CN115729307A - Photovoltaic array reconstruction method and device based on dandelion optimization algorithm - Google Patents
Photovoltaic array reconstruction method and device based on dandelion optimization algorithm Download PDFInfo
- Publication number
- CN115729307A CN115729307A CN202211441214.3A CN202211441214A CN115729307A CN 115729307 A CN115729307 A CN 115729307A CN 202211441214 A CN202211441214 A CN 202211441214A CN 115729307 A CN115729307 A CN 115729307A
- Authority
- CN
- China
- Prior art keywords
- photovoltaic array
- dandelion
- algorithm
- photovoltaic
- stage
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Landscapes
- Control Of Electrical Variables (AREA)
Abstract
The invention discloses a photovoltaic array reconstruction method and device based on a dandelion optimization algorithm, belonging to the technical field of photovoltaic power generation, wherein the method comprises the following steps: establishing an initial photovoltaic array configured in an n x n mesh connection; and reconstructing the initial photovoltaic array under the shielding condition by utilizing a dandelion optimization DO algorithm to obtain a target photovoltaic array. According to the invention, the DO algorithm is applied to photovoltaic reconstruction, so that the photovoltaic power station can output the maximum power under different irradiation conditions, that is, the output power of a photovoltaic array under a shielding condition can be improved, and the efficiency of a photovoltaic power generation system is improved; therefore, the technical problem that the output characteristic of the photovoltaic array is seriously influenced by the cloud coverage condition is solved, and the operation economy of the photovoltaic power station and the stability of grid-connected operation are obviously improved. In addition, scheduling staff can change the internal connection condition of the photovoltaic array through the optimal OAR scheme obtained by the DO algorithm, and the current photovoltaic power station can be guaranteed to operate efficiently.
Description
Technical Field
The invention belongs to the technical field of photovoltaic power generation, and particularly relates to a photovoltaic array reconstruction method and device based on a dandelion optimization algorithm.
Background
In the world, as the demand for energy is increasing and the reserves of fossil energy are decreasing, renewable energy is gaining more and more attention and is gradually replacing most fossil fuels, wherein one of the most promising energy is solar energy. The photovoltaic cell is the core of the photovoltaic power generation system, and the conversion efficiency of the photovoltaic cell determines the practical application capacity of the whole system. The application of photovoltaic power generation systems is complex, and a single photovoltaic cell element cannot meet the power supply requirements of a power system. Therefore, it is necessary to connect a plurality of photovoltaic cell structures together to form a photovoltaic cell array to ensure maximum utilization of the photovoltaic system.
In a photovoltaic power generation system, a photovoltaic cell array may be understood as a combination of photovoltaic cell elements. The higher the number of connected photovoltaic cell elements in the series, the higher the power of the photovoltaic cell array and the greater the conversion capacity, with a constant light intensity. However, mismatch losses and power losses due to partial shading can result in a significant reduction in the energy output of the photovoltaic array, while also resulting in a reduced lifetime of the photovoltaic array. When seeking maximum power output of the photovoltaic array, the power output of the photovoltaic array under the local shadow can be significantly improved by compensating for these power losses by using a reconstruction technique based on a heuristic algorithm. Photovoltaic arrays are widely used as the most common means of obtaining solar energy. The photovoltaic cell is a nonlinear device at the core of photovoltaic power generation, and the output characteristic of the photovoltaic cell enables a photovoltaic array to work under a certain working voltage to generate maximum output power. Some unavoidable damaging factors greatly reduce the efficiency of the photovoltaic array. The Partial Shading Condition (PSC) is one of the conditions, and not only causes the output power curve to have multiple peaks, but also causes damage to the photovoltaic panel, possibly reducing the service life of the photovoltaic panel.
In practical terms, the shielding of large photovoltaic arrays is mainly caused by clouds, the shape and position of which constantly change over time. The output characteristics of a photovoltaic array are severely affected by cloud cover conditions.
Disclosure of Invention
Aiming at the defects or the improvement requirements in the prior art, the invention provides a photovoltaic array reconstruction method and a photovoltaic array reconstruction device based on a dandelion optimization algorithm (DO), aiming at improving the output power of a photovoltaic array under a shielding condition by using an Optical Array Reconstruction (OAR) method based on the dandelion optimization algorithm (DO), so as to improve the efficiency of a photovoltaic power generation system, and thus solving the technical problem that the output characteristic of the photovoltaic array is seriously influenced by the cloud coverage condition.
