Power failure risk analysis method and system for main network voltage fluctuation

Abstract

The invention relates to a power failure risk analysis method and system for main network voltage fluctuation, and the method comprises the steps: firstly determining an initial fault user based on the time of a voltage fluctuation event, and reversely tracing and positioning an influence transformer substation; performing forward extension by taking the transformer substation as a starting point to obtain electrical parameter data of potential equipment, and further identifying an abnormal transformer substation; and finally, based on the equipment covered by the abnormal transformer substation, forming a risk list including medium-voltage lines, power distribution equipment and users. According to the method, through two-way tracing and data verification from the user to the source and then from the source to the full range, the fuzzy perception risk is converted into measurable data, accurate quantification of the power failure risk is achieved, and reliable support is provided for power grid operation and maintenance decision making.

Description

Technical Field

[0001] This invention relates to the field of power system technology, specifically to a method and system for analyzing power outage risks caused by main grid voltage fluctuations. Background Technology

[0002] During power system operation, grid voltage fluctuations are common grid anomalies, and their impact is not limited to individual users but extends along buses and feeders to the entire region. However, current analysis of power outage risks caused by grid voltage fluctuations has significant shortcomings: Firstly, regional impacts are not entirely equivalent to user outages. Some users may experience equipment malfunctions due to voltage fluctuations but be misjudged as experiencing outages, or they may not perceive actual outages due to short-term fluctuations, leading to a significant discrepancy between the "actual impact range" and the "user-reported perception." Secondly, there is a lack of effective analytical methods for assessing customer perception and the actual impact range, making it impossible to accurately quantify the outage risks caused by voltage fluctuations and failing to meet the actual needs of grid operation and maintenance decision-making, fault handling optimization, and user service improvement. Summary of the Invention

[0003] The purpose of this invention is to propose a method and system for analyzing power outage risks caused by main grid voltage fluctuations, thereby solving the problems of discrepancies between the "actual impact range" and "user-reported perceptions" in the prior art, and the lack of effective assessment methods.

[0004] To achieve the above objectives, according to a first aspect of the present invention, a method for analyzing power outage risks due to main grid voltage fluctuations is provided, comprising the following steps: S10, Based on the occurrence time of the voltage fluctuation event, obtain the initial power outage event data from the power metering system, and determine the initial fault user based on the initial power outage event data; S20, based on the power grid topology, reverse trace the initial fault user to locate the affected substation associated with the initial fault user; S30, Starting from the affected substation, a forward expansion is carried out to determine all potentially affected devices covered by the affected substation, and electrical parameter data of all potentially affected devices before and after the voltage fluctuation event occurs are obtained; S40, based on the changes in the electrical parameter data and in conjunction with historical data from the same period, identify the abnormal substations from the affected substations; S50, determine the outage sensing range based on the power grid equipment covered by the abnormal substation, and form a risk list including medium-voltage lines, power distribution equipment and users.

[0005] In some embodiments, step S10 specifically includes: Based on the data upload characteristics of the terminal device, an event analysis cycle is set to ensure coverage of all power outage events associated with the voltage fluctuation event; and within the event analysis cycle, initial power outage event data is obtained from the power metering system.

[0006] In some embodiments, step S20 specifically includes: Based on the power grid topology, the initial fault user is traced back step by step. The tracing path includes the user end, power distribution equipment, medium voltage line and substation end in sequence, in order to locate the affected substation.

[0007] In some embodiments, step S30 specifically includes: Starting from the substation affected by the incident, the expansion proceeds step by step in a positive direction. The expansion path sequentially includes the substation end, the medium-voltage line, and the power distribution equipment end. Extract all medium-voltage lines under the affected substation, and along all medium-voltage lines, sort out all associated power distribution equipment to obtain the electrical parameter data of the power distribution equipment before and after the voltage fluctuation event.

[0008] In some embodiments, step S40 specifically includes: Calculate the electrical parameter change index corresponding to the substation, compare the electrical parameter change index with the preset normal change threshold range, and if the electrical parameter change index exceeds the normal change threshold range, then the corresponding substation is identified as an abnormal substation.

[0009] In some embodiments, step S50 specifically includes: Locate the abnormal medium-voltage line associated with the abnormal substation; Based on the characteristics of the electrical parameter changes in the abnormal medium-voltage line, a judgment threshold is determined. Based on the aforementioned judgment threshold, all medium-voltage lines belonging to the abnormal substation are investigated to identify all medium-voltage lines affected by voltage fluctuations. Based on all affected medium-voltage lines, the associated power distribution equipment and users are counted to form the aforementioned risk list.

[0010] In some embodiments, the determination threshold is the median or magnitude of the electrical parameter change ratio.

[0011] The present invention has the following beneficial effects: By tracing back through the power grid topology, the system pinpoints the core "affected substations" from scattered initial fault users, avoiding misleading information from false or missed user reports and identifying the root cause of the problem. Starting from the substation and expanding forward, it proactively seeks all potentially affected equipment, rather than passively waiting for user reports, effectively solving the problem of "users not noticing actual power outages" and ensuring comprehensive impact coverage. Instead of relying on subjective judgment, it uses objective physical quantities such as "electrical parameter data" of equipment before and after the event for anomaly identification. Only substations with truly abnormal data are judged as abnormal, effectively filtering out "user-misjudged power outages" caused by equipment malfunctions and achieving precise definition of the "true impact range." In summary, this invention solves the problems of discrepancies between the "true impact range" and "user-reported perception" in existing technologies, as well as the lack of effective assessment methods. It improves the accuracy of main grid voltage fluctuation outage risk analysis and better meets the practical needs of power grid operation and maintenance decision-making, fault handling optimization, and user service improvement. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart of a power outage risk analysis method based on main grid voltage fluctuations in an embodiment of the present invention. Detailed Implementation

[0014] The detailed description of the accompanying drawings is intended to illustrate the present embodiments of the invention and is not intended to represent only the forms in which the invention can be implemented. It should be understood that the same or equivalent functions can be accomplished by different embodiments intended to be included within the spirit and scope of the invention.

[0015] like Figure 1 As shown, one embodiment of the present invention provides a method for analyzing power outage risks due to main grid voltage fluctuations. This method aims to solve the problems of discrepancies between the "actual impact range" and "user-reported perception" in the prior art, and the lack of effective assessment methods. The core idea of ​​this embodiment is to "trace the source of the power grid from the faulty user, and then expand the full impact range from the source," which is achieved through the following steps: S10, based on the occurrence time of the voltage fluctuation event, obtain the initial power outage event data from the power metering system, and determine the initial fault user based on the initial power outage event data.

[0016] For example, suppose a voltage fluctuation event occurs in a power grid in a certain region on a certain date (month, year, day). The critical time of occurrence is 4:12:15. Using this time point as a benchmark, the system filters out terminal power outage events before and after 4:12 on that day from the power metering master station, extracting a total of 13,200 valid events. By statistically analyzing these events, the distribution area of ​​the initial power outage users can be determined, for example, mainly distributed in the eastern region A, thus completing the initial fault user identification.

[0017] S20, based on the power grid topology, reverse trace the initial fault user to locate the affected substation associated with the initial fault user.

[0018] Specifically, based on the power grid topology of "user-distribution equipment-medium voltage line-substation", the upstream power grid level is used to trace back the initially identified faulty user. For example, by associating 13,200 events with 280 transformers through user files, and then determining that these 280 transformers belong to 90 10kV lines according to the transformer ownership ledger, and finally clarifying the relationship between the lines and substations, it is determined that these 90 lines belong to 18 substations, thus initially identifying these 18 substations as the core affected substations.

[0019] S30, Starting from the affected substation, a forward expansion is performed to determine all potentially affected devices covered by the affected substation, and electrical parameter data of all potentially affected devices before and after the voltage fluctuation event occurs are obtained.

[0020] Specifically, to avoid omissions, the system starts with 18 substations and traces their entire power grid equipment in a forward direction. For example, it extracts all 410 10kV lines under these 18 substations (including 90 already identified lines and 320 newly added lines associated with the same substation), and traces all 3,420 associated transformers along these 410 lines. The system then obtains electrical parameter data (such as power, current, and voltage) of these transformer terminals at characteristic time points before and after voltage fluctuations (e.g., 4:00 and 4:15).

[0021] S40, based on the changes in the electrical parameter data and in conjunction with historical data from the same period, identify the abnormal substations from the affected substations.

[0022] Specifically, the analysis involves examining changes in electrical parameter data. For example, power data for the same period on the day before (X month Y-1) and the day after (X month Y+1) is collected. The average power change of 18 substations is calculated. A normal change threshold range is set (e.g., 0.9-1.1). If the average power change of a substation exceeds this range, it is identified as an abnormal substation. In this example, it was found that the average power change of 7 substations was less than 0.9, which was consistent with the substations in the reported information collection. Their power change distribution also differed significantly from the other 11 substations, and therefore they were identified as abnormal substations.

