CN113342124B - Photovoltaic MPPT method based on improved wolf optimization algorithm - Google Patents

Photovoltaic MPPT method based on improved wolf optimization algorithm Download PDF

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CN113342124B
CN113342124B CN202110657394.8A CN202110657394A CN113342124B CN 113342124 B CN113342124 B CN 113342124B CN 202110657394 A CN202110657394 A CN 202110657394A CN 113342124 B CN113342124 B CN 113342124B
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李春卫
杜如甫
张增辉
安强
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PowerChina Huadong Engineering Corp Ltd
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Abstract

The invention relates to a photovoltaic MPPT method based on an improved wolf optimization algorithm. The technical scheme of the invention is as follows: s1, selecting N random values in [0, 1] as initial positions of the wolf grey populations, wherein the wolf grey individuals represent duty ratios in the photovoltaic system; s2, acquiring output power of the photovoltaic system under control of each duty ratio, taking the output power as the fitness of the gray wolf individuals, and recording three gray wolfs with the maximum fitness as alpha wolfs, beta wolfs and delta wolfs in sequence; s3, contracting the search range of the wolf population, and updating the upper limit and the lower limit of the search range; s4, carrying out position updating on the wolf population by using an improved position updating formula; s5, executing reverse search, comparing with the grey wolf population fitness, and updating alpha wolf, beta wolf and delta wolf; s6, judging whether a termination condition is met, if so, stopping iteration and stabilizing the photovoltaic system on a duty ratio corresponding to the alpha wolf; otherwise, return to step S2 and continue the iteration.

Description

Photovoltaic MPPT method based on improved wolf optimization algorithm
Technical Field
The invention relates to a photovoltaic MPPT method based on an improved wolf optimization algorithm. The photovoltaic power generation device is suitable for the technical field of photovoltaic power generation.
Background
With the increasing exhaustion of traditional fossil energy, the development and use of renewable energy is an inevitable trend, and solar energy has the characteristics of cleanness, no pollution, unlimited reserves and the like, and is the most ideal renewable energy. In the solar power generation technology, how to reduce the cost and improve the power generation efficiency is a core problem, and since the output characteristics of the solar cell are affected by external conditions such as illumination, temperature and the like, realizing Maximum Power Point Tracking (MPPT) is one of the key technologies for improving the overall efficiency of the system.
In practical engineering, the photovoltaic array can output a P-U characteristic curve to present a multi-peak characteristic due to local shielding, dust accumulation coverage and other reasons. The traditional MPPT method such as a disturbance observation method and a conductance increment method is easy to fall into a local peak point and cannot track the global maximum power. Therefore, group intelligent optimization algorithms with global search capability, such as particle swarm optimization, firefly optimization, wolfsbane optimization and other algorithms, become research hotspots for solving the MPPT problem. However, the existing algorithms still have the problems of low solution precision, slow convergence speed and possibility of falling into local extremum, and need to be improved.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the existing problems, a photovoltaic MPPT method based on an improved grey wolf optimization algorithm is provided to solve the problems that the traditional grey wolf optimization algorithm is low in solving precision and easy to fall into local optimization.
The technical scheme adopted by the invention is as follows: a photovoltaic MPPT method based on an improved wolf optimization algorithm is characterized in that:
s1, selecting N random values in [0, 1] as initial positions of the wolf grey populations, wherein the wolf grey individuals represent duty ratios in the photovoltaic system;
s2, acquiring output power of the photovoltaic system under control of each duty ratio, taking the output power as the fitness of the gray wolf individuals, and recording three gray wolfs with the maximum fitness as alpha wolfs, beta wolfs and delta wolfs in sequence;
s3, shrinking the search range of the wolf population, and updating the upper and lower limits of the search interval, wherein the upper and lower limits of the search interval [ a (t), b (t) ], are updated according to the following formula:
Figure BDA0003113697690000021
s(t)＝1-(1-λ)t/t max
where t is the current iteration algebra, X α Is the position of the alpha wolf, s (t) is a linearly decreasing function, lambda is the final convergence ratio, t max Is the maximum number of iterations of the algorithm, a (0) is 0, b (0) is 1;
s4, carrying out position updating on the wolf population by using an improved position updating formula, wherein the improved position updating formula is as follows:
Figure BDA0003113697690000022
wherein, X old The method is calculated by a position updating formula of a traditional wolf optimization algorithm;
s5, executing reverse search, generating a reverse solution of the current optimal solution, calculating the fitness of the reverse solution, comparing the fitness with the grey wolf population fitness, and updating the alpha wolf, the beta wolf and the delta wolf;
s6, judging whether a termination condition is met, if so, stopping iteration and stabilizing the photovoltaic system on a duty ratio corresponding to the alpha wolf; otherwise, return to step S2 and continue the iteration.
