CN107590572B - IBBO-based complex looped network direction protection MBPS (network Block switch) solving method - Google Patents
IBBO-based complex looped network direction protection MBPS (network Block switch) solving method Download PDFInfo
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Abstract
The invention discloses an IBBO-based complex looped network direction protection MBPS solving method, which adopts a protection incidence matrix to judge the effectiveness of a determined breakpoint, thereby avoiding the formation of a large amount of calculations of all simple directed loops of the whole network; an improved biophysical optimization algorithm is provided, and a migration model, a migration operator and a mutation operator of the traditional BBO algorithm are improved; and the IBBO method is utilized to solve the breakpoint, so that the efficient solution of the minimum breakpoint optimization model is realized. The invention can effectively obtain the MBPS for the extra-high voltage power grid comprising the multi-ring network. Compared with the traditional optimization method, the method has the advantages of high convergence speed, good iteration robustness, high convergence precision and good optimization performance.
Description
Technical Field
The invention relates to the field of power grid optimization, in particular to an IBBO-based complex ring network direction protection MBPS (network management system) solving method.
Background
A group of protection sets capable of breaking the directional loop of the whole network is determined and called a breakpoint set, the group of protection sets is used as initial protection to carry out setting, and setting matching of the rest of protection sets is carried out in sequence after the initial protection sets, so that the premise that direction protection setting calculation of the complex looped network is carried out smoothly is provided.
Since the protection selected as the breakpoint may not be able to cooperate with its main protection, the location and number of breakpoints have an important influence on the overall performance of the full network protection. Therefore, after the concept of the breakpoint set is proposed, scholars at home and abroad perform extensive research work on the aspect of MBPS solution, and the main purpose of the research work is to minimize the dimension of the obtained breakpoint set so as to reduce the situation of protection mismatch. The existing MBPS solving method can be divided into a graph theory method, a protection dependence function method and a method based on an artificial intelligence algorithm. The calculated amount of the graph theory method can be increased sharply along with the increase of the network scale, and the graph theory method is difficult to be suitable for solving the modern complex large-scale power grid breakpoint set. When the protection dependence function method is applied to a complex interconnected power grid, the dimension of the breakpoint set cannot be guaranteed to be minimum. The artificial intelligence algorithm can ensure that the global optimal solution is obtained with the probability close to 1, and is the most common method for obtaining the MBPS at present. However, the existing method for solving MPBS based on artificial intelligence Algorithm still needs to form a whole-network simple loop for constraint processing in the Optimization process, and the performance of the existing Optimization algorithms such as Genetic Algorithm (GA) and Particle Swarm Algorithm (PSO) introduced into the solution of the MBPS can not meet the solution requirement of the large-scale complex interconnected power grid MBPS gradually.
Disclosure of Invention
The invention aims to solve the problem that the existing optimization algorithm cannot meet the solving requirement of MBPS of a large-scale complex interconnected power grid, and provides an IBBO-based method for solving MBPS for protecting the direction of a complex looped network.
In order to realize the purpose, the technical scheme is as follows:
an IBBO-based method for solving MBPS (network protection packet switching) for direction protection of a complex looped network comprises the following steps:
s1: inputting power grid parameters including network topology and direction protection configuration; setting initial parameters of an IBBO algorithm, including a population size N, a maximum iteration number, a maximum immigration probability, a maximum mutation rate and an elite retention parameter t%;
s2: forming a protection incidence matrix R and constructing a fitness evaluation function;
s3: population H for initializing IBBO algorithmi，i＝1，2，…，L；
S4: initializing the iteration time T as 1;
s5: processing the constraint conditions of each individual of each population, and executing a constraint repair strategy for individuals not meeting the constraint;
s6: evaluating the fitness HSI (fitness Index) of each individual of the population;
s7: sorting the population individuals from good to bad according to the fitness;
s8: judging whether the maximum iteration number T is reachedmaxIf yes, go to S15, otherwise go to S9;
s9: calculating the number of individuals, migration-in probability and migration-out probability corresponding to each population according to a cosine migration model;
s10: performing adaptive migration operator and differential mutation operator operation on the population;
s11: evaluating the individual fitness of the new generation of population, and sequencing the population from good to bad according to the fitness;
s12: executing an elite strategy: covering the best individuals of the previous generation population with the worst individuals of the new generation population;
s13: sorting the population from good to bad according to the fitness;
s14: adding 1 to the iteration number, changing T to T +1, and turning to S5;
s15: and (4) after the algorithm is finished, outputting the binary codes corresponding to the optimal individuals, and obtaining the minimum breakpoint set according to the corresponding relation between the binary codes and the system protection.
