Electric energy meter for intelligent panoramic perception of operation state of distribution transformer area

Abstract

The present application relates to the technical field of power grid monitoring, in particular to a kind of electric energy meter for intelligent panorama perception of distribution transformer area operating state;Suspected defect fluctuation degree and suspected defect data point are obtained according to the local data fluctuation characteristics in monitoring data sequence;According to the number characteristics of suspected defect data point, interval characteristics and suspected defect fluctuation degree, obtain historical defect fluctuation characteristic value;According to the time difference characteristics of suspected defect data point of any dimension and other dimensions according to historical defect fluctuation characteristic value, obtain operating smoothness degree;According to the operating smoothness degree corresponding to all dimensions, obtain comprehensive operating smoothness degree.The present application obtains final smoothness degree according to the difference characteristics of the comprehensive operating smoothness degree of other electric energy meter and electric energy meter;According to final smoothness degree, obtain the operating risk sequence of distribution transformer area, improve the efficiency and accuracy of distribution transformer area operating state panorama perception.

Description

Technical Field

[0001] This invention relates to the field of power grid monitoring technology, specifically to an energy meter for intelligent panoramic perception of the operating status of distribution transformer areas. Background Technology

[0002] A distribution transformer area refers to the power supply range or region of a distribution transformer, encompassing power lines, user equipment, and related management objects. A distribution transformer area typically starts from the low-voltage side outgoing switch of the transformer and extends along the low-voltage distribution lines until it connects to the electricity meters of each user. Currently, the monitoring of user-end operating status within the transformer area involves uploading monitoring data from electricity meters to the transformer area management platform for monitoring the operating status of different user-end areas. However, the large number of user-end electricity meters and the large volume of real-time changing information make it impossible to directly analyze the operating status of each user-end area manually. Furthermore, threshold-based monitoring only has anomaly detection capabilities and lacks the ability to analyze and perceive abnormal and unstable operating statuses such as excessively high or low voltage, three-phase voltage imbalance, and frequent instantaneous power outages caused by circuit faults such as aging lines, poor contact, leakage, and abnormal grounding. This affects the efficiency and accuracy of comprehensive monitoring of the distribution transformer area's operating status. Summary of the Invention

[0003] To address the aforementioned technical problems, the present invention aims to provide an energy meter for intelligent panoramic sensing of the operating status of distribution transformer areas. The specific technical solution adopted is as follows:

[0004] The data acquisition module is used to acquire monitoring data sequences from different dimensions of the electricity meters in the distribution transformer area recently.

[0005] The first data analysis module is used to obtain the degree of suspected defect fluctuation of data points based on the local data fluctuation characteristics in the monitoring data sequence; to obtain suspected defect data points based on the degree of suspected defect fluctuation; and to obtain historical defect fluctuation feature values ​​of any dimension based on the quantity characteristics, interval characteristics, and degree of suspected defect fluctuation of the suspected defect data points.

[0006] The second data analysis module is used to obtain the operational stability of the arbitrary dimension based on the historical defect fluctuation characteristic value and the time difference characteristics of the suspected defect data points of the arbitrary dimension and other dimensions; and to obtain the comprehensive operational stability of the electricity meter based on the operational stability of all dimensions.

[0007] The operation status sensing module is used to adjust the overall operation stability based on the differences in the overall operation stability between the energy meters and other energy meters in the same branch of the distribution transformer area, and obtain the final stability; and to sort the energy meters according to the final stability to obtain the operation risk sequence of the distribution transformer area.

[0008] Furthermore, the step of obtaining the degree of suspected defect fluctuation of data points based on the local data fluctuation characteristics in the monitoring data sequence includes:

[0009] In the monitoring data sequence, the absolute value of the difference between a data point and the previous data point is calculated and normalized to obtain the degree of suspected defect fluctuation of the data point.

[0010] Furthermore, the step of obtaining suspected defect data points based on the suspected defect fluctuation level includes:

[0011] Data points whose suspected defect fluctuation exceeds a preset first threshold are designated as suspected defect data points.

