CN116154874B - Coordination control and optimal configuration method for multiple series devices in power distribution network - Google Patents

Coordination control and optimal configuration method for multiple series devices in power distribution network Download PDF

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CN116154874B
CN116154874B CN202310443535.5A CN202310443535A CN116154874B CN 116154874 B CN116154874 B CN 116154874B CN 202310443535 A CN202310443535 A CN 202310443535A CN 116154874 B CN116154874 B CN 116154874B
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dvr
bus
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CN116154874A (en
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杨旭
朱晋
王红树
李建威
肖峥
刘单华
李薛剑
蒋卓宇
熊为军
秦雅岚
魏纯晓
贺兴
罗杰
侯婷婷
侯慧
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Wuhan University of Technology WUT
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract

The invention provides a coordination control and optimal configuration method of a plurality of series devices in a power distribution network, which comprises the following steps: establishing a power distribution network comprising loads and buses, and enabling each bus in the power distribution network to be provided with a DVR; acquiring the output power of the DVR on each bus and the voltage drop of each circuit according to the DVR simplification model; establishing an optimization model of the power distribution network, and determining input parameters, objective functions and constraint conditions of the optimization model of the power distribution network; and optimizing the input parameters of the optimization model of the DVR by adopting a simplex method and a firefly algorithm based on the improved generation, so as to obtain the optimal input parameters and minimize the capacity of all the DVRs in the power distribution network. According to the scheme, the capacity of the DVR is minimized through reasonable configuration, a corresponding optimization model is established, and an objective function and constraint conditions are established to achieve an optimal effect through an optimization algorithm.

Description

Coordination control and optimal configuration method for multiple series devices in power distribution network
Technical Field
The invention relates to the technical field of voltage sag management equipment, in particular to a coordination control and optimal configuration method for a plurality of series equipment in a power distribution network.
Background
With the rapid development of national economy, the types and the number of the power equipment at the user side are continuously increased, the probability of occurrence of voltage drop, fluctuation flicker and other events at the power grid side is gradually increased, so that a plurality of sensitive electrical equipment cannot work normally, and huge economic loss is caused to the user side. The most significant causes of end-user power quality problems are voltage drops and short breaks, most pronounced with voltage drops. IEEE defines the voltage sag as the root mean square value of the voltage of a certain connection point in a power supply system, suddenly drops to 0.1-0.9 times rated voltage, and resumes normal short-time disturbance phenomenon after lasting 0.5 cycle to 1 minute, and events such as short circuit fault, lightning stroke, switch operation, transformer or capacitor switching, high-power induction motor and the like in a circuit are likely to cause the voltage sag. The voltage sag can cause equipment shutdown, production interruption, even personal injury and death, and huge economic loss and safety risks are caused for power users.
Dynamic voltage restorer DVR (Dynamic Voltage Restorer) is one of the effective means of managing voltage sag. The treatment effect depends on the installation position and the capacity of the DVR, and the unordered installation cannot achieve the expected treatment effect and brings great maintenance workload. The Chinese patent application of CN112039082A discloses an optimal configuration method and system for power distribution network voltage regulating equipment based on minimum loss, wherein the scheme is based on the goal of minimum loss, the voltage regulating equipment is selected according to apparent capacity, node loss change values of nodes with unqualified voltages are obtained, and the voltage is regulated to rated voltage until the voltages of all nodes are qualified. However, this solution is to select the DVR based on the goal of minimum loss, not minimum capacity, and when the load has multiple buses and has various voltage requirements, it is not necessarily applicable, and it is easy to cause the capacity waste of the DVR device, and still increases the resources consumed by the DVR. Therefore, it is necessary to optimize the power distribution network for the case of using multiple DVR devices for the power distribution network based on the topology of the power distribution network and the capacities of the DVR devices.
Disclosure of Invention
In view of the above, the invention provides a coordinated control and optimal configuration method of a plurality of series devices in a power distribution network, wherein the capacity of a DVR (digital video recorder) aiming at power grid requirements is used as an optimal target.
The technical scheme of the invention is realized as follows: the invention provides a coordination control and optimal configuration method of a plurality of series devices in a power distribution network, which comprises the following steps:
build inclusion
Figure SMS_1
Personal load +.>
Figure SMS_2
A power distribution network with bus bars, wherein each bus bar in the power distribution network is provided with a DVR;
acquiring the output power of the DVR on each bus and the voltage drop of each circuit according to the DVR simplification model;
establishing an optimization model of the power distribution network, and determining input parameters, objective functions and constraint conditions of the optimization model of the power distribution network;
and optimizing the input parameters of the optimization model of the DVR by adopting a simplex method and a firefly algorithm based on the improved generation, so as to obtain the optimal input parameters and minimize the capacity of all the DVRs in the power distribution network.
On the basis of the above technical solution, preferably, the establishing includes
Figure SMS_3
Personal load +.>
Figure SMS_4
The distribution network of the bus is configured with->
Figure SMS_5
An independent bus is used for dividing a line between a power supply and the bus and between adjacent buses, wherein the total number of the lines is +.>
Figure SMS_6
A load and a DVR are disposed on each line.
Preferably, the obtaining the output power of the DVR on each bus and the voltage drop of each line according to the DVR simplification model is equivalent to the DVR as a controlled voltage source with the same phase as the grid voltage and the amplitude as the difference between the expected value of the load voltage and the grid voltage.
