Electric energy meter on-site real-time detection system and detection method thereof

Abstract

The invention discloses an electric energy meter on-site real-time detection system and a detection method thereof, and relates to the technical field of electric energy metering detection, and the method comprises the following steps: collecting related data of an electric energy meter regularly in real time through a sensor, and reading the electric energy reading of the electric energy meter based on a unit time interval; on the basis of the current and voltage data in the unit time, a theoretical electric energy value is obtained through calculation by using an electric energy calculation formula, electric energy meter error value data is obtained by combining an actually measured electric energy value collected in real time in the unit time, and the electric energy meter is judged and marked on the basis of an error threshold value; an error database is established for the electric energy meters with errors within an allowable range, and error data are recorded independently. By combining the offline fault report and error data analysis method, the accuracy and efficiency of fault diagnosis can be improved, the operation and maintenance strategy can be optimized, the maintenance cost is reduced, more efficient real-time detection and maintenance of the electric energy meter are realized, and the practicability and functionality are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy metering detection, and in particular to a real-time detection system and a detection method for an electric energy meter field. Background Art

[0002] A three-phase energy meter is an instrument used to measure the energy consumption in a three-phase power system. The three-phase power system is a common power supply method in industry and large commercial buildings. It provides electricity through three phase lines. Since the energy meter is related to the calculation of electricity costs, it is necessary to regularly check the energy meter to ensure that the power data is accurate.

[0003] The Chinese patent with the publication number "CN109557497A" discloses "a novel on-site real-time detection system for digital energy meters, including a merging unit, a substation operating digital energy meter, a multi-channel digital standard energy meter and a substation monitoring system; a data stream conforming to the communication protocol is transmitted from the merging unit to the corresponding channels of the substation operating digital energy meter and the multi-channel digital standard energy meter respectively." Although it can complete the detection of the energy meter, it cannot determine the error change rate of the energy meter within a reasonable error range, cannot discover potential faulty energy meters, and cannot determine the fault type of abnormal energy meters, and has problems of low practicality and functionality;

[0004] In view of the above, this case proposes a real-time detection system and a detection method of an electric energy meter on-site to solve the above technical problems. Summary of the invention

[0005] In view of the problems in the related art, the present invention proposes a real-time detection system and a detection method for electric energy meter field to overcome the above technical problems existing in the existing related art.

[0006] To this end, the specific technical solution adopted by the present invention is as follows:

[0007] A method for real-time detection of electric energy meter field, the method comprising the following steps:

[0008] S1. Regularly collect relevant data of the energy meter in real time through sensors, and read the energy reading of the energy meter based on unit time intervals;

[0009] S2. Based on the current and voltage data in unit time, the theoretical electric energy value is calculated using the electric energy calculation formula, and combined with the measured electric energy value collected in real time in unit time, the electric energy meter error value data is obtained, and the electric energy meter is judged and marked based on the error threshold;

[0010] S3. For electric energy meters with errors within the allowable range, an error database is established to record the error data separately, analyze the error change trend, and mark and observe abnormal electric energy meters in advance;

[0011] S4. Based on the online and offline electric energy meter detection data, the error value data of the offline electric energy meter before and after the actual fault is extracted, the obtained error value is further analyzed, and the fault is pre-determined.

[0012] As a preferred embodiment, the S1 includes the following sub-steps:

[0013] S11. Using current sensors and voltage sensors, collect data related to the on-site electric energy meter in real time based on a periodic T, wherein the voltage sensor is used to measure the three-phase voltage value at the electric energy meter inlet, and the current sensor is used to measure the three-phase current value, and attach a timestamp;

[0014] S12. The electric energy meter readings are read in real time through the Modbus communication protocol and timestamps are attached. The three-phase voltage values, three-phase current values ​​and real-time readings of the electric energy meter are uploaded to the electric energy meter database through Wi-Fi.

