CN113724101B - Table relation identification method and system, equipment and storage medium - Google Patents
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Abstract
The invention discloses a method, a system, equipment and a storage medium for identifying the box table relationship of a platform area, wherein the method for identifying the box table relationship of the platform area acquires instantaneous load data of a normal load table box terminal and instantaneous load data of a user table by adopting a freezing window to compare, eliminates the influence of time dyssynchrony, screens out optimal combinations of a plurality of user tables and a plurality of normal load table box terminals by adopting a genetic algorithm of statistics and binary coding, thereby automatically identifying the topological relationship between a plurality of user tables and a plurality of normal load table box terminals, has high identification precision, and can update the box table relationship in time based on the change of the load data.
Description
Technical Field
The present invention relates to the field of topology identification technologies of a platform, and in particular, to a method, a system, a device, and a computer readable storage medium for identifying a box table relationship of a platform.
Background
With the rapid development of the power industry, the power demand is continuously increased, and the power industry tends to be intelligent, digital and modeled. The current low-voltage power supply network construction has low intelligent level, increases the difficulty and workload of daily operation and maintenance of the power grid, and influences the electricity quality of users. The intelligent ammeter is an extremely important sensing terminal in a ubiquitous power internet of things sensing layer and is responsible for a fine-granularity electric energy metering function. Based on the measurement data of the intelligent ammeter, the method can realize advanced applications such as electricity behavior analysis, demand response strategy design, electricity market price formulation and the like, and digital transformation of the supporting point position besides completing electric energy charging. At present, the relation between the traditional household meter and the meter box is that the box meter relation topology is carried out by manually inputting addresses, but the method adopts manual address input to possibly have the problems that the misoperation exists, the household meter change information cannot be changed in time, and the like.
Disclosure of Invention
The invention provides a method, a system, equipment and a computer-readable storage medium for identifying a box table relationship of a platform area, which are used for solving the defects of the existing box table relationship topology by manually inputting addresses.
According to an aspect of the present invention, there is provided a box table relation identifying method of a station area, including:
step S1: periodically freezing the instantaneous load data of each user meter in the same phase, and periodically freezing the instantaneous load data of each normal load meter box terminal in the phase by adopting a freezing window, wherein the freezing window comprises the first n seconds and the last n seconds of each freezing moment;
step S2: constructing a household meter load matrix based on instantaneous load data of a plurality of household meters at the moment t, and constructing a meter box load matrix at each moment in a freezing window based on instantaneous load data of a plurality of normal load meter box terminals in the freezing window corresponding to the moment t, wherein the total is 2n+1 meter box load matrices;
step S3: aiming at a table box load matrix and a household table load matrix at the time t, introducing coefficient matrixes and deviation matrixes of the two matrixes, adopting a binary coded genetic algorithm to perform selection operation on the coefficient matrixes, and calculating to obtain a target coefficient matrix corresponding to the minimum accumulated sum value of each row in the deviation matrix at the time t;
Step S4: for the table box load matrix corresponding to the residual moment in the freezing window, respectively and independently combining the table box load matrix with the household table load matrix at the moment t, repeatedly executing the step S3, calculating to obtain the minimum value of the accumulated sum value of each row in the deviation matrix at each moment in the freezing window, screening out the minimum value from 2n+1 minimum values, and adopting the target coefficient matrix corresponding to the minimum value as the optimal coefficient matrix;
step S5: and (3) repeatedly executing the step (S3) and the step (S4) by adopting a plurality of groups of data, and obtaining the attribution relation between a plurality of user meters and a plurality of normal load meter box terminals based on the optimal coefficient matrix if the optimal coefficient matrix is unchanged.
Further, the step S5 further includes the following:
if the optimal coefficient matrix changes, that is, at least two optimal coefficient matrices exist, respectively adopting each optimal coefficient matrix to calculate separately to obtain the sum of accumulated sum values of each row in the deviation matrix of all the sampled data, screening the smallest one from the sum, and obtaining the attribution relation between a plurality of electricity utilization meters and a plurality of normal load meter box terminals based on the optimal coefficient matrix corresponding to the smallest one.
Further, the method also comprises the following steps:
Step S6: the method comprises the steps of collecting voltage data of each empty household meter in the same phase and voltage data of each small load meter box terminal under the phase, calculating voltage correlation between the voltage data of each empty household meter and the voltage data of each small load meter box terminal by using a pearson correlation coefficient algorithm, finding out the empty household meter with the largest correlation coefficient with each small load meter box terminal, recording the empty household meter with the largest correlation coefficient under each small load meter box terminal if the empty household meter with the largest correlation coefficient corresponding to each small load meter box is not repeated, repeatedly calculating by using multiple groups of voltage data, and attributing the empty household meter with the largest correlation coefficient to the small load meter box terminal if the empty household meter with the largest correlation coefficient corresponding to the small load meter box terminal is consistent.
Further, the step S6 further includes the following:
if the empty household meter with the maximum correlation coefficient corresponding to each small load meter box terminal is repeated, or the empty household meter with the maximum correlation coefficient corresponding to the small load meter box terminal is inconsistent through calculation of multiple groups of voltage data, the empty household meter with the maximum probability under each small load meter box terminal is calculated through a maximum posterior probability estimation algorithm, and the empty household meter is correspondingly attributed to the small load meter box terminal.
Further, the method also comprises the following steps:
step S7: acquiring voltage data of all the empty household tables with unknown attribution relations and all the household tables with known attribution relations, calculating the household table with the known attribution relation nearest to the voltage waveform of the empty household table with each unknown attribution relation by utilizing a neighbor algorithm, and attributing the empty household table with the unknown attribution relation to a table box terminal to which the household table with the known attribution relation belongs.
Further, the step S7 of calculating the household table with the known home relation nearest to the voltage waveform of the empty household table with each unknown home relation by using the neighbor algorithm specifically includes the following steps:
step S71: by L p Calculating the distance between the voltage vector of the empty household meter of each unknown attribution relation and the voltage vectors of all household meters of known attribution relations by a distance formula, and finding the household meter of the known attribution relation closest to the empty household meter of the unknown attribution relation, wherein L is the sum of the distance between the voltage vector of the empty household meter of each unknown attribution relation and the voltage vector of the household meter of all known attribution relations p The distance formula is:
wherein U is i Voltage vector of empty household meter representing unknown attribution relation, U I Voltage vector, L, representing a family table of known attribution relations p (U i ,U I ) L between the voltage vector of the empty household meter representing the unknown home relation and the voltage vector of the household meter of the known home relation p Distance, t, represents the number of samples per set of voltage data;
step S72: repeating calculation by utilizing multiple groups of voltage data, if multiple groups of calculation results are consistent, attributing the empty household meter with unknown attribution relationship to the meter box terminal to which the household meter with the known attribution relationship closest to the unknown attribution relationship belongs; if the multiple groups of calculation results are inconsistent, calculating to obtain a household table with the known attribution relation with the highest probability corresponding to the household table with the unknown attribution relation by adopting a maximum posterior probability estimation algorithm, and attributing the household table with the unknown attribution relation to a table box terminal to which the household table with the known attribution relation with the highest probability belongs.
Further, the transient load data includes at least one of a power consumption, a power, a current, and a voltage.
In addition, the invention also provides a system for identifying the box table relation of the station area, which comprises the following steps:
the instantaneous load data acquisition module is used for periodically freezing the instantaneous load data of each user meter in the same phase and periodically freezing the instantaneous load data of each normal load meter box terminal in the phase by adopting a freezing window, wherein the freezing window comprises the first n seconds and the last n seconds of each freezing moment;
The load matrix construction module is used for constructing a household table load matrix based on the instantaneous load data of a plurality of household tables at the moment t, and constructing a table box load matrix at each moment in a freezing window based on the instantaneous load data of a plurality of normal load table box terminals in the freezing window corresponding to the moment t, wherein the total number of the table box load matrices is 2n+1;
the target coefficient matrix calculation module is used for introducing coefficient matrixes and deviation matrixes of the two matrixes aiming at the table box load matrix and the household table load matrix at the t moment, selecting the coefficient matrixes by adopting a binary coded genetic algorithm, and calculating to obtain a target coefficient matrix corresponding to the minimum accumulated sum value of each row in the deviation matrix at the t moment; and is also used for independently combining the table box load matrix corresponding to the residual moment in the freezing window with the household table load matrix at the moment t to calculate and obtain the minimum value of the accumulated sum value of each row in the deviation matrix at each moment in the freezing window,
the optimal coefficient matrix screening module is used for screening the minimum value from the minimum value of the accumulated sum value of each row in the 2n+1 deviation matrices, and adopting a target coefficient matrix corresponding to the minimum value as an optimal coefficient matrix;
And the box table relation recognition module is used for obtaining attribution relations between the plurality of user tables and the plurality of normal load table box terminals based on the optimal coefficient matrix when the optimal coefficient matrix is unchanged under the condition of adopting a plurality of groups of data calculation.
In addition, the invention also provides a device comprising a processor and a memory, the memory having stored therein a computer program for executing the steps of the method as described above by invoking the computer program stored in the memory.
In addition, the present invention also provides a computer-readable storage medium storing a computer program for identifying a box table relationship of a zone, the computer program executing the steps of the method as described above when run on a computer.
The invention has the following effects:
according to the table area box table relation identification method, the influence of the measurement core, the management core and the HPLC communication delay between the household table and the table box terminal is considered, the instantaneous load data can move in time sequence, in order to protect the electricity safety and the charging accuracy of users, the household table data cannot move, and only the instantaneous value of the freezing moment can be removed, so that the instantaneous load data of each normal load table box terminal is collected by adopting the freezing window, the freezing window comprises the first n seconds and the last n seconds of each freezing moment, namely the instantaneous load data of 2n+1 moments, namely the instantaneous load data of one freezing moment of the household table corresponds to the 2n+1 instantaneous load data of the normal load table box terminal in the freezing window, the situation that the data is distorted due to the fact that the time is not synchronous is eliminated is avoided, and the accuracy of box table relation identification is ensured. Then, constructing a table box load matrix of each moment based on the instantaneous load data of each moment in a freezing window corresponding to the moment t of a plurality of normal load table box terminals, adding up 2n+1 table box load matrices, constructing a household table load matrix based on the instantaneous load data of a plurality of household meters at the moment t, introducing a coefficient matrix and a deviation matrix, adopting a binary coded genetic algorithm to perform selection operation, respectively calculating the deviation values of the 2n+1 table box load matrices and one household table load matrix, namely accumulating sum values of each row in the deviation matrix, and screening out a target coefficient matrix corresponding to the smallest deviation value as an optimal coefficient matrix. And finally, repeatedly calculating through multiple groups of data, and obtaining attribution relations between the multiple user meters and the multiple normal load meter box terminals based on the optimal coefficient matrix if multiple calculation results are consistent, namely the optimal coefficient matrix is unchanged. The method for identifying the box table relationship of the transformer area eliminates the influence of time dyssynchrony by adopting the freezing window to collect and compare the instantaneous load data of the normal load table box terminals with the instantaneous load data of the electricity user tables, and adopts the genetic algorithm of statistics and binary codes to screen out the optimal combination of a plurality of electricity user tables and a plurality of normal load table box terminals, thereby automatically identifying the topological relationship between a plurality of electricity user tables and a plurality of normal load table box terminals, having high identification precision and being capable of updating the box table relationship in time based on the change of the load data.
In addition, the box table relation identifying system, the box table relation identifying device and the box table relation identifying method for the area have the advantages.
In addition to the objects, features and advantages described above, the present invention has other objects, features and advantages. The present invention will be described in further detail with reference to the drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:
fig. 1 is a flowchart of a method for identifying a box table relationship of a zone according to a preferred embodiment of the present invention.
Fig. 2 is a flowchart of a table relationship identifying method of a station according to another embodiment of the present invention.
Fig. 3 is a flowchart of a table relationship identifying method of a station area according to still another embodiment of the present invention.
Fig. 4 is a schematic flow chart of step S7 in fig. 3.
Fig. 5 is a schematic block diagram of a table relationship identifying system of a station according to another embodiment of the present invention.
Detailed Description
Embodiments of the invention are described in detail below with reference to the attached drawing figures, but the invention can be practiced in a number of different ways, as defined and covered below.
As shown in fig. 1, a preferred embodiment of the present invention provides a method for identifying a box table relationship of a station, including the following:
step S1: periodically freezing the instantaneous load data of each user meter in the same phase, and periodically freezing the instantaneous load data of each normal load meter box terminal in the phase by adopting a freezing window, wherein the freezing window comprises the first n seconds and the last n seconds of each freezing moment;
step S2: constructing a household meter load matrix based on instantaneous load data of a plurality of household meters at the moment t, and constructing a meter box load matrix at each moment in a freezing window based on instantaneous load data of a plurality of normal load meter box terminals in the freezing window corresponding to the moment t, wherein the total is 2n+1 meter box load matrices;
step S3: aiming at a table box load matrix and a household table load matrix at the time t, introducing coefficient matrixes and deviation matrixes of the two matrixes, adopting a binary coded genetic algorithm to perform selection operation on the coefficient matrixes, and calculating to obtain a target coefficient matrix corresponding to the minimum accumulated sum value of each row in the deviation matrix at the time t;
step S4: for the table box load matrix corresponding to the residual moment in the freezing window, respectively and independently combining the table box load matrix with the household table load matrix at the moment t, repeatedly executing the step S3, calculating to obtain the minimum value of the accumulated sum value of each row in the deviation matrix at each moment in the freezing window, screening out the minimum value from 2n+1 minimum values, and adopting the target coefficient matrix corresponding to the minimum value as the optimal coefficient matrix;
Step S5: and (3) repeatedly executing the step (S3) and the step (S4) by adopting a plurality of groups of data, and obtaining the attribution relation between a plurality of user meters and a plurality of normal load meter box terminals based on the optimal coefficient matrix if the optimal coefficient matrix is unchanged.
It can be understood that, in the case table relationship identifying method of the table area of the present embodiment, considering that the instantaneous load data may have time sequence movement due to the influence of the delay of metering core, management core and HPLC communication between the household table and the table case terminal, and in order to protect the user electricity safety and charging accuracy, the household table data cannot move, and only the instantaneous value of the freezing moment can be removed, so that the instantaneous load data of each normal load table case terminal is collected by adopting the freezing window, and the freezing window includes the first n seconds and the last n seconds of each freezing moment, that is, includes the instantaneous load data of 2n+1 moments, that is, the instantaneous load data of one freezing moment of the household table corresponds to the 2n+1 instantaneous load data of the normal load table case terminal in the freezing window, so as to eliminate the situation that the time is not synchronous and causes the data distortion, and ensure the accuracy of the case table relationship identification. Then, constructing a table box load matrix of each moment based on the instantaneous load data of each moment in a freezing window corresponding to the moment t of a plurality of normal load table box terminals, adding up 2n+1 table box load matrices, constructing a household table load matrix based on the instantaneous load data of a plurality of household meters at the moment t, introducing a coefficient matrix and a deviation matrix, adopting a binary coded genetic algorithm to perform selection operation, respectively calculating the deviation values of the 2n+1 table box load matrices and one household table load matrix, namely accumulating sum values of each row in the deviation matrix, and screening out a target coefficient matrix corresponding to the smallest deviation value as an optimal coefficient matrix. And finally, repeatedly calculating through multiple groups of data, and obtaining attribution relations between the multiple user meters and the multiple normal load meter box terminals based on the optimal coefficient matrix if multiple calculation results are consistent, namely the optimal coefficient matrix is unchanged. The method for identifying the box table relationship of the transformer area eliminates the influence of time dyssynchrony by adopting the freezing window to collect and compare the instantaneous load data of the normal load table box terminals with the instantaneous load data of the electricity user tables, and adopts the genetic algorithm of statistics and binary codes to screen out the optimal combination of a plurality of electricity user tables and a plurality of normal load table box terminals, thereby automatically identifying the topological relationship between a plurality of electricity user tables and a plurality of normal load table box terminals, having high identification precision and being capable of updating the box table relationship in time based on the change of the load data.
It will be appreciated that in the low voltage area, each household meter is a single-phase household meter, the phase of the household meter is divided into A, B, C three phases, and each meter box terminal also comprises A, B, C three phases of load data. Therefore, in step S1, the load data of the utility meter in a certain phase is analyzed with the load data in the phase in the meter box terminal, which is advantageous in reducing the complexity of calculation and the calculation power demand. In addition, according to the electricity consumption condition, the household meter can be divided into an electricity consumption household meter and an empty household meter, wherein the empty household meter only has voltage data, but does not have load data such as electricity consumption, power, current and the like, and similarly, the meter box terminal can be divided into a normal load meter box terminal (the electricity consumption per day is more than 0.1kw for h) and a small load meter box terminal (the electricity consumption per day is less than or equal to 0.1kw for h), the household meter under the small load meter box terminal is the empty household meter, and the small load meter box terminal also only has voltage data.
