CN113673168A - Model parameter correction method, device, equipment and readable storage medium - Google Patents
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Abstract
The application discloses a model parameter correction method, a device, equipment and a readable storage medium, wherein the method comprises the following steps: acquiring load data of each user in a transformer substation and a jurisdiction thereof; clustering load data of each user according to preset typical electricity utilization industry categories to obtain the category of the load data of each user and clustering center load data of each category; acquiring load model parameters of each typical electricity utilization industry; determining the proportion of the load of each typical power utilization industry according to the load data of the transformer substation and the cluster center load data of each category; and correcting the transformer substation load model parameters based on the proportion of the load model parameters of each typical power utilization industry to the load of each typical power utilization industry. Obviously, the proportion of the load data of the typical power utilization industry in the load data of the transformer substation is analyzed, and the parameters of the load model of the transformer substation are corrected according to the proportion, so that the load model of the transformer substation can accurately reflect the real-time characteristics of the load.
Description
Technical Field
The present application relates to the field of information processing technologies, and in particular, to a method, an apparatus, a device, and a storage medium for model parameter modification.
Background
The simulation of the power system is one of important means for the stability analysis of the power system, and the reasonable modeling of the system is the basis of the simulation technology. The load is an important component of the power system, and the reasonable load model has important significance for the safety and stability analysis of the power system. Because the load has the characteristics of time variability, randomness, high nonlinearity and the like, the identification of load model parameters is an important difficult problem for modeling of the power system.
Most of the existing power system load models are modeled based on historical data. However, the load characteristics of different regions and different time periods under different load conditions have different load parameters, and it is not reliable to describe the load characteristics by using a constant load model.
Therefore, it is important to correct the parameters of the load model so that the load model can accurately reflect the real-time characteristics of the load.
Disclosure of Invention
In view of this, the present application provides a model parameter modification scheme, which is used to modify parameters of a load model, so that the load model can accurately reflect real-time characteristics of a load.
In order to achieve the above object, the following solutions are proposed:
a model parameter modification method, comprising:
acquiring load data of a transformer substation and load data of each user in the district of the transformer substation;
clustering load data of each user according to preset typical electricity utilization industry categories to obtain typical electricity utilization industry categories to which the load data of each user belong and clustering center load data of each typical electricity utilization industry;
acquiring load model parameters of each typical electricity utilization industry;
determining the proportion of the load of each typical electricity utilization industry according to the load data of the transformer substation and the cluster center load data of each typical electricity utilization industry;
and correcting the load model parameters of the transformer substation based on the proportion of the load model parameters of each typical power utilization industry to the load of each typical power utilization industry.
Preferably, the acquiring load data of the transformer substation and load data of each user in the domain of the transformer substation includes:
acquiring daily load curve data of a transformer substation and daily load curve data of each user in the district of the transformer substation.
Preferably, the clustering load data of each user according to preset types of each typical power utilization industry to obtain the type of the typical power utilization industry to which the load data of each user belongs and the clustering center load data of each typical power utilization industry includes:
processing the load data of each user per unit;
clustering the load data of each user after per unit processing by using a clustering analysis principle to obtain a typical power utilization industry category to which the load data of each user belongs and clustering center load data of each typical power utilization industry.
Preferably, the obtaining of the load model parameter of each typical electricity industry includes:
counting the static active power and the static reactive power of each typical electricity consumption industry electric equipment;
carrying out laboratory measurement or reference to empirical values on the characteristic parameter values of the electric equipment to obtain respective weight coefficients of the static active power and the static reactive power;
and performing weighted calculation on the static active power and the static reactive power and the respective weight coefficients to obtain the comprehensive active power and the comprehensive reactive power of each typical power utilization industry, wherein the comprehensive active power and the comprehensive reactive power are used as the load model parameters of each typical power utilization industry.
Preferably, the determining the proportion of the load of each typical electricity utilization industry according to the load data of the substation and the cluster center load data of each typical electricity utilization industry includes:
dividing daily load curve data of the transformer substation into load curve data of a plurality of different time periods;
calculating the proportion of the clustering center load data of each typical power utilization industry in the load curve data of the corresponding time period in the corresponding time period;
and determining the proportion as the proportion of the load of each typical electricity utilization industry in different time periods.
