Photovoltaic MPPT control method based on IALA-PQ algorithm

Abstract

The invention belongs to the technical field of photovoltaic new energy, and discloses an IALA-Pamp-based photovoltaic power generation system. The invention discloses a photovoltaic MPPT control method based on a Q algorithm, and the method comprises the steps: firstly, employing chaotic mapping to initialize population enhancement diversity, and optimizing dynamic parameters in migration and hole digging behaviors; then, a self-adaptive inertia weight and an elite pool strategy are introduced to improve the global search capability; then constructing a double-stage switching mechanism, starting IALA global positioning when shadow or illumination suddenly changes, and switching to the Pamp after approaching the maximum power point; and O, performing local adjustment, and dynamically adjusting a disturbance step length to reduce steady-state fluctuation. The method has the following main beneficial technical effects: compared with a traditional artificial travel mouse algorithm and a grey wolf optimization algorithm, IALA-Pamp is adopted; o is faster in convergence under multiple static peak values, the global maximum power point tracking precision is higher, the response is faster in a dynamic illumination sudden change scene, the steady-state power fluctuation is controlled within 0.7%, the rapidness and stability are effectively considered, and the method is suitable for a complex illumination environment.

Description

Technical Field

[0001] This invention belongs to the field of photovoltaic new energy technology, and in particular discloses a photovoltaic MPPT control method based on the IALA-P&Q algorithm. Background Technology

[0002] Currently, the photovoltaic (PV) power conversion efficiency is only around 20%. The nonlinear output characteristics of PV cells themselves make it difficult for them to operate stably at the maximum power point (MPP). Improving solar energy utilization efficiency and ensuring the efficient operation of PV systems has led to the development of Maximum Power Point Tracking (MPPT) technology. However, frequent partial shading conditions (PSC) caused by clouds, dust, buildings, or trees result in multi-peak characteristics in the power-voltage curve of PV arrays. Traditional MPPT algorithms are prone to getting trapped in local extrema, causing power losses exceeding 30% and triggering safety risks such as hot spots and electric arcs. Achieving rapid response at the Global Maximum Power Point (GMPP) under PSC conditions is a key technical challenge for increasing the annual power generation of PV systems and reducing the cost per kilowatt-hour, demonstrating significant engineering application value and economic benefits.

[0003] With the rapid development of metaheuristic algorithms, their strong global optimization capabilities have garnered widespread attention and application in the photovoltaic MPPT field. Existing technologies have proposed MPPT control strategies based on Particle Swarm Optimization (PSO). Results show that this algorithm tracks faster than traditional methods under uniform illumination; however, it is prone to getting trapped in local optima under local shading conditions due to insufficient population diversity. Existing technologies have also designed MPPT algorithms based on Gray Wolf Optimization (GWO), demonstrating rapid response in the initial stages of dynamic illumination, but insufficient convergence accuracy in multi-peak power curve scenarios and inadequate anti-interference capabilities in complex environments. The Salp Swarm Algorithm (SSA) proposed in existing technologies is simple to implement and has stable convergence performance in low-dimensional optimization problems; however, its leader behavior relies on existing information, resulting in weak global exploration capabilities and a tendency to get trapped in local optima under local shading conditions.

[0004] To balance the tracking speed and convergence accuracy of GMPP in multi-peak environments, the existing Cuckoo Search (CS) algorithm, due to its few parameters and strong global exploration capability, has been used for multi-peak tracking, but its convergence speed is slow and its local optimization accuracy is insufficient in the later stages. Existing technologies also propose MPPT control based on the Improved Differential Squirrel Search Algorithm (IDSSA), which improves global search and local convergence capabilities by initializing the population through an improved Tent chaotic mapping and introducing a differential evolution mechanism. It exhibits a strong ability to escape local optima in multi-peak environments, but its adaptability in dynamic lighting scenarios has not been fully verified. The existing Chaotic Gray Wolf Optimization and Perturbation Observation Combination Algorithm (GWO-P&O) combining cat mapping demonstrates strong global optimization capability and high steady-state accuracy in multi-peak scenarios, but inappropriate step size settings in the local search stage may lead to steady-state oscillations. The proposed Hybrid Capuchin Search Algorithm (HCapSA) integrates Fuch mapping, Lévy flight, and differential strategies, which enhances the global search capability and convergence rate in multi-peak scenes and has high tracking accuracy. However, the switchable mechanism relies on empirical thresholds and lacks adaptive capability, making it prone to misjudgment or response lag in dynamic lighting environments.

