CN115187042A - Probability voltage evaluation method, device, storage medium and system - Google Patents

Probability voltage evaluation method, device, storage medium and system Download PDF

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CN115187042A
CN115187042A CN202210784437.3A CN202210784437A CN115187042A CN 115187042 A CN115187042 A CN 115187042A CN 202210784437 A CN202210784437 A CN 202210784437A CN 115187042 A CN115187042 A CN 115187042A
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wind
preset
voltage
evaluation
probability
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彭穗
雷剧璋
欧仲曦
顾延勋
杨昆
傅明
左郑敏
余浩
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Guangdong Power Grid Co Ltd
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    • 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
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas 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
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/08—Probabilistic or stochastic CAD
    • 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
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy

Abstract

The invention discloses a probabilistic voltage evaluation method, a probabilistic voltage evaluation device, a storage medium and a probabilistic voltage evaluation system. The probability voltage evaluation device comprises a data acquisition unit, a model construction unit and a result output unit. By dividing the wind and light scenes, constructing a probability input model and a voltage stability evaluation model according to different wind and light scenes based on a mixed rattan structure, and calculating and outputting a probability voltage evaluation result through the voltage stability evaluation model according to the inherited Latin hypercube sampling method and the probability input model, the method, the device, the storage medium and the system improve the accuracy of voltage stability evaluation and the efficiency of analysis and evaluation.

Description

Probability voltage evaluation method, device, storage medium and system
Technical Field
The present invention relates to the field of probabilistic voltage evaluation technologies, and in particular, to a probabilistic voltage evaluation method, apparatus, computer readable storage medium, and system.
Background
New energy constructions such as wind power, photovoltaic and the like step into the motorway, and the permeability of new energy in the power grid is further increased. However, the influence of wind power and photovoltaic output on the voltage stability of the power system has complex correlation, and large-scale wind power and photovoltaic grid connection brings severe challenges to the voltage stability of the power grid. However, conventional deterministic voltage stabilization calculations have difficulty accounting for uncertainty in new energy output. In order to deeply reveal the influence of uncertainty of new energy output on Voltage Stability of a power system, the research on a Probabilistic Voltage Stability Evaluation (PVSE) algorithm is of great practical significance.
In the prior art, the wind-solar output correlation is usually described based on Kendall rank correlation coefficients and a Copula function, on the basis, in order to improve and consider the multidimensional correlation random variable probability modeling precision, the Copula function is connected by using C rattan and D rattan structures, and probability input models based on C rattan and D rattan Copula are respectively established.
However, the prior art still has the following defects: the problem that photovoltaic output is not completely the same under different scenes is not considered, but a certain Copula function or a single rattan structure is adopted to establish a probability input model considering relevant variables, so that model application is not matched with actual conditions, and voltage stability evaluation accuracy is not high.
Accordingly, there is a need for a probabilistic voltage evaluation method, apparatus, computer readable storage medium, and system that overcome the above-mentioned deficiencies in the prior art.
Disclosure of Invention
The embodiment of the invention provides a probabilistic voltage evaluation method, a probabilistic voltage evaluation device, a computer readable storage medium and a probabilistic voltage evaluation system, so that the accuracy of voltage stability evaluation is improved.
An embodiment of the present invention provides a probabilistic voltage evaluation method, including: acquiring a preset wind and light historical data set; establishing a probability input model and a voltage stability evaluation model according to the wind-solar historical data group, a preset clustering algorithm, a Copula function group, a preset mixed rattan structure, a preset index evaluation formula and a preset maximum load margin formula group; the mixed rattan structure comprises C rattans and D rattans; and calculating and outputting a probability voltage evaluation result through the voltage stability evaluation model according to the inheritance Latin hypercube sampling method and the probability input model.
As an improvement of the above scheme, establishing a probability input model and a voltage stability evaluation model according to the wind-solar historical data group, a preset clustering algorithm, a Copula function group, a preset mixed rattan structure, a preset index evaluation formula and a preset maximum load margin formula group, specifically including: establishing a probability input model according to the wind-solar historical data group, a preset clustering algorithm, a Copula function group, a preset mixed rattan structure and a preset index evaluation formula; and establishing a voltage stability evaluation model according to the wind and light historical data group and a preset maximum load margin formula group.
As an improvement of the above scheme, establishing a probability input model according to the wind-solar historical data group, a preset clustering algorithm, a Copula function group, a preset mixed rattan structure and a preset index evaluation formula, specifically includes: scene division is carried out on the wind and light historical data groups through a preset clustering algorithm, and a plurality of wind and light scenes and a first wind and light historical data group corresponding to each wind and light scene are obtained; the wind and light historical data set comprises all first wind and light historical data sets; fitting and calculating to obtain a standby Copula function group according to each first wind-solar historical data group and the Copula function group; calculating, evaluating and determining an optimal rattan structure corresponding to each wind and light scene according to a preset mixed rattan structure, the spare Copula function group and a preset index evaluation formula; and constructing a probability input model according to the optimal rattan structure and the wind-light scene.
As an improvement of the above scheme, calculating and evaluating an optimal rattan structure corresponding to each wind and light scene according to a preset mixed rattan structure, the spare Copula function set, and a preset index evaluation formula, specifically including: performing probability modeling respectively aiming at each wind and light scene according to a preset mixed rattan structure and the standby Copula function group to generate a simulation probability input model; and determining the optimal rattan structure corresponding to each wind and light scene according to the simulation probability input model and a preset index evaluation formula.
As an improvement of the above scheme, according to each first wind-solar historical data set and the Copula function set, fitting and calculating to obtain a spare Copula function set specifically includes: fitting each first wind-solar historical data group to obtain a first fitting function; calculating Euclidean distances between the first fitting function and each Copula function in the Copula function group; and selecting 3 Copula functions from the Copula function groups as standby Copula function groups according to the Euclidean distance.
As an improvement of the above scheme, according to the inherited latin hypercube sampling method and the probability input model, calculating and outputting a probability voltage evaluation result through the voltage stability evaluation model, specifically comprising: acquiring a sample point group according to the inheritance Latin hypercube sampling method, the probability input model and the uniform distribution; calculating the sample point group according to the voltage stability evaluation model to obtain a maximum load margin; counting the third moment information of the maximum load margin, and judging whether the maximum load margin is converged or not according to the third moment information; if not, repeating the steps; and if the maximum load margin is converged, outputting the maximum load margin as a probability voltage evaluation result.
As an improvement of the above scheme, the preset index evaluation formula specifically includes:
Figure BDA0003731388270000031
wherein d represents the dimension of a random variable, W (x) is a weight function, C n And C p The cumulative distribution function values of the empirical Copula function and the spare Copula function, respectively.
The probability voltage evaluation device comprises a data acquisition unit, a model construction unit and a result output unit, wherein the data acquisition unit is used for acquiring a preset wind and light historical data set; the model construction unit is used for establishing a probability input model and a voltage stability evaluation model according to the wind and light historical data group, a preset clustering algorithm, a Copula function group, a preset mixed rattan structure, a preset index evaluation formula and a preset maximum load margin formula group; the mixed rattan structure comprises C rattan and D rattan; and the result output unit is used for calculating and outputting a probability voltage evaluation result through the voltage stability evaluation model according to the inheritance Latin hypercube sampling method and the probability input model.
As an improvement of the above solution, the model construction unit is further configured to: establishing a probability input model according to the wind-solar historical data group, a preset clustering algorithm, a Copula function group, a preset mixed rattan structure and a preset index evaluation formula; and establishing a voltage stability evaluation model according to the wind and light historical data group and a preset maximum load margin formula group.
As an improvement of the above scheme, the model construction unit is further configured to perform scene division on the wind and light historical data groups through a preset clustering algorithm to obtain a plurality of wind and light scenes and a first wind and light historical data group corresponding to each wind and light scene; the wind and light historical data set comprises all first wind and light historical data sets; fitting and calculating to obtain a standby Copula function group according to each first wind-solar historical data group and the Copula function group; calculating, evaluating and determining an optimal rattan structure corresponding to each wind and light scene according to a preset mixed rattan structure, the spare Copula function group and a preset index evaluation formula; and constructing a probability input model according to the optimal rattan structure and the wind-light scene.
As an improvement of the above solution, the model construction unit is further configured to: performing probability modeling respectively aiming at each wind and light scene according to a preset mixed rattan structure and the standby Copula function group to generate a simulation probability input model; and determining the optimal rattan structure corresponding to each wind and light scene according to the simulation probability input model and a preset index evaluation formula.
As an improvement of the above solution, the model construction unit is further configured to fit each first wind-solar historical data set to obtain a first fitting function; calculating Euclidean distances between the first fitting function and each Copula function in the Copula function group; and selecting 3 Copula functions from the Copula function groups as standby Copula function groups according to the Euclidean distance.
As an improvement of the above, the result output unit is further configured to: acquiring a sample point group according to the inheritance Latin hypercube sampling method, the probability input model and the uniform distribution; calculating the sample point group according to the voltage stability evaluation model to obtain a maximum load margin; counting the third moment information of the maximum load margin, and judging whether the maximum load margin is converged or not according to the third moment information; if not, repeating the steps; and if the maximum load margin is converged, outputting the maximum load margin as a probability voltage evaluation result.
Another embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program runs, the apparatus on which the computer-readable storage medium is located is controlled to execute the probabilistic voltage evaluation method described above.
Another embodiment of the present invention provides a probabilistic voltage evaluation system, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the probabilistic voltage evaluation method is implemented as described above.
Compared with the prior art, the technical scheme has the following beneficial effects:
the invention provides a probability voltage evaluation method, a probability voltage evaluation device, a computer readable storage medium and a probability voltage evaluation system.
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FIG. 1 is a schematic flow chart illustrating a probabilistic voltage estimation method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a probabilistic voltage evaluation device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, 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 invention.
Detailed description of the preferred embodiment
The embodiment of the invention first describes a probabilistic voltage evaluation method. Fig. 1 is a schematic flow chart of a probabilistic voltage evaluation method according to an embodiment of the present invention.
As shown in fig. 1, the probabilistic voltage evaluation method includes:
s1, acquiring a preset wind and light historical data set.
The wind and light historical data set at least comprises active and reactive reference loads, active and reactive power reference quantities of a traditional generator, wind power, active and reactive power of photovoltaic power, multiplier coefficients of the load and the traditional generator, topological parameters of a power system and the like.
And S2, establishing a probability input model and a voltage stability evaluation model according to the wind and light historical data group, a preset clustering algorithm, a Copula function group, a preset mixed rattan structure, a preset index evaluation formula and a preset maximum load margin formula group.
Wherein the mixed rattan structure comprises C rattan and D rattan.
In the prior art, the difference between various wind and light scenes is not considered, so that the final inaccurate estimation result of the probability voltage stability can be caused. Therefore, different wind and light scenes are divided, so that a probability input model is constructed according to different relations between wind power and photovoltaic output in different wind and light scenes, and accuracy of probability voltage stability evaluation is improved. On this basis, since voltage stability evaluation (which can be generally divided into dynamic analysis and static analysis) is an important means for quantifying the voltage stability level of the power system, and the static voltage stability analysis has the advantages of simplicity, practicability and the like and is widely used in the actual operation and planning of the power system, the embodiment of the invention quantifies the voltage stability level of the system based on the static voltage stability analysis model.
In one embodiment, establishing a probability input model and a voltage stability evaluation model according to the wind and light historical data group, a preset clustering algorithm, a Copula function group, a preset mixed rattan structure, a preset index evaluation formula and a preset maximum load margin formula group specifically includes: establishing a probability input model according to the wind-solar historical data group, a preset clustering algorithm, a Copula function group, a preset mixed rattan structure and a preset index evaluation formula; and establishing a voltage stability evaluation model according to the wind and light historical data group and a preset maximum load margin formula group.
In one embodiment, the preset maximum load margin formula group is specifically:
Figure BDA0003731388270000071
wherein, P Li0 And Q Li0 Respectively representing active and reactive reference loads, P Gi0 And Q Gi0 Reference quantity, P, representing the active and reactive power of a conventional generator Ri And Q Ri Active and reactive power for wind power and photovoltaic output, K Gi And K Li Multiplier coefficients representing the load and the output of a conventional generator, respectively; v denotes c cluster center sets. The maximum value of the load margin parameter epsilon represents the maximum load margin of the system (by epsilon) nose Representation). When epsilon = epsilon nose At this time, the system is at the voltage collapse point (critical state). The maximum load margin formula set can be solved by a predictive correction method.
In one embodiment, establishing a probability input model according to the wind-solar historical data group, a preset clustering algorithm, a Copula function group, a preset mixed rattan structure and a preset index evaluation formula specifically includes: scene division is carried out on the wind and light historical data groups through a preset clustering algorithm, and a plurality of wind and light scenes and a first wind and light historical data group corresponding to each wind and light scene are obtained; the wind and light historical data set comprises all first wind and light historical data sets; fitting and calculating to obtain a standby Copula function group according to each first wind-solar historical data group and the Copula function group; calculating and evaluating to determine the optimal rattan structure corresponding to each wind and light scene according to a preset mixed rattan structure, the spare Copula function group and a preset index evaluation formula; and constructing a probability input model according to the optimal rattan structure and the wind-light scene.
After the optimal vine structure is determined, a probability input model based on mixed vine Copula is constructed based on the optimal vine structure of each scene and in combination with scene data proportion.
Wherein the preset clustering algorithm is Fuzzy C-means (FCM) clustering.
Specifically, assume that X = { X = ×) 1 ,x 2 ,...,x n The historical data of wind speed and illumination is used, and if the historical data is divided into c types, the following relations are required to be satisfied:
Figure BDA0003731388270000081
wherein u is ik Representing data x k Membership to class i.
Make the objective function J m And (U, V) minimizing, thereby acquiring the scene divided by the wind and light data. The objective function is expressed as:
Figure BDA0003731388270000082
wherein U = { U = ik Is a membership matrix; v represents c cluster center sets; x is a radical of a fluorine atom k The kth data to be classified; v. of i Representing the ith cluster center; m is a weighting index, m ∈ [1, + ∞).
Let m =2,d ik 2 (x k ,v i ) The calculation formula of (2) is as follows:
Figure BDA0003731388270000083
in one embodiment, the calculating, evaluating and determining an optimal rattan structure corresponding to each wind and light scene according to a preset mixed rattan structure, the spare Copula function set, and a preset index evaluation formula specifically includes: performing probability modeling respectively aiming at each wind and light scene according to a preset mixed rattan structure and the standby Copula function group to generate a simulation probability input model; and determining the optimal rattan structure corresponding to each wind and light scene according to the simulation probability input model and a preset index evaluation formula.
The method comprises the steps of performing probability modeling on each wind and light scene by using C rattan and D rattan structures respectively to generate sample points, and calculating the AD distance according to the sample points and an AD distance formula (described as a preset index evaluation formula), so that the optimal rattan structure is determined. Specifically, the smaller the AD distance index obtained by calculation, the higher the modeling accuracy of the rattan structure.
In one embodiment, the preset index evaluation formula is specifically:
Figure BDA0003731388270000091
wherein d represents a dimension of a random variable, W (x) is a weight function, C n And C p The cumulative distribution function values of the empirical Copula function and the spare Copula function, respectively.
In an embodiment, according to each first wind-solar historical data set and the Copula function set, fitting and calculating to obtain a standby Copula function set specifically includes: fitting each first wind-solar historical data group to obtain a first fitting function; calculating Euclidean distances between the first fitting functions and each Copula function in the Copula function group; and selecting 3 Copula functions from the Copula function groups as standby Copula function groups according to the Euclidean distance.
In the embodiment of the present invention, an empirical Copula function (described as a "first fitting function" herein) may be obtained by fitting input data using a Copula idf function in Matlab, and then, euclidean distances between each candidate Copula function and the empirical Copula function are calculated, and 3 Copula functions with the smallest euclidean distance value are selected as the spare Copula functions.
The Euclidean distance calculation method comprises the following steps:
Figure BDA0003731388270000092
wherein, C n And C p The cumulative distribution function values of the empirical Copula function and the spare Copula function, respectively.
And S3, calculating and outputting a probability voltage evaluation result through the voltage stability evaluation model according to the inherited Latin hypercube sampling method and the probability input model.
The prior art generally adopts a traditional latin hypercube sampling method, which can greatly improve the efficiency of probability analysis, but the sample points obtained by sampling by the traditional latin hypercube sampling method are highly structured, which makes it difficult to continuously add new sample points to the generated sample points. In contrast, in the embodiment of the invention, the inheritance Latin hypercube sampling method is adopted, a small number of sample points are generated firstly for PVSE calculation, then the number of the sample points is gradually increased according to the convergence condition and the calculation precision requirement until the PVSE calculation is converged, the decision dilemma in the process of determining the number of the sample points can be avoided by adopting the method, and the efficiency of probability analysis is greatly improved by continuously inheriting the sample points and the PVSE calculation results generated before. In addition, since a single deterministic voltage stabilization calculation in PVSE requires repeated iterations to converge, the calculation process is extremely time-consuming, and therefore the use of ILHS in PVSE can significantly increase the calculation speed.
To clearly describe the idea of ILHS algorithm to generate sample points, random variables u are uniformly distributed in 2 dimensions ma And u mb The description is given. Firstly, generating 3 sample points by utilizing a CLHS algorithm; subsequently, 3 new sample points continue to be generated on this basis (the newly added sample points retain the latin hypercube structure). The specific operation steps are as follows: 1) Will random variable u ma And u mb Dividing the space into 6 non-repeated intervals; 2) Finding out a probability information space which is not represented by the previous sample points; 3) New sample points are generated in the probability information space not represented by the sample points. In such a case, the new sample points and the inherited sample points are uniformly distributed over the random variable u ma And u mb In 6 equal intervals, probability distribution information can be characterized without repetition.
Based on the ILHS algorithm, the sample points and the corresponding calculation results can be repeatedly used in the PVSE, so that the calculation efficiency of the PVSE can be greatly improved. The execution steps for processing the multidimensional random variable based on the ILHS algorithm are as follows:
generating p groups of sample points by using a CLHS algorithm; u = [ U ] p,1 ,u p,2 ,…,u p,q ]And the input voltage is stableAnd (5) determining a calculation module.
The execution steps are as follows:
let C _ criterion =1;
let k =2, where k represents the number of iterations;
While C_criterion＝1；
dividing the uniform distribution into kp intervals;
finding out a probability information space which is not represented by the sample points;
generating a new sample point set U new ＝[u kp,1 ,u kp,2 ,…,u kp,q ]；
Transforming the new sample point set back to the original domain;
inputting a new sample point of an original domain into a voltage stabilizing module;
integrating the calculation results of the new sample point and the inherited sample point;
Figure BDA0003731388270000111
in one embodiment, according to the inherited latin hypercube sampling method and the probabilistic input model, the calculating and outputting the probabilistic voltage evaluation result through the voltage stability evaluation model specifically includes: acquiring a sample point group according to the inheritance Latin hypercube sampling method, the probability input model and the uniform distribution; calculating the sample point group according to the voltage stability evaluation model to obtain a maximum load margin; counting the third moment information of the maximum load margin, and judging whether the maximum load margin is converged or not according to the third moment information; if not, repeating the steps; and if the maximum load margin is converged, outputting the maximum load margin as a probability voltage evaluation result.
The embodiment of the invention describes a probability voltage evaluation method, wherein wind and light scenes are divided, a probability input model and a voltage stability evaluation model are built according to different wind and light scenes based on a mixed rattan structure, and a probability voltage evaluation result is calculated and output through the voltage stability evaluation model according to a successive Latin hypercube sampling method and the probability input model.
Detailed description of the invention
Besides the method, the embodiment of the invention also discloses a probability voltage evaluation device. Fig. 2 is a schematic structural diagram of a probabilistic voltage evaluation device according to an embodiment of the present invention.
As shown in fig. 2, the probabilistic voltage estimating apparatus includes a data obtaining unit, a model constructing unit, and a result outputting unit.
The data acquiring unit 11 is configured to acquire a preset wind and light history data set.
The model construction unit 12 is configured to establish a probabilistic input model and a voltage stability evaluation model according to the wind-solar historical data set, a preset clustering algorithm, a Copula function set, a preset mixed rattan structure, a preset index evaluation formula, and a preset maximum load margin formula set. The mixed rattan structure comprises C rattan and D rattan.
And the result output unit 13 is used for calculating and outputting a probability voltage evaluation result through the voltage stability evaluation model according to the inheritance Latin hypercube sampling method and the probability input model.
In one embodiment, the model building unit 12 is further configured to: establishing a probability input model according to the wind-solar historical data group, a preset clustering algorithm, a Copula function group, a preset mixed rattan structure and a preset index evaluation formula; and establishing a voltage stability evaluation model according to the wind and light historical data group and a preset maximum load margin formula group.
In one embodiment, the model building unit 12 is further configured to perform scene division on the wind and light historical data sets through a preset clustering algorithm to obtain a plurality of wind and light scenes and a first wind and light historical data set corresponding to each wind and light scene; the wind and light historical data set comprises all first wind and light historical data sets; fitting and calculating to obtain a standby Copula function group according to each first wind-solar historical data group and the Copula function group; calculating, evaluating and determining an optimal rattan structure corresponding to each wind and light scene according to a preset mixed rattan structure, the spare Copula function group and a preset index evaluation formula; and constructing a probability input model according to the optimal rattan structure and the wind-light scene.
In one embodiment, the model building unit 12 is further configured to: performing probability modeling respectively aiming at each wind and light scene according to a preset mixed rattan structure and the standby Copula function group to generate a simulation probability input model; and determining the optimal rattan structure corresponding to each wind and light scene according to the simulation probability input model and a preset index evaluation formula.
In one embodiment, the model building unit 12 is further configured to fit each first wind-solar historical data set to obtain a first fitting function; calculating Euclidean distances between the first fitting function and each Copula function in the Copula function group; and selecting 3 Copula functions from the Copula function groups as standby Copula function groups according to the Euclidean distance.
In one embodiment, the result output unit 13 is further configured to: acquiring a sample point group according to the inheritance Latin hypercube sampling method, the probability input model and the uniform distribution; calculating the sample point group according to the voltage stability evaluation model to obtain a maximum load margin; counting the third moment information of the maximum load margin, and judging whether the maximum load margin is converged or not according to the third moment information; if not, repeating the steps; and if the maximum load margin is converged, outputting the maximum load margin as a probability voltage evaluation result.
Wherein, the unit integrated with the probability voltage evaluation device can be stored in a computer readable storage medium if the unit is realized in the form of a software functional unit and sold or used as an independent product. Based on such understanding, all or part of the flow in the method according to the above embodiments may be implemented by a computer program, which may be stored in a computer readable storage medium and used by a processor to implement the steps of the above embodiments of the method. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
It should be noted that the above-described device embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiment of the device provided by the invention, the connection relationship between the units indicates that the units have communication connection therebetween, and the connection relationship can be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement it without inventive effort.
The embodiment of the invention discloses a probability voltage evaluation device and a computer readable storage medium, wherein a wind and light scene is divided, a probability input model and a voltage stability evaluation model are constructed according to different wind and light scenes on the basis of a mixed rattan structure, and a probability voltage evaluation result is calculated and output through the voltage stability evaluation model according to an inherited Latin hypercube sampling method and the probability input model.
Detailed description of the preferred embodiment
In addition to the above method and apparatus, a probabilistic voltage evaluation system is also described in the embodiments of the present invention.
The probabilistic voltage evaluation system comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the probabilistic voltage evaluation method when executing the computer program.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is the control center for the device, with various interfaces and lines connecting the various parts of the overall device.
The memory may be used to store the computer programs and/or modules, and the processor may implement the various functions of the apparatus by executing or executing the computer programs and/or modules stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The embodiment of the invention discloses a probability voltage evaluation system, which is characterized in that wind and light scenes are divided, a probability input model and a voltage stability evaluation model are built according to different wind and light scenes based on a mixed rattan structure, and a probability voltage evaluation result is calculated and output through the voltage stability evaluation model according to a successive Latin hypercube sampling method and the probability input model, so that the accuracy of voltage stability evaluation and the efficiency of analysis and evaluation are improved.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.

Claims (10)

1. A probabilistic voltage evaluation method, the probabilistic voltage evaluation method comprising:
acquiring a preset wind and light historical data set;
establishing a probability input model and a voltage stability evaluation model according to the wind-solar historical data group, a preset clustering algorithm, a Copula function group, a preset mixed rattan structure, a preset index evaluation formula and a preset maximum load margin formula group; the mixed rattan structure comprises C rattan and D rattan;
and calculating and outputting a probability voltage evaluation result through the voltage stability evaluation model according to the inheritance Latin hypercube sampling method and the probability input model.
2. The probabilistic voltage evaluation method according to claim 1, wherein the calculating and outputting the probabilistic voltage evaluation result according to the inherited latin hypercube sampling method and the probabilistic input model through the voltage stability evaluation model specifically includes:
acquiring a sample point group according to the inheritance Latin hypercube sampling method, the probability input model and the uniform distribution;
calculating the sample point group according to the voltage stability evaluation model to obtain a maximum load margin;
counting the third moment information of the maximum load margin, and judging whether the maximum load margin is converged or not according to the third moment information;
if not, repeating the steps;
and if the maximum load margin is converged, outputting the maximum load margin as a probability voltage evaluation result.
3. The probabilistic voltage evaluation method according to claim 1, wherein the establishing a probabilistic input model and a voltage stability evaluation model according to the wind-solar historical data set, a preset clustering algorithm, a Copula function set, a preset mixed rattan structure, a preset index evaluation formula and a preset maximum load margin formula set specifically includes:
establishing a probability input model according to the wind-solar historical data group, a preset clustering algorithm, a Copula function group, a preset mixed rattan structure and a preset index evaluation formula;
and establishing a voltage stability evaluation model according to the wind and light historical data group and a preset maximum load margin formula group.
4. The probabilistic voltage evaluation method according to claim 3, wherein the establishing of the probabilistic input model according to the wind-solar historical data set, a preset clustering algorithm, a Copula function set, a preset mixed rattan structure and a preset index evaluation formula specifically includes:
scene division is carried out on the wind and light historical data groups through a preset clustering algorithm, and a plurality of wind and light scenes and a first wind and light historical data group corresponding to each wind and light scene are obtained; the wind and light historical data set comprises all first wind and light historical data sets;
fitting and calculating to obtain a standby Copula function group according to each first wind-solar historical data group and the Copula function group;
calculating and evaluating to determine the optimal rattan structure corresponding to each wind and light scene according to a preset mixed rattan structure, the spare Copula function group and a preset index evaluation formula;
and constructing a probability input model according to the optimal rattan structure and the wind-light scene.
5. The probabilistic voltage evaluation method according to claim 4, wherein the calculating and evaluating to determine the optimal rattan structure corresponding to each wind and light scene according to a preset mixed rattan structure, the spare Copula function set, and a preset index evaluation formula specifically includes:
performing probability modeling respectively aiming at each wind and light scene according to a preset mixed rattan structure and the standby Copula function group to generate a simulation probability input model;
and determining the optimal rattan structure corresponding to each wind and light scene according to the simulation probability input model and a preset index evaluation formula.
6. The probabilistic voltage evaluation method of claim 4, wherein the fitting calculation to obtain the spare Copula function set according to each first wind-solar historical data set and the Copula function set specifically comprises:
fitting each first wind-solar historical data group to obtain a first fitting function;
calculating Euclidean distances between the first fitting function and each Copula function in the Copula function group;
and selecting 3 Copula functions from the Copula function groups as standby Copula function groups according to the Euclidean distance.
7. The probabilistic voltage evaluation method of any one of claims 1 to 6, wherein the predetermined index evaluation formula is specifically:
Figure FDA0003731388260000031
wherein d represents a dimension of a random variable, W (x) is a weight function, C n And C p The cumulative distribution function values of the empirical Copula function and the spare Copula function, respectively.
8. A probabilistic voltage evaluation device comprising a data acquisition unit, a model construction unit, and a result output unit, wherein,
the data acquisition unit is used for acquiring a preset wind and light historical data set;
the model construction unit is used for establishing a probability input model and a voltage stability evaluation model according to the wind and light historical data group, a preset clustering algorithm, a Copula function group, a preset mixed rattan structure, a preset index evaluation formula and a preset maximum load margin formula group; the mixed rattan structure comprises C rattan and D rattan;
and the result output unit is used for calculating and outputting a probability voltage evaluation result through the voltage stability evaluation model according to the inheritance Latin hypercube sampling method and the probability input model.
9. A computer-readable storage medium, comprising a stored computer program, wherein the computer program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the probabilistic voltage evaluation method of any of claims 1 to 7.
10. A probabilistic voltage evaluation system comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor when executing the computer program implementing the probabilistic voltage evaluation method as set forth above.
CN202210784437.3A 2022-07-05 2022-07-05 Probability voltage evaluation method, device, storage medium and system Pending CN115187042A (en)

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