CN110633869A - Optimal configuration method and system for residual capacity of SAPF - Google Patents

Optimal configuration method and system for residual capacity of SAPF Download PDF

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CN110633869A
CN110633869A CN201910904423.9A CN201910904423A CN110633869A CN 110633869 A CN110633869 A CN 110633869A CN 201910904423 A CN201910904423 A CN 201910904423A CN 110633869 A CN110633869 A CN 110633869A
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桂宁
王一帆
裘智峰
张亮
桂卫华
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Abstract

The invention discloses an optimal configuration method and system for residual capacity of SAPF, which takes the compensation quantity of SAPF to the reactive component of fundamental current and the compensation quantity of SAPF to each subharmonic current as the target adaptive function of the residual capacity of SAPF, wherein the target adaptive function has two optimization targets: (1) the residual capacity of the SAPF is close to zero, (2) the current distortion rate of a bus on which the SAPF is installed is close to zero or smaller than a current distortion rate standard; and an optimization algorithm is configured to solve the optimal solution of the target adaptive function, and the SAPF operation is configured according to the compensation quantity of the reactive component of the fundamental current and the compensation quantity of each subharmonic current corresponding to the optimal solution to realize the optimal compensation of the current of the power grid to be compensated. Therefore, the full utilization of the SAPF capacity is realized, and the problem that the residual capacity of the SAPF device is ignored in the existing SAPF control algorithm technology is solved.

Description

Optimal configuration method and system for residual capacity of SAPF
Technical Field
The invention relates to the field of control algorithms of SAPF (cyclic shift keying), in particular to an optimal configuration method and system of SAPF residual capacity.
Background
With the progress of power electronic technology, the use of nonlinear loads and power electronic converters is increasing, which brings a lot of harmonics to the power grid and affects the power quality of the power grid. Parallel active power filters (SAPF) play an important role in power grid harmonic management, reactive compensation and the like, but the selection of compensation capacity is influenced by investment cost and filtering effect. At present, many SAPFs have more residual capacity when compensating nonlinear loads, and the situation that the SAPF capacity is insufficient to completely compensate the nonlinear loads also exists, so that how to improve the utilization rate of the SAPF capacity and how to maximize the compensation effect is also a problem to be faced by power quality management.
To the nonlinear load in the distribution network, its quantity often is more and distribute comparatively scattered complicacy, and the traditional concentrated treatment method effect of installing filter equipment additional is no longer ideal, and the principle of who pollutes who administers is also no longer applicable. In response to these problems, many documents have made researches on the aspects of the control algorithm and circuit structure of the SAPF. In the prior art, technicians respectively use phase compensation and predictive control methods to realize selective detection of any harmonic current of a nonlinear load, and some technicians provide a successive order harmonic detection algorithm on the basis of APF (active power filter) adopting a direct alternating-alternating conversion technology, but the prior art does not consider the capacity limit of SAPF (self adaptive filter function); the SAPF capacity allocation algorithm based on the voltage harmonic responsibility division at the PCC has also been proposed by the skilled person, but the problem of the remaining capacity of the existing SAPF device has not been considered. It can be seen that in the prior art, the control algorithm for the SAPF ignores the residual capacity of the SAPF device, thereby limiting the full utilization of the SAPF capacity and limiting the optimization of the compensation effect of the SAPF on the grid current.
Disclosure of Invention
The invention provides an optimal configuration method and system for residual capacity of SAPF (SAPF), which realizes the full utilization of the SAPF capacity by constructing a target adaptive function and a configuration algorithm of the residual capacity of the SAPF in consideration of the situation that the unused residual capacity exists in the installed SAPF, thereby solving the technical problems that the residual capacity of an SAPF device is ignored by a control algorithm of the SAPF in the prior art, the full utilization of the SAPF capacity is limited, and the optimization of the compensation effect of the SAPF on the power grid current is limited.
In order to solve the technical problems, the technical scheme provided by the invention is as follows:
an optimal configuration method for residual capacity of SAPF comprises the following steps:
acquiring and constructing a target adaptive function which takes compensation quantity of reactive components of the SAPF to the fundamental current and compensation quantity of the SAPF to the harmonic currents as independent variables according to the nominal rated capacity of the SAPF in historical data and the harmonic currents and the load fundamental current of the compensated power grid current, wherein the target adaptive function is provided with two optimization targets which are respectively:
(1) the residual capacity of SAPF is close to zero;
(2) the current distortion rate of the bus mounted with the SAPF is close to zero or smaller than a current distortion rate standard;
obtaining an effective value of load fundamental current, an effective value of each harmonic current and an effective value of the nominal rated capacity of the SAPF of the power grid to be compensated, substituting the effective values into the target adaptation function, and solving an optimal solution;
and configuring the SAPF operation according to the compensation quantity of the reactive component of the fundamental current and the compensation quantity of each subharmonic current corresponding to the optimal solution so as to realize the optimal compensation of the power grid current to be compensated.
