Two-stage distributed photovoltaic MPPT control method

Abstract

The present application relates to a kind of two-stage distributed photovoltaic MPPT control method.The control method will variable step duty cycle scanning method and adaptive incremental conductance method be fused and applied, first variable step duty cycle scanning is executed global scanning, with the size of step size dynamic adjustment by region division, greatly improve scanning speed, scanning is accurately output optimal duty cycle after completion;Subsequently, this duty cycle is imported as initial parameter into adaptive incremental conductance method and carries out more detailed local search, to further improve the tracking efficiency of photovoltaic system, and makes photovoltaic system stable operation in global maximum power point.The control method combines the quick response advantage of variable step duty cycle scanning method and the high-precision tracking characteristics of adaptive incremental conductance method, can significantly improve photovoltaic conversion efficiency, and creates considerable economic benefits for practical engineering application.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic control technology, specifically to a two-stage distributed photovoltaic MPPT control method. Background Technology

[0002] The environmental crisis caused by escalating carbon emissions is driving the widespread adoption of solar photovoltaic arrays through various feasible methods. To ensure that photovoltaic arrays maintain consistently high efficiency, the application of maximum power point tracking (MPPT) technology has become a crucial foundation for research and practice in the field of photovoltaic power generation.

[0003] Traditional photovoltaic (PV) maximum power point tracking (MPPT) systems employ a centralized structure, where the entire PV array shares a single MPPT controller. This controller adjusts the array's operating voltage or current to achieve maximum power point tracking. When some components in the PV array are affected by factors such as shading, dirt, or aging, the "weakest link" effect occurs, meaning the array's output power is limited by the worst-performing component, thus reducing system efficiency. To address this issue, a distributed MPPT structure has emerged. This solution equips each PV module in the array with an independent MPPT controller, enabling module-level autonomous maximum power point tracking. This architecture effectively eliminates the "weakest link" effect; even if some modules are underperforming, the remaining modules can maintain optimal power output, significantly improving the overall power generation efficiency of the PV system.

[0004] Currently, MPPT algorithms are mainly divided into two categories: traditional MPPT algorithms and MPPT algorithms based on optimization algorithms. Traditional MPPT control methods, due to their simplicity and ease of implementation, have become a widely used solution for achieving efficient energy harvesting in photovoltaic power generation systems under uniform illumination. These methods are mainly divided into three types: the ramp-up method, the perturbation-observation method, and the incremental conductance method. While these algorithms can track the maximum power point in real time under uniform illumination, they cannot simultaneously balance tracking speed and efficiency. Furthermore, traditional algorithms only perform well under uniform illumination conditions; their performance will not reach its optimal level when the photovoltaic module is under partial shading.

[0005] The MPPT algorithm, based on optimization algorithms, is essentially implemented through iterative calculations and is widely used because it can converge to the optimal solution corresponding to the global maximum power point. Particle swarm optimization (PSO) is a typical example, and there are also various bio-inspired optimization algorithms such as improved PSO based on neural networks. These algorithms can operate without photovoltaic module parameters, but they suffer from drawbacks such as long tracking time, high oscillation before convergence, and a tendency to converge to local maxima. Furthermore, they are highly complex in real-time calculations, which can lead to some power loss. Summary of the Invention

[0006] To address the shortcomings of traditional MPPT algorithms and optimization-based MPPT algorithms, this paper proposes a two-stage distributed MPPT control method. This method first uses a variable step size duty cycle to scan and locate the approximate duty cycle value at the maximum power point of the photovoltaic module. Then, it uses an adaptive conductivity increment method to further accurately track the maximum power point of the photovoltaic module, effectively improving the overall output power of the photovoltaic array.

