MPPT (Maximum Power Point Tracking) control method based on plant cell population-incremental conductance method composite algorithm
Through the MPPT control method of the plant cell population-conductance increment method compound algorithm, the problem of maximum power point tracking of photovoltaic arrays under local shade is solved, and more efficient photovoltaic power generation system performance is achieved, avoiding local optimal traps and improving Tracking accuracy and speed.
Patent Information
- Application Number
- CN202411203068.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-29
- Publication Date
- 2024-12-06
AI Technical Summary
Traditional maximum power point tracking technology is prone to fall into local optimal values when the photovoltaic array is under local shade, resulting in reduced efficiency and waste of power in the photovoltaic power generation system.
Using the MPPT control method based on the plant cell population-conductance increment method compound algorithm, the cell position is updated through the plant cell population algorithm, and cells with far away from the strongest light position or low sunlight are eliminated, and a perturbation mechanism is introduced, combining the conductance increment The method is further adjusted to avoid falling into local optimality.
It effectively improves the maximum power point tracking accuracy and speed of the photovoltaic power generation system under local shade, avoids the conductance increment method to fall into local optimization, and improves the steady-state and dynamic performance of the system.
Smart Images
Figure CN119088164A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of new energy power generation, and in particular to the application of a plant cell swarm algorithm and a conductance increment method in a photovoltaic power generation system under partial shading. Background Art
[0002] As one of the new energy sources, solar energy is widely used in photovoltaic power generation systems due to its universality, huge energy, clean and pollution-free characteristics. With the increasing popularity of photovoltaic power generation systems, the working environment of photovoltaic arrays has become more and more complex. When each photovoltaic panel in the photovoltaic array is under uniform lighting conditions (same irradiance), its working characteristic curve has only one peak; when the photovoltaic array is partially shaded due to dust, clouds, etc., the irradiance of each photovoltaic panel is different, and the working characteristic curve of the photovoltaic array presents a multi-peak characteristic.
[0003] Traditional Maximum Power Point Tracking (MPPT) technologies, such as the constant voltage method and the perturbation observation method, can quickly and effectively track the maximum power point when dealing with photovoltaic arrays under uniform illumination. However, when the photovoltaic array is in partial shade, due to the presence of multiple peak points in the working characteristic curve of the photovoltaic array, the traditional MPPT technology is prone to fall into the local optimal value, resulting in reduced efficiency of the photovoltaic power generation system and power waste.
[0004] Therefore, developing an algorithm that can better adapt to environmental changes and has relatively good steady-state and dynamic performance plays a vital role in photovoltaic power generation. Summary of the invention
[0005] In view of this, the present invention provides an MPPT control method based on a plant cell group-conductance increment method composite algorithm, which can effectively solve the maximum power point tracking problem of a photovoltaic power generation system under partial shading.
[0006] In order to achieve the above object, the technical solution adopted by the present invention is:
[0007] A MPPT control method based on a plant cell group-conductance increment method composite algorithm, the method comprising the following steps:
[0008] S1, set algorithm-related parameters: number of plant cells N, maximum growth times M, auxin concentration thresholds α, β, γ; initialize plant cell positions;
[0009] S2, calculate the amount of sunlight that each plant cell is exposed to, and update the maximum amount of sunlight that each cell is exposed to and the optimal position. By comparing the sizes, find and update the maximum amount of sunlight that a single cell is exposed to and the corresponding cell position, which is recorded as the strongest light position;
[0010] S3, distributes auxin to cells and calculates cell growth rate;
[0011] S4, the plant cells complete one growth and renew the plant cell population;
[0012] S5, judging whether the termination condition is met, if so, entering the conductivity increment method; otherwise, returning to step S2;
[0013] S6, based on the plant cell swarm algorithm, the conductance increment method is used to further adjust the cell position.
[0014] Further, step S1 includes:
[0015] S101, the effect of auxin concentration on plant cells Figure 1 As shown, α is set as the concentration turning point threshold value of auxin promoting plant cell growth from strong to weak, β is set as the concentration turning point threshold value of auxin from promoting plant cell growth to inhibiting plant cell growth, and γ is set as the concentration turning point threshold value of auxin from inhibiting plant cell growth to killing plant cells;
[0016] S102, the number of cells is equal to the number of photovoltaic panels in the photovoltaic array, the cell positions are initialized, and the plant cell positions are evenly distributed within the range of the Boost circuit duty cycle (0, 1).
[0017] Further, step S2 includes:
[0018] S201, the maximum amount of sunlight that cells are exposed to and the optimal position of cells:
[0019]
[0020] In the formula, i is the cell number; m is the current growth number; f is the calculation function of the amount of sunlight the cell is exposed to; They are respectively the maximum amount of sunlight exposure and the optimal position of the ith cell up to the mth growth; They are respectively the current position, the maximum amount of sunlight exposure, and the optimal position of the ith cell up to the time of the (m+1)th growth;
[0021] S202, the maximum amount of sunlight a single cell is exposed to and the position of the strongest light:
[0022]
[0023] Where M is the maximum number of growth times; The maximum amount of sunlight and the position of the strongest light exposure of a single cell until the (m+1)th growth.
[0024] Further, step S3 includes:
[0025] S301, the calculation method of auxin concentration obtained by cell distribution is:
[0026]
[0027] In the formula, is the position of the ith cell after the mth growth To the strongest light position The Euclidean distance of
[0028] S302, the calculation method of cell growth rate is:
[0029]
[0030] In the formula, a, b, and c are positive numbers. In the algorithm, auxin "inhibits plant cell growth" is considered to be "making plant cells grow in the direction opposite to the position of the strongest light"; auxin "kills plant cells" is considered to be reinitializing the cells.
[0031] Furthermore, the specific method of step S4 is as follows:
[0032] For auxin concentration The method for updating the position of plant cells is: When the auxin concentration When , it means that the plant cells are too far away from the strongest light position, or the amount of sunlight exposed to the plant cells is too little. The existence value of these plant cells is low, so they are randomly initialized again.
[0033] Furthermore, the specific method of step S5 is as follows:
[0034] The termination condition is that the Euclidean distance between each plant cell is less than or equal to 0.05, at which time the plant cells have gathered in the interval where the maximum power point is located; if it is satisfied, the conductance increment method is used to find the maximum power point, otherwise it returns to step S2.
[0035] Furthermore, the specific method of step S6 is as follows:
[0036] The optimal duty cycle obtained by the plant cell swarm algorithm is used as the initial duty cycle of the conductance increment method. After the duty cycle is disturbed, if dI / dU>-I / U, the duty cycle is decreased; if dI / dU<-I / U, the duty cycle is increased; if dI / dU=-I / U, the duty cycle is kept unchanged.
[0037] Furthermore, in step S6, when the environmental conditions of the photovoltaic array change, its maximum power point will also change accordingly, and the maximum power point needs to be found again at this time; the restart condition of the algorithm is: ΔP>0.1, And after restarting, the positions of plant cells should be evenly distributed in [0.5G best ,1.5G best ], where G best It is the strongest light position before restart.
[0038] Furthermore, in step S4, it can be known from the calculation method of the cell growth rate in step S3 that the cell at the strongest light position has a growth rate of 0, so it is necessary to slightly disturb its growth update so that it searches for a better solution near the strongest light position. The growth update formula is: X m+1 =X m +A*0.015, where A is 1 or -1.
[0039] The beneficial effects of the present invention are as follows:
[0040] The present invention provides an MPPT control method based on a plant cell group-conductance increment method composite algorithm, and the invention is used in a photovoltaic power generation system, wherein the photovoltaic power generation system includes a photovoltaic array, a Boost circuit, an MPPT control module, and a load. The method updates the current cell position based on the plant cell group algorithm, eliminates and re-randomly initializes the cells with low search value that are far from the strongest light position or have low sunlight, thereby enhancing the algorithm's global search capability, and introduces a perturbation mechanism to the cells in the best light position, thereby enhancing the algorithm's local search capability. After several growths, the cell positions are gathered in the interval where the maximum power point is located, effectively avoiding the conductance increment method from falling into a local optimum. The optimal position searched by the plant cell group algorithm is used as the initial duty cycle of the conductance increment method. Since the duty cycle is now located in a very small interval containing the maximum power point after being optimized by the plant cell group algorithm, the maximum power point can be found quickly and accurately without falling into a local optimum. BRIEF DESCRIPTION OF THE DRAWINGS
[0041] Figure 1 This is a diagram showing the effect of auxin concentration on plant cells provided by the present invention.
[0042] Figure 2 This is a schematic diagram of a simulation circuit for a photovoltaic power generation system provided by the present invention.
[0043] Figure 3 A flow chart of the MPPT control method based on the plant cell group-conductance increment method composite algorithm provided by the present invention.
[0044] Figure 4PU characteristic curve diagram of the photovoltaic array under two shading conditions provided by the present invention.
[0045] Figure 5 This is a simulation model diagram of the photovoltaic power generation system provided by the present invention in Simulink.
[0046] Figure 6 A comparison chart of the power tracking effects of the MPPT control method of the present invention and the Grey Wolf (GWO) algorithm control method provided by the present invention.
[0047] Figure 7 A comparison chart of the power tracking effects of the MPPT control method of the present invention and the particle swarm (PSO) algorithm control method provided by the present invention. DETAILED DESCRIPTION
[0048] The following is a clear and detailed description of the technical solutions in the embodiments of the invention in conjunction with the accompanying drawings in the embodiments of the invention. Obviously, the described embodiments are only part of the embodiments of the invention.
[0049] Example:
[0050] The present invention provides an MPPT control method based on a plant cell group-conductance increment method composite algorithm. It should be noted that the control method is applied to a photovoltaic power generation system. In this embodiment, the photovoltaic power generation system is mainly composed of a photovoltaic array, a Boost circuit, an MPPT control module and a load. The circuit diagram is shown in FIG. Figure 2 As shown. The photovoltaic array consists of 5 photovoltaic panels connected in series; the MPPT control module is mainly composed of an algorithm module and a pulse width modulator. The working process is as follows: the MPPT module detects the output voltage and output current of the photovoltaic array in real time, finds the duty cycle corresponding to the maximum power point through the algorithm module, and then the pulse width modulator generates a modulation signal according to the required duty cycle, controls and adjusts the Boost circuit, so that the impedance of the photovoltaic array is equal to the external impedance, and according to the impedance matching principle, keeps the photovoltaic array always outputting the maximum power.
[0051] The flow chart of the MPPT control method based on the plant cell group-conductance increment method composite algorithm is as follows: Figure 3 As shown, the following steps are included:
[0052] S1, set algorithm-related parameters: number of plant cells N, maximum growth times M, auxin concentration thresholds α, β, γ.
[0053] Initialize plant cell positions;
[0054] Specifically, the number of cells is equal to 5 (the number of photovoltaic panels connected in series in the photovoltaic array), the cell positions are initialized, and the positions of the plant cells are evenly distributed within the range of the Boost circuit duty cycle (0,1). The effect of auxin concentration on plant cells is as follows Figure 1 As shown, α is set as the concentration turning point threshold value at which the effect of auxin in promoting plant cell growth changes from strong to weak, β is set as the concentration turning point threshold value at which auxin changes from promoting plant cell growth to inhibiting plant cell growth, and γ is set as the concentration turning point threshold value at which auxin changes from inhibiting plant cell growth to killing plant cells.
[0055] S2, calculates the amount of sunlight that each plant cell is exposed to, and updates the maximum amount of sunlight that each cell is exposed to and the optimal position. By comparing the sizes, finds and updates the maximum amount of sunlight that a single cell is exposed to and the corresponding cell position, which is recorded as the position of the strongest light.
[0056] The output power of the photovoltaic array is the amount of sunlight that the cells are exposed to, which is calculated as: the output voltage of the photovoltaic array × the output current of the photovoltaic array.
[0057] The maximum amount of sunlight that the cell is exposed to and the formula for updating the optimal position of the cell are:
[0058]
[0059]
[0060] In the formula, i is the cell number; m is the current growth number; f is the calculation function of the amount of sunlight the cell is exposed to; They are respectively the maximum amount of sunlight exposure and the optimal position of the ith cell up to the mth growth; They are respectively the current position, the maximum amount of sunlight exposure, and the optimal position of the ith cell up to the time of the (m+1)th growth;
[0061] The update formula for the maximum amount of sunlight exposure to a single cell and the position of the strongest illumination is:
[0062]
[0063] Where M is the maximum number of growth times; The maximum amount of sunlight and the position of the strongest light exposure of a single cell until the (m+1)th growth.
[0064] S3, distributes auxin to cells and calculates cell growth rate;
[0065] The calculation method of the auxin concentration obtained by the cell distribution is:
[0066]
[0067] In the formula, is the position of the ith cell after the mth growth To the strongest light position The Euclidean distance of
[0068] The cell growth rate is calculated as follows:
[0069]
[0070] In the formula, a, b, and c are positive numbers. In the algorithm, auxin is considered to "inhibit plant cell growth" as "making plant cells grow in the direction opposite to the position of the strongest light". Auxin is considered to "kill plant cells" as reinitializing the cells.
[0071] S4, the plant cells complete one growth and renew the plant cell population;
[0072] From the calculation method of the auxin concentration obtained by cell distribution in step S3, it can be seen that There are two possibilities for being too small. is too small, which indicates that the position of the ith cell is close to the position of the strongest light, and the ith cell only needs to be searched near it, which has search value; second, If it is too large, it means that the i-th cell is exposed to more sunlight, the light at its location is strong, and there may be an optimal solution around it, which is worth searching. There are two possibilities for oversizing. Too big or The former indicates that the cells are too far away from the strongest light position, and the latter indicates that the plant cells are exposed to too little sunlight. Both of them are not of high search value.
[0073] Specifically, for auxin concentration The method for updating the position of plant cells is: When the auxin concentration , the search value of the cell is not high, so it is randomly initialized again.
[0074] From the calculation method of the cell growth rate in step S3, it can be seen that the cell at the strongest light position is most likely to have the optimal solution nearby, which has a very high search value. The growth rate is 0, and it is impossible to search nearby positions. Therefore, a slight disturbance is introduced to its growth update, so that it searches for a better solution near the strongest light position. Its growth update formula is: m+1 =X m +A*0.015, where A is 1 or -1.
[0075] S5, judging whether the termination condition is met, if so, entering the conductivity increment method; otherwise, returning to step S2;
[0076] Specifically, the termination condition is that the Euclidean distance between each plant cell is less than or equal to 0.05, at which time the plant cells have gathered in the interval where the maximum power point is located. If it is satisfied, the conductance increment method is used to find the maximum power point, otherwise it returns to step S2.
[0077] S6, based on the plant cell swarm algorithm, the conductance increment method is used to further adjust the cell position.
[0078] Specifically, the optimal duty cycle obtained by the plant cell swarm algorithm is used as the initial duty cycle of the conductance increment method. After the duty cycle is disturbed, if dI / dU>-I / U, the duty cycle is decreased; if dI / dU<-I / U, the duty cycle is increased; if dI / dU=-I / U, the duty cycle is kept unchanged.
[0079] Furthermore, when the environmental conditions of the photovoltaic array change, its maximum power point will also change accordingly, and the maximum power point must be found again. The restart condition of the algorithm is: ΔP>0.1, And after restarting, the positions of plant cells should be evenly distributed in [0.5G best ,1.5G best ], where G best It is the strongest light position before restart.
[0080] Figure 4 The PU characteristic curve of the photovoltaic array in the embodiment is shown in Figure 1. Under partial shading conditions, the light intensity of the five photovoltaic panels in the photovoltaic array at t = 0s is 1000, 1000, 800, 800, and 500 W / m 2 , recorded as working condition 1; at t = 0.5s, the light intensity of the five photovoltaic panels in the photovoltaic array suddenly changes to: 1000, 1000, 800, 500, 300W / m 2 , recorded as working condition 2. The system simulation time is 1s. During the entire simulation process, the temperature of the photovoltaic array is 25℃. The maximum power point of working condition 1 is (709.1W, 118.8V), and the maximum power point of working condition 2 is (543.3W, 89.5V).
[0081] according to Figure 2 Schematic diagram of photovoltaic power generation system simulation circuit. A simulation model is built in Matlab / Simulink to test the tracking effect of the algorithm. The model is as follows Figure 5 shown.
[0082] Figure 6It is a comparison chart of the simulation effects of the MPPT control method based on the plant cell group-conductance increment method composite algorithm (the present invention) and the GWO algorithm MPPT control method in the embodiment. Figure 7 It is a comparison chart of the simulation effects of the MPPT control method based on the plant cell group-conductance increment method composite algorithm (the present invention) and the PSO algorithm MPPT control method in the embodiment.
[0083] The comparison of the results of each algorithm under two shading conditions is shown in Table 1.
[0084] Table 1
[0085]
[0086] Depend on Figure 6 As shown in Table 1, under the local shading condition 1, the three algorithms all avoided the local optimum and successfully tracked the maximum power point. Compared with GWO and PSO, the maximum power tracked by the present invention was increased by 4.1 W and 1.6 W, respectively, the tracking accuracy was improved by 0.6% and 0.2%, respectively, and the tracking time was shortened by 0.26 s and 0.10 s, respectively, which was reduced by 61.9% and 38.5% compared with GWO and PSO.
[0087] Depend on Figure 7 As shown in Table 1, under the partial shading condition 2, compared with GWO and PSO, the maximum power point tracked by the present invention is increased by 1.0 W and 0.5 W respectively, and the stable tracking time is shortened by 0.32 s and 0.11 s respectively, which is reduced by 80.0% and 57.9% compared with GWO and PSO, and the power oscillation of the present invention after reaching steady state is very small.
[0088] In summary, the present invention introduces a perturbation mechanism based on the plant cell swarm algorithm to enhance its local search capability; by setting the termination growth condition, the defect of slow convergence of the algorithm in the late iteration is avoided. The improved plant cell swarm algorithm is then combined with the conductance increment method, and the plant cell swarm algorithm is used to first gather the cell positions in the interval where the maximum power point is located, which effectively avoids the conductance increment method from falling into the local optimum, improves the accuracy of maximum power point tracking, and greatly shortens the maximum power point tracking time.
[0089] The above description is only a preferred specific implementation of the present invention and should not be regarded as a limitation on the patent of the present invention. The embodiments and features in the embodiments of the present application can be arbitrarily combined with each other without conflict. The protection scope of the patent of the present invention shall be the technical solution recorded in the claims, including the equivalent replacement scheme of the technical features in the technical solution recorded in the claims. That is, equivalent replacement improvements within this scope are also within the protection scope of the patent of the present invention.
Claims
1. The MPPT control method based on the plant cell group-conductance increment method composite algorithm is characterized by: The following steps are involved: S1, set algorithm-related parameters: number of plant cells N, maximum growth times M, auxin concentration thresholds α, β, γ; initialize plant cell positions; S2, calculate the amount of sunlight that each plant cell is exposed to, and update the maximum amount of sunlight that each cell is exposed to and the optimal position. By comparing the sizes, find and update the maximum amount of sunlight that a single cell is exposed to and the corresponding cell position, which is recorded as the strongest light position; S3, distributes auxin to cells and calculates cell growth rate; S4, the plant cells complete one growth and renew the plant cell population; S5, judging whether the termination condition is met, if so, entering the conductivity increment method; otherwise, returning to step S2; S6, based on the plant cell swarm algorithm, the conductance increment method is used to further adjust the cell position.
2. The MPPT control method based on the plant cell group-conductance increment method composite algorithm according to claim 1 is characterized in that: The specific method of step S1 is as follows: S101, set α as the concentration turning point threshold value at which the effect of auxin on promoting plant cell growth changes from strong to weak, β as the concentration turning point threshold value at which auxin changes from promoting plant cell growth to inhibiting plant cell growth, and γ as the concentration turning point threshold value at which auxin changes from inhibiting plant cell growth to killing plant cells; S102, the number of cells is equal to the number of photovoltaic panels in the photovoltaic array, the cell positions are initialized, and the plant cell positions are evenly distributed within the range of the Boost circuit duty cycle (0, 1).
3. The MPPT control method based on the plant cell group-conductance increment method composite algorithm according to claim 1 is characterized in that: The specific method of step S2 is as follows: S201, the maximum amount of sunlight that cells are exposed to and the optimal position of cells: Where i is the cell number; m is the current growth number; f is the calculation function of the amount of sunlight the cell is exposed to; F i m , They are respectively the maximum amount of sunlight exposure and the optimal position of the ith cell up to the mth growth; F i m+1 , They are respectively the current position, the maximum amount of sunlight exposure, and the optimal position of the ith cell up to the time of the (m+1)th growth; S202, the maximum amount of sunlight a single cell is exposed to and the position of the strongest light: Where M is the maximum number of growth times; The maximum amount of sunlight and the position of the strongest light exposure of a single cell until the (m+1)th growth.
4. The MPPT control method based on the plant cell group-conductance increment method composite algorithm according to claim 1 is characterized in that: The specific method of step S3 is as follows: S301, the calculation method of auxin concentration obtained by cell distribution is: In the formula, is the position of the ith cell after the mth growth To the strongest light position The Euclidean distance of S302, the calculation method of cell growth rate is: In the formula, a, b, and c are positive numbers. In the algorithm, auxin is considered to "inhibit plant cell growth" by "making plant cells grow in the direction opposite to the position of the strongest light"; auxin is considered to "kill plant cells" by reinitializing the cells.
5. The MPPT control method based on the plant cell group-conductance increment method composite algorithm according to claim 1 is characterized in that: The specific method of step S4 is as follows: For auxin concentration The method for updating the position of plant cells is: When the auxin concentration When , it means that the plant cells are too far away from the strongest light position, or the amount of sunlight exposed to the plant cells is too little. The existence value of these plant cells is low, so they are randomly initialized again.
6. The MPPT control method based on the plant cell group-conductance increment method composite algorithm according to claim 1 is characterized in that: The specific method of step S5 is as follows: The termination condition is that the Euclidean distance between each plant cell is less than or equal to 0.05, at which time the plant cells have gathered in the interval where the maximum power point is located; if it is satisfied, the conductance increment method is used to find the maximum power point, otherwise it returns to step S2.
7. The MPPT control method based on the plant cell group-conductance increment method composite algorithm according to claim 1 is characterized in that: The specific method of step S6 is as follows: The optimal duty cycle obtained by the plant cell swarm algorithm is used as the initial duty cycle of the conductance increment method. After the duty cycle is disturbed, if dI / dU>-I / U, the duty cycle is decreased; if dI / dU<-I / U, the duty cycle is increased; if dI / dU=-I / U, the duty cycle is kept unchanged.
8. The MPPT control method based on the plant cell group-conductance increment method composite algorithm according to claim 1 is characterized in that: In step S6, when the environmental conditions of the photovoltaic array change, its maximum power point will also change accordingly, and the maximum power point needs to be found again at this time; the restart condition of the algorithm is: ΔP>0.1, And after restarting, the positions of plant cells should be evenly distributed in [0.5G best ,1.5G best ], where G best It is the strongest light position before restart.
9. The MPPT control method based on the plant cell group-conductance increment method composite algorithm according to claim 3 is characterized in that: In step S4, it can be known from the calculation method of the cell growth rate in step S3 that the cell at the strongest light position has a growth rate of 0, so it is necessary to slightly disturb its growth update so that it searches for a better solution near the strongest light position. The growth update formula is: m+1 =X m +A*0.015, where A is 1 or -1.