[0012] Furthermore, the step of obtaining historical defect fluctuation characteristic values ​​of any dimension based on the quantity characteristics, interval characteristics, and fluctuation degree of the suspected defect data points includes:

[0013] In the formula, R represents the historical defect fluctuation characteristic value; N represents the number of data points in the monitoring data sequence; H represents the number of suspected defect data points in the monitoring data sequence; and T represents the duration of the monitoring data sequence. This represents the time interval between the first suspected defect data point and the last suspected defect data point. This indicates the degree of fluctuation of the suspected defect for the h-th suspected defect data point.

[0014] Furthermore, the step of obtaining the operational stability of the arbitrary dimension based on the historical defect fluctuation characteristic value and the time difference characteristics of the arbitrary dimension and the suspected defect data points of other dimensions includes:

[0015] In the formula, P represents the operational stability, R represents the historical defect fluctuation characteristic value, H represents the number of suspected defect data points in the monitoring data sequence of any dimension, and M represents the number of other dimensions besides the aforementioned arbitrary dimension. This represents the time interval between the h-th suspected defect data point in any dimension and the m-th suspected defect data point in another dimension with the closest time interval.

[0016] Furthermore, the step of obtaining the overall operational stability of the electricity meter based on the operational stability corresponding to all dimensions includes:

[0017] Calculate the average of the operational stability across all dimensions to obtain the overall operational stability of the electricity meter.

[0018] Furthermore, the step of adjusting the overall operational stability based on the differences in overall operational stability between the energy meter and other energy meters within the same branch of the distribution transformer area to obtain the final stability includes:

[0019] In the formula, W represents the final stability level, G represents the overall operational stability level of the energy meter, and D represents the number of other energy meters in the same branch as the energy meter in the distribution transformer area. This indicates the overall operational stability of the d-th other energy meter.

[0020] Furthermore, the step of sorting the electricity meters according to the final stability level to obtain the operational risk sequence of the distribution transformer area includes:

[0021] The electricity meters are sorted in ascending order of final stability to obtain the operational risk sequence of the distribution transformer substations within the distribution transformer substation area.

[0022] The present invention has the following beneficial effects:

[0023] In this invention, acquiring the degree of fluctuation of suspected defects can characterize the fluctuation level of different data points in the monitoring data sequence and the probability of possibly indicating defect anomalies; acquiring suspected defect data points can determine the time when there are obvious defect fluctuations in the monitoring data sequence. Acquiring historical defect fluctuation feature values ​​can characterize the strength of recent defect fluctuation characteristics in that dimension, thereby judging the circuit defect anomalies at the user end monitored by the energy meter. Acquiring the operational stability level of any dimension can more accurately characterize the operational stability characteristics by combining the distribution correlation characteristics of suspected defect data points in other dimensions with those in that arbitrary dimension, improving the accuracy of operational status perception. Acquiring the comprehensive operational stability level can characterize the operational stability characteristics of the user end circuit by combining the operational stability levels of all dimensions of the energy meter. Acquiring the final stability level can avoid the influence of the load distribution of the distribution transformer area on the comprehensive operational stability level of the energy meter, further improving the accuracy of the comprehensive operational stability level in characterizing the circuit defect anomalies at the user end. Finally, acquiring the operational risk sequence of the distribution transformer area can accurately perceive user end areas within the distribution transformer area that may have potential risks, facilitating priority maintenance of such areas and improving the efficiency and accuracy of panoramic perception of the operational status of the distribution transformer area. Attached Figure Description

[0024] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a block diagram of an energy meter for intelligent panoramic perception of the operating status of a distribution transformer area, provided as an embodiment of the present invention. Detailed Implementation

[0026] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an energy meter for intelligent panoramic sensing of the operating status of distribution transformer areas according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0027] 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 invention pertains.

[0028] The following description, in conjunction with the accompanying drawings, details a specific solution for an energy meter provided by the present invention for intelligent panoramic perception of the operating status of a distribution transformer area.

