Remote monitoring method for running state of electric energy meter

Abstract

The invention relates to the technical field of electric energy meter operation monitoring. The invention relates to a remote monitoring method for the running state of an electric energy meter. The method comprises the following steps: S1, obtaining a user to which an electric energy meter belongs, and carrying out region division among the electric energy meters according to the position of the user; s2, collecting current data of the electric energy meter, performing monthly electricity consumption prediction on the user according to historical current data and the weather state of the current month, obtaining predicted current data, and then setting an abnormal threshold value according to the fluctuation trend of the historical current data; according to the method, current feature extraction is performed on the historical current data of the ammeter to which the abnormal current data belongs, and the difference current feature is matched with the historical current feature, so that whether the abnormal current data can return to normal or not can be judged, and if the abnormal current data cannot return to normal, the unmatched current feature and the fault current feature are further detected; and whether the ammeter fails can be accurately determined.

Description

Technical Field

[0001] The invention relates to the technical field of electric energy meter operation monitoring, and in particular to a remote monitoring method for the operation status of an electric energy meter. Background Art

[0002] As a key device for electricity metering, the electric energy meter collects and analyzes the current, voltage and other parameters of the electric energy meter to achieve real-time monitoring of the operating status of the electric energy meter and ensure the stable operation of the power system.

[0003] At present, the operation data of the energy meter is transmitted to the power grid management center through the sensor or communication module installed on the energy meter, and the data is processed and analyzed by a special software system to provide real-time electricity consumption information for the power company. However, since it only relies on simple current and voltage parameters for analysis, it is easily interfered by external environmental factors, such as voltage fluctuations, electromagnetic interference, etc., resulting in misjudgment or missed judgment, which will affect the power company's timely handling of energy meter failures, and may cause inaccurate measurement, incorrect calculation of electricity charges and other problems. In addition, the efficiency of troubleshooting is low. When the energy meter is abnormal, it is often required to conduct manual on-site inspection and analysis, which consumes a lot of time and labor costs. Therefore, a remote monitoring method for the operation status of the energy meter is proposed. Summary of the invention

[0004] The purpose of the present invention is to provide a remote monitoring method for the operating status of an electric energy meter to solve the problems raised in the above background technology.

[0005] To achieve the above object, a remote monitoring method for the operating status of an electric energy meter is provided, comprising the following steps: S1. Obtain the user to which the electric energy meter belongs, and divide the electric energy meters into regions according to the location of the user; S2. Collect the current data of the electric energy meter, make a monthly electricity consumption forecast for the user based on the historical current data and the weather conditions of the month, obtain the predicted current data, and then set the abnormal threshold according to the fluctuation trend of the historical current data; S3, combining the actual current data and the predicted current data at the time of monthly settlement with the abnormal threshold to perform abnormality detection, and then extracting the current features of the abnormal current data and the normal current data adjacent in time and performing difference comparison; S4, extracting current features from historical current data, matching the difference current features obtained in S3 with the historical current features, and then performing ammeter fault detection on the mismatched current features combined with the fault current features; S5. When the fault detection result of S4 shows that there is no fault, the monthly current characteristic difference of the current data in the same area is calculated, and then the difference data corresponding to each user is combined with the current characteristics that S4 did not successfully match to detect electricity theft.

[0006] As a further improvement of the technical solution, the S1 establishes a connection with the power grid management terminal to obtain the right to collect the ammeter data and obtain the installation address of each ammeter.

[0007] As a further improvement of the technical solution, S1 obtains map data and then divides the area by combining the map data with the installation address of the ammeter, so that the ammeters installed in the same building in the community are classified into one area.

[0008] As a further improvement of the technical solution, the steps of S2 are as follows: S2.1. Collect the current data fed back by the ammeter, and save them into historical current data and actual current data. Meanwhile, connect to the weather network to obtain the weather conditions of the current month corresponding to the actual current data. S2.2. Combine the weather conditions of the current month with the historical current data to make a monthly electricity consumption forecast for users, obtain the forecasted current data, and set the abnormal threshold according to the fluctuation trend of the historical current data. The greater the fluctuation, the higher the threshold ratio, and vice versa.

