CN110020713B - Photovoltaic multimodal maximum power tracking method and related device - Google Patents
Photovoltaic multimodal maximum power tracking method and related device Download PDFInfo
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Abstract
The application discloses a photovoltaic multimodal maximum power tracking method, which comprises the following steps: obtaining the maximum value of the absolute value of the updated particle velocity under the same iteration number; judging whether the number of times that the absolute value of the updated particle velocity is the maximum value under the continuous same dimension exceeds a preset number of times; if the number of times exceeds the preset number of times, the same dimension is named as a target dimension, the corresponding updated particle position of which the number of times of continuous maximum value under the target dimension exceeds the preset number of times is replaced by the updated particle position corresponding to the secondary value under the same iteration number, a new updated particle position is obtained, and the replacement number is recorded; and carrying out iterative computation according to the updated particle speed and the new updated particle position, and obtaining local maximum power and global maximum power until the iteration is stopped. Therefore, unnecessary iteration is reduced, and the convergence rate is improved. The application also provides the electronic equipment and the readable storage medium, and the electronic equipment and the readable storage medium have the beneficial effects.
Description
Technical Field
The present disclosure relates to the field of photovoltaic technologies, and in particular, to a photovoltaic multimodal maximum power tracking method, an electronic device, and a computer readable storage medium.
Background
The particle swarm algorithm is an evolutionary computing technique that gradually approximates the optimal value of space by constantly iterating and computing. In the particle swarm MPPT algorithm, the photovoltaic output voltage is taken as particles, and the maximum value is found on the whole multimodal power curve. The existing conventional particle swarm MPPT algorithm can track the actual photovoltaic multimodal maximum power point under most conditions, but the convergence speed is longer, the oscillation is larger, the tracking time is longer, and the power loss is larger.
Therefore, how to provide a solution to the above technical problem is a problem that a person skilled in the art needs to solve at present.
Disclosure of Invention
The purpose of the application is to provide a photovoltaic multimodal maximum power tracking method, a photovoltaic multimodal maximum power tracking device, an electronic device and a computer readable storage medium, which can reduce iteration times and improve convergence speed. The specific scheme is as follows:
the application discloses a photovoltaic multimodal maximum power tracking method, which comprises the following steps:
obtaining the maximum value of the absolute value of the updated particle velocity under the same iteration number;
judging whether the number of times that the absolute value of the updated particle velocity is the maximum value under the continuous same dimension exceeds a preset number of times;
If the number of times exceeds the preset number of times, the same dimension is named as a target dimension, the number of times of continuous maximum values under the target dimension exceeds the preset number of times of corresponding updated particle positions are replaced by the number of times of corresponding updated particle positions under the same iteration number, new updated particle positions are obtained, and the replacement number of times is recorded;
and carrying out iterative computation according to the updated particle speed and the new updated particle position, and obtaining local maximum power and global maximum power until the iteration is stopped.
Optionally, before the obtaining the maximum value of the absolute value of the updated particle velocity at the same iteration number, the method further includes:
judging whether the replacement times reach a preset threshold value or not;
if the preset threshold is reached, executing the step of performing iterative computation according to the updated particle speed and the new updated particle position; if the preset threshold is not reached, executing the step of acquiring the maximum value of the updated particle velocity under the same iteration number;
the preset threshold is N-1, and N is the number of photovoltaic modules.
Optionally, the obtaining the local maximum power and the global maximum power until the iteration stops includes:
Acquiring the local maximum power and the global maximum power;
judging whether the precision of the numerical value of the global maximum power reaches a preset precision;
and if the preset precision is reached, stopping iteration.
Optionally, the obtaining the local maximum power and the global maximum power until the iteration stops includes:
acquiring the local maximum power and the global maximum power;
judging whether the iteration times reach an iteration threshold value or not;
and if the iteration threshold is reached, stopping iteration.
Optionally, the iterative calculation includes:
updating the particle speed and the particle position according to the current global optimal value, the current local optimal value, the learning factor and the inertia weight to obtain the updated particle speed and the updated particle position;
obtaining a particle fitness value according to the updated particle position;
and updating the local optimal value and the global optimal value of each particle according to the particle fitness value corresponding to each particle fitness value and each current local optimal value.
Optionally, the learning factor is determined according to c1=1.5+0.01×nc, c2=2.5-0.01×nc;
wherein c1 and c2 are the learning factors, and Nc is the iteration number.
Optionally, when the number of substitutions is zero, the inertial weight is based on w nc =1.5+0.01×nc;
wherein w is nc And Nc is the iteration number for the inertia weight.
Optionally, when the number of substitutions is not zero, the inertial weight is based on w nc ＝(-0.175×t 2 +0.425×t+1.225) +0.01×nc;
wherein w is nc And for the inertia weight, nc is the iteration number, and t is the substitution number.
The application discloses photovoltaic multimodal maximum power tracking device includes:
the acquisition module is used for acquiring the maximum value of the absolute value of the updated particle speed under the same iteration times;
the first judging module is used for judging whether the number of times that the absolute value of the updated particle velocity is the maximum value under the same continuous dimension exceeds the preset number of times;
a replacing module, configured to, if the number of times exceeds the preset number of times, designate the same dimension as a target dimension, replace a corresponding updated particle position of which the number of times of a continuous maximum value under the target dimension exceeds the preset number of times with an updated particle position corresponding to a secondary value under the same iteration number, obtain a new updated particle position, and record the number of times of replacement;
and the iteration module is used for carrying out iteration calculation according to the updated particle speed and the new updated particle position, and obtaining the local maximum power and the global maximum power until the iteration is stopped.