Preferably, the target adaptation function is:
F＝min f(x)
＝λ1Ir+λ2ITHD+P max(0,Ir-Im)
wherein, F and min F (x) are target adaptive functions, and when the value is minimum, the corresponding fundamental wave reactive current component and each subharmonic current are the optimal solution; i isrIs the remaining capacity of the SAPF, wherein,
Figure BDA0002212850390000021
ITHDis the current distortion rate of the grid bus, wherein,
Figure BDA0002212850390000022
λ1and λ2Respectively, residual capacity IrAnd the current distortion rate I of the corresponding busTHDThe weight values of the two optimization targets satisfy lambda1+λ 21 is ═ 1; p is an out-of-limit penalty coefficient of the residual capacity; i ismAn effective value for the nominal rated capacity of SAPF; i is1For an effective value of the load fundamental current, I1qIs the effective value of the fundamental reactive current component; i isnEffective value of each harmonic current; n is the harmonic order, InAs each harmonic component; ITHDmaxAnd the standard value of the current distortion rate of the bus where the SAPF is located.
Preferably, solving the optimal solution is completed through a particle swarm algorithm, and the method comprises the following steps of:
setting the compensation quantity of the SAPF on the reactive component of the fundamental current as a first dimension component of a position vector of the particle swarm optimization; setting the compensation quantity of the SAPF to each subharmonic current as the second dimension component to the (n + 1) th dimension component of the position vector;
setting the number, the speed vector, the speed updating formula, the position updating formula and the termination condition of the particle swarm algorithm;
initializing a position vector and a velocity vector of the particle swarm algorithm, and guiding particles to iterate towards the optimal solution position direction through a velocity calculation formula and a position update formula;
in each iteration process, calculating the residual capacity and the current distortion rate of the SAPF by using the current position vector of the particle, further calculating the fitness of each particle by using a target adaptation function, and comparing the current adaptation value of each particle with the adaptation values corresponding to the individual historical optimal position and the global optimal position of the particle swarm to determine the global optimal position of the particle swarm; judging whether the global optimal position of the particle after each iteration meets the termination judgment condition or not by judging the termination condition, and entering the next iteration process if the global optimal position of the particle after each iteration does not meet the termination judgment condition; and if the judgment termination condition is met, outputting the global optimal position to obtain the optimal solution corresponding to the global optimal position.
Preferably, the position vector of the particle is:
Figure BDA0002212850390000031
wherein x isiIs the position vector of the particle; alpha is a set existence coefficient, when alpha is 0, the fundamental wave reactive component does not participate in compensation optimization, namely, only each harmonic is subjected to iterative calculation; each dimension of the particle vector is a compensation quantity corresponding to the subharmonic current and is expressed in an exponential form,
Figure BDA0002212850390000032
respectively representing the amplitudes of the fundamental wave and each harmonic compensation quantity; thetainA phase representing an nth harmonic compensation amount;
the velocity vector of the particle is vi＝[vi1,vi2,vi3,…,vin]Wherein v isinThe moving speed of the ith particle is expressed by the nth dimension, i is 1,2, …, m.
Preferably, the velocity update algorithm of the particles is as follows:
Figure BDA0002212850390000033
the position updating algorithm of the particles is as follows:
Figure BDA0002212850390000034
wherein the content of the first and second substances,
Figure BDA0002212850390000035
represents the j-th dimension component in the moving velocity vector of the particle i after the t-th iteration,
Figure BDA0002212850390000036
representing the j-th dimension component in the position vector of the particle i after the t-th iteration; c. C1，c2Are two acceleration constants that adjust the maximum step size, r, of particle learning1，r2Is two value ranges of [0,1 ]]A random function of (a) to increase search randomness; w is a non-negative inertial weight used for adjusting the search range of the solution space; pbestijRepresenting the historical optimal position of the particle individual; gbestijRepresenting a global optimal position of the particle; t represents time;
and the termination condition of the particle swarm algorithm is that the maximum iteration number or the global optimal position limit is met.
Preferably, the harmonic current includes an active direct current component and a reactive direct current component; obtaining a harmonic current of the SAPF, comprising:
obtaining a k-th harmonic current component vector i under a d-q rotating coordinate systemkAnd its corresponding harmonic voltage component vector ukAccording to a synchronous coordinate transformation method, through an improved Park transformation matrix, a voltage current vector under an original d-q rotation coordinate system is rotated clockwise by an angle corresponding to a k-th harmonic voltage initial phase to obtain a new d-q coordinate system, so that a k-th harmonic voltage component vector is overlapped with a d-axis under the new d-q coordinate system, and an active component and a reactive component corresponding to the k-th harmonic current are obtained through the new d-q coordinate system;
sequentially enabling the fundamental frequency multiple n to be equal to +/-1 time of the k-th harmonic current to be extracted, and obtaining positive and negative sequence direct current components of the k-th harmonic current and active and reactive components of the k-th harmonic current through LPF filtering; and obtaining the active direct current component and the reactive direct current component of the kth harmonic current through the positive and negative sequence direct current components of the kth harmonic current and the active component and the reactive component of the kth harmonic current.
