CN115437451A - Photovoltaic MPPT control method based on multi-strategy improved artificial bee colony algorithm - Google Patents
Photovoltaic MPPT control method based on multi-strategy improved artificial bee colony algorithm Download PDFInfo
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Abstract
The photovoltaic MPPT control method based on the multi-strategy improved artificial bee colony algorithm comprises the following steps of: building a photovoltaic array model; building a photovoltaic power generation MPPT control system model; collecting output voltage and current of a photovoltaic cell model, and searching in real time through a multi-strategy improved artificial bee colony algorithm to find an optimal duty ratio corresponding to a maximum output power point; the photovoltaic MPPT control module based on the multi-strategy improved artificial bee colony algorithm outputs the searched optimal duty ratio, generates a corresponding pulse signal through the PWM generator module, and adjusts the voltage of the booster circuit module to enable the photovoltaic power generation system to output the maximum power; judging whether the environment changes, if so, returning to a restarting algorithm; if not, the maximum output power is kept saved. The MPPT method based on the multi-strategy fusion artificial bee colony algorithm provided by the invention can ensure higher tracking precision and has faster tracking speed and less power fluctuation for the maximum power tracking problem of the photovoltaic array in static and dynamic shadow environments.
Description
Technical Field
The invention relates to the technical field of photovoltaic power generation control, in particular to a photovoltaic MPPT control method based on a multi-strategy improved artificial bee colony algorithm.
Background
Solar energy is widely applied to the field of power generation due to the characteristics of green, wide distribution and sufficient resources, the proportion of photovoltaic installed capacity in a Chinese energy structure is increasing day by day, and further improvement of photovoltaic power generation efficiency is an important direction for constructing a high-efficiency green energy network, so that Maximum Power Point Tracking (MPPT) needs to be carried out on a photovoltaic power generation system.
However, the traditional MPPT control technology based on the disturbance observation method and the conductance increment method can successfully track the maximum power point only when the photovoltaic array is in the uniform illumination environment, and the output power has a large fluctuation difference value under the influence of the disturbance step length. In actual life, light received by the photovoltaic array is shielded by clouds, buildings, dust, fallen leaves and the like, so that an output power-voltage curve of the photovoltaic array has a multimodal characteristic, and most of the traditional MPPT control methods cannot track a global maximum power point, so that the power generation efficiency of the photovoltaic array is reduced.
Therefore, many scholars apply population intelligent algorithms such as a particle swarm algorithm, a genetic algorithm and an ant colony algorithm to the photovoltaic MPPT control technology to realize maximum power point tracking under the multimodal condition. Although a certain effect is achieved, the overall tracking precision, tracking speed and steady-state performance are not considered, and the overall performance is low.
Disclosure of Invention
Aiming at the defects of the existing maximum power tracking technology, a photovoltaic MPPT control method based on a multi-strategy fusion artificial bee colony algorithm (MSFABC) is provided, so that the global maximum power point can be quickly, accurately and stably tracked.
The technical scheme adopted by the invention is as follows:
the photovoltaic MPPT control method based on the multi-strategy improved artificial bee colony algorithm comprises the following steps of:
step 1: simplifying a photovoltaic cell equivalent circuit mathematical model under the condition of not influencing simulation precision to obtain a photovoltaic cell mathematical model, and building a photovoltaic array model;
step 2: building a photovoltaic MPPT control module based on a multi-strategy improved artificial bee colony algorithm, a booster circuit module and a PWM generator module to form a photovoltaic power generation MPPT control system model;
and step 3: the photovoltaic MPPT control module collects output voltage and current of a photovoltaic array, real-time search is carried out through a multi-strategy improved artificial bee colony algorithm, and an optimal duty ratio corresponding to the maximum output power point is found;
and 4, step 4: the photovoltaic MPPT control module based on the multi-strategy improved artificial bee colony algorithm outputs the searched optimal duty ratio, generates a corresponding pulse signal through the PWM generator module, and drives the booster circuit module to enable the photovoltaic power generation system to output the maximum power;
and 5: judging whether the environment changes, if so, returning to the step 2, and restarting the algorithm; if not, keeping the step 3 to store the maximum output power.