[0005] CN120566410A discloses a short-term photovoltaic power prediction method based on optimized decomposition and combined neural networks, addressing the technical problems in existing technologies such as prediction accuracy being greatly affected by weather changes, easy mode mixing and residual mismatch in signal decomposition, and insufficient adaptability of single deep learning models. This invention uses a combination of Pearson and Kendall correlation coefficients to screen meteorological factors, improving the comprehensiveness of feature selection; it uses SOM and Kmeans collaborative clustering to classify weather types, improving clustering accuracy; it utilizes an improved artificial lemming optimization algorithm to adaptively optimize VMD parameters, avoiding local optima; it constructs a TCNBiGRU combined neural network model, integrating long-term feature extraction and bidirectional time-series modeling capabilities; and it uses a dynamic weighted average method to reconstruct prediction results, optimizing accuracy and significantly improving the accuracy and efficiency of short-term photovoltaic power prediction, making it suitable for photovoltaic power generation systems under complex weather conditions. This differs from the technical problem addressed by this application. Summary of the Invention

[0006] To address the aforementioned issues, this application presents the IALA-P&Q algorithm, which combines an improved artificial lemming algorithm with a perturbation-observation method. This algorithm not only retains the global optimization capability of metaheuristic algorithms but also achieves rapid response and high tracking accuracy in local shading and dynamic lighting environments through dynamic response mechanisms and fine-tuning methods, providing a new solution for photovoltaic MPPT control in complex environments. The purpose of this invention is to disclose a photovoltaic MPPT control method based on the IALA-P&Q algorithm, implemented using the following technical solutions.

[0007] A photovoltaic MPPT control method based on the IALA-P&Q algorithm is proposed. First, chaotic mapping is used to initialize the population to enhance diversity and optimize dynamic parameters in migration and burrowing behaviors. Then, adaptive inertial weights and an elite pool strategy are introduced to improve global search capability. Subsequently, a two-stage switching mechanism is constructed. When there is a sudden change in shadow or illumination, IALA global positioning is initiated, and after approaching the maximum power point, it switches to P&O local adjustment and dynamically adjusts the disturbance step size to reduce steady-state fluctuations.

[0008] A photovoltaic MPPT control method based on the IALA-P&Q algorithm is characterized by the following steps: First, chaotic mapping is used to initialize the population to enhance diversity and optimize dynamic parameters in migration and burrowing behaviors; then, adaptive inertial weights and an elite pool strategy are introduced to improve global search capability; subsequently, a two-stage switching mechanism is constructed, initiating IALA global positioning when shadow or illumination changes suddenly, switching to P&O local adjustment when approaching the maximum power point, and dynamically adjusting the disturbance step size to reduce steady-state fluctuations.

[0009] The photovoltaic MPPT control method based on the IALA-P&Q algorithm described above is characterized by the use of chaotic mapping to initialize the population and enhance diversity, specifically the Cubic chaotic mapping formula: The Cubic chaotic mapping formula is expressed as: In the formula: a is the chaos parameter; x i+1 x represents the value of the chaotic variable at the (i+1)th iteration; i Let be the value of the chaotic variable at the i-th iteration; the Cubic chaotic mapping is between (0, 1).

[0010] The photovoltaic MPPT control method based on the IALA-P&Q algorithm described above is characterized by introducing an adaptive direction factor F(t) and a dynamic Brownian step size in optimizing dynamic parameters during migration and burrowing behaviors. , expressed as: In the formula: F max F is the maximum direction factor; min Let F be the minimum direction factor; max =2.0, F min =0.5; In the formula: Given the initial step size vector, the improved migration model is as follows: In the formula: This represents the position of the i-th search entity at iteration t+1; Represents the current optimal solution; vector The vector is a random element of dimension 1×Dim. This vector controls the movement of the current best individual and random individuals in the population and is used to represent the interaction between individuals during the migration process. This represents the current position of the i-th searched individual; This represents a search individual randomly selected from the population; a is an integer index between 1 and n. The escape coefficient W decays in a fixed manner, and the Levy flight step size is not associated with the current state. Later, it is prone to getting trapped in local optima due to insufficient escape capability. The adaptive escape coefficient W(t) is as follows: In the formula: W0 is the initial escape coefficient; η is the decay control parameter; take W0=2, η=5;

[0011] The Levy flight stride in relation to distance is: In the formula: Let S be the basic Levy step size, and S be the length of the diagonal of the solution space.

[0012] The photovoltaic MPPT control method based on the IALA-P&Q algorithm described above is characterized by the introduction of adaptive inertia weights and an elite pool strategy to improve global search capability. The new position update formula is as follows: In the formula: The individual is randomly selected from the elite pool; c is a random number in the range [0,1]. It is the Nth best individual in the current population fitness. The weighted average position of the optimal individual reflects the current evolutionary trend of the dominant subgroup; L is a random number related to the current iteration number; Let represent the search individual randomly selected from the population; ω is the inertia weight; represented by a nonlinear function, as follows: In the formula: ω(t) is the adaptive inertia weight.

