Electric energy meter calibration method and calibration system
By constructing a topological correlation structure and an impedance dynamic identification algorithm, sensitive areas of power loss are identified, solving the metering error problem of power meter calibration methods in dynamic power distribution networks, and realizing the accuracy and fairness of power metering.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUANGDONG BOLI TECH CO LTD
- Filing Date
- 2026-02-25
- Publication Date
- 2026-05-05
AI Technical Summary
Existing electricity meter calibration methods are ill-suited to the dynamic characteristics of power distribution networks and cannot accurately identify areas of power loss sensitivity, leading to metering errors and unfair electricity trading.
By constructing a topological interconnection structure and combining node voltage phase disturbance characteristics and impedance dynamic identification algorithms, sensitive areas of power loss are identified and the calibration compensation coefficient of user energy meters is determined, thereby achieving decoupling and reconstruction of dynamic impedance distribution and loss path.
This has improved the accuracy and fairness of electricity metering, reduced metering errors, and ensured the stability and reliability of electricity trading.
Smart Images
Figure CN121978610A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of electricity metering technology, and specifically to an electricity meter calibration method and calibration system. Background Technology
[0002] The accuracy of electricity metering in current power distribution networks is a crucial foundation for ensuring fair electricity trading. However, numerous complex operating conditions in reality make static calibration methods for electricity meters insufficient to fully adapt to the dynamic characteristics of the network, resulting in non-uniform distribution of energy losses. Especially under conditions of severe load fluctuations, traditional metering calibration methods struggle to effectively capture the intrinsic correlation between inter-node impedance characteristics and load changes, further exacerbating metering errors.
[0003] Furthermore, most current electricity metering calibration technologies focus only on the metering deviation of individual electricity meters, neglecting the spatial propagation patterns and mutual influences of voltage and current disturbances in the distribution network topology, thus failing to accurately locate energy loss-sensitive areas. More importantly, existing technologies lack effective methods for identifying the interaction between load current and voltage disturbances when analyzing energy loss paths, causing calibration results to deviate from actual operating conditions and hindering long-term, refined management.
[0004] Therefore, how to develop a power meter calibration method that can describe the sensitive characteristics of power loss within the network and dynamically adapt to changes in actual operating conditions has become an important technical problem that urgently needs to be solved in the field of power distribution metering. Summary of the Invention
[0005] The purpose of this invention is to provide an electricity meter calibration method and calibration system to solve the problems mentioned in the background art.
[0006] To achieve the above objectives, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for calibrating an electricity meter, comprising: Based on the reading differences between the main meter of the distribution area and the electricity meters of each user under multiple typical operating conditions, and combined with the local disturbance characteristics of the node voltage phase, a topological correlation structure is constructed to characterize the local sensitive area of loss during power transmission. An impedance dynamic identification algorithm that integrates the local propagation characteristics of node voltage phase is adopted, and the dynamic impedance distribution of power transmission between nodes is determined with the topological association structure as a constraint. Based on the dynamic impedance distribution, sensitive conduction nodes that cause spatial concentration of power loss are identified by utilizing the local response characteristics of node voltage disturbances. Based on the dynamic impedance distribution of the sensitive conduction node, and combined with the interaction characteristics of the node load current and voltage phase, the power loss path of the user's electricity meter is decoupled and reconstructed, and the calibration compensation coefficient of the user's electricity meter is determined.
[0007] Secondly, the present invention provides an electricity meter calibration system, implemented based on the method described above, comprising: The module is used to construct a topological association structure that characterizes the local sensitive area of power loss during power transmission based on the reading differences between the main meter of the distribution area and the power meters of each user under multiple typical operating conditions, combined with the local disturbance characteristics of the node voltage phase. The determination module is used to determine the dynamic impedance distribution of power transmission between nodes by employing an impedance dynamic identification algorithm that integrates the local propagation characteristics of node voltage phase, with the topological association structure as a constraint. The identification module is used to identify sensitive conduction nodes that cause spatial concentration of power loss based on the dynamic impedance distribution and the local response characteristics of node voltage disturbances. The calibration module is used to decouple and reconstruct the power loss path of the user's electricity meter based on the dynamic impedance distribution of the sensitive conduction node and the interaction characteristics of the node load current and voltage phase, and to determine the calibration compensation coefficient of the user's electricity meter.
[0008] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention effectively achieves a comprehensive understanding of the loss distribution during power transmission by constructing a topological correlation structure that characterizes the power loss sensitive area, thus solving the problem that traditional methods struggle to accurately identify local sensitive areas.
