MPPT (Maximum Power Point Tracking) control method and system of optical storage power generation system based on ISSA-PO algorithm

Abstract

The invention relates to a method based on ISSA-Pamp; the invention discloses an optical storage power generation system MPPT control method and system based on an O algorithm. The method comprises the following steps that ISSA-Pamp is constructed; performing an O algorithm; calculating output power and an initial fitness value according to the collected electrical data; the output power and the initial fitness value are input into the ISSA-Pamp, and the ISSA-Pamp is input into the ISSA-Pamp; and an O algorithm, performing loop iteration calculation until a preset maximum iteration number is reached, and outputting an optimal duty ratio. In the early stage of tracking, the ISSA adopts a chaotic mapping mode to increase population diversity, and the wide search capability of the algorithm is improved; in order to prevent the algorithm from falling into local optimum, a firefly disturbance algorithm is used for carrying out disturbance updating on the sparrow individuals; in the later period of tracking, Pamp is used; o, the system is prevented from oscillating near the maximum power point, stable output of the maximum power point is guaranteed, and the MPPT control method achieves rapid tracking and accurate output in different scenes and has high practical engineering value.

Description

Technical Field

[0001] The present disclosure belongs to the field of photovoltaic power generation control technology, and in particular, relates to an MPPT control method and system for a photovoltaic power generation system based on an ISSA-P&O algorithm. Background Art

[0002] Building a new power system based on new energy has become a trend in the future. Among them, clean energy represented by photovoltaics and energy storage is an important part of new energy power generation. Maximum power point tracking (MPPT) control technology is conducive to improving the efficiency of photovoltaic power generation. However, with the continuous increase in the grid-connected capacity of new energy such as photovoltaics and energy storage, the uneven light intensity causes the output curve of the photovoltaic array to be a multi-peak curve, resulting in the traditional MPPT control algorithm no longer being applicable. Traditional MPPT technology includes the conductance increment method and the perturbation observation method (P&O), which has good effects under uniform light intensity. Because of the advantages of simple algorithms and easy implementation, it is widely used in industry. However, when the actual photovoltaic storage power generation system is in operation, the uneven light intensity causes the output curve of the photovoltaic array to be a multi-peak curve, and the traditional MPPT technology is prone to fall into local optimality.

[0003] Therefore, it is necessary to provide a new MPPT control method and system for a photovoltaic power generation system based on the ISSA-P&O algorithm to solve the above technical problems. Summary of the invention

[0004] The purpose of the present disclosure is to provide a MPPT control method and system for a photovoltaic power generation system based on the ISSA-P&O algorithm in order to solve the above problems.

[0005] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0006] A method for controlling the MPPT of a photovoltaic power generation system based on an ISSA-P&O algorithm comprises the following steps:

[0007] Construct ISSA-P&O algorithm;

[0008] Calculate output power and initial fitness values ​​based on collected electrical data;

[0009] The output power and the initial fitness value are input into the ISSA-P&O algorithm, and after cyclic iterative calculation until the preset maximum number of iterations is reached, the optimal duty cycle is output to realize the MPPT control of the photovoltaic power generation system.

[0010] As a further optimization scheme of the present disclosure, constructing the ISSA-P&O algorithm includes:

[0011] The chaotic mapping method is used to increase the population diversity of the SSA algorithm, and the firefly perturbation algorithm is used to optimize it to obtain the ISSA algorithm.

[0012] The structure of the P&O algorithm is optimized to obtain an adaptive step-size P&O algorithm;

[0013] The ISSA algorithm is combined with the adaptive step-size P&O algorithm to obtain the ISSA-P&O algorithm.

[0014] As a further optimization scheme of the present disclosure, before the output power and the initial fitness value are input into the ISSA-P&O algorithm, the parameters of the ISSA-P&O algorithm need to be initialized, including initializing the population size and the number of iterations, and using chaotic mapping to initialize the individual positions of sparrows, where the sparrow individuals include exploration individuals, tracking individuals and warning individuals.

[0015] As a further optimization scheme of the present disclosure, the output power and the initial fitness value are input into the ISSA-P&O algorithm, and after cyclic iterative calculation until a preset maximum number of iterations is reached, the optimal duty cycle is outputted, including:

[0016] Input the output power and the initial fitness value into the ISSA algorithm, calculate the worst solution and the optimal solution of the global fitness, and determine whether the ISSA reaches a preset maximum number of iterations;

[0017] If the preset maximum number of iterations is reached, the optimal solution of the global fitness is used as the input of the adaptive step size P&O algorithm, and the optimal duty cycle is output after optimization.

[0018] As a further optimization scheme of the present disclosure, if the preset maximum number of iterations is not reached, the individual positions of the sparrows in the ISSA algorithm are iteratively updated, the firefly algorithm is perturbed, and the individual positions of the sparrows are updated to find the optimal solution;

[0019] The optimal solution of the individual sparrow position is substituted into the ISSA-P&O algorithm initialization parameters for updating, and the iterative update process is continued until the preset maximum number of iterations is reached.

[0020] As a further optimization scheme of the present invention, if the preset maximum number of iterations is reached, the optimal solution of the global fitness is used as the input of the adaptive step size P&O algorithm. After the optimal duty cycle is optimized and output, it is determined whether the environmental changes have caused the restart conditions to be met; if so, return to the ISSA-P&O algorithm initialization parameter step; if not, the optimal duty cycle is used as the final output.

[0021] As a further optimization scheme of the present disclosure, the restart condition caused by the environmental change includes that the output power of the photovoltaic power is changed due to environmental factors, and the restart condition is that the output power change rate of the photovoltaic power exceeds a preset threshold.

[0022] An MPPT control system for a photovoltaic power generation system based on an ISSA-P&O algorithm, comprising:

[0023] Algorithm building blocks, used to build the ISSA-P&O algorithm;

[0024] A calculation module, used for calculating output power and initial fitness value according to the collected electrical data;

[0025] The iterative calculation module is used to input the output power and the initial fitness value into the ISSA-P&O algorithm, and after cyclic iterative calculation until a preset maximum number of iterations is reached, the optimal duty cycle is output to achieve MPPT control of the photovoltaic power generation system.

[0026] An electronic device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0027] Memory, for storing computer programs;

[0028] The processor is used to implement the MPPT control method of the photovoltaic power generation system based on the ISSA-P&O algorithm when executing the program stored in the memory.

[0029] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the MPPT control method for a photovoltaic power generation system based on the ISSA-P&O algorithm.

[0030] The beneficial effects of the present disclosure are:

[0031] In the early stage of tracking, the ISSA adopts chaotic mapping to increase population diversity and improve the algorithm's extensive search capability; in order to prevent the algorithm from falling into local optimality, the firefly perturbation algorithm is used to perturb and update the sparrow individuals; in the later stage of tracking, P&O is used to prevent the system from oscillating near the maximum power point to ensure stable output of the maximum power point. The proposed MPPT control method realizes fast tracking and precise output in different scenarios, can be well applied to photovoltaic and energy storage hybrid power generation systems, and has strong practical engineering value. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is a flow chart of the method disclosed herein;

[0033] Figure 2 is a flowchart of a specific method in an embodiment of the present disclosure;

[0034] Figure 3 is a schematic diagram of a photovoltaic storage control system model in an embodiment of the present disclosure;

[0035] Figure 4is a schematic diagram of simulation results of different methods under uniform illumination in an embodiment of the present disclosure;

[0036] Figure 5 is a schematic diagram of simulation results of different methods under static local shadow in an embodiment of the present disclosure;

[0037] Figure 6 is a photovoltaic output curve diagram of each scenario in the embodiment of the present disclosure;

[0038] Figure 7 is a current variation curve diagram of the energy storage device in the embodiment of the present disclosure;

[0039] Figure 8 is a schematic diagram of the change of the charge state of the energy storage device in the embodiment of the present disclosure;

[0040] Fig. 9 is a system structure block diagram in an embodiment of the present disclosure;

[0041] Fig.10 It is a block diagram of the device structure in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0042] The present application is further described in detail below in conjunction with the accompanying drawings. It is necessary to point out here that the following specific implementation methods are only used to further illustrate the present application and cannot be understood as limiting the scope of protection of the present application. Technical personnel in this field can make some non-essential improvements and adjustments to the present application based on the above application content.

[0043] like Figure 1 As shown, a MPPT control method for a photovoltaic power generation system based on an ISSA-P&O algorithm includes the following steps:

[0044] Construct ISSA-P&O algorithm;

[0045] Calculate output power and initial fitness values ​​based on collected electrical data;

[0046] The output power and the initial fitness value are input into the ISSA-P&O algorithm, and after cyclic iterative calculation until the preset maximum number of iterations is reached, the optimal duty cycle is output to realize the MPPT control of the photovoltaic power generation system.

[0047] Building the ISSA-P&O algorithm includes:

[0048] The chaotic mapping method is used to increase the population diversity of the SSA algorithm, and the firefly perturbation algorithm is used to optimize it to obtain the ISSA algorithm.

[0049] The structure of the P&O algorithm is optimized to obtain an adaptive step-size P&O algorithm;

[0050] The ISSA algorithm is combined with the adaptive step-size P&O algorithm to obtain the ISSA-P&O algorithm.

[0051] Before inputting the output power and the initial fitness value into the ISSA-P&O algorithm, the parameters of the ISSA-P&O algorithm need to be initialized, including initializing the population size and the number of iterations, and using chaotic mapping to initialize the individual positions of sparrows, where the sparrow individuals include exploration individuals, tracking individuals and warning individuals.

[0052] The output power and the initial fitness value are input into the ISSA-P&O algorithm, and after cyclic iterative calculation until a preset maximum number of iterations is reached, the optimal duty cycle is outputted, including:

[0053] Input the output power and the initial fitness value into the ISSA algorithm, calculate the worst solution and the optimal solution of the global fitness, and determine whether the ISSA reaches a preset maximum number of iterations;

[0054] If the preset maximum number of iterations is reached, the optimal solution of the global fitness is used as the input of the adaptive step size P&O algorithm, and the optimal duty cycle is output after optimization.

[0055] If the preset maximum number of iterations is not reached, iteratively update the individual positions of the sparrows in the ISSA algorithm, perform firefly algorithm perturbation, and update the individual positions of the sparrows to find the optimal solution;

[0056] The optimal solution of the individual sparrow position is substituted into the ISSA-P&O algorithm initialization parameters for updating, and the iterative update process is continued until the preset maximum number of iterations is reached.

[0057] If the preset maximum number of iterations is reached, the optimal solution of the global fitness is used as the input of the adaptive step size P&O algorithm. After optimizing and outputting the optimal duty cycle, it is determined whether the environmental changes have caused the restart conditions to be met; if so, the step of initializing the parameters of the ISSA-P&O algorithm is returned; if not, the optimal duty cycle is used as the final output.

[0058] The restart condition is reached due to environmental changes, including environmental factors causing the output power of the photovoltaic power to change, and the restart condition is that the output power change rate of the photovoltaic power exceeds a preset threshold.

[0059] In this embodiment, specifically, a method for controlling the MPPT of a photovoltaic power generation system based on the ISSA-P&O algorithm uses a chaotic mapping method to increase the population diversity of the SSA algorithm, uses the firefly perturbation algorithm to perturb and update the individual sparrows, and proposes an adaptive step-size P&O algorithm to ensure the stable output of the maximum power point of MPPT control. It includes the following contents:

[0060] 1. Analyze the characteristics of the SSA algorithm, and adopt the chaotic mapping method to increase the population diversity of the SSA algorithm and improve the wide search ability of the algorithm, specifically including:

[0061] The SSA algorithm is derived from the foraging and predation behaviors of a sparrow population, and includes exploratory individuals, tracking individuals, and warning individuals. The exploratory individuals have the best fitness and the largest energy reserve in all populations, and mainly guide the foraging direction of the population; the following individuals continuously optimize their own fitness and pay attention to the behaviors of the exploratory individuals to optimize the food source; the warning individuals will actively give an alarm when they are in danger. The middle sparrows fly randomly to other sparrows, and the sparrows at the edge of the population fly actively to the safe area.

[0062] Assume that the number of sparrow individuals in the SSA algorithm population is m, and the search space dimension is D. Then the fitness value of the i-th sparrow individual at the spatial position X i =[x i1 ,x i2 ,…,x iD is F i =[f(x i1 ),f(x i2 ),…,f(x iD )].

[0063] The exploratory individuals have the widest search range and can obtain food first. The update formula in the iterative process is:

[0064]

[0065] In the formula, is the d-th dimension position of the i-th sparrow at the t-th iteration; t max is the maximum number of iterations; α and Q are random numbers between 0 and 1 and following a normal distribution respectively; L is a matrix with all elements being 1; R2 is an early warning value with a value range between 0 and 1; ST is a safety value with a value range between 0.5 and 1. If R2 < ST, it means that there is no predator at this time, and the exploratory individuals expand the search range; if R2 ≥ ST, it means that the warning individuals have found the predator and sent out an alarm signal, and all individuals fly to the safe area.

[0066] All sparrows in the entire population except the exploratory individuals are following individuals. The update formula in the iterative process is:

[0067]

[0068] In the formula, is the position of the worst sparrow in the population at this time after t iterations; is the position of the best sparrow after t iterations; A is a matrix with elements randomly assigned to -1 or 1, and A + =A T(AA T ) -1 If i>m / 2, it means that the fitness value of the ith follower is poor and cannot obtain food, so it needs to fly to other places to find food; if i≤m / 2, it means that the fitness value of the ith follower is good and can find food around.

[0069] 15% of the sparrows are randomly selected from the entire population as warning individuals. The update formula of the iterative process is:

[0070]

[0071] In the formula, is the global optimal sparrow position of the population after t iterations; λ is the step length control coefficient that obeys the normal distribution; M is a random number between -1 and 1; δ is a constant with a very small absolute value, and the denominator is prevented from being 0; f i This is the individual fitness at this time; f g is the global optimal fitness; f w is the global worst fitness. i >f g , the sparrows are at the edge of the population and are vulnerable to attack; if f i =f g , the sparrow is in the middle of the flock and, sensing danger, flies towards other sparrows to prevent being attacked.

[0072] The conventional SSA algorithm randomly generates the initial population, which easily causes the problem of sparrow aggregation and low solution space coverage, and cannot achieve extensive search. The present invention uses a chaotic mapping method to initialize the population, improve the population diversity of the SSA algorithm, and thus make the sparrow distribution more uniform. The chaotic mapping calculation formula is:

[0073]

[0074] In the formula, x i is the position of the i-th dimension sparrow in the initial population; mod is the remainder function. The chaotic value after mapping is evenly distributed between 0 and 1.

[0075] Second, use the firefly perturbation algorithm to perturb and update the individual sparrows to prevent the algorithm from falling into the local optimum, including:

[0076] The firefly optimization algorithm is a meta-inspirational algorithm. The algorithm is inspired by the idea of ​​fireflies luminescing and foraging. The fireflies' luminescence is simulated, so the optimization process of the algorithm is like the movement of fireflies. Conventional SSA algorithms are prone to local optimality. To address this problem, the present invention adds firefly algorithm perturbations in the later stages of the algorithm to update the individual positions of sparrows. If the fitness value after the perturbation is better than the fitness value before the perturbation, the position of the sparrow is updated. The position update formula for the i-th firefly attracted by the j-th firefly is:

[0077]

[0078] In the formula, x i is the position of the i-th firefly; γ is the attraction; is the step coefficient between 0 and 1; γ0 is the maximum attraction; ε is the light intensity absorption factor; r ij is the distance between the i-th firefly and the j-th firefly.

[0079] 3. Analyze the structural characteristics of the P&O algorithm and propose an adaptive step-size P&O algorithm to improve tracking accuracy and speed, including:

[0080] The P&O algorithm has the advantages of simple structure and easy implementation, and can be well applied to photovoltaic power generation MPPT. However, the step size of the P&O algorithm optimization process remains unchanged, and the tracking accuracy and speed are contradictory. Based on this, the present disclosure proposes an adaptive step size P&O algorithm, and the calculation expression of the variable step size is:

[0081]

[0082] Where, d i is the step size at the i-th iteration; Δd max is the maximum step length; D ISSA is the sparrow individual after ISSA algorithm optimization, that is, is the duty cycle after ISSA algorithm optimization; D new is the new duty cycle; P i is the photovoltaic output power at the i-th iteration; U i is the PV node voltage value at the i-th iteration; l is the adjustment coefficient, which is mainly used to adjust the step change speed. The smaller l is, the smaller the step change is; the larger l is, the larger the step change is. α is the adjustment factor; in the face of complex and changing environments, the problem of power jitter can be avoided by reasonably setting the value of l.

[0083] The step length is adjusted by calculating the change trend of voltage and power. If the change trend of the two is the same, the step length is increased. If the change trend of the two is opposite, the step length is reduced. The disturbance is stopped when the operation is at the maximum power point. The disturbance step length is updated continuously as the working point changes. The step length is set quickly and adaptively when approaching or moving away from the maximum power point, avoiding large jitters in power output and reducing the process of artificially making up the step length, which greatly improves the power tracking efficiency.

[0084] 4. Apply the ISSA-P&O algorithm to the MPPT control of the photovoltaic power generation system to ensure the stable output of the maximum power point, including:

[0085] The MPPT technology of the photovoltaic power generation system based on the ISSA-P&O algorithm proposed in the present disclosure controls the duty cycle. In the ISSA algorithm, the sparrow individual is set as the duty cycle, and the predation target is the global maximum power tracking. The ISSA algorithm is used for power tracking, and the population diversity is increased through chaotic mapping, the algorithm's extensive search capability is improved, and the firefly perturbation algorithm is used to prevent falling into the local optimum. However, using the ISSA algorithm to track the maximum power all the time can easily increase the burden on the entire system. Therefore, using the P&O algorithm in the later stage of tracking can reduce the amount of calculation while stabilizing the output power, which is beneficial to improving the power generation efficiency of the entire photovoltaic storage system.

[0086] In the early stage of tracking, after multiple iterations of the ISSA algorithm, the individual positions of the sparrows are close, the output power difference decreases, and the search is around the maximum power point. At this time, the ISSA algorithm reaches the maximum number of iterations, stops running, switches to the P&O algorithm, and enters the late stage of tracking.

[0087] When the external environment causes uneven light intensity due to buildings, flying dust, floating clouds, etc., the photovoltaic output power changes, and the ISSA algorithm is restarted to reduce power loss. The restart expression is:

[0088]

[0089] The MPPT technology of the photovoltaic power generation system based on the ISSA-P&O algorithm includes the early tracking of the ISSA algorithm and the late tracking of the P&O algorithm. Figure 2 As shown, the entire algorithm process steps are as follows:

[0090] Step 1: Initialize parameters. Initialize the ISSA algorithm and P&O algorithm parameters, including population size, number of iterations, and use chaotic mapping to initialize the individual positions of sparrows;

[0091] Step 2: Electrical data collection. Measure the photovoltaic output voltage and current, and calculate the output power and initial fitness value based on the measured voltage and current;

[0092] Step 3: Use the output power and initial fitness value as the input of the ISSA algorithm. Calculate the worst solution and the best solution of the global fitness; determine whether the ISSA reaches the maximum number of iterations. If it reaches it, jump to step 5; (the worst solution and the best solution of the global fitness here are respectively f of formula (3) g and f w )

[0093] Step 4: ISSA algorithm optimization. Use formulas (1), (2), and (3) to update the positions of the exploration individual, the following individual, and the warning individual, perform firefly algorithm perturbations, and update the positions of the sparrow individuals to find the optimal solution.

[0094] Step 5: P&O algorithm optimization. The duty cycle optimized by the ISSA algorithm (the optimal solution of the global fitness mentioned above) is used as the input of the adaptive step size P&O algorithm, and a new duty cycle is output after optimization;

[0095] Step 6: Determine whether the environment has changed. According to formula (7), determine whether the restart condition is met. If so, jump to step 1, otherwise jump to step 7;

[0096] Step 7: Output the result. The duty cycle output in step 5 is taken as the optimal duty cycle, and the whole algorithm ends.

[0097] In order to verify the effect of the MPPT control technology of the photovoltaic power generation system based on the ISSA-P&O algorithm proposed in this paper, a Figure 3 The photovoltaic energy storage control system model shown in the figure includes photovoltaic equipment, DC boost circuit, A / D, MPPT algorithm controller, PID controller and energy storage device. The parameters of each component are shown in Table 1.

[0098] Table 1 Component parameters

[0099] name parameter capacitance <![CDATA[C1=5μF;C2=15μF;C3=10μF]]> resistance R=20Ω inductance <![CDATA[L1=L2=10mH]]> IGBT frequency 6kHz Energy storage battery capacity 1000Ah

[0100] The expression for measuring the maximum power tracking effect is:

[0101]

[0102] Where η is the power tracking efficiency; P M , P are the ideal photovoltaic output power and actual output power respectively.

[0103] Under ideal conditions, the illumination is uniform, the outside temperature is 25°C, and the ideal photovoltaic output is 4989.62W. In order to verify the advancedness of the disclosed method, the disclosed method is compared with the particle swarm algorithm, the gray wolf algorithm and the SSA algorithm. The simulation results of different methods under uniform illumination are shown in the figure. Figure 4 The corresponding tracking time and tracking efficiency results are shown in Table 2.

[0104] As can be seen from the figure, the four methods all achieve high tracking efficiency under uniform illumination, and the disclosed method has the highest tracking efficiency and the shortest tracking time. Compared with the particle swarm algorithm, the gray wolf algorithm and the SSA algorithm, the tracking efficiency is increased by 9.65%, 4.92% and 1.42% respectively, and the tracking time is shortened by 0.3s, 0.11s and 0.09s respectively, indicating that the disclosed method can achieve better tracking performance in a shorter time. This is because the ISSA algorithm proposed in the disclosure has higher optimization accuracy and faster speed than the particle swarm algorithm and the gray wolf algorithm. Compared with the SSA algorithm, the ISSA algorithm uses a chaotic mapping method to increase population diversity and improve the algorithm's extensive search capability. It uses the firefly perturbation algorithm to prevent the algorithm from falling into the local optimum, improves the MPPT control accuracy and speed, and verifies the superiority of the disclosed method.

[0105] Table 2 Comparison of results under uniform illumination of different methods

[0106]

[0107] Under partial shadow conditions, the outside temperature is 25°C, the photovoltaic output is a three-peak curve, and the ideal output is 1998.27W. In order to verify the advancement of the disclosed method, the disclosed method is also compared with the particle swarm algorithm, the gray wolf algorithm and the SSA algorithm. The simulation results of different methods under static shadows are shown in the figure. Figure 5 The corresponding tracking time and tracking efficiency results are shown in Table 3.

[0108] It can be seen from the figure that the four methods can also achieve high tracking efficiency under static local shadows, and the disclosed method has the highest tracking efficiency and the shortest tracking time. Compared with the particle swarm algorithm, the gray wolf algorithm and the SSA algorithm, the tracking efficiency is improved by 11.78%, 7.57% and 2.56% respectively, and the tracking time is shortened by 0.34s, 0.23s and 0.15s respectively, indicating that the disclosed method can achieve better tracking performance in a shorter time under static shadows.

[0109] Table 3 Comparison of results of different methods under static local shadow

[0110]

[0111] In actual operation scenarios, changes in different light intensities usually occur. In order to further verify the advancedness of the disclosed method, the disclosed method is compared with the particle swarm algorithm, the gray wolf algorithm and the SSA algorithm under dynamic local shadows, and the simulation is divided into three scenarios. The voltage-power curves of each scenario are as follows: Figure 6The photovoltaic output of scene 1, scene 2, and scene 3 are three-peak curves, four-peak curves, and five-peak curves, respectively, and the temperature is 25°C. Scene 1 is simulated first, and then it is switched to scene 2 after 1s, and then it is switched to scene 3 after another 1s. The results of different methods under partial shadow are shown in Table 4.

[0112] It can be seen from the table that the disclosed method still has the highest tracking efficiency and the fastest tracking speed in dynamic scene switching of different local shadows, which verifies the superiority of the disclosed method under dynamic local shadows and has strong practical application value.

[0113] Table 4 Comparison of results of different methods under dynamic local shadow

[0114]

[0115] Energy device battery current charge and discharge curve Figure 7 As shown, the SOC change curve is as follows: Figure 8 As shown. As can be seen from the figure, from 0 to 1s, it is in scene 1, with sufficient light intensity, the photovoltaic output power is sufficient to meet the load demand, the energy storage device battery is in a charging state, and the SOC curve rises with a larger slope, and the charging speed is faster; when switching to scene 2 at the 1st moment, the light intensity decreases, the photovoltaic output power cannot meet the load demand, the energy storage device battery begins to discharge, and the SOC curve decreases; when switching to scene 3 at 2s, the power generation of the entire system can meet the load demand, the energy storage device battery enters the charging state again, and the SOC curve rises, but the power generation at this time is lower than that of scene 1, so the current size and the slope of the SOC curve are lower than those of scene 1. In summary, the MPPT technology of the photovoltaic storage power generation system based on the ISSA-P&O algorithm proposed in the present disclosure is highly practical in the photovoltaic storage system, and can coordinate the distribution of power balance between photovoltaics, energy storage and loads.

[0116] like Fig. 9 As shown, an embodiment of the present disclosure provides a MPPT control system for a photovoltaic power generation system based on an ISSA-P&O algorithm, including:

[0117] Algorithm building module 11, used to build ISSA-P&O algorithm;

[0118] A calculation module 12, used to calculate the output power and the initial fitness value according to the collected electrical data;

[0119] The iterative calculation module 13 is used to input the output power and the initial fitness value into the ISSA-P&O algorithm, and after cyclic iterative calculation until a preset maximum number of iterations is reached, output the optimal duty cycle to achieve MPPT control of the photovoltaic power generation system.

[0120] The implementation process of the functions and effects of each module in the above system is specifically described in the implementation process of the corresponding steps in the above method, which will not be repeated here.

[0121] For the system embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The system embodiment described above is only schematic, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the disclosed solution. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0122] In the above-mentioned embodiment, any multiple of all modules can be combined in one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least some functions of one or more of these modules can be combined with at least some functions of other modules and implemented in one module. At least one of all modules can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation modes of software, hardware and firmware or in a suitable combination of any of them. Alternatively, at least one of all modules can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be executed.

[0123] See also Fig.10 , an electronic device provided by an embodiment of the present disclosure includes a processor 1110, a communication interface 1120, a memory 1130 and a communication bus 1140, wherein the processor 1110, the communication interface 1120, and the memory 1130 communicate with each other through the communication bus 1140;

[0124] Memory 1130, for storing computer programs;

[0125] The processor 1110 is used to implement the MPPT control method of the photovoltaic power generation system based on the ISSA-P&O algorithm when executing the program stored in the memory 1130.

[0126] The communication bus 1140 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus 1140 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0127] The communication interface 1120 is used for communication between the above electronic device and other devices.

[0128] The memory 1130 may include a random access memory (RAM) or a non-volatile memory, such as at least one disk memory. Optionally, the memory 1130 may also be at least one storage device located away from the processor 1110.

[0129] The above-mentioned processor 1110 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0130] The embodiment of the present disclosure further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the above-mentioned MPPT control method of a photovoltaic power generation system based on the ISSA-P&O algorithm is implemented.

[0131] The computer-readable storage medium may be included in the device / apparatus described in the above embodiment; or it may exist independently without being assembled into the device / apparatus. The above computer-readable storage medium carries one or more programs. When the above one or more programs are executed, the MPPT control method of the photovoltaic power generation system based on the ISSA-P&O algorithm according to the embodiment of the present disclosure is implemented.

[0132] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: 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), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0133] The above-mentioned embodiments only express several implementation methods of the present disclosure, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present disclosure. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present disclosure, and these all belong to the protection scope of the present disclosure.

Claims

1. A MPPT control method for a photovoltaic power generation system based on the ISSA-P&O algorithm, characterized in that: The following steps are involved: Construct ISSA-P&O algorithm; Calculate output power and initial fitness values ​​based on collected electrical data; The output power and the initial fitness value are input into the ISSA-P&O algorithm, and after cyclic iterative calculation until the preset maximum number of iterations is reached, the optimal duty cycle is output to realize the MPPT control of the photovoltaic power generation system.

2. According to claim 1, a method for controlling the MPPT of a photovoltaic power generation system based on the ISSA-P&O algorithm is characterized in that: Building the ISSA-P&O algorithm includes: The chaotic mapping method is used to increase the population diversity of the SSA algorithm, and the firefly perturbation algorithm is used to optimize it to obtain the ISSA algorithm. The structure of the P&O algorithm is optimized to obtain an adaptive step-size P&O algorithm; The ISSA algorithm is combined with the adaptive step-size P&O algorithm to obtain the ISSA-P&O algorithm.

3. The MPPT control method of a photovoltaic power generation system based on ISSA-P&O algorithm according to claim 2 is characterized in that: Before inputting the output power and the initial fitness value into the ISSA-P&O algorithm, the parameters of the ISSA-P&O algorithm need to be initialized, including initializing the population size and the number of iterations, and using chaotic mapping to initialize the individual positions of sparrows, where the sparrow individuals include exploration individuals, tracking individuals and warning individuals.

4. The MPPT control method of a photovoltaic power generation system based on ISSA-P&O algorithm according to claim 1 is characterized in that: The output power and the initial fitness value are input into the ISSA-P&O algorithm, and after cyclic iterative calculation until a preset maximum number of iterations is reached, the optimal duty cycle is outputted, including: Input the output power and the initial fitness value into the ISSA algorithm, calculate the worst solution and the optimal solution of the global fitness, and determine whether the ISSA reaches a preset maximum number of iterations; If the preset maximum number of iterations is reached, the optimal solution of the global fitness is used as the input of the adaptive step size P&O algorithm, and the optimal duty cycle is output after optimization.

5. The MPPT control method of a photovoltaic power generation system based on ISSA-P&O algorithm according to claim 4 is characterized in that: If the preset maximum number of iterations is not reached, iteratively update the individual positions of the sparrows in the ISSA algorithm, perform firefly algorithm perturbation, and update the individual positions of the sparrows to find the optimal solution; The optimal solution of the individual sparrow position is substituted into the ISSA-P&O algorithm initialization parameters for updating, and the iterative update process is continued until the preset maximum number of iterations is reached.

6. The MPPT control method of a photovoltaic power generation system based on ISSA-P&O algorithm according to claim 4 is characterized in that: If the preset maximum number of iterations is reached, the optimal solution of the global fitness is used as the input of the adaptive step size P&O algorithm. After optimizing and outputting the optimal duty cycle, it is determined whether the environment changes and causes the restart condition to be reached; if so, the step of initializing the parameters of the ISSA-P&O algorithm is returned; If not, the optimal duty cycle is used as the final output.

7. The MPPT control method of a photovoltaic power generation system based on ISSA-P&O algorithm according to claim 6 is characterized in that: The restart condition is reached due to environmental changes, including environmental factors causing the output power of the photovoltaic power to change, and the restart condition is that the output power change rate of the photovoltaic power exceeds a preset threshold.

8. A MPPT control system for a photovoltaic power generation system based on the ISSA-P&O algorithm, characterized in that: include: Algorithm building blocks, used to build the ISSA-P&O algorithm; A calculation module, used for calculating output power and initial fitness value according to the collected electrical data; The iterative calculation module is used to input the output power and the initial fitness value into the ISSA-P&O algorithm, and after cyclic iterative calculation until a preset maximum number of iterations is reached, the optimal duty cycle is output to achieve MPPT control of the photovoltaic power generation system.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory, for storing computer programs; The processor is used to implement the MPPT control method of the photovoltaic power generation system based on the ISSA-P&O algorithm described in any one of claims 1 to 7 when executing the program stored in the memory.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the MPPT control method for a photovoltaic power generation system based on the ISSA-P&O algorithm described in any one of claims 1 to 7 is implemented.

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