[0013] The photovoltaic MPPT control method based on the IALA-P&Q algorithm described above is characterized by dividing the photovoltaic MPPT control strategy of the IALA-P&Q algorithm into three parts in the construction of the two-stage switching mechanism: ALA global search stage - P&O local search stage - algorithm restart. The specific steps are as follows:

[0014] Step 1, Initialization Judgment: Generate an initial voltage population using Cubic chaotic mapping, covering the photovoltaic voltage search range [V]. min Vmax The power value corresponding to each individual is used as the fitness value to calculate the power change rate between adjacent time moments in real time. Finally, the coefficient of variation (CV) of the light intensity of each component is calculated. E CV E The expression is as follows: In the formula: The standard deviation of light intensity Let be the average intensity of the illumination, when CV E If more than 10% of respondents believe there is local shading or uneven lighting distribution, an ALA global search will be initiated.

[0015] Step 2, Global Search: When multiple peaks exist, the improved ALA is first used for global optimization. Its optimization strategy quickly finds the global maximum power point. Once the conditions are met, the method switches to the adaptive perturbation step size method. The switching conditions are: In the formula: P gbest For the optimal power of the population, P best This represents the current maximum power.

[0016] Step 3, Local Search: Find the optimal voltage V output by ALA. gbest Using P&O as the starting point, high-precision tracking is achieved. The perturbation step size adopts an adaptive design, so that the perturbation step size ∆V is adjusted with the power gradient, that is: In the formula: ∆V l =0.5%V max ;∆V s =0.1%V max δ is the power gradient threshold (δ=0.01).

[0017] Step 4, Scene Recognition: When the environment changes abruptly, a timely response is required to avoid lag and unnecessary power loss. Scene recognition indicators are introduced as follows: In the formula: P t+1 P t These are the power values ​​for the (t+1)th and tth times, respectively. When the above formula is satisfied, it indicates that a drastic change or large fluctuation in light or temperature has been detected. At this point, the algorithm needs to be restarted for initialization to adapt to the sudden change in the environment.

[0018] This application has the following main beneficial technical effects: Compared with the traditional artificial lemming algorithm and gray wolf optimization algorithm, IALA-P&O converges faster and has higher global maximum power point tracking accuracy under static multi-peak conditions, responds more quickly in dynamic lighting change scenarios, and controls steady-state power fluctuation within 0.7%, effectively balancing speed and stability, and is suitable for complex lighting environments. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the equivalent circuit model of a photovoltaic cell.

[0020] Figure 2 This is a schematic diagram of the photovoltaic array structure under partial shading.

[0021] Figure 3 A graph showing the light intensity received by each module of the photovoltaic array.

[0022] Figure 4 The graphs show the PV characteristic curves of the photovoltaic array under different operating conditions.

[0023] Figure 5 The graphs show the IV characteristic curves of the photovoltaic array under different operating conditions.

[0024] Figure 6 The flowchart is for the IALA-P&Q algorithm.

[0025] Figure 7 This is a graph showing the convergence of the test function.

[0026] Figure 8 This is a schematic diagram of the photovoltaic MPPT simulation model.

[0027] Figure 9 A chart showing the simulation parameters of photovoltaic system components.

[0028] Figure 10 The graphs show the MPPT curves of different algorithms under static uniform illumination.

[0029] Figure 11 The MPPT curves for different algorithms are shown under static shading conditions.

[0030] Figure 12 MPPT curves for different algorithms under dynamic lighting conditions.

[0031] Figure 13 This is a comparison chart of the MPPT strategies of various algorithms under dynamic lighting conditions. Detailed Implementation

[0032] To enable those skilled in the art to better understand and implement this patent, the specific embodiments are further described in detail with reference to the accompanying drawings.

[0033] Please see Figures 1 to 13 This application represents an improvement upon existing technologies. The output characteristics of photovoltaic systems and photovoltaic arrays, and the photovoltaic MPPT control strategy based on the IALA-P&Q algorithm in existing technologies are as follows:

[0034] I. Output Characteristics of Photovoltaic Systems and Arrays

[0035] 1.1 Photovoltaic Cell Mathematical Model: Nonlinear characteristics and changing environmental conditions can easily affect the performance of photovoltaic systems, leading to significant fluctuations in output performance. Therefore, it is crucial to construct a high-precision mathematical model for photovoltaic cells that is both representative and practical for engineering applications. The single-diode topology, by simplifying the circuit configuration, effectively reproduces the IV characteristic curve of photovoltaic devices while ensuring computational efficiency, providing a reliable mathematical foundation for system dynamic response analysis. The equivalent circuit diagram is shown below. Figure 1 As shown. Figure 1 middle, I ph D is the photocurrent; I is the diode; D R is the diode current; s I is the series equivalent resistance of the photovoltaic cell; pv For the output current of the photovoltaic cell; R sh I is the parallel equivalent resistance of the photovoltaic cell; sh For the parallel resistor current; V pv This refers to the output voltage of the photovoltaic cell.

[0036] According to Kirchhoff's current law:

[0037]

[0038]

[0039]

[0040]

[0041]

[0042] Where: A is the diode quality factor; K is the Boltzmann constant; T is the photovoltaic cell temperature; I0 is the diode reverse saturation circuit; q is the electron charge; T n Standard test temperature; K i K represents the temperature coefficient of current. v G is the voltage temperature coefficient; G is the current solar irradiance; G n For standard test irradiance; I sc The short-circuit current under standard test conditions; V oc This is the open-circuit voltage.