In the step 1, the mathematical model expression of the equivalent circuit of the photovoltaic cell is as follows:
in the formula I SC Is a photo-generated current; i is D Is the current flowing through the diode VD; i is 0 Is a diode reverse saturation current; q is an electron charge constant (1.6X 10) -19 C) (ii) a V is the load terminal voltage; i is L Is the load operating current; a is the diode quality factor; r s Is an equivalent series resistance; r sh Is an equivalent parallel resistance; i is Rsh The current is the current flowing through the equivalent parallel resistor; k is Boltzmann constant (1.38X 10) -23 J/K); t is the temperature of the photovoltaic cell;
in view of R therein sh 、A、R S 、I 0 The values of the parameters are related to external environments such as temperature, illumination and the like, are difficult to determine, are inconvenient for engineering application, and have low influence on the accuracy of a photovoltaic cell mathematical model. The mathematical model of the equivalent circuit of the photovoltaic cell is simplified by the following two points:
(1) the method comprises the following steps Under normal conditions
The value of the term is much smaller than the photo-generated current, so the term is ignored;
(2) the method comprises the following steps In the general case of R S Far less than the diode forward on-resistance, set I L ＝I SC ；
And defines the following cases:
a. in an open circuit state: i is L ＝0，V＝V oc ；
b. At the maximum power point: v = V m ，I L ＝I m ；
At this moment, the photovoltaic cell equivalent circuit mathematical model can be simplified as follows:
in the formula I m For an optimum operating current, V m For an optimum operating voltage, V oc Is an open circuit voltage.
Therefore, only I is required SC 、V oc 、I m And V m With four parameters, the characteristics of the photovoltaic cell can be reproduced with higher accuracy.
In the step 2, fig. 3 is a model diagram of a photovoltaic power generation MPPT control system composed of a photovoltaic MPPT control module, a booster circuit module, and a PWM generator module.
The photovoltaic MPPT control module is used for searching an optimal duty ratio corresponding to the maximum output power;
the boost circuit module can boost the output voltage of the photovoltaic array on one hand and can be matched with the MPPT controller to realize the tracking of the maximum output power on the other hand;
the PWM generator module generates a pulse signal corresponding to the optimal duty ratio to drive the booster circuit module;
the modules and the photovoltaic array jointly form a photovoltaic power generation MPPT control system model so as to realize maximum power output. In the step 3, the multi-strategy improved artificial bee colony algorithm searches the duty ratio corresponding to the maximum power point, and the method comprises the following steps:
s3.1, defining parameters: the honey source position represents the duty ratio of the voltage of the control booster circuit module; the honey source nectar amount is the power P output by the photovoltaic array; the optimal honey source searched by the algorithm is the honey source with the largest amount of nectar, namely the optimal duty ratio of the voltage of the control booster circuit module when the photovoltaic array outputs the maximum power.
S3.2, initializing basic parameters: setting the number SN of honey sources, the spatial dimension D of the honey sources and the iteration times t max ；
S3.3, updating the exhaustion parameter threshold value Limit:
the value of Limit is determined by the spatial dimension of the honey sources and the quantity of the honey sources, and the specific expression is as follows:
Limit＝SN×D；
s3.4, initializing a honey source position:
an initialization strategy combining homogenization and randomization is adopted: firstly, honey source x ij Uniformly dividing the j-th dimension interval into subintervals the same as the number SN of the honey sources, firstly selecting the subintervals during initialization operation, then randomly generating initial honey sources in the subintervals, and performing initialization expression combining homogenization and randomization as follows:
where i =1,2, \8230, SN, the number of honey sources (hiring peak, following bee); j =1,2, \8230, D, D is the dimension of the honey source space; alpha (alpha) ("alpha") ij Is a random value between (0, 1);
and
the minimum and maximum values of the j-th dimension component, respectively.
S3.5, calculating the initial honey source nectar amount, selecting the honey source with the most nectar amount, and setting the honey source as an elite individual Gtest S3.6 and updating the weight factor omega (t):
ω (t) is a weight factor that varies nonlinearly with the number of iterations t, and the mathematical expression for ω (t) is:
in the formula, omega max And ω min The maximum value 0.9 and the minimum value 0.1 of the inertia weight omega are respectively the current search times, t max Is the maximum number of searches.
According to the formula, in the initial stage of searching, the quality of the elite individuals is low, the influence of the elite individuals on the candidate solution can be reduced by the self-adaptive weight factor, the quality of the elite individuals is continuously improved along with the increase of the iteration times, and the influence of the elite individuals on the candidate solution can be gradually improved by the self-adaptive weight factor, so that the convergence rate and the tracking accuracy of the algorithm are improved.
S3.7, hiring bee search:
each hiring bee possesses a relative honey source, and the hiring bee searches a new honey source by adopting elite individual guidance and a nonlinear weight factor collaborative search strategy, wherein the mathematical expression is as follows:
x ij '＝x ij +ω(t)(x ij -x kj )+(1-ω(t))(Gbest-x ij )；
in the formula, x ij ' is the new honey source location, x kj For randomly selected honey sources, k =1,2 \8230, SN, and k ≠ i, gbest is elite individuals.
S3.8, greedy selection is carried out on the searched new honey source and the original old honey source by the employing bee, the honey source with more nectar is reserved, and the mathematical expression of the greedy selection is as follows:
in the formula, fit (x) ij ')、fit(x ij ) Respectively represents the nectar amount of the new honey source and the old honey source, V ij The method is a high-quality honey source after greedy selection.
S3.9, selecting a certain hiring bee to follow through the roulette strategy probability according to the honey source information by the following bees, wherein the probability that each hiring bee is selected is as follows:
in the formula, P ij Representing the probability that the ijth hiring bee is selected; from the above formula, it is found that the honey source having a larger amount of nectar is easier to select.
And S3.10, after the following bee selects the following hiring bee, performing neighborhood search near the corresponding honey source, searching for a new honey source, performing greedy selection, and if the honey consumption of the new honey source is more, exchanging identities of the following bee and the hiring bee. The expression for the neighborhood search is:
x ij '＝x ij +ω(t)(x ij -x kj )+(1-ω(t))(Gbest-x ij )；
s3.11, updating the mutation probability P according to the search times:
the variation probability P has great influence on the diversity and convergence speed of the population, the larger the value of P is, the larger the diversity of the population is, the stronger the global search capability of the algorithm is, but the convergence speed is slowed down; conversely, the smaller the P value, the faster the convergence rate, but it cannot effectively help the algorithm jump out of local optimum. Therefore, the P value update formula is set to:
s3.12, in order to avoid trapping in local optimum, random variation occurs with a certain probability P after the search of the employed bees and the following bees is completed, and the specific expression is as follows:
wherein alpha is a random number between (0, 1),
is (-1)And 1) random numbers.
S3.13, searching for scout bees:
if a honey source is not updated for more than the Limit times, the honey source is abandoned. At the moment, the reconnaissance bees reinitialize the abandoned honey source, so that the algorithm is prevented from falling into local optimum and premature convergence is avoided. However, the mechanism also has certain disadvantages, if a certain honey source is the current global optimal honey source in the algorithm searching process and is abandoned due to the overhigh exhaustion parameter Limit, the dominant honey source is lost, and aiming at the problem, directional variation operation is introduced in the process of generating the scout bees by hiring bees, and a twin scout bee is generated while generating random scout bees and always located in the neighborhood of the current exhausted honey source, so that the directional variation can avoid the loss of the searching experience of the optimal honey source, and meanwhile, the neighborhood searching is performed on the optimal honey source, and the searching efficiency of the algorithm is improved. The scout bee search formula for performing the directed mutation operation is as follows:
in the formula, trail ij The number of times that the ijth honey source is not updated, eta is a neighborhood disturbance factor, eta belongs to (-0.05, 0.05), alpha ij Is a random value between (0, 1),
for the twin scout bee produced, limit is a depletion parameter.
S3.14, judging termination conditions: when the positions of the honey sources found by the employed bees are very close, namely the difference between the values of the corresponding duty ratios of the honey sources is less than 0.005, the duty ratio corresponding to the maximum output power is considered to be found, and the mathematical expression is as follows:
|x ij -x kj |<0.005。
in the step 4, the PWM generator module generates corresponding pulse signals with the optimal duty ratio to drive the booster circuit module, and changes the equivalent impedance R of the external circuit eq When the impedance is consistent with the internal impedance of the photovoltaic array, the photovoltaic power generation system outputs the maximum powerAnd (4) rate. External circuit equivalent impedance R eq The relationship with the duty ratio α is shown by the following equation:
R eq ＝R L ×(1-α) 2
in the formula: r L Is an external circuit load.
In the step 5, the basis for judging the environmental change is as follows:
when the environment changes, the output power of the photovoltaic array changes, which results in the loss of the maximum output power, the algorithm needs to be restarted, and the algorithm is realized by detecting the change rate delta P of the output power, so that the algorithm restarting condition is set as:
in the formula, P t Represents the photovoltaic array output power at time t, P max Represents the maximum power tracked, here Δ P =0.05. The invention discloses a photovoltaic MPPT control method based on a multi-strategy improved artificial bee colony algorithm, which has the following technical effects:
1) The invention generates diversified initial honey sources by an initialization strategy combining homogenization and randomization, lays a foundation for subsequent searching, and adopts a cooperative searching strategy of elite individual guidance and adaptive weight factor adjustment to search for the optimal duty ratio, thereby effectively balancing the global and local searching capabilities, reducing the searching time and improving the searching precision. Random-directional double variation strategies of hiring bees, following bees and detecting bees effectively help the algorithm to escape local optimal values.
The strategies are organically combined with the algorithm, so that the overall performance of the algorithm is improved.
2) In static and dynamic environments, the MPPT method based on the multi-strategy fusion artificial bee colony algorithm provided by the invention can ensure higher tracking precision and has faster tracking speed and less power fluctuation for the maximum power tracking problem of the photovoltaic array in static and dynamic shadow environments.
Drawings
Fig. 1 is an equivalent circuit diagram of a photovoltaic cell.
