CN116049156A - Electric quantity data acquisition optimization method and system based on big data technology - Google Patents

Electric quantity data acquisition optimization method and system based on big data technology Download PDF

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CN116049156A
CN116049156A CN202211730449.4A CN202211730449A CN116049156A CN 116049156 A CN116049156 A CN 116049156A CN 202211730449 A CN202211730449 A CN 202211730449A CN 116049156 A CN116049156 A CN 116049156A
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electric quantity
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王顺江
李林
眭冰
左晓晶
李雨珊
宫铭明
楚天丰
东方
于家敏
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State Grid Corp of China SGCC
State Grid Liaoning Electric Power Co Ltd
Electric Power Research Institute of State Grid Liaoning Electric Power Co Ltd
Benxi Power Supply Co of Liaoning Electric Power Co Ltd
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State Grid Liaoning Electric Power Co Ltd
Electric Power Research Institute of State Grid Liaoning Electric Power Co Ltd
Benxi Power Supply Co of Liaoning Electric Power Co Ltd
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Abstract

The electric quantity data acquisition optimization method and system based on the big data technology take acquisition time as an arrangement characteristic, construct the electric quantity data into a data group chain, and define leading data and following data in the data group chain; introducing a nonlinear additional term on the basis of a standard linear mixed model to obtain an observation point electric quantity vector and constructing an objective function; optimizing and solving an objective function based on a group intelligent optimization algorithm, leading data to lead follow data to search a data source in a data group chain as target data of the data group chain, and updating the position of the lead data to obtain an electric quantity global optimal solution; and comparing the global optimal solution of the electric quantity with the original data, and screening out erroneous and abnormal data by taking a 5%o reference error as a discrimination basis. According to the invention, the data accuracy of the electric quantity acquisition terminal of the transformer substation under the condition of interference factors is improved, and the data acquisition algorithm strategy of the electric quantity acquisition terminal is optimized and improved so as to realize intelligent diagnosis and screening of electric quantity data.

Description

Electric quantity data acquisition optimization method and system based on big data technology
Technical Field
The invention belongs to the technical field of data acquisition and processing, and particularly relates to an electric quantity data acquisition optimization method and system based on a big data technology.
Background
Along with the continuous expansion of the construction scale of the intelligent power grid by the national power grid company, the technical level of power generation enterprises is also continuously improved, and the generated energy is increasingly increased. The accurate collection and management of the online electricity quantity is directly related to the service range of the company and the commercial operation efficiency of the electric power. In order to optimize the remote automatic acquisition, processing and statistical analysis functions of the power station output electric quantity data, a scientific decision basis is provided for power supply enterprises, and the operation and maintenance of the electric quantity acquisition terminal are closely related to the safe, stable and economic operation of the power grid.
The electric quantity acquisition terminal is used as an important device for measuring the electric energy consumption of a power generation calculation user, and the technical level and accuracy requirements of the electric quantity acquisition terminal are gradually improved along with the development of a large power grid. The impact load generated in the operation process of the high-power equipment and various reasons of the faults of the metering device can influence the accuracy of electric energy metering of the intelligent electric energy meter, the intelligent electric energy meter is used as an important unit of an electric energy metering system, the accuracy of electric energy metering directly influences the income of an electric power department, and the economic benefit of the operation of the whole power grid is related.
However, in actual engineering, the transformer substation is accompanied with various sudden faults, the power grid structure is increasingly complex, the number of metering points is large, the amount of electric energy information collected by each metering point is huge, the time consumption is long, and the accuracy and timeliness are low; the problems that the operation of the current power grid system is actual and is interfered by various external factors generally exist, the investigation, analysis and prevention and control of the interference factors are not in place, the stability of the power grid system can be influenced, and the whole operation barrier of the power metering device is damaged. For example, the harmonic problem of the power grid can affect fundamental waves, power frequency and the like of the electric power metering device, so that load frequency is generated, metering errors are very large, and even a negative metering state is easy to generate. Because the software system of the partial electric quantity acquisition terminal does not perform fault tolerance and optimization processing functions, bad data cannot be identified, so that error and abnormal data are stored in a database, and an abnormal jump phenomenon of the electric quantity data occurs. Therefore, in order to improve the data accuracy of the electric quantity acquisition terminal of the transformer substation under the condition of interference factors, the data acquisition algorithm strategy of the electric quantity acquisition terminal is optimized and improved to realize the intelligent diagnosis of the electric quantity data and the screening technology, and the method has important engineering application significance.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides the electric quantity data acquisition optimization method and system based on the big data technology, which improve the data accuracy of the electric quantity acquisition terminal of the transformer substation under the condition of interference factors and optimize and improve the data acquisition algorithm strategy of the electric quantity acquisition terminal so as to realize intelligent diagnosis and screening of the electric quantity data.
The invention adopts the following technical scheme.
The invention provides an electric quantity data acquisition optimization method based on a big data technology, which comprises the following steps:
step 1, collecting 4 electric quantity data of forward active power, reverse active power, forward reactive power and reverse reactive power from massive electric quantity data, and constructing the electric quantity data into 4 data group chains by taking the collection time as an arrangement characteristic; defining electric quantity data acquired at the current moment in a data group chain as leading data, and taking the electric quantity data acquired at each moment before the current moment on the data group chain as following data;
step 2, introducing a nonlinear additional term on the basis of a standard linear hybrid model to obtain an electric quantity nonlinear hybrid model, and obtaining an electric quantity vector of an observation point based on the electric quantity nonlinear hybrid model;
step 3, constructing an objective function by using the electric quantity vector of the observation point;
step 4, optimizing and solving the objective function based on a group intelligent optimization algorithm to obtain a global optimal solution of the electric quantity;
and step 5, comparing the global optimal solution of the electric quantity obtained after optimization by the group intelligent optimization algorithm with the original data acquired in real time at the current moment, and screening out erroneous and abnormal data by taking a preset threshold value as a judgment basis.
