Photovoltaic module maximum power point tracking control method and apparatus

Abstract

The application provides a photovoltaic module maximum power point tracking control method and device, and belongs to the technical field of data processing. The method comprises the following steps: obtaining the current value flowing through the parallel diode; when the current value of the parallel diode represents that the photovoltaic module is in the case of shadow shielding, tracking the maximum power point of the photovoltaic module by using an improved firework optimization method, and determining the optimal duty cycle; wherein the initial firework and / or the firework generated through the selection strategy are optimized by using a chaotic optimization method in the improved firework optimization method; and the target pulse signal generated by the PWM generator based on the optimal duty cycle is input into the boost module, so that the output power of the photovoltaic module is maximized. The initial firework and / or the firework generated through the selection strategy are optimized by using the improved firework optimization method fused with the chaotic optimization method, the search range is expanded, and the maximum power can be accurately and quickly tracked when the maximum power point has a multi-peak characteristic due to the shadow shielding of the photovoltaic module.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically to a method for maximum power point tracking control of a photovoltaic module, a device for maximum power point tracking control of a photovoltaic module, and an electronic device. Background Technology

[0002] Commonly used maximum power point tracking (MPPT) control methods include: constant voltage tracking, perturbation and observation (P&O), and incremental conductance. The constant voltage tracking method aims to control the photovoltaic (PV) module's output voltage at its maximum power point (MPP), making the PV module approximately operate at its MPP. The perturbation and observation method (P&O) involves introducing a small variable based on the PV module's power output, observing the results, comparing and analyzing them, and adjusting the PV module's operating point accordingly. Incremental conductance is another commonly used algorithm in MPPT control, achieving MPPT by comparing the PV module's instantaneous conductance and its change.

[0003] However, when the photovoltaic module is under partial shading conditions and the output characteristic curve is a multi-peak curve, all three methods may get stuck in a local maximum power point, which will reduce the output power. Summary of the Invention

[0004] The purpose of this invention is to provide a method, apparatus, and electronic device for maximum power point tracking control of photovoltaic modules, in order to solve the problem that when a photovoltaic module is under partial shading conditions and its output characteristic curve is a multi-peak curve, the above three methods may get stuck in a local maximum power point, resulting in a reduction in output power.

[0005] To achieve the above objectives, embodiments of the present invention provide a photovoltaic module maximum power point tracking control method, applied to perform maximum power point tracking on a photovoltaic module in a physical model. The physical model includes a photovoltaic module, a parallel diode connected in parallel with the photovoltaic module, a PWM generator, a boost module electrically connected to the PWM generator, and a load module electrically connected to the boost module. The method includes:

[0006] Obtain the current value flowing through the parallel diode;

[0007] The current value of the parallel diode represents the photovoltaic module under shading conditions. The photovoltaic module is subjected to maximum power point tracking and the optimal duty cycle is determined by an improved fireworks optimization method. The improved fireworks optimization method optimizes the initial fireworks and / or the fireworks generated by the selection strategy through a chaotic optimization method.

[0008] The PWM generator is controlled to generate a target pulse signal based on the optimal duty cycle and input it to the boost module so as to maximize the output power of the photovoltaic module.

[0009] Optionally, the step of optimizing the initial fireworks using the chaotic optimization method includes:

[0010] In the solution space, the position of each dimension of the initial firework individual is mapped to the chaotic region according to a preset mapping rule;

[0011] The mapped values ​​are iterated according to a preset iteration rule to generate a chaotic sequence;

[0012] The chaotic sequence is inverse mapped to the solution space using a preset inverse mapping rule.

[0013] Optionally, the preset mapping rule is expressed by the following formula:

[0014]

[0015] Where, x i Let a represent the initial individual firework. i b represents the lower bound of the chaotic search range. i y represents the upper bound of the chaotic search range. i This represents the value after mapping;

[0016] The preset iteration rule is expressed by the following formula:

[0017] y i+1 =1-2(y i ) 2 y i ∈(-1, 1);

[0018] Among them, y i This represents the mapped value, (-1, 1) represents the chaotic region, and y i+1 This represents the iteration value; multiple iteration values ​​form a chaotic sequence.

[0019] The preset inverse mapping rule is expressed by the following formula:

[0020]

[0021] Among them, a i b represents the lower bound of the chaotic search range. i y represents the upper bound of the chaotic search range. i Let x represent the value in the chaotic sequence. i This represents the fireworks after the inverse mapping.

[0022] Optionally, the step of optimizing the fireworks generated by the selected strategy using a chaotic optimization method includes:

[0023] In the solution space, the position of each dimension of the individual fireworks generated by the selection strategy is mapped to the chaotic region according to a preset mapping rule.

[0024] The mapped values ​​are iterated according to a preset iteration rule to generate a chaotic sequence;

[0025] The chaotic sequence is inverse mapped to the solution space using a preset inverse mapping rule.

[0026] Optionally, the preset mapping rule is expressed by the following formula:

[0027]

[0028] Where, x i Let a represent the individual fireworks generated by the chosen strategy. i b represents the lower bound of the chaotic search range. i y represents the upper bound of the chaotic search range. i This represents the value after mapping;

[0029] The preset iteration rule is expressed by the following formula:

[0030] y i+1 =1-2(y i ) 2 y i ∈(-1, 1);

[0031] Among them, y i This represents the mapped value, (-1, 1) represents the chaotic region, and y i+1 This represents the iteration value; multiple iteration values ​​form a chaotic sequence.

[0032] The preset inverse mapping rule is expressed by the following formula:

[0033]

[0034] Among them, a i b represents the lower bound of the chaotic search range. i y represents the upper bound of the chaotic search range. i Let x represent the value in the chaotic sequence. i This represents the fireworks after the inverse mapping.

[0035] Optionally, after obtaining the current value flowing through the parallel diode, the method further includes:

[0036] The current value of the parallel diode represents the photovoltaic module under the condition of no shading. The photovoltaic module is subjected to maximum power point tracking and the optimal duty cycle is determined by the improved variable step size perturbation observation method based on the gradually decreasing perturbation step size.

[0037] Optionally, the step of performing maximum power point tracking of photovoltaic modules based on a gradually decreasing perturbation step size using the improved variable step size perturbation observation method includes:

[0038] In each case of switching between positive and negative perturbation, the perturbation step size is reduced based on the scaling factor, and a positive or negative perturbation is applied to the current power value based on the reduced perturbation step size until the maximum power is found.

[0039] On the other hand, embodiments of the present invention also provide a photovoltaic module maximum power point tracking control device, applied to perform maximum power point tracking on a photovoltaic module in a physical model. The physical model includes a photovoltaic module, a parallel diode connected in parallel with the photovoltaic module, a PWM generator, a boost module electrically connected to the PWM generator, and a load module electrically connected to the boost module; the device includes:

[0040] A data acquisition unit is used to acquire the current value flowing through the parallel diode;

[0041] The first tracking unit is used to perform maximum power point tracking of the photovoltaic module and determine the optimal duty cycle by means of an improved fireworks optimization method when the current value of the parallel diode indicates that the photovoltaic module is under shading; wherein the improved fireworks optimization method optimizes the initial fireworks and / or the fireworks generated by the selection strategy through a chaotic optimization method.

[0042] The control unit is used to control the PWM generator to generate a target pulse signal based on the optimal duty cycle and input it to the boost module so as to maximize the output power of the photovoltaic module.

[0043] On the other hand, embodiments of the present invention also provide an MPPT controller, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described photovoltaic module maximum power point tracking control method.

[0044] On the other hand, embodiments of the present invention also provide a chip, including the above-mentioned MPPT controller, photovoltaic module, parallel diode connected in parallel with the photovoltaic module, PWM generator, boost module electrically connected to the PWM generator, and load module electrically connected to the boost module.

[0045] On the other hand, the present invention also provides a machine-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described photovoltaic module maximum power point tracking control method.

[0046] Through the above technical solution, the present invention optimizes the initial fireworks and / or the fireworks generated by the selection strategy by integrating the chaotic optimization method with the improved fireworks optimization method under the condition that the photovoltaic module is under shadow. This expands the search range, avoids the problem of traditional fireworks optimization methods getting stuck in local optima, enhances the optimization ability, and gets rid of local optima. When the photovoltaic module has multi-peak characteristics at the maximum power point due to shadow shading, it can accurately and quickly track the maximum power.

[0047] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0048] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0049] Figure 1 This is one of the flowcharts of the photovoltaic module maximum power point tracking control method provided by the present invention;

[0050] Figure 2 This is a schematic diagram of the physical model provided by existing technology;

[0051] Figure 3 This is a schematic diagram of the equivalent circuit structure of a photovoltaic module provided by existing technology;

[0052] Figure 4 This is a schematic diagram of the output characteristic curves of photovoltaic modules provided by existing technology;

[0053] Figure 5 This is one of the flowcharts of the improved variable step size perturbation observation method provided by the present invention;

[0054] Figure 6 This is the second flowchart of the improved variable step size perturbation observation method provided by the present invention;

[0055] Figure 7 This is a schematic diagram of the structure of the photovoltaic module maximum power point tracking control device provided by the present invention;

[0056] Figure 8 This is a schematic diagram of the structure of the electronic device provided by the present invention;

[0057] Figure 9 This is a schematic diagram of the chip structure provided by the present invention. Detailed Implementation

[0058] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0059] Method Implementation Examples

[0060] Please refer to Figure 1 This invention proposes a maximum power point tracking (MPPT) control method for photovoltaic (PV) modules, applied to MPPT of PV modules in a physical model. Please refer to... Figure 2 The physical model mentioned in this invention includes a photovoltaic module, a parallel diode connected in parallel with the photovoltaic module, a PWM generator for generating a PWM (Pulse Width Modulation) wave, a boost module electrically connected to the PWM generator, and a load module electrically connected to the boost module. The main function of the photovoltaic module is to convert light energy into electrical energy, and its equivalent circuit is as follows: Figure 3 As shown. By Figure 3 It can be seen that the mathematical model of a photovoltaic module is:

[0061]

[0062] In the above formula: I is the current flowing through the load, I ph For photocurrent, I D I is the current flowing through the diode. sh I0 is the current flowing through the parallel resistor, I0 is the reverse saturation current of the diode, and q is the charge amount 1.6 × 102. -19 C and A are the ideal diode coefficients, and K is the Boltzmann constant 1.38 × 10⁻⁶. -23 J / K, U is the output voltage, R s For series resistance, R sh Here, T represents the parallel resistance, and T represents the actual ambient temperature.

[0063] In reality, due to R in the equivalent circuit s Very small, R sh It's very big, so It can be ignored.

[0064] In the open-circuit state, the load resistance is equivalent to infinity, the load current is equivalent to zero, and the load voltage equals the open-circuit voltage. At the maximum power point, U = U m , I = I m The final equivalent circuit of the photovoltaic cell can be simplified as follows:

[0065]

[0066]

[0067]

[0068] Among them, I L This represents the current flowing through the load.

