CN103887791B - A kind of distribution interconnection switch load switching optimization method - Google Patents
A kind of distribution interconnection switch load switching optimization method Download PDFInfo
- Publication number
- CN103887791B CN103887791B CN201410087244.8A CN201410087244A CN103887791B CN 103887791 B CN103887791 B CN 103887791B CN 201410087244 A CN201410087244 A CN 201410087244A CN 103887791 B CN103887791 B CN 103887791B
- Authority
- CN
- China
- Prior art keywords
- interconnection switch
- nci
- node
- chromosome
- population
- 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.)
- Active
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 24
- 238000000034 method Methods 0.000 title claims abstract description 23
- 210000000349 chromosome Anatomy 0.000 claims abstract description 23
- 230000002759 chromosomal effect Effects 0.000 claims abstract description 10
- 230000035772 mutation Effects 0.000 claims abstract description 6
- 238000012423 maintenance Methods 0.000 claims abstract description 5
- 238000004364 calculation method Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 5
- 230000008878 coupling Effects 0.000 abstract description 2
- 238000010168 coupling process Methods 0.000 abstract description 2
- 238000005859 coupling reaction Methods 0.000 abstract description 2
- 238000013461 design Methods 0.000 abstract description 2
- 238000013507 mapping Methods 0.000 abstract description 2
- 238000002360 preparation method Methods 0.000 abstract 1
- 238000004422 calculation algorithm Methods 0.000 description 13
- 230000006870 function Effects 0.000 description 13
- 230000002068 genetic effect Effects 0.000 description 11
- 238000005516 engineering process Methods 0.000 description 5
- 238000010353 genetic engineering Methods 0.000 description 3
- 230000003044 adaptive effect Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 208000026686 chromosomal inheritance Diseases 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000012217 deletion Methods 0.000 description 1
- 230000037430 deletion Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000010429 evolutionary process Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000009396 hybridization Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012804 iterative process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000003012 network analysis Methods 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
Abstract
The present invention discloses a kind of distribution interconnection switch load switching optimization method, belongs to electrical network field of configuration, comprises basic data and obtains and preparation process; Create initial population P step; Calculate each chromosomal fitness value step; Interlace operation and mutation operation step are performed to chromosome and exports optimum results step, said method with existing electric topology for electrical network basis, the given outlet switch Zu Hegong district outside coupling switch list needing maintenance, be the chromosome sequence of variable length by interconnection switch state mapping, design effective fitness function to assess each chromosomal quality, finally provide optimum Distribution Network Load Data handover scheme.By this method, global optimization can be carried out for a large amount of interconnection switches, thus find optimum handover scheme, simplify dispatcher's working strength, reduce artificial error probability.
Description
Technical field
The present invention relates to a kind of distribution interconnection switch load switching optimization method based on Varible-length chromsome genetic algorithm, belong to electrical network field of configuration.
Background technology
Intelligent power distribution automation is based on a rack and equipment, take electrical power distribution automatization system as core, integrated application power electronic technology, the communication technology, computer techno-stress technology, realize the Inspect and control of distribution system, and by the information integerated of related system, realize the scientific management of power distribution automation.Distribution main website is the core of electrical power distribution automatization system, mainly realizes the expanded function such as the basic function such as electric distribution network data collection and monitoring and electrical network analysis application, realizes the communication network of information transmission.IEC61968 standard carrying out and implementing in power distribution automation product, has promoted the integrated application of power distribution automation, for the information interaction between various application creates condition.
In the lexical analysis of power distribution network, dispatcher needs to carry out interpretation and application to the ruuning situation of power distribution network, and the basis analyzed is electric network swim computing function.And existing Load flow calculation algorithm is mainly for a complete grid structure, i.e. the electric network composition of full-mesh.Then, distribution website outdoor electrical topology is due to the generation of various small-sized accident, the carrying out of all kinds of service work, so structure often changes, then cause dispatcher constantly to pay close attention to electric topology change, and time update trend topology is analyzed and evaluation work.
Along with the development of technology, existing power distribution network SCADA(data acquisition and supervisor control) information of all electric components of the automation feeder line outside distribution website all can be carried out classifying and store, and electric topology structure is maintained in its electric network database.
In the daily Load flow calculation process of dispatching services; temporary needs often can be had to overhaul bus and outlet switch; dispatcher is now just needed to analyze confession district's electric power thus supplied and the power network topology of wiretap; search out the interconnection switch switch mode of a reasonable set, ensure the power supply ensureing user when bus and outlet switch overhaul.
But, due to very many, several at least for the external interconnection switch in district, tens at most, so just create various assembled scheme, and dispatcher is many has by virtue of experience carried out such work, there is workload large, inefficiency, the defects such as error probability is large.
A kind of adaptive global optimization probability search method that genetic algorithm is the biological heredity and evolution process in natural environment of simulation and is formed.By introducing the biological concept such as chromosome, gene, individuality, population, adaptive value and selection (copying), intersect the genetic manipulation (operator) such as (hybridization), variation after forming initial population by coding, the task of genetic manipulation is exactly apply certain operation according to them to the degree (Fitness analysis) of environmental adaptation to the individuality of colony, thus realizes the evolutionary process of the survival of the fittest.From the angle of Optimizing Search, genetic manipulation can make the solution of problem, optimization from one generation to the next, and approaches optimal solution.
In view of this, the present inventor studies this, and develop a kind of distribution interconnection switch load switching optimization method based on Varible-length chromsome genetic algorithm specially, this case produces thus.
Summary of the invention
In order to simplify the challenge of load switching in dispatcher's routine work, the present invention is intended to propose a kind of distribution interconnection switch load switching optimization method based on Varible-length chromsome genetic algorithm, described method carries out global optimization for a large amount of interconnection switches, thus find optimum handover scheme, simplify dispatcher's working strength, reduce artificial error probability.
To achieve these goals, solution of the present invention is:
A kind of distribution interconnection switch load switching optimization method, mainly comprises the following steps:
Step 1: basic data obtains and prepares;
1.1, from distribution Scada system acquisition station outdoor electrical topology (based on XML format or binary format), comprise node set N and line set L, and each node Ni represents, each circuit Li represents;
1.2 read from the list of schedules of distribution Scada system the outlet switch list C needing maintenance.Outlet switch list C comprises one or more Ci, and the node type of Ci is outlet switch.Read for district's list G, and the corresponding Ge Gong district Gi of each Ci;
1.3 read interconnection switch set of node NC relevant to list C in topology from distribution Scada system.NC comprises one or more NCi, and the node type of NCi is interconnection switch type.
Step 2: create initial population P;
2.1 generate random number R and, and the scope of Rand is the outlet switch number in 1 to interconnection switch list NC;
2.2 from interconnection switch list NC a random selecting Rand NCi, composition chromosome Pi, and add Pi in population P;
2.3 repeat step 2.1 to 2.2, amount to 30 times, namely produce and comprise 30 chromosomal population P;
2.4 establish iterations t=0.
Step 3: calculate each chromosomal fitness value;
Each Pi in 3.1 traversal population P, arranges back Scada system according to after the state negate of NCi each in Pi, and calls the Load flow calculation function of Scada system;
3.2 read each electriferous state E (Gi) for district Gi from Scada system, and E (Gi) function return value is 0 or 1, if Gi is charged, and E (Gi)=1, if Gi is not charged, E (Gi)=0;
3.3 calculate each chromosomal fitness value Fit (Pi), and wherein Fit (Pi) function account form is as follows:
, the wherein number of NCi that comprises for Pi of n;
If 3.4 iterationses are greater than 100, i.e. t>100, then terminate optimizing process, jump to step 5.
Step 4: interlace operation and mutation operation are performed to chromosome;
4.1 perform interlace operation, and operation operator is Cross(Pi, Pj).Cross operation operator chooses any two chromosome Pi and Pj from population P, and a NCj in a NCi and Pj then in random selecting Pi exchanges;
4.2 perform mutation operation, and operation operator is Mutate(Pi).Mutate operation operator arbitrarily selects a chromosome Pi from population P, from interconnection switch list NC, obtains an interconnection switch NCi join in Pi then at random;
4.3 arrange iterations t=t+1.Repeated execution of steps 3 and step 4.
