A method and system for intelligent tracking of photovoltaic maximum energy collection

Abstract

The invention discloses a method and system for intelligent tracking of photovoltaic maximum energy collection, which relates to the technical field of new energy grid connection and consumption, including: receiving real-time photovoltaic data, inputting the real-time photovoltaic data into a pre-established LambertW function, and outputting a maximum power point parameter; solving the maximum power point parameter by an IPSO algorithm to obtain an accurate output power; comparing the accurate output power with the rated power, and obtaining the maximum power point of the photovoltaic array according to the comparison result. The invention solves the needs of large-scale new energy grid connection maximum energy tracking, realizes high-efficiency calculation of new energy grid connection, and has the potential for online operation.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy grid-connected consumption, and in particular to an intelligent tracking method and system suitable for photovoltaic maximum energy collection. Background Art

[0002] Centralized photovoltaic power generation and distributed photovoltaic power generation have certain cloud cover, solar panel pollution, solar glass panel breakage and other faults. When these factors act on photovoltaic modules, they usually affect the output characteristics of photovoltaic modules. In the actual operation of photovoltaic systems, the current and voltage of the system change in real time due to the above reasons, which causes the maximum power point to be in a dynamic change process, so computers are required to perform online calculations; in actual calculations, the IV equation of solar cells is a nonlinear function. Solving nonlinear functions using iteration methods takes a lot of time and cannot meet the high requirements for data accuracy in online calculations. Summary of the invention

[0003] In order to solve the deficiencies mentioned in the above background technology, the purpose of the present invention is to provide a method and system for intelligent tracking of maximum energy collection of photovoltaics, which can solve the needs of maximum energy tracking of large-scale new energy grid-connected.

[0004] In the first aspect, the purpose of the present invention can be achieved by the following technical solution: a method for intelligent tracking of maximum photovoltaic energy collection, the method comprising the following steps:

[0005] Receive real-time photovoltaic data, input the real-time photovoltaic data into a pre-established LambertW function, and output the maximum power point parameters;

[0006] The IPSO algorithm is used to solve the maximum power point parameters and obtain the accurate output power;

[0007] The precise output power is compared with the rated power, and the maximum power point of the PV array is obtained based on the comparison result.

[0008] In combination with the first aspect, in certain implementations of the first aspect, the method further includes: the real-time photovoltaic data is sampled by a Hall sensor and sent to a host computer through a holding circuit.

[0009] In combination with the first aspect, in some implementations of the first aspect, the method further includes: the pre-established Lambert W function is as follows:

[0010] W(x)exp[(x)]=x

[0011] Where x is an arbitrary complex number, which can be rewritten as: W(x)=xexp(-W(x)).

[0012] In combination with the first aspect, in some implementations of the first aspect, the method further includes: the Lambert W function converts the IV expression of the solar cell into an explicit form:

[0013] Among them, the IV expression of the solar cell is as follows:

[0014]

[0015] The formula converted to explicit form is as follows:

[0016]

[0017] have to

[0018] In the formula, R sh is the parallel resistance, R s is the series resistance, I o is the diode current, I ph is the photocurrent, I is the output current of the battery, V is the output voltage, n is the battery diode quality factor, K is the Boltzmann constant, T is the absolute operating temperature of the photovoltaic module, and q is the electron charge.

[0019] In combination with the first aspect, in certain implementations of the first aspect, the method further includes: inputting the real-time photovoltaic data into a pre-established LambertW function, and outputting a calculation process of obtaining a maximum power point parameter:

[0020]

[0021] It is known that at the maximum power point, The property of , so the voltage partial derivative of the above formula is obtained:

[0022]

[0023] In the formula, R sh is the parallel resistance, R s is the series resistance, I o is the diode current, I ph is the photocurrent, I is the output current of the battery, V is the output voltage, n is the battery diode quality factor, K is the Boltzmann constant, T is the absolute operating temperature of the photovoltaic module, and q is the electron charge.

[0024] In combination with the first aspect, in some implementations of the first aspect, the method further includes: an iteration formula of the IPSO algorithm is as follows:

[0025]

[0026] Where v represents the velocity vector of the particle, x represents the position vector of the particle, ω represents the inertia weight, c1 and c2 are used to adjust the individual optimal position and the global optimal position step, respectively, and r1 and r2 are independent random number sequences in (0,1).

