A method, apparatus and system for identifying and adjusting transformer area anomalies

Abstract

This invention discloses a method, apparatus, and system for identifying and adjusting transformer substation anomalies. The method includes: acquiring the topology of the transformer substation where a user is located; determining a priority hierarchy based on the topology; acquiring meter characteristic data of the priority hierarchy from the lowest level upwards in each preset time interval, and identifying the anomaly level in the priority hierarchy; determining a periodic anomaly level matrix based on the anomaly levels corresponding to all preset time intervals; iteratively analyzing from the highest level of the anomaly level in the periodic anomaly level matrix downwards to determine the transformer substation anomaly identification result for the current anomaly level; and determining and executing an adjustment plan for the transformer substation based on the anomaly identification result to restore the transformer substation to normal. This application solves the technical problem in the prior art that, under limited monitoring data conditions, it is impossible to automatically and accurately distinguish between frequent anomalies and occasional anomalies, thus making it impossible to accurately adjust the abnormal state of the transformer substation.

Description

Technical Field

[0001] This invention relates to the field of power technology, and in particular to a method, apparatus and system for identifying and adjusting transformer area anomalies. Background Technology

[0002] In the daily management of low-voltage distribution transformer areas, accurately identifying anomalies and taking effective adjustment measures are crucial to ensuring power supply reliability and power quality. Currently, based on low-voltage transparent monitoring systems, maintenance personnel can obtain the transformer area topology and massive amounts of meter operation data. Existing technologies typically use threshold comparison methods for anomaly detection; that is, when the monitored meter characteristic data (such as voltage, current, power factor, etc.) exceeds the preset normal operating range, it is judged as an anomaly.

[0003] However, existing technical solutions cannot effectively distinguish between persistent "recurring anomalies" and random "occasional anomalies." Recurring anomalies typically stem from persistent defects at a certain level of the distribution network topology (such as transformer overload or line aging), requiring proactive engineering interventions (such as load adjustments or equipment replacement). Occasional anomalies, on the other hand, are often caused by temporary user-side actions (such as short-term start-ups and shutdowns of high-power equipment) and usually resolve themselves without maintenance personnel intervention. Therefore, how to automatically and accurately distinguish between recurring and occasional anomalies under limited monitoring data conditions, thereby providing precise basis for anomaly adjustment plans and achieving efficient resource allocation, is a pressing issue that needs to be addressed. Summary of the Invention

[0004] This invention provides a method, apparatus, and system for identifying and adjusting transformer substation anomalies, in order to solve the technical problem in the prior art that, under limited monitoring data conditions, it is impossible to automatically and accurately distinguish between recurring anomalies and occasional anomalies, thus making it impossible to accurately adjust the abnormal state of transformer substations.

[0005] To address the aforementioned technical problems, embodiments of the present invention provide a method for identifying and adjusting transformer area anomalies, comprising: Obtain the topology of the user's station area and determine the priority hierarchy based on the topology; In each preset time interval, the meter feature data of the priority hierarchy structure is obtained sequentially from the lowest level upwards. Based on the meter feature data, the level in the priority hierarchy structure that is abnormal is identified, and the highest abnormal level in the current preset time interval is located from the abnormal level. Each preset time interval corresponds to the meter feature data of each level. Based on the anomaly levels corresponding to all preset time intervals, a periodic anomaly level matrix is ​​determined. Starting from the highest level of the anomaly level in the periodic anomaly level matrix, the analysis proceeds iteratively downwards. In each iteration, the anomaly identification result of the current anomaly level is determined by comparing the similarity rate of the anomaly levels in the current periodic anomaly level matrix with the consistency threshold. Based on the anomaly identification result, an anomaly adjustment plan for the current anomaly is determined and executed to restore the current anomaly to normal.

[0006] As a preferred embodiment, obtaining the topology of the user's location and determining the priority hierarchy based on the topology specifically includes: Obtain the topology of the user's local area; Based on the power supply relationships in the topology, the distribution transformer layer, intermediate layer, and load layer of the topology are determined; Based on the aforementioned topology, a priority hierarchy structure is constructed, comprising a distribution transformer layer, an intermediate layer, and a load layer connected sequentially. The intermediate layer includes several branch layers, and the priority of the distribution transformer layer is higher than that of each branch layer. The priorities of each branch layer are different, and the priority of each branch layer is higher than that of the load layer.

[0007] As a preferred embodiment, the step of sequentially acquiring meter feature data of the priority hierarchy structure from the lowest level upwards, identifying abnormal levels in the priority hierarchy structure based on the meter feature data, and locating the highest abnormal level in the current preset time interval from the abnormal levels specifically includes: Obtain the meter characteristic data corresponding to the lowest load level and make anomaly judgments based on the current level's operating threshold range. If the meter characteristic data of the current level is greater than the operating threshold range of the current level, it is determined that there is an anomaly in the current level. The meter characteristic data of the previous level corresponding to the current level is also judged as an anomaly. This process is repeated until there is no anomaly in the current level or the current level is a distribution transformer level. Then the iteration stops and the current level is designated as the abnormal level.

[0008] As a preferred embodiment, in each iteration, the anomaly identification result of the current anomaly level is determined by comparing the similarity rate of the anomaly levels in the current periodic anomaly level matrix with the consistency threshold. This specifically includes: Calculate the similarity rate of the highest-level anomaly in the periodic anomaly hierarchy matrix, and compare the similarity rate with the consistency threshold; When the similarity rate of the anomaly level is greater than the consistency threshold, the anomaly identification result of the current anomaly level is determined to be a recurring anomaly. When the similarity rate of the anomaly level is less than or equal to the consistency threshold, the anomaly identification result of the current anomaly level is determined to be an occasional anomaly.

[0009] As a preferred embodiment, after determining that the anomaly identification result of the current anomaly level is an intermittent anomaly, the method further includes: Remove the anomaly levels currently being compared and analyzed from the periodic anomaly hierarchy matrix to obtain the updated periodic anomaly hierarchy matrix; The updated periodic anomaly hierarchy matrix is ​​compared with the consistency threshold by the same rate of the highest anomaly level. Execution continues until the exception level in the current cycle exception level matrix is ​​the lowest level, at which point execution stops.

[0010] As a preferred embodiment, the step of determining and executing a transformer area anomaly adjustment plan based on the transformer area anomaly identification result specifically includes: When the abnormal identification result of the transformer area is a frequent abnormality, an abnormality adjustment plan for the transformer area is determined based on the meter characteristic data corresponding to the current abnormality level, and the abnormality adjustment plan for the transformer area is executed. If the anomaly identification result of the transformer area is an intermittent anomaly, no operation will be performed.

[0011] As a preferred embodiment, the step of determining a transformer area anomaly adjustment plan based on the meter characteristic data corresponding to the currently determined anomaly level, and executing the transformer area anomaly adjustment plan, specifically includes: The anomaly type is determined based on the meter's characteristic data; wherein, the anomaly type includes three-phase imbalance, power factor anomaly, and voltage deviation anomaly. Based on the three-phase imbalance, power factor abnormality, and voltage deviation abnormality, an adjustment scheme for the transformer area abnormality is generated for the topology nodes corresponding to the abnormality levels.

