CN109038561A - Power failure planning device based on multiple target dragonfly algorithm - Google Patents
Power failure planning device based on multiple target dragonfly algorithm Download PDFInfo
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- CN109038561A CN109038561A CN201810894288.XA CN201810894288A CN109038561A CN 109038561 A CN109038561 A CN 109038561A CN 201810894288 A CN201810894288 A CN 201810894288A CN 109038561 A CN109038561 A CN 109038561A
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- power failure
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- multiple target
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- dragonfly algorithm
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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
-
- 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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a kind of power failure planning devices based on multiple target dragonfly algorithm, on the basis of original multiple target dragonfly algorithm, the external archival maintenance strategy under crowding distance and microhabitat shared mechanism is added, distributing homogeneity and the disaggregation for improving original multiple target dragonfly algorithm solution Pareto disaggregation are of overall importance.
Description
Technical field
Application and planning field the present invention relates to electric power data, and in particular to power failure planning device.
Background technique
Domestic and foreign scholars conduct extensive research in terms of electric network synthetic outage management, but are mostly to carry out stopping for single goal
Electric layout optimization.Such as: the superfine people of [1] Huang Xian converts economy for loss of outage and the cost of overhaul using immune Tabu search algorithm
Objective function carries out single object optimization；[2] Feng Yongqing et al. establishes MIXED INTEGER dual random based on Credibility Theory and obscures not
Determine that model converts economy objectives for the sum of maintenance expense and interruption cost and carries out single object optimization；[3] Xu Xufeng
Et al. utilize scattered date particle swarm optimization, deviate to expire for the overhaul of the equipments time and answer the repair time minimum and maintenance day part
The most reasonable Bi-objective of workload allocations is weighted summation process to carry out single goal solution.
Summary of the invention
The technical problem to be solved by the invention is to provide a kind of power failure plannings based on multiple target dragonfly algorithm
Method realizes multiple-objection optimization, and distributing homogeneity and the disaggregation for promoting original multiple target dragonfly algorithm solution Pareto disaggregation are complete
Office's property.
In order to solve the above technical problems, the present invention adopts the following technical scheme: based on the power failure of multiple target dragonfly algorithm
Preparing method is drawn, is included the following steps,
S1 obtains needed power failure equipments and its electric parameter, topological relation；
S2 obtains the result, electricity price and maintenance resource distribution of period internal loading prediction to be optimized；
S3 is obtained and is protected power supply constraint, the constraint of equipment health, the constraint of power failure window, the maximum duration constraint of overhaul of the equipments, and needle
Plan calculating power failure coordination constraint to not working out, and is calculated for the plan do not worked out and pass through equipment power failure time in homogenization time section
Number is to realize power failure coordination constraint；
S4, above-mentioned constraint and parameter formation can optimize power failure plan collection, realize to have a power failure to candidate by dragonfly algorithm and plan
Collection multiple target combination optimizes calculating, obtains the non-bad plan mix scheme for meeting multiple target and multi-constraint condition；
Population initial position and speed is randomly generated in S5, initiation parameter, and setting external archival maximum capacity, maximum change
Generation number, radius and each weight initial value；
S6 calculates the target value function of each individual in population；
S7 constructs disaggregation according to Pareto dominance relation, and external archival is added, to promote the equal of Pareto disaggregation distribution
Even property and population diversity, and whether Dynamic Maintenance is executed according to external archival Capacity Selection；
S8 according to microhabitat shared mechanism selection individual, and carries out weight factor and radius parameter according to individual adaptation degree
Update；
S9 translates the low meter of priority according to day according to constraint violation situation and target value functional value for unit backward
It draws, repeats step S6；
S9 such as reaches maximum number of iterations, exports all solutions in external archival.
Preferably, the electric parameter includes rated capacity, minimum technology power output, voltage rating electric current.
Preferably, the target value includes reliability, economy, balance of distribution, redundant index.
The present invention by adopting the above technical scheme, on the basis of original multiple target dragonfly algorithm, is added outer under crowding distance
Portion achieves maintenance strategy and microhabitat shared mechanism, and the distribution for improving original multiple target dragonfly algorithm solution Pareto disaggregation is equal
Even property and disaggregation are of overall importance.
Detailed description of the invention
Present invention will be further described below with reference to the accompanying drawings and specific embodiments:
Fig. 1 is flow chart of the present invention.
Specific embodiment
Illustrate that the present invention is based on the power failure planning devices of multiple target dragonfly algorithm below with reference to Fig. 1, including such as
Lower step,
S1 obtains needed power failure equipments and its electric parameter, topological relation；
S2 obtains the result, electricity price and maintenance resource distribution of period internal loading prediction to be optimized；
S3 is obtained and is protected power supply constraint, the constraint of equipment health, the constraint of power failure window, the maximum duration constraint of overhaul of the equipments, and needle
Plan calculating power failure coordination constraint to not working out, and is calculated for the plan do not worked out and pass through equipment power failure time in homogenization time section
Number is to realize power failure coordination constraint；
S4, above-mentioned constraint and parameter formation can optimize power failure plan collection, realize to have a power failure to candidate by dragonfly algorithm and plan
Collection multiple target combination optimizes calculating, obtains the non-bad plan mix scheme for meeting multiple target and multi-constraint condition；
Population initial position and speed is randomly generated in S5, initiation parameter, and setting external archival maximum capacity, maximum change
Generation number, radius and each weight initial value；
S6 calculates the target value function of each individual in population；
S7 constructs disaggregation according to Pareto dominance relation, and external archival is added, to promote the equal of Pareto disaggregation distribution
Even property and population diversity, and whether Dynamic Maintenance is executed according to external archival Capacity Selection；
S8 according to microhabitat shared mechanism selection individual, and carries out weight factor and radius parameter according to individual adaptation degree
Update；
S9 translates the low meter of priority according to day according to constraint violation situation and target value functional value for unit backward
It draws, repeats step S6,；
S9 such as reaches maximum number of iterations, exports all solutions in external archival.