Further comprising:
and S7, continuously monitoring whether the output power of the photovoltaic system changes suddenly, and returning to the step S1 to restart the algorithm when the change rate of the output power is greater than a set threshold value.
The judgment formula for judging whether the output power is abruptly changed in step S7 is:
Figure BDA0003113697690000023
in the formula: p real Is the power, P, collected in real time m Is the maximum power before restart.
The performing a reverse search to produce a reverse solution to the current optimal solution comprises:
searching for the inverse solution X of alpha wolf rd The calculation formula is as follows:
Figure BDA0003113697690000031
the termination conditions include: judging whether the algorithm reaches the maximum iteration time tmax; whether the standard deviation sigma of the whole gray wolf positions is smaller than a set threshold value theta is calculated.
A photovoltaic MPPT device based on improve grey wolf optimization algorithm, its characterized in that includes:
the initialization module is used for selecting N random values in [0, 1] as initial positions of the grey wolf population, and the grey wolf individuals represent duty ratios in the photovoltaic system;
the fitness calculation module is used for acquiring the output power of the photovoltaic system under the control of each duty ratio, taking the output power as the fitness of the gray wolf individuals, and recording the three gray wolfs with the highest fitness as alpha wolfs, beta wolfs and delta wolfs in sequence;
the search range contraction module is used for contracting the search range of the wolf population and updating the upper limit and the lower limit of the search interval, and the upper limit and the lower limit of the search interval [ a (t), b (t) ] are updated according to the following formula:
Figure BDA0003113697690000032
s(t)＝1-(1-λ)t/t max
where t is the current iteration algebra, X α Is the position of the alpha wolf, s (t) is a linearly decreasing function, lambda is the final convergence ratio, t max Is the maximum number of iterations of the algorithm, a (0) is 0, b (0) is 1;
a location update module, configured to perform a location update on the wolf population using an improved location update formula, where the improved location update formula is:
Figure BDA0003113697690000033
wherein, X old The method is calculated by a position updating formula of a traditional wolf optimization algorithm;
the reverse search module is used for executing reverse search, generating a reverse solution of the current optimal solution, calculating the fitness of the reverse solution, comparing the fitness with the grey wolf population fitness, and updating the alpha wolf, the beta wolf and the delta wolf;
the judging module is used for judging whether a termination condition is met, if so, stopping iteration and stabilizing the photovoltaic system on a duty ratio corresponding to the alpha wolf; otherwise, return to step S2 and continue the iteration.
Further comprising:
and the restarting module is used for continuously monitoring whether the output power of the photovoltaic system changes suddenly or not, and when the change rate of the output power is greater than a set threshold value, the step S1 is returned, and the algorithm is restarted.
A storage medium having stored thereon a computer program executable by a processor, the computer program comprising: the computer program when executed implements the steps of the improved grayish optimization algorithm based photovoltaic MPPT method.
An MPPT controller having a memory and a processor, the memory having stored thereon a computer program executable by the processor, the MPPT controller comprising: the computer program when executed implements the steps of the improved grayish optimization algorithm based photovoltaic MPPT method.
A photovoltaic maximum power point tracking system is characterized in that: the MPPT controller is provided.
The invention has the beneficial effects that: aiming at the two problems of low accuracy and local optimum trapping existing in the traditional gray wolf optimization algorithm, the invention carries out coordination solution by two strategies, wherein the two strategies are respectively as follows: firstly, the convergence speed of the algorithm is improved through a search interval contraction strategy, and the calculation power is concentrated near the optimal solution to search for more accurate results; secondly, the population diversity is increased through a reverse optimization strategy, and the algorithm is effectively helped to jump out of a local optimal solution. The method converts the group search cost of the algorithm into the individual search cost of the reverse solution, so that the photovoltaic system can track the maximum power point more quickly and accurately, and is more suitable for the transient response process of the circuit.
Drawings
Fig. 1 is a schematic diagram of a Boost circuit structure adopted by a photovoltaic MPPT system in an embodiment.
FIG. 2 is a schematic flow chart of an embodiment.
