Electric energy metering device state prediction method

Abstract

The invention discloses an electric energy metering device state prediction method, and belongs to the field of electric energy metering devices. The electric energy metering device state prediction method comprises the following steps: S1, predicting fault time and residual life of an electric energy meter; s2, judging an operation state; s3, predicting the health degree of the electric energy metering device; s4, predicting the error state of the electric energy metering device; and S5, comprehensively evaluating the state of the electric energy metering device. The method solves the problem that the existing operation state evaluation method cannot predict the health and fault conditions of the electric energy metering device and thus cannot make corresponding countermeasures in advance, can timely know the residual life of the time machine when the fault of the electric energy metering device occurs, facilitates the timely making of a maintenance plan, and improves the maintenance efficiency of the electric energy metering device. The workload of the electric energy metering device is reduced, the accuracy of the prediction result is improved, and the maintenance efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy metering devices, and in particular to a state prediction method for electric energy metering devices. Background Art

[0002] The electric energy metering device is a metering device used to measure and record the amount of power generated, the amount of power supplied, the amount of power consumed by the plant, the amount of power lost by the line and the amount of power consumed by the user. The electric energy metering device refers to the whole composed of the electric energy meter, the metering transformer and the secondary connecting wire. The electric energy metering device is the sum of all meters, components, circuits and their related transformers, lead seals, etc. in the meter box (or meter cabinet). It is used to measure the user's power consumption and has anti-destruction function.

[0003] The Chinese patent with publication number CN104880691A discloses a method for evaluating the operating status of a gateway electric energy metering device, which is characterized by comprising the following steps: 1) collecting the original data of the on-site gateway electric energy metering device through the online monitoring system of the remote metering device, and uploading the original data to the data server of the upper PC; 2) the upper PC classifies the original data, and establishes a mathematical model for evaluating the operation of the gateway electric energy metering device according to the classified original data; 3) according to the established mathematical model for evaluating the operation of the gateway electric energy metering device, the operation status level of the gateway electric energy metering device is evaluated, and a gateway metering device operation evaluation system is established, and the operation trend of the gateway electric energy metering device is predicted according to the gateway metering device operation evaluation system and an early warning is issued. Compared with the prior art, this patent has the advantages of scientific rationality, a large number of specific research objects, a wide range of coverage, systematization, and intelligence.

[0004] In actual use, the operating status evaluation method of the electric energy metering device in the above patent can only evaluate the operating status of the electric energy metering device, and cannot predict the health and fault conditions of the electric energy metering device. Therefore, corresponding countermeasures cannot be made in advance for the health status of the electric energy metering device, which affects the use of the electric energy metering device; it does not meet the existing needs. In this regard, we propose a state prediction method for electric energy metering devices. Summary of the invention

[0005] The object of the present invention is to provide a state prediction method for an electric energy metering device, which facilitates and timely formulates a maintenance plan by predicting the failure time and remaining life of the electric energy meter, avoids temporarily customizing a maintenance plan when a failure occurs, resulting in a long maintenance time and affecting the normal use of the electric energy metering device, can timely understand the operating state of the electric energy metering device, make corresponding adjustments according to the operating state, reduce the workload of the electric energy metering device, can timely understand the health state of the electric energy metering device, avoid the electric energy metering device working for a long time in a poor health state, affecting normal work, can analyze and predict the state of the electric energy metering device in combination with different situations, improves the accuracy of the prediction result, improves the pertinence of the operation and maintenance of the electric energy metering device, saves maintenance time, improves maintenance efficiency, and solves the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a method for predicting the state of an electric energy metering device, comprising the following steps:

[0007] S1: Obtain the historical operation fault data of the electric energy metering device, calculate the probability of fault triggering, use the homogeneous Poisson process to describe the relationship between the number of abnormal events and time, and predict the fault time and remaining life of the electric energy meter through the fault prediction module.

[0008] S2: Obtain historical operating status data of the electric energy metering device, and determine the operating status through an operating status evaluation module based on the historical operating status data.

[0009] S3: Obtain the operating time and seal data of the electric energy metering device, and predict the health of the electric energy metering device through a health status assessment module according to the operating time, seal data and historical operating fault data of the electric energy metering device.

[0010] S4: Obtain historical error data of the electric energy metering device, analyze the historical error sequence, and predict the error state of the electric energy metering device according to the analysis result.

[0011] S5: Comprehensively evaluate the status of the electric energy metering device through a comprehensive evaluation module according to the predicted failure time, operating status, health status and error status of the electric energy metering device, and formulate a dynamic maintenance plan according to the comprehensive evaluation results of the electric energy metering device.

[0012] Preferably, the prediction module comprises:

[0013] The data storage module is used to obtain the operating time, seal data and historical operating fault data of the electric energy meter, current transformer, voltage transformer and secondary circuit in the electric energy metering device, and classify and store the obtained data.