[0009] This invention dynamically identifies the impedance distribution between nodes by integrating the interaction characteristics of node voltage phase disturbance and load current, making the impedance parameters more consistent with the actual operating state and effectively reducing the power metering error caused by static impedance parameters.
[0010] This invention determines sensitive conduction nodes based on dynamic impedance distribution and decouples and reconstructs energy loss paths, thereby reasonably determining the calibration compensation coefficient of user energy meters, improving the fairness and accuracy of power distribution network metering, and ensuring the stability and reliability of power trading. Attached Figure Description
[0011] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.
[0012] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a framework diagram of the system of the present invention. Detailed Implementation
[0013] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided to make the description of this application more complete and comprehensive, and to fully convey the concept of the exemplary embodiments to those skilled in the art. The drawings are merely illustrative illustrations of this application and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.
[0014] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more exemplary embodiments. Numerous specific details are provided in the following description to give a full understanding of the exemplary embodiments disclosed in this application. However, those skilled in the art will recognize that the technical solutions disclosed in this application can be practiced with one or more specific details omitted, or other methods, components, steps, etc., can be employed. In other instances, well-known structures, methods, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of the disclosure of this application.
[0015] Example 1
[0016] like Figure 1 As shown in the figure, this embodiment discloses a method for calibrating an electricity meter, including: S101: Based on the reading differences between the main meter of the distribution area and the electricity meters of each user under multiple typical operating conditions, and combined with the local disturbance characteristics of the node voltage phase, a topological correlation structure is constructed to characterize the local sensitive area of loss during the power transmission process. It should be noted that the aforementioned topology is established based on the fact that power transmission losses are uneven in the power distribution network. By analyzing the metering differences between the main power meter and the user power meters, local areas with concentrated or sensitive power losses are identified in the network. This structure can intuitively reflect the power loss correlation between nodes.
[0017] Specifically, the construction of the topological correlation structure characterizing the loss-sensitive region during power transmission includes: Extract the reading differences between the total meter reading of the distribution area and the user's electricity meter under multiple typical operating conditions, and determine the initial sensitive area based on the difference in the spatial diffusion rate of node voltage phase disturbance; It should be understood that typical operating conditions mainly refer to representative periods of significant power load fluctuations, such as peak electricity consumption periods (e.g., 18:00-21:00) and off-peak periods (e.g., 00:00-05:00). Under these typical operating conditions, the differences in electricity readings between the main electricity meter and each user's electricity meter within the same metering cycle (e.g., 30 minutes or 60 minutes) are recorded. By statistically analyzing these differences, areas with relatively concentrated power losses can be preliminarily identified.
[0018] It should be noted that the spatial propagation velocity difference of node voltage phase disturbances refers to the difference in the propagation speed of voltage disturbances from different nodes to adjacent nodes. For example, after a drastic change in the load of a node causes a voltage phase disturbance, the voltage disturbance responses of neighboring nodes show different delay times and intensity changes. This difference can serve as a reference for spatially locating energy loss-sensitive areas.
[0019] For example, in a specific transformer substation, selecting the peak load period of 18:00-18:30 in the evening, if the total electricity meter measures 200 kWh during this period, while the total electricity meter readings of all subordinate users are 185 kWh, then the total metering difference is 15 kWh. Simultaneously, considering the differences in the propagation speed of voltage phase disturbances at each node—for example, a voltage disturbance at node A propagates rapidly to node B within 1 second, while it takes 3 seconds to propagate to node C—the area between nodes A and B is initially identified as the initial sensitive area.
[0020] Based on the path topological connectivity of node voltage phase disturbance propagation, and combined with the spatial distribution concentration trend of voltage disturbance in the initial sensitive region, local sensitive regions of power loss are identified. Specifically, topological connectivity is an indicator that measures the strength of the correlation between voltage disturbance propagation paths between nodes, and is achieved by quantitatively evaluating the connection strength of voltage disturbance propagation paths between nodes. Preferably, topological connectivity can be represented by the propagation probability or strength of voltage disturbances between nodes.
[0021] It should be noted that the spatial distribution concentration trend of voltage disturbances refers to the degree of concentration of voltage disturbance intensity and frequency exhibited by different nodes within the initial sensitive region. When the frequency of voltage disturbances at multiple nodes within this region is high and the propagation path correlation is strong, this region is further identified as a local sensitive region for power loss.