FIG. 2 (a) is a graph of the output current-voltage characteristic of a photovoltaic array;
fig. 2 (b) is a graph of the output power-voltage characteristic of the photovoltaic array.
Fig. 3 is a model diagram of a photovoltaic power generation MPPT control system.
Fig. 4 is a flow chart of an MPPT control method based on the MSFABC algorithm.
Fig. 5 (a) is an MPPT effect diagram of the MSFABC algorithm under a static environment (working condition 1);
FIG. 5 (b) is an MPPT effect diagram of the MSFABC algorithm under a static environment (working condition 2);
fig. 5 (c) is an MPPT effect diagram of the MSFABC algorithm in a static environment (condition 3);
fig. 5 (d) is an MPPT effect diagram of the MSFABC algorithm in the static environment (condition 4).
FIG. 6 (a) is a diagram of MPPT effect of the (local shading) MSFABC algorithm in a dynamic environment;
fig. 6 (b) is a diagram of the MPPT effect of the MSFABC algorithm in a dynamic environment (shading disappearance).
Detailed Description
The method is based on an artificial bee colony algorithm with multi-strategy fusion, and maximum power tracking of a photovoltaic array in static and dynamic environments is achieved. Firstly, simplifying the equivalent circuit of the photovoltaic cell shown in fig. 1 to obtain a mathematical model for engineering, and then building the MPPT control system for photovoltaic power generation shown in fig. 3. The MPPT control module based on the multi-strategy fused artificial bee colony algorithm collects the output voltage and current of the photovoltaic array in real time, finds out the duty ratio corresponding to the maximum output power through the multi-strategy fused artificial bee colony algorithm, outputs the duty ratio to the PWM generator, generates corresponding pulse signals to drive the booster circuit to change the output voltage of the photovoltaic array, and achieves the purpose of outputting the maximum power.
Step 1: simplifying a photovoltaic cell equivalent circuit mathematical model under the condition of not influencing simulation precision to obtain a photovoltaic cell mathematical model suitable for engineering application, and building a photovoltaic array model;
the equivalent circuit of the photovoltaic cell is shown in fig. 1, and the mathematical expression is as follows:
in the formula I SC Is a photo-generated current; I.C. A D Is the current flowing through the diode VD; i is 0 Is a diode reverse saturation current; q is an electron charge constant (1.6X 10) -19 C) (ii) a V is the load terminal voltage; I.C. A L A load operating current; a is the diode quality factor; rs is an equivalent series resistor; r sh Is an equivalent parallel resistance; k is Boltzmann constant (1.38X 10) -23 J/K); and T is the temperature of the photovoltaic cell.
In view of R therein sh 、A、R S 、I 0 The values of the parameters are related to external environments such as temperature and illumination, are difficult to determine, are inconvenient for engineering application, and have low influence on the accuracy of the photovoltaic cell model. The mathematical model of the photovoltaic cell is simplified as follows:
(1) in the normal case
The value of the term is much smaller than the photo-generated current, so the term is ignored;
(2) in the general case of R S Far less than the diode forward on-resistance, set I L ＝I SC ；
And defines the following cases:
a. in an open circuit state: i is L ＝0，V＝V oc ；
b. At the maximum power point: v = V m ，I L ＝I m ；
At this moment, the photovoltaic cell equivalent circuit mathematical model can be simplified as:
in the formula I m For optimum operating current, V m For an optimum operating voltage, V oc Is an open circuitAnd (5) pressing. Therefore, only I is required SC 、V oc 、I m And V m With four parameters, the characteristics of the photovoltaic cell can be reproduced with higher accuracy.
A photovoltaic cell model is built by using Matlab/Simulink, and four parameters of a single photovoltaic cell are respectively as follows: v oc ＝43.6V、I sc ＝8.35A、V m ＝35V、I m =7.6A. Different conditions were set as shown in table 1.
TABLE 1 photovoltaic array temperature and irradiation intensity parameters under different working conditions
The output characteristic curves of the photovoltaic array shown in fig. 2 (a) and fig. 2 (b) are obtained by respectively simulating the 4 working conditions. As can be seen from fig. 2 (a) and 2 (b), under the condition of uniform illumination, the photovoltaic P-U characteristic curve is a unimodal curve; under the condition of local shadows, the photovoltaic P-U characteristic curve is a multi-peak curve, and the voltage positions of maximum power points under different shadows are different, so that the traditional MPPT algorithm is easy to fall into local optimization, and the power generation efficiency is reduced.
Step 2: and building a photovoltaic MPPT control module based on a multi-strategy improved artificial bee colony algorithm, a booster circuit module and a PWM generator module to form a photovoltaic power generation MPPT control system model diagram shown in figure 3.
And step 3: the photovoltaic MPPT control module collects the output voltage and current of the photovoltaic array, real-time search is carried out through a multi-strategy improved artificial bee colony algorithm, and the optimal duty ratio corresponding to the maximum output power point is found.
