CN110988783A - Intelligent electric meter precision online monitoring method and device - Google Patents
Intelligent electric meter precision online monitoring method and device Download PDFInfo
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- CN110988783A CN110988783A CN201911380287.4A CN201911380287A CN110988783A CN 110988783 A CN110988783 A CN 110988783A CN 201911380287 A CN201911380287 A CN 201911380287A CN 110988783 A CN110988783 A CN 110988783A
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Abstract
The application discloses smart electric meter precision online monitoring method and device, through the uncertainty of different channels output in the last monitoring period, the total occupied time of each channel in the last monitoring period, the scheduling table of each set channel in the next monitoring period is obtained, through a channel allocation mechanism, the precision data and the uncertainty data of the smart electric meter are continuously updated and optimized, so that the smart electric meter precision online monitoring is optimized, the online monitoring precision level is improved, effective support is provided for realizing stable and reliable online monitoring of the smart electric meter precision, the technical problem that the monitoring precision is limited by time when the existing smart electric meter precision online monitoring mode is used for improving the application time of a single channel injection signal is solved, and the online monitoring precision level is difficult to improve is solved.
Description
Technical Field
The application relates to the technical field of intelligent electric meter quality detection, in particular to an intelligent electric meter precision online monitoring method and device.
Background
With the wide application of the smart meters, the number of the smart meters in the network is increased day by day, and the operation and maintenance thereof become more and more difficult due to the huge number and the complex and variable operation environment, wherein the operation and maintenance comprise daily maintenance management, inventory management, product replacement and the like. The intelligent electric meter is used as a legal metering device for new generation electric energy consumption, is the only basis for electric energy consumption and payment, and the accuracy of metering is related to the aspects of social activities because the current electric energy is used as the most important energy form for social production and life, so that the accuracy of the on-line operation of the electric energy meter is ensured to be controllable and measurable.
The traditional precision monitoring mode aiming at the online electric energy meter is very limited, the electric energy meters in a certain area and a certain batch can be subjected to sampling inspection only in a manual inspection mode, the sampling inspection method can be used for carrying out precision inspection on the online running electric energy meter by using a field calibrator, or a certain number of electric energy meters can be disassembled from the field to a laboratory for precision inspection, the precision distribution condition of the online running electric energy meter is estimated by a statistical method and a relevant probability model according to the precision value measured by the current sample electric energy meter, so that a basis is provided for service life estimation and stock management of the electric energy meter, but the manual processing mode has the defects of limited sampling coverage rate, huge manual investment and deviation of an analysis result.
Aiming at the traditional monitoring mode, a metering chip with the function of on-line monitoring of the precision is developed in the prior art, signals are injected into a sensor impedance loop, the change of the sensor loop impedance value can be obtained by monitoring the change of the injected return signals, and therefore the function of on-line monitoring of the precision of the electric energy meter can be achieved. Generally, a unidirectional electric energy meter has 3 electric energy information acquisition channels, including voltage, live line current and zero line current, and since 3 loops are independent of each other, a metering chip needs to add an injection signal to each loop to monitor the impedance change value of each metering loop respectively. Because the internal resources of the metering chip are limited and only one set of injection signal extraction and processing unit is provided, the injection signal of each channel can be applied only in a time-sharing manner, signal extraction and data processing are performed in a time-sharing manner, and finally the precision monitoring of the channel is realized.
Under the general condition, an anti-aliasing filter circuit is added at the sampling front end of a metering chip, and a digital filter circuit is added in the metering chip, so that the effective filtering of a non-power frequency signal can be realized, but the non-power frequency signal in a power grid still exists and has the characteristic of uncertain frequency distribution, therefore, in order to avoid the overlapping of an injection signal and the non-power frequency signal in the power grid as much as possible, an effective injection signal is applied, the impedance change of a loop is accurately detected, the application time of a single-channel injection signal needs to be prolonged, the metering chip can select a proper injection signal frequency to apply through a frequency hopping technology, but the application time of other channel injection signals needs to be ensured, and the mode of prolonging the application time of the single-channel injection signal is limited, so that the online monitoring precision of the intelligent electric meter is optimized, the, The reliable online monitoring intelligent electric meter provides effective support, which is a technical problem to be solved urgently by technical personnel in the field.