[0007] This invention provides a two-stage distributed photovoltaic MPPT control method, which includes:

[0008] Starting from the initial duty cycle, the maximum power point and the corresponding optimal duty cycle of the photovoltaic module are updated in real time using the variable step size duty cycle scanning method;

[0009] When the output voltage of the photovoltaic module exceeds the preset voltage threshold, the variable step size duty cycle scan is completed, the latest updated maximum power point and the corresponding optimal duty cycle are recorded, and the system switches to the adaptive conductivity incremental method mode.

[0010] Using the recorded optimal duty cycle as the initial control parameter input, the duty cycle is dynamically adjusted in real time using the adaptive conductance incremental method mode to track the maximum power point of the photovoltaic module.

[0011] If the power change of the photovoltaic module between adjacent time periods exceeds a preset change threshold, the above steps are repeated starting from the initial duty cycle.

[0012] As a preferred embodiment, the real-time updating of the maximum power point and corresponding optimal duty cycle of the photovoltaic module using the variable step size duty cycle scanning method includes:

[0013] Starting from the set initial duty cycle, perform small-step duty cycle scanning and detect the output power of the photovoltaic module in real time;

[0014] During the small step duty cycle scan, the duty cycle is decreased sequentially with small step intervals. In response to the photovoltaic module's output power changing from an upward trend to a downward trend, the output power of the photovoltaic module at the current moment is determined to be the maximum power point and the corresponding optimal duty cycle is recorded. Then, the scan is switched to a large step duty cycle.

[0015] During the large step duty cycle scan, the duty cycle is decreased sequentially with large step intervals. In response to the detection that the output power of the photovoltaic module exceeds the previously recorded maximum power point, the scan is switched to a small step duty cycle.

[0016] During the scanning process of switching back to small step duty cycle scanning, in response to the photovoltaic module's output power changing from an upward trend to a downward trend again, the maximum power point and the corresponding optimal duty cycle are updated, and the scanning process switches back to large step duty cycle scanning.

[0017] Repeat the variable step size duty cycle scanning process until the output voltage of the photovoltaic module is greater than the preset voltage threshold.

[0018] As a preferred embodiment, the initial duty cycle is set to 0.8.

[0019] As a preferred embodiment, the duty cycle is dynamically adjusted in real time using the adaptive conductivity increment method to track the maximum power point of the photovoltaic module, including:

[0020] The duty cycle step size at the current moment is determined based on the maximum power point recorded when the variable step size duty cycle scan is completed and the output power of the photovoltaic module at the current operating point.

[0021] The change in conductance of the photovoltaic module at the current moment is determined based on the collected voltage and current changes of the photovoltaic module.

[0022] The recorded optimal duty cycle is used as the initial control parameter input, and the duty cycle is adjusted in real time according to the change in conductivity and the duty cycle step size.

[0023] As a preferred embodiment, adjusting the duty cycle at the current moment based on the change in conductance and the duty cycle step size includes:

[0024] If the change in conductivity is greater than zero, increase the duty cycle step by the current duty cycle step.

[0025] If the change in conductivity is less than zero, the duty cycle at the current moment is reduced by the duty cycle step size;

[0026] If the change in conductivity is zero, the duty cycle at the current moment will not be adjusted.

[0027] As a preferred embodiment, the expression for the duty cycle step size ΔD is:

[0028] ΔD=N×|PP best |

[0029] Where N is the proportionality coefficient, P is the output power of the photovoltaic module at the current operating point, and P best This is the maximum power point recorded when the variable step size duty cycle scan is completed.

[0030] As a preferred embodiment, the expression for the change in conductivity ΔG is:

[0031] ΔG=G(x)-G(x-1)

[0032]

[0033] Where G represents the conductance of the photovoltaic module, G(x) and G(x-1) are the conductance values ​​at time x and x-1 respectively, U(x) and U(x-1) are the voltage of the photovoltaic module at time x and x-1 respectively, and I(x) and I(x-1) are the current of the photovoltaic module at time x and x-1 respectively.

[0034] As a preferred embodiment, the power change of the photovoltaic module between adjacent time points exceeding a preset change threshold is expressed as follows:

[0035]

[0036] Where P(x) is the output power of the photovoltaic module at time x, P(x-1) is the output power of the photovoltaic module at time x-1, and ΔP is the power change.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] This invention's control method integrates a variable step-size duty cycle scanning method with an adaptive conductivity increment method. First, a global scan is performed using a variable step-size duty cycle scan, dynamically adjusting the step size through region division to significantly improve scanning speed. After scanning, the optimal duty cycle is accurately output. Subsequently, this duty cycle is used as an initial parameter to perform a more detailed local search using the adaptive conductivity increment method, thereby further improving the tracking efficiency of the photovoltaic system and ensuring its stable operation at the global maximum power point. This control method combines the fast response advantage of the variable step-size duty cycle scanning method with the high-precision tracking characteristics of the adaptive conductivity increment method, significantly improving photovoltaic conversion efficiency and creating considerable economic benefits for practical engineering applications. The algorithm proposed in this paper effectively reduces power oscillations during maximum power point tracking, achieving the fastest tracking speed while maintaining tracking accuracy. Attached Figure Description

[0039] Figure 1 Photovoltaic MPPT control circuit structure diagram in this embodiment of the invention

[0040] Figure 2 Schematic diagram of step size adjustment in the variable step size duty cycle scanning method in this embodiment of the invention.

[0041] Figure 3 Flowchart of the adaptive conductivity incremental method control in this embodiment of the invention;

[0042] Figure 4 Flowchart of variable step size duty cycle scanning and adaptive conductance increment control in this embodiment of the invention;

[0043] Figure 5 Photovoltaic system model in the embodiments of the present invention;

[0044] Figure 6 Comparison of simulation results in the embodiments of the present invention. Detailed Implementation

[0045] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0046] This embodiment provides a two-stage distributed photovoltaic MPPT control method. First, the structure diagram of the distributed photovoltaic MPPT control circuit is explained, combined with… Figure 1 Traditional centralized photovoltaic MPPT control circuit structure, such as Figure 1 As shown in (a), the photovoltaic array is uniformly connected to a single Boost circuit and MPPT control module. This single module adjusts the duty cycle of the Boost circuit to achieve maximum power point tracking control of the entire photovoltaic array. However, under non-uniform illumination, it is impossible to make individual photovoltaic modules operate at their maximum power point. The distributed MPPT control circuit structure used in this paper is as follows: Figure 1 As shown in (b), each photovoltaic module is connected to an independent Boost circuit and MPPT control module. Each module adjusts the duty cycle of the corresponding Boost circuit to perform maximum power point tracking control on a single photovoltaic module. Under various lighting conditions, the photovoltaic module can work independently at the maximum power point, effectively improving the overall output power of the photovoltaic array.

[0047] The distributed MPPT control method in this embodiment includes the variable step size duty cycle scanning method and the adaptive conductance increment method.

[0048] The variable step size duty cycle scanning method uses different step sizes to change the duty cycle D, and measures the corresponding output power and duty cycle after each change. When the duty cycle scan ends, the maximum power point and the corresponding optimal duty cycle value D for each photovoltaic module can be obtained. best .

[0049] The variable step size duty cycle scanning method refines the search within the effective region and accelerates the skipping of invalid regions, thus speeding up the duty cycle scanning process. Figure 2 Taking the case in the example, a small step size duty cycle scanning method is first used for fine searching, which can accurately identify the first local maximum power point (e.g., Figure 2 (Point A shown). During this process, the system will record the power value P corresponding to this local maximum power point. A and voltage value V A This serves as a baseline parameter. Subsequently, the algorithm continues the duty cycle scanning process. When it detects that the photovoltaic module's output voltage continues to increase while the output power is lower than the previously recorded P, the algorithm proceeds. AWhen the value is reached, the system will automatically switch to a large step scan mode to speed up the skipping of invalid regions (A). When skipping region (A), the output power of the photovoltaic array will be greater than P. A At this point, the system will switch to a small-step scanning mode to refine the search until the next maximum power point is found. This strategy shift is based on the principle of isopower lines: such as... Figure 2 As shown, the power output of regions (A), (B), and (C) is lower than P. A P B and P C These are all inefficient working areas. Using a large step size scan can quickly traverse these inefficient areas, significantly improving tracking efficiency. The specific control flow of the variable step size duty cycle scan method is as follows:

[0050] (1) Set the initial duty cycle D of the DC-DC converter to 0.8, and then decrease the duty cycle D sequentially in small steps D1. During this process, monitor and collect the output voltage and current of the photovoltaic module in real time, and synchronously record the maximum power point P identified in each state. mpp And the corresponding optimal duty cycle D best .

[0051] (2) When the current output power value is detected to be lower than the recorded P mpp If the maximum power point does not exist in the region, then the duty cycle D is decreased sequentially with a large step size D2 to accelerate the skipping of invalid regions.

[0052] (3) When the current output power value is detected to be higher than the recorded P mpp When the timeout is reached, it indicates that the invalid region has been skipped. At this point, the step size change ΔD is switched to a small step size D1 for duty cycle scanning, and the optimal duty cycle D is updated in real time. best Otherwise, continue with step (2).

[0053] (4) Determine the output voltage V of the photovoltaic module pv Is it greater than 0.9V? oc If V pv Exceeding this threshold indicates that the duty cycle scan has been completed, and the maximum output power P is reached. mpp With the optimal duty cycle D best Otherwise, continue with variable step size duty cycle scanning.

[0054] Traditional incremental conductance methods, employing a fixed step size, cannot dynamically adjust the step size based on the actual operating state of the photovoltaic module. This results in slow tracking speeds when far from the maximum power point, while excessively large step sizes near the maximum power point can cause power oscillations, making it difficult to balance dynamic response speed and steady-state tracking accuracy. Therefore, this paper proposes an adaptive incremental conductance method. The step size of this algorithm is positively correlated with the degree to which the photovoltaic module deviates from the maximum power point. By introducing a proportional coefficient and the relative position of the photovoltaic module's current operating point to the maximum power point, an adaptive variable step size control strategy is constructed, effectively addressing the shortcomings of the traditional incremental conductance method. The designed adaptive step size control strategy satisfies:

[0055] ΔD=N×|PP best | (1)

[0056] Where N is the proportionality coefficient, P is the current operating point of the photovoltaic module, and P best The maximum power point is the starting point. When the current operating point is detected to be far from the maximum power point, the step size is automatically increased to accelerate the tracking speed; when approaching the maximum power point, the step size is gradually decreased to improve tracking accuracy. This adaptive adjustment strategy allows the adaptive conductance incremental method to flexibly adjust the step size according to the actual operating conditions, respond quickly to rapid environmental changes, and accurately locate near steady state, achieving an adaptive balance between dynamic response speed and steady-state accuracy. The control flowchart of the adaptive conductance incremental method is as follows: Figure 3 As shown, the specific operating steps are as follows:

[0057] (1) Sample the current voltage U and current I, and calculate the change in voltage dU and the change in current dI according to formula (2).

[0058]

[0059] Where U(x) and U(x-1) are the voltages of the photovoltaic module at times x and x-1, respectively, and I(x) and I(x-1) are the currents of the photovoltaic module at times x and x-1, respectively.

[0060] (2) Calculate the conductivity G of the photovoltaic module according to formula (3).

[0061]

[0062] (3) Calculate the change in the conductivity of the photovoltaic module ΔG according to formula (4).

[0063] ΔG=G(x)-G(x-1) (4)

[0064] Where G(x) and G(x-1) are the conductance values ​​at times x and x-1, respectively.

[0065] (4) Calculate the step size ΔD according to formula (1).

[0066] (5) If ΔG>0, the working point is to the left of MPP and the duty cycle needs to be increased, D=D+ΔD; if ΔG<0, the working point is to the right of MPP and the duty cycle needs to be decreased, D=D-ΔD; if ΔG=0, the current working point is in MPP and no change is needed.

[0067] The distributed MPPT algorithm based on variable step-size duty cycle scanning and adaptive conductance increment employs a two-stage optimization control strategy. First, a variable step-size duty cycle scanning method is used to quickly track the power-voltage characteristic curve of the photovoltaic module, thereby locating the approximate position of the maximum power point. Then, the algorithm switches to a more precise adaptive conductance increment method stage for finer tracking. Considering that photovoltaic modules may face complex dynamic illumination changes in actual operating environments, the algorithm designs the following restart mechanism to ensure that the system always operates at the maximum power point:

[0068]

[0069] Where P(x) is the output power of the photovoltaic module at time x, P(x-1) is the output power of the photovoltaic module at time x-1, and ΔP is the power change. When ΔP is detected to satisfy equation (5), the algorithm will automatically restart the complete optimization process. By re-executing the initial variable step size duty cycle scan and the subsequent adaptive conductance increment method tracking, the algorithm can achieve continuous and accurate tracking of the maximum power point, thereby effectively improving the energy conversion efficiency of the photovoltaic module under various illumination conditions.

[0070] The overall control flow of the proposed algorithm is as follows: Figure 4 As shown, the corresponding operation steps are as follows:

[0071] (1) The initial duty cycle D is set to 0.8. Small step duty cycle scanning is performed to monitor and collect the output voltage and current of the photovoltaic module in real time, and the maximum power point P identified in the current state is recorded. mpp and its corresponding optimal duty cycle D best .

[0072] (2) When the current output power value is detected to be lower than the recorded P mpp Immediately switch to large step duty cycle scanning to accelerate skipping invalid regions.

[0073] (3) When the current output power value is detected to exceed P again mpp Immediately switch to small step duty cycle scanning and update the optimal duty cycle D in real time. best Otherwise, continue with step (2).

[0074] (4) Determine the output voltage V of the photovoltaic module pv Is it greater than 0.9V? oc If V pv If the threshold is exceeded, proceed to the next step; otherwise, the system continues to maintain the variable step size duty cycle scanning mode.

[0075] (5) Switch to the adaptive conductivity incremental method mode and record the previously optimal duty cycle D. best As initial control parameter input.

[0076] (6) Adjust the step size of the adaptive conductance increment method dynamically according to formula (1) and perform maximum power point tracking.

[0077] (7) Determine whether the algorithm needs to be restarted according to formula (5); if the restart condition is met, initialize the parameters and return to step (1); otherwise, continue to execute step (6).

[0078] In MATLAB / Simulink, construct as follows Figure 5 The distributed photovoltaic system shown is used to evaluate the performance of the proposed algorithm. The irradiance of the three photovoltaic panels is 1000 W / m². 2 800W / m 2 and 400W / m 2 The ambient temperature was set to 25℃, and the maximum power point of the photovoltaic array was 476.91W. Figure 6 The simulation results compare the proposed distributed MPPT algorithm with the centralized MPPT algorithm using particle swarm optimization. The tracking powers of the two algorithms are 476.76W and 361.48W, respectively, with corresponding tracking times of 0.22s and 0.75s. The simulation results show that the proposed algorithm effectively reduces power oscillations during maximum power point tracking, achieving the fastest tracking speed while maintaining tracking accuracy.

[0079] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A two-stage distributed photovoltaic MPPT control method, characterized in that, include: Starting from the initial duty cycle, the maximum power point and the corresponding optimal duty cycle of the photovoltaic module are updated in real time using the variable step size duty cycle scanning method; When the output voltage of the photovoltaic module exceeds the preset voltage threshold, the variable step size duty cycle scan is completed, the latest updated maximum power point and the corresponding optimal duty cycle are recorded, and the system switches to the adaptive conductivity incremental method mode. Using the recorded optimal duty cycle as the initial control parameter input, the duty cycle is dynamically adjusted in real time using the adaptive conductance incremental method mode to track the maximum power point of the photovoltaic module. If the power change of the photovoltaic module in adjacent time periods exceeds the preset change threshold, the above steps are repeated starting from the initial duty cycle. The method of using variable step size duty cycle scanning to update the maximum power point and corresponding optimal duty cycle of the photovoltaic module in real time includes: Starting from the set initial duty cycle, perform small-step duty cycle scanning and detect the output power of the photovoltaic module in real time; During the small step duty cycle scan, the duty cycle is decreased sequentially with small step intervals. In response to the photovoltaic module's output power changing from an upward trend to a downward trend, the output power of the photovoltaic module at the current moment is determined to be the maximum power point and the corresponding optimal duty cycle is recorded. Then, the scan is switched to a large step duty cycle. During the large step duty cycle scan, the duty cycle is decreased sequentially with large step intervals. In response to the detection that the output power of the photovoltaic module exceeds the previously recorded maximum power point, the scan is switched to a small step duty cycle. During the scanning process of switching back to small step duty cycle scanning, in response to the photovoltaic module's output power changing from an upward trend to a downward trend again, the maximum power point and the corresponding optimal duty cycle are updated, and the scanning process switches back to large step duty cycle scanning. Repeat the variable step size duty cycle scanning process until the output voltage of the photovoltaic module is greater than the preset voltage threshold.

2. The two-stage distributed photovoltaic MPPT control method according to claim 1, characterized in that, The initial duty cycle is set to 0.

8.

3. The two-stage distributed photovoltaic MPPT control method according to claim 1, characterized in that, The method of dynamically adjusting the duty cycle step size in real time to track the maximum power point of a photovoltaic module using the adaptive conductivity increment method includes: The duty cycle step size at the current moment is determined based on the maximum power point recorded when the variable step size duty cycle scan is completed and the output power of the photovoltaic module at the current operating point. The change in conductance of the photovoltaic module at the current moment is determined based on the collected voltage and current changes of the photovoltaic module. The recorded optimal duty cycle is used as the initial control parameter input, and the duty cycle is adjusted in real time according to the change in conductivity and the duty cycle step size.

4. The two-stage distributed photovoltaic MPPT control method according to claim 3, characterized in that, Adjusting the duty cycle at the current moment based on the change in conductance and the duty cycle step size includes: If the change in conductivity is greater than zero, increase the duty cycle step by the current duty cycle step. If the change in conductivity is less than zero, the duty cycle at the current moment is reduced by the duty cycle step size; If the change in conductivity is zero, the duty cycle at the current moment will not be adjusted.

5. The two-stage distributed photovoltaic MPPT control method according to claim 3, characterized in that, The expression for the duty cycle step size ΔD is: in, This is the proportionality coefficient. This represents the output power of the photovoltaic module at its current operating point. This is the maximum power point recorded when the variable step size duty cycle scan is completed.

6. The two-stage distributed photovoltaic MPPT control method according to claim 3, characterized in that, The expression for the change in conductivity ΔG is: Where G represents the conductivity of the photovoltaic module, and G(x) and G(x-1) are the values ​​at time x and x-1, respectively. The conductance at time 1, U(x) and U(x-1) are the voltages of the photovoltaic module at times x and x-1, respectively, and I(x) and I(x-1) are the currents of the photovoltaic module at times x and x-1, respectively.

7. The two-stage distributed photovoltaic MPPT control method according to claim 1, characterized in that, The statement that the power change of a photovoltaic module between adjacent time points exceeds a preset threshold is as follows: Where P(x) is the output power of the photovoltaic module at time x, P(x-1) is the output power of the photovoltaic module at time x-1, and ΔP is the power change.

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