CN108828494B - Intelligent electric energy meter function verification method based on genetic algorithm - Google Patents
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Abstract
The invention discloses an intelligent electric energy meter function verification method based on a genetic algorithm, which comprises the steps of firstly setting an event to be verified of an intelligent electric energy meter and power supply parameters involved in the intelligent electric energy meter function verification according to actual needs, obtaining a trigger condition table of the event to be verified, and then obtaining a value range of each power supply parameter according to the trigger condition table; then, taking the source modulation parameter vector as an individual chromosome, and sequentially obtaining each source modulation parameter vector by adopting a genetic algorithm, wherein the fitness value of the individual is the number of events to be determined triggered by the corresponding source modulation parameter vector; and finally, setting power supply parameters of the intelligent electric energy meter according to the source-adjusting parameter vector to verify the functions of the intelligent electric energy meter. The invention can automatically generate the source-adjusting parameter vectors required by the intelligent electric energy meter function verification, can complete the verification of all events to be verified by adopting a small number of source-adjusting parameter vectors, can reduce the workload of testers and improve the efficiency of the intelligent electric energy meter function verification.
Description
Technical Field
The invention belongs to the technical field of intelligent electric energy meters, and particularly relates to an intelligent electric energy meter function verification method based on a genetic algorithm.
Background
The electric energy meter is used as a main tool for current electric energy metering and economic settlement, and the accuracy of the electric energy meter is directly related to the economic benefits of the country and users. At present, various domestic large electric energy meter manufacturers mainly produce intelligent electric energy meters, unqualified products cannot be avoided in the production process of the electric energy meters, and meanwhile, the situation that the electric energy meters are unqualified can also occur in the use process of a user. The accurate evaluation of whether the electric energy meter is qualified is a very important aspect, so that the verification of the intelligent electric energy meter is a very important link for putting the intelligent electric energy meter into field application after the intelligent electric energy meter is produced. The items of the electric energy meter function detection comprise more than dozens of events such as current loss, current cutoff, overcurrent and overload, undervoltage, voltage loss, power failure and the like. For a three-phase electric energy meter, most events need to be measured respectively for each phase event and a phase combination event, so that the workload is increased by more than three times. The workload of manual item-by-item verification is too large, and the current more suitable scheme is to complete the test through an automatic test platform.
Fig. 1 is a structural diagram of an automatic test platform of an intelligent electric energy meter. As shown in fig. 1, it mainly includes a PC, a power source and a measured object (electric energy meter). The general working process is divided into 3 steps:
● regulating the parameters: the PC machine adjusts parameters of the electric energy meter, such as threshold voltage for voltage loss triggering and recovery, current conditions and the like;
● Source adjustment: the PC adjusts the power source, and controls the power source to output voltage and current to the electric energy meter according to a test flow (formulated in a software platform) formulated for realizing. (if the ammeter is normal, judging whether the event occurs or not according to the parameters stored in the first step, and recording);
● data reading: and the PC reads the recorded data of the electric energy meter at proper time (the specific data is determined by the test items) according to the test flow and the scheme, compares the data with the expected data and gives a test conclusion.
Compared with manual testing, the automatic testing platform can greatly reduce the manual workload and avoid errors. Even so, the test time required for testing a three-phase electric energy meter is still unbearable by the verification unit. The test time is mainly consumed in two aspects:
firstly, the test scheme is time-consuming: the current test protocol requires the tester to make a single schedule, which takes about 10 minutes/100 to 1000 minutes, i.e., about 17 hours, for an electric energy meter containing nearly 100 events.
Secondly, the testing time is long: the test for each event is divided into three steps as described previously: tuning parameters, tuning sources and reading data. The time for adjusting parameters and adjusting sources is short, but after the sources are adjusted, the time for reading data needs to be kept about 60 seconds (required by detection specifications) and the current time for reading data is also long in minutes. The power source is then restored for 60 seconds. Testing of each event therefore takes around 3 minutes. According to the test protocol, each event needs to be tested 10 times (ensuring 10 times of unloading data correctness), so the total test time of one event needs 0.5 hour. The 100 items required about 50 hours.