Preferably, the establishing of the optimization model of the power distribution network is to obtain values of equivalent resistance and reactance, load power, power grid voltage rated value, power factor and maximum drop ratio on each bus respectively, and take the values of equivalent resistance and reactance, load power, power grid voltage rated value, power factor and maximum drop ratio on each bus as input parameters of the optimization model of the power distribution network of the DVR; let the same phase compensation voltage of DVR on each bus be
Figure SMS_7
Wherein->
Figure SMS_8
The method comprises the steps of carrying out a first treatment on the surface of the Let the current flowing through each DVR be +.>
Figure SMS_9
The method comprises the steps of carrying out a first treatment on the surface of the The compensation capacity for each DVR in the distribution network is +.>
Figure SMS_10
The objective function is the minimum total capacity of all DVRs in the distribution network +.>
Figure SMS_11
。
Preferably, constraint conditions of the optimization model of the power distribution network respectively comprise load constraint, bus voltage constraint and loop constraint; wherein:
the load constraint is to include
Figure SMS_13
Personal load and->
Figure SMS_15
Distribution network with bus bars, and voltage after power supply sag is +.>
Figure SMS_17
The same phase of the same phase compensation voltage as DVR, the voltage after power supply sudden rising is +.>
Figure SMS_19
In contrast to the phase of the in-phase compensation voltage of DVR, only amplitude compensation is performed so that each load is +.>
Figure SMS_21
Is>
Figure SMS_23
Maintained in the working voltage range, i.e.
Figure SMS_25
Wherein->
Figure SMS_12
，/>
Figure SMS_14
，/>
Figure SMS_16
For load->
Figure SMS_18
Lower limit of the operating voltage of>
Figure SMS_20
For load->
Figure SMS_22
Upper limit of the operating voltage of>
Figure SMS_24
For the grid-side residual voltage after the occurrence of a voltage sag, +.>
Figure SMS_26
To compensate the voltage;
bus voltage constraint is that for a power distribution network, the requirements are satisfied
Figure SMS_28
，/>
Figure SMS_30
Wherein->
Figure SMS_31
Is bus bar->
Figure SMS_32
Voltage on>
Figure SMS_33
；/>
Figure SMS_34
Is the voltage on the bus of the upper stage; />
Figure SMS_35
Is bus bar->
Figure SMS_27
The lower voltage limit is the maximum lower limit of the working voltage of all loads on the bus; />
Figure SMS_29
Is the upper limit of the bus voltage, is the bus +.>
Figure SMS_36
The upper limit of the working voltage of all the loads is minimum; />
Figure SMS_37
Is bus bar->
Figure SMS_38
And->
Figure SMS_39
A voltage difference therebetween;
each line meets KVL and KCL, the current of each line flows to the load on the current line and the next bus, and the voltage vectors of all lines of the power distribution network meet the following relations:
Figure SMS_40
。
preferably, the optimization of the input parameters of the optimization model of the DVR by adopting the improved simplex method and firefly algorithm comprises the following steps:
the first step: setting and initializing parameters of a firefly algorithm, including firefly number
Figure SMS_41
Step size factor->
Figure SMS_42
Random step factor->
Figure SMS_43
Medium absorption factor->
Figure SMS_44
Attraction factor->
Figure SMS_45
And maximum number of iterationsN；
And a second step of: random initialization
Figure SMS_46
Spatial location of firefliesS，/>
Figure SMS_47
The method comprises the steps of carrying out a first treatment on the surface of the The magnitude of the attraction degree of fireflies is in direct proportion to the magnitude of the brightness, and the brightness is determined by an objective function; to make firefly at space positionSThe relation of the brightness of (2) and its objective function is +.>
Figure SMS_48
，/>
Figure SMS_49
Is an objective function; the respective target values are recalculated as the maximum luminance of firefly +.>
Figure SMS_50
；
And a third step of: calculating the relative brightness between fireflies
Figure SMS_51
According to the relative brightness->
Figure SMS_52
Determining the movement direction of fireflies;
fourth step: updating the position of the firefly, and randomly disturbing the firefly at the best position;
fifth step: updating fireflies at non-optimal positions according to a simplex method, and recalculating brightness of the fireflies after the position updating;
sixth step: judgingtIf the moment meets the ending condition, jumping to a seventh step; otherwise att+1Jumping to the third step at the moment to search for the next time;
seventh step: and outputting the optimal position and the optimal solution.
Preferably, the fourth step updates the position of the firefly, randomly perturbing the firefly at the best position to obtain the maximum brightness of each firefly
Figure SMS_54
Calculating the relative brightness of firefly +.>
Figure SMS_56
The calculation formula of the relative brightness is
Figure SMS_58
Wherein->
Figure SMS_59
For the brightness of firefly with respect to its Euclidean distance of 0, +.>
Figure SMS_61
Is the bottom of natural logarithm, marked by +.>
Figure SMS_63
Is two different fireflieshAnd (3) withjEuropean distance between->
Figure SMS_65
The method comprises the steps of carrying out a first treatment on the surface of the Behavior of firefly:
Figure SMS_53
wherein->
Figure SMS_55
And->
Figure SMS_57
Fireflies respectivelyhAnd (3) withjIs provided with a plurality of spatial positions,
Figure SMS_60
is fireflyhUpdated position after random perturbation, +.>
Figure SMS_62
As an attractive factor->
Figure SMS_64
Is fireflyhIn spatial position->
Figure SMS_66
Relative brightness of>
Figure SMS_67
Is fireflyjThe relative brightness at the spatial location(s),randis interval [0,1 ]]Obeys a random factor of both and distribution.
