Identification and processing methods of abnormal voltage in substations supported by data center

Abstract

The purpose of the present invention is to address the problem that the causes of abnormal voltage in substations in the prior art are difficult to identify and handle. A method for identifying and handling abnormal voltage in substations supported by a data center is proposed, which is used to automatically analyze low-voltage and high-voltage abnormal conditions. The abnormal identification and judgment module is connected to multiple data systems. The abnormal identification and judgment module establishes an identification model based on the measurement and ledger information provided by the multiple data systems, and performs multi-level resolution and judgment in sequence based on information from different voltage detection and acquisition devices. The abnormal fault library provides maintenance personnel with abnormal handling solutions for the abnormal types. The present invention establishes an identification model through the abnormal identification and judgment module, automatically identifies various possible abnormal situations through the identification model, and automatically launches corresponding abnormal handling solutions through the abnormal fault library, which facilitates staff to quickly carry out corresponding maintenance work.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric energy monitoring, and specifically relates to a method for identifying and processing voltage anomalies in a substation supported by a data center. Background Art

[0002] When the user voltage is higher than the rated voltage range designed for the equipment, the equipment may overheat due to excessive current, and long-term operation will shorten the life of the equipment or even burn the equipment. When the user voltage is lower than the rated voltage range designed for the equipment, the equipment may not be able to operate normally. Therefore, in order to prevent the power equipment from not operating normally or being damaged due to low voltage or high voltage, it is necessary to identify abnormalities in the substation voltage during work. In the existing technology, the identification of substation voltage abnormalities can only be carried out by going to the work site through equipment after observing the fluctuation. Even so, due to the time difference between the on-site detection and the actual occurrence of voltage abnormalities, it is sometimes impossible to distinguish the cause of the abnormality. Such detection also requires high work experience, detection time and skills of the operator. Summary of the Invention

[0003] The purpose of the present invention is to address the problems in the prior art that the identification of substation voltage anomalies requires on-site practical instruments for investigation, the identification of causes is difficult, and the processing method relies on experience. A substation voltage anomaly identification and processing method supported by a data center is proposed, which is used to automatically analyze low voltage and high voltage abnormal conditions, including an abnormality identification and judgment module, wherein: the abnormality identification and judgment module is connected to multiple sets of data systems, and the abnormality identification and judgment module establishes an identification model based on the measurement information and ledger information provided by the multiple sets of data systems. After the identification model is built, multi-level resolution and judgment are performed in sequence according to the acquired data, and the actual type of abnormality is determined according to the conditions such as the substation total meter voltage difference and the three-phase imbalance. The abnormality identification and judgment module is also connected to an editable abnormal fault library, and the abnormal fault library provides maintenance personnel with abnormality handling solutions for the abnormal types.

[0004] Preferably, the data acquired by the anomaly identification and determination module from various data systems includes: medium-voltage line measurement data from distribution automation; measurement data from the main switch on the low-voltage side of the distribution transformer; user meter data; and PMS3.0 substation and line ledger data. The mutual verification of these data ensures a solid and stable foundation for building the data model.

[0005] Preferably, when low voltage or high voltage occurs to the user, the identification model includes three levels of abnormal identification conditions, wherein the first level abnormal identification condition is based on the maximum difference in the three-phase voltages of ABC in the substation main meter; the second level abnormal identification condition is based on the upper line voltage and whether the three-phase current of the substation main meter is balanced; the third level abnormal identification condition is based on the voltage drop from the transformer to the user, photovoltaic power generation conditions, reactive power transmission conditions, line parameters, power supply radius, and transformer model to finally make an accurate judgment.

[0006] Preferably, when low voltage or high voltage occurs to the user, the first-level abnormality identification condition is that the maximum difference in the three-phase voltage of the substation main meter ABC is greater than 10V, and the second-level abnormality identification condition is to determine whether the three phases of the substation main meter are balanced.

[0007] Preferably, when low voltage or high voltage occurs to the user, the first-level abnormality identification condition is that the maximum difference between the three-phase voltages of the substation main meter ABC is <10V, and the second-level abnormality identification condition is to determine whether the upper-level line voltage is within the normal range.

[0008] Preferably, the abnormality handling scheme recorded in the voltage abnormality fault library is matched by comprehensive data features to handle problems such as transformer gear, three-phase load imbalance, 10kV line voltage, photovoltaic reverse transmission, wire diameter being too thin, power supply radius being too long, power factor being too low, high resistance at the tapping point, and old transformer model. The problems cover all known voltage abnormalities in substations.

