MPPT (Maximum Power Point Tracking) control method of offshore floating type photovoltaic power generation system

Abstract

An MPPT (maximum power point tracking) control method of an offshore floating photovoltaic power generation system comprises the following steps: establishing a dynamically updated historical optimal database of the maximum power point of the offshore floating photovoltaic power generation system by using the time-varying periodicity of the maximum power point of an offshore floating photovoltaic array; optimizing population initial distribution of the biological heuristic algorithm by using a maximum power point historical optimal database and a support vector machine method, and improving an elimination mechanism of bad individuals in an iteration process of the biological heuristic algorithm; an improved biological heuristic algorithm is combined with a traditional MPPT technology, and a hybrid improved MPPT control method is provided. Respectively taking an improved cuckoo and variable step size perturbation and observation method as well as an improved grey wolf algorithm and a variable step size conductance increment method as examples to carry out mixed improvement, and further explaining the proposed control technical method in combination with actual engineering data of a certain sea area; according to the invention, the offshore floating photovoltaic array can meet the requirements of maximum power tracking speed and precision under various sea conditions.

Description

(I) Technical field:

[0001] The present invention relates to the technical field of photovoltaic power generation, and in particular to an MPPT (Maximum Power Point Tracking) control method for an offshore floating photovoltaic power generation system. (II) Background technology:

[0002] Compared with land-based photovoltaic power generation, there are no tall buildings such as trees around offshore floating photovoltaic power generation, and there are sufficient light resources, humid air and more precipitation, which are conducive to the heat dissipation and cleaning of photovoltaic modules. After adopting highly weather-resistant double-sided glass photovoltaic modules, the power generation of offshore floating photovoltaic power generation is theoretically much ahead of inland photovoltaic power stations. However, the power generation of offshore floating photovoltaic power generation systems is not only affected by light resources and the power generation efficiency of photovoltaic modules, but also by factors such as the wind and wave environment in the sea area and the MPPT efficiency.

[0003] Affected by sea breezes, ocean currents, and irregular waves, offshore floating photovoltaic modules move with six degrees of freedom along the floating structure, including translation and rotation along all axes. Therefore, the radiation energy received by offshore floating double-sided glass photovoltaic modules is not only affected by the weather environment (such as cloud movement) and the inclination of module installation, but also changes dramatically with the increase in sea conditions. The complex and changeable marine environment will cause the PV curve of the same MPPT photovoltaic array to fluctuate rapidly and may have multiple peaks, which may reach dozens in severe cases, increasing the technical difficulty of the photovoltaic array MPPT and hindering the efficient and economical operation of offshore floating photovoltaic power stations.

[0004] The methods for reducing the impact of rapid changes and uneven irradiance of photovoltaic arrays can be divided into two types: adding hardware facilities and using software algorithms. However, the operating environment at sea is harsh, and the electrical devices in offshore photovoltaic power stations need to have strong weather resistance and reliability. The additional circuit method and reconstruction method increase the electrical control devices of the station, which not only increases the complexity and cost of the system, but also increases the possibility of failure during the operation of the system. Therefore, the efficiency of MPPT should be improved from the perspective of software algorithms. Software methods can be roughly divided into two categories. The first category is traditional MPPT technology, which mainly includes perturbation observation method, conductance increment, fractional open circuit voltage and other methods. Traditional MPPT technology is very popular and can operate effectively under stable and uniform irradiation, but they cannot track the MPPT under rapidly changing sunshine and partially uneven irradiance of offshore floating photovoltaic modules. The second category mainly includes fuzzy logic control, artificial neural network and swarm intelligence algorithms. Among them, fuzzy logic control and artificial neural network calculation methods require designers to have a large amount of data training, which is not suitable for early project development research. Therefore, in order to promote the large-scale development and utilization of offshore floating photovoltaics and ensure the safe operation and economic production of offshore floating photovoltaic power stations, it is urgently necessary to propose maximum power tracking technology suitable for offshore floating photovoltaics for the project sea area, which will not only be utilized for the preliminary site selection survey of the project, but also help to improve the economic efficiency of the project production and operation. (III) Summary of the invention:

[0005] The purpose of the present invention is to provide an MPPT control method for an offshore floating photovoltaic power generation system, which can solve the shortcomings of the existing MPPT control technology and is a simple and easy-to-implement MPPT control method. The method reduces the complexity and cost of the offshore floating photovoltaic system and can effectively improve the reliability of the system during operation and the efficiency of the MPPT process.

[0006] The technical solution of the present invention is a MPPT control method for an offshore floating photovoltaic power generation system, characterized in that it comprises the following steps:

[0007] (1) By using the movement of seawater in the sea area at multiple time scales and the time-varying periodicity of the maximum power point of the offshore floating photovoltaic array to a certain extent, a dynamically updated historical optimal database of the maximum power point of the offshore floating photovoltaic array is established (d GMPPi ,V GMPPi ,P GMPPi ,t i ), narrowing the initial distribution range of the population of the traditional biologically inspired algorithm, where d GMPPi For the historical moment i The corresponding historical optimal duty cycle, V GMPPi For the historical moment i The corresponding historical optimal voltage, P GMPPi For the historical momenti The corresponding historical maximum power;

[0008] In the step (1), a dynamically updated historical optimal database of maximum power points of offshore floating photovoltaic arrays is established, and the initial distribution range of the population of the traditional bio-inspired algorithm is narrowed, specifically referring to:

[0009] (1-1) The changes in irradiance and temperature of floating photovoltaic arrays at sea are related to factors such as the rotation of the earth, the revolution of the earth around the sun, and seasonal wind and waves. i There may be a similar maximum power point, denoted as (d GMPPi ,V GMPPi ,P GMPPi );Since the MPPT algorithm needs to output the historical optimal duty cycle d GMPPi Value vs. time t i Therefore, the global maximum power point searched during the operation of the offshore floating photovoltaic array is recorded in order to establish a dynamically updated maximum power point historical optimal database (d GMPPi ,V GMPPi ,P GMPPi ,t i );

[0010] (1-2) Considering that the maximum power point of the photovoltaic array has certain seasonal variation characteristics, in order to reduce the storage scale of the maximum power point historical optimal database and reduce the initial distribution range of the population of the bio-inspired algorithm, when the data scale reaches the upper limit set by the maximum power point historical optimal database, the number of optimal data points will no longer be increased, and the individuals with the highest dispersion in the maximum power point historical optimal database will be replaced through dynamic updates;

[0011] The steps (1-2) specifically refer to:

[0012] (1-2-1) Assume that the number of maximum power points stored in the maximum power point historical optimal database is m c According to the time sequence of data entry, these data are sorted according to the proportion k 1 With 1-k 1 Divided into two parts: online database and offline database;

[0013] (1-2-2) After each MPPT optimization process is completed, the current latest maximum power point (d GMPPi ,V GMPPi ,P GMPPi ,t i ) and store it in an online database;

[0014] (1-2-3) When storing the current latest maximum power point in step (1-2-2), if the number of online database storages has reached the upper limit k1 m c , then a historical maximum power point is randomly selected from the online database and migrated into the offline database;

[0015] (1-2-4) In step (1-2-3), when a historical maximum power point is randomly selected from the online database and migrated to the offline database, if the number of offline database storages has reached the upper limit (1-k 1 )m c , then calculate the data in the offline database (d GMPPi ,V GMPPi ,P GMPPi ), the maximum power point with the largest variance value, i.e., the highest discreteness, in the offline database is cleared, thereby providing space for data migration in the online database; at the same time, clearing the data with the highest discreteness in the offline database can screen out the erroneous data actually collected and the very few extreme condition data in the seasonal cycle, thereby reducing the maximum power point historical time t in the historical optimal database. i The corresponding historical optimal duty cycle d GMPPi The distribution range of

[0016] (2) Using the dynamically updated maximum power point historical optimal database provided by step (1), and based on probability statistics and support vector machine method, the initial distribution positions of n population individuals at time 0 of the traditional bio-inspired algorithm, i.e., the initial duty cycle d 1 0 ~d n 0 , reduce the power fluctuation and tracking time in the early stage of maximum power tracking;

[0017] The step (2) specifically comprises the following steps:

[0018] (2-1) Considering that the purpose of the MPPT algorithm is to provide a suitable duty cycle d, so as to help the photovoltaic array output operate at the maximum power point; therefore, the support vector machine method can be simplified to partition the multidimensional data in the maximum power point historical optimal database, and the maximum power point historical optimal database (d GMPPi ,V GMPPi ,P GMPPi ,t i ) in historical moment t i The corresponding historical optimal duty cycle d GMPPi , use the support vector machine method to find the historical optimal duty cycle d at this moment GMPPi The array is used to classify one-dimensional data. In this case, there is no need to pre-train the support vector machine model. When the support vector machine performs partitioning, it only needs to find the historical moment t i The corresponding historical optimal duty cycle d GMPPiThe maximum interval of adjacent data can be used, and the average value of adjacent data can be used as the basis for dividing the area; therefore, the support vector machine method is used to convert the historical time t i The corresponding historical optimal duty cycle d GMPPi According to the size of the value, it is divided into z partitions, which are recorded as: [d 0 ,d 1 ],[d 1 ,d 2 ]…[d z-1 ,d z ];