The observation point electric quantity vector is as follows:
Figure BDA0004031376200000021
in the method, in the process of the invention,
Figure BDA0004031376200000022
and->
Figure BDA0004031376200000023
The observation point vector y, the electric quantity vector m and the abundance of the 4 electric quantity acquisition items corresponding to the forward active power, the reverse active power, the forward reactive power and the reverse reactive power respectivelyA matrix formed by the degree vector a and the nonlinear vector r; wherein (1)>
Figure BDA0004031376200000024
Additional terms for introduced nonlinearity.
The objective function is as follows:
Figure BDA0004031376200000025
in the method, in the process of the invention,
Figure BDA0004031376200000026
as a function of the object to be processed,
d (|) represents the K-L divergence formula,
lambda represents the regularization parameter and,
Figure BDA0004031376200000031
is a residual constraint term.
Step 4 comprises:
step 4.1, optimizing and solving an objective function based on a group intelligent optimization algorithm, namely leading data to lead to follow data to coordinate and move, searching a data source in a data group chain as target data of the data group chain, and carrying out an abundance vector matrix in the objective function
Figure BDA0004031376200000032
Mapping to a data source location in a chain of data groups;
step 4.2, updating the positions of the leading data and the following data in the search iteration process at the same time, namely using a spiral update position method as a search method to obtain the abundance vector matrix in the step 4.1
Figure BDA0004031376200000033
The element of the (2) is subjected to fitness calculation, the fitness value of each data is compared, the data corresponding to the minimum fitness value is used as the leading data of the next iteration, and the data is calculatedIterative updating of data positions is carried out on iteration paths of leading data and following data according to a group intelligent optimization algorithm, and finally abundance vectors are output>
Figure BDA0004031376200000034
Is a solution to the optimization of (3).
Step 4.1 comprises:
step 4.1.1, discretizing the data group chain to obtain an electric quantity data signal sequence x it [n]The method comprises the steps of carrying out a first treatment on the surface of the Wherein n represents the dimension of the electric quantity data signal sequence, it represents the iteration number; when iteration is started, it is 0 in initial value and the output signal sequence is y 0 [n]；
Step 4.1.2, smoothing y 0 [n]Obtaining an optimal frequency band for the energy spectrum of (a)
Figure BDA0004031376200000035
Step 4.1.3, initializing the residual threshold PS th And iteration threshold T th ；
Specifically, in each iterative decomposition, in order to extract a band of a larger amplitude within a search area while reducing the search area, it is necessary to set a residual threshold PS in advance th Residual threshold PS th And the optimal frequency band
Figure BDA0004031376200000036
The relationship between them is as follows:
Figure BDA0004031376200000037
in the method, in the process of the invention,
Figure BDA0004031376200000038
for a sequence x of charge data signals that has been filtered using an SG filter it [n]The signal after the fourier transform is processed,
Figure BDA0004031376200000039
for a sequence x of charge data signals filtered without SG filters it [n]The signal after the fourier transform is processed,
w is the frequency band of the search area,
SGfilter (·) is the SG filter function;
step 4.1.4, with residual Signal
Figure BDA0004031376200000041
Is a transverse component, in the best frequency band +.>
Figure BDA0004031376200000042
For the longitudinal component, a search space is determined and constantly optimized, wherein +.>
Figure BDA0004031376200000043
For a sequence x of charge data signals that has been filtered using an SG filter it-1 [n]Fourier transformed signals;
step 4.1.5 output signal sequence y for the (it-1) th discrete filter iteration using a side window filter (Side Window Filter, SWF) algorithm it-1 [n]Filtering is carried out, and the iterative deviation T is calculated according to the following relation:
Figure BDA0004031376200000044
if T<T th Extracting the filtered signal as an input signal sequence x 'of the ith discrete filtering iteration' it [n]And obtain the output signal sequence y of the it discrete filtering iteration it [n]Step 4.1.6 is performed; otherwise, it=it+1 is set and step 4.1.1 is repeatedly executed;
step 4.1.6, obtaining residual Signal x it+1 [n]＝x it [n]-x′ it [n]If (3)
Figure BDA0004031376200000045
Stopping discrete filtering iteration; based on the electric quantity vector of the observation point, according to x it [n]And y it [n]Relationship betweenOutput abundance vector matrix->
Figure BDA0004031376200000049
Otherwise repeating step 4.1.1, wherein, < ->
Figure BDA0004031376200000046
Representing the residual signal x it+1 [n]Fourier transformed signals.
Residual threshold PS th And iteration threshold T th The value ranges of the (E) are [0.01,0.99 ]]。
The relation between the ripple window parameter delta and the edge window width D in the SWF algorithm is set by adopting a genetic algorithm as follows:
Figure BDA0004031376200000047
in the method, in the process of the invention,
|Y δ,D (it) | and |S monl (it) | is the SWF filtered output signal sequence y [ b ], respectively]And a sequence of electrical quantity data signals x [ n ]]The signal after the fourier transform is processed,
wherein y [ n ]]SWF is used for controlling the parameters delta of the ripple window, the width D of the side window and the input signal S multi [n]A sequence of output signals at the time of the sequence,
S monl [n]is a set of single component signals, and
Figure BDA0004031376200000048
where q represents the number of power harvesting items.
By and with the current S monl (it) comparing to obtain the ripple window parameter delta and the edge window width D, and determining the most similar oscillation component by the SWF through the ripple window parameter delta and the edge window width D.
Step 4.2 comprises:
step 4.2.1, generating a spiral equation according to the position relation between the current leading data and the historical leading data to simulate the movement of the data group chain, wherein the movement is shown in the following relation:
X(t+1)＝D’·e bl ·cos(2πl)+X * (t)
in the method, in the process of the invention,
x (t) is a position vector of the current lead data corresponding to time t,
X * (t) is a position vector of history leading data corresponding to time t,
D’＝|X * (t) -X (t) | represents the distance of each current lead data and the history lead data corresponding to the time t,
b is a constant for modeling the shape of the logarithmic spiral,
l is a random variable in [ -1,1 ];
step 4.2.2, the objective function is added to
Figure BDA0004031376200000051
As a fitness function of the group intelligent optimization algorithm, the current leading data is respectively formed +.>
Figure BDA0004031376200000052
Is composed of historical leading data>
Figure BDA0004031376200000053
Substitution of fitness function->
Figure BDA0004031376200000054
Comparing the fitness value corresponding to the current leader data and the historical leader data, and if the fitness value of the current leader data is smaller than that of the historical leader data, taking the current leader data as the leader data of the next iteration; otherwise, the historical leading data is reserved as leading data of the next iteration; if the maximum iteration number of the group is reached, outputting a global optimal solution meeting the fitness function in the current data group chain, thereby updating the abundance vector matrix +.>
Figure BDA0004031376200000055
Step 4.2.3, updating the position of the leading data according to the following relation:
Figure BDA0004031376200000056
in the method, in the process of the invention,
Figure BDA0004031376200000057
represented as the individual in the j-th dimension herd chain, at position 1, i.e. the leader,
F j representing the target source location in the j-th dimension,
Figure BDA0004031376200000061
and->
Figure BDA0004031376200000062
Representing the upper and lower bounds of the search space respectively,
c 2 is [0,1]The random numbers are distributed uniformly and the random numbers are distributed uniformly,
c 3 is [ -1,1]The random numbers are distributed uniformly and the random numbers are distributed uniformly,
c 1 parameters which play a role in balancing the exploration and development of the optimal targets of the data swarm chain are defined as follows:
Figure BDA0004031376200000063
wherein it represents the number of iterations, L max The maximum iteration number;
step 4.2.4, updating the position of the following data according to the following relation:
Figure BDA0004031376200000064
in the method, in the process of the invention,
Figure BDA0004031376200000065
the position of the ith following data in the j-th dimension is represented, i.gtoreq.2.
In step 5, presetting a reference error with a threshold value of 5 per mill; after the wrong and abnormal data are screened out, the wrong data are corrected according to the type of the sampled data, and abnormal data jump caused by the loss of a data uploading mechanism of the electric quantity acquisition terminal is eliminated.
The invention also provides an electric quantity data acquisition optimization system based on the big data technology, which comprises the following steps: the system comprises an acquisition module, a data group chain module, an objective function module, an optimal solution module and a screening module;
the acquisition module is used for acquiring 4 electric quantity data of forward active power, reverse active power, forward reactive power and reverse reactive power from the massive electric quantity data;
the data group chain module is used for constructing electric quantity data into 4 data group chains by taking acquisition time as an arrangement characteristic; defining electric quantity data acquired at the current moment in a data group chain as leading data, and taking the electric quantity data acquired at each moment before the current moment on the data group chain as following data;
the objective function module is used for introducing a nonlinear additional term on the basis of the standard linear hybrid model to obtain an electric quantity nonlinear hybrid model, and obtaining an electric quantity vector of an observation point on the basis of the electric quantity nonlinear hybrid model; constructing an objective function by using the electric quantity vector of the observation point;
the optimal solution module is used for carrying out optimal solution on the objective function based on a group intelligent optimization algorithm so as to obtain an electric quantity global optimal solution;
the screening module is used for comparing the electric quantity global optimal solution obtained after optimization by the group intelligent optimization algorithm with the original data acquired in real time at the current moment, and screening out erroneous and abnormal data by taking a preset threshold value as a judgment basis.
Compared with the prior art, the optimization method has the beneficial effects that error and abnormal data are screened out by carrying out fault-tolerant screening optimization on the electric quantity acquisition process under the condition of interference factors of the electric power system, and the abnormal data jump phenomenon caused by the loss of a data uploading mechanism is eliminated.
The optimization method has few control parameters and high exploratory property. In each iteration, the group keeps the best solution and searching trend and trend so far, the searching capability is better than the group intelligent optimization algorithm (swarm intelligence optimization algorithm, SIOA) such as the particle swarm optimization algorithm (particle swarm optimization, PSO) and the artificial bee colony optimization algorithm (artificial bee colony optimization algorithm, ABC), and the group intelligent optimization algorithm has better optimization performance for processing the unimodal and multi-modal problems.
Drawings
FIG. 1 is a flow chart of an electric quantity data acquisition optimization method based on big data technology;
FIG. 2 is a schematic diagram of a cluster chain model of a simulated goblet sea squirt as proposed in the examples of the present invention;
FIG. 3 is a diagram of an overall control framework of the electrical quantity data acquisition optimization system based on big data technology;
the reference numerals in fig. 3 are explained as follows:
L s 、R s respectively representing the internal inductance and the internal resistance of the alternating current power supply; p (P) s 、Q s Respectively representing the active power and the reactive power of an alternating current system; u (u) sa 、u sb 、u sc And i sa 、i sb 、i sc Respectively representing alternating voltage and current collected by an alternating current loop; u (U) sabc 、I sabc Respectively representing three-phase alternating voltage and current collected by the multifunctional watt-hour meter.
Fig. 4 is a graph comparing simulation results of electric quantity according to an embodiment of the present invention, wherein a solid line represents electric quantity data obtained by a conventional method, and a dotted line represents electric quantity data obtained by a method according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. The embodiments described herein are merely some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art without inventive faculty, are within the scope of the invention, based on the spirit of the invention.
The invention provides an electric quantity data acquisition optimization method based on big data technology, which aims at the situation that an electric quantity acquisition terminal in a transformer substation has no data fault tolerance and optimization processing function, and introduces a group intelligent optimization algorithm (the group intelligent optimization algorithm is a novel intelligent optimization algorithm provided by carrying out random search simulation on certain biological evolutions or social behaviors in nature, the inspiration of the algorithm is from group behaviors of the sea squirts during foraging in the deep sea), the group behaviors of the sea squirts during foraging in the deep sea are simulated, and mass electric quantity data acquired from each electric meter in the transformer substation are divided into two types by means of a group self-adaption mechanism: a leader and follower; the leader is at the forefront of the cluster chain, and the rest of the cluster chain is called a follower, and the leader brings the follower to perform coordinated movement, so that better food sources are explored and developed in the search space as targets of the cluster. The leader and the follower continuously update their own positions in the search iteration process and gradually trend towards the global optimal target, and the traditional mechanism that the electric quantity acquisition terminal adopts direct acquisition, no identification and no screening on the electric quantity data is abandoned, so that the classification identification on massive electric quantity data is realized, the wrong and abnormal data are screened out, and the wrong data are corrected in a targeted manner according to the type of the sampled data.