Step 5: export optimum results;
5.1 choose Fit(Pi from current population P) maximum chromosome Pmax;
Each interconnection switch NCi in maximum chromosome Pmax exports by 5.2, and this Output rusults is final optimization pass result.
The above-mentioned distribution interconnection switch load switching optimization method based on Varible-length chromsome genetic algorithm, with existing electric topology for electrical network basis, the given outlet switch Zu Hegong district outside coupling switch list needing maintenance, be the chromosome sequence of variable length by interconnection switch state mapping, design effective fitness function to assess each chromosomal quality, finally provide optimum Distribution Network Load Data handover scheme.By this method, global optimization can be carried out for a large amount of interconnection switches, thus find optimum handover scheme, simplify dispatcher's working strength, reduce artificial error probability.
Below in conjunction with drawings and the specific embodiments, the present invention is described in further detail.
Accompanying drawing explanation
Fig. 1 is the flow chart of the distribution interconnection switch load switching optimization method based on Varible-length chromsome genetic algorithm;
Fig. 2 is the structure chart of the distribution interconnection switch load switching optimization system based on Varible-length chromsome genetic algorithm.
Embodiment
With reference to Fig. 1, based on the distribution interconnection switch load switching optimization method of Varible-length chromsome genetic algorithm, comprise the following steps:
Step 101: from distribution Scada system acquisition station outdoor electrical topology (based on XML format or binary format), comprise node set N and line set L, each node Ni represents, each circuit Li represents;
Step 201: read from the list of schedules of distribution Scada system the outlet switch list C needing maintenance, outlet switch list C comprises one or more Ci, and the node type of Ci is outlet switch, reads for district's list G, and the corresponding Ge Gong district Gi of each Ci;
Step 301: read interconnection switch set of node NC relevant to outlet switch list C in topology from distribution Scada system, NC comprises one or more NCi, and the node type of NCi is interconnection switch type;
Step 401: create initial population P, comprise 30 chromosome Pi altogether, if iterations t=0.The method generating each Pi is: generate random number R and, and the scope of Rand is the outlet switch number in 1 to interconnection switch list NC, a random selecting Rand NCi from interconnection switch list NC, composition chromosome Pi, and adds Pi in population P;
Step 501: each Pi in traversal population P, arranges back Scada system according to after the state negate of NCi each in Pi, and call the Load flow calculation function of Scada system.Read each electriferous state E (Gi) for district Gi from Scada system, E () function return value is 0 or 1, if Gi is charged, and E (Gi)=1, if Gi is not charged, E (Gi)=0.Calculate each chromosomal fitness value Fit (Pi), wherein Fit () function account form is as follows:
, the wherein number of NCi that comprises for Pi of n;
Step 601: if iterations is greater than 100, i.e. t>100, then terminate optimizing process, jumps to step 1001;
Step 701: perform interlace operation, operation operator is Cross(Pi, Pj).Cross operation operator chooses any two chromosome Pi and Pj from population P, and a NCj in a NCi and Pj then in random selecting Pi exchanges;
Step 801: perform mutation operation, operation operator is Mutate(Pi).Mutate operation operator arbitrarily selects a chromosome Pi from population P, from interconnection switch list NC, obtains an interconnection switch NCi join in Pi then at random;
Step 901: put iterations t=t+1, jump to step 501;
Step 1001: choose Fit(Pi from current population P) maximum chromosome Pmax;
Step 1101: exported by each interconnection switch NCi in maximum chromosome Pmax, this Output rusults is final optimization pass result.
With reference to Fig. 2, the distribution interconnection switch load switching optimization system based on Varible-length chromsome genetic algorithm that application this method realizes, mainly comprises existing system interface 1 and optimizes subsystem 2.
Described existing system interface comprises:
(1) SCADA power network topology interface module 11: this module can be docked with the electric topology of SCADA system, can the outer topological structure of extraction station, for district's information, service information etc.;
(2) SCADA Load flow calculation interface module 12: after the amendment of contact on off state, the Load flow calculation function of SCADA system can be called by this module, trend is calculated to amended electrical structure, obtain for zone electrical property state.
