Improved Gray Wolf MPPT control method and device for photovoltaic arrays used for charging energy storage power stations

Abstract

An improved Grey Wolf MPPT control method and device for a photovoltaic array suitable for charging an energy storage power station comprises: constructing an improved Grey Wolf algorithm (THW‑GWO‑P&O), combining a traditional Grey Wolf algorithm (GWO) with maximum power point tracking (MPPT), improving the GWO algorithm, and combining it with a traditional P&O algorithm to form an improved Grey Wolf algorithm (THW‑GWO‑P&O); setting simulation conditions and designing operating data for a photovoltaic array under different conditions; building a simulation model of a photovoltaic power generation system in Matlab / Simulink, and controlling a circuit duty cycle through an MPPT controller using the THW‑GWO‑P&O algorithm; running the simulation model of the photovoltaic power generation system, analyzing the difference between the actual voltage, current, and power and the ideal voltage, current, and power of the THW‑GWO‑P&O algorithm under different conditions, and comparatively analyzing the efficiency of different algorithms under the same and different conditions; and simulating global maximum power point tracking under uniform illumination and shading conditions using the simulation model to verify the accuracy and speed of the THW‑GWO‑P&O algorithm in tracking the maximum power point under complex illumination conditions.

Description

Technical Field

[0001] The present invention relates to the field of inverter control technology, and in particular to an improved Grey Wolf MPPT control method and device for a photovoltaic array suitable for charging an energy storage power station. Background Art

[0002] With the rapid development of renewable energy (especially photovoltaic and wind energy) and the increasing demand for energy security, environmental protection and energy transformation, energy storage technology has become an important component of achieving efficient, reliable and green power systems. Photovoltaic power generation, as a clean and renewable power generation technology, has unique advantages in resource utilization and ecological environment, and has attracted widespread attention from scholars at home and abroad.

[0003] The output of a photovoltaic array exhibits nonlinear characteristics under certain fixed operating conditions, with a peak output power at its maximum power point (MPPT). To improve the efficiency of photovoltaic power generation systems and avoid output power loss, the MPPT algorithm for photovoltaic arrays is crucial. Traditional MPPT algorithms modify the output characteristics of the photovoltaic array by altering the structure of the photovoltaic modules, converting multiple peaks in output power into a single peak, thereby controlling the output of the photovoltaic array at its maximum power point.

[0004] However, in real life, due to the influence of external obstructions, the photovoltaic array is in partial shading conditions, and there are multiple peaks in the output power. The maximum power point tracking of the photovoltaic array is more complicated. The traditional MPPT algorithm is unable to jump out of the local optimum, and has great limitations in dynamic response and stability accuracy, making it impossible to complete accurate maximum power point tracking. As a result, the inverter cannot achieve maximum power point tracking under complex lighting conditions, affecting the efficiency of the energy storage system. Summary of the Invention

[0005] The present invention aims to overcome the above-mentioned shortcomings of the prior art and provide an improved Grey Wolf MPPT control method for a photovoltaic array suitable for charging an energy storage power station.

[0006] The present invention introduces the traditional Grey Wolf Algorithm (GWO) based on swarm intelligence, adjusts its convergence factor, weighted distance, position update weight, and jumping out of local optimum, and combines it with the disturbance observation method (P&O), thereby adjusting the strategy of the maximum power point tracking algorithm, so that the energy storage inverter can operate at the maximum power point to the greatest extent, thereby improving the efficiency of the energy storage inverter. In addition, the present invention integrates the advantages of different MPPT algorithms and has more advantages in dynamic response and stability accuracy than a single MPPT control method.

[0007] To achieve the above objectives, a first aspect of the present invention relates to an improved Grey Wolf MPPT control method for a photovoltaic array suitable for charging an energy storage power station. The improved Grey Wolf algorithm THW-GWO-P&O is used to adjust the duty cycle of the Boost circuit through an MPPT controller to maintain the output power of the photovoltaic array near the maximum power point. The input of the photovoltaic array is solar irradiance and temperature, and the output is voltage, current, power, and efficiency. The method comprises the following steps:

[0008] S1. Construct an improved Grey Wolf Algorithm (THW-GWO-P&O). This algorithm combines the traditional Grey Wolf Algorithm (GWO) with Maximum Power Tracking (MPPT) to improve the GWO algorithm. By improving the convergence factor and adjusting the weighted distance and position update weights, a dual-wolf-led improved Grey Wolf Algorithm (THW-GWO) is formed. Finally, this algorithm is combined with the traditional P&O algorithm to form the improved Grey Wolf Algorithm (THW-GWO-P&O).