[0019] (2-2) Count the z partitions obtained in step (2-1) [d 0 ,d 1 ],[d 1 ,d 2 ]…[d z-1 ,d z ] historical optimal duty cycle d GMPPi The number of 1 ~N z ), and based on the obtained historical optimal duty cycle number (N 1 ~N z ) Statistical calculation of the historical optimal duty cycle d GMPPi In the j interval [d j-1 ,d j ] probability distribution rate F j ; Finally, according to the probability distribution F of the interval j Determine the number of randomly distributed populations D in the interval j at the initial time of the biologically inspired algorithm j , as shown in formula (1):

[0020]

[0021] D j =nθ c F j ,j∈{1,2,3...z}

[0022] In formula (1), n ​​is the number of algorithm populations; θ c is a constant on the interval [0, 1];

[0023] Considering that the corresponding duty cycle of the maximum power point in the actual operation process may appear at the historical optimal duty cycle d GMPPi Outside the minimum and maximum range, that is, min(d GMPPi )~max(d GMPPi ), so not all individuals in the population have their initial positions determined by the historical optimal database, only the total number of nθ cThe positions of the individuals are determined by formula (1); the number of remaining individuals is n(1-θ c ), randomly distributed in the region [0,min(d GMPPi )] and [max(d GMPPi ),d uplimit ], the initial distribution of the bioinspired algorithm population can be completed by steps (2-1) and (2-2), that is, the initial distribution positions of n population individuals at time 0 are obtained, that is, the initial duty cycle d 1 0 ~d n 0 ;

[0024] The constant θ in step (2-2) c The value range of is generally 0.55~0.75. If θ>0.75, then min(d GMPPi )~max(d GMPPi ) is small, and the interval where the maximum power point of the photovoltaic array output is located may be missed; if θ < 0.55, the role of the maximum power point historical optimal database in the algorithm will be weakened, and the convergence time of the algorithm will be extended accordingly.

[0025] (3) Improve the elimination mechanism of bad individuals in the iterative process of the bio-inspired algorithm from the perspective of elimination probability and the generation mechanism of new population individuals after bad individuals are eliminated, and improve the convergence speed of the maximum power tracking in the middle stage;

[0026] The improved bioinspired algorithm in step (3) is based on the initial optimized distribution position d of the population individuals at time 0 provided in step (2). i 0 ~d n 0 The cuckoo algorithm with improved elimination rules for bad individuals is implemented, including:

[0027] ① Set the number of bird nests n and the elimination probability p a And the iteration termination criterion,

[0028] ②The initial distribution position d of the population determined by step (2) 1 0 ~d n 0 , confirm the initial positions of n bird nests in the improved cuckoo algorithm, that is, the initial duty cycle d 1 0 ~d n 0 ;

[0029] ③ The position of the bird's nest at time t, that is, the duty cycle d at time t 1 t ~d nt (t≥0) is applied to the DC-DC rectifier unit connected to the offshore floating photovoltaic array in turn, and the DC-DC rectifier unit outputs the duty cycle d at time t respectively. 1 t ~d n t The corresponding offshore floating photovoltaic array output power P at time t pv (d 1 t )~P pv (d n t ), compare and record the maximum power P max And the bird's nest position corresponding to this power, that is, the optimal duty cycle d best ;

[0030] ④ Check the position of each bird's nest, that is, the duty cycle d at time t 1 t ~d n t , the corresponding photovoltaic array output power P at time t pv (d 1 t )~P pv (d n t ) and the maximum output power point P of the photovoltaic array during the iteration process max Is the difference greater than the set threshold ε, that is:

[0031]

[0032] d i t is the duty cycle of the i-th bird's nest, i.e., d i At the position of the tth iteration, if the result satisfies formula (3), then go to step ⑤, that is, use the Levy flight and the elimination mechanism of bad bird nests to update the positions of n bird nests, that is, the duty cycle d at time t 1 t ~d n t ;

[0033] If the position of each bird's nest, that is, the duty ratio d at time t 1 t ~d n t , the corresponding photovoltaic array output power P at time t pv (d 1 t )~P pv (d n t ) and the maximum output power point P of the photovoltaic array during the iteration process maxWhen the difference is less than or equal to the set threshold ε, that is:

[0034]

[0035] Then record the latest maximum power point (d GMPPi ,V GMPPi ,P GMPPi ,t i ), and store it in the online database of step (1). At this time, the improved cuckoo algorithm iteration ends and goes to step (4), that is, the maximum power point (d GMPPi ,V GMPPi ,P GMPPi ) is used as the initial position of the variable step size perturbation observation or the conductance increment method for perturbation;

[0036] ⑤Use Levy flight to update the positions of n bird nests, that is, the duty cycle d at time t 1 t ~d n t Value:

[0037]

[0038] In formula (5), t is the number of iterations in the iterative process, is the i-th bird's nest, i.e., duty cycle d i At the position of the tth iteration, represents point-to-point multiplication, α is the limiting coefficient of the flight step length, and conforms to the standard normal distribution, L is the Lévy search path, that is, the step length during flight, γ c The flight scale for Lévy flight; β c =3 / 2, d best Indicates the position of the bird's nest corresponding to the maximum power point in the iteration process, that is, the optimal duty cycle, and u and v both obey uniform distribution, that is, and Γ is the standard gamma function;

[0039] ⑥ In order to improve the convergence speed of the algorithm, the bad individual elimination mechanism in the cuckoo biological inspiration algorithm is used, and the elimination probability p is used to eliminate the bad individuals. a Abandon the minimum output power P of the photovoltaic array at time t pv (d t worst ) The corresponding bird's nest is the bad duty ratio d at time t t worst , and updated to the new duty cycle d t worst,new ;

[0040] The step ⑥ specifically refers to: in order to improve the convergence speed of the algorithm and reduce the steady-state oscillation, it is not necessary to perform a calculation on each bird's nest, that is, the duty cycle d after each iteration.i (i≤n) are eliminated, and only the worst nest, i.e., the worst duty cycle d at time t, needs to be eliminated after each iteration. t worst Eliminate and replace the neighboring bird nests of the bad bird nests in the traditional cuckoo algorithm Replaced by the optimal bird's nest, that is, the optimal duty cycle d best , as shown in formulas (6) and (7):

[0041]

[0042] That is, after each iteration, the output power of the offshore floating photovoltaic array is selected The minimum value in pv (d i t ),i≤n}, the corresponding d t worst As a bad individual; take a random number r for the bad individual i ∈(0,1), when r i Greater than the elimination probability p a When the new individual generation mechanism of formula (7) is used, the bad duty ratio d at time t is t worst Replace with the new duty cycle d t worst,new , we get the improved n bird nests, that is, the duty cycle d at time t 1 t ~d n t , and proceed to step ③:

[0043] The improved bioinspired algorithm in step (3) is based on the initial optimized distribution position of the population individuals at time 0 provided in step (2), i.e., the initial duty cycle d 1 0 ~d n 0 , and the gray wolf algorithm with improved bad individual elimination rules, including:

[0044] (I) Set the number of gray wolves n and the elimination probability p a and iteration termination criteria;

[0045] (II) The initial population distribution d determined by step (2) 1 0 ~d n 0 , the initial positions of n gray wolf individuals in the improved gray wolf algorithm, that is, the initial duty cycle d 1 0 ~d n 0 ;

[0046] (III) The position of the gray wolf at time t, that is, the duty ratio d at time t 1 t ~d n t (t≥0) is applied to the DC-DC rectifier unit connected to the offshore floating photovoltaic array in turn, and the DC-DC rectifier unit outputs the duty cycle d at time t respectively. 1 t ~d n t The corresponding offshore floating photovoltaic array output power P at time t pv (d 1 t )~P pv (d n t ), compare and record the maximum power P max And the gray wolf position corresponding to this power, that is, the duty cycle d best ;

[0047] (IV) Check the position of each individual gray wolf, that is, the duty cycle d at time t 1 t ~d n t , the corresponding photovoltaic array output power P at time t pv (d 1 t )~P pv (d n t ) and the maximum output power point P of the photovoltaic array during the iteration process max Is the difference greater than the set threshold ε, that is:

[0048]

[0049] is the position of the i-th gray wolf, i.e., the duty cycle, at the t-th iteration. If the result satisfies equation (8), then go to step (V), i.e., update the positions of n gray wolf individuals, i.e., the duty cycle variable;

[0050] If the position of each gray wolf is the duty ratio d at time t 1 t ~d n t The corresponding photovoltaic array output power P at time t pv (d 1 t )~P pv (d n t ) and the maximum output power point P of the photovoltaic array during the iteration process maxThe difference between is less than or equal to the set threshold ε, that is, it satisfies formula (9), then the latest maximum power point (d GMPPi ,V GMPPi ,P GMPPi ,t i ), and store it in the online database of step (1). At this time, the improved grey wolf algorithm iteration ends, and the process goes to step (4), that is, the maximum power point (d GMPPi ,V GMPPi ,P GMPPi ) is used as the initial position of the variable step size perturbation observation or the conductance increment method for perturbation;

