Electric energy metering abnormity monitoring method based on space-time correlation characteristics

Abstract

The invention discloses an electric energy metering abnormity monitoring method based on space-time correlation characteristics, and relates to the technical field of electric quantity monitoring, and the method comprises the following five steps: 1, collecting space-time characteristic data in real time, and carrying out the preprocessing; 2, performing comprehensive analysis to obtain space-time anomaly factors; 3, judging whether abnormity occurs or not according to the judgment result, if yes, ending early warning, and if not, entering 4; 4, further analyzing to obtain an abnormal trend factor; and 5, judging whether the trend is abnormal according to the judgment result, if so, early warning and returning, otherwise, directly returning. According to the method, the comprehensive analysis model is constructed by fusing the spatio-temporal characteristics, cross-dimensional three-dimensional description of the operation state of the equipment is realized through the spatio-temporal abnormal factors, the abnormity is accurately identified, misjudgment and missed judgment of traditional single-dimensional monitoring are avoided, a dynamic trend analysis mechanism is established, the spatio-temporal abnormal change trend is tracked through the abnormal trend factors, and the accuracy of monitoring is improved. Abnormal risk prospective early warning is achieved, operation and maintenance are promoted to be converted into active prevention, and the system risk and cost are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of electric quantity monitoring, and in particular to a method for monitoring electric energy metering anomalies based on spatiotemporal correlation characteristics. Background Art

[0002] As the data cornerstone of the power system, electric energy metering equipment, with its characteristics of covering all aspects of power generation, transmission, transformation, distribution and consumption, undertakes the core functions of accurate measurement, real-time recording and reliable transmission of electric energy data. From the real-time collection of electricity consumption by smart meters on the user side to support fair settlement of electricity bills, to the monitoring of power flow in transmission lines by metering terminals at substations to provide a basis for grid dispatching decisions, to the foundation laid for the management of new energy consumption by distributed energy metering devices, its importance runs through key areas such as the economic operation of the power system, system regulation and the construction of the energy Internet.

[0003] However, traditional electric energy metering equipment has abnormalities during data transmission and is only monitored through a single feature of the spatial dimension or time dimension. It is difficult to fully and accurately reflect the operating status of the electric energy metering equipment, thereby reducing the accuracy of monitoring. Summary of the Invention

[0004] Technical problems solved In response to the shortcomings of the existing technology, the present invention provides a method for monitoring electric energy metering anomalies based on spatiotemporal correlation features, which solves the problem that when traditional electric energy metering equipment transmits abnormalities, it is difficult to fully and accurately reflect its operating status through only single feature monitoring in the spatial or temporal dimension, thereby reducing the monitoring accuracy.

[0005] Technical Solution To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for monitoring electric energy metering anomalies based on spatiotemporal correlation characteristics, comprising the following specific methods: step one: start real-time collection of spatial feature data and temporal feature data, and perform preprocessing; step two: perform comprehensive analysis on the spatial feature data and temporal feature data to obtain a spatiotemporal anomaly factor; step three: analyze whether the electric energy metering is abnormal based on the spatiotemporal anomaly factor; if an abnormality is found, issue an early warning and end the monitoring; if normal is found, execute step four; step four: further analyze the spatiotemporal anomaly factor to obtain an abnormal trend factor; step five: analyze whether the electric energy metering tends to be abnormal based on the abnormal trend factor; if the normal trend is found, return directly to step one; if the abnormal trend is found, issue an early warning and return to step one.

[0006] Furthermore, in step one, the spatial characteristic data and the temporal characteristic data are cleaned, and the spatial characteristic data include the equipment operating temperature and the actual load of the equipment, and the temporal characteristic data include each data transmission time and the interval time of data transmission.

[0007] Furthermore, the specific method for obtaining the spatiotemporal anomaly factor is as follows: the operating temperature of the equipment and the actual load of the equipment are standardized and comprehensively calculated to obtain a spatial anomaly parameter; each data transmission time and the interval time of data transmission are comprehensively calculated to obtain a temporal anomaly parameter; the spatial anomaly parameter and the temporal anomaly parameter are standardized and comprehensively calculated to obtain a spatiotemporal anomaly factor; ;in, represents the spatiotemporal anomaly factor, represents the spatial anomaly parameter, Represents the temporal anomaly parameter.

