CN105634117A - Smart distribution network reconstruction method on the basis of multiple targets - Google Patents

Smart distribution network reconstruction method on the basis of multiple targets Download PDF

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Publication number
CN105634117A
CN105634117A CN201410588590.4A CN201410588590A CN105634117A CN 105634117 A CN105634117 A CN 105634117A CN 201410588590 A CN201410588590 A CN 201410588590A CN 105634117 A CN105634117 A CN 105634117A
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distribution network
step
formula
network
branch road
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CN201410588590.4A
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Chinese (zh)
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邵雯
张俊芳
毕月
解坤
史媛
许辉
林莎
褚智亮
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南京理工大学
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Abstract

The present invention provides a smart distribution network reconstruction method on the basis of multiple targets. The distribution network is equivalent to an equivalent network formed by a line and a switch; branch parameters, node parameters and fault line parameters of the equivalent network are generated; and an optimal network after reconstruction is obtained by updating the particle position and speed through a binary particle algorithm. The smart distribution network reconstruction method on the basis of multiple targets is able to automatically identify the running state of a distribution network and provide distribution network multi-target smart distribution network reconstruction in different running states.

Description

A kind of based on multiobject intelligent distribution network reconstructing method

Technical field

This patent belongs to power system automatic field, particularly to one based on multiobject intelligent distribution network reconstructing method.

Background technology

The economy China develops rapidly, and the demand of electric power is also in quick growth. Power distribution network, as the pith of power system, is also power system main loads output part. As the important infrastructure ensureing social and economic stability development, power distribution network reconfiguration has great significance.

The feature of power distribution network is ring network structure, the radial operation of open loop. Power distribution network reconfiguration is the structure changing power distribution network by changing the folding of network breaker in middle, makes up to the method for operation of the best. Power distribution network reconfiguration is a multi-objective optimization question, optimization aim has to reduce network active loss for target, can also be the economy to improve operation of power networks, to improve security of distribution network and power supply quality be target, or combined by several different targets and carry out multiobject reconstruction and optimization. But power distribution network reconfiguration is used in tradition example, does not use in actual power distribution network always. And the prioritization scheme that existing power distribution network reconfiguration is when can only draw properly functioning, it is impossible to prioritization scheme is provided when power distribution network breaks down. The present invention not only for power distribution network properly functioning time prioritization scheme is provided, and provide scientific and effective decision scheme when distribution network failure for the scheduling of electrical network, recover rapidly the power supply of power distribution network.

Summary of the invention

It is an object of the invention to identification power distribution network running status automatically, it is provided that power distribution network Multiobjective Intelligent reconstruction method of power distribution network under different running statuses.

In order to solve above-mentioned technical problem, the present invention provides a kind of based on multiobject intelligent distribution network reconstructing method, comprises the following steps:

Step one, the equivalent network that power distribution network Equivalent Network one-tenth is made up of circuit and switch;

Step 2, the generation branch parameters of equivalent network, node parameter, faulty line parameter;

Parameter required for step 3, initialization power distribution network reconfiguration;

Step 4, generation initial population;

Step 5, renewal particle position and speed;

Step 6, judge whether network is radiation network, it may be judged whether for radial, if not radial, then return step 5 and continue to update particle position and speed, until for till radial; If radial, then carry out step 7; ;

Step 7, calculating reconstruct the minima of object function, and meet voltage constraints and tributary capacity constraints;

Step 8, judge whether the network representated by current group is optimal network, if optimum, enter step 9, if not then returning step 5, until obtaining optimal network;

Step 9, judge whether to reach maximum iteration time, if it is export reconstruction result, if not then returning step 5; The output reconstruction result when reaching colony's optimum and reaching maximum iteration time.