[0009] S2. Set up simulation conditions and design the operating data of the photovoltaic array under different operating conditions, where the operating conditions are divided into uniform illumination and partial shading conditions. The operating data is the different solar irradiance and temperature of each single photovoltaic cell in the photovoltaic array;

[0010] S3. Build a simulation model of a photovoltaic power generation system in Matlab / Simulink, including components such as the photovoltaic array, MPPT controller, boost circuit, and load. The photovoltaic array consists of multiple photovoltaic cells, and the circuit duty cycle is controlled by the MPPT controller using the THW-GWO-P&O algorithm.

[0011] S4. Using the different solar irradiance and temperature of each single photovoltaic cell in the photovoltaic array as data input, and the voltage, current, power, and efficiency as data output, run the simulation model of the photovoltaic power generation system, observe the gap between the actual voltage, current, and power and the ideal voltage, current, and power of the THW-GWO-P&O algorithm under different conditions, and compare and analyze the efficiency of different algorithms under the same and different conditions; S5. The constructed simulation model simulates the global maximum power point tracking under uniform lighting conditions and shade conditions respectively. By comparing and analyzing with the P&O algorithm, GWO algorithm, and THW-GWO algorithm, the accuracy and speed of the THW-GWO-P&O algorithm in tracking the maximum power point under complex lighting conditions are verified.

[0012] Preferably, the step S1 specifically includes:

[0013] S101 and the GWO algorithm seek the optimal solution. The GWO algorithm is based on the principles of search, encirclement, and hunting. The gray wolves adjust their positions and iterate continuously, gathering towards the optimal solution and ultimately achieving their goal.

[0014] S102. Formulate the optimization rules of the GWO algorithm. After calculating the fitness of the gray wolf each time, compare it with the α wolf, β wolf, and δ wolf to determine the new leader. The update process is as follows: First, compare the average fitness of the gray wolf with that of the α wolf. If it exceeds, it becomes the new leader; otherwise, it remains unchanged. Next, compare its fitness with that of the β wolf. If it exceeds, it is replaced. If its fitness is lower than that of the α wolf and the β wolf, compare it with the δ wolf. If it exceeds, it becomes the new δ wolf. If the gray wolf's fitness is lower than that of the α wolf, the β wolf, and the δ wolf, the leadership level remains unchanged.

[0015] S103: Obtaining global maximum power, using the real-time power of the PV array as the fitness function, with the position of the gray wolf corresponding to the duty cycle. With each round of updates, the gray wolf pack gradually approaches the global maximum power point. By setting different PV array input data, the operating data of the GWO algorithm under different operating conditions is obtained. During this process, the fitness of wolf α is considered to be the maximum output power.

[0016] Preferably, the step S103 specifically includes:

[0017] S1031. Calculate the efficiency of the GWO algorithm. Under current uniform and partially shaded lighting conditions, record the difference between the actual voltage, current, and power of the GWO algorithm under different conditions and the ideal voltage, current, and power to determine the efficiency.

[0018] S1032. Extract the variation characteristics of the GWO algorithm. Study the GWO algorithm under the condition that the solar photovoltaic cell is partially shaded, track the unique global maximum power point existing in multiple peaks of the characteristic curve, and extract the variation characteristics between the ideal power and the actual power.

[0019] Preferably, the step S1 further comprises:

[0020] S104. Improve the convergence factor and weighted distance. The global and local search capabilities of the GWO algorithm are affected by parameter A. When |A|≤1, the wolf pack tends to follow the leader to hunt; when |A|>1, the wolf pack tends to disperse in search of prey. Parameter A changes with the size of the convergence factor a. By improving the GWO algorithm by updating the weights based on the position, we can solve the problem that the GWO algorithm may cause a poor balance between global exploration and local development when linear changes occur.

[0021] S105. Introducing a nonlinear dual convergence factor. In the GWO algorithm, the control algorithm of the first half of the iteration is used to globally survey the optimal value, and the second half of the iteration is used to locally search for the optimal value. Although the convergence factor a changes linearly, this may lead to a situation where the global and local optimization cannot reach a balance. In order to solve this problem, THW-GWO proposed a new method. First, the average fitness value of the wolf pack is calculated. The wolves with a fitness value higher than the average are hunting wolves, while the wolves with a fitness value lower than the average are scout wolves. The corresponding nonlinear dual convergence factor is determined based on the classification. The improvement of the convergence factor ensures that the convergence factor a1 decreases slowly in the early and late stages and decreases rapidly in the middle stage, achieving a balance between global search and local development, thereby improving the convergence speed of the algorithm, and making the convergence factor a2 decrease from nonlinearity to 0, enhancing the global exploration ability of the scout wolves;

[0022] S106. The global leader γ is introduced to update the global optimal value, improving the GWO algorithm. By introducing adaptive weight coefficients and Levy flight strategies, the problem of the GWO algorithm that may lead to a poor balance between global exploration and local development when linear changes occur is resolved. The global leader γ is denoted as the global optimal value during the iteration process. The job of the γ wolf is to communicate with the hunting wolf, update the global optimal solution, and guide the scout wolf in its search for prey. The position of the γ wolf is denoted as Pbest. This position is updated using Levy flight in the update formula. To improve the scout wolf's search efficiency, if it obtains a poor position after a Levy flight, the position is not changed and the scout wolf's position is updated again.