Specifically, taking the electricity consumption meter in the phase a as an example, the specific phase can be selected according to actual needs, and instantaneous load data of all the electricity consumption meters in the phase a are periodically frozen, wherein the instantaneous load data comprises at least one of electricity consumption, power, current and voltage. In consideration of the influence of communication delay of a metering core, a management core and HPLC (high performance liquid chromatography) between a user meter and a normal load meter box terminal, instantaneous load data can move in time sequence, and in order to protect user electricity safety and charging accuracy, the user meter data cannot move but only can freeze instantaneous values at the moment, so that the instantaneous load data of each normal load meter box terminal is collected by adopting a freezing window. The freezing window comprises the first n seconds and the last n seconds of each freezing moment, namely 2n+1 instantaneous load data are arranged in the freezing window, the length of the instantaneous load data time sequence of the normal load meter box terminal is 2n+1 times of the instantaneous load data time sequence of the electricity consumption meter at each freezing moment, namely the instantaneous load data of one freezing moment of the electricity consumption meter simultaneously correspond to 2n+1 instantaneous load data of the normal load meter box terminal in the freezing window, and the influence of time dyssynchrony between the electricity consumption meter and the meter box terminal can be effectively eliminated. The freezing period may be set as needed, and may be set as an hour freezing period, a 15 minute freezing period, or a minute freezing period, and it is generally preferable that the freezing period be 15 minutes.
It can be understood that in the step S2, the user table load matrix is constructed based on the instantaneous load data of the plurality of user tables at the time t, and the table box load matrix at each time in the freezing window is constructed based on the instantaneous load data of the plurality of normal load table box terminals in the freezing window corresponding to the time t, and 2n+1 table box load matrices can be obtained because 2n+1 times are included in the freezing window.
For example, setting n=1, instantaneous load data adopts the electricity consumption W, the power P and the current I, and the household table load matrix is constructed as follows:
wherein A is t Representing the household meter load matrix of 1-i A-phase household meters at the moment t,indicating the electricity consumption of the ith A-phase electricity meter at the time t,/day>Indicating the instantaneous power of the ith A-phase electricity meter at time t,/for the electricity meter>The instant current of the ith A-phase ammeter at the time t is shown.
The constructed 3 table box load matrixes are respectively as follows:
wherein B is t-1 Meter box load matrix representing t-1 time of 1-j normal load meter box terminals, B t A meter box load matrix representing 1-j normal load meter box terminals at t time, B t+1 A meter box load matrix of 1-j normal load meter box terminals at the time t+1,indicating the electricity consumption of the normal load meter box terminal j at the time t-1,/for the time of the normal load meter box terminal j >Indicating the instantaneous power of the normal load meter box terminal j at time t-1,/>Instantaneous current of normal load meter box terminal j at t-1 momentAnd others by analogy. In addition, i is more than or equal to j, namely the number of the user meters is more than or equal to the number of the terminals of the normal load meter box.
It will be appreciated that in said step S3, the terminal load matrix B of the table box for time t t Sum household meter load matrix A t The coefficient matrix X and the bias matrix Λ of the two matrices are introduced. Since the subscriber's list under each normal load box terminal is not repeatable, i.e. the coefficient matrix X has a size of i X j, in particular, the coefficient matrixThen B is t ＝X×A t +Λ，x ij The coefficient between the user meter i and the normal load meter box terminal j is represented.
Then, the coefficient elements in the coefficient matrix X are converted by adopting binary coding, and the value of each element is takenThe genetic algorithm is used for selection operation, the element with better adaptability is selected to be 1, the element with poor adaptability is selected to be 0, and each row of vector of the coefficient matrix X is only 1 and is positioned at different positions. Thus, a plurality of coefficient matrices X can be obtained by performing a selection operation by a binary-coded genetic algorithm.
And then, carrying out optimal solution solving by using a genetic algorithm. Specifically, Λ=b t -XA t Judging whether the combination is an optimal combination according to the accumulated sum value of each row in the deviation matrix lambda, and when the accumulated sum value of each row in the deviation matrix lambda is minimum, namely the deviation valueAnd when the minimum value is the minimum value, the coefficient matrix corresponding to the global optimum combination, namely the minimum deviation value, is the optimum coefficient matrix at the time t, and the optimum coefficient matrix is used as a target coefficient matrix. Wherein DeltaP k 、ΔW k 、ΔI k And respectively representing the power difference, the power consumption difference and the current difference of each normal load meter box terminal and the power consumption meter below the normal load meter box terminal. Wherein the specific working principle of the genetic algorithm belongs to the prior artAnd are of a technology and will not be described in detail herein.
It will be appreciated that in the step S4, the calculation process in the step S3 is repeatedly performed by individually combining the table box load matrix corresponding to the remaining time in the freezing window and the household table load matrix at the time t, for example, the table box load matrix B at the time t-1 t-1 And t+1 time table box load matrix B t+1 Respectively comparing the load matrix with a table box load matrix A at the time t t Performing combined calculation, namely B in the calculation process t Respectively replace with B t-1 And B t+1 And (3) obtaining the product. Therefore, 2n+1 minimum deviation values and target coefficient matrixes can be obtained, and then the smallest target coefficient matrix is screened out from the 2n+1 minimum deviation values, and the target coefficient matrix corresponding to the smallest target coefficient matrix is used as the final optimal coefficient matrix, so that the influence of time asynchronism can be effectively eliminated. Wherein 2n+1 minimum deviation values may be expressed as
It can be understood that in the step S5, the calculation in the step S3 and the step S4 is repeated by using the frozen data in a plurality of periods, and if the optimal coefficient matrix X obtained by the calculation result is consistent, that is, does not change, the topology relationship between the plurality of user meters and the plurality of normal load meter box terminals is obtained based on the optimal coefficient matrix X, so as to complete the box table relationship identification of the platform area.
It will be appreciated that step S5 further includes the following:
if the optimal coefficient matrix changes, that is, at least two optimal coefficient matrices exist, respectively adopting each optimal coefficient matrix to calculate separately to obtain the sum of accumulated sum values of each row in the deviation matrix of all the sampled data, screening the smallest one from the sum, and obtaining the attribution relation between a plurality of electricity utilization meters and a plurality of normal load meter box terminals based on the optimal coefficient matrix corresponding to the smallest one.
For example, when three normal load meter box terminals M1, M2, M3 exist, six user meters h1, h2, h3, h4, h5, h6, two optimal coefficient matrices are obtained through freezing data calculation in a plurality of periods, and are respectively:
then, the optimal coefficient matrix X is adopted when all instantaneous load data of a plurality of sampling periods are calculated independently 1 When calculating, the sum of the accumulated sum values of each row in the obtained deviation matrix, namely the sum of the deviation valuesCalculating the optimal coefficient matrix X for all instantaneous load data of multiple sampling periods 2 When the calculation is performed, the sum of the accumulated sum values of each row in the obtained deviation matrix, namely the sum of the deviation values +.>Wherein m represents the number of groups of the sampled data, ε T Representing the deviation value of the sample data of the T-th group.
Finally, selectAnd->And obtaining the attribution relation between the plurality of user meters and the plurality of normal load meter box terminals based on the optimal coefficient matrix corresponding to the minimum.
It can be understood that the steps S1 to S5 are repeated for the box table relation identification of the remaining phases, so that the topological relation between all the user tables and the normal load table box terminals in the transformer area can be accurately identified.
It will be appreciated that, as shown in fig. 2, in another embodiment of the present invention, the method for identifying a table relationship of a station area further includes the following:
step S6: the method comprises the steps of collecting voltage data of each empty household meter in the same phase and voltage data of each small load meter box terminal under the phase, calculating voltage correlation between the voltage data of each empty household meter and the voltage data of each small load meter box terminal by using a pearson correlation coefficient algorithm, finding out the empty household meter with the largest correlation coefficient with each small load meter box terminal, recording the empty household meter with the largest correlation coefficient under each small load meter box terminal if the empty household meter with the largest correlation coefficient corresponding to each small load meter box is not repeated, repeatedly calculating by using multiple groups of voltage data, and attributing the empty household meter with the largest correlation coefficient to the small load meter box terminal if the empty household meter with the largest correlation coefficient corresponding to the small load meter box terminal is consistent.
Through the steps S1-S5, the topological relation between all the electricity consumption meters and all the normal load meter box terminals can be identified, but for a low-voltage station area with lower occupancy rate, empty meter boxes and small load meter box terminals can exist. The empty household meter and the small load meter box terminal only have voltage data, and the attribution relation of each empty household meter and the meter box is usually identified by adopting a voltage correlation algorithm at present, but because the voltage waveform similarity of the adjacent meter box terminals under the same branch is higher, the empty household meter adopts the voltage correlation to easily find out the adjacent meter boxes. The invention finds the empty household meter with the largest voltage correlation under each small load meter box terminal through the pearson correlation coefficient algorithm, only identifies the empty household meter with the largest voltage correlation, ensures the accuracy of box meter topological relation identification, and identifies the remaining empty household meters through the subsequent identification process.
For example, voltage data of a plurality of A-phase space account numbers and voltage data of a plurality of small load meter box terminals in A-phase are adopted, and then a pearson correlation coefficient algorithm is utilized to calculate voltage correlation between the voltage data of each space account number and the voltage data of each small load meter box terminal. The pearson correlation coefficient calculation formula is:
Wherein ρ representsThe larger the correlation coefficient, the higher the voltage correlation, T represents the length of the voltage sequence,voltage data representing the terminal of the small load meter box at time t,/>Voltage average value of voltage sequence representing small load meter box terminal, +.>Voltage data representing the time t of the empty user meter,/->A voltage average representing a voltage sequence of an empty household meter,variance of voltage sequence representing small load meter box terminal, +.>Representing the variance of the voltage sequence of the air subscriber list.
The rho under each small-load meter box terminal can be calculated through the above method max If the empty household meter corresponds to rho of each small load meter box max The empty household meter is not repeated, and the rho under each small load meter box terminal is recorded respectively max An address of the empty subscriber table.
Repeating calculation by using multiple groups of voltage data, if rho of small load meter box terminal is calculated max If the empty household meter is unchanged, determining the rho max The empty household meter belongs to the small load meter box terminal, so that an empty household meter below the small load meter box terminal is accurately identified for each small load meter box terminal.
It will be appreciated that step S6 further includes the following:
if the empty household meter with the maximum correlation coefficient corresponding to each small load meter box terminal is repeated, or the empty household meter with the maximum correlation coefficient corresponding to the small load meter box terminal is inconsistent through calculation of multiple groups of voltage data, the empty household meter with the maximum probability under each small load meter box terminal is calculated through a maximum posterior probability estimation algorithm, and the empty household meter is correspondingly attributed to the small load meter box terminal.
Specifically, if ρ of the multiple calculation results is present max The empty household meters are inconsistent, or rho corresponding to a plurality of small load meter box terminals max The method for judging the maximum correlation coefficient is not applicable any more when the repetition condition of the empty user meter occurs. Aiming at the problem, the invention can find the empty household meter with the highest probability under each small load meter box terminal through the maximum posterior probability estimation algorithm, and correspondingly belongs to the small load meter box terminal.
For example, there are known i empty user meters and j small load meter box terminals, wherein the maximum a posteriori probability estimation calculation formula is:
wherein,represents the estimated maximum probability, P (θ) i |M j ) Representing the small load meter box terminal as M j Space-time household meter theta i Probability under the bin terminal, P (M j ) Representing small load meter box terminal selection M j Probability of->P(M j |θ i ) Indicating that when the air household meter is theta i The belonged list box is M j Probability of P (θ) i ) Representing the indoor household meter as theta in all sample data i Probability of P (M) j |θ i ) And P (θ) i ) Obtained by actual statistics.
From the maximum posterior probability estimation calculation formula, it can be seen thatThe maximum posterior probability incorporates the a priori distribution of the quantity to be estimated therein, and is derived from the desired risk minimization criteria. By deriving the distribution from the sample data, the prior experimental hypothesis (i.e. judging P (M j |θ i ) The effect is remarkable in the case of a comparison of the leaning spectrum, and the dominant effect of the prior assumption on the model parameters is gradually weakened as the data volume is increased. Therefore, the maximum probability of the empty household meter under each small-load meter box terminal can be obtained based on the maximum posterior probability estimation algorithm, and therefore an accurate box meter relation is obtained.
It can be understood that, through the above step S6, an empty household meter under each small load meter box terminal can be identified, so far, an indoor meter has been accurately identified under each meter box terminal, and only the remaining empty household meters have not identified the attribution relationship. As shown in fig. 3, in another embodiment of the present invention, the method for identifying a table relationship of a station area further includes the following:
step S7: acquiring voltage data of all the empty household tables with unknown attribution relations and all the household tables with known attribution relations, calculating the household table with the known attribution relation nearest to the voltage waveform of the empty household table with each unknown attribution relation by utilizing a neighbor algorithm, and attributing the empty household table with the unknown attribution relation to a table box terminal to which the household table with the known attribution relation belongs.
The household table with known attribution relation can be an empty household table or a power utilization household table.
It can be understood that, as shown in fig. 4, the process of calculating the household table with the known home relation nearest to the voltage waveform of the empty household table with each unknown home relation in the step S7 by using the neighbor algorithm specifically includes the following steps:
step S71: by L p Calculating the distance between the voltage vector of the empty household meter of each unknown attribution relation and the voltage vectors of all household meters of known attribution relations by a distance formula, and finding the household meter of the known attribution relation closest to the empty household meter of the unknown attribution relation, wherein L is the sum of the distance between the voltage vector of the empty household meter of each unknown attribution relation and the voltage vector of the household meter of all known attribution relations p The distance formula is:
wherein U is i A voltage vector representing an empty household meter of unknown home relation,U I voltage vector of user table representing known attribution relation, < ->t represents the number of voltage data in the voltage vector, t is the time of day node length for a 15 minute frozen acquisition frequency, t=96, i.e. the voltage vector comprises 96 sample points of data. L (L) p (U i ,U I ) L between the voltage vector of the empty household meter representing the unknown home relation and the voltage vector of the household meter of the known home relation p Distance. P=1, 2, 3, …, and can be set as necessary.
Step S72: repeating calculation by utilizing multiple groups of voltage data, if multiple groups of calculation results are consistent, attributing the empty household meter with unknown attribution relationship to the meter box terminal to which the household meter with the known attribution relationship closest to the unknown attribution relationship belongs; if the multiple groups of calculation results are inconsistent, calculating to obtain a household table with the known attribution relation with the highest probability corresponding to the household table with the unknown attribution relation by adopting a maximum posterior probability estimation algorithm, and attributing the household table with the unknown attribution relation to a table box terminal to which the household table with the known attribution relation with the highest probability belongs. The specific maximum a posteriori probability estimation algorithm is the same as that in step S6, and therefore will not be described here again. It can be understood that the problem of box table relation topology under the condition that the occupied space of the station area is relatively large can be effectively solved through the scheme.
In addition, as shown in fig. 5, another embodiment of the present invention further provides a system for identifying a box table relationship of a station, preferably adopting the identification method of the foregoing embodiment, where the identification system includes:
the instantaneous load data acquisition module is used for periodically freezing the instantaneous load data of each user meter in the same phase and periodically freezing the instantaneous load data of each normal load meter box terminal in the phase by adopting a freezing window, wherein the freezing window comprises the first n seconds and the last n seconds of each freezing moment;
the load matrix construction module is used for constructing a household table load matrix based on the instantaneous load data of a plurality of household tables at the moment t, and constructing a table box load matrix at each moment in a freezing window based on the instantaneous load data of a plurality of normal load table box terminals in the freezing window corresponding to the moment t, wherein the total number of the table box load matrices is 2n+1;
the target coefficient matrix calculation module is used for introducing coefficient matrixes and deviation matrixes of the two matrixes aiming at the table box load matrix and the household table load matrix at the t moment, selecting the coefficient matrixes by adopting a binary coded genetic algorithm, and calculating to obtain a target coefficient matrix corresponding to the minimum accumulated sum value of each row in the deviation matrix at the t moment; and is also used for independently combining the table box load matrix corresponding to the residual moment in the freezing window with the household table load matrix at the moment t to calculate and obtain the minimum value of the accumulated sum value of each row in the deviation matrix at each moment in the freezing window,
The optimal coefficient matrix screening module is used for screening the minimum value from the minimum value of the accumulated sum value of each row in the 2n+1 deviation matrices, and adopting a target coefficient matrix corresponding to the minimum value as an optimal coefficient matrix;
and the box table relation recognition module is used for obtaining attribution relations between the plurality of user tables and the plurality of normal load table box terminals based on the optimal coefficient matrix when the optimal coefficient matrix is unchanged under the condition of adopting a plurality of groups of data calculation.
It can be understood that, in the case table relationship identifying system of the table area of the present embodiment, considering that the instantaneous load data may have time sequence movement due to the influences of the measurement core, the management core and the HPLC communication delay between the household table and the table case terminal, and in order to protect the user electricity safety and the charging accuracy, the household table data cannot move, and only the instantaneous value of the freezing moment can be removed, so that the instantaneous load data of each normal load table case terminal is collected by adopting the freezing window, and the freezing window includes the first n seconds and the last n seconds of each freezing moment, that is, includes the instantaneous load data of 2n+1 moments, that is, the instantaneous load data of one freezing moment of the household table corresponds to the 2n+1 instantaneous load data of the normal load table case terminal in the freezing window, so as to eliminate the situation that the time is not synchronous and causes the data distortion, and ensure the accuracy of the case table relationship identification. Then, constructing a table box load matrix of each moment based on the instantaneous load data of each moment in a freezing window corresponding to the moment t of a plurality of normal load table box terminals, adding up 2n+1 table box load matrices, constructing a household table load matrix based on the instantaneous load data of a plurality of household meters at the moment t, introducing a coefficient matrix and a deviation matrix, adopting a binary coded genetic algorithm to perform selection operation, respectively calculating the deviation values of the 2n+1 table box load matrices and one household table load matrix, namely accumulating sum values of each row in the deviation matrix, and screening out a target coefficient matrix corresponding to the smallest deviation value as an optimal coefficient matrix. And finally, repeatedly calculating through multiple groups of data, and obtaining attribution relations between the multiple user meters and the multiple normal load meter box terminals based on the optimal coefficient matrix if multiple calculation results are consistent, namely the optimal coefficient matrix is unchanged. The box table relation recognition system for the transformer area, provided by the invention, has the advantages that the instantaneous load data of the box terminals of the normal load table are acquired through adopting the freezing window and compared with the instantaneous load data of the power consumption meters, the influence of time dyssynchrony is eliminated, and the optimal combination of a plurality of power consumption meters and a plurality of normal load table box terminals is screened out through adopting a genetic algorithm of statistics and binary coding, so that the topological relation between a plurality of power consumption meters and a plurality of normal load table box terminals is automatically recognized, the recognition precision is high, and the box table relation can be updated in time based on the change of the load data.
It can be understood that each module in the system of the present embodiment corresponds to each step of the above method embodiment, so the working process of each module is not described herein, and only needs to refer to the above method embodiment.
In addition, another embodiment of the present invention also provides an apparatus, including a processor and a memory, the memory storing a computer program, the processor being configured to perform the steps of the method as described above by calling the computer program stored in the memory.
In addition, another embodiment of the present invention also provides a computer-readable storage medium storing a computer program for identifying a box table relationship of a zone, the computer program executing the steps of the method as described above when run on a computer.
Forms of general computer-readable storage media include: a floppy disk (floppy disk), a flexible disk (flexible disk), hard disk, magnetic tape, any other magnetic medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a Random Access Memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a FLASH erasable programmable read-only memory (FLASH-EPROM), any other memory chip or cartridge, or any other medium from which a computer can read. The instructions may further be transmitted or received over a transmission medium. The term transmission medium may include any tangible or intangible medium that may be used to store, encode, or carry instructions for execution by a machine, and includes digital or analog communications signals or their communications with intangible medium that facilitate communication of such instructions. Transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise a bus for transmitting a computer data signal.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The method for identifying the box table relation of the station area is characterized by comprising the following steps:
step S1: periodically freezing the instantaneous load data of each user meter in the same phase, and periodically freezing the instantaneous load data of each normal load meter box terminal in the phase by adopting a freezing window, wherein the freezing window comprises the first n seconds and the last n seconds of each freezing moment;
step S2: constructing a household meter load matrix based on instantaneous load data of a plurality of household meters at the moment t, and constructing a meter box load matrix at each moment in a freezing window based on instantaneous load data of a plurality of normal load meter box terminals in the freezing window corresponding to the moment t, wherein the total is 2n+1 meter box load matrices;
step S3: aiming at a table box load matrix and a household table load matrix at the time t, introducing coefficient matrixes and deviation matrixes of the two matrixes, adopting a binary coded genetic algorithm to perform selection operation on the coefficient matrixes, and calculating to obtain a target coefficient matrix corresponding to the minimum accumulated sum value of each row in the deviation matrix at the time t;
Step S4: for the table box load matrix corresponding to the residual moment in the freezing window, respectively and independently combining the table box load matrix with the household table load matrix at the moment t, repeatedly executing the step S3, calculating to obtain the minimum value of the accumulated sum value of each row in the deviation matrix at each moment in the freezing window, screening out the minimum value from 2n+1 minimum values, and adopting the target coefficient matrix corresponding to the minimum value as the optimal coefficient matrix;
step S5: and (3) repeatedly executing the step (S3) and the step (S4) by adopting a plurality of groups of data, and obtaining the attribution relation between a plurality of user meters and a plurality of normal load meter box terminals based on the optimal coefficient matrix if the optimal coefficient matrix is unchanged.
2. The method for identifying a box table relationship of a station area according to claim 1, wherein the step S5 further comprises the following steps:
if the optimal coefficient matrix changes, that is, at least two optimal coefficient matrices exist, respectively adopting each optimal coefficient matrix to calculate separately to obtain the sum of accumulated sum values of each row in the deviation matrix of all the sampled data, screening the smallest one from the sum, and obtaining the attribution relation between a plurality of electricity utilization meters and a plurality of normal load meter box terminals based on the optimal coefficient matrix corresponding to the smallest one.
3. The method for identifying a box table relationship of a station area according to claim 1, further comprising:
step S6: the method comprises the steps of collecting voltage data of each empty household meter in the same phase and voltage data of each small load meter box terminal under the phase, calculating voltage correlation between the voltage data of each empty household meter and the voltage data of each small load meter box terminal by using a pearson correlation coefficient algorithm, finding out the empty household meter with the largest correlation coefficient with each small load meter box terminal, recording the empty household meter with the largest correlation coefficient under each small load meter box terminal if the empty household meter with the largest correlation coefficient corresponding to each small load meter box is not repeated, repeatedly calculating by using multiple groups of voltage data, and attributing the empty household meter with the largest correlation coefficient to the small load meter box terminal if the empty household meter with the largest correlation coefficient corresponding to the small load meter box terminal is consistent.
4. The method for identifying a box table relationship of a station area according to claim 3, wherein said step S6 further comprises the following steps:
if the empty household meter with the maximum correlation coefficient corresponding to each small load meter box terminal is repeated, or the empty household meter with the maximum correlation coefficient corresponding to the small load meter box terminal is inconsistent through calculation of multiple groups of voltage data, the empty household meter with the maximum probability under each small load meter box terminal is calculated through a maximum posterior probability estimation algorithm, and the empty household meter is correspondingly attributed to the small load meter box terminal.
5. The method for identifying a box table relationship of a station area according to claim 4, further comprising:
step S7: acquiring voltage data of all the empty household tables with unknown attribution relations and all the household tables with known attribution relations, calculating the household table with the known attribution relation nearest to the voltage waveform of the empty household table with each unknown attribution relation by utilizing a neighbor algorithm, and attributing the empty household table with the unknown attribution relation to a table box terminal to which the household table with the known attribution relation belongs.
6. The method for identifying the bin table relationship of the station area according to claim 5, wherein the step S7 of calculating the household table of the known home relationship nearest to the voltage waveform of the empty household table of each unknown home relationship by using a neighbor algorithm specifically comprises the following steps:
step S71: by L p Calculating the distance between the voltage vector of the empty household meter of each unknown attribution relation and the voltage vectors of all household meters of known attribution relations by a distance formula, and finding the household meter of the known attribution relation closest to the empty household meter of the unknown attribution relation, wherein L is the sum of the distance between the voltage vector of the empty household meter of each unknown attribution relation and the voltage vector of the household meter of all known attribution relations p The distance formula is:
wherein U is i Voltage vector of empty household meter representing unknown attribution relation, U I Voltage vector, L, representing a family table of known attribution relations p (U i ,U I ) L between the voltage vector of the empty household meter representing the unknown home relation and the voltage vector of the household meter of the known home relation p Distance, t, represents the number of samples per set of voltage data;
step S72: repeating calculation by utilizing multiple groups of voltage data, if multiple groups of calculation results are consistent, attributing the empty household meter with unknown attribution relationship to the meter box terminal to which the household meter with the known attribution relationship closest to the unknown attribution relationship belongs; if the multiple groups of calculation results are inconsistent, calculating to obtain a household table with the known attribution relation with the highest probability corresponding to the household table with the unknown attribution relation by adopting a maximum posterior probability estimation algorithm, and attributing the household table with the unknown attribution relation to a table box terminal to which the household table with the known attribution relation with the highest probability belongs.
7. The method for identifying a box table relationship of a station area according to any one of claims 1 to 6, wherein the instantaneous load data includes at least one of a power consumption amount, a power, a current, and a voltage.
8. A box table relationship identification system for a station, comprising:
the instantaneous load data acquisition module is used for periodically freezing the instantaneous load data of each user meter in the same phase and periodically freezing the instantaneous load data of each normal load meter box terminal in the phase by adopting a freezing window, wherein the freezing window comprises the first n seconds and the last n seconds of each freezing moment;
The load matrix construction module is used for constructing a household table load matrix based on the instantaneous load data of a plurality of household tables at the moment t, and constructing a table box load matrix at each moment in a freezing window based on the instantaneous load data of a plurality of normal load table box terminals in the freezing window corresponding to the moment t, wherein the total number of the table box load matrices is 2n+1;
the target coefficient matrix calculation module is used for introducing coefficient matrixes and deviation matrixes of the two matrixes aiming at the table box load matrix and the household table load matrix at the t moment, selecting the coefficient matrixes by adopting a binary coded genetic algorithm, and calculating to obtain a target coefficient matrix corresponding to the minimum accumulated sum value of each row in the deviation matrix at the t moment; and is also used for independently combining the table box load matrix corresponding to the residual moment in the freezing window with the household table load matrix at the moment t to calculate and obtain the minimum value of the accumulated sum value of each row in the deviation matrix at each moment in the freezing window,
the optimal coefficient matrix screening module is used for screening the minimum value from the minimum value of the accumulated sum value of each row in the 2n+1 deviation matrices, and adopting a target coefficient matrix corresponding to the minimum value as an optimal coefficient matrix;
And the box table relation recognition module is used for obtaining attribution relations between the plurality of user tables and the plurality of normal load table box terminals based on the optimal coefficient matrix when the optimal coefficient matrix is unchanged under the condition of adopting a plurality of groups of data calculation.
9. An apparatus comprising a processor and a memory, the memory having stored therein a computer program for executing the steps of the method according to any of claims 1-7 by invoking the computer program stored in the memory.
10. A computer readable storage medium storing a computer program for identifying box table relationships of a zone, wherein the computer program when run on a computer performs the steps of the method according to any one of claims 1 to 7.
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