Compared with prior art, it has the great advantage that actual complex power distribution network is carried out simplification process by (1) present invention to the present invention, improves the optimization ability calculating speed and power distribution network; (2) present invention is reconstructed under power distribution network difference running status by the binary particle swarm algorithm improved, network time not only properly functioning can be optimized, network during fault can also obtain optimum reconfiguration scheme, fast quick-recovery power distribution network is powered, and the scheduling that actual electric network is run has good decision guidance effect; (3) power distribution network is carried out multiple target reconstruct by the present invention, not only can ensure that when power distribution network runs, active power loss is minimum, and it is also ensured that balancing the load index is minimum, variation volume index is also minimum, thus ensure that electrical network is in optimal operational condition.

Accompanying drawing explanation

Fig. 1It it is flow process of the present inventionFigure��

Fig. 2It it is the network in one embodiment of the invention after the simplification of actual power distribution networkFigure��

Fig. 3It it is the network in another embodiment of the present invention after the simplification of actual power distribution networkFigure��

Detailed description of the invention

Such as Fig. 1, the present invention, based on multiobject intelligent distribution network reconstructing method, comprises the following steps:

Step one, the equivalent network that power distribution network Equivalent Network one-tenth is made up of circuit and switch, particularly as follows:

Several load around same switch is merged into a load, and the circuit at same switch place is considered as a branch road, delete and uncharge branch and apart from shorter branch road, only retain main line and important branch road. Equivalent network after simplification can to calculate and to calculate speed.

Step 2, the generation branch parameters of equivalent network, node parameter, faulty line parameter; Wherein,

Branch parameters includes: branch number, branch road start node, branch road end-node, branch resistance, branch road reactance, switch names;

Node parameter includes: node serial number, node active power, node reactive power;

Faulty line parameter includes: faulty line is numbered, and corresponding to this numbering and branch number, wherein 0 represents circuit fault-free.

Parameter required for step 3, initialization power distribution network reconfiguration, parameter includes:

The scale of particle: n the molecular colony of grain, n can be arranged automatically, and n is more big, and colony is more big, and the probability searching optimal network is also just maximum;

The dimension of particle, the i.e. number of branch road;

Iterations;

Studying factors, namely searches for the ability of optimal network

Step 4, generation initial population;

Step 5, update particle position and speed, described particle position and speed renewal process and be:

In the search volume of a M dimension, the positional information of particle j is expressed as Xj=(xj1,xj2,��xjM) ', velocity information is expressed as Vj=(vj1,vj2,��vjM) ', particle j is finding personal best particle gbestWith colony optimal location pbestAfterwards, more shown in the position of new particle self and the update method such as formula (1) and (2) of velocity information, position and speed,

v jm k + 1 = wv jm k + c 1 rand ( 0,1 ) 1 k ( p best , jm k - x jm k ) + c 2 rand ( 0,1 ) 2 k ( g best , jm k - x jm k ) - - - ( 1 )

x jm k + 1 = x jm k + v jm k + 1 - - - ( 2 )

In formula (1) and (2),Speed after updating for particle;Searching position after updating for particle; W is inertia weight, and when population is less, inertia weight w can between value [0.9,1.2], and convergence is better; c1��c2For accelerated factor, being typically set to fixed value, value is between [0,2]; Rand (0,1) is the function randomly generating the arithmetic number between [0,1];For the position corresponding to k+1 rear individual optimal value of particle j iteration;For the position corresponding to particle j iteration k+1 Ci Hou colony optimal value.

When particle is relatively big in speed previous stage of search procedure, when the latter half search speed is less, for avoiding result to be absorbed in locally optimal solution and search speed can reach the fastest, the present invention is to c1��c2Do improvement, as shown in formula (3),

c 1 = c 1 max - ( c 1 max - c 1 min ) × t / N c 2 = c 2 max - ( c 2 max - c 2 min ) × t / N - - - ( 3 )

In formula (3): c1max��c1minRespectively c1Minimum and maximum value; c2max��c2minRespectively c2Minimum and maximum value; T is current iteration number of times; N is maximum iteration time.