Optionally, the method further comprises:
the second judging module is used for judging whether the replacement times reach a preset threshold value or not;
the iteration module is used for executing the step of carrying out iterative computation according to the updated particle speed and the new updated particle position if the preset threshold value is reached; if the preset threshold is not reached, executing the step of acquiring the maximum value of the updated particle velocity under the same iteration number;
the preset threshold is N-1, and N is the number of photovoltaic modules.
Optionally, the iteration module includes:
an acquisition unit configured to acquire the local maximum power and the global maximum power;
the judging unit is used for judging whether the precision of the numerical value of the global maximum power reaches the preset precision;
and the execution unit is used for stopping iteration if the preset precision is reached.
Optionally, the iteration module includes:
an acquisition unit configured to acquire the local maximum power and the global maximum power;
the judging unit is used for judging whether the iteration times reach an iteration threshold value or not;
and the execution unit is used for stopping iteration if the iteration threshold is reached.
Optionally, the iteration module includes:
The first updating unit is used for updating the particle speed and the particle position according to the current global optimal value, the current local optimal value, the learning factor and the inertia weight to obtain the updated particle speed and the updated particle position;
a determining unit, configured to obtain each particle fitness value according to the updated particle position;
and a second updating unit for updating the local optimum and the global optimum of each particle according to the particle fitness value and the particle fitness value corresponding to the local optimum of each particle.
Optionally, the first updating unit includes:
a determining subunit for determining the learning factor according to c1=1.5+0.01×nc, c2=2.5-0.01×nc;
wherein c1 and c2 are the learning factors, and Nc is the iteration number.
Optionally, the first updating unit includes:
an inertial weight determining subunit for determining the inertial weight according to w when the number of substitutions is zero nc =1.5+0.01×nc;
wherein w is nc And Nc is the iteration number for the inertia weight.
Optionally, the first updating unit includes:
an inertial weight determining subunit for determining the inertial weight according to w when the number of substitutions is not zero nc ＝(-0.175×t 2 +0.425×t+1.225) +0.01×nc;
wherein w is nc And for the inertia weight, nc is the iteration number, and t is the substitution number.
The application discloses electronic equipment includes:
a memory for storing a computer program;
a processor for implementing the steps of the multimodal maximum power tracking method for photovoltaic as described above when executing the computer program.
The present application discloses a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of a multimodal maximum power tracking method of photovoltaic as described above.
The application provides a photovoltaic multimodal maximum power tracking method, which comprises the following steps: obtaining the maximum value of the absolute value of the updated particle velocity under the same iteration number; judging whether the number of times that the absolute value of the updated particle velocity is the maximum value under the continuous same dimension exceeds a preset number of times; if the number of times exceeds the preset number of times, the same dimension is named as a target dimension, the corresponding updated particle position of which the number of times of continuous maximum value under the target dimension exceeds the preset number of times is replaced by the updated particle position corresponding to the secondary value under the same iteration number, a new updated particle position is obtained, and the replacement number is recorded; and carrying out iterative computation according to the updated particle speed and the new updated particle position, and obtaining local maximum power and global maximum power until the iteration is stopped.
As can be seen, when the number of times that the velocity of the updated particles in the same continuous dimension is the maximum value exceeds the preset number of times, the same dimension is named as the target dimension, and the position of the updated particles corresponding to the value of the continuous maximum value in the target dimension exceeding the preset number of times is replaced by the position of the updated particles corresponding to the value of the next same iteration number, so that a new position of the updated particles is obtained; iterative computation is carried out according to the updated particle speed and the new updated particle position, so that the particle position is readjusted according to the updated particle speed value, the self-adaptive adjustment of particle layout is realized, unnecessary iteration is reduced, and the convergence speed is improved. The application also provides an electronic device and a computer readable storage medium, which have the beneficial effects and are not described in detail herein.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings may be obtained according to the provided drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flowchart of a method for tracking peak maximum power of a photovoltaic according to an embodiment of the present disclosure;
FIG. 2 is a flowchart of an iterative computation provided in an embodiment of the present application;
FIG. 3 is a flow chart of another method for tracking peak maximum power of a photovoltaic according to an embodiment of the present disclosure;
FIG. 4 is a flow chart of another method for multimodal maximum power tracking of photovoltaic provided in embodiments of the present application;
FIG. 5 is a flow chart of another method for tracking multimodal maximum power of a photovoltaic provided in an embodiment of the present application;
fig. 6 illustrates three shielding situations for providing photovoltaic modules in a photovoltaic string according to an embodiment of the present disclosure;
fig. 7 is a graph showing output characteristics of a photovoltaic string under three shielding conditions according to an embodiment of the present application;
FIGS. 8-10 are graphs comparing results before and after the particle swarm algorithm is improved under three shielding conditions according to the embodiments of the present application;
fig. 11 is a schematic structural diagram of a photovoltaic multi-peak maximum power tracking device according to an embodiment of the present disclosure.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The existing conventional particle swarm MPPT algorithm can track the actual photovoltaic multimodal maximum power point under most conditions, but the convergence speed is longer, the oscillation is larger, the tracking time is longer, and the power loss is larger. Based on the above technical problems, the present embodiment provides a photovoltaic multimodal maximum power tracking method, when the number of times that the updated particle velocity is the maximum value in the same continuous dimension exceeds a preset number of times, and the same dimension is named as a target dimension, the updated particle position corresponding to the secondary value in the same iteration number is replaced by the updated particle position corresponding to the number of times that the continuous maximum value in the target dimension exceeds the preset number of times, and a new updated particle position is obtained; according to the updated particle velocity and the new updated particle position, the particle position is readjusted according to the updated velocity value, the particle layout is adaptively adjusted, unnecessary iteration is reduced, the convergence speed is improved, and referring to fig. 1 specifically, fig. 1 is a flowchart of a photovoltaic multimodal maximum power tracking method provided by an embodiment of the present application, and specifically includes:
s101, obtaining the maximum value of the absolute value of the updated particle velocity under the same iteration times.
The updated particle velocity is a set of the calculated particle velocities for each iteration, wherein each row in the set represents the calculated updated particle velocity for each iteration, and each column represents each dimension. And obtaining the maximum value of the absolute value of the updated particle velocity under each iteration number, namely obtaining the maximum value in each row. It will be appreciated that the present embodiment uses the absolute value of the velocity of the updated particles as a reference, since the velocity of the updated particles is calculated as a positive and negative number.
S102, judging whether the number of times that the absolute value of the updated particle velocity is the maximum value in the same continuous dimension exceeds the preset number of times.
The purpose of this step is to determine that the maximum value of the absolute value of the velocity of the updated particles in the set continuously exceeds a preset number of times in the same dimension, i.e. in the same column.
And S103, if the number of times exceeds the preset number of times, the same dimension is named as a target dimension, the corresponding updated particle position of which the number of times of the continuous maximum value under the target dimension exceeds the preset number of times is replaced by the updated particle position corresponding to the secondary value under the same iteration number of times, the new updated particle position is obtained, and the replacement number of times is recorded.
Wherein each maximum is a corresponding maximum in the target dimension that is consecutive more than a preset number of times. And when the number of times exceeds the preset number, the same dimension is marked as a target dimension, and the updated particle speed under the target degree is replaced by a secondary value under the same iteration number.
For example, when updating the set of particle velocity components to
The corresponding set of updated particle positions is +.>
Setting the preset number of times to 3, it is known that the particle velocity is updated more than 3 times to obtain +.>
When updating the set of particle velocity components to
The corresponding set of updated particle positions is +.>
Setting the preset times to 2, it is known that the particle velocity is updated more than 2 times to obtain +.>
It is noted that the examples provided in this embodiment are for illustrative purposes only of alternatives.
S104, carrying out iterative computation according to the updated particle speed and the new updated particle position, and obtaining local maximum power and global maximum power until the iteration is stopped.
And carrying out iterative computation according to the updated particle speed and the new updated particle position, and obtaining local maximum power and global maximum power until the iteration is stopped. The present embodiment does not limit the condition for stopping the iteration, and may be that the iteration is stopped when the accuracy of the global maximum power value reaches a preset value, or the iteration number reaches a preset number, and the global maximum power is output.
Further, performing iterative computation according to the updated particle velocity and the new updated particle position, specifically including: judging whether the corresponding updated particle speed of which the number of times of continuous maximum values in the target dimension exceeds the preset number of times is larger than the open circuit voltage or not; if the current value is larger than the open-circuit voltage, replacing the corresponding updated particle speed with the number of times of which the continuous maximum value in the target dimension exceeds the preset number of times with the preset speed, and acquiring a new updated particle speed; and carrying out iterative calculation according to the new updated particle speed and the new updated particle position.
Specifically, the preset speed is not limited in this embodiment, and may be 0.1, 0.5, 1, 2, 3, or the like, as long as the purpose of this embodiment can be satisfied. The purpose of this step is to reduce the iteration number, and when the update particle speed is greater than the open circuit voltage, it can be understood that the next iteration will be far away from the local optimum, so that the iteration can be reduced after the replacement of the update particle is achieved in this step, and the algorithm is simplified, and detailed description of this embodiment is omitted.
Further, referring specifically to fig. 2, fig. 2 is a flowchart of iterative computation provided in an embodiment of the present application, including:
And S201, updating the particle speed and the particle position according to the current global optimal value, the current local optimal value, the learning factor and the inertia weight to obtain the updated particle speed and the new updated particle position.
Specific updates may utilize the following formula:
V nc+1 ＝w nc ×V nc +c1×r1×(PPbest nc -Vm nc )+c2×r2×(GPbest nc -Vm nc )；
Vm nc+1 ＝Vm nc +V nc+1 ；
wherein V is nc+1 The speed of the particle voltage moving to the optimal solution in the next iteration is the updated particle speed;
w nc is the inertial weight factor of the current iteration;
V nc is the current particle velocity, i.e., the velocity at which the particle voltage of the current iteration moves toward the optimal solution;
c1 and c2 are learning factors;
r1 and r2 are random numbers within 0 to 1;
PPbest nc the local optimal value is the current iterative local optimal value;
Vm nc is the current particle voltage location;
GPbest nc the current iteration global optimal value is the current global optimal value;
Vm nc+1 the particle voltage position of the next iteration is the updated particle position;
notably, w nc The inertia weight factor of the current iteration is not limited any more, and may be a fixed value or may be a linear adaptive adjustment mode, and of course, the inertia weight factor may also be automatically adjusted according to an obtained particle evaluation value, where the particle evaluation value refers to whether the number of times of determining whether the updated particle velocity in the same continuous dimension is the maximum value exceeds a preset number of times.
c1 and c2 are learning factors, which are not limited in this embodiment, and may be fixed values or linear adaptive adjustment.
Further, the learning factor is determined according to c1=1.5+0.01xnc, c2=2.5-0.01xnc; wherein c1 and c2 are learning factors, and Nc is the number of iterations.
Based on the technical means, a linear self-adaptive mode is adopted for the learning factors c1 and c2, and the c1 gradually increases along with the increase of the iteration times, so that the local search speed is accelerated; along with the increase of the iteration times, the iteration power is gradually close to an optimal value, at the moment, c2 is gradually reduced, the global searching speed is reduced, the optimal value is beneficial to finding, and the iteration power span can be reduced to be overlarge by adopting a linear self-adaptive mode.
Further, the inertia weight value is larger, has better global convergence capacity, and is smaller, and has stronger searching capacity. The particle swarm algorithm with decreasing inertia weight has better global searching capability in the early stage, and better later convergence speed, but the algorithm with slow convergence speed and increasing inertia weight has higher earlier convergence speed but poorer later local searching capability.
Therefore, when the number of substitutions is zero, the inertial weight is based on w nc =1.5+0.01×nc;
wherein w is nc As inertia weight, nc is the number of iterations.
When the number of substitution times is not zero, the inertia weight is according to w nc ＝(-0.175×t 2 +0.425×t+1.225) +0.01×nc;
wherein w is nc For inertial weight, nc is the number of iterations, and t is the number of substitutions.
It is known that the output voltage of a single photovoltaic module is relatively small, and that multiple photovoltaic modules need to be connected in series in a photovoltaic string to obtain a higher voltage. In the in-service use, the photovoltaic module in the photovoltaic group string is often shielded by different degrees, and because the bypass diode is arranged in the photovoltaic module, the photovoltaic module can bypass the battery pieces to ensure normal power generation of other battery pieces when the photovoltaic cell is shielded, so that the photovoltaic module has the advantages of preventing the problem of hot spots, reducing the risk of ignition and prolonging the service life of the photovoltaic module. When the photovoltaic cell is shielded, the output power curve of the photovoltaic string is multimodal due to the conduction of the bypass diode. The maximum power in the multimodal shape will appear at different locations, typically shown to the left, in the middle and to the right, due to the different occlusion locations and the different degree of occlusion. Based on the technical means, the self-adaptive inertia weight w is adjusted nc Firstly, the inertia weight value adopts a linear self-adaptive mode, the convergence speed can be improved along with the gradual reduction of iteration, but the photovoltaic multimodal maximum power point mainly appears at the left, middle and right positions, and if the inertia weight is only in a linear self-adaptive mode, the algorithm is easily trapped into a local optimal value or junctionThe method has low precision, so that after each particle layout adjustment, a new smaller self-adaptive inertia weight is adopted, the convergence speed can be improved, the algorithm precision can be improved, and the situation that the particle layout falls into a local optimal value is avoided.
S202, obtaining a particle fitness value according to the updated particle position;
and S203, updating the local optimal value and the global optimal value of each particle according to the particle fitness value corresponding to each particle fitness value and each current local optimal value.
Based on the above technical solution, in this embodiment, when the number of times that the absolute value of the updated particle velocity in the same continuous dimension is the maximum value exceeds the preset number of times, and the same dimension is named as the target dimension, the updated particle position corresponding to the value of the continuous maximum value in the target dimension exceeding the preset number of times is replaced by the updated particle position corresponding to the value in the same iteration number, so as to obtain a new updated particle position; iterative computation is carried out according to the updated particle speed and the new updated particle position, so that the particle position is readjusted according to the updated particle speed value, the self-adaptive adjustment of particle layout is realized, unnecessary iteration is reduced, and the convergence speed is improved.
Based on the foregoing embodiments, the present embodiment provides a method for tracking multi-peak maximum power of a photovoltaic, and referring to fig. 3, fig. 3 is a flowchart of another method for tracking multi-peak maximum power of a photovoltaic according to the embodiments of the present application, including:
s301, judging whether the replacement times reach a preset threshold value.
The purpose of this step is to obtain the magnitude of the preset threshold and the number of substitutions.
S302, if the preset threshold is not reached, obtaining the maximum value of the absolute value of the updated particle velocity under the same iteration number.
The preset threshold is N-1, and N is the number of photovoltaic modules. The purpose of setting the threshold is to perform the particle layout N-1 times so as to efficiently reduce the iteration number and improve the convergence speed.
If the preset threshold is not reached, obtaining the maximum value of the updated particle velocity under the same iteration number;
and if the preset threshold value is reached, executing the step of iterative calculation according to the updated particle speed and the new updated particle position.
S303, judging whether the number of times that the absolute value of the updated particle velocity is the maximum value in the same continuous dimension exceeds the preset number of times.
And S304, if the number of times exceeds the preset number of times, the same dimension is named as a target dimension, the corresponding updated particle position of which the number of times of the continuous maximum value under the target dimension exceeds the preset number of times is replaced by the updated particle position corresponding to the secondary value under the same iteration number of times, the new updated particle position is obtained, and the replacement number of times is recorded.
And S305, carrying out iterative computation according to the updated particle speed and the new updated particle position, and obtaining the local maximum power and the global maximum power until the iteration is stopped.
Based on the above technical solution, in this embodiment, when the number of times of replacement does not reach the preset threshold, and when the number of times that the velocity of the updated particles in the same continuous dimension is the maximum value exceeds the preset number of times, and the same dimension is named as the target dimension, the corresponding updated particle position in which the number of times of the continuous maximum value in the target dimension exceeds the preset number of times is replaced with the updated particle position corresponding to the secondary value in the same iteration number, so as to obtain a new updated particle position; iterative computation is carried out according to the updated particle speed and the new updated particle position, so that the particle position is readjusted according to the updated particle speed value, the self-adaptive adjustment of particle layout is realized, unnecessary iteration is reduced, and the convergence speed is improved
Based on the foregoing embodiments, the present embodiment provides a method for tracking multi-peak maximum power of a photovoltaic, and referring to fig. 4, fig. 4 is a flowchart of the method for tracking multi-peak maximum power of the photovoltaic provided in the embodiment of the present application, which includes:
S401, obtaining the maximum value of the absolute value of the updated particle velocity under the same iteration times.
S402, judging whether the number of times that the absolute value of the updated particle velocity is the maximum value in the same continuous dimension exceeds the preset number of times.
