CN104133922A - Optimized configuration of power distribution network filtering device for eliminating mutual influence of multiple harmonic sources - Google Patents
Optimized configuration of power distribution network filtering device for eliminating mutual influence of multiple harmonic sources Download PDFInfo
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Abstract
For preventing the defects that multiple harmonic sources in an intelligent electric power system mutually influence each other, phenomena of mutual superposition, counteraction or attenuation and the like of harmonic waves may occur, and the safe, stable and economic operation of a power distribution network is seriously threatened, the invention discloses the study of suppressing harmonic waves for the power distribution network with the multiple harmonic sources by using an improved self-adaptive fuzzy particle swarm algorithm. The improved algorithm adopts a self-adaptive inertia coefficient for adjusting an inertia weight coefficient; an individual optimal weighted mean of all particles in the speed and position updating process of the particle swarm algorithm is used for replacing a global optimum value; the guide performance of all individuals on the group activities is considered for regulating the speed and the position of the particles; and the particle position upgrading is subjected to fuzzy control, so that the algorithm can be effectively prevented from falling into the local optimum. The simulation result shows that the algorithm has the advantages that the installing places and corresponding parameters of active and passive filtering devices can be optimized in a unified way in a given power grid range; the system loss is reduced; and the goal of effectively suppressing the harmonic waves is achieved.
Description
Technical field
The present invention relates to unified infield and the corresponding parameter of optimizing active and passive filtration unit within the scope of power distribution network, reduce the loss of system, voltage, power factor etc. are kept within the limits prescribed.
Background technology
Many type of power in intelligent grid various energy resources converting units such as (etc.) the various energy resources input such as wind energy, sun power and internal combustion engine, accumulator systems operation is uncertain strong, there is intermittence, complicacy, diversity, instable feature, its quality of power supply feature and conventional electric power system have very big-difference, increasing distributed power source and power quality adjusting device infiltration are in distribution system infrastructure, make single-phase trend in traditional electrical network face the problem of bi-directional current, and cause between harmonic wave and influence each other; When a plurality of harmonic sources are done the used time simultaneously, due to humorous wave frequency, amplitude, phase place is different, and harmonic wave suffered impact in transmitting procedure, make the inner harmonic wave of intelligent grid very complicated, bring serious harmonic pollution, there is the situations such as coupling interaction impact in multi-inverter device particularly, will cause harmonic wave to amplify, its negative effect is the decline of the quality of power supply, is having a strong impact on the safety and economic operation of confession, consumer simultaneously and is causing huge economic loss; In user or electrical network, installing wave filter is a kind of effective measures that suppress harmonic wave, can reduce and control harmonic current and the compensating reactive power loss of injecting electrical network, so that the harmonic voltage of each node meets corresponding harmonic standard in power distribution network
In order to adapt to the random variation of harmonic source and network parameter, consider that the complex optimum configuration of two kinds of wave filters is the trend that current quality of power supply engineering suppresses harmonic wave measure; Be subject to the impact of actual electric network operation complicacy, the harmonic wave constraint that an APF is difficult to meet whole network is installed, can consider the optimization allocation of a plurality of APF; The advantage such as compare with other traditional algorithms that particle cluster algorithm has that empirical parameter simple to operate, that rely on is few, speed fast and optimizing ability is strong, but it exists late convergence slow, optimizing precision is low and be easily absorbed in the deficiency of local optimum; Fuzzy theory is effective mathematical tool of research uncertain problem, there is good dirigibility and stronger adaptive faculty, herein the multiple objective function optimization of wave filter is analyzed, application fuzzy theory, proposes a kind of improved adaptive fuzzy particle swarm optimization algorithm this complicated multi-objective optimization question is solved.
Summary of the invention
The advantages such as particle cluster algorithm is simple to operate with it, fast convergence rate are to carry out in multi-objective optimization question application wider, but when in algorithm, inertia weight coefficient is larger, particle may produce to the fine search of optimum solution the adverse consequences that search precision is not high owing to lacking; By adopting adaptive inertial coefficient to adjust inertia weight coefficient, larger inertia weight value is conducive to jump out local optimum, is adapted to search volume to develop on a large scale; Less inertia weight value can improve the precision of algorithm and be beneficial to algorithm local convergence, is applicable to exploitation among a small circle; Conventionally PSO algorithm only relies on optimal value and does not make full use of the information of other particles in iterative process, and when problem is comparatively complicated, algorithm is easy to be absorbed in local optimum.For fear of the generation of this situation, the weighted mean value with the individuality optimum of all particles in the speed of particle cluster algorithm and position updating process replaces global optimum, considers that all individualities adjust speed and the position of particle to the guidance quality of group activity; In standard particle group algorithm, only stipulate the limit value of particle rapidity, but particle position limit value has not been determined, made algorithm easily be absorbed in local optimum; By the renewal of position being carried out to fuzzy control, can effectively avoid being absorbed in local optimum.
its beneficial effect is:
1) this algorithm has the population of avoiding and is absorbed in local optimum, and inertia weight is carried out to adaptive control energy;
2) unified infield and the corresponding parameter of optimizing active and passive filtration unit in given distribution system, reduce the loss of system, line voltage current distortion rate is controlled in national regulation limit value, in the situation that guaranteeing power distribution network safe and stable operation, reach the object of filter first cost minimum;
3) greatly reduce quantity and the capacity of required current transformer, reached good filter effect and optimistic economic benefit, there is actual application value;
Below in conjunction with accompanying drawing, the invention will be further described.
accompanying drawing explanation
Fig. 1 is algorithm flow chart.
embodiment
The search volume of a D dimension, by m the molecular population of grain
, the
ithe position of individual particle is
, present speed is
; In each iteration, the desired positions that particle individuality searches is
be called individual optimum and be denoted as P
best; In colony all particle search to desired positions be
be called global optimum, be denoted as G
best.Particle upgrades respectively oneself speed and position according to formula (1) and formula (2):
(1)
i = 1,2,…,M (2)
tfor iterations; c
1and c
2for the study factor, rand
1and rand
2it is [0,1] interval interior equally distributed random number;
wfor inertia weight.The speed of particle is limited to [v
max, v
max] between
Adopt adaptive inertial coefficient to adjust inertia weight coefficient:
(3)
In formula: λ regulates
wthe positive coefficient of pace of change,
tfor current iteration number of times,
w 0 for the upper limit of w (t), t
maxfor maximum iteration time; Larger
wvalue is conducive to jump out local optimum, is adapted to search volume to develop on a large scale; Less
wvalue can improve the precision of algorithm and be beneficial to algorithm local convergence, is applicable to exploitation among a small circle
The optimum weighted mean value of individuality of all particles is expressed as:
(4)
be weight vectors, reacted the percentage contribution of i particle and met
, the formula that the position of particle is upgraded becomes:
(5)
In standard particle group algorithm, only stipulate the limit value of particle rapidity, but particle position limit value has not been determined, made algorithm easily be absorbed in local optimum; The renewal of position is carried out to fuzzy control herein and can effectively avoid being absorbed in local optimum, formula 5 is carried out to fuzzy control and draw:
（6）
（7）
Wherein,
for
sshape subordinate function,
tbe a given threshold values, with t
maxclosely related, a, c is constant.When
time,
get 1, during this time, the variation of particle position is larger; When t>T, the change of particle will slow down, and while arriving certain iterations, the variation of particle can add hurry up again, can effectively avoid being absorbed in local optimum.
The mathematical model that in the power distribution network of generation of electricity by new energy, filter unification is distributed rationally
Objective function
(1) harmonic voltage resultant distortion rate
With each node harmonic voltage resultant distortion rate of power distribution network
tHDU i mean value be objective function, that is:
(8)
h=(2,3…H) (9)
In formula,
ifor grid nodes label, N is the total nodes of network;
hfor overtone order, Y
hi be
inode
hthe admittance matrix of subharmonic; U
li for node
ifundamental voltage effective value, U
hi for node
i's
hsubharmonic voltage effective value;
u tHUUifor node
ivoltage harmonic aberration rate
(2) filter economy
The mathematical model of passive filter and active filter show that the economy that the objective function of distributing rationally is filter is minimum, that is:
(10)
In formula,
,
whether represent installing filter branch road;
f(Q
cNij) the expression expense of passive filter and the funtcional relationship of branch road capacitor rated capacity;
f(S
ni) relevant with the capacity of compensation harmonic with the expense of active filter, funtcional relationship is as follows:
(11)
(12)
Q
cNijbe the jot constant volume of the installation capacitor of i node j bar branch road, S
nithe capacity that represents active filter; Coefficient a
0ij, a
1ij, b
0ij,b
1ijchoosing value adopt market price decision method to determine effectively to avoid blindness
The rated capacity of passive filter is comprised of fundamental wave reactive power capacity and harmonic wave reactive capability.Get nonlinear-load power factor 0.65～0.85 herein.Passive filter is at node
ithe specified installed capacity of smallest capacitor be:
(13)
(14)
In formula, Q
1 fundamental wave reactive power capacity; Q
hi harmonic wave reactive capability; C
i for node
ithe capacitance of wave filter;
Active filter compensation capacity S
i be decided by compensated total harmonic current effective value, that is:
(15)
In formula, U
1i be
ithe fundamental current of node; U
1i be
iindividual node
hinferior voltage effective value; I
hi be
inodes active filter compensate
hsubharmonic current value; Capacity S
i irrelevant with fundamental current;
(3) general objective function
Above-mentioned distributing rationally is typical multiple goal, non-linear, uncertain combinatorial optimization problem.It is impossible making above-mentioned multiple objective function reach in actual applications minimum value simultaneously, can only be by the relation between coordination function, make as far as possible both to reach more excellent, above-mentioned two indexs are normalized, can solve the skimble-scamble problem of the order of magnitude between each target
(16)
(17)
Limit
,
value between 0～1, adopts the mode of linear weighted functions to obtain general objective function to two objective functions to be:
(18)
In formula
for the weight of (1) (2) two objective functions, meet
, and
,
;
Constraint condition
(1) in searching process, the constraint of network harmonic trend:
(19)
In formula, C
tHDUthe limit value that represents the total percent harmonic distortion of voltage.According to GB GB/T14549-93 regulation, calculate
. the voltage distortion rate limit value of the power distribution network national regulation of different electric pressures is different, and general public electric wire net harmonic limits and line voltage grade are closely related, and electric pressure is higher, and Harmonic is stricter
(2) safe operation of passive filter constraint
Because harmonic wave has harm to capacitor itself, therefore, when passive filtering branch road is designed, should consider the impact of harmonic wave on wave filter rated current, voltage and capacity, following formula is voltage, electric current and the capacity-constrained in passive filter branch road:
(20)
(21)
(22)
The electric current of general capacitor can not overrate 135%, in formula, K
u , K
i , K
q be respectively permission piezoelectric voltage, excess current and the overcapacity coefficient of capacitor
(4) safe operation of active filter constraint
The capacity-constrained of active filter:
(23)
The overcapacity coefficient K that active filter allows
s represent
In sum, the Parametric optimization problem of active filter and passive filter is meeting under above constraint condition exactly, makes general objective function reach the search problem of optimum solution.
The structure of fitness function
According to above-mentioned optimization problem, adopt mixing penalty function method that above-mentioned Solution of Nonlinear Optimal Problem is become to unconstrained optimization problem herein, that is:
(24)
being penalty factor, is an indefinite array successively decreasing; h
i (X) be
iindividual equality constraints functions
i=(1,2 ..., l);
be
jindividual inequality constrain function, j=(1,2 ..., k), herein
initial value gets 1, order
, and C=1/2; By constantly reducing penalty factor, carry out taking turns without Constrain Searching, every constraint condition being met all comes in obstacle item, the constraint condition being not being met is all arranged in penalty term, from the inside and outside optimum solution of approaching on border, the optimum solution finally obtaining is exactly the optimum solution of single goal belt restraining problem respectively.
Claims (1)
1. improve adaptive fuzzy particle cluster algorithm, that the inertial coefficient of typical particle cluster algorithm is carried out to adaptive control, weighted mean value with the individuality optimum of all particles in the speed of particle cluster algorithm and position updating process replaces global optimum, consider that all individualities adjust speed and the position of particle to the guidance quality of group activity, and the renewal of particle position is carried out to fuzzy control, can effectively avoid algorithm to be absorbed in local optimum like this;
When in algorithm, inertia weight coefficient is larger, particle may produce to the fine search of optimum solution the adverse consequences that search precision is not high owing to lacking, and adopts adaptive inertial coefficient to adjust inertia weight coefficient:
In formula: λ is the positive coefficient that regulates ω pace of change,
tfor current iteration number of times, w
0for the upper limit of w (t),
for maximum iteration time, larger ω value is conducive to jump out local optimum, is adapted to that search volume is developed to less ω value on a large scale and can improves the precision of algorithm and be beneficial to algorithm local convergence, is applicable to exploitation among a small circle;
The optimum weighted mean value of individuality of all particles is expressed as:
be weight vectors, reacted the percentage contribution of i particle and met
, the formula that the position of particle is upgraded becomes:
The renewal of position is carried out to fuzzy control and can effectively avoid being absorbed in local optimum, above formula formula is carried out to fuzzy control and draw:
Wherein,
for S shape subordinate function, T is a given threshold values, with
closely related, a, c is constant; When
time,
get 1, during this time, the variation of particle position is larger; When t>T, the change of particle will slow down, and while arriving certain iterations, the variation of particle can add hurry up again, can effectively avoid being absorbed in local optimum.
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Cited By (3)
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| CN107800135A (en) * | 2017-06-21 | 2018-03-13 | 中南大学 | A kind of different subharmonic for SAPF become more meticulous compensation method |
| CN117424273A (en) * | 2023-10-26 | 2024-01-19 | 国网山西省电力公司电力科学研究院 | Distributed harmonic wave treatment method suitable for high-voltage direct-current transmission system |
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|---|---|---|---|---|
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| CN107121617B (en) * | 2017-03-09 | 2019-09-27 | 昆明理工大学 | A kind of DC power transmission line k nearest neighbor distance measuring method using filter branches electric current |
| CN107800135A (en) * | 2017-06-21 | 2018-03-13 | 中南大学 | A kind of different subharmonic for SAPF become more meticulous compensation method |
| CN107800135B (en) * | 2017-06-21 | 2021-04-23 | 中南大学 | Different-order harmonic refinement compensation method for SAPF |
| CN117424273A (en) * | 2023-10-26 | 2024-01-19 | 国网山西省电力公司电力科学研究院 | Distributed harmonic wave treatment method suitable for high-voltage direct-current transmission system |
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