[0012] As a preferred embodiment, the transformer area anomaly adjustment scheme includes: when the three phases are unbalanced, performing load cut-off or installing an automatic phase-switching device at the corresponding topology node; when the power factor is abnormal, installing a reactive power compensation device at the corresponding topology node; and when the voltage deviation is abnormal, installing a line voltage regulation device at the corresponding topology node.

[0013] As a preferred embodiment, determining the anomaly type based on the meter characteristic data specifically includes: The apparent power value is calculated based on the reactive power value in the meter's characteristic data, and the power factor is calculated based on the active power value in the meter's characteristic data and the apparent power value; wherein, the apparent power value is the product of the total voltage and the total current. If the power factor is less than the preset power factor, then there is a power factor anomaly. The range method is used to calculate the three-phase current in the characteristic data of the meter to obtain the three-phase imbalance. If the three-phase imbalance is greater than the preset balance, then there is a three-phase imbalance.

[0014] As a preferred embodiment, the execution of the adjustment scheme based on the transformer area anomaly specifically includes: The abnormal adjustment plan for the transformer area is displayed through a human-computer interaction interface; wherein, the human-computer interaction interface includes an LCD display and a touch input device.

[0015] In response to user confirmation, the control executes the aforementioned transformer area anomaly adjustment plan.

[0016] Accordingly, the present invention also provides a transformer area anomaly identification and adjustment device, comprising: The hierarchical structure module is used to obtain the topology of the user's station area and determine the priority hierarchy based on the topology. The anomaly level module is used to sequentially obtain the meter feature data of the priority hierarchy structure from the lowest level upwards in each preset time interval, and identify the levels with anomalies in the priority hierarchy structure based on the meter feature data, and locate the highest anomaly level in the current preset time interval from the levels with anomalies; wherein, each preset time interval corresponds to the meter feature data of each level. The anomaly identification module is used to determine a periodic anomaly level matrix based on the anomaly levels corresponding to all preset time intervals. Starting from the highest level of the anomaly level in the periodic anomaly level matrix, the module iteratively analyzes each level downwards. In each iteration, the module determines the anomaly identification result of the current anomaly level by comparing the similarity rate of the anomaly levels in the current periodic anomaly level matrix with the consistency threshold. Based on the anomaly identification result, the module determines and executes an anomaly adjustment plan for the current anomaly level to restore the current anomaly level to normal.

[0017] Accordingly, the present invention also provides a transformer area anomaly identification and adjustment system for implementing the transformer area anomaly identification and adjustment method described in any of the above claims, comprising: a core processing unit, a communication unit, a human-machine interface, a storage unit, a power management unit, and a monitoring unit; The core processing unit is connected to the communication unit, the human-machine interface, the storage unit, and the power management unit, respectively. The communication unit is connected to the monitoring unit and the power management unit, respectively, and the power management unit is connected to the human-machine interface and the storage unit, respectively.

[0018] Accordingly, the present invention also provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the station area anomaly identification and adjustment method as described above.

[0019] Accordingly, the present invention also provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the transformer area anomaly identification and adjustment method as described above.

[0020] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The technical solution of this invention determines the priority hierarchy structure through the topology of the transformer substation and, within the analysis cycle, identifies the highest level of anomalies for each preset time interval, constructing an anomaly hierarchy matrix for the current analysis cycle. This combines the time-dimensional anomaly data with the spatial-dimensional topological hierarchy, forming a spatiotemporal correlation analysis model, providing a data foundation for accurately locating the root cause of anomalies. Iterating from the highest level of the matrix, the similarity rate of the highest level is calculated and compared with a consistency threshold. If the similarity rate is higher than the threshold, it is determined to be a recurring anomaly and adjustment is performed. If the similarity rate is lower than the threshold, the highest level is lowered by one level and iteration continues. If the threshold is still not met at the lowest level, the transformer substation anomaly is determined to be an occasional anomaly, and no adjustment is made. This introduces an iterative judgment mechanism based on a consistency threshold, which achieves automatic and accurate differentiation between recurring and occasional anomalies. This ensures that resources are accurately allocated to adjusting persistent, root-cause recurring anomalies, while avoiding unnecessary intervention in temporary, self-healing occasional anomalies, thereby significantly improving the intelligence level and economic efficiency of low-voltage transformer substation anomaly adjustment and management. Attached Figure Description

[0021] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating a method for identifying and adjusting transformer area anomalies according to an embodiment of the present invention. Figure 2 This is a schematic diagram of a topology provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of an anomaly level determination method provided by an embodiment of the present invention; Figure 4 This is a schematic diagram of an anomaly identification method based on a periodic matrix provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of a transformer area anomaly identification and adjustment system provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of another transformer area anomaly identification and adjustment system provided in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0025] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0026] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0027] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0028] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0029] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0030] Example 1 Please refer to Figure 1 The present invention provides a method for identifying and adjusting anomalies in a transformer substation, comprising the following steps S101-S103: S101: Obtain the topology of the user's station area and determine the priority hierarchy based on the topology.

[0031] As a preferred embodiment, obtaining the topology of the user's location and determining the priority hierarchy based on the topology specifically includes: Obtain the topology of the user's local area; Based on the power supply relationships in the topology, the distribution transformer layer, intermediate layer, and load layer of the topology are determined; Based on the aforementioned topology, a priority hierarchy structure is constructed, comprising a distribution transformer layer, an intermediate layer, and a load layer connected sequentially. The intermediate layer includes several branch layers, and the priority of the distribution transformer layer is higher than that of each branch layer. The priorities of each branch layer are different, and the priority of each branch layer is higher than that of the load layer.

[0032] In this embodiment, by specifically defining the "priority hierarchy" as the distribution transformer layer, branch layer, and load layer from high to low, the relatively abstract "priority hierarchy" is clearly associated with and concretized with the real and well-known physical topology of the power system. This allows the judgment results of the entire scheme to directly correspond to specific physical equipment or areas in the distribution area (such as "transformer problem" or "branch box problem"), which greatly improves the interpretability and operability of the scheme output. This enables managers to quickly understand the problem and take practical actions, thus enhancing the practical value of the method.

[0033] S102: In each preset time interval, the meter feature data of the priority hierarchy structure is obtained sequentially from the lowest level upwards, and the abnormal level in the priority hierarchy structure is identified based on the meter feature data, and the highest abnormal level in the current preset time interval is located from the abnormal level; wherein, each preset time interval corresponds to the meter feature data of each level.

[0034] As a preferred embodiment, the step of sequentially acquiring meter feature data of the priority hierarchy structure from the lowest level upwards, identifying abnormal levels in the priority hierarchy structure based on the meter feature data, and locating the highest abnormal level in the current preset time interval from the abnormal levels specifically includes: Obtain the meter characteristic data corresponding to the lowest load level and make anomaly judgments based on the current level's operating threshold range. If the meter characteristic data of the current level is greater than the operating threshold range of the current level, it is determined that there is an anomaly in the current level. The meter characteristic data of the previous level corresponding to the current level is also judged as an anomaly. This process is repeated until there is no anomaly in the current level or the current level is a distribution transformer level. Then the iteration stops and the current level is designated as the abnormal level.