And S403, if the number of times exceeds the preset number of times, the same dimension is named as a target dimension, the corresponding updated particle position of which the number of times of the continuous maximum value under the target dimension exceeds the preset number of times is replaced by the updated particle position corresponding to the secondary value under the same iteration number of times, the new updated particle position is obtained, and the replacement number of times is recorded.
S404, performing iterative computation according to the updated particle speed and the new updated particle position to obtain local maximum power and global maximum power.
With specific reference to the foregoing embodiments, the details of this embodiment are not repeated.
S405, judging whether the accuracy of the numerical value of the global maximum power reaches a preset accuracy.
The embodiment does not limit the preset precision, and the user can set the device according to the actual requirement, and the device can be two bits after the decimal point or three bits after the decimal point. Taking the last two bits of the decimal point as an example, the formula for setting the iteration termination is:
wherein Vm is (nc+1)t The optimal solution obtained after t times of particle layout adjustment, i.e. t is the number of substitutions, vm nc+1 The N-dimensional optimal solution found for the particle voltage, namely the global maximum power value, round (x, -2) is the data x, so that the data x is accurate to the position 2 bits after the decimal point.
And S406, if the preset precision is reached, stopping iteration.
The goal of this step is to stop the iteration, at which time the global maximum power is obtained, successfully tracking the maximum power point of the photovoltaic multimodal.
Based on the above technical scheme, when the accuracy of the numerical value of the global maximum power reaches the preset accuracy, the iteration is stopped at the moment, and the global maximum power of the photovoltaic multimodal is obtained.
Based on the above embodiments, the present embodiment provides a method for tracking peak maximum power of a photovoltaic, and referring to fig. 5, fig. 5 is a flowchart of a method for tracking peak maximum power of a photovoltaic according to the embodiment of the present application
S501, obtaining the maximum value of the absolute value of the updated particle velocity under the same iteration times.
S502, judging whether the number of times that the absolute value of the updated particle velocity is the maximum value in the same continuous dimension exceeds the preset number of times.
And S503, if the number of times exceeds the preset number of times, the same dimension is named as a target dimension, the corresponding updated particle position of which the number of times of the continuous maximum value under the target dimension exceeds the preset number of times is replaced by the updated particle position corresponding to the secondary value under the same iteration number, the new updated particle position is obtained, and the replacement number is recorded. Referring specifically to the above embodiments, the details of this embodiment are not repeated.
S504, carrying out iterative computation according to the updated particle speed and the new updated particle position to obtain the local maximum power and the global maximum power.
S505, judging whether the iteration times reach an iteration threshold.
The step does not limit the iteration threshold, and the user can set any value of 50, 80, 100, 120, 150, 180, and 200 according to actual requirements, and of course, other user-defined values are also possible, and it is worth noting that, in this embodiment, when the number of times that the update particle velocity in the same continuous dimension is the maximum value exceeds the preset number of times, the same dimension is named as the target dimension, the corresponding update particle position in which the number of times that the continuous maximum value in the target dimension exceeds the preset number of times is replaced with the update particle position in which the secondary value in the same iteration number is corresponding, so as to obtain a new update particle position, and the replacement number is recorded; according to the updated particle speed and the new updated particle position, the particle position is readjusted according to the updated particle speed value, the particle layout is self-adaptively adjusted, unnecessary iteration is reduced, and the convergence speed is improved, so that the iteration threshold value can be set smaller, and the energy consumption can be effectively reduced.
S506, if the iteration threshold is reached, the iteration is stopped.
Based on the above technical scheme, the present embodiment obtains the global maximum power of the photovoltaic multimodal by reaching the iteration threshold through the iteration number and stopping the iteration at this time.
The embodiment provides a specific photovoltaic multimodal maximum power tracking method, which comprises the following steps:
s1, inputting photovoltaic data, wherein the photovoltaic string voltage V is obtained pv And current I pv Then calculate P pv ＝V pv ×I pv ，P pv And the power value is the photovoltaic string power value.
S2, initializing basic parameters of an algorithm, wherein initial positions of particles are as follows
V oc The photovoltaic module string open circuit voltage is obtained, N is the number of photovoltaic modules, and the maximum iteration number is Nmax.
S3, determining an adaptive learning factor, wherein the learning factor is used for adjusting the proportion of particles moving to an extreme value, and the self-adaptive learning factor is performed in a linear self-adaptive mode according to the following formula:
c1＝1.5+0.01×Nc；
c2＝2.5-0.01×Nc；
wherein c1 and c2 are learning factors, wherein c1 is the specific gravity of the particles moving to the local extremum, c2 is the specific gravity of the particles moving to the global extremum, and Nc is the number of iterations, i.e. the number of iterations.
S4, determining self-adaptive inertia weight w nc Due to the inertial weight w nc Is a very important parameter in the algorithm, relates to convergence speed and tracking precision, adopts a self-adaptive linear mode (when the replacement times are 0) at the initial stage of algorithm iteration, and is carried out according to the following formula:
w nc =1.5+0.01×nc; nc is the number of iterations.
S5, updating the particle speed and the position, namely obtaining a new particle speed and a new particle voltage position of the next iteration, namely obtaining the updated particle speed and the new updated particle position, and updating according to the following formula:
V nc+1 ＝w nc ×V nc +c1×r1×(PPbest nc -Vm nc )+c2×r2×(GPbest nc -Vm nc )；
Vm nc+1 ＝Vm nc +V nc+1 ；
wherein V is nc+1 The speed of the particle voltage moving to the optimal solution in the next iteration is the updated particle speed;
w nc is the inertial weight factor of the current iteration;
V nc is the current particle velocity, i.e., the velocity at which the particle voltage of the current iteration moves toward the optimal solution;
c1 and c2 are learning factors;
r1 and r2 are random numbers within 0 to 1;
PPbest nc the local optimal value is the current iterative local optimal value;
Vm nc is the current particle voltage location;
GPbest nc the current iteration global optimal value is the current global optimal value;
Vm nc+1 the particle voltage position of the next iteration is the updated particle position;
s6, acquiring an adaptive value of the particle voltage according to the current particle voltage Vm nc And the updated particle velocity V obtained after each iteration nc+1 By GetPower [ V ] pv ，P pv ]Acquiring corresponding power values, namely determining fitness values of the particles according to the updated particle positions; wherein GetPower [ V ] pv ，P pv ]The value obtained by the current sensor and the voltage sensor in real time can further obtain a corresponding power value, and further, the value can be stored in a memory so as to be checked and extracted.
The N-dimensional particle voltage Vm nc Corresponding power value Pm nc Comparing to obtain the global maximum power value GPbest of the current iteration nc I.e. the current global optimum; the N-dimensional particle voltage Vm nc And Vm (Vm) nc +V nc+1 Corresponding Pm nc And Pm nc+1 Comparing to obtain the local maximum power value PPbest of the current iteration nc I.e. the current local optimum.
S7, updating the local optimal value and the global optimal value of the particles, wherein the local optimal value and the global optimal value are the local optimal value PPbest after the current iteration is compared nc And global optimum GPbest nc With the local optimum PPbest of the last iteration nc-1 And global optimum GPbest nc-1 And comparing, namely replacing the original smaller value by the larger value.
S8, adjusting the particle layout, namely obtaining the maximum value of the updated particle speed under the same iteration number; judging whether the number of times that the updated particle velocity is the maximum value under the continuous same dimension exceeds a preset number of times; if the number of times exceeds the preset number of times, the same dimension is named as a target dimension, the corresponding maximum value of which the number of times of continuous maximum values under the target dimension exceeds the preset number of times is replaced by a secondary value under the same iteration number, the speed of each updated particle is obtained, and the replacement number is recorded; and carrying out iterative calculation according to the updated particle velocity and the new updated particle position. Adjusting the particle layout may also refer to calculating V from each iteration nc+1 Making a judgment if V of N dimension nc+1 And if the internal certain dimension is continuously the maximum value, replacing the particle voltage of the maximum value with the particle voltage of a smaller value, and continuing to iteratively execute S3-S7 along with the algorithm, continuing to judge and replace until the replacement times t is equal to N-1, and not replacing any more, wherein N is the number of photovoltaic groups.
Wherein when the number of substitutions is 0, the adaptive inertial weight w is determined according to S4 nc ；
When the number of substitution times is not 0, the self-adaptive inertia weight w is adjusted nc Means that after each adjustment of the primary particle layout, the adaptive inertia weight w needs to be adjusted nc The adjustment formula proceeds as follows:
w nc ＝(-0.175×t 2 +0.425×t+1.225)+0.01×Nc；
and S10, judging whether the accuracy of the numerical value of the global maximum power reaches the preset accuracy, and if so, stopping iteration. The termination condition formula proceeds as follows:
in the formula, vm (nc+1)t For the particle layout, the optimal solution, vm, obtained after t times of adjustment is adopted nc+1 The N-dimensional optimal solution found for the particle voltage, round (x, -2), is the data x, which is made to be accurate to the 2 bits after the decimal point.
S11, outputting the global maximum power, namely when the iteration termination condition is met, according to GetPower [ Vm ] (nc+1)t ，P pv ]And acquiring a global optimal value Ppvmax, and outputting the tracked maximum power point as an actual photovoltaic multimodal maximum power point.
Further, fig. 6 is three shielding situations for providing a photovoltaic module in a photovoltaic string according to the embodiment of the present application, and fig. 7 is a photovoltaic string output characteristic curve under the three shielding situations provided by the embodiment of the present application; FIG. 8 is a graph showing the comparison of the results of the particle swarm algorithm before and after improvement in the shielding situation 1 according to the embodiment of the present application; FIG. 9 is a graph showing the comparison of the results before and after the particle swarm optimization in the shielding case 2 according to the embodiment of the present application; fig. 10 is a comparison chart of the results before and after the particle swarm algorithm is improved in the shielding case 3 according to the embodiment of the present application.
As shown in fig. 6, in order to better show the multi-peak maximum power point of the photovoltaic string and appear at different positions, 1 photovoltaic string is formed by connecting 5 320W photovoltaic modules in series. Under standard conditions, the electrical parameters of the photovoltaic module are open-circuit voltage uoc=40.8v, short-circuit current isc=10.05a, maximum power point operating voltage um=33.48v, and maximum power point operating current im=9.56A. Under the shielding conditions 1,2 and 3, the illumination received by the photovoltaic module is respectively [1000, 900, 300, 200 and 100], [1000, 900, 800, 500 and 400], [1000, 900, 800, 700 and 600]. As shown in fig. 7, in the case of the shielding cases 1,2, and 3, the output characteristic curves of the photovoltaic string are multimodal, and the actual maximum power points appear at the left, middle, and right, respectively.
To verify the advantages of the improved particle swarm algorithm, the tracking effect comparison is performed under three different shielding conditions by adopting a basic particle swarm algorithm and an improved particle swarm algorithm. The number of basic particle swarm algorithm particles is 5, c1=2, c2= 2,w =0.2, and the maximum iteration number is 200. The improved particle swarm algorithm is carried out by adopting the method.
Shielding case 1:
in case of the blocking mode 1, the actual maximum output power point position of the string of photovoltaic groups is [61.93v,575.7477w ]. The MPPT tracking results of the basic particle swarm, namely the basic PSO algorithm MPPT and the improved particle swarm algorithm, namely the improved PSO algorithm MPPT tracking results are shown in fig. 8, it can be seen that the 2 algorithms find the actual maximum power point, but the basic particle swarm algorithm is iterated for 40 times, the improved particle swarm algorithm is iterated for 24 times, the iteration times are reduced by 40%, meanwhile, the power oscillation in the improved algorithm process is smaller, and the power loss is reduced.
Shielding case 2:
in the case of the blocking mode 2, the actual maximum output power point position of the string of photovoltaic groups is [92.91v,771.6861w ]. The tracking results of the basic particle swarm and the improved particle swarm algorithm are shown in fig. 9, it can be seen that the 2 algorithms find the actual maximum power point, but the basic particle swarm algorithm is iterated for 42 times, the improved particle swarm algorithm is iterated for 22 times, the iteration number is reduced by 48%, and meanwhile, the power oscillation in the improved algorithm process is obviously reduced, so that the power loss is obviously reduced.
Shielding case 3:
in the case of the shielding mode 3, the actual maximum output power point position of the photovoltaic string is in [155.01V,961.6814W ], the tracking results of the basic particle swarm and the modified particle swarm algorithm are shown in fig. 10, the modified particle swarm algorithm obtains the maximum power point to be 961.6814W through 67 iterations, and the basic particle swarm algorithm obtains the maximum power point to be 915.0036W through 71 iterations, and the basic particle swarm algorithm is already trapped in a local optimal value. Through multiple experiments, if the self-adaptive weight factors are not adjusted along with the particle voltage layout, the algorithm can be sunk into a local optimal value or the tracked maximum power point is not high in accuracy.
It can be known that, the learning factors c1 and c2 adopt a linear self-adaptive mode, and the c1 gradually increases along with the increase of the iteration times, so that the local search speed is increased; with iteration timesThe number is increased to be closer to the optimal value, at the moment, c2 is gradually reduced, the global searching speed is reduced, the optimal value is beneficial to finding, and the iteration power span can be reduced to be overlarge by adopting a linear self-adaption mode. Further, the particle layout is adaptively adjusted, and the particle position is adjusted according to the characteristics of the initial position of the voltage of the placed particles and the new speed value calculated in each iteration, so that unnecessary searching cost is reduced. Further, the adaptive inertia weight w is adjusted nc Firstly, the inertia weight value adopts a linear self-adaptive mode, the convergence speed can be improved along with the gradual reduction of iteration, but the photovoltaic multimodal maximum power point mainly appears at the left, middle and right positions, if the inertia weight is only in the linear self-adaptive mode, the algorithm is easy to sink into a local optimal value or the result precision is not high, so that after each particle layout adjustment, the new smaller self-adaptive inertia weight is adopted, the convergence speed can be improved, the algorithm precision can be improved, and the sinking into the local optimal value is avoided.
The following description is presented with reference to a multi-peak maximum power tracking device of a photovoltaic provided in an embodiment of the present application, and the multi-peak maximum power tracking device of a photovoltaic and the multi-peak maximum power tracking method of a photovoltaic described in the following description may be referred to correspondingly, and referring to fig. 11, fig. 11 is a schematic structural diagram of the multi-peak maximum power tracking device of a photovoltaic provided in an embodiment of the present application, where the multi-peak maximum power tracking device of a photovoltaic includes:
an obtaining module 10, configured to obtain a maximum value of an absolute value of an updated particle velocity at the same iteration number;
a first judging module 20, configured to judge whether the number of times of updating the absolute value of the particle velocity to be the maximum value in the same dimension in succession exceeds a preset number of times;
The replacing module 30 is configured to, if the number of times exceeds the preset number of times, designate the same dimension as a target dimension, replace a corresponding updated particle position of which the number of times of continuous maximum value under the target dimension exceeds the preset number of times with a corresponding updated particle position of which the number of times under the same iteration number, obtain a new updated particle position, and record the number of times of replacement;
and the iteration module 40 is used for carrying out iterative calculation according to the updated particle speed and the new updated particle position, and obtaining the local maximum power and the global maximum power until the iteration is stopped.
In some specific embodiments, further comprising:
the second judging module is used for judging whether the replacement times reach a preset threshold value or not;
the execution module is used for executing the step of iterative computation according to the updated particle speed and the new updated particle position if the preset threshold value is reached; if the preset threshold is not reached, executing the step of obtaining the maximum value of the updated particle speed under the same iteration number;
the preset threshold is N-1, and N is the number of photovoltaic modules.
In some particular embodiments, the iteration module 40 includes:
the acquisition unit is used for acquiring local maximum power and global maximum power;
the judging unit is used for judging whether the precision of the numerical value of the global maximum power reaches the preset precision;
And the execution unit is used for stopping iteration if the preset precision is reached.
In some particular embodiments, the iteration module 40 includes:
the acquisition unit is used for acquiring local maximum power and global maximum power;
the judging unit is used for judging whether the iteration times reach an iteration threshold value or not;
and the execution unit is used for stopping iteration if the iteration threshold is reached.
In some particular embodiments, the iteration module 40 includes:
the first updating unit is used for updating the particle speed and the particle position according to the current global optimal value, the current local optimal value, the learning factor and the inertia weight to obtain the updated particle speed and the new updated particle position;
a determining unit for obtaining each particle fitness value according to the updated particle position;
and a second updating unit for updating the local optimum and the global optimum of each particle according to the particle fitness value and the particle fitness value corresponding to the local optimum of each particle.
In some specific embodiments, the first updating unit comprises:
a determination subunit for determining a learning factor according to c1=1.5+0.01xnc, c2=2.5-0.01xnc;
wherein c1 and c2 are learning factors, and Nc is the number of iterations.
In some specific embodiments, the first updating unit comprises:
an inertial weight determining subunit for determining inertial weight according to w when the number of substitutions is zero nc =1.5+0.01×nc;
wherein w is nc As inertia weight, nc is the number of iterations.
In some specific embodiments, the first updating unit comprises:
an inertial weight determining subunit for determining inertial weight according to w when the number of substitutions is not zero nc ＝(-0.175×t 2 +0.425×t+1.225) +0.01×nc;
wherein w is nc For inertial weight, nc is the number of iterations, and t is the number of substitutions.
Since the embodiments of the multi-peak maximum power tracking device portion of the photovoltaic and the embodiments of the multi-peak maximum power tracking method portion of the photovoltaic correspond to each other, the embodiments of the multi-peak maximum power tracking device portion of the photovoltaic are referred to for description of the embodiments of the multi-peak maximum power tracking method portion of the photovoltaic, and are not repeated herein.
An electronic device provided in the embodiments of the present application is described below, where the electronic device described below and the above-described method for tracking the peak maximum power of a photovoltaic may be referred to correspondingly.
The present embodiment provides an electronic device including:
a memory for storing a computer program;
A processor for implementing the steps of the multimodal maximum power tracking method for photovoltaic as described above when executing a computer program.
Since the embodiments of the electronic device portion correspond to the embodiments of the photovoltaic multi-peak maximum power tracking method portion, the embodiments of the electronic device portion are described in detail herein for brevity.
A computer readable storage medium provided in the embodiments of the present application is described below, where the computer readable storage medium described below and the method for tracking the peak maximum power of photovoltaic described above may be referred to correspondingly.
The present embodiment provides a computer readable storage medium having a computer program stored thereon, which when executed by a processor implements the steps of the above-described photovoltaic multimodal maximum power tracking method.
Since the embodiments of the computer readable storage medium portion and the embodiments of the photovoltaic multimodal maximum power tracking method portion correspond to each other, the embodiments of the computer readable storage medium portion are described with reference to the embodiments of the photovoltaic multimodal maximum power tracking method portion, and are not repeated herein.
In the description, each embodiment is described in a progressive manner, and each embodiment is mainly described by the differences from other embodiments, so that the same similar parts among the embodiments are mutually referred. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above describes a photovoltaic multi-peak maximum power tracking method, a photovoltaic multi-peak maximum power tracking device, an electronic device and a computer readable storage medium. Specific examples are set forth herein to illustrate the principles and embodiments of the present application, and the description of the examples above is only intended to assist in understanding the methods of the present application and their core ideas. It should be noted that it would be obvious to those skilled in the art that various improvements and modifications can be made to the present application without departing from the principles of the present application, and such improvements and modifications fall within the scope of the claims of the present application.
Claims (9)
1. A method for tracking the peak maximum power of a photovoltaic, comprising:
obtaining the maximum value of the absolute value of the updated particle velocity under the same iteration number;
judging whether the number of times that the absolute value of the updated particle velocity is the maximum value under the continuous same dimension exceeds a preset number of times;
if the number of times exceeds the preset number of times, the same dimension is named as a target dimension, the number of times of continuous maximum values under the target dimension exceeds the preset number of times of corresponding updated particle positions are replaced by the number of times of corresponding updated particle positions under the same iteration number, new updated particle positions are obtained, and the replacement number of times is recorded;
Performing iterative computation according to the updated particle speed and the new updated particle position to obtain local maximum power and global maximum power until the iteration is stopped;
the iterative computation includes:
updating the particle speed and the particle position according to the current global optimal value, the current local optimal value, the learning factor and the inertia weight to obtain the updated particle speed and the updated particle position;
obtaining a particle fitness value according to the updated particle position;
and updating the local optimal value and the global optimal value of each particle according to the particle fitness value corresponding to each particle fitness value and each current local optimal value.
2. The method of claim 1, wherein before the step of obtaining the maximum value of the absolute value of the updated particle velocity for the same number of iterations, further comprises:
judging whether the replacement times reach a preset threshold value or not;
if the preset threshold is reached, executing the step of performing iterative computation according to the updated particle speed and the new updated particle position; if the preset threshold is not reached, executing the step of acquiring the maximum value of the updated particle velocity under the same iteration number;
The preset threshold is N-1, and N is the number of photovoltaic modules.
3. The method of claim 1, wherein the obtaining the local maximum power and the global maximum power until the iteration stops comprises:
acquiring the local maximum power and the global maximum power;
judging whether the precision of the numerical value of the global maximum power reaches a preset precision;
and if the preset precision is reached, stopping iteration.
4. The method of claim 1, wherein the obtaining the local maximum power and the global maximum power until the iteration stops comprises:
acquiring the local maximum power and the global maximum power;
judging whether the iteration times reach an iteration threshold value or not;
and if the iteration threshold is reached, stopping iteration.
5. The method of claim 1, wherein the learning factor is determined from c1=1.5+0.01xnc, c2=2.5-0.01xnc;
wherein c1 and c2 are the learning factors, and Nc is the iteration number.
6. The method of claim 1, wherein the inertial weight is based on w when the number of substitutions is zero nc =1.5+0.01×nc;
wherein w is nc And Nc is the iteration number for the inertia weight.
7. The method of claim 1, wherein the inertial weight is based on w when the number of substitutions is non-zero nc ＝(-0.175×t 2 +0.425×t+1.225) +0.01×nc;
wherein w is nc And for the inertia weight, nc is the iteration number, and t is the substitution number.
8. An electronic device, comprising:
a memory for storing a computer program;
processor for implementing the steps of the multimodal maximum power tracking method of a photovoltaic according to any of claims 1 to 7 when executing said computer program.
9. A computer readable storage medium, characterized in that it has stored thereon a computer program which, when executed by a processor, implements the steps of the multimodal maximum power tracking method of photovoltaics according to any of claims 1 to 7.
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