In conclusion, about 8.5 working days are needed for completing the conventional detection of one electric energy meter, and about 3 days are needed even if the detection platform works all the day.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an intelligent electric energy meter function verification method based on a genetic algorithm, which can automatically generate a source-adjusting parameter vector required during the function verification of the intelligent electric energy meter, reduce the workload of testers and improve the efficiency of the function verification of the intelligent electric energy meter.
In order to achieve the purpose, the intelligent electric energy meter function verification method based on the genetic algorithm comprises the following steps:
s1: setting an event to be detected of the intelligent electric energy meter and power parameters related to the function verification of the intelligent electric energy meter according to actual needs, obtaining a trigger condition table D of the event to be verified, and obtaining an element D in the trigger condition table DijRepresenting the condition that the power parameter j needs to satisfy when the event i to be verified is triggered, and if the event i to be verified is triggered and the power parameter j does not need to satisfy the specific condition, corresponding element dijNull, where i is 0,1, …, M-1, j is 0,1, …, N-1, M indicates the number of events to be verified, N indicates the number of power supply parameters;
s2: for the power supply parameter j, if the power supply parameter j is a phase parameter, the value range C is setjIs [0,180 ]]Otherwise, setting the value range according to the trigger condition table D of the event to be verified, wherein the specific method comprises the following steps: acquiring trigger conditions corresponding to events to be verified related to the power supply parameter j from the trigger condition table D to obtain power supply parameter j critical values of the trigger conditions, recording the number of the acquired critical values as Q, and arranging all the critical values from large to small to obtain a critical value sequence
The value range C of the power supply parameter jjIs composed of
Wherein α is more than 1, 0 is more than or equal to β and less than 1;
s3: making the sequence number k of the tuning source parameter vector equal to 0;
s4: judging whether the trigger condition table D is empty, if so, completing the acquisition of the tuning source parameter vector required by the function verification of the intelligent electric energy meter, and entering step S8, otherwise, entering step S5;
s5: adopting genetic algorithm to obtain modulation source parameter vector P [ k ] with sequence number k]In the genetic algorithm, the vector of the source-regulating parameter is taken as an individual chromosome, namely the genetic algorithm individual X ═ X1,x2,…,xN]Wherein x isjThe value of the power supply parameter j is represented, and the corresponding value range is CjThe fitness value of the individual is the number of events to be determined triggered by the corresponding source adjustment parameter vector;
s6: acquiring a set B [ k ] of events to be detected triggered by a modulated source parameter vector P [ k ] according to a current trigger condition table D, and deleting data of the events to be detected in the set B [ k ] of the events to be detected from the current trigger condition table D;
s7: return to step S4 when k is k + 1;
s8: and recording the number of finally obtained source adjusting parameter vectors as K, sequentially setting power supply parameters of the intelligent electric energy meter according to the source adjusting parameter vectors P [ K ], wherein K is 0,1, … and K-1, then recovering to the nominal value of the power supply parameters, judging whether the triggered events are consistent with the events in the corresponding to-be-detected event set B [ K ] according to the output data of the intelligent electric energy meter, if so, judging that the events in the to-be-detected event set B [ K ] are verified to be passed, otherwise, searching for inconsistent events, and judging that the events are not verified to be passed.
The invention relates to an intelligent electric energy meter function verification method based on a genetic algorithm, which comprises the steps of firstly setting an event to be verified of an intelligent electric energy meter and power supply parameters related to the intelligent electric energy meter function verification according to actual needs, obtaining a trigger condition table of the event to be verified, and then obtaining a value range of each power supply parameter according to the trigger condition table; then, taking the source modulation parameter vector as an individual chromosome, and sequentially obtaining each source modulation parameter vector by adopting a genetic algorithm, wherein the fitness value of the individual is the number of events to be determined triggered by the corresponding source modulation parameter vector; and finally, setting power supply parameters of the intelligent electric energy meter according to the source-adjusting parameter vector to verify the functions of the intelligent electric energy meter. The invention can automatically generate the source-adjusting parameter vectors required by the intelligent electric energy meter function verification, can complete the verification of all events to be verified by adopting a small number of source-adjusting parameter vectors, can reduce the workload of testers and improve the efficiency of the intelligent electric energy meter function verification.
Drawings
FIG. 1 is a structural diagram of an automatic test platform of an intelligent electric energy meter;
fig. 2 is a flowchart of an embodiment of the method for verifying the function of the intelligent electric energy meter based on the genetic algorithm.
Detailed Description
The following description of the embodiments of the present invention is provided in order to better understand the present invention for those skilled in the art with reference to the accompanying drawings. It is to be expressly noted that in the following description, a detailed description of known functions and designs will be omitted when it may obscure the subject matter of the present invention.
Examples
Fig. 2 is a flowchart of an embodiment of the method for verifying the function of the intelligent electric energy meter based on the genetic algorithm. As shown in fig. 2, the method for verifying the function of the intelligent electric energy meter based on the genetic algorithm comprises the following specific steps:
s201: acquiring a trigger condition table of an event to be verified:
setting an event to be detected of the intelligent electric energy meter and power parameters related to the function verification of the intelligent electric energy meter according to actual needs, obtaining a trigger condition table D of the event to be verified, and obtaining an element D in the trigger condition table DijRepresenting the condition that the power parameter j needs to satisfy when the event i to be verified is triggered, and if the event i to be verified is triggered and the power parameter j does not need to satisfy the specific condition, corresponding element dijIs empty, wherein i is 0,1, …, M-1, j is 0,1, …, N-1, M indicates the substance to be assayedThe number of pieces, N, represents the number of power supply parameters.
Table 1 is an example of a trigger condition table D of a to-be-detected event of the three-phase intelligent electric energy meter.
TABLE 1
The parameters in the trigger conditions in table 1 are as follows:
a) a current-loss event current trigger lower limit lccl (3% -10%), a current trigger upper limit lccu (0.5% -2%), a voltage trigger lower limit lcvl (60% -90%), and a decision delay time lct (10s-90 s);
b) the lower voltage trigger limit cfvl (60-85%), the upper current trigger limit cfcu (0.5-5%), and the delay time cft (10-90 s) of the cutoff event;
c) overcurrent event current trigger lower limit occl (0.5-1.5I)max) Judging the delay time oct (10s-90 s);
d) overload event active power trigger lower limit olpl (0.5-1.5 Ima)x) Determining the delay time olt (10s-90 s);
e) overvoltage event voltage trigger lower limit ovvl (110% -130%), decision delay time ovt (10s-90 s);
f) the voltage triggering upper limit uvvu (70% -90%) of the undervoltage event, and the judgment delay time uvt (10s-90 s);
g) a voltage loss event current triggering lower limit lvcl (0.5-5%), a voltage triggering upper limit lvvu (70-90%), a voltage recovery lower limit lvvrl (lvvu-90%) and a time delay lvt (10s-90 s);
h) judging the delay time alvt (10s-90s) by using the critical voltage u (60%) of the voltage meter of the total voltage loss event;
i) phase failure event voltage triggering upper limit pfvu (70% -90%), current triggering upper limit pfcu (0.5% -5%), and determination delay time pft (10s-90 s);
j) the active power reverse event active power triggering lower limit aprl (0.5% -5%), and the delay time aprt (10s-90s) are judged;
k) the active power triggering lower limit prl (0.5% -5%) of the tidal current reverse event and the delay time prlt (10s-90s) are judged;
l) current unbalance rate ubcr (10% -90%) of current unbalance event, severe unbalance rate hubcr (20% -90%), and determining delay event (10s-90 s);
m) voltage unbalance rate ubvr (10% -90%), serious unbalance rate (20% -90%), and time delay (10s-90s) are judged;
n) "u" represents "critical voltage";
o) "| |" indicates or, that is, at least 1 of a plurality of trigger conditions with "| |" identification in a row is satisfied, and the trigger condition without "| |" identification is necessary to be satisfied;
p) "/" indicates that specific conditions need not be satisfied.