FIG. 3 is a diagram illustrating a section contraction strategy in an embodiment.
Detailed Description
Fig. 1 is a circuit structure of a Boost circuit based photovoltaic maximum power point tracking system in an embodiment, which includes a photovoltaic system, an MPPT controller, and a PWM generator, where the photovoltaic system includes a photovoltaic array formed by connecting 4 × 1 photovoltaic cells in series; the MPPT controller adjusts output duty ratio according to the collected photovoltaic array output power, controls the working voltage of the photovoltaic array and further realizes maximum power point tracking.
The MPPT controller has a memory and a processor, the memory having stored thereon a computer program executable by the processor, the computer program when executed implementing the steps of the improved grey wolf optimization algorithm based photovoltaic MPPT method.
As shown in fig. 2, the photovoltaic MPPT method based on the improved grayish wolf optimization algorithm in this embodiment includes the following steps:
s1, selecting N random values in [0, 1] as initial positions of the wolf population, and initializing relevant parameters a, A and C of the wolf optimization algorithm.
The size N of the wolf population is determined according to the series-parallel connection structure of the photovoltaic array in the actual circuit. The parameter a is at the maximum number of iterations t max The convergence factor, which decreases linearly from 2 to 0, the parameters a and C are calculated as:
Figure BDA0003113697690000051
Figure BDA0003113697690000052
wherein r is 1 And r 2 Is [0, 1]]Random numbers within a range.
And S2, measuring the output power of the photovoltaic system under the control of each duty ratio, taking the output power as the fitness of the gray wolf, and recording the three gray wolfs with the maximum fitness as an alpha wolf, a beta wolf and a delta wolf in sequence.
In the circuit structure shown in fig. 1, the MPPT controller outputs the duty ratio represented by the position of the individual gray wolf, and the circuit operates at the duty ratio, and records the output power at the time of stabilization as the fitness of the individual gray wolf when the output power of the circuit is stabilized. After all the wolf individuals are calculated, sorting according to the fitness, recording the maximum as alpha wolf, the second largest as beta wolf and the third largest as delta wolf.
S3, narrowing the search range of the sirius population, and updating the upper and lower limits of the search interval, where the interval narrowing strategy is as shown in fig. 3, the original search interval is [0, 1], the lower limit a (0) is 0, the upper limit b (0) is 1, and the search interval [ a (t), b (t) ] is updated according to the iteration:
Figure BDA0003113697690000053
s(t)＝1-(1-λ)t/t max
where t is the current iteration algebra, X α Is the position of the alpha wolf, i.e. the position of the current optimal solution, s (t) is a linearly decreasing function, lambda is the final convergence ratio, set to 0.5, t max Is the maximum number of iterations of the algorithm. Therefore, as iteration is carried out, the search range is gradually reduced, and the wolf population gradually gathers to the position of the alpha wolf, so that the search calculation power is concentrated near the optimal solution, and excessive search in the area with poor fitness is avoided. The convergence speed of the wolf population is further accelerated, and the final solving precision is improved.
And S4, updating the location of the wolf population by using the improved location updating formula. The position updating formula of the traditional gray wolf optimization algorithm is as follows:
Figure BDA0003113697690000061
Figure BDA0003113697690000062
Figure BDA0003113697690000063
wherein, D represents the distance between the alpha, beta and delta three wolves and other gray wolves, X represents the position update of the gray wolves under the guidance of the alpha, beta and delta three wolves respectively, and the updated position X (t +1) obtained by solving the average value is obtained.
The improvement of the improved position updating formula is that on the basis of the original position updating formula, the gray wolf population is gradually gathered to the position of the alpha wolf, and the calculation formula is as follows:
Figure BDA0003113697690000064
wherein, X old Calculated from the position update formula of the traditional wolf optimization algorithm, X new Calculated by the improved position updating formula. It can be seen that the wolf population will search within the contracted search interval.
And S5, executing reverse search, generating a reverse solution of the current optimal solution, calculating the fitness of the reverse solution, comparing the fitness with the grey wolf population fitness, and updating the alpha wolf, the beta wolf and the delta wolf.
The reverse search strategy is to increase the diversity of the population by searching the reverse solution of the current optimal solution, and further enhance the search capability of the algorithm, so that the local optimal solution can be skipped at the later stage. Searching for the inverse solution X of alpha wolf rd The calculation formula is as follows:
Figure BDA0003113697690000065
s6, judging whether the algorithm meets a termination condition, if so, stopping iteration and stabilizing the photovoltaic system on a duty ratio corresponding to the alpha wolf; otherwise, return to step S2 and continue the iteration.
Two termination conditions are provided, one is to judge whether the algorithm reaches the maximum iteration number t max (ii) a And secondly, calculating whether the standard deviation sigma of all the gray wolf positions is smaller than a set threshold value theta. If any condition is met, stopping iteration and stabilizing the photovoltaic system on a duty ratio corresponding to an alpha wolf, namely the maximum power point; otherwise, return to step S2.
And S7, continuously detecting whether the output power of the photovoltaic system changes suddenly, and returning to the step S1 to restart the algorithm when the change rate of the output power is greater than a set threshold value.
When the illumination condition changes due to external environmental factors, the output power characteristic of the photovoltaic array also changes, and at this time, the maintained duty ratio may no longer correspond to the maximum power point, and a new maximum power point needs to be searched by the restart algorithm. The judgment formula for judging whether the output power is mutated is as follows:
Figure BDA0003113697690000071
in the formula: p real Is the power, P, collected in real time m Is the maximum power before restart.
The embodiment also provides a photovoltaic MPPT apparatus based on the improved grey wolf optimization algorithm, including: the device comprises an initialization module, a fitness calculation module, a search range contraction module, a position updating module, a reverse search module, a judgment module and a restart module.
In this example, the initialization module is used to select N random values within [0, 1] as the initial positions of the population of sirius, where the sirius individuals represent the duty cycles in the photovoltaic system. The fitness calculation module is used for acquiring the output power of the photovoltaic system under the control of each duty ratio, and taking the output power as the fitness of the gray wolf individuals, and recording three gray wolfs with the maximum fitness as alpha wolfs, beta wolfs and delta wolfs in sequence.
In this embodiment, the search range contraction module is configured to contract the search range of the sirius population, and update the upper and lower limits of the search interval, where the upper and lower limits of the search interval [ a (t), b (t) ], are updated according to the following formula:
Figure BDA0003113697690000072
s(t)＝1-(1-λ)t/t max
where t is the current iteration algebra, X α Is the position of the alpha wolf, s (t) is a linearly decreasing function, lambda is the final convergence ratio, t max Is the maximum number of iterations of the algorithm, with a (0) equal to 0 and b (0) equal to 1.
The location update module in this example is configured to perform a location update on the wolf population using an improved location update formula, where the improved location update formula is:
Figure BDA0003113697690000073
wherein, X old The method is calculated by a position updating formula of a traditional wolf optimization algorithm.
The reverse search module in this embodiment is configured to perform a reverse search, generate a reverse solution of the current optimal solution, calculate a fitness of the reverse solution, compare the fitness with the grey wolf population fitness, and update the α wolf, the β wolf, and the δ wolf. The judging module is used for judging whether a termination condition is met, if so, stopping iteration and stabilizing the photovoltaic system on a duty ratio corresponding to the alpha wolf; otherwise, return to step S2 and continue the iteration. The restarting module is used for continuously monitoring whether the output power of the photovoltaic system changes suddenly, and when the change rate of the output power is larger than a set threshold value, the step S1 is returned, and the algorithm is restarted.
The above description is only for the purpose of describing the embodiments of the present invention with reference to the accompanying drawings, and it should be noted that, for those skilled in the art, various modifications and decorations can be made on the technical solution of the present invention, and the content of the present description should not be understood as the limitation of the present invention.

Claims (10)

1. A photovoltaic MPPT method based on an improved wolf optimization algorithm is characterized in that:
s1, selecting N random values in [0, 1] as initial positions of the wolf grey populations, wherein the wolf grey individuals represent duty ratios in the photovoltaic system;
s2, acquiring output power of the photovoltaic system under control of each duty ratio, taking the output power as the fitness of the gray wolf individuals, and recording three gray wolfs with the maximum fitness as alpha wolfs, beta wolfs and delta wolfs in sequence;
s3, shrinking the search range of the wolf population, and updating the upper and lower limits of the search interval, wherein the upper and lower limits of the search interval [ a (t), b (t) ], are updated according to the following formula:
Figure FDA0003674421830000011
s(t)＝1-(1-λ)t/t max
where t is the current iteration algebra, X α Is the position of the alpha wolf, s (t) is a linearly decreasing function, lambda is the final convergence ratio, t max Is the maximum of the algorithmThe number of iterations, a (0) being 0 and b (0) being 1;
s4, carrying out position updating on the wolf population by using an improved position updating formula, wherein the improved position updating formula is as follows:
Figure FDA0003674421830000012
wherein, X old The method is calculated by a position updating formula of a traditional wolf optimization algorithm;
s5, executing reverse search, generating a reverse solution of the current optimal solution, calculating the fitness of the reverse solution, comparing the fitness with the grey wolf population fitness, and updating the alpha wolf, the beta wolf and the delta wolf;
s6, judging whether a termination condition is met, if so, stopping iteration and stabilizing the photovoltaic system on a duty ratio corresponding to the alpha wolf; otherwise, return to step S2 and continue the iteration.
2. The improved grayish optimization algorithm-based photovoltaic MPPT method according to claim 1, characterized by further comprising:
and S7, continuously monitoring whether the output power of the photovoltaic system changes suddenly, and returning to the step S1 to restart the algorithm when the change rate of the output power is greater than a set threshold value.
3. The improved grayish wolf optimization algorithm-based photovoltaic MPPT method according to claim 2, characterized in that the judgment formula for judging whether the output power is mutated in step S7 is as follows:
Figure FDA0003674421830000021
in the formula: p real Is the power, P, collected in real time m Is the maximum power before restart.
4. The improved grayish wolf optimization algorithm-based photovoltaic MPPT method according to claim 1, wherein the performing of the reverse search, resulting in a reverse solution to the current optimal solution, comprises:
searching for the inverse solution X of alpha wolf rd The calculation formula is as follows:
Figure FDA0003674421830000022
5. the improved grayish optimization algorithm-based photovoltaic MPPT method according to claim 1, characterized in that the termination condition includes: judging whether the algorithm reaches the maximum iteration time tmax; whether the standard deviation sigma of the whole gray wolf positions is smaller than a set threshold value theta is calculated.
6. A photovoltaic MPPT device based on improve grey wolf optimization algorithm, its characterized in that includes:
the initialization module is used for selecting N random values in [0, 1] as initial positions of the grey wolf population, and the grey wolf individuals represent duty ratios in the photovoltaic system;
the fitness calculation module is used for acquiring the output power of the photovoltaic system under the control of each duty ratio, taking the output power as the fitness of the gray wolf individuals, and recording the three gray wolfs with the highest fitness as alpha wolfs, beta wolfs and delta wolfs in sequence;
the search range contraction module is used for contracting the search range of the wolf population and updating the upper limit and the lower limit of the search interval, and the upper limit and the lower limit of the search interval [ a (t), b (t) ] are updated according to the following formula:
Figure FDA0003674421830000023
s(t)＝1-(1-λ)t/t max
where t is the current iteration algebra, X α Is the position of the alpha wolf, s (t) is a linearly decreasing function, lambda is the final convergence ratio, t max Is the maximum number of iterations of the algorithm, a (0) is 0, b (0) is 1;
a location update module, configured to perform a location update on the wolf population using an improved location update formula, where the improved location update formula is:
Figure FDA0003674421830000024
wherein, X old The method is calculated by a position updating formula of a traditional wolf optimization algorithm;
the reverse search module is used for executing reverse search, generating a reverse solution of the current optimal solution, calculating the fitness of the reverse solution, comparing the fitness with the grey wolf population fitness, and updating the alpha wolf, the beta wolf and the delta wolf;
the judging module is used for judging whether a termination condition is met, if so, stopping iteration and stabilizing the photovoltaic system on a duty ratio corresponding to the alpha wolf; otherwise, returning to the fitness calculation module and continuing iteration.
7. The improved grayish optimization algorithm-based photovoltaic MPPT apparatus according to claim 6, characterized by further comprising:
and the restarting module is used for continuously monitoring whether the output power of the photovoltaic system is suddenly changed, and when the change rate of the output power is greater than a set threshold value, the restarting module returns to the initialization module to restart the algorithm.
8. A storage medium having stored thereon a computer program executable by a processor, the computer program comprising: the computer program when executed implements the steps of the improved grayling optimization algorithm based photovoltaic MPPT method of any one of claims 1 to 5.
9. An MPPT controller having a memory and a processor, the memory having stored thereon a computer program executable by the processor, the MPPT controller comprising: the computer program when executed implements the steps of the improved grayling optimization algorithm based photovoltaic MPPT method of any one of claims 1 to 5.
10. A photovoltaic maximum power point tracking system is characterized in that: having the MPPT controller of claim 9.
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