[0015] As a preferred embodiment, S2 includes the following sub-steps:

[0016] S21. Through the electric energy meter database, extract the three-phase current value and the three-phase voltage value of the electric energy meter to be tested within a unit time based on the timestamp, and obtain the theoretical electric energy value of the electric energy meter to be tested within a unit time t through the calculation formula. The algorithm formula is:

[0017]

[0018] Among them, W 0 Represents the theoretical electric energy value of the electric energy meter to be tested within unit time t, Represents the average three-phase voltage value per unit time t, Represents the average three-phase current value within unit time t;

[0019] S22. Calculate the actual electric energy value of the electric energy meter to be tested within unit time t through the electric energy meter database. The specific algorithm formula is:

[0020] ΔW=W 最终 -W 起始 ;

[0021] Among them, ΔW represents the actual measured energy value of the energy meter to be tested, W 最终 Represents the actual reading of the energy meter at the end of unit time t, W 起始 Represents the actual reading of the energy meter at the beginning of unit time t;

[0022] S23, based on the theoretical electric energy value of the electric energy meter to be tested and the actual electric energy value of the electric energy meter to be tested, calculate and obtain the error value of the electric energy meter to be tested, and the specific algorithm formula is:

[0023] W1 =|ΔW-W 0 |;

[0024] Among them, W 1 Represents the error value of the electric energy meter, by comparing the error threshold W θ , determine the operating status of the electric energy meter to be tested:

[0025] When W 1 ≥W θ When it is, it means that the electric energy meter to be tested has obvious errors, and the electric energy meter file to be tested in the electric energy meter database is marked in red;

[0026] When W 1 <W θ When , it means that there is no obvious error in the electric energy meter to be tested, and the error value at that time is recorded in the file.

[0027] As a preferred implementation, S3 includes the following sub-steps:

[0028] S31. Establish an error database in the electric energy meter files under the electric energy meter database, wherein the database includes the error value within the normal range of each record;

[0029] S32. Analyze the error value change trend in the error database, calculate the error change rate, and mark the electric energy meter with abnormal change rate.

[0030] As a preferred implementation, the S32 includes the following sub-steps:

[0031] S321. Based on the error data in the error database, the error rate of two adjacent continuous errors is calculated. The specific algorithm formula is:

[0032]

[0033] Among them, e i represents the error rate, E i+1 、E i Represents two adjacent error data in the error database, statistically calculates the error rate and calculates the error change rate Δe based on the two adjacent error rates i , and its algorithm formula is:

[0034] Δe i =e i+1 -e i ;

[0035] Among them, e i+1 、e i Respectively represent two adjacent error rate data;

[0036] S322, based on the error change rate Δe i, analyze the change trend of the current archive electric energy meter, and the specific steps are:

[0037] When any one Δe i With Δe i+1 are greater than 0, and Δe i+1 >Δe θ When , it means that the current electric energy meter error is gradually increasing, and the speed of increase exceeds the change rate threshold Δe θ , mark the current electricity meter file in blue.

[0038] As a preferred implementation, the S322 includes the following sub-steps:

[0039] S3221, for the blue-marked electric energy meter database file, based on the change rate threshold Δe θ , the current electric energy meter is continuously error-checked. The specific steps are: i+1 The data was re-judged, when 1.25Δe θ ≤Δe i+1 <1.5Δe θ When N times in a row based on cycle, obtain the three-phase current data and three-phase voltage data of the relevant electric energy meter through the sensor, obtain the short-term error change rate, and make a short-term continuous judgment on the error change rate. The short-term error change rate is greater than or equal to 1.25Δe θ When the relevant electric energy meter file is marked in red;

[0040] When 1.5Δe θ ≤Δe i+1 <1.75Δe θ When N times in a row based on cycle, obtain the three-phase current data and three-phase voltage data of the relevant electric energy meter through the sensor, obtain the short-term error change rate, and make a short-term continuous judgment on the error change rate. The short-term error change rate is greater than or equal to 1.5Δe θ When the relevant electric energy meter file is marked in red;

[0041] When Δe i+1 ≥1.75Δe θ When N times in a row based on cycle, obtain the three-phase current data and three-phase voltage data of the relevant electric energy meter through the sensor, obtain the short-term error change rate, and make a short-term continuous judgment on the error change rate. The short-term error change rate is greater than or equal to 1.75Δe θ When the relevant electricity meter files are marked in red.

[0042] As a preferred implementation, S4 comprises the following steps:

[0043] S41, based on the fault report during offline electric energy meter fault detection, retrieve relevant electric energy meter files in the electric energy meter database, collect and organize the error data of the electric energy meter before and after the fault occurs, and generate an error change curve model;

[0044] S42. Based on the error change curve model, a curve is generated for the error data in the existing red mark file, and the error change curve model is matched by cosine similarity to pre-judge the fixed type of the electric energy meter and record it in the relevant electric energy meter file.

[0045] As a preferred embodiment, the S41 includes the following sub-steps:

[0046] S411, based on the fault report during offline electric energy meter fault detection, determine the fault type of the relevant electric energy meter, including phase misalignment, frequency mismatch, open circuit fault, and current transformer fault, and collect error data groups in the files of relevant electric energy meters under the same fault in group D;

[0047] S412, smoothing the data by moving average method, and drawing the error curve by Python, the specific steps are:

[0048]

[0049] Among them, S t is the smoothed value at time t, E t-i Represents the value of the original error data at time t i positions forward, and n represents the size of the moving average window;

[0050] Visualize the smoothed error data using Python;

[0051] S413. Based on the Euclidean distance method, similar curve segments are selected for the error curves of group D under the same fault, similar line segments are clustered through hierarchical clustering, the line segment at the cluster center is selected as the representative line segment, and the final fault curve is synthesized in the order of timestamps.

[0052] A real-time detection system for electric energy meter on-site, including a data acquisition module, a real-time detection module, an error trend analysis module, and a fault matching module:

[0053] The data acquisition module includes a current sensor and a voltage sensor, which are used to regularly collect data related to the electric energy meter in real time, and to read the electric energy of the electric energy meter based on a unit time interval, establish an electric energy meter database, and transmit the collected data to subsequent modules;

[0054] The real-time detection module obtains the theoretical electric energy value based on the electric energy calculation formula by using the current and voltage data in unit time, and obtains the electric energy meter error value data by combining the measured electric energy value collected in real time in unit time, and judges and marks the electric energy meter based on the error threshold;

[0055] The error trend analysis module establishes an error database for electric energy meters with errors within the allowable range, records the error data separately, analyzes the error change trend, and marks and observes abnormal electric energy meters in advance;

[0056] The fault matching module extracts the error value data before and after the actual fault of the offline electric energy meter based on the online and offline electric energy meter detection data, further analyzes the obtained error value, generates a fault type curve, and pre-judges the red file error data in the electric energy meter database.

[0057] The beneficial effects of the present invention are:

[0058] 1. The present invention collects error data of electric energy meters, performs trend analysis on the error change rate of electric energy meters within the error allowable range, discovers potential faulty electric energy meters, and marks potential faulty electric energy meters, so as to perform maintenance before the fault actually occurs, thereby avoiding a wider range of power loss or equipment damage;

[0059] 2. The present invention can dynamically adjust the detection cycle according to the risk interval of the error change rate by setting different secondary detection threshold intervals, ensuring that data can be acquired in a targeted manner under different risk levels. At the same time, for risk intervals with a small error change rate, the frequency of data upload is reduced and the judgment threshold is increased, thereby reducing the occupation of communication bandwidth and reducing the probability of misjudgment. For high-risk intervals, high-frequency data upload and lowering of the judgment threshold ensure that abnormalities can be discovered in time and measures can be taken to avoid failure delays;

[0060] 3. The present invention collects error data of related faulty electric energy meters by combining offline electric energy meter fault reports, generates curves for error values, and combines multiple groups of error curves with the same fault to determine the final fault judgment curve. The error curves are matched for abnormally marked electric energy meters in the electric energy meter database, thereby pre-judging electric energy meter faults and improving the work efficiency of operation and maintenance personnel;