Disclosure of Invention
The application provides an intelligent electric meter precision online monitoring method and device, which are used for solving the technical problems that the monitoring precision is improved by improving the signal application time of single channel injection in the existing intelligent electric meter precision online monitoring mode, the monitoring precision is limited by time, and the online monitoring precision level is difficult to improve.
In view of this, the first aspect of the present application provides an online precision monitoring method for a smart meter, including:
sequentially applying injection signals to each electric energy information acquisition channel of the intelligent electric meter in an equal time period, and acquiring the precision and uncertainty of each output current electric energy information acquisition channel when a preset monitoring period is reached;
calculating the total occupied time of each electric energy information acquisition channel in the last preset monitoring period according to the uncertainty of each electric energy information acquisition channel output in the last preset monitoring period;
generating a scheduling table of each electric energy information acquisition channel of the next preset monitoring period based on the time occupied by each electric energy information acquisition channel;
and controlling each electric energy information acquisition channel to execute execution arrangement of the scheduling table according to the scheduling table, and updating the precision and uncertainty of the output electric energy information acquisition channel.
Optionally, the calculating the total occupied time of each electric energy information collecting channel in the last preset monitoring period according to the uncertainty of each electric energy information collecting channel output in the last preset monitoring period includes:
the uncertainty of each electric energy information acquisition channel output according to the previous preset monitoring period;
calculating the average uncertainty of each electric energy information acquisition channel in the last preset monitoring period;
and calculating the total occupied time of each electric energy information acquisition channel in the last preset monitoring period based on the average uncertainty of each electric energy information acquisition channel.
Optionally, said calculating a total occupancy time of each of said electrical energy information collection channels in a last said preset monitoring period based on said average uncertainty of each of said electrical energy information collection channels comprises:
calculating the relative uncertainty of each electric energy information acquisition channel according to a preset relative uncertainty calculation formula on the basis of the average uncertainty of each electric energy information acquisition channel;
and calculating the channel occupancy rate of the electric energy information acquisition channels according to a preset channel occupancy rate calculation formula based on the relative uncertainty to obtain the total occupied time of each electric energy information acquisition channel in the last preset monitoring period.
Optionally, the generating a schedule of each of the electric energy information collection channels for a next preset monitoring period based on the time occupied by each of the electric energy information collection channels includes:
on the basis of the channel occupancy rate, calculating the channel distribution time of each electric energy information acquisition channel according to a preset channel distribution time calculation formula;
and on the basis of the channel distribution time, calculating the number of time windows for applying injection signals by each electric energy information acquisition channel according to a preset time window number calculation formula, and generating a scheduling table of each electric energy information acquisition channel in the next preset monitoring period.
Optionally, the preset relative uncertainty calculation formula is:
wherein, PRIO [ i ] is the priority of three channels of a live wire current AI channel, a zero wire current BI channel and a voltage AV channel, the value of i is [0,2], PRIO [0] is the priority of the live wire current AI channel, PRIO [1] is the priority of the zero wire current BI channel, and PRIO [2] is the priority of the voltage AV channel.
Optionally, the preset channel occupancy rate calculation formula is:
the CertAvg array is an array storing average uncertainty of a live wire current AI channel, a zero wire current BI channel and a voltage AV channel in the last monitoring period, CertAvg [0] is an average uncertainty array of the live wire current AI channel, CertAvg [1] is an average uncertainty array of the zero wire current BI channel, and CertAvg [2] is an average uncertainty array of the voltage AV channel.
Optionally, the preset channel allocation time calculation formula is:
T[x]＝Tmon*aURatio[x]
wherein Tmon is the online monitoring period.
Optionally, the preset time window number calculation formula is as follows:
N[x]＝T[x]/Tw
where Tw is the time when a single channel applies an injection signal at a single time.