In order to achieve the above object, according to an aspect of the present invention, there is provided a photovoltaic array reconstruction method based on a dandelion optimization algorithm, including:
s1: establishing an initial photovoltaic array configured by n x n mesh connections;
s2: reconstructing the initial photovoltaic array under the shielding condition by utilizing a dandelion optimization DO algorithm to obtain a target photovoltaic array; the method comprises the following specific steps:
s21: initializing the position of a population, and setting system parameters related to a boundary value, the population quantity and the iteration times;
s22: calculating the fitness value of each solution and selecting the optimal fitness value;
s23: in the dandelion rising stage, updating the position according to an iterative formula in the rising stage;
s24: a dandelion descending stage, wherein the position is updated by using an iterative formula of the descending stage;
s25: in the landing stage of the dandelion, the position is updated by using an iterative formula in the landing stage;
s26: outputting an optimal result when the iteration times reach a threshold, otherwise, repeating S22-S25 until the iteration times reach the threshold, and outputting a corresponding optimal result; and taking the optimal result as the target photovoltaic array.
In one embodiment, the initial photovoltaic array in S1 is represented as:
wherein, V out Is the output voltage of the initial photovoltaic array; i is out Is the output current of the initial photovoltaic array; v maxa Is the row a maximum output voltage; i is ab Is the node current for row a and column b.
In one embodiment, the iterative formula of the rise phase is represented as:
on a sunny day, the wind speed can be regarded as lognormal distribution lnY-N (mu, sigma) 2 )，X t+1 ＝X t +a*v x *v y *lnY*(X s -X t )，X t Representing the positions of dandelion seeds in an iterative process; xs denotes a randomly selected position in the search space during iteration t, X s = rand (1, dim) × (UB-LB) + LB, lnY denotes obedience μ =0 and σ 2 A log-normal distribution of =1,
y represents a standard normal distribution N (0, 1); a is an adaptive parameter for adjusting the search step size,
randn () is a random number that conforms to a standard normal distribution; v. of x And v y Showing the coefficient of the lift component, v, of the dandelion due to the vortex effect of the separation x ＝r*cosθ，y x ＝r*sinθ，
Theta has a value in the range of [ -pi, pi [ -pi [ ]]；
In case of rain, the update position is expressed as: x t+1 ＝X t * k; k is used to normalize the dandelion's local search field, k =1-rand ()' q,
in one embodiment, the iterative formula for the descent phase is represented as:
X t+1 ＝X t -a*β t *(X mean_t -a*β t *X t )；
in the formula, beta t Representing brownian motion, is a random number from a standard normal distribution; x mwan_t Indicating the average position of the population in the ith iteration,
in one embodiment, the iterative formula for the landing phase is represented as: x t+1 ＝X elite -levy(λ)*a*(X elite -X t *δ)；
Wherein, X elite Representing the optimal position of dandelion seeds in i iterations; levy (λ) denotes the Levy function
Is [0,2]]A random number in between; s is a fixed constant; w and τ are [0,1]A random number in between, and a random number,
beta is fixed to be 1.5; delta is [0,2]]A linear increasing function of the number of the first and second,
in one embodiment, the method further comprises:
s3: and analyzing and evaluating the performance parameters of the target photovoltaic array by adopting three evaluation indexes of mismatch loss, filling factor and standard deviation.
In one embodiment, the mismatch loss is expressed as: p is mi ＝P G(unshaded) -P G(shaded) ；P G(unshaded) Maximum output power, P, of the target photovoltaic array without shading G(shaded) Is the maximum output power of the shaded target photovoltaic array;
the fill factor is expressed as:
V p and I p Voltage and current at the local maximum power point; v o And I s Is the open circuit voltage and short circuit current of the target photovoltaic array;
the maximum output power corresponding to the standard deviation is:
I a and V a Respectively representing the output current and the output voltage of the a-th row.