[0061] 4. By combining offline fault reporting and error data analysis methods, the present invention can not only improve the accuracy and efficiency of fault diagnosis, but also optimize operation and maintenance strategies, reduce maintenance costs, achieve more efficient real-time detection and maintenance of electric energy meters, and enhance practicality and functionality. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0063] Figure 1 is a flow chart of a method for real-time detection of electric energy performance field according to an embodiment of the present invention;

[0064] Figure 2 It is a block diagram of a real-time detection system for electric energy performance field according to an embodiment of the present invention. DETAILED DESCRIPTION

[0065] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in the field should be able to understand other possible implementations and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0066] According to an embodiment of the present invention, a real-time detection system and a detection method for an electric energy meter are provided. The present invention is further described in conjunction with the accompanying drawings and specific implementation methods.

[0067] Embodiment 1:

[0068] like Figure 1 As shown, according to a method for real-time detection of electric energy performance field according to an embodiment of the present invention, the method comprises the following steps:

[0069] S1. Regularly collect relevant data of the energy meter in real time through sensors, and read the energy reading of the energy meter based on unit time intervals;

[0070] S11. Using current sensors and voltage sensors, collect data related to the on-site electric energy meter in real time based on a periodic T, wherein the voltage sensor is used to measure the three-phase voltage value at the electric energy meter inlet, and the current sensor is used to measure the three-phase current value, and attach a timestamp;

[0071] S12. The electric energy meter readings are read in real time through the Modbus communication protocol and timestamps are attached. The three-phase voltage values, three-phase current values ​​and real-time readings of the electric energy meter are uploaded to the electric energy meter database through Wi-Fi.

[0072] It should be noted that the electricity meter database includes the files of all the electricity meters under its jurisdiction, each of which includes the current location, installation time, and usage time of the electricity meter. At the same time, the three-phase voltage value, three-phase current value and real-time reading of the electricity meter are received in real time via Wi-Fi, and a timestamp is attached and recorded in the relevant electricity meter file. The parameters of the electricity meter are collected once based on the periodic T. Depending on the usage scenario of the three-phase electricity meter, the periodic T is generally 18-36 hours.

[0073] S2. Based on the current and voltage data in unit time, the theoretical electric energy value is calculated using the electric energy calculation formula, and combined with the measured electric energy value collected in real time in unit time, the electric energy meter error value data is obtained, and the electric energy meter is judged and marked based on the error threshold;

[0074] S21. Through the electric energy meter database, extract the three-phase current value and the three-phase voltage value of the electric energy meter to be tested within a unit time based on the timestamp, and obtain the theoretical electric energy value of the electric energy meter to be tested within a unit time t through the calculation formula. The algorithm formula is:

[0075]

[0076] Among them, W 0 Represents the theoretical electric energy value of the electric energy meter to be tested within unit time t, Represents the average three-phase voltage value per unit time t, Represents the average three-phase current value within unit time t;

[0077] S22. Calculate the actual electric energy value of the electric energy meter to be tested within unit time t through the electric energy meter database. The specific algorithm formula is:

[0078] ΔW=W 最终 -W 起始 ;

[0079] Among them, ΔW represents the actual measured energy value of the energy meter to be tested, W 最终 Represents the actual reading of the energy meter at the end of unit time t, W 起始 Represents the actual reading of the energy meter at the beginning of unit time t;

[0080] S23, based on the theoretical electric energy value of the electric energy meter to be tested and the actual electric energy value of the electric energy meter to be tested, calculate and obtain the error value of the electric energy meter to be tested, and the specific algorithm formula is:

[0081] W 1 =|ΔW-W 0 |;

[0082] Among them, W 1 Represents the error value of the electric energy meter, by comparing the error threshold W θ, determine the operating status of the electric energy meter to be tested:

[0083] When W 1 ≥W θ When it is, it means that the electric energy meter to be tested has obvious errors, and the electric energy meter file to be tested in the electric energy meter database is marked in red;

[0084] When W 1 <W θ When , it means that there is no obvious error in the electric energy meter to be tested, and the error value at that time is recorded in the file.