Preferably, the fifth step of updating the firefly at the non-optimal position according to the simplex method, and recalculating the brightness of the firefly after the position update, comprises the steps of: let the initial vertex be
Figure SMS_68
The number of vertices is->
Figure SMS_69
=8, the objective function value of all initial points is calculated, the barycenter of simplex is +.>
Figure SMS_70
The method comprises the steps of carrying out a first treatment on the surface of the Calculating objective function values of all search points to find out optimal pointW1Selecting secondary advantagesW2，/>
Figure SMS_71
，/>
Figure SMS_72
，tFor scaling factor, let the corresponding objective function valuef(W1)Andf(W2)the method comprises the steps of carrying out a first treatment on the surface of the Make the best pointW1Sum and secondary advantagesW2Is at the center position ofW4，W4=（W1+W2）/2The method comprises the steps of carrying out a first treatment on the surface of the And randomly finding out a plurality of non-optimal firefly positions and non-suboptimal firefly positions, taking one of the positions asW3The objective function value is recorded asf (W3)For a pair ofW3Performing a reflection operation to obtain a reflection pointW5，W5=W4+μ（W4-W3），μIs the reflection coefficient; if the objective function value of the reflection pointf(W5)>f(W1)Indicating that the reflection direction is correct, and continuing to perform the expansion operation to obtain an expansion pointW6，W6=W4+λ（W5- W4），λIs the expansion coefficient; objective function value of expansion pointf(W6)>f(W1)Then use the expansion pointW6Substitution ofW3If the objective function value of the expansion pointf(W6)<f(W1)Then use the reflection pointW5Substitution ofW3The method comprises the steps of carrying out a first treatment on the surface of the If the objective function value of the reflection pointf(W5)<f (W3)Indicating the reflection direction is poor, and performing compression operation to obtain compression pointsW7，W7=W4+ρ（W3-W4），ρIs the compression coefficient; objective function value of compression pointf(W7)>f(W3)Then use the compression pointW7Instead ofW3The method comprises the steps of carrying out a first treatment on the surface of the If it isf(W3)<Objective function value of reflection pointf(W5)<f(W1)Performing a contraction operation to obtain a contraction pointW8，W8=W4-τ（W3-W4），τIs the coefficient of contraction; objective function value of contraction pointf(W8)>f(W3)Then use the contraction pointW8Substitution ofW3Otherwise using reflection pointsW5Substitution ofW3The method comprises the steps of carrying out a first treatment on the surface of the When the simplex shape is no longer changed, the brightness of each firefly is again calculated.
Preferably, the reflection coefficientμThe value of (2) is 1; coefficient of expansionλThe value of (2); compression coefficientρThe value of (2) is 0.5; coefficient of contractionτThe value of (2) is 0.5.
Compared with the prior art, the coordination control and optimal configuration method for the plurality of series devices in the power distribution network has the following beneficial effects:
according to the scheme, the minimum total capacity of the DVR is defined as an optimization target, a corresponding objective function and optimization conditions are set, and an optimal solution is searched through iteration, so that the capacity waste phenomenon of the DVR is reduced;
the firefly algorithm has the defects of low convergence rate and easiness in sinking into a local optimal solution, the local search speed is improved through an improved simplex method, and the characteristic of globally searching for the optimal point by combining with the firefly algorithm is avoided sinking into the local optimal solution, so that the optimization precision and accuracy are improved.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a power distribution network with a method for coordinated control and optimal configuration of multiple devices connected in series in the power distribution network according to the present invention;
FIG. 2 is a schematic diagram of a simplified structure of a power distribution network of a method for coordinated control and optimal configuration of multiple devices in series in the power distribution network according to the present invention;
FIG. 3 is a load voltage equation diagram of a coordination control and optimal configuration method of a plurality of series devices in a power distribution network according to the present invention;
FIG. 4 is a schematic diagram illustrating steps of a method for coordinated control and optimal configuration of multiple series devices in a power distribution network according to the present invention;
FIG. 5 is a flow chart of a single-purity method-based improved firefly algorithm for a coordinated control and optimal configuration method of multiple series devices in a power distribution network;
fig. 6 is a schematic diagram of a simplex method space search for a coordinated control and optimal configuration method of multiple series devices in a power distribution network.
Description of the embodiments
The following description of the embodiments of the present invention will clearly and fully describe the technical aspects of the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.
As shown in fig. 1-5, the invention provides a coordination control and optimal configuration method for a plurality of series devices in a power distribution network, which comprises the following steps:
s1: and (3) establishing a power distribution network comprising loads and buses, and enabling each bus in the power distribution network to be provided with a DVR.
As shown in fig. 1 in combination with fig. 2, the build contain
Figure SMS_73
Personal load +.>
Figure SMS_74
Bus barDistribution network, is provided with->
Figure SMS_75
An independent bus is used for dividing a line between a power supply and the bus and between adjacent buses, wherein the total number of the lines is +.>
Figure SMS_76
A load and a DVR are disposed on each line. In fig. 1, it can be seen that a plurality of bus bars are arranged in series, starting from a voltage source, each line comprising a bus bar, the impedance of the bus bar, the load and the DVR. Taking line 1 as an example, the equivalent resistance of the bus is R 1 The equivalent impedance of the bus is X 1 The DVR on the line is DVR1, and the corresponding load is loadnAnd so on.
To simplify the complexity of the model, a special case is illustrated in fig. 2, in which the illustrated distribution network has only 2 loads and 2 buses, and the active power loss of the line 2 is P z2 =I 2 2 R 2 The reactive power loss of line 2 is Q z2 = I 2 2 X 2 The compensation capacity of the DVR of line 2 is
Figure SMS_78
，/>
Figure SMS_79
Active power output for DVR2, +.>
Figure SMS_80
Reactive power output for DVR 2; />
Figure SMS_81
For the compensation voltage of DRV2,I 2 is the current flowing through line 2; the compensation capacity of a DVR of a similar line 1 is +.>
Figure SMS_82
The method comprises the steps of carrying out a first treatment on the surface of the From the above two formulas, it can be deduced that when multiple DVRs are used, the sum of the compensation capacities of the DVRs is +.>
Figure SMS_83
，/>
Figure SMS_84
The method comprises the steps of carrying out a first treatment on the surface of the The current flowing through each DVR is
Figure SMS_77
。
S2: and obtaining the output power of the DVR on each bus and the voltage drop of each line according to the DVR simplified model.
As shown in fig. 3, the step S2 of obtaining the output power of the DVR on each bus and the voltage drop of each line according to the DVR simplification model is equivalent to a controlled voltage source with the same phase as the line voltage and the amplitude being the difference between the expected load voltage and the grid voltage. The DVR is generally composed of an energy storage unit, a series transformer, an inversion unit and a filtering unit. When the voltage of the power grid fluctuates, the DVR outputs the compensation voltage in an extremely short time
Figure SMS_85
For maintaining load side voltage +>
Figure SMS_86
And the energy storage unit is used for converting energy of the DVR in the voltage compensation process. The DVR in the scheme adopts the same-phase compensation strategy, the compensation voltage of the same-phase compensation strategy is the same as the phase of the line voltage after the sag, and is opposite to the phase of the line voltage after the sudden rise, when in actual use, only the instantaneous voltage of the line is considered, and the phase factor is not considered, so that the simplified model of the DVR is the controlled current source in the figures 1, 2 and 3.
S3: and establishing an optimization model of the power distribution network, and determining input parameters, objective functions and constraint conditions of the optimization model of the power distribution network.
The optimization model of the distribution network is built by respectively obtaining the equivalent resistance and reactance, load power, power grid voltage rated value, power factor and maximum drop ratio values of all buses, and using the equivalent resistance and reactance, load power and power grid voltage rated value of all busesThe value of the power factor and the maximum drop ratio is used as the input parameter of an optimization model of the power distribution network of the DVR; let the same phase compensation voltage of DVR on each bus be
Figure SMS_87
Wherein->
Figure SMS_88
The method comprises the steps of carrying out a first treatment on the surface of the Let the current flowing through each DVR be +.>
Figure SMS_89
The method comprises the steps of carrying out a first treatment on the surface of the The compensation capacity for each DVR in the distribution network is +.>
Figure SMS_90
According to the deduction process of the above-mentioned content combining step S1, determining the objective function as the minimum total capacity of all DVRs in the distribution network
Figure SMS_91
。
The constraint conditions of the optimization model of the power distribution network are respectively load constraint, bus voltage constraint and loop constraint; wherein:
the load constraint is to include
Figure SMS_92
Personal load and->
Figure SMS_93
Distribution network with bus bars, and voltage after power supply sag is +.>
Figure SMS_94
The same phase of the same phase compensation voltage as DVR, the voltage after power supply sudden rising is +.>
Figure SMS_95
In contrast to the phase of the in-phase compensation voltage of DVR, only amplitude compensation is performed so that each load is +.>
Figure SMS_96
Is>
Figure SMS_97
Maintained in the working voltage range, i.e.
Figure SMS_98
Wherein->
Figure SMS_99
，/>
Figure SMS_100
，/>
Figure SMS_101
For load->
Figure SMS_102
Lower limit of the operating voltage of>
Figure SMS_103
For load->
Figure SMS_104
Upper limit of the operating voltage of>
Figure SMS_105
For the grid-side residual voltage after the occurrence of a voltage sag, +.>
Figure SMS_106
To compensate for the voltage.
Bus voltage constraint is that for a power distribution network, the requirements are satisfied
Figure SMS_108
，/>
Figure SMS_110
Wherein
Figure SMS_112
Is bus bar->
Figure SMS_114
Voltage on>
Figure SMS_116
；/>
Figure SMS_118
Is the voltage on the bus of the upper stage; />
Figure SMS_119
Is bus bar->
Figure SMS_107
The lower voltage limit is the maximum lower limit of the working voltage of all loads on the bus; />
Figure SMS_109
Is bus bar->
Figure SMS_111
The upper voltage limit is the minimum upper working voltage limit of all loads on the bus; />
Figure SMS_113
Is bus bar->
Figure SMS_115
And->
Figure SMS_117
A voltage difference therebetween;
each line meets KVL and KCL, the current of each line flows to the load on the current line and the next bus, and the voltage vectors of all lines of the power distribution network meet the following relations:
Figure SMS_120
. Each line voltage meets kirchhoff voltage law, the circuit meets kirchhoff current law, one part of line current flows to a load, and the other part flows to a bus of a next-stage line. Considering that the line voltage drops under in-phase compensation, the compensated voltage of the DVR is the same as the phase of the grid voltage, and the voltage vector relationship can be simply represented as a scalar relationship.