[0022] The identification of locally sensitive areas of power loss includes: A spatial diffusion network of node voltage phase perturbations in the initial sensitive region is established, and the local key nodes of perturbation propagation are obtained based on the spatial propagation stability characteristics of the network. It should be noted that the spatial diffusion connectivity network is constructed using graph theory, where each node represents an actual user node, and the connections between nodes represent the path of voltage phase disturbance propagation from one node to another.
[0023] Specifically, spatial propagation stability characteristics are reflected in the stability of the propagation frequency and amplitude during the propagation of node voltage phase disturbances. If a node has a high disturbance propagation frequency and stable amplitude, then the node is defined as a locally critical node. For example, if node X exhibits more than 5 stable disturbance propagation records within 30 minutes, and the amplitude change of each propagation is less than 5%, then node X is identified as a locally critical node.
[0024] Based on the spatial concentration of voltage disturbance propagation in local key nodes, identify local sensitive areas of power loss; Specifically, sensitive areas of power loss are determined by analyzing the intersection of disturbance propagation paths of multiple local critical nodes. When the propagation paths of multiple local critical nodes intersect at the same or adjacent nodes, the intersection area is identified as a locally sensitive area of power loss.
[0025] For example, if the disturbance propagation paths of local critical nodes X, Y, and Z all converge at node Q, and the disturbance propagation records at node Q account for more than 60% of the total propagation times, then the node region centered on node Q is identified as a local sensitive area for power loss.
[0026] Based on the spatial node connection relationships of the identified local sensitive areas of power loss, a topological association structure is constructed. It should be understood that the topological association structure is used to express the actual electrical connection relationship between nodes in the local sensitive area of power loss and the relationship of power transmission direction. Its construction basis is the primary wiring structure and actual operating topology of the power distribution network.
[0027] Specifically, the physical nodes in the local power loss sensitive area are first used as the topology node set. The physical nodes include, but are not limited to: low-voltage side nodes of distribution transformers, branch box nodes, line tap nodes, and each user's access node.
[0028] It should be noted that the connection relationships between nodes are not simply determined based on physical distance, but rather on the actual electrical connections. Specifically, this is achieved by reading the primary wiring diagram of the distribution area or the topology data from the distribution management system, extracting the line connection relationships between nodes, and constructing a topology structure in the form of an adjacency matrix or adjacency table.
[0029] Specifically, if the set of nodes is represented as: The connection relationship between nodes can be represented as an adjacency matrix. ;in, , representing a node With nodes There is a direct electrical connection. , representing a node With nodes There is no direct electrical connection.
[0030] Preferably, based on the constructed adjacency matrix, the nodes in the locally sensitive areas of power loss identified in S101.2 are marked to form a weighted topological association structure. The weights are set according to whether the node belongs to a sensitive area or is a local key node.
[0031] For example, if the set of nodes is ,in and If it belongs to a locally sensitive area of power loss, then in the adjacency matrix, the pairs with... , Connected edges are assigned a weight coefficient of 1.5, while other ordinary connected edges are assigned a weight of 1.0, thus forming a weighted topological association structure.
[0032] Understandably, this topological association structure not only reflects the physical connection relationship between nodes, but also embeds spatial information about power loss sensitivity, enabling subsequent impedance identification and loss path analysis to be carried out under the constraints of this structure.
[0033] It should be further noted that when constructing the topology association structure, the node numbers should be consistent with the actual power distribution system numbers to ensure that the node mapping relationships are not confused during subsequent analysis. Those skilled in the art can reproduce the complete topology association structure based on the above-described node set construction method, adjacency matrix definition method, and weight setting method.
[0034] S102: An impedance dynamic identification algorithm that integrates the local propagation characteristics of node voltage phase is adopted, and the dynamic impedance distribution of power transmission between nodes is determined with the topological association structure as a constraint. It should be noted that the dynamic impedance distribution refers to the actual impedance characteristics between nodes under different operating conditions, rather than traditional static impedance parameters. Through dynamic identification algorithms, the actual impedance changes in the distribution network under different operating conditions can be reflected more precisely.
[0035] Specifically, determining the dynamic impedance distribution of power transmission between nodes includes: Based on the cross-connectivity characteristics of the propagation path of voltage phase disturbance in nodes within the topological association structure, an initial propagation association matrix between nodes is constructed. Understandably, the initial propagation correlation matrix between nodes is based on the topological correlation structure and records the cross-propagation relationships between the propagation paths of voltage phase disturbances between nodes. The values of each element in this matrix reflect the strength of the path correlation for voltage disturbance propagation between nodes.