[0009] As a further improvement of this technical solution, the steps of S3 are as follows: S3.1. The actual current data and the predicted current data at the time of monthly settlement are combined with the abnormal threshold to perform abnormal detection. When the difference ratio between the actual current data and the predicted current data exceeds the abnormal threshold, the actual current data is marked as abnormal current data. On the contrary, when the difference ratio between the actual current data and the predicted current data does not exceed the abnormal threshold, the monitoring is continued; S3.2. Extract the normal current data whose feedback time is closest to the abnormal current data, and then extract the current features by combining the normal current data with the abnormal current data to obtain the current features of the normal current data and the current features of the abnormal current data. Then, perform a difference comparison between the two current features to obtain the difference current features.

[0010] As a further improvement of the technical solution, the step of S4 is as follows: S4.1. When abnormal current data appears, current feature extraction is performed on the historical current data of the current meter to which the abnormal current data belongs, thereby obtaining the historical current feature; S4.2, matching the difference current feature obtained in S3.2 with the historical current feature, when the difference current feature is exactly the same as the historical current feature, the abnormal current data is restored to normal, otherwise, when the difference current feature is different from the historical current feature, the different current feature is used as the unmatched current feature; S4.3. Collect fault current characteristics at the power grid management end, and then combine the mismatched current characteristics with the fault current characteristics for fault detection. When the mismatched current characteristics and the fault current characteristics have the same current characteristics, the ammeter is determined to be faulty, and the maintenance personnel are notified at the power grid management end. Conversely, when the mismatched current characteristics and the fault current characteristics do not have the same current characteristics, the ammeter is determined to be not faulty.

[0011] As a further improvement of this technical solution, the formula of S4.3 is as follows: Among them, F unmatched ={f1, f2, f3, ...} is the mismatch current feature set, F fault ={g1, g2, g3, ...} is the fault current characteristic set, F intersection For the same feature set, when F intersection If it is not an empty set, it means that they have the same current characteristics and the ammeter is judged to be faulty. Otherwise, when F intersection If it is an empty set, it is determined that the ammeter is not faulty.

[0012] As a further improvement of the technical solution, the step of S5 is as follows: S5.1, monitor the fault detection result of S4.3, and when the fault detection result shows that there is no fault, extract the current data fed back by other ammeters in the same area as the abnormal current data, and calculate the current characteristic difference of the current data adjacent in time to obtain the differential current characteristics corresponding to other ammeters in the same area; S5.2. Set the electricity theft detection threshold, and then perform electricity theft detection by combining the difference current characteristics and mismatch current characteristics corresponding to other ammeters in the same area with the electricity theft detection threshold. When the similarity rate of the difference current characteristics and the mismatch current characteristics is greater than the electricity theft detection threshold, it is determined that the electricity meter has been stolen and feedback is given to the maintenance personnel. Conversely, when the similarity rate of the difference current characteristics and the mismatch current characteristics are both less than the electricity theft detection threshold, the abnormal mark of the actual current data is removed to end the fault detection.

[0013] As a further improvement of the technical solution, the formula of S5.2 is as follows: Where n is F unmatched The number of features of F otherDiff The number of features, N total is the total number of features; F unmatched ={f1, f2, f3, ...} is the mismatched current feature set, and the difference current feature set corresponding to other ammeters in the same area is F otherDiff ={g1, g2, g3, ...}, for each feature, traverse and search in, when there is g j So that f i and g jare considered the same, then the same feature N same The quantity increases by one; Among them, R is the same rate, T theft is the power theft detection threshold; When R ≥ T theft , it is determined that the electric energy meter has been stolen; When R<T theft , then the abnormal mark of the actual current data is removed and the fault detection is ended.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. In the remote monitoring method of the operating status of the electric energy meter, by extracting the current characteristics of the historical current data of the ammeter to which the abnormal current data belongs, the difference current characteristics are matched with the historical current characteristics, so as to judge whether the abnormal current data can be restored to normal. If it cannot be restored to normal, the mismatched current characteristics are further detected with the fault current characteristics to accurately determine whether the ammeter is faulty. This step-by-step in-depth fault troubleshooting method improves the efficiency of fault handling.