Described optimization subsystem comprises:
(1) to stand outdoor electrical Topology Management module 21: electric component and electric wiring are managed, comprise increase, deletion, amendment, merge and importing/export function;
(2) genetic algorithm iteration module 22: the Optimized Iterative process completing variable length chromosomal inheritance algorithm;
(3) scheme conversion with merge module 23: complete the genomic function before optimization electric topology being converted to genetic algorithm, complete optimum chromosome to be interpreted as contact after optimization terminates and to open the light load switching scheme;
(4) the automatic administration module of internal storage data 24: the data structure all modules set up in internal memory carries out unified management, prevents the situations such as RAM leakage, improves reliability.
Above-described embodiment and graphic and non-limiting product form of the present invention and style, any person of an ordinary skill in the technical field, to its suitable change done or modification, all should be considered as not departing from patent category of the present invention.
Claims (1)
1. a distribution interconnection switch load switching optimization method, is characterized in that mainly comprising the following steps:
Step 1: basic data obtains and prepares;
1.1 from distribution Scada system acquisition station outdoor electrical topology, and comprise node set N and line set L, each node Ni represents, each circuit Li represents;
1.2 read from the list of schedules of distribution Scada system the outlet switch list C needing maintenance, and outlet switch list C comprises one or more Ci, and the node type of Ci is outlet switch, reads for district's list G, and the corresponding Ge Gong district Gi of each Ci;
1.3 read interconnection switch set of node NC relevant to outlet switch list C in topology from distribution Scada system, and interconnection switch set of node NC comprises one or more NCi, and the node type of NCi is interconnection switch type;
Step 2: create initial population P;
2.1 generate random number R and, and the scope of Rand is the outlet switch number in 1 to interconnection switch set of node NC;
2.2 from interconnection switch set of node NC a random selecting Rand NCi, composition chromosome Pi, and add Pi in population P;
2.3 repeat step 2.1 to 2.2, amount to 30 times, namely produce and comprise 30 chromosomal population P;
2.4 establish iterations t=0;
Step 3: calculate each chromosomal fitness value;
Each Pi in 3.1 traversal population P, arranges back Scada system according to after the state negate of NCi each in Pi, and calls the Load flow calculation function of Scada system;
3.2 read each electriferous state E (Gi) for district Gi from Scada system, and E (Gi) function return value is 0 or 1, if Gi is charged, and E (Gi)=1, if Gi is not charged, E (Gi)=0;
3.3 calculate each chromosomal fitness value Fit (Pi), and wherein Fit (Pi) function account form is as follows:
, the wherein number of NCi that comprises for Pi of n;
If 3.4 iterationses are greater than 100, i.e. t>100, then terminate optimizing process, jump to step 5;
Step 4: interlace operation and mutation operation are performed to chromosome;
4.1 perform interlace operation, and operation operator is Cross(Pi, Pj);
Cross operation operator chooses any two chromosome Pi and Pj from population P, and a NCj in a NCi and Pj then in random selecting Pi exchanges;
4.2 perform mutation operation, and operation operator is Mutate(Pi);
Mutate operation operator arbitrarily selects a chromosome Pi from population P, from interconnection switch set of node NC, obtains an interconnection switch NCi join in Pi then at random;
4.3 arrange iterations t=t+1;
Repeated execution of steps 3 and step 4;
Step 5: export optimum results;
5.1 choose Fit(Pi from current population P) maximum chromosome Pmax;
Each interconnection switch NCi in maximum chromosome Pmax exports by 5.2, and this Output rusults is final optimization pass result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410087244.8A CN103887791B (en) | 2014-03-11 | 2014-03-11 | A kind of distribution interconnection switch load switching optimization method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410087244.8A CN103887791B (en) | 2014-03-11 | 2014-03-11 | A kind of distribution interconnection switch load switching optimization method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103887791A CN103887791A (en) | 2014-06-25 |
| CN103887791B true CN103887791B (en) | 2016-03-23 |
Family
ID=50956564
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410087244.8A Active CN103887791B (en) | 2014-03-11 | 2014-03-11 | A kind of distribution interconnection switch load switching optimization method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103887791B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110932399B (en) * | 2019-11-28 | 2022-02-25 | 上海宏力达信息技术股份有限公司 | Intelligent switch regulation and control method and system for power grid, storage medium and terminal |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101242103A (en) * | 2008-03-13 | 2008-08-13 | 上海交通大学 | Intelligent optimization method for power system stabilizer parameter |
| CN103050970A (en) * | 2013-01-15 | 2013-04-17 | 华北电力大学 | Stability analyzing and optimizing method suitable for layering and zoning of ultra-high voltage electric network |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH07231565A (en) * | 1994-02-16 | 1995-08-29 | Mitsubishi Heavy Ind Ltd | Optimal load distribution system |
-
2014
- 2014-03-11 CN CN201410087244.8A patent/CN103887791B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101242103A (en) * | 2008-03-13 | 2008-08-13 | 上海交通大学 | Intelligent optimization method for power system stabilizer parameter |
| CN103050970A (en) * | 2013-01-15 | 2013-04-17 | 华北电力大学 | Stability analyzing and optimizing method suitable for layering and zoning of ultra-high voltage electric network |
Non-Patent Citations (1)
| Title |
|---|
| 配电网开关优化配置的改进遗传算法;王俊峰等;《电网技术》;20051031;第29卷(第19期);第27至32页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103887791A (en) | 2014-06-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhou et al. | Game-theoretical energy management for energy Internet with big data-based renewable power forecasting | |
| CN102722764B (en) | Integration network optimization computer-aided decision support System | |
| Miranda et al. | Evolutionary computation in power systems | |
| Tan et al. | Optimization of distribution network incorporating distributed generators: An integrated approach | |
| Rahmani‐Andebili | Dynamic and adaptive reconfiguration of electrical distribution system including renewables applying stochastic model predictive control | |
| Syahputra | Fuzzy multi-objective approach for the improvement of distribution network efficiency by considering DG | |
| Pezzini et al. | Optimization techniques to improve energy efficiency in power systems | |
| CN102801790A (en) | Microgrid energy management system based on IEC (International Electrotechnical Commission) 61850 and multi-agent method | |
| Gutiérrez‐Alcaraz et al. | Two‐stage heuristic methodology for optimal reconfiguration and Volt/VAr control in the operation of electrical distribution systems | |
| Maffei et al. | A cyber-physical systems approach for implementing the receding horizon optimal power flow in smart grids | |
| Raju et al. | Iot based autonomous demand side management of a micro-grid using arduino and multi agent system | |
| Ing et al. | Gravitational search algorithm and selection approach for optimal distribution network configuration based on daily photovoltaic and loading variation | |
| Kumar et al. | An optimized framework of the integrated renewable energy and power quality model for the smart grid | |
| Montoya et al. | Hybrid optimization strategy for optimal location and sizing of DG in distribution networks | |
| CN109670981A (en) | Active distribution network planing method based on balance of interest and planning operation alternative optimization | |
| CN103887791B (en) | A kind of distribution interconnection switch load switching optimization method | |
| Koodalsamy et al. | Fuzzified artificial bee colony algorithm for nonsmooth and nonconvex multiobjective economic dispatch problem | |
| CN115879742A (en) | Power distribution network transformation method and system, electronic equipment and storage medium | |
| CN116187723A (en) | Resource scheduling method and device applied to distribution line loss reduction scene | |
| CN103178517B (en) | Multilevel topological analysis method for power system | |
| Xu et al. | Fault recovery of distribution network containing distributed generation based on heuristic search algorithm and multi-population genetic algorithm | |
| Schumilin et al. | Semantic CIM2Matpower: Ontology-based network topology processor and converter for CIM node-breaker models | |
| Santos et al. | Distribution power network reconfiguration in the smart grid | |
| Abdolrasol | M.; Hannan, MA; Hussain, SMS; Ustun, TS; Sarker, MR; Ker, PJ Energy Management Scheduling for Microgrids in the Virtual Power Plant System Using Artificial Neural Networks. Energies 2021, 14, 6507 | |
| Breve et al. | Flow-Network-Based Method for the Reliability Analysis of Decentralized Power System Topologies With a Sequential Monte-Carlo Simulation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant |