CN101252308A - Power electronic circuit optimizing method based on paralleling ant cluster algorithm - Google Patents
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Abstract
Disclosed is a power electronic circuit optimization method based on parallel ant colony algorithm, relating to the field of intelligent computation and the field of power electronics. Firstly, the power electronic circuit is decoupled into a power transmission part and a feedback network part, then the two parts are respectively optimized through two optimization processes based on ant colony algorithm. In the first optimization process, the parameters of the elements of the power transmission part are adjustable, while the parameters of the feedback network are fixed. In the second optimization process, the parameters of the elements of the power transmission part are not adjustable, while the parameters of the feedback network are adjustable. The two optimization processes are not isolated, the adjustable parameters in one optimization process will be migrated to the other optimization process to serve as the fixed parameters, and the migration strategy is self-adaptive to control. The optimization method is quite suitable for parallel processes of computers and effectively improves optimization speed.
Description
Technical field:
The present invention relates to artificial intelligence and power electronic two big fields, relate generally to the optimization and the design of power electronic circuit.
Technical background:
In recent two decades, small-signal model is extensively applied in the middle of the design of power electronic circuit.In numerous methods, mean state and derivation usage thereof are the most general one.Because switch converters contains an output capacitance, its cut-off frequency is significantly less than switching frequency, so just can obtain the model of a linear time invariant, the power electronic circuit that becomes when being similar on operating point.Use traditional control theory, feedback network part that just can design circuit.Though this method is very simple and easy, it only is applicable to specific circuit and control method usually, and requires that the running of circuit is had comprehensive understanding.In addition, when circuit is converted into Mathematical Modeling, its state variable averages out, and just can not obtain the details of any relevant response wave shape.Under the situation of large-signal, circuit designers is difficult to the response of accurately predicting circuit.Along with the development of power electronic technology, increasing to the demand that circuit automatic generates, the component parameters of optimizing circuit is to reach the performance of appointment.Before 20 years, the automated design engineering of analog circuit begins to occur greatly.These methods comprise, heuritic approach, the algorithm of knowledge base, simulated annealing and other optimization circuit.Traditional optimization method, as gradient method, hill climbing etc. are utilization to some extent all.But these algorithms are absorbed in the local optimum point easily, cause the component parameters of suboptimum, have limited its utilization in complex space.
Ant group algorithm is a kind of novel simulated evolutionary algorithm, finds the solution a lot of combinatorial optimization problems by the behavior of real ant search food source in simulating nature circle.In the natural world, ant carries out interchange between the individuality by release pheromone, can find a path the shortest between ant nest and food.Because positive feedback, concurrency, strong convergence and robustness that ant group algorithm itself has make it that good performance be arranged in combinatorial optimization problem, as traveling salesman problem, scheduling problem, quadratic assignment problem etc.Compare with other first heuritic approach, ant group algorithm has stronger ability of searching optimum and optimizing ability, and the steady quality of separating also has higher search efficiency.In the Automatic Optimal Design of power electronic circuit, can often relate to a large amount of circuit simulation tests, will be very consuming time so in serial computer, carry out single optimization process.Ant group algorithm has natural concurrency, very be suitable for realizing on parallel computer, so paralleling ant cluster algorithm will improve the speed of finding the solution of circuit optimization problem greatly.
Summary of the invention:
On a computer, use ant group algorithm to find the solution the slow excessively problem of power electronic circuit speed for solving.The present invention applies to paralleling ant cluster algorithm in the optimal design of power electronic circuit, the advantage of this method is: the paralleling ant cluster algorithm of proposition is suitable for realizing on parallel computer, therefore can improve the speed of finding the solution of optimization for power electronic circuit problem greatly.
The concrete grammar of utilization paralleling ant cluster algorithm optimal design power electronic circuit is as follows.
With the power electronic circuit decoupling zero is power delivery and feedback network two parts.Wherein power delivery partly comprises I
PIndividual resistance, J
PIndividual inductance and K
PIndividual electric capacity; Feedback network partly comprises I
FIndividual resistance, J
FIndividual inductance and K
FIndividual electric capacity.Use two passive components in vector representation two parts respectively
Wherein,
The optimization process of two ant group algorithms of initialization is respectively in order to optimize Θ
PAnd Θ
FWherein in process one, the component value Θ of feedback network part
FFix the component value Θ of power delivery part
PBe adjustable.And in process two, the element of power delivery part is fixed, and the element of feedback network part is adjustable.
On parallel computer, move the optimization process of two ant group algorithms simultaneously.Two processes of ant group algorithm include following step:
1) turns to a series of nominal value with each the element value in the power electronic circuit is discrete, and these nominal values are mapped to an ant group algorithm optimize in the structural map of power electronic circuit.Wherein, each element all uses a tabulation to represent, each node in the table is represented the nominal value that may obtain of this element.Wherein each node also has been endowed certain pheromones, is used to guide the search of ant.
2) pheromones of each node of initialization is an initial value τ
o, initialization N
PIndividual ant is used to search for best element value combination.
3) allow every ant be followed successively by element adjustable in the optimization process and choose a nominal value, thereby form a paths according to the pheromones size on the node.Ant is selected the node C of element j
jThe probability of [i] is
τ wherein
JiPheromones on i the node of expression element j.N is the node sum of this element, the nominal value number that just may obtain, and what preserve in N+1 node is value on the successive dynasties optimal path, this makes optimal value to produce bigger influence to whole search.In the process of selecting, the selecteed probability of the node that pheromones is big more is just high more, and the selecteed chance of node that opposite pheromones is more little is more little.
4) after all ants have all constructed the path, circuit simulation test is carried out in the path of each ant, and according to the adaptive value of adaptive value function phi calculating path.Adaptive value function in two optimization processes is identical,
Wherein, ph represents the path that ant is selected.v
InBe input voltage, it is with step-length δ v
InFrom V
In, minChange to V
In, maxR
LWei load value, it is with step-length δ R
LFrom R
L, minChange to R
L, maxOF
1, OF
2, OF
3, OF
4Respectively as giving a definition.
OF
1Be used to be evaluated at the steady-state error of output voltage.Define a variance accumulation equation E
2, in order to assessment output voltage v
oWith reference voltage v
RefAt N
sThe degree of closeness of individual simulated point
If E
2Value less, then steady-state error is little, OF
1Can be bigger.Formula OF
1Be defined as follows
Wherein, K
1Be OF
1The maximum that can reach, K
2In order to adjust OF
1To E
2Susceptibility.
OF
2Be used to be evaluated at the settling time of output voltage between the starting period, maximum overshoot and Xia Chong.OF
4Be used for the dynamic property of evaluation circuits when input voltage and output resistance disturbance.During startup or external disturbance, a transient response v will appear
d, wherein
v
d=v
ref-v
o
OF
2And OF
4In order to assessment v
d, comprise 1) and maximum overshoot, 2) maximum dash 3 down) and during startup or disturbance, the settling time of response.OF
6And OF
8Citation form can be expressed as follows
OF
2=OV(R
L,v
in,ph)+UV(R
L,v
in,ph)+ST(R
L,v
in,ph)
N wherein
TBe the input and the number of times of load disturbance in performance test.
In above formula, OV, UV and ST minimize maximum overshoot, maximum dashing down and v
dThe target function of settling time.They are as giving a definition:
K wherein
3Be the maximum that this target function can reach, M
P0Be maximum overshoot, M
pBe actual overshoot, K
4It is the passband constant.
K wherein
5Be the maximum that this target function can reach, M
V0Be to dash M under the maximum
vBe actual following dashing, K
6It is the passband constant.
K wherein
7Be the maximum that this target function can reach, T
S0Be a constant, T
sBe actual settling time, K
8Be used to adjust susceptibility.T
sBe defined as v
dFall into the settling time of α ± σ % passband.Just,
|v
d(t)|≤0.01σ,t≥T
s
OF
3With the stable ripple voltage on the assessment output voltage.v
oOn ripple voltage must be at expection output v
O, expNear ± Δ v
oIn the limit.At OF
3The method of the middle path ch of measurement is to calculate at N
sIn the individual simulated point, v
oExceed v
O, exp± Δ v
oThe simulated point number.OF
3Be defined as follows
Wherein, K
9Be OF
3The maximum that can reach, K
10Be attenuation constant, A
1Be to exceed the simulated point number that allows sideband.As seen, work as A
1When increasing, OF
3Reduce.
5) pheromones on the new node more.Pheromones for the node on the m paths is preferably strengthened, and more new formula is
τ
ji=α×τ
ji+β×Δτ
Wherein, α is an evaporation coefficient, and β is the plain coefficient that increases of control information, and Δ τ is the unit value that pheromones increases.For best path β=m, second-best path β=m-1, by that analogy.
For all the other relatively poor paths, only the pheromones of node on these paths is evaporated
τ
ji=α×τ
ji
The pheromones minimum value that is provided with on the node is τ
Min, the pheromones on node is reduced to τ
MinThe time, will no longer this node be carried out the evaporation of pheromones.
6) the successive dynasties optimal path that ant is selected carries out a Local Search renewal at random, and its element value is carried out Local Search in a restricted portion.For i element, its search radius is R
i, when optimal path was carried out Local Search, the value that each element is original was changed into another nominal value in the search radius, and new path is assessed.
If new path is better than original route, then replaces former optimal path, and strengthen the search radius of all nodes with new path
Shrink ∈ (0,1) is a zoom factor.
If new path is worse than original route, then keeps original route constant, and reduce the search radius R of all nodes
i=R
i* shrink.
7) continuous repeating step 3) to step 6) to reach the purpose of ant group optimizing.
In the paralleling ant cluster algorithm that proposes, two optimization processes are not what isolate.The adaptive value of the optimal path in k generation is Φ (k) in the suppose process.Owing to taked the strategy of optimum reservation, so Φ (k) 〉=Φ (k-1) is arranged.Calculate each variation, ΔΦ (k)=Φ (k)-Φ (k-1) for Φ (k).If ΔΦ (k)>0 is then to a migration counter N
+Add 1.If N
+Greater than a reference value v, illustrate that then this process has been ready to move.When two processes can be moved, with power delivery subelement value Θ adjustable in the process one
PMove in the process two as fixing parameter adjustable feedback network subelement value Θ in the process two
FMove in the process one as fixing parameter.Generally speaking, too frequent migration will make paralleling ant cluster algorithm similar with traditional ant group algorithm effect.And very few migration can cause two processes to be isolated relatively, makes them be absorbed in separately locally optimal solution easily.Therefore, the value of v is the key factor that influences efficiency of algorithm.
V is by comparing the bound Φ of Φ (k) and expectation in the present invention
Max(k) and Φ
Min(k) and self adaptation adjusts.In generation, pass through a some U at k by constructing one
1: [k-2, Φ
MaxAnd U (k-2)]
2: [k-1, Φ
Max(k-1)] straight line is predicted the upper limit
The utilization linear extrapolation,
Can be expressed as
Same, the desired value of lower limit also can be expressed as
If,
So
Otherwise the utilization linear interpolation is Φ
Max(k) and Φ
Min(k) calculate new value Φ
Min' (k) and Φ
Max' (k).
Φ
max′(k)=Φ(k)+ΔΦ(k)=2Φ(k)-Φ(k-1)
Φ
min′(k)=Φ(k)-ΔΦ(k)=Φ(k-1)
Based on above method, divide following four kinds of situations to adjust v.
Situation 1: if
Keep the value of v constant.
Situation 2: if
Keep the value of v constant,
Situation 3: if
The collaborative of this non-adjustable parameter in this optimization process of explanation and adjustable parameter is very high.Adjustable parameter in current process probably can further be optimized.Therefore, the migration between the process should be delayed v=v+1.Simultaneously, Φ
Max(k)=Φ
Max' (k), Φ
Min(k)=Φ
Min' (k).
Situation 4: if
The collaborative of this non-adjustable parameter in this optimization process of explanation and adjustable parameter is very low.Further optimization possibility is lower for adjustable parameter in the current process.Therefore, the migration v=v-1 between the quickening process.Simultaneously, Φ
Max(k)=Φ
Max' (k), Φ
Min(k)=Φ
Min' (k).
V adjusts in the optimization of whole paralleling ant cluster algorithm adaptively as can be seen.V is controlling the frequency that two processes are moved as adaptive parameter.The migration frequency will not increase when a process has potentiality to do more deep optimization, otherwise the migration frequency can reduce when a process has potential to do more deep optimization.
Description of drawings:
The implementation schematic diagram of Fig. 1 paralleling ant cluster algorithm
Fig. 2
The time the computational methods schematic diagram
Fig. 3
The time the computational methods schematic diagram
Fig. 4
The time the computational methods schematic diagram
Fig. 5
The time the computational methods schematic diagram
Embodiment:
The invention will be further described below in conjunction with the drawings and specific embodiments.
The implementation of paralleling ant cluster algorithm as shown in Figure 1.That the band shade is nonadjustable parameter Θ in process one
F, that the band shade is nonadjustable parameter Θ in process two
PThe process of two ant group algorithms is carried out on parallel computer simultaneously, and step is as follows:
1. upgrade the path that ant is selected in the optimization process one according to the step in the ant group algorithm, and calculate adaptive value function phi (k) and ΔΦ (k).If ΔΦ (k)>0, N
+=N
++ 1.Otherwise, N
+Remain unchanged.
2. according to formula
3. if
v=v+1。According to formula
Φ
max(k)=Φ
max′(k)=2Φ(k)-Φ(k-1),Φ
min(k)=Φ
min′(k)=Φ(k-1)
Calculate Φ
Max(k) and Φ
Max(k), as shown in Figure 2.
4. if
v=v-1。According to formula
Φ
max(k)=Φ
max′(k)=2Φ(k)-Φ(k-1),Φ
min(k)=Φ
min′(k)=Φ(k-1)
Calculate Φ
Max(k) and Φ
Max(k), as shown in Figure 3.
5. if
Keep v constant,
6. if
The same v that keeps is constant.According to formula
Calculate Φ
Max(k) and Φ
Max(k), as shown in Figure 5.
7. if N
+<v, process one repeating step 1 to 6.If N
+=v, process once in best adjustable parameter be ready to the process of moving to two and suffered.Process is waited for up to process two for a moment and also is ready for migration.Be noted that step 1 to 6 also execution in process two simultaneously.
8. when two processes all are ready to, will carry out the step of migration.The Θ of optimum in process one
PWill move to the fixing parameter of conduct in the process two, and the Θ of optimum in the process two
FThen will move in the process one as fixing parameter.
9. after migration is carried out, N
+Can be set to 1, continue as two process repeating steps 1 to 9 then.
At last, if move at certain algebraically N
MaxAfter still do not take place, will carry out an enforceable migration.Can prevent that like this two processes from sinking into their locally optimal solution, optimize processes and become two independently mutual incoherent ant group algorithms.In addition, if N
SMSpontaneous migration does not take place more than inferior, can think that then algorithm has reached optimal solution.In this case, the optimal solution in two processes is exactly to optimize the end value that obtains.
Claims (3)
1, a kind of method of optimization for power electronic circuit based on paralleling ant cluster algorithm is characterized in that, this method is specific as follows: with the power electronic circuit decoupling zero is power delivery and feedback network two parts.The optimization process of two ant group algorithms of initialization.Wherein in process one, the component value of feedback network part is fixed, and the component value of power delivery part is adjustable.And in process two, the element of power delivery part is fixed, and the element of feedback network part is adjustable.Move the optimization process of two ant group algorithms on parallel computer simultaneously, these two optimization processes are not what isolate.The adaptive value of the optimal path in k generation is Φ (k) in the suppose process, calculates each variation for Φ (k), ΔΦ (k)=Φ (k)-Φ (k-1).If ΔΦ (k)>0 is then to a migration counter N
+Add 1.If N
+Greater than a reference value v, illustrate that then this process has been ready to move.When two processes can be moved, the optimum component value of power delivery adjustable in the process one part is moved in the process two as fixing parameter, and the optimum component value of feedback network part adjustable in the process two is moved in the process one as the parameter of fixing.
2, based on the described a kind of method of optimization for power electronic circuit based on paralleling ant cluster algorithm of claim 1, it is characterized in that: in paralleling ant cluster algorithm, v is by comparing the bound Φ of Φ (k) and expectation
Max(k) and Φ
Min(k) and self adaptation adjusts.In generation, pass through a some U at k by constructing one
1: [k-2, Φ
MaxAnd U (k-2)]
2: [k-1, Φ
Max(k-1)] straight line is predicted the upper limit
The utilization linear extrapolation,
Can be expressed as
Same, the desired value of lower limit also can be expressed as
If,
So
Otherwise the utilization linear interpolation is Φ
Max(k) and Φ
Min(k) calculate new value Φ
Min' (k) and Φ
Max' (k).
Φ
max′(k)=Φ(k)+ΔΦ(k)=2Φ(k)-Φ(k-1)
Φ
min′(k)=Φ(k)-ΔΦ(k)=Φ(k-1)
Based on above method, divide following four kinds of situations to adjust v.
Situation 1: if
Keep the value of v constant.
Situation 2: if
Keep the value of v constant,
Situation 3: if
The collaborative of this non-adjustable parameter in this optimization process of explanation and adjustable parameter is very high.Adjustable parameter in current process probably can further be optimized.Therefore, the migration between the process should be delayed v=v+1.Simultaneously,
Φ
max(k)=Φ
max′(k),Φ
min(k)=Φ
min′(k)。
Situation 4: if
The collaborative of this non-adjustable parameter in this optimization process of explanation and adjustable parameter is very low.The possibility that adjustable parameter in the current process can further be optimized is lower.Therefore, the migration v=v-1 between the quickening process.Simultaneously, Φ
Max(k)=Φ
Max' (k), Φ
Min(k)=Φ
Min' (k).
3, based on the described a kind of method of optimization for power electronic circuit of claim 1, it is characterized in that: if migration is at certain algebraically N based on paralleling ant cluster algorithm
MaxAfter still do not take place, will carry out an enforceable migration.Can prevent that like this two processes from sinking into their locally optimal solution, optimize processes and become two independently mutual incoherent ant group algorithms.In addition, if N
SMSpontaneous migration does not take place more than inferior, can think that then algorithm has reached optimal solution.In this case, the optimal solution in two processes is exactly to optimize the end value that obtains.
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Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104793691A (en) * | 2015-03-30 | 2015-07-22 | 南昌大学 | Photovoltaic array whole situation MPPT method based on ant colony algorithm under partial shadow |
CN109670210A (en) * | 2018-11-26 | 2019-04-23 | 华南理工大学 | A kind of method of optimization for power electronic circuit based on parallel distributed particle swarm algorithm |
-
2008
- 2008-01-07 CN CNA2008100256722A patent/CN101252308A/en active Pending
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104793691A (en) * | 2015-03-30 | 2015-07-22 | 南昌大学 | Photovoltaic array whole situation MPPT method based on ant colony algorithm under partial shadow |
CN104793691B (en) * | 2015-03-30 | 2016-06-15 | 南昌大学 | A kind of photovoltaic array under local shadow based on ant group algorithm overall situation MPPT method |
CN109670210A (en) * | 2018-11-26 | 2019-04-23 | 华南理工大学 | A kind of method of optimization for power electronic circuit based on parallel distributed particle swarm algorithm |
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