Fig. 4 is a flowchart of a MPPT controller searching for a duty ratio corresponding to a maximum power point based on a multi-strategy improved artificial bee colony algorithm. The specific steps of searching the duty ratio corresponding to the maximum power point by the multi-strategy improved artificial bee colony algorithm in the step 3 comprise:
s3.1, defining parameters: the honey source position represents the duty ratio of the voltage of the control booster circuit module; the honey source nectar amount is the power P output by the photovoltaic array; the optimal honey source searched by the algorithm is the honey source with the largest amount of nectar, namely the optimal duty ratio of the voltage of the control booster circuit module when the maximum power is output by the photovoltaic array.
S3.2, initializing basic parameters: setting the number SN of honey sources, the spatial dimension D of the honey sources and the iteration number t max ；
S3.3, updating the exhaustion parameter threshold value Limit:
the value of Limit is determined by the spatial dimension of the honey sources and the quantity of the honey sources, and the specific expression is as follows:
Limit＝SN×D；
s3.4, initializing a honey source position:
an initialization strategy combining homogenization and randomization is adopted: firstly, honey source x ij The j-th dimension interval is uniformly divided into subintervals the same as the number SN of the honey sources, the subintervals are selected firstly during initialization operation, then the initial honey sources are randomly generated in the subintervals, and the initialization expression combining homogenization and randomization is as follows:
where i =1,2, \8230, SN, the number of honey sources (hiring peak, following bee); j =1,2, \ 8230, D, D is the dimension of the honey source space; alpha (alpha) ("alpha") ij Is a random value between (0, 1);
and
the minimum and maximum values of the j-th dimension component, respectively.
S3.5, calculating the initial honey source nectar amount, selecting the honey source with the most nectar amount, and setting the honey source as elite individual Gbest
S3.6, update weight factor ω (t):
ω (t) is a weight factor that varies nonlinearly with the number of iterations t, and the mathematical expression for ω (t) is:
in the formula, omega max And ω min The maximum value 0.9 and the minimum value 0.1 of the inertia weight omega are respectively the current search times, t max Is the maximum number of searches.
According to the formula, in the initial stage of searching, the quality of the elite individuals is low, the influence of the elite individuals on the candidate solution can be reduced by the self-adaptive weight factor, the quality of the elite individuals is continuously improved along with the increase of the iteration times, and the influence of the elite individuals on the candidate solution can be gradually improved by the self-adaptive weight factor, so that the convergence speed and the tracking accuracy of the algorithm are improved.
S3.7, hiring bee search:
each hiring bee has a relative honey source, and the hiring bee adopts elite individual guidance and nonlinear weight factor collaborative search strategy to search a new honey source, wherein the mathematical expression is as follows:
x ij '＝x ij +ω(t)(x ij -x kj )+(1-ω(t))(Gbest-x ij )；
in the formula, x ij ' is the new honey source location, x kj For randomly selected honey sources, k =1,2 \8230, SN, and k ≠ i, gbest is elite individuals.
S3.8, greedy selection is performed on the searched new honey sources and the original old honey sources by the hiring bees, the honey sources with more nectar are reserved, and the mathematical expression of the greedy selection is as follows:
in the formula, fit (x) ij ')、fit(x ij ) Respectively represents the nectar amount of the new and old nectar sources, V ij The method is a high-quality honey source after greedy selection.
S3.9, selecting a certain hiring bee to follow through roulette strategy probability according to honey source information by the following bees, wherein the probability of each hiring bee being selected is as follows:
in the formula, P ij Representing the probability that the ijth hiring bee is selected; as can be seen from the above formula, the honey source having a larger amount of nectar is easier to select.
And S3.10, after the following bees select the following employed bees, performing neighborhood search near the corresponding honey sources, searching for new honey sources, performing greedy selection, and if the honey amount of the new honey sources is more, exchanging identities of the following bees and the employed bees. The expression for the neighborhood search is:
x ij '＝x ij +ω(t)(x ij -x kj )+(1-ω(t))(Gbest-x ij )；
s3.11, updating the mutation probability P according to the search times:
the variation probability P has great influence on the diversity and convergence speed of the population, the larger the value of P is, the larger the diversity of the population is, the stronger the global search capability of the algorithm is, but the convergence speed is slowed down; conversely, the smaller the P value, the faster the convergence rate, but it cannot effectively help the algorithm jump out of local optimum. Therefore, the P-value update formula is set to:
s3.12, in order to avoid trapping in local optimum, random variation occurs with a certain probability P after the search of the employed bees and the following bees is completed, and the specific expression is as follows:
wherein alpha is a random number between (0, 1),
is a random number between (-1, 1).