[0085] It should be noted that the error threshold of the electricity meter can be set according to the relevant regulations or provisions in the "DL / T 645-2017 Technical Specifications for Electricity Meters" or the International Electrotechnical Commission (IEC) standards, or it can be set based on empirical methods by consulting experts in related fields. At the same time, according to the usage scenarios and accuracy levels of different electricity meters, the threshold can be flexibly adjusted to meet different usage scenarios. The red mark file indicates that the electricity meter is abnormal and needs offline calibration or maintenance.

[0086] Embodiment 2:

[0087] S3. For electric energy meters with errors within the allowable range, an error database is established to record the error data separately, analyze the error change trend, and mark and observe abnormal electric energy meters in advance;

[0088] S31. Establish an error database in the electric energy meter files under the electric energy meter database, wherein the database includes the error value within the normal range of each record;

[0089] S32. Analyze the error value change trend in the error database, calculate the error change rate, and mark the electric energy meter with abnormal change rate.

[0090] S321. Based on the error data in the error database, the error rate of two adjacent continuous errors is calculated. The specific algorithm formula is:

[0091]

[0092] Among them, e i represents the error rate, E i+1 、E i Represents two adjacent error data in the error database, statistically calculates the error rate and calculates the error change rate Δe based on the two adjacent error rates i , and its algorithm formula is:

[0093] Δe i =e i+1 -e i ;

[0094] Among them, e i+1 、e i Respectively represent two adjacent error rate data;

[0095] S322, based on the error change rate Δe i , analyze the change trend of the current archive electric energy meter, and the specific steps are:

[0096] When any one Δe i With Δe i+1 are greater than 0, and Δe i+1 >Δe θ When , it means that the current electric energy meter error is gradually increasing, and the speed of increase exceeds the change rate threshold Δe θ , mark the current electricity meter file in blue.

[0097] It should be noted that when Δe i When it is greater than 0, it means that the error is changing faster, that is, the error is gradually increasing. i When it is less than 0, it means that the error change rate is slowing down, that is, the error is gradually getting smaller. θ Adjustments can be made to grasp the error change rate according to specific scenario needs, mark possible potential faulty electricity meters, and calculate the statistical characteristics of the change rate by analyzing previous error change rate data, including the mean, standard deviation, and maximum value, and set the change rate threshold based on the statistical results.

[0098] S3221, for the blue-marked electric energy meter database file, based on the change rate threshold Δe θ , the current electric energy meter is continuously error-checked. The specific steps are: i+1 The data was re-judged, when 1.25Δe θ ≤Δe i+1 <1.5Δe θ When N times in a row based on cycle, obtain the three-phase current data and three-phase voltage data of the relevant electric energy meter through the sensor, obtain the short-term error change rate, and make a short-term continuous judgment on the error change rate. The short-term error change rate is greater than or equal to 1.25Δe θ When the relevant electric energy meter file is marked in red;

[0099] When 1.5Δe θ ≤Δe i+1 <1.75Δe θ When N times in a row based on cycle, obtain the three-phase current data and three-phase voltage data of the relevant electric energy meter through the sensor, obtain the short-term error change rate, and make a short-term continuous judgment on the error change rate. The short-term error change rate is greater than or equal to 1.5Δe θ When the relevant electric energy meter file is marked in red;

[0100] When Δe i+1 ≥1.75Δe θ When N times in a row based on cycle, obtain the three-phase current data and three-phase voltage data of the relevant electric energy meter through the sensor, obtain the short-term error change rate, and make a short-term continuous judgment on the error change rate. The short-term error change rate is greater than or equal to 1.75Δe θ When the relevant electricity meter files are marked in red.

[0101] It should be noted that, by making a secondary judgment on the error change rate, based on different error change rate values, different continuous detection schemes are adopted to perform multiple continuous detections on the relevant electric energy meters, where T is the normal detection period T of the relevant electric energy meters. By shortening the regular detection cycle, according to different situations, error data is continuously obtained at different periods to assist in the secondary judgment. The value of N is generally 20, and it can also be set according to the needs of specific usage scenarios. The secondary judgment is divided into intervals to reduce resource waste when uploading continuous data.

[0102] S4. Based on the online and offline electric energy meter detection data, the error value data before and after the actual fault of the offline electric energy meter is extracted, the obtained error value is further analyzed, and the fault is pre-determined;

[0103] S41, based on the fault report during offline electric energy meter fault detection, retrieve relevant electric energy meter files in the electric energy meter database, collect and organize the error data of the electric energy meter before and after the fault occurs, and generate an error change curve model;

[0104] S411, based on the fault report during offline electric energy meter fault detection, determine the fault type of the relevant electric energy meter, including phase misalignment, frequency mismatch, open circuit fault, and current transformer fault, and collect error data groups in the files of relevant electric energy meters under the same fault in group D;

[0105] S412, smoothing the data by moving average method, and drawing the error curve by Python, the specific steps are:

[0106]

[0107] Among them, S t is the smoothed value at time t, E t-iRepresents the value of the original error data at time t i positions forward, and n represents the size of the moving average window;

[0108] Visualize the smoothed error data using Python;

[0109] S413. Based on the Euclidean distance method, similar curve segments are selected for the error curves of group D under the same fault, similar line segments are clustered through hierarchical clustering, the line segment at the cluster center is selected as the representative line segment, and the final fault curve is synthesized in the order of timestamps.

[0110] It should be noted that the error curves of multiple groups under the same fault are processed based on the Euclidean distance method, and the final fault curve is synthesized. Not only the overall similarity of the curves is considered, but also the appropriate representative curves are selected by clustering and central segments to ensure the representativeness and accuracy of the final fault curve. The number of groups D is generally 15, and the number of groups can also be added or reduced according to actual needs.

[0111] S42. Based on the error change curve model, a curve is generated for the error data in the existing red mark file, and the error change curve model is matched by cosine similarity to pre-judge the fixed type of the electric energy meter and record it in the relevant electric energy meter file.

[0112] It should be noted that when the corresponding error curve is not matched, no recording operation is performed. By pre-determining the fault type in advance, offline maintenance personnel can be assisted in preparing for the maintenance of the electric energy meter fault.

[0113] Embodiment 3:

[0114] like Figure 2 As shown, a real-time detection system for electric energy meter on-site includes a data acquisition module, a real-time detection module, an error trend analysis module, and a fault matching module:

[0115] The data acquisition module includes a current sensor and a voltage sensor, which are used to regularly collect data related to the electric energy meter in real time, and to read the electric energy of the electric energy meter based on the unit time interval, establish an electric energy meter database, and transmit the collected data to the subsequent modules;

[0116] The real-time detection module obtains the theoretical electric energy value based on the electric energy calculation formula through the current and voltage data in unit time, and obtains the electric energy meter error value data based on the actual electric energy value collected in real time in unit time, and judges and marks the electric energy meter based on the error threshold;

[0117] The error trend analysis module establishes an error database for electric energy meters with errors within the allowable range, records the error data separately, analyzes the error change trend, and marks and observes abnormal electric energy meters in advance;

[0118] The fault matching module, based on the online and offline electric energy meter detection data, extracts the error value data before and after the actual fault of the offline electric energy meter, further analyzes the obtained error value, generates a fault type curve, and pre-judges the red file error data in the electric energy meter database.

[0119] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for real-time detection of electric energy meter field, characterized in that: The method comprises the following steps: S1. Regularly collect relevant data of the energy meter in real time through sensors, and read the energy reading of the energy meter based on unit time intervals; S2. Based on the current and voltage data in unit time, the theoretical electric energy value is calculated using the electric energy calculation formula, and combined with the measured electric energy value collected in real time in unit time, the electric energy meter error value data is obtained, and the electric energy meter is judged and marked based on the error threshold; S3. For electric energy meters with errors within the allowable range, an error database is established to record the error data separately, analyze the error change trend, and mark and observe abnormal electric energy meters in advance; S4. Based on the online and offline electric energy meter detection data, the error value data of the offline electric energy meter before and after the actual fault is extracted, the obtained error value is further analyzed, and the fault is pre-determined.

2. The real-time detection method of electric energy performance field according to claim 1 is characterized in that: The S1 comprises the following sub-steps: S11. Using current sensors and voltage sensors, collect data related to the on-site electric energy meter in real time based on a periodic T, wherein the voltage sensor is used to measure the three-phase voltage value at the electric energy meter inlet, and the current sensor is used to measure the three-phase current value, and attach a timestamp; S12. The electric energy meter readings are read in real time through the Modbus communication protocol and timestamps are attached. The three-phase voltage values, three-phase current values ​​and real-time readings of the electric energy meter are uploaded to the electric energy meter database through Wi-Fi.

3. The real-time detection method of electric energy performance field according to claim 1 is characterized in that: The S2 comprises the following sub-steps: S21. Through the electric energy meter database, extract the three-phase current value and the three-phase voltage value of the electric energy meter to be tested within a unit time based on the timestamp, and obtain the theoretical electric energy value of the electric energy meter to be tested within a unit time t through the calculation formula. The algorithm formula is: Among them, W0 represents the theoretical electric energy value of the electric energy meter to be tested within unit time t, Represents the average three-phase voltage value per unit time t, Represents the average three-phase current value within unit time t; S22. Calculate the actual electric energy value of the electric energy meter to be tested within unit time t through the electric energy meter database. The specific algorithm formula is: ΔW=W 最终 -IN 起始 ; Among them, ΔW represents the actual measured energy value of the energy meter to be tested, W 最终 Represents the actual reading of the energy meter at the end of unit time t, W 起始 Represents the actual reading of the energy meter at the beginning of unit time t; S23, based on the theoretical electric energy value of the electric energy meter to be tested and the actual electric energy value of the electric energy meter to be tested, calculate and obtain the error value of the electric energy meter to be tested, and the specific algorithm formula is: W1=|ΔW-W0|; Among them, W1 represents the error value of the electric energy meter. By comparing the error threshold W θ , determine the operating status of the electric energy meter to be tested: When W1≥W θ When it is, it means that the electric energy meter to be tested has obvious errors, and the electric energy meter file to be tested in the electric energy meter database is marked in red; When W1<W θ When , it means that there is no obvious error in the electric energy meter to be tested, and the error value at that time is recorded in the file.

4. The method for real-time detection of electric energy performance field according to claim 1, characterized in that: The S3 comprises the following sub-steps: S31. Establish an error database in the electric energy meter files under the electric energy meter database, wherein the database includes the error value within the normal range of each record; S32. Analyze the error value change trend in the error database, calculate the error change rate, and mark the electric energy meter with abnormal change rate.

5. The real-time detection method of electric energy performance field according to claim 4 is characterized in that: The S32 includes the following sub-steps: S321. Based on the error data in the error database, the error rate of two adjacent continuous errors is calculated. The specific algorithm formula is: Among them, e i represents the error rate, E i+1 、E i Represents two adjacent error data in the error database, statistically calculates the error rate and calculates the error change rate Δe based on the two adjacent error rates i , and its algorithm formula is: Δe i =e i+1 -e i ; Among them, e i+1 、e i Respectively represent two adjacent error rate data; S322, based on the error change rate Δe i , analyze the change trend of the current archive electric energy meter, and the specific steps are: When any one Δe i With Δe i+1 are greater than 0, and Δe i+1 >Δe θ When , it means that the current electric energy meter error is gradually increasing, and the speed of increase exceeds the change rate threshold Δe θ , mark the current electricity meter file in blue.

6. The real-time detection method of electric energy performance field according to claim 5 is characterized in that: The S322 includes the following sub-steps: S3221, for the blue-marked electric energy meter database file, based on the change rate threshold Δe θ , the current electric energy meter is continuously error-checked. The specific steps are: i+1 The data was re-judged, when 1.25Δe θ ≤Δe i+1 <1.5Δe θ When N times in a row based on cycle, obtain the three-phase current data and three-phase voltage data of the relevant electric energy meter through the sensor, obtain the short-term error change rate, and make a short-term continuous judgment on the error change rate. The N short-term error change rates are all greater than or equal to 1.25Δe θ When the relevant electric energy meter file is marked in red; When 1.5Δe θ ≤Δe i+1 <1.75Δe θ When N times in a row based on cycle, obtain the three-phase current data and three-phase voltage data of the relevant electric energy meter through the sensor, obtain the short-term error change rate, and make a short-term continuous judgment on the error change rate. The N short-term error change rates are all greater than or equal to 1.5Δe θ When the relevant electric energy meter file is marked in red; When Δe i+1 ≥1.75Δe θ When N times in a row based on cycle, obtain the three-phase current data and three-phase voltage data of the relevant electric energy meter through the sensor, obtain the short-term error change rate, and make a short-term continuous judgment on the error change rate. The N short-term error change rates are all greater than or equal to 1.75Δe θ When the relevant electricity meter files are marked in red.

7. The real-time detection method of electric energy performance field according to claim 1 is characterized in that: The S4 comprises the following steps: S41, based on the fault report during offline electric energy meter fault detection, retrieve relevant electric energy meter files in the electric energy meter database, collect and organize the error data of the electric energy meter before and after the fault occurs, and generate an error change curve model; S42. Based on the error change curve model, a curve is generated for the error data in the existing red mark file, and the error change curve model is matched by cosine similarity to pre-judge the fixed type of the electric energy meter and record it in the relevant electric energy meter file.

8. The method for real-time detection of electric energy performance field according to claim 7, characterized in that: The S41 comprises the following sub-steps: S411, based on the fault report during offline electric energy meter fault detection, determine the fault type of the relevant electric energy meter, including phase misalignment, frequency mismatch, open circuit fault, and current transformer fault, and collect error data groups in the files of relevant electric energy meters under the same fault in group D; S412, smoothing the data by moving average method, and drawing the error curve by Python, the specific steps are: Among them, S t is the smoothed value at time t, E t-i Represents the value of the original error data at time t i positions forward, and n represents the size of the moving average window; Visualize the smoothed error data using Python; S413. Based on the Euclidean distance method, similar curve segments are selected for the error curves of group D under the same fault, similar line segments are clustered through hierarchical clustering, the line segment at the cluster center is selected as the representative line segment, and the final fault curve is synthesized in the order of timestamps.

9. A real-time detection system for electric energy meter field, characterized in that: The method adopts the real-time detection method of the electric energy meter field according to any one of claims 1 to 8, including a data acquisition module, a real-time detection module, an error trend analysis module, and a fault matching module: The data acquisition module includes a current sensor and a voltage sensor, which are used to regularly collect data related to the electric energy meter in real time, and at the same time, read the electric energy of the electric energy meter based on the unit time interval, establish an electric energy meter database, and transmit the collected data to the subsequent modules; The real-time detection module obtains the theoretical electric energy value based on the electric energy calculation formula by using the current and voltage data in unit time, and obtains the electric energy meter error value data by combining the measured electric energy value collected in real time in unit time, and judges and marks the electric energy meter based on the error threshold; The error trend analysis module establishes an error database for electric energy meters with errors within the allowable range, records the error data separately, analyzes the error change trend, and marks and observes abnormal electric energy meters in advance; The fault matching module extracts the error value data before and after the actual fault of the offline electric energy meter based on the online and offline electric energy meter detection data, further analyzes the obtained error value, generates a fault type curve, and pre-judges the red file error data in the electric energy meter database.

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