In binary particle swarm algorithm, the position at each particle place is set as 1 or 0, determines that the position that particle is corresponding is 1 or 0 according to speed. When speed is bigger, it is that the probability of 1 is big, and speed hour, it is that the probability of 0 is big. In binary particle swarm algorithm, the speed sigmoid function of particle updates, shown in sigmoid function such as formula (4),

sigmoid ( d ) = 1 1 + e - d - - - ( 4 )

In formula (4), x represents the speed of particle, for preventing sigmoid function saturated, the speed of particle can be set within the specific limits, be set at this in [-4,4] scope, then formula (4) can be further represented as formula (5)

sigmoid ( d ) = 0.98 d > 4 1 1 + e - x - d < x < 4 - 0.98 d < - 4 - - - ( 5 )

Therefore the particle position in binary particle swarm algorithm is updated shown in formula (6),

x jm k + 1 = 1 &rho; jm k + 1 < sigmoid ( v jm k + 1 ) x jm k + 1 = 0 &rho; jm k + 1 > sigmoid ( v jm k + 1 ) - - - ( 6 )

In formula (6),For the random arithmetic number between [0,1].

Step 6, judge whether network is radiation network, it may be judged whether for radial, if not radial, then return step 5 and continue to update particle position and speed, until for till radial; If radial, then carry out step 7; ;

Step 7, network being carried out Load flow calculation adaptive value, namely adaptive value reconstructs the minima of object function, even if active power loss, balancing the load index, voltage deviation index reach minimum, and meets voltage constraints and tributary capacity constraints.

Shown in described reconstruct object function such as formula (7),

Minf=[f1,f2,f3]T(7)

In formula (7), f1Representing active power loss, mathematic(al) representation isWherein, nbSum for the branch road of power distribution network; riIt is the resistance of i-th branch road;For flowing through the electric current of i-th branch road; kiFor switching the state of i, 0 represents disjunction, and 1 represents Guan Bi.

In formula (7), f2Representing balancing the load index, mathematic(al) representation is:Wherein, nbSum for the branch road of power distribution network; SiRepresent the power passed through on branch road i; SimaxIndicate that nbThe maximum of the power passed through on bar branch road.

In formula (7), f3Representing variation volume index, mathematic(al) representation is:Wherein n is power distribution network nodes; VnLoad voltage value for node j.

Shown in described voltage constraints such as formula (8),

Uj,min��Uj��Uj,max(8)

In formula (8), Uj,minAnd Uj,maxThe respectively voltage upper lower limit value of node j.

Shown in described tributary capacity constraints such as formula (9),

Si��Si,max(9)

In formula (9), SiFor the performance number flow through on branch road i; Si,maxFor the maximum power value allowed to flow through on branch road i;

Step 8, judge whether the network representated by current group is optimal network, if optimum, enter step 9, if not then returning step 5, until obtaining optimal network;

Step 9, judge whether to reach maximum iteration time, if it is export reconstruction result, if not then returning step 5; The output reconstruction result when reaching colony's optimum and reaching maximum iteration time.

Below in conjunction with embodiment, the present invention is done further detailed description:

Embodiment

The feasibility of Multiobjective Intelligent power distribution network reconfiguration that the present invention proposes is verified with a part for the actual power distribution network in In A Certain Locality, Jiangsu Province district.

Actual power distribution network simplify after network asLower Fig. 2Shown in, this section is 10kV power distribution network, has 8 buses, 7 union switch, 23 regular taps, altogether 30 branch roads, and total load is 7806.7+j3030kVA.

Distribution power system load flow calculation adopts forward-backward sweep method, initializes population scale 50, maximum iteration time 100. The non-update times of colony's optimum is 20. The circuit of input power distribution network and node parameter, parameterSuch as table 1��Table 2��Table 3Shown in.

Table 1Power distribution network branch parameters

Branch number Branch road start node Branch road end-node Branch resistance Branch road reactance 2-1 1 2 0.425 0.85 2-2 2 3 0.0255 0.051 2-3 3 4 0.0085 0.017 2-4 4 5 0.0595 0.119 2-5 5 6 0.0476 0.0952 2-6 6 7 0.0255 0.051 2-7 7 8 0.1773 0.3546 2-8 1 8 0.1955 0.391 2-9 6 9 0.1907 0.118 2-10 1 10 0.2861 0.1677

[0069] 2-11 10 11 0.5722 0.3354 2-12 1 12 0.2988 0.1751 2-13 12 13 0.5404 0.3168 2-14 13 14 0.17 0.34 2-15 14 15 0.1275 0.255 2-16 15 16 0.1105 0.221 2-17 1 17 0.034 0.068 2-18 13 17 0.102 0.204 2-19 1 18 0.425 0.85 2-20 18 19 0.255 0.51 2-21 19 20 0.017 0.034 2-22 20 21 0.3179 0.1863 2-23 21 22 0.17 0.34 2-24 1 22 0.3349 0.6698 2-25 1 23 0.4352 0.8704 2-26 23 24 0.0908 0.1816 2-27 21 24 0.0187 0.0374 2-28 2 15 0.3179 0.1863 2-29 8 10 0.3179 0.1863 2-30 6 21 0.3179 0.1863

Table 2Power distribution network node parameter

Node serial number Node active power Node reactive power 1 0 0 2 189.7 100 3 154.1 50 4 47.4 20 5 179.6 60 6 534.6 110 7 385.3 20 8 819.6 400 9 331.3 100 10 193.2 20 11 254.3 100 12 47.4 10 13 1015.4 500

[0072] 14 301.7 100 15 84.8 20 16 246 100 17 84.8 70 18 683.4 220 19 647.3 320 20 364.5 150 21 325.4 20 22 481.3 300 23 71.1 50 24 364.5 200

Table 3Faulty line parameter

Faulty line is numbered 2-17

Power distribution network reconfiguration is in two kinds of situation: power distribution network is properly functioning and during failure operation.

1, power distribution network time properly functioning being reconstructed, now faulty line is 0.

When network is properly functioning, it is only necessary to find an optimum operating scheme, reduce network loss, each switch is involved in reconstruct, the reconstruction result obtainedSuch as table 4Shown in:

Table 4Reconstruction result time properly functioning

Disconnect switch Target f1(kW) Target f2 Target f3 Before reconstruct 5��18��21��23��28��29��30 64.8117 3.8761 0.0210 After reconstruct 5��13��21��27��28��29��30 39.4527 2.1748 0.0138

From the results, it was seen that distribution network loss but changes very big after reconstruct, being reduced to 39.4527kW from original 64.8117kW, balancing the load index is reduced to 2.1748 by original 3.8761, and variation volume index is also reduced to 0.0138 from 0.0210. Visible reconstruct also can be used well in real network, and effect is obvious.

2, power distribution network when breaking down is reconstructed,

When bar branch road a certain in network breaks down, faulty line is not involved in reconstruct, and wherein faulty line can be ordinary branch can also be power supply output line road. When breaking down in power supply output line road, this power supply then exits the operation of power distribution network. When breaking down such as 2-17 branch road, original state networkSuch as Fig. 3Shown in, fromFig. 3In can be seen that No. 17 nodes become " isolated island ", this state can not normally ensure the normal power supply of user, it is necessary to is reconstructed:

Again read off power distribution network branch road and node and faulty line data, the reconstruction result obtainedSuch as table 5Shown in:

Table 5Reconstruction result during failure operation

Disconnect switch Target f1(kW) Target f2 Target f3 Before reconstruct 5��17��21��23��28��29��30 68.3417 3.9779 0.0221 After reconstruct 5��14��17��21��27��29��30 54.6505 2.4862 0.0138

It seems that from result, power distribution network is reconstructed during 2-17 line failure, distribution network loss is reduced to 54.6505kW by original 68.3417kW. Balancing the load index is reduced to 2.4862 by original 3.9779, and variation volume index is also reduced to 0.02145 from 0.0221. During visible fault, power distribution network reconfiguration result effect is also apparent from.

Claims (6)

1. one kind based on multiobject intelligent distribution network reconstructing method, it is characterised in that comprise the following steps:
Step one, the equivalent network that power distribution network Equivalent Network one-tenth is made up of circuit and switch;
Step 2, the generation branch parameters of equivalent network, node parameter, faulty line parameter;
Parameter required for step 3, initialization power distribution network reconfiguration;
Step 4, generation initial population;
Step 5, renewal particle position and speed;
Step 6, judge whether network is radiation network, it may be judged whether for radial, if not radial, then return step 5 and continue to update particle position and speed, until for till radial; If radial, then carry out step 7; ;
Step 7, calculating reconstruct the minima of object function, and meet voltage constraints and tributary capacity constraints;
Step 8, judge whether the network representated by current group is optimal network, if optimum, enter step 9, if not then returning step 5, until obtaining optimal network;
Step 9, judge whether to reach maximum iteration time, if it is export reconstruction result, if not then returning step 5; The output reconstruction result when reaching colony's optimum and reaching maximum iteration time.
2. as claimed in claim 1 based on multiobject intelligent distribution network reconstructing method, it is characterized in that, in step, several load around same switch is merged into a load, and the circuit at same switch place is considered as a branch road, delete and uncharge branch and apart from shorter branch road, only retain main line and important branch road.
It is 3. as claimed in claim 1 based on multiobject intelligent distribution network reconstructing method, it is characterised in that in step 2,
Branch parameters includes: branch number, branch road start node, branch road end-node, branch resistance, branch road reactance, switch names;
Node parameter includes: node serial number, node active power, node reactive power;
Faulty line parameter includes: faulty line is numbered.
4. as claimed in claim 1 based on multiobject intelligent distribution network reconstructing method, it is characterised in that in step 3, the parameter required for initializing power distribution network reconfiguration includes:
The scale of particle: n the molecular colony of grain;
Represent the dimension of the particle of the number of branch road;
Iterations;
Represent the Studying factors of search optimal network ability.
It is 5. as claimed in claim 1 based on multiobject intelligent distribution network reconstructing method, it is characterised in that in step 5, shown in the update method of described particle position such as formula (1),
In formula (1),For the random arithmetic number between [0,1],For update after position,For particle rapidity,Update method such as formula (2) shown in,
In formula (2), w is inertia weight; Rand (0,1) is the function randomly generating the arithmetic number between [0,1];For the position corresponding to k+1 rear individual optimal value of particle j iteration;For the position corresponding to particle j iteration k+1 Ci Hou colony optimal value; c1��c2For accelerated factor, as shown in formula (3),
c1=c1max-(c1max-c1min)��t/N(3)
c2=c2max-(c2max-c2min)��t/N
In formula (3): c1max��c1minRespectively c1Minimum and maximum value; c2max��c2minRespectively c2Minimum and maximum value; T is current iteration number of times; N is maximum iteration time;
In formula (1), shown in function sigmoid such as formula (4),
In formula (4), d represents the speed of particle
It is 6. as claimed in claim 1 based on multiobject intelligent distribution network reconstructing method, it is characterised in that in step 7,
Shown in described reconstruct object function such as formula (5),
Minf=[f1,f2,f3]T(5)
In formula (5), f1Representing active power loss, mathematic(al) representation isWherein, nbSum for the branch road of power distribution network; riIt is the resistance of i-th branch road;For flowing through the electric current of i-th branch road; kiFor switching the state of i, 0 represents disjunction, and 1 represents Guan Bi;
In formula (5), f2Representing balancing the load index, mathematic(al) representation is:Wherein, nbSum for the branch road of power distribution network; SiRepresent the power passed through on branch road i; SimaxIndicate that nbThe maximum of the power passed through on bar branch road;
In formula (5), f3Representing variation volume index, mathematic(al) representation is:Wherein n is power distribution network nodes; VnLoad voltage value for node j;
Shown in described voltage constraints such as formula (6),
Uj,min��Uj��Uj,max(6)
In formula (6), Uj,minAnd Uj,maxThe respectively voltage upper lower limit value of node j;
Shown in described tributary capacity constraints such as formula (7),
Si��Si,max(7)
In formula (7), SiFor the performance number flow through on branch road i; Si,maxFor the maximum power value allowed to flow through on branch road i.
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