Preferably, the modifying the load model parameter of the substation based on the proportion of the load model parameter of each typical power utilization industry to the load of each typical power utilization industry includes:
weighting and calculating the proportion of the comprehensive active power and the comprehensive reactive power of each typical power utilization industry to the load of each typical power utilization industry in different time periods to obtain the active power and the reactive power of each typical power utilization industry in different time periods;
the active power and the reactive power of each typical electricity utilization industry in different time periods are counted to obtain the active power and the reactive power of the total load of the transformer substation in different time periods;
and correcting the active power and the reactive power of the total load of the transformer substation in different time periods into load model parameters of the transformer substation in different time periods.
A model parameter modification apparatus comprising:
the data acquisition unit is used for acquiring load data of a transformer substation and load data of each user in the domain of the transformer substation;
the industry clustering unit is used for clustering the load data of each user according to the preset type of each typical electricity utilization industry to obtain the type of the typical electricity utilization industry to which the load data of each user belongs and the clustering center load data of each typical electricity utilization industry;
the parameter acquisition unit is used for acquiring the load model parameters of each typical electricity utilization industry;
the proportion determining unit is used for determining the proportion of the load of each typical electricity utilization industry according to the load data of the transformer substation and the cluster center load data of each typical electricity utilization industry;
and the parameter correcting unit is used for correcting the load model parameters of the transformer substation based on the proportion of the load model parameters of each typical power utilization industry to the load of each typical power utilization industry.
Preferably, the data acquiring unit includes:
and the daily data acquisition unit is used for acquiring daily load curve data of the transformer substation and daily load curve data of each user in the district of the transformer substation.
A model parameter modification apparatus comprising a memory and a processor;
the memory is used for storing programs;
the processor is configured to execute the program to implement the steps of the model parameter modification method.
A readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned model parameter modification method.
As can be seen from the above solution, the model parameter modification solution provided by the present application includes: acquiring load data of a transformer substation and load data of each user in the district of the transformer substation; clustering load data of each user according to preset typical electricity utilization industry categories to obtain typical electricity utilization industry categories to which the load data of each user belong and clustering center load data of each typical electricity utilization industry; acquiring load model parameters of each typical electricity utilization industry; determining the proportion of the load of each typical electricity utilization industry according to the load data of the transformer substation and the cluster center load data of each typical electricity utilization industry; and correcting the load model parameters of the transformer substation based on the proportion of the load model parameters of each typical power utilization industry to the load of each typical power utilization industry. Therefore, the proportion of the load data of the typical power utilization industry in the load data of the transformer substation is analyzed, and the parameters of the load model of the transformer substation are corrected in real time according to the proportion, so that the load model of the transformer substation can accurately reflect the real-time characteristics of the load.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic flowchart of a model parameter modification method according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a model parameter modification apparatus according to an embodiment of the present disclosure;
fig. 3 is a block diagram of a hardware structure of a model parameter correction device according to an embodiment of the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, fig. 1 is a schematic flow chart of a model parameter correction method provided in an embodiment of the present application, where the method includes:
step S100: acquiring load data of a transformer substation and load data of each user in the district of the transformer substation.
Specifically, the step may obtain the load data of the substation in various ways, for example: the load Data of the transformer substation can be acquired from a Data acquisition And monitoring Control system of a power grid, namely an SCADA (supervisory Control And Data acquisition) system.
In addition, load data of each user in the jurisdiction of the substation can be acquired in various ways, such as: the load data of each user in the district of the transformer substation can be obtained from a load control system of the power grid.
The above-mentioned methods for obtaining the load data of the transformer substation and the load data of each user in the domain of the transformer substation are only examples of an optional method, and those skilled in the art may also obtain the above-mentioned load data in other manners.
The load characteristics are considered to be time-varying and random. The load characteristics are determined by the characteristics of various electric equipment forming the load and the living habits, climates, seasons, industrial and agricultural production and the like of people in the area. In addition, the load varies in size and composition at different times due to unpredictable and uncontrollable characteristics of the user operation, and thus the dynamic characteristics vary.
Therefore, in order to make the load model more time-efficient, daily load curve data of the transformer substation and daily load curve data of each user in the district of the transformer substation can be obtained. Acquiring the load data by taking the day as a unit can reduce the influence caused by the factors and can reflect the real-time characteristic of the load data.