S3.13, searching for scout bees:
if a honey source is not updated more than the Limit times, it is abandoned. At the moment, the reconnaissance bees reinitialize the abandoned honey source, so that the algorithm is prevented from falling into local optimum and premature convergence is avoided. However, the mechanism also has certain disadvantages, if a certain honey source is the current global optimal honey source in the algorithm searching process and is abandoned due to the overhigh exhaustion parameter Limit, the dominant honey source is lost, and aiming at the problem, directional variation operation is introduced in the process of generating the scout bees by hiring bees, and a twin scout bee is generated while generating random scout bees and always located in the neighborhood of the current exhausted honey source, so that the directional variation can avoid the loss of the searching experience of the optimal honey source, and meanwhile, the neighborhood searching is performed on the optimal honey source, and the searching efficiency of the algorithm is improved. The scout bee search formula for performing the directed mutation operation is as follows:
in the formula, trail ij The number of times that the ijth honey source is not updated, eta is a neighborhood disturbance factor, eta belongs to (-0.05, 0.05), alpha ij Is a random value between (0, 1),
for the twin scout bee produced, limit is a depletion parameter.
S3.14, judging termination conditions: when the positions of the honey sources found by the employed bees are very close, namely the difference between the values of the corresponding duty ratios of the honey sources is less than 0.005, the duty ratio corresponding to the maximum output power is considered to be found, and the mathematical expression is as follows:
|x ij -x kj |<0.005。
and 4, step 4: the photovoltaic MPPT control module based on the multi-strategy improved artificial bee colony algorithm outputs the searched optimal duty ratio, and the PWM generator generates corresponding pulse signals to drive the booster circuit module, so that the photovoltaic power generation system outputs the maximum power.
And 5: judging whether the environment changes, if so, returning to the step 2, and restarting the algorithm; if not, keeping the step 3 to store the maximum output power.
The basis for judging the environmental change in the step 5 is as follows: when the environment changes, the output power of the photovoltaic array changes, which causes the loss of the maximum output power, and the algorithm needs to be restarted, and the algorithm can be realized by detecting the change rate delta P of the output power, so that the algorithm restarting condition is set as follows:
in the formula, P t Representing the photovoltaic array output power at time t, P max Represents the maximum power tracked, Δ P =0.05.
Under the static condition and the dynamic shadow condition, the MSFABC algorithm and the P & O, PSO and ABC algorithms provided by the invention are adopted for simulation analysis, and the static condition is set to be 4 working conditions in the table 1. The dynamic shadow settings are two scenarios in table 2:
table 2 table of change of illumination data
| Context | Change of operating condition state | Change in irradiance/(W.m) -2 ) |
| 1 | |
1000/1000/1000→1000/1000/800 |
| 2 | |
1000/1000/800→1000/800/600 |
The tracking effect of the algorithm in static condition 4 is shown in fig. 5 (a) to 5 (d). In the case of uniform illumination, as shown in fig. 5 (a), the maximum power point can be found by all four algorithms, but the power fluctuation of the MSFANC algorithm in the search process is small. And the convergence rate is fast. Under different local shadow working conditions, as shown in fig. 5 (b), 5 (c) and 5 (d), the P & O algorithm falls into a local maximum power point, and compared with the PSO algorithm and the ABC algorithm, the MSFABC algorithm has smaller power fluctuation in the searching process, is stable in convergence earlier and has a higher stable value after convergence.
Table 3 shows the tracking power, tracking accuracy, power fluctuation difference after steady state, and tracking time value of the MSFABC algorithm and the other 3 algorithms under 4 working conditions. Analyzing the data in Table 3 shows that:
table 34 simulation results of different algorithms under working conditions
For the tracking accuracy, under 4 working conditions, the MSFABC algorithm has the highest value, the lowest value is 99.3%, the PSO and ABC algorithms also have the tracking accuracy between 98% and 99%, but the P & O algorithms fall into the local maximum power point under the local shading working condition, and the tracking accuracy is 47.7% to 74.9%. Therefore, it is more critical to improve the tracking speed and reduce the tracking power fluctuation on the premise of ensuring the tracking accuracy.
For the tracking time, under 4 working conditions, the MSFABC algorithm is obviously faster than the PSO and ABC algorithms, and under the working condition 4, the MSFABC algorithm consumes 0.042s of time for searching, which is 65.9% of the time consumed by the PSO algorithm and 24.9% of the time consumed by the ABC algorithm.
For power fluctuation, the MSFABC algorithm not only causes less power fluctuation in the searching process, but also has the minimum power fluctuation difference after stabilization.
Under the dynamic condition, after the local shading, the P & O algorithm searches again but falls into a local maximum power point; after the shading disappears, although the vicinity of the maximum power point is searched, the power fluctuation is large after the stabilization. Compared with PSO and ABC algorithms, the MSFABC algorithm can be converged to a stable value earlier before local shading and shading disappear, and the searching precision is higher; after the local shading and the shading disappear, the MSFABC algorithm is restarted, compared with the rest 3 algorithms, the MSFABC algorithm has the advantages that the tracking speed is higher, and less power fluctuation is accompanied in the searching process.