[0043] 1.2 Analysis of photovoltaic array output characteristics under partial shading conditions: When a photovoltaic array of size {m×n} (where m is the number of series-connected battery modules in each branch and n is the number of parallel branches) is under partial shading conditions, the array receives different light intensities. The voltage difference between modules with different light intensities in the branches will activate the bypass diodes of the modules with lower light intensities, causing the PV curve of the photovoltaic array to exhibit significant multi-peak characteristics.

[0044] To reveal the distribution mechanism of multiple peak power values ​​under non-uniform irradiation, a 4×2 photovoltaic array was used as the observation object, and its electrical structure was illustrated. Figure 2 The irradiance quantification values ​​corresponding to each sub-condition are summarized in Figure 3 D1, D2, D3, D14, D4, D5, D6, and D13 are parallel diodes between the positive and negative output terminals of the photovoltaic module, respectively; D10 and D11 are anti-reverse diodes configured in the parallel circuit to prevent current backflow between the parallel branches; S1, S2, S3, and S4 are the light intensities corresponding to each photovoltaic module, respectively.

[0045] Based on the mathematical model of monocrystalline silicon cells and their series-parallel topology in the array, a Simulink simulation platform was built to simulate the dynamic characteristics of the photovoltaic array under different operating conditions. Its power-voltage and current-voltage output characteristics are shown below. Figure 4 and Figure 5 , Figure 4 Under medium uniform illumination, the maximum output power of the photovoltaic array is 1750W; under operating condition 2, the GMPP is 1150W; and under operating condition 3, the GMPP is 800W. Figure 4 It is known that the output power P of a photovoltaic array varies with the output voltage V, and exhibits multi-peak characteristics under non-uniform illumination, resulting in a "multi-peak" phenomenon. Essentially, this is because multiple "sub-IV curves" exist in parallel output within the same photovoltaic array, each with different short-circuit currents. The array consists of several series-parallel connected components. As the voltage gradually increases, the shaded series is the first to "withdraw" from power supply, thus cutting the overall IV curve into several segments, each corresponding to a current drop. The PV curve then exhibits multiple local extrema due to the V×I product. Simultaneously, the MPP shifts with changes in operating parameters. To achieve maximum power point tracking (MPPT) of the photovoltaic array, the output voltage V needs to be dynamically adjusted to ensure the system always operates at the optimal power output state corresponding to the current environment. This is typically achieved by using an external boost circuit and adjusting the duty cycle of the electronic switching transistors in the circuit. Therefore, photovoltaic MPPT is essentially an optimization process with the duty cycle deltaD as the controlled variable and the output power P as the objective. To address the multi-maximum power output characteristics of photovoltaic arrays under local shading conditions, a metaheuristic algorithm with the ability to escape local optima, fast convergence speed, and high convergence accuracy needs to be introduced to ensure that the system always tracks the global maximum power point.

[0046] II. Photovoltaic MPPT Control Strategy Based on IALA-P&Q Algorithm

[0047] 2.1 Artificial Lemmings Optimization Algorithm: To balance the response speed and tracking accuracy of GMPP in dynamic multi-peak environments, the Artificial Lemmings Algorithm (ALA) is introduced to mathematically model four different behaviors of lemmings in nature, including long-distance migration, burrowing, foraging, and evading predators.

[0048] The initialization operation of an artificial lemming population can be represented as:

[0049]

[0050] In the formula: The initial lemming population location matrix; z i,j Let be the position value of the i-th lemming individual in the j-th dimension; rand is a random value in the range [0,1]; LB j UB is the lower bound of the j-th dimension; j is the upper bound of the j-th dimension; N is the population size; Dim is the dimension of the search space.

[0051] 1) Exploration Phase

[0052] When lemmings experience food shortages due to overpopulation during long-distance migration, they will randomly embark on long-distance migrations. At this time, lemmings will explore and search their space based on their current location and the locations of random individuals within the population, seeking food-rich habitats to obtain better survival conditions and resources. It is important to note that the direction and distance of lemming migration are not fixed and are influenced by various factors such as the ecological environment. The following equation is proposed to simulate this behavior:

[0053]

[0054] In the formula: This represents the position of the i-th search entity at iteration t+1; This represents the current optimal solution; F is the direction adjustment factor. A vector of random numbers representing Brownian motion; a vector The vector is a random element of dimension 1×Dim. This vector controls the movement of the current best individual and random individuals in the population and is used to represent the interaction between individuals during the migration process. This represents the current position of the i-th searched individual; This represents a search individual randomly selected from the population; a is an integer index between 1 and n. Parameters F and... It is difficult to adapt to the needs of "global exploration → local development" in the iteration process, which can easily lead to premature development in the later stages of exploration.

