CN110676855A - Intelligent optimization and adjustment method for reactive voltage control parameters of power distribution network - Google Patents
Intelligent optimization and adjustment method for reactive voltage control parameters of power distribution network Download PDFInfo
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- 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
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/16—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by adjustment of reactive power
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- 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
- H02J3/18—Arrangements for adjusting, eliminating or compensating reactive power in networks
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Abstract
The invention relates to the technical field of optimization and adjustment of reactive voltage control parameters of a power distribution network, in particular to an intelligent optimization and adjustment method of reactive voltage control parameters of the power distribution network. According to the invention, research and analysis can be carried out on the three-level coordination control parameters of the reactive voltage of the power distribution network, and the target parameters of the three-level coordination control of the power distribution network are intelligently optimized and adjusted, so that the reactive voltage level of the whole power distribution network is improved.
Description
Technical Field
The invention relates to the technical field of optimization and adjustment of reactive voltage control parameters of a power distribution network, in particular to an intelligent optimization and adjustment method of the reactive voltage control parameters of the power distribution network.
Background
Aiming at inaccurate data measurement of the power distribution network, some students propose that a time sequence method and a RBF neural network are adopted to identify and correct data, but the students do not optimally learn the measured data of the power distribution network and give more meaningful guidance; the scholars also adopt a gray GM (1,1) model algorithm to correct bad data of the distribution network load system, but do not study the corrected load data of the distribution network area and the parameter optimization target of reactive voltage; the problem is solved by adopting an improved data elimination algorithm when bad data exist in a large amount of data by a scholars, and the scholars do not perform application analysis on an actual power distribution network.
Disclosure of Invention
The invention aims to overcome the defect that the existing method cannot carry out intelligent optimization on the reactive voltage control parameters of the power distribution network, and provides an intelligent optimization and adjustment method for the reactive voltage control parameters of the power distribution network.
In order to solve the technical problems, the invention adopts the technical scheme that:
the method for intelligently optimizing and adjusting the reactive voltage control parameters of the power distribution network comprises the following steps:
s1, collecting reactive voltage control parameters of a power distribution network, and preprocessing bad parameters in the reactive voltage control parameters;
s2, taking the power factor of the three-level coordination control of the power grid as a target parameter, and then carrying out optimization adjustment on the target parameter based on the RBF neural network;
and S3, transmitting the optimized and adjusted parameters to the three-level voltage coordination controller of the power distribution network in real time.
The invention relates to an intelligent optimization and adjustment method for reactive voltage control parameters of a power distribution network, which can be used for researching and analyzing the reactive voltage three-level coordination control parameters of the power distribution network and intelligently optimizing and adjusting target parameters of the three-level coordination control of the power distribution network so as to improve the reactive voltage level of the whole power distribution network.
Further, the specific steps of step S1 are as follows:
s11, removing obviously abnormal parameters, and performing subset regression on the remaining parameters in the abnormal parameter range;
and S12, repairing the missing parameters by utilizing a Lagrange difference algorithm.
Further, in step S11, the following linear regression model is utilized:
wherein M + N ═ K, MIN ═ phi, MYN ═ 1,2, Λ, K };
remember Y ═ Y1,Y2,,ΛYK)T，X＝(X1,X2,,ΛXK)T，H＝(hii)K×K＝X(XTX)-1XT，δ＝(W1,W2,Λ,WK)T＝(I-H)Y，
Wherein, { eiI ∈ M } independently obeys N (0, e)2) Set of random variables, { ZjJ ∈ N } is independently obeyed to N (0, be)2) And M, N, two unknown index sets are represented, M represents the number of indexes in the unknown index set M, and N represents the number of indexes in the unknown index set N.
Further, in step S11, the following stopping rule is used to prevent the normal parameters from being culled:
E||W||2＝(K-L)e2，
Yie-2～i2(1)，
when the probability is 0.995, there are
For the linear regression model, take
When in use
Consider YjIs an abnormal parameter;
get
When Y isi<T||W||2Is considered to be YiIs a normal parameter.