[0035] In this embodiment, the specific logic for determining the anomaly level is as follows: starting from the lowest load layer, iteratively judging upwards level by level until the first level where an anomaly occurs is found. This is a "bottom-up, first-hit" search strategy. Its advantage lies in its high computational efficiency. In most cases, the anomaly originates from the load layer, and this method can quickly locate and stop the search, avoiding unnecessary computational overhead that might occur from traversing downwards from the transformer layer. At the same time, it naturally guarantees that the "highest level where the anomaly exists" (i.e., the anomaly level closest to the root cause) is found. This provides a specific means to quickly and efficiently locate the most root cause anomaly level, improving the execution efficiency and response speed of the entire method.

[0036] S103: Determine the periodic anomaly level matrix based on the anomaly levels corresponding to all preset time intervals, and iterate downwards from the highest level of the anomaly level in the periodic anomaly level matrix. In each iteration, the anomaly identification result of the current anomaly level is determined by comparing the same rate of the anomaly levels in the current periodic anomaly level matrix with the consistency threshold. Based on the anomaly identification result, the anomaly adjustment plan for the current anomaly level is determined and executed to restore the current anomaly level to normal.

[0037] As a preferred embodiment, in each iteration, the anomaly identification result of the current anomaly level is determined by comparing the similarity rate of the anomaly levels in the current periodic anomaly level matrix with the consistency threshold. This specifically includes: Calculate the similarity rate of the highest-level anomaly in the periodic anomaly hierarchy matrix, and compare the similarity rate with the consistency threshold; When the similarity rate of the anomaly level is greater than the consistency threshold, the anomaly identification result of the current anomaly level is determined to be a recurring anomaly. When the similarity rate of the anomaly level is less than or equal to the consistency threshold, the anomaly identification result of the current anomaly level is determined to be an occasional anomaly.

[0038] As a preferred embodiment, after determining that the anomaly identification result of the current anomaly level is an intermittent anomaly, the method further includes: Remove the anomaly levels currently being compared and analyzed from the periodic anomaly hierarchy matrix to obtain the updated periodic anomaly hierarchy matrix; The updated periodic anomaly hierarchy matrix is ​​compared with the consistency threshold by the same rate of the highest anomaly level. Execution continues until the exception level in the current cycle exception level matrix is ​​the lowest level, at which point execution stops.

[0039] In this embodiment, anomalies are concretized into three typical types: three-phase imbalance, power factor anomaly, and voltage deviation anomaly. Specific and executable hardware adjustment schemes (such as load cutover, installation of compensation / commutation / regulation devices) are specified for each anomaly type. This advances anomaly judgment from a binary "is it abnormal?" to a precise diagnosis of "what type of anomaly?" The analysis conclusions are directly linked to engineering practice, making the output adjustment schemes no longer mere principle suggestions but highly executable specific technical measures. This completes the closed loop from "intelligent diagnosis" to "precise treatment," greatly enhancing the final implementation value and industrialization potential of the entire technical solution.

[0040] As a preferred embodiment, the step of determining and executing a transformer area anomaly adjustment plan based on the transformer area anomaly identification result specifically includes: When the abnormal identification result of the transformer area is a frequent abnormality, an abnormality adjustment plan for the transformer area is determined based on the meter characteristic data corresponding to the current abnormality level, and the abnormality adjustment plan for the transformer area is executed. If the anomaly identification result of the transformer area is an intermittent anomaly, no operation will be performed.

[0041] As a preferred embodiment, the step of determining a transformer area anomaly adjustment plan based on the meter characteristic data corresponding to the currently determined anomaly level, and executing the transformer area anomaly adjustment plan, specifically includes: The anomaly type is determined based on the meter's characteristic data; wherein, the anomaly type includes three-phase imbalance, power factor anomaly, and voltage deviation anomaly. Based on the three-phase imbalance, power factor abnormality, and voltage deviation abnormality, an adjustment scheme for the transformer area abnormality is generated for the topology nodes corresponding to the abnormality levels.

[0042] As a preferred embodiment, the transformer area anomaly adjustment scheme includes: when the three phases are unbalanced, performing load cut-off or installing an automatic phase-switching device at the corresponding topology node; when the power factor is abnormal, installing a reactive power compensation device at the corresponding topology node; and when the voltage deviation is abnormal, installing a line voltage regulation device at the corresponding topology node.

[0043] As a preferred embodiment, determining the anomaly type based on the meter characteristic data specifically includes: The apparent power value is calculated based on the reactive power value in the meter's characteristic data, and the power factor is calculated based on the active power value in the meter's characteristic data and the apparent power value; wherein, the apparent power value is the product of the total voltage and the total current. If the power factor is less than the preset power factor, then there is a power factor anomaly. The range method is used to calculate the three-phase current in the characteristic data of the meter to obtain the three-phase imbalance. If the three-phase imbalance is greater than the preset balance, then there is a three-phase imbalance.

[0044] In this embodiment, specific and quantifiable calculation formulas and judgment criteria for two key characteristic parameters—power factor and three-phase imbalance—are provided. This provides an objective, unified, and repeatable metrological basis for anomaly type determination, avoiding deviations caused by subjective judgment, ensuring the scientific rigor and accuracy of the method in the detection stage, and laying a reliable data foundation for subsequent correct type determination and solution generation, thereby enhancing the credibility and authority of the entire technical solution.

[0045] As a preferred embodiment, the execution of the adjustment scheme based on the transformer area anomaly specifically includes: The abnormal adjustment plan for the transformer area is displayed through a human-computer interaction interface; wherein, the human-computer interaction interface includes an LCD display and a touch input device.

[0046] In response to user confirmation, the control executes the aforementioned transformer area anomaly adjustment plan.

[0047] In this embodiment, a human-computer interaction interface is added to display the solution, making it not a closed "black box" but a human-computer collaborative system. Managers can intuitively obtain the decision-making basis (displayed solution), improving the system's usability and on-site adaptability.

[0048] Implementing the above embodiments has the following effects: The technical solution of this invention determines the priority hierarchy structure through the topology of the transformer substation and, within the analysis cycle, identifies the highest level of anomalies for each preset time interval, constructing an anomaly hierarchy matrix for the current analysis cycle. This combines the time-dimensional anomaly data with the spatial-dimensional topological hierarchy, forming a spatiotemporal correlation analysis model, providing a data foundation for accurately locating the root cause of anomalies. Iterating from the highest level of the matrix, the similarity rate of the highest level is calculated and compared with a consistency threshold. If the similarity rate is higher than the threshold, it is determined to be a recurring anomaly and adjustment is performed. If the similarity rate is lower than the threshold, the highest level is lowered by one level and iteration continues. If the threshold is still not met at the lowest level, the transformer substation anomaly is determined to be an occasional anomaly, and no adjustment is made. This introduces an iterative judgment mechanism based on a consistency threshold, which achieves automatic and accurate differentiation between recurring and occasional anomalies. This ensures that resources are accurately allocated to adjusting persistent, root-cause recurring anomalies, while avoiding unnecessary intervention in temporary, self-healing occasional anomalies, thereby significantly improving the intelligence level and economic efficiency of low-voltage transformer substation anomaly adjustment and management.

[0049] Example 2 This embodiment also provides various preferred implementation schemes, as follows: As a preferred embodiment, the step of obtaining the topology of the user's location and determining the priority hierarchy based on the topology specifically includes: Obtain the topology of the user's transformer area; based on the power supply relationship in the topology, determine the distribution transformer layer, intermediate layer, and load layer of the topology; based on the distribution transformer layer, intermediate layer, and load layer of the topology, construct a priority hierarchy structure; wherein, the priority hierarchy structure includes the distribution transformer layer, intermediate layer, and load layer connected in sequence, the intermediate layer includes several branch layers, and the priority of the distribution transformer layer is higher than that of each branch layer, the priority of each branch layer is different, and the priority of each branch layer is higher than that of the load layer.

[0050] In this embodiment, local communication technologies such as high-speed power line carrier (HPLC) or low-power wireless are used to issue topology identification commands through the master station system. The concentrators under the low-voltage transformers and each smart meter mutually detect and report communication characteristics (such as signal strength and signal-to-noise ratio). The master station system of the distribution area analyzes the detected data using impedance methods, correlation coefficient methods, or uplink feature identification algorithms to automatically generate a distribution area topology diagram with the distribution transformer as the root node, branch boxes as intermediate nodes, and smart meters as leaf nodes, as shown below. Figure 2The diagram shows a topology. The low-voltage outlet monitoring unit is used to collect current, voltage, phase angle, and power data from the low-voltage outlet side, and can be a smart measurement terminal, smart gateway, or other devices. The branch monitoring unit is used to collect current, voltage, phase angle, and power data from the branch, and can be a smart switch, branch monitoring device, or other devices.

[0051] In this embodiment, each branch level has a relevant label. The load monitoring unit is used to collect current, voltage, phase angle, power, and energy data from the load side, and can be a smart meter, smart load switch, or other similar device. According to a preset priority rule, the priority of each topology node in the platform topology is automatically mapped and labeled to obtain a priority hierarchy structure.

[0052] In this embodiment, the system reads the generated distribution area topology connection tree and assigns a priority value label to each device node in the topology according to preset mapping and assignment rules, thereby constructing a digital priority hierarchy structure. For example, the rules are as follows: Rule 1 (Load Layer): Mark all user smart meter nodes located at the very end of the topology tree as priority 1. Rule 2 (Branch Layers): Mark all branch box nodes as priority 2. If there are multiple levels of branches, they can be further subdivided into priority 2, priority 3, etc., according to their logical distance from the root node. The larger the value, the higher the level and the closer to the root node (distribution transformer). Rule 3 (Distribution Transformer Layer): Mark the distribution transformer (or its concentrator) node, which serves as the power supply source, as the highest priority. For example, if a distribution area has three levels of branches from the low-voltage outlet side to the end load, the load priority weights for that distribution area are as follows: [Low-voltage outlet side, first-level branch, second-level branch, third-level branch, load] = [5, 4, 3, 2, 1].

[0053] As a preferred embodiment, the step of sequentially acquiring the meter feature data of the priority hierarchy structure from the lowest level upwards, identifying the levels with anomalies in the priority hierarchy structure based on the meter feature data, and locating the highest abnormal level in the current preset time interval from the abnormal levels specifically includes: Obtain the meter characteristic data corresponding to the lowest load level and perform anomaly judgment based on the current level's operating threshold range. If the meter characteristic data of the current level is greater than the current level's operating threshold range, it is determined that the current level has an anomaly, and the meter characteristic data of the previous level corresponding to the current level is also judged to have an anomaly. This process is repeated until there is no anomaly in the current level or the current level is a distribution transformer level, at which point the iteration stops, and the current level is designated as the anomaly level.

[0054] In this embodiment, for each preset time interval within a preset analysis period, the meter feature data can be obtained step by step based on the priority hierarchy structure to determine whether there is an anomaly at each level. The highest level with an anomaly is determined as the anomaly level of the current time interval. The preset analysis period includes multiple preset time intervals, and the meter feature data includes current, voltage, phase angle, and power data.

[0055] In this embodiment, based on user complaint information from a specific low-voltage distribution area, the user's unique meter identification number is obtained to locate the user, abnormal meter data is retrieved, and the anomaly determination process begins. The preset analysis period is one day, and 96% near real-time analysis is performed. Therefore, the preset time interval is 15 minutes. Near real-time analysis is performed on data every 15 minutes within the characteristic period, and a periodic anomaly hierarchy matrix is ​​obtained.

[0056] Specifically, the monitoring equipment freezes and uploads a set of meter characteristic data every 15 minutes as a preset time interval, including three-phase voltage (Ua, Ub, Uc), three-phase current (Ia, Ib, Ic), phase angle of each phase, active power (P), and reactive power (Q). In the main station analysis platform, each topology level is preset with its own independent normal operating threshold range, and hierarchical anomaly diagnosis is performed. For a given time interval, the analysis starts from the load level (a single meter). Specifically, the characteristic data of that meter is retrieved and compared with the preset normal operating threshold range of the load level (e.g., voltage 198V-235V, power factor qualification standard greater than or equal to 0.85; three-phase imbalance less than or equal to 15%). If the characteristic data of the meter does not fall within the normal operating threshold range, the meter is marked as abnormal. Based on the topology, the system locates the branch box to which the meter belongs. Then, it retrieves the feature data reported by the branch box monitoring terminal (FTU) and compares it with the normal operating threshold range for the branch level. If the meter's feature data does not fall within the normal operating threshold range, the branch box is marked as abnormal. Similarly, if a branch box under a transformer is determined to be abnormal, the system retrieves the feature data reported by the transformer monitoring terminal (TTU) and compares it with the normal operating threshold range for the transformer level to determine if the transformer itself is abnormal. The system traverses from bottom to top, finding the level (highest level) in the topology closest to the transformer that is determined to be abnormal using its own data within the given time interval, and assigns this level the priority label corresponding to the abnormal level for that time interval.

[0057] It should be noted that the preset analysis cycle can be set via the human-machine interface (daily, weekly, monthly). Adjustment strategies are then provided based on the cycle. For example, if the analysis cycle is set to daily, the data from 0:00 to 23:59 of the previous calendar day will be analyzed and calculated after 0:00 each day; if the cycle is set to weekly, the data from 0:00 to 23:59 of the previous seven calendar days will be analyzed and calculated each day; and if the analysis cycle is set to monthly, the data from 0:00 to 23:59 of the previous thirty calendar days will be analyzed and calculated each day. Given that current power system anomaly analysis primarily uses daily data, the default design cycle is daily.

[0058] In this embodiment, the judgment criteria are consistent at each level, and all nodes in the entire low-voltage distribution area are judged according to the same criteria. For example... Figure 3The diagram illustrates a method for determining anomaly levels. Initialization and startup: The current level of the iteration is initialized to the lowest priority level—the load level (priority 1). First iteration: Load level analysis. Meter characteristic data of a single smart meter (e.g., representing a household) within the given time interval is retrieved from the database and compared with preset load level operating threshold ranges (e.g., lower voltage limit 198V, upper voltage limit 235V). Scenario A (Load Level Anomaly): If the meter voltage is 190V (below the threshold), the system determines that the load level is abnormal. At this point, the system does not immediately confirm this as the final anomaly level but initiates the next iteration, elevating the current level to the next higher branch level (priority 2) to investigate whether the problem is more fundamental. Scenario B (Load Level Normal): If all meter data is normal, the system determines that the load level is not abnormal. The iteration stops, and the anomaly level record for the current time interval is empty (or marked as "normal"). If the load layer corresponds to situation A, the second iteration begins: Each branch layer is analyzed. Based on the topology, the branch box to which the meter belongs is located, and the meter characteristic data reported by the FTU of that branch box is retrieved from the database. This branch layer data is compared with the preset branch layer operating threshold range (e.g., line load rate upper limit 80%). Situation A (Branch Layer Abnormal): If the load rate of the branch box is 95% (above the threshold), the system determines that the branch layer is abnormal. Similarly, the system continues to start the next iteration, elevating the current level to the distribution transformer layer (priority 3). Situation B (Branch Layer Normal): If the data of the branch box itself is normal, the iteration stops. The system determines the load layer identified in the previous round as the abnormal level for the current time interval. If the branch layer corresponds to situation A, the third iteration begins: Distribution transformer layer analysis is performed. The meter characteristic data reported by the transformer TTU (such as transformer total load rate, outlet voltage, three-phase imbalance) is retrieved, and this data is compared with the preset distribution transformer layer operating threshold range. Scenario A (Distribution transformer level anomaly): If the total transformer load rate is 105% (too high), the system determines that there is an anomaly in the distribution transformer level. Since the highest level has been reached, iteration stops. The system designates this level as the final anomaly level. Scenario B (Distribution transformer level normal): If the transformer's own data is normal, iteration stops. The system designates the branch level identified in the previous round as the final anomaly level.

[0059] As a preferred embodiment, in each iteration, the determination of the transformer area anomaly identification result at the current anomaly level based on the comparison between the similarity rate of the anomaly levels in the current periodic anomaly level matrix and the consistency threshold specifically includes: Calculate the similarity rate of the highest level of the anomaly hierarchy in the periodic anomaly hierarchy matrix, and compare the similarity rate with the consistency threshold. When the similarity rate of the anomaly hierarchy is greater than the consistency threshold, the anomaly identification result of the current anomaly hierarchy is determined to be a recurring anomaly. When the similarity rate of the anomaly hierarchy is less than or equal to the consistency threshold, the anomaly identification result of the current anomaly hierarchy is determined to be an occasional anomaly.

[0060] As a preferred embodiment, after determining that the anomaly identification result of the current anomaly level is an intermittent anomaly, the method further includes: Remove the currently analyzed abnormal level from the periodic abnormality level matrix and use it as the updated periodic abnormality level matrix; compare the sameness rate of the highest level of abnormality in the updated periodic abnormality level matrix with the consistency threshold; repeat the process until the abnormal level in the current periodic abnormality level matrix is ​​the lowest level, then stop the process.

[0061] In this embodiment, a periodic anomaly level matrix is ​​determined by identifying the anomaly levels corresponding to all preset time intervals. Iteration proceeds level by level, starting from the highest level of the anomaly level in the matrix. In the current iteration, it is determined whether the sameness rate of the highest level of the anomaly level in the current periodic anomaly level matrix is ​​greater than a consistency threshold. If so, the transformer area anomaly identification result is determined to be a recurring anomaly, an adjustment plan is determined and executed, and the meter characteristic data is restored to normal. If not, the highest level of the anomaly level in the current periodic anomaly level matrix is ​​lowered by one level to obtain an updated periodic anomaly level matrix. The next iteration is then performed based on the updated periodic anomaly level matrix until the highest level of the anomaly level in the current periodic anomaly level matrix is ​​the lowest level, at which point the iteration stops. For anomalies with a sameness rate less than or equal to the consistency threshold, the transformer area anomaly identification result is determined to be an intermittent anomaly, and no adjustment is performed.

[0062] In this embodiment, using a database and data analysis software (such as Python Pandas, SQL, etc.), the anomaly level labels of all time intervals within an analysis period (e.g., one day, with 96 15-minute intervals) are arranged chronologically to form a 1×N row vector matrix. For example, taking a feature period of 1 day and a similarity threshold of 90% as an example, 96 near real-time analyses are first performed to form a 1*96 anomaly level matrix, such as [3,4,3,1,3,3,3...3]. Figure 4The diagram illustrates an anomaly identification method based on a periodic matrix. In the first iteration, the highest priority level in the matrix is ​​determined to be 4. The similarity rate of 4 (i.e., the number of times "4" appears / the total number of time intervals) is calculated. If this similarity rate is greater than the consistency threshold 'a' (e.g., 90%), it is judged as a recurring anomaly. Based on the meter characteristic data causing the level 4 anomaly (e.g., long-term overload, low power factor), the system automatically matches a preset adjustment scheme from the strategy library. For example, "install a reactive power compensation device on the branch side corresponding to level 4" or "suggest load cutover," with the goal of restoring the subsequently collected meter characteristic data such as power factor and voltage to normal. If the similarity rate is only 1.04%, which is less than 90%, then all "4" level labels are downgraded by one level to "3", and the "4" level is removed to obtain a new periodic anomaly level matrix. Then, the second round of iteration begins, and the above calculation and judgment process is repeated until the similarity rate of a certain level reaches the standard. For example, if the similarity rate of level "3" is 98.9%, which is greater than the threshold of 90%, then a frequent anomaly and adjustment plan are output, and the current level 3 is judged as a frequent anomaly. Alternatively, if there is still no level with a similarity rate greater than the threshold of 90% after iterating to the lowest level (load layer), then it is judged as an occasional anomaly. The system generates a no-adjustment plan, only records the event in the database and marks it as "observation", without the need to send out maintenance personnel.

[0063] As a preferred embodiment, the step of determining and executing a transformer area anomaly adjustment plan based on the transformer area anomaly identification result specifically includes: If the abnormality identification result of the transformer area is a frequent abnormality, then an abnormality adjustment plan for the transformer area is determined based on the meter characteristic data corresponding to the current abnormality level, and the plan is executed accordingly; if the abnormality identification result of the transformer area is an occasional abnormality, no operation is performed.

[0064] As a preferred embodiment, the step of determining a transformer area anomaly adjustment plan based on the meter characteristic data corresponding to the currently determined anomaly level, and executing the transformer area anomaly adjustment plan, specifically includes: The anomaly type is determined based on the meter characteristic data; wherein, the anomaly type includes three-phase imbalance, power factor anomaly, and voltage deviation anomaly; based on the three-phase imbalance, power factor anomaly, and voltage deviation anomaly, an adjustment scheme for the transformer area anomaly corresponding to the topology node of the anomaly level is generated.

[0065] As a preferred embodiment, the transformer area anomaly adjustment scheme includes: when the three phases are unbalanced, performing load cut-off or installing an automatic phase-switching device on the corresponding topology node; when the power factor is abnormal, installing a reactive power compensation device on the corresponding topology node; and when the voltage deviation is abnormal, installing a line voltage regulation device on the corresponding topology node.

[0066] In this embodiment, if the anomaly is a recurring anomaly, the adjustment plan is determined according to the type of the anomaly data. Specifically, if the anomaly type is three-phase imbalance, the load is cut off or an automatic phase switching device is installed at the anomaly node; if the anomaly type is low power factor, a reactive power compensation device is installed at the anomaly node; if the anomaly type is voltage deviation, an automatic voltage regulation device for low-voltage lines is installed at the anomaly node.

[0067] As a preferred embodiment, determining the anomaly type based on the meter characteristic data specifically includes: The apparent power value is calculated based on the reactive power value in the meter's characteristic data, and the power factor is calculated based on the active power value and the apparent power value in the meter's characteristic data; wherein, the apparent power value is the product of the total voltage and the total current; if the power factor is less than a preset power factor, there is a power factor anomaly; the three-phase current in the meter's characteristic data is calculated using the range method to obtain the three-phase imbalance degree; if the three-phase imbalance degree is greater than a preset balance degree, there is a three-phase imbalance.

[0068] In this embodiment, the power factor *a* and three-phase unbalance of a node are calculated according to the existing general formulas for power grids, and are expressed as: *a* = P / (P² + Q²)⁰.⁵; three-phase unbalance = (Max(Ia, Ib, Ic) - Min(Ia, Ib, Ic)) / Max(Ia, Ib, Ic). Where Ua, Ub, and Uc are the three-phase voltages A, B, and C respectively, Ia, Ib, and Ic are the three-phase currents A, B, and C respectively, P is the active power at that point, and Q is the reactive power at that point.

[0069] As a preferred embodiment, the execution of the transformer area anomaly adjustment scheme specifically includes: The abnormal adjustment plan for the transformer area is displayed through a human-machine interface; wherein, the human-machine interface includes an LCD display and a touch input device; in response to user confirmation, the abnormal adjustment plan for the transformer area is executed.

[0070] In this embodiment, the human-machine interaction function is implemented through an industrial touch screen all-in-one machine deployed in the monitoring center or on-site. Its LCD screen clearly displays the analysis conclusions (such as "frequent anomalies" and specific load cut-off solutions, or "occasional anomalies" and observation suggestions). Maintenance personnel can use the touch screen to confirm execution, generate work orders, or adjust algorithm parameters, thereby achieving efficient interaction and closed-loop management with the analysis system.

[0071] Example 3 Please see Figure 5 The present invention provides a transformer area anomaly identification and adjustment device, comprising: Hierarchical structure module 201 is used to obtain the topology of the user's station area and determine the priority hierarchy based on the topology; The anomaly level module 202 is used to sequentially obtain the meter feature data of the priority hierarchy structure from the lowest level to the highest level in each preset time interval, and identify the level with anomalies in the priority hierarchy structure based on the meter feature data, and locate the highest anomaly level in the current preset time interval from the level with anomalies; wherein, each preset time interval corresponds to the meter feature data of each level; The anomaly identification module 203 is used to determine a periodic anomaly level matrix based on the anomaly levels corresponding to all preset time intervals, and to iteratively analyze from the highest level of the anomaly level in the periodic anomaly level matrix downwards, so that in each iteration, the anomaly identification result of the current anomaly level is determined by comparing the same rate of the anomaly levels in the current periodic anomaly level matrix with the consistency threshold, and the anomaly identification result of the current anomaly level is determined and executed based on the anomaly identification result of the current anomaly level matrix, so as to restore the current anomaly level to normal.

[0072] As a preferred embodiment, obtaining the topology of the user's location and determining the priority hierarchy based on the topology specifically includes: Obtain the topology of the user's local area; Based on the power supply relationships in the topology, the distribution transformer layer, intermediate layer, and load layer of the topology are determined; Based on the aforementioned topology, a priority hierarchy structure is constructed, comprising a distribution transformer layer, an intermediate layer, and a load layer connected sequentially. The intermediate layer includes several branch layers, and the priority of the distribution transformer layer is higher than that of each branch layer. The priorities of each branch layer are different, and the priority of each branch layer is higher than that of the load layer.

[0073] As a preferred embodiment, the step of sequentially acquiring meter feature data of the priority hierarchy structure from the lowest level upwards, identifying abnormal levels in the priority hierarchy structure based on the meter feature data, and locating the highest abnormal level in the current preset time interval from the abnormal levels specifically includes: Obtain the meter characteristic data corresponding to the lowest load level and make anomaly judgments based on the current level's operating threshold range. If the meter characteristic data of the current level is greater than the operating threshold range of the current level, it is determined that there is an anomaly in the current level. The meter characteristic data of the previous level corresponding to the current level is also judged as an anomaly. This process is repeated until there is no anomaly in the current level or the current level is a distribution transformer level. Then the iteration stops and the current level is designated as the abnormal level.

[0074] As a preferred embodiment, in each iteration, the anomaly identification result of the current anomaly level is determined by comparing the similarity rate of the anomaly levels in the current periodic anomaly level matrix with the consistency threshold. This specifically includes: Calculate the similarity rate of the highest-level anomaly in the periodic anomaly hierarchy matrix, and compare the similarity rate with the consistency threshold; When the similarity rate of the anomaly level is greater than the consistency threshold, the anomaly identification result of the current anomaly level is determined to be a recurring anomaly. When the similarity rate of the anomaly level is less than or equal to the consistency threshold, the anomaly identification result of the current anomaly level is determined to be an occasional anomaly.

[0075] As a preferred embodiment, after determining that the anomaly identification result of the current anomaly level is an intermittent anomaly, the method further includes: Remove the anomaly levels currently being compared and analyzed from the periodic anomaly hierarchy matrix to obtain the updated periodic anomaly hierarchy matrix; The updated periodic anomaly hierarchy matrix is ​​compared with the consistency threshold by the same rate of the highest anomaly level. Execution continues until the exception level in the current cycle exception level matrix is ​​the lowest level, at which point execution stops.

[0076] As a preferred embodiment, the step of determining and executing a transformer area anomaly adjustment plan based on the transformer area anomaly identification result specifically includes: When the abnormal identification result of the transformer area is a frequent abnormality, an abnormality adjustment plan for the transformer area is determined based on the meter characteristic data corresponding to the current abnormality level, and the abnormality adjustment plan for the transformer area is executed. If the anomaly identification result of the transformer area is an intermittent anomaly, no operation will be performed.

[0077] As a preferred embodiment, the step of determining a transformer area anomaly adjustment plan based on the meter characteristic data corresponding to the currently determined anomaly level, and executing the transformer area anomaly adjustment plan, specifically includes: The anomaly type is determined based on the meter's characteristic data; wherein, the anomaly type includes three-phase imbalance, power factor anomaly, and voltage deviation anomaly. Based on the three-phase imbalance, power factor abnormality, and voltage deviation abnormality, an adjustment scheme for the transformer area abnormality is generated for the topology nodes corresponding to the abnormality levels.

