CN106503844B - A kind of power circuit path optimization method using genetic algorithm - Google Patents
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Abstract
The invention discloses a kind of power circuit path optimization methods using genetic algorithm, cable ring-system path is optimized using genetic algorithm, the following steps are included: being encoded to comprising n cable Single-ring network cabinet and/or cable dual-ring network cabinet, the random section that generates is [1, n] n integer random alignment, form a chromosome；It is selected to be used to judge chromosome to the fitness function of the adaptability of targetDetermine population quantity N, maximum algebra Gmax, crossover probability pc and mutation probability pm；The big individual of adaptive value is chosen as male parent using roulette mode, roulette wheel is that selection is carried out by the fitness of individual, and the big individual of adaptive value is then chosen, the small individual of adaptive value then removes, to each evaluated chromosome in new group, optimum individual is saved, and exports optimal solution.Compared to hand layouts, not only reduce cost of labor, but also improve design efficiency, and flexibility and reliability, saves entreprise cost.
Description
Technical field
The present invention relates to a kind of power circuit path optimization methods using genetic algorithm.
Background technique
Current Electric Power Network Planning power circuit wiring in, generally use manual routing, manual routing generally by qualification compared with
Deep expert carries out, and significantly limits human resources, in addition, manual routing's efficiency is lower, it is time-consuming larger, it improves again
Cost of labor, last manual routing can not find optimal route every time, cause routing path elongated, cost of investment mentions
It is high.For by the Chinese valley section Jin Gu, A divides plot.It needs to form Single-ring network to 9 ring network cabinets, the route chosen using expertise
As shown in Figure 1, being 2.4km.
Summary of the invention
The object of the present invention is to provide a kind of power circuit path optimization methods using genetic algorithm, promote electric load
The goodness of fit between prediction, power grid construction timing and power demand, and optimization medium voltage power lines path are considering power supply half
It proposes in the case of diameter with the path optimization method of the minimum objective function of layout of roads year comprehensive cost.To achieve the above object,
The present invention adopts the following technical solutions:
A kind of power circuit path optimization method using genetic algorithm, comprising the following steps:
1), foundation includes the layout of roads year the smallest objective function of comprehensive cost of the investment cost of route, cost of losses
Equation group:
Wherein, α is unit length track investment expense, r0For discount rate, m is substation low-voltage side route depreciable life, N
For substation's sum, lijFor the length of i-th substation's j-th strip basic routing line, JiGo out the total of route for i-th of substation
Number, PjFor the load of j-th strip basic routing line institute band, β is route network loss conversion factor；
2), in the Electric Power Network Planning of section, the load in each sub- plot is obtained according to load prediction, determines the sub- plot needs
Ring network cabinet quantity, ring network cabinet position is arranged according to sub- plot actual conditions, referring to actual cable pipe trench path, by each looped network
Cabinet concatenation, forms Single-ring network or dual-ring network wiring construction；
3), cable ring-system path is optimized using genetic algorithm, abstract constraint condition and target are to find most short electricity
Cable path, specifically includes the following steps:
A), symbolization coding mode is encoded to comprising n cable Single-ring network cabinet and/or cable dual-ring network cabinet, with
Machine generates the random alignment for the n integer that section is [1, n], forms a chromosome；
B), select and be used to judge chromosome to the fitness function of the adaptability of target, fitness function equation isWherein, SijRepresent the actual range between i-th of ring network cabinet and j-th of ring network cabinet；
C), genetic algorithm main control parameters select, and determine population quantity N, maximum algebra Gmax, crossover probability pc and change
Different Probability p m；
D), selection course is based on rotating roulette wheel 100 times, and rotation all selects an individual for new population every time,
The big individual of adaptive value is chosen as male parent using roulette mode, roulette wheel is to carry out selection by the fitness of individual, is adapted to
The big individual of value is then chosen, and the small individual of adaptive value then removes；
E), crossover operator designs: the parent of crossover operation is determined using partial mapped crossover, by 100 samples group two-by-two
Conjunction is divided into 50 groups, generates 2 random numbers b1 and b2 from closed interval [0,1] first, enables r1 be equal to b1 × 100 and b2 × 100, really
Fixed 2 positions, intersect the data among 2 positions；After intersection, duplicate ring network cabinet is had in same sample, is not repeated
Number retain, it is duplicate number using partial mapped crossover method eliminate repeat；
F), mutation operator designs: using inversion alternative method, randomly chooses 2 point c1 and c2, exchanges position, and will be between 2 points
Number since c2 inverted order place；
G), to each evaluated chromosome in new group, optimum individual is saved, and exports optimal solution.
Further, in step d), the big individual of adaptive value is chosen as male parent using roulette mode, calculates each dye
Colour solid viAdaptive value eval (vi) (i=1,2....N) and group always just whenCalculate each chromosome vi's
Select probability pi=eval (vi)/F (i=1,2....N)；Calculate each chromosome viAccumulated probability(i=1,
2....N)；Wheel disc is rotated 100 times, selects a single chromosome according to the methods below every time: generating one in section
The random number r of [0,1], if r < q1, select first chromosome v1New group is added, otherwise selection is so that qi-1<r<qiIt sets up
I-th of chromosome viNew group is added in (2≤i≤N).
The invention has the advantages that
Passage path optimization technology of the present invention, can optimize cable trace, to shorten cable run investment.For quantity
More ring network cabinets, or when the primary looped network for forming 5 or more, it is obvious to save cable.Hand layouts are compared simultaneously, are both subtracted
Lack cost of labor, and improved design efficiency, and flexibility and reliability, saves entreprise cost.
Detailed description of the invention
Fig. 1 is the looped network path profile using expertise selection；
Fig. 2 is a kind of power circuit path optimization method using genetic algorithm；
Fig. 3 is that 10kV cable outlet segments is 5, conductor cross-section 400mm2, copper core cable " monocyclic physical model
Figure；
Fig. 4 is that 10kV cable outlet segments is 5, conductor cross-section 400mm2, copper core cable " dicyclic physical model
Figure；
Fig. 5 is the looped network path profile using genetic algorithm selection；
Fig. 6 is effect picture after the optimization of the section Han Yu cable trace.
Specific embodiment
As shown in Fig. 2, a kind of power circuit path optimization method using genetic algorithm, comprising the following steps:
1), foundation includes the layout of roads year the smallest objective function of comprehensive cost of the investment cost of route, cost of losses
Equation group:
Wherein, α is unit length track investment expense, r0For discount rate, m is substation low-voltage side route depreciable life, N
For substation's sum, lijFor the length of i-th substation's j-th strip basic routing line, JiGo out the total of route for i-th of substation
Number, PjFor the load of j-th strip basic routing line institute band, β is route network loss conversion factor；
2), in the Electric Power Network Planning of section, the load in each sub- plot is obtained according to load prediction, determines the sub- plot needs
Ring network cabinet quantity, ring network cabinet position is arranged according to sub- plot actual conditions, referring to actual cable pipe trench path, by each looped network
Cabinet concatenation, forms Single-ring network or dual-ring network wiring construction.By taking cable system as an example, the optimizing of power circuit path is discussed.According to load
Prediction obtains the load in each sub- plot, determines the ring network cabinet quantity that the sub- plot needs；According to load character (significance level),
Determine cable ring-system type (monocyclic or dicyclic).With " 10kV cable outlet segments be 5, conductor cross-section 400mm2, copper
For core cable ", illustrate the relationship of load Yu attaching capacity." 10kV cable outlet segments be 5, conductor cross-section 400mm2,
The transimission power analysis of copper core cable " is as shown in table 1.
1 10kV cable outlet segments of table is 5, conductor cross-section 400mm2, the transimission power of copper core cable
As shown in Figure 3, Figure 4, dicyclic is only the superposition of monocyclic, and " two monocycles " and the difference of " one bicyclic " are to match
The two-way power supply of electric room equipment is respectively from a ring network cabinet still respectively from two ring network cabinets.Illustrate by taking monocyclic as an example
Each ring network cabinet can attaching capacity.It is every when different electrical equipment (distribution transforming) load factors in the case where not considering load moment
A ring network cabinet can attaching capacity it is as shown in table 2.
Table 210kV cable outlet segments is 5, conductor cross-section 400mm2, each ring network cabinet of copper core cable can attaching
Capacity
If selected distribution transformer load rate is 40%, and considers simultaneity factor (choosing 0.65), then a ring network cabinet can attaching distribution transforming
Capacity is 4MVA, is otherwise unsatisfactory for route N-1.If the load W in known sub- plotz(t) (MW), the then looped network that the sub- plot needs
Cabinet quantity is nh=Wz(t)/(4×0.95×0.4)。
3), cable ring-system path is optimized using genetic algorithm, abstract constraint condition and target are to find most short electricity
Cable path, specifically includes the following steps:
A), symbolization coding mode, for optimizing comprising 9 ring network cabinet cable Single-ring network (or dual-ring network) path lengths
Problem.Using with symbol coding, each number represents 1 ring network cabinet, random to generate 9 integers that section is [1,9]
Random alignment forms a chromosome.
B), fitness function is the effect for evaluating chromosome to the adaptability of target, selected to be used to judge chromosome to mesh
The fitness function of target adaptability, fitness function equation areWherein, SijRepresent i-th of ring network cabinet and
Actual range between j ring network cabinet, if the limitation two o'clock due to cable pipe trench is different, then it is assumed that SijFor infinity, in reality
Border is replaced in calculating with one big number.
C), genetic algorithm main control parameters select, and determine population quantity N, maximum algebra Gmax, crossover probability pc and change
Different Probability p m；A possibility that population size is bigger, and mode handled by GA is more, falls into local solution is smaller, easily fall into not at
Ripe convergence, but scale is excessive will increase calculation amount, influences efficiency of algorithm, chooses 40 here.The number of iterations is few, target value convergence effect
Fruit is bad, and the number of iterations is excessive, and long operational time chooses 500 here.Hybridization is that the important recombination of one of genetic algorithm is calculated
Son, probability of crossover pc are a parameters of algorithm, and it is pc*N, crossover probability one that this probability, which provides the number for being expected to be hybridized,
As choose 0.2-0.9, choose 0.8 here；Variation is also an important genetic operator, is executed on the basis of one one
, it is contemplated that variation digit be pm*N, therefore make a variation be equal to aberration rate probability change one or several genes, entire group
Each in all chromosomes in body has equal opportunity experience variation, and general mutation probability pm chooses 0.01-0.1,
Here 0.1 is chosen.
D), the big individual of adaptive value is chosen as male parent using roulette mode, calculates each chromosome viAdaptive value
eval(vi) (i=1,2....40) and group always just whenCalculate each chromosome viSelect probability pi=
eval(vi)/F (i=1,2....40)；Calculate each chromosome viAccumulated probability(i=1,2....40)；To wheel
Disk rotates 100 times, selects a single chromosome according to the methods below every time: generating one in the random of section [0,1]
Number r, if r < q1, select first chromosome that new group is added, otherwise selection is so that qi-1<r<qiI-th of the chromosome set up
viNew group is added in (2≤i≤40)；
E), crossover operator designs: the parent of crossover operation is determined using partial mapped crossover, by 100 samples group two-by-two
Conjunction is divided into 50 groups, generates 2 random numbers b1 and b2 from closed interval [0,1] first, enables r1 be equal to b1 × 100 and b2 × 100, really
Fixed 2 positions, intersect the data among 2 positions；After intersection, duplicate ring network cabinet is had in same sample, is not repeated
Number retain, it is duplicate number using partial mapped crossover method eliminate repeat；
F), mutation operator designs: using inversion alternative method, randomly chooses 2 point c1 and c2, exchanges position, and will be between 2 points
Number since c2 inverted order place；
G), to each evaluated chromosome in new group, optimum individual is saved, and exports optimal solution.
For by the Chinese valley section Jin Gu, A divides plot.It needs to form Single-ring network to 9 ring network cabinets, be chosen using expertise
Route as shown in Figure 1, be 2.4km.Shorten 8.3% as shown in figure 5, for 2.2km in the path for using genetic algorithm to choose.It is logical
Path optimization technology is crossed, cable trace can be optimized, to shorten cable run investment.For the concatenation of 9 ring network cabinets,
Cable is saved to be not obvious.But for more ring network cabinet, or when the primary looped network for forming 5 or more, save
Cable is obvious.As shown in fig. 6, the effect picture after the entire Chinese valley section Jin Gu optimization, is saved under the premise of guaranteeing power supply reliability
It 31 kilometers of cable length, is calculated by 1,000,000 yuan/kilometer of cable run, saves 31,000,000 yuan altogether.Hand layouts, effect are compared simultaneously
Rate improves significant.
Above-mentioned, although the foregoing specific embodiments of the present invention is described with reference to the accompanying drawings, not protects model to the present invention
The limitation enclosed, those skilled in the art should understand that, based on the technical solutions of the present invention, those skilled in the art are not
Need to make the creative labor the various modifications or changes that can be made still within protection scope of the present invention.
Claims (2)
1. a kind of power circuit path optimization method using genetic algorithm, which comprises the following steps:
1), foundation includes the smallest objective function equation of layout of roads year comprehensive cost of the investment cost of route, cost of losses
Group:
Wherein, α is unit length track investment expense, r0For discount rate, m is the substation low-voltage side route depreciable life, and N is to become
Power station sum, lijFor the length of i-th substation's j-th strip basic routing line, JiGo out the sum of route, P for i-th of substationj
For the load of j-th strip basic routing line institute band, β is route network loss conversion factor；
2), in the Electric Power Network Planning of section, the load in each sub- plot is obtained according to load prediction, determines the ring that the sub- plot needs
Net cabinet quantity arranges ring network cabinet position according to sub- plot actual conditions, referring to actual cable pipe trench path, by each ring network cabinet string
It connects, forms Single-ring network or dual-ring network wiring construction；
3), cable ring-system path is optimized using genetic algorithm, abstract constraint condition and target are to find most stub cable road
Diameter, specifically includes the following steps:
A), symbolization coding mode is encoded to comprising n cable Single-ring network cabinet and/or cable dual-ring network cabinet, random raw
At the random alignment for the n integer that section is [1, n], a chromosome is formed；
B), select and be used to judge chromosome to the fitness function of the adaptability of target, fitness function equation isWherein, SijRepresent the actual range between i-th of ring network cabinet and j-th of ring network cabinet；
C), genetic algorithm main control parameters select, and determine that population quantity N, maximum algebra Gmax, crossover probability pc and variation are general
Rate pm；
D), selection course is based on rotating roulette wheel 100 times, and rotation all selects an individual for new population every time, is used
Roulette mode chooses the big individual of adaptive value as male parent, and roulette wheel is to carry out selection by the fitness of individual, and adaptive value is big
Individual then choose, adaptive value it is small individual then remove；
E), crossover operator designs: the parent of crossover operation is determined using partial mapped crossover, by 100 sample combination of two point
It is 50 groups, generates 2 random numbers b1 and b2 from closed interval [0,1] first, enables r1 be equal to b1 × 100 and b2 × 100, determine 2
A position intersects the data among 2 positions；After intersection, duplicate ring network cabinet, unduplicated number are had in same sample
Word retains, and duplicate number is eliminated using partial mapped crossover method to be repeated；
F), mutation operator designs: using inversion alternative method, randomly chooses 2 point c1 and c2, exchanges position, and by the number between 2 points
Word inverted order since c2 is placed；
G), to each evaluated chromosome in new group, optimum individual is saved, and exports optimal solution.
2. a kind of power circuit path optimization method using genetic algorithm as described in claim 1, which is characterized in that step
D) in, the big individual of adaptive value is chosen as male parent using roulette mode, calculates each chromosome viAdaptive value eval (vi)
(i=1,2....N) and group always just whenCalculate each chromosome viSelect probability pi=eval (vi)/F
(i=1,2....N)；Calculate each chromosome viAccumulated probabilityTo wheel disc rotation 100
It is secondary, it selects a single chromosome according to the methods below every time: generating a random number r in section [0,1], if r <
q1, select first chromosome v1New group is added, otherwise selection is so that qi-1<r<qiI-th of the chromosome v set upi(2≤i≤
N new group) is added.
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| CN107515003B (en) * | 2017-07-19 | 2020-08-11 | 中国南方电网有限责任公司超高压输电公司检修试验中心 | Method for planning flight route of airplane for patrolling power transmission line |
| CN111027738B (en) * | 2019-10-18 | 2023-04-21 | 国网浙江省电力有限公司嘉兴供电公司 | Genetic algorithm-based power communication optical cable laying optimization method |
| CN110796297B (en) * | 2019-10-21 | 2020-11-10 | 浙江大学 | Electric power system structure optimization method based on balance degree variance and reliability |
| CN110763953B (en) * | 2019-10-30 | 2022-04-22 | 国网四川省电力公司电力科学研究院 | Troubleshooting line patrol path planning method under distribution automation condition |
| CN111597668A (en) * | 2020-05-28 | 2020-08-28 | 江苏蔚能科技有限公司 | Power path topology method based on genetic algorithm |
| CN111881534B (en) * | 2020-07-03 | 2024-05-21 | 吴仉华 | Indoor wiring optimization method and device |
| CN112199803A (en) * | 2020-09-01 | 2021-01-08 | 华南理工大学 | Cable group loop arrangement optimization method based on cultural gene algorithm |
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