[0014] The fault database is used to collect historical fault types, fault type-related parameters and parameter fault thresholds of electric energy meters, current transformers, voltage transformers and secondary circuits in electric energy metering devices.

[0015] The monitoring module is used to monitor the real-time status of the electric energy meter, current transformer, voltage transformer and various parameters of the secondary circuit in real time according to the parameter status threshold in the fault database.

[0016] The diagnostic module is used to analyze the collected data on the abnormal parameter status of the electric energy meter, current transformer, voltage transformer and secondary circuit, and use a two-part judgment method consisting of probabilistic judgment and continuous judgment to diagnose whether the abnormal status is an instantaneous abnormal alarm or a fault. If a fault occurs, the fault point is marked.

[0017] The prediction module is used to predict the future status of parameters based on the historical data of the power meter, current transformer, voltage transformer and secondary circuit parameter status, and to diagnose the future status of the power meter, current transformer, voltage transformer and secondary circuit parameters based on the various parameters in the fault database, and predict the time when the fault will occur.

[0018] Preferably, the data storage module comprises:

[0019] A real-time acquisition module, used for real-time acquisition of data information in the electric energy metering device, wherein the types of data information include the operation time, seal data and historical operation fault data of the electric energy meter, current transformer, voltage transformer and secondary circuit;

[0020] The first unit time setting module is used to set the first unit time length, wherein the first unit time length is obtained by the following formula:

[0021] T 01 =(1-(X 01 -X 02 ))·T max

[0022]

[0023] Among them, T 01 Indicates the length of the first unit of time; X 01 and X 02 Represent the first parameter and the second parameter respectively; T max Indicates the maximum value of the data collection time interval in the data type; n indicates the number of data information types; T i represents the data collection time interval of the i-th data type; T p represents the average time interval for data collection; T minIndicates the minimum value of the data collection time interval in the data type; λ indicates the adjustment parameter, and the value range of λ is 0.32-0.37;

[0024] A type extraction module, used to extract and classify the data information collected within a first unit time length, and obtain the data information type of the data collected within the first unit time length;

[0025] A comparison module, configured to compare the data information type of the data collected within the first unit time length with the type of data information, and determine whether the number of the data information type of the data collected within the first unit time length and the number of the type of data information are the same;

[0026] A first collection control module, configured to collect data information in the electric energy metering device according to the first unit time length when the data information type of the data collected within the first unit time length is the same as the number of the data information types;

[0027] A second unit time setting module, configured to adjust the first unit time length to obtain a second unit time length when the data information type of the data collected within the first unit time length is different from the number of data information types;

[0028] A second collection control module, used for collecting data information in the electric energy metering device according to the second unit time length;

[0029] The collected data information is classified and stored according to the type of data information.

[0030] Preferably, the second unit time setting module includes:

[0031] a target data type extraction module, configured to extract the type of data information missing in the first unit time length as the target data type when the data information type of the data collected in the first unit time length is different from the number of types of data information;

[0032] A time position extraction module, used to extract the time positions where all target data types appear within two consecutive first unit time lengths;

[0033] A first adjustment module, configured to adjust the first unit time length by the following formula to obtain a second unit time length if the time positions at which all the target data types appear are less than 38% of the first unit time length;

[0034] T 02 =T 01 +ΔT

[0035]

[0036] Among them, T 02 represents the second unit time length; ΔT represents the compensation time amount; T 01 represents the first unit time length; P represents the ratio of the time positions of all occurrences of the target data type to the first unit time length; n represents the number of data information types; T i represents the data collection time interval of the i-th data type; T fi Represents the floating change in the data collection time interval of the i-th data type;

[0037] A second adjustment module is configured to adjust the first unit time length by using the following formula to obtain a second unit time length if the time positions at which all the target data types appear are not less than 38% of the first unit time length;

[0038] T 02 =2T 01 -ΔT

[0039]

[0040] Among them, T 02 represents the second unit time length; ΔT represents the compensation time amount; T 01 represents the first unit time length; P represents the ratio of the time positions of all occurrences of the target data type to the first unit time length; n represents the number of data information types; T i represents the data collection time interval of the i-th data type; T fi Indicates the floating change in the data collection time interval of the i-th data type.

[0041] Preferably, the monitoring process of the monitoring module specifically includes:

[0042] Firstly, the real-time status of the energy meter, current transformer, voltage transformer and various parameters of the secondary circuit are monitored in real time according to the parameter status thresholds in the fault database.

[0043] If the parameter status exceeds the threshold limit, it is considered that the system has an abnormal condition, and the abnormal condition is transmitted to the diagnosis module.

[0044] If the parameter status does not cross the threshold limit, the status is transmitted to the prediction module.

