CN113595071A - Transformer area user identification and voltage influence evaluation method - Google Patents

Transformer area user identification and voltage influence evaluation method Download PDF

Info

Publication number
CN113595071A
CN113595071A CN202110871498.9A CN202110871498A CN113595071A CN 113595071 A CN113595071 A CN 113595071A CN 202110871498 A CN202110871498 A CN 202110871498A CN 113595071 A CN113595071 A CN 113595071A
Authority
CN
China
Prior art keywords
users
voltage
matrix
user
data
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202110871498.9A
Other languages
Chinese (zh)
Inventor
熊文
李欣
刘艳萍
曾顺奇
吴杰康
蔡志宏
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangzhou Power Supply Bureau of Guangdong Power Grid Co Ltd
Original Assignee
Guangzhou Power Supply Bureau of Guangdong Power Grid Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangzhou Power Supply Bureau of Guangdong Power Grid Co Ltd filed Critical Guangzhou Power Supply Bureau of Guangdong Power Grid Co Ltd
Priority to CN202110871498.9A priority Critical patent/CN113595071A/en
Publication of CN113595071A publication Critical patent/CN113595071A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Educational Administration (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Tourism & Hospitality (AREA)
  • Theoretical Computer Science (AREA)
  • Marketing (AREA)
  • General Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Development Economics (AREA)
  • Physics & Mathematics (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Game Theory and Decision Science (AREA)
  • Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a transformer area user identification and voltage influence evaluation method, which comprises the following steps: constructing a data matrix of active values of users in a transformer area and a time sequence matrix of low-side voltage of a transformer; preprocessing the data matrix to obtain data after dimensionality reduction; based on the data matrix and the time sequence matrix, calculating the relation between each user and the voltage fluctuation of the low-voltage transformer area so as to construct a transformer association factor; and establishing a target function by taking the membership as an Euclidean distance, and clustering by taking the Pearson correlation coefficient and the membership sum as constraint conditions, thereby obtaining the classification condition of the influence degrees of different user grades in a specific influence factor range, further obtaining the relation between the user success value between the users and the voltage value of the low-voltage side of the transformer in the transformer area, and carrying out influence evaluation according to the relation. The method has important significance for knowing the topology of the power distribution network and building the smart power grid, and provides necessary technical support for further power quality management.