[0051]

[0052] (V) The gray wolf position, i.e., the duty cycle d at time t 1 t ~d n t The corresponding photovoltaic array output power P pv (d 1 t )~P pv (d n t ) are sorted from large to small, and P pv (d 1 t )~P pv (d n t ) Maximum value P max The corresponding gray wolf position is Named α wolf, P pv (d 1 t )~P pv (d n t )The gray wolf position corresponding to the second largest value, that is, Named β wolf, P pv (d 1 t )~P pv (d n t )The gray wolf position corresponding to the third largest value is Named as δ wolf, the positions of the other wolves are Divided into ω wolves; for each ω wolf, that is Its new position is calculated according to the update formula of the wolf position in the search process involving the positions of α wolf, β wolf and δ wolf shown in formula (10), which aims to imitate the hunting behavior of gray wolves and help explore to obtain better solutions;

[0053]

[0054] In formula (10), is the position of the i-th wolf in ω at the t-th iteration, is the position of the i-th wolf in ω at the t+1th iteration, is the prey, that is, the maximum output power point P of the photovoltaic array during the iteration process max Corresponding position, A and C are coefficient vectors, and D is the distance between the gray wolf individual and the prey, that is and distance, a is the convergence factor of the random iteration number linearly decreasing in the interval [0,2], r 1 With r 2 is a random vector with values ​​in the interval [0,1];

[0055] (VI) When the position of the prey is determined, that is, the optimal duty cycle is obtained, the wolf α, the wolf β, and the wolf δ lead the wolf ω to surround the prey, and the position update formula is shown in (11):

[0056]

[0057] In formula (9), D a , D β , D δ is the distance between the gray wolf individual position and the positions of α wolf, β wolf and δ wolf. and The distance of 1 , A 2 , A 3 , C 1 , C 2 , C 3 is the coefficient vector described in formula (2), d 1 d 2 With d 3 is the distance and direction that the ω wolf moves toward the α wolf, β wolf, and δ wolf. Towards distance and direction;

[0058] (VII) In order to improve the convergence speed of the algorithm, an improved bad individual elimination mechanism is used, that is, after each iteration, the bad individuals are eliminated according to the elimination probability p. a Abandon the gray wolf position corresponding to the minimum output power of the photovoltaic array, that is Corresponding Updated to d t worst,new , during the algorithm iteration, randomly select r i ∈(0,1), when r i Greater than the elimination probability p a When , the worst gray wolf individual position is improved The improved positions of n gray wolves, i.e., the duty cycle d at time t, are obtained. 1 t ~d n t , and proceed to step (III).

[0059]

[0060] (4) Based on steps (1), (2) and (3), a variable step size MPPT algorithm is used to improve the tracking speed and accuracy of the maximum power point tracking. After detecting the power fluctuation caused by environmental changes, the improved bio-inspired algorithm is restarted to ensure that the offshore floating photovoltaic array always runs quickly and accurately near the theoretical maximum power point.

[0061] The step (4) specifically refers to: using the improved bioinspired algorithm in step (3) to iterate the population, and after each iteration of the population, comparing the i-th individual in the population The corresponding power value and the optimal individual d in the iterative process best The corresponding power value P max The difference between When both are less than the threshold ε, the latest maximum power point (d GMPPi ,V GMPPi ,P GMPPi ,t i ) and store it in the online database of step (1), and use the newly recorded (d GMPPi ,V GMPPi ,P GMPPi ) as the initial position of variable step perturbation observation or conductance increment method for maximum power tracking; when using variable step perturbation observation or conductance increment method for tracking, when the output power value P at time t is detected pv (V t ) and the new record maximum power P GMPPi The difference is greater than the set ratio θ, indicating that the external environment has changed and caused the maximum power point to change. At this time, it is necessary to return to step (2) and restart the improved bio-inspired algorithm, which specifically includes the following contents:

[0062] (4-1) The disturbance voltage of variable step-size perturbation observation or variable step-size conductance increment method can be calculated by formula (13):

[0063]

[0064] Where ΔV is the variable step size disturbance voltage, dP pv and dV are the output power fluctuation and voltage fluctuation of the photovoltaic array at the last disturbance moment, λ is the step scaling factor, ΔV 1 With ΔV2 are the upper and lower limits of the step length respectively;

[0065] (4-2) When the variable step size MPPT technology is used, when the irradiance of the photovoltaic panels is uneven or the illumination conditions change due to the marine environment and meteorological conditions, the output power of the offshore floating photovoltaic system also changes accordingly. In order to reduce power loss and ensure that the offshore floating photovoltaic system can work quickly and accurately near the theoretical maximum power point, it is necessary to return to step (2) in time and restart the improved bio-inspired algorithm for maximum power tracking, that is:

[0066] When the output power value P is detected at time t pv (V t ) and the latest maximum power P at the end of the iteration of step (3) GMPP,i When the difference is greater than the set ratio θ, that is, when equation (14) is satisfied, the process returns to step (2) and restarts the improved bio-inspired algorithm for maximum power tracking;

[0067]

[0068] When the output power value P is detected at time t pv (V t ) and the latest maximum power P at the end of the iteration of step (3) GMPP,i When the difference is less than or equal to the set ratio θ, that is, when equation (15) is satisfied, return to step (4-1) and continue the disturbance operation to ensure that the offshore floating photovoltaic array always runs quickly and accurately near the theoretical maximum power point.

[0069]

[0070] Working principle of the present invention: Although the improved bio-inspired algorithm reduces the power oscillation in the early and middle optimization process, there is still the problem of slow convergence speed in the later stage. Therefore, in order to further improve the convergence speed and accuracy of MPPT, the improved bio-inspired algorithm can be combined with the traditional MPPT technology, namely the perturbation observation method and the conductance increment method, and the good local search ability and small convergence oscillation of the perturbation observation method and the conductance increment method are used to optimize the speed and accuracy of MPPT tracking in the later stage. The perturbation observation method and the conductance increment method have basically the same principles. Both use the power fluctuation caused by the voltage disturbance to judge the position of the maximum power point and perform the next disturbance. According to the size of the voltage disturbance, the fixed step perturbation observation method and the conductance increment algorithm can be divided into two types. The large step method can quickly find the maximum power point by using a large duty cycle disturbance, but the oscillation at the maximum power point is too large, which reduces the tracking accuracy. The small step method has a small oscillation at the maximum power point, which improves the maximum power point tracking accuracy, but reduces the tracking speed. After comprehensive consideration, in order to improve the convergence speed of the algorithm in the later stage, reduce the power fluctuation after convergence, and improve the MPPT tracking efficiency, the improved bio-inspired algorithm can be combined with the traditional variable step-size algorithms such as the variable step-size perturbation observation method and the variable step-size conductance increment method.

[0071] Advantages of the present invention:

[0072] (1) The MPPT control method proposed in the present invention does not need to use redundant external sensors including irradiance and temperature sensors in the marine environment. During the operation of the photovoltaic power station, the maximum power point historical optimal database (d GMPPi ,V GMPPi ,P GMPPi ,t i ) can be adaptively updated without the need for additional electrical devices, which reduces the complexity and cost of the system and increases the reliability of the system during operation.

[0073] (2) When establishing the maximum power point historical optimal database, the present invention incorporates the wind and wave environment periodicity of the target engineering sea area into the maximum power point historical optimal database update principle, realizes the screening of bad data and the protection of valid data, and reduces the historical optimal duty cycle d in the database. GMPPi The distribution range of the maximum power point historical optimal database improves the performance of optimizing population distribution.

[0074] (3) In order to simplify the data partitioning by support vector machine method, it is proposed to extract the historical optimal duty cycle d from the historical optimal database of maximum power point. GMPPi Classification of 1D data is performed, so that there is no need for a large amount of historical electrical data to pre-train the support vector machine method, ensuring the maximum power tracking performance of newly built offshore floating photovoltaic power stations that lack historical electrical data.

[0075] (4) The present invention utilizes the establishment of a dynamically updated maximum power point historical optimal database, probability statistics and support vector machine processing of low-dimensional arrays to assist the bio-inspired algorithm in optimizing the initial distribution of the population, proposes a mechanism for eliminating and replacing bad individuals, optimizes the population distribution and convergence step of the bio-inspired algorithm, and reduces the power oscillation and tracking time in the early and middle stages of tracking.

[0076] (5) Considering the problem of slow convergence speed in the later stage of the bio-inspired algorithm, in order to further improve the convergence speed and accuracy of MPPT, the present invention proposes to combine the improved bio-inspired algorithm with the traditional MPPT technology, namely the perturbation observation method and the conductance increment method, and utilize the good local search ability and small convergence oscillation of the traditional MPPT technology to achieve the effect of optimizing the speed and accuracy of the later stage of MPPT tracking. (IV) Description of the drawings:

[0077] Figure 1 The present invention is a flowchart of an MPPT control method for an offshore floating photovoltaic power generation system.

[0078] Figure 2 The present invention is a schematic diagram of a method for establishing a dynamically updated maximum power point historical optimal database in an MPPT control method for an offshore floating photovoltaic power generation system.