Preferably, the active component i of the kth harmonic currentd' and reactive component iq' is:
Figure BDA0002212850390000041
wherein the content of the first and second substances,
Figure BDA0002212850390000042
the positive sequence component amplitude of the nth harmonic,
Figure BDA0002212850390000043
the amplitude of the negative sequence component of the nth harmonic;
Figure BDA0002212850390000044
is the initial phase angle of the nth harmonic current positive sequence component,
Figure BDA0002212850390000045
is the initial phase angle of the negative sequence component of the nth harmonic current; k is the number of harmonic waves to be solved; omega is angular frequency;
Figure BDA0002212850390000046
the initial phase of the voltage of the kth harmonic, i.e. the phase compensation amount of the kth harmonic,
Figure BDA0002212850390000047
the expression is as follows:
Figure BDA0002212850390000048
wherein u iskqAnd ukdRespectively representing a q-axis component and a d-axis component of the k-th harmonic voltage after dq decomposition.
The modified Park transformation matrix Cdq' is:
Figure BDA0002212850390000049
active direct current component of the kth harmonic current
Figure BDA00022128503900000410
And a reactive DC component
Figure BDA00022128503900000411
Comprises the following steps:
Figure BDA00022128503900000412
wherein ikd' and ikqThe d-axis component and the q-axis component of the kth harmonic current obtained after phase compensation and LPF filtering are obtained;andthe initial phase angles of the positive sequence and negative sequence components of the kth harmonic current, respectively.
A computer system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of any of the methods described above when executing the computer program.
The invention has the following beneficial effects:
1. the invention provides an optimal configuration method and system of SAPF residual capacity, which is characterized in that under the condition that the unused residual capacity exists in the installed SAPF, an optimal solution of the optimal adaptation function is solved by constructing an objective adaptation function of the SAPF residual capacity, wherein the objective adaptation function takes compensation quantity of the SAPF to a reactive component of fundamental current and compensation quantity of the SAPF to each subharmonic current as an independent variable, and the operation of the SAPF is configured according to the compensation quantity of the reactive component of the fundamental current and the compensation quantity of each subharmonic current corresponding to the optimal solution so as to realize the optimal compensation of the power grid current to be compensated. Therefore, the full utilization of the SAPF capacity is realized, the problem that the residual capacity of the SAPF device is ignored in the existing SAPF control algorithm technology is solved, and the technical problem that the compensation effect of the SAPF on the power grid current is optimized is realized.
2. In the preferred scheme, the optimization of the SAPF residual capacity and the current distortion rate of a power grid bus and the optimization selection of each harmonic compensation amount are realized by calculating the SAPF residual capacity, designing an improved harmonic extraction algorithm and fusing a particle swarm optimization algorithm, and finally, the current harmonic content of the power grid is greatly reduced while the utilization rate of the SAPF capacity is improved.
In addition to the objects, features and advantages described above, other objects, features and advantages of the present invention are also provided. The present invention will be described in further detail below with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of a particle swarm algorithm for SAPF residual capacity of the preferred embodiment of the present invention;
FIG. 2 is a grid A-phase current waveform with sufficient remaining SAPF capacity in accordance with a preferred embodiment of the present invention;
FIG. 3 is a compensated grid current waveform of the optimal configuration method of the preferred embodiment of the present invention, wherein (a) is a compensated grid current waveform of the conventional 5 th harmonic, (b) is a compensated grid current waveform of the conventional 7 th harmonic, (c) is a compensated grid current waveform of the conventional equal proportion, and (d) is a compensated grid current waveform of the optimal configuration;
FIG. 4 shows the harmonic content of the grid current in four compensation methods according to the preferred embodiment of the present invention;
fig. 5 is a flowchart of an optimal configuration method of the remaining capacity of the SAPF according to the present invention.
Detailed Description
The embodiments of the invention will be described in detail below with reference to the drawings, but the invention can be implemented in many different ways as defined and covered by the claims.
The first embodiment is as follows:
as shown in fig. 5, the present invention discloses an optimal configuration method for residual capacity of SAPF, which comprises the following steps:
acquiring and constructing a target adaptive function which takes compensation quantity of reactive components of the SAPF to the fundamental current and compensation quantity of the SAPF to the harmonic currents as independent variables according to the nominal rated capacity of the SAPF in historical data and the harmonic currents and the load fundamental current of the compensated power grid current, wherein the target adaptive function is provided with two optimization targets which are respectively:
(1) the residual capacity of SAPF is close to zero;
(2) the current distortion rate of the bus mounted with the SAPF is close to zero or smaller than a current distortion rate standard;
obtaining an effective value of load fundamental current, an effective value of each harmonic current and an effective value of the nominal rated capacity of the SAPF of the power grid to be compensated, substituting the effective values into the target adaptation function, and solving an optimal solution;
and configuring the SAPF operation according to the compensation quantity of the reactive component of the fundamental current and the compensation quantity of each subharmonic current corresponding to the optimal solution so as to realize the optimal compensation of the power grid current to be compensated.
In addition, the embodiment also discloses a computer system, which includes a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor implements the steps of any one of the methods of the above embodiments when executing the computer program.