[0045] Preferably, the operating status assessment module includes:

[0046] Collect basic information data and on-site operation data of the electric energy metering device, and calculate the operation status value of the electric energy metering device based on the collected operation data.

[0047] The operating state information is analyzed according to the operating state value of the electric energy metering device, and the state information includes short-term state information and long-term state information.

[0048] A short-term comprehensive state information matrix is ​​established according to the short-term state information, and the operating states of the electric energy meter, current transformer and voltage transformer are short-term evaluated through the short-term comprehensive state information matrix.

[0049] A long-term comprehensive status information matrix is ​​established based on the long-term status information, and the operating status of the electric energy meter, current transformer and voltage transformer is comprehensively evaluated through the long-term comprehensive status information matrix.

[0050] Preferably, the running status value calculation includes:

[0051] The basic information data and on-site operation data of the electric energy metering device are obtained respectively. The basic information data and on-site operation data of the electric energy metering device include basic information data, detection data, monitoring data, family defect data of the electric energy meter, current transformer and voltage transformer, and detection data and monitoring data of the secondary circuit.

[0052] The operation status value of the electric energy meter is calculated based on the basic information data of the electric energy meter, the detection data of the electric energy meter, the monitoring data of the electric energy meter, and the family defect data of the electric energy meter.

[0053] The current transformer operating status value is calculated based on the current transformer basic information data, current transformer detection data, current transformer monitoring data, and current transformer family defect data.

[0054] The voltage transformer operating status value is calculated based on the voltage transformer basic information data, voltage transformer detection data, voltage transformer monitoring data, and voltage transformer family defect data.

[0055] The secondary circuit operating status value and the operating status value of the metering device are calculated based on the secondary circuit detection data and the secondary circuit monitoring data.

[0056] Preferably, the assessment process of the health status assessment module specifically includes:

[0057] Obtain health influencing factors of the electric energy metering device and scores of the corresponding health influencing factors, where the health influencing factors include: lifespan of the electric energy metering device, performance of the electric energy metering device, equipment error of the electric energy metering device, temperature of the environment in which the electric energy metering device is located, and humidity of the environment in which the electric energy metering device is located.

[0058] Based on the acquired health influencing factors, a comparison matrix is ​​established. The comparison matrix represents the relative importance between any two health influencing factors. Based on the scores of the health influencing factors and the comparison matrix, a health score is obtained.

[0059] Preferably, the historical error data acquisition of the energy metering device specifically includes:

[0060] According to the historical operation status of the electric energy metering device, the historical power and electric energy of the electric energy metering device during a certain period of time are calculated and converted into a first pulse signal.

[0061] The error test equipment is used to calculate the operating power of the electric energy metering device within the same time period and convert it into a second pulse signal.

[0062] The error state is calculated according to the first pulse signal and the second pulse signal, and the historical error data of the electric energy metering device is obtained.

[0063] Preferably, the comprehensive evaluation module includes:

[0064] The acquisition module is used to obtain comprehensive evaluation indicators of the electric energy metering device, and the evaluation indicators include: fault prediction time, operating status, error status, health status and configuration mode of the electric energy metering device.

[0065] The processing module is used to calculate the score of the state of the electric energy metering device according to the obtained comprehensive evaluation index, and calculate the normalized element matrix according to the scoring scheme.

[0066] The judgment module is used to score the judgment criteria in the normalized element matrix in pairs to obtain the weight matrix of the judgment criteria.

[0067] The evaluation module is used to evaluate and score the electric energy metering device, analyze the evaluation results of the electric energy metering device, and determine the state of the electric energy metering device according to the analysis results.

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] By predicting the failure time and remaining life of the electric energy meter, the present invention can timely understand the failure time and remaining life of the electric energy metering device, facilitate timely formulation of maintenance plans, avoid temporarily customizing maintenance plans when a failure occurs, resulting in long maintenance time and affecting the normal use of the electric energy metering device, and by determining the operating status, the operating status of the electric energy metering device can be timely understood, and corresponding adjustments can be made according to the operating status, thereby reducing the workload of the electric energy metering device; by predicting the health status, the health status of the electric energy metering device can be timely understood, and the electric energy metering device can be avoided from working for a long time in a poor health status, affecting normal work; by comprehensively evaluating the electric energy metering device, the status of the electric energy metering device can be analyzed and predicted in combination with different situations, thereby improving the accuracy of the prediction results; and a dynamic maintenance plan can be formulated according to the health evaluation results of the electric energy metering device, thereby improving the pertinence of the operation and maintenance of the electric energy metering device, saving maintenance time and improving maintenance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 A prediction flow chart of a method for predicting a state of an electric energy metering device according to the present invention;

[0071] Figure 2 It is a module schematic diagram of a method for predicting the state of an electric energy metering device according to the present invention. DETAILED DESCRIPTION

[0072] 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.

[0073] In order to solve the problem that the existing evaluation method of the operating status of the electric energy metering device can only evaluate the operating status of the electric energy metering device in actual use, but cannot predict the health and fault conditions of the electric energy metering device, so it is impossible to make corresponding countermeasures in advance for the health status of the electric energy metering device, which affects the use of the electric energy metering device, please refer to Figure 1 , this embodiment provides the following technical solutions:

[0074] A method for predicting the state of an electric energy metering device comprises the following steps:

[0075] S1: Obtain the historical operation fault data of the electric energy metering device, calculate the probability of fault triggering, use the homogeneous Poisson process to describe the relationship between the number of abnormal events and time, and predict the fault time and remaining life of the electric energy meter through the fault prediction module. It can timely understand the time when the fault occurs and the remaining life of the electric energy metering device, facilitate timely formulation of maintenance plans, and avoid temporary customization of maintenance plans when a fault occurs, resulting in long maintenance time and affecting the normal use of the electric energy metering device.

[0076] S2: Obtain the historical operating status data of the electric energy metering device, and determine the operating status through the operating status evaluation module based on the historical operating status data, so as to timely understand the operating status of the electric energy metering device and make corresponding adjustments according to the operating status, thereby reducing the workload of the electric energy metering device.

[0077] S3: Obtain the operating time and seal data of the electric energy metering device, and predict the health of the electric energy metering device through the health status assessment module based on the operating time, seal data and historical operating fault data of the electric energy metering device. By predicting the health status, the health status of the electric energy metering device can be understood in time to avoid the electric energy metering device working in a poor health state for a long time, which affects normal work.

[0078] S4: Obtain historical error data of the electric energy metering device, analyze the historical error sequence, predict the error state of the electric energy metering device based on the analysis results, and better predict the state of the electric energy metering device based on the error state, make corresponding solutions in advance, and ensure the normal operation of the electric energy metering device.

[0079] S5: Based on the predicted failure time, operating status, health status and error status of the electric energy metering device, the comprehensive evaluation module is used to comprehensively evaluate the status of the electric energy metering device, and a dynamic maintenance plan is formulated based on the comprehensive evaluation results of the electric energy metering device. This improves the pertinence of the operation and maintenance of the electric energy metering device to a certain extent. The electric energy metering device with a higher health assessment score indicates that its operation is stable and the on-site inspection of the next cycle can be skipped. The electric energy metering device with a lower health assessment score requires an on-site inspection immediately or as soon as possible to eliminate risks in time.

[0080] Prediction module, including:

[0081] The data storage module is used to obtain the operating time, seal data and historical operating fault data of the electric energy meter, current transformer, voltage transformer and secondary circuit in the electric energy metering device, and classify and store the obtained data.

[0082] The fault database is used to collect historical fault types, fault type-related parameters and parameter fault thresholds of electric energy meters, current transformers, voltage transformers and secondary circuits in electric energy metering devices.

[0083] The monitoring module is used to monitor the real-time status of the electric energy meter, current transformer, voltage transformer and various parameters of the secondary circuit in real time according to the parameter status threshold in the fault database to realize autonomous fault diagnosis.

[0084] The diagnostic module is used to analyze the collected data on the abnormal parameter status of the electric energy meter, current transformer, voltage transformer and secondary circuit, and uses a two-part judgment method consisting of probabilistic judgment and continuous judgment to diagnose whether the abnormal status is an instantaneous abnormal alarm or a fault. If a fault occurs, the fault point is marked so that the fault can be handled in time, thereby improving the efficiency of fault handling.

[0085] The prediction module is used to predict the future state of parameters based on the historical data of the electric energy meter, current transformer, voltage transformer and secondary circuit parameter state, and to diagnose the future state of the electric energy meter, current transformer, voltage transformer and secondary circuit parameters based on the various parameters in the fault database, predict the time of occurrence of the fault, and thus complete the fault prediction. It can realize fault prediction of the electric energy metering device, and then understand the working state of the electric energy metering device. At the same time, it can prevent faults according to the results of fault prediction, thereby ensuring the normal operation of the electric energy metering device.

[0086] Specifically, the data storage module includes:

[0087] A real-time acquisition module, used for real-time acquisition of data information in the electric energy metering device, wherein the types of data information include the operation time, seal data and historical operation fault data of the electric energy meter, current transformer, voltage transformer and secondary circuit;

[0088] The first unit time setting module is used to set the first unit time length, wherein the first unit time length is obtained by the following formula:

[0089] T 01 =(1-(X 01 -X 02 ))·T max

[0090]

[0091] Among them, T 01 Indicates the length of the first unit of time; X 01 and X 02 Represent the first parameter and the second parameter respectively; T maxIndicates the maximum value of the data collection time interval in the data type; n indicates the number of data information types; T i represents the data collection time interval of the i-th data type; T p represents the average time interval for data collection; T min Indicates the minimum value of the data collection time interval in the data type; λ indicates the adjustment parameter, and the value range of λ is 0.32-0.37;

[0092] A type extraction module, used to extract and classify the data information collected within a first unit time length, and obtain the data information type of the data collected within the first unit time length;

[0093] A comparison module, configured to compare the data information type of the data collected within the first unit time length with the type of data information, and determine whether the number of the data information type of the data collected within the first unit time length and the number of the type of data information are the same;

[0094] A first collection control module, configured to collect data information in the electric energy metering device according to the first unit time length when the data information type of the data collected within the first unit time length is the same as the number of the data information types;

[0095] A second unit time setting module, configured to adjust the first unit time length to obtain a second unit time length when the data information type of the data collected within the first unit time length is different from the number of data information types;

[0096] A second collection control module, used for collecting data information in the electric energy metering device according to the second unit time length;

[0097] The collected data information is classified and stored according to the type of data information.

[0098] The technical effects of the above technical solution are: Real-time data collection: The module can collect the operation time, seal data and historical operation fault data of the electric energy meter, current transformer, voltage transformer and secondary circuit in real time. This helps to obtain various data information of the electric energy metering device in a timely manner.

[0099] Dynamic unit time setting: By using formulas and parameters for calculation, the first unit time length can be set to best suit the collection needs of different data information types. This allows the frequency of data collection to be adjusted according to specific circumstances.

[0100] Data classification and comparison: The data extraction module is used to classify the collected data information to determine the type of data information collected in the first unit time. The comparison module is used to compare the information type of the collected data with the known data information type to ensure that the collected data type matches the expected data type quantity.

[0101] Adaptive acquisition control: The first acquisition control module ensures that when the type of data information collected is the same as the number of data information types, data acquisition is performed according to the first unit time length. If the number does not match, the second unit time setting module will dynamically adjust the time length and use the second unit time length for data acquisition. This adaptive control ensures efficient data acquisition.

[0102] Data storage: The collected data information will be classified and stored according to its type for subsequent data processing and analysis.

[0103] The technical effect of the above technical solution of this embodiment is to realize an intelligent and adaptive data acquisition system, which can dynamically adjust the acquisition frequency according to the different types of data information to ensure the accuracy and efficiency of the data. This is of great significance for the monitoring and data management of electric energy metering devices.

[0104] Specifically, the second unit time setting module includes:

[0105] a target data type extraction module, configured to extract the type of data information missing in the first unit time length as the target data type when the data information type of the data collected in the first unit time length is different from the number of types of data information;

[0106] A time position extraction module, used to extract the time positions where all target data types appear within two consecutive first unit time lengths;

[0107] A first adjustment module, configured to adjust the first unit time length by the following formula to obtain a second unit time length if the time positions at which all the target data types appear are less than 38% of the first unit time length;

[0108] T 02 =T 01 +ΔT

[0109]

[0110] Among them, T 02 represents the second unit time length; ΔT represents the compensation time amount; T 01represents the first unit time length; P represents the ratio of the time positions of all occurrences of the target data type to the first unit time length; n represents the number of data information types; T i represents the data collection time interval of the i-th data type; T fi Represents the floating change in the data collection time interval of the i-th data type;

[0111] A second adjustment module is configured to adjust the first unit time length by using the following formula to obtain a second unit time length if the time positions at which all the target data types appear are not less than 38% of the first unit time length;

[0112] T 02 =2T 01 -ΔT

[0113]

[0114] Among them, T 02 represents the second unit time length; ΔT represents the compensation time amount; T 01 represents the first unit time length; P represents the ratio of the time positions of all occurrences of the target data type to the first unit time length; n represents the number of data information types; T i represents the data collection time interval of the i-th data type; T fi Indicates the floating change in the data collection time interval of the i-th data type.

[0115] The technical effects of the above technical solution are: Target data type extraction: When the data information type collected in the first unit time does not match the known number of data information types, this module is used to extract the missing data information type as the target data type. This helps the system understand the type of data that needs to be collected and ensures the integrity of data collection.

[0116] Time position extraction: The time position extraction module is used to determine the time positions of all occurrences of the target data type within two consecutive first unit time lengths. This allows the system to understand the time distribution of the target data type.

[0117] Dynamic time adjustment: The first adjustment module and the second adjustment module are respectively used to dynamically adjust the first unit time length according to the time distribution of the target data type to obtain the second unit time length. If the position of the target data type in the first unit time is lower than 38%, the second unit time length will be increased to better capture these data. On the contrary, if the position of the target data type in the first unit time is not lower than 38%, the second unit time length can be reduced to improve the efficiency of acquisition.

[0118] The technical effect of the above technical solution of this embodiment is to realize an intelligent and adaptive data acquisition system, which can dynamically adjust the data acquisition frequency according to the different types of data information and their time distribution to ensure the efficiency and integrity of data acquisition. This is of great significance for the monitoring and data management of electric energy metering devices, and can better adapt to actual data conditions and improve the accuracy and efficiency of data acquisition.

[0119] The monitoring process of the monitoring module specifically includes:

[0120] Firstly, the real-time status of the energy meter, current transformer, voltage transformer and various parameters of the secondary circuit are monitored in real time according to the parameter status thresholds in the fault database.

[0121] If the parameter status exceeds the threshold limit, it is considered that the system has an abnormal condition, and the abnormal condition is transmitted to the diagnosis module.

[0122] If the parameter status does not cross the threshold limit, the status is transmitted to the prediction module.

[0123] Operation status assessment module, including:

[0124] Collect basic information data and on-site operation data of the electric energy metering device, and calculate the operation status value of the electric energy metering device based on the collected operation data.

[0125] The operating state information is analyzed according to the operating state value of the electric energy metering device, and the state information includes short-term state information and long-term state information.

[0126] A short-term comprehensive state information matrix is ​​established according to the short-term state information, and the operating states of the electric energy meter, current transformer and voltage transformer are short-term evaluated through the short-term comprehensive state information matrix.

[0127] A long-term comprehensive status information matrix is ​​established based on the long-term status information, and the operating status of the electric energy meter, current transformer and voltage transformer is comprehensively evaluated through the long-term comprehensive status information matrix.

[0128] Operation status value calculation, including:

[0129] The basic information data and on-site operation data of the electric energy metering device are obtained respectively. The basic information data and on-site operation data of the electric energy metering device include basic information data, detection data, monitoring data, family defect data of the electric energy meter, current transformer and voltage transformer, and detection data and monitoring data of the secondary circuit.

[0130] The operation status value of the electric energy meter is calculated based on the basic information data of the electric energy meter, the detection data of the electric energy meter, the monitoring data of the electric energy meter, and the family defect data of the electric energy meter.

[0131] The current transformer operating status value is calculated based on the current transformer basic information data, current transformer detection data, current transformer monitoring data, and current transformer family defect data.

[0132] The voltage transformer operating status value is calculated based on the voltage transformer basic information data, voltage transformer detection data, voltage transformer monitoring data, and voltage transformer family defect data.

[0133] The secondary circuit operating status value and the operating status value of the metering device are calculated based on the secondary circuit detection data and the secondary circuit monitoring data.

[0134] The assessment process of the health status assessment module specifically includes:

[0135] Obtain health influencing factors of the electric energy metering device and scores of the corresponding health influencing factors, where the health influencing factors include: lifespan of the electric energy metering device, performance of the electric energy metering device, equipment error of the electric energy metering device, temperature of the environment in which the electric energy metering device is located, and humidity of the environment in which the electric energy metering device is located.

[0136] Based on the acquired health influencing factors, a comparison matrix is ​​established. The comparison matrix represents the relative importance between any two health influencing factors. Based on the scores of the health influencing factors and the comparison matrix, a health score is obtained.

[0137] Acquisition of historical error data of energy metering devices, including

[0138] According to the historical operation status of the electric energy metering device, the historical power and electric energy of the electric energy metering device during a certain period of time are calculated and converted into a first pulse signal.

[0139] The error test equipment is used to calculate the operating power of the electric energy metering device within the same time period and convert it into a second pulse signal.

[0140] The error state is calculated according to the first pulse signal and the second pulse signal, and the historical error data of the electric energy metering device is obtained.

[0141] Comprehensive evaluation module, including:

[0142] The acquisition module is used to obtain comprehensive evaluation indicators of the electric energy metering device, and the evaluation indicators include: fault prediction time, operating status, error status, health status and configuration mode of the electric energy metering device.

[0143] The processing module is used to calculate the score of the state of the electric energy metering device according to the obtained comprehensive evaluation index, and calculate the normalized element matrix according to the scoring scheme.

[0144] The judgment module is used to score the judgment criteria in the normalized element matrix in pairs to obtain the weight matrix of the judgment criteria.

[0145] The evaluation module is used to evaluate and score the electric energy metering device and analyze the evaluation results of the electric energy metering device. The status of the electric energy metering device is judged according to the analysis results. The higher the score, the better the status of the electric energy metering device and the higher the reliability. The status of the electric energy metering device can be analyzed and predicted in combination with different situations, thereby improving the accuracy of the prediction results. A dynamic maintenance plan can be formulated according to the health evaluation results of the electric energy metering device, thereby improving the pertinence of the operation and maintenance of the electric energy metering device, saving maintenance time and improving maintenance efficiency.

[0146] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0147] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for predicting the state of an electric energy metering device, characterized in that; The following steps are involved: S1: Obtain the historical operation fault data of the electric energy metering device, calculate the probability of fault triggering, use the homogeneous Poisson process to describe the relationship between the number of abnormal events and time, and predict the fault time and remaining life of the electric energy meter through the fault prediction module; S2: Acquire historical operating status data of the electric energy metering device, and determine the operating status of the device through an operating status evaluation module based on the historical operating status data; S3: Obtaining the operating time and seal data of the electric energy metering device, and predicting the health of the electric energy metering device through a health status assessment module according to the operating time, seal data and historical operating fault data of the electric energy metering device; S4: Acquire historical error data of the electric energy metering device, analyze the historical error sequence, and predict the error state of the electric energy metering device according to the analysis result; S5: Comprehensively evaluate the status of the electric energy metering device through a comprehensive evaluation module according to the predicted failure time, operating status, health status and error status of the electric energy metering device, and formulate a dynamic maintenance plan according to the comprehensive evaluation results of the electric energy metering device.

2. The method for predicting the state of an electric energy metering device according to claim 1, characterized in that: The prediction module comprises: A data storage module is used to obtain the operation time, seal data and historical operation fault data of the electric energy meter, current transformer, voltage transformer and secondary circuit in the electric energy metering device, and classify and store the obtained data; A fault database is used to collect historical fault types, fault type-related parameters and parameter fault thresholds of electric energy meters, current transformers, voltage transformers and secondary circuits in electric energy metering devices; A monitoring module is used to monitor the real-time status of the electric energy meter, current transformer, voltage transformer and various parameters of the secondary circuit in real time according to the parameter status threshold in the fault database; The diagnosis module is used to analyze the collected data of abnormal parameter status of electric energy meters, current transformers, voltage transformers and secondary circuits, and use a two-part judgment method consisting of probabilistic judgment and continuous judgment to diagnose whether the abnormal status is an instantaneous abnormal alarm or a fault. If a fault occurs, the fault point is marked; The prediction module is used to predict the future status of parameters based on the historical data of the power meter, current transformer, voltage transformer and secondary circuit parameter status, and to diagnose the future status of the power meter, current transformer, voltage transformer and secondary circuit parameters based on the various parameters in the fault database, and predict the time when the fault will occur.

3. The method for predicting the state of an electric energy metering device according to claim 2, characterized in that: Data storage module, including: A real-time acquisition module, used for real-time acquisition of data information in the electric energy metering device, wherein the types of data information include the operation time, seal data and historical operation fault data of the electric energy meter, current transformer, voltage transformer and secondary circuit; The first unit time setting module is used to set the first unit time length, wherein the first unit time length is obtained by the following formula: T 01 =(1-(X 01 -X 02 ))·T max Among them, T 01 Indicates the length of the first unit of time; X 01 and X 02 Represent the first parameter and the second parameter respectively; T max Indicates the maximum value of the data collection time interval in the data type; n indicates the number of data information types; T i represents the data collection time interval of the i-th data type; T p represents the average time interval for data collection; T min Indicates the minimum value of the data collection time interval in the data type; λ indicates the adjustment parameter, and the value range of λ is 0.32-0.37; A type extraction module, used to extract and classify the data information collected within a first unit time length, and obtain the data information type of the data collected within the first unit time length; A comparison module, configured to compare the data information type of the data collected within the first unit time length with the type of data information, and determine whether the number of the data information type of the data collected within the first unit time length and the number of the type of data information are the same; A first collection control module, configured to collect data information in the electric energy metering device according to the first unit time length when the data information type of the data collected within the first unit time length is the same as the number of the data information types; A second unit time setting module, configured to adjust the first unit time length to obtain a second unit time length when the data information type of the data collected within the first unit time length is different from the number of data information types; A second collection control module, used for collecting data information in the electric energy metering device according to the second unit time length; The collected data information is classified and stored according to the type of data information.

4. The method for predicting the state of an electric energy metering device according to claim 2, characterized in that: The second unit time setting module includes: a target data type extraction module, configured to extract the type of data information missing in the first unit time length as the target data type when the data information type of the data collected in the first unit time length is different from the number of types of data information; A time position extraction module, used to extract the time positions where all target data types appear within two consecutive first unit time lengths; A first adjustment module, configured to adjust the first unit time length by the following formula to obtain a second unit time length if the time positions at which all the target data types appear are less than 38% of the first unit time length; T 02 =T 01 +ΔT Among them, T 02 represents the second unit time length; ΔT represents the compensation time amount; T 01 represents the first unit time length; P represents the ratio of the time positions of all occurrences of the target data type to the first unit time length; n represents the number of data information types; T i represents the data collection time interval of the i-th data type; T fi Represents the floating change in the data collection time interval of the i-th data type; A second adjustment module is configured to adjust the first unit time length by using the following formula to obtain a second unit time length if the time positions at which all the target data types appear are not less than 38% of the first unit time length; T 02 =2T 01 -ΔT Among them, T 02 represents the second unit time length; ΔT represents the compensation time amount; T 01 represents the first unit time length; P represents the ratio of the time positions of all occurrences of the target data type to the first unit time length; n represents the number of data information types; T i represents the data collection time interval of the i-th data type; T fi Indicates the floating change in the data collection time interval of the i-th data type.

5. The method for predicting the state of an electric energy metering device according to claim 2, characterized in that: The monitoring process of the monitoring module specifically includes: First, the real-time status of the energy meter, current transformer, voltage transformer and various parameters of the secondary circuit are monitored in real time according to the parameter status thresholds in the fault database; If the parameter status exceeds the threshold limit, it is considered that the system has an abnormal condition, and the abnormal condition is transmitted to the diagnosis module; If the parameter status does not cross the threshold limit, the status is transmitted to the prediction module.

6. The method for predicting the state of an electric energy metering device according to claim 1, characterized in that: The operating status assessment module comprises: Collect basic information data and on-site operation data of the electric energy metering device, and calculate the operation status value of the electric energy metering device based on the collected operation data; Analyze the operation status information according to the operation status value of the electric energy metering device, the status information includes short-term status information and long-term status information; A short-term comprehensive state information matrix is ​​established based on the short-term state information, and the operation states of the electric energy meter, the current transformer and the voltage transformer are evaluated in the short term through the short-term comprehensive state information matrix; A long-term comprehensive status information matrix is ​​established based on the long-term status information, and the operating status of the electric energy meter, current transformer and voltage transformer is comprehensively evaluated through the long-term comprehensive status information matrix.

7. The method for predicting the state of an electric energy metering device according to claim 6, characterized in that: The running status value calculation includes: The basic information data and field operation data of the electric energy metering device are respectively obtained, and the basic information data and field operation data of the electric energy metering device include basic information data, detection data, monitoring data, family defect data of the electric energy meter, current transformer and voltage transformer, and detection data and monitoring data of the secondary circuit; The basic information data and field operation data of the electric energy metering device include the basic information data, detection data, monitoring data, family defect data of the electric energy meter, current transformer and voltage transformer, and the detection data and monitoring data of the secondary circuit; Calculate the running status value of the electric energy meter according to the basic information data of the electric energy meter, the detection data of the electric energy meter, the monitoring data of the electric energy meter, and the family defect data of the electric energy meter; Calculate the current transformer operating status value according to the current transformer basic information data, current transformer detection data, current transformer monitoring data, and current transformer family defect data; Calculate the voltage transformer operating status value according to voltage transformer basic information data, voltage transformer detection data, voltage transformer monitoring data, and voltage transformer family defect data; The secondary circuit operating status value and the operating status value of the metering device are calculated based on the secondary circuit detection data and the secondary circuit monitoring data.

8. The method for predicting the state of an electric energy metering device according to claim 1, characterized in that: The evaluation process of the health status evaluation module specifically includes: Obtaining health influencing factors of the electric energy metering device and scores of the corresponding health influencing factors, the health influencing factors including: life of the electric energy metering device, performance of the electric energy metering device, equipment error of the electric energy metering device, temperature of the environment in which the electric energy metering device is located, and humidity of the environment in which the electric energy metering device is located; Based on the acquired health influencing factors, a comparison matrix is ​​established. The comparison matrix represents the relative importance between any two health influencing factors. Based on the scores of the health influencing factors and the comparison matrix, a health score is obtained.

9. The method for predicting the state of an electric energy metering device according to claim 1, characterized in that: The historical error data acquisition of the energy metering device specifically includes: According to the historical operation of the electric energy metering device, the historical power and electric energy of the electric energy metering device during a certain period of time are calculated, and converted into a first pulse signal; Calculate the operating power of the electric energy metering device in the same time period using the error test device and convert it into a second pulse signal; The error state is calculated according to the first pulse signal and the second pulse signal, and the historical error data of the electric energy metering device is obtained.

10. The method for predicting the state of an electric energy metering device according to claim 1, characterized in that: The comprehensive evaluation module includes: An acquisition module is used to acquire comprehensive evaluation indicators of the electric energy metering device, the evaluation indicators including: fault prediction time, operating state, error state, health state and configuration mode of the electric energy metering device; A processing module, used to calculate the score of the state of the electric energy metering device according to the obtained comprehensive evaluation index, and calculate a normalized element matrix according to the scoring scheme; A judgment module is used to score the judgment criteria in the normalized element matrix in pairs to obtain a weight matrix of the judgment criteria; The evaluation module is used to evaluate and score the electric energy metering device, analyze the evaluation results of the electric energy metering device, and determine the state of the electric energy metering device according to the analysis results.

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