[0029] Please see Figure 1 The diagram illustrates a block diagram of an energy meter for intelligent panoramic perception of the operating status of a distribution transformer area, according to an embodiment of the present invention. The energy meter includes the following modules:

[0030] The data acquisition module S1 is used to acquire monitoring data sequences of different dimensions from the recent monitoring of electricity meters in the distribution transformer area.

[0031] First, recent monitoring data of different dimensions of all electricity meters in the distribution transformer area are obtained. In this embodiment of the invention, the monitoring dimensions include voltage, current and power. The monitoring frequency is once every 10 seconds, and historical data within one day is selected to construct a monitoring data sequence. Finally, the monitoring data sequence of each dimension of all electricity meters in the distribution transformer area is obtained. The implementer can determine the monitoring dimensions and collection process according to the implementation scenario.

[0032] The first data analysis module S2 is used to obtain the degree of suspected defect fluctuation of data points based on the local data fluctuation characteristics in the monitoring data sequence; to obtain suspected defect data points based on the degree of suspected defect fluctuation; and to obtain historical defect fluctuation characteristic values ​​of any dimension based on the quantity characteristics, interval characteristics, and degree of suspected defect fluctuation of suspected defect data points.

[0033] First, fluctuating data points in the monitoring data sequence that may indicate defects or anomalies in that dimension are acquired. The difference between a data point and the previous data point is taken as the fluctuation amplitude of that data point. Compared to the stable and normal data points and the background floating data points with small fluctuations in the electricity meter monitoring, the fluctuation amplitude of data points that may indicate defects or anomalies is larger, and the number of data points indicating defects or anomalies is smaller. Therefore, suspected defect data points that may indicate defects or anomalies can be obtained by normalizing the fluctuation amplitude. Thus, the degree of suspected defect fluctuation of a data point is obtained based on the local data fluctuation characteristics in the monitoring data sequence. Preferably, in this embodiment of the invention, the step of obtaining the degree of suspected defect fluctuation of a data point includes: calculating and normalizing the absolute value of the difference between a data point and the previous data point in the monitoring data sequence to obtain the degree of suspected defect fluctuation of that data point. The normalization method is maximum and minimum value normalization; the maximum value is the maximum absolute value of the difference in the monitoring data sequence, and the minimum value is the minimum absolute value of the difference in the monitoring data sequence. The larger the absolute value of the difference between the data point and the previous data point, the larger the fluctuation amplitude, the greater the degree of suspected defect fluctuation of the data point, and the more likely the data point is to indicate a defect or anomaly.

[0034] Furthermore, suspected defect data points can be obtained based on the degree of fluctuation of suspected defects. Preferably, in this embodiment of the invention, the step of obtaining suspected defect data points includes: taking data points whose degree of fluctuation of suspected defects exceeds a preset first threshold as suspected defect data points; the value range of the degree of fluctuation of suspected defects obtained by normalization is from 0 to 1. When the fluctuation amplitude of the data point is smaller, the degree of fluctuation of suspected defects is closer to 0; when the fluctuation amplitude of the data point is larger, the degree of fluctuation of suspected defects is closer to 1. Therefore, the degree of fluctuation of suspected defects of data points with stable changes or small fluctuations tends to 0, while the degree of fluctuation of suspected defects of data points that may represent defect anomalies tends to 1, and the two are distributed at both ends of the value range. Therefore, in this embodiment of the invention, the preset first threshold is 0.5, which can be determined by the implementer according to the implementation scenario. Suspected defect data points represent that the operating state of this dimension is relatively unstable at this moment. When the number of recent suspected defect data points in this dimension is more, the distribution period in the recent period is wider, and the fluctuation amplitude is larger, it indicates that there are more times of fluctuation in this dimension of the electricity meter, the occurrence of abnormal data of the electricity meter is maintained for a longer period of time, and the corresponding operating stability of the electricity meter monitoring user terminal is weaker. Furthermore, historical defect fluctuation characteristic values ​​of any dimension can be obtained based on the quantity characteristics, interval characteristics, and fluctuation degree of suspected defect data points; preferably, in this embodiment of the invention, the step of obtaining historical defect fluctuation characteristic values ​​includes:

[0035]

[0036] In the formula, R represents the historical defect fluctuation characteristic value; N represents the number of data points in the monitoring data sequence; and H represents the number of suspected defect data points in the monitoring data sequence. The larger the value, the more data points in that monitoring dimension of the electricity meter show suspected defect fluctuations, and the stronger the degree of defect fluctuation characteristics at the user end. T represents the duration of the monitoring data sequence. This represents the time interval between the first suspected defect data point and the last suspected defect data point. The larger the value, the longer the abnormal data in the monitoring data sequence lasts, and the wider the distribution of historical defect fluctuations. This indicates the degree of fluctuation in the suspected defect for the h-th suspected defect data point. The larger the value, the greater the overall defect fluctuation in that dimension recently. Therefore, the larger the suspected defect fluctuation characteristic value, the more obvious the historical defect fluctuation characteristics in that dimension, and the more unstable the recent operating status of the user terminal monitored by the electricity meter.

[0037] The second data analysis module S3 is used to obtain the operational stability of any dimension based on historical defect fluctuation characteristics and the time difference characteristics of suspected defect data points in any dimension and other dimensions; and to obtain the comprehensive operational stability of the electricity meter based on the operational stability of all dimensions.

[0038] Since a single change in a dimension monitored by the electricity meter could be noise, current fluctuations due to increased or decreased power consumption at the user end, or temporary voltage changes due to load variations, and noise is not included in stability assessment, current fluctuations caused by user power consumption behavior or voltage fluctuations caused by load changes cannot indicate that there is a circuit defect at the user end causing operational instability at that moment, it is necessary to combine multi-dimensional analysis to improve the accuracy of operational status perception and monitoring. The more dimensions with defect fluctuations at similar times, and the closer the times, the more likely there is a circuit defect at the suspected defect data point causing abnormalities in voltage, current, and power attributes. The credibility of the suspected defect data point having a real defect anomaly is higher, and the operational stability is lower. Therefore, the operational stability of any dimension can be obtained based on historical defect fluctuation characteristic values ​​and the time difference characteristics between any dimension and suspected defect data points in other dimensions. Preferably, in this embodiment of the invention, the step of obtaining the operational stability includes:

[0039]

[0040] In the formula, P represents the operational stability, R represents the historical defect fluctuation characteristic value. The smaller the historical defect fluctuation characteristic value, the weaker the defect fluctuation characteristic, and the higher the operational stability of that dimension. H represents the number of suspected defect data points in the monitoring data sequence of any dimension, and M represents the number of other dimensions besides the aforementioned arbitrary dimension. This represents the time interval between the h-th suspected defect data point in any dimension and the m-th suspected defect data point in other dimensions with the closest time interval. When The smaller the value, the closer the occurrence time of suspected defective data points across different dimensions, and the stronger the correlation; therefore, when The smaller the value, the greater the possibility of a real defect or anomaly, and the less stable the operation. Conversely, a higher degree of operational stability means that the energy meter monitors the operational status of any dimension at the user end more smoothly.

[0041] Furthermore, the operational stability of the user end can be comprehensively evaluated by combining the operational stability of all dimensions of the electricity meter. Therefore, the comprehensive operational stability of the electricity meter is obtained based on the operational stability corresponding to all dimensions. Preferably, in this embodiment of the invention, the step of obtaining the comprehensive operational stability includes: calculating the average value of the operational stability corresponding to all dimensions to obtain the comprehensive operational stability of the electricity meter. The higher the comprehensive operational stability, the lower the probability that the electricity meter is detecting circuit defects or abnormalities at the user end; conversely, the lower the comprehensive operational stability, the higher the probability that the electricity meter is detecting circuit defects or abnormalities at the user end.