[0055] Another behavior of lemmings is digging complex tunnels in their habitat to provide themselves with safe hiding places and food storage spaces. They will randomly dig new burrows based on the existing burrow locations and the location of any individual in the population. This practice helps them quickly escape the threat of predators and find food more efficiently. The formula for updating burrow locations is as follows:

[0056]

[0057] In the formula: L is a random number related to the current iteration number; L represents a search individual randomly selected from the population; b is a random integer index value between 1 and n. Used to describe the interactions between individual lemmings when digging new burrows.

[0058] 2) Development Phase

[0059] Lemmings use their keen sense of smell and hearing to locate food sources by moving extensively and randomly within their burrows. Based on the abundance and availability of food, they typically establish a small foraging range within their habitat, and will randomly roam within this range to obtain more food. The model at this stage employs a spiral winding mechanism, as detailed below:

[0060]

[0061]

[0062] In the formula: s is a function simulating the lemming spiraling around the optimal position; r is the spiral radius; Z best,j (t) represents the j-th dimension component of the optimal lemming position at time t; Z i,j (t) represents the j-th dimension component of the i-th lemming position at time t.

[0063] The core of the final stage of modeling is the lemming's avoidance and self-protection behavior when encountering danger. These burrows serve as hiding places for lemmings. If they detect a predator, they use their unique running ability to return to the burrow and will also perform deceptive maneuvers to escape the predator's pursuit. The corresponding mathematical expression is:

[0064]

[0065] In the formula: W is the dynamic escape gain, which decreases with the iteration process to quantify the decay of the probability of an individual lemming escaping predation; Levy(x) is the Levy flight function, reflecting the escape trajectory; t is the current iteration number; T max This represents the maximum number of iterations.

[0066] The ALA algorithm often suffers from decreased diversity due to the random generation of the initial population, and it tends to converge prematurely during the iteration process, making it difficult to balance speed and accuracy.

[0067] A photovoltaic MPPT control method based on the IALA-P&Q algorithm is characterized by the following steps: First, chaotic mapping is used to initialize the population to enhance diversity and optimize dynamic parameters in migration and burrowing behaviors; then, adaptive inertial weights and an elite pool strategy are introduced to improve global search capability; subsequently, a two-stage switching mechanism is constructed, initiating IALA global positioning when shadow or illumination changes suddenly, switching to P&O local adjustment when approaching the maximum power point, and dynamically adjusting the disturbance step size to reduce steady-state fluctuations.

[0068] The photovoltaic MPPT control method based on the IALA-P&Q algorithm described above is characterized by the use of chaotic mapping to initialize the population and enhance diversity, specifically the Cubic chaotic mapping formula: The Cubic chaotic mapping formula is expressed as: In the formula: a is the chaos parameter; x i+1 x represents the value of the chaotic variable at the (i+1)th iteration; i Let be the value of the chaotic variable at the i-th iteration; the Cubic chaotic map has good ergodicity between (0, 1).

[0069] The Cubic chaotic map outputs stable and highly ergodic, and using it to replace random initialization allows particles to be evenly distributed in the solution space, providing a good foundation for subsequent searches and thus enhancing the global optimization effect.

[0070] The photovoltaic MPPT control method based on the IALA-P&Q algorithm described above is characterized by the following optimization of dynamic parameters in migration and burrowing behaviors: the introduction of an adaptive direction factor F(t) and a dynamic Brownian step size. , expressed as formula - .

[0071]

[0072] In the formula: F max F is the maximum direction factor; min Let F be the minimum direction factor; max =2.0, F min =0.5.

[0073]

[0074] In the formula: Given the initial step size vector, the improved migration model is as follows:

[0075]

[0076] The escape coefficient W decays in a fixed manner, and the Levy flight step size is not correlated with the current state, making it prone to getting trapped in local optima later due to insufficient escape capability. The adaptive escape coefficient W(t) is designed as follows:

[0077]

[0078] In the formula: W0 is the initial escape coefficient; η is the decay control parameter; take W0=2, η=5.

[0079] The Levy flight stride in relation to distance is:

[0080]

[0081] In the formula: Let S be the basic Levy step size, and S be the length of the diagonal of the solution space.

[0082] The photovoltaic MPPT control method based on the IALA-P&Q algorithm described above is characterized by the introduction of adaptive inertia weights and an elite pool strategy to improve global search capability. The new position update formula is as follows:

[0083]

[0084] In the formula: The individual is randomly selected from the elite pool; c is a random number in the range [0,1]. It is the Nth best individual in the current population fitness. ω represents the weighted average position of the optimal individual, reflecting the current evolutionary trend of the dominant subgroup; ω is the inertial weight.

[0085] When ω is large, lemmings tend to conduct a broader global search, while when ω is small, they tend to conduct a local search. The linearly decreasing inertial weight strategy causes ω to decrease at a constant rate, which often weakens the exploration ability prematurely, easily leads to local optima, and causes significant oscillations. Therefore, a nonlinear function is used instead, which can be expressed as:

[0086]

[0087] In the formula: ω(t) is the adaptive inertia weight.

[0088] The original ALA algorithm relies on a single optimal individual to guide the search process, which is not conducive to finding the global optimum. To address this issue, an elite pool mechanism and an inertial weight strategy are introduced to retain historical optimal solutions and guide the population to develop towards better regions, balancing the ability of global exploration and local development to avoid premature convergence.

[0089] The photovoltaic MPPT control method based on the IALA-P&Q algorithm described above is characterized in that, in constructing the dual-stage switching mechanism, the photovoltaic MPPT control strategy of the IALA-P&Q algorithm is implemented in three parts: ALA global search stage - P&O local search stage - algorithm restart. The specific steps are as follows.

[0090] Step 1, Initialization Judgment: Generate an initial voltage population using Cubic chaotic mapping, covering the photovoltaic voltage search range [V]. min V max The power value corresponding to each individual is used as the fitness value to calculate the power change rate between adjacent time moments in real time. Finally, the coefficient of variation (CV) of the light intensity of each component is calculated. E CV E The expression is as follows:

[0091]

[0092] In the formula: The standard deviation of light intensity Let be the average intensity of the illumination, when CV E If more than 10% of respondents believe there is local shading or uneven lighting distribution, an ALA global search will be initiated.

[0093] Step 2, Global Search: When multiple peaks exist, the improved ALA is first used for global optimization. Its optimization strategy quickly finds the global maximum power point. Once the conditions are met, the method switches to the adaptive perturbation step size method. The switching conditions are:

[0094]

[0095] In the formula: P gbest For the optimal power of the population, P best This represents the current maximum power.

[0096] Step 3, Local Search: Conventional P&Q algorithms with fixed step sizes struggle to balance speed and accuracy. Larger step sizes accelerate tracking but result in significant steady-state oscillations (fluctuations of 5%~8%), while smaller step sizes suppress oscillations but suffer from slow response. The optimal voltage V output by ALA is... gbestUsing P&O as the starting point, high-precision tracking is achieved. The perturbation step size adopts an adaptive design, so that the perturbation step size ∆V is adjusted with the power gradient, that is:

[0097]

[0098] Where: ∆V l =0.5%V max ;∆V s =0.1%V max δ is the power gradient threshold (δ=0.01).

[0099] Step 4, Scene Recognition: When the environment changes abruptly, a timely response is required to avoid lag and unnecessary power loss. Scene recognition indicators are introduced as follows:

[0100]

[0101] In the formula: P t+1 P t These are the power values ​​for the (t+1)th and tth iterations, respectively. When the above equation is satisfied, it indicates that a drastic change or large fluctuation in light or temperature has been detected, and the algorithm needs to be restarted to initialize and adapt to the sudden change in the environment.

[0102] The photovoltaic MPPT control flowchart based on IALA-P&Q is as follows: Figure 6 As shown.

[0103] Algorithm Performance Verification and Analysis: To systematically evaluate the convergence characteristics and global optimization capability of the IALA-P&Q algorithm, the population size was uniformly set to 50, and the maximum number of iterations was fixed at 400. A specific function from the CEC2017 standard test function set was selected as the performance benchmark, and the function is expressed as:

[0104]

[0105] In the formula: D is the dimension of the function; i is the index variable; x i It is the i-th component in the D-dimensional decision vector; the search range is [-600, 600], and the optimal value is 0.

[0106] Comparative simulation experiments were conducted with ALA and GWO algorithms, and the results are as follows: Figure 7 As shown, Figure 7 Three algorithms were demonstrated in the formula The convergence curve on the test function.

[0107] from Figure 7It can be seen that the ALA and IALA-P&Q algorithms converge to lower fitness values ​​with fewer iterations, and IALA-P&Q shows better convergence, indicating that its global exploration and local exploitation capabilities are superior to GWO. GWO, on the other hand, has a slower response speed, fails to converge properly, and has insufficient optimization accuracy, verifying the effectiveness of the introduced elite pool and inertial weight mechanism in escaping local optima.

[0108] Simulation Analysis: To verify the effectiveness of the proposed method, a 4×2 photovoltaic array MPPT simulation model was built using Simulink, as shown below. Figure 8 As shown. The parameter values ​​for the photovoltaic array and the boost circuit are as follows. Figure 9 As shown.

[0109] To verify the combined performance of the original ALA algorithm, the GWO algorithm, and the improved algorithm under static conditions, this paper designs two types of static lighting conditions at a constant temperature of 25°C: uniform lighting and local shading. The specific settings are as follows:

[0110] (1) Uniform illumination condition: The irradiance is uniformly set to 1000W / m 2 The corresponding standard test conditions (STC) are used to obtain the steady-state tracking accuracy, oscillation amplitude, and maximum power point efficiency benchmark.

[0111] (2) Local shading condition: adopt Figure 3 The shaded 1 distribution shown induces multi-peak IV characteristics within the array through differentiated irradiance, which is used to verify the algorithm's global optimization capability and steady-state error suppression capability.

[0112] MPPT curves of different algorithms under static uniform illumination are shown below. Figure 10 As shown. MPPT curves of different algorithms under static shading conditions are as follows. Figure 11 As shown.

[0113] from Figure 10 and Figure 11 It can be seen that the IALA-P&Q, GWO, and ALA algorithms can all eventually track an approximate maximum power. However, the IALA-P&Q algorithm has the fastest power rise rate, approaching the maximum power in a shorter time, and exhibits minimal oscillation amplitude in the power curve after stabilization, demonstrating superior steady-state performance. In contrast, the GWO and ALA algorithms have a slower power convergence process and exhibit more pronounced fluctuations during convergence, resulting in overall MPPT performance weaker than the IALA-P&Q algorithm.

[0114] In real-world applications, to more realistically evaluate the performance of the IALA-P&Q algorithm, dynamic lighting conditions were used to test the three algorithms.

[0115] During the test, the lighting conditions were set as follows: all modules were kept at 1000W / m² for 0~0.5s. 2 Uniform illumination; 0.5~1s array according to Figure 3 Condition 2 in the simulation changes to shaded state; from 1 to 1.5 seconds, it changes to condition 3 to simulate dynamic environmental changes. Figure 12 The MPPT test results of each algorithm under dynamic lighting conditions are presented.

[0116] from Figure 12 It can be seen that the IALA-P&Q algorithm exhibits a significant speed advantage in the initial tracking phase, quickly locking onto the maximum power point. When illumination conditions change dynamically, this algorithm is particularly sensitive to power fluctuations, not only exhibiting smaller fluctuation amplitudes but also being able to more smoothly and quickly recapture the corresponding maximum power point. In contrast, ALA and GWO, under the same conditions, show a larger power oscillation range, and the time required to re-stabilize to the target power is significantly longer. This phenomenon indicates that, in terms of overall MPPT performance, IALA-P&Q is indeed superior to the other two methods.

[0117] Figure 4 The global maximum power values ​​of the photovoltaic array under three operating conditions have been given. To further compare the advantages and disadvantages of the three algorithms, this application designs the MPPT tracking performance of each under dynamic illumination. The statistical results are shown in [link to statistical results]. Figure 13 .

[0118] from Figure 13 It can be seen that under uniform illumination, IALA-P&Q can quickly track objects in just 0.2s with a tracking accuracy of 99.71%, while ALA and GWO require 0.25s and 0.23s respectively, with tracking accuracies of 99.09% and 98.57%. During the first instantaneous dynamic change, IALA-P&Q's response time is 0.25s with a tracking accuracy of 99.3%, while ALA and GWO require 0.32s and 0.34s respectively, with tracking accuracies of 98.26% and 97.57%. During the second dynamic shadow change, IALA-P&Q's response time is also only 0.22s with a tracking accuracy of 99.63%, while ALA and GWO require 0.25s and 0.28s respectively, with tracking accuracies of 98.75% and 97.75%. IALA-P&Q is superior in both convergence time and tracking accuracy.

[0119] When cloud cover or building obstruction causes uneven light exposure to photovoltaic (PV) modules, their PV characteristic curves exhibit multi-peak characteristics. Traditional maximum power point tracking (MPPT) strategies, due to gradient information misleading, are prone to getting stuck in local extrema, significantly limiting the available power of the array. To improve PV utilization efficiency, this application proposes an IALA-P&Q composite MPPT control method that integrates an improved artificial lemming algorithm and a perturbation-observation method. Simulation results show that:

[0120] (1) The optimized IALA has a wider global search coverage and a more prominent ability to get rid of local optima, with a 20% increase in convergence rate.

[0121] (2) The combination of IALA and P&Q algorithms retains the global tracking advantage of IALA for multi-peak curves while demonstrating the local control accuracy of P&Q, with tracking deviation controlled within 0.7%.

[0122] (3) In static partial occlusion scenes, IALA-P&Q can accurately capture GMPP; under dynamic lighting changes, the response time is as low as 0.25 seconds, and it has excellent dynamic and steady-state performance.

[0123] The photovoltaic MPPT control method based on the IALA-P&Q algorithm described in this application is characterized by: firstly, using chaotic mapping to initialize the population to enhance diversity and optimize dynamic parameters in migration and burrowing behaviors; then, introducing adaptive inertial weights and an elite pool strategy to improve global search capabilities; subsequently, constructing a two-stage switching mechanism, initiating IALA global positioning when shadow or illumination changes abruptly, switching to P&O local adjustment when approaching the maximum power point, and dynamically adjusting the disturbance step size to reduce steady-state fluctuations.

[0124] This application has the following main beneficial technical effects: Compared with the traditional artificial lemming algorithm and gray wolf optimization algorithm, IALA-P&O converges faster and has higher global maximum power point tracking accuracy under static multi-peak conditions, responds more quickly in dynamic lighting change scenarios, and controls steady-state power fluctuation within 0.7%, effectively balancing speed and stability, and is suitable for complex lighting environments.

[0125] The above embodiments are merely preferred technical solutions of the present invention and should not be considered as limitations on the present invention. The scope of protection of the present invention should be limited to the technical solutions described in the claims, including equivalent substitutions of the technical features described in the claims. That is, equivalent substitutions and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. A photovoltaic MPPT control method based on IALA-P&Q algorithm, characterized in that It includes the following steps: first, using chaotic mapping to initialize population to enhance diversity, and optimizing dynamic parameters in migration and digging behavior; then, introducing adaptive inertia weight and elite pool strategy to improve global search ability; Then, a two-stage switching mechanism is constructed, which starts IALA global positioning when shadow or light mutation occurs, switches to P&O local regulation when close to the maximum power point, and dynamically adjusts the perturbation step to reduce steady-state fluctuations.

2. A photovoltaic MPPT control method based on IALA-P&Q algorithm according to claim 1, characterized in that In the method for initializing population by using chaotic mapping to enhance diversity, a Cubic chaotic mapping formula is used, which is expressed as: wherein a is a chaotic parameter; x i+1 represents a chaotic variable value at the i+1th iteration; x i is a chaotic variable value at the ith iteration; and the Cubic chaotic mapping is between 0 and 1.

3. The photovoltaic MPPT control method based on IALA-P&Q algorithm according to claim 1, characterized in that, In the optimization of dynamic parameters in migration and hole digging behavior: introduce adaptive direction factor F(t) and dynamic Brownian step , expressed as formula: , in the formula: F max is the maximum direction factor; F min is the minimum direction factor; F max =2.0, F min =0.5; , in the formula: is the initial step vector, and the improved migration model is: , the escape coefficient W is fixed in the attenuation mode, the Levy flight step is not associated with the current state, and in the later period, it is easy to fall into local optimum due to insufficient escape ability, and the adaptive escape coefficient W(t) is as follows: , in the formula: W0 is the initial escape coefficient; η is the attenuation control parameter; take W0=2, η=5; The Levy flight step size associated with distance is: where: is the base Levy step size and S is the diagonal length of the solution space.

4. A photovoltaic MPPT control method based on IALA-P&Q algorithm according to claim 1, characterized by introducing In order to improve the global search ability, the elite pool and adaptive inertia weight are introduced. The new position update formula is as follows: , wherein: is a randomly selected individual in the elite pool; c is a random number in [0, 1]; is the Nth optimal individual in the current population fitness; is the weighted average position of the optimal individual, which reflects the evolution trend of the current dominant subpopulation; ω is the inertia weight; a nonlinear function is used instead, which is expressed as: , wherein: ω(t) is the adaptive inertia weight.

5. The photovoltaic MPPT control method based on IALA-P&Q algorithm according to claim 1, characterized in that In the construction of the two-stage switching mechanism, the photovoltaic MPPT control strategy of IALA-P&Q algorithm is divided into three parts: ALA global search stage, P&O local search, and algorithm restart. The specific steps are as follows: Step 1, initialization judgment; Step 2, global search; Step 3, local search; Step 4, scene recognition.

6. The photovoltaic MPPT control method based on IALA-P&Q algorithm according to claim 5, characterized in that In step 1: The Cubic chaotic mapping is used to generate an initial voltage population, covering the photovoltaic voltage search range [V]. min V max The power value corresponding to each individual is used as the fitness value to calculate the power change rate between adjacent time moments in real time. Finally, the coefficient of variation (CV) of the light intensity of each component is calculated. E CV E The expression is as follows: In the formula: The standard deviation of light intensity Let be the average intensity of the illumination, when CV E If more than 10% of respondents believe there is local shading or uneven lighting distribution, an ALA global search will be initiated.

7. The photovoltaic MPPT control method based on IALA-P&Q algorithm according to claim 5, characterized in that In step 2: when there are multiple peaks, firstly, the improved ALA is used for global optimization, and the global maximum power point is quickly found by using its optimization strategy, and after meeting the condition, the adaptive perturbation step method is switched, and the switching condition is: , wherein: P gbest is the population optimal power, P best is the current maximum power.

8. The photovoltaic MPPT control method based on IALA-P&Q algorithm according to claim 5, characterized in that In step 3: the optimal voltage V gbest As a starting point, high-precision tracking is achieved by P&O, and the perturbation step is adaptively designed, so that the perturbation step AV adjusts with the power gradient, i.e.: , wherein: AV l = 0.5%V max ; AV s = 0.1%V max ; and δ is a power gradient threshold, δ = 0.

01.

9. The photovoltaic MPPT control method based on IALA-P&Q algorithm according to claim 5, characterized in that In step 4: when the environment mutates, it is necessary to respond in time to avoid unnecessary power loss caused by lag, and introduce a scene recognition flag: , wherein: P t+1 , P t are the power values of the t+1th and tth times respectively; when the above formula is satisfied, it indicates that the light or temperature changes sharply or fluctuates widely, and at this moment, the algorithm initialization needs to be restarted to adapt to the mutation of the environment.

Images