[0069] According to the short-circuit current I under standard conditions provided by the photovoltaic module manufacturer SC Open circuit voltage U OC Maximum power point current I m Maximum power point voltage U m This will give you the output characteristic curve under standard operating conditions (standard conditions).

[0070] Under other operating conditions, temperature and irradiance are analyzed first:

[0071] ΔT=TT ref ;

[0072]

[0073]

[0074] U′ OC =U OC (1-cΔT)ln(1+bΔS);

[0075]

[0076] U′ m =U m (1-cΔT)ln(1+bΔS);

[0077] In the formula, I SC S is the short-circuit current under standard conditions, and S is the actual illumination intensity. ref Standard illuminance is 1000 W / m 2 T is the actual ambient temperature. ref Given a standard ambient temperature of 25℃, a = 0.0025, b = 0.5, and c = 0.0028, the parameters for other operating conditions can be calculated using the above formula, I′. SC For short-circuit current under other operating conditions, U′ OC For open-circuit voltage under other operating conditions, I′ m For the maximum power point current under other operating conditions, U′ m This is the voltage at the maximum power point under other operating conditions.

[0078] This invention allows the construction of photovoltaic module models using MATLAB or Simulink. The four parameters of a single photovoltaic module are: U OC =37.3V, I SC =8.84, U m =30.2、I m=8.29, set the photovoltaic array irradiance parameters under different modes as shown in Table 1.

[0079] Table 1

[0080] No shadow occlusion <![CDATA[1000W / m 2 ]]> <![CDATA[1000W / m 2 ]]> <![CDATA[1000W / m 2 ]]> There is shadow <![CDATA[600W / m 2 ]]> <![CDATA[800W / m 2 ]]> <![CDATA[1000W / m 2 ]]> There is shadow <![CDATA[800W / m 2 ]]> <![CDATA[900W / m 2 ]]> <![CDATA[1000W / m 2 ]]>

[0081] Simulations were performed on different modes to obtain... Figure 4 The output characteristic curve of the photovoltaic module is shown below. Figure 4 It can be seen that the characteristic curve is a single-peak curve when there is no shading, and a multi-peak curve when there is shading. In this case, regardless of whether it is the constant voltage tracking method, the perturbation observation method, or the incremental conductance method, there is a possibility of getting stuck in a local maximum power point, which will reduce the output power.

[0082] In view of this, the present invention proposes a maximum power point tracking control method for photovoltaic modules to solve the above problems. Please refer to... Figure 1 The maximum power point tracking control method for photovoltaic modules includes:

[0083] Step 100: Obtain the current value flowing through the parallel diode.

[0084] This invention allows a current sensor to be installed on the bypass diode (parallel diode) of a photovoltaic module prone to shading, in order to detect whether the bypass diode is conducting. The current sensor transmits the current value of the parallel diode to the MPPT (Maximum Power Point Tracking) controller. The MPPT controller obtains the output voltage and output current of the photovoltaic module and the current value of the parallel diode, and then determines which maximum power point tracking control method to use based on the current value flowing through the parallel diode.

[0085] Step 200: When the current value of the parallel diode indicates that the photovoltaic module is under shade, the photovoltaic module is subjected to maximum power point tracking by an improved fireworks optimization method, and the optimal duty cycle is determined.

[0086] When the current value of the parallel diode is greater than 0, it indicates that current flows through the parallel diode, signifying that the photovoltaic module is under shading. An improved fireworks optimization method is used to perform maximum power point tracking on the photovoltaic module and determine the optimal duty cycle. Specifically, the improved fireworks optimization method uses chaotic optimization to optimize the initial fireworks and / or the fireworks generated by the selection strategy.

[0087] In one embodiment, an improved fireworks optimization method is used under shading conditions. This method is selected to optimize the maximum power of the photovoltaic module, and after obtaining the maximum power, the duty cycle is calculated. The specific steps are as follows:

[0088] Step 1: Parameter initialization, including the number of firework species, the number of explosion sparks, the explosion radius, the number of Gaussian sparks, and the constraint conditions.

[0089] Step 2: Use the chaos optimization method to optimize the initial fireworks, and select the best N fireworks as the final determined initial fireworks.

[0090] Step 3: Calculate the initial firework's adaptability, i.e., its output power.

[0091] Step 4: Calculate the explosion radius of each firework except the optimal firework and the number of explosions of all fireworks.

[0092] Step 5: Generate Gaussian sparks.

[0093] Step 6: Calculate the fitness value for all Gaussian sparks.

[0094] Step 7: Calculate the optimal firework radius.

[0095] Step 8: Generate the next generation of fireworks based on the selected strategy.

[0096] Step 9: Use chaotic optimization to optimize the fireworks generated by the selection strategy, and select the N best fireworks as the next generation of fireworks.

[0097] Step 10: If the termination condition is not met, proceed to step 4.

[0098] Step 11: Output the optimal fireworks and their fitness values, from which the maximum power can be obtained.

[0099] By employing a chaotic optimization method to optimize the initial fireworks, the best N fireworks are selected as the final determined initial fireworks. This increases the search range in the early stages of traditional fireworks optimization methods, avoiding the fireworks algorithm from getting stuck in local optima. By optimizing the fireworks generated by the selection strategy through chaotic optimization, the original fireworks are diverged during the generation of the next generation of fireworks, further expanding the search range, enhancing the optimization ability, and getting rid of local optima. Under multi-peak characteristics, it can accurately and quickly track the maximum power.

[0100] Understandably, in other embodiments, the initial fireworks can be optimized solely using chaotic optimization methods, selecting the best N fireworks as the final determined initial fireworks. This expands the search range in the early stages of traditional fireworks optimization methods, preventing the fireworks algorithm from getting trapped in local optima, thus enhancing the optimization capability, escaping local optima, and accurately and quickly tracking the maximum power under multi-peak characteristics. Alternatively, the fireworks generated by the selection strategy can be optimized solely using chaotic optimization methods, and the original fireworks can be diverged during the generation of the next generation of fireworks to further expand the search range, enhance the optimization capability, escape local optima, and accurately and quickly track the maximum power under multi-peak characteristics.

[0101] The specific steps of step 4 are as follows:

[0102] Calculate the blast radius A of all fireworks except the optimal fireworks using the following two formulas. i and the total number of fireworks explosions S i ;

[0103]

[0104]

[0105] In the above formula: and m are the maximum explosion radius and the maximum number of sparks produced by the explosion, respectively; Y min and Y max It represents the fitness values ​​of the best and worst performing individuals; f(x) i ) for individual x i The fitness value; ε is a very small constant; N is the number of fireworks produced.

[0106] To avoid uneven spark count affecting performance, S i Change to:

[0107]

[0108] In the above formula: round() is the floor function, and a and b are given constants.

[0109] The specific steps for step 5 are as follows:

[0110] Gaussian sparks are generated; the formula for calculating a Gaussian spark is:

[0111]

[0112] Where e is a random number that follows a Gaussian distribution with mean 0 and variance 1; x b k Let be the position of the firework with the best fitness value in the current population in the k-th dimension.

[0113] For Gaussian sparks exceeding the range, a random mapping rule is applied, and the calculation formula is as follows:

[0114]

[0115] Where, x k max and x k min Let U(0,1) be the upper and lower bounds of the k-th dimension boundary, respectively, and let U(0,1) be a uniformly distributed random number in the interval [0,1].

[0116] The specific steps for step 7 are as follows:

[0117] Calculate the fitness values ​​of all Gaussian sparks and then calculate the optimal firework radius. The optimal firework radius is calculated as follows: To calculate the adaptive radius, an individual needs to be selected, and the distance between it and the optimal individual is used as the radius of the next explosion. This individual must satisfy two conditions:

[0118] 1. The fitness value is worse than that of this generation of fireworks:

[0119] f(s i )>f(X);

[0120] Where f(s) i ) represents the fitness value of all fireworks, and f(X) represents the fitness value of fireworks in this generation.

[0121] 2. The distance to the optimal individual is the shortest distance r among the individuals that satisfy condition 1:

[0122] r = min(d(s) i s * ));

[0123] Where s * d represents the best fitness value among all Gaussian sparks and fireworks. i and s * The distance. The initial radius is the entire solution range.

[0124] The specific steps for step 8 are as follows:

[0125] The next generation of fireworks is selected using an elite-random selection strategy: first, the individual s with the best fitness in the population is selected. * :

[0126] s * =min(s);

[0127] Where s represents the fitness of all Gaussian sparks and fireworks.

[0128] Then, a random strategy is used to select the remaining fireworks:

[0129] x = rand(ss) * ).

[0130] x represents the position of all fireworks except the optimal one.

[0131] Given the maximum power of the photovoltaic module, the optimal duty cycle corresponding to the maximum power value is determined to be 1.

[0132] Step 400: Control the PWM generator to generate a target pulse signal based on the optimal duty cycle and input it to the boost module so as to maximize the output power of the photovoltaic module.

[0133] The MPPT controller controls the PWM generator module to generate corresponding pulse signals according to the optimal duty cycle. These pulse signals enter the boost module (DC-DC circuit module, optionally a boost circuit), changing the equivalent input resistance. When the equivalent input resistance is the same as the resistance of the load module, the photovoltaic module's output power is maximized. Equivalent input resistance R eq The relationship with the optimal duty cycle D is as follows:

[0134] R eq =R(1-D) 2 ;

[0135] In the above formula: R is the resistance of the load module.

[0136] This invention optimizes the initial fireworks and / or fireworks generated by a selection strategy by integrating a chaotic optimization method with an improved fireworks optimization method under the condition that the photovoltaic module is shaded. This expands the search range, avoids the problem of traditional fireworks optimization methods getting stuck in local optima, enhances the optimization ability, and gets rid of local optima. When the photovoltaic module has a multi-peak characteristic of the maximum power point due to shading, it can accurately and quickly track the maximum power.

[0137] In other aspects of embodiments of the present invention, the step of optimizing the initial fireworks using a chaotic optimization method includes:

[0138] Step 21: In the solution space, assign the initial firework individual x i Each dimension of the position is mapped to a chaotic region according to a preset mapping rule; where the chaotic region is (-1, 1).

[0139] In one embodiment, the preset mapping rule is expressed by the following formula:

[0140]

[0141] Where, x i Let a represent the initial individual firework. i b represents the lower bound of the chaotic search range. i y represents the upper bound of the chaotic search range. i This represents the value after mapping.

[0142] Step 22: Iterate the mapped values ​​according to the preset iteration rules to generate a chaotic sequence.

[0143] In one embodiment, the preset iteration rule is expressed by the following formula:

[0144] y i+1 =1-2(y i ) 2 y i ∈(-1, 1);

[0145] Among them, y i This represents the mapped value, (-1, 1) represents the chaotic region, and y i+1 This represents the iteration value; multiple iteration values ​​form a chaotic sequence.

[0146] Step 23: Inverse map the chaotic sequence to the solution space using a preset inverse mapping rule.

[0147] In one embodiment, the preset inverse mapping rule is expressed by the following formula:

[0148]

[0149] Among them, a i b represents the lower bound of the chaotic search range. i y represents the upper bound of the chaotic search range. i Let x represent the value in the chaotic sequence. i This represents the fireworks after the inverse mapping.

[0150] Compared to traditional fireworks optimization methods that randomly generate fireworks and select the best N fireworks as initial fireworks, this invention employs a chaotic optimization method to optimize the initial fireworks and selects the best N fireworks as the final determined initial fireworks. By increasing the search range in the early stages of traditional fireworks optimization methods, it avoids the fireworks algorithm getting stuck in local optima, enhances the optimization ability, and can accurately and quickly track the maximum power under multi-peak characteristics.

[0151] In other aspects of embodiments of the present invention, the step of optimizing the fireworks generated by the selection strategy using a chaotic optimization method includes:

[0152] Step 91: In the solution space, map the position of each dimension of the individual fireworks generated by the selection strategy to the chaotic region according to the preset mapping rule; where the chaotic region is (-1, 1).

[0153] In one embodiment, the preset mapping rule is expressed by the following formula:

[0154]

[0155] Where, x i Let a represent the individual fireworks generated by the chosen strategy. i b represents the lower bound of the chaotic search range. i y represents the upper bound of the chaotic search range. i This represents the value after mapping.

[0156] Step 92: Iterate the mapped values ​​according to the preset iteration rules to generate a chaotic sequence.

[0157] In one embodiment, the preset iteration rule is expressed by the following formula:

[0158] y i+1 =1-2(y i ) 2 y i ∈(-1, 1);

[0159] Among them, y i This represents the mapped value, (-1, 1) represents the chaotic region, and y i+1 This represents the iteration value; multiple iteration values ​​form a chaotic sequence.

[0160] Step 93: Inverse map the chaotic sequence to the solution space using a preset inverse mapping rule.

[0161] In one embodiment, the preset inverse mapping rule is expressed by the following formula:

[0162]

[0163] Among them, a i b represents the lower bound of the chaotic search range. i y represents the upper bound of the chaotic search range. i Let x represent the value in the chaotic sequence. i This represents the fireworks after the inverse mapping.

[0164] Compared to traditional fireworks optimization methods that select the best N fireworks from those generated by a selection strategy as the next generation, this invention optimizes the fireworks generated by the selection strategy using a chaotic optimization method. During the generation of the next generation, the existing fireworks are expanded, further increasing the search range, enhancing optimization capabilities, and escaping local optima. Under multi-peak characteristics, it can accurately and quickly track the maximum power.

[0165] Please refer to Figure 1 In other aspects of this embodiment of the invention, after step 100, obtaining the current value flowing through the parallel diode, the method further includes:

[0166] Step 300: When the current value of the parallel diode indicates that the photovoltaic module is in a state of no shading, the photovoltaic module is subjected to maximum power point tracking based on a gradually decreasing perturbation step size by an improved variable step size perturbation observation method, and the optimal duty cycle is determined.

[0167] When the current flowing through the parallel diode is 0, it indicates that the photovoltaic module is in a state of no shading. The maximum power point tracking of the photovoltaic module is performed by the improved variable step size perturbation observation method based on the gradually decreasing perturbation step size, and the optimal duty cycle is determined.

[0168] The basic principle of the traditional variable-step-size perturbation observation method is: within a given time period, a perturbation is applied at regular intervals, and the voltage U at that time is detected. i and current I i Calculate the current power P i The system compares the power output with the previous power. If the power output is greater than the previous power, a positive perturbation is applied; otherwise, a negative perturbation is applied, until the maximum power is found. For the variable-step perturbation observation method, the two most important control parameters are the time interval and the perturbation step size. If the perturbation step size is too small, the system's optimization time is long, and it is prone to getting trapped in local optima under shadow conditions. If the perturbation step size is too large, oscillations are likely to occur near the optimal value, resulting in energy loss.

[0169] This invention employs an improved variable step size perturbation observation method in the absence of shading. The perturbation step size is designed to be variable. By using the improved variable step size perturbation observation method to perform maximum power point tracking of photovoltaic modules based on a gradually decreasing perturbation step size, both tracking speed and tracking accuracy can be guaranteed, enabling accurate and rapid tracking to the maximum power.

[0170] Furthermore, the method of performing maximum power point tracking of photovoltaic modules by improving the variable step size perturbation observation method based on a gradually decreasing perturbation step size includes: each time there is a switch between positive and negative perturbation, reducing the perturbation step size based on a scaling factor, and applying a positive or negative perturbation to the current power value based on the reduced perturbation step size until the maximum power is found.

[0171] The flowchart of the improved variable step size perturbation observation method of this invention is as follows: Figure 5 As shown, when switching between forward and reverse perturbations, the perturbation step size is reduced. Specifically, when switching from a forward to a reverse perturbation, the perturbation step size ΔU is changed according to the scaling factor δ, i.e., ΔU = δΔU; when switching from a reverse to a forward perturbation, the perturbation step size is changed according to the scaling factor δ, i.e., ΔU = δΔU. The perturbation is terminated when the optimal value is found. Here, forward and reverse perturbations are voltage perturbations in opposite directions. For example, the currently detected voltage U... i Increasing the perturbation step size ΔU is a positive perturbation, i.e., U i +ΔU represents a positive disturbance; the currently detected voltage U i Subtracting the perturbation step size ΔU gives the reverse perturbation, i.e., U i -ΔU represents the reverse perturbation.

[0172] It should be noted that, in this embodiment of the invention, the perturbation step size is reduced by multiplying the original perturbation step size by a scaling factor δ each time the perturbation switches between positive and negative perturbations, thus gradually decreasing the perturbation step size. In other embodiments, the perturbation step size can also be gradually reduced by subtracting a fixed value from it each time the perturbation switches between positive and negative perturbations.

[0173] For example, please refer to Figure 6 As shown, when point 1 is to the left of the peak (i.e., the maximum power point), it moves to point 2 to the right according to the perturbation step size ΔU. If the Y value of point 2 is greater than that of point 1, and it is still to the left of the peak, it continues to move the perturbation step size ΔU to the right, moving to point 3. If the Y value of point 3 is less than that of point 2, and the system is switching between positive and negative perturbation, the perturbation step size ΔU is reduced, and the system moves in the opposite direction by a step size δΔU, moving to point 4. This process continues until the search terminates when the perturbation step size is less than the set value.

[0174] Therefore, when the current value of the parallel diode represents the photovoltaic module under the condition of no shading, the perturbation step size is reduced each time the forward and reverse perturbation are switched, so that the perturbation step size gradually becomes smaller. This can ensure both tracking speed and tracking accuracy, and achieve rapid tracking of the maximum power point of the photovoltaic module.

[0175] In summary, this invention employs a control strategy that alternates between two algorithms. Under conditions without occlusion, an improved variable-step-size perturbation observation method reduces the perturbation step size during each switch between forward and reverse perturbations, gradually decreasing the step size to ensure both tracking speed and accuracy. Under conditions with occlusion, a combination of fireworks optimization and chaotic optimization methods enhances the optimization capability, eliminating local optima and enabling accurate and rapid tracking of maximum power even under multi-peak characteristics. This invention can quickly and accurately track the maximum power point under both occlusion and non-occlusion conditions, making it suitable for complex environments.

[0176] In other aspects of this embodiment, after step 400, which involves controlling the PWM generator to generate a target pulse signal based on the optimal duty cycle and inputting it to the boost module to maximize the output power of the photovoltaic module, the method further includes: if the rate of change of the output power of the photovoltaic module exceeds a set threshold, re-executing steps 100 to 400 to find the maximum power value. If the rate of change of the output power of the photovoltaic module does not exceed the set threshold, the output power of the photovoltaic module remains unchanged.

[0177] Specifically, when the external conditions of the photovoltaic module change, its output power also changes, and the maximum output power changes accordingly. Therefore, the maximum power point tracking (MPPT) control method for the photovoltaic module needs to be restarted for optimization, and steps 100 to 400 need to be re-executed to find the maximum power value. The decision to restart is made by detecting the rate of change of the photovoltaic module's output power, where the rate of change is:

[0178]

[0179] Among them, P i P represents the output power of the photovoltaic module at time i. max This represents the maximum power value tracked. ΔP can be set manually, for example, to 0.001.

[0180] Device Examples

[0181] Please refer to Figure 7 On the other hand, embodiments of the present invention also provide a photovoltaic module maximum power point tracking control device, applied to perform maximum power point tracking on a photovoltaic module in a physical model. The physical model includes a photovoltaic module, a parallel diode connected in parallel with the photovoltaic module, a PWM generator, a boost module electrically connected to the PWM generator, and a load module electrically connected to the boost module; the device includes:

[0182] The data acquisition unit 701 is used to acquire the current value flowing through the parallel diode;

[0183] The first tracking unit 702 is used to perform maximum power point tracking on the photovoltaic module and determine the optimal duty cycle by means of an improved fireworks optimization method when the current value of the parallel diode indicates that the photovoltaic module is under shadow. The improved fireworks optimization method optimizes the initial fireworks and / or the fireworks generated by the selection strategy by means of a chaotic optimization method.

[0184] The control unit 703 is used to control the PWM generator to generate a target pulse signal based on the optimal duty cycle and input it to the boost module so as to maximize the output power of the photovoltaic module.

[0185] Optionally, the step of optimizing the initial fireworks using the chaotic optimization method includes:

[0186] In the solution space, the position of each dimension of the initial firework individual is mapped to the chaotic region according to a preset mapping rule;

[0187] The mapped values ​​are iterated according to a preset iteration rule to generate a chaotic sequence;

[0188] The chaotic sequence is inverse mapped to the solution space using a preset inverse mapping rule.

[0189] Optionally, the preset mapping rule is expressed by the following formula:

[0190]

[0191] Where, x i Let a represent the initial individual firework. i b represents the lower bound of the chaotic search range. i y represents the upper bound of the chaotic search range. i This represents the value after mapping;

[0192] The preset iteration rule is expressed by the following formula:

[0193] y i+1 =1-2(y i ) 2 y i ∈(-1, 1);

[0194] Among them, y i This represents the mapped value, (-1, 1) represents the chaotic region, and y i+1 This represents the iteration value; multiple iteration values ​​form a chaotic sequence.

[0195] The preset inverse mapping rule is expressed by the following formula:

[0196]

[0197] Among them, a i b represents the lower bound of the chaotic search range. i y represents the upper bound of the chaotic search range. i Let x represent the value in the chaotic sequence. i This represents the fireworks after the inverse mapping.

[0198] Optionally, the step of optimizing the fireworks generated by the selected strategy using a chaotic optimization method includes:

[0199] In the solution space, the position of each dimension of the individual fireworks generated by the selection strategy is mapped to the chaotic region according to a preset mapping rule.

[0200] The mapped values ​​are iterated according to a preset iteration rule to generate a chaotic sequence;

[0201] The chaotic sequence is inverse mapped to the solution space using a preset inverse mapping rule.

[0202] Optionally, the preset mapping rule is expressed by the following formula:

[0203]

[0204] Where, xi Let a represent the individual fireworks generated by the chosen strategy. i b represents the lower bound of the chaotic search range. i y represents the upper bound of the chaotic search range. i This represents the value after mapping;

[0205] The preset iteration rule is expressed by the following formula:

[0206] y i+1 =1-2(y i ) 2 y i ∈(-1, 1);

[0207] Among them, y i This represents the mapped value, (-1, 1) represents the chaotic region, and y i+1 This represents the iteration value; multiple iteration values ​​form a chaotic sequence.

[0208] The preset inverse mapping rule is expressed by the following formula:

[0209]

[0210] Among them, a i b represents the lower bound of the chaotic search range. i y represents the upper bound of the chaotic search range. i Let x represent the value in the chaotic sequence. i This represents the fireworks after the inverse mapping.

[0211] Optionally, the device further includes:

[0212] The second tracking unit is used to perform maximum power point tracking of the photovoltaic module based on a gradually decreasing perturbation step size using an improved variable step size perturbation observation method, and to determine the optimal duty cycle, when the current value of the parallel diode indicates that the photovoltaic module is in a state of no shading.

[0213] Optionally, the step of performing maximum power point tracking of photovoltaic modules based on a gradually decreasing perturbation step size using the improved variable step size perturbation observation method includes:

[0214] In each case of switching between positive and negative perturbation, the perturbation step size is reduced based on the scaling factor, and a positive or negative perturbation is applied to the current power value based on the reduced perturbation step size until the maximum power is found.

[0215] The photovoltaic module maximum power point tracking control device includes a processor and a memory. The data acquisition unit 701, the first tracking unit 702, and the control unit 703 are all stored in the memory as program units. The processor executes the program units stored in the memory to realize the corresponding functions.

[0216] A processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured.

[0217] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0218] Figure 8 An example of a schematic diagram of the physical structure of an MPPT controller is shown, such as... Figure 8 As shown, the MPPT controller may include a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other through the communication bus 840. The processor 810 can call logic instructions in the memory 830 to execute a maximum power point tracking control method for photovoltaic modules. This method includes: acquiring the current value flowing through the parallel diode; performing maximum power point tracking on the photovoltaic module using an improved fireworks optimization method, based on the current value of the parallel diode indicating that the photovoltaic module is under shading; and determining the optimal duty cycle; wherein the improved fireworks optimization method optimizes the initial fireworks and / or the fireworks generated by a selection strategy using a chaotic optimization method; and controlling the PWM generator to generate a target pulse signal based on the optimal duty cycle and input it to the boost module to maximize the output power of the photovoltaic module.

[0219] The MPPT controller of this invention optimizes the initial fireworks and / or fireworks generated by the selection strategy by integrating the improved fireworks optimization method with the chaotic optimization method when the photovoltaic module is under shadow. This expands the search range, avoids the problem of traditional fireworks optimization methods getting stuck in local optima, enhances the optimization ability, and gets rid of local optima. When the photovoltaic module has multi-peak characteristics of maximum power point due to shadowing, it can accurately and quickly track the maximum power.

[0220] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0221] Please refer to Figure 9 This invention also provides a chip comprising the aforementioned photovoltaic module 10, a parallel diode 20 connected in parallel with the photovoltaic module, an MPPT controller 30, a PWM generator 40, a boost module 50 electrically connected to the PWM generator, and a load module 60 electrically connected to the boost module. The MPPT controller is used to acquire the current value flowing through the parallel diode; when the current value of the parallel diode indicates that the photovoltaic module is under shading, it performs maximum power point tracking on the photovoltaic module using an improved fireworks optimization method and determines the optimal duty cycle; wherein, the improved fireworks optimization method optimizes the initial fireworks and / or fireworks generated through a selection strategy using a chaotic optimization method; when the current value of the parallel diode indicates that the photovoltaic module is not under shading, it performs maximum power point tracking on the photovoltaic module based on a gradually decreasing perturbation step size using an improved variable step size perturbation observation method and determines the optimal duty cycle; it controls the PWM generator to generate a target pulse signal based on the optimal duty cycle and input it to the boost module to maximize the output power of the photovoltaic module.

[0222] The chip of this invention employs a control strategy that switches between two algorithms. Under conditions without shadow occlusion, an improved variable-step perturbation observation method reduces the perturbation step size during each switch between forward and reverse perturbations, gradually decreasing the step size to ensure both tracking speed and accuracy. Under conditions with shadow occlusion, a combination of fireworks optimization and chaotic optimization methods enhances the optimization capability, eliminating local optima and enabling accurate and rapid tracking of maximum power even under multi-peak characteristics. This invention can quickly and accurately track the maximum power point under both shadow-free and shadowed conditions, adapting to complex environments.

[0223] In another aspect, the present invention also provides a machine-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a maximum power point tracking control method for a photovoltaic module. The method includes: acquiring the current value flowing through the parallel diode; performing maximum power point tracking on the photovoltaic module using an improved fireworks optimization method, based on the current value of the parallel diode indicating that the photovoltaic module is under shading; and determining an optimal duty cycle; wherein the improved fireworks optimization method optimizes the initial fireworks and / or fireworks generated through a selection strategy using a chaotic optimization method; and controlling the PWM generator to generate a target pulse signal based on the optimal duty cycle and input it to the boost module to maximize the output power of the photovoltaic module.

[0224] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0225] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0226] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for maximum power point tracking control of a photovoltaic module, characterized in that, The method is applied to perform maximum power point tracking on photovoltaic modules in a physical model, wherein the physical model includes photovoltaic modules, parallel diodes connected in parallel with the photovoltaic modules, a PWM generator, a boost module electrically connected to the PWM generator, and a load module electrically connected to the boost module; the method includes: Obtain the current value flowing through the parallel diode; The current value of the parallel diode represents the photovoltaic module under shading conditions. The photovoltaic module is subjected to maximum power point tracking and the optimal duty cycle is determined by an improved fireworks optimization method. The improved fireworks optimization method optimizes the initial fireworks and / or the fireworks generated by the selection strategy through a chaotic optimization method. The PWM generator is controlled to generate a target pulse signal based on the optimal duty cycle and input to the boost module so as to maximize the output power of the photovoltaic module. The steps for optimizing the initial fireworks using the chaos optimization method include: In the solution space, the position of each dimension of the initial firework individual is mapped to the chaotic region according to a preset mapping rule; The mapped values ​​are iterated according to a preset iteration rule to generate a chaotic sequence; The chaotic sequence is inverse mapped to the solution space using a preset inverse mapping rule; The steps of optimizing the fireworks generated by the selected strategy using the chaotic optimization method include: In the solution space, the position of each dimension of the individual fireworks generated by the selection strategy is mapped to the chaotic region according to a preset mapping rule. The mapped values ​​are iterated according to a preset iteration rule to generate a chaotic sequence; The chaotic sequence is inverse mapped to the solution space using a preset inverse mapping rule.

2. The photovoltaic module maximum power point tracking control method according to claim 1, characterized in that, The preset mapping rule is expressed by the following formula: ； in, x i This represents the initial individual firework. a i This represents the lower boundary of the chaotic search range. b i This represents the upper bound of the chaotic search range. y i This represents the value after mapping; The preset iteration rule is expressed by the following formula: ； in, y i This represents the mapped value, and (-1, 1) represents the chaotic region. y i+1 This represents the iteration value; multiple iteration values ​​form a chaotic sequence. The preset inverse mapping rule is expressed by the following formula: in, a i This represents the lower boundary of the chaotic search range. b i This represents the upper bound of the chaotic search range. y i Represents the values ​​in a chaotic sequence. x i This represents the fireworks after the inverse mapping.

3. The photovoltaic module maximum power point tracking control method according to claim 1, characterized in that, The preset mapping rule is expressed by the following formula: ； in, x i This represents the individual fireworks generated by the chosen strategy. a i This represents the lower boundary of the chaotic search range. b i This represents the upper bound of the chaotic search range. y i This represents the value after mapping; The preset iteration rule is expressed by the following formula: ； in, y i This represents the mapped value, and (-1, 1) represents the chaotic region. y i+1 This represents the iteration value; multiple iteration values ​​form a chaotic sequence. The preset inverse mapping rule is expressed by the following formula: in, a i This represents the lower boundary of the chaotic search range. b i This represents the upper bound of the chaotic search range. y i Represents the values ​​in a chaotic sequence. x i This represents the fireworks after the inverse mapping.

4. The photovoltaic module maximum power point tracking control method according to claim 1, characterized in that, After obtaining the current value flowing through the parallel diode, the method further includes: The current value of the parallel diode represents the photovoltaic module under the condition of no shading. The photovoltaic module is subjected to maximum power point tracking and the optimal duty cycle is determined by the improved variable step size perturbation observation method based on the gradually decreasing perturbation step size.

5. The photovoltaic module maximum power point tracking control method according to claim 4, characterized in that, The method of performing maximum power point tracking of photovoltaic modules based on a gradually decreasing perturbation step size through an improved variable step size perturbation observation method includes: In each case of switching between positive and negative perturbation, the perturbation step size is reduced based on the scaling factor, and a positive or negative perturbation is applied to the current power value based on the reduced perturbation step size until the maximum power is found.

6. A maximum power point tracking control device for photovoltaic modules, characterized in that, This device is used for maximum power point tracking of photovoltaic modules in a physical model, wherein the physical model includes a photovoltaic module, a parallel diode connected in parallel with the photovoltaic module, a PWM generator, a boost module electrically connected to the PWM generator, and a load module electrically connected to the boost module; the device includes: A data acquisition unit is used to acquire the current value flowing through the parallel diode; The first tracking unit is used to perform maximum power point tracking on the photovoltaic module and determine the optimal duty cycle by means of an improved fireworks optimization method when the current value of the parallel diode indicates that the photovoltaic module is under shading; wherein the improved fireworks optimization method optimizes the initial fireworks and / or the fireworks generated by the selection strategy through a chaotic optimization method. The control unit is used to control the PWM generator to generate a target pulse signal based on the optimal duty cycle and input it to the boost module so as to maximize the output power of the photovoltaic module. The steps for optimizing the initial fireworks using the chaos optimization method include: In the solution space, the position of each dimension of the initial firework individual is mapped to the chaotic region according to a preset mapping rule; The mapped values ​​are iterated according to a preset iteration rule to generate a chaotic sequence; The chaotic sequence is inverse mapped to the solution space using a preset inverse mapping rule; The steps of optimizing the fireworks generated by the selected strategy using the chaotic optimization method include: In the solution space, the position of each dimension of the individual fireworks generated by the selection strategy is mapped to the chaotic region according to a preset mapping rule. The mapped values ​​are iterated according to a preset iteration rule to generate a chaotic sequence; The chaotic sequence is inverse mapped to the solution space using a preset inverse mapping rule.

7. An MPPT controller, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the photovoltaic module maximum power point tracking control method according to any one of claims 1 to 5.

8. A chip comprising the MPPT controller of claim 7, a photovoltaic module, a parallel diode connected in parallel with the photovoltaic module, a PWM generator, a boost module electrically connected to the PWM generator, and a load module electrically connected to the boost module.

9. A machine-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the photovoltaic module maximum power point tracking control method according to any one of claims 1 to 5.

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