In S1, the population size N is 300, the maximum iteration number is 500, the maximum immigration probability is 1.0, the maximum mutation rate is 0.01, and the elite retention parameter is 10%.
In S2, the protection association matrix R is defined as follows:
rijrepresents the ith row and the jth column element in R;
the fitness evaluation function was used as follows:
wherein x isiSequentially corresponding to the ith directional protection in the system, if the protection is set as a breakpoint, xiIs 1, otherwise is 0.
In S5, the specific steps of processing the constraint condition of each population individual and executing the constraint repair policy for individuals not meeting the constraint are as follows:
s5.1: inputting a protection incidence matrix R and a population individual X;
s5.2: for the element with the median value of 1 in X, the corresponding protection is a breakpoint, and the row and the column corresponding to the protection in the protection incidence matrix are deleted;
s5.3: judging whether all zero rows exist in the protection incidence matrix R, if so, indicating that the protection corresponding to the row is unlinked from the complex ring network, calculating a fixed value, and deleting the row and the column corresponding to the protection;
s5.4: s5.2 is repeated until there are no rows in the protection association matrix R with all zero elements.
If R is empty after the execution of the steps is finished, X is a breakpoint set and meets constraint conditions; if not, the constraint condition is not satisfied. Executing a constraint repair strategy on X which does not satisfy the constraint: all the protections which can not be released from the ring network are set as breakpoints, and the corresponding elements of the protections in X are corrected to be 1.
In S9, the immigration probability λ corresponding to each population is calculated according to a cosine migration modeliAnd the probability of emigration muiThe formula of (1) is as follows:
wherein, I and E respectively represent the maximum immigration probability and the maximum immigration probability; k is a radical ofiIs the number of individuals of the ith population, N ═ SmaxThe maximum number of individuals that a population can accommodate.
In S10, the formula for performing the adaptive migration operator operation on the population is as follows:
Hi(SIV)←αHi(SIV)+(1-α)Hj(SIV)
wherein Hi(SIV) and Hj(SIV) two individuals H in the populationiAnd HjIs a characteristic fitness vector, HSIiAnd HSIjRepresenting the fitness of the two individuals, T is the iteration number, TmaxAlpha is a real number between (0,1) and epsilon is a minimum value, and the denominator is not zero; ae of going to H ← representing the right-hand side base member HiAnd HjThe new feature vector obtained by the feature vector calculation replaces the individual HiAnd the original characteristic vector realizes the individual migration.
In S10, the formula for performing the differential mutation operator operation on the population is as follows:
Hi(SIV)←Hr1(SIV)+F·(Hr2(SIV)+Hr3(SIV))
wherein Hr1(SIV)、Hr2(SIV)、Hr3(SIV) are different from each other and are Hi(SIV) different individuals; f is a scale factor greater than 0.
Compared with the prior art, the invention has the beneficial effects that:
1) a constraint processing method based on a protection incidence matrix is adopted, so that a large amount of calculation for forming all simple directed loops of the whole network is avoided;
2) the migration model, the migration operator and the mutation operator of the traditional BBO algorithm are improved, the IBBO method is used for solving the breakpoint, and the efficient solution of the minimum breakpoint optimization model is realized.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a system diagram of IEEE14 nodes;
FIG. 3 is a system diagram of IEEE30 nodes;
FIG. 4 is a topological structure diagram of a provincial 500kV system;
FIG. 5 is a graph comparing fitness index curves of the minimum dimension of MBPS calculated by using a conventional BBO algorithm, a GA algorithm, a PSO algorithm and the method of the present invention;
FIG. 6 is a diagram showing the result of MBPS solution performed on an IEEE14 node system, an IEEE30 node system and a provincial 500kV system by using the method of the present invention;
FIG. 7 is a dimension number graph obtained by MBPS solution of IEEE14 node and IEEE30 node system by using graph theory method, protection dependence function method and the method of the present invention.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
the invention is further illustrated below with reference to the figures and examples.
Example 1
An IBBO-based method for obtaining MBPS for direction protection of a complex ring network, as shown in fig. 1, includes the following steps:
s1: inputting power grid parameters including network topology and direction protection configuration; setting initial parameters of an IBBO algorithm, including a population size N, a maximum iteration number, a maximum immigration probability, a maximum mutation rate and an elite retention parameter t%;
s2: forming a protection incidence matrix R and constructing a fitness evaluation function;
s3: population H for initializing IBBO algorithmi，i＝1，2，…，L；
S4: initializing the iteration time T as 1;
s5: processing the constraint conditions of each individual of each population, and executing a constraint repair strategy for individuals not meeting the constraint;
s6: evaluating the fitness HSI (fitness Index) of each individual of the population;
s7: sorting the population individuals from good to bad according to the fitness;
s8: judging whether the maximum iteration number T is reachedmaxIf yes, go to S15, otherwise go to S9;
s9: calculating the number of individuals, migration-in probability and migration-out probability corresponding to each population according to a cosine migration model;
s10: performing adaptive migration operator and differential mutation operator operation on the population;
s11: evaluating the individual fitness of the new generation of population, and sequencing the population from good to bad according to the fitness;
s12: executing an elite strategy: covering the best individuals of the previous generation population with the worst individuals of the new generation population;
s13: sorting the population from good to bad according to the fitness;
s14: adding 1 to the iteration number, changing T to T +1, and turning to S5;
s15: and (4) after the algorithm is finished, outputting the binary codes corresponding to the optimal individuals, and obtaining the minimum breakpoint set according to the corresponding relation between the binary codes and the system protection.
In S1, the population size N is 300, the maximum iteration number is 500, the maximum immigration probability is 1.0, the maximum mutation rate is 0.01, and the elite retention parameter is 10%.
In S2, the protection association matrix R is defined as follows:
rijrepresents the ith row and the jth column element in R;
the fitness evaluation function was used as follows:
wherein x isiSequentially corresponding to the ith directional protection in the system, if the protection is set as a breakpoint, xiIs 1, otherwise is 0.
In S5, the specific steps of processing the constraint condition of each population individual and executing the constraint repair policy for individuals not meeting the constraint are as follows:
s5.1: inputting a protection incidence matrix R and a population individual X;
s5.2: for the element with the median value of 1 in X, the corresponding protection is a breakpoint, and the row and the column corresponding to the protection in the protection incidence matrix are deleted;
s5.3: judging whether all zero rows exist in the protection incidence matrix R, if so, indicating that the protection corresponding to the row is unlinked from the complex ring network, calculating a fixed value, and deleting the row and the column corresponding to the protection;
s5.4: s5.2 is repeated until there are no rows in the protection association matrix R with all zero elements.
If R is empty after the execution of the steps is finished, X is a breakpoint set and meets constraint conditions; if not, the constraint condition is not satisfied. Executing a constraint repair strategy on X which does not satisfy the constraint: all the protections which can not be released from the ring network are set as breakpoints, and the corresponding elements of the protections in X are corrected to be 1.
In S9, the immigration probability λ corresponding to each population is calculated according to a cosine migration modeliAnd the probability of emigration muiThe formula of (1) is as follows:
wherein, I and E respectively represent the maximum immigration probability and the maximum immigration probability; k is a radical ofiIs the number of individuals of the ith population, N ═ SmaxThe maximum number of individuals that a population can accommodate.
In S10, the formula for performing the adaptive migration operator operation on the population is as follows:
Hi(SIV)←αHi(SIV)+(1-α)Hj(SIV)
wherein Hi(SIV) and Hj(SIV) two individuals H in the populationiAnd HjIs a characteristic fitness vector, HSIiAnd HSIjRepresenting the fitness of the two individuals, T is the iteration number, TmaxAlpha is a real number between (0,1) and epsilon is a minimum value, and the denominator is not zero; ae of going to H ← representing the right-hand side base member HiAnd HjThe new feature vector obtained by the feature vector calculation replaces the individual HiAnd the original characteristic vector realizes the individual migration.
In S10, the formula for performing the differential mutation operator operation on the population is as follows:
Hi(SIV)←Hr1(SIV)+F·(Hr2(SIV)+Hr3(SIV))
wherein Hr1(SIV)、Hr2(SIV)、Hr3(SIV) are different from each other and are Hi(SIV) different individuals; f is a scale factor greater than 0.
Fig. 2, 3 and 4 are topological structure diagrams of an IEEE14 node system, an IEEE30 node system and a certain provincial 500kV system. The MBPS of the three systems of FIGS. 2, 3 and 4 obtained by the present invention is shown in FIG. 6. It can be seen that the invention adopts the constraint processing method based on the protection incidence matrix, and avoids the formation of a large amount of calculations of all simple directed loops of the whole network; a minimum set of breakpoints for the system can be obtained.
The calculation is carried out on the IEEE14 node and the IEEE30 node by adopting a graph theory method, a protection dependent function method and the invention, and the MBPS is shown in figure 7. Compared with a graph theory method and a protection dependent function method, the method can effectively reduce the dimensionality of the MBPS.
The traditional BBO algorithm, GA algorithm and PSO algorithm are adopted to perform simulation calculation on the provincial 500kV system shown in the figure 4, and the fitness index contrast curve of the respective optimal scheme in 30 independent simulations is shown in the figure 4. As can be seen from fig. 5, the convergence speed of IBBO algorithm has a significant advantage over the other 3 algorithms.
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 (5)
1. An IBBO-based method for solving MBPS (network protection packet switching) for direction protection of a complex looped network is characterized by comprising the following steps of:
s1: inputting power grid parameters including network topology and direction protection configuration; setting initial parameters of an IBBO algorithm, including a population size N, a maximum iteration number, a maximum immigration probability, a maximum mutation rate and an elite retention parameter t%;
s2: forming a protection incidence matrix R and constructing a fitness evaluation function;
s3: population H for initializing IBBO algorithmi，i＝1，2，…，L；
S4: initializing the iteration time T as 1;
s5: processing the constraint conditions of each individual of each population, and executing a constraint repair strategy for individuals not meeting the constraint;
s6: evaluating the fitness HSI (fitness Index) of each individual of the population;
s7: sorting the population individuals from good to bad according to the fitness;
s8: judging whether the maximum iteration number T is reachedmaxIf yes, go to S15, otherwise go to S9;
s9: calculating the number of individuals, migration-in probability and migration-out probability corresponding to each population according to a cosine migration model;
s10: performing adaptive migration operator and differential mutation operator operation on the population;
s11: evaluating the individual fitness of the new generation of population, and sequencing the population from good to bad according to the fitness;
s12: executing an elite strategy: covering the best individuals of the previous generation population with the worst individuals of the new generation population;
s13: sorting the population from good to bad according to the fitness;
s14: adding 1 to the iteration number, changing T to T +1, and turning to S5;
s15: after the algorithm is finished, outputting a binary code corresponding to the optimal individual, and obtaining a minimum breakpoint set according to the corresponding relation between the binary code and system protection;
in S2, the protection association matrix R is defined as follows:
rijrepresents the ith row and the jth column element in R;
the fitness evaluation function was used as follows:
wherein x isiSequentially corresponding to the ith directional protection in the system, if the protection is set as a breakpoint, xiIs 1, otherwise is 0.
2. The method of claim 1, wherein the method for obtaining the MBPS for the direction protection of the complex looped network based on the IBBO comprises the following steps: in S1, the population size N is 300, the maximum iteration number is 500, the maximum immigration probability is 1.0, the maximum mutation rate is 0.01, and the elite retention parameter is 10%.
3. The method of claim 1, wherein the method for obtaining the MBPS for the direction protection of the complex looped network based on the IBBO comprises the following steps: in S5, the specific steps of processing the constraint condition of each population individual and executing the constraint repair policy for individuals not meeting the constraint are as follows:
s5.1: inputting a protection incidence matrix R and a population individual X;
s5.2: for the element with the median value of 1 in X, the corresponding protection is a breakpoint, and the row and the column corresponding to the protection in the protection incidence matrix are deleted;
s5.3: judging whether all zero rows exist in the protection incidence matrix R, if so, indicating that the protection corresponding to the row is unlinked from the complex ring network, calculating a fixed value, and deleting the row and the column corresponding to the protection;
s5.4: repeating S5.2 until no row with all zero elements exists in the protection incidence matrix R;
if R is empty after the execution of the steps is finished, X is a breakpoint set and meets constraint conditions; if not, the constraint condition is not satisfied; executing a constraint repair strategy on X which does not satisfy the constraint: all the protections which can not be released from the ring network are set as breakpoints, and the corresponding elements of the protections in X are corrected to be 1.
4. The complex looped network direction protection MBPS based on IBBO of claim 1The calculation method is characterized by comprising the following steps: in the step S9, the migration probability λ corresponding to each population is calculated according to the cosine migration modeliAnd the probability of emigration muiThe formula of (1) is as follows:
wherein, I and E respectively represent the maximum immigration probability and the maximum immigration probability; k is a radical ofiIs the number of individuals of the ith population, N ═ SmaxThe maximum number of individuals that a population can accommodate.
5. The method of claim 1, wherein the method for obtaining the MBPS for the direction protection of the complex looped network based on the IBBO comprises the following steps: in S10, the formula for performing the adaptive migration operator operation on the population is as follows:
Hi(SIV)←αHi(SIV)+(1-α)Hj(SIV)
wherein Hi(SIV) and Hj(SIV) two individuals H in the populationiAnd HjIs a characteristic fitness vector, HSIiAnd HSIjRepresenting the fitness of the two individuals, T is the iteration number, TmaxAlpha is a real number between (0,1) and epsilon is a minimum value, and the denominator is not zero; ae of going to H ← representing the right-hand side base member HiAnd HjThe new feature vector obtained by the feature vector calculation replaces the individual HiThe original characteristic vector realizes the individual migration;
in S10, the formula for performing the differential mutation operator operation on the population is as follows:
Hi(SIV)←Hr1(SIV)+F·(Hr2(SIV)+Hr3(SIV))
wherein Hr1(SIV)、Hr2(SIV)、Hr3(SIV) are different from each other and are Hi(SIV) different individuals; f is a scale factor greater than 0.
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Effective date of registration: 20210723 Address after: No.75, Meihua Road, Yuexiu District, Guangzhou, Guangdong 510000 Patentee after: ELECTRIC POWER DISPATCHING CONTROL CENTER OF GUANGDONG POWER GRID Co.,Ltd. Address before: No.75, Meihua Road, Yuexiu District, Guangzhou, Guangdong 510000 Patentee before: ELECTRIC POWER DISPATCHING CONTROL CENTER OF GUANGDONG POWER GRID Co.,Ltd. Patentee before: HUAZHONG University OF SCIENCE AND TECHNOLOGY |