[0008] Furthermore, the specific method for obtaining the spatial anomaly parameters is as follows: comprehensively calculate the equipment operating temperature of each equipment to obtain the equipment temperature fluctuation value, set the equipment temperature fluctuation threshold, compare the equipment temperature fluctuation value with the equipment temperature fluctuation threshold, if the equipment temperature fluctuation value is greater than the equipment temperature fluctuation threshold, then calculate the difference between the equipment temperature fluctuation value and the equipment temperature fluctuation threshold to obtain the temperature fluctuation deviation value, if the equipment temperature fluctuation value is less than or equal to the equipment temperature fluctuation threshold, then continue to compare the equipment temperature fluctuation value with the equipment temperature fluctuation threshold, set the equipment load upper limit value, perform standardization according to the equipment load upper limit value, the temperature fluctuation deviation value and the actual load of the equipment, and perform comprehensive calculation to obtain the spatial anomaly parameters.

[0009] Furthermore, the specific method for obtaining the device temperature fluctuation value is as follows: summing and averaging the device operating temperatures of each device to obtain the device operating temperature mean, taking the difference between the device operating temperature of each device and the device operating temperature mean and then squaring it to obtain the degree of deviation of the device operating temperature of each device from the device operating temperature mean, and then summing the degree of deviation of the device operating temperature of each device from the device operating temperature mean to obtain the device temperature fluctuation value.

[0010] Furthermore, the specific method for obtaining the time anomaly parameter is as follows: the next data transmission time and the previous data transmission time are comprehensively calculated to obtain the transmission anomaly time, and then the interval time of data transmission is calculated by the variance method to obtain the interval time fluctuation value, and standardization processing is performed according to the number of data transmission times, the transmission anomaly time and the interval time fluctuation value, and a comprehensive calculation is performed to obtain the time anomaly parameter.

[0011] Furthermore, in step three, a time-space threshold is set, and the time-space anomaly factor is compared with the time-space threshold. If the time-space anomaly factor is greater than the time-space threshold, it indicates that the electric energy metering is abnormal. If the time-space anomaly factor is less than or equal to the time-space threshold, it indicates that the electric energy metering is normal.

[0012] Furthermore, the specific method of obtaining the abnormal trend factor is as follows: a two-dimensional coordinate system is established according to the time series and the spatiotemporal abnormal factor to obtain the trend coordinates, the trend coordinates are averaged according to the number of trend coordinates to obtain the trend center coordinates, the trend center coordinates of the next time series and the trend center coordinates of the previous time series are averaged according to the time series to obtain the abnormal trend slope, and the quotient of two consecutive abnormal trend slopes is calculated to obtain the abnormal trend factor.

[0013] Furthermore, the specific method of obtaining the abnormal trend slope is as follows: ;in, represents the abnormal trend slope, The vertical coordinate representing the trend center coordinate of the next time series, The vertical coordinate represents the trend center coordinate of the previous time series. The horizontal coordinate representing the trend center coordinate of the next time series, The horizontal coordinate represents the trend center coordinate of the previous time series. represents a positive real number between zero and one.

[0014] Furthermore, in step five, the abnormal trend factor is compared with one. If the abnormal trend factor is greater than one, it indicates that the electric energy metering trend is abnormal. If the abnormal trend factor is less than or equal to one, it indicates that the electric energy metering trend is normal.

[0015] Beneficial effects Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: 1. By integrating spatial feature data such as equipment operating temperature and actual load with temporal feature data such as data transmission time and interval time, a comprehensive analysis model for spatiotemporal anomaly factors is constructed. By standardizing and performing cross-dimensional calculations on the two types of features, a three-dimensional portrayal of the equipment operating status is achieved. This model can accurately identify complex scenarios such as transmission anomalies caused by the synergy of high temperature and high load, breaking through the limitations of traditional single-dimensional monitoring that relies only on local features such as communication frequency or equipment area, effectively avoiding misjudgments and missed judgments caused by the fragmentation of spatiotemporal dimensions, and significantly improving the comprehensiveness and accuracy of anomaly detection.

[0016] 2. Establish a dynamic trend analysis mechanism based on time series. By constructing a two-dimensional coordinate system of spatiotemporal anomaly factors and time, calculating the trend center coordinates and the abnormal trend slope, and obtaining the abnormal trend factor by taking the quotient of continuous slopes, this mechanism can not only detect the current abnormal state in real time, but also predict the abnormal development trend through signals with trend factors greater than one, trigger early warnings and intervene in operation and maintenance, and promote the transformation of metering equipment operation and maintenance from passive response to faults to active prediction of risks, effectively reducing the risk of power grid shutdown caused by abnormal sudden events and the cost of manual inspections, and improving the reliability and economy of power system operation.

[0017] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This invention is a flow chart of a method for monitoring abnormality in electric energy metering based on spatiotemporal correlation characteristics. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0020] It should be noted that, in this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include," "comprise," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0021] like Figure 1 As shown, an embodiment of the present invention provides a method for monitoring abnormality of electric energy metering based on spatiotemporal correlation characteristics, which includes the following specific steps: Step 1: Start collecting spatial feature data and temporal feature data in real time, and perform data cleaning on the spatial feature data and temporal feature data to help remove redundant data from the spatial feature data and temporal feature data and improve the data quality of the spatial feature data and temporal feature data. Spatial feature data includes the equipment operating temperature and the actual load of the equipment. Temporal feature data includes the time of each data transmission and the interval between data transmissions.

[0022] Step 2: Standardize the spatial feature data and perform comprehensive calculations to help eliminate the dimensional differences of the spatial feature data and convert the different orders of magnitude values of the spatial feature data into a unified numerical range to obtain spatial anomaly parameters. Comprehensively calculate the temporal feature data to obtain temporal anomaly parameters. Standardize the spatial anomaly parameters and the temporal anomaly parameters to help eliminate the dimensional differences of the spatial anomaly parameters and the temporal anomaly parameters and convert the different orders of magnitude values of the spatial anomaly parameters and the temporal anomaly parameters into a unified numerical range. Comprehensively calculate and obtain the spatiotemporal anomaly factors. ; in, Indicates the spatiotemporal anomaly factor, reflecting whether the electric energy measurement is abnormal. Indicates the spatial anomaly parameter, reflecting whether the spatial feature data is abnormal. Due to the abnormality of spatial feature data, it will affect the temporal feature data when transmitting data. Indicates the time anomaly parameter, reflecting whether the time feature data is abnormal.

[0023] The specific method of obtaining spatial anomaly parameters is as follows: The device operating temperature of each device is calculated using the variance method to obtain a device temperature fluctuation value. The larger the device temperature fluctuation value, the greater the probability of device abnormality. A device temperature fluctuation threshold is set, that is, the temperature fluctuation value of the device during normal operation. The device temperature fluctuation value is compared with the device temperature fluctuation threshold. If the device temperature fluctuation value is greater than the device temperature fluctuation threshold, the difference between the device temperature fluctuation value and the device temperature fluctuation threshold is calculated to obtain a temperature fluctuation deviation value. If the device temperature fluctuation value is less than or equal to the device temperature fluctuation threshold, the device temperature fluctuation value is further compared with the device temperature fluctuation threshold. Set the upper limit of the equipment load, that is, the maximum load limit of the equipment, perform standardization based on the upper limit of the equipment load, the temperature fluctuation deviation value, and the actual load of the equipment, and perform comprehensive calculations to obtain the spatial anomaly parameters; ; in, represents the spatial anomaly parameter, Indicates the temperature fluctuation deviation value. The larger the temperature fluctuation deviation value, the more abnormal the spatial anomaly parameter is. Indicates the upper limit of the equipment load. Indicates the actual load of the equipment. The smaller the difference between the upper limit of the equipment load and the actual load of the equipment, the more abnormal the spatial abnormality parameter is. It is a positive real number to avoid meaningless spatial anomaly parameters.

[0024] The specific method for obtaining the device temperature fluctuation value is as follows: The device operating temperature of each device is summed and averaged to obtain the device operating temperature mean, which is used as a standard for measuring the fluctuation of the device operating temperature. The device operating temperature of each device is subtracted from the device operating temperature mean and then squared to obtain the degree of deviation of the device operating temperature of each device from the device operating temperature mean. The degree of deviation of the device operating temperature of each device from the device operating temperature mean is then summed to obtain the device temperature fluctuation value.

[0025] The specific method of obtaining time anomaly parameters is as follows: Since the increase in the operating temperature of the equipment will cause the performance of the equipment to deteriorate and the processing speed to decrease, and the high load will cause competition for resources such as the CPU, the two together will lead to an increase in data transmission time, thereby extending the time of each data transmission. Therefore, the next data transmission time and the previous data transmission time are comprehensively calculated to obtain the transmission abnormality time. The interval time of data transmission also fluctuates greatly due to the instability of the equipment operating temperature and the actual load of the equipment. Therefore, the interval time of data transmission is calculated by the variance method to obtain the interval time fluctuation value. According to the number of data transmission times, the transmission abnormality time and the interval time fluctuation value, they are standardized and comprehensively calculated to obtain the time abnormality parameter. ; in, Indicates the time anomaly parameter, reflecting whether the time characteristic data is abnormal. Indicates the interval time fluctuation value. The larger the interval time fluctuation value, the more abnormal the time abnormality parameter word is. Indicates the next data transmission time. Indicates the last data transmission time. Indicates the number of data transmission times.

[0026] Step 3: Set the time and space threshold through historical experiments, which serves as a measure of whether the electric energy metering is abnormal. Compare the time and space anomaly factor with the time and space threshold. If the time and space anomaly factor is greater than the time and space threshold, it means that the electric energy metering is abnormal, and an early warning is issued and the monitoring ends. If the time and space anomaly factor is less than or equal to the time and space threshold, it means that the electric energy metering is normal, and execute step 4.

[0027] Step 4: Comprehensively calculate the spatiotemporal anomaly factors to obtain the anomaly trend factor.

[0028] The specific method of obtaining the abnormal trend factor is as follows: A two-dimensional coordinate system is established according to the time series and the spatiotemporal anomaly factor to obtain the trend coordinates, wherein the horizontal axis of the two-dimensional coordinate system is the time series and the vertical axis is the spatiotemporal anomaly factor. The trend coordinates are averaged according to the number of trend coordinates to obtain the trend center coordinates, which is convenient for reducing the computational complexity. According to the time series, the slope of the trend center coordinates of the next time series and the trend center coordinates of the previous time series is calculated to obtain the abnormal trend slope. The quotient of two consecutive abnormal trend slopes is calculated to obtain the abnormal trend factor, that is, when the latter slope becomes larger, the quotient of the latter slope and the previous slope is greater than one, reflecting that the electric energy metering trend is abnormal; otherwise, it is less than or equal to one, reflecting that the electric energy metering trend is normal.

[0029] The specific method of obtaining the abnormal trend slope is as follows: ; in, represents the abnormal trend slope, The vertical coordinate representing the trend center coordinate of the next time series, The vertical coordinate represents the trend center coordinate of the previous time series. The horizontal coordinate representing the trend center coordinate of the next time series, The horizontal coordinate represents the trend center coordinate of the previous time series. It represents a positive real number between zero and one to avoid meaningless abnormal trend slopes.

[0030] Step 5: Compare the abnormal trend factor with one. If the abnormal trend factor is greater than one, it indicates that the electric energy metering trend is abnormal, an early warning is issued, and the process returns to step one. If the abnormal trend factor is less than or equal to one, it indicates that the electric energy metering trend is normal, and the process returns directly to step one.

[0031] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for monitoring abnormality of electric energy metering based on spatiotemporal correlation characteristics, characterized by: The specific steps include: Step 1: Start collecting spatial feature data and temporal feature data in real time and perform preprocessing; Step 2: Comprehensively analyze the spatial and temporal feature data to obtain the spatiotemporal anomaly factors; Step 3: Analyze whether the electric energy metering is abnormal based on the spatiotemporal abnormality factors. If abnormality is found, issue an early warning and end the monitoring. If normal, proceed to step 4. Step 4: Further analyze the spatiotemporal anomaly factors to obtain the anomaly trend factors; Step 5: Analyze whether the electric energy metering is trending abnormally based on the abnormal trend factor. If the analysis shows that the trend is normal, directly return to step 1. If the analysis shows that the trend is abnormal, issue an early warning and return to step 1.

2. The method for monitoring abnormality of electric energy metering based on spatiotemporal correlation characteristics according to claim 1, characterized in that: In step 1, data cleaning is performed on the spatial feature data and the temporal feature data, wherein the spatial feature data includes the operating temperature of the equipment and the actual load of the equipment, and the temporal feature data includes the time of each data transmission and the interval time of data transmission.

3. The method for monitoring abnormality of electric energy metering based on spatiotemporal correlation characteristics according to any one of claims 1 to 2, characterized in that: The specific method of obtaining the spatiotemporal anomaly factor is as follows: The equipment operating temperature and the actual load of the equipment are standardized and comprehensively calculated to obtain the spatial anomaly parameter. The data transmission time and the interval between data transmissions are comprehensively calculated to obtain the temporal anomaly parameter. The spatial anomaly parameter and the temporal anomaly parameter are standardized and comprehensively calculated to obtain the spatiotemporal anomaly factor. ； in, represents the spatiotemporal anomaly factor, represents the spatial anomaly parameter, Represents the temporal anomaly parameter.

4. The method for monitoring abnormality of electric energy metering based on spatiotemporal correlation characteristics according to claim 3 is characterized in that: The specific method of obtaining the spatial anomaly parameters is as follows: A comprehensive calculation is performed on the equipment operating temperature of each equipment to obtain the equipment temperature fluctuation value, and the equipment temperature fluctuation threshold is set. The equipment temperature fluctuation value is compared with the equipment temperature fluctuation threshold. If the equipment temperature fluctuation value is greater than the equipment temperature fluctuation threshold, the equipment temperature fluctuation value and the equipment temperature fluctuation threshold are subtracted to obtain the temperature fluctuation deviation value. If the equipment temperature fluctuation value is less than or equal to the equipment temperature fluctuation threshold, the equipment temperature fluctuation value and the equipment temperature fluctuation threshold are further compared. The equipment load upper limit value is set, and standardization processing is performed according to the equipment load upper limit value, the temperature fluctuation deviation value and the actual load of the equipment, and a comprehensive calculation is performed to obtain the spatial anomaly parameter.

5. The method for monitoring abnormality of electric energy metering based on spatiotemporal correlation characteristics according to claim 4 is characterized in that: The specific method for obtaining the device temperature fluctuation value is as follows: The device operating temperature of each device is summed and averaged to obtain the device operating temperature mean. The device operating temperature of each device is subtracted from the device operating temperature mean and then squared to obtain the degree of deviation of the device operating temperature of each device from the device operating temperature mean. The degree of deviation of the device operating temperature of each device from the device operating temperature mean is then summed to obtain the device temperature fluctuation value.

6. The method for monitoring abnormality of electric energy metering based on spatiotemporal correlation characteristics according to claim 3, characterized in that: The specific method of obtaining the time anomaly parameters is as follows: The next data transmission time and the previous data transmission time are comprehensively calculated to obtain the transmission anomaly time. Therefore, the data transmission interval is calculated by the variance method to obtain the interval time fluctuation value. The data transmission times, the transmission anomaly time and the interval time fluctuation value are standardized and comprehensively calculated to obtain the time anomaly parameter.

7. The method for monitoring abnormality of electric energy metering based on spatiotemporal correlation characteristics according to claim 1, characterized in that: In step three, a spatiotemporal threshold is set, and the spatiotemporal anomaly factor is compared with the spatiotemporal threshold. If the spatiotemporal anomaly factor is greater than the spatiotemporal threshold, it indicates that the electric energy metering is abnormal. If the spatiotemporal anomaly factor is less than or equal to the spatiotemporal threshold, it indicates that the electric energy metering is normal.

8. The method for monitoring abnormality of electric energy metering based on spatiotemporal correlation characteristics according to claim 1, characterized in that: The specific method of obtaining the abnormal trend factor is as follows: A two-dimensional coordinate system is established based on the time series and the spatiotemporal anomaly factor to obtain the trend coordinates. The trend coordinates are averaged according to the number of trend coordinates to obtain the trend center coordinates. The slope of the trend center coordinates of the next time series and the trend center coordinates of the previous time series is calculated based on the time series to obtain the abnormal trend slope. The abnormal trend factor is obtained by calculating the quotient of two consecutive abnormal trend slopes.

9. The method for monitoring abnormality of electric energy metering based on spatiotemporal correlation characteristics according to claim 8, characterized in that: The specific method of obtaining the abnormal trend slope is as follows: ； in, represents the abnormal trend slope, The vertical coordinate representing the trend center coordinate of the next time series, The vertical coordinate represents the trend center coordinate of the previous time series. The horizontal coordinate representing the trend center coordinate of the next time series, The horizontal coordinate represents the trend center coordinate of the previous time series. represents a positive real number between zero and one.

10. The method for monitoring abnormality of electric energy metering based on spatiotemporal correlation characteristics according to claim 1, characterized in that: In step five, the abnormal trend factor is compared with one. If the abnormal trend factor is greater than one, it indicates that the electric energy metering trend is abnormal. If the abnormal trend factor is less than or equal to one, it indicates that the electric energy metering trend is normal.

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