[0023] Preferably, the step S104 specifically includes:

[0024] A new escape mechanism is used to avoid the dilemma caused by misjudgment by the leading wolf α in the GWO algorithm. If the position of the α wolf remains unchanged after n consecutive iterations, the wolf pack will perform a Levy flight to continue searching for other optimal solutions until the algorithm terminates.

[0025] Preferably, the step S106 specifically includes:

[0026] The GWO algorithm uses an average weight update coefficient, which significantly slows down hunting. The THW-GWO algorithm introduces a two-headed wolf mechanism, with one responsible for hunting wolves and the other for scouting. Since the hunting wolves' goal is to surround and kill prey, while the scout wolves' mission is to search for prey, the THW-GWO algorithm proposes a strategy in which the hunting wolves' movement weight is determined by the speed of their fitness value decrease. This strategy prevents individual gray wolves from converging on wolf α, balances the influence among wolf α, wolf β, and wolf δ, and thus enhances the algorithm's exploration capabilities.

[0027] Preferably, the step S2 specifically includes:

[0028] Working condition 1: set under uniform lighting conditions.

[0029] Working condition 2: set under partial shading conditions.

[0030] Simulation information was set up, and four aspects were analyzed: photovoltaic output power, output current, output voltage, and photoelectric conversion efficiency. MPPT tracking efficiency is defined as the ratio of the maximum output power actually tracked by the photovoltaic system to the theoretically achievable maximum output power under the same environmental conditions (solar irradiance and temperature). A simulation model of the photovoltaic power generation system was built in Matlab / Simulink, and the photovoltaic array adopted a 5×1 series structure. Two simulation conditions were set up during the simulation, and the temperature was always maintained at 25°C. The control effects of the photovoltaic array under four MPPT methods, namely P&O, GWO, THW-GWO, and THW-GWO-P&O, were compared and analyzed. The specific application of each algorithm was implemented through the S-function module.

[0031] Preferably, the step S5 specifically includes:

[0032] S501. Initialize the algorithm and initialize the positions of 10 wolves. These positions are evenly distributed between 0.1 and 1, indicating the duty cycle of the photovoltaic array.

[0033] S502: Evaluate the algorithm's fitness, select output power P as the PV array's fitness function, and evaluate each wolf's performance. Select the top three wolves in fitness and use their location information to guide the other wolves.

[0034] S503: Update the position of the wolves according to a specific formula to make them move closer to the maximum power point. This stage uses the global search performance of the THW-GWO algorithm to quickly converge the wolf pack to the maximum power point.

[0035] S504: Perform a local search on the algorithm. When the maximum number of iterations is reached or the MPP is approached, a small-step-size P&O algorithm is used for the local search. The P&O algorithm has excellent local search performance and fast convergence, enabling precise regulation near the maximum power point. In this stage, the small-step-size P&O algorithm performs a local search until the most accurate MPP position is determined. When the termination criteria are met, the optimal value and optimal duty cycle are output.

[0036] The second aspect of the present invention relates to an improved Gray Wolf MPPT control device for a photovoltaic array suitable for charging an energy storage power station, comprising a memory and one or more processors, wherein the memory stores executable code, and when the one or more processors execute the executable code, they are used to implement the improved Gray Wolf MPPT control method for a photovoltaic array suitable for charging an energy storage power station of the present invention.

[0037] A third aspect of the present invention relates to a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the improved Grey Wolf MPPT control method of the present invention for a photovoltaic array charged by an energy storage power station.

[0038] Compared with the existing technology, the advantages of the present invention are: using the different solar irradiance and temperature of each single photovoltaic cell in the photovoltaic array as data input, and using voltage, current, power, and efficiency as data output, running the simulation model of the photovoltaic power generation system, observing the gap between the actual voltage, current, and power of the THW-GWO-P&O algorithm under different conditions and the ideal voltage, current, and power, and comparing and analyzing the efficiency of different algorithms under the same and different conditions, the constructed simulation model simulates the global maximum power point tracking under uniform illumination conditions and shade conditions respectively, and through comparison with the P&O algorithm, GWO algorithm, and THW-GWO algorithm, the simulation model of the photovoltaic power generation system is realized. A comparative analysis was conducted using the THW-GWO-P&O algorithm to verify the accuracy and speed of the THW-GWO-P&O algorithm in tracking the maximum power point under complex lighting conditions. The algorithm introduced the traditional Grey Wolf algorithm (GWO) based on swarm intelligence, adjusted its convergence factor, weighted distance, position update weight, and escape from local optimum, and combined it with the disturbance-observation method (P&O). This adjusted the strategy of the maximum power point tracking algorithm, allowing the energy storage inverter to operate at the maximum power point to the greatest extent possible, thereby improving the efficiency of the energy storage inverter. Furthermore, the present invention integrates the advantages of different MPPT algorithms and has greater advantages in dynamic response and stability accuracy than a single MPPT control method. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a flowchart of the method of the present invention;