In one aspect, the present invention proposes a method for optimizing electric quantity data collection based on big data technology, as shown in fig. 1, including:
step 1, collecting 4 electric quantity data of forward active power, reverse active power, forward reactive power and reverse reactive power from massive electric quantity data, and constructing the electric quantity data into 4 data group chains by taking the collection time as an arrangement characteristic; and defining the electric quantity data acquired at the current moment in the data group chain as leading data, and taking the electric quantity data acquired at each moment before the current moment on the data group chain as following data.
The electric quantity collection terminal in the transformer substation can collect electric quantity collection items such as voltage, current, frequency, forward active, reverse active, forward reactive and reverse reactive of each line interval in the transformer substation through the kilowatt-hour meter, in this embodiment, only 4 electric quantity collection items such as forward active, reverse active, forward reactive and reverse reactive are used as optimal objects, electric quantity data collection time is used as an arrangement characteristic, electric quantity data are utilized to construct a data group chain, as shown in fig. 2, electric quantity data which are collected latest at the current moment in the data group chain are defined as leading data, and electric quantity data which are collected at each moment before the current moment in the data group chain are used as following data.
Step 2, introducing a nonlinear additional term on the basis of a standard linear hybrid model to obtain an electric quantity nonlinear hybrid model, and obtaining an electric quantity vector of an observation point based on the electric quantity nonlinear hybrid model, wherein the electric quantity vector is as follows:
Figure BDA0004031376200000081
in the method, in the process of the invention,
Figure BDA0004031376200000082
and->
Figure BDA0004031376200000083
The system is a matrix formed by an observation point vector y, an electric quantity vector m, an abundance vector a and a nonlinear vector r of 4 electric quantity acquisition items corresponding to forward active power, reverse active power, forward reactive power and reverse reactive power respectively. Wherein (1)>
Figure BDA0004031376200000084
Additional terms for introduced nonlinearity.
In step 2, to describe more precisely the interaction relationship among the electric quantity data of forward active power, reverse active power, forward reactive power, reverse reactive power and the like, a nonlinear additional term is introduced based on a standard linear mixed model
Figure BDA0004031376200000091
To express the nonlinear effect and treat it as an outlier with sparse distribution, representing the higher order scattering generated between the electrical quantities, thus constituting an electrical quantity nonlinear hybrid model (electric nonlinear mixing model, ENMM).
Taking 4 electric quantity acquisition items of forward active power, reverse active power, forward reactive power and reverse reactive power as examples, the observation points of the electric quantity values of the line intervals of a single transformer substation are as follows:
Figure BDA0004031376200000092
in the method, in the process of the invention,
y＝[y 1 ,y 2 ,y 3 ,y 4 ] T observation points for electrical quantity data of single substation line interval, wherein y 1 ,y 2 ,y 3 ,y 4 The system comprises an observation point of forward active data, an observation point of reverse active data, an observation point of forward reactive data and an observation point of reverse reactive data;
m k ＝[m 1,k ,m 2,k ,m 3,k ,m 4,k ] T is the kth power vector, where k=1, 2,3, …, K represents the dimension of the data cluster chain; m is m 1,k ,m 2,k ,m 3,k ,m 4,k The positive active power kth electric quantity vector, the reverse active power kth electric quantity vector, the positive reactive power kth electric quantity vector and the reverse reactive power kth electric quantity vector are respectively adopted;
a＝[a 1 ,a 2 ,…,a K ] T the abundance vectors of K elements in a single electric quantity acquisition item represent the proportion of each electric quantity value in each electric quantity acquisition item;
o＝[o 1 ,o 2 ,o 3 ,o 4 ] T is Gaussian white noise;
r＝[r 1 ,r 2 ,…,r 4 ] T is a non-negative outlier to account for nonlinear effects.
And 3, constructing an objective function by using the electric quantity vector of the observation point, wherein the objective function is as follows:
Figure BDA0004031376200000093
in the method, in the process of the invention,
d (|) represents the K-L divergence formula,
lambda represents the regularization parameter and,
Figure BDA0004031376200000094
is a residual constraint term.
And 4, carrying out optimization solution on the objective function based on a group intelligent optimization algorithm, namely leading the data to lead the following data to carry out coordinated movement, searching a data source in the data group chain as the target data of the data group chain, and updating the positions of the leading data and the following data in the searching iteration process at the same time so as to obtain the global optimal solution of the electric quantity.
In the process of data identification, the minimum search area is dynamically extracted in each iterative decomposition process, the leading data lead the rest following data to gradually approach the optimal target position in a spiral position updating mode, and meanwhile, the optimal search area is repeatedly calculated for multiple times according to the mutual linkage and logic relation between the data sources in the group chain, the search path is continuously updated, so that groups can be finally gathered in the place with the most abundant data sources, and the global optimal solution is obtained. And (3) comparing the actual acquired data at the current moment with the optimal solution obtained after optimization, and screening out erroneous and abnormal data, so as to eliminate the abnormal data jump phenomenon of the electric quantity acquisition terminal device caused by the lack of a data uploading mechanism.
Step 4 comprises:
step 4.1, optimizing and solving an objective function based on a group intelligent optimization algorithm, and carrying out an abundance vector matrix in the objective function
Figure BDA0004031376200000101
Mapped to a data source location within the chain of data clusters.