[0078] As a preferred embodiment, the transformer area anomaly adjustment scheme includes: when the three phases are unbalanced, performing load cut-off or installing an automatic phase-switching device at the corresponding topology node; when the power factor is abnormal, installing a reactive power compensation device at the corresponding topology node; and when the voltage deviation is abnormal, installing a line voltage regulation device at the corresponding topology node.

[0079] As a preferred embodiment, determining the anomaly type based on the meter characteristic data specifically includes: The apparent power value is calculated based on the reactive power value in the meter's characteristic data, and the power factor is calculated based on the active power value in the meter's characteristic data and the apparent power value; wherein, the apparent power value is the product of the total voltage and the total current. If the power factor is less than the preset power factor, then there is a power factor anomaly. The range method is used to calculate the three-phase current in the characteristic data of the meter to obtain the three-phase imbalance. If the three-phase imbalance is greater than the preset balance, then there is a three-phase imbalance.

[0080] As a preferred embodiment, the execution of the adjustment scheme based on the transformer area anomaly specifically includes: The abnormal adjustment plan for the transformer area is displayed through a human-computer interaction interface; wherein, the human-computer interaction interface includes an LCD display and a touch input device.

[0081] In response to user confirmation, the control executes the aforementioned transformer area anomaly adjustment plan.

[0082] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0083] Implementing the above embodiments has the following effects: The technical solution of this invention determines the priority hierarchy structure through the topology of the transformer substation and, within the analysis cycle, identifies the highest level of anomalies for each preset time interval, constructing an anomaly hierarchy matrix for the current analysis cycle. This combines the time-dimensional anomaly data with the spatial-dimensional topological hierarchy, forming a spatiotemporal correlation analysis model, providing a data foundation for accurately locating the root cause of anomalies. Iterating from the highest level of the matrix, the similarity rate of the highest level is calculated and compared with a consistency threshold. If the similarity rate is higher than the threshold, it is determined to be a recurring anomaly and adjustment is performed. If the similarity rate is lower than the threshold, the highest level is lowered by one level and iteration continues. If the threshold is still not met at the lowest level, the transformer substation anomaly is determined to be an occasional anomaly, and no adjustment is made. This introduces an iterative judgment mechanism based on a consistency threshold, which achieves automatic and accurate differentiation between recurring and occasional anomalies. This ensures that resources are accurately allocated to adjusting persistent, root-cause recurring anomalies, while avoiding unnecessary intervention in temporary, self-healing occasional anomalies, thereby significantly improving the intelligence level and economic efficiency of low-voltage transformer substation anomaly adjustment and management.

[0084] Example 4 Please see Figure 6 This invention provides an anomaly identification and adjustment system for implementing any of the above-described method embodiments of the present invention, providing a method for anomaly identification and adjustment in a transformer area. The system includes a core processing unit, a communication unit, a human-machine interface, a storage unit, a power management unit, and a monitoring unit 560. The core processing unit is connected to the communication unit, the human-machine interface, the storage unit, and the power management unit. The communication unit is connected to the monitoring unit and the power management unit. The power management unit is connected to the human-machine interface and the storage unit. The core processing unit uses a high-speed ARM chip and is responsible for analyzing abnormal situations in the distribution area based on the operating data of the distribution area and formulating adjustment strategies. The communication unit includes an RS485 communication module / 4G module. The 4G module can retrieve data from the low-voltage output side monitoring unit, branch monitoring unit, load monitoring unit, and transformer area information after low-voltage topology identification from the back-end server. The RS485 communication module can be used to realize local information interaction with each monitoring unit. The human-machine interface is responsible for human-machine interaction functions. It is equipped with an LCD display and touch input function, which can display governance strategies for easy reference by front-line operators. At the same time, it can display key parameters of equipment such as feature analysis cycle and set values ​​of transformer access strategy adjustment for maintenance personnel. The storage unit is responsible for storing the data transmitted from the communication unit and the governance strategies analyzed and calculated by the core processing unit. The power management unit provides power to the system; The monitoring unit includes a low-voltage outlet-side monitoring unit, a branch monitoring unit, and a load monitoring unit. It can acquire data from these units, along with transformer area information after low-voltage topology identification, via 4G wireless communication / RS485 or other communication methods. This data is used for anomaly identification and adjustment analysis. The low-voltage outlet-side monitoring unit collects current, voltage, phase angle, and power data from the low-voltage outlet side and can be a smart metering terminal, smart gateway, or similar device. The branch monitoring unit collects current, voltage, phase angle, and power data from branches and can be a smart switch, branch monitoring system, or similar device. In this solution, each branch level has relevant markings. The load monitoring unit collects current, voltage, phase angle, power, and energy data from the load side and can be a smart meter, smart load switch, or similar device.

[0085] It should be noted that the specific implementation of the monitoring device is existing technology. This patent only considers how to use this data to conduct power quality analysis of the low-voltage distribution area after these monitoring units have been installed.

[0086] Example 5 Accordingly, the present invention also provides a terminal device, comprising: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the transformer area anomaly identification and adjustment method as described in any of the above embodiments.

[0087] The terminal device in this embodiment includes a processor, a memory, and a computer program and computer instructions stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps described in Embodiment 1 above, for example... Figure 1 The steps S101 to S103 are shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above-described device embodiment, such as the exception level module 202.

[0088] For example, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the terminal device. For example, the anomaly level module 202 is used to sequentially obtain meter characteristic data of the priority hierarchy structure from the lowest level upwards in each preset time interval, and based on the meter characteristic data, identify the levels in the priority hierarchy structure that are abnormal, and locate the highest abnormal level in the current preset time interval from the abnormal levels.

[0089] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the schematic diagram is merely an example of a terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than illustrated, or combine certain components, or different components. For example, the terminal device may also include input / output devices, network access devices, buses, etc.

[0090] 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. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0091] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, applications required for at least one function, etc.; the data storage area may store data created based on the use of the mobile terminal, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital card (SD card), flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0092] Wherein, if the modules / units integrated in the terminal device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, it can implement the steps of the various method embodiments described above. Wherein, the computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0093] Example 6 Accordingly, the present invention also provides a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the transformer area anomaly identification and adjustment method as described in any of the above embodiments.

[0094] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for identifying and adjusting anomalies in transformer substations, characterized in that, include: Obtain the topology of the user's station area and determine the priority hierarchy based on the topology; In each preset time interval, the meter feature data of the priority hierarchy structure is obtained sequentially from the lowest level upwards. Based on the meter feature data, the level in the priority hierarchy structure that is abnormal is identified, and the highest abnormal level in the current preset time interval is located from the abnormal level. Each preset time interval corresponds to the meter feature data of each level. Based on the anomaly levels corresponding to all preset time intervals, a periodic anomaly level matrix is ​​determined. Starting from the highest level of the anomaly level in the periodic anomaly level matrix, the analysis proceeds iteratively downwards. In each iteration, the anomaly identification result of the current anomaly level is determined by comparing the similarity rate of the anomaly levels in the current periodic anomaly level matrix with the consistency threshold. Based on the anomaly identification result, an anomaly adjustment plan for the current anomaly is determined and executed to restore the current anomaly to normal.

2. The method for identifying and adjusting transformer area anomalies as described in claim 1, characterized in that, The process of obtaining the topology of the user's location and determining the priority hierarchy based on the topology specifically includes: Obtain the topology of the user's local area; Based on the power supply relationships in the topology, the distribution transformer layer, intermediate layer, and load layer of the topology are determined; Based on the aforementioned topology, a priority hierarchy structure is constructed, comprising a distribution transformer layer, an intermediate layer, and a load layer connected sequentially. The intermediate layer includes several branch layers, and the priority of the distribution transformer layer is higher than that of each branch layer. The priorities of each branch layer are different, and the priority of each branch layer is higher than that of the load layer.

3. The method for identifying and adjusting transformer area anomalies as described in claim 2, characterized in that, The process of sequentially acquiring meter feature data from the lowest level upwards in the priority hierarchy, identifying abnormal levels within the priority hierarchy based on the meter feature data, and locating the highest abnormal level within the current preset time interval from the abnormal levels specifically includes: Obtain the meter characteristic data corresponding to the lowest load level and make anomaly judgments based on the current level's operating threshold range. If the meter characteristic data of the current level is greater than the operating threshold range of the current level, it is determined that there is an anomaly in the current level. The meter characteristic data of the previous level corresponding to the current level is also judged as an anomaly. This process is repeated until there is no anomaly in the current level or the current level is a distribution transformer level. Then the iteration stops and the current level is designated as the abnormal level.

4. The method for identifying and adjusting transformer area anomalies as described in claim 3, characterized in that, In each iteration, the anomaly identification result of the current anomaly level is determined by comparing the similarity rate of the anomaly levels in the current periodic anomaly level matrix with the consistency threshold. Specifically, this includes: Calculate the similarity rate of the highest-level anomaly in the periodic anomaly hierarchy matrix, and compare the similarity rate with the consistency threshold; When the similarity rate of the anomaly level is greater than the consistency threshold, the anomaly identification result of the current anomaly level is determined to be a recurring anomaly. When the similarity rate of the anomaly level is less than or equal to the consistency threshold, the anomaly identification result of the current anomaly level is determined to be an occasional anomaly.

5. The method for identifying and adjusting transformer area anomalies as described in claim 4, characterized in that, After determining that the anomaly identification result of the current anomaly level is an intermittent anomaly, the following steps are also included: Remove the anomaly levels currently being compared and analyzed from the periodic anomaly hierarchy matrix to obtain the updated periodic anomaly hierarchy matrix; The updated periodic anomaly hierarchy matrix is ​​compared with the consistency threshold by the same rate of the highest anomaly level. Execution continues until the exception level in the current cycle exception level matrix is ​​the lowest level, at which point execution stops.

6. A method for identifying and adjusting transformer area anomalies as described in any one of claims 4 or 5, characterized in that, The step of determining and implementing an anomaly adjustment plan for the transformer area based on the anomaly identification results specifically includes: When the abnormal identification result of the transformer area is a frequent abnormality, an abnormality adjustment plan for the transformer area is determined based on the meter characteristic data corresponding to the current abnormality level, and the abnormality adjustment plan for the transformer area is executed. If the anomaly identification result of the transformer area is an intermittent anomaly, no operation will be performed.

7. The method for identifying and adjusting transformer area anomalies as described in claim 6, characterized in that, The step of determining an anomaly adjustment plan for the transformer substation based on the meter characteristic data corresponding to the currently determined anomaly level, and executing the anomaly adjustment plan for the transformer substation, specifically includes: The anomaly type is determined based on the meter's characteristic data; wherein, the anomaly type includes three-phase imbalance, power factor anomaly, and voltage deviation anomaly. Based on the three-phase imbalance, power factor abnormality, and voltage deviation abnormality, an adjustment scheme for the transformer area abnormality is generated for the topology nodes corresponding to the abnormality levels.

8. The method for identifying and adjusting transformer area anomalies as described in claim 7, characterized in that, The abnormal adjustment scheme for the transformer area includes: when the three phases are unbalanced, performing load cut-off or installing an automatic phase-switching device at the corresponding topology node; when the power factor is abnormal, installing a reactive power compensation device at the corresponding topology node; and when the voltage deviation is abnormal, installing a line voltage regulation device at the corresponding topology node.

9. The method for identifying and adjusting transformer area anomalies as described in claim 8, characterized in that, The step of determining the anomaly type based on the meter characteristic data specifically includes: The apparent power value is calculated based on the reactive power value in the meter's characteristic data, and the power factor is calculated based on the active power value in the meter's characteristic data and the apparent power value; wherein, the apparent power value is the product of the total voltage and the total current. If the power factor is less than the preset power factor, then there is a power factor anomaly. The range method is used to calculate the three-phase current in the characteristic data of the meter to obtain the three-phase imbalance. If the three-phase imbalance is greater than the preset balance, then there is a three-phase imbalance.

10. The method for identifying and adjusting transformer area anomalies as described in claim 6, characterized in that, The execution of the adjustment plan based on the transformer area anomaly specifically includes: The abnormal adjustment plan for the transformer area is displayed through a human-computer interaction interface; wherein, the human-computer interaction interface includes an LCD display and a touch input device; In response to user confirmation, the control executes the aforementioned transformer area anomaly adjustment plan.

11. A transformer area anomaly identification and adjustment device, characterized in that, include: The hierarchical structure module is used to obtain the topology of the user's station area and determine the priority hierarchy based on the topology. The anomaly level module is used to sequentially obtain the meter feature data of the priority hierarchy structure from the lowest level upwards in each preset time interval, and identify the levels with anomalies in the priority hierarchy structure based on the meter feature data, and locate the highest anomaly level in the current preset time interval from the levels with anomalies; wherein, each preset time interval corresponds to the meter feature data of each level. The anomaly identification module is used to determine a periodic anomaly level matrix based on the anomaly levels corresponding to all preset time intervals. Starting from the highest level of the anomaly level in the periodic anomaly level matrix, the module iteratively analyzes each level downwards. In each iteration, the module determines the anomaly identification result of the current anomaly level by comparing the similarity rate of the anomaly levels in the current periodic anomaly level matrix with the consistency threshold. Based on the anomaly identification result, the module determines and executes an anomaly adjustment plan for the current anomaly level to restore the current anomaly level to normal.

12. A transformer area anomaly identification and adjustment system, characterized in that, The method for implementing the transformer area anomaly identification and adjustment method as described in any one of claims 1-10 includes: a core processing unit, a communication unit, a human-machine interface, a storage unit, a power management unit, and a monitoring unit; The core processing unit is connected to the communication unit, the human-machine interface, the storage unit, and the power management unit, respectively. The communication unit is connected to the monitoring unit and the power management unit, respectively, and the power management unit is connected to the human-machine interface and the storage unit, respectively.

13. A terminal device, characterized in that, The method includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the transformer area anomaly identification and adjustment method as described in any one of claims 1 to 10.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the transformer area anomaly identification and adjustment method as described in any one of claims 1 to 10.

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