[0079] Figure 3 The present invention is a schematic diagram of optimizing the initial distribution of a bio-inspired algorithm population by using a maximum power point historical optimal database and a support vector machine method in an MPPT control method for an offshore floating photovoltaic power generation system.

[0080] Figure 4 It is a schematic diagram of a variable step-size perturbation observation method in an MPPT control method for an offshore floating photovoltaic power generation system involved in the present invention.

[0081] Figure 5 It is a schematic diagram of the MPPT control circuit of the offshore floating photovoltaic array in the embodiment of the present invention.

[0082] Figure 6 It is a schematic diagram of the uneven irradiance scenario of the offshore floating photovoltaic array in the embodiment of the present invention.

[0083] Figure 7 It is a schematic diagram of the PV curve of the offshore floating photovoltaic array in the uneven irradiance scenario in the embodiment of the present invention.

[0084] Figure 8 This is a schematic diagram of the photovoltaic array output power curve in Example 1 of the present invention.

[0085] Fig. 9This is a schematic diagram of the photovoltaic array output power curve in Example 2 of the present invention. (V) Specific implementation methods:

[0086] In order to make the purpose, technical scheme and advantages of the present invention clearer, the embodiments of the present invention are further described in detail below. It should be noted that the embodiments described below are only some embodiments of the present invention, not all embodiments. In the absence of conflict, the embodiments and embodiment features in the present invention can be combined with each other.

[0087] Embodiment 1: The embodiment of the present invention provides an MPPT control method applicable to an offshore floating photovoltaic power generation system, as shown in the attached Figure 1 As shown, the photovoltaic array adjusts the photovoltaic array output voltage through the DC-DC rectifier unit to ensure that the photovoltaic array operates at the maximum power point. The method includes the following steps: population initial distribution optimization driven by the maximum power point historical optimal database, improved cuckoo algorithm iteration, variable step size perturbation observation method, and algorithm restart.

[0088] 101: Establish a dynamically updated maximum power point historical optimal database, use the maximum power point historical optimal database and support vector machine to optimize the initial distribution and convergence step of the bio-inspired algorithm population, and reduce the power oscillation and tracking time in the early stage of tracking;

[0089] Record the global maximum power point searched during the run, (d GMPPi ,V GMPPi ,P GMPPi ,t i ). Set the number of maximum power points stored in the maximum power point historical optimal database to m c According to the time sequence of data entry, these data are sorted according to the proportion k 1 With 1-k 1 It is divided into online database and offline database. After each MPPT process, the maximum power point with the highest dispersion, i.e., the variance, in the offline database is cleared, and a maximum power point is randomly selected from the online database and migrated to the offline database. Finally, the new maximum power point obtained by MPPT is migrated to the online database. Figure 2 .

[0090] In order to simplify the partition of multidimensional data in the maximum power point historical optimal database by support vector machine method, the maximum power point historical optimal database (d GMPPi ,V GMPPi ,P GMPPi ,t i ) in the historical optimal duty cycle d GMPPi , using support vector machine method to d GMPPiThe array is used to classify 1D data. In this case, there is no need to pre-train the support vector machine model. When the support vector machine performs partitioning, it only needs to find d GMPPi The maximum interval between adjacent data and the average value of adjacent data are used as the basis for dividing the area. GMPPi Divided into 3 partitions, interval [d 0 ,d 1 ],[d 1 ,d 2 ],[d 2 ,d 3 ],See Figure 3 .

[0091] Statistics of 3 partitions [d 0 ,d 1 ],[d 1 ,d 2 ],[d 2 ,d 3 ] GMPPi The number of historical optimal duty cycles (N 1 ,N 2 ,N 3 ), statistical calculation d GMPPi The probability distribution rate (F 1 ,F 2 ,F 3 )Finally, the number of randomly distributed populations in each interval at the initial moment of the bioinspired algorithm is determined according to the probability distribution of the interval (D 1 ,D 2 ,D 3 ), as shown in formula (1).

[0092]

[0093] D j =nθ c F j ,j∈{1,2,3}

[0094] In formula (1), n ​​is the number of algorithm populations; θ c is a constant in the interval [0, 1]. Considering that the maximum power point may appear at min(d GMPPi ) to max(d GMPPi ), so not all individuals in the population have their initial positions determined by the historical optimal database, only the total number of nθ c The positions of the individuals are determined by formula (1). Except for the region 1 to z, the number of the remaining individuals is n(1-θ c ), randomly distributed in the region [0,min(d GMPPi )] and [max(d GMPPi ),duplimit The above steps are completed to realize the initial distribution of the biologically inspired algorithm population and obtain d 1 0 ~d n 0 .

[0095] θ c The value range of is generally 0.55~0.75. If θ>0.75, then d min ~d max The number of populations distributed outside is small, and the interval where the maximum power point of the photovoltaic array output is located may be missed. If θ < 0.55, the role of the maximum power point historical optimal database in the algorithm will be weakened, and the algorithm convergence time will be extended accordingly. Each time after the bio-inspired algorithm runs, the maximum power point historical optimal database needs to be updated in time, and the latest maximum power point is added to replace the historical data. In addition, in order to improve the convergence speed of the traditional bio-inspired algorithm and reduce power oscillation during the convergence process, the iteration step size and the replacement of bad individuals can be improved according to the characteristics of the bio-inspired algorithm.

[0096] 102: The present invention is further described below by taking the improved cuckoo algorithm in the bio-inspired algorithm and the variable step size perturbation observation method in the traditional MPPT technology as examples, but this is not intended to limit the present invention.

[0097] The cuckoo algorithm is based on the parasitic reproduction of some cuckoo birds and the Lévy flight mechanism of birds. The cuckoo algorithm is applied to the MPPT control of photovoltaic systems. The position of the bird's nest corresponds to the duty cycle d, and the quality of the bird's nest is the output power P of the photovoltaic array corresponding to the duty cycle d. pv Since the cuckoo method abandons the general isotropic random walk and relies on Levy flight to enhance the search effect, in theory, the cuckoo method has a better search path than the particle swarm algorithm based on random walk of particles, and the optimization speed is faster. In photovoltaic MPPT applications, it can track the global maximum power point faster and more accurately. In addition, according to the discovery probability p a The improved nest position also enables the cuckoo algorithm to more effectively jump out of the local maximum power point, thereby reducing the power mismatch loss.

[0098] The improved cuckoo algorithm is used to iterate. First, the number of bird nests (i.e., the number of duty cycles n) and the elimination probability p are set. a And the iteration termination criterion. The initial positions d of n bird nests in the improved cuckoo algorithm determined by 101 1 0 ~d n 0 , and apply them to the DC-DC rectifier unit in turn, according to the output power P of the corresponding offshore floating photovoltaic array pv (d 10 )~P pv (d n 0 ), compare and record the maximum output power P of the photovoltaic array during the iteration process max And the corresponding bird's nest position is the optimal duty cycle d best Then, the positions of the n bird nests at time t are updated using Levy flight, that is, the duty cycle d at time t. 1 t ~d n t :

[0099]

[0100] In formula (2), t is the current iteration number, is the duty cycle of the i-th bird's nest, i.e., d i At the position of the tth iteration, represents point-to-point multiplication, α is the limiting coefficient of the flight step length, and conforms to the standard normal distribution, L is the Lévy search path, that is, the step length during flight, γ c The flight scale for Lévy flight; β c =3 / 2u and v both obey uniform distribution, that is, and Γ is the standard gamma function.

[0101] In order to improve the convergence speed of the algorithm, the traditional cuckoo algorithm uses the bird's nest elimination mechanism to select the best candidate according to the probability p. a Abandon the nest and update it, that is, during the algorithm iteration, for each bird nest, the duty cycle is randomly selected r i ∈(0,1), when r i Greater than the elimination probability p a When improving the nest position,

[0102]

[0103] In formula (3), For To improve the convergence speed of the algorithm, the bad individual elimination mechanism in the cuckoo biological inspiration algorithm is used, and the elimination probability p is used to eliminate the bad individuals. a Abandon the minimum output power P of the photovoltaic array pv (d t worst ) The corresponding bird's nest is the bad duty ratio d at time t t worst , and updated to the new duty cycle d t worst,new , and the adjacent duty cycles of the bad duty cycles in the traditional cuckoo algorithm Replaced by the optimal duty cycle d best That is, after each iteration, the power value is selected The minimum value in min{P pv (d i t ),i≤n} corresponding to d t worst . t worst Get a random number r i ∈(0,1), when r i Greater than the elimination probability p a When the new individual generation mechanism is adopted, d t worst Replace with d t worst,new , thereby improving the convergence speed of the biological population:

[0104]

[0105] The iterative position of n bird nests in the improved cuckoo algorithm is determined by the duty cycle d at time t 1 t ~d n t , and apply them to the DC-DC rectifier unit in turn, according to the output power P of the corresponding offshore floating photovoltaic array pv (d 1 t )~P pv (d n t ), compare and record the maximum power P max And the corresponding bird's nest position is the optimal duty cycle d best If the duty ratio of each bird's nest at time t is d 1 t ~d n t The corresponding photovoltaic array output power P at time t pv (d 1 t )~P pv (d n t ) The maximum output power point P of the photovoltaic array during the iteration process max When the difference is still greater than the set threshold ε, continue to use equations (2) and (4), that is, the Levy flight and the elimination mechanism of bad bird nests to update the positions of n bird nests, that is, the duty cycle d at time t 1 t ~d n t :

[0106]

[0107] When the duty ratio of each bird's nest is d at time t 1 t ~d n t The corresponding photovoltaic array output power P at time t pv (d 1 t )~P pv (d n t ) and the maximum output power point P of the photovoltaic array during the iteration process max When the difference is less than the set threshold ε:

[0108]

[0109] Record the latest maximum power point (d GMPPi ,V GMPPi ,P GMPPi ,t i ), and store it in the online database in step 101. At this time, the improved cuckoo algorithm iteration ends, and the process goes to step 103, that is, using the latest maximum power point (d GMPPi ,V GMPPi ,P GMPPi ) is used as the initial position of the variable step size perturbation observation for perturbation;

[0110] 103: The variable step-size perturbation observation method is adopted, which has the advantages of good local search and small convergence oscillation;

[0111] At the moment when the variable step size perturbation and observation method is enabled, the latest maximum power point (d GMPPi ,V GMPPi ,P GMPPi ) to conduct voltage disturbance, according to the output power P of the offshore floating photovoltaic array at time t pv (V t ), voltage V t The output power P at time t-1 pv (V t-1 ), voltage V t-1 The difference ΔP pv , ΔV t Determine the disturbance direction to perform voltage disturbance, as shown in formula (7).

[0112] Specific perturbation principle Figure 4 As shown, the closer to the maximum power point, the closer the power disturbance caused by the voltage disturbance is to zero.

[0113]

[0114] The voltage perturbation step size ΔV of each voltage perturbation in the variable step size perturbation observation method is:

[0115]

[0116] In formula (8), ΔV is the variable step size, λ is the step size scaling factor, and ΔV 1 With ΔV 2 are the upper and lower limits of the voltage perturbation step length respectively. pv is larger, so the tracking step is larger and the tracking speed is faster; in the later stage of optimization, ΔP pv It gradually decreases, so the step size is reduced, which will not cause large fluctuations around the MPP, improve the tracking accuracy, and then reduce the power loss.

[0117] 104: Determine whether to restart the algorithm;

[0118] When the irradiance of the photovoltaic panels is uneven or the lighting conditions change due to the marine environment and meteorological conditions, the output power of the offshore floating photovoltaic system also changes accordingly, and the output power of the photovoltaic power generation system also changes accordingly. In order to reduce power loss, the variable step-size perturbation observation method needs to be terminated in time, and the improved cuckoo algorithm is restarted to perform maximum power tracking when returning to step 101.

[0119] When the output power value P is detected at time t pv (V t ) and the latest maximum power P at the end of step 102 iteration GMPPi When the difference is greater than the set ratio θ, that is, when equation (9) is satisfied, the algorithm returns to step 102 and restarts the improved cuckoo algorithm.

[0120]

[0121] When the output power value P is detected at time t pv (V t ) and the latest maximum power P at the end of step 102 iteration GMPPi When the difference is less than or equal to the set ratio θ, that is, when equation (10) is satisfied, continue to use equations (7) and (8) for disturbance operation.

[0122]

[0123] A maximum power tracking test is carried out using a floating photovoltaic power generation system at sea as an example. The DC-DC rectifier unit is selected as a Boost rectifier circuit, and the MPPT control circuit is as follows: Figure 5 The offshore floating photovoltaic array has a capacity of about 30kW. Each photovoltaic array string contains 26 photovoltaic modules. There are two photovoltaic strings connected to the same MPPT control circuit. Under the action of waves, the photovoltaic array has unevenly distributed irradiance. Figure 6 The photovoltaic module parameters are shown in Table 1.

[0124] Table 1 Bifacial photovoltaic module parameters

[0125]

[0126] The PV curve corresponding to the unevenly distributed irradiance of the photovoltaic array under the action of waves is as follows Figure 7 As shown in the figure, there are 4 local maximum power points of the photovoltaic array at this time, among which the global maximum power point (1286V, 13.820kW) is located on the far right of the curve. Figure 8 It can be seen that in the uneven irradiance scenario, the hybrid improved algorithm based on the improved cuckoo algorithm and the variable step-size perturbation observation method tracks the global maximum power point (1286V, 13.820kW), the algorithm convergence time is about 19ms, and the average tracking efficiency of the algorithm is about 99.88%, which can meet the maximum power tracking requirements of the offshore floating photovoltaic power generation system.

[0127] Although specific bio-inspired algorithms, traditional MPPT techniques, and implementations are used to describe the present invention, it should be clear that these algorithms and implementations are merely examples of the principles and applications of the present invention. Therefore, it should be understood that many modifications may be made to the exemplary embodiments, and other arrangements may be designed, as long as they do not deviate from the spirit and scope of the present invention as defined by the appended claims.

[0128] Embodiment 2: The embodiment of the present invention provides an MPPT control method applicable to an offshore floating photovoltaic power generation system, as shown in the attached Figure 1 As shown, the photovoltaic array adjusts the photovoltaic array output voltage through the DC-DC rectifier unit to ensure that the photovoltaic array operates at the maximum power point. The method includes the following steps: population initial distribution optimization driven by the maximum power point historical optimal database, improved grey wolf algorithm iteration, variable step size conductance increment method, and algorithm restart.

[0129] 101: Establish a dynamically updated maximum power point historical optimal database, use the maximum power point historical optimal database and support vector machine to optimize the initial distribution and convergence step of the bio-inspired algorithm population, and reduce the power oscillation and tracking time in the early stage of tracking;

[0130] Record the global maximum power point searched during the run, (d GMPPi ,V GMPPi ,P GMPPi ,t i ). Set the number of maximum power points stored in the maximum power point historical optimal database to m c According to the time sequence of data entry, these data are sorted according to the proportion k 1 With 1-k 1It is divided into online database and offline database. After each MPPT process, the maximum power point with the highest dispersion, i.e., the variance, in the offline database is cleared, and a maximum power point is randomly selected from the online database and migrated to the offline database. Finally, the new maximum power point obtained by MPPT is migrated to the online database, such as Figure 2 shown.

[0131] In order to simplify the partition of multidimensional data in the maximum power point historical optimal database by support vector machine method, the historical optimal database (d GMPPi ,V GMPPi ,P GMPPi ,t i ) GMPPi , using support vector machine method to d GMPPi The array is used to classify 1D data. In this case, there is no need to pre-train the support vector machine model. When the support vector machine performs partitioning, it only needs to find d GMPPi The maximum interval between adjacent data and the average value of adjacent data are used as the basis for dividing the area. GMPPi Divided into 3 partitions, interval [d 0 ,d 1 ],[d 1 ,d 2 ],[d 2 ,d 3 ] as shown in the figure.

[0132] Statistics of 3 partitions [d 0 ,d 1 ],[d 1 ,d 2 ],[d 2 ,d 3 ] GMPPi The number of historical optimal duty cycles (N 1 ,N 2 ,N 3 ), statistical calculation d GMPPi The probability distribution rate (F 1 ,F 2 ,F 3 )Finally, the number of randomly distributed populations in each interval at the initial moment of the bioinspired algorithm is determined according to the probability distribution of the interval (D 1 ,D 2 ,D 3 ), as shown in formula (1).

[0133]

[0134] D j =nθ c F j ,j∈{1,2,3}

[0135] In formula (1), n ​​is the number of algorithm populations; θ c is a constant in the interval [0, 1]. Considering that the maximum power point may appear at d min ~d max In addition, not all individuals in the population have their initial positions determined by the historical optimal database, only the total number of nθ c The positions of the individuals are determined by formula (1). Except for the region 1 to z, the number of the remaining individuals is n(1-θ c ), randomly distributed in the region [0,d min ] and [d max ,d uplimit The above steps are completed to realize the initial distribution of the biologically inspired algorithm population and obtain d 1 0 ~d n 0 .

[0136] θ c The value range of is generally 0.55~0.75. If θ>0.75, then d min ~d max The number of populations distributed outside is small, and the interval where the maximum power point of the photovoltaic array output is located may be missed. If θ < 0.55, the role of the historical optimal database in the algorithm will be weakened, and the algorithm convergence time will be extended accordingly. Each time after the bio-inspired algorithm runs, the maximum power point historical optimal database needs to be updated in time, and the latest maximum power point is added to replace the historical data. In addition, in order to improve the convergence speed of the traditional bio-inspired algorithm and reduce power oscillation during the convergence process, the iteration step size and the replacement of bad individuals can be improved according to the characteristics of the bio-inspired algorithm.

[0137] 102: The present invention is further described below by taking the improved Grey Wolf algorithm in the bio-inspired algorithm and the variable step size conductance increment method in the traditional MPPT technology as examples, but this is not intended to be a limitation of the present invention.

[0138] The Gray Wolf Algorithm is based on the hierarchy of wolves and their hunting characteristics. The Gray Wolf Algorithm is applied to the MPPT control of photovoltaic systems. The position of each wolf in the wolf pack corresponds to the duty cycle d, and the position of the wolf pack corresponds to the output power P of the photovoltaic array. pv The Grey Wolf Algorithm has few parameters and is easy to implement. It has an information sharing mechanism and has strong algorithm convergence ability.

[0139] Using the improved gray wolf iteration, first set the number of gray wolves n and the elimination probability p a And the iteration termination criterion. The initial positions d of n gray wolves in the improved gray wolf algorithm determined by 101 1 0 ~d n0 , and apply them to the DC-DC rectifier unit in turn, according to the initial output power P of the corresponding offshore floating photovoltaic array pv (d 1 0 )~P pv (d n 0 ), compare and record the maximum output power P of the photovoltaic array in the next iteration process max And the corresponding gray wolf position is the optimal duty cycle d best .

[0140] The gray wolf position, i.e. the duty cycle at time t, is d 1 t ~d n t (t≥0) The corresponding photovoltaic array output power P at time t pv (d 1 t )~P pv (d n t ) are sorted from large to small, and P pv (d 1 t )~P pv (d n t ) Maximum value P max The corresponding gray wolf position is Named α Wolf, P pv (d 1 t )~P pv (d n t )The position of the gray wolf corresponding to the second largest value is Named β wolf, P pv (d 1 t )~P pv (d n t )The position of the gray wolf corresponding to the third largest value is Named as δ wolf, the positions of the other wolves are Divided into ω wolves; for each ω wolf, The new position is calculated based on an equation involving the positions of alpha, beta, and delta wolves. The update formula for the wolf pack’s position during the search is:

[0141]

[0142] In formula (2) is the position of the i-th wolf in ω at the t-th iteration, is the position of the i-th wolf in ω at the t+1th iteration, The maximum output power point P of the photovoltaic array is the prey max Corresponding position, A and C are coefficient vectors, and D is the distance between the gray wolf individual and the prey, that is and distance, a is the convergence factor of the random iteration number linearly decreasing in the interval [0,2], r 1 With r 2 is a random vector with values ​​in the interval [0,1].

[0143] When the position of the prey is determined (optimal duty cycle), the wolves α, β, and δ lead the ω wolves to surround the prey, and the position update formula is:

[0144]

[0145] In formula (3), D a , D β , D δ is the distance between the gray wolf individual position and the positions of α wolf, β wolf and δ wolf. and The distance of 1 , A 2 , A 3 , C 1 , C 2 , C 3 is the coefficient vector described in formula (2), d 1 d 2 With d 3 is the distance and direction that the ω wolf moves toward the α wolf, β wolf, and δ wolf. Towards distance and direction.

[0146] In order to improve the convergence speed of the algorithm, the improved bad individual elimination mechanism is used, that is, after each iteration, according to the elimination probability p a Abandon the gray wolf position corresponding to the minimum output power of the photovoltaic array, that is, the bad duty cycle at time t Right now Corresponding Updated to d t worst,new , during the algorithm iteration, randomly select r i ∈(0,1), when r i Greater than the elimination probability p a When the worst individual position of the gray wolf is improved, that is, the bad duty ratio at time t The improved positions of n gray wolves, i.e., the duty cycle d at time t, are obtained. 1 t ~d n t :

[0147]

[0148] The position of n gray wolf individuals in the improved gray wolf algorithm is determined by the duty cycle d at time t 1 t ~d n t , and apply them to the DC-DC rectifier unit in turn, according to the output power P of the corresponding offshore floating photovoltaic array at time t pv (d 1 t )~P pv (d n t ), compare and record the maximum power P max And the corresponding wolf pack position, i.e. the optimal duty cycle d best If the duty ratio of each wolf at time t is d 1 t ~d n t The corresponding photovoltaic array output power P at time t pv (d 1 t )~P pv (d n t ) and the maximum output power point P of the photovoltaic array during the iteration process max When the difference is still greater than the set threshold ε, continue to use formulas (2), (3), and (4) in the gray wolf algorithm to update the n gray wolf positions, that is, the duty cycle positions:

[0149]

[0150] When the position of each gray wolf is the duty ratio d at time t 1 t ~d n t The corresponding photovoltaic array output power P at time t pv (d 1 t )~P pv (d n t ) and the maximum power P of the photovoltaic array during the iteration process max When the difference is less than the set threshold ε:

[0151]

[0152] Record the latest maximum power point (d GMPPi ,V GMPPi ,P GMPPi ,t i), and store it in the online database in step 101. At this time, the improved gray wolf algorithm iteration ends, and the process goes to step 103, which is to use the latest maximum power point (d GMPPi ,V GMPPi ,P GMPPi ) is used as the initial position of the variable step size conductance increment method for perturbation;

[0153] 103: The variable step size conductance increment method is adopted, which has the advantages of good local search and small convergence oscillation;

[0154] At the moment when the variable step size conductance increment method is enabled, the maximum power point (d GMPPi ,V GMPPi ,P GMPPi ) to conduct voltage disturbance, according to the output power P of the offshore floating photovoltaic array at time t pv (V t ), voltage V t The output power P at time t-1 pv (V t-1 ), voltage V t-1 The difference in dP pv , dV t Determine the disturbance direction to perform voltage disturbance, as shown in equation (8).

[0155] Specific perturbation principle Figure 4 As shown, the closer to the maximum power point, the closer the power disturbance caused by the voltage disturbance is to zero.

[0156]

[0157] The voltage perturbation step length ΔV of the variable step-size conductivity increment method is:

[0158]

[0159] In formula (9), ΔV is the variable step size, λ is the step size scaling factor, and ΔV 1 With ΔV 2 are the upper and lower limits of the voltage perturbation step, respectively. pv Larger, so the tracking step is larger and the tracking speed is faster; in the later stage of optimization, dP pv It gradually decreases, so the step size is reduced, which will not cause large fluctuations around the MPP, improve the tracking accuracy, and then reduce the power loss.

[0160] 104: Determine whether to restart the algorithm;

[0161] When the irradiance of the photovoltaic panels is uneven or the lighting conditions change due to the marine environment and meteorological conditions, the output power of the photovoltaic power generation system also changes accordingly. In order to reduce power loss, it is necessary to terminate the variable step size perturbation observation method in time and return to step 101 to restart the improved grey wolf algorithm for maximum power tracking.

[0162] When the output power value P is detected at time t pv (V t ) and the latest maximum power P at the end of step 102 iteration GMPPi When the difference is greater than the set ratio θ, that is, when equation (10) is satisfied, the algorithm returns to step 102 and restarts the improved grey wolf algorithm.

[0163]

[0164] When the output power value P is detected at time t pv (V t ) and the latest maximum power P at the end of step 102 iteration GMPPi When the difference is less than or equal to the set ratio θ, that is, when equation (11) is satisfied, the system returns to step 103 and continues to use equations (8) and (9) for disturbance operation.

[0165]

[0166] In order to verify the effectiveness of the method proposed in this invention, a maximum power tracking test is carried out using a floating photovoltaic power generation system at sea as an example. The DC-DC rectifier unit is selected as a Boost rectifier circuit, and the MPPT control circuit is as follows: Figure 5 The offshore floating photovoltaic array has a capacity of about 30kW. Each photovoltaic array string contains 26 photovoltaic modules. There are two photovoltaic strings connected to the same MPPT control circuit. Under the action of waves, the photovoltaic array has unevenly distributed irradiance. Figure 6 The photovoltaic module parameters are shown in Table 1.

[0167] Table 1 Bifacial photovoltaic module parameters

[0168]

[0169] The PV curve corresponding to the unevenly distributed irradiance of the photovoltaic array under the action of waves is as follows Figure 7 As shown in the figure, there are 4 local maximum power points of the photovoltaic array at this time, among which the global maximum power point (1286V, 13.820kW) is located on the far right of the curve. Fig. 9It can be seen that in the uneven irradiance scenario, the hybrid improved algorithm based on the improved grey wolf algorithm and the variable step size perturbation observation method tracks the global maximum power point (1286V, 13.820kW), the algorithm convergence time is about 12ms, and the average tracking efficiency of the algorithm is about 99.89%, which can meet the maximum power tracking requirements of the offshore floating photovoltaic power generation system.

[0170] Although specific bio-inspired algorithms, traditional MPPT techniques, and implementations are used to describe the present invention, it should be clear that these algorithms and implementations are merely examples of the principles and applications of the present invention. Therefore, it should be understood that many modifications may be made to the exemplary embodiments, and other arrangements may be designed, as long as they do not deviate from the spirit and scope of the present invention as defined by the appended claims.

Claims

1. A MPPT control method for an offshore floating photovoltaic power generation system, characterized in that It includes the following steps: (1) By using the movement of seawater in the sea area at multiple time scales and the time-varying periodicity of the maximum power point of the offshore floating photovoltaic array to a certain extent, a dynamically updated historical optimal database of the maximum power point of the offshore floating photovoltaic array is established (d GMPPi ,V GMPPi ,P GMPPi ,t i ), narrowing the initial distribution range of the population of the traditional biologically inspired algorithm, where d GMPPi For the historical moment i The corresponding historical optimal duty cycle, V GMPPi For the historical moment i The corresponding historical optimal voltage, P GMPPi For the historical moment i The corresponding historical maximum power; (2) Using the dynamically updated maximum power point historical optimal database provided by step (1), and based on probability statistics and support vector machine method, the initial distribution position of n population individuals at time 0 of the traditional bio-inspired algorithm, i.e., the initial duty cycle d1 0 ~d n 0 , reduce the power fluctuation and tracking time in the early stage of maximum power tracking; (3) Improve the elimination mechanism of bad individuals in the iterative process of the bio-inspired algorithm from the perspective of elimination probability and the generation mechanism of new population individuals after bad individuals are eliminated, and improve the convergence speed of the maximum power tracking in the middle stage; (4) Based on steps (1), (2) and (3), a variable step size MPPT algorithm is used to improve the tracking speed and accuracy of the maximum power point tracking. After detecting the power fluctuation caused by environmental changes, the improved bio-inspired algorithm is restarted to ensure that the offshore floating photovoltaic array always runs quickly and accurately near the theoretical maximum power point.

2. According to claim 1, a MPPT control method for an offshore floating photovoltaic power generation system is characterized in that In the step (1), a dynamically updated historical optimal database of maximum power points of offshore floating photovoltaic arrays is established, and the initial distribution range of the population of the traditional bio-inspired algorithm is narrowed, specifically referring to: (1-1) The changes in irradiance and temperature of floating photovoltaic arrays at sea are related to factors such as the rotation of the earth, the revolution of the earth around the sun, and seasonal wind and waves. i There may be a similar maximum power point, denoted as (d GMPPi ,V GMPPi ,P GMPPi );Since the MPPT algorithm needs to output the historical optimal duty cycle d GMPPi Value vs. time t i Therefore, the global maximum power point searched during the operation of the offshore floating photovoltaic array is recorded in order to establish a dynamically updated maximum power point historical optimal database (d GMPPi ,V GMPPi ,P GMPPi ,t i ); (1-2) Considering that the maximum power point of the photovoltaic array has certain seasonal variation characteristics, in order to reduce the storage scale of the maximum power point historical optimal database and reduce the initial distribution range of the population of the bio-inspired algorithm, when the data scale reaches the upper limit set by the maximum power point historical optimal database, the number of optimal data points will no longer be increased, and the individuals with the highest discreteness in the maximum power point historical optimal database will be replaced through dynamic updates.

3. According to claim 2, a MPPT control method for an offshore floating photovoltaic power generation system is characterized in that The steps (1-2) specifically refer to: (1-2-1) Assume that the number of maximum power points stored in the maximum power point historical optimal database is m c , according to the time sequence of data entry, the data are divided into two parts, online database and offline database, in proportion to k1 and 1-k1; (1-2-2) After each MPPT optimization process is completed, the current latest maximum power point (d GMPPi ,V GMPPi ,P GMPPi ,t i ) and store them in an online database; (1-2-3) When storing the current latest maximum power point in step (1-2-2), if the number of online database storages has reached the upper limit k1m c , then a historical maximum power point is randomly selected from the online database and migrated into the offline database; (1-2-4) In step (1-2-3), when a historical maximum power point is randomly selected from the online database and migrated to the offline database, if the number of offline database storages has reached the upper limit (1-k1)m c , then calculate the data in the offline database (d GMPPi ,V GMPPi ,P GMPPi ), the maximum power point with the largest variance value, i.e., the highest discreteness, in the offline database is cleared, thereby providing space for data migration in the online database; at the same time, clearing the data with the highest discreteness in the offline database can screen out the erroneous data actually collected and the very few extreme condition data in the seasonal cycle, thereby reducing the maximum power point historical time t in the historical optimal database. i The corresponding historical optimal duty cycle d GMPPi distribution range.

4. According to claim 1, a MPPT control method for an offshore floating photovoltaic power generation system is characterized in that The step (2) specifically comprises the following steps: (2-1) Considering that the purpose of the MPPT algorithm is to provide a suitable duty cycle d, so as to help the photovoltaic array output operate at the maximum power point; therefore, the support vector machine method can be simplified to partition the multidimensional data in the maximum power point historical optimal database, and the maximum power point historical optimal database (d GMPPi ,V GMPPi ,P GMPPi ,t i ) in historical moment t i The corresponding historical optimal duty cycle d GMPPi , use the support vector machine method to find the historical optimal duty cycle d at this moment GMPPi The array is used to classify one-dimensional data. In this case, there is no need to pre-train the support vector machine model. When the support vector machine performs partitioning, it only needs to find the historical moment t i The corresponding historical optimal duty cycle d GMPPi The maximum interval of adjacent data can be used, and the average value of adjacent data can be used as the basis for dividing the area; therefore, the support vector machine method is used to convert the historical time t i The corresponding historical optimal duty cycle d GMPPi According to the size of the value, it is divided into z partitions, which are recorded as: [d0, d1], [d1, d2]…[d z-1 ,d z ]; (2-2) Count the z partitions [d0, d1], [d1, d2]…[d z-1 ,d z ] historical optimal duty cycle d GMPPi The number (N1~N z ), and based on the obtained historical optimal duty cycle number of each partition (N1~N z ) Statistical calculation of the historical optimal duty cycle d GMPPi In the j interval [d j-1 ,d j ] probability distribution rate F j ; Finally, according to the probability distribution F of the interval j Determine the number of randomly distributed populations D in the interval j at the initial time of the biologically inspired algorithm j , as shown in formula (1): D j =nθ c F j ,j∈{1,2,3...z} In formula (1), n ​​is the number of algorithm populations; θ c is a constant on the interval [0, 1]; Considering that the corresponding duty cycle of the maximum power point in the actual operation process may appear at the historical optimal duty cycle d GMPPi Outside the minimum and maximum range, that is, min(d GMPPi )~max(d GMPPi ), so not all individuals in the population have their initial positions determined by the historical optimal database, only the total number of nθ c The positions of the individuals are determined by formula (1); the number of remaining individuals is n(1-θ c ), randomly distributed in the region [0,min(d GMPPi )] and [max(d GMPPi ),d uplimit ], the initial distribution of the bioinspired algorithm population can be completed by steps (2-1) and (2-2), that is, the initial distribution positions of n population individuals at time 0 are obtained, that is, the initial duty cycle d1 0 ~d n 0 .

5. According to claim 4, a MPPT control method for an offshore floating photovoltaic power generation system is characterized in that The constant θ in step (2-2) c The value range of is generally 0.55~0.

75.

6. According to claim 1, a MPPT control method for an offshore floating photovoltaic power generation system is characterized in that The improved bioinspired algorithm in step (3) is based on the initial optimized distribution position d of the population individuals at time 0 provided in step (2). i 0 ~d n 0 The cuckoo algorithm with improved elimination rules for bad individuals is implemented, including: ① Set the number of bird nests n and the elimination probability p a And the iteration termination criterion, ②The initial distribution position d1 of the population determined by step (2) 0 ~d n 0 , confirm the initial position of n bird nests in the improved cuckoo algorithm, that is, the initial duty cycle d1 0 ~d n 0 ; ③ The position of the bird's nest at time t, that is, the duty cycle d1 at time t t ~d n t (t≥0) is applied to the DC-DC rectifier unit connected to the offshore floating photovoltaic array in turn, and the DC-DC rectifier unit outputs the duty cycle d1 at time t respectively. t ~d n t The corresponding offshore floating photovoltaic array output power P at time t pv (d1 t )~P pv (d n t ), compare and record the maximum power P max And the bird's nest position corresponding to this power, that is, the optimal duty cycle d best ; ④ Check the position of each bird's nest, that is, the duty cycle d1 at time t t ~d n t , the corresponding photovoltaic array output power P at time t pv (d1 t )~P pv (d n t ) and the maximum output power point P of the photovoltaic array during the iteration process max Is the difference greater than the set threshold ε, that is: d i t is the i-th bird's nest, i.e., duty cycle d i At the position of the tth iteration, if the result satisfies formula (3), then go to step ⑤, that is, use the Levy flight and the elimination mechanism of bad bird nests to update the positions of n bird nests, that is, the duty cycle d1 at time t t ~d n t ; If the position of each bird's nest, that is, the duty ratio d1 at time t t ~d n t , the corresponding photovoltaic array output power P at time t pv (d1 t )~P pv (d n t ) and the maximum output power point P of the photovoltaic array during the iteration process max When the difference is less than or equal to the set threshold ε, that is: Then record the latest maximum power point (d GMPPi ,V GMPPi ,P GMPPi ,t i ), and store it in the online database of step (1). At this time, the improved cuckoo algorithm iteration ends and goes to step (4), that is, the maximum power point (d GMPPi ,V GMPPi ,P GMPPi ) is used as the initial position of the variable step size perturbation observation or the conductance increment method for perturbation; ⑤Use Levy flight to update the positions of n bird nests, that is, the duty cycle d1 at time t t ~d n t Value: In formula (5), t is the number of iterations in the iterative process, d i t is the duty cycle of the i-th bird's nest, i.e., d i At the position of the tth iteration, represents point-to-point multiplication, α is the limiting coefficient of the flight step length, and conforms to the standard normal distribution, L is the Lévy search path, that is, the step length during flight, γ c The flight scale for Lévy flight; β c =3 / 2, d best Indicates the position of the bird's nest corresponding to the maximum power point in the iteration process, that is, the optimal duty cycle, and u and v both obey uniform distribution, that is, and Γ is the standard gamma function; ⑥ In order to improve the convergence speed of the algorithm, the bad individual elimination mechanism in the cuckoo biological inspiration algorithm is used, and the elimination probability p is used to eliminate the bad individuals. a Abandon the minimum output power P of the photovoltaic array at time t pv (d t worst ) The corresponding bird's nest is the bad duty ratio d at time t t worst , and updated to the new duty cycle d t worst,new .

7. The MPPT control method of an offshore floating photovoltaic power generation system according to claim 6, characterized in that The step ⑥ specifically refers to: in order to improve the convergence speed of the algorithm and reduce the steady-state oscillation, it is not necessary to perform a calculation on each bird's nest, that is, the duty cycle d after each iteration. i (i≤n) are eliminated, and only the worst nest, i.e., the worst duty cycle d at time t, needs to be eliminated after each iteration. t worst Eliminate and replace the neighboring bird nests of the bad bird nests in the traditional cuckoo algorithm, i.e., d j t , replaced by the optimal bird's nest, that is, the optimal duty cycle d best , as shown in formulas (6) and (7): That is, after each iteration, the output power of the offshore floating photovoltaic array is selected The minimum value in pv (d i t ),i≤n}, corresponding As a bad individual; take a random number r for the bad individual i ∈(0,1), when r i Greater than the elimination probability p a When the new individual generation mechanism of formula (7) is used, the bad duty ratio d at time t is t worst Replace with the new duty cycle d t worst,new , we get the improved n bird nests, that is, the duty cycle d1 at time t t ~d n t , and go to step ③.

8. The MPPT control method of an offshore floating photovoltaic power generation system according to claim 1, characterized in that The improved bioinspired algorithm in step (3) is based on the initial optimized distribution position of the population individuals at time 0 provided in step (2), that is, the initial duty cycle d1 0 ~d n 0 , and the gray wolf algorithm with improved bad individual elimination rules, including: (I) Set the number of gray wolves n and the elimination probability p a and iteration termination criteria; (II) The initial population distribution d1 determined by step (2) 0 ~d n 0 , the initial position of n gray wolf individuals in the improved gray wolf algorithm, that is, the initial duty cycle d1 0 ~d n 0 ; (III) The position of the gray wolf at time t, that is, the duty cycle d1 at time t t ~d n t (t≥0) is applied to the DC-DC rectifier unit connected to the offshore floating photovoltaic array in turn, and the DC-DC rectifier unit outputs the duty cycle d1 at time t respectively. t ~d n t The corresponding offshore floating photovoltaic array output power P at time t pv (d1 t )~P pv (d n t ), compare and record the maximum power P max And the gray wolf position corresponding to this power, that is, the duty cycle d best ; (IV) Check the position of each individual gray wolf, that is, the duty cycle d1 at time t t ~d n t , the corresponding photovoltaic array output power P at time t pv (d1 t )~P pv (d n t ) and the maximum output power point P of the photovoltaic array during the iteration process max Is the difference greater than the set threshold ε, that is: d i t is the position of the i-th gray wolf, i.e., the duty cycle, at the t-th iteration. If the result satisfies equation (8), then go to step (V), i.e., update the positions of n gray wolf individuals, i.e., the duty cycle variable; If the position of each gray wolf is the duty ratio d1 at time t t ~d n t The corresponding photovoltaic array output power P at time t pv (d1 t )~P pv (d n t ) and the maximum output power point P of the photovoltaic array during the iteration process max The difference between is less than or equal to the set threshold ε, that is, it satisfies formula (9), then the latest maximum power point (d GMPPi ,V GMPPi ,P GMPPi ,t i ), and store it in the online database of step (1). At this time, the improved grey wolf algorithm iteration ends, and the process goes to step (4), that is, the maximum power point (d GMPPi ,V GMPPi ,P GMPPi ) is used as the initial position of the variable step size perturbation observation or the conductance increment method for perturbation; (V) The gray wolf position, i.e., the duty cycle d1 at time t t ~d n t The corresponding photovoltaic array output power P pv (d1 t )~P pv (d n t ) are sorted from large to small, and P pv (d1 t )~P pv (d n t ) Maximum value P max The corresponding gray wolf position is Named α wolf, P pv (d1 t )~P pv (d n t )The gray wolf position corresponding to the second largest value, that is, Named β wolf, P pv (d1 t )~P pv (d n t )The gray wolf position corresponding to the third largest value is Named as δ wolf, the positions of the other wolves are Divided into ω wolves; for each ω wolf, that is Its new position is calculated according to the update formula of the wolf position in the search process involving the positions of α wolf, β wolf and δ wolf shown in formula (10), which aims to imitate the hunting behavior of gray wolves and help explore to obtain better solutions; In formula (10), is the position of the i-th wolf in ω at the t-th iteration, is the position of the i-th wolf in ω at the t+1th iteration, is the prey, that is, the maximum output power point P of the photovoltaic array during the iteration process max Corresponding position, A and C are coefficient vectors, and D is the distance between the gray wolf individual and the prey, that is and , a is the convergence factor of the random iteration number linearly decreasing in the interval [0,2], r1 and r2 are random vectors with values ​​in the interval [0,1]; (VI) When the position of the prey is determined, that is, the optimal duty cycle is obtained, the wolf α, the wolf β, and the wolf δ lead the wolf ω to surround the prey, and the position update formula is shown in (11): In formula (9), D a , D β , D δ is the distance between the gray wolf individual position and the positions of α wolf, β wolf and δ wolf. and A1, A2, A3, C1, C2, C3 are the coefficient vectors described in formula (2), d1, d2 and d3 are the distance and direction of the ω wolf individual to the α wolf, β wolf and δ wolf, that is, Towards distance and direction; (VII) In order to improve the convergence speed of the algorithm, an improved bad individual elimination mechanism is used, that is, after each iteration, the bad individuals are eliminated according to the elimination probability p. a Abandon the gray wolf position corresponding to the minimum output power of the photovoltaic array, that is Corresponding Updated to d t worst,new , during the algorithm iteration, randomly select r i ∈(0,1), when r i Greater than the elimination probability p a When , the worst gray wolf individual position is improved The improved positions of n gray wolves, i.e. the duty ratio d1 at time t, are obtained. t ~d n t , and proceed to step (III).

9. The MPPT control method of an offshore floating photovoltaic power generation system according to claim 1, characterized in that The step (4) specifically refers to: using the improved bioinspired algorithm in step (3) to iterate the population, and after each iteration of the population, comparing the i-th individual d i t The corresponding power value P pv (d i t ) and the optimal individual d in the iterative process best The corresponding power value P max The difference between |P pv (d i t )-P max When all the values ​​of | are not greater than the threshold ε, the latest maximum power point (d GMPPi ,V GMPPi ,P GMPPi ,t i ) and store it in the online database of step (1), and use the newly recorded (d GMPPi ,V GMPPi ,P GMPPi ) as the initial position of variable step perturbation observation or conductance increment method for maximum power tracking; when using variable step perturbation observation or conductance increment method for tracking, when the output power value P at time t is detected pv (V t ) and the new record maximum power P GMPPi The difference is greater than the set ratio θ, indicating that the external environment has changed and caused the maximum power point to change. At this time, it is necessary to return to step (2) and restart the improved bio-inspired algorithm, which specifically includes the following contents: (4-1) The disturbance voltage of variable step-size perturbation observation or variable step-size conductance increment method can be calculated by formula (13): Where ΔV is the variable step size disturbance voltage, dP pv and dV are the output power fluctuation and voltage fluctuation of the photovoltaic array at the last disturbance moment, λ is the step length scaling factor, ΔV1 and ΔV2 are the upper and lower limits of the step length respectively; (4-2) When the variable step size MPPT technology is used, when the irradiance of the photovoltaic panels is uneven or the illumination conditions change due to the marine environment and meteorological conditions, the output power of the offshore floating photovoltaic system also changes accordingly. In order to reduce power loss and ensure that the offshore floating photovoltaic system can work quickly and accurately near the theoretical maximum power point, it is necessary to return to step (2) in time and restart the improved bio-inspired algorithm for maximum power tracking, that is: When the output power value P is detected at time t pv (V t ) and the latest maximum power P at the end of the iteration of step (3) GMPP,i When the difference is greater than the set ratio θ, that is, when equation (14) is satisfied, the process returns to step (2) and restarts the improved bio-inspired algorithm for maximum power tracking; When the output power value P is detected at time t pv (V t ) and the latest maximum power P at the end of the iteration of step (3) GMPP,i When the difference is less than or equal to the set ratio θ, that is, when equation (15) is satisfied, return to step (4-1) and continue the disturbance operation to ensure that the offshore floating photovoltaic array always runs quickly and accurately near the theoretical maximum power point.

Images