As shown in table 1, table 1 shows the trigger conditions of the to-be-verified events of the three-phase smart power meter part, each column represents one power supply parameter (phase voltage, current and phase), each row represents one to-be-verified event, and the row shows the trigger conditions (which can be determined by a power supply vector with a length of 9) of the current to-be-verified event. Taking the first row as an example, when the voltage of the phase a is greater than the current loss event voltage trigger lower limit lcvl, the phase a current is less than the current trigger upper limit lccu, and one of the phase BC currents is greater than the current trigger lower limit lccl, it is considered that the phase a current loss event occurs, the electric energy meter should have a corresponding record, otherwise, it is considered that the function of the electric energy meter is abnormal.
S202: setting the value range of power supply parameters:
for the power supply parameter j, if the power supply parameter j is a phase parameter, the value range C is setjIs [0,180 ]]Otherwise, setting the value range according to the trigger condition table D of the event to be verified, wherein the specific method comprises the following steps: acquiring trigger conditions corresponding to events to be verified related to the power supply parameter j from the trigger condition table D to obtain power supply parameter j critical values of the trigger conditions, recording the number of the acquired critical values as Q, and arranging all the critical values from large to small to obtain a critical value sequence
The value range C of the power supply parameter jjIs composed of
Wherein α is more than 1, 0 is more than or equal to β and less than 1.
In order to trigger as many events as possible in the modulation source parameter vector corresponding to the chromosome individual in the subsequent genetic algorithm and improve the iteration efficiency of the genetic algorithm, the value range of the power supply parameter of the non-phase parameter is further optimized in the embodiment, so that the power supply parameter is valued between all critical values, is larger than the maximum value of the critical values and is smaller than the minimum value of the critical values, that is, the value range of the power supply parameter j is a set
S203: let the tuning-source parameter vector sequence number k equal to 0.
S204: judging whether the trigger condition table D is empty, if so, completing the acquisition of the modulation source parameter vector required by the function verification of the intelligent electric energy meter, and entering step S208, otherwise, entering step S205;
s205: adopting a genetic algorithm to obtain a source-regulating parameter vector:
adopting genetic algorithm to obtain modulation source parameter vector P [ k ] with sequence number k]In the genetic algorithm, the vector of the source-regulating parameter is taken as an individual chromosome, namely the genetic algorithm individual X ═ X1,x2,…,xN]Wherein x isjThe value of the power supply parameter j is represented, and the corresponding value range is CjAnd the fitness value of the individual is the number of events to be detected triggered by the corresponding adjusting source parameter vector, and obviously, the more the number of the triggered events to be detected is, the better the individual is.
The genetic algorithm is a common algorithm, and the basic process is as follows: initializing each individual in the population to obtain an initial population, calculating the fitness value of each individual in the population, generating a next generation population through selection, intersection and variation, selecting the optimal individual from the current population as the final optimal individual if the iteration ending condition is met, and continuing to generate the next generation population if the iteration ending condition is not met.
S206: updating a trigger condition table:
and acquiring a set B [ k ] of the events to be detected triggered by the adjusted source parameter vector P [ k ] according to the current trigger condition table D, and deleting data of the events to be detected in the set B [ k ] of the events to be detected from the current trigger condition table D.
S207: let k be k +1, return to step S204.
S208: function verification of the intelligent electric energy meter:
and recording the number of finally obtained source adjusting parameter vectors as K, sequentially setting power supply parameters of the intelligent electric energy meter according to the source adjusting parameter vectors P [ K ], wherein K is 0,1, … and K-1, then recovering to the nominal value of the power supply parameters, judging whether the triggered events are consistent with the events in the corresponding to-be-detected event set B [ K ] according to the output data of the intelligent electric energy meter, if so, judging that the events in the to-be-detected event set B [ K ] are verified to be passed, otherwise, searching for inconsistent events, and judging that the events are not verified to be passed.
In order to better explain the technical scheme of the invention, a specific example is adopted to describe the implementation process of the invention in detail. Table 2 is a trigger condition table D of events to be verified in this embodiment.
TABLE 2
As shown in table 2, the intelligent electric energy meter in this embodiment is a three-phase electric energy meter, that is, the number of power parameters is 9, and the number of events to be detected is 32. When the data in table 2 is-1, it indicates that the event to be verified is triggered without the corresponding power parameter satisfying the specific condition, and the other data is the default critical value and can be reset by the user before the test. The 1 st event to be detected, phase A loss, is taken as an example for explanation, wherein the 1 st column is' UA”>70% represents that the A phase voltage must be greater than 0.7 times the standardVoltage value, column 4 "UA”<0.5% represents that the current must be less than 0.005 times the standard current value.
And then setting the value range of the power supply parameter. By power supply parameter UAFor example, U can be seen from column 1 of Table 2AThe critical values of (1.2), (0.9), (0.78), (0.7), (0.6), (0.3) are 6, and in the embodiment, the setting parameter α is 1.1, and the setting parameter β is 0.5, so the range of the setting parameter is Cj＝[1.32，0.15]. Then further optimizing the value ranges to [1.32, 1.1, 0.84, 0.74, 0.65, 0.45, 0.15 ]]. The value ranges of other 8 genes are obtained by the same method.
Taking the modulation source parameter vector as an individual chromosome, obviously, the length of the genetic algorithm individual is 9, and acquiring each modulation source parameter vector by adopting the genetic algorithm, wherein the specific process is as follows:
the first step is as follows: the genetic algorithm generates a tuning source parameter vector with a sequence number of 0, which is marked as P [0], searches the event to be determined triggered by the current tuning source parameter vector P [0] in the table 2, and marks the set as B [0], which is as follows:
P[0]＝[0.65，1.32，0.65，1.32，1.32，1.32，180，180，180]
B[0]＝[18，7，29，28，30，6，17，20，31，15，10，13，8]
then deleting the data of the events to be detected in the event set B [0] to be detected from the table 2.
The second step is that: based on the updated table 2, the genetic algorithm generates a modulation source parameter vector P [1], and the event to be determined triggered by the current modulation source parameter vector is searched in the updated table 2, and the set is recorded as B [1], specifically as follows:
P[1]＝[1.32，0.74，1.32，0.0025，0.0025，1.32，0，0，0]
B[1]＝[14，11，12，0，24，1，16，4，3]
and then deleting the data of the events to be detected in the event set B [1] to be detected from the updated table 2.
And repeating the steps to obtain a final intelligent electric energy meter function verification test scheme. Table 3 shows the test scheme for verifying the function of the intelligent electric energy meter obtained in this embodiment. And table 4 shows the to-be-detected event corresponding to each source-adjusting parameter vector in the intelligent electric energy meter function verification test scheme in table 3.
TABLE 3
TABLE 4
As shown in tables 3 and 4, in this embodiment, the number of the source adjustment parameter vectors required for the function verification of the three-phase intelligent electric energy meter is 6, that is, the function verification of 32 events to be verified can be completed by setting the power supply parameters for 6 times, and 10 times of calculation is performed for each event verification according to 60 seconds for each source adjustment, which is required in total: 60 seconds per 60 minutes per 10 times. As described in the background, the total test time of one event in the prior art is 0.5 hours, and then the total test time of 32 events to be detected in this example is 16 hours. The invention can automatically generate the intelligent electric energy meter function verification test scheme which is reasonable, thereby effectively reducing the workload of testers and improving the efficiency of the intelligent electric energy meter function verification.
