Photovoltaic maximum power point tracking method, device and storage medium based on weight factor improved tanh function

Abstract

The present invention discloses a photovoltaic maximum power point tracking method, device and storage medium based on a weight factor improved tanh function, which adopts a perturbation observation method with an adaptive step length, and the method includes: determining the relative position relationship between the current power point and the maximum power point on the power P-voltage U curve, and if the current power point is located on the right side of the maximum power point, the tanh function adaptive step length algorithm is used to adjust the step length; if the current power point is located on the left side of the maximum power point, the weight factor improved tanh function adaptive step length algorithm is used to adjust the step length. The method can effectively improve the maximum power point tracking speed and accuracy, ensure the stable output of the photovoltaic system, and has the superior performance of fast response, accurate tracking, stable and reliable.

Description

Technical Field

[0001] The invention belongs to the technical field of photovoltaic new energy, and relates to a photovoltaic maximum power point tracking method, equipment and storage medium based on a weight factor improved tanh function. Background Art

[0002] The most commonly used methods for tracking the maximum power point of photovoltaic systems are the constant voltage method (CVT), the conductance increment method (INC), and the perturbation observation method (P&O). Each of the three methods has its own advantages and disadvantages. The control logic of the constant voltage method is relatively simple and easy to implement, but it ignores the influence of environmental conditions such as temperature, so the tracking accuracy is low; although the conductance increment method has high tracking accuracy and stability, it requires real-time calculation of voltage and current differentials, which requires high hardware and computing power; the perturbation observation method is relatively simple to implement and has strong applicability, so it is widely used in actual control systems, but there will also be some power oscillation and misjudgment problems. The traditional fixed-step perturbation observation method cannot take into account both tracking speed and stability. Therefore, many domestic and foreign scholars and experts have studied and improved algorithms to achieve variable step control in order to optimize tracking speed and accuracy.

[0003] Reference [1] proposed a variable step size perturbation observation method based on an optimization algorithm. However, due to the complexity of the calculation model, it does not have the significance for practical application and promotion. Reference [2] proposed an improved power prediction variable step size photovoltaic MPPT algorithm based on the sigmoid function. According to the characteristics of the sigmoid curve, the sigmoid function is used as the step size correction coefficient to update the perturbation step size in real time. However, due to the lack of dP / dU normalization, the photovoltaic power plant enters the step size correction interval of the sigmoid function too early under low power output conditions, and tracks with a small step size for a long time, reducing the tracking speed. At the same time, the step size weights on both sides of the maximum power point are not adjusted, resulting in the possibility of an out-of-bounds misjudgment problem on the right side due to the perturbation step size being too large.

[0004] In summary, for the perturbation and observation method (P&O) which is widely used in the current photovoltaic maximum power point tracking algorithm, the traditional fixed step size algorithm cannot control the perturbation step size, cannot adjust the tracking speed and accuracy, and is prone to misjudgment problems; while most variable step size algorithms have problems such as the inability to balance tracking speed and accuracy, out-of-bounds misjudgment, and complex calculations. Based on this, the present invention proposes a new P&O method.

[0005] [1] Liu Qiuhua, Zhang Xiujin, Fan Chen. MPPT control algorithm based on power difference variable step size perturbation observation method [J]. Power Technology, 2020, 44(7): 1035-1039.

[0006] [2] Zheng Yang. Research on Maximum Power Point Tracking Control Algorithm for Photovoltaic Power Generation System[D]. Qingdao University of Technology, 2023. Summary of the invention

[0007] The purpose of the present invention is to address the problems and difficulties in the prior art and propose a photovoltaic maximum power point tracking method, device and storage medium based on a weight factor improved tanh function. The method judges the relative position of the current power point and the maximum power point, and adopts different step adjustment strategies based on the tanh function for different position conditions. The method can effectively improve the maximum power point tracking speed and accuracy, and ensure the stable output of the photovoltaic system during the tracking process. The method has the superior performance of fast response, accurate tracking, stable and reliable.

[0008] The technical solution adopted by the present invention is as follows:

[0009] A photovoltaic maximum power point tracking method based on a weight factor improved tanh function is a perturbation observation method with an adaptive step size, including the following:

[0010] Determine the relative position relationship between the current power point and the maximum power point on the PU curve. If the current power point is on the right side of the maximum power point, the tanh function adaptive step size algorithm is used to adjust the step size. If the current power point is on the left side of the maximum power point, the weight factor improved tanh function adaptive step size algorithm is used to adjust the step size.