The application second aspect provides a smart electric meter precision on-line monitoring device, includes:
the injection signal applying module is used for sequentially applying injection signals to each electric energy information acquisition channel of the intelligent electric meter in an equal time period, and acquiring the precision and uncertainty of each output current electric energy information acquisition channel when a preset monitoring period is reached;
the calculation module is used for calculating the total occupied time of each electric energy information acquisition channel in the last preset monitoring period according to the uncertainty of each electric energy information acquisition channel output in the last preset monitoring period;
the scheduling table generating module is used for generating a scheduling table of each electric energy information acquisition channel in the next preset monitoring period based on the time occupied by each electric energy information acquisition channel;
and the scheduling module is used for controlling each electric energy information acquisition channel to execute execution arrangement of the scheduling table according to the scheduling table, and updating the precision and uncertainty of the output electric energy information acquisition channel.
Optionally, the calculation module is specifically configured to:
the uncertainty of each electric energy information acquisition channel output according to the previous preset monitoring period;
calculating the average uncertainty of each electric energy information acquisition channel in the last preset monitoring period;
calculating the relative uncertainty of each electric energy information acquisition channel according to a preset relative uncertainty calculation formula on the basis of the average uncertainty of each electric energy information acquisition channel;
based on the relative uncertainty, calculating the channel occupancy rate of the electric energy information acquisition channels according to a preset channel occupancy rate calculation formula to obtain the total occupied time of each electric energy information acquisition channel in the last preset monitoring period;
the schedule table generating module is specifically configured to:
on the basis of the channel occupancy rate, calculating the channel distribution time of each electric energy information acquisition channel according to a preset channel distribution time calculation formula;
and on the basis of the channel distribution time, calculating the number of time windows for applying injection signals by each electric energy information acquisition channel according to a preset time window number calculation formula, and generating a scheduling table of each electric energy information acquisition channel in the next preset monitoring period.
According to the technical scheme, the embodiment of the application has the following advantages:
the application provides an online monitoring method for precision of an intelligent electric meter, which comprises the following steps: sequentially applying injection signals to each electric energy information acquisition channel of the intelligent electric meter in an equal time period, and acquiring the precision and uncertainty of each output current electric energy information acquisition channel when a preset monitoring period is reached; calculating the total occupied time of each electric energy information acquisition channel in the last preset monitoring period according to the uncertainty of each electric energy information acquisition channel output in the last preset monitoring period; generating a scheduling table of each electric energy information acquisition channel of the next preset monitoring period based on the time occupied by each electric energy information acquisition channel; and controlling each electric energy information acquisition channel to execute execution arrangement of the scheduling table according to the scheduling table, and updating the precision and uncertainty of the output electric energy information acquisition channel. According to the intelligent electric meter precision online monitoring method, uncertainty of different channels output in the previous monitoring period is used, total occupied time of each channel in the previous monitoring period is obtained, a scheduling table of each set channel of the next monitoring period is obtained, precision data and uncertainty data of an intelligent electric meter are continuously updated and optimized through a channel allocation mechanism, intelligent electric meter precision online monitoring is optimized, online monitoring precision level is improved, effective support is provided for achieving stable and reliable online monitoring of intelligent electric meter precision, the problem that monitoring precision is limited by time when the existing intelligent electric meter precision online monitoring mode is used for improving single channel injection signal application time is solved, and online monitoring precision level is difficult to improve is solved.
Drawings
Fig. 1 is a schematic flowchart of an online precision monitoring method for a smart meter according to an embodiment of the present disclosure;
fig. 2 is another schematic flow chart of a method for monitoring the accuracy of an intelligent electric meter on line in the embodiment of the present application;
fig. 3 is a schematic structural diagram of an online precision monitoring device for an intelligent electric meter, provided in an embodiment of the present application.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
For easy understanding, please refer to fig. 1, the present application provides an embodiment of an online precision monitoring method for a smart meter, including:
It should be noted that, in the embodiment of the present application, injection signals are first applied to each channel according to an equal time period, for example, the time period for outputting an online monitoring accuracy report once (i.e., the online monitoring period) is 1 hour, the time period for applying an injection signal once to each channel is 1 minute, based on a hardware basis with only one set of injection signal extraction and processing unit, three channels need to be polled for one time for 3 minutes, that is, the live current channel is monitored in the 1 st minute, the neutral current channel is monitored in the 2 nd minute, the voltage channel is monitored in the 3 rd minute, and the live current channel is monitored in the 4 th minute, and subsequent monitoring is performed in sequence according to this rule, which is an equal time allocation mechanism. When the regular monitoring time of each channel is finished, two parameters are output, wherein one parameter is the precision of the current channel, the other parameter is uncertainty, and the uncertainty is used for evaluating the reliability of the current output precision, so that the parameter is required to evaluate the reliability of the obtained precision data.
