CN106503844A - A kind of power circuit optimum path search method of employing genetic algorithm - Google Patents
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Abstract
The invention discloses a kind of power circuit optimum path search method of employing genetic algorithm, is optimized to cable ring-system path using genetic algorithm, is comprised the following steps：To encoding comprising n cable Single-ring network cabinet and/or cable dual-ring network cabinet, the random random alignment for generating the interval n integer for [1, n] forms a chromosome；Selecting, population quantity N, maximum algebraically Gmax, crossover probability pc and mutation probability pm is determined to the adaptive fitness function of target for passing judgment on chromosome；The big individuality of adaptive value is chosen as male parent using roulette mode, and roulette wheel is selection to be carried out by individual fitness, and the big individuality of adaptive value is then chosen, the little individuality of adaptive value is then removed, to each evaluated chromosome in new colony, optimum individual is preserved, and exports optimal solution.Hand layouts are compared, cost of labor has both been reduced, design efficiency, and flexibility and reliability is improve again, has been saved entreprise cost.
Description
Technical field
The present invention relates to a kind of power circuit optimum path search method of employing genetic algorithm.
Background technology
Current Electric Power Network Planning power circuit wiring in, typically adopt manual routing, manual routing typically by qualifications and record of service compared with
Deep expert significantly limit human resourcess carrying out, additionally, manual routing is less efficient, takes larger, improves again
Cost of labor, last manual routing can not find optimal route every time, cause routing path elongated, and cost of investment is carried
High.By taking Chinese valley Jin Gu sections A point of plot as an example.Need to form Single-ring network to 9 ring main units, the circuit that chooses using expertise
As shown in figure 1, being 2.4km.
Content of the invention
It is an object of the invention to provide a kind of power circuit optimum path search method of employing genetic algorithm, lifts electric load
Prediction, the goodness of fit between power grid construction sequential and need for electricity, and optimize medium voltage power lines path, in consideration power supply half
Propose in the case of footpath in the optimum path search method of the minimum object function of layout of roads year comprehensive cost.For achieving the above object,
The present invention adopts following technical proposals：
A kind of power circuit optimum path search method of employing genetic algorithm, comprises the following steps：
1), set up include the investment cost of circuit, cost of losses layout of roads year comprehensive cost minimum object function
Equation group：
Wherein, α be unit length track investment expense, r0For discount rate, m is the substation low-voltage side circuit depreciable life, N
For transformer station's sum, lijFor the length of i-th transformer station's j-th strip basic routing line, JiGo out the total of circuit for i-th substation
Number, PjFor the load of j-th strip basic routing line institute band, β is circuit network loss conversion factor；
2), in the Electric Power Network Planning of section, according to the load that load prediction obtains each sub- plot, the sub- plot needs are determined
Ring main unit quantity, ring main unit position is arranged according to sub- plot practical situation, with reference to actual cable pipe trench path, by each looped network
Cabinet is concatenated, and 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 the most short electricity of searching
Cable path, specifically includes following steps：
A), symbolization coded system, to encoding comprising n cable Single-ring network cabinet and/or cable dual-ring network cabinet, with
Machine generates the random alignment of the interval n integer for [1, n], forms a chromosome；
B), select and be used for passing judgment on adaptive fitness function of the chromosome to target, fitness function equation isWherein, SijRepresent the actual range between i-th ring main unit and j-th ring main unit；
C), genetic algorithm main control parameters are selected, and determine population quantity N, maximum algebraically Gmax, crossover probability pc and change
Different Probability p m；
D), selection course is that rotation every time all be a new population selection individual based on rotating roulette wheel 100 times,
The big individuality of adaptive value is chosen as male parent using roulette mode, and roulette wheel is selection to be carried out by individual fitness, adapts to
The big individuality of value is then chosen, and the little individuality of adaptive value is then removed；
E), crossover operator design：The parent for determining crossover operation using partial mapped crossover, by 100 samples group two-by-two
Conjunction is divided into 50 groups, produces 2 randoms number b1 and b2 first from closed interval [0,1], makes r1 be equal to b1 × 100 and b2 × 100, really
Data in the middle of 2 positions are intersected by fixed 2 positions；After intersection, the ring main unit of repetition is had in same sample, is not repeated
Numeral reservation, the numeral of repetition eliminated using partial mapped crossover method and repeated；
F), mutation operator design：Using inversion alternative method, randomly choose 2 point c1 and c2, exchange position, and by 2 points between
Numeral from c2 start inverted order placement；
G), to each evaluated chromosome in new colony, optimum individual is preserved, and exports optimal solution.
Further, in step d), the big individuality of adaptive value is chosen as male parent using roulette mode, calculates each dye
The adaptive value v of colour solidrAdaptive value eval (vr) (i=1,2....N) and colony always just whenCalculate each
Chromosome vrSelect probability pr=eval (vr)/F (i=1,2....N)；Calculate each chromosome vrAccumulated probabilityWheel disc is rotated 100 times, select a single chromosome every time according to the methods below：
A random number r in interval [0,1] is produced, if r<q1, select first chromosome v1New colony is added, otherwise selects to make
Obtain qr-1＜ r ＜ qrThe i-th chromosome v for setting upr(2≤i≤N) adds new colony.
The invention has the beneficial effects as follows,
The present invention, can be with optimization cable trace, to shorten cable run investment by optimum path search technology.For quantity
More ring main units, or when once forming the looped network of 5 or more, save cable obvious.Compare hand layouts simultaneously, both subtracted
Cost of labor is lacked, has improve design efficiency, and flexibility and reliability again, saved entreprise cost.
Description of the drawings
Fig. 1 is the looped network pathway figure selected using expertise；
Fig. 2 is a kind of power circuit optimum path search method of employing genetic algorithm；
Fig. 3 is that 10kV cable outlets segments is 5, and conductor cross-section is 400mm3, copper core cable " monocyclic physical model
Figure；
Fig. 4 is that 10kV cable outlets segments is 5, and conductor cross-section is 400mm3, copper core cable " dicyclic physical model
Figure；
Fig. 5 is the looped network pathway figure selected using genetic algorithm；
Fig. 6 is design sketch after Han Yu sections cable trace optimizes.
Specific embodiment
As shown in Fig. 2 a kind of power circuit optimum path search method of employing genetic algorithm, comprises the following steps：
1), set up include the investment cost of circuit, cost of losses layout of roads year comprehensive cost minimum object function
Equation group：
Wherein, α be unit length track investment expense, r0For discount rate, m is the substation low-voltage side circuit depreciable life, N
For transformer station's sum, lijFor the length of i-th transformer station's j-th strip basic routing line, JiGo out the total of circuit for i-th substation
Number, PjFor the load of j-th strip basic routing line institute band, β is circuit network loss conversion factor；
2), in the Electric Power Network Planning of section, according to the load that load prediction obtains each sub- plot, the sub- plot needs are determined
Ring main unit quantity, ring main unit position is arranged according to sub- plot practical situation, with reference to actual cable pipe trench path, by each looped network
Cabinet is concatenated, and forms Single-ring network or dual-ring network wiring construction.By taking cable system as an example, power circuit optimum path search is discussed.According to load
Prediction obtains the load in each sub- plot, determines the ring main unit quantity that the sub- plot needs；According to load character (significance level),
Determine cable ring-system type (monocyclic or dicyclic).So that " 10kV cable outlets segments as 5, conductor cross-section is 400mm2, copper
The relation of load and attaching capacity, as a example by core cable ", is described." 10kV cable outlets segments is 5, and conductor cross-section is 400mm2,
The through-put power analysis of copper core cable " is as shown in table 1.
Table 110kV cable outlets segments is 5, and conductor cross-section is 400mm2, the through-put power of copper core cable
As shown in Figure 3, Figure 4, dicyclic is only the superposition of monocyclic, and " two monocyclic " is to match somebody with somebody with the difference of " bicyclic "
The two-way power supply of electric room equipment is respectively from a ring main unit still respectively from two ring main units.Illustrate by taking monocyclic as an example
Each ring main unit can attaching capacity.In the case where load moment is not considered, every during different electrical equipment (distribution transformings) load factor
Individual ring main unit can attaching capacity as shown in table 2.
Table 210kV cable outlets segments is 5, and conductor cross-section is 400mm2, copper core cable each ring main unit can attaching
Capacity
If it is 40% to select distribution transforming load factor, and considers simultaneity factor (choosing 0.65), then a ring main unit can attaching distribution transforming
Capacity is 4MVA, is otherwise unsatisfactory for circuit N-1.If the load W in known sub- plotr(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 the most short electricity of searching
Cable path, specifically includes following steps：
A), symbolization coded system, for comprising 9 ring main unit cable Single-ring network (or dual-ring network) path optimizations
Problem.Using symbol coding is used, 1 ring main unit of each digitized representation, random generation interval are 9 integers of [1,9]
Random alignment, forms a chromosome.
B), fitness function is to evaluate adaptive effect of the chromosome to target, selectes and is used for passing judgment on chromosome to mesh
The adaptive fitness function of target, fitness function equation isWherein, SijRepresent i-th ring main unit and
Actual range between j ring main unit, if due to 2 points of differences of restriction of cable pipe trench, then it is assumed that SijFor infinity, in reality
Border is replaced with a big number in calculating.
C), genetic algorithm main control parameters are selected, and determine population quantity N, maximum algebraically Gmax, crossover probability pc and change
Different Probability p m；Population size is bigger, and the pattern handled by GA is more, and the probability for being absorbed in local solution is less, be easily absorbed in not into
Ripe convergence, but scale crosses conference increase amount of calculation, affects efficiency of algorithm, chooses 40 here.Iterationses are few, desired value convergence effect
Really bad, iterationses are excessive, long operational time, choose 500 here.Hybridization is that an important restructuring of genetic algorithm is calculated
Son, probability of crossover pc is a parameter of algorithm, and it is pc*N that this probability provides the number for being expected to be hybridized, crossover probability one
As choose 0.2-0.9, here choose 0.8；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, whole group
Each in all chromosomes in body has equal opportunity experience variation, general mutation probability pm to choose 0.01-0.1,
0.1 is chosen here.
D) the big individuality of adaptive value is chosen as male parent using roulette mode, the adaptive value v of each chromosome is calculatedr
Adaptive value eval (vr) (i=1,2....40) and colony always just whenCalculate each chromosome vrSelection
Probability pr=eval (vr)/F (i=1,2....40)；Calculate each chromosome vrAccumulated probabilityWheel disc is rotated 100 times, select a single dyeing every time according to the methods below
Body：A random number r in interval [0,1] is produced, if r<q1, select first chromosome to add new colony, otherwise select
So that qr-1＜ r ＜ qrThe i-th chromosome v for setting upr(2≤i≤40) add new colony；
E), crossover operator design：The parent for determining crossover operation using partial mapped crossover, by 100 samples group two-by-two
Conjunction is divided into 50 groups, produces 2 randoms number b1 and b2 first from closed interval [0,1], makes r1 be equal to b1 × 100 and b2 × 100, really
Data in the middle of 2 positions are intersected by fixed 2 positions；After intersection, the ring main unit of repetition is had in same sample, is not repeated
Numeral reservation, the numeral of repetition eliminated using partial mapped crossover method and repeated；
F), mutation operator design：Using inversion alternative method, randomly choose 2 point c1 and c2, exchange position, and by 2 points between
Numeral from c2 start inverted order placement；
G), to each evaluated chromosome in new colony, optimum individual is preserved, and exports optimal solution.
By taking Chinese valley Jin Gu sections A point of plot as an example.Need Single-ring network to be formed to 9 ring main units, chosen using expertise
Circuit as shown in figure 1, be 2.4km.The path for adopting genetic algorithm to choose is as shown in figure 5, for 2.2km, shorten 8.3%.Logical
Path optimization technology is crossed, can be with optimization cable trace, to shorten cable run investment.For the concatenation of 9 ring main units,
Save cable unobvious.But for more ring main unit, or when once forming the looped network of 5 or more, save
Cable is obvious.As shown in fig. 6, the design sketch after whole Chinese valley Jin Gu sections optimization, saves under the premise of power supply reliability is ensured
31 kilometers of cable length, is calculated by 1,000,000 yuan/kilometer of cable run, saves 31,000,000 yuan altogether.Compare hand layouts simultaneously, imitate
Rate is improved significantly.
Although the above-mentioned accompanying drawing that combines is described to the specific embodiment of the present invention, not to present invention protection model
The restriction that encloses, one of ordinary skill in the art should be understood that on the basis of technical scheme those skilled in the art are not
The various modifications that makes by needing to pay creative work or deformation are still within protection scope of the present invention.
Claims (2)
1. a kind of power circuit optimum path search method of employing genetic algorithm, it is characterised in that comprise the following steps：
1), set up include the investment cost of circuit, cost of losses layout of roads year comprehensive cost minimum object function equation
Group：
Wherein, α be unit length track investment expense, r0For discount rate, m is the substation low-voltage side circuit depreciable life, and N is to become
Power station sum, lijFor the length of i-th transformer station's j-th strip basic routing line, JiFor the sum that i-th substation goes out circuit, Pj
For the load of j-th strip basic routing line institute band, β is circuit network loss conversion factor；
2), in the Electric Power Network Planning of section, according to the load that load prediction obtains each sub- plot, determine the ring that the sub- plot needs
Net cabinet quantity, arranges ring main unit position according to sub- plot practical situation, with reference to actual cable pipe trench path, by each ring main unit string
Connect, form 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 searching most stub cable road
Footpath, specifically includes following steps：
A), symbolization coded system, to encoding comprising n cable Single-ring network cabinet and/or cable dual-ring network cabinet, random raw
Into the random alignment of the interval n integer for [1, n], a chromosome is formed；
B), select and be used for passing judgment on adaptive fitness function of the chromosome to target, fitness function equation isWherein, SijRepresent the actual range between i-th ring main unit and j-th ring main unit；
C), genetic algorithm main control parameters are selected, and determine that population quantity N, maximum algebraically Gmax, crossover probability pc and variation are general
Rate pm；
D), selection course is that rotation every time all be that new population selects an individual, employing based on rotating roulette wheel 100 times
Roulette mode chooses the big individuality of adaptive value as male parent, and roulette wheel is to carry out selection by individual fitness, and adaptive value is big
Individuality then choose, the little individuality of adaptive value is then removed；
E), crossover operator design：The parent for determining crossover operation using partial mapped crossover, by 100 sample combination of two point
For 50 groups, 2 randoms number b1 and b2 are produced from closed interval [0,1] first, make r1 be equal to b1 × 100 and b2 × 100, determine 2
Data in the middle of 2 positions are intersected by individual position；After intersection, the ring main unit of repetition, unduplicated number in same sample, is had
Word retains, and the numeral of repetition is eliminated using partial mapped crossover method and repeated；
F), mutation operator design：Using inversion alternative method, randomly choose 2 point c1 and c2, exchange position, and by 2 points between number
Word starts inverted order placement from c2；
G), to each evaluated chromosome in new colony, optimum individual is preserved, and exports optimal solution.
2. a kind of power circuit optimum path search method of employing genetic algorithm as claimed in claim 1, it is characterised in that step
D), in, the big individuality of adaptive value is chosen as male parent using roulette mode, the adaptive value v of each chromosome is calculatedrAdaptation
Value eval (vr) (i=1,2....N) and colony always just whenCalculate each chromosome vrSelect probability pr=
eval(vr)/F (i=1,2....N)；Calculate each chromosome vrAccumulated probability(i=1,2....N)；To wheel disc
Rotate 100 times, select a single chromosome every time according to the methods below：Produce a random number in interval [0,1]
R, if r<q1, select first chromosome v1New colony is added, otherwise selects to cause qr-1＜ r ＜ qrI-th dyeing that sets up
Body vr(2≤i≤N) adds new colony.
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| CN110796297A (en) * | 2019-10-21 | 2020-02-14 | 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 |
| CN110763953A (en) * | 2019-10-30 | 2020-02-07 | 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 |
| CN111881534A (en) * | 2020-07-03 | 2020-11-03 | 吴仉华 | Indoor wiring optimization method and device |
| 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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| CN112529278A (en) * | 2020-12-02 | 2021-03-19 | 中国人民解放军93209部队 | Method and device for planning navigation network based on connection matrix optimization |
| CN112859931A (en) * | 2021-01-11 | 2021-05-28 | 暨南大学 | Unmanned aerial vehicle flight path planning method, forest fire prevention system and computer readable storage medium |
| CN116924287A (en) * | 2023-09-18 | 2023-10-24 | 临工重机股份有限公司 | Control method, device, equipment and medium of hydraulic compensation leveling mechanism |
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