CN105740977A - Multi-target particle swarm-based power outage management optimization method - Google Patents
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Abstract
The invention relates to a multi-target particle swarm-based power outage management optimization method. The method comprises the following steps: 1, reading original data of a power outage circuit and obtaining indexes of the power outage circuit; 2, initializing a particle swarm, setting parameters of a particle swarm algorithm and randomly initializing the particle swarm to form an initial feasible solution; 3, respectively calculating the fitness of each particle to obtain the local optimum and global optimum of each particle; 4, punishing the non-feasible solutions by adopting a penalty function method, and storing the optimum solutions; 5, updating the speeds and positions of the particles by adopting a solution updating policy; 6, judging whether a termination condition is satisfied, if the judging result is positive, ending the algorithm, otherwise, returning to step 2; and 7, outputting a specific power outage plan. The method is beneficial for comprehensively optimizing the reliability, economy and resource configuration of the large-scale power outage management of power grids.
Description
Technical field
The present invention relates to electric power network technical field, particularly a kind of outage management optimization method based on multi-objective particle swarm.
Background technology
Along with development and the social progress of scientific and technological level, people are for the requirement of electric power, and degree of dependence improves constantly, and this makes Utilities Electric Co. pay much attention to the reliability of power supply.Each big city, world reliability index is added up by Song Yunting etc., and China's urban distribution network reliability index is far below world average level.In the face of big data age, huge user number, how scientific and effective each department are carried out power failure planning management by ten hundreds of circuit, voltage transformer station, it is achieved the complex optimum of grid equipment the best power off time, number of devices and resource requirement becomes particularly significant.
Electric network synthetic outage management is studied and is achieved certain achievement by domestic and international experts and scholars, it is thus proposed that minimum for target with system System average interruption duration, sets up overhaul of the equipments Optimized model；Someone introduces the thought of " composition decomposition ", and to have a power failure, cost minimization turns to target, it is proposed to a kind of Integer programming carries out method for solving；Someone is in conjunction with the advantage of heuristic search Yu successive approximation algorithm, it is proposed to a kind of adopt minimum short of electricity amount and etc. standby piecewise function be the method that target carries out unit maintenance Optimum.The studies above successfully considers the many factors involved by grid power blackout, but only will wherein be modeled as unique object function in a certain respect, fails to realize multiobject global optimization.
Summary of the invention
It is an object of the invention to provide a kind of outage management optimization method based on multi-objective particle swarm, the method is conducive to the reliability making electrical network massive blackout manage, economy and resource distribution to reach comprehensive optimum.
For achieving the above object, the technical scheme is that a kind of outage management optimization method based on multi-objective particle swarm, comprise the following steps:
Step 1, reads dead line initial data, obtains the indices of dead line；
Step 2, arranges the parameter of particle cluster algorithm, random initializtion population, and each particle represents the initial feasible solution comprising each equipment power failure time started and the end time that has a power failure；
Step 3, calculates the fitness of each particle respectively, obtains the local optimum of each particle and overall global optimum；
Step 4, adopts penalty function method that infeasible solution is punished, preserves optimal solution；
Step 5, adopts solution more New Policy that speed and the position of each particle are updated；
Step 6, it may be judged whether meet end condition, if meeting, then algorithm terminates, and otherwise returns step 2；
Step 7, exports concrete power failure plan.
Further, in described step 2, the parameter of the particle cluster algorithm of setting includes: accelerated factorc 1Withc 2, maximum iteration timeMaxIter, stablize iterationsStableIterAnd inertia weight coefficientω, described inertia weight coefficientωThe method adopting linear decrease calculates and obtains, and its computing formula is as follows:
Wherein,ω 1Withω 2Being initial value and the end value of Inertia Weight, CurIter represents current iteration number of times.