The equal application time period of the injection signal in the embodiment of the present application may be understood as a period in which the metering chip continuously applies the injection signal to a single channel and finally outputs precision data and uncertainty data, and the period may be adjusted to generally conform to the characteristic that the longer the application period is, the more accurate the precision data is, but the longer the period is, the better the precision is, because the trend that the precision data becomes better with the increase of the injection signal time may be weakened.
And 102, calculating the total occupied time of each electric energy information acquisition channel in the last preset monitoring period according to the uncertainty of each electric energy information acquisition channel output in the last preset monitoring period.
And 103, generating a scheduling table of each electric energy information acquisition channel in the next preset monitoring period based on the time occupied by each electric energy information acquisition channel.
It should be noted that, according to the uncertainty data output by each channel in the previous monitoring period, a schedule table of the monitoring channel in the next monitoring period is generated, that is, which channel is arranged in different time windows to apply the injection signal, and the specific method may be to calculate the average uncertainty of each channel in the previous period, calculate the total occupied time of each channel in the monitoring period through the average uncertainty, and then complete the schedule table of applying the injection signal to the monitoring channel by using the respective occupied time of the three channels.
And 104, controlling each electric energy information acquisition channel to execute execution arrangement of the scheduling table according to the scheduling table, and updating the precision and uncertainty of the output electric energy information acquisition channel.
It should be noted that, according to the newly generated schedule table, the arrangement of the schedule table is sequentially executed, and when each channel is executed, the channel precision and the uncertainty are sequentially output, and a record is formed, and after the whole monitoring period is completed, the schedule table of the next monitoring period is generated according to the method from step 102 to step 103.
According to the intelligent electric meter precision online monitoring method provided in the embodiment of the application, through the uncertainty of different channels output in the previous monitoring period, the total occupied time of each channel in the previous monitoring period is obtained, the scheduling table of each set channel in the next monitoring period is obtained, through a channel allocation mechanism, the precision data and the uncertainty data of the intelligent electric meter are continuously updated and optimized, the intelligent electric meter precision online monitoring is optimized, the online monitoring precision level is improved, effective support is provided for realizing stable and reliable online monitoring of the intelligent electric meter precision, the technical problem that the monitoring precision is limited by time when the existing intelligent electric meter precision online monitoring mode is used for improving the application time of injection signals of a single channel, and the online monitoring precision level is difficult to improve is solved.
For easy understanding, please refer to fig. 2, the present application provides another embodiment of an online smart meter precision monitoring method, including:
It should be noted that step 201 in this embodiment of the present application is the same as step 101 in the previous embodiment, and is not described herein again.
And 202, according to the uncertainty of each electric energy information acquisition channel output in the previous preset monitoring period.
And step 203, calculating the average uncertainty of each electric energy information acquisition channel in the last preset monitoring period.
And step 204, calculating the relative uncertainty of each electric energy information acquisition channel according to a preset relative uncertainty calculation formula based on the average uncertainty of each electric energy information acquisition channel.
And step 205, based on the relative uncertainty, calculating the channel occupancy rate of the electric energy information acquisition channels according to a preset channel occupancy rate calculation formula to obtain the total occupied time of each electric energy information acquisition channel in the last preset monitoring period.
It should be noted that, in this embodiment of the present application, the occupied time of the channel in the next period may be calculated according to the average uncertainty of the channel in the previous period, and the way of calculating the occupied time of the channel in the next period according to the average uncertainty of the channel in the previous period may be:
firstly, the relative uncertainty of the channel is calculated by using a preset relative uncertainty calculation formula, wherein the preset relative uncertainty calculation formula can be as follows:
wherein, PRIO [ i ] is the priority of three channels of a live wire current AI channel, a zero wire current BI channel and a voltage AV channel, the value of i is [0,2], PRIO [0] is the priority of the live wire current AI channel, PRIO [1] is the priority of the zero wire current BI channel, and PRIO [2] is the priority of the voltage AV channel. The smaller the priority value, the higher the priority, and the lower the priority of the neutral line current BI channel if the neutral line metering is not considered.
Then, the preset channel occupancy rate calculation formula is used for calculating the channel occupancy rate, and the preset channel occupancy rate calculation formula can be as follows:
the CertAvg array is an array storing average uncertainty of a live wire current AI channel, a zero wire current BI channel and a voltage AV channel in the last monitoring period, CertAvg [0] is an average uncertainty array of the live wire current AI channel, CertAvg [1] is an average uncertainty array of the zero wire current BI channel, and CertAvg [2] is an average uncertainty array of the voltage AV channel.
And after the channel occupancy rate is calculated, the total occupied time of each electric energy information acquisition channel in the last preset monitoring period can be obtained.
And step 206, calculating the channel distribution time of each electric energy information acquisition channel according to a preset channel distribution time calculation formula based on the channel occupancy rate.
And step 207, based on the channel distribution time, calculating the number of time windows for each electric energy information acquisition channel to apply the injection signal according to a preset time window number calculation formula, and generating a scheduling table of each electric energy information acquisition channel in the next preset monitoring period.
It should be noted that, planning a schedule table requires calculating the time allocated to each channel, in this embodiment of the present application, based on the channel occupancy rate, the channel allocation time of each electric energy information acquisition channel is calculated according to a preset channel allocation time calculation formula, where the preset channel allocation time calculation formula may be:
T[x]＝Tmon*aURatio[x]
wherein Tmon is an online monitoring period, which is generally set to one hour.
The number of time windows for applying the injection signal to each channel can be calculated by a preset time window number calculation formula, and the preset time window number calculation formula can be as follows:
N[x]＝T[x]/Tw
where Tw is the time for a single channel to apply an injection signal, typically 1 minute. The generation of the scheduling table is determined by N [ x ], the generation list of the scheduling table and the channel execution sequence are not limited, each channel is only required to execute N [ x ] times in the monitoring period, and the general scheduling strategy is as follows: the injection signals are cyclically applied to different channels at intervals.
And step 208, controlling each electric energy information acquisition channel to execute execution arrangement of the scheduling table according to the scheduling table, and updating the accuracy and uncertainty of the output electric energy information acquisition channel.
It should be noted that, the execution schedule of each electric energy information acquisition channel execution schedule is controlled according to the schedule, and the accuracy and uncertainty of the output electric energy information acquisition channel are updated. The scheduling execution strategy is to realize the balance between the signal injection application time of a single channel and the overall monitoring precision through the configuration of the channel application time, realize the overall precision maximization, namely, the channels with large uncertainty and large noise have more corresponding distribution time, thereby realizing that the precision level of each channel tends to be average, and realizing the optimal overall precision.
According to the intelligent ammeter precision online monitoring method provided by the embodiment of the application, uncertainty parameters of different channels output in the previous monitoring period are used, the weights or priorities of the different channels are considered, the proportion value of the different channels output in the previous monitoring period in the whole monitoring period is calculated, and finally the number of time windows for applying injection signals to each channel is calculated, so that the scheduling method with the optimal overall precision data is obtained.
For easy understanding, please refer to fig. 3, an embodiment of an online smart meter precision monitoring apparatus is provided in the present application, including:
and the injection signal applying module is used for sequentially applying injection signals to each electric energy information acquisition channel of the intelligent electric meter in an equal time period, and acquiring the precision and uncertainty of each output current electric energy information acquisition channel when a preset monitoring period is reached.
And the calculating module is used for calculating the total occupied time of each electric energy information acquisition channel in the last preset monitoring period according to the uncertainty of each electric energy information acquisition channel output in the last preset monitoring period.
And the scheduling table generating module is used for generating a scheduling table of each electric energy information acquisition channel in the next preset monitoring period based on the time occupied by each electric energy information acquisition channel.
And the scheduling module is used for controlling each electric energy information acquisition channel to execute the execution arrangement of the scheduling table according to the scheduling table and updating the precision and the uncertainty of the output electric energy information acquisition channel.