The invention provides an optimal configuration method and system of SAPF residual capacity, which is characterized in that under the condition that the unused residual capacity exists in the installed SAPF, an optimal solution of the optimal adaptation function is solved by constructing an objective adaptation function of the SAPF residual capacity, wherein the objective adaptation function takes compensation quantity of the SAPF to a reactive component of fundamental current and compensation quantity of the SAPF to each subharmonic current as an independent variable, and the operation of the SAPF is configured according to the compensation quantity of the reactive component of the fundamental current and the compensation quantity of each subharmonic current corresponding to the optimal solution so as to realize the optimal compensation of the power grid current to be compensated. Therefore, the full utilization of the SAPF capacity is realized, the problem that the residual capacity of the SAPF device is ignored in the existing SAPF control algorithm technology is solved, and the technical problem that the compensation effect of the SAPF on the power grid current is optimized is realized.
Example two:
the second embodiment is an extended embodiment of the first embodiment, and is different from the first embodiment in that how to construct an objective adaptive function and a configuration optimization algorithm are refined, and how to obtain the nominal rated capacity of the SAPF and how to refine harmonic current and load fundamental current of the grid current compensated by the nominal rated capacity of the SAPF.
With the emergence of new energy sources popularizing energy internet, the energy planning design of the power system draws more and more attention, and the proposition of the topological structure and the control algorithm needs to quantize the power components and compensation equipment. For a power network containing a grid, a nonlinear load and a SAPF, the grid current iSMainly comprising a load current iLAnd compensation current i of SAPFcAs shown in formula (1).
iS＝iL+ic (1)
The nonlinear load often includes inductive and capacitive elements, and the load current divided by the harmonic component inBesides, the device also comprises a fundamental wave active component i1pAnd a fundamental reactive component i1qAs shown in formula (2).
iL＝i1p+i1q+in (2)
According to the Thevenin equivalent model before and after compensation of the power grid and the SAPF, the SAPF can output compensation current which is equal to the load harmonic current in magnitude and opposite in direction, and the nonlinear load is compensated into an equivalent resistor. The SAPF output compensation current needs to compensate the harmonic current and also needs to compensate the fundamental wave reactive component, and the formula (3) is shown in the moment.
ic＝-(i1q+in) (3)
If the compensation capacity of the SAPF is sufficient, let the nominal rated capacity of the SAPF be ImThen the remaining capacity I of SAPF at this timerCan be expressed as
In the formula (4) I1For loading a fundamental current, I1pIs the effective value of the fundamental active current, InIs the effective value of each harmonic current, n isThe number of harmonics.
In an ideal situation, however, the grid only needs to deliver the fundamental positive sequence active current, i.e. the minimum current required for the operation of the load, to the load. Neglecting the zero sequence component, the load current can be decomposed into a combination of fundamental positive sequence, fundamental negative sequence and harmonics, as shown in equation (5).
In the formula (5), iLa、iLb、iLcA, B, C three phases representing nonlinear load currents, respectively;
Figure BDA0002212850390000073
the fundamental positive sequence A, B, C three phases respectively representing the nonlinear load current,a, B, C three phases representing fundamental negative sequences of non-linear load currents, respectively; i.e. ian、ibn、icnRespectively representing the total harmonic current of each phase in the load current; the signs represent positive and negative sequences, respectively, where the fundamental positive sequence component can be expressed as:
in the formula (6), a is an operator; i.e. i1abcIs a nonlinear load fundamental component; t is+An operator matrix is represented. At this time, if the SAPF needs to compensate the fundamental positive sequence reactive component in addition to the harmonic and fundamental negative sequence components, the output compensation current i of the SAPFcCan be expressed as:
having an effective value of IcThen the remaining capacity I of SAPF at this timerAs shown in formula (8).
Ir＝Im-Ic (8)
By the calculation and analysis of the residual capacity of the SAPF and the harmonic currents, when the available capacity of the SAPF is insufficient, an objective adaptive function comprising the following two optimization objectives can be constructed:
1) SAPF residual capacity IrShould be close to zero;
2) current distortion rate I of bus mounted with SAPFTHDShould be close to zero or meet the national standards as much as possible, as shown in equation (16).
Figure BDA0002212850390000081
Wherein ITHDmaxIs the current distortion rate standard of the bus where the SAPF is located.
From the above, in consideration of two optimization targets of the residual capacity and the current distortion rate of the bus, a target adaptation function shown in formula (18) is constructed, and since the constructed target adaptation function is a multi-target, a multi-target optimization algorithm needs to be configured for solving, the principle of the particle swarm optimization algorithm is inspired by the foraging behavior of the bird swarm, and the position and the speed of the particles are evaluated and adjusted by integrating the adaptation function every period, so that the nonlinear multivariate optimization problem is solved.
The target adaptation function is:
Figure BDA0002212850390000082
wherein, F and min F (x) are target adaptive functions, the smaller the value is, the more the group of solutions conforms to the optimal solution, that is, when the value of F and min F (x) is the minimum, the corresponding fundamental wave reactive current component and each subharmonic current are the optimal solution, and the corresponding fundamental wave reactive current component and each subharmonic current are the compensation quantity of the fundamental wave reactive current component by the SAPF and the compensation quantity of the SAPF to each subharmonic current; i isrIs the remaining capacity of the SAPF, wherein,
Figure BDA0002212850390000083
ITHDis the current distortion rate of the grid bus, wherein,
Figure BDA0002212850390000084
λ1and λ2Respectively, residual capacity IrAnd the current distortion rate I of the corresponding busTHDThe weight values of the two optimization targets satisfy lambda1+λ21 is ═ 1; p is an out-of-limit penalty coefficient of the residual capacity; i ismIs an effective value of the nominal rated capacity of SAPF, I1For an effective value of the load fundamental current, I1qIs the effective value of the fundamental reactive current component; i isnFor the effective value of each harmonic current, n is the harmonic number InAs each harmonic component; ITHDmaxAnd the standard value of the current distortion rate of the bus where the SAPF is located. In equation (18), P is an out-of-limit penalty coefficient for remaining capacity. Namely, the residual capacity of the SAPF is used as much as possible for harmonic compensation to reduce the current distortion rate of the bus, but in each harmonic compensation configuration scheme obtained by each optimization calculation, the sum of the used residual capacities cannot exceed the nominal capacity of the SAPF, so that the constraint condition of the residual capacity needs to be embodied in the target adaptive function in the form of a penalty function.
Therefore, in this embodiment, a particle swarm algorithm is constructed to solve the optimal solution of the target adaptive function, and the compensation amount of the SAPF for the reactive component of the fundamental current is set as the first dimension component of the position vector of the particle swarm algorithm; setting the compensation quantity of the SAPF to each subharmonic current as the second dimension component to the (n + 1) th dimension component of the position vector; setting the number, the speed vector, the speed updating formula, the position updating formula and the termination condition of the particle swarm algorithm; initializing a position vector and a velocity vector of the particle swarm algorithm, and guiding particles to iterate towards the optimal solution position direction through a velocity calculation formula and a position update formula; in each iteration process, calculating an adaptive value of each particle through a target adaptive function, and comparing the current adaptive value of each particle with the adaptive values corresponding to the individual historical optimal position and the global optimal position of each particle to determine the global optimal position of the particle swarm; judging whether the global optimal position of the particle in each iteration meets the termination judgment condition or not by judging the termination condition, and entering the next iteration process if the global optimal position of the particle in each iteration does not meet the termination judgment condition; and if the judgment termination condition is met, outputting the global optimal position to obtain the optimal solution corresponding to the global optimal position.
The particle swarm algorithm is constructed by the following steps:
(1) setting the number, position vector and speed vector of particles;
in a three-phase power system, the number of harmonic currents n typically comprises only 6k ± 1, where k is a positive integer. Therefore, each particle in the constructed particle group should be a vector of n +1 dimensions, so that a 1-dimensional component representing the compensation amount of the SAPF for the reactive component of the fundamental current, and 2-dimensional to n + 1-dimensional components representing the compensation amount of the SAPF for the respective harmonic currents are constructed, and the position vectors of the particles are shown in formula (17).
Figure BDA0002212850390000091
Wherein x isiIs the position vector of the particle; alpha is a set existence coefficient, when alpha is 0, the fundamental wave reactive component does not participate in compensation optimization, namely, only each harmonic is subjected to iterative calculation; each dimension of the particle vector is a compensation quantity corresponding to the subharmonic current and is expressed in an exponential form,
Figure BDA0002212850390000092
respectively representing the amplitudes of the fundamental wave and each harmonic compensation quantity; thetainRepresenting the phase of the nth harmonic compensation quantity.
The number of particles in the initial population is set to be m, and m can be a large value in order to prevent the result from falling into local optimum. Position vector x of the particleiGiven in section 4.1, the velocity vector of the particle is also set to vi＝[vi1,vi2,vi3,…,vin]Wherein v isinThe moving speed of the ith particle is expressed by the nth dimension, i is 1,2, …, m.
The optimizing process of the particle swarm algorithm is a process that the speed and the position of the particles are changed constantly, namely, the current adaptive value of each particle is compared with the adaptive values corresponding to the individual historical optimal position and the global optimal position of the particle, and the particle is guided to move to the optimal solution position by using a speed calculation formula and a position updating formula.
(2) Setting a velocity update formula, a position update formula, and a termination condition of the particle swarm algorithm
The velocity update formula for setting particles is shown in formula (19), and the position update formula is shown in formula (20).
Wherein
Figure BDA0002212850390000102
Represents the j-th dimension component in the moving velocity vector of the particle i after the t-th iteration,
Figure BDA0002212850390000103
representing the j-th component, c, of the position vector of the particle i after the t-th iteration1，c2Are two acceleration constants that are used to adjust the maximum step size for particle learning, typically set to 1.5, r1，r2Is two value ranges of [0,1 ]]W is a non-negative inertial weight, and is used to adjust the search range of the solution space, typically set to 0.5. The velocity update formula for the particle includes three parts, namely the previous velocity of the particle, the cognitive part and the social part, pbestijRepresenting individual historical optimal positions of particles, gbestijThe global optimal position of the particle is represented, and the three parts act together to ensure that the particle moves towards the direction of the optimal solution.
And finally, setting a termination condition of the particle swarm algorithm, wherein the termination condition of the particle swarm algorithm is that the maximum iteration times or the global optimal position limit is met.
By combining the above analysis, a particle swarm optimization configuration algorithm flow of the residual capacity of the SAPF can be given, as shown in fig. 1.
Step 1: initializing a particle swarm, namely initializing a position vector, a speed vector and the number of particles;
step 2: calculating the residual capacity and the current distortion rate of the SAPF by using the current position vector, and further calculating the fitness of each particle by using a target adaptive function;
and step 3: comparing the fitness of the current position of the particle with the fitness of the individual historical best position of the particle: if the fitness of the current position of the particle is smaller than the fitness of the individual historical optimal position of the particle, directly entering step 4; if the fitness of the current position of the particle is larger than the fitness of the individual historical optimal position of the particle, setting the current position as the individual historical optimal position, and then entering the step 4:
and 4, step 4: comparing the fitness of the individual historical optimal position with the fitness of the global optimal position of the particle, and if the fitness of the individual historical optimal position is smaller than the fitness of the global optimal position of the particle, directly entering the step 5; and if the fitness of the individual historical optimal position is greater than the fitness of the global optimal position of the particle, setting the individual historical optimal position as the global optimal position, and then entering the step 5.
And 5: and updating the speed and the position of the particle through a position updating formula and a speed following formula.
Step 6: and judging a termination condition, outputting a global optimal position if the position vector of the particle meets the maximum iteration times or the global optimal position limit, and otherwise, turning to the step 2.
To verify the effectiveness of the algorithm presented herein, a simulation model of SAPF, the grid and several nonlinear loads was constructed using MATLAB/SIMULINK. Setting the line voltage of a power grid to be 380V, the frequency to be 50Hz, the resistance of a power grid bus to be 1 omega, the filter inductance of the SAPF to be 2.4mH, the filter capacitance to be 10 muF, the direct-current side voltage to be 800V, and the sampling time to be 1 multiplied by 10-6。
When the residual capacity of the SAPF is sufficient, harmonic compensation of the input nonlinear load is sufficiently completed, when 0s is set, the nonlinear load is input, when 0.1s, the SAPF under the optimized configuration algorithm is input for compensation, and simulation observation shows that A-phase oscillograms before and after grid current compensation are shown in FIG. 2.
The FFT analysis is carried out on the bus current of the power grid, the current distortion rate before compensation is 22.47%, the current distortion rate after compensation is reduced to 1.16%, the current waveform after compensation is very close to a sine wave, the compensation effect is obvious, and the effective compensation capacity used by the SAPF is 25.31A.
When the residual compensation capacity of the SAPF is insufficient, namely the harmonic compensation of the newly-input nonlinear load is not fully completed, the residual effective compensation capacity is limited to 20A by using the simulation current limiting module. Four compensation methods are used, and in the case of residual capacity limitation, the input nonlinear load is compensated in turn, and the compensation effect of each compensation method is compared. The first two methods are single harmonic compensation, namely, the first 5 th harmonic and the second 7 th harmonic with higher harmonic content are compensated independently, the third method is a traditional equal proportion compensation method for each harmonic, and the fourth method is a particle swarm optimization configuration compensation method. The phase current waveform diagrams of the grid bus a obtained after compensation by the four compensation methods are respectively shown in (a), (b), (c) and (d) of fig. 3.
Comparing the power grid current waveform diagrams under the four compensation methods in fig. 2, it can be known that the power grid current waveforms obtained by the optimal configuration compensation method and the conventional 5 th harmonic compensation method are closer to a sine wave. Fig. 4 shows a histogram of distortion rates of each harmonic obtained by FFT analysis after compensation by the four SAPF compensation methods, which indicates that the conventional 5 th harmonic compensation cannot completely realize full compensation of the 5 th harmonic under the condition of limited residual capacity of the SAPF, and the 7 th harmonic has less content and can realize full compensation. After the compensation of the traditional equal proportion compensation method and the optimized configuration compensation method, the content of each harmonic wave is reduced, but the compensation proportioning effect of each harmonic wave under the optimized configuration compensation method is better.
The effective capacity of the SAPF detected in the simulation process of the four compensation methods is compared with the total harmonic distortion of the power grid bus, and the result is shown in Table 1.
TABLE 1 four compensation methods compensation capacity and THD value comparison
Figure BDA0002212850390000111
As can be seen from table 1, the compensation capacity used in the 7 th harmonic compensation method is small, the current distortion rate of the power grid bus is high, and the compensation effect is poor. Although the SAPF capacity used by the equal proportion compensation is closer to the limit value 20A, the utilization rate is higher, but the current distortion rate of the bus is higher, and the compensation effect is still not ideal. Compared with 5-order harmonic compensation, the optimized configuration compensation method has the advantages that the compensation capacity of the SAPF used by the optimized configuration compensation method is higher, the compensation configuration of each order of harmonic is more optimized and reasonable, the utilization rate of the residual capacity of the SAPF is higher, and the current distortion rate of the power grid bus after compensation is lower.
In summary, the invention provides an optimal configuration method and system for the residual capacity of the SAPF, which is characterized in that under the condition that the unused residual capacity exists in the installed SAPF, an optimal solution of the target adaptive function is solved by constructing a target adaptive function of the residual capacity of the SAPF, wherein the target adaptive function takes the compensation quantity of the SAPF on the reactive component of the fundamental current and the compensation quantity of the SAPF on each subharmonic current as self-variables, and the operation of the SAPF is configured according to the compensation quantity of the reactive component of the fundamental current and the compensation quantity of each subharmonic current corresponding to the optimal solution so as to realize the optimal compensation of the grid current to be compensated. Thereby realizing the full utilization of the SAPF capacity. Therefore, the problem that the control algorithm of the SAPF ignores the residual capacity of the SAPF device in the prior art is solved, the full utilization of the SAPF capacity is realized, and the technical problem of optimizing the compensation effect of the SAPF on the power grid current is solved.
In the preferred scheme, the optimization of the SAPF residual capacity and the current distortion rate of a power grid bus and the optimization selection of each harmonic compensation amount are realized by calculating the SAPF residual capacity, designing an improved harmonic extraction algorithm and fusing a particle swarm optimization algorithm, and finally, the current harmonic content of the power grid is greatly reduced while the utilization rate of the SAPF capacity is improved. The feasibility and the practicability of the compensation algorithm proposed by the method are proved by comparing the optimized configuration compensation method with other three traditional compensation methods.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (8)

1. An optimal configuration method for residual capacity of SAPF is characterized by comprising the following steps:
acquiring and constructing a target adaptive function which takes compensation quantity of reactive components of the SAPF to the fundamental current and compensation quantity of the SAPF to the harmonic currents as independent variables according to the nominal rated capacity of the SAPF in historical data and the harmonic currents and the load fundamental current of the compensated power grid current, wherein the target adaptive function is provided with two optimization targets which are respectively:
(1) the residual capacity of SAPF is close to zero;
(2) the current distortion rate of the bus mounted with the SAPF is close to zero or smaller than a current distortion rate standard;
obtaining an effective value of load fundamental current, an effective value of each harmonic current and an effective value of the nominal rated capacity of the SAPF of the power grid to be compensated, substituting the effective values into the target adaptation function, and solving an optimal solution;
and configuring the SAPF operation according to the compensation quantity of the reactive component of the fundamental current and the compensation quantity of each subharmonic current corresponding to the optimal solution so as to realize the optimal compensation of the power grid current to be compensated.
2. The method of claim 1, wherein the objective adaptive function is:
F＝min f(x)
＝λ1Ir+λ2ITHD+P max(0,Ir-Im)
f and minf (x) are target adaptive functions, and when the value of the target adaptive function is minimum, the corresponding fundamental wave reactive current component and each subharmonic current are optimal solutions; i isrIs the residual volume of SAPFAn amount of, wherein,
Figure FDA0002212850380000011
ITHDis the current distortion rate of the grid bus, wherein,
Figure FDA0002212850380000012
λ1and λ2Respectively, residual capacity IrAnd the current distortion rate I of the corresponding busTHDThe weight values of the two optimization targets satisfy lambda1+λ21 is ═ 1; p is an out-of-limit penalty coefficient of the residual capacity; i ismAn effective value for the nominal rated capacity of SAPF; i is1For an effective value of the load fundamental current, I1qIs the effective value of the fundamental reactive current component; i isnEffective value of each harmonic current; n is the harmonic order, InAs each harmonic component; ITHDmaxAnd the standard value of the current distortion rate of the bus where the SAPF is located.
3. The optimal configuration method of the remaining capacity of SAPF of claim 2, wherein solving the optimal solution is accomplished by particle swarm optimization, comprising the steps of:
setting the compensation quantity of the SAPF on the reactive component of the fundamental current as a first dimension component of a position vector of the particle swarm optimization; setting the compensation quantity of the SAPF to each subharmonic current as the second dimension component to the (n + 1) th dimension component of the position vector;
setting the number, the speed vector, the speed updating formula, the position updating formula and the termination condition of the particle swarm algorithm;
initializing a position vector and a velocity vector of the particle swarm algorithm, and guiding particles to iterate towards the optimal solution position direction through a velocity calculation formula and a position update formula;
in each iteration process, calculating the residual capacity and the current distortion rate of the SAPF by using the current position vector of the particle, further calculating the fitness of each particle by using a target adaptation function, and comparing the current adaptation value of each particle with the adaptation values corresponding to the individual historical optimal position and the global optimal position of the particle swarm to determine the global optimal position of the particle swarm; judging whether the global optimal position of the particle after each iteration meets the termination judgment condition or not by judging the termination condition, and entering the next iteration process if the global optimal position of the particle after each iteration does not meet the termination judgment condition; and if the judgment termination condition is met, outputting the global optimal position to obtain the optimal solution corresponding to the global optimal position.
4. The optimal configuration method of SAPF residual capacity of claim 3, wherein the position vector of the particles is:
Figure FDA0002212850380000021
wherein x isiIs the position vector of the particle; alpha is a set existence coefficient, when alpha is 0, the fundamental wave reactive component does not participate in compensation optimization, namely, only each harmonic is subjected to iterative calculation; each dimension of the particle vector is a compensation quantity corresponding to the subharmonic current and is expressed in an exponential form,
Figure FDA0002212850380000022
respectively representing the amplitudes of the fundamental wave and each harmonic compensation quantity; thetainA phase representing an nth harmonic compensation amount;
the velocity vector of the particle is vi＝[vi1,vi2,vi3,…,vin]Wherein v isinThe moving speed of the ith particle is expressed by the nth dimension, i is 1,2, …, m.