Further, in described step 2, formal similarity design is as follows:
The solution that each particle represents was made up of the power failure time started of each equipment and the end time that has a power failure, due to the overhaul of the equipments timet repair For fixed value, therefore only design has a power failure the time startedx istart , have a power failure the end timex iend By the time started that has a power failurex istart Plus the overhaul of the equipments timet repair Obtain:
x iend =x istart +t repair
Wherein,x istart For the power failure time started of i-th power failure equipments,x iend For the power failure end time of i-th power failure equipments,i=1,2,…,n,nFor power failure equipments number,t repair For the overhaul of the equipments time span in units of half an hour；Design the power off time of each equipment comprise sky and hour, and in units of half an hour, namely the form designing power off time is ddHH, wherein, front two dd represents that power failure occurs in which sky of month, and span is [01,31], rear two HH represent that power failure occurred in which and a half hours of this day, and span is [00,47]；
The constraints of feasible solution includes:
1. for limiting the trend constraint of the trend value of circuit:
|S l |≤S lmax
Wherein,S l For circuitlTrend value,S lmaxFor circuitlAllow the trend limit value of transmission；
2. for limiting the power off time constraint of line outage time started:
t imin≤t i ≤t imax
Wherein,t iminWitht imaxThe minima of time started of respectively having a power failure and maximum, i.e. power failure earliest start time and power failure Late Start；
3. it is used for limiting circuit transformer substation voltage value, so that its node voltage constraint in certain voltage load:
V imin≤V i ≤V imax
Wherein,V iminWithV imaxRespectively equipment voltageV i Lower limit and the upper limit；
4. for ensureing that the workload spent by interruption maintenance reaches the workload harmony constraint of optimum.
Further, in described step 3, the computing formula of the fitness of each particle is as follows:
Wherein,FFor object function,f 1For reliability objectives,f 2For economical target,f 3For workload harmony target,ω i Weight coefficient for i-th target；
Reliability objectivesf 1Consider the impact that user is caused that has a power failure, be calculated according to conventional power failure frequency, it is achieved have a power failure minimizing customer impact, and its computing formula is:
Wherein,F i For the power failure frequency of equipment i,N i Have a power failure for equipment i and affect number of users,N s For total number of users,φFor power failure equipments set；
Economy objectivesf 2Refer to the losses in economic advantages that grid equipment power failure causes, comprise the sale of electricity loss owing to the minimizing of the load caused that has a power failure causes and equipment power failure carries out overhauling the cost of overhaul caused and uses, realizing minimizing of power failure equipments loss of outage and the cost of overhaul, its computing formula is:
Wherein,C i Have a power failure the electricity charge loss caused for equipment i,R i The cost of overhaul for equipment i is used；
The harmonious target of workf 3Refer to the mean square deviation of the workload of the construction working caused that has a power failure, it is achieved every day, the difference of interruption maintenance workload minimized, and its computing formula is:
Wherein,w i It is the number in man-hour of i-th day,For the average hour of work in the power failure cycle,N d For the working days in the power failure cycle.
Further, in described step 4, penalty function refers to that restricted problem is become unconstrained problem solves, " punishment " item for certain combination composition of constraint function, is loaded on original object function and forces iteration point to approach feasible zone；<A,f> in, A is the set of feasible solution meeting constraints,For object function, then object function minima solve as follows:
For constraints, problem with inequality constraint is converted into below equation constraint:
Wherein,F(x,M) for penalty function, M is penalty factor, when M is sufficiently large,F(x,M) optimal solution approach the optimal solution of restricted problem.