Description

Transformer area user identification and voltage influence evaluation method
Technical Field
The invention relates to the field of transformer area relation identification research and electric energy management, in particular to a transformer area user identification and voltage influence evaluation method.
Background
Topological information in the power distribution system has important significance for platform load balance management, and uneven load distribution not only can increase operating line loss, but also can seriously affect the service life of equipment. With the advance of the digitization process of the power grid, power grid companies start to perform partition management on low-voltage distribution areas of the power distribution network. In recent years, the low-voltage transformer area has realized the full coverage of the intelligent electric meter, the quality of the electricity consumption measurement data of the transformer area is improved, and the possibility is provided for the data-driven user topology identification. With the access of a large number of Distributed Generation (DG) of an active power Distribution network and an electric automobile, the load Distribution is uneven and the DG output is difficult to predict; in addition, the structure and the operation mode of the power system with the expanded power distribution network node size, the enhanced load dynamic performance and the like are gradually complicated, the traditional data acquisition method is difficult to meet the data analysis requirement under the large background of the smart power grid, the defects of data loss, low data precision and the like are not easily existed, for example, the SCADA acquisition resolution is 5min, the phase and the accurate value of the data cannot be accurately obtained, and the RTU and the AMI are all functional modules which are packaged based on the smart power meter and have the function of acquiring real-time data. However, the resolutions of the data are 10min and 15min respectively, and the data analysis requirements of modern smart power grids cannot be met. In addition to the data measurement system provided above, mass user load data collected by using a PMU (phasor measurement unit) can also provide data support for operations and maintenance tasks of a distribution area such as line loss analysis and load prediction.
Three indexes of evaluating the power quality, namely voltage, frequency and waveform, have the prominent low-voltage problem in a power distribution network, and the power supply voltage limit values of different voltage grades are regulated in the power quality power supply voltage deviation (GB/T12325-2008): the deviation of three-phase voltage of 20kv and below is the nominal voltage; the 220V single-phase power supply voltage is deviated to a nominal voltage. The reasons for the low voltage problem mainly include three aspects of power supply, power grid and load.
Aiming at the load side, along with the rapid development of urban and rural economic construction in China in recent years, the power utilization load is increased rapidly, some small users are developed into high-power users, the power utilization load exceeds the reserved space of a line, some small users are still small-power users, and some small users are medium-load type users. The contradiction between the power supply capacity of the power distribution network and the rapidly-increased power consumption demand is more and more prominent, so that the problem that the voltage of a transformer area and the voltage of a user side are low frequently occurs, the power consumption experience of the user is seriously influenced, the power supply reliability is reduced, and a severe test is brought to a power supply company. The power load levels of different subscribers can cause the voltage levels on both the platform and the subscriber to be higher or lower. The voltage influence of high-power users on the transformer area in the peak and the valley of power load may cause the increase of line loss, and the overall voltage of the transformer area is reduced; secondly, medium load users may have secondary effects; there are some small users, and although the influence of a single user on the platform area is not obvious in some peak electricity utilization intervals, if there is a small user with similar electricity utilization behavior, the line loss or voltage of the platform area may be influenced to some extent in peak electricity utilization or valley electricity utilization. Aiming at the problem that a smart power grid is established at present, real-time and effective management of a load end by a management department is realized, and the power consumption quality of a user is ensured, a clear relation is not established in the relation identification of a platform house in the traditional low-voltage platform area power quality management, and the relation identification research, the power management and the line loss management of the platform house are not facilitated.
Disclosure of Invention
The invention aims to provide a transformer area user identification and voltage influence evaluation method, which provides technical support for power quality management.
In order to realize the task, the invention adopts the following technical scheme:
a method for identifying users in a distribution room and evaluating voltage influence comprises the following steps:
acquiring active power data of a user side at a set resolution and time point, and constructing a data matrix of active values of users in a distribution area; acquiring a time sequence active value under the transformer area voltage in a unidirectional way, and constructing a time sequence matrix of the voltage at the low side of the transformer;
preprocessing the data matrix by adopting a multi-dimensional scaling method to obtain data after dimension reduction;
based on the data matrix and the time sequence matrix, calculating the relation between each user and the voltage fluctuation of the low-voltage transformer area so as to construct a transformer association factor;
and establishing a target function by taking the membership as an Euclidean distance, and clustering by taking the Pearson correlation coefficient and the membership sum as constraint conditions, thereby obtaining the classification condition of the influence degrees of different user grades in a specific influence factor range, further obtaining the relation between the user success value between the users and the voltage value of the low-voltage side of the transformer in the transformer area, and carrying out influence evaluation according to the relation.
Further, the preprocessing the data matrix by using a multidimensional scaling method to obtain the data after dimensionality reduction includes:
for m users, each user acquires D-dimensional data, and calculates a distance matrix B epsilon R in the original spacem×DIts ith row and j column element distijFor any one user sample xiTo xjThe goal is to obtain a representation matrix Z ∈ R in d' dimensional spacem×d′D 'is less than or equal to D, and the Euclidean distance of any two samples in the D' dimensional space is equal to the distance of the original space: | | zi-zj||＝distij，zi,zjRepresenting the reduced dimension sample, sample xi、xjAs a data matrix XpThe user data in (1);
let E be ZTZ∈Rm×mAnd decentralizing the matrix Z after dimension reduction, simplifying the matrix Z by combining decentralization constraint conditions, calculating E, and performing characteristic decomposition on the E to obtain E ═ etaTWherein Λ ═ diag [ λ ═ d [ lambda ] ]1,λ2,…,λn]A diagonal matrix formed by the eigenvalues, wherein eta is an eigenvector matrix; selecting an eigenvector matrix W [ [ eta ] η ] corresponding to the first 95% contribution eigenvalue according to the contribution of the eigenvalue1,η2,…,ηd′]Obtaining an eigenvalue matrix by E matrix eigen decompositionAnd sorting the first d' pieces from big to small: lambda [ alpha ]1≥λ2≥…≥λq≥λd′And obtaining a matrix Z after final dimensionality reduction as follows:
Z＝WTX。
further, the value of d' is chosen according to the following contribution expression:
Figure BDA0003189343790000031
further, the calculating the relationship between each user and the voltage fluctuation of the low-voltage transformer area comprises:
and sequentially calculating the overall mean value and the overall covariance between each user and the voltage fluctuation of the low-voltage area, and then calculating to obtain the overall Pearson correlation coefficient.
Further, when clustering is performed, the construction process for the fuzzy matrix center includes:
data Z epsilon after dimensionality reduction is Rm×d′Three users are randomly selected as a clustering center, and original user data is divided into three categories: large users, medium users and small users, and constructing a clustering center vector of the data set: c. Ci＝{ci,1,ci,2,...,ci,kWhere c is 1,2,3i,kAnd representing the characteristic value of the k-th dimension of the ith cluster center.
Further, when clustering is performed, the set objective function and constraint condition are expressed as:
Figure BDA0003189343790000032
Figure BDA0003189343790000033
Figure BDA0003189343790000034
where γ is a membership factor, m represents the number of all samples, and the membership u exists assuming that each sample j belongs to a class iijThe relationship of (1); c denotes the center of the cluster, ciDenotes the ith cluster center, dijRepresenting the distance, X, of a sample point from a central pointjRepresents XpUser vector of (1) | phiXi,VAnd | represents the overall pearson correlation coefficient.
Furthermore, in order to obtain the minimum value of the target function under the constraint condition, the original target function introduces a Lagrange multiplier and a relaxation variable, changes inequality constraint into equality constraint, converts the problem of solving the minimum value of the original problem into the problem of solving the convex optimization of quadratic programming, and reconstructs the target function by integrating the original target function and the constraint condition:
Figure BDA0003189343790000041
zeta in the formulajRepresenting a lagrange multiplier; h (-) represents a membership function; mu.sjRepresents a relaxation variable; g (-) represents a correlation coefficient function.
Further, the objective function satisfies the following KKT condition:
Figure BDA0003189343790000042
in the formula
Figure BDA0003189343790000043
Representing the derivation of the target function;
Figure BDA0003189343790000044
representing an equality constraint in the objective function;
Figure BDA0003189343790000045
an inequality constraint representing an objective function;
Figure BDA0003189343790000046
representing a solution that makes the objective function partial derivative 0.
Further, the clustering with the pearson correlation coefficient and the sum of membership as constraint conditions includes:
and (3) adopting a fuzzy clustering analysis method, taking the data subjected to dimensionality reduction as a clustering object through an iterative computation mode, and obtaining a final classification cluster through iteration by combining with an improved fuzzy optimal constraint condition KKT.
Further, in the fuzzy clustering process, with the objective function as a convergence condition, a specific iteration process includes:
1) setting a membership factor gamma, an iteration stop error epsilon and the maximum iteration times;
2) calculating an initial distance matrix;
3) updating the membership degree between the user and the clustering center, if the distance between the user and the clustering center is 0, the membership degree is 1, otherwise, determining the membership degree according to a derivation formula, wherein the membership degree updating formula is as follows:
Figure BDA0003189343790000051
wherein (t) represents the t-th iteration, and d () represents the distance from the sample point to the cluster center;
4) updating a clustering center:
Figure BDA0003189343790000052
5) recalculating the distance formula and calculating a target function;
6) comparing whether the target function is smaller than a set error epsilon or whether the iteration times meet an iteration ending condition, otherwise, turning to the step 3) to recalculate the membership degree until a constraint condition is met and jumping out of an iteration loop; and obtaining preset class clusters after the iteration is finished, wherein each class cluster has a corresponding correlation coefficient value.
Further, obtaining a relationship between a user active value and a transformer low-voltage side voltage value of the transformer area between the users comprises:
large users and the influence factors are weakly correlated above 0.2; large users and the influence factors are more than 0.4 and are related to a medium degree; the large users have strong correlation when the influence factor is more than 0.6; the influence factors of medium users are weakly correlated above 0.2; moderate users and the influence factors are moderately correlated above 0.4; medium users and strong correlation of influence factors above 0.6; small users and the influence factors are weakly correlated above 0.2; small users and the influence factors are moderately correlated above 0.4; small users and impact factors are strongly correlated above 0.6.
Further, performing impact evaluation according to the relationship, including:
obtaining which users belong to large users, medium users, small users and which users have strong influence factors according to clustering; users with influence factors above 0.6 need to take electric measures to defend the users, so as to prevent accidents; the users with the influence factors above 0.4 need to increase the monitoring strength; users with influence factors above 0.2 need to periodically check the system operation to see whether the system is abnormal or not, and provide certain attention to the users; users with an impact factor above 0.2 do not need to intervene.
A terminal device comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor executes the computer program to realize the steps of the station area user identification and voltage influence evaluation method.
A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, implements the steps of the aforementioned station area user identification and voltage impact evaluation method.
Compared with the prior art, the invention has the following technical characteristics:
the method can distinguish large, medium and small clusters of a certain phase of users under a single transformer area, simultaneously reflects the influence factor of the users in each cluster by combining the Pearson correlation coefficient, can directly judge the relation between the users in a certain grade and the voltage fluctuation of the low-voltage side of the transformer, has important significance for knowing the topology of a power distribution network and building a smart power grid, and provides necessary technical support for further power quality management.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a detailed flow chart of an improved optimal fuzzy clustering algorithm.
Detailed Description
Referring to fig. 1, the invention provides a method for identifying users in a distribution room and evaluating voltage influence, which is based on Multi-Dimensional Scaling (MDS) combined with pearson influence factors to improve a fuzzy C-means clustering method, can quickly distinguish user grades and identify influence degrees of users with different grades on voltage fluctuation of a low-voltage distribution room, and finally obtains a cluster with a large influence degree of different users on the distribution room. The power management is normalized and continuous, and the user types cannot be judged according to data of a certain day or two days by distinguishing the user grades, so that the relationship of the users is unknown, the power management is distorted, and management redundancy or insufficient management strength is generated. The data mining is generally carried out on the electricity utilization historical data in the same station area with the collection interval of 7d and different users 7d, so that N users are divided into three types, namely large users, medium users and small users. The technical solution of the present invention will be further described in detail with reference to the accompanying drawings.
A transformer area user identification and voltage influence evaluation method comprises the following steps:
construction of S1 data matrix
The station-user relationship identification provided by the invention is based on historical data of the station area and users, wherein the influence of the power peak-valley interval on the voltage of the station area is based on the historical data of the station area and the users. The user active power data used by the invention is obtained by historical collected data of a synchronous vector Measurement Unit (PMU), and m user side active power data of a certain phase of the transformer at D time points are obtained according to the resolution ratio of Mmin. Data matrix X of active values of station users acquired from historical data of power utilization acquisition systemP∈Rm×D(R represents the real number domain), where m × D represents the m users co-samplingD time points are collected, and are specifically expressed as follows:
Figure BDA0003189343790000071
wherein
Figure BDA0003189343790000072
Respectively being user end 1,2, m number users at tjThe unit of the active measurement value at a moment is as follows: kw, j ═ 1, 2.
Obtaining the time sequence active value under the single-phase of the platform area voltage, and synchronously obtaining the single-phase voltage data according to the resolution and the acquisition interval, wherein the unit is as follows: volts. Constructing a voltage time sequence matrix of the low-voltage side of the transformer:
Figure BDA0003189343790000073
in the formula VTA matrix formed by D voltage values collected in a period under a certain transformer single phase (for example, A phase) in the transformer area;
Figure BDA0003189343790000074
and the active measurement voltage value of the single phase at the time D is shown.
Data preprocessing of S2 multidimensional scaling method
The Multi-Dimensional Scaling (MDS) is a typical dimension reduction algorithm, which reduces the dimension of original data and minimizes the phenomenon of data "distortion" on the principle of keeping the characteristics of the original data to the maximum. Therefore, the calculation amount of the data can be reduced, and the original characteristics of the data can be kept as much as possible. For m users, each user acquires D-dimensional data, and a distance matrix B epsilon R in the original space can be calculatedm×DIts ith row and j column element distijFor a sample (any one user) xiTo xjThe goal is to obtain a representation matrix Z ∈ R in d' dimensional spacem×d′D 'is less than or equal to D, and the Euclidean form of any two samples in D' dimension spaceThe distance is equal to the distance of the original space, i.e. | | zi-zj||＝distij，zi,zjThe reduced dimension samples are represented. Wherein the sample xi、xjIs XpThe user data in (1) is
Figure BDA0003189343790000075
And the like.
Let E be ZTZ∈Rm×mWherein E is an inner product matrix of the reduced samples,
Figure BDA0003189343790000076
comprises the following steps:
Figure BDA0003189343790000077
wherein the content of the first and second substances,
Figure BDA0003189343790000081
making the matrix Z after dimension reduction belong to Rm×d′Decentralization, i.e.
Figure BDA0003189343790000082
Figure BDA0003189343790000083
Figure BDA0003189343790000084
Figure BDA0003189343790000085
And (3) simplifying the formula by combining the decentralized constraint condition:
Figure BDA0003189343790000086
Figure BDA0003189343790000087
Figure BDA0003189343790000088
wherein, disti.、distj.、distijThe average distance is indicated.
Obtained by the above formula
Figure BDA0003189343790000089
Respectively calculating E-Z from the above formulaTZ∈Rm×mAnd performing characteristic decomposition on the E to obtain E ═ eta Λ etaTWherein Λ ═ diag [ λ ═ d [ lambda ] ]1,λ2,…,λn]And the eta is a feature vector matrix. In reality, for effective dimension reduction, the distance after dimension reduction is often only required to be as close as possible to the distance of the original space, and is not necessarily strictly equal. Selecting an eigenvector matrix W [ [ eta ] η ] corresponding to the first 95% contribution eigenvalue according to the contribution of the eigenvalue1,η2,…,ηd′]. And (3) obtaining an eigenvalue matrix by E matrix eigen decomposition, and sequencing the eigenvalue matrix from large to small (d' first): lambda [ alpha ]1≥λ2≥…≥λq≥λd′. The value of d' is expressed according to the contribution degree:
Figure BDA0003189343790000091
the matrix Z after dimension reduction belongs to Rm×d′Can be expressed as:
Z＝WTX
wherein W is ∈ RD×d′Is a transition matrix, Z ∈ Rm×d′Is a sample space XpAnd (5) reducing the expression of the new space.
S3 construction of platform relationship factor
For XpM sets of user vectors { x }1,x2,x3,...,xm(for convenience of representation, subscript t is omitted)j) And corresponding low-voltage platform voltage matrix
Figure BDA0003189343790000092
And calculating the relation between each user and the voltage fluctuation of the low-voltage transformer area through the Pearson correlation coefficient:
overall mean value:
Figure BDA0003189343790000093
overall covariance:
Figure BDA0003189343790000094
wherein, Xi、ViRespectively representing the active numerical value and the voltage vector of the transformer area of a single user;
overall pearson correlation coefficient:
Figure BDA0003189343790000095
σX，σVare each XpAnd VTStandard deviation of (a):
Figure BDA0003189343790000101
s4 improved fuzzy clustering method station area user identification
The target object of the clustering algorithm obtained in the step S2 is the data feature with 95% of original data after dimensionality reduction. At present, the common clustering algorithms in China are roughly divided into two categories: direct and indirect processes. The direct method is used for directly clustering data, and is usually subjected to K-means, FCM, SOM and the like, but with the continuous increase of data scale and the influence of data noise, data residual errors, default values and the like, the direct method not only brings the challenges of poor clustering effect, large storage capacity, low calculation efficiency and the like. Clustering is carried out according to a traditional clustering algorithm, only users with set clusters can be obtained, the relation between the users and the distribution area voltage cannot be rapidly identified, and further Pearson correlation coefficient analysis needs to be combined.
Based on the above, an improved fuzzy clustering algorithm is provided, which can quickly obtain the definite relationship among the platform-to-user relationships, maintain the characteristics of the original data, greatly reduce the calculation amount of the clustering algorithm and improve the calculation efficiency. The membership degree is used as Euclidean distance to establish an objective function, and the Pearson correlation coefficient and the membership degree sum are used as constraint conditions to perform clustering, wherein the processing process is as follows:
s4.1 construction of fuzzy clustering center
Adopting a fuzzy clustering analysis method, and enabling data Z after dimensionality reduction to belong to Rm×d′Three users are randomly selected as a clustering center, and original user data is divided into three categories: large users, medium users and small users. Constructing a clustering center vector of the data set: c. Ci＝{ci,1,ci,2,…,ci,kWhere c is 1,2,3i,kAnd representing the characteristic value of the k-th dimension of the ith cluster center.
S4.2 optimal fuzzy clustering constraint condition setting and reconstruction objective function
The traditional fuzzy clustering algorithm has the constraint condition that the sum of the membership degrees of each particle to each clustering center is always 1. In order to directly identify users in different clusters, for particles with a fixed loudness in the platform region voltage, a Pearson correlation coefficient is added as a constraint condition, so that the user class can be obtained finally, and the user number with a certain influence factor can be identified. Objective function and constraints:
Figure BDA0003189343790000102
Figure BDA0003189343790000111
Figure BDA0003189343790000112
wherein gamma is a membership factor, and m represents the number of all samples, i.e. the number of users; assuming that each sample j belongs to a certain class i, there is a degree of membership uijThe relationship of (1); c denotes the center of the cluster, ciDenotes the ith cluster center, dijRepresenting the distance, X, of a sample point from a central pointjRepresents XpThe user vector of (1).
The traditional fuzzy clustering algorithm has the constraint condition that the sum of the membership degrees of each particle to each clustering center is always 1. In order to directly identify users in different clusters, for particles with a fixed loudness in the platform region voltage, a Pearson correlation coefficient is added as a constraint condition, so that the user class can be obtained finally, and the user number with a certain influence factor can be identified. According to the original objective function, in order to obtain the minimum value of the objective function under the constraint condition, equality constraint and inequality constraint are carried out: introducing a Lagrange multiplier and a relaxation variable, changing inequality constraint into equality constraint, converting the problem of solving the minimum value of the original problem into the problem of solving convex optimization of quadratic programming, and reconstructing an objective function by integrating the original objective function and the constraint condition:
Figure BDA0003189343790000113
zeta in the formulajRepresenting a lagrange multiplier; h (-) represents a membership function; mu.sjRepresents a relaxation variable; g (-) represents a correlation coefficient function.
S4.3 Lagrange multiplier and relaxation variables
Because the original objective function meets the KKT condition, the KKT condition is a sufficient necessary condition for solving the optimization problem. An SMO heuristic can be applied, whose basic idea is: two variables are selected, other variables are fixed, and a quadratic programming problem is constructed aiming at the two variables. The quadratic programming subproblems of the two variables should be closer to the solution of the original quadratic programming problem, because the new variable values can make the original objective function smaller, and more importantly, the subproblems are solved by an analytical method, so that the overall calculation speed of the algorithm is greatly improved. The SMO algorithm continuously decomposes the original problem into sub-problems and solves the sub-problems, thereby achieving the purpose of solving the original problem.
KKT condition:
Figure BDA0003189343790000121
in the formula
Figure BDA0003189343790000122
Representing the derivation of the target function;
Figure BDA0003189343790000123
representing an equality constraint in the objective function;
Figure BDA0003189343790000124
an inequality constraint representing an objective function; xj *Representing a solution that makes the objective function partial derivative 0.
S4.4 iterative process of optimized fuzzy clustering matrix and optimized fuzzy clustering objective function
By adopting a fuzzy clustering analysis method, obtaining a final classification cluster through iteration by taking a feature matrix subjected to dimensionality reduction obtained by S2 as a clustering object and combining with an improved fuzzy optimal constraint condition KKT in an iterative computation mode; in the fuzzy clustering process, the objective function is taken as a convergence condition, and the specific iteration process is as follows:
1) setting a membership factor gamma, an iteration stop error epsilon and a maximum iteration number (LOOP);
2) calculating an initial distance matrix;
3) updating the membership degree between the user and the clustering center, wherein d (-) is a distance function from the sample point to the sample center, if the distance between the user and the clustering center is 0, the membership degree is 1, otherwise, the membership degree is determined according to a derivation formula, and the updating formula of the membership degree is as follows:
Figure BDA0003189343790000125
where (t) represents the t-th iteration and d () represents the distance of the sample point to the cluster center.
4) Updating a clustering center:
Figure BDA0003189343790000126
5) recalculating the distance formula and calculating a target function;
6) and (3) comparing whether the target function is smaller than a set error epsilon or whether the iteration times meet an iteration ending condition, and otherwise, turning to the step 3) to recalculate the membership degree until a constraint condition is met and jumping out of an iteration loop. And obtaining preset class clusters after the iteration is finished, wherein each class cluster has a corresponding correlation coefficient value.
S4.5 clustering results analysis
The traditional fuzzy clustering algorithm is applied to the identification of the relationship between the users, the clustering objects adopt MDS characteristic values, the clustering results only can show the clustering results of different user characteristics, and the user objects with larger influence factors can not be quickly obtained. Based on the constraint conditions of the traditional fuzzy clustering algorithm, the physical meaning of the Pearson correlation coefficient is referred to in Table 1, and the Pearson influence factor is added
Figure BDA0003189343790000131
As a constraint condition of user clustering, the method aims to quickly obtain the classification condition of the influence degrees of different user grades in a specific influence factor range and quickly obtain the relationship between the user work value and the transformer low-voltage side voltage value of the transformer in a transformer area between the users. With the previous clustering algorithm, the clustering result is: large users and influence factor at 0More than 2 are weakly correlated; large users and the influence factors are more than 0.4 and are related to a medium degree; the large users have strong correlation when the influence factor is more than 0.6; the influence factors of medium users are weakly correlated above 0.2; moderate users and the influence factors are moderately correlated above 0.4; medium users and strong correlation of influence factors above 0.6; small users and the influence factors are weakly correlated above 0.2; small users and the influence factors are moderately correlated above 0.4; small users and impact factors are strongly correlated above 0.6.
Influence and evaluation: through the clustering result, specific users belonging to a large user, a medium user and a small user can be obtained, and the users having strong influence factors can be obtained. Users with influence factors above 0.6 (including users with large, medium and small grades) need to take electric measures to defend the users, so as to prevent accidents; the users with the influence factors above 0.4 need to increase the monitoring strength; users with influence factors above 0.2 need to periodically check the system operation to see whether the system is abnormal or not, and provide certain attention to the users; users with an impact factor above 0.2 do not need to intervene.
The method adopts an MDS algorithm to extract dimensionality reduction features of data features on the basis of original data, maintains the characteristics of the original data, greatly reduces the calculated amount of a clustering algorithm, improves the calculation efficiency, improves the traditional fuzzy clustering method, combines classification and influence factors, obtains user categories and the influence factors simultaneously from clustering results, and finally adopts proper intervention measures according to the physical meanings of the influence factors.
TABLE 1
Figure BDA0003189343790000132
Figure BDA0003189343790000141
The embodiment of the application further provides a terminal device, which can be a computer or a server; the method comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor realizes the steps of the station area user identification and voltage influence evaluation method when executing the computer program.
The computer program may also be partitioned into one or more modules/units, which are stored in the memory and executed by the processor to accomplish the present application. One or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of a computer program in a terminal device.
The implementation of the present application provides a computer-readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the steps of the above-mentioned station user identification and voltage impact evaluation method.
The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.

Claims (10)

1. A method for identifying users in a distribution room and evaluating voltage influence is characterized by comprising the following steps:
acquiring active power data of a user side at a set resolution and time point, and constructing a data matrix of active values of users in a distribution area; acquiring a time sequence active value under the transformer area voltage in a unidirectional way, and constructing a time sequence matrix of the voltage at the low side of the transformer;
preprocessing the data matrix by adopting a multi-dimensional scaling method to obtain data after dimension reduction;
based on the data matrix and the time sequence matrix, calculating the relation between each user and the voltage fluctuation of the low-voltage transformer area so as to construct a transformer association factor;
and establishing a target function by taking the membership as an Euclidean distance, and clustering by taking the Pearson correlation coefficient and the membership sum as constraint conditions, thereby obtaining the classification condition of the influence degrees of different user grades in a specific influence factor range, further obtaining the relation between the user success value between the users and the voltage value of the low-voltage side of the transformer in the transformer area, and carrying out influence evaluation according to the relation.
2. The method of claim 1, wherein the preprocessing the data matrix by using a multidimensional scaling method to obtain dimension-reduced data comprises:
for m users, each user acquires D-dimensional data, and calculates a distance matrix B epsilon R in the original spacem×DIts ith row and j column element distijFor any one user sample xiTo xjThe goal is to obtain a representation matrix Z ∈ R in d' dimensional spacem×d′D 'is less than or equal to D, and the Euclidean distance of any two samples in the D' dimensional space is equal to the distance of the original space: | | zi-zj||＝distij，zi,zjRepresenting the reduced dimension sample, sample xi、xjAs a data matrix XpThe user data in (1);
let E be ZTZ∈Rm×mAnd decentralizing the matrix Z after dimension reduction, simplifying the matrix Z by combining decentralization constraint conditions, calculating E, and performing characteristic decomposition on the E to obtain E ═ etaTWherein Λ ═ diag [ λ ═ d [ lambda ] ]1,λ2,…,λn]A diagonal matrix formed by the eigenvalues, wherein eta is an eigenvector matrix; selecting an eigenvector matrix W [ [ eta ] η ] corresponding to the first 95% contribution eigenvalue according to the contribution of the eigenvalue1,η2,…,ηd′]And decomposing the characteristic of the E matrix to obtain a characteristic value matrix, and sorting the characteristic value matrix according to the first d' numbers from large to small: lambda [ alpha ]1≥λ2≥…≥λq≥λd′To obtain the finalAnd D, reducing the dimension of the matrix Z.
3. The method of claim 2, wherein the value of d' is selected according to the following expression of contribution degree:
Figure FDA0003189343780000021
4. the method of claim 1, wherein the calculating the relationship between each user and the voltage fluctuation of the low voltage transformer area comprises:
and sequentially calculating the overall mean value and the overall covariance between each user and the voltage fluctuation of the low-voltage area, and then calculating to obtain the overall Pearson correlation coefficient.
5. The method of claim 1, wherein the step of constructing the fuzzy matrix center during clustering comprises:
data Z epsilon after dimensionality reduction is Rm×d′Three users are randomly selected as a clustering center, and original user data is divided into three categories: large users, medium users and small users, and constructing a clustering center vector of the data set: c. Ci＝{ci,1,ci,2,…,ci,kWhere c is 1,2,3i,kAnd representing the characteristic value of the k-th dimension of the ith cluster center.
6. The method of claim 1, wherein the objective function and constraint conditions are set as follows:
Figure FDA0003189343780000022
Figure FDA0003189343780000023
Figure FDA0003189343780000024
where γ is a membership factor, m represents the number of all samples, and the membership u exists assuming that each sample j belongs to a class iijThe relationship of (1); c denotes the center of the cluster, ciDenotes the ith cluster center, dijRepresenting the distance, X, of a sample point from a central pointjRepresents XpThe vector of the user in (1) is,
Figure FDA0003189343780000025
representing the overall pearson correlation coefficient.
7. The transformer area user identification and voltage influence evaluation method according to claim 6, wherein a Lagrange multiplier and a relaxation variable are introduced from an original objective function in order to obtain a minimum value of the objective function under a constraint condition, an inequality constraint is changed into an equality constraint, the problem of solving the minimum value of the original problem is converted into a convex optimization problem of solving a quadratic plan, and the objective function is reconstructed by integrating the original objective function and the constraint condition:
Figure FDA0003189343780000031
zeta in the formulajRepresenting a lagrange multiplier; h (-) represents a membership function; mu.sjRepresents a relaxation variable; g (-) represents a correlation coefficient function.
8. The station area user identification and voltage impact evaluation method of claim 1, wherein the objective function satisfies the following KKT condition:
Figure FDA0003189343780000032
in the formula
Figure FDA0003189343780000033
Representing the derivation of the target function;
Figure FDA0003189343780000034
representing an equality constraint in the objective function;
Figure FDA0003189343780000035
an inequality constraint representing an objective function;
Figure FDA0003189343780000036
representing a solution that makes the objective function partial derivative 0.
9. The method of claim 1, wherein the obtaining a relationship between a user activity value between users and a voltage value at a low-voltage side of a transformer of the transformer:
large users and the influence factors are weakly correlated above 0.2; large users and the influence factors are more than 0.4 and are related to a medium degree; the large users have strong correlation when the influence factor is more than 0.6; the influence factors of medium users are weakly correlated above 0.2; moderate users and the influence factors are moderately correlated above 0.4; medium users and strong correlation of influence factors above 0.6; small users and the influence factors are weakly correlated above 0.2; small users and the influence factors are moderately correlated above 0.4; small users and impact factors are strongly correlated above 0.6.
10. The method of claim 1, wherein performing impact evaluation based on the relationship comprises:
obtaining which users belong to large users, medium users, small users and which users have strong influence factors according to clustering; users with influence factors above 0.6 need to take electric measures to defend the users, so as to prevent accidents; the users with the influence factors above 0.4 need to increase the monitoring strength; users with influence factors above 0.2 need to periodically check the system operation to see whether the system is abnormal or not, and provide certain attention to the users; users with an impact factor above 0.2 do not need to intervene.
CN202110871498.9A 2021-07-30 2021-07-30 Transformer area user identification and voltage influence evaluation method Pending CN113595071A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110871498.9A CN113595071A (en) 2021-07-30 2021-07-30 Transformer area user identification and voltage influence evaluation method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110871498.9A CN113595071A (en) 2021-07-30 2021-07-30 Transformer area user identification and voltage influence evaluation method

Publications (1)

Publication Number Publication Date
CN113595071A true CN113595071A (en) 2021-11-02

Family

ID=78252656

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110871498.9A Pending CN113595071A (en) 2021-07-30 2021-07-30 Transformer area user identification and voltage influence evaluation method

Country Status (1)

Country Link
CN (1) CN113595071A (en)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116154972A (en) * 2023-04-21 2023-05-23 新风光电子科技股份有限公司 Distributed power grid power quality monitoring method and system
CN116796213A (en) * 2023-02-24 2023-09-22 南昌工程学院 Power distribution network line transformation relation identification method based on clustering algorithm
CN118011074A (en) * 2024-04-08 2024-05-10 广东电网有限责任公司广州供电局 Method, device, system and storage medium for monitoring voltage fluctuation of transformer area
CN118011074B (en) * 2024-04-08 2024-07-09 广东电网有限责任公司广州供电局 Method, device, system and storage medium for monitoring voltage fluctuation of transformer area

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116796213A (en) * 2023-02-24 2023-09-22 南昌工程学院 Power distribution network line transformation relation identification method based on clustering algorithm
CN116796213B (en) * 2023-02-24 2023-11-21 南昌工程学院 Power distribution network line transformation relation identification method based on clustering algorithm
CN116154972A (en) * 2023-04-21 2023-05-23 新风光电子科技股份有限公司 Distributed power grid power quality monitoring method and system
CN118011074A (en) * 2024-04-08 2024-05-10 广东电网有限责任公司广州供电局 Method, device, system and storage medium for monitoring voltage fluctuation of transformer area
CN118011074B (en) * 2024-04-08 2024-07-09 广东电网有限责任公司广州供电局 Method, device, system and storage medium for monitoring voltage fluctuation of transformer area

Similar Documents

Publication Publication Date Title
CN111199016B (en) Daily load curve clustering method for improving K-means based on DTW
CN109873501B (en) Automatic identification method for low-voltage distribution network topology
CN108199404B (en) Spectral clustering cluster division method of high-permeability distributed energy system
CN109546659B (en) Power distribution network reactive power optimization method based on random matrix and intelligent scene matching
CN110569316A (en) low-voltage distribution area user topology identification method based on t-SNE dimension reduction technology and BIRCH clustering
CN113595071A (en) Transformer area user identification and voltage influence evaluation method
CN111339491A (en) Evaluation method for urban power distribution network transformation scheme
CN114519514B (en) Low-voltage transformer area reasonable line loss value measuring and calculating method, system and computer equipment
CN111654392A (en) Low-voltage distribution network topology identification method and system based on mutual information
CN111539657A (en) Typical electricity consumption industry load characteristic classification and synthesis method combined with user daily electricity consumption curve
CN112070121A (en) Intelligent electric meter data filling method based on variational self-encoder
CN112819649A (en) Method and device for determining station area subscriber change relationship
CN111091223B (en) Matching short-term load prediction method based on intelligent sensing technology of Internet of things
CN113591322A (en) Low-voltage transformer area line loss rate prediction method based on extreme gradient lifting decision tree
CN112101673A (en) Power grid development trend prediction method and system based on hidden Markov model
Zhang et al. User power interaction behavior clustering analysis that is based on the self-organizing-center K-means algorithm
CN115051363B (en) Distribution network area user change relation identification method and device and computer storage medium
CN113989073B (en) Photovoltaic high-duty distribution network voltage space-time multidimensional evaluation method based on big data mining
CN115186882A (en) Clustering-based controllable load spatial density prediction method
CN110852628A (en) Rural medium and long term load prediction method considering development mode influence
Huang et al. Latin hypercube sampling and spectral clustering based typical scenes generation and analysis for effective reserve dispatch
şen Yildiz et al. Cascaded clustering analysis of electricity load profile based on smart metering data
CN115663801B (en) Low-voltage area topology identification method based on spectral clustering
Peng et al. A Deep Convolutional Embedded Clustering Method for Scenario Reduction of Production Simulation
Li et al. Research on smart grid big data’s curve mean clustering algorithm for edge-cloud collaborative application

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication