A photovoltaic system mppt control method

Abstract

The application provides a photovoltaic system MPPT control method, which realizes rapidity and precision of MPP tracking. The method comprises the following steps: S1, analyzing physical characteristics of the photovoltaic system, and designing a reasonable model of the photovoltaic system; S2, determining an optimization target under an MPPT strategy of the photovoltaic system; and S3, designing an MPPT optimization strategy, which divides the MPPT control into a rapid positioning stage and an accurate search stage based on an improved royal battle optimization algorithm and a poplar optimization algorithm, so as to realize rapid positioning of the maximum power point.

Description

Technical Field

[0001] This invention relates to the technical field of grid-connected optimization control of photovoltaic systems, specifically to an MPPT control method for photovoltaic systems. Background Technology

[0002] A photovoltaic (PV) array consists of photovoltaic modules connected in series or parallel, absorbing solar energy and coordinating current and voltage to output power. However, PV power generation is intermittent and uncertain, and the output characteristics of PV cells are greatly affected by irradiance and temperature. Optimizing the maximum power point control strategy for PV systems helps them better match grid load indicators and maintain system safety and stability.

[0003] Ideally, a PV array has only one maximum power point under uniform illumination. However, in actual operation, local shading is unavoidable, resulting in multiple local maximum power points in the system. The purpose of the MPPT controller is to maintain the PV array's power generation at the global maximum power point under any illumination conditions, including partial shading. Therefore, there is an urgent need to develop an MPPT control method for photovoltaic systems, so that the optimization algorithm can compensate for the maximum power point optimization under power point tracking and improve maximum power point tracking performance. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides a photovoltaic system MPPT control method. It improves the Royal Campaign optimization algorithm and Poplar optimization to reasonably distinguish different shading modes and irradiance intensities, balancing exploration and exploitation in MPPT. The Poplar optimization algorithm quickly locates the coarse search region near the global peak in the early stages of the MPPT algorithm, ensuring the diversity of its early search and preventing premature convergence. The Royal Campaign optimization significantly improves search accuracy while maintaining computational speed, thereby achieving both speed and accuracy in MPP tracking.

[0005] A method for controlling MPPT in a photovoltaic system, characterized by comprising the following steps:

[0006] S1. Analyze the physical characteristics of the photovoltaic system and design a reasonable model of the photovoltaic system;

[0007] S2. Determine the optimization objective under the MPPT strategy for the photovoltaic system;

[0008] S3: Design an MPPT optimization strategy, which divides MPPT control into rapid positioning and precise search stages based on the improved Royal Battle optimization algorithm and Poplar optimization algorithm, to achieve rapid positioning of the maximum power point.

[0009] Its further features are:

[0010] The specific steps of step S1 are as follows:

[0011] Step S1.1: Analyze the characteristics of the photovoltaic system, including the features of photovoltaic cells, photovoltaic arrays, and the photovoltaic system itself. The photovoltaic array consists of photovoltaic modules connected in series or parallel, and each photovoltaic module contains a certain number of interconnected photovoltaic cells. Determine the expression for the photocurrent generated by the photovoltaic cells under the influence of solar irradiance and cell temperature:

[0012]

[0013] Step S1.2: Considering different shading conditions, the PV curve of the photovoltaic system exhibits multiple peaks. Analyze its global characteristics and study the structure and characteristics of the DC-DC converter. Study the principle of the DC-DC boost converter under different illumination conditions, determine the transistor duty cycle expression, and analyze the conditions that the maximum power output of the PV array must meet.

[0014] Step S2 specifically involves determining the objective function for the maximum power performance of the PV array and determining the constraints on the optimization parameters. The specific operations are as follows:

[0015] The MPPT algorithm achieves maximum power output from the photovoltaic cell by adjusting the duty cycle until the equivalent resistance of the photovoltaic cell equals the load impedance. Using the duty cycle d as the optimization parameter and the output power P as the fitness function, the MPPT optimization objective is defined as:

[0016] maxP:R oi =R in

[0017]

[0018] In the formula R oi R represents the output impedance. in d is the internal resistance of the battery. min ,d max These represent the upper and lower limits of the duty cycle in the second stage of optimization, respectively.

[0019] Step S3's MPPT optimization strategy is divided into two stages. The first stage uses the poplar optimization algorithm to perform a coarse search for the position of the duty cycle d. The poplar optimization algorithm simulates the sexual and asexual reproduction principles of poplar trees, employing a backtracking search algorithm mutation strategy to maintain population diversity, thereby locating the coarse search area near the peak. The second stage uses the Royal Battle optimization algorithm, simulating a "Royal Battle" game, narrowing down from multiple peak positions towards the highest peak to achieve both speed and accuracy in MPP tracking. The specific steps of step S3 are as follows:

[0020] Step S3.1: The first stage consists of improved poplar optimization, where each individual in the population searches near the current optimal solution. Thanks to the sexual reproduction process of poplars, the population achieves a wide-range search near the optimal solution while maintaining diversity. Asexual reproduction ensures that the duty cycle meets the iteration termination condition. Its key process is as follows:

[0021] Step S3.1.1: The population undergoes sexual reproduction.

[0022] After initialization, the population first undergoes a sexual reproduction phase. To simplify the algorithm, each parent tree produces only one seed. This seed is affected by various external factors such as wind and gravity, and its final position follows the formula below:

[0023]

[0024] Step S3.1.2: The population undergoes asexual reproduction.

[0025] This stage further narrows the search scope. The asexual reproduction process iterates using the best-fitting individual in the current population and historical information from certain individuals. The best-fitting individual serves as a guide, and historical information is beneficial for diversity. This operation further reduces the search scope, preparing for the subsequent second-stage search. The position update formula is:

[0026] X new (i,j)=X old (s,j)+(1-C)·(X best (j)-X old (i,j))+C·(X oldp (s,j)-X oldp (i,j))

[0027] Step S3.1.3, Transition Phase: When the poplar optimization meets the maximum number of iterations or reaches a suitable search range, the optimization begins to transition to the second phase. The transition between the two phases will affect the optimization accuracy of the final algorithm. The following formula is selected as the transition condition:

[0028]

[0029] Step S3.2.1, Transitional Stage: The final result of the poplar optimization will provide a reference for the second stage optimization. The upper and lower limits of the optimization parameters in the second stage are determined by the optimization results of the first stage.

[0030]

[0031] Step S3.2.2: Entering the precise search phase. In this phase, Royal Campaign Optimization further reduces the search space and obtains the final optimized result. In order to focus on exploration, individuals will move towards historical locations and the best locations found so far:

[0032] x new (i,j)=x old (i,j)+r(x best (j)-x old (i,j))

[0033] Step S3.2.3: Determine the damage level of individuals. Once the damage level of an individual exceeds a preset threshold, these individuals will die and be reborn in the feasible problem space.

[0034] x new (i,j)=r(ub(j)-lb(j))+lb(j)

[0035] Step S3.2.4: Narrowing the population location space. After each iteration, the feasible search space of the problem begins to shrink towards the optimal solution. This interaction helps in exploration and development, and the upper and lower bounds will be updated as follows:

[0036]

[0037] In the first and second stages of the MPPT algorithm, population initialization is a stochastic process, avoiding dependence on the initial population position; the combination of the two algorithms ensures the speed and accuracy of tracking; the poplar optimization algorithm ensures rapid localization and narrowing of the optimization parameter range in the early stage of the algorithm, while the Royal Battle optimization increases the search accuracy and convergence speed; therefore, this two-stage optimization algorithm can achieve rapid localization of the maximum power point of a photovoltaic system under local shading conditions.

[0038] This invention combines Royal Battle optimization and Poplar optimization algorithms to provide a novel two-stage MPPT tracking scheme to address the problem of faster and more accurate maximum power point tracking of photovoltaic systems under different shading conditions.

[0039] This invention proposes a combined poplar optimization and royal campaign optimization algorithm to achieve more intelligent and flexible maximum power tracking. In the early stage, poplar optimization locates the coarse search region near the global peak in a short time, while in the later stage, royal campaign optimization achieves simultaneous optimization of position and universe of discourse.

[0040] This invention optimizes the MPPT control strategy for photovoltaic arrays under shading conditions in both common and special environments, aiming to solve the problem of maximum power point tracking easily getting trapped in local optima, and providing an effective reference for quickly and accurately obtaining the optimal power operating point of the photovoltaic array. Attached Figure Description

[0041] Figure 1 This is a framework diagram corresponding to the MPPT control method for photovoltaic systems of the present invention;

[0042] Figure 2 This is a flowchart of the improved Royal Battle optimization and Poplar optimization algorithm in this invention. Detailed Implementation

[0043] Figure 1 The diagram below illustrates the framework of this invention. The method described in this invention is implemented using the Matlab software platform and consists of the following three steps:

[0044] S1: Analyze the physical characteristics of the photovoltaic system and design a reasonable model of the photovoltaic system;

[0045] S2: Determine the optimization objective under the MPPT strategy for the photovoltaic system;

[0046] S3: Design MPPT optimization strategies.

[0047] Normal illumination, uniform illumination, and partial shading are three common irradiation conditions for photovoltaic arrays. Photovoltaic cells exhibit different output characteristics under different irradiation conditions. Under uniform illumination, a photovoltaic cell has a unique maximum power point, and its output characteristic curve is non-linear. Under partial shading, the PV curve of the photovoltaic array will show multiple peaks. Based on this, step S1 can be specified as follows:

[0048] S1.1: Analyze the characteristics of the photovoltaic system, including the features of photovoltaic cells, photovoltaic arrays, and the photovoltaic system as a whole.

[0049] A photovoltaic array consists of photovoltaic modules connected in series or parallel, with each module containing a number of interconnected photovoltaic cells. These cells are connected in parallel with diodes to reduce the hotspot effect and minimize power waste caused by localized shading.

[0050] Photovoltaic cells generate photocurrent due to the influence of solar irradiance and cell temperature. Therefore, the IV characteristic of an M×N photovoltaic array can be expressed as:

[0051] Where ns is the number of photovoltaic cells in the genetic link of the photovoltaic module, B represents the diode ideality coefficient, and V T R represents thermal voltage. s ,R th The internal impedance of a photovoltaic cell;

[0052] The short-circuit current and open-circuit voltage under standard test conditions are respectively represented by I. ss-STC and V oc-STC The photocurrent can be expressed as:

[0053]

[0054] Where k i This represents the temperature coefficient, where G and T are the actual solar irradiance and battery temperature, respectively. Simultaneously, the anti-saturation current I...o The calculation is as follows:

[0055]

[0056] Where k v For V oc-STC Temperature coefficient.

[0057] S1.2: Considering different shading conditions, the PV curve of the photovoltaic system exhibits multiple peaks. Analyze its global characteristics and study the structure and characteristics of the DC-DC converter. Under partial shading conditions, the power generation efficiency of the photovoltaic system drops significantly, and the multi-peak characteristics of the PV feature increase the difficulty of global maximum power point tracking. Since the existence of local maximum power points can mislead the optimization trend, Royal Battle optimization and Poplar optimization are chosen to improve the search capability and convergence speed of the MPPT algorithm.

[0058] A DC-DC boost converter is a simple and efficient transformer system composed of a capacitor, an inductor, a diode, and an insulated-gate bipolar transistor. The energy absorbed by the inductor is equal to the energy released by the transistor in one on / off cycle t, which can be expressed as:

[0059] U in I L t on =(U out -U in )I L t off ,t=t on +t off (4)

[0060] U in and U out These are the input and output voltages of the transistor, t. on and t off These represent the on-cycle and off-cycle of the transistor, respectively.

[0061] The duty cycle d is defined as:

[0062]

[0063] The change in duty cycle causes changes in output voltage and current. As long as the output impedance matches the battery's internal resistance, the power of the PV array reaches its maximum. Therefore, the duty cycle is optimized, and the optimization target is first defined in S2.

[0064] S2: Maximum Power Point Tracking (MPPT) technology is a crucial technology in grid-connected solar photovoltaic (PV) power generation. When illumination conditions change, the MPPT algorithm adjusts the output voltage and current of the PV array to ensure the array always operates at its maximum power point. The MPPT algorithm also adjusts the duty cycle to make the equivalent resistance of the PV cells equal to the load impedance, thus achieving maximum power output. Using the duty cycle d as the optimization parameter and the output power P as the fitness function, the MPPT optimization objective is defined as:

[0065]

[0066] In the formula R oi R represents the output impedance. in d is the internal resistance of the battery. min ,d max These represent the upper and lower limits of the duty cycle in the second stage of optimization, respectively.

[0067] Based on the above photovoltaic system characteristic analysis and optimization objectives, step S3 provides a detailed explanation of the MPPT tracking strategy method based on improved Royal Battle optimization and Poplar optimization:

[0068] The MPPT optimization strategy in step S3 is divided into two stages (see...). Figure 2 The first stage uses poplar optimization to perform a coarse search for the position of duty cycle d. Poplar optimization simulates the sexual and asexual reproduction principles of poplar trees and adopts a backtracking search algorithm mutation strategy to maintain population diversity, thereby locating the coarse search area near the peak. The second stage uses the Royal Battle optimization algorithm to simulate the "Royal Battle" game, shrinking from multiple peak positions towards the highest peak to achieve the speed and accuracy of MPP tracking.

[0069] S3.1: Unlike the classic poplar optimization algorithm, each individual in the population searches near the current optimal solution. Thanks to the sexual reproduction process of poplars, the population achieves a wide-range search near the optimal solution while maintaining diversity. Asexual reproduction ensures that the duty cycle meets the iteration termination condition. Its key process is as follows:

[0070] S3.1.1: After initialization, the population first undergoes a sexual reproduction stage. To simplify the algorithm, each parent tree produces only one seed. The seed's position is affected by various external factors such as wind and gravity, and follows the formula below:

[0071]

[0072] in Let H(i) be the predicted new position of the i-th tree in the j-th dimension, and H(i) be the height of the i-th tree.

[0073] The actual falling process is very complex, so it is simplified using a chaos factor, and its final position is shown in the following equation:

[0074]

[0075] In the formula, r1 and r3 are random numbers between [0, 1], and r2 is a random number in the range [-1, 1].

[0076] F(i) is the chaos factor. A simple model with logical mapping is selected for mapping:

[0077] F k+1 (i)=μ·F k (i)·(1-F k (i)) (9)

[0078] F k (i),F k+1 (i) represent the chaos factors of the i-th poplar tree in the k-th and k+1-th iterations, respectively. μ is the chaos operator, and μ = 4 is chosen to completely map the population individuals into the chaotic space.

[0079] To ensure that the update magnitude is larger in the initial evolution stage and smaller in the later evolution stage, a change factor rr is used to correct the linear descent model of angle θ. The formula for rr is as follows:

[0080] rr=10-8·(maxgen-gen) / maxgen (10)

[0081] Next, the height of the i-th tree is determined. In this algorithm, the height of a tree is determined by the fitness of all trees, assuming the maximum fitness value in the current iteration is H. max The height of the i-th tree is defined as:

[0082]

[0083] In the formula, α is a random number in the range [0,1], fit(i) is the fitness value of the i-th individual, and ε is a small constant to ensure that the algorithm value does not overflow. The height of an individual has an adaptive characteristic; for the maximum optimization problem, individuals with higher fitness have relatively larger heights.

[0084] S3.1.2: Asexual reproduction is performed to initially improve search accuracy and further narrow the search range. The asexual reproduction process iterates using the individual with the best fitness in the current population and historical information of some individuals. The best individual plays a guiding role, and historical information is beneficial to its diversity. This operation will further reduce the search range, preparing for the subsequent second-stage search. Its position update formula is:

[0085] X new(i,j)=X old (s,j)+(1-C)·(X best (j)-X old (i,j))+C·(X oldp (s,j)-X oldp (i,j))(12)

[0086] C = f best / (f best +f oldp,s (13)

[0087] In the formula, C is the adaptive factor, s is a random individual, and X... best (j) represents the optimal position in the j-th dimension, X oldp (·) represents the historical position in the historical population, which is obtained by the backtracking search optimization algorithm, as shown in the following formula:

[0088]

[0089] oldp:=permuting(oldp) (15)

[0090] In the formula, oldp represents the historical population, and P represents the evolutionary population. r4 and r5 are random numbers between [0,1].

[0091] S3.1.3: When the poplar optimization meets the maximum number of iterations or reaches a suitable search range, the optimization begins to transition to the second stage. The transition between the two stages will affect the optimization accuracy of the final algorithm. Therefore, the following formula is chosen as the transition condition:

[0092]

[0093] In the formula, β is the transition constant, N is the population size and β > N.

[0094] S3.2.1: In the second stage, Royal Campaign optimization further reduces the search space and obtains the final optimization result. The upper and lower limits of the optimization parameters in this stage are determined by the optimization results of the first stage, namely:

[0095]

[0096] S3.2.2: Royal Battle Optimization. This simulates a "Royal Battle" type of game where an individual immediately changes position after taking damage, allowing them to attack opponents on the other side. Therefore, to focus on exploration, individuals will move towards historical locations and the best positions found so far.

[0097] x new (i,j)=x old (i,j)+r(x best(j)-x old (i,j)) (18)

[0098] Where r represents a random number in the range [0,1].

[0099] S3.2.3: Once the damage to an individual exceeds a preset threshold, these individuals will die and be reborn in the feasible problem space:

[0100] x new (i,j)=r(ub(j)-lb(j))+lb(j) (19)

[0101] Where lb(j) and ub(j) are the upper and lower bounds of the j-th dimension, respectively.

[0102] S3.2.4: After each iteration, the feasible search space of the problem begins to shrink toward the optimal solution. This interaction helps in exploration and development, and the upper and lower bounds will be updated as follows:

[0103]

[0104] This represents the standard deviation of the entire population in the j-th dimension.

[0105] In the two-stage MPPT algorithm described above, population initialization is a stochastic process, avoiding dependence on the initial population position. The combination of the two algorithms ensures both speed and accuracy in tracking. Poplar optimization ensures rapid localization and narrows the range of optimization parameters in the early stages of the algorithm, while Royal Campaign optimization increases the search accuracy and convergence speed. Therefore, this two-stage optimization algorithm can achieve rapid localization of the maximum power point of a photovoltaic system under partial shading conditions.

[0106] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0107] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for MPPT control in a photovoltaic system, characterized in that, It includes the following steps: S1. Analyze the physical characteristics of the photovoltaic system and design a reasonable model of the photovoltaic system; S2. Determine the optimization objective under the MPPT strategy for the photovoltaic system; S3: Design an MPPT optimization strategy, which divides MPPT control into a rapid positioning and precise search stage based on the improved Royal Battle optimization algorithm and Poplar optimization algorithm, to achieve rapid positioning of the maximum power point; The specific steps of step S1 are as follows: Step S1.1: Analyze the characteristics of the photovoltaic system, including the features of photovoltaic cells, photovoltaic arrays, and the photovoltaic system itself. The photovoltaic array consists of photovoltaic modules connected in series or parallel, and each photovoltaic module contains a certain number of interconnected photovoltaic cells. Determine the expression for the photocurrent generated by the photovoltaic cells under the influence of solar irradiance and cell temperature: Step S1.2: Considering different shading conditions, the PV curve of the photovoltaic system shows multiple peaks. Analyze its global characteristics and study the structure and characteristics of the DC-DC converter. Study the principle of the DC-DC boost converter under different illumination conditions, determine the transistor duty cycle expression, and analyze the conditions that the maximum power output of the PV array must meet. Step S2 specifically involves determining the objective function for the maximum power performance of the PV array and determining the constraints on the optimization parameters. The specific operations are as follows: The MPPT algorithm achieves maximum power output from the photovoltaic cell by adjusting the duty cycle to make the equivalent resistance of the photovoltaic cell equal to the load impedance. Using the duty cycle d as the optimization parameter and the output power P as the fitness function, the MPPT optimization objective is defined as: In the formula Indicates the output impedance. This refers to the battery's internal resistance. These are the upper and lower limits of the duty cycle in the second stage of optimization, respectively; In step S3, the MPPT optimization strategy is divided into two stages. In the first stage, the poplar optimization algorithm is used to perform a coarse search on the position of the duty cycle d. The poplar optimization algorithm simulates the sexual and asexual reproduction principles of poplar trees and adopts a backtracking search algorithm mutation strategy to maintain population diversity, thereby locating the coarse search area near the peak. The second stage employs the Royal Battle optimization algorithm, simulating the "Royal Battle" game, and zooms in from multiple peak positions toward the highest peak to achieve both speed and accuracy in MPP tracking.

2. The MPPT control method for a photovoltaic system as described in claim 1, characterized in that: The specific steps of step S3 are as follows: Step S3.1: The first stage consists of improved poplar optimization, where each individual in the population searches near the current optimal solution. Thanks to the sexual reproduction process of poplars, the population achieves a wide-range search near the optimal solution while maintaining diversity. Asexual reproduction ensures that the duty cycle meets the iteration termination condition. The key process is as follows: Step S3.1.1: The population undergoes sexual reproduction. After initialization, the population first undergoes a sexual reproduction phase. To simplify the algorithm, each parent tree produces only one seed. This seed is affected by various external factors such as wind and gravity, and its final position follows the formula below: Step S3.1.2: The population undergoes asexual reproduction. This stage further narrows the search scope. The asexual reproduction process iterates using the best-fitting individual in the current population and historical information from certain individuals. The best-fitting individual serves as a guide, and historical information is beneficial for diversity. This operation further reduces the search scope, preparing for the subsequent second-stage search. The position update formula is: Step S3.1.3, Transition Phase: When the poplar optimization meets the maximum number of iterations or reaches a suitable search range, the optimization begins to transition to the second phase. The transition between the two phases will affect the optimization accuracy of the final algorithm. The following formula is selected as the transition condition: ； Step S3.2.1, Transitional Stage: The final result of the poplar optimization will provide a reference for the second stage optimization. The upper and lower limits of the optimization parameters in the second stage are determined by the optimization results of the first stage. Step S3.2.2: Entering the precise search phase. In this phase, Royal Campaign Optimization further reduces the search space and obtains the final optimized result. In order to focus on exploration, individuals will move towards historical locations and the best locations found so far: Step S3.2.3: Determine the damage level of individuals. Once the damage level of an individual exceeds a preset threshold, these individuals will die and be reborn in the feasible problem space. Step S3.2.4: Narrowing the population location space. After each iteration, the feasible search space of the problem begins to shrink towards the optimal solution. This interaction helps in exploration and development, and the upper and lower bounds will be updated as follows: 。

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