5. The optimal configuration method of remaining capacity of SAPF of claim 4,
the speed updating algorithm of the particles is as follows:
Figure FDA0002212850380000023
the position updating algorithm of the particles is as follows:
Figure FDA0002212850380000024
wherein the content of the first and second substances,
Figure FDA0002212850380000025
represents the j-th dimension component in the moving velocity vector of the particle i after the t-th iteration,
Figure FDA0002212850380000026
representing the j-th dimension component in the position vector of the particle i after the t-th iteration; c. C1，c2Are two acceleration constants that adjust the maximum step size, r, of particle learning1，r2Is two value ranges of [0,1 ]]A random function of (a) to increase search randomness; w is a non-negative inertial weight used for adjusting the search range of the solution space; pbestijRepresenting the historical optimal position of the particle individual; gbestijRepresenting a global optimal position of the particle; t represents time;
and the termination condition of the particle swarm algorithm is that the maximum iteration number or the global optimal position limit is met.
6. The optimal configuration method of the remaining capacity of SAPF according to any of claims 1 to 5, wherein the harmonic current comprises a real DC component and a reactive DC component; obtaining a harmonic current of the SAPF, comprising:
obtaining a k-th harmonic current component vector i under a d-q rotating coordinate systemkAnd its corresponding harmonic voltage component vector ukAccording to a synchronous coordinate transformation method, through an improved Park transformation matrix, a voltage current vector under an original d-q rotation coordinate system is rotated clockwise by an angle corresponding to a k-th harmonic voltage initial phase to obtain a new d-q coordinate system, so that a k-th harmonic voltage component vector is overlapped with a d-axis under the new d-q coordinate system, and an active component and a reactive component corresponding to the k-th harmonic current are obtained through the new d-q coordinate system;
sequentially enabling the fundamental frequency multiple n to be equal to +/-1 time of the k-th harmonic current to be extracted, and obtaining positive and negative sequence direct current components of the k-th harmonic current and active and reactive components of the k-th harmonic current through LPF filtering; and obtaining the active direct current component and the reactive direct current component of the kth harmonic current through the positive and negative sequence direct current components of the kth harmonic current and the active component and the reactive component of the kth harmonic current.
7. The optimal configuration method of remaining capacity of SAPF of claim 6,
the active component i of the kth harmonic currentd' and reactive component iq' is:
wherein the content of the first and second substances,
Figure FDA0002212850380000032
the positive sequence component amplitude of the nth harmonic,
Figure FDA0002212850380000039
the amplitude of the negative sequence component of the nth harmonic;
Figure FDA0002212850380000033
is the initial phase angle of the nth harmonic current positive sequence component,
Figure FDA0002212850380000034
is the initial phase angle of the negative sequence component of the nth harmonic current; k is the number of harmonic waves to be solved; omega is angular frequency;
Figure FDA0002212850380000035
the initial phase of the voltage of the kth harmonic, i.e. the phase compensation amount of the kth harmonic,the expression is as follows:
Figure FDA0002212850380000037
wherein u iskqAnd ukdRespectively representing a q-axis component and a d-axis component of the k-th harmonic voltage after dq decomposition;
the modified Park transformation matrix Cdq' is:
Figure FDA0002212850380000038
active direct current component of the kth harmonic current
Figure FDA0002212850380000041
And a reactive DC component
Figure FDA0002212850380000042
Comprises the following steps:
Figure FDA0002212850380000043
wherein ikd' and ikqThe d-axis component and the q-axis component of the kth harmonic current obtained after phase compensation and LPF filtering are obtained;
Figure FDA0002212850380000044
andthe initial phase angles of the positive sequence and negative sequence components of the kth harmonic current, respectively.
8. A computer system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method of any of the preceding claims 1 to 7 are performed when the computer program is executed by the processor.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111952976A (en) * 2020-08-25 2020-11-17 中冶赛迪电气技术有限公司 Steel mill substation harmonic current optimization method based on multi-target particle swarm algorithm

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105470947A (en) * 2015-11-23 2016-04-06 杭州电子科技大学 Micro-power-grid scheduling method based on quantum-behaved particle swarm optimization
US20180356803A1 (en) * 2017-06-12 2018-12-13 Hefei University Of Technology Method and system for batch scheduling uniform parallel machines with different capacities based on improved genetic algorithm
CN110021940A (en) * 2019-04-25 2019-07-16 国网重庆市电力公司璧山供电分公司 A kind of capacitor placement optimization method based on improvement particle swarm algorithm

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105470947A (en) * 2015-11-23 2016-04-06 杭州电子科技大学 Micro-power-grid scheduling method based on quantum-behaved particle swarm optimization
US20180356803A1 (en) * 2017-06-12 2018-12-13 Hefei University Of Technology Method and system for batch scheduling uniform parallel machines with different capacities based on improved genetic algorithm
CN110021940A (en) * 2019-04-25 2019-07-16 国网重庆市电力公司璧山供电分公司 A kind of capacitor placement optimization method based on improvement particle swarm algorithm

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111952976A (en) * 2020-08-25 2020-11-17 中冶赛迪电气技术有限公司 Steel mill substation harmonic current optimization method based on multi-target particle swarm algorithm

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