In conclusion, the comprehensive tracking performance of the MSFABC algorithm provided by the invention is better than that of other 3 algorithms through the comparative analysis of simulation experiments of the algorithms in static and dynamic environments.
Claims (6)
1. The photovoltaic MPPT control method based on the multi-strategy improved artificial bee colony algorithm is characterized by comprising the following steps of:
step 1: simplifying a photovoltaic cell equivalent circuit mathematical model to obtain a photovoltaic cell mathematical model, and building a photovoltaic array model;
step 2: building a photovoltaic MPPT control module, a booster circuit module and a PWM generator module to form a photovoltaic power generation MPPT control system model;
and step 3: the photovoltaic MPPT control module collects the output voltage and current of a photovoltaic array, real-time search is carried out through a multi-strategy improved artificial bee colony algorithm, and the optimal duty ratio corresponding to the maximum output power point is found;
and 4, step 4: the photovoltaic MPPT control module based on the multi-strategy improved artificial bee colony algorithm outputs the searched optimal duty ratio, generates a corresponding pulse signal through the PWM generator module, and drives the booster circuit module to enable the photovoltaic power generation system to output the maximum power;
and 5: judging whether the environment changes, if so, returning to the step 2, and restarting the algorithm; if not, keeping the step 3 to store the maximum output power.
2. The multi-strategy improved artificial bee colony algorithm-based photovoltaic MPPT control method according to claim 1, characterized in that: in the step 1, the mathematical model expression of the equivalent circuit of the photovoltaic cell is as follows:
in the formula I SC Is a photo-generated current; I.C. A D Is the current flowing through the diode VD; i is 0 Is a diode reverse saturation current; q is an electron charge constant; v is the load terminal voltage; I.C. A L Is the load operating current; a is the diode quality factor; r s Is an equivalent series resistance; r is sh Is an equivalent parallel resistance; i is Rsh The current is the current flowing through the equivalent parallel resistor; k is Boltzmann constant; t is the temperature of the photovoltaic cell;
the mathematical model of the equivalent circuit of the photovoltaic cell is simplified by the following two points:
(1) the method comprises the following steps Under normal conditions
The value of the term is much smaller than the photo-generated current, so the term is ignored;
(2) the method comprises the following steps In the general case of R S Far less than the diode forward on-resistance, set I L ＝I SC ；
And defines the following cases:
a. in an open circuit state: i is L ＝0，V＝V oc ；
b. At the maximum power point: v = V m ，I L ＝I m ；
At this moment, the photovoltaic cell equivalent circuit mathematical model can be simplified as:
in the formula I m For optimum operating current, V m For an optimum operating voltage, V oc Is an open circuit voltage;
therefore, only I is required SC 、V oc 、I m And V m With four parameters, the characteristics of the photovoltaic cell can be reproduced with high accuracy.
3. The multi-strategy improved artificial bee colony algorithm-based photovoltaic MPPT control method according to claim 1, characterized in that: in the step (2),
the photovoltaic MPPT control module is used for searching the optimal duty ratio corresponding to the maximum output power;
the boost circuit module is used for boosting the output voltage of the photovoltaic array;
the PWM generator module is used for generating a pulse signal corresponding to the optimal duty ratio to drive the booster circuit module;
the modules and the photovoltaic array jointly form a photovoltaic power generation MPPT control system model so as to realize maximum power output.
4. The multi-strategy improved artificial bee colony algorithm-based photovoltaic MPPT control method according to claim 1, characterized in that: in the step 3, the multi-strategy improved artificial bee colony algorithm searches the duty ratio corresponding to the maximum power point, and the method comprises the following steps:
s3.1, defining parameters: the honey source position represents the duty ratio of the voltage of the control booster circuit module; the honey source nectar amount is the power P output by the photovoltaic array; the optimal honey source searched by the algorithm is the honey source with the largest amount of nectar, namely the optimal duty ratio of the voltage of the control booster circuit module when the photovoltaic array outputs the maximum power;
s3.2, initializing basic parameters: setting the number SN of honey sources, the spatial dimension D of the honey sources and the iteration number t max ；
S3.3, updating the exhaustion parameter threshold value Limit:
the value of Limit is determined by the spatial dimension of the honey sources and the quantity of the honey sources, and the specific expression is as follows:
Limit＝SN×D；
s3.4, initializing a honey source position:
firstly, honey source x is firstly mixed ij Uniformly dividing the j-th dimension interval into subintervals with the same SN as the honey source number, and initiallyDuring the transformation operation, a subinterval is selected firstly, then an initial honey source is randomly generated in the subinterval, and an initialization expression combining homogenization and randomization is as follows:
where i =1,2, \8230, SN, the number of honey sources (hiring peak, following bee); j =1,2, \ 8230, D, D is the dimension of the honey source space; alpha is alpha ij Is a random value between (0, 1);
and
respectively the minimum value and the maximum value of the jth dimension component;
s3.5, calculating the initial honey source nectar amount, selecting the honey source with the most nectar amount, and setting the honey source as elite individual Gbest
S3.6, update weight factor ω (t):
ω (t) is a weight factor that varies nonlinearly with the number of iterations t, and the mathematical expression for ω (t) is:
in the formula, omega max And omega min Respectively the maximum value and the minimum value of the inertia weight omega, t is the current search frequency, t max The maximum number of searches;
s3.7, hiring bee search:
each hiring bee possesses a relative honey source, and the hiring bee searches a new honey source by adopting elite individual guidance and a nonlinear weight factor collaborative search strategy, wherein the mathematical expression is as follows:
x ij '＝x ij +ω(t)(x ij -x kj )+(1-ω(t))(Gbest-x ij )；
in the formula, x ij ' is the new honey source location, x kj Is a randomly selected honey source, k =1,2 \8230, SN, k ≠ i, and Gtest is an elite individual;
s3.8, greedy selection is carried out on the searched new honey source and the original old honey source by the employing bee, the honey source with more nectar is reserved, and the mathematical expression of the greedy selection is as follows:
in the formula, fit (x) ij ')、fit(x ij ) Respectively represents the nectar amount of the new honey source and the old honey source, V ij Selecting high-quality honey sources for greedy selection;
s3.9, selecting a certain hiring bee to follow through roulette strategy probability according to honey source information by the following bees, wherein the probability of each hiring bee being selected is as follows:
in the formula, P ij Representing the probability that the ijth hiring bee is selected; from the above formula, the honey source with more nectar is easier to select;
s3.10, after the following bees select the following employed bees, performing neighborhood search near the corresponding honey sources, searching for new honey sources, performing greedy selection, and if the honey amount of the new honey sources is more, exchanging identities of the following bees and the employed bees; the expression for the neighborhood search is:
x ij '＝x ij +ω(t)(x ij -x kj )+(1-ω(t))(Gbest-x ij )；
s3.11, updating the mutation probability P according to the search times:
the variation probability P has great influence on the diversity and convergence speed of the population, the larger the value of P is, the larger the diversity of the population is, the stronger the global search capability of the algorithm is, but the convergence speed is slowed down; on the contrary, the smaller the P value is, the faster the convergence speed is, but the algorithm cannot be effectively helped to jump out of the local optimum; therefore, the P-value update formula is set to:
s3.12, after the search of the employed bees and the following bees is completed, random variation occurs with a certain probability P, and the specific expression is as follows:
wherein alpha is a random number between (0, 1),
is a random number between (-1, 1);
s3.13, searching for scout bees:
directional variation operation is introduced in the process of generating the scout bees by the hiring bees, and a twin scout bee is generated while a random scout bee is generated and is always positioned in the neighborhood of the currently depleted honey source; the scout bee search formula for performing the directed mutation operation is as follows:
in the formula, trail ij The number of times that the ijth honey source is not updated, eta is a neighborhood disturbance factor, eta belongs to (-0.05, 0.05), alpha ij Is a random value between (0, 1),
for the generated twin scout bees, limit is a depletion parameter;
s3.14, judging termination conditions: when the positions of the honey sources found by the employed bees are very close, namely the difference between the values of the corresponding duty ratios of the honey sources is less than 0.005, the duty ratio corresponding to the maximum output power is considered to be found, and the mathematical expression is as follows:
|x ij -x kj |<0.005。
5. the photovoltaic MPPT control method based on the multi-strategy improved artificial bee colony algorithm according to claim 1, characterized in that: in the step 4, the PWM generator module generates corresponding pulse signals with the optimal duty ratio to drive the booster circuit module, and changes the equivalent impedance R of the external circuit eq When the impedance is consistent with the internal impedance of the photovoltaic array, the photovoltaic power generation system outputs the maximum power; external circuit equivalent impedance R eq The relationship with the duty ratio α is shown by the following equation:
R eq ＝R L ×(1-α) 2
in the formula: r L Is an external circuit load.
6. The photovoltaic MPPT control method based on the multi-strategy improved artificial bee colony algorithm according to claim 1, characterized in that: in the step 5, the basis for judging the environmental change is as follows:
when the environment changes, the output power of the photovoltaic array changes, which results in the loss of the maximum output power, the algorithm needs to be restarted, and the algorithm is realized by detecting the change rate delta P of the output power, so that the algorithm restarting condition is set as:
in the formula, P t Representing the photovoltaic array output power at time t, P max Representing the maximum power tracked.
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