Specifically, step 4.1 includes:
step 4.1.1, discretizing the data group chain to obtain an electric quantity data signal sequence x it [n]The method comprises the steps of carrying out a first treatment on the surface of the Wherein n represents the dimension of the electric quantity data signal sequence, it represents the iteration number; when iteration is started, it is 0, and the signal sequence is outputListed as y 0 [n]；
Step 4.1.2, smoothing y 0 [n]Obtaining an optimal frequency band for the energy spectrum of (a)
Figure BDA0004031376200000102
Step 4.1.3, initializing the residual threshold PS th And iteration threshold T th ；
Further, the residual threshold PS th And iteration threshold T th The value ranges of the (E) are [0.01,0.99 ]]。
Specifically, in each iterative decomposition, in order to extract a band of a larger amplitude within a search area while reducing the search area, it is necessary to set a residual threshold PS in advance th Residual threshold PS th And the optimal frequency band
Figure BDA0004031376200000103
The relationship between them is as follows:
Figure BDA0004031376200000104
/>
in the method, in the process of the invention,
Figure BDA0004031376200000105
for a sequence x of charge data signals that has been filtered using an SG filter it [n]The signal after the fourier transform is processed,
Figure BDA0004031376200000106
for a sequence x of charge data signals filtered without SG filters it [n]The signal after the fourier transform is processed,
w is the frequency band of the search area,
SGfilter (·) is the SG filter function;
step 4.1.4, with residual Signal
Figure BDA0004031376200000111
Is a transverse component, in the best frequency band +.>
Figure BDA0004031376200000112
For the longitudinal component, a search space is determined and constantly optimized, wherein +.>
Figure BDA0004031376200000113
For a sequence x of charge data signals that has been filtered using an SG filter it-1 [n]Fourier transformed signals.
Step 4.1.5 output signal sequence y for the (it-1) th discrete filter iteration using a side window filter (Side Window Filter, SWF) algorithm it-1 [n]Filtering is carried out, and the iterative deviation T is calculated according to the following relation:
Figure BDA0004031376200000114
if T<T th Extracting the filtered signal as an input signal sequence x 'of the ith discrete filtering iteration' it [n]And obtain the output signal sequence y of the it discrete filtering iteration it [n]Step 4.1.6 is performed; otherwise, it=it+1 is set and step 4.1.1 is repeatedly executed;
specifically, the relation between the ripple window parameter δ and the edge window width D in the SWF algorithm is set by using the genetic algorithm as follows:
Figure BDA0004031376200000115
in the method, in the process of the invention,
|Y δ,D (it) | and |S monl (it) | is the SWF filtered output signal sequence y [ n ], respectively]And a sequence of electrical quantity data signals x [ n ]]The signal after the fourier transform is processed,
wherein y [ n ]]SWF is used for controlling the parameters delta of the ripple window, the width D of the side window and the input signal S multi [n]A sequence of output signals at the time of the sequence,
S monl [n]is a set of single component signals, and
Figure BDA0004031376200000116
where q represents the number of power harvesting items.
That is to say by means of the current S monl (it) comparing to obtain the ripple window parameter delta and the edge window width D, and determining the most similar oscillation component by the SWF through the ripple window parameter delta and the edge window width D.
Step 4.1.6, obtaining residual Signal x it+1 [n]＝x it [n]-x′ it [n]If (3)
Figure BDA0004031376200000117
Stopping discrete filtering iteration; based on the electric quantity vector of the observation point, according to x it [n]And y it [n]The relation between them outputs an abundance vector matrix +.>
Figure BDA0004031376200000118
Otherwise repeating step 4.1.1, wherein, < ->
Figure BDA0004031376200000119
Representing the residual signal x it+1 [n]Fourier transformed signals.
Step 4.2, using a spiral update position method as a search method, and performing the search on the abundance vector matrix obtained in the step 4.1
Figure BDA0004031376200000121
The fitness calculation is carried out on the elements of the data, the data corresponding to the minimum fitness value is used as the leading data of the next iteration by comparing the fitness values of the data, the iteration paths of the leading data and the following data are obtained by calculation, the iteration update of the data positions is carried out according to a group intelligent optimization algorithm, and finally the abundance vector is output>
Figure BDA0004031376200000122
Is a solution to the optimization of (3).
Preferably, the fitness value of the data at the position near the leading data is selected for comparison, and the optimal leading data of the next iteration is determined according to the fitness value minimization.
Specifically, step 4.2 includes:
step 4.2.1, generating a spiral equation according to the position relation between the current leading data and the historical leading data to simulate the movement of the data group chain, wherein the movement is shown in the following relation:
X(t+1)＝D’·e bl ·cos(2πl)+X * (t)
in the method, in the process of the invention,
x (t) is a position vector of the current lead data corresponding to time t,
X * (t) is a position vector of history leading data corresponding to time t,
D’＝|X * (t) -X (t) | represents the distance of each current lead data and the history lead data corresponding to the time t,
b is a constant for modeling the shape of the logarithmic spiral,
l is a random variable in [ -1,1 ].
Step 4.2.2, the objective function is added to
Figure BDA0004031376200000123
As a fitness function of the group intelligent optimization algorithm, the current leading data is respectively formed +.>
Figure BDA0004031376200000124
Is composed of historical leading data>
Figure BDA0004031376200000125
Substitution of fitness function->
Figure BDA0004031376200000126
Comparing the fitness value corresponding to the current leader data and the historical leader data, and if the fitness value of the current leader data is smaller than that of the historical leader data, taking the current leader data as the leader data of the next iteration; otherwise, the historical leading data is reserved as leading data of the next iteration; if the maximum iteration number of the group is reached, outputting a global optimal solution meeting the fitness function in the current data group chain, thereby updating the abundance vector matrix +.>
Figure BDA0004031376200000127
Specifically, in each iterative decomposition, the search method includes, but is not limited to, a spiral update location method.
Specifically, in each iteration decomposition, the data group chains need to randomly search for targets by depending on the positions of the data group chains, and the data group chains are repeatedly searched and evolved through the mutual linkage among the data group chains to continuously move and update the positions of the data group chains, so that the data group chains can be finally gathered in the place with the most abundant food sources, and the global optimal solution is obtained.
Step 4.2.3, updating the position of the leading data according to the following relation:
Figure BDA0004031376200000131
in the method, in the process of the invention,
Figure BDA0004031376200000132
represented as the individual in the j-th dimension herd chain, at position 1, i.e. the leader,
F j representing the target source location in the j-th dimension,
Figure BDA0004031376200000133
and->
Figure BDA0004031376200000134
Representing the upper and lower bounds of the search space respectively,
c 2 is [0,1]The random numbers are distributed uniformly and the random numbers are distributed uniformly,
c 3 is [ -1,1]The random numbers are distributed uniformly and the random numbers are distributed uniformly,
c 1 the most important parameters in the swarm intelligent optimization algorithm play a role in balancing the exploration and development of the optimal target of the data swarm chain, and are defined as follows:
Figure BDA0004031376200000135
/>
wherein it represents the number of iterations, L max Is the maximum number of iterations.