[0042] The operation status sensing module S4 is used to adjust the overall operation stability based on the differences in the overall operation stability of other energy meters within the same branch of the distribution transformer area to obtain the final stability; and to sort the energy meters according to the final stability to obtain the operation risk sequence of the distribution transformer area.

[0043] Since different energy meters within the same branch of the same distribution transformer area are under the same load distribution, the more similar the overall operational stability of similar energy meters, the more likely it is that even if there are significant fluctuations in the monitored operating status leading to a lower overall operational stability, it is more likely due to abnormal load distribution within the transformer area. Therefore, the overall operational stability of that energy meter should be higher. Thus, the overall operational stability can be adjusted based on the differences in overall operational stability between other energy meters within the same branch of the distribution transformer area to obtain the final stability. Preferably, in this embodiment of the invention, the step of obtaining the final stability includes:

[0044]

[0045] In the formula, W represents the final stability level, G represents the overall operational stability level of the energy meter, and D represents the number of other energy meters in the same branch as the given energy meter within the distribution transformer area. This indicates the overall operational stability of the d-th other energy meter. When The smaller the value, the smaller the difference in overall operational stability between this energy meter and other energy meters within the same branch. This means a higher similarity in the operational stability of energy meters within the same distribution transformer area. Even if the operational stability of this energy meter is low, it is more likely due to the same load distribution rather than user-side issues. Therefore, it is necessary to improve the overall operational stability of this energy meter. Thus, a value of [value missing] is taken. The inverse form of the equation is used to correct the overall operational stability of the electricity meter, thereby obtaining a more accurate final stability.

[0046] Furthermore, the final stability level can characterize the circuit defects and anomalies monitored by the electricity meter at the user end. The lower the final stability level, the more likely the user end is to experience circuit faults such as aging lines, poor contact, leakage, or abnormal grounding. Finally, the electricity meters can be sorted according to their final stability levels to obtain the operational risk sequence of the distribution transformer area. Preferably, in this embodiment of the invention, the step of obtaining the operational risk sequence includes: sorting the electricity meters according to their final stability levels from smallest to largest to obtain the operational risk sequence of the distribution transformer area. The higher the ranking of the electricity meter in the operational risk sequence, the more unstable the operating status of the user end monitored by that electricity meter is, and the more likely there are circuit defects and anomalies. This electricity meter should be prioritized for display in the distribution transformer area management platform, thereby prioritizing the investigation of user end areas with abnormal risks. Thus, by calculating the final stability level of electricity meters within the same distribution transformer area to obtain the operational risk sequence, intelligent panoramic perception of the circuit operating status within the distribution transformer area is achieved, improving the efficiency and accuracy of panoramic perception of the distribution transformer area's operating status.

[0047] In summary, this invention provides an energy meter for intelligent panoramic perception of the operating status of distribution transformer substations. It obtains the degree of suspected defect fluctuations and suspected defect data points based on local data fluctuation characteristics in the monitoring data sequence; it obtains historical defect fluctuation characteristic values ​​based on the quantity, interval, and degree of suspected defect data points; it obtains the operational stability based on the historical defect fluctuation characteristic values ​​and the time difference characteristics between suspected defect data points in any dimension and other dimensions; and it obtains the comprehensive operational stability based on the operational stability corresponding to all dimensions. This invention obtains the final stability based on the difference characteristics between other energy meters and the comprehensive operational stability of the energy meter; and it obtains the operational risk sequence of the distribution transformer substation based on the final stability, thus improving the efficiency and accuracy of panoramic perception of the operating status of the distribution transformer substation.

[0048] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0049] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. An energy meter for intelligent panoramic perception of the operating status of distribution transformer areas, characterized in that, The electricity meter includes the following modules: The data acquisition module is used to acquire monitoring data sequences from different dimensions of the electricity meters in the distribution transformer area recently. The first data analysis module is used to obtain the degree of suspected defect fluctuation of data points based on the local data fluctuation characteristics in the monitoring data sequence; to obtain suspected defect data points based on the degree of suspected defect fluctuation; and to obtain historical defect fluctuation feature values ​​of any dimension based on the quantity characteristics, interval characteristics, and degree of suspected defect fluctuation of the suspected defect data points. The second data analysis module is used to obtain the operational stability of the arbitrary dimension based on the historical defect fluctuation characteristic value and the time difference characteristics of the suspected defect data points of the arbitrary dimension and other dimensions; and to obtain the comprehensive operational stability of the electricity meter based on the operational stability of all dimensions. The operation status sensing module is used to adjust the overall operation stability based on the differences in the overall operation stability between the energy meters and other energy meters in the same branch of the distribution transformer area, and obtain the final stability; and to sort the energy meters according to the final stability to obtain the operation risk sequence of the distribution transformer area.

2. The energy meter for intelligent panoramic perception of the operating status of distribution transformer areas according to claim 1, characterized in that, The step of obtaining the suspected defect fluctuation degree of data points based on the local data fluctuation characteristics in the monitoring data sequence includes: In the monitoring data sequence, the absolute value of the difference between a data point and the previous data point is calculated and normalized to obtain the degree of suspected defect fluctuation of the data point.

3. The energy meter for intelligent panoramic perception of the operating status of distribution transformer areas according to claim 1, characterized in that, The step of obtaining suspected defect data points based on the suspected defect fluctuation level includes: Data points whose suspected defect fluctuation exceeds a preset first threshold are designated as suspected defect data points.

4. The energy meter for intelligent panoramic perception of the operating status of distribution transformer areas according to claim 1, characterized in that, The step of obtaining historical defect fluctuation feature values ​​of any dimension based on the quantity characteristics, interval characteristics, and fluctuation degree of the suspected defect data points includes: In the formula, R represents the historical defect fluctuation characteristic value; N represents the number of data points in the monitoring data sequence; H represents the number of suspected defect data points in the monitoring data sequence; and T represents the duration of the monitoring data sequence. This represents the time interval between the first suspected defect data point and the last suspected defect data point. This indicates the degree of fluctuation of the suspected defect for the h-th suspected defect data point.

5. An energy meter for intelligent panoramic perception of the operating status of a distribution transformer area according to claim 1, characterized in that, The step of obtaining the operational stability of the arbitrary dimension based on the historical defect fluctuation characteristic value and the time difference characteristics of the arbitrary dimension and the suspected defect data points of other dimensions includes: In the formula, P represents the operational stability, R represents the historical defect fluctuation characteristic value, H represents the number of suspected defect data points in the monitoring data sequence of any dimension, and M represents the number of other dimensions besides the aforementioned arbitrary dimension. This represents the time interval between the h-th suspected defect data point in any dimension and the m-th suspected defect data point in another dimension with the closest time interval.

6. The energy meter for intelligent panoramic perception of the operating status of distribution transformer areas according to claim 1, characterized in that, The steps for obtaining the overall operational stability of the electricity meter based on the operational stability corresponding to all dimensions include: Calculate the average of the operational stability across all dimensions to obtain the overall operational stability of the electricity meter.

7. An energy meter for intelligent panoramic perception of the operating status of a distribution transformer area according to claim 1, characterized in that, The step of adjusting the overall operational stability based on the differences in overall operational stability between the energy meter and other energy meters within the same branch of the distribution transformer area to obtain the final stability includes: In the formula, W represents the final stability level, G represents the overall operational stability level of the energy meter, and D represents the number of other energy meters in the same branch as the energy meter in the distribution transformer area. This indicates the overall operational stability of the d-th other energy meter.

8. The energy meter for intelligent panoramic perception of the operating status of distribution transformer areas according to claim 1, characterized in that, The step of sorting the electricity meters according to the final stability level to obtain the operational risk sequence of the distribution transformer area includes: The electricity meters are sorted in ascending order of final stability to obtain the operational risk sequence of the distribution transformer substations within the distribution transformer substation area.

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