S4: and optimizing the input parameters of the optimization model of the DVR by adopting a simplex method and a firefly algorithm based on the improved generation, so as to obtain the optimal input parameters and minimize the capacity of all the DVRs in the power distribution network.
The source of the firefly algorithm is based on the brightness of fireflies and the attraction of fireflies to other fireflies, and the higher the brightness of fireflies, the better the position of the fireflies is, and the greater the attraction of fireflies to other fireflies is. Each firefly moves and updates according to the brightness and attraction degree of the peers in the field, so that the aim of position optimization is fulfilled. In the firefly algorithm, the attraction degree of fireflies is proportional to the brightness of the fireflies, and the brightness is closely related to the objective function of the firefly algorithm. The fluorescence of fireflies is also affected by the propagation medium and absorbed partly during the transmission, so that the magnitude of the attraction is related to both the distance and the medium absorption factor.
The improved firefly algorithm based on the simplex method is adopted to optimize the input parameters of the DVR optimization model, and comprises the following steps:
the first step: setting and initializing parameters of a firefly algorithm, including firefly number
Figure SMS_121
Step size factor->
Figure SMS_122
Random step factor->
Figure SMS_123
Medium absorption factor->
Figure SMS_124
Attraction factor->
Figure SMS_125
And maximum number of iterationsN。
And a second step of: random initialization
Figure SMS_126
Spatial location of firefliesS，/>
Figure SMS_127
The method comprises the steps of carrying out a first treatment on the surface of the The magnitude of the attraction degree of fireflies is in direct proportion to the magnitude of the brightness, and the brightness is determined by an objective function; to make firefly at space positionSThe relation of the brightness of (2) and its objective function is +.>
Figure SMS_128
，/>
Figure SMS_129
Is an objective function; the respective target values are recalculated as the maximum luminance of firefly +.>
Figure SMS_130
。
And a third step of: calculating the relative brightness between fireflies
Figure SMS_131
According to the relative brightness->
Figure SMS_132
The size of (2) determines the direction of movement of the firefly.
Fourth step: and updating the position of the firefly, and randomly disturbing the firefly at the best position.
The specific content of the step is that the maximum brightness of each firefly is obtained
Figure SMS_133
Calculating the relative brightness of fireflies
Figure SMS_134
The calculation formula of the relative brightness is +.>
Figure SMS_135
Wherein->
Figure SMS_136
For the brightness of firefly with respect to its Euclidean distance of 0, +.>
Figure SMS_137
Is the bottom of natural logarithm, marked by +.>
Figure SMS_138
Is two different fireflieshAnd (3) withjThe euclidean distance between the two,
Figure SMS_139
。
behavior of firefly:
Figure SMS_141
wherein->
Figure SMS_143
And->
Figure SMS_144
Fireflies respectivelyhAnd (3) withjSpatial position of->
Figure SMS_145
Is fireflyhUpdated position after random perturbation, +.>
Figure SMS_146
In order to attract the factors of interest,
Figure SMS_147
is fireflyhIn spatial position->
Figure SMS_148
Relative brightness of>
Figure SMS_140
Is fireflyjIn spatial position->
Figure SMS_142
Is used for the control of the relative brightness of the (c),randis interval [0,1 ]]Obeys a random factor of both and distribution. The better the spatial position of the firefly, the higher its relative brightness, and the relative brightness of the firefly is positively correlated with the objective function of its firefly algorithm.
The firefly algorithm can achieve the purpose of rapid optimization of position updating by improving the step length, and the searching process is better realized. Two different strategies for adjusting the step size may be provided depending on the current state:
(1) If firefly is a fireflyhIf the globally optimal solution is not found, a method of gradually reducing the step length is adopted, namely
Figure SMS_149
，/>
Figure SMS_150
Is fireflyhUpdating the new position after the step size, < > and>
Figure SMS_151
the iteration times; in each iteration, the firefly algorithm updates the position of the firefly according to the position and brightness of the firefly, adjusts the brightness of the firefly, and the iteration times are used for controlling the change amplitude and speed of the step length.
(2) If firefly is a fireflyhWhen the global optimal solution is found, a method of gradually increasing step length is adopted, namely
Figure SMS_152
，/>
Figure SMS_153
Is fireflyhUpdating the new position after the step size, < > and>
Figure SMS_154
is a random step size factor.
In this scheme, step length factor
Figure SMS_155
And->
Figure SMS_156
The value is set to 0.2, and the medium absorption factor is +.>
Figure SMS_157
The value is 1.
However, the firefly algorithm has the defects of low convergence speed, easy sinking into a local optimal solution and low precision. Therefore, the scheme further provides a method for globally searching the optimal solution by combining the simplex method with the firefly algorithm.
Fifth step: and updating fireflies at non-optimal positions according to a simplex method, and recalculating the brightness of the fireflies after the position updating.
The method comprises the following steps: let the initial vertex be
Figure SMS_158
The number of vertices is->
Figure SMS_159
=8, the objective function value of all initial points is calculated, the barycenter of simplex is +.>
Figure SMS_160
The method comprises the steps of carrying out a first treatment on the surface of the Calculating objective function values of all search points to find out optimal pointW1Selecting secondary advantagesW2，/>
Figure SMS_161
，/>
Figure SMS_162
，tFor scaling factor, let the corresponding objective function valuef (W1)Andf(W2)the method comprises the steps of carrying out a first treatment on the surface of the Make the best pointW1Sum and secondary advantagesW2Is at the center position ofW4，W4=（W1+W2）/2The method comprises the steps of carrying out a first treatment on the surface of the And randomly finding out a plurality of non-optimal firefly positions and non-suboptimal firefly positions, taking one of the positions asW3The objective function value is recorded asf(W3)For a pair ofW3Performing a reflection operation to obtain a reflection pointW5，W5=W4+μ（W4-W3），μIs the reflection coefficient; if the objective function value of the reflection pointf (W5)>f(W1)Indicating that the reflection direction is correct, and continuing to perform the expansion operation to obtain an expansion pointW6，W6=W4+λ（W5-W4），λIs the expansion coefficient; objective function value of expansion pointf(W6)>f(W1)Then use the expansion pointW6Substitution ofW3If the objective function value of the expansion pointf(W6)<f(W1)Then use the reflection pointW5Substitution ofW3The method comprises the steps of carrying out a first treatment on the surface of the If the objective function value of the reflection pointf(W5)<f(W3)Indicating the reflection direction is poor, and performing compression operation to obtain pressurePinch pointW7，W7=W4+ρ（W3-W4），ρIs the compression coefficient; objective function value of compression pointf(W7)>f(W3)Then use the compression pointW7Instead ofW3The method comprises the steps of carrying out a first treatment on the surface of the If it isf(W3)<Objective function value of reflection pointf(W5)<f (W1)Performing a contraction operation to obtain a contraction pointW8，W8=W4-τ（W3-W4），τIs the coefficient of contraction; objective function value of contraction pointf(W8)>f(W3)Then use the contraction pointW8Substitution ofW3Otherwise using reflection pointsW5Substitution ofW3The method comprises the steps of carrying out a first treatment on the surface of the When the simplex shape is no longer changed, the brightness of each firefly is again calculated.
In this scheme, the reflection coefficientμThe value of (2) is 1; coefficient of expansionλThe value of (2); compression coefficientρThe value of (2) is 0.5; coefficient of contractionτThe value of (2) is 0.5.
The conventional simplex method is to construct a polyhedron in a space, find out the vertices of the polyhedron and compare them to find out the optimal point, secondary advantage and worst point, i.e. the above-mentionedW1、W2AndW3. Updating the worst point by reflection, compression, expansion, or the likeW3Forming a new polyhedron, wherein the reflection operation can enable the individual to search in the opposite direction, and the individual search space is enlarged; the expansion operation enables the individual to be far away from the optimal solution, and prevents the algorithm from sinking into local minimum value points; the compression and contraction operations enable the individual to more closely approximate the optimal position. The present solution therefore also improves on the simplex method. Taking three-dimensional space as an example, referring to FIG. 6, 8 points are randomly generated, corresponding to the aboveW1、W2、……、W8And introduces the concept of a center of gravity.
Sixth step: judgingtIf the moment meets the ending condition, jumping to a seventh step; otherwise att+1And jumping to the third step to search for the next time.
Seventh step: and outputting the optimal position and the optimal solution.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.

Claims (3)

1. A coordination control and optimal configuration method for a plurality of series devices in a power distribution network is characterized by comprising the following steps:
build inclusion
Figure QLYQS_1
Personal load +.>
Figure QLYQS_2
A power distribution network with bus bars, wherein each bus bar in the power distribution network is provided with a DVR;
acquiring the output power of the DVR on each bus and the voltage drop of each circuit according to the DVR simplification model;
establishing an optimization model of the power distribution network, and determining input parameters, objective functions and constraint conditions of the optimization model of the power distribution network;
optimizing input parameters of an optimization model of the DVR by adopting a simplex method and a firefly algorithm based on an improved generation, obtaining the optimal input parameters, and minimizing the capacity of all DVRs in the power distribution network;
wherein the establishing comprises
Figure QLYQS_3
Personal load +.>
Figure QLYQS_4
The distribution network of the bus is configured with->
Figure QLYQS_5
An independent bus is used for dividing a line between a power supply and the bus and between adjacent buses, wherein the total number of the lines is +.>
Figure QLYQS_6
A load and a DVR are correspondingly configured on each line;
the DVR output power on each bus and the voltage drop of each line are obtained according to a DVR simplification model, and the DVR is equivalent to a controlled voltage source which has the same phase as the power grid voltage and has the amplitude of the difference between the load voltage expected value and the power grid voltage;
the method comprises the steps of establishing an optimization model of the power distribution network, namely respectively obtaining values of equivalent resistance and reactance, load power, power grid voltage rated value and power factor and maximum drop ratio on each bus, and taking the values of equivalent resistance and reactance, load power, power grid voltage rated value and power factor and maximum drop ratio on each bus as input parameters of the optimization model of the power distribution network of the DVR; let the same phase compensation voltage of DVR on each bus be
Figure QLYQS_7
Wherein->
Figure QLYQS_8
The method comprises the steps of carrying out a first treatment on the surface of the Let the current flowing through each DVR be +.>
Figure QLYQS_9
The method comprises the steps of carrying out a first treatment on the surface of the The compensation capacity for each DVR in the distribution network is +.>
Figure QLYQS_10
The objective function is the minimum total capacity of all DVRs in the distribution network +.>
Figure QLYQS_11
；
Constraint conditions of the optimization model of the power distribution network respectively comprise load constraint, bus voltage constraint and loop constraint; wherein:
the load constraint is to include
Figure QLYQS_13
Personal load and->
Figure QLYQS_15
Distribution network with bus bars, and voltage after power supply sag is +.>
Figure QLYQS_17
The same phase of the same phase compensation voltage as DVR, the voltage after power supply sudden rising is +.>
Figure QLYQS_18
In contrast to the phase of the in-phase compensation voltage of DVR, only amplitude compensation is performed so that each load is +.>
Figure QLYQS_19
Is>
Figure QLYQS_20
Maintained in the working voltage range, i.e.
Figure QLYQS_21
Wherein->
Figure QLYQS_12
，/>
Figure QLYQS_14
，/>
Figure QLYQS_16
For load->
Figure QLYQS_22
Lower limit of the operating voltage of>
Figure QLYQS_23
For load->
Figure QLYQS_24
Upper limit of the operating voltage of>
Figure QLYQS_25
In order to generate a grid-side residual voltage after a voltage sag,
Figure QLYQS_26
to compensate the voltage;
bus voltage constraint, which is to power distributionIn the case of a net, the following are satisfied
Figure QLYQS_27
，/>
Figure QLYQS_29
Wherein->
Figure QLYQS_31
Is bus bar->
Figure QLYQS_33
Voltage on>
Figure QLYQS_35
；/>
Figure QLYQS_37
Is the voltage on the bus of the upper stage; />
Figure QLYQS_39
Is bus bar->
Figure QLYQS_28
The lower voltage limit is the maximum lower limit of the working voltage of all loads on the bus; />
Figure QLYQS_30
Is bus bar->
Figure QLYQS_32
The upper voltage limit is the minimum upper working voltage limit of all loads on the bus; />
Figure QLYQS_34
Is bus bar->
Figure QLYQS_36
And->
Figure QLYQS_38
A voltage difference therebetween;
each line meets KVL and KCL, the current of each line flows to the load on the current line and the next bus, and the voltage vectors of all lines of the power distribution network meet the following relations:
Figure QLYQS_40
；
the method for optimizing the input parameters of the DVR optimization model by adopting the improved simplex method and firefly algorithm comprises the following steps:
the first step: setting and initializing parameters of a firefly algorithm, including firefly number
Figure QLYQS_41
Step size factor->
Figure QLYQS_42
Random step factor->
Figure QLYQS_43
Medium absorption factor->
Figure QLYQS_44
Attraction factor->
Figure QLYQS_45
And maximum number of iterationsN；
And a second step of: random initialization
Figure QLYQS_46
Spatial location of firefliesS，/>
Figure QLYQS_47
The method comprises the steps of carrying out a first treatment on the surface of the The magnitude of the attraction degree of fireflies is in direct proportion to the magnitude of the brightness, and the brightness is determined by an objective function; to make firefly at space positionSThe relation of the brightness of (2) and the objective function thereof is that
Figure QLYQS_48
，/>
Figure QLYQS_49
Is an objective function; the respective target values are recalculated as the maximum luminance of firefly +.>
Figure QLYQS_50
；
And a third step of: calculating the relative brightness between fireflies
Figure QLYQS_51
According to the relative brightness->
Figure QLYQS_52
Determining the movement direction of fireflies;
fourth step: updating the position of the firefly, and randomly disturbing the firefly at the best position;
fifth step: updating fireflies at non-optimal positions according to a simplex method, and recalculating brightness of the fireflies after the position updating;
sixth step: judgingtIf the moment meets the ending condition, jumping to a seventh step; otherwise att+1Jumping to the third step at the moment to search for the next time;
seventh step: outputting an optimal position and an optimal solution;
the fourth step updates the position of fireflies, randomly disturbing fireflies at the best position to obtain the maximum brightness of each firefly
Figure QLYQS_55
Calculating the relative brightness of firefly +.>
Figure QLYQS_57
The calculation formula of the relative brightness is +.>
Figure QLYQS_59
Wherein->
Figure QLYQS_61
For the brightness of firefly with respect to its Euclidean distance of 0, +.>
Figure QLYQS_63
Is the bottom of natural logarithm, marked by +.>
Figure QLYQS_65
Is two different fireflieshAnd (3) withjEuropean distance between->
Figure QLYQS_67
The method comprises the steps of carrying out a first treatment on the surface of the Behavior of firefly:
Figure QLYQS_54
wherein->
Figure QLYQS_56
And->
Figure QLYQS_58
Fireflies respectivelyhAnd (3) withjSpatial position of->
Figure QLYQS_60
Is fireflyhUpdated position after random perturbation, +.>
Figure QLYQS_62
As an attractive factor->
Figure QLYQS_64
Is fireflyhIn spatial position->
Figure QLYQS_66
Relative brightness of>
Figure QLYQS_68
Is fireflyjIn spatial position->
Figure QLYQS_53
Is used for the control of the relative brightness of the (c),randis interval [0,1 ]]A random factor obeying the uniform and distributed;
the firefly algorithm achieves the aim of quick optimization of position updating by improving the step length, and the searching process is better realized; two different strategies for adjusting the step size may be provided depending on the current state:
(1) If firefly is a fireflyhIf the globally optimal solution is not found, a method of gradually reducing the step length is adopted, namely
Figure QLYQS_69
，/>
Figure QLYQS_70
Is fireflyhUpdating the new position after the step size, < > and>
Figure QLYQS_71
the iteration times; in each iteration, updating the position of the firefly according to the position and the brightness of the firefly by a firefly algorithm, adjusting the brightness of the firefly, and controlling the change amplitude and the change speed of the step length by the iteration times;
(2) If firefly is a fireflyhWhen the global optimal solution is found, a method of gradually increasing step length is adopted, namely
Figure QLYQS_72
，/>
Figure QLYQS_73
Is fireflyhUpdating the new position after the step size, < > and>
Figure QLYQS_74
is a random step size factor.
2. The method for coordinated control and optimal configuration of a plurality of series devices in a power distribution network according to claim 1, wherein the fifth step updates the non-optimal state according to a simplex methodThe method for recalculating the brightness of the firefly with updated positions by using the firefly with good positions comprises the following steps: let the initial vertex be
Figure QLYQS_75
The number of vertices is->
Figure QLYQS_76
=8, the objective function value of all initial points is calculated, the barycenter of simplex is +.>
Figure QLYQS_77
The method comprises the steps of carrying out a first treatment on the surface of the Calculating objective function values of all search points to find out optimal pointW1Selecting secondary advantagesW2，/>
Figure QLYQS_78
，/>
Figure QLYQS_79
tFor scaling factor, let the corresponding objective function valuef(W1)Andf(W2)the method comprises the steps of carrying out a first treatment on the surface of the Make the best pointW1Sum and secondary advantagesW2Is at the center position ofW4，W4=（W1+W2）/2The method comprises the steps of carrying out a first treatment on the surface of the And randomly finding out a plurality of non-optimal firefly positions and non-suboptimal firefly positions, taking one of the positions asW3The objective function value is recorded asf(W3)For a pair ofW3Performing a reflection operation to obtain a reflection pointW5，W5=W4+μ（W4-W3），μIs the reflection coefficient; if the objective function value of the reflection pointf (W5)>f(W1)Indicating that the reflection direction is correct, and continuing to perform the expansion operation to obtain an expansion pointW6，W6=W4+λ（W5-W4），λIs the expansion coefficient; objective function value of expansion pointf(W6)>f(W1)Then use the expansion pointW6Substitution ofW3If the objective function value of the expansion pointf(W6)<f(W1)Then use the reflection pointW5Substitution ofW3The method comprises the steps of carrying out a first treatment on the surface of the If the objective function value of the reflection pointf(W5)<f(W3)Indicating the reflection direction is poor, and performing compression operation to obtain compression pointsW7，W7=W4+ρ（W3-W4），ρIs the compression coefficient; objective function value of compression pointf(W7)>f(W3)Then use the compression pointW7Instead ofW3The method comprises the steps of carrying out a first treatment on the surface of the If it isf(W3)<Objective function value of reflection pointf(W5)<f (W1)Performing a contraction operation to obtain a contraction pointW8，W8=W4-τ（W3-W4），τIs the coefficient of contraction; objective function value of contraction pointf(W8)>f(W3)Then use the contraction pointW8Substitution ofW3Otherwise using reflection pointsW5Substitution ofW3The method comprises the steps of carrying out a first treatment on the surface of the When the simplex shape is no longer changed, the brightness of each firefly is again calculated.
3. The method for coordinated control and optimal configuration of a plurality of series devices in a power distribution network according to claim 2, wherein the reflection coefficient isμThe value of (2) is 1; coefficient of expansionλThe value of (2); compression coefficientρThe value of (2) is 0.5; coefficient of contractionτThe value of (2) is 0.5.
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110823229A (en) * 2019-12-16 2020-02-21 湖北工业大学 Mobile robot path planning method and system based on firefly optimization algorithm
CN112333723A (en) * 2020-11-03 2021-02-05 西安建筑科技大学 Wireless sensor node deployment method, storage medium and computing device

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109508343A (en) * 2018-08-30 2019-03-22 广西民族大学 A kind of cuckoo searching method of simplex method and its application
CN112003281B (en) * 2020-08-26 2022-04-01 广东电网有限责任公司广州供电局 Optimal configuration method, device and equipment for dynamic voltage restorer

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110823229A (en) * 2019-12-16 2020-02-21 湖北工业大学 Mobile robot path planning method and system based on firefly optimization algorithm
CN112333723A (en) * 2020-11-03 2021-02-05 西安建筑科技大学 Wireless sensor node deployment method, storage medium and computing device

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
Jia Zhao ; Wenping Chen ; Jun Ye ; .Firefly Algorithm Based on Level-Based Attracting and Variable Step Size.IEEE Access.2020,第8卷第58700-58716页. *
基于精英个体划分的变步长萤火虫算法的特征选择方法;刘磊;罗蓉;尹胜;;重庆邮电大学学报(自然科学版)(02);第313-321页 *
改进的区间二型模糊聚类遥感影像变化检测;苏艺凡; 党建武;测绘通报(第7期);第44-51页 *

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