[0040] Figure 2 It is the topological diagram of the inverter and photovoltaic power generation system of the present invention;

[0041] Figure 3 is a graph showing the output characteristics of the photovoltaic array under partial shading according to the present invention;

[0042] Figure 4 Schematic diagram of the GWO algorithm search process of the present invention;

[0043] Figure 5is a flow chart of the photovoltaic array THW-GWO-P&O composite MPPT control of the present invention;

[0044] Figure 6 It is a schematic diagram of the device of the present invention. DETAILED DESCRIPTION

[0045] In order to explain the technical content, structural features, achieved objectives and effects of the present invention in detail, the following is a detailed description in conjunction with the embodiments and the accompanying drawings.

[0046] Example 1

[0047] See also Figure 1-Figure 5 As shown, the improved Grey Wolf MPPT control method for a photovoltaic array used for charging an energy storage power station in this embodiment is characterized by using an improved Grey Wolf algorithm THW-GWO-P&O to adjust the duty cycle of the Boost circuit through an MPPT controller to maintain the output power of the photovoltaic array near the maximum power point. The input of the photovoltaic array is solar irradiance and temperature, and the output is voltage, current, power, and efficiency. The method includes the following steps:

[0048] S1. Construct an improved Grey Wolf algorithm (THW-GWO-P&O). This algorithm combines the traditional Grey Wolf algorithm (GWO) with Maximum Power Tracking (MPPT). The GWO algorithm is improved by improving the convergence factor and adjusting the weighted distance and position update weights to form the improved Grey Wolf algorithm (THW-GWO) led by two wolves. Finally, this algorithm is combined with the traditional P&O algorithm to form the improved Grey Wolf algorithm (THW-GWO-P&O).

[0049] S2. Set up simulation conditions and design the operating data of the photovoltaic array under different operating conditions, where the operating conditions are divided into uniform illumination and partial shading conditions. The operating data is the different solar irradiance and temperature of each single photovoltaic cell in the photovoltaic array;

[0050] S3. Build a simulation model of a photovoltaic power generation system in Matlab / Simulink, including components such as the photovoltaic array, MPPT controller, boost circuit, and load. The photovoltaic array consists of multiple photovoltaic cells, and the circuit duty cycle is controlled by the MPPT controller using the THW-GWO-P&O algorithm.

[0051] S4. Using the different solar irradiance and temperature of each single photovoltaic cell in the photovoltaic array as data input, and the voltage, current, power, and efficiency as data output, run the simulation model of the photovoltaic power generation system, observe the gap between the actual voltage, current, and power and the ideal voltage, current, and power of the THW-GWO-P&O algorithm under different conditions, and compare and analyze the efficiency of different algorithms under the same and different conditions; S5. The constructed simulation model simulates the global maximum power point tracking under uniform lighting conditions and shade conditions respectively. By comparing and analyzing with the P&O algorithm, GWO algorithm, and THW-GWO algorithm, the accuracy and speed of the THW-GWO-P&O algorithm in tracking the maximum power point under complex lighting conditions are verified.

[0052] Preferably, the step S1 specifically includes:

[0053] S101 and the GWO algorithm seek the optimal solution. The GWO algorithm is based on the principles of search, encirclement, and hunting. The gray wolves adjust their positions and iterate continuously, gathering towards the optimal solution and ultimately achieving their goal.

[0054] S102. Formulate the optimization rules of the GWO algorithm. After calculating the fitness of the gray wolf each time, compare it with the α wolf, β wolf, and δ wolf to determine the new leader. The update process is as follows: First, compare the average fitness of the gray wolf with that of the α wolf. If it exceeds, it becomes the new leader; otherwise, it remains unchanged. Next, compare its fitness with that of the β wolf. If it exceeds, it is replaced. If its fitness is lower than that of the α wolf and the β wolf, compare it with the δ wolf. If it exceeds, it becomes the new δ wolf. If the gray wolf's fitness is lower than that of the α wolf, the β wolf, and the δ wolf, the leadership level remains unchanged.

[0055] S103: Obtaining global maximum power, using the real-time power of the PV array as the fitness function, with the position of the gray wolf corresponding to the duty cycle. With each round of updates, the gray wolf pack gradually approaches the global maximum power point. By setting different PV array input data, the operating data of the GWO algorithm under different operating conditions is obtained. During this process, the fitness of wolf α is considered to be the maximum output power.

[0056] Preferably, the step S103 specifically includes:

[0057] S1031. Calculate the efficiency of the GWO algorithm. Under current uniform and partially shaded lighting conditions, record the difference between the actual voltage, current, and power of the GWO algorithm under different conditions and the ideal voltage, current, and power to determine the efficiency.

[0058] S1032. Extract the variation characteristics of the GWO algorithm. Study the GWO algorithm under the condition that the solar photovoltaic cell is partially shaded, track the unique global maximum power point existing in multiple peaks of the characteristic curve, and extract the variation characteristics between the ideal power and the actual power.

[0059] Preferably, the step S1 further comprises:

[0060] S104. Improve the convergence factor and weighted distance. The global and local search capabilities of the GWO algorithm are affected by parameter A. When |A|≤1, the wolf pack tends to follow the leader to hunt; when |A|>1, the wolf pack tends to disperse in search of prey. Parameter A changes with the size of the convergence factor a. By improving the GWO algorithm by updating the weights based on the position, we can solve the problem that the GWO algorithm may cause a poor balance between global exploration and local development when linear changes occur.

[0061] S105. Introducing a nonlinear dual convergence factor. In the GWO algorithm, the control algorithm of the first half of the iteration is used to globally survey the optimal value, and the second half of the iteration is used to locally search for the optimal value. Although the convergence factor a changes linearly, this may lead to a situation where the global and local optimization cannot reach a balance. In order to solve this problem, THW-GWO proposed a new method. First, the average fitness value of the wolf pack is calculated. The wolves with a fitness value higher than the average are hunting wolves, while the wolves with a fitness value lower than the average are scout wolves. The corresponding nonlinear dual convergence factor is determined based on the classification. The improvement of the convergence factor ensures that the convergence factor a1 decreases slowly in the early and late stages and decreases rapidly in the middle stage, achieving a balance between global search and local development, thereby improving the convergence speed of the algorithm, and making the convergence factor a2 decrease from nonlinearity to 0, enhancing the global exploration ability of the scout wolves;

[0062] S106. The global leader γ is introduced to update the global optimal value, improving the GWO algorithm. By introducing adaptive weight coefficients and Levy flight strategies, the problem of the GWO algorithm that may lead to a poor balance between global exploration and local development when linear changes occur is resolved. The global leader γ is denoted as the global optimal value during the iteration process. The job of the γ wolf is to communicate with the hunting wolf, update the global optimal solution, and guide the scout wolf in its search for prey. The position of the γ wolf is denoted as Pbest. This position is updated using Levy flight in the update formula. To improve the scout wolf's search efficiency, if it obtains a poor position after a Levy flight, the position is not changed and the scout wolf's position is updated again.

[0063] Preferably, the step S104 specifically includes:

[0064] A new escape mechanism is used to avoid the dilemma caused by misjudgment by the leading wolf α in the GWO algorithm. If the position of the α wolf remains unchanged after n consecutive iterations, the wolf pack will perform a Levy flight to continue searching for other optimal solutions until the algorithm terminates.

[0065] Preferably, the step S106 specifically includes:

[0066] The GWO algorithm uses an average weight update coefficient, which significantly slows down hunting. The THW-GWO algorithm introduces a two-headed wolf mechanism, with one responsible for hunting wolves and the other for scouting. Since the hunting wolves' goal is to surround and kill prey, while the scout wolves' mission is to search for prey, the THW-GWO algorithm proposes a strategy in which the hunting wolves' movement weight is determined by the speed of their fitness value decrease. This strategy prevents individual gray wolves from converging on wolf α, balances the influence among wolf α, wolf β, and wolf δ, and thus enhances the algorithm's exploration capabilities.

[0067] Preferably, the step S2 specifically includes:

[0068] Working condition 1: Under uniform illumination conditions, the entire photovoltaic array experiences a sudden change in illumination at 2s, i.e., the solar irradiance is uniformly 1000W / m2 within 0-2s. 2 , the solar irradiance is uniformly 600W / m within 2-4s 2 .

[0069] Working condition 2: under partial shading conditions, the solar irradiance of each photovoltaic cell in 0-2s is 1000 W / m 2 , 900W / m 2 , 800 W / m 2 , 700 W / m 2 , 600W / m 2 , the solar irradiance of each photovoltaic cell within 2-4s is 800W / m 2 , 600W / m 2 , 400W / m 2 、300W / m 2 , 200W / m 2 .

[0070] Simulation information was set up, and four aspects were analyzed: photovoltaic output power, output current, output voltage, and photoelectric conversion efficiency. MPPT tracking efficiency is defined as the ratio of the maximum output power actually tracked by the photovoltaic system to the theoretically achievable maximum output power under the same environmental conditions (solar irradiance and temperature). A simulation model of the photovoltaic power generation system was built in Matlab / Simulink, and the photovoltaic array adopted a 5×1 series structure. Two simulation conditions were set up during the simulation, and the temperature was always maintained at 25°C. The control effects of the photovoltaic array under four MPPT methods, namely P&O, GWO, THW-GWO, and THW-GWO-P&O, were compared and analyzed. The specific application of each algorithm was implemented through the S-function module.

[0071] Preferably, the step S5 specifically includes:

[0072] S501. Initialize the algorithm and initialize the positions of 10 wolves. These positions are evenly distributed between 0.1 and 1, indicating the duty cycle of the photovoltaic array.

[0073] S502: Evaluate the algorithm's fitness, select output power P as the PV array's fitness function, and evaluate each wolf's performance. Select the top three wolves in fitness and use their location information to guide the other wolves.

[0074] S503: Update the position of the wolves according to a specific formula to make them move closer to the maximum power point. This stage uses the global search performance of the THW-GWO algorithm to quickly converge the wolf pack to the maximum power point.

[0075] S504: Perform a local search on the algorithm. When the maximum number of iterations is reached or the MPP is approached, a small-step-size P&O algorithm is used for the local search. The P&O algorithm has excellent local search performance and fast convergence, enabling precise regulation near the maximum power point. In this stage, the small-step-size P&O algorithm performs a local search until the most accurate MPP position is determined. When the termination criteria are met, the optimal value and optimal duty cycle are output.

[0076] Combine Figure 1 Figure 5 The present invention uses the different solar irradiance and temperature of each single photovoltaic cell in the photovoltaic array as data input, and uses voltage, current, power, and efficiency as data output to run the simulation model of the photovoltaic power generation system, observe the gap between the actual voltage, current, and power of the THW-GWO-P&O algorithm under different conditions and the ideal voltage, current, and power, and compare and analyze the efficiency of different algorithms under the same and different conditions. The constructed simulation model simulates the global maximum power point tracking under uniform illumination conditions and shade conditions, and compares it with the P&O algorithm, GWO algorithm, and THW-GWO algorithm. The THW-GWO-P&O algorithm is analyzed and verified for its accuracy and speed in tracking the maximum power point under complex lighting conditions. By introducing the traditional Grey Wolf algorithm (GWO) based on swarm intelligence, adjusting its convergence factor, weighted distance, position update weight, and jumping out of local optimality, and combining it with the perturbation and observation method (P&O), the maximum power point tracking algorithm strategy is adjusted, allowing the energy storage inverter to operate at the maximum power point to the greatest extent, thereby improving the efficiency of the energy storage inverter. Furthermore, the present invention integrates the advantages of different MPPT algorithms and has greater advantages in dynamic response and stable accuracy than a single MPPT control method.

[0077] Example 2

[0078] Reference Figure 6This embodiment relates to an improved Gray Wolf MPPT control device for a photovoltaic array suitable for charging an energy storage power station, including a memory and one or more processors. The memory stores executable code. When the one or more processors execute the executable code, they are used to implement the improved Gray Wolf MPPT control method for a photovoltaic array suitable for charging an energy storage power station of Example 1.

[0079] Example 3

[0080] This embodiment relates to a computer-readable storage medium having a program stored thereon. When the program is executed by a processor, the improved Grey Wolf MPPT control method for a photovoltaic array for charging an energy storage power station according to embodiment 1 is implemented.

[0081] The above disclosure is only the preferred embodiment of the present invention, which certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the scope of the patent application of the present invention are still within the scope of the present invention.

Claims

1. An improved Gray Wolf MPPT control method for photovoltaic arrays used for charging energy storage power stations, characterized in that: The improved Grey Wolf algorithm THW-GWO-P&O is used to adjust the duty cycle of the Boost circuit through the MPPT controller to maintain the output power of the photovoltaic array near the maximum power point. The input of the photovoltaic array is solar irradiance and temperature, and the output is voltage, current, power, and efficiency. The steps include: S1. Construct an improved Grey Wolf Algorithm (THW-GWO-P&O). This algorithm combines the traditional Grey Wolf Algorithm (GWO) with Maximum Power Tracking (MPPT) to improve the GWO algorithm. By improving the convergence factor and adjusting the weighted distance and position update weights, a dual-wolf-led improved Grey Wolf Algorithm (THW-GWO) is formed. Finally, the improved Grey Wolf Algorithm (THW-GWO-P&O) is combined with the traditional P&O algorithm to form the improved Grey Wolf Algorithm (THW-GWO-P&O). The improved convergence factor specifically includes: S104. Improve the convergence factor and weighted distance. The global and local search capabilities of the GWO algorithm are both affected by parameter A. When |A|≤1, wolves tend to follow the leader in their hunts. When |A|>1, wolves tend to disperse in their search for prey. Parameter A changes with the convergence factor a. The GWO algorithm is improved by updating the weights based on position, addressing the issue of poor balance between global exploration and local exploitation in the linearly changing GWO algorithm. S105. Introducing a nonlinear dual convergence factor. In the GWO algorithm, the control algorithm in the first half of the iteration is used to globally explore the optimal value, and the second half of the iteration is used to locally search for the optimal value. Although the convergence factor a changes linearly, a situation occurs in which the global and local optimization cannot reach a balance. To solve this problem, THW-GWO proposes a new method. First, the average fitness value of the wolf pack is calculated. Wolves with a fitness value higher than the average are hunting wolves, while wolves with a fitness value lower than the average are scout wolves. The corresponding nonlinear dual convergence factor is determined based on the classification. The improvement of the convergence factor ensures that the convergence factor a1 decreases slowly in the early and late stages and decreases rapidly in the middle stage, achieving a balance between global search and local development, thereby improving the convergence speed of the algorithm and making the convergence factor a2 decrease from nonlinearity to 0, enhancing the global exploration capability of the scout wolves. S106. Introduce the global alpha wolf γ to update the global optimal value and improve the GWO algorithm. By introducing the adaptive weight coefficient and Levy flight strategy, the problem of poor balance between global exploration and local development caused by linear changes in the GWO algorithm is integrated. The global alpha wolf γ is denoted as the global optimal value in the iterative process. The work content of the γ wolf is to communicate with the hunting wolf, update the global optimal solution, and guide the scout wolf to search for prey. The position of the γ wolf is denoted as P best , the position is updated. Levy flight is used in the update formula. In order to improve the search efficiency of the scout wolf, if it obtains a poor position after Levy flight, the position is not changed and the scout wolf position is updated again; S2. Set up simulation conditions and design the operating data of the photovoltaic array under different operating conditions, where the operating conditions are divided into uniform illumination and partial shading conditions. The operating data is the different solar irradiance and temperature of each single photovoltaic cell in the photovoltaic array; S3. Build a simulation model of a photovoltaic power generation system in Matlab / Simulink, including the photovoltaic array, MPPT controller, boost circuit, and load components. The photovoltaic array consists of multiple photovoltaic cells, and the MPPT controller uses the THW-GWO-P&O algorithm to control the circuit duty cycle. S4. Using the different solar irradiance and temperature of each single photovoltaic cell in the photovoltaic array as data input, and the voltage, current, power, and efficiency as data output, run the simulation model of the photovoltaic power generation system, observe the gap between the actual voltage, current, and power and the ideal voltage, current, and power of the THW-GWO-P&O algorithm under different conditions, and compare and analyze the efficiency of different algorithms under the same and different conditions; S5. The constructed simulation model simulates the global maximum power point tracking under uniform lighting conditions and shade conditions respectively. By comparing and analyzing with the P&O algorithm, GWO algorithm, and THW-GWO algorithm, the accuracy and speed of the THW-GWO-P&O algorithm in tracking the maximum power point under complex lighting conditions are verified.

2. The improved Gray Wolf MPPT control method for a photovoltaic array suitable for charging an energy storage power station according to claim 1, characterized in that: The step S1 of combining the traditional Grey Wolf algorithm GWO and maximum power tracking includes: S101 and the GWO algorithm seek the optimal solution. The GWO algorithm is based on the principles of search, encirclement, and hunting. The gray wolves adjust their positions and iterate continuously, gathering towards the optimal solution and ultimately achieving their goal. S102. Formulate the optimization rules of the GWO algorithm. After calculating the fitness of the gray wolf each time, compare it with the α wolf, β wolf, and δ wolf to determine the new leader. The update process is as follows: First, compare the average fitness of the gray wolf with that of the α wolf. If it exceeds, it becomes the new leader; otherwise, it remains unchanged. Next, compare its fitness with that of the β wolf. If it exceeds, it is replaced. If its fitness is lower than that of the α wolf and the β wolf, compare it with the δ wolf. If it exceeds, it becomes the new δ wolf. If the gray wolf's fitness is lower than that of the α wolf, the β wolf, and the δ wolf, the leadership level remains unchanged. S103. Obtain the global maximum power, using the real-time power of the photovoltaic array as the fitness function, and the position of the gray wolf corresponds to the duty cycle; with each round of updates, the gray wolf group gradually approaches the global maximum power point; by setting different photovoltaic array input data, the operating data of the GWO algorithm under different working conditions is obtained. In this process, the fitness of wolf α is considered to be the maximum output power.

3. The improved Grey Wolf MPPT control method for a photovoltaic array used for charging an energy storage power station according to claim 2, characterized in that: The step S103 of obtaining the operating data of the GWO algorithm under different working conditions specifically includes: S1031. Calculate the efficiency of the GWO algorithm. Under current uniform and partially shaded lighting conditions, record the difference between the actual voltage, current, and power of the GWO algorithm under different conditions and the ideal voltage, current, and power to determine the efficiency. S1032. Extract the variation characteristics of the GWO algorithm. Study the GWO algorithm under the condition that the solar photovoltaic cell is partially shaded, track the unique global maximum power point existing in multiple peaks of the characteristic curve, and extract the variation characteristics between the ideal power and the actual power.

4. The improved Grey Wolf MPPT control method for a photovoltaic array used for charging an energy storage power station according to claim 1, characterized in that: The updating of the weight by location in step S104 specifically includes: A new jump-out mechanism is adopted to avoid the dilemma caused by the misjudgment of the α wolf leading the hunt in the GWO algorithm. When the position of the α wolf remains unchanged after n consecutive iterations, the wolf pack is allowed to perform a Levy flight to continue searching for other optimal solutions until the algorithm ends.

5. The improved Grey Wolf MPPT control method for a photovoltaic array used for charging an energy storage power station according to claim 1, characterized in that: The introduction of the adaptive weight coefficient in step S106 includes: The adaptive weight coefficient of the two-headed wolf mechanism is introduced. The GWO algorithm uses an average weight update coefficient, but this strategy slows down the hunting speed. The THW-GWO algorithm introduces a two-headed wolf mechanism, which is responsible for hunting wolves and scouting wolves respectively. Since the goal of hunting wolves is to surround and kill prey, and the task of scouting wolves is to search for prey, the THW-GWO algorithm proposes a strategy in which the movement weight of hunting wolves is determined by the speed of fitness value decrease. This strategy prevents all gray wolves from converging on wolf α, balances the influence among wolf α, wolf β and wolf δ, and thus enhances the algorithm's exploration ability.

6. The improved Grey Wolf MPPT control method for a photovoltaic array suitable for charging an energy storage power station according to claim 1, characterized in that: Setting the simulation working condition in step S2 specifically includes: Working condition 1: set under uniform lighting conditions; Working condition 2: set under partial shading conditions; Simulation information was set up. During the simulation, four aspects, namely photovoltaic output power, output current, output voltage, and photoelectric conversion efficiency, were selected for analysis. MPPT tracking efficiency was defined as the ratio of the maximum output power actually tracked by the photovoltaic system to the theoretically achievable maximum output power under the same environmental conditions. A simulation model of the photovoltaic power generation system was built in Matlab / Simulink, and the photovoltaic array adopted a 5×1 series structure. Two simulation operating conditions were set up during the simulation, and the temperature was always maintained at 25°C. The control effects of the photovoltaic array under four MPPT methods, namely P&O, GWO, THW-GWO, and THW-GWO-P&O, were compared and analyzed, and the specific application of each algorithm was implemented through the S-function module.

7. The improved Grey Wolf MPPT control method for a photovoltaic array used for charging an energy storage power station according to claim 1, characterized in that: The simulation model constructed in step S5 simulates global maximum power point tracking under uniform illumination conditions and shade conditions. By comparing and analyzing the P&O, GWO, and THW-GWO algorithms, the accuracy and speed of the THW-GWO-P&O algorithm in tracking the maximum power point under complex illumination conditions are verified. Specifically, the following are performed: S501, initializing the algorithm and initializing the positions of 10 wolves, where these positions are equally distributed between 0.1 and 1, representing the duty cycle of the photovoltaic array; S502: Evaluate the algorithm's fitness, select the output power P as the photovoltaic array's fitness function, and evaluate the performance of each wolf. Select the top three wolves in fitness, and use their location information to guide the other gray wolves. S503: Update the position of the algorithm and update the positions of the wolves according to the formula so that they are close to the maximum power point. In this stage, the global search performance of the THW-GWO algorithm is used to make the wolf pack quickly converge to the vicinity of the maximum power point. S504. Perform a local search on the algorithm. When the maximum number of iterations is reached or the MPP is approached, a small-step-size P&O algorithm is used for local search. The P&O algorithm has good local search performance and fast convergence performance, and can achieve precise adjustment near the maximum power point. In this stage, a small-step-size P&O algorithm is used for local search until the most accurate MPP position is determined. When the termination condition is met, the optimal value and the optimal duty cycle are output.

8. An improved Gray Wolf MPPT control device for photovoltaic arrays used for charging energy storage power stations, characterized in that: The invention comprises a memory and one or more processors, wherein the memory stores executable code, and when the one or more processors execute the executable code, they are used to implement the improved Grey Wolf MPPT control method for a photovoltaic array suitable for charging an energy storage power station according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that A program is stored thereon, and when the program is executed by a processor, the improved Grey Wolf MPPT control method for a photovoltaic array suitable for charging an energy storage power station as described in any one of claims 1 to 7 is implemented.

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