The basic principle of multiple target dragonfly algorithm can refer to the prior art, it will be appreciated by persons skilled in the art that
For the present invention on the basis of original multiple target dragonfly algorithm, the external archival maintenance strategy and microhabitat under addition crowding distance are total
Mechanism is enjoyed, distributing homogeneity and the disaggregation for improving original multiple target dragonfly algorithm solution Pareto disaggregation are of overall importance.
Wherein, in step S1, the electric parameter include rated capacity, minimum technology power output, voltage rating electric current, when
It so, also may include conventional electrical parameter known to other skilled in the art.
In step S2, the result of load prediction is stored in advance, electricity price and maintenance resource distribution by computing staff according to calculating
Time is manually entered.
In step S3, constrained obtain obtained by the regular data stored in expert system.
In step S6, the target value includes reliability, economy, balance of distribution, redundant index.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, is familiar with
The those skilled in the art should be understood that the present invention includes but is not limited to content described in specific embodiment above.It is any
Modification without departing from function and structure principle of the invention is intended to be included in the range of claims.
Claims (3)
1. the power failure planning device based on multiple target dragonfly algorithm, it is characterised in that include the following steps,
S1 obtains needed power failure equipments and its electric parameter, topological relation；
S2 obtains the result, electricity price and maintenance resource distribution of period internal loading prediction to be optimized；
S3 is obtained and is protected power supply constraint, the constraint of equipment health, the constraint of power failure window, the maximum duration constraint of overhaul of the equipments, and for not
Work out plan and calculate power failure coordination constraint, and for the plan do not worked out calculate by equipment frequency of power cut in homogenization time section with
Realize power failure coordination constraint；
S4, above-mentioned constraint and parameter formation can optimize power failure plan collection, be realized by dragonfly algorithm more to candidate's power failure plan collection
Objective cross optimizes calculating, obtains the non-bad plan mix scheme for meeting multiple target and multi-constraint condition；
Population initial position and speed, setting external archival maximum capacity, greatest iteration time is randomly generated in S5, initiation parameter
Number, radius and each weight initial value；
S6 calculates the target value function of each individual in population；
S7 constructs disaggregation according to Pareto dominance relation, and external archival is added, to promote the uniformity of Pareto disaggregation distribution
And population diversity, and whether Dynamic Maintenance is executed according to external archival Capacity Selection；
S8 according to microhabitat shared mechanism selection individual, and carries out weight factor and radius parameter more according to individual adaptation degree
Newly；
S9 translates the low plan of priority according to day according to constraint violation situation and target value functional value for unit, weight backward
Multiple step S6；
S9 such as reaches maximum number of iterations, exports all solutions in external archival.
2. the power failure planning device according to claim 1 based on multiple target dragonfly algorithm, which is characterized in that described
Electric parameter includes rated capacity, minimum technology power output, voltage rating electric current.
3. the power failure planning device according to claim 1 based on multiple target dragonfly algorithm, which is characterized in that described
Target value includes reliability, economy, balance of distribution, redundant index.
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| CN201810894288.XA CN109038561A (en) | 2018-08-08 | 2018-08-08 | Power failure planning device based on multiple target dragonfly algorithm |
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| CN201810894288.XA CN109038561A (en) | 2018-08-08 | 2018-08-08 | Power failure planning device based on multiple target dragonfly algorithm |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109980700A (en) * | 2019-04-09 | 2019-07-05 | 广东电网有限责任公司 | A kind of distributed generation resource multi-objection optimization planning method, apparatus and equipment |
| CN111080031A (en) * | 2019-12-27 | 2020-04-28 | 圆通速递有限公司 | Vehicle path optimization method and system based on improved dragonfly algorithm |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102521673A (en) * | 2011-12-01 | 2012-06-27 | 嘉兴电力局 | Method for optimizing power-failure plan based on genetic algorithm |
| CN105740977A (en) * | 2016-01-28 | 2016-07-06 | 福州大学 | Multi-target particle swarm-based power outage management optimization method |
-
2018
- 2018-08-08 CN CN201810894288.XA patent/CN109038561A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102521673A (en) * | 2011-12-01 | 2012-06-27 | 嘉兴电力局 | Method for optimizing power-failure plan based on genetic algorithm |
| CN105740977A (en) * | 2016-01-28 | 2016-07-06 | 福州大学 | Multi-target particle swarm-based power outage management optimization method |
Non-Patent Citations (1)
| Title |
|---|
| 谢昶: ""电网检修计划优化编制方法研究与应用"", 《中国博士学位论文全文数据库工程科技Ⅱ辑》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109980700A (en) * | 2019-04-09 | 2019-07-05 | 广东电网有限责任公司 | A kind of distributed generation resource multi-objection optimization planning method, apparatus and equipment |
| CN109980700B (en) * | 2019-04-09 | 2023-01-10 | 广东电网有限责任公司 | Multi-objective optimization planning method, device and equipment for distributed power supply |
| CN111080031A (en) * | 2019-12-27 | 2020-04-28 | 圆通速递有限公司 | Vehicle path optimization method and system based on improved dragonfly algorithm |
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