CN109741091B - User load classification method based on basic load reduction strategy - Google Patents
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- 238000007621 cluster analysis Methods 0.000 description 1
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Abstract
The invention discloses a user load classification method based on a basic load reduction strategy, which comprises the following steps: step 1, extracting user load data and preprocessing the load data; step 2, selecting a load reduction reference value, taking the polymerization degree as an evaluation parameter, and distinguishing the load types by adopting a method of reducing a reference load value; and 3, clustering the load data by adopting a fuzzy C-means algorithm according to the load type distinguishing result. Aiming at the problem that the user type is identified by mistake due to the user scale in the current load clustering method, the invention provides an improved load clustering algorithm processing skill, and the accuracy of the algorithm provided by the invention is verified by using a fuzzy algorithm. The method adopted by the invention can effectively eliminate the interference of the user scale on the user type.
Description
Technical Field
The invention belongs to the field of load classification of power systems, and particularly relates to a user load classification method based on a basic load reduction strategy.
Background
Load clustering is to integrate a large number of users into a single different aggregate through a certain mathematical means. Aiming at the condition of real-time operation of a power grid, different types of polymers are reasonably guided to orderly use electricity, and huge economic benefits can be generated. The existing clustering method generally clusters the trend and the numerical value of the load. However, due to the fact that the scales and the number of users are different in different regions, users of the same type or the same power utilization rule cannot be identified, and therefore the classification result is not fine enough. Aiming at the problems, the invention provides a clustering method based on load reduction, and the clustering result is verified through a fuzzy algorithm.
In the prior art, there are also methods for realizing load classification by other methods. For example, chinese patent application No. 201810382946.7 discloses a load classification method based on fuzzy C-clustering of decision trees, which first determines the optimal classification number through an agglomeration-type hierarchical clustering algorithm, and then clusters load data by using a fuzzy C-means clustering algorithm, thereby implementing load classification.
Disclosure of Invention
The invention aims to provide a user load classification method based on a basic load reduction strategy aiming at the problems in the prior art so as to solve the problem that the user type is wrongly identified due to the user scale in the current load clustering method.
In order to achieve the purpose, the invention adopts the technical scheme that:
the user load classification method based on the basic load reduction strategy comprises the following steps:
and 3, clustering the load data by adopting a fuzzy C-means algorithm according to the load type distinguishing result.
Preferably, the pretreatment step in step 1 comprises: the deviation ratio eta is defined and,
wherein, y i The load data representing a certain point is shown,
representing the average of the load data over a period of time.
Preferably, when eta is more than 500%, whether the data is a bad data point needs to be checked, and if most load points of the current load curve exceed the standard, the data is determined to be normal measurement data; if only the individual points exceed the standard, the data points are determined to be bad data points.
Preferably, the specific process of step 2 is:
2.1, selecting a load reduction reference value;
2.2, data standardization;
2.3, calculating a data gradient;
and 2.4, judging whether the polymerization degree meets the requirement, if so, outputting the distinguished load type, otherwise, reselecting the load reduction reference value, and turning to 2.2.
Preferably, step 3 further comprises:
3.1, determining the classification number, the power index and the membership matrix;
3.2, calculating the clustering centrality;
3.3, modifying the membership function and the target function;
and 3.4, stopping iteration when the membership function meets the termination limit or the maximum step length, and otherwise, turning to 3.2.
Preferably, for the membership function, a termination limit ε is given J >0 or define the maximum step size l when satisfied
And stopping iteration when the maximum step length is met.
Preferably, the iterative formula of the membership degree and the cluster centrality is as follows:
I j ＝{1≤i≤c,d ij ＝0}
when the temperature is higher than the set temperature
When the temperature of the water is higher than the set temperature,
when in use
When the temperature of the water is higher than the set temperature,
when the temperature of the water is higher than the set temperature,
preferably, step 3 further comprises: let sample be taken from p-ary sample population, sample X 1 ,X 2 ,L X n The total is divided into c types, wherein the value of c is not less than 2, v i Denotes the ith cluster center, and sets the variable u ij Representing the degree of membership of the ith sample to the jth sample, the objective function is set as follows:
constraint conditions are as follows:
u ij ∈[0,1]1≤j≤n,1≤i≤c
calculating an optimal solution according to a Lagrange multiplier method:
and optimizing in MATLAB according to given constraint conditions.
Compared with the prior art, the invention has the beneficial effects that: aiming at the problem that the user type is identified by mistake due to the user scale in the current load clustering method, the invention provides an improved load clustering algorithm processing skill, and the accuracy of the algorithm provided by the invention is verified by using a fuzzy algorithm; the algorithm provided by the invention has a better clustering effect, and can perform more effective clustering on the load according to the actual requirements of projects or companies.
Drawings
FIG. 1 is a schematic flow chart of a user load classification method based on a basic load shedding policy according to the present invention;
FIGS. 2(a) - (e) are schematic diagrams illustrating conventional fuzzy clustering results;
FIGS. 3(a) - (c) are schematic diagrams of clustering results obtained by the method of the present invention;
fig. 4 is a diagram illustrating the relationship between the cluster center and the contour value when c is 3;
fig. 5 is a diagram illustrating the relationship between the cluster center and the contour value when c is 5;
in the figure: q-load; t-time; t-cluster center; s (i) -contour values.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it is to be understood 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.
As shown in fig. 1, the present invention provides a user load classification method based on a basic load reduction strategy, comprising the following steps:
and 2, selecting a load reduction reference value, taking the polymerization degree as an evaluation parameter, and distinguishing the load types by adopting a method of reducing the reference load value. In order to eliminate the loads of the same type, the clustering results are different due to the difference of the area scale and the number of users, the invention adopts a method of reducing the reference load value, amplifies the difference of the load curve, and enlarges the gradient of the load curve, thereby distinguishing the load types.
The specific process of the step 2 is as follows:
2.1, selecting a load reduction reference value;
2.2, data standardization;
2.3, calculating a data gradient;
and 2.4, judging whether the polymerization degree meets the requirement, if so, outputting the distinguished load type, otherwise, reselecting the load reduction reference value, and turning to 2.2.
And 3, clustering the load data by adopting a fuzzy C-means algorithm according to the load type distinguishing result. The invention carries out direct clustering on load curves by combining the electricity utilization habits and the electricity utilization characteristics of users by means of the theory of a fuzzy set proposed by L.A.Zadeh (the 60 th of the 20 th century) and the fuzzy clustering algorithm proposed by Ruspine and Bezdek.
3.1, determining the classification number, the power index and the membership matrix;
3.2, calculating the clustering centrality;
3.3, modifying the membership function and the target function;
and 3.4, stopping iteration when the membership function meets the termination limit or the maximum step length, and otherwise, turning to 3.2.
After the above steps, the elements and the cluster centers in the final membership function matrix can be determined, so that the value of the objective function is minimized.
The method has important practical significance in preprocessing the load data at the beginning of algorithm starting, and eliminating the influence of the area scale and the number of users on the clustering result. And clustering is performed according to the characteristics of different types of users, so that the effect is better.
Examples
The data of the invention adopts the data of the American PJM power market, selects 24-hour load data, and performs example analysis demonstration on the load data. In the embodiment, load data of different areas of the American PJM power market are selected, and 113 groups of load data of different areas on a uniform working day are selected for cluster analysis in order to eliminate interference of other factors.
(1) The invention firstly preprocesses the data, eliminates the data with severe deviation and prevents the influence of the extreme number on the classification. Defining the deviation ratio η:
in the formula y i The load data representing a certain point is shown,
representing the average of the load data over 24 h.
When eta is more than 500%, checking whether the data is a bad data point, and if most load points of the load curve exceed the standard, determining the data as normal measurement data; if only the individual point exceeds the standard, the data point is determined to be bad.
(2) The reference load reduction value is selected.
In order to eliminate the loads of the same type, the clustering results are different due to the difference of the area scale and the number of users, the invention adopts a method of reducing the reference load value, amplifies the difference of the load curve, and enlarges the gradient of the load curve, thereby distinguishing the load types.
(3) Fuzzy algorithm
The embodiment combines the electricity utilization habits and the electricity utilization characteristics of users by means of the theory of fuzzy sets proposed by L.A.Zadeh (20 th century 60 years) and the fuzzy clustering algorithm proposed by Ruspine and Bezdek to directly cluster the load curves. Let samples be taken from p-gram sample population. Sample X 1 ,X 2 ,L X n The population is designated as class c, where c has a value of not less than 2. v. of i Representing the ith cluster center. Set variable u ij Representing the degree of membership of the ith sample to the jth sample.
The objective function is as follows:
constraint conditions are as follows:
u ij ∈[0,1]1≤j≤n,1≤i≤c
calculating an optimal solution according to a Lagrange multiplier method:
and optimizing in MATLAB according to the constraint conditions given by the system.
Further, according to the lagrange multiplier method, an optimal solution of J (u, c) can be obtained. The iterative formulas of membership and cluster centrality are as follows:
I j ＝{1≤i≤c,d ij ＝0}
when in use
When the temperature of the water is higher than the set temperature,
when in use
When the temperature of the water is higher than the set temperature,
when the temperature of the water is higher than the set temperature,
the fuzzy algorithm comprises the following steps:
1) determining the number of classes c and the power exponent m>1 and membership matrix
The simultaneous treatment is to remove [0, 1]]The random number above serves as matrix initial data. And l is 1 as step 1 iteration.
2) And calculating the clustering centrality according to the formula.
3) Modifying membership function U (l) And an objective function J (l) 。
Wherein,
4) for a given membership function termination limit ε J >0 or define a maximum step size l. When it is satisfied with
And stopping iteration when the maximum step length is met. Otherwise, jumping to the step 2).
After the above steps, the elements in the final membership function matrix U and the clustering center V may be determined so that the value of J (U, V) of the objective function is minimized.
Aiming at the problem that the user type is identified by mistake due to the user scale in the current load clustering method, the invention provides an improved load clustering algorithm processing skill, and the accuracy of the algorithm provided by the invention is verified by using a fuzzy algorithm.
The conventional load clustering algorithm is compared with the improved load clustering algorithm of the present invention, as shown in the accompanying drawings. Fig. 2(a) - (e) are schematic diagrams of conventional fuzzy clustering results. FIGS. 3(a) - (c) are schematic diagrams of clustering results obtained by the method of the present invention. Compared with the clustering effect of the traditional method, the method provided by the invention can effectively eliminate the interference of the user scale on the user type.
For the conventional method, according to the fuzzy algorithm result, the load can be roughly classified into 5 classes, and the following distinction is made among the classes:
(1) the time points at which the load peaks and valleys occur differ. The second and fifth types of loads are compared to the fourth type of load and the peaks occur at 9:00 and 8:00 respectively, with significant time differences.
(2) The load curves are not uniform. The second, fourth and fifth types of loads exhibit double peaks, while the first and third types are always in a high load state from 9:00 to 21: 00.
(3) There is a difference in the number of peaks and valleys. The second kind load and the fourth kind peak value are 4 x 10 4 MW, valley value slightly less than 3X 10 4 MW. Compared with the first three-five types of loads, the peak-to-valley value is reduced by 5 multiplied by 10 3 MW is about.
In the traditional method, because the algorithm has the problem of unclear boundaries, the cross phenomenon among categories is serious. For the power system, the distinguishing point (3) is not beneficial to load clustering. Because some regions are not distinguished separately when the peak-to-valley values are inconsistent due to the size of the region.
The invention adopts a load reduction method, increases the load change gradient and can solve the problem of false identification caused by different user scales. The results are shown in FIGS. 3(a) - (c).
Comparing fig. 2 and fig. 3, it can be seen that when the classification result is changed into 3 classes, the regularity of the curve is obviously enhanced, and the weight according to the change trend in the clustering basis is increased, which is beneficial to identifying the electricity utilization law of the user. According to fig. 3, the load can be clearly classified into a steady type load, a bimodal type load, and a unimodal type load. And a good experimental basis is provided for better making a strategy of demand side response for users who do not use regions and electricity habits.
The invention uses the contour value as an index of the algorithm.
In particular, a comparative analysis is carried out using the contour values S (i),
wherein a represents the normalized distance between the ith point and the homogeneous point; b represents the normalized distance from the different classification points. The value range of the contour value S (i) is [ -1,1], and the larger the value of S (i) is, the more reasonable the classification is. When S (i) < 0, it indicates that the point is not reasonable to classify, and there is a more reasonable classification method.
As shown in the drawing, fig. 4 is a schematic diagram of the relationship between the cluster center and the contour value when c is 3. Fig. 5 is a diagram illustrating the relationship between the cluster center and the contour value when c is 5.
According to the processing skill provided by the invention, after the load value is reduced by a certain reference value, the change gradient between the loads is enlarged, and the load is divided into clustering standards again. As can be seen from FIG. 4, when the clustering criterion is classified into 5 classes, the membership degree of the sample in the class 1 and the class 2 shows a negative value, which indicates that the sample has a low membership degree to the clustering center, and a better clustering mode exists. As can be seen from fig. 5, when the samples are classified into 3 classes, the sample contour values are all positive, and for most samples, the membership degree is higher, which is a more reasonable classification method. And the classification mode avoids the phenomenon of misclassification caused by the scale of the power load data.
Compared with the traditional method, the clustering effect is seen, and when the classification number is reduced from 5 classes to 3 classes, the comparison of fig. 4 and 5 shows that the classification result of the algorithm is more accurate.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (4)
1. The user load classification method based on the basic load reduction strategy is characterized by comprising the following steps:
step 1, extracting user load data and preprocessing the load data;
the pretreatment step comprises the following steps: defining deviation ratio
，
Wherein,
the load data of a certain point is represented,
represents an average value of the load data over a period of time;
when in use
In the process, whether the data are bad data points or not needs to be checked, if most load points of the current load curve exceed the standard, the data are determined to be normal measurement data; if only the individual point exceeds the standard, determining the data point as a bad data point;
step 2, selecting a load reduction reference value, taking the polymerization degree as an evaluation parameter, and distinguishing the load types by adopting a method of reducing a reference load value; the specific process is as follows:
2.1, selecting a load reduction reference value;
2.2, data standardization;
2.3, calculating a data gradient;
2.4, judging whether the polymerization degree meets the requirement, if so, outputting the distinguished load type, and if not, reselecting a load reduction reference value and turning to 2.2;
step 3, clustering the load data by adopting a fuzzy C-means algorithm according to the load type distinguishing result; further comprising:
3.1, determining the classification number, the power index and the membership matrix;
3.2, calculating the clustering centrality;
3.3, modifying the membership function and the target function;
and 3.4, stopping iteration when the membership function meets the termination limit or the maximum step length, and otherwise, turning to 3.2.
2. The method of claim 1, wherein a termination limit is given for the membership function
Or define a maximum step size
When it is satisfied
And stopping iteration when the maximum step length is met.
3. The method for classifying user loads based on the basic load shedding strategy according to claim 1, wherein the iterative formulas of the membership degree and the clustering center degree are as follows:
when in use
When the temperature of the water is higher than the set temperature,
when in use
When the temperature of the water is higher than the set temperature,
when the temperature of the water is higher than the set temperature,
4. the method of claim 1 wherein step 3 further comprises the step of classifying the user load based on the basic load shedding strategyThe method comprises the following steps: provided that the sample is taken from
Meta-sample population, sample
Is divided into
Class I, wherein
The value of (a) is not less than 2,
is shown as
Individual cluster center, set variable
Is shown as
And the membership degree of each sample to the jth sample, setting an objective function as follows:
constraint conditions are as follows:
calculating an optimal solution according to a Lagrange multiplier method:
and optimizing in MATLAB according to given constraint conditions.
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