Further, the calculation module is specifically configured to:
the uncertainty of each electric energy information acquisition channel output according to the previous preset monitoring period;
calculating the average uncertainty of each electric energy information acquisition channel in the last preset monitoring period;
based on the average uncertainty of each electric energy information acquisition channel, calculating the relative uncertainty of each electric energy information acquisition channel according to a preset relative uncertainty calculation formula;
and on the basis of the relative uncertainty, calculating the channel occupancy rate of the electric energy information acquisition channels according to a preset channel occupancy rate calculation formula to obtain the total occupied time of each electric energy information acquisition channel in the last preset monitoring period.
The scheduling table generation module is specifically configured to:
calculating the channel distribution time of each electric energy information acquisition channel according to a preset channel distribution time calculation formula based on the channel occupancy rate;
and based on the channel distribution time, calculating the number of time windows for applying the injection signals by each electric energy information acquisition channel according to a preset time window number calculation formula, and generating a scheduling table of each electric energy information acquisition channel in the next preset monitoring period.
Further, the preset relative uncertainty calculation formula is as follows:
wherein, PRIO [ i ] is the priority of three channels of a live wire current AI channel, a zero wire current BI channel and a voltage AV channel, the value of i is [0,2], PRIO [0] is the priority of the live wire current AI channel, PRIO [1] is the priority of the zero wire current BI channel, and PRIO [2] is the priority of the voltage AV channel.
The preset channel occupancy rate calculation formula is as follows:
the CertAvg array is an array storing average uncertainty of a live wire current AI channel, a zero wire current BI channel and a voltage AV channel in the last monitoring period, CertAvg [0] is an average uncertainty array of the live wire current AI channel, CertAvg [1] is an average uncertainty array of the zero wire current BI channel, and CertAvg [2] is an average uncertainty array of the voltage AV channel.
The preset channel distribution time calculation formula is as follows:
T[x]＝Tmon*aURatio[x]
wherein Tmon is the online monitoring period.
The preset time window number calculation formula is as follows:
N[x]＝T[x]/Tw
where Tw is the time when a single channel applies an injection signal at a single time.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer system (which may be a personal computer, a server, or a network system) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. An intelligent electric meter precision online monitoring method is characterized by comprising the following steps:
sequentially applying injection signals to each electric energy information acquisition channel of the intelligent electric meter in an equal time period, and acquiring the precision and uncertainty of each output current electric energy information acquisition channel when a preset monitoring period is reached;
calculating the total occupied time of each electric energy information acquisition channel in the last preset monitoring period according to the uncertainty of each electric energy information acquisition channel output in the last preset monitoring period;
generating a scheduling table of each electric energy information acquisition channel of the next preset monitoring period based on the time occupied by each electric energy information acquisition channel;
and controlling each electric energy information acquisition channel to execute execution arrangement of the scheduling table according to the scheduling table, and updating the precision and uncertainty of the output electric energy information acquisition channel.
2. The method for monitoring the accuracy of the intelligent electric meter in an online manner according to claim 1, wherein the step of calculating the total occupied time of each electric energy information acquisition channel in the last preset monitoring period according to the uncertainty of each electric energy information acquisition channel output in the last preset monitoring period comprises the following steps:
the uncertainty of each electric energy information acquisition channel output according to the previous preset monitoring period;
calculating the average uncertainty of each electric energy information acquisition channel in the last preset monitoring period;
and calculating the total occupied time of each electric energy information acquisition channel in the last preset monitoring period based on the average uncertainty of each electric energy information acquisition channel.
3. The method for monitoring the accuracy of the smart meter according to claim 2, wherein the step of calculating the total occupancy time of each of the electric energy information collecting channels in the last preset monitoring period based on the average uncertainty of each of the electric energy information collecting channels comprises:
calculating the relative uncertainty of each electric energy information acquisition channel according to a preset relative uncertainty calculation formula on the basis of the average uncertainty of each electric energy information acquisition channel;
and calculating the channel occupancy rate of the electric energy information acquisition channels according to a preset channel occupancy rate calculation formula based on the relative uncertainty to obtain the total occupied time of each electric energy information acquisition channel in the last preset monitoring period.
4. The method for monitoring the precision of the intelligent electric meter on line according to claim 3, wherein the step of generating a schedule table of each electric energy information acquisition channel of the next preset monitoring period based on the time occupied by each electric energy information acquisition channel comprises the following steps:
on the basis of the channel occupancy rate, calculating the channel distribution time of each electric energy information acquisition channel according to a preset channel distribution time calculation formula;
and on the basis of the channel distribution time, calculating the number of time windows for applying injection signals by each electric energy information acquisition channel according to a preset time window number calculation formula, and generating a scheduling table of each electric energy information acquisition channel in the next preset monitoring period.
5. The method for monitoring the accuracy of the intelligent electric meter according to claim 4, wherein the preset relative uncertainty calculation formula is as follows:
wherein, PRIO [ i ] is the priority of three channels of a live wire current AI channel, a zero wire current BI channel and a voltage AV channel, the value of i is [0,2], PRIO [0] is the priority of the live wire current AI channel, PRIO [1] is the priority of the zero wire current BI channel, and PRIO [2] is the priority of the voltage AV channel.
6. The method for monitoring the accuracy of the smart meter according to claim 5, wherein the preset passage occupancy rate calculation formula is as follows:
the CertAvg array is an array storing average uncertainty of a live wire current AI channel, a zero wire current BI channel and a voltage AV channel in the last monitoring period, CertAvg [0] is an average uncertainty array of the live wire current AI channel, CertAvg [1] is an average uncertainty array of the zero wire current BI channel, and CertAvg [2] is an average uncertainty array of the voltage AV channel.
7. The method for monitoring the accuracy of the intelligent electric meter according to claim 6, wherein the preset channel allocation time calculation formula is as follows:
T[x]＝Tmon*aURatio[x]
wherein Tmon is the online monitoring period.
8. The method for monitoring the accuracy of the intelligent electric meter according to claim 7, wherein the preset time window number calculation formula is as follows:
N[x]＝T[x]/Tw
where Tw is the time when a single channel applies an injection signal at a single time.
9. The utility model provides an online monitoring devices of smart electric meter precision which characterized in that includes:
the injection signal applying module is used for sequentially applying injection signals to each electric energy information acquisition channel of the intelligent electric meter in an equal time period, and acquiring the precision and uncertainty of each output current electric energy information acquisition channel when a preset monitoring period is reached;
the calculation module is used for calculating the total occupied time of each electric energy information acquisition channel in the last preset monitoring period according to the uncertainty of each electric energy information acquisition channel output in the last preset monitoring period;
the scheduling table generating module is used for generating a scheduling table of each electric energy information acquisition channel in the next preset monitoring period based on the time occupied by each electric energy information acquisition channel;
and the scheduling module is used for controlling each electric energy information acquisition channel to execute execution arrangement of the scheduling table according to the scheduling table, and updating the precision and uncertainty of the output electric energy information acquisition channel.
10. The smart meter accuracy online monitoring device of claim 9, wherein the computing module is specifically configured to:
the uncertainty of each electric energy information acquisition channel output according to the previous preset monitoring period;
calculating the average uncertainty of each electric energy information acquisition channel in the last preset monitoring period;
calculating the relative uncertainty of each electric energy information acquisition channel according to a preset relative uncertainty calculation formula on the basis of the average uncertainty of each electric energy information acquisition channel;
based on the relative uncertainty, calculating the channel occupancy rate of the electric energy information acquisition channels according to a preset channel occupancy rate calculation formula to obtain the total occupied time of each electric energy information acquisition channel in the last preset monitoring period;
the schedule table generating module is specifically configured to:
on the basis of the channel occupancy rate, calculating the channel distribution time of each electric energy information acquisition channel according to a preset channel distribution time calculation formula;
and on the basis of the channel distribution time, calculating the number of time windows for applying injection signals by each electric energy information acquisition channel according to a preset time window number calculation formula, and generating a scheduling table of each electric energy information acquisition channel in the next preset monitoring period.
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Address after: Room 501-503, annex building, Huaye building, No.1-3 Chuimao new street, Xihua Road, Yuexiu District, Guangzhou City, Guangdong Province 510000 Applicant after: China Southern Power Grid Power Technology Co.,Ltd. Address before: Room 501-503, annex building, Huaye building, No.1-3 Chuimao new street, Xihua Road, Yuexiu District, Guangzhou City, Guangdong Province 510000 Applicant before: GUANGDONG DIANKEYUAN ENERGY TECHNOLOGY Co.,Ltd. |
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Application publication date: 20200410 |