[0009] Preferably, before processing, a targeted processing plan is designed to guide on-site personnel based on the abnormality identification results, and the corresponding tools required to be carried are marked. After the operator has processed the abnormality, the processing result is imported into the abnormal fault library. After the operator has repaired the abnormality, the processing result is backfilled with the processing status, and the voltage abnormality data before maintenance is marked and closed-loop. The abnormal fault library evaluates the corresponding processing plan based on the system operation status after the later operation is completed, and tracks the actual operation status. Based on the actual operation status, it proposes modification suggestions for the corresponding processing plan to the corresponding staff.

[0010] Furthermore, the present invention also includes an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, it implements the steps of the method for identifying and processing substation voltage anomalies supported by the data center.

[0011] Furthermore, the present invention also includes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the method for identifying and processing substation voltage anomalies supported by the data center.

[0012] The present invention achieves the following beneficial effects: After acquiring various types of voltage data from the substation users and substation master tables, an abnormality identification and judgment module is used to establish a recognition model. This recognition model automatically identifies various possible abnormal situations and automatically develops corresponding treatment plans based on the abnormal fault database, facilitating staff to quickly carry out corresponding maintenance work. This is particularly helpful in guiding new employees with limited work experience to adopt the correct treatment methods.

[0013] The above content of the invention is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Other features, objects, and advantages of the present invention will become more apparent upon reading the detailed description of the non-limiting embodiments made with reference to the following drawings. The drawings are for the purpose of illustrating preferred embodiments only and are not to be construed as limiting the present invention. Like reference characters are used throughout the drawings to designate like parts.

[0015] Figure 1 This is a diagram showing the connection relationship of relevant modules of the method for identifying and processing abnormal voltage in a substation supported by the data center provided in an embodiment of the present invention.

[0016] Figure 2 It is a specific flow chart of the method for identifying and processing abnormal voltage in the substation supported by the data center provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0018] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be performed in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. A process can be terminated when its operations are completed, but can also have additional steps not included in the figures; a process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0019] The terms "first", "second", "third", "fourth", etc. (if any) in the description of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. It should also be understood that in various embodiments of the present invention, the size of the sequence number of each process does not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0020] It should be understood that, in the present invention, "plurality" refers to two or more. "And / or" is merely a variable relationship that describes associated objects, indicating that three relationships can exist. It should be understood that, in the present invention, "B corresponding to A," "B corresponding to A," "A corresponds to B," or "B corresponds to A" means that B is associated with A and B can be determined based on A. Determining B based on A does not mean determining B based solely on A; B can also be determined based on A and / or other information. A and B match when the similarity between A and B is greater than or equal to a preset threshold.

[0021] The data center of the present invention supports the method for identifying and processing abnormal voltage in the substation area, which is used to automatically analyze low voltage and high voltage abnormal conditions, including an abnormality identification and judgment module. Figure 1 、 Figure 2 As shown, wherein: the abnormality identification and determination module is connected to multiple data systems, and the abnormality identification and determination module establishes an identification model based on the voltage information provided by the multiple data systems,

[0022] The voltage information provided by these multiple data systems specifically refers to medium-voltage line measurement data from distribution automation; measurement data from distribution transformer master switches; user meter data; and PMS3.0 ledger data. The cross-validation of these data sets ensures a robust and stable foundation for building the data model. These four sets of data are all recorded during power operations, but existing technologies have not yet integrated and compared them for data analysis.

[0023] Distribution automation measurement data acquisition is accomplished through a series of monitoring and control systems, primarily relying on various sensors, measuring instruments, and data acquisition terminals installed throughout the distribution network. These devices collect power parameters such as voltage, current, power factor, and energy consumption in real time or periodically, and transmit this data via a communication system to a data concentrator or master station system for processing and analysis. The corresponding data collected by the multiple voltage detection and acquisition devices employed in this invention also constitutes this set of system data.

[0024] The measurement data of the distribution transformer's main switch is collected by sensors, measuring instruments, and data acquisition systems installed near the transformer's main switch. These devices monitor and record power parameters such as voltage, current, and power factor in real time or periodically, and transmit this data to a data center or master station system via a communication system for processing and analysis.

[0025] The data recorded by the user meter is the direct reading on the user meter, and this set of data can be directly obtained from the user terminal device.

[0026] The PMS 3.0 described in this article refers to the Power Equipment Asset Management System version 3.0. This system is a next-generation lean asset management system built by State Grid Corporation of China to achieve the "four digitalization" goals of equipment, operations, management, and collaboration. With the power grid resource business platform at its core, the system integrates extensive smart devices, aggregates IoT sensor data, and flexibly customizes micro-application clusters based on the grid's organizational structure to support field operations, business management and control, analytical decision-making, and ecosystem sharing. In PMS 3.0, the ledger is the foundation for the system's refined management of power equipment, providing comprehensive information throughout the equipment lifecycle. This enables equipment management: The ledger provides a clear overview of basic information such as equipment name, model, installation location, and commissioning date, facilitating daily operation, maintenance, and inspection. Condition monitoring: The equipment operating status data recorded in the ledger, such as operating hours and number of failures, helps identify potential equipment issues and provides a basis for preventive maintenance. Decision support: The analysis and mining of ledger data supports decision-making by power companies, such as the development of equipment upgrades and maintenance plans.

[0027] After the identification model is established, it performs multi-level discrimination and judgment based on data from different voltage detection and collection devices. The identification model generally uses three levels of abnormality identification conditions. The first level of abnormality identification conditions is based on the maximum difference in the three-phase voltages of the substation main meter ABC. It determines whether the maximum difference in the three-phase voltages of the substation main meter ABC is greater than 10V or less than 10V. The second level of abnormality identification conditions is based on whether the upstream line voltage is within the normal range of 10-11kV and whether the three-phase load of the substation main meter is balanced. The third level of abnormality identification conditions is based on the voltage drop from the transformer to the user, photovoltaic power generation, reactive power transmission, line parameters, power supply radius, and transformer model, and finally implements specific parameters to make accurate judgments based on these specific parameters. If the first level of abnormality identification conditions is that the maximum difference in the three-phase voltages of the substation main meter ABC is greater than 10V, the second level of abnormality identification conditions is to determine whether the three phases of the substation main meter are balanced. If the first level of abnormality identification conditions is that the maximum difference in the three-phase voltages of the substation main meter ABC is less than 10V, the second level of abnormality identification conditions is to determine whether the upstream line voltage is within the normal range. After acquiring relevant data from different voltage detection and acquisition devices, the corresponding data systems are cross-checked and used as the basis for abnormality determination. The actual abnormality type is determined based on the voltage difference and three-phase imbalance conditions. The abnormality identification and determination module is also connected to an editable abnormal fault library, which provides maintenance personnel with abnormality handling solutions for the specific abnormality type.

[0028] The practical effects of the present invention are described below with specific embodiments:

[0029] After the voltage detection and collection device collects voltage information, the abnormality identification and judgment module first determines whether an abnormal state has occurred. This is especially true when the user's voltage fluctuates. Based on the national standard, the normal voltage range is between +7% and -10% of the rated voltage of 220V. Voltages above this range are considered high voltage, and voltages below this range are considered low voltage.

[0030] When the abnormality identification module determines that an abnormality has occurred, it identifies the fault based on the model. By analyzing the data, it determines what fault or abnormality has occurred on site and guides on-site personnel to bring appropriate tools to solve the problem.

[0031] Embodiment 1:

[0032] First-level abnormality determination: Determine the maximum difference in the three-phase voltages of the substation main meter ABC. When the maximum difference in the three-phase voltages of the substation main meter ABC is greater than 10V, a second-level abnormality determination is performed to determine whether the substation main meter is three-phase balanced. When the three-phase current imbalance of the substation main meter is greater than 15%, and the current of a phase is greater than 30% of the rated current of the transformer, the model determines that the abnormality is caused by three-phase imbalance. At this time, the abnormal fault library proposes specific solutions for load phase switching and user diversion based on the specific values. To quickly obtain the three-phase current imbalance, the calculation formula used in this embodiment is: Three-phase current imbalance = (maximum phase current - minimum phase current) / maximum phase current × 100%.

[0033] Example 2:

[0034] First-level abnormality determination: Determine the maximum difference in the three-phase voltages of ABC. When the maximum difference in the three-phase voltages of ABC is greater than 10V, a second-level abnormality determination is performed to determine whether the three phases of the substation main meter are balanced. If the three phases of the substation main meter are unbalanced, and the three-phase current imbalance is less than 15%, and the transformer model is S11 or earlier, the model determines that the abnormality is caused by the aging of the transformer. At this time, the abnormal fault library proposes a specific solution for replacing the new transformer based on the specific values.

[0035] Example 3:

[0036] First-level abnormality judgment: determine the maximum difference in the three-phase voltage of the substation main meter ABC; when the maximum difference in the three-phase voltage of the substation main meter ABC is greater than 10V, perform the second-level abnormality judgment to determine whether the three phases of the substation main meter are balanced; when the three-phase load of the substation main meter is balanced, if the total meter measurement data suddenly changes at the moment of voltage abnormality and the user's electricity consumption curve does not change significantly, it indicates a measurement problem of the substation main meter. At this time, the abnormal fault library proposes a specific solution of replacing the total meter or the total meter CT based on the specific values.

[0037] Based on other conditions when the three-phase load of the substation meter is balanced, it is determined that the transformer is faulty or the transformer internal resistance is too high. At this time, the abnormal fault database is used to propose a specific solution of replacing the new transformer based on the specific values.

[0038] Embodiment 4:

[0039] The first-level abnormality determination determines the maximum difference in the three-phase voltages of the A, B, and C phases of the substation main meter. If the maximum difference is less than 10V, the second-level abnormality determination is performed to determine whether the upstream line voltage is within the normal range of 10-11kV. If the upstream line voltage is within the normal range of 10-11kV, the third-level abnormality determination is performed: if the highest phase voltage of the substation main meter is greater than 235V or the lowest phase voltage of the substation main meter is less than 205V, the model determines that the abnormality is caused by an incorrect transformer gear setting, that is, the transformer gear setting is too high or too low. The abnormal fault library proposes a short-term power outage plan, a specific solution for transformer gear adjustment, and identifies technical points that require attention during the operation.

[0040] Example 5:

[0041] The first-level abnormality determination involves determining the maximum voltage difference between the three phases A, B, and C of the substation meter. If the maximum voltage difference is less than 10V, the second-level abnormality determination is performed to determine whether the upstream line voltage is within the normal range of 10-11kV. If the upstream line voltage is within the normal range of 10-11kV, the third-level abnormality determination is performed: if the highest voltage phase of the substation meter is less than 235V and the lowest voltage phase of the substation meter is greater than 205V, and the user low voltage is less than 198V and the voltage difference is less than 30V, the PMS3.0 substation line parameter data and substation user load data are used to deduce the line voltage drop. If the substation line length is greater than 300 or the substation line diameter is less than the standard specification, resulting in excessive voltage drop, the model determines that the abnormality is caused by an excessively large power supply radius or an undersized wire diameter. The abnormal fault database proposes specific solutions for line modification and the use of flexible DC voltage regulation, and also identifies technical points to note during operation. The voltage difference here is the value of the total meter phase voltage minus the user meter phase voltage. If it is a three-phase meter, it is the maximum value of the ABC three-phase voltage difference.

[0042] Example 6:

[0043] The first-level anomaly determination is to determine the maximum voltage difference between the three phases A, B, and C of the substation main meter. If the maximum voltage difference between the three phases A, B, and C of the substation main meter is less than 10V, the second-level anomaly determination is performed to determine whether the upstream line voltage is within the normal range of 10-11kV. If the upstream line voltage is within the normal range of 10-11kV, the third-level anomaly determination is performed: if the highest voltage phase of the substation main meter is less than 235V and the lowest voltage phase of the substation main meter is greater than 205V, and the user low voltage is less than 198V and the voltage difference is greater than 30V. At this point, the line voltage drop is deduced using PMS3.0 substation line parameter data and substation user load data. If the PMS3.0 substation line length and wire diameter meet the requirements for a reasonable voltage drop, the power flow deduction model determines that the anomaly is caused by high resistance in the meter wiring circuit, aging connectors, or an internal fault in the meter. The anomaly database provides specific solutions for on-site meter replacement or wiring replacement at the user access point, and identifies key technical points to note during the operation. The voltage difference here is the value of the total meter phase voltage minus the user meter phase voltage. If it is a three-phase meter, it is the maximum value of the maximum three-phase voltage difference.

[0044] Example 7:

[0045] The first-level abnormality determination involves determining the maximum voltage difference between the three phases A, B, and C at the substation's main meter. If the maximum voltage difference is less than 10V, the second-level abnormality determination is performed to determine whether the upstream line voltage is within the normal range of 10-11kV. If the upstream line voltage is within the normal range, the third-level abnormality determination is performed if the substation's highest voltage phase is less than 235V, the lowest voltage phase is greater than 205V, the customer's high voltage is greater than 248V, the voltage difference is less than -10V, and the high voltage period occurs between 10am and 4pm with a hit rate exceeding 60%. If the marketing system records indicate that there are customers with distributed photovoltaic installations in the substation, the model determines that the abnormality is caused by PV reverse transmission. The abnormality fault database proposes specific solutions to reduce PV voltage drop using active support inverters or end-of-line power electronics (AUCs). The voltage difference is the total meter phase voltage minus the customer meter phase voltage. For three-phase meters, the maximum voltage difference between the three phases A, B, and C is used.

[0046] Example 8:

[0047] The first-level abnormality determination involves assessing the maximum voltage difference between phases A, B, and C at the substation's main meter. If this difference is less than 10V, the second-level abnormality determination is performed to determine whether the upstream line voltage is within the normal range of 10-11kV. If this is the case, the third-level abnormality determination is performed: if the highest phase voltage at the substation's main meter is less than 235V and the lowest phase voltage at the substation's main meter is greater than 205V, and if the customer's low voltage is less than 198V, the customer's branch line power factor is less than 0.85, and the voltage difference is greater than 10V, the model determines that the abnormality is caused by excessive voltage drop due to reactive power. The abnormality fault database proposes specific solutions such as adding a terminal reactive power compensation device or a power electronics AUC. The voltage difference here is the total meter phase voltage minus the customer meter phase voltage. For a three-phase meter, the maximum voltage difference between phases A, B, and C is used.

[0048] Example 9:

[0049] First-level abnormality determination: Determine the maximum difference in the three-phase voltages of the A, B, and C lines in the substation's master meter. If the maximum difference is less than 10V, a second-level abnormality determination is performed to determine whether the upstream line voltage is within the normal range of 10-11kV. If the upstream line voltage is not within this range, the model directly determines that the abnormality is caused by high or low voltages due to factors such as photovoltaic power generation, hydropower generation, and line length. The abnormal fault database proposes specific solutions, including installing a voltage-regulating transformer on the 10kV line or replacing an automatic voltage-regulating transformer on the substation side.

[0050] It is worth mentioning that the above embodiments cover all known abnormal situations and their solutions. The abnormal handling solutions recorded in the abnormal fault library are matched by comprehensive data features to handle problems such as transformer gear, three-phase load imbalance, 10kV line voltage, photovoltaic reverse transmission, too thin wire diameter, too long power supply radius, too low power factor, high resistance of the connection point, and old transformer models. The problems cover all known abnormal voltage conditions in the substation. After the operator handles the abnormality, the processing results will be imported into the abnormal fault library. The abnormal fault library will evaluate the corresponding handling solution based on the system operation status after the completion of the later operation, and track the actual operation status. Based on the actual operation status, the operator will make modification suggestions for the corresponding handling solution to the corresponding staff.

[0051] An embodiment of the present invention further provides a computer device comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor. The processor invokes the computer program stored in the memory to execute the steps of the method for analyzing and processing power quality in a substation based on measured data provided in an embodiment of the present invention, achieving the same technical effects as described in the above embodiment and will not be repeated here.

[0052] It should be pointed out that those skilled in the art will understand that the electronic device in the embodiment of the present invention is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc. The electronic device can be a computing device such as a desktop computer, a notebook, a handheld computer, and a cloud server. The electronic device can perform human-computer interaction through a keyboard, a mouse, a remote control, a touchpad, or a voice-controlled device.

[0053] An embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the various processes and steps in the substation power quality analysis and processing method based on measurement data provided in the embodiment of the present invention are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0054] It should be noted that readable storage media include flash memory, hard disks, multimedia cards, card-type memories (e.g., SD or DX memories), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disks, optical disks, and the like. In some embodiments, the memory may be an internal storage unit of an electronic device, such as the hard disk or internal memory of the electronic device. In other embodiments, the memory may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash memory card, and the like. Of course, the memory may also include both the internal storage unit and external storage devices of the electronic device. In this embodiment, the memory is typically used to store operating devices installed in the electronic device and various application software, such as program code for a substation power quality analysis and processing method based on measured data. In addition, the memory may also be used to temporarily store various data that has been output or is about to be output.

[0055] Those skilled in the art will appreciate that all or part of the processes in the above-described embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-readable storage medium. When executed, the program can include the processes of various embodiments of the above-described method for analyzing and processing power quality in a substation area based on measured data. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0056] The above specific implementation is a preferred implementation of the substation power quality analysis and processing method based on measurement data of the present invention, and is not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to this specific implementation. Any equivalent changes made in accordance with the shape and structure of the present invention are within the scope of protection of the present invention.

Claims

1. A method for identifying and processing voltage anomalies in a substation supported by a data center, which is used to automatically analyze low voltage and high voltage anomalies, characterized in that: It includes an abnormality identification and judgment module, wherein: the abnormality identification and judgment module is connected to multiple data systems, and the abnormality identification and judgment module establishes an identification model according to the measurement and ledger information provided by the multiple data systems. After the identification model is established, multi-level resolution and judgment are performed in sequence according to the data from the data center. The identification model adopts three-level abnormality identification conditions, wherein the first-level abnormality identification condition is based on the maximum difference in the three-phase voltage of the substation total meter ABC; it is determined whether the maximum difference in the three-phase voltage of the substation total meter ABC is greater than 10V or the maximum difference in the three-phase voltage of the substation total meter ABC is less than 10V; the second-level abnormality identification condition is based on whether the upper-level line voltage is within the normal range of 10-11kV and whether the three-phase load of the substation total meter is balanced; the third-level abnormality identification condition is based on the total meter measurement data from the transformer to the user, The user's electricity consumption curve, the voltage phase of the substation main meter, the voltage drop, the line parameters, the power supply radius, the transformer model, and the photovoltaic power generation situation are ultimately implemented in specific parameters, and accurate judgments are made based on the specific parameters; when the first-level abnormality identification condition is that the maximum difference in the three-phase voltage of the substation main meter ABC is greater than 10V, the second-level abnormality identification condition is to determine whether the three phases of the substation main meter are balanced; and when the first-level abnormality identification condition is that the maximum difference in the three-phase voltage of the substation main meter ABC is less than 10V, the second-level abnormality identification condition is to determine whether the voltage of the upper line is within the normal range; after obtaining the relevant data from different voltage detection and acquisition devices, the corresponding data systems are cross-checked and used as the basis for abnormality judgment; the actual type of abnormality is determined according to the voltage difference and the three-phase imbalance conditions. The abnormality identification and judgment module is also connected to an editable abnormal fault library, which provides maintenance personnel with abnormality handling solutions for the abnormal types.

2. The method for identifying and processing abnormal voltage in a substation supported by a data center according to claim 1, characterized in that: The data obtained by the abnormality identification and judgment module from various data systems include: medium-voltage line measurement data of distribution automation; measurement data of the main switch on the low-voltage side of the distribution transformer; user meter measurement data and PMS3.0 substation and line ledger data.

3. The method for identifying and processing abnormal voltage in a substation supported by a data center according to claim 1 or 2, characterized in that: By matching comprehensive data features with the processing solutions recorded in the voltage anomaly fault library, it is used to handle problems including transformer gear, three-phase load imbalance, 10kV line voltage, photovoltaic reverse transmission, wire diameter being too thin, power supply radius being too long, power factor being too low, high resistance at the connection point, and old transformer models. The problems described cover all known voltage anomalies in substations.

4. The method for identifying and processing abnormal voltage in a substation supported by a data center according to claim 3 is characterized in that: Before handling, a targeted handling plan is designed to guide on-site personnel based on the abnormality identification results, and the corresponding tools required are marked. After the operator handles the abnormality, the handling results are imported into the abnormal fault library. The abnormal fault library evaluates the corresponding handling plan based on the system operation status after the subsequent operation is completed, and tracks the actual operation status. Based on the actual operation status, it proposes modification suggestions for the corresponding handling plan to the corresponding staff.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for identifying and processing voltage anomalies in a substation supported by the data center according to any one of claims 1 to 4 are implemented.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for identifying and processing voltage anomalies in a substation supported by a data center according to any one of claims 1 to 4 are implemented.

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