[0011] Furthermore, the relative position relationship between the current power point and the maximum power point is determined based on the sign of the sampling point dP / dU. When dP / dU < 0, the current power point is located to the right of the maximum power point; when dP / dU > 0, the current power point is located to the left of the maximum power point.

[0012] Furthermore, the tanh function adaptive step length algorithm is specifically as follows: using |ΔP| as an input variable of the tanh function, taking the output of the tanh function as a step length correction coefficient, and multiplying it by a preset initial reference step length to obtain an adaptive correction step length.

[0013] Furthermore, the weight factor improved tanh function adaptive step size algorithm is specifically as follows: first normalize the dP / dU and tanh functions respectively to obtain the x(t) and tanh(x)′ functions, then use x(t) as the input variable of the tanh(x)′ function, and the value of tanh(x)′ is the step size correction coefficient, which is multiplied by the weight factor and then multiplied by the preset initial reference step size to obtain the adaptive correction step size.

[0014] Furthermore, dP / dU is normalized to obtain x(t), which is:

[0015]

[0016] Where I(t) is the photovoltaic current at time t.

[0017] Furthermore, the tanh function is normalized to obtain the tanh(x)′ function, which is:

[0018]

[0019] Furthermore, the weight factor K3 is:

[0020]

[0021] A photovoltaic maximum power point tracking system based on a weight factor improved tanh function, comprising:

[0022] A judgment module is used to judge the relative position relationship between the current power point and the maximum power point on the PU curve;

[0023] The step size adjustment module is used to adjust the preset initial reference step size according to the result of the judgment module. If the current power point is on the right side of the maximum power point, the tanh function adaptive step size algorithm is used to adjust the step size; if the current power point is on the left side of the maximum power point, the weight factor improved tanh function adaptive step size algorithm is used to adjust the step size.

[0024] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements any of the above-mentioned photovoltaic maximum power point tracking methods based on a weight factor improved tanh function.

[0025] An electronic device, comprising:

[0026] one or more processors;

[0027] A memory for storing one or more programs;

[0028] When the one or more programs are executed by the one or more processors, the one or more processors implement any of the above-mentioned photovoltaic maximum power point tracking methods based on the weight factor improved tanh function.

[0029] The beneficial effects of the present invention are:

[0030] Based on the perturbation observation method to realize photovoltaic maximum power point tracking, the present invention proposes an adaptive step length method based on a weight factor improved tanh function. By determining the location of the current power point on the photovoltaic system PU curve, if it is located on the right side of the maximum power point, the tanh function is fitted to |ΔP| as the step length correction coefficient to adjust the step length; if it is located on the left side of the maximum power point, the dP / dU and tanh functions are normalized and then fitted to obtain the step length correction coefficient, and then the weight factor is used to perform adaptive step length adjustment. The method can effectively improve the maximum power point tracking speed and accuracy, and ensure the stability of the photovoltaic system output during the tracking process, and has the superior performance of fast response, accurate tracking, stability and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is the principle diagram of the perturbation and observation method;

[0032] Figure 2 is the tanh function curve;

[0033] Figure 3 is the tanh derivative function curve;

[0034] Figure 4 It is the |dP / dU|-U curve;

[0035] Figure 5 are x(t)-U curves under three different external input conditions;

[0036] Figure 6 is the curve corresponding to the x>0 region after tanh(x) normalization;

[0037] Figure 7 It is the K3-dP / dU curve;

[0038] Figure 8 It is a schematic diagram of the process of the present invention;

[0039] Fig. 9 Output power curves of three different methods under temperature changes;

[0040] Fig.10 Output power curves of three different methods under changing illumination.

[0041] Fig.11 Output power curves of three different methods under simultaneous changes in light and temperature. DETAILED DESCRIPTION

[0042] The technical solution, principle basis and specific implementation process of the present invention are further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0043] 1) Perturbation and Observation (P&O) Principle

[0044] The perturbation and observation method (P&O) is to periodically apply a positive or negative voltage disturbance to the system, and determine the direction of the next disturbance by comparing the changes in the system output power before and after the disturbance. That is, if the photovoltaic output power increases after the disturbance, continue to apply the disturbance in the same direction, and if the output power decreases, apply the disturbance in the opposite direction. In essence, it is a continuous optimization process for the output power, and ultimately the working point of the photovoltaic system is always near the maximum power point. The principle diagram is detailed in Figure 1 .

[0045] Depend on Figure 1 It can be seen that the maximum power point P of the power P-voltage U curve max The absolute values ​​of the slopes of the curves on both sides are different. If the same algorithm is used for the left and right sides, it will cause disturbance out of bounds. Therefore, the photovoltaic maximum power point tracking method based on the weight factor improved tanh function of the present invention adopts different adaptive step length algorithms for both sides of the maximum power point, and judges whether the sampling point is on the left or right side of the maximum power point according to the sign of the sampling point dP / dU. If dP / dU<0, it is on the right side of the maximum power point, and the tanh function adaptive step length algorithm is adopted; if dP / dU>0, it is on the left side of the maximum power point, and the adaptive step length algorithm based on the weight factor improved tanh function is adopted. Using different algorithms for different areas can effectively ensure the tracking speed and accuracy, while avoiding oscillation and misjudgment.

[0046] 2) About the tanh function:

[0047] Tanh function, full name hyperbolic tangent function, mathematical formula:

[0048]

[0049] Its function curve is as follows Figure 2 As shown, the derivative function curve is as follows Figure 3 shown.

[0050] The tanh function has the following characteristics:

[0051] (1) Smooth and continuous;

[0052] (2) Nonlinear monotonically increasing;

[0053] (3) The curve passes through the origin and is symmetrical about the origin;

[0054] (4) The derivative is continuous, increasing near the origin and decreasing near the ends;

[0055] (5) The output range is normalized to [-1, 1] and the output absolute value range is [0, 1].

[0056] The smooth and continuous characteristics of the tanh function can effectively reduce the sudden changes of the control signal, making the system operation more stable; the limiting feature can ensure that the output is always within the effective range, which is suitable as a correction coefficient to avoid excessive adjustment; the curve characteristics of the derivative meet the requirements of adaptive step size, especially near the maximum power point, which can achieve more precise adjustment.

[0057] 3) For the right side of the maximum power point, an adaptive step size algorithm based on the tanh function is used

[0058] Depend on Figure 1 It can be seen that the PU curve presents a single-peak characteristic. For the traditional fixed-step perturbation observation method, on the right side of the maximum power point, with the same voltage step size ΔU changing, the closer to the maximum power point, the smaller the absolute value of ΔP.

[0059] Combining the characteristics of the tanh function and the requirements of the adaptive step size algorithm, |ΔP| can be used as the input variable of the tanh function to map the input to the output interval [0,1]. When approaching the maximum power point, |ΔP| tends to 0, the output of the tanh function is close to 0, and the step size is automatically reduced to avoid crossing the boundary to ensure tracking accuracy; when far away from the maximum power point, a larger |ΔP| value will cause a larger tanh output, and the step size will be automatically increased to accelerate convergence and ensure tracking speed. Define the output of tanh as the step size correction coefficient, multiply it by the preset initial reference step size to obtain the adaptive correction step size.

[0060] For the right side of the maximum power point curve (dP / dU<0), |ΔP| is input as a variable into the tanh function:

[0061]

[0062] The output result is the step correction coefficient K1 of the curve on the right side of the maximum power point. The final correction step length of the curve on the right side of the maximum power point is:

[0063] ΔU1=K1×Uset

[0064] Where ΔU1 is the final correction step size of the curve on the right side of the maximum power point, K1 is the step size correction coefficient, and Uset is the reference step size.

[0065] 4) For the left side of the maximum power point, an adaptive step size algorithm based on the improved tanh function with weight factor is used

[0066] ① Improved tanh function adaptive step size algorithm

[0067] Under the same voltage change ΔU on the left side of the maximum power point, the change rate of ΔP in most areas far away from the maximum power point is not significant. Therefore, the aforementioned method of using |ΔP| as the input variable of the tanh function is not suitable for the left side of the maximum power point.

[0068] Depend on Figure 1 It can be seen that the slope of the PU curve is dP / dU. The closer to the maximum power point, the smaller the absolute value of the curve slope |dP / dU|. Draw the |dP / dU|-U curve, as shown Figure 4 shown.

[0069] Figure 4 As shown in the |dP / dU|-U curve, on the left side of the maximum power point, the |dP / dU| curves corresponding to the three different external conditions (where curves a, b, and c correspond to three different light intensities) are quite different, and |dP / dU| has different values ​​under the same voltage. It is necessary to perform adaptive step correction on the interval where the |dP / dU| slope changes greatly, so that the step size becomes smaller as it is closer to the maximum power point.

[0070] Combination Figure 2 Tanh function curve and Figure 3 Tanh derivative function curve, define the step correction interval as the interval where the slope of the tanh curve changes greatly, that is, the interval where the tanh function derivative is large. Set the interval boundary point to the point where the absolute value of its derivative is 0.1, and solve the equation:

[0071] tanh′(x)=1-tanh 2 (x) = 0.1

[0072] The obtained x is the boundary point of the step correction area. The calculation shows that x≈1.818, so its step correction area is [0,1.818].

[0073] for Figure 4 In the |dPc / dU| curve in the |dP / dU|-U curve, under this external condition, |dP / dU| is smaller than the boundary point of the step correction zone 1.818 in the initial stage. If |dPc / dU| is used as the tanh function variable, the initial state is already in the step correction zone. As it approaches the maximum power point, the step size is further reduced, and it will always be impossible to track with the reference step size, which is not conducive to ensuring the tracking speed.

[0074] Therefore, for the curve on the left side of the maximum power point (dP / dU>0), if dP / dU is directly used as a variable to input the tanh function, the step size correction area will be entered too early.

[0075] To solve this problem, a mapping between the dP / dU slope change area and the tanh step correction area is established to ensure that the step correction area will not be entered too early or too late, so as not to affect the tracking speed and accuracy. |dP / dU| under different external conditions is normalized and its interval is limited to [0,1]. The step adjustment input variable x(t) is defined as:

[0076]

[0077] Where I(t) is the photovoltaic current at time t.

[0078] For details of the x(t)-U curves under three different external input conditions, see Figure 5 , we can see that for the left part of the maximum power point x(t) = 0, before the slope of each curve changes, x(t) is 1, and at the maximum power point x(t) is 0, effectively limiting the left side of the maximum power point x(t) to [0,1].

[0079] Correspondingly, tanh(x) is also normalized, and its step size correction interval is also limited to [0,1]. After normalization, it is:

[0080]

[0081] The curve corresponding to the x>0 region after tanh(x) normalization is shown in Figure 6 .

[0082] For the left part of the maximum power point of the PU curve, the above improved tanh function adaptive step size algorithm is used, and x(t) is used as the input variable of tanh(x)′. The value of tanh(x)′ is the step size correction coefficient K2.

[0083] ② Step size correction based on weight factor

[0084] According to the PU curve, the voltage variation range of the curve on the left side of the maximum power point is about 4 times that of the curve on the right side. In order to ensure that the tracking speeds of the left and right sides are similar when the absolute value of the step correction coefficient is the same, the step weight factor K3 is defined, and the interval of K3 is [1,4]. If there is no weight factor K3 for correction, when the reference step lengths on the left and right sides of the maximum power point of the PU curve are the same, the tracking speed on the right side changes too fast, and the tracking is easy to cross the boundary.

[0085] On the left side of the maximum power point, we hope to achieve:

[0086] (1) The closer to the maximum power point, the smaller the step size, that is, the step size weight factor approaches 1;

[0087] (2) The farther away from the maximum power point, the larger the step size, that is, the step size weight factor approaches 4;

[0088] (3) The rate of change of the weight factor increases from 1 to 4.

[0089] Therefore, the weight factor curve can also be fitted using the tanh function:

[0090]

[0091] K3-dP / dU curve Figure 7 shown.

[0092] In summary, the modified step size of the curve on the left side of the maximum power point is obtained by the improved tanh function adaptive step size algorithm, and then the weight factor is corrected. The final step size after correction is:

[0093] ΔU2=K3×K2×Uset

[0094] Wherein, ΔU2 is the final correction step size of the curve on the left side of the maximum power point, K2 is the step size correction coefficient, K3 is the weight factor, and Uset is the reference step size.

[0095] like Figure 8 FIG. 1 is a schematic diagram of a specific implementation process of the method of the present invention, which generally includes the following steps:

[0096] Step 1: Sample the photovoltaic system voltage U(t-1) and current I(t-1) at time t-1;

[0097] Step 2: Sample the PV system voltage U(t) and current I(t) at time t;

[0098] Step 3: Calculate the output power of the photovoltaic system at time t-1 P(t-1)=U(t-1)*I(t-1), and calculate the output power of the photovoltaic system at time t P(t)=U(t)*I(t);

[0099] Step 4: Calculate dP and dU, dP = P(t) - P(t-1), dU = U(t) - U(t-1);

[0100] Step 5: Determine whether dP / dU is 0. If it is 0, do not apply disturbance voltage, so that the voltage at the next moment is equal to U(t) and return to step 3; if it is 0, go to step 6;

[0101] Step 6: Determine whether dP / dU is less than 0. If it is less than 0, it is determined that the power point is located on the right side of the maximum power point. The adaptive step size algorithm based on the tanh function is used to calculate the step size correction coefficient K1. The disturbance step size voltage at the next moment is ΔU1=K1*Uset, and Uset is the preset reference step size. Therefore, the voltage at the next moment is U(t+1)=U(t)+ΔU1, and return to step 3.

[0102] If dP / dU is greater than 0, it is determined that the power point is located on the left side of the maximum power point at this time. The adaptive step size algorithm based on the improved tanh function based on the weight factor is used. First, the step size correction coefficient K2 based on the improved tanh function algorithm is calculated, and then the weight factor K3 is calculated to obtain the disturbance step size voltage ΔU2=K2*K3*Uset at the next moment. Uset is the preset reference step size. Therefore, the voltage U(t+1)=U(t)+ΔU2 at the next moment, and return to step three.

[0103] Simulation analysis:

[0104] In order to verify the effectiveness and superiority of the adaptive step-size perturbation observation method based on the weight factor improved tanh function proposed in the present invention, a simulation model was built using the Matlab / Simulink software platform.

[0105] The photovoltaic system parameters of the adaptive step-size perturbation observation MPPT algorithm based on the weight factor improved tanh function are shown in Table 1.

[0106] Table 1 PV system parameters of adaptive step-size perturbation observation MPPT algorithm based on weight factor improved tanh function

[0107]

[0108]

[0109] The maximum power point tracking speed and accuracy of three disturbance observation methods, namely the traditional fixed step size algorithm, a certain improved variable step size algorithm and the weight factor-based improved tanh function adaptive step size algorithm of the present invention, are compared in three scenarios. The method of the certain improved variable step size is: setting a power change threshold, and comparing the power difference between the current moment and the previous sampling moment with the power change threshold to determine whether to use the set large step size or small step size for tracking (such as the method used in "Research on Control Strategy of Photovoltaic Microgrid Hybrid Energy Storage System"). The speed evaluation index is defined as the time from the beginning of the change in environmental conditions to the tracking of the maximum power point, and the accuracy evaluation index is the steady-state power ripple: the difference between the maximum power and the minimum power in the steady state, the smaller the difference, the higher the accuracy.

[0110] Scenario 1: Constant light and changing temperature. The initial ambient temperature is 20°C and the light intensity is 1000W / m 2 , the temperature rises from 20℃ to 40℃ in 3s, and the light intensity remains unchanged. The output power curves of the three algorithms are: Fig. 9 shown.

[0111] In this scenario, at the start stage and 3s, the power tracking speed and accuracy of the three algorithms are shown in Table 2.

[0112] Table 2 Power tracking speed and accuracy of three algorithms under temperature changes

[0113]

[0114] from Fig. 9 As shown in Table 2, in the scenario where the light remains unchanged but the temperature changes, the output power of the traditional fixed-step algorithm has a large oscillation in the tracking process, and the oscillation time is relatively long; the output power oscillation amplitude of the improved variable-step algorithm is slightly smaller than that of the fixed-step algorithm, and the tracking speed is also slightly improved; the power amplitude of the adaptive algorithm proposed in the present invention is significantly smaller than that of the traditional fixed-step and improved variable-step algorithms in the tracking process, and the tracking speed is also greatly improved, and it has the highest tracking accuracy among the three algorithms. It can be seen from the above that in the scenario of temperature changes, the method of the present invention has obvious advantages in speed and accuracy in tracking the maximum power point.

[0115] Scenario 2: Constant temperature, varying light intensity. Set the ambient temperature to 25°C and the initial light intensity to 1000W / m 2 The light intensity increases to 1500W / m in 3s 2 , the temperature remains unchanged. The output power curves of the three algorithms are: Fig.10 shown.

[0116] In this scenario, at the start stage and at 3s, the power tracking speed and accuracy of the three algorithms are shown in Table 3.

[0117] Table 3 Power tracking speed and accuracy of three algorithms under illumination changes

[0118]

[0119] from Fig.10 It can be seen from Table 3 that in the initial stage, the output power amplitude of the improved variable step-size algorithm is the largest, while the traditional fixed step-size algorithm has the slowest tracking speed among the three algorithms. At 3s, the output power of the traditional fixed step-size algorithm takes a long time (110ms) to track the maximum power and the power amplitude is the largest; although the tracking speed of the improved variable step-size algorithm is improved, the effect is not obvious, and there is no obvious improvement in the amplitude; the adaptive step-size algorithm proposed in the present invention quickly tracks the maximum power at the fastest speed (21ms) and the smallest amplitude. From the tracking accuracy, it can be seen that the algorithm proposed in the present invention has obvious advantages. It can be seen from the above that in the scene of changing illumination, the algorithm of the present invention has a faster tracking speed, smaller amplitude and higher accuracy for the maximum power point.

[0120] Scenario 3: Temperature and light intensity change simultaneously. Set the initial light intensity to 500W / m 2 The initial ambient temperature is 40°C. At 3 seconds, the light intensity increases to 1500W / m 2, the temperature drops to 20℃. The output power curves of the three algorithms are: Fig.11 shown.

[0121] In this scenario, at the start stage and 3s, the power tracking speed and accuracy of the three algorithms are shown in Table 4.

[0122] Table 4 Power tracking speed and accuracy of three algorithms under simultaneous changes in light and temperature

[0123]

[0124] from Fig.11 It can be seen from Table 4 that the three algorithms can eventually track the maximum power point. However, when the temperature and light intensity change at the same time, the conventional fixed step size algorithm and the improved variable step size algorithm have certain misjudgments, resulting in a significant decrease in output power. The algorithm of the present invention does not have any misjudgments, and shows great advantages in both tracking speed and tracking accuracy, proving that under the complex environmental conditions of real scenes, the algorithm of the present invention has stronger robustness.

[0125] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0126] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0127] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0128] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0129] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention are included in the protection scope of the present invention.

Claims

1. A photovoltaic maximum power point tracking method based on a weight factor improved tanh function, characterized in that: The perturbation and observation method with adaptive step size is adopted, including the following: Determine the relative position relationship between the current power point and the maximum power point on the power P-voltage U curve. If the current power point is on the right side of the maximum power point, the tanh function adaptive step length algorithm is used to adjust the step length. If the current power point is on the left side of the maximum power point, the weight factor improved tanh function adaptive step length algorithm is used to adjust the step length. The weight factor improved tanh function adaptive step size algorithm is specifically as follows: firstly, dP / dU and tanh function are normalized respectively to obtain x(t) and tanh(x)′ functions, then x(t) is used as the input variable of tanh(x)′ function, and the value of tanh(x)′ is obtained as the step size correction coefficient, which is multiplied by the weight factor and then multiplied by the preset initial reference step size to obtain the adaptive correction step size; The tanh function is normalized to obtain the tanh(x)′ function, which is: The weight factor K3 is:

2. The photovoltaic maximum power point tracking method based on the weight factor improved tanh function according to claim 1 is characterized in that: The relative position relationship between the current power point and the maximum power point is determined based on the sign of the sampling point dP / dU. When dP / dU < 0, the current power point is located to the right of the maximum power point; when dP / dU > 0, the current power point is located to the left of the maximum power point.

3. The photovoltaic maximum power point tracking method based on the weight factor improved tanh function according to claim 1 is characterized in that: The tanh function adaptive step length algorithm specifically comprises: using |ΔP| as the input variable of the tanh function, taking the output of the tanh function as the step length correction coefficient, and multiplying it by a preset initial reference step length to obtain an adaptive correction step length.

4. The photovoltaic maximum power point tracking method based on the weight factor improved tanh function according to claim 1, characterized in that: Normalizing dP / dU gives x(t), which is: Where I(t) is the photovoltaic current at time t.

5. A photovoltaic maximum power point tracking system based on a weight factor improved tanh function, characterized in that: The method for implementing any one of claims 1 to 4 comprises: A judgment module is used to judge the relative position relationship between the current power point and the maximum power point on the PU curve; The step size adjustment module is used to adjust the preset initial reference step size according to the result of the judgment module. If the current power point is on the right side of the maximum power point, the tanh function adaptive step size algorithm is used to adjust the step size; if the current power point is on the left side of the maximum power point, the weight factor improved tanh function adaptive step size algorithm is used to adjust the step size.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the photovoltaic maximum power point tracking method based on the weight factor improved tanh function as described in any one of claims 1 to 4 is implemented.

7. An electronic device, characterized in that: The device comprises: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the photovoltaic maximum power point tracking method based on the weight factor improved tanh function as described in any one of claims 1 to 4.

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