CN116561507A - Photovoltaic array reconstruction method based on improved suburban wolf optimization algorithm - Google Patents
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Abstract
The invention relates to a photovoltaic array reconstruction method based on an improved suburban wolf optimization algorithm, which comprises the following steps: firstly, establishing a mathematical model of a Photovoltaic (PV) array, taking the maximum output power of the PV array as a target, establishing an objective function to be optimized, and determining constraint conditions which are required to be met by the state quantity of an electrical switch of the PV array; then, in order to further improve the searching capability and convergence capability of suburban wolf optimization algorithm (COA) in the model solving process, the standard COA is improved, and a measurement index of the output characteristics after the PV array is reconstructed is established; finally, the effectiveness and superiority of the proposed method are verified by software simulation based on Improved COA (ICOA) for PV array reconstruction. Through simulation analysis, the method can effectively relieve the influence of partial shading on the active output of the PV array, reduce the power loss of the system and improve the condition of the PV array under the shading conditionI‑UAndP‑Uthe problem that the output characteristic curve has multiple peaks is solved, and compared with the traditional method, the improved algorithm has better global searching capability and convergence characteristic.
Description
Technical Field
The invention relates to the technical field of photovoltaic power generation, in particular to a photovoltaic array reconstruction method based on an improved suburban wolf optimization algorithm.
Background
Solar energy is widely paid attention to as an inexhaustible energy source because of its clean and pollution-free properties. However, the problems that the photovoltaic cell has low conversion efficiency, is easily influenced by external environment, has imperfect self-construction process and the like are still a great current problem to be solved urgently. The photovoltaic array can not avoid the problem of shielding partial shadow caused by factors such as bird, cloud layer, nearby buildings, unreasonable interval arrangement and the like in the operation process, and the random irregular partial shadow shielding can cause the photovoltaic output to become unstable. While the shaded cells in the photovoltaic array have reduced output current, resulting in an overall output voltage of the array being applied to the cells, causing them to become load consuming electrical energy to generate heat, damaging the cells and the system.
When the photovoltaic array works under the condition of partial shadow, energy is lost due to power mismatch, so that the output characteristic curve has multiple peaks, and the problem of partial shadow shielding cannot be absolutely avoided. The reconstruction technology is one of the effective means for solving the problem, and combines the arrangement modes of the components in the array in different forms, so that when partial shading is generated, the actual positions of the shadows are evenly dispersed, and the aims of balancing each row of current and reducing the number of multimodal values are achieved. The technology not only reduces the requirement of the photovoltaic system on the global maximum power point tracking precision, but also improves the power output and changes the I-U, P-U characteristic of the array under the shadow condition, so that the invention provides an effective photovoltaic array reconstruction method which has extremely important practical significance.
Disclosure of Invention
Aiming at overcoming the defects of the prior art, the invention aims at solving the problems that the prior research mostly adopts a group intelligent algorithm to reconstruct a photovoltaic array, but the output power of the method is limited, the mismatch loss is higher, the power hoisting percentage is lower, and the convergence speed is low and the convergence precision is low, and the invention provides a photovoltaic array reconstruction method based on an improved suburban wolf optimization algorithm.
The invention adopts the technical scheme that: a photovoltaic array reconstruction method based on an improved suburban wolf optimization algorithm comprises the following steps:
step 1: establishing a mathematical model of the photovoltaic array;
step 2: establishing an objective function to be optimized by taking the maximum output power of the photovoltaic array as a target, and determining constraint conditions which are required to be met by the electrical switching state quantity of the photovoltaic array;
step 3: improving a standard suburban wolf optimization algorithm;
step 4: establishing a measurement index of the output characteristics of the reconstructed photovoltaic array;
step 5: the photovoltaic array is reconstructed based on an improved suburban wolf optimization algorithm.
Specifically, in the step 1, a mathematical model of the photovoltaic array is built.
The photovoltaic array mostly adopts a full cross type (TCT) structure, and the photovoltaic modules are connected in series to form a module string, and the module strings are connected in parallel to form the TCT structure. For photovoltaic arrays of size "mxn", where row i and column j correspond to component M ij In irradiance of light G ij The current generated is I ij The calculation formula is as follows:
in the method, in the process of the invention,representation component M ij In irradiance of light G 0 The current generated.
The output power of the photovoltaic array is:
wherein V is i 、I i Representing the voltage and current, respectively, of row i of the PV array.
Specifically, in the step 2, the maximum output power of the photovoltaic array is taken as a target, an objective function to be optimized is established, and constraint conditions which are required to be met by the electrical switching state quantity of the photovoltaic array are determined. The photovoltaic array reconstruction is to determine that the sum of the power of each row of the photovoltaic array is maximum in the optimal connection mode by adjusting the connection mode of components in the photovoltaic panel according to different shadow modes, so that an objective function is established:
wherein P is a Is the output power W when the bypass diode is not used f For its weight coefficient, typically 10 is taken; e (E) e Is the maximum row current I of the photovoltaic array max And a single row current I i Sum of errors (W) e For its weight coefficient, generally 10), the calculation formula is:
each component only exchanges with another component in the same column, i.e., the component changes the row number. Therefore, the reconstruction variable constituted by the electrical switch states should satisfy the constraint:
wherein x is i,j The serial numbers of the components in the ith row and the jth column.
Specifically, in the step 3, a standard suburban wolf optimization algorithm is improved. The standard suburban wolf optimization algorithm updates the social adaptability of suburban wolves by simulating activities such as birth, growth, death, outlier and addition of new groups of suburban wolves, and performs winner and worse elimination on suburban wolves, so that the social state of suburban wolves with the best social adaptability, namely the optimal solution of the problem, is determined.
1) And initializing a population. Suburban wolf optimization algorithm firstly randomly divides the whole population into N p Groups, each group having N c Only wolves are suburban. The social conditions of the j-th dimension of the c suburban wolf of the p-th group are as follows:
in ub j 、lb j Upper and lower boundaries of the j-th dimension vector; r is (r) j Is [0,1]Random numbers within.
2) Suburban wolves grow up. The growth process of suburban wolves can be expressed as:
in the method, in the process of the invention,respectively representing the social state of suburban wolves before and after growth; r is (r) 1 、r 2 Is [0,1]Random numbers uniformly distributed in the inner part; delta 1 And delta 2 Respectively representing the optimal suburban wolf factor in the group and the cultural trend factor in the group, wherein the calculation formula is as follows:
in the method, in the process of the invention,is the social state of two suburban wolves randomly selected in the p groups; soc best Is the most optimal suburban wolf social state in the group. Group internalization cult for group p p Is the median of all suburban wolf social states in the group, and can be expressed as:
wherein O is p Represents the median of the ranking of suburban wolves in the p groups in order of the social status from small to large.
3) Suburban wolves born and die. Mating suburban wolves in a group, randomly selecting two suburban wolves from suburban wolves, and obtaining new individuals according to a mating rule. The social state of pup newly-born individuals is jointly influenced by the genetic action and environmental variation of the suburban wolves, so that the j-th-dimension social state calculation formula of pup is as follows
In the method, in the process of the invention,and->A social status of a parent suburban wolf representing a new individual pup; r is R j Is a random number within the j-th dimensional social state variable range. Probability of divergence P d Associated probability P a Are all functions of the problem dimension D, and their expressions are as follows:
the determination of suburban wolf dead individuals is determined by determining whether the new suburban wolf pup is the suburban wolf with the worst social adaptability in the group. When the social adaptability of other suburban wolves in the group is worse than pup, the suburban wolves with the worst social adaptability die, otherwise pup dies.
4) Outliers of suburban wolves and addition of new groups. Under a certain probability, suburban wolves are evicted from the original group to be added into the new group. The eviction probability is:
in order to overcome the defects that COA is easy to sink into local optimum and the convergence rate is low, levy flight and Circle chaotic mapping are adopted to improve the growth process of suburban wolves, so that diversity and stability of groups are improved, meanwhile, the operation of converging towards an origin is removed, the global searching capability is improved, and an improved formula is as follows:
wherein Levy (lambda) represents a Levy flight function; a, a k Representing the introduced Circle chaotic map;representing the multiplication of the corresponding elements. The specific mathematical formula is as follows:
1) The Levy flight strategy mathematical expression is:
where λ takes a value between [0.75,195], typically 1.5; u, v obey normal distribution, i.e
Where Γ (·) is the gamma function.
2) Sinusoidal chaos mapping mathematical expression:
wherein a is k Representing the kth position, mod (·) is a remainder function, and the remainder is taken to obtain the kth+1th position.
Specifically, in the step 4, a measurement index of the output characteristic of the reconstructed photovoltaic array is established. Using mismatch loss P mis And the power boost percentage lambda is used for measuring the output characteristics of the photovoltaic array after reconstruction, and the specific calculation formula is as follows:
P mis ＝P PV -P PV.PS (17)
wherein P is PV Representing the maximum output power of the hybrid system without partial shading; p (P) PV,PS Representing the maximum output power of the hybrid system in a partially obscured condition; p (P) PV1 Maximum output power of the hybrid system before reconstruction; p (P) PV2 Representing the maximum output power of the hybrid system after reconstruction.
Specifically, in the step 5, the photovoltaic array is reconstructed based on an improved suburban wolf optimization algorithm. The method comprises the following main steps:
1) Collecting data such as output current, voltage, irradiance and the like of the PV array through a sensor;
2) Initializing algorithm initial parameters, and adding P mis The minimum and the lambda maximum are used as fitness functions of the algorithm, and initial fitness values are calculated;
3) Determining an objective function and constraint conditions of the photovoltaic array reconstruction;
4) Solving the reconstruction model by utilizing an improved COA, updating a local optimal solution, determining a global optimal solution, and continuously updating the optimal solution according to a fitness function;
5) Judging whether the maximum iteration times are reached, if so, outputting the optimal array arrangement and the maximum output power, otherwise, continuously executing the step 4) until the algorithm termination condition is met.
Compared with the prior art, the invention has the following beneficial effects:
in order to solve the defects of the prior art, the invention provides a PV array reconstruction method based on an improved suburban wolf optimization algorithm, which utilizes an improved COA to reconstruct the PV array so that the photovoltaic array obtains the maximum power output, the convergence performance of the improved algorithm is further improved compared with that of the standard COA, and smaller mismatch loss and larger power improvement percentage can be obtained compared with that of the traditional intelligent algorithm.
Drawings
FIG. 1 is a flow chart of a method for reconstructing a PV array based on an improved COA of the present invention;
FIG. 2 is a schematic illustration of a 9×9 PV array in accordance with an embodiment of the present invention;
FIG. 3 is a graph showing the variation of output power with the number of algorithm iterations obtained by four comparison algorithms in the PV array reconstruction process in an embodiment of the present invention;
FIG. 4 illustrates the irradiation of a 9 x 9 PV array before and after reconstruction in a wide shade condition in accordance with an embodiment of the present invention;
FIG. 5 illustrates the reconstruction of a front and rear PV array for a 9 x 9 PV array in a short narrow shade in accordance with an embodiment of the present invention;
FIG. 6 is a graph of I-U and P-U of a 9X 9 PV array in accordance with an embodiment of the present invention reconstructed using ICOA in a wide shade condition;
FIG. 7 shows the I-U and P-U curves of a 9X 9 PV array in accordance with an embodiment of the present invention after reconstruction using ICOA in a short narrow shade.
Detailed Description
The effectiveness of the method is verified by using the embodiment, and the technical scheme in the embodiment of the invention is clearly and completely described by combining the attached drawings. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in the predictive flow of fig. 1, a photovoltaic array reconstruction method based on an improved suburban wolf optimization algorithm mainly comprises the following main steps:
step 1: establishing a mathematical model of the photovoltaic array;
step 2: establishing an objective function to be optimized by taking the maximum output power of the photovoltaic array as a target, and determining constraint conditions which are required to be met by the electrical switching state quantity of the photovoltaic array;
step 3: improving a standard suburban wolf optimization algorithm;
step 4: establishing a measurement index of the output characteristics of the reconstructed photovoltaic array;
step 5: the photovoltaic array is reconstructed based on an improved suburban wolf optimization algorithm.
According to step 1, as shown in a schematic diagram of a photovoltaic array of size 9×9 in fig. 2, the array reconstruction performance of ICOA is evaluated by using shadows caused by 2 typical irradiation types (short wide type, long narrow type) of a PV array simulated by MATLAB/Simulink simulation software, and compared with standard COA, particle Swarm Optimization (PSO), genetic Algorithm (GA), and Cuckoo Search Algorithm (CSA), the performance of ICOA is explored. Under the condition of short and wide shading, the output of the PV array is limited by the irradiance of five light with the irradiance of 900W/m respectively 2 、600W/m 2 、500W/m 2 、400W/m 2 、200W/m 2 The method comprises the steps of carrying out a first treatment on the surface of the Under the condition of long and narrow shading, the output of the PV array is limited to five light irradiance of 900W/m respectively 2 、800W/m 2 、700W/m 2 、400W/m 2 、300W/m 2 。
For better set control comparisons, the number of iterations of all algorithms was set to 200 and the population size was set to 30. Through simulation, the output active power of the array before reconstruction under two shading conditions is 9.74kW and 14.60kW respectively, the maximum output power of the PV array under the condition of full illumination is 18.56kW, the mismatch loss and the power improvement percentage of each comparison method are obtained after reconstruction, the short wide shading condition is taken as an example, the adaptation loss is taken as an adaptability function, and the curve of the output active power of each comparison method in the situation changing along with the iteration times is obtained as shown in figure 3. Under two shading conditions, five algorithms are utilized to reconstruct the array, the irradiation conditions before and after reconstruction are obtained as shown in figures 4 and 5, and I-U and P-U curves before and after reconstruction of the PV array under the two shading conditions are obtained through simulation as shown in figures 6 and 7.
Table 1 mismatch loss contrast
Table 2 power boost percentage comparison
As can be seen from fig. 3, the COA is fast converged after 20 iterations, but the obtained output power is the lowest (about 14.41 kW), the PSO algorithm also only obtains about 13.64kW of output power, the optimizing effect is not ideal, the ICOA is converged after 13 iterations, the convergence speed is better than COA, GA (71 times), PSO (46 times), CSA (35), and the highest output power is obtained. Thus, the ICOA can find a relatively optimal solution in a fewer number of iterations. Based on the above discussion, the ICOA has improved global searching capability and convergence accuracy compared to the pre-improvement algorithm.
As can be seen from fig. 4 and 5, irradiance of the whole array is unbalanced before reconstruction, and after reconstruction, partial shading conditions of the array are uniformly distributed in the whole array, so that influence of shading on active output of the PV array can be effectively reduced. As can be seen from table 1, the PV array is reconstructed by using the algorithm under two typical shading conditions, the mismatch loss after reconstruction is reduced, the output power is improved, the mismatch loss after reconstruction based on ICOA is the lowest, the reconstruction effect is the best, and the mismatch loss under two shading conditions is reduced by 4.16kW and 2.54kW respectively. As can be seen from table 2, the average power boost percentage after the ICOA-based reconstruction is highest, with the most significant effect on mitigating the shading effect. As can be seen from the curves in fig. 6 and 7, the reconstruction of the PV array by using the algorithm alleviates the problem of multimodal output characteristics of the I-U and P-U under shade conditions. In the case of shading, the problem of multiple peaks in the output power curve easily leads to problems with local maxima at maximum power point tracking. Through graph analysis, compared with a standard COA, the global searching capability and convergence accuracy of the improved algorithm are obviously improved, the effectiveness of the improved method is verified, and compared with other optimized algorithms, higher power output can be obtained after reconstruction, so that the superiority of the improved method is illustrated.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (6)
1. The photovoltaic array reconstruction method based on the improved suburban wolf optimization algorithm is characterized by comprising the following steps of:
step 1: establishing a mathematical model of the photovoltaic array;
step 2: establishing an objective function to be optimized by taking the maximum output power of the photovoltaic array as a target, and determining constraint conditions which are required to be met by the electrical switching state quantity of the photovoltaic array;
step 3: improving a standard suburban wolf optimization algorithm;
step 4: establishing a measurement index of the output characteristics of the reconstructed photovoltaic array;
step 5: the photovoltaic array is reconstructed based on an improved suburban wolf optimization algorithm.
2. The photovoltaic array reconstruction method based on the improved suburban wolf optimization algorithm of claim 1, wherein the following steps 1: establishing a mathematical model of the photovoltaic array;
the photovoltaic array mostly adopts a full cross type (TCT) structure, photovoltaic modules are connected in series to form module strings, and the module strings are connected in parallel to form the TCT structure; for photovoltaic arrays of size "mxn", where row i and column j correspond to component M ij In irradiance of light G ij The current generated is I ij The calculation formula is as follows:
in the method, in the process of the invention,representation component M ij In irradiance of light G 0 A current generated by the lower part;
the output power of the photovoltaic array is:
wherein V is i 、I i Representing the voltage and current, respectively, of row i of the PV array.
3. The photovoltaic array reconstruction method based on the improved suburban wolf optimization algorithm of claim 1, wherein the step 2: establishing an objective function to be optimized by taking the maximum output power of the photovoltaic array as a target, and determining constraint conditions which are required to be met by the electrical switching state quantity of the photovoltaic array; the photovoltaic array reconstruction is to determine that the sum of the power of each row of the photovoltaic array is maximum in the optimal connection mode by adjusting the connection mode of components in the photovoltaic panel according to different shadow modes, so that an objective function is established:
wherein P is a Is the output power W when the bypass diode is not used f For its weight coefficient, typically 10 is taken; e (E) e Is the maximum row current I of the photovoltaic array max And a single row current I i Sum of errors (W) e For its weight coefficient, generally 10), the calculation formula is:
each component only exchanges with another component in the same column, i.e., the component changes the row number; therefore, the reconstruction variable constituted by the electrical switch states should satisfy the constraint:
wherein x is i,j The serial numbers of the components in the ith row and the jth column.
4. The photovoltaic array reconstruction method based on the improved suburban wolf optimization algorithm of claim 1, wherein the step 3: improving a standard suburban wolf optimization algorithm; in order to overcome the defects that COA is easy to sink into local optimum and the convergence speed is low, an improved COA which fuses Levy flight and Circle chaotic mapping is provided so as to enhance the global searching capability of the COA; in order to overcome the defects that COA is easy to sink into local optimum and the convergence speed is low, levy flight and Circle chaotic mapping are adopted to improve the growth process of suburban wolves so as to enhance the global searching capability of COA; the improved formula is as follows:
in the method, in the process of the invention,respectively representing the social state of suburban wolves before and after growth; delta, delta respectively represents the optimal suburban wolf factor in the group and the cultural trend factor in the group; levy (λ) represents a Levy flight function; a, a k Representing the introduced Circle chaotic map; the specific mathematical formula is as follows:
1) The Levy flight strategy mathematical expression is:
where λ takes a value between [0.75,195], typically 1.5; u, v obey normal distribution, i.e
Wherein Γ (·) is a gamma function;
2) Sinusoidal chaos mapping mathematical expression:
wherein a is k Representing the kth position, mod (·) is a remainder function, which is left to obtain the kth+1 position.
5. A photovoltaic array reconstruction method based on an improved suburban wolf optimization algorithm as claimed in claims 1 and 5, wherein said step 4: establishing a measurement index of the output characteristics of the reconstructed photovoltaic array; using mismatch loss P mis And the power boost percentage lambda is used for measuring the output characteristics of the photovoltaic array after reconstruction, and the specific calculation formula is as follows:
P mis ＝P PV -P PV.PS
wherein P is PV Representing the maximum output power of the hybrid system without partial shading; p (P) PV,PS Representing the maximum output power of the hybrid system in a partially obscured condition; p (P) PV1 Maximum output power of the hybrid system before reconstruction; p (P) PV2 Representing the maximum output power of the hybrid system after reconstruction.
6. The photovoltaic array reconstruction method based on the improved suburban wolf optimization algorithm of claim 1, wherein the step 5: reconstructing the photovoltaic array based on an improved suburban wolf optimization algorithm; the method comprises the following main steps:
1) Collecting data such as output current, voltage, irradiance and the like of the PV array through a sensor;
2) Initializing algorithm initial parameters, and adding P mis The minimum and the lambda maximum are used as fitness functions of the algorithm, and initial fitness values are calculated;
3) Determining an objective function and constraint conditions of the photovoltaic array reconstruction;
4) Solving the reconstruction model by utilizing an improved COA, updating a local optimal solution, determining a global optimal solution, and continuously updating the optimal solution according to a fitness function;
5) Judging whether the maximum iteration times are reached, if so, outputting the optimal array arrangement and the maximum output power, otherwise, continuously executing the step 4) until the algorithm termination condition is met.
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