Further, in described step 5, solution more New Policy is adopted to be updated comprising the following steps to speed and the position of particle:
Step 501: the permissible power outage duration scope of every equipment is different, for each solution vectorx i Arrange independent speed update codomain [v min,v max]；
Step 502: in order to meet constraints 4., before updating the power failure time started of each equipment, first according to the power failure earliest start time of following steps more new equipmentt mminWith power failure Late Startt mmax, so that the power off time excursion of the equipment of same circuit is merged into same interval:
(1) record in power failure planning chart is pressed dead line packet, the power failure plan of the equipment of same circuit is assigned to same group；
(2) with probabilityp l Select the some equipment in same group；
(3) take the power failure earliest start time of selected device and power failure Late Start to <t imin,t imax>common factor<t mmin,t mmax>；
(4) if occur simultaneously <t mmin,t mmax> exist, then with it as the new power failure earliest start time of selected device and power failure Late Start, otherwise, still retain the original power failure earliest start time of selected device and power failure Late Start；
(5) with probabilityp e Select the power failure plan (same equipment is likely to have a power failure repeatedly in month) of same equipment in same group, again perform step (3) ~ (5)；
Suitable by arrangingp l Withp e Value, and makep l <p e , make same circuit equipment the power failure time started close proximity to, and the power failure time started of same equipment closer to some；Meanwhile, the randomness of solution is remained in that；Additionally, in order to ensure that solution procedure can be close to meeting constraints solution 4. quickly, adopt penalty function method to reduce the fitness violating constraints solution 4.；
Step 503: in order to meet constraints 2., while the interval that trapped particle speed updates, limits the renewal solved, with interval without departing from the power off time allowed；Adopting the solution of a kind of delivery strategy guarantee particle in allowed limits, formula is as follows:
Wherein, the new power failure time startedx i,new Power failure time started during equal to last iterationx i Increase a new rate of change valuev i,new , then value is carried out modulo operation and makes it meet constraints 2.；And new rate of change valuev i,new Calculate according to population speed more new formula again, and be maintained in permission time range；Complementation in formula ensures to have a power failure the time started not over the power failure time started interval allowed.
Further, in described step 6, as long as one met in following two end conditions, algorithm terminates:
A) number of times that algorithm runs reaches maximum iteration timeMaxIter；
B) iterations that optimal solution is constant continuously reaches stable iterationsStableIter。
A kind of outage management optimization method based on multi-objective particle swarm of offer is provided, the solution structure that the method design power cut problem adapts, and the more New Policy of new particle position and speed, effectively prevent particle cluster algorithm and be absorbed in the situation of locally optimal solution, it is achieved the reliability of electrical network massive blackout management, economy and resource distribution reach comprehensive optimum.
Accompanying drawing explanation
Fig. 1 is the flowchart of the embodiment of the present invention.
Fig. 2 be the embodiment of the present invention step 5 in solve the flowchart of renewal.
Detailed description of the invention
Below in conjunction with drawings and the specific embodiments, the present invention is described in further detail.
The present invention provides a kind of outage management optimization method based on multi-objective particle swarm, as it is shown in figure 1, comprise the following steps:
For achieving the above object, the technical scheme is that a kind of outage management optimization method based on multi-objective particle swarm, comprise the following steps:
Step 1, reads dead line initial data, obtains the indices of dead line.
Step 2, arranges the parameter of particle cluster algorithm, random initializtion population, and each particle represents the initial feasible solution comprising each equipment power failure time started and the end time that has a power failure.
Wherein, the parameter of the particle cluster algorithm of setting includes: accelerated factorc 1Withc 2, maximum iteration timeMaxIter, stablize iterationsStableIterAnd inertia weight coefficientω, described inertia weight coefficientωThe method adopting linear decrease calculates and obtains, and its computing formula is as follows:
Wherein,ω 1Withω 2Being initial value and the end value of Inertia Weight, CurIter represents current iteration number of times.
Formal similarity design is as follows:
The solution that each particle represents was made up of the power failure time started of each equipment and the end time that has a power failure, due to the overhaul of the equipments timet repair For fixed value, therefore only design has a power failure the time startedx istart , have a power failure the end timex iend By the time started that has a power failurex istart Plus the overhaul of the equipments timet repair Obtain:
x iend =x istart +t repair
Wherein,x istart For the power failure time started of i-th power failure equipments,x iend For the power failure end time of i-th power failure equipments,i=1,2,…,n,nFor power failure equipments number,t repair For the overhaul of the equipments time span in units of half an hour；Design the power off time (time started that namely has a power failure, have a power failure end time) of each equipment comprise sky and hour, and in units of half an hour, namely the form designing power off time is ddHH, wherein, front two dd represents that power failure occurs in which sky of month, and span is [01,31], rear two HH represent that power failure occurred in which and a half hours of this day, and span is [00,47]；
The constraints of feasible solution includes:
1. for limiting the trend constraint of the trend value of circuit:
|S l |≤S lmax
Wherein,S l For circuitlTrend value,S lmaxFor circuitlAllow the trend limit value of transmission；
2. for limiting the power off time constraint of line outage time started:
t imin≤t i ≤t imax
Wherein,t iminWitht imaxThe minima of time started of respectively having a power failure and maximum, i.e. power failure earliest start time and power failure Late Start；
3. it is used for limiting circuit transformer substation voltage value, so that its node voltage constraint in certain voltage load:
V imin≤V i ≤V imax
Wherein,V iminWithV imaxRespectively equipment voltageV i Lower limit and the upper limit；
4. for ensureing that the workload spent by interruption maintenance reaches the workload harmony constraint of optimum.
Step 3, calculates the fitness of each particle respectively, obtains the local optimum of each particle and overall global optimum.
Wherein, the computing formula of the fitness of each particle is as follows:
Wherein,FFor object function,f 1For reliability objectives,f 2For economical target,f 3For workload harmony target,ω i Weight coefficient for i-th target；
Reliability objectivesf 1Consider the impact that user is caused that has a power failure, be calculated according to conventional power failure frequency, it is achieved have a power failure minimizing customer impact, and its computing formula is:
Wherein,F i For the power failure frequency of equipment i,N i Have a power failure for equipment i and affect number of users,N s For total number of users,φFor power failure equipments set；
Economy objectivesf 2Refer to the losses in economic advantages that grid equipment power failure causes, comprise the sale of electricity loss owing to the minimizing of the load caused that has a power failure causes and equipment power failure carries out overhauling the cost of overhaul caused and uses, realizing minimizing of power failure equipments loss of outage and the cost of overhaul, its computing formula is:
Wherein,C i Have a power failure the electricity charge loss caused for equipment i,R i The cost of overhaul for equipment i is used；
The harmonious target of workf 3Refer to the mean square deviation of the workload of the construction working caused that has a power failure, it is achieved every day, the difference of interruption maintenance workload minimized, and its computing formula is:
Wherein,w i It is the number in man-hour of i-th day,For the average hour of work in the power failure cycle,N d For the working days in the power failure cycle.
Step 4, adopts penalty function method that infeasible solution is punished, preserves optimal solution.
Wherein, penalty function refers to that restricted problem is become unconstrained problem solves, " punishment " item for certain combination composition of constraint function, is loaded on original object function and forces iteration point to approach feasible zone；<A,f> in, A is the set of feasible solution meeting constraints,For object function, then object function minima solve as follows:
For constraints, problem with inequality constraint is converted into below equation constraint:
Wherein,F(x,M) for penalty function, M is penalty factor, when M is sufficiently large,F(x,M) optimal solution approach the optimal solution of restricted problem.
Step 5, adopts solution more New Policy that speed and the position of each particle are updated.As in figure 2 it is shown, specifically include following steps:
Step 501: the permissible power outage duration scope of every equipment is different, for each solution vectorx i Arrange independent speed update codomain [v min,v max]；
Step 502: in order to meet constraints 4., before updating the power failure time started of each equipment, first according to the power failure earliest start time of following steps more new equipmentt mminWith power failure Late Startt mmax, so that the power off time excursion of the equipment of same circuit is merged into same interval:
(1) record in power failure planning chart is pressed dead line packet, the power failure plan of the equipment of same circuit is assigned to same group；
(2) with probabilityp l Select the some equipment in same group；
(3) take the power failure earliest start time of selected device and power failure Late Start to <t imin,t imax>common factor<t mmin,t mmax>；
(4) if occur simultaneously <t mmin,t mmax> exist, then with it as the new power failure earliest start time of selected device and power failure Late Start, otherwise, still retain the original power failure earliest start time of selected device and power failure Late Start；
(5) with probabilityp e Select the power failure plan (same equipment is likely to have a power failure repeatedly in month) of same equipment in same group, again perform step (3) ~ (5)；
Suitable by arrangingp l Withp e Value, and makep l <p e , make same circuit equipment the power failure time started close proximity to, and the power failure time started of same equipment closer to some；Meanwhile, the randomness of solution is remained in that；Additionally, in order to ensure that solution procedure can be close to meeting constraints solution 4. quickly, adopt penalty function method to reduce the fitness violating constraints solution 4.；
Step 503: in order to meet constraints 2., while the interval that trapped particle speed updates, limits the renewal solved, with interval without departing from the power off time allowed；Adopting the solution of a kind of delivery strategy guarantee particle in allowed limits, formula is as follows:
Wherein, the new power failure time startedx i,new Power failure time started during equal to last iterationx i Increase a new rate of change valuev i,new , then value is carried out modulo operation and makes it meet constraints 2.；And new rate of change valuev i,new Calculate according to population speed more new formula again, and be maintained in permission time range；Complementation in formula ensures to have a power failure the time started not over the power failure time started interval allowed.
Step 6, it may be judged whether meet end condition, if meeting, then algorithm terminates, and otherwise returns step 2.Wherein, as long as one met in following two end conditions, algorithm terminates:
A) number of times that algorithm runs reaches maximum iteration timeMaxIter；
B) iterations that optimal solution is constant continuously reaches stable iterationsStableIter。
Step 7, exports concrete power failure plan.
It is above presently preferred embodiments of the present invention, all changes made according to technical solution of the present invention, when produced function is without departing from the scope of technical solution of the present invention, belong to protection scope of the present invention.
Claims (7)
1. the outage management optimization method based on multi-objective particle swarm, it is characterised in that comprise the following steps:
Step 1, reads dead line initial data, obtains the indices of dead line；
Step 2, arranges the parameter of particle cluster algorithm, random initializtion population, and each particle represents the initial feasible solution comprising each equipment power failure time started and the end time that has a power failure；
Step 3, calculates the fitness of each particle respectively, obtains the local optimum of each particle and overall global optimum；
Step 4, adopts penalty function method that infeasible solution is punished, preserves optimal solution；
Step 5, adopts solution more New Policy that speed and the position of each particle are updated；
Step 6, it may be judged whether meet end condition, if meeting, then algorithm terminates, and otherwise returns step 2；
Step 7, exports concrete power failure plan.
2. a kind of outage management optimization method based on multi-objective particle swarm according to claim 1, it is characterised in that in described step 2, the parameter of the particle cluster algorithm of setting includes: accelerated factorc 1Withc 2, maximum iteration timeMaxIter, stablize iterationsStableIterAnd inertia weight coefficientω, described inertia weight coefficientωThe method adopting linear decrease calculates and obtains, and its computing formula is as follows:
Wherein,ω 1Withω 2Being initial value and the end value of Inertia Weight, CurIter represents current iteration number of times.
3. a kind of outage management optimization method based on multi-objective particle swarm according to claim 2, it is characterised in that in described step 2, formal similarity design is as follows:
The solution that each particle represents was made up of the power failure time started of each equipment and the end time that has a power failure, due to the overhaul of the equipments timet repair For fixed value, therefore only design has a power failure the time startedx istart , have a power failure the end timex iend By the time started that has a power failurex istart Plus the overhaul of the equipments timet repair Obtain:
x iend =x istart +t repair
Wherein,x istart For the power failure time started of i-th power failure equipments,x iend For the power failure end time of i-th power failure equipments,i=1,2,…,n,nFor power failure equipments number,t repair For the overhaul of the equipments time span in units of half an hour；Design the power off time of each equipment comprise sky and hour, and in units of half an hour, namely the form designing power off time is ddHH, wherein, front two dd represents that power failure occurs in which sky of month, and span is [01,31], rear two HH represent that power failure occurred in which and a half hours of this day, and span is [00,47]；
The constraints of feasible solution includes:
1. for limiting the trend constraint of the trend value of circuit:
|S l |≤S lmax
Wherein,S l For circuitlTrend value,S lmaxFor circuitlAllow the trend limit value of transmission；
2. for limiting the power off time constraint of line outage time started:
t imin≤t i ≤t imax
Wherein,t iminWitht imaxThe minima of time started of respectively having a power failure and maximum, i.e. power failure earliest start time and power failure Late Start；
3. it is used for limiting circuit transformer substation voltage value, so that its node voltage constraint in certain voltage load:
V imin≤V i ≤V imax
Wherein,V iminWithV imaxRespectively equipment voltageV i Lower limit and the upper limit；
4. for ensureing that the workload spent by interruption maintenance reaches the workload harmony constraint of optimum.
4. a kind of outage management optimization method based on multi-objective particle swarm according to claim 3, it is characterised in that in described step 3, the computing formula of the fitness of each particle is as follows:
Wherein,FFor object function,f 1For reliability objectives,f 2For economical target,f 3For workload harmony target,ω i Weight coefficient for i-th target；
Reliability objectivesf 1Consider the impact that user is caused that has a power failure, be calculated according to conventional power failure frequency, it is achieved have a power failure minimizing customer impact, and its computing formula is:
Wherein,F i For the power failure frequency of equipment i,N i Have a power failure for equipment i and affect number of users,N s For total number of users,φFor power failure equipments set；
Economy objectivesf 2Refer to the losses in economic advantages that grid equipment power failure causes, comprise the sale of electricity loss owing to the minimizing of the load caused that has a power failure causes and equipment power failure carries out overhauling the cost of overhaul caused and uses, realizing minimizing of power failure equipments loss of outage and the cost of overhaul, its computing formula is:
Wherein,C i Have a power failure the electricity charge loss caused for equipment i,R i The cost of overhaul for equipment i is used；
The harmonious target of workf 3Refer to the mean square deviation of the workload of the construction working caused that has a power failure, it is achieved every day, the difference of interruption maintenance workload minimized, and its computing formula is:
Wherein,w i It is the number in man-hour of i-th day,For the average hour of work in the power failure cycle,N d For the working days in the power failure cycle.
5. a kind of outage management optimization method based on multi-objective particle swarm according to claim 4, it is characterized in that, in described step 4, penalty function refers to that restricted problem is become unconstrained problem solves, " punishment " item for certain combination composition of constraint function, is loaded on original object function and forces iteration point to approach feasible zone；<A,f> in, A is the set of feasible solution meeting constraints,For object function, then object function minima solve as follows:
For constraints, problem with inequality constraint is converted into below equation constraint:
Wherein,F(x,M) for penalty function, M is penalty factor, when M is sufficiently large,F(x,M) optimal solution approach the optimal solution of restricted problem.
6. a kind of outage management optimization method based on multi-objective particle swarm according to claim 5, it is characterised in that in described step 5, adopts solution more New Policy to be updated comprising the following steps to speed and the position of particle:
Step 501: the permissible power outage duration scope of every equipment is different, for each solution vectorx i Arrange independent speed update codomain [v min,v max]；
Step 502: in order to meet constraints 4., before updating the power failure time started of each equipment, first according to the power failure earliest start time of following steps more new equipmentt mminWith power failure Late Startt mmax, so that the power off time excursion of the equipment of same circuit is merged into same interval:
(1) record in power failure planning chart is pressed dead line packet, the power failure plan of the equipment of same circuit is assigned to same group；
(2) with probabilityp l Select the some equipment in same group；
(3) take the power failure earliest start time of selected device and power failure Late Start to <t imin,t imax>common factor<t mmin,t mmax>；
(4) if occur simultaneously <t mmin,t mmax> exist, then with it as the new power failure earliest start time of selected device and power failure Late Start, otherwise, still retain the original power failure earliest start time of selected device and power failure Late Start；
(5) with probabilityp e Select the power failure plan (same equipment is likely to have a power failure repeatedly in month) of same equipment in same group, again perform step (3) ~ (5)；
Suitable by arrangingp l Withp e Value, and makep l <p e , make same circuit equipment the power failure time started close proximity to, and the power failure time started of same equipment closer to some；Meanwhile, the randomness of solution is remained in that；Additionally, in order to ensure that solution procedure can be close to meeting constraints solution 4. quickly, adopt penalty function method to reduce the fitness violating constraints solution 4.；
Step 503: in order to meet constraints 2., while the interval that trapped particle speed updates, limits the renewal solved, with interval without departing from the power off time allowed；Adopting the solution of a kind of delivery strategy guarantee particle in allowed limits, formula is as follows:
Wherein, the new power failure time startedx i,new Power failure time started during equal to last iterationx i Increase a new rate of change valuev i,new , then value is carried out modulo operation and makes it meet constraints 2.；And new rate of change valuev i,new Calculate according to population speed more new formula again, and be maintained in permission time range；Complementation in formula ensures to have a power failure the time started not over the power failure time started interval allowed.
7. a kind of outage management optimization method based on multi-objective particle swarm according to claim 6, it is characterised in that in described step 6, as long as one met in following two end conditions, algorithm terminates:
A) number of times that algorithm runs reaches maximum iteration timeMaxIter；
B) iterations that optimal solution is constant continuously reaches stable iterationsStableIter。
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| CN106157174A (en) * | 2016-07-21 | 2016-11-23 | 中国南方电网有限责任公司电网技术研究中心 | To reduce power off time and the electricity power failure planning optimization decision method as target |
| CN106779442A (en) * | 2016-12-26 | 2017-05-31 | 国网山东省电力公司泰安供电公司 | Have a power failure the generation method and device planned |
| CN109038561A (en) * | 2018-08-08 | 2018-12-18 | 国网浙江省电力有限公司绍兴供电公司 | Power failure planning device based on multiple target dragonfly algorithm |
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| CN106157174B (en) * | 2016-07-21 | 2019-08-06 | 中国南方电网有限责任公司电网技术研究中心 | To reduce the power failure planning optimization decision-making technique of power off time and electricity as target |
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| CN109767051B (en) * | 2018-10-26 | 2022-12-09 | 国网天津市电力公司 | Transformer power failure planning method based on big data analysis |
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| CN110059406A (en) * | 2019-04-18 | 2019-07-26 | 成都德杉科技有限公司 | Aerial array optimization method, system, storage medium and the terminal of value function are adapted to using contiguous segmentation |
| CN111292002A (en) * | 2020-02-24 | 2020-06-16 | 福州大学 | Method and system for identifying and grouping reading and answering based on genetic-particle swarm algorithm |
| CN111292002B (en) * | 2020-02-24 | 2022-07-01 | 福州大学 | Method and system for identifying and grouping reading and answering based on genetic-particle swarm algorithm |
| CN111709632A (en) * | 2020-06-09 | 2020-09-25 | 国网安徽省电力有限公司安庆供电公司 | Power failure plan automatic arrangement method based on artificial intelligence and multi-target constraint |
| CN112163389A (en) * | 2020-09-07 | 2021-01-01 | 华南理工大学 | Power electronic circuit optimization method based on self-adaptive distributed particle swarm optimization algorithm |
| CN114444762A (en) * | 2021-12-15 | 2022-05-06 | 国网江苏省电力有限公司徐州供电分公司 | Power failure maintenance optimization method based on multi-target particle swarm |
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