Step 4.2.4, updating the position of the following data according to the following relation:
Figure BDA0004031376200000136
in the method, in the process of the invention,
Figure BDA0004031376200000137
the position of the ith following data in the j-th dimension is represented, i.gtoreq.2.
And 5, comparing the global optimal solution of the electric quantity obtained after optimization by the group intelligent optimization algorithm with the original data acquired in real time at the current moment, and screening out erroneous and abnormal data by taking a 5%o reference error as a discrimination basis.
Further, according to the type of the sampled data, error data are corrected, so that the phenomenon of abnormal data jump caused by the loss of a data uploading mechanism of the electric quantity acquisition terminal is eliminated.
After the error and abnormal data are screened out through the intelligent optimization algorithm of the large data group, the error data are corrected in a targeted manner according to the type of the sampled data. The data collected by the electric quantity collection terminal in the transformer substation are generally divided into current I, voltage U, active power P and reactive power Q. The specific scheme is as follows:
1) Current I: according to kirchhoff's law, simulating and calculating real-time current I of a current line through bus current of the current line and current of other lines hung under the bus, and carrying out data correction on the current;
2) Voltage U: carrying out data correction through the voltage of the bus where the current line is located;
3) Active power P: according to
Figure BDA0004031376200000141
Calculating real-time active power P of a current line through simulation of line voltage and current acquired in real time, and carrying out data correction on the active power;
4) Reactive power Q: according to
Figure BDA0004031376200000142
And calculating the real-time reactive power Q of the current line through the line voltage and current simulation acquired in real time, and carrying out data correction on the active power.
The invention also provides an electric quantity data acquisition optimization system based on the big data technology, which comprises the following steps: the system comprises an acquisition module, a data group chain module, an objective function module, an optimal solution module and a screening module;
the acquisition module is used for acquiring 4 electric quantity data of forward active power, reverse active power, forward reactive power and reverse reactive power from the massive electric quantity data;
the data group chain module is used for constructing electric quantity data into 4 data group chains by taking acquisition time as an arrangement characteristic; defining electric quantity data acquired at the current moment in a data group chain as leading data, and taking the electric quantity data acquired at each moment before the current moment on the data group chain as following data;
the objective function module is used for introducing a nonlinear additional term on the basis of the standard linear hybrid model to obtain an electric quantity nonlinear hybrid model, and obtaining an electric quantity vector of an observation point on the basis of the electric quantity nonlinear hybrid model; constructing an objective function by using the electric quantity vector of the observation point;
the optimal solution module is used for carrying out optimal solution on the objective function based on a group intelligent optimization algorithm, namely leading data to lead follow data to carry out coordinated movement, searching a data source in a data group chain to serve as target data of the data group chain, and updating the positions of the leading data and the follow data in a searching iteration process at the same time so as to obtain an electric quantity global optimal solution;
the screening module is used for comparing the electric quantity global optimal solution obtained after optimization by the group intelligent optimization algorithm with the original data acquired in real time at the current moment, and screening out error and abnormal data by taking a 5 per mill reference error as a discrimination basis.
In the embodiment of the invention, in order to verify the effectiveness of the provided electric quantity acquisition optimization method, a simulation model is built through a Matlab/Simulink module, and the equipment factory configuration and the configuration of the optimization method provided herein are respectively applied to the electric quantity acquisition management simulation model, wherein the topological structure and the overall control framework are shown in figure 3. Fig. 4 is a graph comparing simulation results of electric quantity according to an embodiment of the present invention, wherein a solid line represents electric quantity data obtained by a conventional method, and a dotted line represents electric quantity data obtained by a method according to the present invention. In fig. 4, the disturbance variable is a random pulse signal to simulate bad data occurring in the field. And the corresponding electric quantity data curve is obtained by simulation comparison through adopting non-optimized configuration and configuration of the optimization method provided herein.
As can be seen from fig. 4, under the original configuration, the electric quantity acquisition terminal frequently generates an electric quantity data jump phenomenon under the influence of the interference quantity, which affects the accuracy and economy of the electric quantity metering system; after the optimization method provided by the invention is introduced, interference data can be effectively identified and filtered, the data jump phenomenon is eliminated, effective collection and accurate verification and reliable uploading of electric quantity data are realized, classification identification of massive electric quantity data is realized, incorrect and abnormal data are screened out, stable and reliable electric quantity data can be provided for a dispatching master station under the condition of interference factors, and the electric power commercial operation efficiency of a national power grid company is ensured.
Taking a certain type of electric quantity acquisition terminal as an example, the electric quantity acquisition terminal acquires electric quantity data information from an electric meter at intervals of 5 minutes on the lower side and transmits the electric quantity data information to a dispatching electric quantity master station at intervals of 15 minutes on the upper side. However, the software system does not perform fault tolerance and optimization processing, so that error and abnormal data can be stored in the database, the accuracy of the data in a certain period of time is reduced, abnormal jump of the data is caused, and the economic benefit of the whole power grid operation can be directly influenced under serious conditions.
In order to solve the problems of missing and jumping of metering data of the substation electric quantity acquisition terminal under the condition of interference factors, the optimization method provided by the invention is used for realizing intelligent diagnosis and screening of electric quantity data, and the data accuracy of the substation electric quantity acquisition terminal can be improved under the condition of interference factors. The following conclusions can be obtained through theoretical analysis and experimental verification:
1) In the running process of the high-power equipment, various reasons of impact load and self faults of the metering device can influence the accuracy of electric energy metering of the intelligent electric energy meter, and an abnormal jump phenomenon of electric quantity data is caused, and the intelligent optimization algorithm of the large data group is introduced by adopting the electric quantity acquisition optimization method provided by the invention, so that intelligent diagnosis and screening of the electric quantity data are realized, and the data accuracy of the electric quantity acquisition terminal of the transformer substation is improved.
2) According to case analysis, fault-tolerant screening optimization is carried out on the electric quantity acquisition process under the condition of the interference factors of the electric power system, wrong and abnormal data are screened out, the phenomenon of abnormal data jump caused by the loss of a data uploading mechanism is eliminated, and therefore the proposed electric quantity acquisition optimization method can provide stable and reliable electric quantity data for a dispatching master station under the condition of the interference factors.
The present disclosure may be a system, method, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for causing a processor to implement aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.
Computer program instructions for performing the operations of the present disclosure can be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present disclosure are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information of computer readable program instructions, which can execute the computer readable program instructions.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.

Claims (10)

1. The electric quantity data acquisition optimization method based on the big data technology is characterized by comprising the following steps of:
step 1, collecting 4 electric quantity data of forward active power, reverse active power, forward reactive power and reverse reactive power from massive electric quantity data, and constructing the electric quantity data into 4 data group chains by taking the collection time as an arrangement characteristic; defining electric quantity data acquired at the current moment in a data group chain as leading data, and taking the electric quantity data acquired at each moment before the current moment on the data group chain as following data;
step 2, introducing a nonlinear additional term on the basis of a standard linear hybrid model to obtain an electric quantity nonlinear hybrid model, and obtaining an electric quantity vector of an observation point based on the electric quantity nonlinear hybrid model;
step 3, constructing an objective function by using the electric quantity vector of the observation point;
step 4, optimizing and solving the objective function based on a group intelligent optimization algorithm to obtain a global optimal solution of the electric quantity;
and step 5, comparing the global optimal solution of the electric quantity obtained after optimization by the group intelligent optimization algorithm with the original data acquired in real time at the current moment, and screening out erroneous and abnormal data by taking a preset threshold value as a judgment basis.
2. The method for optimizing power data collection based on big data technology according to claim 1, wherein,
the observation point electric quantity vector is as follows:
Figure FDA0004031376190000011
in the method, in the process of the invention,
Figure FDA0004031376190000012
Figure FDA0004031376190000013
and->
Figure FDA0004031376190000014
The system comprises an observation point vector y, an electric quantity vector m, an abundance vector a and a nonlinear vector r, wherein the observation point vector y corresponds to 4 electric quantity acquisition items of forward active power, reverse active power, forward reactive power and reverse reactive power; wherein (1)>
Figure FDA0004031376190000015
Additional terms for introduced nonlinearity.
3. The method for optimizing power data collection based on big data technology according to claim 2, wherein,
the objective function is as follows:
Figure FDA0004031376190000016
in the method, in the process of the invention,
Figure FDA0004031376190000017
as a function of the object to be processed,
d (|) represents the K-L divergence formula,
lambda represents the regularization parameter and,
Figure FDA0004031376190000021
is a residual constraint term.
4. The method for optimizing power data collection based on big data technology according to claim 3, wherein,
step 4 comprises:
step 4.1, optimizing and solving an objective function based on a group intelligent optimization algorithm, namely leading data to lead to follow data to coordinate and move, searching a data source in a data group chain as target data of the data group chain, and carrying out an abundance vector matrix in the objective function
Figure FDA0004031376190000022
Mapping to a data source location in a chain of data groups;
step 4.2, updating the positions of the leading data and the following data in the search iteration process at the same time, namely using a spiral update position method as a search method to obtain the abundance vector matrix in the step 4.1
Figure FDA0004031376190000023
The fitness calculation is carried out on the elements of the data, the data corresponding to the minimum fitness value is used as the leading data of the next iteration by comparing the fitness values of the data, the iteration paths of the leading data and the following data are obtained by calculation, the iteration update of the data positions is carried out according to a group intelligent optimization algorithm, and finally the abundance vector is output>
Figure FDA0004031376190000024
Is a solution to the optimization of (3).
5. The method for optimizing power data collection based on big data technology according to claim 4, wherein,
step 4.1 comprises:
step 4.1.1, discretizing the data group chain to obtain an electric quantity data signal sequence x it [n]The method comprises the steps of carrying out a first treatment on the surface of the Wherein n represents the dimension of the electric quantity data signal sequence, it represents the iteration number; when iteration is started, it is 0 in initial value and the output signal sequence is y 0 [n]；
Step 4.1.2, smoothing y 0 [n]Obtaining an optimal frequency band for the energy spectrum of (a)
Figure FDA0004031376190000025
Step 4.1.3, initializing the residual threshold PS th And iteration threshold T th ；
Specifically, in each iterative decomposition, in order to extract a band of a larger amplitude within a search area while reducing the search area, it is necessary to set a residual threshold PS in advance th Residual threshold PS th And the optimal frequency band
Figure FDA0004031376190000026
The relationship between them is as follows:
Figure FDA0004031376190000027
in the method, in the process of the invention,
Figure FDA0004031376190000031
for a sequence x of charge data signals that has been filtered using an SG filter it [n]The signal after the fourier transform is processed,
Figure FDA0004031376190000032
for a sequence x of charge data signals filtered without SG filters it [n]The signal after the fourier transform is processed,
w is the frequency band of the search area,
SGfilter (·) is the SG filter function;
step 4.1.4, with residual Signal
Figure FDA0004031376190000033
Is a transverse component, in the best frequency band +.>
Figure FDA0004031376190000034
For the longitudinal component, a search space is determined and constantly optimized, wherein +.>
Figure FDA0004031376190000035
For a sequence x of charge data signals that has been filtered using an SG filter it-1 [n]Fourier transformed signals;
step 4.1.5 output signal sequence y for the (it-1) th discrete filter iteration using a side window filter (Side Window Filter, SWF) algorithm it-1 [n]Filtering is carried out, and the iterative deviation T is calculated according to the following relation:
Figure FDA0004031376190000036
if T<T th Extracting the filtered signal as an input signal sequence x 'of the ith discrete filtering iteration' it [n]And obtain the output signal sequence y of the it discrete filtering iteration it [n]Step 4.1.6 is performed; otherwise, it=it+1 is set and step 4.1.1 is repeatedly executed;
step 4.1.6, obtaining residual Signal x it+1 [n]＝x it [n]-x′ it [n]If (3)
Figure FDA0004031376190000037
Stopping discrete filtering iteration; based on the electric quantity vector of the observation point, according to x it [n]And y it [n]The relation between them outputs an abundance vector matrix +.>
Figure FDA0004031376190000038
Otherwise repeating step 4.1.1, wherein, < ->
Figure FDA0004031376190000039
Representing the residual signal x it+1 [n]Fourier transformed signals.
6. The method for optimizing power data collection based on big data technology according to claim 5, wherein,
residual threshold PS th And iteration threshold T th The value ranges of the (E) are [0.01,0.99 ]]。
7. The method for optimizing power data collection based on big data technology according to claim 5, wherein,
the relation between the ripple window parameter delta and the edge window width D in the SWF algorithm is set by adopting a genetic algorithm as follows:
Figure FDA0004031376190000041
in the method, in the process of the invention,
|Y δ,D (it) | and |S monl (it) | is the SWF filtered output signal sequence y [ n ], respectively]And a sequence of electrical quantity data signals x [ n ]]The signal after the fourier transform is processed,
wherein y [ n ]]SWF is used for controlling the parameters delta of the ripple window, the width D of the side window and the input signal S multi [n]A sequence of output signals at the time of the sequence,
S monl [n]is a set of single component signals, and
Figure FDA0004031376190000042
where q represents the number of electrical quantity acquisition terms,
by and with the current S monl (it) comparing to obtain the ripple window parameter delta and the edge window width D, and determining the most similar oscillation component by the SWF through the ripple window parameter delta and the edge window width D.
8. The method for optimizing power data collection based on big data technology according to claim 4, wherein,
step 4.2 comprises:
step 4.2.1, generating a spiral equation according to the position relation between the current leading data and the historical leading data to simulate the movement of the data group chain, wherein the movement is shown in the following relation:
X(t+1)＝D’·e bl ·cos(2πl)+X * (t)
in the method, in the process of the invention,
x (t) is a position vector of the current lead data corresponding to time t,
X * (t) is a position vector of history leading data corresponding to time t,
D’＝|X * (t) -X (t) | represents the distance of each current lead data and the history lead data corresponding to the time t,
b is a constant for modeling the shape of the logarithmic spiral,
l is a random variable in [ -1,1 ];
step 4.2.2, the objective function is added to
Figure FDA0004031376190000043
As a fitness function of the group intelligent optimization algorithm, the current leading data is respectively formed +.>
Figure FDA0004031376190000044
Is composed of historical leading data>
Figure FDA0004031376190000045
Substitution of fitness function->
Figure FDA0004031376190000046
Comparing the fitness value corresponding to the current leader data and the historical leader data, and if the fitness value of the current leader data is smaller than that of the historical leader data, taking the current leader data as the leader data of the next iteration; otherwise, the historical leading data is reserved as leading data of the next iteration; if the maximum iteration number of the group is reached, outputting a global optimal solution meeting the fitness function in the current data group chain, thereby updating the abundance vector matrix +.>
Figure FDA0004031376190000051
Step 4.2.3, updating the position of the leading data according to the following relation:
Figure FDA0004031376190000052
in the method, in the process of the invention,
Figure FDA0004031376190000053
represented as the individual in the j-th dimension herd chain, at position 1, i.e. the leader,
F j representing the target source location in the j-th dimension,
Figure FDA0004031376190000054
and->
Figure FDA0004031376190000055
Representing the upper and lower bounds of the search space respectively,
c 2 is [0,1]The random numbers are distributed uniformly and the random numbers are distributed uniformly,
c 3 is [ -1,1]The random numbers are distributed uniformly and the random numbers are distributed uniformly,
c 1 parameters which play a role in balancing the exploration and development of the optimal targets of the data swarm chain are defined as follows:
Figure FDA0004031376190000056
/>
wherein it represents the number of iterations, L max The maximum iteration number;
step 4.2.4, updating the position of the following data according to the following relation:
Figure FDA0004031376190000057
in the method, in the process of the invention,
Figure FDA0004031376190000058
the position of the ith following data in the j-th dimension is represented, i.gtoreq.2.
9. The method for optimizing power data collection based on big data technology according to claim 1, wherein,
in step 5, presetting a reference error with a threshold value of 5 per mill; after the wrong and abnormal data are screened out, the wrong data are corrected according to the type of the sampled data, and abnormal data jump caused by the loss of a data uploading mechanism of the electric quantity acquisition terminal is eliminated.
10. An electric quantity data acquisition optimization system based on big data technology, which is used for realizing the method of any one of the claims 1 to 9,
comprising the following steps: the system comprises an acquisition module, a data group chain module, an objective function module, an optimal solution module and a screening module;
the acquisition module is used for acquiring 4 electric quantity data of forward active power, reverse active power, forward reactive power and reverse reactive power from the massive electric quantity data;
the data group chain module is used for constructing electric quantity data into 4 data group chains by taking acquisition time as an arrangement characteristic; defining electric quantity data acquired at the current moment in a data group chain as leading data, and taking the electric quantity data acquired at each moment before the current moment on the data group chain as following data;
the objective function module is used for introducing a nonlinear additional term on the basis of the standard linear hybrid model to obtain an electric quantity nonlinear hybrid model, and obtaining an electric quantity vector of an observation point on the basis of the electric quantity nonlinear hybrid model; constructing an objective function by using the electric quantity vector of the observation point;
the optimal solution module is used for carrying out optimal solution on the objective function based on a group intelligent optimization algorithm so as to obtain an electric quantity global optimal solution;
the screening module is used for comparing the electric quantity global optimal solution obtained after optimization by the group intelligent optimization algorithm with the original data acquired in real time at the current moment, and screening out erroneous and abnormal data by taking a preset threshold value as a judgment basis.
CN202211730449.4A 2022-12-30 2022-12-30 Electric quantity data acquisition optimization method and system based on big data technology Pending CN116049156A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117541126A (en) * 2024-01-05 2024-02-09 福建省政务门户网站运营管理有限公司 Big data government affair data processing and correcting system

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117541126A (en) * 2024-01-05 2024-02-09 福建省政务门户网站运营管理有限公司 Big data government affair data processing and correcting system
CN117541126B (en) * 2024-01-05 2024-04-12 福建省政务门户网站运营管理有限公司 Big data government affair data processing and correcting system

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