Step S110: and clustering the load data of each user according to preset typical electricity utilization industry categories to obtain the typical electricity utilization industry category to which the load data of each user belongs and the clustering center load data of each typical electricity utilization industry.
Specifically, the power consumption situation of each user is different, and the load characteristics of all users are different. Therefore, the types of the plurality of typical electricity utilization industries can be preset, the load data of each user can be analyzed and clustered, and the type of the typical electricity utilization industry to which the load data of each user belongs can be obtained.
In addition, after the load data of each user is analyzed and clustered, the cluster center load data of each typical power utilization industry can be obtained.
Step S120: and acquiring the load model parameters of each typical electricity utilization industry.
In an alternative mode, the load model parameters of each typical electricity utilization industry can be obtained according to the electric equipment data of the power grid.
Specifically, the configuration conditions of the electric equipment in each typical electric industry can be investigated and counted, and then the load model parameters of each typical electric industry can be obtained according to the counting result.
Step S130: and determining the proportion of the load of each typical electricity utilization industry according to the load data of the transformer substation and the cluster center load data of each typical electricity utilization industry.
Specifically, in order to calculate the power utilization industry composition ratio of the transformer substation, in an optional manner, the proportion of the cluster center load data of each typical power utilization industry in the load data of the transformer substation may be calculated, and then the proportion of the load of each typical power utilization industry may be obtained.
Step S140: and correcting the load model parameters of the transformer substation based on the proportion of the load model parameters of each typical power utilization industry to the load of each typical power utilization industry.
Specifically, the proportion of the load model parameter of each typical electricity consumption industry obtained in the above steps to the load of each typical electricity consumption industry can be weighted and calculated, and then the weighted load model parameter of each typical electricity consumption industry can be obtained.
Further, the weighted load model parameters of each typical electricity industry can be counted to obtain the sum of the weighted load model parameters, and the load model parameters of the transformer substation are corrected to be the sum of the weighted load model parameters.
According to the embodiment, the load model parameters of each typical user industry are weighted and calculated according to the proportion of the load of each typical power utilization industry, the weighted and calculated load model parameters are collected and corrected into the load model parameters of the transformer substation, and therefore the load model of the transformer substation can accurately reflect real-time load characteristics.
In some embodiments of the present application, step S110 is introduced, the load data of each user is clustered according to preset each typical electricity consumption industry category, so as to obtain the typical electricity consumption industry category to which the load data of each user belongs and the clustering center load data of each typical electricity consumption industry, and then the process is further described.
Specifically, the process may include:
and S1, per unit processing the load data of each user.
It can be understood that the obtained load data of the users are all absolute values, and for more convenient processing of the load data, the load data of each user can be subjected to per unit processing and converted into load data of relative values.
And S2, clustering the load data of each user after per unit processing by using a clustering analysis principle to obtain a typical power utilization industry category to which the load data of each user belongs and clustering center load data of each typical power utilization industry.
Specifically, according to the clustering analysis principle, a fuzzy clustering algorithm can be applied to perform clustering analysis on the load curve of each user. The present application provides an alternative implementation, namely, a Fuzzy C Mean (FCM) clustering method is used to implement the above clustering analysis process.
The process of clustering using the fuzzy C-means clustering method may include: the load data of each user can be classified into a certain clustering center according to a certain fuzzy membership degree. Firstly, selecting corresponding clustering centers according to preset typical electricity industry categories, endowing load data of all users with certain fuzzy membership degrees to the clustering centers, then continuously correcting the clustering centers through an iteration method, and taking the weighted sum of the distances from the load data of all the users to all the clustering centers and the membership degrees as an optimization target in the iteration process.
After the iteration is finished, the typical power utilization industry category to which the load data of each user belongs can be obtained according to the membership degree, and the clustering center load data of each typical power utilization industry can be obtained.
Next, the above-described process of obtaining the typical electricity consumption industry category to which the load data of each user belongs and the cluster center load data of each typical electricity consumption industry will be described with reference to specific examples.
Specifically, according to a preset typical electricity utilization industry, the user load can be classified according to heavy industry, light industry, business and residential load, and the number of initial clustering categories is defined as c-4. Then, the initial clustering center can be selected, and particularly, users with any heavy industry, light industry, business and residential load of known load types can be selected as the initial clustering center, and the initial clustering centers can respectively use x*1,...,x*cTo indicate.
Then, a membership matrix U, U may be introducedik(i 1.. m.; k.. 1.. c.) denotes the ith sample xi(load data of i-th user here) for class AkThe sum of the membership degrees of one sample to each clustering center is 1, namely, the following conditions are met:
dikdenotes the ith sample xiWith class k centers vkThe euclidean distance between, there are:
l represents the number of iterations, having:
the cluster centers are updated as follows:
and (5) repeatedly calculating the membership degree and the clustering center of the sample, and finishing iteration when the algorithm is converged.
Let uij＝max{ui1,...,uicH, the ith sample xiAnd the data belongs to the jth category, and a final clustering result can be sequentially obtained, wherein the final clustering result comprises a typical power utilization industry category to which the load data of each user belongs and a clustering center of each typical power utilization industry.
In some embodiments of the present application, a process of obtaining the load model parameters of each typical electricity industry in step S120 is described, and the process will be further described below.
Specifically, the process may include:
and S1, counting the static active power and the static reactive power of each typical electricity utilization industry electric equipment.
Specifically, the configuration conditions of the electric equipment in each typical electric industry can be investigated and counted, and then the static active power and the static reactive power of the electric equipment in each typical electric industry can be counted.
And S2, carrying out laboratory measurement or reference to empirical values on the characteristic parameter values of the electric equipment to obtain respective weight coefficients of the static active power and the static reactive power.
Because the static active power and the static reactive power obtained through statistics may have errors, in order to obtain more accurate data, laboratory measurement may be performed on the characteristic parameter values of the electric equipment or the respective weight coefficients of the static active power and the static reactive power may be determined by referring to empirical values, and the errors may be reduced as much as possible after the respective weighting calculations.
And S3, performing weighted calculation on the static active power and the static reactive power and the respective weight coefficients to obtain the comprehensive active power and the comprehensive reactive power of each typical power utilization industry, wherein the comprehensive active power and the comprehensive reactive power are used as the load model parameters of each typical power utilization industry.
Specifically, the comprehensive active power and reactive power obtained through weighting calculation can accurately reflect the comprehensive characteristics of each typical power utilization industry, and can be used as load model parameters of each typical power utilization industry.
The above process will be described next with reference to specific examples.
Specifically, the static active power and the static reactive power of each typical electricity utilization industry can be obtained based on historical data.
Firstly, within the scope of a transformer substation jurisdiction, a plurality of representative typical users can be selected for users of each type of typical power utilization industry to conduct investigation, and the constitution conditions of power utilization equipment and the capacity proportion of each type of power utilization equipment are determined. And further, the comprehensive characteristics of each typical power utilization industry can be determined according to the average characteristics of each type of power utilization equipment of typical users of each typical power utilization industry.
The selection method of the typical user may include multiple modes, and an optional mode provided in the embodiment of the present application is used for implementing a process of selecting the typical user, where the mode may include: according to the typical power utilization industry classification, a plurality of users capable of reflecting the production characteristics of the typical power utilization industry are selected from each typical power utilization industry according to practical experience.
Further, the device proportion of a typical user of each typical electricity utilization industry can be calculated, and the implementation process can include: and performing weighted calculation according to the capacity of the electric equipment to determine the proportion of the electric equipment in each typical electric industry.
Specifically, m typical users can be selected in a certain type of typical power utilization industry, n types of power utilization equipment in the industry are provided, and the capacity ratio of the certain type of power utilization equipment is as follows:
in the formula: k is a radical ofijRepresents the capacity ratio, P, of the j-th class of electric equipment of the i-th typical useriAnd the total capacity of the electric equipment of the ith typical user is shown.
Comprehensive characteristics of industry users: setting the active and reactive power of the i-th class of electric equipment as Pi、QiCoefficient of static characteristics of pui、pfi、qui、qfiThe static characteristics of the i-th class of electric equipment are as follows:
the active power and the reactive power of the total load of the users in the typical power utilization industry are respectively as follows:
the above is an example of the calculation process of the total load power of a certain typical power utilization industry, and the calculation process of the total load power of other typical power utilization industries may refer to the above calculation process, which is not described herein again.
In some embodiments of the present application, a process of determining a proportion of the load of each typical electricity industry according to the load data of the substation and the cluster center load data of each typical electricity industry is described in step S130, and the process will be further described below.
Specifically, the process may include:
and S1, dividing daily load curve data of the transformer substation into load curve data of a plurality of different time intervals.
Specifically, the daily load curve data can be divided into a plurality of load curve data of different time periods according to the change condition of the daily load curve data of the transformer substation.
And S2, calculating the proportion of the cluster center load data of each typical electricity utilization industry in the load curve data of the corresponding time period in the corresponding time period.
Specifically, the clustering center curve of each typical electricity industry obtained in step S110 is divided according to the plurality of time periods divided in step S1 to obtain clustering center segment load data in different time periods, and then the proportion of the clustering center segment load data of each typical electricity industry in the load curve data of the corresponding time period in the corresponding time period can be calculated.
And S3, determining the proportion as the proportion of the load of each typical electricity utilization industry in different time periods.
Specifically, the proportion of the clustering center segment load data of each typical electricity utilization industry obtained in S2 in different time periods of the daily load data of the transformer substation is determined as the proportion of the load of each typical electricity utilization industry in different time periods of the transformer substation.
Next, the above-described process will be described with reference to specific examples.
Specifically, a typical daily load curve may be taken as an example. The typical daily load curve shows a trend of two peaks one by one and one valley, so the daily load curve can be divided into four time intervals for analysis according to the change condition of the daily load curve. The load of each time interval can be regarded as the superposition of load curves of each typical electricity utilization industry, and the proportion of the load of each typical electricity utilization industry is different in different time intervals.
In different time periods, the proportion of the load of each typical electricity utilization industry is calculated according to the following formula:
in the formula: rhoi (j)Represents the proportion of the ith industry load in the jth time interval, Pi (j)The ith industry load power in the jth time period is shown, and m represents the industry number.
In some embodiments of the present application, step S140 is introduced, and a process of correcting the load model parameter of the substation based on the proportion of the load model parameter of each typical electricity industry to the load of each typical electricity industry is further described below.
Specifically, the process may include:
and S1, carrying out weighted calculation on the proportion of the comprehensive active power and the comprehensive reactive power of each typical electricity utilization industry to the load of each typical electricity utilization industry in different time periods to obtain the active power and the reactive power of each typical electricity utilization industry in different time periods.
Specifically, the total active power and the total reactive power of each typical electricity consumption industry obtained in step S120 and the proportion of the load of each typical electricity consumption industry obtained in step S130 in different time periods may be weighted and calculated, so that the active power and the reactive power of each typical electricity consumption industry in different time periods may be obtained.
And S2, counting the active power and the reactive power of each typical electricity utilization industry in different time periods to obtain the active power and the reactive power of the total load of the transformer substation in different time periods.
Specifically, the active power and the reactive power of all typical electricity utilization industries in each time period are counted, and then the active power and the reactive power of the total load of the transformer substation in different time periods can be obtained.
And S3, correcting the active power and the reactive power of the total load of the transformer substation in different time periods into load model parameters of the transformer substation in different time periods.
Specifically, the load model parameters of the substation in different time periods may be modified to the active power and the reactive power of the total load in the time period corresponding to the substation obtained in S2.
Therefore, the transformer substation load model of the parameters is corrected according to the proportion of the load of each typical power utilization industry, and the real-time characteristic of the load can be accurately reflected.
Next, the above-described procedure will be described with reference to specific examples.
Specifically, the example process may include: and (4) performing weighted calculation by combining the comprehensive active power and the comprehensive reactive power of each typical power utilization industry and the proportion of the load of each typical power utilization industry in different time periods to complete the correction of the load model parameters of the transformer substation.
The parameter correction formula of the transformer substation load model is as follows:
in the formula: p, Q denotes the total load active and reactive power, p, of the substation, respectivelyiRepresents the proportion of the ith industry load, P∑i、Q∑iRespectively representActive power and reactive power of the ith industry total load.
The following describes the model parameter correction device provided in the embodiment of the present application, and the model parameter correction device described below and the model parameter correction device described above may be referred to correspondingly.
First, referring to fig. 2, a model parameter modification apparatus will be described, and as shown in fig. 2, the model parameter modification apparatus may include:
the data acquisition unit 100 is used for acquiring load data of a transformer substation and load data of each user in the domain of the transformer substation;
the industry clustering unit 110 is configured to cluster the load data of each user according to preset typical electricity utilization industry categories to obtain a typical electricity utilization industry category to which the load data of each user belongs and clustering center load data of each typical electricity utilization industry;
a parameter obtaining unit 120, configured to obtain a load model parameter of each typical electricity consumption industry;
the proportion determining unit 130 is configured to determine a proportion of the load of each typical electricity utilization industry according to the load data of the substation and the cluster center load data of each typical electricity utilization industry;
and the parameter correcting unit 140 is configured to correct the load model parameter of the substation based on a ratio of the load model parameter of each typical power consumption industry to the load of each typical power consumption industry.
Optionally, the industry clustering unit 110 may include:
the per-unit is used for per-unit processing the load data of each user;
and the industry clustering subunit is used for clustering the load data of each user after per unit processing by using a clustering analysis principle to obtain a typical power utilization industry category to which the load data of each user belongs and clustering center load data of each typical power utilization industry.
Optionally, the parameter obtaining unit 120 may include:
the first statistical unit is used for counting the static active power and the static reactive power of each typical electricity consumption industry electric equipment;
the weight coefficient determining unit is used for carrying out laboratory measurement or reference experience value on the characteristic parameter value of the electric equipment to obtain the respective weight coefficients of the static active power and the static reactive power;
and the parameter obtaining subunit is configured to perform weighted calculation on the static active power and the static reactive power and the respective weight coefficients to obtain a comprehensive active power and a comprehensive reactive power of each typical power consumption industry, which are used as load model parameters of each typical power consumption industry.
Optionally, the data obtaining unit 100 may include:
and the daily data acquisition unit is used for acquiring daily load curve data of the transformer substation and daily load curve data of each user in the district of the transformer substation.
Optionally, the ratio determining unit 130 may include:
the dividing unit is used for dividing daily load curve data of the transformer substation into a plurality of load curve data in different time periods;
the proportion calculation unit is used for calculating the proportion of the clustering center load data of each typical power utilization industry in the load curve data of the corresponding time period in the corresponding time period;
and the proportion determining subunit is used for determining the proportion as the proportion of the load of each typical electricity industry in different time periods.
Optionally, the parameter modification unit 140 may include:
the parameter calculation unit is used for weighting and calculating the proportion of the comprehensive active power and the comprehensive reactive power of each typical power utilization industry to the load of each typical power utilization industry in different time periods to obtain the active power and the reactive power of each typical power utilization industry in different time periods;
the second statistical unit is used for calculating the active power and the reactive power of each typical electricity utilization industry in different time periods to obtain the active power and the reactive power of the total load of the transformer substation in different time periods;
and the parameter correction subunit is used for correcting the active power and the reactive power of the total load of the transformer substation in different time periods into the load model parameters of the transformer substation in different time periods.
The model parameter correction device provided by the embodiment of the application can be applied to model parameter correction equipment. Fig. 3 is a block diagram showing a hardware configuration of the model parameter correction apparatus, and referring to fig. 3, the hardware configuration of the model parameter correction apparatus may include: at least one processor 1, at least one communication interface 2, at least one memory 3 and at least one communication bus 4;
in the embodiment of the application, the number of the processor 1, the communication interface 2, the memory 3 and the communication bus 4 is at least one, and the processor 1, the communication interface 2 and the memory 3 complete mutual communication through the communication bus 4;
the processor 1 may be a central processing unit CPU, or an application Specific Integrated circuit asic, or one or more Integrated circuits configured to implement embodiments of the present invention, etc.;
the memory 3 may include a high-speed RAM memory, and may further include a non-volatile memory (non-volatile memory) or the like, such as at least one disk memory;
wherein the memory stores a program and the processor can call the program stored in the memory, the program for:
acquiring load data of a transformer substation and load data of each user in the district of the transformer substation;
clustering load data of each user according to preset typical electricity utilization industry categories to obtain typical electricity utilization industry categories to which the load data of each user belong and clustering center load data of each typical electricity utilization industry;
acquiring load model parameters of each typical electricity utilization industry;
determining the proportion of the load of each typical electricity utilization industry according to the load data of the transformer substation and the cluster center load data of each typical electricity utilization industry;
and correcting the load model parameters of the transformer substation based on the proportion of the load model parameters of each typical power utilization industry to the load of each typical power utilization industry.
Alternatively, the detailed function and the extended function of the program may be as described above.
Embodiments of the present application further provide a storage medium, where a program suitable for execution by a processor may be stored, where the program is configured to:
acquiring load data of a transformer substation and load data of each user in the district of the transformer substation;
clustering load data of each user according to preset typical electricity utilization industry categories to obtain typical electricity utilization industry categories to which the load data of each user belong and clustering center load data of each typical electricity utilization industry;
acquiring load model parameters of each typical electricity utilization industry;
determining the proportion of the load of each typical electricity utilization industry according to the load data of the transformer substation and the cluster center load data of each typical electricity utilization industry;
and correcting the load model parameters of the transformer substation based on the proportion of the load model parameters of each typical power utilization industry to the load of each typical power utilization industry.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A model parameter modification method, comprising:
acquiring load data of a transformer substation and load data of each user in the district of the transformer substation;
clustering load data of each user according to preset typical electricity utilization industry categories to obtain typical electricity utilization industry categories to which the load data of each user belong and clustering center load data of each typical electricity utilization industry;
acquiring load model parameters of each typical electricity utilization industry;
determining the proportion of the load of each typical electricity utilization industry according to the load data of the transformer substation and the cluster center load data of each typical electricity utilization industry;
and correcting the load model parameters of the transformer substation based on the proportion of the load model parameters of each typical power utilization industry to the load of each typical power utilization industry.
2. The method of claim 1, wherein the obtaining load data of the substation and load data of each user in the substation jurisdiction comprises:
acquiring daily load curve data of a transformer substation and daily load curve data of each user in the district of the transformer substation.
3. The method according to claim 1, wherein the clustering load data of each user according to preset typical electricity utilization industry categories to obtain the typical electricity utilization industry category to which the load data of each user belongs and the clustering center load data of each typical electricity utilization industry comprises:
processing the load data of each user per unit;
clustering the load data of each user after per unit processing by using a clustering analysis principle to obtain a typical power utilization industry category to which the load data of each user belongs and clustering center load data of each typical power utilization industry.
4. The method of claim 2, wherein the obtaining load model parameters for each typical electricity industry comprises:
counting the static active power and the static reactive power of each typical electricity consumption industry electric equipment;
carrying out laboratory measurement or reference to empirical values on the characteristic parameter values of the electric equipment to obtain respective weight coefficients of the static active power and the static reactive power;
and performing weighted calculation on the static active power and the static reactive power and the respective weight coefficients to obtain the comprehensive active power and the comprehensive reactive power of each typical power utilization industry, wherein the comprehensive active power and the comprehensive reactive power are used as the load model parameters of each typical power utilization industry.
5. The method of claim 4, wherein the determining the proportion of the load of each typical electricity industry according to the load data of the substation and the cluster center load data of each typical electricity industry comprises:
dividing daily load curve data of the transformer substation into load curve data of a plurality of different time periods;
calculating the proportion of the clustering center load data of each typical power utilization industry in the load curve data of the corresponding time period in the corresponding time period;
and determining the proportion as the proportion of the load of each typical electricity utilization industry in different time periods.
6. The method of claim 5, wherein the modifying the load model parameters of the substation based on the proportion of the load model parameters of each typical electricity industry to the load of each typical electricity industry comprises:
weighting and calculating the proportion of the comprehensive active power and the comprehensive reactive power of each typical power utilization industry to the load of each typical power utilization industry in different time periods to obtain the active power and the reactive power of each typical power utilization industry in different time periods;
the active power and the reactive power of each typical electricity utilization industry in different time periods are counted to obtain the active power and the reactive power of the total load of the transformer substation in different time periods;
and correcting the active power and the reactive power of the total load of the transformer substation in different time periods into load model parameters of the transformer substation in different time periods.
7. A model parameter correction apparatus, characterized by comprising:
the data acquisition unit is used for acquiring load data of a transformer substation and load data of each user in the domain of the transformer substation;
the industry clustering unit is used for clustering the load data of each user according to the preset type of each typical electricity utilization industry to obtain the type of the typical electricity utilization industry to which the load data of each user belongs and the clustering center load data of each typical electricity utilization industry;
the parameter acquisition unit is used for acquiring the load model parameters of each typical electricity utilization industry;
the proportion determining unit is used for determining the proportion of the load of each typical electricity utilization industry according to the load data of the transformer substation and the cluster center load data of each typical electricity utilization industry;
and the parameter correcting unit is used for correcting the load model parameters of the transformer substation based on the proportion of the load model parameters of each typical power utilization industry to the load of each typical power utilization industry.
8. The apparatus of claim 7, wherein the data acquisition unit comprises:
and the daily data acquisition unit is used for acquiring daily load curve data of the transformer substation and daily load curve data of each user in the district of the transformer substation.
9. A model parameter modification apparatus comprising a memory and a processor;
the memory is used for storing programs;
the processor, which executes the program, implements the steps of the model parameter correction method according to any one of claims 1 to 6.
10. A readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the model parameter modification method according to any one of claims 1 to 6.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2012205432A (en) * | 2011-03-25 | 2012-10-22 | Tokyo Electric Power Co Inc:The | Load estimation method and load estimation system for transformer |
| CN103279803A (en) * | 2013-04-27 | 2013-09-04 | 深圳供电局有限公司 | Load modeling method and system based on comprehensive information theory and modern interior point theory |
| CN104200275A (en) * | 2014-06-24 | 2014-12-10 | 国家电网公司 | Power utilization mode classification and control method based on user behavior characteristics |
| CN105528660A (en) * | 2016-03-09 | 2016-04-27 | 湖南大学 | Substation load model parameter prediction method based on daily load curve |
| CN107977771A (en) * | 2017-11-07 | 2018-05-01 | 国家电网公司 | A kind of substation's characteristic analysis method based on multivariate clustering model Yu two-phase analyzing method correction algorithm |
| CN110601174A (en) * | 2019-07-06 | 2019-12-20 | 天津大学 | Load modeling and online correction method based on deep learning |
| CN111199016A (en) * | 2019-09-29 | 2020-05-26 | 国网湖南省电力有限公司 | DTW-based improved K-means daily load curve clustering method |
| CN111539657A (en) * | 2020-05-30 | 2020-08-14 | 国网湖南省电力有限公司 | Typical electricity consumption industry load characteristic classification and synthesis method combined with user daily electricity consumption curve |
| CN113033596A (en) * | 2020-12-30 | 2021-06-25 | 国网河南省电力公司南阳供电公司 | Refined identification method for user electricity consumption behavior category and typical electricity consumption mode |
-
2021
- 2021-08-27 CN CN202110994158.5A patent/CN113673168B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2012205432A (en) * | 2011-03-25 | 2012-10-22 | Tokyo Electric Power Co Inc:The | Load estimation method and load estimation system for transformer |
| CN103279803A (en) * | 2013-04-27 | 2013-09-04 | 深圳供电局有限公司 | Load modeling method and system based on comprehensive information theory and modern interior point theory |
| CN104200275A (en) * | 2014-06-24 | 2014-12-10 | 国家电网公司 | Power utilization mode classification and control method based on user behavior characteristics |
| CN105528660A (en) * | 2016-03-09 | 2016-04-27 | 湖南大学 | Substation load model parameter prediction method based on daily load curve |
| CN107977771A (en) * | 2017-11-07 | 2018-05-01 | 国家电网公司 | A kind of substation's characteristic analysis method based on multivariate clustering model Yu two-phase analyzing method correction algorithm |
| CN110601174A (en) * | 2019-07-06 | 2019-12-20 | 天津大学 | Load modeling and online correction method based on deep learning |
| CN111199016A (en) * | 2019-09-29 | 2020-05-26 | 国网湖南省电力有限公司 | DTW-based improved K-means daily load curve clustering method |
| CN111539657A (en) * | 2020-05-30 | 2020-08-14 | 国网湖南省电力有限公司 | Typical electricity consumption industry load characteristic classification and synthesis method combined with user daily electricity consumption curve |
| CN113033596A (en) * | 2020-12-30 | 2021-06-25 | 国网河南省电力公司南阳供电公司 | Refined identification method for user electricity consumption behavior category and typical electricity consumption mode |
Non-Patent Citations (1)
| Title |
|---|
| 徐振华;李欣然;钱军;陈辉华;宋军英;: "变电站用电行业负荷构成比例的在线修正方法", 电网技术, no. 07, pages 53 - 56 * |
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