[0036] Specifically, if the node set is defined as: The initial propagation correlation matrix between nodes is then expressed as: ;in, Represents a node With nodes The cross-propagation strength of voltage disturbance propagation paths between them; For example, in the node set With nodes There are two intersecting propagation paths, path one with a propagation strength of 0.8 and path two with a propagation strength of 0.6. Then the matrix elements... The calculation method is as follows: ; Preferably, in the actual calculation process, the cross propagation intensity is determined by comprehensively calculating the voltage disturbance amplitude attenuation and propagation delay time obtained from actual on-site measurements.
[0037] Based on the spatial difference variation pattern of voltage disturbance propagation in the initial propagation correlation matrix, the trend of initial impedance variation between nodes is identified. It should be noted that the spatial variation pattern of voltage disturbance propagation reflects the variation of the voltage disturbance propagation intensity between nodes under different typical operating conditions. By analyzing this variation pattern, the changing trend of impedance between nodes can be revealed.
[0038] The initial impedance change trend between the identification nodes includes: Extract the spatial alternation pattern of node voltage phase perturbation propagation from the initial propagation correlation matrix; It should be understood that the alternating pattern of spatial intensity reflects the alternating changes in the propagation intensity of voltage disturbances between nodes over time or under different operating conditions. Specifically, this manifests as regular fluctuations in the propagation intensity of voltage disturbances between the same pair of nodes under different typical operating conditions. For example, the propagation intensity is lower under low-load conditions at night, while it is higher under peak conditions during the day.
[0039] For example, in actual measurements, node pairs With nodes The transmission intensity was 0.4 during the nighttime trough and 0.9 during the daytime peak, showing a clear spatial alternation pattern of strong and weak transmission.
[0040] Spatial differences in initial impedance between nodes are identified by using a spatial alternation pattern of strong and weak impedances, and the trend of initial impedance variation is obtained. Specifically, the impedance variation trend is negatively correlated with the voltage disturbance propagation intensity; that is, paths with higher voltage disturbance propagation intensity have relatively lower inter-node impedance, while paths with lower propagation intensity have relatively higher inter-node impedance. Therefore, the initial impedance variation trend can be derived from the variation amplitude of the spatial alternation pattern of strong and weak disturbances.
[0041] For example, with the above-mentioned nodes and For example, based on the alternating pattern of propagation intensity (0.4 at night, 0.9 during the day), the initial impedance change trend of this path can be inferred to be: the impedance is higher at night and lower during the day. The trend curve reflects a typical daily cycle change pattern.
[0042] By integrating the spatial coupling characteristics of the node load current disturbance response and the initial impedance change trend, the dynamic impedance distribution of power transmission between nodes is determined. It should be understood that the initial impedance change trend obtained solely from the propagation characteristics of voltage phase disturbances is still a derivation of a single physical quantity, while the actual transmission state of electrical energy in the distribution network is also affected by changes in load current. Therefore, it is necessary to spatially couple the node load current disturbance response with the aforementioned initial impedance change trend to obtain a dynamic impedance distribution that better reflects the operating state.
[0043] Specifically, firstly, load current time series data of the participating nodes under the same typical operating conditions are obtained, and then the disturbance component of the load current is extracted. The load current disturbance component can be obtained by mean filtering the original current series to obtain the residual series, i.e.: ;in, Let be the current value of node i at time t; Let i be the average current value of node i during the current operating cycle; Let be the current disturbance component at node i.
[0044] It is understandable that the current disturbance component reflects the current fluctuation amplitude caused by load changes and is coupled with the voltage phase disturbance.
[0045] Subsequently, the node voltage phase perturbation sequence With current disturbance component Spatial coupling analysis is performed. Spatial coupling characteristics can be obtained by calculating the normalized correlation coefficient between the two: ;in, is the spatial coupling coefficient of current-voltage perturbation at node i; T is the total number of sampling points in the current analysis period.
[0046] It should be noted that the coupling coefficient The value range is from -1 to 1. When When the value is close to 1, it indicates that the current disturbance and the voltage phase disturbance change in the same direction to a relatively high degree; when... When it is close to -1, it indicates a high degree of reverse change; when When the value is close to 0, it indicates a weak correlation.
[0047] Furthermore, in obtaining the initial impedance variation trend and coupling coefficient Then, the initial impedance is dynamically corrected to obtain the dynamic impedance value of the node. The dynamic impedance can be expressed as: ;in, Let i be the dynamic impedance of node i; The coupling effect coefficient is determined by fitting historical operating data.
[0048] Understandably, the above correction method makes the impedance value change dynamically with the load disturbance state, thus more accurately reflecting the actual impedance distribution of the power transmission path under different operating conditions.
[0049] Preferably, all participating nodes are included in the analysis. The summaries form a dynamic impedance distribution vector: The distribution vector is spatially mapped under the constraints of the topological association structure to obtain the dynamic impedance distribution matrix of power transmission between nodes.
[0050] S103: Based on the dynamic impedance distribution, the sensitive conduction nodes that cause the spatial concentration of power loss are identified by utilizing the local response characteristics of node voltage disturbances. It should be understood that sensitive transmission nodes refer to nodes in the power transmission path that have a significant impact on the concentration of power loss. By identifying sensitive transmission nodes, the source of loss can be accurately located, providing a valid basis for the accurate calibration of electricity meters.
[0051] Specifically, the identification of sensitive conduction nodes that cause spatial concentration of power loss includes: The spatial anomaly region of impedance is determined based on the spatial abrupt change characteristics of dynamic impedance distribution. It should be noted that the spatially anomalous impedance region refers to the area where the dynamic impedance between nodes changes drastically in space, that is, the part where the local impedance gradient is significantly greater than that of the adjacent region. Abrupt changes in dynamic impedance indicate a significant increase in power loss or a significant decrease in transmission efficiency within this region, and are an important basis for identifying sensitive conduction nodes.
[0052] The determination of the impedance spatial anomaly region includes: Calculate the spatial variation gradient of dynamic impedance distribution between adjacent nodes to obtain the spatial abrupt change characteristics of impedance. Specifically, for any pair of adjacent nodes in the topological association structure The impedance gradient between adjacent nodes is calculated based on the aforementioned dynamic impedance distribution. The impedance gradient is calculated as follows: ;in, and They are nodes and The dynamic impedance value; For nodes With nodes Electrical distances between them, such as line lengths or topology level distances.
[0053] It should be noted that a larger impedance gradient indicates a more prominent impedance difference between nodes and a more concentrated energy loss.
[0054] Based on the degree of spatial clustering of impedance spatial abrupt change characteristics in the node set, the impedance spatial anomaly region is determined; Specifically, spatial clustering algorithms (such as DBSCAN density clustering algorithm) are used to calculate the impedance gradient between all node pairs. Clustering is performed, and the set of high-gradient nodes formed by the clustering is the impedance space anomaly region.
[0055] For example, if the DBSCAN algorithm is used in actual analysis, with impedance gradient threshold... The node density threshold uses three nodes as clustering parameters to obtain the node set. They cluster together to form an impedance spatial anomaly region.
[0056] Preferably, the clustering threshold parameter can be set based on historical data statistical analysis, making the identification results more stable and reliable.
[0057] Extract the spatial synchronization response characteristics of node voltage disturbance response within the impedance spatial anomaly region; It is understandable that the spatial synchronous response characteristic refers to the synchronous change characteristics of the node voltage disturbance response in time within the impedance spatial anomaly region, that is, the voltage disturbance change trends of different nodes are highly consistent.
[0058] The specific implementation method is as follows: For all nodes in the impedance space anomaly region, calculate the time series correlation coefficient of the voltage disturbance response between nodes, and extract the spatial synchronization response characteristics in this way.
[0059] For example, nodes within the region and For example, if the Pearson correlation coefficient between the voltage disturbance response sequences of the two nodes reaches 0.9 or higher, it is considered that there is a high degree of spatial synchronization response characteristics between the nodes.
[0060] It should be noted that the more obvious the spatial synchronization response characteristics, the more likely the nodes in the region are to be sensitive transmission nodes of concentrated power loss phenomena.
[0061] Based on the concentrated distribution pattern of node voltage disturbances in spatial synchronization response characteristics, sensitive conduction nodes are identified; It should be understood that the concentrated distribution pattern of node voltage disturbances refers to the spatial concentration trend of the amplitude or phase change of node voltage disturbances. That is, in the impedance anomaly region, nodes with larger voltage disturbance characteristics and higher spatial synchronicity are more likely to become sensitive transmission nodes for power loss.
[0062] Specifically, the detailed process of identifying sensitive conduction nodes is as follows: First, for each node within the impedance anomaly region, calculate the deviation of its voltage disturbance amplitude from the region's average value. The specific calculation method is as follows: ;in, For nodes The voltage disturbance amplitude; This indicates the relative deviation of node voltage disturbances; the larger the value, the more significant the difference between the node voltage disturbance amplitude and the regional average.
[0063] Preferably, the sensitivity of a node is further determined by combining the aforementioned spatial synchronization response characteristics with a comprehensive scoring method, i.e., defining the node's sensitivity transmission coefficient. ;in, This is the average correlation coefficient between the node and the voltage disturbance response of other nodes in the region; These are weighting coefficients; for example, their values could be: The weight values are determined through statistical fitting of historical data.
[0064] By using the above method, the sensitive conduction coefficients of all nodes in the impedance anomaly region are obtained, and finally, nodes with coefficients higher than a set threshold are identified as sensitive conduction nodes.
[0065] Preferably, the threshold for determining sensitive transmission nodes can be selected based on the statistical characteristics of historical data. For example, if the threshold is set to 0.4, then the aforementioned nodes... Sensitive conductivity coefficient It is above the threshold, therefore it can be determined It is a sensitive conduction node.
[0066] Understandably, the identification results of sensitive transmission nodes are used to accurately locate the main concentrated areas of power loss, providing an accurate basis for determining the compensation coefficient for subsequent user electricity meter calibration.
[0067] S104: Based on the dynamic impedance distribution of the sensitive conduction node, and combined with the interaction characteristics of the node load current and voltage phase, the power loss path of the user's energy meter is decoupled and reconstructed, and the calibration compensation coefficient of the user's energy meter is determined. It should be understood that the dynamic impedance distribution of sensitive conduction nodes reflects the actual local impedance differences in the user's power consumption path, while the interaction characteristics of the node load current and voltage phase reflect the specific impact of user load changes on power transmission. Therefore, combining the two can accurately achieve the decoupling and reconstruction of the user's power loss path, thus providing an effective compensation basis for user energy meter calibration.
[0068] Specifically, determining the calibration compensation coefficient of the user's electricity meter includes: Based on the dynamic impedance distribution of sensitive conduction nodes and the spatial interaction characteristics of node load current, the preliminary coupling region of the power loss path is determined. It should be noted that the initial coupling region refers to the area where power loss is relatively concentrated due to the combined effect of user load current disturbance and dynamic impedance during power transmission.
[0069] The preliminary coupling region for determining the power loss path includes: Extract the spatial phase difference distribution between the dynamic impedance of the sensitive conduction node and the load current of the user node; Specifically, for sensitive transmission nodes User nodes connected to it The voltage phase sequence and load current sequence of the two nodes are obtained respectively, and the corresponding spatial phase difference is calculated. The specific calculation method is as follows: ;in, Sensitive conduction node The voltage phase at time t; For user nodes The phase of the load current at time t; It should be noted that the greater the spatial phase difference, the higher the degree of phase mismatch between voltage and current between nodes, and the more likely power loss is to be concentrated in that path region.
[0070] Based on the spatial concentration pattern of spatial phase difference distribution, the preliminary coupling region of power loss path is determined; Specifically, regarding the spatial phase difference obtained above... The sequence is statistically analyzed, and the average phase difference is used as the judgment standard. When the average spatial phase difference is greater than the set threshold, the region between the corresponding nodes is determined to be a preliminary coupling region.
[0071] For example, if the set spatial phase difference threshold is Then the aforementioned node pair Average phase difference If the value is greater than the threshold, it can be determined that it belongs to the initial coupling region.
[0072] Preferably, the spatial phase difference threshold is determined by the statistical characteristics of historical data, such as taking the average of the spatial phase differences of all nodes plus a standard deviation.
[0073] Based on the spatial alternation response pattern of node voltage phase and load current in the initial coupling region, the power loss path is spatially decoupled and reconstructed. It should be understood that the spatial decoupling and reconfiguration process involves analyzing the timing characteristics of the alternating responses of voltage phase and load current to clarify the specific sources and contribution ratios of each user's actual losses in the path.
[0074] Specifically, the detailed process of spatial decoupling and reconstruction is as follows: First, spatial alternation correlation analysis is performed on the time series data of voltage phase and load current of nodes in the initial coupling region to define the node contribution coefficient. The contribution coefficient is specifically calculated as the cross-correlation coefficient of the voltage-current disturbance response between nodes: ;in, For nodes With user nodes Contribution coefficients between them; They are nodes Voltage phase and nodes Load current data at time t; These are the time averages of the corresponding variables; T is the total number of sampling points.
[0075] It should be noted that the closer the contribution coefficient is to 1, the stronger the consistency between the user's load current disturbance and the node voltage disturbance response, and the greater the user's contribution to the node's power loss.
[0076] For example, if node With user nodes If the contribution coefficient is 0.75, then this user makes a significant contribution to the energy loss path.
[0077] Secondly, by using the aforementioned contribution coefficients, the actual loss contribution ratio of each user node in the power loss path can be clarified, thereby achieving spatial decoupling of the path.
[0078] Based on the spatial characteristics of the energy loss path after decoupling and reconstruction, the calibration compensation coefficient of the user's energy meter is determined; It should be understood that the calibration compensation coefficient is used to correct the difference between the actual meter reading of the user's electricity meter and the user's actual electricity consumption, ensuring the accuracy and fairness of the user's electricity metering results.
[0079] Specifically, the detailed process for determining the calibration compensation coefficient of a user's electricity meter is as follows: First, based on the user node contribution coefficients obtained during the aforementioned spatial decoupling and reconstruction process... Further determine the loss contribution weighting coefficient for each user node. The specific calculation method is as follows: Where N represents the total number of user nodes within the initial coupling region.
[0080] Preferably, the energy loss share of each user node is determined based on the weighting coefficient of each user node's loss contribution, combined with the difference between the actual measured readings of the total meter and the user meters. The specific calculation method is as follows: ;in, Represents user node The actual amount of electrical energy lost; The difference between the actual measured total meter reading and the readings of all users' electricity meters in the area represents the total energy loss in the area.
[0081] Secondly, based on the actual amount of loss borne by the user And the actual electricity consumption measured by the user's electricity meter. Calculate the calibration compensation coefficient of the user's electricity meter. The specific formula for calculating the compensation coefficient is as follows: ; It should be noted that the calibration compensation coefficient The larger the value, the greater the error between the actual usage measured by the user's electricity meter and the true value, requiring a larger compensation.
[0082] It should be understood that the calibration compensation coefficient It should be updated regularly to ensure it remains applicable to current changes in electricity load, thereby continuously guaranteeing the accuracy of metering.
[0083] Example 2
[0084] like Figure 2 As shown in the example, the parts not detailed in this embodiment are as shown in Example 1. This embodiment discloses an electricity meter calibration system, including: Module 201 is used to construct a topological association structure that characterizes the local sensitive area of power loss during power transmission based on the reading difference between the total meter of the distribution area and the power meters of each user under multiple typical operating conditions, combined with the local disturbance characteristics of the node voltage phase. The determination module 202 is used to determine the dynamic impedance distribution of power transmission between nodes by employing an impedance dynamic identification algorithm that integrates the local propagation characteristics of node voltage phase, with the topological association structure as a constraint. The identification module 203 is used to identify sensitive conduction nodes that cause spatial concentration of power loss based on the dynamic impedance distribution and by utilizing the local response characteristics of node voltage disturbances. The calibration module 204 is used to decouple and reconstruct the power loss path of the user's electricity meter based on the dynamic impedance distribution of the sensitive conduction node and the interaction characteristics of the node load current and voltage phase, and to determine the calibration compensation coefficient of the user's electricity meter.
[0085] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters, weights, and thresholds in the formulas are set by those skilled in the art according to the actual situation.
[0086] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.
Claims
1. A method for calibrating an electricity meter, characterized in that, include: Based on the reading differences between the main meter of the distribution area and the electricity meters of each user under multiple typical operating conditions, and combined with the local disturbance characteristics of the node voltage phase, a topological correlation structure is constructed to characterize the local sensitive area of loss during power transmission. An impedance dynamic identification algorithm that integrates the local propagation characteristics of node voltage phase is adopted, and the dynamic impedance distribution of power transmission between nodes is determined with the topological association structure as a constraint. Based on the dynamic impedance distribution, sensitive conduction nodes that cause spatial concentration of power loss are identified by utilizing the local response characteristics of node voltage disturbances. Based on the dynamic impedance distribution of the sensitive conduction node, and combined with the interaction characteristics of the node load current and voltage phase, the power loss path of the user's electricity meter is decoupled and reconstructed, and the calibration compensation coefficient of the user's electricity meter is determined.
2. The method according to claim 1, characterized in that, The construction of the topological correlation structure characterizing the locally sensitive regions of power loss during power transmission includes: Extract the reading differences between the total meter reading of the distribution area and the user's electricity meter under multiple typical operating conditions, and determine the initial sensitive area based on the difference in the spatial diffusion rate of node voltage phase disturbance; Based on the path topological connectivity of node voltage phase disturbance propagation, and combined with the spatial distribution concentration trend of voltage disturbance in the initial sensitive region, local sensitive regions of power loss are identified. A topological association structure is constructed based on the spatial node connection relationships of the identified local sensitive areas of power loss.
3. The method according to claim 2, characterized in that, The identification of localized sensitive areas of power loss includes: A spatial diffusion network of node voltage phase perturbations in the initial sensitive region is established, and the local key nodes of perturbation propagation are obtained based on the spatial propagation stability characteristics of the network. Based on the spatial concentration of voltage disturbance propagation in local key nodes, local sensitive areas of power loss can be identified.
4. The method according to claim 1, characterized in that, Determining the dynamic impedance distribution of power transmission between nodes includes: Based on the cross-connectivity characteristics of the propagation path of voltage phase disturbance in nodes within the topological association structure, an initial propagation association matrix between nodes is constructed. Based on the spatial difference variation pattern of voltage disturbance propagation in the initial propagation correlation matrix, the trend of initial impedance variation between nodes is identified. By integrating the spatial coupling characteristics of the load current disturbance response and the initial impedance change trend of the nodes, the dynamic impedance distribution of power transmission between nodes is determined.
5. The method according to claim 4, characterized in that, The initial impedance change trend between the identification nodes includes: Extract the spatial alternation pattern of node voltage phase perturbation propagation from the initial propagation correlation matrix; The spatial difference in initial impedance between nodes is identified by using the spatial alternation pattern of strong and weak impedances, and the trend of initial impedance variation is obtained.
6. The method according to claim 1, characterized in that, The identification of sensitive transmission nodes that cause spatial concentration of power loss includes: The spatial anomaly region of impedance is determined based on the spatial abrupt change characteristics of dynamic impedance distribution. Extract the spatial synchronization response characteristics of node voltage disturbance response within the impedance spatial anomaly region; Sensitive conduction nodes are identified based on the concentrated distribution pattern of node voltage disturbances in spatial synchronization response characteristics.
7. The method according to claim 6, wherein determining the impedance spatial anomaly region comprises: Calculate the spatial variation gradient of dynamic impedance distribution between adjacent nodes to obtain the spatial abrupt change characteristics of impedance. Based on the degree of spatial clustering of impedance spatial abrupt change characteristics in the node set, the impedance spatial anomalous region is determined.
8. The method according to claim 1, wherein determining the calibration compensation coefficient of the user's electricity meter includes: Based on the dynamic impedance distribution of sensitive conduction nodes and the spatial interaction characteristics of node load current, the preliminary coupling region of the power loss path is determined. Based on the spatial alternation response pattern of node voltage phase and load current in the initial coupling region, the power loss path is spatially decoupled and reconstructed. Based on the spatial characteristics of the energy loss path after decoupling and reconstruction, the calibration compensation coefficient of the user's energy meter is determined.
9. The method according to claim 8, wherein determining the preliminary coupling region of the power loss path includes: Extract the spatial phase difference distribution between the dynamic impedance of the sensitive conduction node and the load current of the user node; Based on the spatial concentration pattern of spatial phase difference distribution, the preliminary coupling region of power loss path is determined.
10. An electricity meter calibration system, implemented based on the method of any one of claims 1-9, characterized in that, include: The module is used to construct a topological association structure that characterizes the local sensitive area of power loss during power transmission based on the reading differences between the main meter of the distribution area and the power meters of each user under multiple typical operating conditions, combined with the local disturbance characteristics of the node voltage phase. The determination module is used to determine the dynamic impedance distribution of power transmission between nodes by employing an impedance dynamic identification algorithm that integrates the local propagation characteristics of node voltage phase, with the topological association structure as a constraint. The identification module is used to identify sensitive conduction nodes that cause spatial concentration of power loss based on the dynamic impedance distribution and the local response characteristics of node voltage disturbances. The calibration module is used to decouple and reconstruct the power loss path of the user's electricity meter based on the dynamic impedance distribution of the sensitive conduction node and the interaction characteristics of the node load current and voltage phase, and to determine the calibration compensation coefficient of the user's electricity meter.