According to another aspect of the present invention, there is provided a photovoltaic array reconstruction apparatus based on a dandelion optimization algorithm, for executing the photovoltaic array reconstruction method, including:
the system comprises an establishing module, a calculating module and a calculating module, wherein the establishing module is used for establishing an initial photovoltaic array of an n multiplied by n mesh connection configuration;
the reconstruction module is used for reconstructing the initial photovoltaic array under the shielding condition by using a dandelion optimized DO algorithm to obtain a target photovoltaic array, and is specifically used for:
initializing the position of a population, and setting system parameters related to a boundary value, the population quantity and the iteration times;
calculating the fitness value of each solution, and selecting the optimal fitness value;
in the dandelion rising stage, updating the position according to an iterative formula in the rising stage;
in the dandelion descending stage, the position is updated by using an iterative formula in the descending stage;
in the dandelion landing stage, the position is updated by using an iterative formula in the landing stage;
outputting an optimal result when the iteration times reach a threshold value, otherwise, repeating S22-S25 until the iteration times reach the threshold value, and outputting a corresponding optimal result; and taking the optimal result as the target photovoltaic array.
According to another aspect of the invention, an electronic device is provided, comprising a memory storing a computer program and a processor implementing the steps of the method when executing the computer program.
According to another aspect of the invention, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method.
In general, compared with the prior art, the above technical solution contemplated by the present invention can achieve the following beneficial effects:
1. the invention provides a photovoltaic array reconfiguration (OAR) method based on a dandelion optimization algorithm (DO), wherein the DO algorithm is applied to photovoltaic reconfiguration, so that a photovoltaic power station can output maximum power under different irradiation conditions, namely the output power of a photovoltaic array under a shielding condition can be improved, and the efficiency of a photovoltaic power generation system is improved; therefore, the technical problem that the output characteristic of the photovoltaic array is seriously influenced by the cloud coverage condition is solved, and the operation economy of the photovoltaic power station and the stability of grid-connected operation are obviously improved.
2. The scheduling staff can change the internal connection condition of the photovoltaic array through the optimal OAR scheme obtained by the DO algorithm, and the current photovoltaic power station can be guaranteed to operate efficiently.
Drawings
FIG. 1 is a diagram of a photovoltaic array reconstruction model of a TCT structure in accordance with an embodiment of the present invention;
FIG. 2 is a flow chart of an OAR based DO algorithm in one embodiment of the present invention;
FIG. 3 is a graph of the light shading for an initial photovoltaic array within 9 minutes according to one embodiment of the present invention;
fig. 4 is a graph of the light shading condition corresponding to the photovoltaic array optimized based on the DO algorithm within 9 minutes in the embodiment of the present invention;
fig. 5a and 5b are the output I-U and P-U curves for 9 minutes before and after optimization of the various algorithms and the photovoltaic array, respectively.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The invention provides a photovoltaic array reconfiguration (OAR) method based on a Dandelion Optimizer (DO) algorithm, which is used for improving the output power of a photovoltaic array and improving the efficiency of a photovoltaic power generation system. The method comprises the following steps:
the invention establishes a photovoltaic array reconstruction model of 9 multiplied by 9 mesh connection configuration, and the method comprises the following steps:
in this work a 9 x 9TCT connected photovoltaic array was used, as shown in figure 1. TCT configurations are the most widely used connections, proven to be the most stable topology of photovoltaic arrays, and can exhibit better performance when faced with PSCs. It is worth noting that this configuration technique does not change the original position of the photovoltaic array, but changes its electrical connections. The total output voltage of the photovoltaic array can be written as the following equation:
in the formula, V out Is the photovoltaic array output voltage; I.C. A out Is the photovoltaic array output current; v maxa Is the maximum output voltage of row a; i is ab Is the node current for row a and column b.
The present invention utilizes three evaluation criteria to measure the simulation results of the proposed DO method.
(1) Mismatch loss:
P mi ＝P G(unshaded) -P G(shaded) (3)
in the formula, P G(unshaded) Maximum output power of unshaded photovoltaic model, P G(shaded) Is the maximum output power of the shaded photovoltaic model.
(2) Fill factor
The fill factor is a key criterion for representing the power loss of a photovoltaic system under PSC, and the formula is as follows:
in the formula, V p And I p Voltage and current at local maximum power point; v o And I s Are the open circuit voltage and the short circuit current of the photovoltaic array, which are generally given by the manufacturer.
(3) Standard deviation of rotation
The invention adopts standard deviation (STD) to evaluate the reconfiguration stability of the heuristic algorithm.
When photovoltaic arrays are operating properly under PSC conditions, they can operate at a point that is offset from their maximum power point, resulting in a drop in output power. In order to reduce the influence of mismatch loss caused by PSC occurrence, a reconstruction method is adopted to homogenize irradiation of a photovoltaic array, so that the output power is maximized, which can be expressed as:
in the formula I a And V a Respectively representing the output current and the output voltage of each row.
The DO algorithm simulates the flight process of dandelion seeds, and dandelion is one of the most representative plants which depend on wind for seed propagation. Under appropriate conditions, its seeds can fly for tens of kilometers with the wind. When the dandelion seed flies, it will form two vortices, creating an upward drag. The two vortices above become larger and symmetrical as the seeds fall at a lower speed. A symmetrical vortex ensures the stable falling of the seeds; that is, the filaments are level with the ground and the fruit is pointed downward. The dandelion seeds fly a great distance and they need to be kept at a relatively stable height. The vortex of separation is maintained at a fixed distance below the dandelion crown. Surprisingly, the porosity of the taraxacum crown appears to be precisely adjusted to stabilize the swirl ring. The crown bristles are made of elongated filaments radiating outwardly from a central handle, similar to spokes on a bicycle wheel. This consistency is critical to vortex stabilization above dandelion seeds and thus helps to keep seeds stable during long flights. Wind speed and weather are two major factors affecting dandelion seed transmission. Wind speed is used to determine whether the seed flies far or short. Weather controls whether dandelion seeds can fly or not, and influences the ability of dandelion to grow in nearby or distant spaces. Dandelion seeds are propagated through three stages, as follows. In the rising stage, a vortex is generated above the dandelion seeds and rises under the action of the drag force in sunny days and windy days. Conversely, if the weather is rainy, there will be no eddies on the seeds. In this case, the search can only be performed locally. In the descent phase, the seeds descend steadily as they rise to a certain height. In the landing stage, dandelion seeds finally fall randomly in a place under the influence of wind and weather, and new dandelion grows. Dandelion carries out population evolution in three stages by transferring seeds to the next generation. The DO algorithm operates as follows.
(1) Initialization
Similar to other natural heuristic algorithms, the DO algorithm realizes population evolution and iterative optimization on the basis of population initialization. In the proposed DO algorithm, it is assumed that each dandelion seed represents a candidate solution, whose population is represented as:
where pop represents the population size and Dim is the dimension of the variable; each candidate solution is a randomly generated Upper Bound (UB) and Lower Bound (LB) of the given problem, the ith candidate solution x i Comprises the following steps:
X i ＝rand×(UB-LB)+LB (7)
wherein i is an integer between 1 and pop, and rand represents a random number between (0, 1). LB and UB are expressed as:
LB＝[ιb 1 ，...，ιb Dim ]，UB＝[ub 1 ，...，ub Dim ] (8)
during initialization, DO considers an individual as the optimal fitness value as the initial elite, which is approximately considered to be the most suitable location for dandelion seed to grow. Taking the minimum value as an example, an initial elite X of a mathematical expression is given elite Comprises the following steps:
f best ＝min(f(X i ))，X elite ＝X(find(f best ＝＝f(X i ))) (9)
where find () represents two indices having equal values.
(2) Rising phase
In the rise phase, dandelion seeds need to reach a certain height in order to float away from their mother plant. The dandelion seeds rise to different heights under the influence of wind speed, air humidity, etc. The weather here is divided into the following two cases.
In the first case, on a sunny day, the wind speed can be regarded as lognormal distribution lnY-N (mu, sigma) 2 ). Under this distribution, the random numbers are more distributed along the Y-axis, which increases the chances of dandelion seeds spreading to distant areas. Therefore, in this case, DO emphasizes exploration. In the search space, it is possible to search for,the dandelion seeds are blown randomly to different positions by wind. The rising height of dandelion seeds is determined by the wind speed. The stronger the wind, the higher the dandelion flies, and the farther the seeds are scattered. Under the influence of wind speed, the vortex above the dandelion seeds is continuously adjusted, so that they rise in a spiral shape. The corresponding mathematical expression in this case is:
X t+1 ＝X t +a*v x *v y *lnY*(X s -X t ) (10)
in the formula, X t Representing the positions of dandelion seeds in an iterative process; xs denotes a randomly selected position in the search space during iteration t. Equation (11) provides an expression for the randomly generated position.
X s ＝rand(1，Dim)*(UB-LB)+LB (11)
lnY denotes obedience to μ =0 and σ 2 Lognormal distribution of =1, whose mathematical formula is:
in the formula, y represents a normal distribution N (0, 1). a is the adaptive parametric mathematical expression for adjusting the search step size is:
in which a is [0,1]]The random parameter in between, in the non-linear decreasing process, approaches 0. The fluctuation enables the algorithm to focus on global search in the early stage and turn to local search in the later stage, and the algorithm is favorable for ensuring accurate convergence after the global search is completed. v. of x And v y Showing the coefficient of the lift component of the dandelion due to the detached vortex effect. The force variable is calculated using equation (14).
In the formula, the value range of theta is [ -pi, pi ].
In the second case, in rainy days, dandelion seeds cannot properly rise with the wind due to the influence of factors such as air resistance, humidity and the like. In this case, dandelion seeds were developed in their local area, with the corresponding mathematical expression:
X t+1 ＝X t *k (15)
where k is used to specify the dandelion local search field and equation (16) is used to calculate the definition field.
In the formula, k shows downward convex oscillation, which is beneficial to the algorithm that the stride is large in the early stage and the stride is small in the later stage. And when the iteration is finished, the parameter k gradually approaches to 1 so as to ensure that the population finally converges to the optimal search area.
In summary, the mathematical expression of the rising stage of dandelion seeds is:
in the formula, randn () is a random number that conforms to a standard normal distribution.
Firstly, in sunny weather, the dandelion seeds are updated according to randomly selected position information, and the searching process is emphasized. The vortex above the seed acts on the movement vector by multiplying by the x and y components to correct the direction in which the dandelion is moving in the helix. In the second case, dandelion seeds are widely utilized in local populations. And the development and exploration are dynamically controlled by utilizing the normal distribution of the random numbers. To make the algorithm more global search-oriented, the cut-off point is set to 1.5. This arrangement allows the dandelion seeds to traverse the entire search space as much as possible in the first stage, providing the right direction for the iterative optimization in the next stage.
(3) Lowering phase
At this stage, the proposed DO algorithm also emphasizes exploration. The dandelion seeds steadily descend after rising to a certain distance. In DO, the Brownian motion is used to simulate the trajectory of dandelion. Since brownian motion follows a normal distribution at each change, individuals easily traverse more search populations during the iterative update process. To reflect the decreasing stability of the dandelion, the average position information after the rising stage was used. This contributes to the development of a promising population for the population as a whole. The corresponding mathematical expression is:
X t+1 ＝X t -a*β t *(X mean_t -a*β t *X t ) (18)
in the formula, beta t Which represents brownian motion, is a random number from a standard normal distribution. X mean_t Represents the average position of the population in the ith iteration and its mathematical expression is:
the above formula shows the regeneration process of dandelion seeds during descent. The average position information of the population is important for iterative updating of the individuals, and directly determines the evolution direction of the individuals. This irregular movement causes the search agent to escape from local extrema with a high probability during the iterative update process, thereby forcing the population to search toward the globally optimal vicinity.
(4) And (3) a landing stage:
according to the first two stages, dandelion seeds were randomly selected where to fall. As the iteration progresses, the algorithm is likely to converge to a globally optimal solution. Therefore, the optimal solution obtained is the approximate location where dandelion seeds are most likely to survive. To converge accurately to global optima, search media borrow the outstanding information of the current best solution, developing in their vicinity. With the evolution of the population, a global optimal solution can be finally found. This behavior is shown in equation (20).
X t+1 ＝X elite -levy(λ)*a*(X elite -X t *δ) (20)
In the formula, X elite Indicates the species of Dandelion in i iterationsThe optimal position of the child; levy (λ) represents the Levy function, calculated using equation (21):
wherein beta is a random number between [0,2 ]; s is a fixed constant of 0.01; w and t are random numbers between [0,1 ]. The mathematical expression of σ is:
in the formula, β is fixed to 1.5.δ is a linear increasing function between [0,2], calculated by equation (23).
In order to accurately converge to the global optimum, a linear increasing function is adopted for the individuals, and excessive development is avoided. This stage uses the Levy flight coefficients to model individual motion steps. The reason is that under gaussian distribution, levy flight coefficients can be spanned by the medium to other locations with a large probability, developing more local search domains under a limited number of iterations.
As shown in fig. 2, the algorithm flow of DO is:
(1) Initializing the position of a population, and setting a boundary value, the number of the population, the iteration times and the like;
(2) Calculating the fitness value of each solution, and selecting the optimal fitness value;
(3) A dandelion rising stage, and updating the position according to the formulas (10) - (17);
(4) In the dandelion descending stage, updating the positions by using the formulas (18) to (19);
(5) A dandelion landing stage, and the positions are updated by utilizing (20) - (23);
(6) And judging whether the iteration times are reached, if so, outputting an optimal result, and otherwise, repeating the second step to the fifth step.
4. The invention adopts an ASW-280M solar photovoltaic panel, and the specific parameters are shown in the following table 1.
TABLE 1 specific parameters of photovoltaic modules
To verify the effectiveness of the reconstruction algorithm, different sizes of photovoltaic arrays and different shading patterns are often simulated. This section uses the most commonly used 9 x 9 photovoltaic array to verify the effect of DO and performs a 9 minute moving cloud shadow to fit the simulation experiment herein. The simulation tool used was MATLAB 2021b. And constructing reconstruction methods such as GA, PSO and the like for performance comparison. The run time, number of iterations, and overall number of DO algorithms are set to 20, 200, and 20. Fig. 3 shows the photovoltaic array shaded for OAR within 9 minutes.
The invention aims to reconstruct the shielded photovoltaic array by introducing a heuristic algorithm, change the connection mode of the shielded photovoltaic array according to a simulation result, increase the output power and improve the operation benefit of a system. The present invention introduces a 25 megawatt photovoltaic power plant with 20 identical subsystems, each consisting of a photovoltaic array in a 9 x 9TCT configuration, to evaluate the performance of the invention. The introduced partially shaded photovoltaic array simulates slow movement of the cloud in nine minutes, with different colors representing different irradiance. The working temperature is set to be 25 ℃, and the irradiation distribution of the photovoltaic array per minute is shown in figure 4.
And when the DO algorithm is adopted to optimize and reconstruct the photovoltaic array, the internal connection of the photovoltaic array is changed according to the optimal optimization result of DO algorithm simulation. The maximum optimized output power and the minimum output power obtained by the simulation of each heuristic algorithm, and the mismatch loss and the fill factor of each method are shown in table 2. Among all algorithms, the DO algorithm obtains P max And P min All higher than the other algorithms. And alsoThe STD of the DO algorithm is smaller than that of other algorithms, which shows that the DO algorithm has higher stability. It is clear that mismatch loss and ff of the DO perform better in all algorithms. The optimal mismatch loss obtained with the DO algorithm was 38.13%, 3.75% and 0.51% lower than before optimization, GA and PSO, respectively. The optimal ff obtained by using the DO algorithm is respectively improved by 20.70%, 1.15% and 0.13% compared with that before optimization, GA and PSO.
Fig. 5a and 5b are the output I-U and P-U curves for 9 minutes before and after optimization of the various algorithms and the photovoltaic array, respectively. It was observed that the more shading, the less smooth the output characteristic curve. The output I-U curve typically has many inflection points and the output P-U curve has many peaks. Taking minute 4 as an example, there are 6 power peaks before reconfiguration. DO reduces the number of energy peaks to one, which completely clears the local peaks. Thus, DO achieves significant reconstruction effects compared to other methods of the present invention.
A DO-based OAR technique is presented. The method is realized by discretizing an original DO algorithm and combining a photovoltaic reconstruction method. DO may provide an optimal solution in real time, thereby avoiding a locally optimal solution. The superiority of DO is demonstrated by quantitatively comparing the output power, mismatch loss, fill factor, standard deviation and output characteristic curve of DO, GA and PSO.
TABLE 2 comparison of simulation results for each algorithm
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A photovoltaic array reconstruction method based on a dandelion optimization algorithm is characterized by comprising the following steps:
s1: establishing an initial photovoltaic array configured in an n x n mesh connection;
s2: reconstructing the initial photovoltaic array under the shielding condition by utilizing a dandelion optimization DO algorithm to obtain a target photovoltaic array; the method comprises the following specific steps:
s21: initializing the position of a population, and setting system parameters related to a boundary value, the population quantity and the iteration times;
s22: calculating the fitness value of each solution and selecting the optimal fitness value;
s23: a dandelion rising stage, wherein the position is updated according to an iterative formula of the rising stage;
s24: in the dandelion descending stage, the position is updated by using an iterative formula in the descending stage;
s25: in the landing stage of the dandelion, the position is updated by using an iterative formula in the landing stage;
s26: outputting an optimal result when the iteration times reach a threshold value, otherwise, repeating S22-S25 until the iteration times reach the threshold value, and outputting a corresponding optimal result; and taking the optimal result as the target photovoltaic array.
2. The method for reconstructing a photovoltaic array based on the dandelion optimization algorithm as claimed in claim 1, wherein the initial photovoltaic array in S1 is represented by:
wherein, V out Is the output voltage of the initial photovoltaic array; i is out Is the output current of the initial photovoltaic array; v maxa Is the row a maximum output voltage; I.C. A ab Is the node current for row a and column b.
3. The photovoltaic array reconstruction method based on the dandelion optimization algorithm as recited in claim 1, wherein the iterative formula of the ascending phase is represented as:
in a sunny day, the wind speed is regarded as lognormal distribution lnY-N (mu, sigma) 2 ) The update location is represented as: x t+1 ＝X t +a*v x *v y *lnY*(X s -X t )；X t Representing the position of dandelion seeds during iteration t; x s Representing randomly selected positions, X, in the search space during an iteration t s = rand (1, dim) × (UB-LB) + LB, lnY denotes obedience μ =0 and σ 2 A log-normal distribution of =1,
y denotes a normal distribution N (0, 1), a is an adaptive parameter for adjusting a search step size,
randn () is a random number that conforms to a standard normal distribution; v. of x And v y Showing the coefficient of the lift component, v, of the dandelion due to the vortex effect of the separation x ＝r*cosθ，y x ＝r*sinθ，
Theta is in the range of [ -pi, pi [ -pi [ ]]；
In case of rain, the update position is expressed as: x t+1 ＝X t * k; k is used to specify the local search domain for dandelion,
in the formula, T represents the maximum number of iterations.
4. The method for reconstructing a photovoltaic array based on the dandelion optimizing algorithm as recited in claim 3, wherein the iterative formula of the descent phase is represented as:
X t+1 ＝X t -a*β t *(X mean_t -a*β t *X t )；
in the formula, beta t Representing brownian motion, is a random number from a standard normal distribution; x mean_t Indicating the average position of the population in the ith iteration,
5. the photovoltaic array reconstruction method based on the dandelion optimizing algorithm as recited in claim 4, characterized in that the iterative formula of the landing stage is expressed as: x t+1 ＝X elite -levy(λ)*a*(X elite -X t *δ)；
Wherein, X elite Representing the optimal position of dandelion seeds in i iterations; levy (λ) denotes the Levy function
Is [0,2]]A random number in between; s is a fixed constant; w and τ are [0,1]]A random number in between, and a random number,
beta is fixed to be 1.5; delta is [0,2]]A linear increasing function of the number of the first and second,
6. the method for photovoltaic array reconstruction based on dandelion optimization algorithm according to claim 1, characterized in that said method further comprises:
s3: and analyzing and evaluating the performance parameters of the target photovoltaic array by adopting three evaluation indexes of mismatch loss, filling factor and standard deviation.
7. The method of claim 6, wherein the photovoltaic array reconstruction method based on the dandelion optimization algorithm,
the mismatch loss is expressed as: p is mi ＝P G(unshaded) -P G(shaded) ；P G(unshaded) Maximum output power, P, of target photovoltaic array without shading G(shaded) Is the maximum output power of the shaded target photovoltaic array;
the fill factor is expressed as:
V p and I p Voltage and current at the local maximum power point; v o And I s Is the open circuit voltage and short circuit current of the target photovoltaic array;
the maximum output power corresponding to the standard deviation is:
I a and V a Respectively representing the output current and the output voltage of the a-th row.
8. A photovoltaic array reconstruction apparatus based on dandelion optimization algorithm, for performing the photovoltaic array reconstruction method according to any one of claims 1-7, comprising:
the system comprises an establishing module, a calculating module and a calculating module, wherein the establishing module is used for establishing an initial photovoltaic array of an n multiplied by n mesh connection configuration;
a reconstruction module, configured to reconstruct the initial photovoltaic array under the shielding condition by using a dandelion optimized DO algorithm to obtain a target photovoltaic array, and specifically configured to:
initializing the position of a population, and setting system parameters related to a boundary value, the population quantity and the iteration times;
calculating the fitness value of each solution, and selecting the optimal fitness value;
in the dandelion rising stage, updating the position according to an iterative formula in the rising stage;
in the dandelion descending stage, the position is updated by using an iterative formula in the descending stage;
in the landing stage of the dandelion, the position is updated by using an iterative formula in the landing stage;
outputting an optimal result when the iteration times reach a threshold value, otherwise, repeating S22-S25 until the iteration times reach the threshold value, and outputting a corresponding optimal result; and taking the optimal result as the target photovoltaic array.
9. An electronic device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor realizes the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211441214.3A CN115729307A (en) | 2022-11-17 | 2022-11-17 | Photovoltaic array reconstruction method and device based on dandelion optimization algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211441214.3A CN115729307A (en) | 2022-11-17 | 2022-11-17 | Photovoltaic array reconstruction method and device based on dandelion optimization algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115729307A true CN115729307A (en) | 2023-03-03 |
Family
ID=85296308
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211441214.3A Pending CN115729307A (en) | 2022-11-17 | 2022-11-17 | Photovoltaic array reconstruction method and device based on dandelion optimization algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115729307A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116646927A (en) * | 2023-05-30 | 2023-08-25 | 淮阴工学院 | Wind power prediction method based on segmented filtering and longitudinal and transverse clustering |
| CN116842846A (en) * | 2023-07-14 | 2023-10-03 | 云南师范大学 | Array antenna pattern comprehensive design method based on improved DO algorithm |
-
2022
- 2022-11-17 CN CN202211441214.3A patent/CN115729307A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116646927A (en) * | 2023-05-30 | 2023-08-25 | 淮阴工学院 | Wind power prediction method based on segmented filtering and longitudinal and transverse clustering |
| CN116842846A (en) * | 2023-07-14 | 2023-10-03 | 云南师范大学 | Array antenna pattern comprehensive design method based on improved DO algorithm |
| CN116842846B (en) * | 2023-07-14 | 2024-03-08 | 云南师范大学 | Array antenna pattern comprehensive design method based on improved DO algorithm |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115729307A (en) | Photovoltaic array reconstruction method and device based on dandelion optimization algorithm | |
| Wu et al. | Parameter identification of photovoltaic cell model based on improved ant lion optimizer | |
| JP7407894B1 (en) | Control method, device and electronic equipment for hybrid energy storage system | |
| Chang | Wind energy assessment incorporating particle swarm optimization method | |
| CN111709109B (en) | Photovoltaic absorption capacity calculation method and device considering source-load time sequence correlation | |
| CN107591807B (en) | Optimization method for power transmission network planning under new energy access | |
| CN109062314B (en) | Improved cuckoo photovoltaic global maximum power tracking method under local shielding condition | |
| CN104734175A (en) | Method for intelligently modifying wind speed and power curve of wind turbine generator | |
| CN116306303A (en) | Photovoltaic array reconstruction method based on improved Harris eagle optimization algorithm | |
| CN107332241A (en) | Method and system for assessing the distribution network reliability containing distributed new | |
| Ibrahim et al. | Comparative study of solar PV system performance under partial shaded condition utilizing different control approaches | |
| Jiang et al. | A novel hybrid maximum power point tracking method based on improving the effectiveness of different configuration partial shadow | |
| CN112327997A (en) | Photovoltaic global maximum power tracking control method based on improved dragonfly algorithm | |
| Stonier | A novel PV array interconnection scheme to extract maximum power based on global shade dispersion using grey wolf optimization algorithm under partial shading conditions | |
| Li et al. | Global maximum power point tracking for solar power systems using the hybrid artificial fish swarm algorithm | |
| CN114706445A (en) | Photovoltaic maximum power point tracking method based on DE-GWO algorithm | |
| CN114243696A (en) | Approximate probability scene generation method for photovoltaic output of typical sunlight of photovoltaic power generation | |
| CN116578830A (en) | Photovoltaic array reconstruction method based on improved marine predator algorithm | |
| CN116362007A (en) | Photovoltaic array reconstruction method of laser energy supply unmanned aerial vehicle receiving end based on IGWO algorithm | |
| Raj et al. | Numerical Simulation and Comparative Assessment of Improved Cuckoo Search and PSO based MPPT System for Solar Photovoltaic System Under Partial Shading Condition | |
| CN107104429B (en) | power system load scheduling method considering distributed energy storage system | |
| CN115912331A (en) | Power adjusting method of intelligent wind-solar hybrid power generation system | |
| Maharana et al. | Performance analysis of different PV array configurations under partial shading condition | |
| CN112596575B (en) | Maximum power point tracking method based on NPSO algorithm and hierarchical automatic restart | |
| Pingali et al. | Novel Maximum Power Point Tracking Implementation Using Satin Bowerbird Algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |