CN107590572A - A kind of complicated Directional protection in loops MBPS acquiring methods based on IBBO - Google Patents
A kind of complicated Directional protection in loops MBPS acquiring methods based on IBBO Download PDFInfo
- Publication number
- CN107590572A CN107590572A CN201711003753.8A CN201711003753A CN107590572A CN 107590572 A CN107590572 A CN 107590572A CN 201711003753 A CN201711003753 A CN 201711003753A CN 107590572 A CN107590572 A CN 107590572A
- Authority
- CN
- China
- Prior art keywords
- population
- protection
- individuals
- ibbo
- mbps
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention discloses a kind of complicated Directional protection in loops MBPS acquiring methods based on IBBO, judge to determine the validity of breakpoint using protection incidence matrix, avoid a large amount of calculating to form all simple directed cycles of the whole network；Propose and improve biogeography optimized algorithm, migration models, transfer operator and the mutation operator of traditional BBO algorithms are improved；Breakpoint is solved using IBBO methods, realizes the Efficient Solution of minimum break point Optimized model.The present invention can effectively try to achieve MBPS for the supergrid comprising multi-ring network.Compared with traditional optimization, fast convergence rate of the present invention, iteration robustness are good and convergence precision is high, have 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 disconnecting the directional loop of the whole ring network is determined and called a breakpoint set, the group of protection sets are used as initial protection for setting, and setting matching of the rest of protection sets is performed sequentially after the initial protection sets are determined, so that the premise that direction protection setting calculation of the complex ring network is performed smoothly is provided.
Since the protection selected as a breakpoint may not be able to cooperate with its main protection, the location and number of breakpoints have a significant impact on the overall performance of the protection of the entire network. 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 conventional MBPS (molecular beam position relationship) 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 a 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 commonly used 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 large-scale complex interconnected network MBPS, and provides an IBBO-based complex ring network direction protection MBPS solving method.
In order to realize the purpose of the invention, 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, wherein the initial parameters comprise 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 algorithm i ，i＝1，2，…，L；
S4: initializing iteration times T =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 HIS (Habitat Suitability 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 reached max If yes, turning to S15, otherwise, entering 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: and (3) 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: the iteration times are added with 1,T= T +1, and then the step goes 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 =300, the maximum number of iterations =500, the maximum immigration probability =1.0, the maximum mutation rate =0.01, and the elite retention parameter =10%.
In S2, the protection association matrix R is defined as follows:
the fitness evaluation function was used as follows:
wherein x is i Sequentially corresponding to the ith directional protection in the system, if the protection is set as a breakpoint, x i Is 1, otherwise is 0.
In S5, the specific steps of processing the constraint condition of each population individual and executing the constraint repairing policy for individuals that do not satisfy 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 corresponding rows and columns of 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 the constraint condition; 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 lambda corresponding to each population is calculated according to a cosine migration model i And the probability of emigration mu i The 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 of i Number of individuals of ith population, N = S max The 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:
H i (SIV)←αH i (SIV)+(1-α)H j (SIV)
wherein epsilon is a minimum value, and the denominator is not zero.
In S10, the formula for performing the differential mutation operator operation on the population is as follows:
H i (SIV)←H r1 (SIV)+F·(H r2 (SIV)+H r3 (SIV))
wherein H r1 、H r2 、H r3 Are different from each other and H i (ii) different individuals; f: (F)>, 0) is a scale factor.
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 solving of the minimum breakpoint optimization model is achieved.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a system diagram of an IEEE14 node;
FIG. 3 is a system diagram of an IEEE30 node;
FIG. 4 is a topology 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 systems 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 by the following figures and examples.
Example 1
An IBBO-based method for obtaining MBPS for directional 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, wherein the initial parameters comprise 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 algorithm i ，i＝1，2，…，L；
S4: initializing the iteration number T =1;
s5: processing the constraint conditions of each individual of each population, and executing a constraint repairing strategy for individuals which do not meet the constraint;
s6: evaluating the fitness HIS (Habitat Suitability 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 reached max If yes, turning to S15, otherwise, entering 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 the operation of an adaptive migration operator and a differential mutation operator 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: and (3) 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: the iteration times are added with 1,T= T +1, and then the step goes to S5;
s15: and (5) 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 =300, the maximum iteration number =500, the maximum immigration probability =1.0, the maximum mutation rate =0.01, and the elite retention parameter =10%.
In S2, the protection association matrix R is defined as follows:
the fitness evaluation function was used as follows:
wherein x is i Sequentially corresponding to the ith directional protection in the system, if the protection is set as a breakpoint, x i Is 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 that do not satisfy 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 the constraint condition; 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 lambda corresponding to each population is calculated according to a cosine migration model i And the probability of emigration mu i The 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 of formula i Number of individuals of ith population, N = S max The 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:
H i (SIV)←αH i (SIV)+(1-α)H j (SIV)
wherein epsilon is a minimum value, and the denominator is not zero.
In S10, the formula for performing the differential mutation operator operation on the population is as follows:
H i (SIV)←H r1 (SIV)+F·(H r2 (SIV)+H r3 (SIV))
wherein H r1 、H r2 、H r3 Are different from each other and H i (ii) different individuals; f (>, 0) is a scale factor.
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 fig. 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 (6)
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, wherein the initial parameters comprise 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 algorithm i ，i＝1，2，…，L；
S4: initializing iteration times T =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 HIS (Habitut Suitability 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 reached max If yes, turning to S15, otherwise, entering 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 by L multiplied by t percent with the worst individuals of the new generation population by L multiplied by t percent;
s13: sorting the population from good to bad according to the fitness;
s14: the iteration times are added with 1,T = T +1, and then S5 is carried out;
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.
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 =300, the maximum iteration number =500, the maximum immigration probability =1.0, the maximum mutation rate =0.01, and the elite retention parameter =10%.
3. The method for solving the MBPS for the direction protection of the complex looped network based on the IBBO as claimed in claim 1, wherein: in S2, the protection association matrix R is defined as follows:
the fitness evaluation function was used as follows:
wherein x is i Sequentially corresponding to the ith directional protection in the system, if the protection is set as a breakpoint, x i Is 1, otherwise is 0.
4. 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 repairing policy for individuals that do not satisfy the constraint are as follows:
s5.1: and inputting a protection incidence matrix R and population individuals X.
S5.2: for the element with value 1 in X, the corresponding protection is a breakpoint, and the row and column corresponding to these protections in the protection association matrix are deleted.
S5.3: and 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. And executing a constraint repair strategy on X which does not satisfy the constraint: all the protections that cannot be released from the ring network are set as breakpoints, and the corresponding elements of these protections in X are corrected to 1.
5. The method for solving the MBPS for the direction protection of the complex looped network based on the IBBO as claimed in claim 1, wherein: in S9, the immigration probability lambda corresponding to each population is calculated according to the cosine migration model i And the probability of emigration mu i The 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 of i Number of individuals of ith population, N = S max The maximum number of individuals that a population can accommodate.
6. 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, a formula for performing the adaptive migration operator operation on the population is as follows:
H i (SIV)←αH i (SIV)+(1-α)H j (SIV)
wherein epsilon is a minimum value, and the denominator is not zero.
In S10, the formula for performing the differential mutation operator operation on the population is as follows:
H i (SIV)←H r1 (SIV)+F·(H r2 (SIV)+H r3 (SIV))
wherein H r1 、H r2 、H r3 Are different from each other and H i (ii) different individuals; f (>, 0) is a scale factor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711003753.8A CN107590572B (en) | 2017-10-24 | 2017-10-24 | IBBO-based complex looped network direction protection MBPS (network Block switch) solving method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711003753.8A CN107590572B (en) | 2017-10-24 | 2017-10-24 | IBBO-based complex looped network direction protection MBPS (network Block switch) solving method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107590572A true CN107590572A (en) | 2018-01-16 |
| CN107590572B CN107590572B (en) | 2021-05-04 |
Family
ID=61044265
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711003753.8A Active CN107590572B (en) | 2017-10-24 | 2017-10-24 | IBBO-based complex looped network direction protection MBPS (network Block switch) solving method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107590572B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109447349A (en) * | 2018-10-29 | 2019-03-08 | 浙江财经大学 | A kind of manufacturing service supply chain optimization method of Based on Networked correlation perception |
| CN110365006A (en) * | 2019-04-26 | 2019-10-22 | 太原理工大学 | A kind of sub-area division method based on nwbbo algorithm |
| CN116600365A (en) * | 2023-04-18 | 2023-08-15 | 中国科学院上海微系统与信息技术研究所 | Clustering routing method and device for wireless sensor network, storage medium and terminal |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103345661A (en) * | 2013-07-10 | 2013-10-09 | 大连海事大学 | Ship grid reconstruction method based on ring topology gauss dynamic particle swarm optimization algorithm |
| CN105896528A (en) * | 2016-04-21 | 2016-08-24 | 国网重庆市电力公司电力科学研究院 | Power distribution network reconstruction method based on isolation ecological niche genetic algorithm |
-
2017
- 2017-10-24 CN CN201711003753.8A patent/CN107590572B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103345661A (en) * | 2013-07-10 | 2013-10-09 | 大连海事大学 | Ship grid reconstruction method based on ring topology gauss dynamic particle swarm optimization algorithm |
| CN105896528A (en) * | 2016-04-21 | 2016-08-24 | 国网重庆市电力公司电力科学研究院 | Power distribution network reconstruction method based on isolation ecological niche genetic algorithm |
Non-Patent Citations (2)
| Title |
|---|
| MING CHEN等: "Minimum break relay dependency set approach for coordination of directional relays in multi-loop networks", 《IET GENERATION, TRANSMISSION & DISTRIBUTION》 * |
| 冯思玲: "生物地理学优化算法及其在生物序列模式发现中的应用", 《中国博士学位论文全文数据库基础科学辑》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109447349A (en) * | 2018-10-29 | 2019-03-08 | 浙江财经大学 | A kind of manufacturing service supply chain optimization method of Based on Networked correlation perception |
| CN109447349B (en) * | 2018-10-29 | 2022-04-19 | 浙江财经大学 | Manufacturing service supply chain optimization method facing networked relevance perception |
| CN110365006A (en) * | 2019-04-26 | 2019-10-22 | 太原理工大学 | A kind of sub-area division method based on nwbbo algorithm |
| CN110365006B (en) * | 2019-04-26 | 2022-05-03 | 太原理工大学 | Power grid partitioning method based on nwbbo algorithm |
| CN116600365A (en) * | 2023-04-18 | 2023-08-15 | 中国科学院上海微系统与信息技术研究所 | Clustering routing method and device for wireless sensor network, storage medium and terminal |
| CN116600365B (en) * | 2023-04-18 | 2024-02-23 | 中国科学院上海微系统与信息技术研究所 | Clustering routing method and device for wireless sensor network, storage medium and terminal |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107590572B (en) | 2021-05-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107590572B (en) | IBBO-based complex looped network direction protection MBPS (network Block switch) solving method | |
| CN110460091B (en) | Method for acquiring optimal planning of power transmission network under new energy access | |
| CN105117517B (en) | A kind of Distribution system method based on improvement particle cluster algorithm | |
| CN104505820B (en) | Based on the power distribution network intelligent reconstruction method that multi-information correlation is utilized | |
| CN110162041A (en) | A kind of robot path planning method based on self-adapted genetic algorithm | |
| CN104332995A (en) | Improved particle swarm optimization based power distribution reconstruction optimization method | |
| CN106611379A (en) | Improved culture gene algorithm for solving multi-objective flexible job shop scheduling problem | |
| CN110635478B (en) | Optimization method for power transmission network planning under new energy access based on single target | |
| CN107591807B (en) | Optimization method for power transmission network planning under new energy access | |
| CN106777449A (en) | Distribution Network Reconfiguration based on binary particle swarm algorithm | |
| CN104867062A (en) | Low-loss power distribution network optimization and reconfiguration method based on genetic algorithm | |
| CN107517201A (en) | A kind of network vulnerability discrimination method removed based on sequential | |
| CN110059405A (en) | High-quality Steiner minimum tree construction method with differential evolution under X structure | |
| CN110110395A (en) | A kind of multi-state System Reliability appraisal procedure based on markov and general generating function | |
| CN111191955B (en) | Power CPS risk area prediction method based on dependent Markov chain | |
| Wang et al. | A research on the optimal design of BP neural network based on improved GEP | |
| CN115963731A (en) | Command control system network structure optimization method based on improved genetic algorithm | |
| CN108710742B (en) | PGSA-GA hybrid algorithm-based fault section positioning method | |
| CN110879778A (en) | Novel dynamic feedback and improved patch evaluation software automatic restoration method | |
| Torres-Jimenez et al. | Reconfiguration of power distribution systems using genetic algorithms and spanning trees | |
| CN112488314B (en) | System elasticity recovery method and system based on improved genetic algorithm | |
| Mansour et al. | Dynamic economic load dispatch of thermal power system using genetic algorithm | |
| CN110571791B (en) | Optimal configuration method for power transmission network planning under new energy access | |
| CN111429302B (en) | Initial value calculation method for natural gas system in steady-state energy flow calculation of comprehensive energy system | |
| CN113029150A (en) | Intelligent aircraft track planning method under multi-constraint condition |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
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 |