[0023] S50, determine the outage sensing range based on the power grid equipment covered by the abnormal substation, and form a risk list including medium-voltage lines, power distribution equipment and users.

[0024] Specifically, based on the power grid equipment covered by the aforementioned seven abnormal substations, the final outage detection range was calculated, abnormal medium-voltage lines were located, and their electrical parameter change characteristics were analyzed to determine the judgment threshold. Based on this threshold, all related lines were comprehensively investigated to identify all affected medium-voltage lines (including those initially identified and newly added). Finally, information on all affected medium-voltage lines, distribution equipment, and related users was compiled to form a three-tiered risk list of "medium-voltage lines - distribution equipment - users," thus quantifying the outage risk.

[0025] In some embodiments, step S10 specifically includes: Based on the data upload characteristics of the terminal device, an event analysis cycle is set to ensure coverage of all power outage events associated with the voltage fluctuation event; and within the event analysis cycle, initial power outage event data is obtained from the power metering system.

[0026] Specifically, the system sets an event analysis period based on the data upload characteristics of the terminal devices. These characteristics include, but are not limited to, data upload delays from electricity meters, clock deviations, and signal coverage differences in remote areas. For example, in a voltage fluctuation event on day Y of month X, considering that some electricity meters may experience upload delays or weak signals leading to delayed data reporting, the system sets the event analysis period to be from 4:12 AM to 12:00 AM that day. This ensures coverage of all power outage events associated with the voltage fluctuation event. In this way, the system can capture all power outage events that may be delayed in reporting due to the voltage fluctuation within a wider time window, thereby avoiding the omission of initial fault users due to an excessively short analysis period and laying a solid foundation for the accuracy of subsequent analysis.

[0027] In some embodiments, step S20 specifically includes: Based on the power grid topology, the initial fault user is traced back step by step. The tracing path includes the user end, power distribution equipment, medium voltage line and substation end in sequence, in order to locate the affected substation.

[0028] Specifically, the system traces the initial fault user step-by-step back based on the power grid topology. The tracing path sequentially includes the user end, distribution equipment, medium-voltage lines, and substation end. In the case of X month Y day, this process is as follows: User terminal → Power distribution equipment: Through user profile information, 13,200 initial power outage events are associated with 280 transformers (power distribution equipment).

[0029] Power distribution equipment → Medium voltage lines: According to the line ledger to which the transformers belong, it was determined that 280 transformers belong to 90 10kV lines (medium voltage lines).

[0030] Medium-voltage lines → substation end: By identifying the relationship between 10kV lines and substations, it is determined that these 90 lines belong to 18 substations, thereby locating the substations that are affected.

[0031] This hierarchical tracing method is logically clear and has well-defined steps. It can accurately converge from scattered user points to the core substation, providing an accurate starting point for subsequent scope expansion.

[0032] In some embodiments, step S30 specifically includes: Starting from the substation affected by the incident, the expansion proceeds step by step in a positive direction. The expansion path sequentially includes the substation end, the medium-voltage line, and the power distribution equipment end. Extract all medium-voltage lines under the affected substation, and along all medium-voltage lines, sort out all associated power distribution equipment to obtain the electrical parameter data of the power distribution equipment before and after the voltage fluctuation event.

[0033] Specifically, the system expands forward step by step, starting from the affected substation. The expansion path sequentially includes the substation end, medium-voltage lines, and distribution equipment end. In the case of X month Y day, this process manifests as follows: Substation end → Medium voltage lines: The system extracts all 10kV lines under the jurisdiction of 18 affected substations, totaling 410 lines. The word "all" here is crucial; it includes not only the 90 fault-related lines already identified in S20, but also 320 related lines within the same substation, thus avoiding omissions in the scope of impact.

[0034] Medium voltage lines → power distribution equipment: The system traces all 3,420 related transformers (power distribution equipment) along these 410 lines.

[0035] Acquiring electrical parameter data: The system acquires electrical parameter data of the power distribution equipment before and after the voltage fluctuation event, such as the power data of these 3420 transformers at 4:00 and 4:15, to provide a data basis for the next step of anomaly identification.

[0036] In some embodiments, step S40 specifically includes: Calculate the electrical parameter change index corresponding to the substation, compare the electrical parameter change index with the preset normal change threshold range, and if the electrical parameter change index exceeds the normal change threshold range, then the corresponding substation is identified as an abnormal substation.

[0037] Specifically, the system calculates the electrical parameter change indicators affecting the substations, such as the average power change value of all distribution equipment under 18 substations. Then, the system compares these electrical parameter change indicators with a preset normal change threshold range. This threshold range can be dynamically adjusted according to the power grid type and equipment characteristics, for example, set to 0.9-1.1. If the electrical parameter change indicator exceeds the normal change threshold range, for example, if the average power change of a certain substation is less than 0.9, the corresponding substation is identified as an abnormal substation. In this example, 7 substations are therefore identified as abnormal. This method based on data threshold comparison transforms subjective judgment into objective quantitative standards, greatly improving the accuracy and consistency of anomaly identification.

[0038] In some embodiments, step S50 specifically includes: Locate the abnormal medium-voltage line associated with the abnormal substation; Based on the characteristics of the electrical parameter changes in the abnormal medium-voltage line, a judgment threshold is determined. Based on the aforementioned judgment threshold, all medium-voltage lines belonging to the abnormal substation are investigated to identify all medium-voltage lines affected by voltage fluctuations. Based on all affected medium-voltage lines, the associated power distribution equipment and users are counted to form the aforementioned risk list.

[0039] Specifically, the abnormal medium-voltage lines were located: first, 65 medium-voltage lines associated with the 7 abnormal substations that had power outage alarms or abnormal electrical parameters were located.

[0040] Determine the judgment threshold: Based on the electrical parameter change characteristics of the abnormal medium-voltage lines, for example, by analyzing the power change ratio of these 65 lines, a judgment threshold is determined (e.g., taking the median of 0.648).

[0041] Investigation and Identification: Based on the aforementioned judgment thresholds, all medium-voltage lines belonging to the 7 abnormal substations were investigated to identify all medium-voltage lines affected by voltage fluctuations. In this example, 122 lines were investigated, and 10 new affected lines were added.

[0042] List formation: Based on all affected medium-voltage lines (75 in total), the system statistically analyzes the associated power distribution equipment and users to form the aforementioned risk list, which is the final three-level list of "medium-voltage lines-power distribution equipment-users".

[0043] In some embodiments, the determination threshold is the median or magnitude of the electrical parameter change ratio.

[0044] Specifically, the determination threshold can be determined in several ways. A preferred method is the median of the electrical parameter change ratio, as described in Example 6, which is determined by calculating the median (0.648) of the known power change ratios of affected lines. This method is adaptive and can reflect the typical impact of the event. Another method is the change magnitude threshold. For example, a fixed change magnitude value (e.g., a power drop exceeding 30kW) can be preset based on operation and maintenance experience. Any line whose power change magnitude exceeds this value is judged to be affected. These two methods provide the present invention with a flexible threshold selection strategy to adapt to different power grid environments and analysis needs.

[0045] Another embodiment of the present invention provides a power outage risk analysis system for main grid voltage fluctuations, including a module for performing the power outage risk analysis method for main grid voltage fluctuations described in the above embodiments. The system may include the following modules: Event Analysis Module: Used to execute step S10, obtain data from the power metering system, and identify the initial faulty user.

[0046] Topology tracing module: Used to execute step S20, perform reverse tracing based on the power grid topology relationship, and locate the affected substation.

[0047] Range extension module: Used to execute step S30, starting from the substation affected, to perform forward expansion and obtain electrical parameter data of potentially affected equipment.

[0048] Anomaly identification module: Used to execute step S40 and identify abnormal substations based on changes in electrical parameter data.

[0049] Risk Quantification Module: Used to execute the S50 steps, determine the power outage detection range based on the equipment covered by the abnormal substation, and generate a risk list.

[0050] These modules are connected via a data bus or central controller and work together to achieve automated and precise analysis of the risk of power outages due to voltage fluctuations in the main grid.

[0051] It should be noted that the system provided in this embodiment can be used to execute the methods described in the above embodiments. Therefore, the contents not described in detail in this embodiment can be obtained by referring to the contents of the methods in the above embodiments, and will not be repeated here.

[0052] Another embodiment of the present invention provides an electronic device, comprising: A communication interface used for communicating with other electronic devices; Memory is used to store computer program instructions; A processor is configured to execute the computer program instructions to support the electronic device in implementing the methods described in the embodiments above.

[0053] In this embodiment, the memory mainly includes a program storage area and a data storage area. The program storage area can store the operating device, applications required for at least one function, etc., and the data storage area can store related data, etc. Furthermore, the memory can be a high-speed random access memory, or a non-volatile memory, such as a plug-in hard disk, a smart media card (SMC), a secure digital card (SD), and a flash card, or other volatile solid-state storage devices.

[0054] The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or the processor can be any conventional processor. The processor is the control center of the electronic device and uses various interfaces and lines to connect the various parts of the electronic device.

[0055] Another embodiment of the present invention provides a computer program product including computer program instructions that instruct a computer device to perform operations corresponding to the methods described in the above embodiments.

[0056] Specifically, the computer program product can be a tangible or non-transitory computer-readable medium, such as a hard disk, solid-state drive, optical disk, or network download package. The computer program product includes a series of computer program instructions, which are codes written in a computer program. These instructions define how to perform specific operations. These instructions are designed to be loaded onto a computer device and instruct the device to perform specific operations, which refer to the various steps in the power outage risk analysis method for main grid voltage fluctuations described in the above embodiments. In this way, the computer program product of this embodiment provides a complete software solution that can run on various computer devices to implement the power outage risk analysis method for main grid voltage fluctuations described in the above embodiments.

[0057] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used in these embodiments is chosen to best explain the principles, practical applications, or technological improvements to the embodiments in the market, or to enable other those skilled in the art to understand the embodiments disclosed herein.

Claims

1. A power outage risk analysis method of a main grid voltage fluctuation, characterized by, The method comprises the following steps: S10, obtaining initial power outage event data from a power metering system based on the occurrence time of a voltage fluctuation event, and determining initial fault users according to the initial power outage event data; S20, performing reverse tracing on the initial fault users based on a power grid topology relationship to locate an influence substation associated with the initial fault users; S30, performing forward expansion starting from the influence substation to determine all potential influence devices covered by the influence substation, and obtaining electrical parameter data of the all potential influence devices before and after the voltage fluctuation event; S40, identifying an abnormal substation from the influence substation according to the change of the electrical parameter data and in combination with historical data of the same period; S50, determining a power outage sensing range based on power grid devices covered by the abnormal substation to form a risk list containing medium-voltage lines, distribution devices and users.

2. The method of claim 1, wherein, The step S10 specifically comprises: In combination with the data uploading characteristics of terminal devices, an event analysis period is set to ensure that all power outage events associated with the voltage fluctuation event are covered, and initial power outage event data is obtained from the power metering system within the event analysis period.

3. The method of claim 2, wherein, The step S20 specifically comprises: Based on the power grid topology relationship, the initial fault users are traced in reverse step by step, and the tracing path comprises a user end, a distribution device, a medium-voltage line and a substation end in sequence to locate the influence substation.

4. The method of claim 3, wherein, The step S30 specifically comprises: The influence substation is expanded forward step by step, and the expansion path comprises a substation end, a medium-voltage line and a distribution device end in sequence; All medium-voltage lines under the jurisdiction of the influence substation are extracted, and all associated distribution devices are sorted along the all medium-voltage lines to obtain electrical parameter data of the distribution devices before and after the voltage fluctuation event.

5. The method of claim 4, wherein, The step S40 specifically comprises: An electrical parameter change index corresponding to the influence substation is calculated, the electrical parameter change index is compared with a preset normal change threshold range, and if the electrical parameter change index exceeds the normal change threshold range, the corresponding substation is identified as an abnormal substation.

6. The method of claim 5, wherein, The step S50 specifically comprises: An abnormal medium-voltage line associated with the abnormal substation is located; A determination threshold is determined based on the electrical parameter change characteristics of the abnormal medium-voltage line; All medium-voltage lines affected by the voltage fluctuation are locked by searching all medium-voltage lines belonging to the abnormal substation according to the determination threshold; Based on all affected medium-voltage lines, associated distribution devices and users are counted to form the risk list.

7. The method of claim 5, wherein, The determination threshold is a median of an electrical parameter change ratio or a change amplitude.

8. A power outage risk analysis system for main grid voltage fluctuations, characterized by The electronic device comprises a module for executing the method in any one of claims 1 to 7.

9. An electronic device, comprising: The electronic device comprises: a communication interface for communicating with other electronic devices; a memory for storing computer program instructions; a processor for executing the computer program instructions to support the electronic device to implement the method in any one of claims 1 to 7.

10. A computer program product, characterised in that, comprise computer program instructions directed to instructing a computer device to perform operations corresponding to the method of any one of claims 1 to 7.

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