Transformer area voltage abnormity identification and processing method supported by transformer in data

Abstract

The objective of the invention is to solve the problem that reasons of voltage abnormity in a transformer area are difficult to identify and process in the prior art. The method is used for automatically analyzing low-voltage and high-voltage abnormal conditions, an abnormal recognition and judgment module is connected with multiple sets of data systems, and the abnormal recognition and judgment module establishes a recognition model according to measurement and machine account information provided by the multiple sets of data systems. And according to different voltage detection and acquisition devices, carrying out multi-stage distinguishing and determination in sequence, and providing an exception handling scheme for the exception type to maintenance personnel by the exception fault library. According to the invention, the identification model is established through the abnormity identification and determination module, various abnormal conditions which may occur are automatically identified through the identification model, and a processing scheme corresponding to the abnormity is automatically pushed out through the abnormity fault database, so that workers can conveniently and rapidly 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 abnormal voltage in the substation during work. In the prior art, the identification of substation voltage anomalies can only be carried out by detecting the equipment at the work site after observing the fluctuation. Even so, due to the time difference between the on-site detection and the actual occurrence of voltage anomalies, it is sometimes impossible to distinguish the cause of the anomaly. Such detection also has high requirements on the operator's work experience, detection time point and skills. 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 method for identifying and processing substation voltage anomalies 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 voltage difference of the substation total meter 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 obtained by the abnormal identification and determination 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 area and line ledger data. The mutual verification of these data can ensure that the foundation for establishing the data model is substantial and stable.

[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 of abnormal identification conditions is based on the maximum difference in the three-phase voltages of ABC in the substation main meter; the second level of abnormal identification conditions is based on the upper-level line voltage and whether the three-phase current of the substation main meter is balanced; the third level of abnormal identification conditions is based on the voltage drop from the transformer to the user, the photovoltaic power generation situation, the reactive power transmission situation, the line parameters, the power supply radius, and the transformer model to finally make an accurate judgment.

[0006] Preferably, when low voltage or high voltage occurs to the user, the first-level abnormal identification condition is that the maximum difference in the three-phase voltage of ABC in the substation main meter is >10V, and the second-level abnormal 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 abnormal identification condition is that the maximum difference between the three-phase voltages of the substation ABC is less than 10V, and the second-level abnormal 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 joint, and old transformer model. The problems cover all known voltage abnormalities in all known substations.

[0009] Preferably, before processing, a processing plan is designed to guide on-site personnel to have a targeted processing plan based on the abnormality identification result, 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 situation, and the abnormal voltage data before the maintenance is marked and closed-loop. The abnormal fault library evaluates the corresponding processing plan according to the system operating status after the subsequent operation is completed, and tracks the actual operating status, and makes modification suggestions for the corresponding processing plan to the corresponding staff based on the actual operating status.

[0010] Furthermore, the present invention also includes an electronic device, including 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 a method for identifying and processing voltage anomalies in a substation supported by a data center are implemented.

[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 voltage anomalies in substations supported by the data center.

[0012] The beneficial effects achieved by the present invention are as follows: after acquiring various types of voltage data from the users in the substation area and the substation area master table, a recognition model is established through the abnormal recognition and judgment module, various possible abnormal situations are automatically recognized through the recognition model, and the corresponding abnormal processing solutions are automatically introduced through the abnormal fault library, so that the staff can quickly carry out the corresponding maintenance work. In particular, it can guide new employees with little work experience to use the correct processing methods.

[0013] The above invention content 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 according to 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 by reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the drawings to represent the same parts.

[0015] Figure 1 It is a connection relationship diagram of related modules 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.

[0016] Figure 2 It is a specific flow chart of the method for identifying and processing voltage anomalies in a substation supported by a 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 clearly understood, the present invention is further described in detail below in conjunction with 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 implemented 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 accompanying drawings; a process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0019] The terms "first", "second", "third", "fourth", etc. (if any) in the specification 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, and the execution order 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, "multiple" means two or more. "And / or" is only a variable relationship that describes associated objects, indicating that three relationships may 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 only on A, but B can also be determined based on A and / or other information. The match between A and B means that 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 the abnormal conditions of low voltage and high voltage, and includes an abnormal identification and determination module. Figure 1 , Figure 2 As shown, wherein: the abnormal identification and determination module is connected to multiple data systems, and the abnormal identification and determination module establishes an identification model according to the voltage information provided by the multiple data systems. The voltage information provided by multiple data systems specifically refers to the medium-voltage line measurement data of distribution automation; the measurement data of the distribution transformer master switch; the user meter data and the PMS3.0 ledger data. The mutual verification of these data has ensured that the foundation for establishing the data model is substantial and stable. These four sets of data are all recorded in the process of power work, but the existing technology has not integrated them with each other for data analysis.

[0022] Among them: the measurement data acquisition of distribution automation is completed through a series of monitoring and control systems, mainly relying on various sensors, measuring instruments and data acquisition terminals installed in the distribution network. These devices collect power parameters such as voltage, current, power factor, power consumption, etc. in real time or regularly, and transmit the data to the data concentrator or master station system for processing and analysis through the communication system. The corresponding data collected by the multiple voltage detection and acquisition devices used in the present invention also belongs to this group of system data.

[0023] The measurement data of the distribution transformer main switch is achieved through sensors, measuring instruments and data acquisition systems installed near the transformer main switch. These devices can monitor and record the power parameters of the transformer main switch in real time or regularly, such as voltage, current, power factor, etc., and transmit these data to the data center or master station system through the communication system for processing and analysis.

[0024] The data recorded by the user meter is the direct reading on the user meter, and this group of data can be directly obtained from the user terminal device.

[0025] The PMS3.0 described in the present invention refers to the 3.0 version of the power equipment asset management system, which is a new generation of equipment asset lean management system built by State Grid Corporation of China to achieve the "four digitalization" goals of equipment, operation, management, and collaboration. The system takes the power grid resource business middle platform as the core, widely accesses intelligent devices, gathers IoT perception data, and flexibly customizes micro-application groups on demand according to the power grid organizational structure to support field operations, business management and control, analysis and decision-making, ecological sharing and other business development. In PMS3.0, the ledger is the basis for the system to carry out refined management of power equipment, and the full life cycle information of the equipment can be fully understood through the ledger. Realize equipment management: that is, through the ledger, you can clearly grasp the basic information of the equipment such as the name, model, installation location, and commissioning date, which provides convenience for the daily operation and maintenance of the equipment. Status monitoring: that is, the equipment operation status data recorded in the ledger, such as the operating time, the number of failures, etc., helps to timely discover potential problems of the equipment and provide a basis for preventive maintenance. And decision support: that is, through the analysis and mining of the ledger data, it can provide support for the decision-making of power companies, such as equipment replacement and maintenance plan formulation.

[0026] After the recognition model is built, multi-level discrimination and judgment are carried out in sequence according to different voltage detection and collection devices. The recognition model usually adopts three-level abnormal recognition conditions, wherein the first-level abnormal recognition condition is based on the maximum difference of the three-phase voltage of the total meter ABC in the substation; it is determined whether the maximum difference of the three-phase voltage of the total meter ABC in the substation is >10V or the maximum difference of the three-phase voltage of the total meter ABC in the substation is <10V; the second-level abnormal recognition condition is based on whether the voltage of the upper line is within the normal range of 10-11kV and whether the three-phase load of the total meter in the substation is balanced; the third-level abnormal recognition condition is based on the voltage drop from the transformer to the user, the photovoltaic power generation, the reactive power transmission, the line parameters, the power supply radius, and the transformer model, and finally implemented in specific parameters, and accurate judgment is made based on the specific parameters. When the first-level abnormal recognition condition is that the maximum difference of the three-phase voltage of the total meter ABC in the substation is >10V, the second-level abnormal recognition condition is to determine whether the three phases of the total meter in the substation are balanced. When the first-level abnormal recognition condition is that the maximum difference of the three-phase voltage of the total meter ABC in the substation is <10V, the second-level abnormal recognition condition is to determine whether the voltage of the upper line is within the normal range. After obtaining 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 according to the voltage difference and the three-phase imbalance condition. 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 abnormal type.

[0027] The practical effects of the present invention are described below with specific embodiments: After the voltage detection and collection device collects the voltage information, the abnormal identification and judgment module first determines whether an abnormal state occurs. Especially when the user voltage fluctuates, the normal voltage range based on the national standard is between +7% and -10% of the rated voltage 220V. Voltages above this range are high voltages, and voltages below this range are low voltages.

[0028] When the abnormality identification module determines that an abnormality has occurred, it will identify the fault according to the model. By analyzing the data, it will determine what fault or abnormality has occurred on site, and guide the on-site personnel to bring appropriate tools to solve the problem.

[0029] Embodiment 1: First-level abnormality determination: determine the maximum difference in the three-phase voltage of the ABC in the substation; when the maximum difference in the three-phase voltage of the ABC in the substation is >10V, perform the second-level abnormality determination to determine whether the three-phase of the substation is balanced; when the three-phase imbalance of the substation current is >15%, and the current of a certain phase is >30% of the rated current of the transformer, the model determines that the abnormality is caused by the three-phase imbalance. At this time, the abnormal fault library proposes specific solutions for load phase change and user diversion based on the specific values. In order to quickly obtain the three-phase imbalance of the current, the calculation formula used in this embodiment is: three-phase imbalance of current = (maximum phase current - minimum phase current) / maximum phase current × 100%.

[0030] Embodiment 2: The first-level abnormality judgment: judge the maximum difference of the three-phase voltage of ABC; when the maximum difference of the three-phase voltage of ABC is >10V, the second-level abnormality judgment is carried out to check whether the three-phase of the substation main meter is balanced; when the three-phase of the substation main meter is unbalanced, and the three-phase imbalance of the current is <15% and the transformer model is before S11, it is judged according to the model that the abnormality is caused by the old transformer. At this time, the abnormal fault library proposes a specific solution of replacing a new transformer based on the specific values.

[0031] Embodiment 3: The 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 >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 has no significant change, it indicates a measurement problem of the substation main meter. At this time, the abnormal fault library proposes a specific solution of replacing the main meter or the main meter CT based on the specific values.

[0032] Based on other conditions when the three-phase load of the total meter in the substation is balanced, it is determined that the transformer is faulty or the internal resistance of the transformer is too high. At this time, the specific solution of replacing a new transformer is proposed according to the specific values ​​through the abnormal fault library.

[0033] Embodiment 4: First-level abnormality judgment: determine the maximum difference in the three-phase voltage of the ABC in the substation; when the maximum difference in the three-phase voltage of the ABC in the substation is <10V, the second-level abnormality judgment is performed to determine whether the voltage of the upper line is within the normal range of 10-11kV; when the voltage of the upper line is within the normal range of 10-11kV, the third-level abnormality judgment: when the highest voltage phase of the substation is >235V or the lowest voltage phase of the substation is <205V, the model determines that this abnormality is caused by the wrong transformer gear, that is, the transformer gear is set too high or too low. The abnormal fault library proposes a specific solution for applying for a short-term power outage plan and adjusting the transformer gear, and marks the technical points that need to be paid attention to during the operation.

[0034] Embodiment 5: First-level abnormality judgment: determine the maximum difference of the three-phase voltage of the ABC in the substation; when the maximum difference of the three-phase voltage of the ABC in the substation is <10V, the second-level abnormality judgment is performed to determine whether the voltage of the upper line is within the normal range of 10-11kV; when the voltage of the upper line is within the normal range of 10-11kV, the third-level abnormality judgment: the highest voltage phase of the substation is <235V and the lowest voltage phase of the substation is >205V, and the user low voltage is <198V and the voltage difference is <30V. At this time, the PMS3.0 substation line parameter data and substation user load data are called to deduce the line voltage drop. If it is found that the substation line length is >300 or the substation line diameter is <standard specifications, causing excessive voltage drop, then according to the model, it is determined that this abnormality is caused by too large a power supply radius or too thin a wire diameter. The abnormal fault library proposes specific solutions for line transformation and the use of flexible direct current voltage regulation, and marks the technical points that need to be paid attention to 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 ABC three-phase voltage difference.

[0035] Embodiment 6: First-level abnormality judgment: determine the maximum difference of the three-phase voltage of the ABC in the substation; when the maximum difference of the three-phase voltage of the ABC in the substation is <10V, the second-level abnormality judgment is performed to determine whether the voltage of the upper line is within the normal range of 10-11kV; when the voltage of the upper line is within the normal range of 10-11kV, the third-level abnormality judgment: the highest voltage phase of the substation is <235V, and the lowest voltage phase of the substation is >205V, and the user low voltage is <198V, and the voltage difference is >30V. At this time, the PMS3.0 substation line parameter data and the substation user load data are called to deduce the line voltage drop. If it is found that the length and diameter of the PMS3.0 substation line meet the conditions and the voltage drop is reasonable, then according to the power flow deduction model, it is determined that this abnormality is caused by high resistance of the meter wiring circuit, aging of the joint, and internal fault of the meter. The abnormal fault library proposes a specific solution to replace the meter or replace the meter wiring at the on-site user access point, and marks the technical points that need to be paid attention to 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.

[0036] Embodiment 7: First-level abnormality judgment: determine the maximum difference of the three-phase voltage of the total meter ABC in the substation; when the maximum difference of the three-phase voltage of the total meter ABC in the substation is <10V, the second-level abnormality judgment is performed to determine whether the voltage of the upper line is within the normal range of 10-11kV; when the voltage of the upper line is within the normal range of 10-11kV, the third-level abnormality judgment: the highest voltage phase of the total meter in the substation is <235V, and the lowest voltage phase of the total meter in the substation is >205V, and the user's high voltage is >248V and the voltage difference is <-10V and the high voltage period is distributed between 10am-4pm. The hit rate exceeds 60%, and it is found through the marketing system account that there are users who have installed distributed photovoltaics in the substation. According to the model, it is determined that this abnormality is caused by photovoltaic reverse transmission. The abnormal fault library proposes a specific solution to reduce photovoltaic voltage drop by using active support inverters or terminal power electronics AUC. 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.

[0037] Embodiment 8: First-level abnormality judgment: determine the maximum difference in the three-phase voltage of the ABC in the substation; when the maximum difference in the three-phase voltage of the ABC in the substation is <10V, the second-level abnormality judgment is performed to determine whether the voltage of the upper line is within the normal range of 10-11kV; when the voltage of the upper line is within the normal range of 10-11kV, the third-level abnormality judgment: the highest voltage phase of the substation is <235V and the lowest voltage phase of the substation is >205V, and at the same time, the user's low voltage is <198V, the power factor of the branch line where the user is located is <0.85, and the voltage difference is >10V. According to the model, it is determined that this abnormality is caused by excessive voltage drop due to reactive power. The abnormal fault library proposes a specific solution of adding a terminal reactive power compensation device or a power electronic AUC. 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.

[0038] Embodiment 9: First-level abnormality determination: determine the maximum difference in the three-phase voltage of the ABC in the substation; when the maximum difference in the three-phase voltage of the ABC in the substation is <10V, perform the second-level abnormality determination to determine whether the voltage of the upper line is within the normal range of 10-11kV; when the voltage of the upper line is not within the normal range of 10-11kV, the model can directly determine that the abnormality is caused by high and low voltages caused by photovoltaic, hydropower and line length of the upper line. The abnormal fault library proposes specific solutions for installing a voltage-regulating transformer on the 10kV line or replacing the self-regulating transformer on the substation side.

[0039] 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 overlap point, and old transformer models. The problems cover all known abnormal voltage situations in the substation. After the operator handles the abnormality, the processing result is imported into the abnormal fault library. The abnormal fault library evaluates the corresponding processing plan according to the system operation status after the completion of the later operation, and tracks the actual operation status. Based on the actual operation status, the corresponding staff is given modification suggestions for the corresponding processing plan.

[0040] The embodiment of the present invention further provides a computer device, which includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. The processor calls the computer program stored in the memory to execute each step of the power quality analysis and processing method for the substation area based on the measured data provided in the embodiment of the present invention, and can achieve the same technical effect, which is described in the above embodiment and will not be repeated here.

[0041] It should be pointed out that those skilled in the art can 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 (ASIC), field-programmable gate arrays (FPGA), digital signal processors (DSP), 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.

[0042] 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.

[0043] It should be noted that the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), 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, disk, optical disk, etc. In some embodiments, the memory may be an internal storage unit of an electronic device, such as a hard disk or memory of the electronic device. In other embodiments, the memory may also be an external storage device of an electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Of course, the memory may also include both the internal storage unit of the electronic device and its external storage device. In this embodiment, the memory is generally used to store operating devices and various application software installed in the electronic device, such as program codes for power quality analysis and processing methods of the substation based on measured data, etc. In addition, the memory may also be used to temporarily store various types of data that have been output or are to be output.

[0044] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of various embodiments of the above-mentioned power quality analysis and processing method for the substation based on the measured data. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0045] The above specific implementation is a preferred implementation of the power quality analysis and processing method of the substation based on the measured 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 the present specific implementation. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope 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 abnormal conditions, characterized in that: It includes an abnormality identification and determination module, wherein: the abnormality identification and determination module is connected to multiple data systems, and the abnormality identification and determination 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 determination are performed in sequence according to the data from the data center, and the actual abnormality type is determined according to conditions including the phase-to-phase voltage difference, the voltage drop from the line to the user, the photovoltaic power generation situation, the reactive power transmission situation, the line parameters, the power supply radius, the transformer model, the upper power supply voltage situation and the three-phase imbalance. The abnormality identification and determination 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.

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 abnormal 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 area 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, characterized in that: When low voltage or high voltage occurs to the user, the identification model includes three levels of abnormal identification conditions, wherein the first level of abnormal identification conditions is based on the maximum difference of the three-phase voltages of the ABC in the total meter of the substation area; The second-level abnormality identification conditions are based on the upper-level line voltage and whether the three-phase load of the substation total meter is balanced; the third-level abnormality identification conditions are 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 ultimately make an accurate judgment.

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: When low voltage or high voltage occurs to the user, the first-level abnormal identification condition is that the maximum difference of the three-phase voltage of ABC in the substation main meter is >10V, and the second-level abnormal identification condition is to determine whether the three phases of the substation main meter are balanced.

5. The method for identifying and processing abnormal voltage in a substation supported by a data center according to claim 3 is characterized in that: When low voltage or high voltage occurs to the user, the first-level abnormal identification condition is that the maximum difference between the three-phase voltages of ABC in the substation main meter is less than 10V, and the second-level abnormal identification condition is to determine whether the voltage of the upper-level line is within the normal range.

6. The method for identifying and processing abnormal voltage in a substation supported by a data center according to any one of claims 1 to 5, characterized in that: The processing solutions recorded in the voltage abnormality fault library are matched by comprehensive data features to handle problems including 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 model. The problems described cover all known voltage abnormalities in all substations.

7. The method for identifying and processing abnormal voltage in a substation supported by a data center according to claim 6 is characterized in that: Before processing, a targeted processing plan is designed to guide on-site personnel based on the abnormality identification results, and the corresponding tools required are marked. After the operator has processed the abnormality, the processing result is imported into the abnormal fault library. The abnormal fault library evaluates the corresponding processing 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 makes suggestions for modifying the corresponding processing plan to the corresponding staff.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: 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 7 are implemented.

9. 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 7 are implemented.

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