protection according to claim 6, characterized in that: The real-time monitoring of the power supply quality and the operating status of the equipment includes installing sensors on critical load equipment and temporary power supply equipment to collect power parameters, equipment temperature, and vibration in real time; preprocessing the collected data, including data cleaning, format conversion, and outlier detection; The abnormal status includes setting corresponding thresholds for power parameters and operating status information; When the collected data exceeds the corresponding threshold, it is regarded as an abnormal status.

8. A system for intelligent identification and adaptive scheduling of abnormal power supply guarantee, according to any one of claims 1-7, characterized in that: It includes: A data acquisition module, a correspondence establishment module, a sub-region division module, a risk region determination module, and a scheduling module; The data acquisition module is used to obtain the location information, real-time power consumption data, historical power consumption data of all smart meters in the target area, and the line topology diagram of all smart meters and power sources; The correspondence establishment module is used to set the first correspondence between each smart meter and the corresponding branch line, and the second correspondence between each branch line and the main line according to the line topology diagram; The sub-region division module is used to divide the target area into sub-regions according to the location of the smart meters according to the first correspondence, and set corresponding power consumption ranges for different power consumption times of each sub-region according to the historical power consumption data of each sub-region; The risk region determination module is used to detect the power consumption situation of each sub-region according to the real-time power consumption data, detect whether the power consumption of each sub-region is within the corresponding power consumption range, and mark the region that continuously fails to be within the corresponding power consumption range as a risk region; The scheduling module is used to calculate the power consumption change rate and the power consumption standard deviation according to the real-time power consumption data of the risk region; perform scheduling on the risk region based on the power consumption change rate and the power consumption standard deviation through a scheduling control strategy.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the power supply guarantee abnormal intelligent identification and adaptive scheduling method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the power supply guarantee abnormal intelligent identification and adaptive scheduling method according to any one of claims 1 to 7 are implemented.

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