Although illustrative embodiments of the present invention have been described above to facilitate the understanding of the present invention by those skilled in the art, it should be understood that the present invention is not limited to the scope of the embodiments, and various changes may be made apparent to those skilled in the art as long as they are within the spirit and scope of the present invention as defined and defined by the appended claims, and all matters of the invention which utilize the inventive concepts are protected.
Claims (2)
1. An intelligent electric energy meter function verification method based on a genetic algorithm is characterized by comprising the following steps:
s1: setting the event to be detected of the intelligent electric energy meter and the power supply parameter involved in the function verification of the intelligent electric energy meter according to actual needs to obtainTrigger condition table D of event to be verified, element D in trigger condition table DijRepresenting the condition that the power parameter j needs to satisfy when the event i to be verified is triggered, and if the event i to be verified is triggered and the power parameter j does not need to satisfy the specific condition, corresponding element dijNull, where i is 0,1, …, M-1, j is 0,1, …, N-1, M indicates the number of events to be verified, N indicates the number of power supply parameters;
s2: for the power supply parameter j, if the power supply parameter j is a phase parameter, the value range C is setjIs [0,180 ]]Otherwise, setting the value range according to the trigger condition table D of the event to be verified, wherein the specific method comprises the following steps: acquiring trigger conditions corresponding to events to be verified related to the power supply parameter j from the trigger condition table D to obtain power supply parameter j critical values of the trigger conditions, recording the number of the acquired critical values as Q, and arranging all the critical values from large to small to obtain a critical value sequence
The value range C of the power supply parameter jjIs composed of
Wherein α is more than 1, 0 is more than or equal to β and less than 1;
s3: making the sequence number k of the tuning source parameter vector equal to 0;
s4: judging whether the trigger condition table D is empty, if so, completing the acquisition of the tuning source parameter vector required by the function verification of the intelligent electric energy meter, and entering step S8, otherwise, entering step S5;
s5: adopting genetic algorithm to obtain modulation source parameter vector P [ k ] with sequence number k]In the genetic algorithm, the vector of the source-regulating parameter is taken as an individual chromosome, namely the genetic algorithm individual X ═ X1,x2,…,xN]Wherein x isjThe value of the power supply parameter j is represented, and the corresponding value range is CjThe fitness value of the individual is the number of events to be determined triggered by the corresponding source adjustment parameter vector;
s6: acquiring a set B [ k ] of events to be detected triggered by a modulated source parameter vector P [ k ] according to a current trigger condition table D, and deleting data of the events to be detected in the set B [ k ] of the events to be detected from the current trigger condition table D;
s7: return to step S4 when k is k + 1;
s8: and recording the number of finally obtained source adjusting parameter vectors as K, sequentially setting power supply parameters of the intelligent electric energy meter according to the source adjusting parameter vectors P [ K ], wherein K is 0,1, … and K-1, then recovering to the nominal value of the power supply parameters, judging whether the triggered events are consistent with the events in the corresponding to-be-detected event set B [ K ] according to the output data of the intelligent electric energy meter, if so, judging that the events in the to-be-detected event set B [ K ] are verified to be passed, otherwise, searching for inconsistent events, and judging that the events are not verified to be passed.
2. The method for verifying the function of an intelligent electric energy meter according to claim 1, wherein the value range C of the power supply parameter of the non-phase parameter in the step S2jIs a set, i.e.
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| 电能表自动化检定系统设计;杨霖;《中国优秀硕士学位论文全文数据库 工程科技II辑》;20170315;C042-3448 * |
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