CN116820179A - Efficient solar cell control method based on improved seagull algorithm - Google Patents

Efficient solar cell control method based on improved seagull algorithm Download PDF

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CN116820179A
CN116820179A CN202310035717.9A CN202310035717A CN116820179A CN 116820179 A CN116820179 A CN 116820179A CN 202310035717 A CN202310035717 A CN 202310035717A CN 116820179 A CN116820179 A CN 116820179A
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iteration
seagull
photovoltaic system
individual
algorithm
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刘光宇
朱凌
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Hangzhou Dianzi University
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Hangzhou Dianzi University
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05F—SYSTEMS FOR REGULATING ELECTRIC OR MAGNETIC VARIABLES
    • G05F1/00—Automatic systems in which deviations of an electric quantity from one or more predetermined values are detected at the output of the system and fed back to a device within the system to restore the detected quantity to its predetermined value or values, i.e. retroactive systems
    • G05F1/66—Regulating electric power
    • G05F1/67—Regulating electric power to the maximum power available from a generator, e.g. from solar cell
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers

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  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Power Engineering (AREA)
  • Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Automation & Control Theory (AREA)
  • Photovoltaic Devices (AREA)

Abstract

The invention discloses a high-efficiency solar cell control method based on an improved seagull algorithm. The method comprises the following steps: 1. randomly selecting a group of discrete solar photovoltaic system input values; 2. initializing the individual quantity of the seagulls and the initial position of the individual seagulls based on the input value of the photovoltaic system in the step 1; 3. converting the obtained input value of the discrete solar photovoltaic system into a continuous input value, and inputting the continuous input value into the photovoltaic system to obtain an output value of the solar photovoltaic system, wherein the output value is used as the adaptability of a seagull individual; 4. updating the individual position of the seagull according to the improved iterative formula; 5. sequencing all the obtained seagull positions; 6. judging whether the iteration termination condition is met, stopping iteration if the iteration termination condition is met, and repeating the steps 3-5 if the iteration termination condition is not met. The method disclosed by the invention improves the power oscillation and the burr phenomenon in the control process, increases the early search range and the later convergence speed, and greatly improves the power generation efficiency of the solar cell.

Description

Efficient solar cell control method based on improved seagull algorithm
Technical Field
The invention relates to a photovoltaic control technology, in particular to a tracking control method for a maximum power point of a solar photovoltaic cell.
Background
Solar photovoltaic systems are used as a common energy source for small electric equipment and are applied to all corners of towns. In China, the new energy industry also belongs to an emerging industry, and related control technologies are not mature. The solar photovoltaic modules in the current market are mostly composed of silicon wafers, and although electron hole pairs exist in the solar photovoltaic modules, the current of the single modules is not large, so that a solar photovoltaic panel needs to be arranged on a large scale to realize large power supply, and therefore, the current movable solar equipment is less.
In order to obtain larger power, workers often deploy thousands of photovoltaic cells to form a photovoltaic array to generate electricity simultaneously in an actual working scene. Under the condition of uniform illumination, the output characteristics of the photovoltaic module can show the characteristics of a single peak value, but under the condition of shielding, for example, when the photovoltaic module is covered by shadows such as surrounding buildings or trees, the output efficiency of a part of the photovoltaic modules can be reduced. When part of the components are blocked, the output current of the components is almost negligible, and meanwhile, the output current value of surrounding components is relatively high, so that the situation that the surrounding components charge the blocked components is caused, the hot spot effect is very easy to generate, and the output P-V curve of the components also has the multi-peak condition. Furthermore, aging, temperature non-uniformity, etc. have a great influence on tracking the maximum power point. In order to avoid the similar phenomenon, in the photovoltaic array, an output end of each component needs to be connected with a reverse diode in parallel, but the introduction of a bypass diode also causes the solar photovoltaic system to generate mismatching multimodal phenomenon. The traditional disturbance observation method is based on the tracking of the position relation between the actual working point and the maximum power point of the photovoltaic array in the P-V curve, so that the working point is aligned to the maximum power point, but tends to be easily trapped into the local maximum power point. In order to solve the problems of global optimization and control performance improvement of a solar photovoltaic system, the invention discloses a high-efficiency solar cell control method based on an improved seagull optimization algorithm, and the method has a wide application value.
Disclosure of Invention
The invention aims at overcoming the defects of the prior art and provides a high-efficiency solar cell control method based on an improved seagull algorithm.
In order to achieve the technical purpose, the invention adopts the following technical scheme:
step 1, randomly selecting a group of discrete solar photovoltaic system input values.
And 2, initializing the individual quantity of the seagulls and the initial position of the individual seagulls based on the input value of the photovoltaic system in the step 1.
And step 3, converting the obtained input value of the discrete solar photovoltaic system into a continuous input value, and inputting the continuous input value into the photovoltaic system to obtain an output value of the solar photovoltaic system, wherein the output value is used as the adaptability of the individual seagulls.
And step 4, updating individual seagull positions according to the improved iteration formula.
And 5, sequencing all the obtained seagull positions.
And step 6, judging whether the iteration termination condition is met, stopping iteration if the iteration termination condition is met, and repeating the steps 3, 4 and 5 if the iteration termination condition is not met.
Firstly, the step 1 is specifically implemented as follows:
wherein ,for the first iteration the i-th input value, d max Inputting maximum value for photovoltaic system, d min The minimum value is input to the photovoltaic system, rand is a random number of 0-1, and n is the number of the output of the improved seagull algorithm iterative calculation.
The step 2 is to initialize the number n of seagulls and the initial position of individual seagulls
The step 3 is specifically to utilize a discrete-continuous algorithm to obtain the input value of the discrete photovoltaic systemThe method is converted into a continuous input quantity u (t), and is concretely realized as follows:
wherein ,and inputting a signal for the photovoltaic system at the ith moment of the kth iteration. Continuous input signal +.>The output value of the kth photovoltaic system can be obtained when the output value is input into the solar photovoltaic system>
Said step 4 comprises the sub-steps of:
(4.1) to avoid collisions between different seagull individuals, the seagull positions are updated as follows:
in the formula ,indicating the position of the sea gull where no collision will occur, < ->The method is characterized in that the current individual position of the seagull is represented, t is the iteration number of the seagull population, A represents the movement parameter of the individual of the seagull, and the method is concretely realized as follows:
wherein ε represents the constraint coefficient, k max Representing the maximum number of iterations.
(4.2) in order to ensure that each seagull in the population moves towards the optimal individual direction, the following is realized:
wherein ,Pbs For the best in the iterative processBody position, P s As the location of the current individual,representing the change in position in the position update process based on the position guidance of the best individual, parameter B represents the balance parameter approaching the best individual, and is implemented as follows:
B＝2×A 2 ×c (6)
wherein c represents a random variable ranging from [0,1 ].
(4.3) updating the individual seagull positions based on the step (4.1) and the step (4.2), and the specific implementation is as follows:
wherein ,representing the adjustment distance between the gull individuals and the optimal individuals in the population.
Judging the individual adaptability of the obtained seagullsWhether or not it is the best fitness value y best The method is concretely realized as follows:
and step 5 is specifically to sort the k+1st solar photovoltaic system input values solved in step 4 by using a sorting algorithm.
Further, the step 5 includes the following sub-steps:
(5.1) determining the order at the kth iteration as ascending or descending.
(5.2) if the order at the kth iteration is in ascending order, the order at the (k+1) th iteration is in descending order; if the order of the kth iteration is descending, the order of the (k+1) th iteration is ascending; if this time is the first iteration, then ascending sort is performed. The specific implementation steps of the step (5.2) are as follows:
and 6, judging whether the method meets the condition of ending iteration or not. If the algorithm meets the condition of ending the iteration, ending the iteration of the algorithm; if the algorithm does not meet the iteration termination condition, repeating the iteration steps 3, 4 and 5 until the iteration termination condition in the step 6 is met. The iteration termination condition described in step 5 is as follows:
wherein ,and inputting the input value of the solar photovoltaic system obtained by the k-th iteration into the solar photovoltaic system to obtain the maximum power output value. d, d i k The ith solar photovoltaic system input value for the kth iteration is represented.
The invention has the following beneficial effects:
the individual searched by the basic seagull algorithm has the characteristic that the spiral is close to the optimal value in the searching process, and the characteristic enables the phenomenon that the seagull algorithm easily falls into the local extremum when searching the global optimal. Under the condition that the P-V characteristic of the photovoltaic array shows multimodal characteristics, MPPT control is performed by using a seagull algorithm with a certain probability, so that the output power of the photovoltaic array is converged to a local optimal value, the output power is reduced, and the output power of the photovoltaic array has larger oscillation and burr phenomena in the searching process. The high-efficiency solar cell control method based on the improved seagull algorithm improves power oscillation and burr phenomena in the control process, increases the early search range and the later convergence speed, and greatly improves the power generation efficiency of the solar cell.
Drawings
FIG. 1 is a flow chart of a maximum power point tracking control method
FIG. 2 is a case result diagram of the maximum power point tracking control method
Detailed Description
The invention is further described below with reference to the accompanying drawings.
The invention aims at overcoming the defects of the prior art and provides a high-efficiency solar cell control method based on an improved seagull algorithm.
In order to achieve the technical purpose, the invention adopts the following technical scheme:
step 1, randomly selecting a group of discrete solar photovoltaic system input values.
And 2, initializing the individual quantity of the seagulls based on the step 1 and the step 2, and initializing the individual initial positions of the seagulls.
And step 3, converting the obtained input value of the discrete solar photovoltaic system into a continuous input value, and inputting the continuous input value into the photovoltaic system to obtain an output value of the solar photovoltaic system, wherein the output value is used as the adaptability of the individual seagulls.
And step 4, updating individual seagull positions according to the improved iteration formula.
And 5, sequencing the obtained seagull positions.
And step 6, judging whether the iteration termination condition is met, stopping iteration if the iteration termination condition is met, and repeating the steps 3, 4 and 5 if the iteration termination condition is not met.
Firstly, the step 1 is specifically implemented as follows:
wherein ,for the first iteration the i-th input value, d max Inputting maximum value for photovoltaic system, d min The minimum value is input to the photovoltaic system, rand is a random number of 0-1, and n is the number output by one iteration of the algorithm.
The step 2 is concretely to initialize the seaGull number n and gull individual initial position
The step 3 is specifically to utilize a discrete-continuous algorithm to obtain the input value of the discrete photovoltaic systemThe method is converted into a continuous input quantity u (t), and is concretely realized as follows:
wherein ,and inputting a signal for the photovoltaic system at the ith moment of the kth iteration. Continuous input signal +.>The output value of the kth photovoltaic system can be obtained when the output value is input into the solar photovoltaic system>
Said step 4 comprises the sub-steps of:
(4.1) to avoid collisions between different seagull individuals, the seagull positions are updated as follows:
in the formula ,indicating the position of the sea gull where no collision will occur, < ->The individual position of the current seagull is represented, t isThe iteration coefficient of the seagull population, A represents the movement parameter of individual seagull, and the implementation is as follows:
(4.2) in order to ensure that each seagull in the population moves towards the optimal individual direction, the following is realized:
wherein Pbs For the location of the best individual in the iterative process,representing the change in position in the position update process based on the position guidance of the best individual, parameter B represents the balance parameter approaching the best individual, and is implemented as follows:
B＝2×A 2 ×c (16)
wherein c represents a random variable ranging from [0,1 ].
(4.3) updating the individual seagull positions based on the step (4.1) and the step (4.2), and the specific implementation is as follows:
wherein Representing the adjusted distance between the population of individuals and the best individual.
Judging the individual adaptability of the obtained wolvesWhether the value is the optimal fitness value is specifically realized as follows:
and step 5 is specifically to sort the k+1st solar photovoltaic system input values solved in step 6 by using a sorting algorithm.
Further, the step 5 includes the following sub-steps:
(5.1) determining whether the order at the kth iteration is in ascending or descending order.
(5.2) if the order at the kth iteration is in ascending order, the order at the (k+1) th iteration is in descending order; if the order of the kth iteration is descending, the order of the (k+1) th iteration is ascending; if this time is the first iteration, then ascending sort is performed. The specific implementation steps of the step (5.2) are as follows:
and 6, judging whether the method meets the condition of ending iteration or not. If the algorithm meets the condition of ending the iteration, ending the iteration of the algorithm; if the algorithm does not meet the iteration termination condition, repeating the iteration steps 3, 4 and 5 until the iteration termination condition in the step 6 is met. The iteration termination condition in the fifth step is as follows:
wherein ,and inputting the input value of the solar photovoltaic system obtained by the k-th iteration into the solar photovoltaic system to obtain the maximum power output value.
Example 1:
the solar photovoltaic system is easy to work and is easy to be interfered by the environment, and the load resistance value is required to be changed continuously to realize maximum power generation. In general, the DC/DC chopper is used for realizing impedance matching of a load, and the DC/DC chopper and the load resistor are regarded as a whole in use, and the equivalent resistance is thatThe actual resistance of the photovoltaic array access is adjusted by adjusting the on-off time proportion of the DC/DC chopper. The control method disclosed by the invention can quickly find the maximum power of the solar photovoltaic systemInput duty cycle corresponding to power generation +.>And the searching speed is high, and the buffeting of the system is small. As shown in fig. 2, the method proposed by the present invention only generates 3 oscillations in the process of finding the optimal input duty cycle, and the optimal input duty cycle is found only with 0.03 s. Therefore, the method disclosed by the invention greatly improves the dynamic performance and the steady-state performance of the solar photovoltaic system, and improves the power generation efficiency of the solar photovoltaic system.

Claims (6)

1. The high-efficiency solar cell control method based on the improved seagull algorithm is characterized by comprising the following steps of:
step 1, randomly selecting a group of discrete solar photovoltaic system input values;
step 2, initializing the individual quantity of the seagulls and the initial position of the individual seagulls based on the input value of the photovoltaic system in the step 1;
step 3, converting the obtained input value of the discrete solar photovoltaic system into a continuous input value, and inputting the continuous input value into the photovoltaic system to obtain an output value of the solar photovoltaic system, wherein the output value is used as the adaptability of a seagull individual;
step 4, updating individual seagull positions according to an improved iteration formula;
step 5, sequencing all the obtained seagull positions;
and step 6, judging whether the iteration termination condition is met, stopping iteration if the iteration termination condition is met, and repeating the steps 3-5 if the iteration termination condition is not met.
2. The method for controlling the high-efficiency solar cell based on the improved seagull algorithm as claimed in claim 1, wherein the step 1 is specifically implemented as follows:
wherein ,for the first iteration the i-th input value, d max Inputting maximum value for photovoltaic system, d min The minimum value is input to the photovoltaic system, rand is a random number of 0-1, and n is the number of the output of the improved seagull algorithm iterative calculation.
3. The method for controlling a solar cell with high efficiency based on the improved gull algorithm as claimed in claim 2, wherein the step 3 is specifically to use a discrete-continuous algorithm to obtain the input value d of the discrete photovoltaic system i k The method is converted into a continuous input quantity u (t), and is concretely realized as follows:
wherein n is the number of initialized seagulls,the initial position of the sea gull is the individual initial position; />For the input signal of the photovoltaic system at the ith moment of the kth iteration, the continuous input signal +.>The output value of the kth photovoltaic system can be obtained when the output value is input into the solar photovoltaic system>
4. The method for controlling the solar cell according to claim 3, wherein the step 4 is specifically implemented as follows:
(4.1) to avoid collisions between different seagull individuals, the seagull positions are updated as follows:
in the formula ,indicating the position of the sea gull where no collision will occur, < ->The method is characterized in that the current individual position of the seagull is represented, t is the iteration number of the seagull population, A represents the movement parameter of the individual of the seagull, and the method is concretely realized as follows:
wherein ε represents the constraint coefficient, k max Representing a maximum number of iterations;
(4.2) in order to ensure that each seagull in the population moves towards the optimal individual direction, the following is realized:
wherein ,Pbs For the position of the best individual in the iterative process, P s As the location of the current individual,representing the change in position in the position update process based on the position guidance of the best individual, parameter B represents the balance parameter approaching the best individual, and is implemented as follows:
B＝2×A 2 ×c (6)
wherein c represents a random variable ranging from [0,1 ];
(4.3) updating the individual seagull positions based on the step (4.1) and the step (4.2), and the specific implementation is as follows:
wherein ,representing the adjustment distance between the gull individuals and the optimal individuals in the population;
judging the individual adaptability of the obtained seagullsWhether or not it is the best fitness value y best The method is concretely realized as follows:
5. the method for controlling the high-efficiency solar cell based on the improved seagull algorithm according to claim 4, wherein the step 5 is specifically to sort the k+1st solar photovoltaic system input values solved in the step 4 by using a sorting algorithm, and specifically comprises the following steps:
(5.1) judging the order of the kth iteration to be ascending or descending;
(5.2) if the order at the kth iteration is in ascending order, the order at the (k+1) th iteration is in descending order; if the order of the kth iteration is descending, the order of the (k+1) th iteration is ascending; if the iteration is the first iteration, performing ascending sort; the specific implementation steps of the step (5.2) are as follows:
6. the method for controlling the solar cell with high efficiency based on the improved seagull algorithm as claimed in claim 5, wherein the step 6 is specifically implemented as follows:
if the algorithm meets the condition of ending the iteration, ending the iteration of the algorithm; if the algorithm is judged not to meet the iteration termination condition, repeating the iteration steps 3-5 until the iteration termination condition in the step 6 is met; the iteration termination condition described in step 6 is as follows:
wherein ,the maximum power output value obtained after the input value of the solar photovoltaic system solved for the kth iteration is input into the solar photovoltaic system; d, d i k The ith solar photovoltaic system input value for the kth iteration is represented.
CN202310035717.9A 2023-01-10 2023-01-10 Efficient solar cell control method based on improved seagull algorithm Pending CN116820179A (en)

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