[0027] In combination with the first aspect, in some implementations of the first aspect, the method further includes: the processing steps of the IPSO algorithm are as follows:

[0028] Initialize the particle swarm: randomly generate the position vectors and velocity vectors of all particles, and use the initial positions of the particles to initialize the single optimal position P of the particles. best , initialize the global optimal position G with the best position corresponding to the fitness value in all initial best ;

[0029] For each particle, the corresponding fitness value and the optimal position P experienced by the particle are best The corresponding fitness values ​​are compared. If the fitness value corresponding to the new position is larger, the local optimal value P is updated. best ;

[0030] For each particle, the corresponding fitness value is compared with the global optimal position G best The corresponding fitness values ​​are compared. If the fitness value corresponding to the new position is larger, the global optimal value G is updated. best ;

[0031] Update particle speed and position in real time according to the iterative formula;

[0032] If the iterative calculation meets the termination condition, the iterative process is terminated, the search ends, and the optimal solution is output; if not, the iteration continues, and the termination condition is: the maximum number of iterations or the fitness value reaches the lower limit.

[0033] In combination with the first aspect, in some implementations of the first aspect, the method further includes: a calculation formula for the fitness value is as follows:

[0034] F pv,array =max[P1(I1,G1,T1),P2(I2,G2,T2),P3(I3,G3,T3),P4(I4,G4,T4)].

[0035] Where P1, P2, P3, P4 are the real-time powers of the four photovoltaic modules in the simulation, I1, I2, I3, I4 are the output currents, G1, G2, G3, G4 are the light intensity of each module, and T1, T2, T3, T4 are the ambient temperatures of each module.

[0036] In a second aspect, in order to achieve the above-mentioned purpose, the present invention discloses an intelligent tracking system suitable for photovoltaic maximum energy collection, comprising:

[0037] A calculation module is used to receive real-time photovoltaic data, input the real-time photovoltaic data into a pre-established LambertW function, and output a maximum power point parameter;

[0038] Algorithm solving module, used to solve the maximum power point parameters using IPSO algorithm to obtain accurate output power;

[0039] The comparison acquisition module is used to compare the precise output power with the rated power and obtain the maximum power point of the photovoltaic array according to the comparison result.

[0040] In combination with the second aspect, in certain implementations of the second aspect, the system further includes: the real-time photovoltaic data in the calculation module is sent to the processor through a collection device.

[0041] The LambertW function pre-established in the calculation module is as follows:

[0042] W(x)exp[(x)]=x

[0043] Rewrite as: W(x) = xexp(-W(x))

[0044] The LambertW function in the calculation module converts the IV expression of the solar cell into an explicit form:

[0045] Among them, the IV expression of the solar cell is as follows:

[0046]

[0047] The formula converted to explicit form is as follows:

[0048]

[0049] have to

[0050] In the formula, R sh is the parallel resistance, R s is the series resistance, I o is the diode current, I ph is the photocurrent, I is the output current of the battery, V is the output voltage, n is the battery diode quality factor, K is the Boltzmann constant, T is the absolute operating temperature of the photovoltaic module, and q is the electron charge;

[0051] The calculation module inputs real-time photovoltaic data into the pre-established Lambert W function, and outputs the calculation process of the maximum power point parameters:

[0052]

[0053] It is known that at the maximum power point, The property of , so the voltage partial derivative of the above formula is obtained:

[0054]

[0055] In the formula, R sh is the parallel resistance, R s is the series resistance, I o is the diode current, I ph is the photocurrent, I is the output current of the battery, V is the output voltage, n is the battery diode quality factor, K is the Boltzmann constant, T is the absolute operating temperature of the photovoltaic module, and q is the electron charge.

[0056] Preferably, the iteration formula of the IPSO algorithm in the algorithm solution module is as follows:

[0057]

[0058] In the formula, v represents the velocity vector of the particle, x represents the position vector of the particle, ω represents the inertia weight, c1 and c2 are used to adjust the individual optimal position and the global optimal position step size respectively, and r1 and r2 are independent random number sequences in (0,1);

[0059] Preferably, the processing steps of the IPSO algorithm in the algorithm solving module are as follows:

[0060] Initialize the particle swarm: randomly generate the position vectors and velocity vectors of all particles, and use the initial positions of the particles to initialize the single optimal position P of the particles. best , initialize the global optimal position G with the best position corresponding to the fitness value in all initial best ;

[0061] For each particle, the corresponding fitness value and the optimal position P experienced by the particle are best The corresponding fitness values ​​are compared. If the fitness value corresponding to the new position is larger, the local optimal value P is updated. best ;

[0062] For each particle, the corresponding fitness value is compared with the global optimal position G best The corresponding fitness values ​​are compared. If the fitness value corresponding to the new position is larger, the global optimal value G is updated. best ;

[0063] Update particle speed and position in real time according to the iterative formula;

[0064] If the iterative calculation meets the termination condition, the iterative process is terminated, the search is ended, and the optimal solution is output; if not, the iteration continues, and the termination condition is: the maximum number of iterations or the fitness value reaches the lower limit;

[0065] Preferably, the calculation formula for the fitness value in the algorithm solution module is as follows:

[0066] F pv,array =max[P1(I1,G1,T1),P2(I2,G2,T2),P3(I3,G3,T3),P4(I4,G4,T4)].

[0067] Where P1, P2, P3, P4 are the real-time powers of the four photovoltaic modules in the simulation, I1, I2, I3, I4 are the output currents, G1, G2, G3, G4 are the light intensity of each module, and T1, T2, T3, T4 are the ambient temperatures of each module.

[0068] Beneficial effects of the present invention:

[0069] The present invention solves the needs of maximum energy tracking for large-scale renewable energy grid connection, realizes high-efficiency calculation for renewable energy grid connection, and has the potential for online operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0071] Figure 1 It is a schematic flow chart of the method of the present invention;

[0072] Figure 2 It is a schematic diagram of the photovoltaic array under the SP, HC, BL, and TCT structures of the present invention;

[0073] Figure 3 It is a schematic diagram of the relationship between IU and PU under partial shadow conditions of the present invention;

[0074] Figure 4 It is a simplified model schematic diagram of the MPPT photovoltaic array of the present invention;

[0075] Figure 5 It is a flow chart of the improved algorithm based on ImPSO of the present invention; DETAILED DESCRIPTION

[0076] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0077] Embodiment 1:

[0078] The following is an introduction to the relevant terms involved in the embodiments of the present application:

[0079] IPSO: Particle swarm optimization algorithm has the advantages of simple structure, easy implementation, fast convergence speed, and few algorithm parameters. It is often used in optimization calculations. The principle of its algorithm is to imitate the predation behavior of birds. When looking for food, the flock of birds will use their own experience and the experience of the group to update their position, so as to achieve the purpose of obtaining food faster. Each bird in the flock is a particle in the particle swarm algorithm, that is, the potential optimal solution we need to obtain. Each particle is represented by position, speed and fitness characteristics. The position of the particle is usually randomly initialized, the fitness is determined by the optimization function, and the speed controls the flight distance and direction of the particle. It is controlled in real time by the movement of the particle and changes according to the experience of the population, so as to achieve the optimization of the individual in space. In addition to individual optimization, global optimization will also change the current position of the particle. Global optimization refers to the position of the particle to find the optimal fitness function value. Each time the fitness function value of the particle is updated, the new fitness value is compared with the optimal value of the individual and the group, so as to update the optimal position of the individual and the optimal position of the group.

[0080] Photovoltaic modules, also known as solar panels, are the core part of solar power generation systems, responsible for converting solar energy into electrical energy. They are usually composed of high-efficiency single-crystal or multi-crystal solar cells, low-iron ultra-white velvet tempered glass, packaging materials (such as EVA, POE, etc.), functional backplanes, interconnection strips, busbars, junction boxes, and aluminum alloy frames. The service life of photovoltaic modules can reach 15-25 years, and they have the characteristics of high photoelectric conversion efficiency and high reliability.

[0081] like Figure 1 As shown, a method for intelligent tracking of maximum photovoltaic energy collection is provided, characterized in that the method comprises the following steps:

[0082] Receive real-time photovoltaic data, input the real-time photovoltaic data into a pre-established LambertW function, and output the maximum power point parameters;

[0083] Among them, real-time photovoltaic data is collected by sensors such as temperature, light, output current and voltage, and sent to the processor for online calculation. It is sampled by the Hall sensor and sent to the host computer through the holding circuit.

[0084] The pre-built LambertW functions are as follows:

[0085] W(x)exp[(x)]=x

[0086] Where x is any complex number, rewritten as: W(x) = xexp(-W(x))

[0087] The LambertW function converts the IV expression for a solar cell into explicit form:

[0088] Among them, the IV expression of the solar cell is as follows:

[0089]

[0090] The formula converted to explicit form is as follows:

[0091]

[0092] have to

[0093] In the formula, R sh is the parallel resistance, R s is the series resistance, I o is the diode current, I ph is the photocurrent, I is the output current of the battery, V is the output voltage, n is the battery diode quality factor, K is the Boltzmann constant, T is the absolute operating temperature of the photovoltaic module, and q is the electron charge.

[0094] Input the real-time photovoltaic data into the pre-established LambertW function and output the calculation process of the maximum power point parameters:

[0095]

[0096] It is known that at the maximum power point, Therefore, the voltage partial derivative of the above formula is obtained:

[0097]

[0098] In the formula, R sh is the parallel resistance, R s is the series resistance, I o is the diode current, I ph is the photocurrent, I is the output current of the battery, V is the output voltage, n is the battery diode quality factor, K is the Boltzmann constant, T is the absolute operating temperature of the photovoltaic module, and q is the electron charge.

[0099] The IPSO algorithm is used to solve the maximum power point parameters and obtain the accurate output power;

[0100] The iterative formula of the IPSO algorithm is as follows:

[0101]

[0102] In the formula, v represents the velocity vector of the particle, x represents the position vector of the particle, ω represents the inertia weight, c1 and c2 are used to adjust the individual optimal position and the global optimal position step size respectively, and r1 and r2 are independent random number sequences in (0,1);

[0103] A simple photovoltaic array model is Figure 4 As shown in the figure, it includes a boost converter, an MPPT algorithm module and a PWM generator. In this module, the MPPT algorithm block collects the output voltage and current of the photovoltaic array, and then uses MPPT algorithms such as curve fitting method, perturbation observation method and incremental conductance method to calculate the duty cycle of the PWM controller, thereby generating the corresponding PWM signal. The PWM signal adjusts the output voltage of the photovoltaic array by controlling the on and off of the MOSFET.

[0104] However, in reality, due to the influence of various meteorological and ambient factors, photovoltaic arrays do not always operate under standard conditions. When photovoltaic arrays operate under non-uniform irradiance conditions, shading will occur. The appearance of partial shadows will cause the photovoltaic power output characteristic curve to be stepped. When partial shading occurs on the surface of the photovoltaic panel, the output of the entire module may be reduced. There are two main reasons: (1) The irradiance on the panel surface is reduced, and the photocurrent is reduced. (2) The surface temperature of the photovoltaic array changes. This paper studies the photovoltaic surface shadow situation. There are three types of surface shadows: horizontal shading, vertical shading, and uniform shading. The corresponding photovoltaic output characteristic curves are different. This study uses the photovoltaic panel parameters of an actual photovoltaic power station for simulation. Figure 2 The photovoltaic array is connected using the series-parallel (SP) method, and the photovoltaic array model is used to simulate the output power of the photovoltaic array under partial shading conditions.

[0105] The precise output power is compared with the rated power, and the maximum power point of the PV array is obtained based on the comparison result.

[0106] In order to study the output power characteristics of photovoltaic arrays under different shading conditions, this study chooses to study the output characteristics of horizontal shading, vertical shading and uniform shading. After horizontal shading, the light intensity of the first and second rows is 1000W / m 2 , the third row light intensity is 800W / m 2 , the fourth row light intensity is 500W / m 2 After the vertical shadow is blocked, the light intensity of the first and second columns is 1000w / m2, and the light intensity of the third and fourth columns is 700w / m2. After the shadow is evenly blocked, the light intensity is 800w / m2. Figure 3It can be seen that the IU and PU characteristic curves of the photovoltaic array under uniform shadow and vertical shadow are the same as the IU and PU characteristic curves of the photovoltaic cell under normal irradiance conditions. However, when the lateral shadow is blocked, the photovoltaic array will produce multiple maximum power point peaks, which may cause the MPPT calculation to fall into the local maximum power situation. That is, the local maximum power point (Local Max PowerPoint, LMPP), which actually needs to be the global maximum power point (Global Max PowerPoint, GMPP). Figure 3 In (b), the green arrow indicates the LMPP point, and the red arrow indicates the GMMP point. If the PV array always works at the local maximum power point, the actual output value of the PV array will be less than the set value. Therefore, it is necessary to find a suitable MPPT mode to jump out of the local optimal situation and find the global optimal maximum power point.

[0107] Through the above simulation analysis, we can deduce the mechanism of the multi-peak phenomenon of photovoltaic array output power under partial shading environment. Figure 3 Take the horizontal shading situation in the example. When the photovoltaic array works at low voltage, the output current of the photovoltaic array is higher than the maximum parallel current of other modules, so that the bypass diode reaches the conduction condition, and the photovoltaic array is short-circuited. At this time, the photovoltaic array PU curve begins to produce the first local maximum power point (l mpp When the operating current is less than the maximum short-circuit current of the fourth row, the bypass diode is cut off and all PV modules generate power at the same time, so the global maximum power point (G MMP ). In summary, under partial shading conditions, there will be multiple maximum power points, but the entire photovoltaic system has only one maximum power point G MPP Therefore, in this case, the traditional MPPT algorithm, such as the hill climbing method or the conductance increment method, may not be able to track the global maximum power point, so it is necessary to find a suitable method to track the maximum power point under partial shadow conditions.

[0108] Under partial shadow conditions, the PU output characteristics of the photovoltaic array will have multiple peaks, which is very different from the output characteristics under normal light irradiance. In this case, it is difficult for traditional MPPT algorithms to find the global maximum power point, such as curve fitting method, perturbation observation method, incremental conductance method, etc. Therefore, it is necessary to find a technology with good global search capability. An MPPT algorithm with good convergence is very important, and the particle swarm algorithm (PSO) is suitable for solving nonlinear optimization problems that are difficult to solve with traditional search algorithms. However, the traditional particle swarm algorithm has the disadvantages of poor convergence and low efficiency. Therefore, the improved particle swarm optimization method is used to adjust the photovoltaic array to the global optimal working point to ensure that the output power of the photovoltaic array is always maintained at the maximum value.

[0109] The processing steps of the IPSO algorithm are as follows:

[0110] Initialize the particle swarm: randomly generate the position vectors and velocity vectors of all particles, and use the initial positions of the particles to initialize the single optimal position P of the particles. best , initialize the global optimal position G with the best position corresponding to the fitness value in all initial best ;

[0111] For each particle, the corresponding fitness value and the optimal position P experienced by the particle are best The corresponding fitness values ​​are compared. If the fitness value corresponding to the new position is larger, the local optimal value P is updated. best ;

[0112] For each particle, the corresponding fitness value is compared with the global optimal position G best The corresponding fitness values ​​are compared. If the fitness value corresponding to the new position is larger, the global optimal value G is updated. best ;

[0113] Update particle speed and position in real time according to the iterative formula;

[0114] If the iterative calculation meets the termination condition, the iterative process is terminated, the search ends, and the optimal solution is output; if not, the iteration continues, and the termination condition is: the maximum number of iterations or the fitness value reaches the lower limit.

[0115] Through the study of the output characteristics of the photovoltaic array under the SP connection mode in the previous section, it is not difficult to find that the PU characteristics of the photovoltaic array under partial shading conditions may have multi-peak conditions, and it is difficult to find the global optimal point according to the traditional MPPT algorithm. Therefore, it is believed that the particle swarm algorithm is suitable for solving multi-peak function optimization problems. However, the traditional particle swarm algorithm is prone to local optimization and premature convergence, so this paper adopts an improved particle swarm algorithm to solve this problem.

[0116] The parameters c1 and c2 in the particle velocity update formula are the acceleration coefficients of the particles, which represent the influence of the particle's self-cognition and social cognition on the particle's motion trajectory. Generally speaking, the acceleration coefficient is c1 = c2 = 2. There are many ways to improve computational efficiency. This paper proposes an adaptive method to improve the particle search capability. The acceleration coefficients c1 and c2 are improved. c1 takes a larger value in the previous stage of search, and c2 takes a smaller value, so that the particle can move from its own optimal P best , and less from the socially optimal G bestLearning in the process enhances the global search ability of particles. As the number of iterations increases, the values ​​of c1 and c2 will also change. When the number of iterations is small, c1 and c2 are increased rapidly to make the particles approach the target value quickly. When the iteration scale increases, in order to ensure the accuracy of the particles in finding the optimal value, the values ​​of c1 and c2 are improved. c1 takes a larger value in the previous stage, and c2 takes a smaller value, which increases slowly to ensure that the particles can approach the optimal value. Therefore, this paper proposes an inverse cosine nonlinear symmetrical variation acceleration coefficient based on the number of iterations, as shown in the formula.

[0117]

[0118] Where c 1,start =2.7,c 1,end =1.2,c 2,start =0.5,c 2,end =2.2; N = current iteration, N max is the maximum number of iterations. The inertia weight value ω is the parameter with the greatest impact in the PSO algorithm. How to set the appropriate ω in each iteration is crucial to the PSO algorithm. There are many research results to make the algorithm have a good convergence speed, which can be mainly divided into linear adjustment and nonlinear adjustment. This paper uses the nonlinear adaptive ω in the following formula to calculate

[0119]

[0120] Where P N is the current PV array output power, P ave is the average output power of the photovoltaic array, P Nmax is the rated maximum output power of the photovoltaic array. Therefore, the inertia weight ω will change with the change of the objective function value. The particle speed update formula is as follows. r1 and r2 are random numbers between (0, 1), which increases the randomness of the particle swarm algorithm and prevents the particles from being premature due to different initial value selections.

[0121] v Nnext =w1·v1+c1·r1·(P Nmax -V o,N )+c2·r2·(V max -V o,N )

[0122] Through this method, the global search ability and local search ability of the algorithm are greatly improved.

[0123] The improved PV partial shading acceleration coefficient particle swarm optimization algorithm under the MPPT control logic is: first initialize various parameters of the particles, then initialize the objective function and the fitting value of each particle for calculation, then take the output voltage of multiple photovoltaic modules as multidimensional variables, set the total output power of the photovoltaic array as the objective function, and finally apply the improved particle swarm algorithm, that is: use the adaptive acceleration coefficient and inertia weight optimization algorithm to optimize the total output maximization of the power line. The specific optimization process is: as the number of iterations increases, the rate of increase of the acceleration coefficient decreases to ensure that the particles have good search capabilities, which is conducive to getting closer to the optimal solution. The photovoltaic array composed of 4 photovoltaic modules in series is simulated, and the calculation method of its fitness value can be expressed as formula ()

[0124] F pv,array =max[P1(I1,G1,T1),P2(I2,G2,T2),P3(I3,G3,T3),P4(I4,G4,T4)]

[0125] Where P1, P2, P3, P4 are the real-time powers of the four photovoltaic modules in the simulation, I1, I2, I3, I4 are the output currents, G1, G2, G3, G4 are the light intensity of each module, and T1, T2, T3, T4 are the ambient temperatures of each module.

[0126] When evaluating particles in the population, the current individual position and fitness value are stored in the P of each particle. best and round it off, best The position and fitness value of the best individual in is stored in Gbest; according to the updated inertia weight, the speed and position formula of the particle is calculated from its corresponding speed and position. For each particle, its fitness value is set to P best , compared with the best experience. If the current fitness value is good, the current particle position and adaptive value are used as the current best position P best Save; compare the fitness value of each particle with the best position of global experience G best If the current fitness value is better, the current particle position and fitness value are saved as the global maximum optimal position G best The last step is to see if the final term is reached. If so, stop the algorithm and output the optimal solution (maximum power value). If not, continue to update the particles and search for options, and increase the number of particles until the algorithm finds the optimal value or reaches the maximum iteration time. The flowchart of IPSO is as follows: Figure 1 Shown:

[0127] The present invention provides an improved particle swarm optimization algorithm for the case of a series array of four photovoltaic modules through Matlab / Simunlink, and compiles the IPSO MPPT algorithm through Matlab / S-Function, which is analyzed as part of the entire photovoltaic array. The MPPT block uses two optimal algorithms, one is the traditional particle swarm algorithm, and the other is a particle swarm algorithm for comparison. The irradiance of each photovoltaic is different: PV1=1000W / m2, PV2=800W / m2, PV3=600W / m2, PV4=400W / m2. By changing the input irradiance, the PU curve of the PV array can be made to have a multi-peak situation. The simulation results are shown in the figure below. When the number of particles is 4, we can find that in the multi-peak state, the output power of the photovoltaic module found by these four particles is close to the maximum power point. The output voltage deviation value is limited to within 0.5%. As can be seen from Table 1, the maximum power output is 494.38W, which is very close to the calculated value of 503W, with a difference of only 9W.

[0128] Table 1: Particle swarm optimization results for the voltage and power output of the four modules

[0129]

[0130] Embodiment 2: In the second aspect, in order to achieve the above-mentioned purpose, the present invention discloses an intelligent tracking system suitable for photovoltaic maximum energy collection, comprising:

[0131] A calculation module is used to receive real-time photovoltaic data, input the real-time photovoltaic data into a pre-established Lambert W function, and output a maximum power point parameter;

[0132] Algorithm solving module, used to solve the maximum power point parameters using IPSO algorithm to obtain accurate output power;

[0133] The comparison acquisition module is used to compare the precise output power with the rated power and obtain the maximum power point of the photovoltaic array according to the comparison result.

[0134] In combination with the second aspect, in some implementations of the second aspect, the system further includes: the real-time photovoltaic data in the calculation module is collected by the sensor to obtain temperature, light, output current and voltage, and sent to the processor for online calculation.

[0135] The Lambert W function pre-established in the calculation module is as follows:

[0136] W(x)exp[(x)]=x

[0137] Rewrite as: W(x) = xexp(-W(x))

[0138] Or the Lambert W function in the calculation module converts the IV expression of the solar cell into an explicit form:

[0139] Among them, the IV expression of the solar cell is as follows:

[0140]

[0141] The formula converted to explicit form is as follows:

[0142]

[0143] have to

[0144] In the formula, R sh is the parallel resistance, R s is the series resistance, I o is the diode current, I ph is the photocurrent, I is the output current of the battery, V is the output voltage, n is the battery diode quality factor, K is the Boltzmann constant, T is the absolute operating temperature of the photovoltaic module, and q is the electron charge.

[0145] Or the calculation module inputs the real-time photovoltaic data into the pre-established Lambert W function, and outputs the calculation process of the maximum power point parameters:

[0146]

[0147] It is known that at the maximum power point, The property of , so the voltage partial derivative of the above formula is obtained:

[0148]

[0149] In the formula, R sh is the parallel resistance, R s is the series resistance, I o is the diode current, I ph is the photocurrent, I is the output current of the battery, V is the output voltage, n is the battery diode quality factor, K is the Boltzmann constant, T is the absolute operating temperature of the photovoltaic module, and q is the electron charge.

[0150] Preferably, the iteration formula of the IPSO algorithm in the algorithm solution module is as follows:

[0151]

[0152] In the formula, v represents the velocity vector of the particle, x represents the position vector of the particle, ω represents the inertia weight, c1 and c2 are used to adjust the individual optimal position and the global optimal position step size respectively, and r1 and r2 are independent random number sequences in (0,1);

[0153] Preferably, the processing steps of the IPSO algorithm in the algorithm solving module are as follows:

[0154] Initialize the particle swarm: randomly generate the position vectors and velocity vectors of all particles, and use the initial positions of the particles to initialize the single optimal position P of the particles. best , initialize the global optimal position G with the best position corresponding to the fitness value in all initial best ;

[0155] For each particle, the corresponding fitness value and the optimal position P experienced by the particle are best The corresponding fitness values ​​are compared. If the fitness value corresponding to the new position is larger, the local optimal value P is updated. best ;

[0156] For each particle, the corresponding fitness value is compared with the global optimal position G best The corresponding fitness values ​​are compared. If the fitness value corresponding to the new position is larger, the global optimal value G is updated. best ;

[0157] Update particle speed and position in real time according to the iterative formula;

[0158] If the iterative calculation meets the termination condition, the iterative process is terminated, the search is ended, and the optimal solution is output; if not, the iteration continues, and the termination condition is: the maximum number of iterations or the fitness value reaches the lower limit;

[0159] Preferably, the calculation formula for the fitness value in the algorithm solution module is as follows:

[0160] F pv,array =max[P1(I1,G1,T1),P2(I2,G2,T2),P3(I3,G3,T3),P4(I4,G4,T4)]

[0161] Where P1, P2, P3, P4 are the real-time powers of the four photovoltaic modules in the simulation, I1, I2, I3, I4 are the output currents, G1, G2, G3, G4 are the light intensity of each module, and T1, T2, T3, T4 are the ambient temperatures of each module.

[0162] Based on the same inventive concept, the present invention also provides a computer device, which includes: one or more processors, and a memory for storing one or more computer programs; the program includes program instructions, and the processor is used to execute the program instructions stored in the memory. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is used to implement one or more instructions, specifically for loading and executing one or more instructions in a computer storage medium to implement the above method.

[0163] It needs to be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium, on which a computer program is stored, and the computer program is executed by a processor to execute the above method. The storage medium can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0164] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0165] The above shows and describes the basic principles, main features and advantages of the present disclosure. Those skilled in the art should understand that the present disclosure is not limited by the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present disclosure. Without departing from the spirit and scope of the present disclosure, the present disclosure may have various changes and improvements, and these changes and improvements fall within the scope of the present disclosure to be protected.

Claims

1. An intelligent tracking method for maximum photovoltaic energy collection, characterized in that: The method comprises the following steps: Receive real-time photovoltaic data, input the real-time photovoltaic data into a pre-established LambertW function, and output the maximum power point parameters; The real-time photovoltaic data is input into the pre-established Lambert W function, and the calculation process of the maximum power point parameters is obtained as output: It is known that at the maximum power point, Therefore, the voltage partial derivative of the above formula is obtained: In the formula, R sh is the parallel resistance, R s is the series resistance, I o is the diode current, I ph is the photocurrent, I is the output current of the battery, V is the output voltage, n is the battery diode quality factor, K is the Boltzmann constant, T is the absolute operating temperature of the photovoltaic module, and q is the electron charge; The IPSO algorithm is used to solve the maximum power point parameters and obtain the accurate output power; The iteration formula of the IPSO algorithm is as follows: In the formula, V represents the velocity vector of the particle, X represents the position vector of the particle, ω represents the inertia weight, c1 and c2 are used to adjust the individual optimal position and the global optimal position step size respectively, r1 and r2 are independent random number sequences in (0,1); The processing steps of the IPSO algorithm are as follows: Initialize the particle swarm: randomly generate the position vectors and velocity vectors of all particles, and use the initial positions of the particles to initialize the single optimal position P of the particles. best , initialize the global optimal position G with the best position corresponding to the fitness value in all initial best ; For each particle, the corresponding fitness value and the optimal position P experienced by the particle are best The corresponding fitness values ​​are compared. If the fitness value corresponding to the new position is larger, the local optimal value P is updated. best ; For each particle, the corresponding fitness value is compared with the global optimal position G best The corresponding fitness values ​​are compared. If the fitness value corresponding to the new position is larger, the global optimal value G is updated. best ; Update particle speed and position in real time according to the iterative formula; If the iterative calculation meets the termination condition, the iterative process is terminated, the search is ended, and the optimal solution is output; if not, the iteration continues, and the termination condition is: the maximum number of iterations or the fitness value reaches the lower limit; The calculation formula of the fitness value is as follows: F pv,array =max[P1(I1,G1,T1),P2(I2,G2,T2),P3(I3,G3,T3),P4(I4,G4,T4)] Where P1, P2, P3, and P4 are the real-time powers of the four photovoltaic modules in the simulation, I1, I2, I3, and I4 are the output currents, G1, G2, G3, and G4 are the light intensities of each module, and T1, T2, T3, and T4 are the ambient temperatures of each module; The precise output power is compared with the rated power, and the maximum power point of the photovoltaic array is obtained based on the comparison result.

2. According to claim 1, a method for intelligent tracking of photovoltaic maximum energy collection is characterized in that: The real-time photovoltaic data is sampled by a Hall sensor and sent to a host computer via a holding circuit.

3. The method for intelligent tracking of photovoltaic maximum energy collection according to claim 1, characterized in that: The pre-established LambertW function is as follows, where x is any complex number: W(x)exp[W(x)]=x Rewrite as: W(x)=xexp(-W(x)).

4. The method for intelligent tracking of photovoltaic maximum energy collection according to claim 3 is characterized in that: The LambertW function converts the IV expression for a solar cell into explicit form: Among them, the IV expression of the solar cell is as follows: The formula converted to explicit form is as follows: have to In the formula, R sh is the parallel resistance, R s is the series resistance, I o is the diode current, I ph is the photocurrent, I is the output current of the battery, V is the output voltage, n is the battery diode quality factor, K is the Boltzmann constant, T is the absolute operating temperature of the photovoltaic module, and q is the electron charge.

5. An intelligent tracking system for maximum photovoltaic energy collection, characterized in that: include: A calculation module is used to receive real-time photovoltaic data, input the real-time photovoltaic data into a pre-established LambertW function, and output a maximum power point parameter; The calculation module inputs real-time photovoltaic data into the pre-established Lambert W function, and outputs the calculation process of the maximum power point parameters: It is known that at the maximum power point, Therefore, the voltage partial derivative of the above formula is obtained: In the formula, R sh is the parallel resistance, R s is the series resistance, I o is the diode current, I ph is the photocurrent, I is the output current of the battery, V is the output voltage, n is the battery diode quality factor, K is the Boltzmann constant, T is the absolute operating temperature of the photovoltaic module, and q is the electron charge; Algorithm solving module, used to solve the maximum power point parameters using IPSO algorithm to obtain accurate output power; The iterative formula of the IPSO algorithm in the algorithm solution module is as follows: The sensor collects temperature, light, output current and voltage, and sends them to the processor for online calculation. In the formula, V represents the velocity vector of the particle, X represents the position vector of the particle, ω represents the inertia weight, c1 and c2 are used to adjust the individual optimal position and the global optimal position step size respectively, r1 and r2 are independent random number sequences in (0,1); Preferably, the processing steps of the IPSO algorithm in the algorithm solving module are as follows: Initialize the particle swarm: randomly generate the position vectors and velocity vectors of all particles, and use the initial positions of the particles to initialize the single optimal position P of the particles. best , initialize the global optimal position G with the best position corresponding to the fitness value in all initial best ; For each particle, the corresponding fitness value and the optimal position P experienced by the particle are best The corresponding fitness values ​​are compared. If the fitness value corresponding to the new position is larger, the local optimal value P is updated. best ; For each particle, the corresponding fitness value is compared with the global optimal position G best The corresponding fitness values ​​are compared. If the fitness value corresponding to the new position is larger, the global optimal value G is updated. best ; Update particle speed and position in real time according to the iterative formula; If the iterative calculation meets the termination condition, the iterative process is terminated, the search is ended, and the optimal solution is output; if not, the iteration continues, and the termination condition is: the maximum number of iterations or the fitness value reaches the lower limit; Preferably, the calculation formula for the fitness value in the algorithm solution module is as follows: F pv,array =max[P1(I1,G1,T1),P2(I2,G2,T2),P3(I3,G3,T3),P4(I4,G4,T4)] Where P1, P2, P3, and P4 are the real-time powers of the four photovoltaic modules in the simulation, I1, I2, I3, and I4 are the output currents, G1, G2, G3, and G4 are the light intensities of each module, and T1, T2, T3, and T4 are the ambient temperatures of each module; The comparison acquisition module is used to compare the precise output power with the rated power and obtain the maximum power point of the photovoltaic array according to the comparison result.

6. The intelligent tracking system for photovoltaic maximum energy collection according to claim 5 is characterized in that: The real-time photovoltaic data in the calculation module is collected by sensors such as temperature, light, output current and voltage, and sent to the processor for online calculation; The LambertW function pre-established in the calculation module is as follows: W(x)exp[W(x)]=x Rewrite as: W(x) = xexp(-W(x)) The LambertW function in the calculation module converts the IV expression of the solar cell into an explicit form: Among them, the IV expression of the solar cell is as follows: The formula converted to explicit form is as follows: have to In the formula, R sh is the parallel resistance, R s is the series resistance, I o is the diode current, I ph is the photocurrent, I is the output current of the battery, V is the output voltage, n is the battery diode quality factor, K is the Boltzmann constant, T is the absolute operating temperature of the photovoltaic module, and q is the electron charge.

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