Method for monitoring running state of intelligent electric energy meter

Abstract

The invention relates to the technical field of data processing, in particular to an intelligent electric energy meter operation state monitoring method. The method comprises the following steps: acquiring historical maintenance data of the intelligent electric energy meter and power, voltage, temperature and humidity in a historical time period; determining the load change degree according to the relative change condition of the power and the voltage in the historical time period; determining temperature stability and humidity stability, and carrying out weighted summation on the temperature stability and the humidity stability to obtain an environment change degree; correcting scale parameters in a Weibull distribution model according to the load change degree and the environment change degree; and based on the corrected scale parameters, performing fault prediction on the historical maintenance data according to a Weibull distribution model. According to the method, the accuracy of running state monitoring and fault prediction of the intelligent electric energy meter can be improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and more specifically, to a method for monitoring the operating status of a smart electric energy meter. Background Art

[0002] Smart energy meters are an important infrastructure. However, they are prone to failure due to long-term use, environmental factors, electrical interference, etc. Failures not only affect the accuracy of power metering, but may also cause power supply interruptions. By monitoring the operating status of smart energy meters, the time of failure can be predicted in advance, so that corresponding maintenance measures can be taken, thereby reducing the failure rate of smart energy meters, extending the service life of the equipment, and ensuring the stability of power supply.

[0003] Generally, the fault of smart electric energy meter can be predicted by Weibull distribution. Weibull distribution is a probability distribution model commonly used to describe the life of equipment or failure time, which can effectively evaluate the reliability of smart electric energy meter and predict faults. In the Weibull distribution model, the scale parameter directly affects the time scale of fault occurrence. It reflects the average life of the equipment under specific conditions and is one of the most critical parameters in reliability analysis. However, the scale parameter is sensitive to changes in usage conditions and environment. In the existing methods, most of them use the maximum likelihood estimation method to determine the parameter. For example, a Chinese patent application document with publication number CN110261811A discloses a smart meter batch warning method and system, which obtains the scale parameter in the Weibull distribution model by the maximum likelihood estimation method. However, in the above process, the scale parameter obtained by the maximum likelihood estimation method is based on the fact that the data distribution remains unchanged throughout the estimation process, while the operating parameters of the smart electric energy meter will change due to environmental influences, and its data distribution will also change. At this time, fault prediction based on the obtained scale parameter will reduce the accuracy of the monitoring result, resulting in false alarms. Summary of the invention

[0004] In order to solve the technical problem of low fault prediction accuracy of the above-mentioned smart electric energy meter, the present invention provides solutions in the following aspects.

[0005] The present invention provides a method for monitoring the operating status of a smart electric energy meter, comprising: obtaining historical maintenance data of the smart electric energy meter and power, voltage, temperature and humidity in a historical time period; determining the load change degree according to the relative changes of the power and the voltage in the historical time period; determining temperature stability and humidity stability, and performing weighted summation of the temperature stability and the humidity stability to obtain the degree of environmental change; wherein the weight of the temperature stability The calculation formula is: ; and the weight of the humidity stability The calculation formula is: ; In the formula, For the The temperature collected at the reference time, For the The temperature collected at the reference time, For the The temperature collected at the reference time, For the The humidity collected at the reference time, For the The humidity collected at the reference time, For the The humidity collected at the reference time, is the number of reference moments in the historical time period; the scale parameter in the Weibull distribution model is corrected according to the load change degree and the environment change degree, and the corrected calculation formula is: ; In the formula, is the corrected scale parameter, is the scale parameter before correction, is the degree of environmental change, is the load change degree; based on the modified scale parameter, the fault prediction is performed on the historical maintenance data according to the Weibull distribution model, is the ceiling function.

[0006] Beneficial effects: By correcting the obtained scale parameters according to the influence of temperature and humidity on the failure rate of smart electricity meters, as well as the failure performance of smart electricity meters under different load conditions, the interference of environmental factors on smart electricity meters during the monitoring process is reduced, thereby improving the accuracy of smart electricity meter operation status monitoring and fault prediction.

[0007] Furthermore, the degree of load change is determined according to the relative changes of the power and the voltage in the historical time period, including: determining the load change characteristics according to the voltage change and the power change at a reference moment, wherein the load change characteristics are positively correlated with the power change and negatively correlated with the voltage change; determining the degree of load change according to the variance of the load change characteristics in the historical time period, or the mean of the difference between the load change characteristics and the reference load change characteristics.

[0008] Beneficial effect: When the load of the smart energy meter changes, the voltage will also be affected to a certain extent. Therefore, by comprehensively considering the changes in voltage and power, the load changes of the smart energy meter can be better reflected, thereby improving the accuracy of subsequent correction of scale parameters according to the degree of load change.

[0009] Furthermore, the load variation degree The calculation formula is: ; In the formula, For the Load variation characteristics at the reference time, is the average value of the load change characteristic in the historical time period, is the number of reference moments in the historical time period.

[0010] Furthermore, the voltage variation The calculation formula is: ; In the formula, For the The voltage collected at the reference time, For the The voltage collected at the previous moment of the reference moment, For the The voltage sampled at the next instant after the reference instant.

[0011] Beneficial effect: By obtaining the voltage change at the reference moment based on the voltage at the reference moment and the voltages at two moments adjacent to the reference moment, the change of the voltage at the moment can be accurately reflected, thereby improving the accuracy of calculating the degree of load change.

[0012] Furthermore, in the process of determining temperature stability and humidity stability, it includes: determining the temperature stability according to the mean of the difference between the temperature and the reference temperature in the historical time period or the variance of the temperature; determining the humidity stability according to the mean of the difference between the humidity and the reference humidity in the historical time period or the variance of the humidity.

[0013] Furthermore, the calculation formula of the temperature stability is: ; In the formula, is the temperature stability of the smart energy meter during the historical period, is the number of reference moments in the historical time period, For the The temperature collected at the reference time, is the reference temperature.

[0014] Furthermore, the reference temperature is a median value of all temperatures collected during the historical time period.

[0015] Beneficial effect: By taking the median of the temperatures collected at all reference moments as the reference temperature, it can better reflect a relatively stable temperature that can represent the overall temperature level. At the same time, it can avoid the problem of frequent changes in reference temperature due to frequently fluctuating data, thereby improving the accuracy of the temperature changes of the smart electricity meter obtained in the historical time period.

[0016] Furthermore, fault prediction is performed on the historical maintenance data according to the Weibull distribution model, including: determining a reliability function of the smart power meter according to the probability density function of the Weibull distribution model; determining the reliability of the smart power meter according to the reliability function; and issuing a fault warning in response to the reliability being less than a preset threshold.

[0017] The beneficial effect of the present invention is that the method of the present invention corrects the obtained scale parameters by considering the influence of temperature and humidity on the service life of the smart energy meter and the performance of the smart energy meter under different load conditions, thereby improving the accuracy of the smart energy meter operation status monitoring and fault prediction. Furthermore, according to the predicted fault of the smart energy meter, corresponding maintenance measures are adopted to ensure the stability of power, thereby avoiding the loss caused by the failure of the smart energy meter. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein: Figure 1 FIG. 4 is a flow chart schematically illustrating a method for monitoring the operating status of a smart electric energy meter according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] 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 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0020] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0021] Figure 1FIG. 4 is a flow chart schematically illustrating a method for monitoring the operating status of a smart electric energy meter according to an embodiment of the present invention.

[0022] like Figure 1 As shown, the present invention provides a method for monitoring the operating status of a smart electric energy meter, the method comprising: S101. Obtain historical maintenance data of the smart electric energy meter and power, voltage, temperature and humidity within a historical time period.

[0023] Environmental factors such as temperature, humidity and vibration will directly affect the working state of electronic components inside smart energy meters. For example, high temperature may cause accelerated aging of electronic components, while high humidity may cause short circuit or corrosion of the internal circuit of smart energy meters. Therefore, when predicting faults of smart energy meters, the scale parameters in the Weibull distribution model can be corrected by considering the impact of temperature and humidity on the service life of smart energy meters, so as to reduce the interference of environmental factors on the operating status monitoring of smart energy meters, thereby improving the accuracy of fault prediction of smart energy meters, so as to more accurately reflect the actual operating status and fault risk of smart energy meters.

[0024] Furthermore, load changes will change the operating characteristics of smart energy meters. Overload or unstable load may cause failures of smart energy meters. Therefore, by considering the impact of load changes on the failure rate of smart energy meters, the scale parameters in the Weibull distribution model can be corrected to reduce the interference of load changes on the monitoring of the operating status of smart energy meters, thereby improving the accuracy of smart energy meter fault prediction. Specifically, since the power fluctuation of smart energy meters can reflect the load changes of smart energy meters, the load changes of smart energy meters can be monitored by power. However, voltage instability will also cause power fluctuations to a certain extent. When the voltage change is relatively small (the voltage is relatively stable), the greater the power change, the greater the load change. Therefore, when determining the load change, by comprehensively considering the changes in voltage and power, the accuracy of determining the load changes of smart energy meters can be improved.

[0025] In one embodiment, monitoring equipment (such as temperature sensors, humidity sensors, etc.) can be installed in the smart electric energy meter to obtain the information before the prediction time. The voltage, power, temperature, humidity and other data of the smart energy meter during operation within a certain period of time (i.e. historical period), where the sensor collection frequency can be set to 1Hz (i.e. once per second). The sensor collects the voltage, power, temperature, humidity and other data of the smart energy meter during operation in real time, ensuring the real-time correction of scale parameters.

[0026] In one embodiment, Can be one month, time period It includes multiple reference times, where the reference time is the same as the predicted time in each day within the time period T. For example, if the predicted time is 15:36:28, then the reference time is the time period 15 hours, 36 minutes and 28 seconds every day.

[0027] Furthermore, historical maintenance data of the smart energy meter may be obtained from the maintenance log of the smart energy meter.

[0028] S102: Determine a load change degree according to a relative change of the power and the voltage within the historical time period.

[0029] Specifically, the load change characteristics are determined based on the voltage change and power change at a reference moment, wherein the load change characteristics are positively correlated with the power change and negatively correlated with the voltage change; the load change degree is determined based on the variance of the load change characteristics within the historical time period, or the mean of the difference between the load change characteristics and the reference load change characteristics.

[0030] In one embodiment, the voltage variation at any reference time can be determined based on the difference between the voltage at the reference time and the voltages at two adjacent times of the reference time on the same day. Specifically, the calculation expression of the voltage variation is as follows: ; In the formula, For the The voltage change at the reference time, For the The voltage collected at the reference time, For the The voltage collected at the previous moment of the reference moment, For the The voltage sampled at the next moment after the reference moment.

[0031] By obtaining the voltage change at the reference moment based on the voltage at the reference moment and the voltages at two moments adjacent to the reference moment, the change of the voltage at the moment can be accurately reflected, thereby improving the accuracy of calculating the load change degree.

[0032] Furthermore, the power variation at any reference time can be determined based on the difference between the power at the reference time and the power at two adjacent times of the reference time on the same day. Specifically, the power variation is calculated as follows: ; In the formula, For the The power change at the reference time, For the The power collected at the reference time, For the The power collected at the previous moment of the reference moment, For the The power collected at the next moment after the reference moment.

[0033] By obtaining the power variation amount based on the power at a reference moment and the power at two moments adjacent to the reference moment, the variation of the power at the moment can be accurately reflected, thereby improving the accuracy of obtaining the degree of load variation.

[0034] Furthermore, the load change characteristics of the smart electric energy meter at the reference time are determined according to the ratio of the voltage change to the power change at the same reference time. It can be understood that when the voltage change is relatively small, the greater the power change, the greater the load change of the smart electric energy meter at that moment.

[0035] In this embodiment, according to the time period The variance of the load change characteristics within a period determines the smart energy meter in this period It is understood that the load variation degree represents the time period. The discrete degree of relative changes in internal voltage and power. Specifically, the calculation expression for the load change degree is as follows: ; In the formula, For time period The load variation degree of the smart energy meter inside, For the Load variation characteristics at the reference time, For time period The mean value of the load variation characteristics within For time period The number of internal reference moments.

[0036] In other optional embodiments, the time period may also be The average of the difference between the load change characteristics within a period and the reference load change characteristics determines the smart energy meter in this period The reference load change characteristic can be set by user, or can be set to the median or mean of all the obtained load change characteristics.

[0037] By comprehensively considering the changes in voltage and power, the actual load condition of the smart energy meter can be better reflected, thereby improving the accuracy of subsequent corrections to scale parameters based on the degree of load change.

[0038] S103: Determine temperature stability and humidity stability, and perform weighted summation of the temperature stability and the humidity stability to obtain a degree of environmental change.

[0039] In one embodiment, the time period The temperature stability is determined by the difference between the temperature collected at all reference moments and the reference temperature. Specifically, the calculation expression of temperature stability is as follows: ; In the formula, For time period Temperature stability of smart energy meters, For the The temperature collected at the reference time, is the reference temperature, For time period The number of internal reference moments.

[0040] In the calculation expression of temperature stability, For time period The difference between the temperature collected at all reference moments and the reference temperature. The smaller the value, the lower the temperature in this time period. The higher the temperature stability of the smart energy meter inside.

[0041] In one embodiment, the reference temperature may be a time period The median value of the temperatures collected at all reference moments in the period. Since the mean value is insensitive to fluctuations in a short period of time and is easily affected by extreme values, the median value of the temperatures collected at all reference moments is used as the reference temperature, which can better reflect a relatively stable temperature that can represent the overall temperature level. At the same time, it can avoid the problem of frequent changes in the reference temperature due to frequently fluctuating data, thereby improving the accuracy of the smart energy meter obtained in the time period. The accuracy of internal temperature changes.

[0042] In other optional embodiments, the time period may also be The variance of the temperature collected within the temperature sensor is used to obtain the temperature stability, and technicians in this field can set it according to actual needs.

[0043] Furthermore, the time period can be The weight of the temperature stability is determined by the temperature difference between all reference moments and the two adjacent reference moments. Specifically, the weight of the temperature stability is The calculation expression is: ; In the formula, For the The temperature collected at the reference time, For the The temperature collected at the reference time, For the The temperature collected at the reference time, For time period The number of internal reference moments.

[0044] The temperature stability of different time periods is dynamically adjusted by determining the weight of temperature stability according to the temperature difference between each reference moment and its adjacent moments. The temperature stability within the environment can better reflect the actual temperature changes, thereby improving the accuracy of subsequent corrections to scale parameters based on the degree of environmental changes.

[0045] In one embodiment, the time period The humidity stability is determined by the difference between the humidity collected at all reference moments and the reference humidity. The calculation expression is as follows: ; In the formula, For the The humidity collected at the reference time, is the reference humidity, For time period The number of internal reference moments. For time period The difference between the humidity at all reference moments in the period and the reference humidity. The smaller the value, the higher the humidity stability of the smart energy meter during this period, that is, the smaller the change amplitude of the humidity during this period.

[0046] In one embodiment, the reference humidity may be a time period Compared with the mean value, by taking the median value of humidity collected at all reference times as the reference humidity, it can better reflect a relatively stable humidity that can represent the overall humidity level. At the same time, it can avoid the problem of frequent changes in reference humidity due to frequent fluctuations in data, thus improving the accuracy of the smart energy meter in the time period. The accuracy of humidity changes inside the instrument.

[0047] In other optional embodiments, the time period may also be The variance of the humidity collected within the humidity is used to obtain the humidity stability, and technicians in this field can set it according to actual needs.

[0048] Furthermore, the time period can be The weight of humidity stability is determined by the humidity difference between all reference moments and the two adjacent reference moments. Specifically, the weight of humidity stability is The calculation expression is: ; In the formula, For the The humidity collected at the reference time, For the The humidity collected at the reference time, For the The humidity collected at the reference time, For time period The number of internal reference moments.

[0049] In one embodiment, the time period The degree of environmental change within The calculation expression is: ; In the formula, For time period The weight of the temperature stability of the smart energy meter, For time period Temperature stability of smart energy meters, For time period The weight of humidity stability within the smart energy meter, For time period Wet stability of smart energy meters.

[0050] In other optional embodiments, the time period The degree of environmental change within The calculation expression is: ; In the formula, For time period The weight of the temperature stability of the smart energy meter, For time period Temperature stability of smart energy meters, For time period Wet stability of smart energy meters.

[0051] Since the degree of change in the smart meter environment is mainly described by the degree of change in temperature and humidity, by integrating the two, the time period can be comprehensively and accurately evaluated. The degree of environmental change within the meter can be improved, thereby improving the accuracy of scale parameter correction, and further improving the accuracy of operating status monitoring and fault prediction of smart electricity meters.

[0052] S104: modifying the scale parameter in the Weibull distribution model according to the load change degree and the environment change degree.

[0053] Specifically, according to the time period The voltage data collected in the system are used to obtain the scale parameter in the Weibull distribution model using the maximum likelihood estimation method.

[0054] Since the failure time of the smart energy meter is relatively short, the accuracy of the obtained scale parameters can be improved by using voltage data with more samples to obtain the scale parameters.

[0055] Furthermore, since the smart energy meter will have an increased risk of failure when operating for a long time under high load or unstable load, the scale parameter can be reduced to reflect the higher failure rate of the smart energy meter under high load or unstable load. In addition, the greater the degree of environmental change, the more complex and harsh the working environment of the smart energy meter, which increases the risk of failure of the smart energy meter. Therefore, the scale parameter can also be reduced to reflect the higher failure rate of the smart energy meter under severe environment. The scale parameters are modified according to the load change degree and environmental change degree within the system to reduce the interference caused by environmental factors and high load and unstable load. Specifically, the process of modifying the scale parameters is as follows: ; In the formula, is the corrected scale parameter, is the scale parameter before correction, is the degree of environmental change, is the load variation degree, is the ceiling function.

[0056] By correcting the scale parameters according to the environment and load conditions of the smart energy meter, the accuracy of smart energy meter fault prediction is improved, so that corresponding preventive measures can be taken according to the prediction results, thereby avoiding the losses caused by smart energy meter failures, and at the same time improving the accuracy of smart energy meter operating status monitoring.

[0057] S105 . Based on the corrected scale parameter, perform fault prediction on the historical maintenance data according to a Weibull distribution model.

[0058] Specifically, the corrected scale parameters replace the original scale parameters in the Weibull distribution model, and the reliability function of the smart electric energy meter is determined according to the probability density function of the replaced Weibull distribution model; further, based on historical maintenance data, the reliability of the smart electric energy meter is determined by the reliability function, and in response to the reliability being less than a preset threshold, a fault warning is issued.

[0059] It can be understood that reliability is the probability of a smart energy meter operating without failure within a given time. When the reliability of a smart energy meter is less than a preset threshold, it indicates that the smart energy meter can be maintained to avoid losses caused by its failure. In one embodiment, the preset threshold is 0.8.

[0060] In the description of this specification, “plurality” means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0061] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A method for monitoring the operating status of a smart electric energy meter, characterized in that: include: Obtain historical maintenance data of smart energy meters as well as power, voltage, temperature and humidity within historical time periods; Determining the load change degree according to the relative changes of the power and the voltage in the historical time period; Determine the temperature stability and humidity stability, and perform weighted summation of the temperature stability and the humidity stability to obtain the degree of environmental change; wherein the weight of the temperature stability is The calculation formula is: ； and the weight of the humidity stability The calculation formula is: ； In the formula, For the The temperature collected at the reference time, For the The temperature collected at the reference time, For the The temperature collected at the reference time, For the The humidity collected at the reference time, For the The humidity collected at the reference time, For the The humidity collected at the reference time, is the number of reference moments in the historical time period; The scale parameter in the Weibull distribution model is corrected according to the load change degree and the environment change degree, and the corrected calculation formula is: ； In the formula, is the corrected scale parameter, is the scale parameter before correction, is the degree of environmental change, is the load variation degree, is the ceiling rounding function; Based on the modified scale parameter, fault prediction is performed on the historical maintenance data according to the Weibull distribution model.

2. The method for monitoring the operating status of a smart electric energy meter according to claim 1, characterized in that: Determining the load change degree according to the relative change of the power and the voltage in the historical time period includes: Determining a load variation characteristic according to a voltage variation and a power variation at a reference time, wherein the load variation characteristic is positively correlated with the power variation and negatively correlated with the voltage variation; The load variation degree is determined according to the variance of the load variation characteristic within the historical time period, or the mean of the difference between the load variation characteristic and a reference load variation characteristic.

3. The method for monitoring the operating status of a smart electric energy meter according to claim 2, characterized in that: The load variation degree The calculation formula is: ； In the formula, For the Load variation characteristics at the reference time, is the average value of the load change characteristic in the historical time period, is the number of reference moments in the historical time period.

4. The method for monitoring the operating status of a smart electric energy meter according to claim 2, characterized in that: The voltage change The calculation formula is: ； In the formula, For the The voltage collected at the reference time, For the The voltage collected at the previous moment of the reference moment, For the The voltage sampled at the next instant after the reference instant.

5. The method for monitoring the operating status of a smart electric energy meter according to claim 1, characterized in that: In the process of determining temperature stability and humidity stability, it includes: determining the temperature stability according to the mean of the difference between the temperature and the reference temperature in the historical time period or the variance of the temperature; determining the humidity stability according to the mean of the difference between the humidity and the reference humidity in the historical time period or the variance of the humidity.

6. The method for monitoring the operating status of a smart electric energy meter according to claim 1 or 5, characterized in that: The calculation formula for the temperature stability is: ； In the formula, is the temperature stability of the smart energy meter during the historical period, is the number of reference moments in the historical time period, For the The temperature collected at the reference time, is the reference temperature.

7. The method for monitoring the operating status of a smart electric energy meter according to claim 5, characterized in that: The reference temperature is the median value of all temperatures collected during the historical time period.

8. The method for monitoring the operating status of a smart electric energy meter according to claim 1, characterized in that: Performing fault prediction on the historical maintenance data according to the Weibull distribution model includes: Determining a reliability function of the smart electric energy meter according to the probability density function of the Weibull distribution model; Determining the reliability of the smart electric energy meter according to the reliability function; In response to the reliability being less than a preset threshold, a fault warning is issued.

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