Further, in step S11, the number of remaining parameters is K2＝K-t1-m1The number of remaining abnormal parameters is n1＝K·10％-t1(ii) a Wherein the content of the first and second substances,1and m1Respectively representing the number of abnormal parameters and normal parameters, K representing the total number of the parameters, K2Indicates the number of remaining parameters, n1Indicating the number of remaining abnormal parameters, and the number K of remaining parameters2By passing
Sum of squares of residualsFinding an anomalous dataset J, wherein:
further, the specific step of step S12 is:
s121. suppose X ═ X0,x1,x2,Λ,xnIs the set of interpolated samples, Y ═ Y0,y1,y2,ΛynThe values of the corresponding X function are used, n represents the number of nodes, and X represents the node of interpolation;
s122, inputting n +1 interpolation sample points x0,x1,x2,Λ,xnAnd the corresponding function value y0,y1,y2,Λ,yn；
S123, calculating an n-order Lagrange basis function:
wherein i is 0,1,2,. n;
s124, calculating an n-order Lagrange interpolation function:
in the formula Ii(x) Representing an nth order Lagrangian basis function;
and S125, inputting an interpolation point x and substituting the interpolation point x into the interpolation function in the step S124 to obtain a calculation result.
Further, the specific step of step S2 is:
s21, carrying out self-adaptive adjustment on the corrected parameters based on an RBF neural network algorithm;
and S22, optimizing and adjusting the three-level coordination control target parameters of the power distribution network aiming at the fact that the three-level coordination control target parameters of the power distribution network mainly comprise voltage and power factors of all levels.
Further, the specific step of step S21 is:
s211, selecting a Gaussian function
i is 1,2, Λ, k as a basis function,
wherein c represents the center of the basis function and σ represents the width of the radial basis function;
s212, acquiring a basis function center c, wherein the constraint conditions are as follows:
di＝min||xp-ci||,(p＝1,2,Λ,P,i＝1,2,Λ,k)，
in the formula, xp(P ═ 1,2, Λ, P) represents training data, ciRepresents the starting center;
s213, obtaining the width sigma of the radial basis function, wherein the calculation formula of the width sigma is as follows:
σi＝bdi,i＝1,2,Λ,k，
di＝min||ci-cg||，
in the formula, ci,cjEach representing the center of a certain category, b representing the overlap factor, diRepresenting a constraint;
s214, obtaining a weight W, wherein the calculation formula of the weight W is as follows:
Wj＝(HTH)-1HTTj,j＝1,2,Λ,m，
wherein H ═ H1,h2,Λ,hk]Representing intermediate layer output vector, h representing radial basis function, k representing intermediate layer number, m representing output layer number, T representing network output target quantity, TjThe j-th exit layer unit is shown.
Further, the specific step of step S22 is:
s221, setting a target voltage and a target power factor;
s222, establishing an RBF neural network with a plurality of input vectors and a plurality of voltage and power factor output vectors of the three-level network of the power distribution network;
s223, constructing a training sample, carrying out normalization processing on the training sample, and then training the RBF neural network through the processed data;
s224, inputting the input vector into the RBF neural network which completes training in the step S222 to obtain a group of corresponding output values, and comparing the output values with the target voltage and the target power factor in the step S221;
s225, evaluating the accuracy of the corrected output result by using the relative error, and evaluating the correction capability of the RBF neural network; the evaluation calculation formula is as follows:
wherein Z represents an actual numerical value, ZfThe corrected value is indicated.
Further, in step S222, the input vectors include distribution network voltages, currents, phase angles, active powers, and reactive powers of different voltage classes.
Compared with the prior art, the invention has the beneficial effects that:
the invention relates to an intelligent optimization and adjustment method for reactive voltage control parameters of a power distribution network, which can reduce the action times of reactive voltage control equipment by preprocessing bad parameters, intelligently optimizing and adjusting power factors of three-level coordination control of the power distribution network based on a RBF neural network and transmitting an optimized target value to a three-level voltage coordination controller of the power distribution network in real time so as to improve the reactive voltage level of the whole power distribution network.
Drawings
Fig. 1 is a flow chart of an intelligent optimization and adjustment method for reactive voltage control parameters of a power distribution network according to the invention.
Detailed Description
The present invention will be further described with reference to the following embodiments. Wherein the showings are for the purpose of illustration only and are shown by way of illustration only and not in actual form, and are not to be construed as limiting the present patent; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by the terms "upper", "lower", "left", "right", etc. based on the orientation or positional relationship shown in the drawings, it is only for convenience of describing the present invention and simplifying the description, but it is not intended to indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and are not to be construed as limiting the present patent, and the specific meaning of the terms may be understood by those skilled in the art according to specific circumstances.
Example 1
Fig. 1 shows a first embodiment of an intelligent optimization and adjustment method for reactive voltage control parameters of a power distribution network, which includes the following steps:
s1, collecting reactive voltage control parameters of a power distribution network, and preprocessing bad parameters in the reactive voltage control parameters;
s2, taking the power factor of the three-level coordination control power box of the power grid as a target parameter, and then carrying out optimization adjustment on the target parameter based on the RBF neural network;
and S3, transmitting the optimized and adjusted parameters to the three-level voltage coordination controller of the power distribution network in real time.
The preprocessing of the bad parameters is to correct or eliminate the problems of abnormity and errors of the acquired parameters in the power distribution system. The reactive voltage analysis of the historical power distribution data is usually parameters within one year, and the parameter quantity is huge, so that obviously abnormal parameter points are found out by adopting a point-by-point elimination method, then normal parameters are determined, and finally all possible subset regression is carried out on the residual parameters within the range of the abnormal parameters. In addition, data loss or abnormality can be caused due to power failure of equipment for maintenance and failure or aging of acquisition channels of acquisition equipment in the power distribution network, and therefore the lost parameters need to be repaired. The specific steps of step S1 are as follows:
s11, removing obviously abnormal parameters, and performing subset regression on the remaining parameters in the abnormal parameter range;
and S12, repairing the missing parameters by utilizing a Lagrange difference algorithm.
Specifically, in step S11, the following linear regression model is utilized:
wherein M + N ═ K, MIN ═ phi, MYN ═ 1,2, Λ, K };
remember Y ═ Y1,Y2,,ΛYK)T，X＝(X1,X2,,ΛXK)T，H＝(hii)K×K＝X(XTX)-1XT，δ＝(W1,W2,Λ,WK)T＝(I-H)Y，
Wherein, { eiI ∈ M } independently obeys N (0, e)2) Set of random variables, { ZjJ ∈ N } is independently obeyed to N (0, be)2) And M, N, two unknown index sets are represented, M represents the number of indexes in the unknown index set M, and N represents the number of indexes in the unknown index set N.
To prevent normal parameters from being culled, the following stopping rule is utilized:
E||W||2＝(K-L)e2，
Yie-2～i2(1)，
when the probability is 0.995, there are
For the linear regression model, take
When in use
Consider YjIs an abnormal parameter;
get
When Y isi<T||W||2Is considered to be YiIs a normal parameter.
The number of the rest parameters K is obtained through the steps2＝K-t1-m1The number of remaining abnormal parameters is n1＝K·10％-t1(ii) a Wherein the content of the first and second substances,1and m1Respectively representing the number of abnormal parameters and normal parameters, K representing the total number of the parameters, K2Indicates the number of remaining parameters, n1Indicating the number of remaining abnormal parameters, and the number K of remaining parameters2By passing
Sum of squares of residualsFinding an anomalous dataset J, wherein:
specifically, the specific steps of step S12 are:
s121. suppose X ═ X0,x1,x2,Λ,xnIs the set of interpolated samples, Y ═ Y0,y1,y2,ΛynThe values of the corresponding X function are used, n represents the number of nodes, and X represents the node of interpolation;
s122, inputting n +1 interpolation sample points x0,x1,x2,Λ,xnAnd the corresponding function value y0,y1,y2,Λ,yn；
S123, calculating an n-order Lagrange basis function:
wherein i is 0,1,2,. n;
s124, calculating an n-order Lagrange interpolation function:
in the formula Ii() Representing an nth order Lagrangian basis function;
and S125, inputting an interpolation point x and substituting the interpolation point x into the interpolation function in the step S124 to obtain a calculation result.
Example 2
The present embodiment is similar to embodiment 1, except that the specific step of step S2 in the present embodiment is:
s21, carrying out self-adaptive adjustment on the corrected parameters based on an RBF neural network algorithm;
s22, optimizing and adjusting three-level coordination control target parameters of the power distribution network, wherein the three-level coordination control target parameters of the power distribution network mainly comprise voltages and power factors of all levels; the control target parameters of the reactive voltage are voltage U and power factor of a transformer substation, a feeder line and a transformer area
Specifically, the specific steps of step S21 are:
s211, selecting a Gaussian function
i is 1,2, Λ, k as a basis function,
wherein c represents the center of the basis function and σ represents the width of the radial basis function;
s212, acquiring a basis function center c, wherein the constraint conditions are as follows:
di＝min||xp-ci||,(p＝1,2,Λ,P,i＝1,2,Λ,k)，
in the formula, xp(P ═ 1,2, Λ, P) represents training data, ciRepresents the starting center;
s213, obtaining the width sigma of the radial basis function, wherein the calculation formula of the width sigma is as follows:
σi＝bdi,i＝1,2,Λ,k，
di＝min||ci-cg||，
in the formula, ci,cjEach representing the center of a certain category, b representing the overlap factor, diRepresenting a constraint;
s214, obtaining a weight W, wherein the calculation formula of the weight W is as follows:
Wj＝(HTH)-1HTTj,j＝1,2,Λ,m，
wherein H ═ H1,h2,Λ,hk]Representing intermediate layer output vector, h representing radial basis function, k representing intermediate layer number, m representing output layer number, T representing network output target quantity, TjThe j-th exit layer unit is shown.
Specifically, the specific steps of step S22 are:
s221, setting a target voltage and a target power factor;
s222, establishing an RBF neural network with a plurality of input vectors and a plurality of voltage and power factor output vectors of the three-level network of the power distribution network; the input vectors comprise power distribution network voltages, currents, phase angles, active power and reactive power of different voltage grades;
s223, constructing a training sample, carrying out normalization processing on the training sample, and then training the RBF neural network through the processed data;
s224, inputting the input vector into the RBF neural network which completes training in the step S222 to obtain a group of corresponding output values, and comparing the output values with the target voltage and the target power factor in the step S221;
s225, evaluating the accuracy of the corrected output result by using the relative error, and evaluating the correction capability of the RBF neural network; the evaluation calculation formula is as follows:
wherein Z represents an actual numerical value, ZfThe corrected value is indicated.
The target values of the reactive power and the voltage of the power distribution network based on RBF neural network optimization learning are transmitted to a three-level voltage coordination controller of the power distribution network in real time, so that the action times of reactive voltage control equipment are reduced, and the reactive voltage level of the whole power distribution network is improved.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (10)
1. An intelligent optimization and adjustment method for reactive voltage control parameters of a power distribution network is characterized by comprising the following steps:
s1, collecting reactive voltage control parameters of a power distribution network, and preprocessing bad parameters in the reactive voltage control parameters;
s2, taking the power factor of the three-level coordination control of the power grid as a target parameter, and then carrying out optimization adjustment on the target parameter based on the RBF neural network;
and S3, transmitting the optimized and adjusted parameters to the three-level voltage coordination controller of the power distribution network in real time.
2. The intelligent optimization and adjustment method for the reactive voltage control parameters of the power distribution network according to claim 1, wherein the specific steps of the step S1 are as follows:
s11, removing obviously abnormal parameters, and performing subset regression on the remaining parameters in the abnormal parameter range;
and S12, repairing the missing parameters by utilizing a Lagrange difference algorithm.
3. The method for intelligently optimizing and adjusting the reactive voltage control parameters of the power distribution network according to claim 2, wherein in step S11, the following linear regression model is used:
wherein M + N ═ K, mn ═ Φ, and M Y N ═ 1,2, Λ, K };
remember Y ═ Y1,Y2,,ΛYK)T，X＝(X1,X2,,ΛXK)T，H＝(hii)K×K＝X(XTX)-1XT，δ＝(W1,W2,Λ,WK)T＝(I-H)Y，
Wherein, { eiI ∈ M } independently obeys N (0, e)2) Set of random variables, { ZjJ ∈ N } is independently obeyed to N (0, be)2) And M, N, two unknown index sets are represented, M represents the number of indexes in the unknown index set M, and N represents the number of indexes in the unknown index set N.
4. The method for intelligently optimizing and adjusting the reactive voltage control parameters of the power distribution network according to claim 3, wherein in step S11, the following stopping rules are used to prevent the normal parameters from being eliminated:
E||W||2＝(K-L)e2，
Yie-2～i2(1)，
when the probability is 0.995, there are
For the linear regression model, take
When in use
Consider YjIs an abnormal parameter;
get
When Y isi<T||W||2Is considered to be YiIs a normal parameter.
5. The intelligent optimization and adjustment method for the reactive voltage control parameters of the power distribution network according to claim 4, wherein in step S11, the number of the remaining parameters is K2＝K-t1-m1The number of remaining abnormal parameters is n1＝K·10％-t1(ii) a Wherein the content of the first and second substances,1and m1Respectively representing the number of abnormal parameters and normal parameters, K representing the total number of the parameters, K2Indicates the number of remaining parameters, n1Indicating the number of remaining abnormal parameters, and the number K of remaining parameters2By passing
Sum of squares of residualsFinding an anomalous dataset J, wherein:
6. the intelligent optimization and adjustment method for the reactive voltage control parameters of the power distribution network according to claim 2, wherein the specific steps of the step S12 are as follows:
s121. suppose X ═ X0,x1,x2,Λ,xnIs the set of interpolated samples, Y ═ Y0,y1,y2,ΛynThe values of the corresponding X function are used, n represents the number of nodes, and X represents the node of interpolation;
s122, inputting n +1 interpolation sample points x0,x1,x2,Λ,xnAnd the corresponding function value y0,y1,y2,Λ,yn；
S123, calculating an n-order Lagrange basis function:
wherein i is 0,1,2,. n;
s124, calculating an n-order Lagrange interpolation function:
in the formula Ii(x) Representing an nth order Lagrangian basis function;
and S125, inputting an interpolation point x and substituting the interpolation point x into the interpolation function in the step S124 to obtain a calculation result.
7. The intelligent optimization and adjustment method for the reactive voltage control parameters of the power distribution network according to claim 1, wherein the specific steps of the step S2 are as follows:
s21, carrying out self-adaptive adjustment on the corrected parameters based on an RBF neural network algorithm;
and S22, optimizing and adjusting the three-level coordination control target parameters of the power distribution network aiming at the fact that the three-level coordination control target parameters of the power distribution network mainly comprise voltage and power factors of all levels.
8. The intelligent optimization and adjustment method for the reactive voltage control parameters of the power distribution network according to claim 7, wherein the specific steps of the step S21 are as follows:
s211, selecting a Gaussian function
As a function of the basis function,
wherein c represents the center of the basis function and σ represents the width of the radial basis function;
s212, acquiring a basis function center c, wherein the constraint conditions are as follows:
di＝min||xp-ci||,(p＝1,2,Λ,P,i＝1,2,Λ,k)，
in the formula, xp(P ═ 1,2, Λ, P) represents training data, ciRepresents the starting center;
s213, obtaining the width sigma of the radial basis function, wherein the calculation formula of the width sigma is as follows:
σi＝bdi,i＝1,2,Λ,k，
di＝min||ci-cg||，
in the formula, ci,cjEach representing the center of a certain category, b representing the overlap factor, diRepresenting a constraint;
s214, obtaining a weight W, wherein the calculation formula of the weight W is as follows:
Wj＝(HTH)-1HTTj,j＝1,2,Λ,m，
wherein H ═ H1,h2,Λ,hk]Representing intermediate layer output vector, h representing radial basis function, k representing intermediate layer number, m representing output layer number, T representing network output target quantity, TjThe j-th exit layer unit is shown.
9. The intelligent optimization and adjustment method for the reactive voltage control parameters of the power distribution network according to claim 7, wherein the specific steps of the step S22 are as follows:
s221, setting a target voltage and a target power factor;
s222, establishing an RBF neural network with a plurality of input vectors and a plurality of voltage and power factor output vectors of the three-level network of the power distribution network;
s223, constructing a training sample, carrying out normalization processing on the training sample, and then training the RBF neural network through the processed data;
s224, inputting the input vector into the RBF neural network which completes training in the step S222 to obtain a group of corresponding output values, and comparing the output values with the target voltage and the target power factor in the step S221;
s225, evaluating the accuracy of the corrected output result by using the relative error, and evaluating the correction capability of the RBF neural network; the evaluation calculation formula is as follows:
wherein Z represents an actual numerical value, ZfThe corrected value is indicated.
10. The method according to claim 9, wherein in step S222, the input vectors include distribution network voltages, currents, phase angles, active powers, and reactive powers of different voltage classes.
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