[0015] 2. In the remote monitoring method of the operating status of the electric energy meter, by setting the power theft detection threshold, the difference current characteristics and mismatch current characteristics corresponding to other ammeters in the same area are combined for power theft detection. When the same rate is greater than the power theft detection threshold, it can be determined in time whether the electric energy meter has been stolen and feedback can be given to the maintenance personnel, which effectively prevents power theft and protects the legitimate rights and interests of the power company and the normal operation of the power system.

[0016] 3. In the remote monitoring method of the operating status of the electric energy meter, the actual operating status of the electric energy meter can be accurately grasped through the real-time collection and classified storage of the electric energy meter current data. The distinction between historical current data and actual current data is helpful for long-term trend analysis and short-term status monitoring, which improves the accuracy of judging the operating status of the electric energy meter. Then, by combining the weather conditions to make monthly electricity consumption forecasts for users, the impact of external environmental factors on electricity consumption is fully considered, making the forecast results closer to the actual situation. At the same time, the abnormal threshold is set according to the fluctuation trend of the historical current data, which can timely discover abnormal conditions in the operation of the electric energy meter and improve the sensitivity of fault detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is the overall flow chart of the present invention; Figure 2 A flowchart of the present invention for obtaining the weather conditions of the current month corresponding to the actual current data; Figure 3 A flowchart of obtaining differential current characteristics of the present invention; Figure 4A flowchart of the present invention using the different current characteristics as unmatched current characteristics; Figure 5 This is a flowchart of the present invention for ending fault detection. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0019] See also Figure 1-Figure 5 As shown, the purpose of this embodiment is to provide a remote monitoring method for the operating status of an electric energy meter, comprising the following steps: S1. Obtain the user to which the electric energy meter belongs, and divide the electric energy meters into regions according to the location of the user; S1 establishes a connection with the power grid management terminal to obtain the right to collect ammeter data and the installation address of each ammeter. The specific steps are as follows: Establish a connection with the power grid management end: determine the communication protocol and interface specifications of the power grid management end, use appropriate communication technology to attempt to connect with the power grid management end, and perform identity authentication and authorization to ensure that you have the authority to obtain the current meter data collection; Obtaining ammeter data and installation address: Sending a request to the grid management end to obtain relevant information of all ammeters, including current data and installation address, and parsing and organizing the received data to ensure the accuracy and completeness of the data.

[0020] S1 obtains map data and then divides the area by combining the map data with the installation address of the ammeter, so that the ammeters installed in the same building in the community are classified into one area. The specific steps are as follows: Obtain map data: Obtain the map data of the community through the online map service API and determine the coordinate system and scale of the map to match it with the installation address of the ammeter; Area division: According to the map data and the installation address of the ammeter, the location of each ammeter on the map is determined, and the ammeters installed in the same building are grouped into one area using a spatial clustering algorithm.

[0021] S2. Collect the current data of the electric energy meter, make a monthly electricity consumption forecast for the user based on the historical current data and the weather conditions of the month, obtain the predicted current data, and then set the abnormal threshold according to the fluctuation trend of the historical current data; The steps of S2 are as follows: S2.1. Collect the current data fed back by the ammeter and save them into historical current data and actual current data. At the same time, connect to the weather network to obtain the weather conditions of the month corresponding to the actual current data. The specific steps are as follows: Collecting ammeter data: The power grid management end collects the current data fed back by the ammeter; Classify and save data: Create a database or data storage structure to save historical current data and actual current data. The latest feedback current data is the actual current data, and other saved current data are historical current data; Connect to the weather network to obtain weather conditions: Find a reliable weather data provider, understand its API interface specifications and usage methods, use the network request library in the programming language, send a request to the weather network's API, and pass the necessary parameters (such as geographic location, time, etc.) to obtain the weather conditions of the corresponding area for the current month; Correlate current data and weather conditions: Match the actual current data with the corresponding weather conditions of the month through timestamps and geographic locations.

[0022] S2.2. Combine the weather conditions of the current month with the historical current data to predict the monthly electricity consumption of users, obtain the predicted current data, and set the abnormal threshold according to the fluctuation trend of the historical current data. The greater the fluctuation, the higher the threshold ratio. Conversely, the greater the fluctuation, the lower the threshold ratio. The formula is as follows: in, is the average current value of w under specific weather conditions, n w is the number of historical current data points under a specific weather state w, I i,w is each current data point under a specific weather condition; For monthly electricity consumption forecast, we can first find the corresponding historical average current value as the basic forecast value according to the weather conditions of the month, and then adjust it in combination with other factors; Where S is the standard deviation of the historical current data, n is the total number of historical current data points, I i is each historical current data point, is the total average current value of historical current data; Among them, T R is the abnormal threshold ratio, α and β are adjustment coefficients, which can be determined according to actual conditions and are used to adjust the degree of change of the threshold ratio with the standard deviation; Where T is the abnormal threshold, For the predicted monthly average current data, Make a prediction.

[0023] S3, combining the actual current data and the predicted current data at the time of monthly settlement with the abnormal threshold to perform abnormality detection, and then extracting the current features of the abnormal current data and the normal current data adjacent in time and performing difference comparison; The steps for S3 are as follows: S3.1. The actual current data and the predicted current data at the time of monthly settlement are combined with the abnormal threshold to perform abnormal detection. When the difference ratio between the actual current data and the predicted current data exceeds the abnormal threshold, the actual current data is marked as abnormal current data. On the contrary, when the difference ratio between the actual current data and the predicted current data does not exceed the abnormal threshold, the monitoring is continued; S3.2, extract the normal current data whose feedback time is closest to the abnormal current data, and then extract the current characteristics of the normal current data and the abnormal current data in combination, and then compare the difference between the two current characteristics to obtain the difference current characteristics. The specific steps are as follows: Find the closest normal current data: Before and after the feedback time of the abnormal current data, search for the closest normal current data, and find the normal current data point closest to the abnormal current data in time by traversing in chronological order; Current feature extraction: Current feature extraction is performed on normal current data and abnormal current data respectively, and amplitude features are extracted using the peak detection method; Difference comparison to obtain difference current features: compare the features of normal current data and abnormal current data item by item. The formula is as follows: Among them, A n is the amplitude of normal current data, A a is the amplitude of abnormal current data, and △A is the amplitude difference feature.

[0024] S4, extracting current features from historical current data, matching the difference current features obtained in S3 with the historical current features, and then performing ammeter fault detection on the mismatched current features combined with the fault current features; The steps of S4 are as follows: S4.1. When abnormal current data appears, current feature extraction is performed on the historical current data of the current meter to which the abnormal current data belongs, thereby obtaining the historical current feature; S4.2, matching the difference current feature obtained in S3.2 with the historical current feature, when the difference current feature is exactly the same as the historical current feature, the abnormal current data is restored to normal, otherwise, when the difference current feature is different from the historical current feature, the different current feature is used as the unmatched current feature; S4.3. Collect fault current characteristics at the power grid management end, and then combine the mismatch current characteristics with the fault current characteristics for fault detection. When the mismatch current characteristics and the fault current characteristics have the same current characteristics, the ammeter is determined to be faulty, and the maintenance personnel are notified at the power grid management end. Conversely, when the mismatch current characteristics and the fault current characteristics do not have the same current characteristics, the ammeter is determined to be not faulty. The specific steps are as follows: Collect fault current characteristics: At the power grid management end, various monitoring devices and systems are used to collect known fault current characteristics, which may include overcurrent amplitude, specific harmonic content, asymmetric current characteristics, etc. Combine mismatched current features for fault detection: compare the mismatched current features obtained previously with the fault current features. If there are the same features between the mismatched current features and the fault current features, that is, one or some features appear in both the mismatched current features and the fault current features, and if there are no same features between the mismatched current features and the fault current features, it is determined that the ammeter is not faulty. Notify maintenance personnel: When the ammeter is determined to be faulty, the maintenance personnel are notified through a specific communication channel at the power grid management end, and a notification containing information such as the location of the ammeter and a description of the fault characteristics is sent. The formula is as follows: Among them, F unmatched ={f1, f2, f3, ...} is the mismatch current feature set, F fault ={g1, g2, g3, ...} is the fault current characteristic set, F intersection For the same feature set, when F intersection If it is not an empty set, it means that they have the same current characteristics and the ammeter is judged to be faulty. Otherwise, when F intersection If it is an empty set, it is determined that the ammeter is not faulty.

[0025] S5. When the fault detection result of S4 shows that there is no fault, the monthly current characteristic difference of the current data in the same area is calculated, and then the difference data corresponding to each user is combined with the current characteristics that S4 did not successfully match to detect electricity theft.

[0026] The steps of S5 are as follows: S5.1, monitor the fault detection result of S4.3, and when the fault detection result shows that there is no fault, extract the current data fed back by other ammeters in the same area as the abnormal current data, and calculate the current characteristic difference of the current data adjacent in time to obtain the differential current characteristics corresponding to other ammeters in the same area; The current characteristic difference is calculated for the current data adjacent in time, wherein the current characteristic difference calculation method is the same as S3.2.

[0027] S5.2, set the power theft detection threshold, and then combine the difference current characteristics and mismatch current characteristics corresponding to other ammeters in the same area with the power theft detection threshold to perform power theft detection. When the difference current characteristics and mismatch current characteristics have a similarity rate greater than the power theft detection threshold, it is determined that the electric energy meter has been stolen and feedback is sent to the maintenance personnel. On the contrary, when the difference current characteristics and mismatch current characteristics have a similarity rate less than the power theft detection threshold, the abnormal mark of the actual current data is eliminated and the fault detection is ended. The formula is as follows: Where n is F unmatched The number of features of F otherDiff The number of features, N total is the total number of features; F unmatched ={f1, f2, f3, ...} is the mismatched current feature set, and the difference current feature set corresponding to other ammeters in the same area is F otherDiff ={g1, g2, g3, ...}, for each feature, traverse and search in, when there is g j So that f i and g j are considered the same, then the same feature N same The quantity increases by one; Among them, R is the same rate, T theft is the power theft detection threshold; When R ≥ T theft , it is determined that the electric energy meter has been stolen; When R<T theft , then the abnormal mark of the actual current data is removed and the fault detection is ended.

[0028] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A remote monitoring method for the operating status of an electric energy meter, characterized in that: The following steps are involved: S1. Obtain the user to which the electric energy meter belongs, and divide the electric energy meters into regions according to the location of the user; S2. Collect the current data of the electric energy meter, make a monthly electricity consumption forecast for the user based on the historical current data and the weather conditions of the month, obtain the predicted current data, and then set the abnormal threshold according to the fluctuation trend of the historical current data; S3, combining the actual current data and the predicted current data at the time of monthly settlement with the abnormal threshold to perform abnormality detection, and then extracting the current features of the abnormal current data and the normal current data adjacent in time and performing difference comparison; S4, extracting current features from historical current data, matching the difference current features obtained in S3 with the historical current features, and then performing ammeter fault detection on the mismatched current features combined with the fault current features; S5. When the fault detection result of S4 shows that there is no fault, the monthly current characteristic difference of the current data in the same area is calculated, and then the difference data corresponding to each user is combined with the current characteristics that S4 did not successfully match to detect electricity theft.

2. The remote monitoring method of the operating status of an electric energy meter according to claim 1 is characterized in that: The S1 establishes a connection with the power grid management terminal to obtain the right to collect the ammeter data and the installation address of each ammeter.

3. The remote monitoring method of the operating status of an electric energy meter according to claim 1, characterized in that: The S1 obtains map data and then divides the area by combining the map data with the installation address of the ammeter, so that the ammeters installed in the same building in the community are classified into one area.

4. The remote monitoring method of the operating status of an electric energy meter according to claim 1, characterized in that: The steps of S2 are as follows: S2.

1. Collect the current data fed back by the ammeter, and save them into historical current data and actual current data. At the same time, connect to the weather network to obtain the weather conditions of the current month corresponding to the actual current data. S2.

2. Combine the weather conditions of the current month with the historical current data to make a monthly electricity consumption forecast for users, obtain the forecasted current data, and set the abnormal threshold according to the fluctuation trend of the historical current data. The greater the fluctuation, the higher the threshold ratio, and vice versa.

5. The remote monitoring method of the operating status of an electric energy meter according to claim 1, characterized in that: The steps of S3 are as follows: S3.

1. The actual current data and the predicted current data at the time of monthly settlement are combined with the abnormal threshold to perform abnormal detection. When the difference ratio between the actual current data and the predicted current data exceeds the abnormal threshold, the actual current data is marked as abnormal current data. On the contrary, when the difference ratio between the actual current data and the predicted current data does not exceed the abnormal threshold, the monitoring is continued; S3.

2. Extract the normal current data whose feedback time is closest to the abnormal current data, and then extract the current features by combining the normal current data with the abnormal current data to obtain the current features of the normal current data and the current features of the abnormal current data. Then, perform a difference comparison between the two current features to obtain the difference current features.

6. The remote monitoring method of the operating status of an electric energy meter according to claim 1, characterized in that: The steps of S4 are as follows: S4.

1. When abnormal current data appears, current feature extraction is performed on the historical current data of the current meter to which the abnormal current data belongs, thereby obtaining the historical current feature; S4.2, matching the difference current feature obtained in S3.2 with the historical current feature, when the difference current feature is exactly the same as the historical current feature, the abnormal current data is restored to normal, otherwise, when the difference current feature is different from the historical current feature, the different current feature is used as the unmatched current feature; S4.

3. Collect fault current characteristics at the power grid management end, and then combine the mismatched current characteristics with the fault current characteristics for fault detection. When the mismatched current characteristics and the fault current characteristics have the same current characteristics, the ammeter is determined to be faulty, and the maintenance personnel are notified at the power grid management end. Conversely, when the mismatched current characteristics and the fault current characteristics do not have the same current characteristics, the ammeter is determined to be not faulty.

7. The remote monitoring method of the operating status of the electric energy meter according to claim 6 is characterized in that: The formula of S4.3 is as follows: Among them, F unmatched ={f1, f2, f3, ...} is the mismatch current feature set, F fault ={g1, g2, g3, ...} is the fault current characteristic set, F intersection For the same feature set, when F intersection If it is not an empty set, it means that they have the same current characteristics and the ammeter is judged to be faulty. Otherwise, when F intersection If it is an empty set, it is determined that the ammeter is not faulty.

8. The remote monitoring method for the operation status of an electric energy meter according to claim 1, characterized in that: The steps of S5 are as follows: S5.1, monitor the fault detection result of S4.3, and when the fault detection result shows that there is no fault, extract the current data fed back by other ammeters in the same area as the abnormal current data, and calculate the current characteristic difference of the current data adjacent in time to obtain the differential current characteristics corresponding to other ammeters in the same area; S5.

2. Set the electricity theft detection threshold, and then perform electricity theft detection by combining the difference current characteristics and mismatch current characteristics corresponding to other ammeters in the same area with the electricity theft detection threshold. When the similarity rate of the difference current characteristics and the mismatch current characteristics is greater than the electricity theft detection threshold, it is determined that the electricity meter has been stolen and feedback is given to the maintenance personnel. Conversely, when the similarity rate of the difference current characteristics and the mismatch current characteristics are both less than the electricity theft detection threshold, the abnormal mark of the actual current data is removed to end the fault detection.

9. The remote monitoring method of the operating status of an electric energy meter according to claim 8, characterized in that: The formula of S5.2 is as follows: Where n is F unmatched The number of features of F otherDiff The number of features, N total is the total number of features; F unmatched ={f1, f2, f3, ...} is the mismatched current feature set, and the difference current feature set corresponding to other ammeters in the same area is F otherDiff ={g1, g2, g3, ...}, for each feature, traverse and search in, when there is g j So that f i and g j are considered the same, then the same feature N same The quantity increases by one; Among them, R is the same rate, T theft is the power theft detection threshold; When R ≥ T theft , it is determined that the electric energy meter has been stolen; When R<T theft , then the abnormal mark of the actual current data is removed and the fault detection is ended.

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