Perturbation and observation method combining particle swarm optimization and adaptive step length adjustment coefficient

Abstract

A perturbation and observation method combining particle swarm optimization and an adaptive step length adjustment coefficient changes output power by applying perturbation to voltage in each working period of a photovoltaic cell so as to realize power optimization. By introducing a self-adaptive step length adjustment coefficient, dynamic adjustment of the step length of the perturbation and observation method is realized, and illumination changes and system dynamic responses under different working conditions are coped with. Therefore, the tracking speed and stability are improved. A fixed step length is multiplied by a step length adjustment coefficient, the step length adjustment coefficient is calculated and determined by an absolute value of a derivative of a power increment to a voltage increment and a specific value obtained by dividing a square value of a current current value by a square value plus 1, an upper limit and a lower limit are determined, and the adjustment coefficient is subjected to partition setting. And the optimal voltage value is searched in the solution space in combination with the particle swarm optimization algorithm, power tracking secondary optimization is carried out, the perturbation and observation method has higher maximum power point tracking speed and higher tracking precision, and the stable maximum power point can be converged more quickly.

Description

Technical Field

[0001] The invention belongs to the technical field of MPPT control in photovoltaic power generation, and specifically relates to a disturbance observation method combining particle swarm optimization with an adaptive step length adjustment coefficient. Background Art

[0002] The sun is one of the most abundant energy sources on earth. The sun releases huge amounts of energy every day, and the total amount of solar radiation energy on earth exceeds the global energy demand by thousands of times. With the popularization of new energy, solar energy has become a potential clean and renewable energy source. At the same time, photovoltaic grid-connected technology continues to improve, and photovoltaic power generation systems have become an integral part of people's lives. Photovoltaic power generation systems can help individuals, families and businesses reduce their dependence on traditional power supply and improve energy independence. This is very beneficial for remote areas and post-disaster recovery. However, the performance of photovoltaic systems is highly dependent on weather conditions, such as sunny days and abundant sunshine. On cloudy, rainy days or at night, the output of photovoltaic power generation will be significantly reduced, affecting the power generation efficiency. At this time, MPPT technology came into being. The MPPT controller can monitor the output of solar cells and dynamically adjust the operating point to ensure that the system can operate at the maximum power point under different lighting conditions. At present, there are many MPPT algorithms, such as the perturbation observation method and the conductance increment method, which are widely used, but at the same time they themselves have certain shortcomings.

[0003] Technology comparison:

[0004] (1) Technical comparison with the MPPT control method of patent CN 116009643 A that integrates the improved particle swarm algorithm and the improved disturbance observation method

[0005] CN 116009643 A Method used

[0006] Patent CN 116009643 A proposes an MPPT control method that integrates an improved particle swarm algorithm and an improved perturbation observation method. It uses a particle swarm to optimize the output power and output voltage of the photovoltaic array at the current sampling moment, and finally uses the perturbation observation method with different step lengths to track the partition position based on the slope.

[0007] According to the actual situation of the photovoltaic array, the basic parameters of the particle swarm algorithm are determined, and the output power and output voltage U of the photovoltaic array and the duty cycle D of the Boost converter are optimized.

[0008] The output power Pt and output voltage Ut are determined based on the slope k value.

[0009] The duty cycle D of the Boost converter is adjusted and tracked by the perturbation and observation method with different step sizes.

[0010] 3. The partition is divided into zone A, zone B and zone C from left to right along the curve according to the relationship curve between the output power and the output voltage of the photovoltaic array. When the slope k is greater than k1, it is zone A; when the slope k is less than k2, it is zone C; the rest is zone B, k1 is greater than 0, and k2 is less than 0;

[0011] 4. In area A, a fixed large step length S1 is used. In area B, when k is greater than 0, a variable small step length S2 related to the slope is used to increase the output voltage to the right. In area B, when k is less than 0, a variable small step length S2 related to the slope is used to reduce the output voltage to the left. In area C, a fixed large step length S1 is used. S2 = |k|*M, where the M value is set according to the actual situation.

[0012] This method first determines the basic parameters of the particle swarm according to the photovoltaic array, reduces the number of iterations by improving the particle quality, and then reduces the time required in the tracking process, quickly converging to the vicinity of the global optimal point; after reaching the maximum number of natural selections, it switches to the partitioned variable step-size perturbation observation method for local precise tracking to improve tracking accuracy.

[0013] Method of the present invention

[0014] The present invention proposes a perturbation observation method that combines particle swarm optimization and adaptive step-size adjustment coefficient. First, by introducing an adaptive step-size adjustment coefficient, the dynamic adjustment of the step size of the perturbation observation method is realized to cope with the illumination changes and system dynamic response under different working conditions. Then, the particle swarm optimization algorithm is combined to search for the optimal voltage value in the solution space and perform power tracking secondary optimization. The core innovations of the method are:

[0015] Adaptive step adjustment coefficient: In order to improve the maximum power point tracking speed and accuracy, the fixed step size is multiplied by the step adjustment coefficient. The step adjustment coefficient is calculated by the absolute value of the derivative of the power increment to the voltage increment and the current current value squared divided by the square value plus 1. The upper and lower limits are determined, and the adjustment coefficient is set in different areas. By introducing the square value of the current current to determine the step adjustment coefficient, the oscillation when approaching the maximum power point can be effectively reduced, avoiding power fluctuations caused by excessive adjustment, thereby improving the overall stability of the system.

[0016] Particle swarm power secondary optimization: In order to converge to the stable maximum power point faster, the particle swarm optimization algorithm is combined to search for the optimal voltage value in the solution space and perform power tracking secondary optimization.

[0017] Comparison of technical advantages;

[0018] Higher tracking accuracy and tracking speed: The adaptive step adjustment coefficient in the present invention is calculated based on the absolute value of the derivative of the power increment to the voltage increment and the ratio of the square of the current current value divided by the square value plus 1, which can more directly reflect the actual working state of the photovoltaic module, thereby improving the tracking accuracy. In addition, the dynamic adjustment mechanism of the adaptive step size allows the system to make more detailed step size adjustments when approaching the maximum power point, which not only meets the pursuit of accuracy, but also has a fast tracking speed, thereby more accurately locking the maximum power point.

[0019] Strong stability: In the present invention, by introducing the square value of the current current to determine the step adjustment coefficient, the oscillation near the maximum power point can be effectively reduced, and the power fluctuation caused by over-adjustment can be avoided, thereby improving the overall stability of the system. The mechanism of dynamically adjusting the step size enables the system to smoothly transition during operation, reducing the instability caused by frequent switching of the step size.

[0020] Higher computational efficiency: The present invention requires partition setting and parameter optimization in the early stage, but once the setting is completed, combined with the particle swarm optimization algorithm, the system can effectively converge quickly in the solution space and optimize the voltage value. Due to the mechanism of secondary optimization, the system can continuously improve performance, and the overall computational efficiency will increase over time.

[0021] In contrast, the method in CN 116009643 A uses the slope to determine the partition position and uses the perturbation observation method with different step lengths for tracking. The tracking accuracy and tracking speed are greatly limited at the same time. The slope judgment will also cause response delays and large oscillations near the maximum power point. Although the efficiency is high in the short term, the reliance on real-time data and frequent calculations may lead to reduced efficiency in long-term operation, especially when the light changes dramatically.

[0022] In summary, the perturbation observation method combining particle swarm optimization and adaptive step-size adjustment coefficient proposed in the present invention is superior to CN 116009643 A in tracking accuracy, tracking speed, stability and computational efficiency, and can ensure that the system operates stably near the maximum power point. These advantages enable the perturbation observation method with adaptive step-size adjustment coefficient to not only respond quickly to environmental changes and accurately adjust the step size, but also have global search capabilities to avoid local optimality, ensuring more efficient and accurate maximum power point tracking under complex dynamic conditions.

[0023] (2) Comparison with patent CN 115189455 A: A hybrid energy power scheduling method and system technology based on power balance

[0024] CN 115189455 A Method used

[0025] Patent CN 115189455 A uses an adaptive particle swarm optimization algorithm to adjust the weight coefficients to obtain the optimal solution of the dual-objective optimization model; according to the operating conditions, the hybrid energy system is scheduled using the control working mode corresponding to the optimal solution of the dual-objective optimization model, and the voltage of the hybrid energy system is adjusted based on the variable step size perturbation observation method of segmented sampling.

[0026] 1. Based on the linear weighted method, a dual-objective optimization model is established using the primary energy utilization rate of the hybrid energy system and the total cost of a single operation of the hybrid energy system; the adaptive particle swarm optimization algorithm is used to adjust the weight coefficient to obtain the optimal solution of the dual-objective optimization model;

[0027] 2. Set the threshold of the variable step length; when the variable step length is less than the threshold, the output power of the hybrid energy system reaches the maximum power point and the maximum power point tracking is stopped; when the variable step length is greater than or equal to the threshold, the maximum power point tracking is maintained.

[0028] 3. According to the operating conditions, the hybrid energy system is scheduled using the control working mode corresponding to the optimal solution of the dual-objective optimization model, and the voltage of the hybrid energy system is adjusted based on the variable step size disturbance observation method of segmented sampling. The positive and negative values ​​of the voltage change dU and the power change dP determine the direction of the voltage disturbance added in the next sampling period;

[0029] 4. In the next sampling period, the voltage disturbance is added by multiplying |dP / dU| with the speed variable of the variable step size, and the step size is adjusted adaptively.

[0030] This method, based on the linear weighted method, uses the primary energy utilization rate of the hybrid energy system and the total cost of a single operation of the hybrid energy system to establish a dual-objective optimization model; the adaptive particle swarm optimization algorithm is used to adjust the weight coefficient ω1 to obtain the optimal solution of the dual-objective optimization model minF. Then, the positive and negative multiplication of |dP / dU| and the speed variable with a variable step size determines the direction of adding voltage disturbances in the next sampling period, which increases the speed and accuracy of MPPT.

[0031] The method of the present invention:

[0032] The present invention proposes a perturbation observation method that combines particle swarm optimization and adaptive step-size adjustment coefficient. First, by introducing an adaptive step-size adjustment coefficient, the dynamic adjustment of the step size of the perturbation observation method is realized to cope with the illumination changes and system dynamic response under different working conditions. Then, the particle swarm optimization algorithm is combined to search for the optimal voltage value in the solution space and perform power tracking secondary optimization. The core innovations of the method are:

[0033] Adaptive step adjustment coefficient: In order to improve the maximum power point tracking speed and accuracy, the fixed step size is multiplied by the step adjustment coefficient. The step adjustment coefficient is calculated by the absolute value of the derivative of the power increment to the voltage increment and the current current value squared divided by the square value plus 1. The upper and lower limits are determined, and the adjustment coefficient is set in different areas. By introducing the square value of the current current to determine the step adjustment coefficient, the oscillation when approaching the maximum power point can be effectively reduced, avoiding power fluctuations caused by excessive adjustment, thereby improving the overall stability of the system.

[0034] Particle swarm power secondary optimization: In order to converge to the stable maximum power point faster, the particle swarm optimization algorithm is combined to search for the optimal voltage value in the solution space and perform power tracking secondary optimization.

[0035] Comparison of technical advantages:

[0036] More precise step size adjustment: The adaptive step size adjustment coefficient in the present invention is calculated based on the absolute value of the derivative of the power increment to the voltage increment and the ratio of the square of the current current value divided by the square value plus 1. The upper and lower limits of the step size adjustment coefficient are set, and adaptive partitioning is performed, which not only meets the pursuit of accuracy, but also has a fast tracking speed, thereby more accurately locking the maximum power point.

[0037] Higher stability: Compared with the patent CN 115189455 A, which uses the positive or negative multiplication of |dP / dU| and the speed variable of the variable step size to determine the direction of adding voltage disturbance in the next sampling period, the present invention determines the step size adjustment coefficient by introducing the square value of the current current, which can suppress the error caused by excessively large or small adjustment steps to a certain extent and improve the stability of the system.

[0038] Lower computational complexity: After the particle swarm optimization algorithm is finally used in the present invention, it can effectively converge quickly in the solution space, optimize the voltage value, and perform secondary optimization of power. In contrast, the dual-objective optimization model in CN 115189455 A involves the simultaneous optimization of primary energy utilization and operating cost, and adjusts the weight coefficient through an adaptive particle swarm algorithm, which means that the computational complexity is higher.

[0039] In contrast, CN 115189455 A may require more computing resources when adjusting weights and iteratively optimizing models, especially when the system is large or the operating conditions change frequently. And there is a fixed threshold limit for adjusting the step size based on the threshold. When the variable step size is greater than the threshold, the system continues to track; when the step size is less than the threshold, the tracking stops. This setting may cause tracking to stop prematurely or be over-adjusted in some cases, especially in scenarios where light or load changes frequently, and it cannot flexibly respond to changes in external conditions.

[0040] In summary, the perturbation observation method combining particle swarm optimization and adaptive step size adjustment coefficient proposed in the present invention has advantages in step size adjustment accuracy, stability and computational complexity, especially in application scenarios requiring fast tracking and high precision.

[0041] Technical comparison with the improved particle swarm-perturbation observation method of photovoltaic global maximum power point tracking method based on patent CN 110703847 B

[0042] CN 110703847 B Method used

[0043] Patent CN 110703847 B samples the voltage and current of the series photovoltaic array. Use the global maximum power point estimation algorithm (GMPPEA) to estimate the global maximum power point voltage. Estimate the global maximum power point voltage by linear interpolation. Based on the estimation results, call the perturbation and observation method (P&O) or the variable population structure particle swarm optimization algorithm (VPSPSO) to track the global maximum power point. When the perturbation and observation method converges, the tracking is completed.

[0044] 1. Based on the four-parameter model of the photovoltaic array, a new algorithm is proposed to estimate the global maximum power point voltage (Vgm_isc), which improves the accuracy of the tracking algorithm.

[0045] 2. The linear interpolation method is used to estimate the global maximum power point voltage (Vgm_inter), which is combined with the GMPPEA algorithm to further improve the estimation accuracy of the global maximum power point.

[0046] 3. Based on the traditional particle swarm optimization algorithm, improvements have been made. By setting the search voltage range of the two sub-particle swarms, the global search capability of the algorithm is improved, thereby enhancing the anti-premature maturity ability.

[0047] 4. According to the size of the voltage difference (Delta_Vgm), dynamically choose to use the perturbation observation method or the variable population structure particle swarm optimization algorithm to track the global maximum power point

[0048] This method, based on the linear weighted method, uses the primary energy utilization rate of the hybrid energy system and the total cost of a single operation of the hybrid energy system to establish a dual-objective optimization model; the adaptive particle swarm optimization algorithm is used to adjust the weight coefficient w1 to obtain the optimal solution of the dual-objective optimization model minF. Then, the positive and negative multiplication of |dP / dU| and the speed variable with a variable step size determines the direction of adding voltage disturbances in the next sampling period, which increases the speed and accuracy of MPPT.

[0049] Method of the present invention

[0050] The present invention proposes a perturbation observation method that combines particle swarm optimization and adaptive step-size adjustment coefficient. First, by introducing an adaptive step-size adjustment coefficient, the dynamic adjustment of the step size of the perturbation observation method is realized to cope with the illumination changes and system dynamic response under different working conditions. Then, the particle swarm optimization algorithm is combined to search for the optimal voltage value in the solution space and perform power tracking secondary optimization. The core innovations of the method are:

[0051] Adaptive step adjustment coefficient: In order to improve the maximum power point tracking speed and accuracy, the fixed step size is multiplied by the step adjustment coefficient. The step adjustment coefficient is calculated by the absolute value of the derivative of the power increment to the voltage increment and the current current value squared divided by the square value plus 1. The upper and lower limits are determined, and the adjustment coefficient is set in different areas. By introducing the square value of the current current to determine the step adjustment coefficient, the oscillation when approaching the maximum power point can be effectively reduced, avoiding power fluctuations caused by excessive adjustment, thereby improving the overall stability of the system.

[0052] Particle swarm power secondary optimization: In order to converge to the stable maximum power point faster, the particle swarm optimization algorithm is combined to search for the optimal voltage value in the solution space and perform power tracking secondary optimization.

[0053] Comparison of technical advantages

[0054] Flexibility and adaptability of step size adjustment: The adaptive step size adjustment coefficient in the present invention is calculated based on the absolute value of the derivative of the power increment to the voltage increment and the ratio of the square of the current current value divided by the square value plus 1. The upper and lower limits of the step size adjustment coefficient are set, and adaptive partitioning is set, which not only meets the pursuit of accuracy, but also has a fast tracking speed, so as to more accurately lock the maximum power point. On the contrary, the step size adjustment mentioned in CN 110703847 B mainly depends on the voltage difference (Delta_Vgm), and determines which method to use for tracking by selecting the perturbation observation method or the particle swarm optimization algorithm. Although the flexibility of the search is improved to a certain extent, in actual applications, the global search of particle swarm optimization often takes a long time, especially in complex or rapidly changing working environments, and excessive iteration of the particle swarm may cause response lag.

[0055] Lower computational complexity: After the particle swarm optimization algorithm is finally used in the present invention, it can effectively converge quickly in the solution space, optimize the voltage value, and perform power secondary optimization. On the contrary, CN 110703847 B estimates and tracks the global maximum power point through a four-parameter model of the photovoltaic array, a linear interpolation method, and an improved particle swarm algorithm. Although these techniques improve the accuracy of estimation and search, they also increase the complexity of the algorithm, especially the use of twin particle swarms, which may require more computing resources. In real-time operation, complex estimation and search steps may lead to a reduction in response speed, especially under high-frequency illumination changes or system disturbances, and the computing efficiency may not be ideal.

[0056] In contrast, the complex combination of multiple algorithms in the patent method of CN 110703847 B may have higher hardware requirements, especially the particle swarm optimization algorithm requires more computing resources and may be limited when running on a lower performance hardware platform. In addition, the system may require additional time for parameter adjustment and optimization, which will increase the hardware burden.

[0057] In summary, the perturbation observation method combining particle swarm optimization and adaptive step size adjustment coefficient proposed in the present invention has advantages in the flexibility and adaptability of step size adjustment and computational complexity. Its simple and efficient design makes it suitable for application in systems with high real-time requirements and dynamically changing environments, especially for hardware platforms with limited resources.

[0058] Comparison with patent CN 111474978 B A photovoltaic MPPT control method with intelligent step size change and its system storage medium technology

[0059] CN 111474978 B Method used

[0060] Patent CN 111474978 B combines the improved neural network (RBF / BP) of the particle swarm algorithm (PSO) with the perturbation observation method. It dynamically adjusts the step size to approach the maximum power point (MPP), and uses the BP neural network to switch to a small step size in the later stage of the algorithm to continue convergence, thereby improving the maximum power point tracking accuracy and stability of the photovoltaic system under multi-peak conditions and reducing the oscillation at the maximum power point.

[0061] 1. Collect 50 sets of four major input parameters: light, temperature, voltage, and current. Set the corresponding reference maximum power point (MPP) voltage as the output parameter. Use the radial basis function (RBF) neural network to integrate these parameters and perform training and learning. Input another 50 sets of new data to obtain the 50 sets of voltage values ​​at the maximum power point predicted by the RBF neural network.

[0062] 2. Calculate the difference between the MPP voltage value predicted by the RBF network and the reference MPP voltage value. Take the absolute value of the difference as the reference value for switching the convergence step.

[0063] 3. Use the particle swarm algorithm (PSO) to improve and optimize the relevant parameters and architecture of the BP neural network. Use the BP neural network improved by the particle swarm algorithm for training and learning. The MPP voltage value and switching step reference value are obtained through the improved BP neural network.

[0064] 4. Combined with the perturbation observation method, the step length reference value is used as the measurement and the MPP voltage value is used as the center. Switch one step to the left and one to the right respectively to obtain the corresponding voltage values. Determine whether the power corresponding to the original center MPP voltage value is the maximum power. If it is the maximum power, return to the environmental parameter collection to continue the maximum power point tracking. If not, proceed to the next step. Select the voltage value corresponding to the maximum power as the algorithm output value and output it.

[0065] Together, these steps form the core method of this patent, which improves the efficiency and accuracy of the photovoltaic system in tracking the maximum power point in the face of environmental changes by intelligently adjusting the step size and combining advanced optimization algorithms.

[0066] Method of the present invention

[0067] The present invention proposes a perturbation observation method that combines particle swarm optimization and adaptive step-size adjustment coefficient. First, by introducing an adaptive step-size adjustment coefficient, the dynamic adjustment of the step size of the perturbation observation method is realized to cope with the illumination changes and system dynamic response under different working conditions. Then, the particle swarm optimization algorithm is combined to search for the optimal voltage value in the solution space and perform power tracking secondary optimization. The core innovations of the method are:

[0068] Adaptive step adjustment coefficient: In order to improve the maximum power point tracking speed and accuracy, the fixed step size is multiplied by the step adjustment coefficient. The step adjustment coefficient is calculated by the absolute value of the derivative of the power increment to the voltage increment and the current current value squared divided by the square value plus 1. The upper and lower limits are determined, and the adjustment coefficient is set in different areas. By introducing the square value of the current current to determine the step adjustment coefficient, the oscillation when approaching the maximum power point can be effectively reduced, avoiding power fluctuations caused by excessive adjustment, thereby improving the overall stability of the system.

[0069] Particle swarm power secondary optimization: In order to converge to the stable maximum power point faster, the particle swarm optimization algorithm is combined to search for the optimal voltage value in the solution space and perform power tracking secondary optimization.

[0070] Comparison of technical advantages

[0071] Flexibility and adaptability of step size adjustment: The adaptive step size adjustment coefficient in the present invention is calculated based on the absolute value of the derivative of the power increment to the voltage increment and the ratio of the square of the current current value divided by the square value plus 1. The upper and lower limits of the step size adjustment coefficient are set, and adaptive partitioning is performed, which not only meets the pursuit of accuracy, but also has a fast tracking speed, so as to more accurately lock the maximum power point. In contrast, CN 111474978 B uses a neural network and a particle swarm optimization algorithm to predict the step size and voltage difference, and then fine-tunes them through the perturbation observation method. This step size adjustment relies on the prediction effect of the network.

[0072] Lower computational complexity: After the particle swarm optimization algorithm is finally used in the present invention, it can effectively converge quickly in the solution space, optimize the voltage value, and perform power secondary optimization. On the contrary, CN 111474978 B needs to train and optimize the neural network, especially PSO optimization of BP neural network will increase the computational complexity and may reduce the response speed in practical applications.

[0073] Anti-noise and anti-interference: The present invention determines the step adjustment coefficient by introducing the square value of the current current, which can respond faster and adjust the step size in the face of dynamic changes, and has strong anti-interference ability. On the contrary, CN 111474978 B does not perform as well as the adaptive method when the photovoltaic system is subject to sudden interference or the environment changes drastically, because the neural network is more sensitive to abnormal data.

[0074] In contrast, the patent CN 111474978 B uses RBF and BP neural networks to predict the maximum power point and improves accuracy through particle swarm optimization algorithm. This method predicts by learning historical data, but requires a lot of training data and is affected by the training effect of the neural network.

[0075] In summary, the perturbation observation method combining particle swarm optimization and adaptive step size adjustment coefficient proposed in the present invention has advantages in flexibility and adaptability of step size adjustment, computational complexity, and noise and interference resistance. These advantages enable the perturbation observation method of adaptive step size adjustment coefficient to not only quickly respond to environmental changes and accurately adjust the step size, but also have global search capabilities to avoid local optimality, ensuring more efficient and accurate maximum power point tracking under complex dynamic conditions.

[0076] Comparison with the maximum power point tracking method of wind power generation system under variable wind speed conditions in patent CN 103414414A

[0077] CN 103414414 AMethod used

[0078] Patent CN 103414414 A proposes a maximum power point tracking method for a wind power generation system under variable wind speed conditions. By adaptively combining the perturbation observation method and the particle swarm optimization algorithm, it achieves efficient and precise control of the output power of the wind turbine under different wind speed conditions, effectively improving the tracking speed, output efficiency and power quality of the system.

[0079] 1. During the start-up or low wind speed stage of the fan, if the output power of the fan is less than 0.5 times the rated power, use the perturbation observation method for MPPT (maximum power point tracking). The perturbation observation method adjusts the fan speed by increasing or decreasing the duty cycle of PWM (pulse width modulation) to make the fan work at the maximum power point.

[0080] 2. During the operation of the wind turbine, if the wind speed increases and changes rapidly, and the output power is greater than 0.5 times the rated power and continues to rise, use the particle swarm optimization algorithm for MPPT. The particle swarm optimization algorithm can quickly find the maximum power point through global search capabilities and has strong adaptability and stability.

[0081] 3. According to the different conditions of wind speed and output power, intelligently choose to use the perturbation observation method or particle swarm optimization algorithm to adapt to wind speed changes.

[0082] 4. Set the PWM duty cycle D, disturbance amount and allowable error range. The duty cycle can be adjusted by comparing the voltage, current and power before and after the disturbance. According to the characteristic curve of the wind turbine, it is determined whether the wind turbine is running on the left or right side of the maximum power point, and the PWM duty cycle is increased or decreased accordingly.

[0083] These steps together constitute the core method of this patent, which more effectively performs maximum power point tracking under changing wind speed conditions, thereby improving the efficiency and stability of wind power generation systems.

[0084] Method of the present invention

[0085] The present invention proposes a perturbation observation method that combines particle swarm optimization and adaptive step-size adjustment coefficient. First, by introducing an adaptive step-size adjustment coefficient, the dynamic adjustment of the step size of the perturbation observation method is realized to cope with the illumination changes and system dynamic response under different working conditions. Then, the particle swarm optimization algorithm is combined to search for the optimal voltage value in the solution space and perform power tracking secondary optimization. The core innovations of the method are:

[0086] Adaptive step adjustment coefficient: In order to improve the maximum power point tracking speed and accuracy, the fixed step size is multiplied by the step adjustment coefficient. The step adjustment coefficient is calculated by the absolute value of the derivative of the power increment to the voltage increment and the current current value squared divided by the square value plus 1. The upper and lower limits are determined, and the adjustment coefficient is set in different areas. By introducing the square value of the current current to determine the step adjustment coefficient, the oscillation when approaching the maximum power point can be effectively reduced, avoiding power fluctuations caused by excessive adjustment, thereby improving the overall stability of the system.

[0087] Particle swarm power secondary optimization: In order to converge to the stable maximum power point faster, the particle swarm optimization algorithm is combined to search for the optimal voltage value in the solution space and perform power tracking secondary optimization.

[0088] Comparison of technical advantages

[0089] Flexibility of step size adjustment: The adaptive step size adjustment coefficient in the present invention is calculated based on the absolute value of the derivative of the power increment to the voltage increment and the ratio of the square of the current current value divided by the square value plus 1. The upper and lower limits of the step size adjustment coefficient are set, and adaptive partitioning is set, which not only meets the pursuit of accuracy, but also has a fast tracking speed, so as to more accurately lock the maximum power point. In contrast, CN 103414414 A uses a neural network and a particle swarm optimization algorithm to predict the step size and voltage difference, and then fine-tunes them through the perturbation observation method. This step size adjustment depends on the prediction effect of the network.

[0090] Algorithm switching complexity: The perturbation observation method with adaptive step size adjustment coefficient in the present invention does not need to switch between different algorithms, but adapts to system changes by dynamically adjusting the step size. This makes the algorithm more continuous and concise, reduces the delay and error introduced by switching, and improves the real-time performance of the system. On the contrary, the maximum power point tracking of CN 103414414 A needs to dynamically switch different algorithms based on the estimation results, such as switching from the perturbation observation method (P&O) to the variable population structure particle swarm optimization algorithm (VPSPSO). This frequent algorithm switching increases the complexity of the system and requires additional judgment conditions and switching logic to decide which algorithm to use, which not only prolongs the calculation time of the tracking process, but also may cause unnecessary delays and errors in the switching process.

[0091] Global search dependency: The perturbation observation method of the adaptive step size adjustment coefficient of the present invention focuses on dynamically adjusting the step size according to the current system state, avoiding the lengthy calculation time caused by the global search. Especially in local tracking, the step size can be quickly reduced to ensure rapid convergence to the maximum power point. On the contrary, although the CN 103414414 A patent method combines multiple algorithms, it still relies on fixed settings in the selection of step size and cannot adapt to different photovoltaic operating conditions, which may result in slower tracking speed or insufficient accuracy. When the system approaches the maximum power point, the fixed step size cannot be further refined, which may cause excessive disturbances and even affect the stability of the system.

[0092] In contrast, the algorithm switching of CN 103414414A is complex and the computational cost of global search is high. It relies on the particle swarm optimization algorithm to search for the global maximum power point. Although it has strong global search capabilities, it is easy to cause excessive consumption of computing resources in photovoltaic systems with high real-time requirements. In addition, the step size adjustment of this patent lacks flexibility and cannot be adjusted dynamically in real time according to the system status. Especially when approaching the maximum power point, the fixed step size may lead to insufficient tracking accuracy and reduced system stability.

[0093] In summary, the perturbation observation method combining particle swarm optimization and adaptive step size adjustment coefficient proposed in the present invention has advantages in the flexibility of step size adjustment, algorithm switching complexity and global search dependency. These advantages enable the perturbation observation method of adaptive step size adjustment coefficient to not only quickly respond to environmental changes and accurately adjust the step size, but also have global search capabilities, avoid local optimality, and ensure more efficient and accurate maximum power point tracking under complex dynamic conditions.

[0094] Comparison with the photovoltaic MPPT control method, system, medium and equipment technology with improved step size factor introduced in patent CN 113687684A

[0095] Method used in CN 113687684A

[0096] Patent CN 113687684A proposed the concept of dynamically adjusting the MPPT perturbation step size through the step size factor. This innovation enables the system to adaptively adjust the step size according to the changes in the output power and voltage of the photovoltaic cell, ensuring that it can maintain a faster convergence speed and improve accuracy during the tracking process.

[0097] 1. By adding a step factor The disturbance step size is dynamically adjusted according to the power difference and voltage difference to ensure faster convergence speed and high-precision tracking at both ends of the photovoltaic power curve.

[0098] 2. The disturbance direction is determined based on the product of the voltage difference and the power difference. When the result is positive, the disturbance direction is correct. Otherwise, the direction is adjusted to further optimize the tracking efficiency.

[0099] 3. Through variable step-size control, the system can respond more quickly to changes in light intensity and temperature, ensuring that the photovoltaic system quickly stabilizes at the maximum power point under different conditions.

[0100] By introducing the step size factor, the oscillation or slow speed problems caused by the fixed step size of the traditional perturbation observation method are improved, so that the system can adapt to different working conditions and improve the tracking efficiency and accuracy.

[0101] Method of the present invention

[0102] The present invention proposes a perturbation observation method that combines particle swarm optimization and adaptive step-size adjustment coefficient. First, by introducing an adaptive step-size adjustment coefficient, the dynamic adjustment of the step size of the perturbation observation method is realized to cope with the illumination changes and system dynamic response under different working conditions. Then, the particle swarm optimization algorithm is combined to search for the optimal voltage value in the solution space and perform power tracking secondary optimization. The core innovations of the method are:

[0103] Adaptive step adjustment coefficient: In order to improve the maximum power point tracking speed and accuracy, the fixed step size is multiplied by the step adjustment coefficient. The step adjustment coefficient is calculated by the absolute value of the derivative of the power increment to the voltage increment and the current current value squared divided by the square value plus 1. The upper and lower limits are determined, and the adjustment coefficient is set in different areas. By introducing the square value of the current current to determine the step adjustment coefficient, the oscillation when approaching the maximum power point can be effectively reduced, avoiding power fluctuations caused by excessive adjustment, thereby improving the overall stability of the system.

[0104] Particle swarm power secondary optimization: In order to converge to the stable maximum power point faster, the particle swarm optimization algorithm is combined to search for the optimal voltage value in the solution space and perform power tracking secondary optimization.

[0105] Comparison of technical advantages

[0106] Flexibility of step size adjustment: The adaptive step size adjustment coefficient in the present invention is calculated based on the absolute value of the derivative of the power increment to the voltage increment and the ratio of the square of the current current value divided by the square value plus 1. The upper and lower limits of the step size adjustment coefficient are set, and adaptive partitioning is performed, which not only meets the pursuit of accuracy, but also has a fast tracking speed, thereby locking the maximum power point more accurately. On the contrary, the step size factor in CN 113687684A Although the step size can be adjusted dynamically, its adjustment mechanism is completely dependent on the power difference. When the power difference is large, the step size increases rapidly, which is suitable for the initial tracking stage; but when the power difference is small, the step size decreases rapidly, which may cause the step size to be too small and affect the tracking speed, especially when the system is close to the maximum power point, the adjustment may not be accurate enough, and even cause the tracking process to be conservative.

[0107] Combination of global and local optimization: In the present invention, by introducing particle swarm optimization (PSO), your method can perform secondary optimization in the power tracking process. This means that even if the perturbation observation method finds the local maximum power point, the particle swarm optimization algorithm can further optimize the power in the global scope, avoid falling into the local optimum, and ensure that the true global maximum power point is tracked. This is especially important when the photovoltaic array is partially blocked or in a complex working environment. On the contrary, CN113687684A tracks by adjusting the step size locally. Although it can make fine adjustments near the local maximum power point, its global search capability is weak, especially in multi-peak situations (such as partially blocked photovoltaic arrays), it may fall into the local optimum and it is difficult to find the global maximum power point.

[0108] System adaptability and anti-interference ability: The present invention can more comprehensively reflect the system status through the comprehensive calculation of power increment, voltage increment and current square, especially when the power, voltage and current change at the same time, the step size adjustment is more reasonable. This method has a high anti-interference ability when dealing with external interference, especially in scenarios with drastic light or load fluctuations, it can keep the system stable and respond quickly. On the contrary, the step size factor of the CN 113687684A patent method is only adjusted according to the power difference. In the case of frequent power fluctuations or drastic environmental changes in the system, the step size may be too sensitive or improperly adjusted.

[0109] In contrast, although the step size factor calculation of CN 113687684A is simple, it may lose the balance between accuracy and speed during the tracking process. Moreover, a single local step size adjustment may fall into a local optimum in the case of multiple peaks (such as a partially blocked photovoltaic array).

[0110] In summary, the perturbation observation method combining particle swarm optimization and adaptive step size adjustment coefficient proposed in the present invention has advantages in the flexibility of step size adjustment, the combination of global and local optimization, and the system adaptability and anti-interference ability. These advantages enable the perturbation observation method of adaptive step size adjustment coefficient to not only quickly respond to environmental changes and accurately adjust the step size, but also have global search capabilities, avoid local optimality, and ensure more efficient and accurate maximum power point tracking under complex dynamic conditions. Summary of the invention

[0111] To solve the above technical problems, the present invention proposes a perturbation observation method combining particle swarm optimization and an adaptive step adjustment coefficient. The present invention determines the introduced step adjustment coefficient through the absolute value of the derivative of the power increment with respect to the voltage increment and the current value at the current moment, and adaptively adjusts the step. At the same time, a particle swarm is introduced to perform a secondary optimization of the power tracking, so that the system can stably operate at the maximum power point.

[0112] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0113] A perturbation observation method combining particle swarm optimization and an adaptive step adjustment coefficient, comprising the following steps:

[0114] Step 1: Obtain the voltage U(k) and current I(k) of the current control period;

[0115] Step 2: Using the obtained voltage U(k) and current I(k), calculate the current power P(k), and subtract it from the power P(k-1) at the previous moment to obtain the power difference ΔP(k); at the same time, subtract the voltage U(k) at the current moment from the voltage U(k-1) at the previous moment to obtain the voltage difference ΔU(k); similarly, the current difference ΔI(k) can also be obtained. Then, determine the step adjustment coefficient X(k) from the absolute value of the derivative of the power difference ΔP with respect to the voltage difference ΔU and the current value I(k) at the current moment;

[0116] Step 3: Judge the relationship between the current step adjustment coefficient X(k) and the lower limit value a and the upper limit value b. If a < X(k) < b, output the step as the product value of the fixed reference step and the step adjustment coefficient; if X(k) < a, output the step as the product value of the fixed reference step and the lower limit value a of the step adjustment coefficient; if X(k) > b, output the step as the product value of the fixed reference step and the upper limit value b of the step adjustment coefficient. Define the step output in Step 3 as Step, and then enter Step 4;

[0117] Step 4: When judging the output step, if ΔP(k) = 0 and ΔU(k) is 0, then output the step as 0, and U(k) = U(k-1); if, on the premise that ΔP(k) > 0, when ΔU(k) > 0, then output the step as Step, U(k) = U(k-1) + Step, when ΔU(k) < 0, then output the step as -Step, U(k) = U(k-1) - Step; if, on the premise that ΔP(k) < 0, when ΔU(k) > 0, then output the step as -Step, U(k) = U(k-1) - Step. When ΔU(k) < 0, then output the step as Step, U(k) = U(k-1) + Step, and then enter Step 5;

[0118] Step 5: Update U(k-1) and I(k-1), U(k-1) = U(k), I(k-1) = I(k), and finally send the updated value to the particle swarm optimization algorithm, power tracking secondary optimization, and the updated voltage is input into the particle swarm optimization algorithm as part of the initial particle position. The position of each particle represents a voltage setting, and the speed of the particle represents the direction and speed of change during the search process. Through iterative optimization, the particle swarm algorithm will continuously adjust the voltage setting until the maximum power is reached or the upper limit of the number of iterations is reached.

[0119] Furthermore, in step 1, the voltage U(k) and the current I(k) of the current control cycle are the initial voltage and the initial current.

[0120] Further, in step 2, the current power P(k) is the product of the acquired voltage U(k) and current I(k), the power increment mentioned is the product of the voltage difference and the current difference, the voltage increment is the voltage difference, the current increment is the current difference, that is, the difference between the voltage value of the current control cycle and the voltage value of the previous control cycle, and the current difference is the difference between the current value of the current control cycle and the current value of the previous control cycle; X(k) is the adjustment coefficient of the step size, and the specific expression is as follows.

[0121]

[0122] ΔP(k) / ΔU(k) ​​is the derivative of power with respect to voltage in the current control cycle.

[0123] Further, in the step 3, the values ​​of the lower limit a and the upper limit b of the step size adjustment coefficient are determined. The value of the lower limit a determines the tracking accuracy of the maximum power point. If the value of a is too large, the maximum power point tracking speed will become faster, but the tracking accuracy will decrease; if the value of a is too small, the tracking accuracy of the maximum power point increases, but the tracking speed will slow down. Here, a is considered to be 0.5; the value of the upper limit b determines the tracking speed of the maximum power point. If b is too large, the maximum power point tracking speed will become faster, but the tracking accuracy will decrease; if b is too small, the tracking accuracy of the maximum power point increases, but the tracking speed will slow down. Here, b is considered to be 2.

[0124] Furthermore, in step 4, Step is the adjusted step length, Step = X(k)U ref Among them, U ref is a fixed reference step size, U ref Take 0.01.

[0125] Furthermore, in step 5, in PSO, a set of random solutions is first set, and then these solutions are continuously optimized through multiple iterations. Each solution can be represented as a position vector and a velocity vector in N-dimensional space. Each solution has a corresponding fitness value, which is determined by the function to be optimized. Assume that the position and velocity of the i-th particle in N-dimensional space are the array X i =(x i1 ,x i2 ..x in) and V i =(v i1 ,v i2 ..v in ), in each iteration, the particle tracks the individual extreme value pbest, that is, Pi = (p i1 ,p i2 ..p in ), and track the global optimal solution gbest, i.e. G i After finding it, use the following formula to update your speed and position;

[0126] vi(k+1)=ωvi(k)+c1r1[pi(k)-xi(k)]+c2r2[gi(k)-xi(k)]

[0127] xi(k+1)=xi(k)+vi(k+1)

[0128] P i With G i Defined as:

[0129]

[0130] Gi=max{f(p0),f(p1),...f(p n )}

[0131] Among them, the control parameters that determine the performance of the particle swarm algorithm are: w is the inertia weight, which determines the influence of the previous iteration speed on the current iteration speed; k is the number of iterations; c1 and c2 are learning factors; the maximum speed is V max .

[0132] Furthermore, the updated value in step 5 is fed into the particle swarm optimization algorithm, and the specific steps of power tracking secondary optimization are as follows:

[0133] Initialization: First, the parameters of the particle swarm algorithm are set, including the number of particles, number of iterations, inertia weight, cognitive coefficient and social coefficient. Then, the particle position, velocity, individual optimal position and fitness, as well as the global optimal position and fitness are initialized.

[0134] Iterative optimization: In each iteration, the fitness of each particle is first calculated. The fitness function considers the product of the current voltage and current as power. Then, the individual optimal position and the global optimal position of each particle are updated. Then, according to the formula of the particle swarm algorithm, the speed and position of the particle are updated.

[0135] Inertia weight adjustment: During the iteration process, the inertia weight is adjusted in a linear decreasing manner to reduce the power oscillation in the initial stage. The inertia weight determines the degree of inertia of the particle during movement. A high inertia weight can maintain a higher speed, while a low inertia weight can converge to the global optimal solution faster.

[0136] Return to the optimal solution: Finally, return the global optimal voltage as the optimized voltage setting. Compared with the existing technology, the beneficial effects of the present invention are:

[0137] The present invention can dynamically adjust the step size according to the current operating point and power change rate, and can perform fine-tuning with a smaller step size to accurately lock the MPP, while being able to converge to the MPP faster because it automatically reduces the step size when approaching the MPP. At the same time, a particle swarm algorithm is added on the basis of the variable step size to perform secondary optimization on the power tracking, thereby improving the efficiency of the system. The traditional fixed-step MPPT method usually changes the operating point with a fixed step value, which may cause the system to not accurately lock to the maximum power point (MPP), especially when the lighting conditions change rapidly. This may cause the efficiency of the system to decrease because it cannot fully utilize the potential of solar cells. Therefore, the present invention helps to reduce the oscillation problem that occurs in the system when the lighting conditions change, and improves the stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0138] Figure 1 This is a system structure diagram based on the perturbation and observation method combining particle swarm optimization with adaptive step size adjustment coefficient;

[0139] Figure 2 It is a flow chart of the perturbation and observation method based on the combination of particle swarm optimization and adaptive step size adjustment coefficient;

[0140] Figure 3 This is a simulation model diagram based on the perturbation and observation method combining particle swarm optimization and adaptive step size adjustment coefficient;

[0141] Figure 4 It is the simulation waveform of the traditional fixed-step perturbation and observation method.

[0142] Figure 5 The present invention is based on a simulation waveform of a disturbance observation method combining particle swarm optimization with an adaptive step size adjustment coefficient. DETAILED DESCRIPTION

[0143] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments:

[0144] like Figure 1 As shown in the figure, this is a perturbation observation method system structure that combines particle swarm optimization with adaptive step size adjustment coefficient, including the following components: photovoltaic array, sampling module, MPPT calculation module, PWM drive module, Boost boost module and load.

[0145] Photovoltaic Array: A photovoltaic array is a collection of solar panels that convert the sun's rays into electricity.

[0146] Sampling module: The sampling module consists of a voltage sampling module and a current sampling module, which are connected to the output end of the photovoltaic array respectively. The function of the voltage sampling module and the current sampling module is to collect the output voltage and output current data of the photovoltaic array in real time and transmit these data to the MPPT calculation module.

[0147] MPPT calculation module: The input of the MPPT calculation module is connected to the output of the sampling module, and it receives data from the voltage sampling module and the current sampling module. The main task of the MPPT calculation module is to execute the maximum power point tracking algorithm, calculate the current maximum power point based on the sampled voltage and current data, and pass this information to the PWM drive module.

[0148] PWM drive module: The PWM drive module receives the maximum power point information from the MPPT calculation module, and then controls the operation of the photovoltaic array to make it operate at the maximum power point. It generates a PWM signal to control the voltage and current output of the photovoltaic array.

[0149] Boost module: Boost module is usually used to increase the output voltage of photovoltaic array to ensure that it can meet the voltage requirements of the load. This module can boost the power of photovoltaic array to the required voltage level.

[0150] Load: Loads are devices or applications connected to the system that receive power from the Boost module to perform actual work or provide power.

[0151] The core idea of ​​the present invention is to achieve accurate tracking of MPP by dynamically adjusting the step size, and add particle swarm power tracking secondary optimization. Therefore, under different lighting conditions, it can quickly and accurately find and lock to the maximum power point. This method has good adaptability and can adapt to different photovoltaic arrays and environmental conditions, so that the system can respond to changes in light in real time and maximize the use of solar energy resources.

[0152] like Figure 2 As shown, this method includes the following specific steps:

[0153] Step 1: Obtain the voltage U(k) and current I(k) of the current control period.

[0154] Step 2: Using the obtained voltage U(k) and current I(k), calculate the current power P(k), and take the difference from the power P(k-1) at the previous moment to obtain the power difference ΔP(k); at the same time, take the difference between the voltage U(k) at the current moment and the voltage U(k-1) at the previous moment to obtain the voltage difference ΔU(k); similarly, the current difference ΔI(k) can also be obtained. Then determine the step size adjustment coefficient X(k) based on the absolute value of the derivative of the power difference ΔP with respect to the voltage difference ΔU and the current value I(k) at the current moment. If the incremental power is greater than 0, increase the voltage step size; if the incremental power is less than 0, decrease the voltage step size.

[0155] Step 3: Determine the relationship between the current step size adjustment coefficient X(k) and the lower limit value a and the upper limit value b. If a < X(k) < b, output the step size as the product value of the fixed reference step size and the step size adjustment coefficient; if X(k) < a, output the step size as the product value of the fixed reference step size and the lower limit value a of the step size adjustment coefficient; if X(k) > b, output the step size as the product value of the fixed reference step size and the upper limit value b of the step size adjustment coefficient. Set the step size output in Step 3 as Step, and then proceed to Step 4.

[0156] Step 4: When judging the output step size, if ΔP(k) = 0 and ΔU(k) is 0, then output the step size as 0, and U(k) = U(k-1); if under the premise of ΔP(k) > 0, when ΔU(k) > 0, then output the step size as Step, and U(k) =

[0157] U(k-1) + Step. When ΔU(k) < 0, then output the step size as -Step, and U(k) = U(k-1) - Step; if under the premise of ΔP(k) < 0, when ΔU(k) > 0, then output the step size as -Step, and U(k) = U(k-1) - Step. When ΔU(k) < 0, then output the step size as Step, and U(k) = U(k-1) + Step. Then proceed to Step 5.

[0158] Step 5: Update U(k-1) and I(k-1), U(k-1) = U(k), I(k-1) = I(k). Finally, send the updated values into the particle swarm optimization algorithm for secondary power tracking optimization. The updated voltage is input into the particle swarm optimization algorithm as part of the initial particle position. The position of each particle represents a voltage setting, and the velocity of the particle represents the change direction and speed during the search process. Through iterative optimization, the particle swarm algorithm will continuously adjust the voltage setting until the maximum power is reached or the iteration count limit is reached.

[0159] Specifically, in step 1, the voltage U(k) and the current I(k) of the current control cycle are the initial voltage and the initial current;

[0160] Specifically, in step 2, the current power P(k) is the product of the obtained voltage U(k) and current I(k), the power increment mentioned is the product of the voltage difference and the current difference, the voltage increment is the voltage difference, and the current increment is the current difference, that is, the difference between the voltage value of the current control cycle and the voltage value of the previous control cycle, and the current difference is the difference between the current value of the current control cycle and the current value of the previous control cycle; X(k) is the adjustment coefficient of the step size, and the specific expression is as follows.

[0161]

[0162] ΔP(k) / ΔU(k) ​​is the derivative of power with respect to voltage in the current control cycle.

[0163] Specifically, in step 3, the values ​​of the lower limit a and the upper limit b of the step size adjustment coefficient are determined. The value of the lower limit a determines the tracking accuracy of the maximum power point. If a is too large, the maximum power point tracking speed will become faster, but the tracking accuracy will decrease; if a is too small, the tracking accuracy of the maximum power point increases, but the tracking speed will slow down. Here, a is considered to be 0.5; the value of the upper limit b determines the tracking speed of the maximum power point. If b is too large, the maximum power point tracking speed will become faster, but the tracking accuracy will decrease; if b is too small, the tracking accuracy of the maximum power point increases, but the tracking speed will slow down. Here, b is considered to be 2.

[0164] Specifically, in step 4, Step is the adjusted step length, Step = X(k)U ref Among them, U ref is a fixed reference step size.

[0165] U ref Take 0.01.

[0166] Specifically, in step 5, the PSO algorithm is derived from the observation of bird flock predation behavior. It is an iteration-based optimization tool. In PSO, a set of random solutions is first set, and then these solutions are continuously optimized through multiple iterations. Each solution can be represented as a position vector and a velocity vector in N-dimensional space. Each solution has a corresponding fitness value, which is determined by the function to be optimized. Assume that the position and velocity of the i-th particle in N-dimensional space are the array X i =(x i1 ,x i2 ..x in) and V i =(v i1 ,vi2 ..v in ), in each iteration, the particle tracks the individual extreme value pbest, that is, Pi = (p i1 ,p i2 ..p in ), and track the global optimal solution gbest, i.e. G i After finding it, use the following formula to update your speed and position.

[0167] vi(k+1)=ωvi(k)+c1r1[pi(k)-xi(k)]+c2r2[gi(k)-xi(k)]

[0168] xi(k+1)=xi(k)+vi(k+1)

[0169] P i With G i Defined as:

[0170]

[0171] Gi=max{f(p0),f(p1),...f(p n )}

[0172] Among them, the control parameters that determine the performance of the particle swarm algorithm are: w is the inertia weight, which determines the influence of the previous iteration speed on the current iteration speed; k is the number of iterations; C1 and C2 are learning factors; the maximum speed is V max .

[0173] The specific steps of particle swarm power tracking secondary optimization are:

[0174] Initialization: First, the parameters of the particle swarm algorithm are set, including the number of particles, number of iterations, inertia weight, cognitive coefficient and social coefficient. Then, the particle position, speed, individual optimal position and fitness, and global optimal position and fitness are initialized.

[0175] Iterative optimization: In each iteration, the fitness of each particle is first calculated. The fitness function considers the product of the current voltage and current as power. Then, the individual optimal position and the global optimal position of each particle are updated. Then, according to the formula of the particle swarm algorithm, the speed and position of the particle are updated.

[0176] Inertia weight adjustment: During the iteration process, the inertia weight is adjusted in a linear decreasing manner to reduce the power oscillation in the initial stage. The inertia weight determines the degree of inertia of the particle during movement. A higher inertia weight can maintain a higher speed, while a lower inertia weight can converge to the global optimal solution faster.

[0177] Return the optimal solution: Finally, return the global optimal voltage as the optimized voltage setting.

[0178] In the Matlab / Simulink environment, a composite MPPT control model of the photovoltaic array boost circuit is built. The initial simulation conditions are a temperature of 25°C and a light intensity of 900W / m 2 The light intensity suddenly changes to 1000W / m in 0.5s. 2 .

[0179] In the perturbation observation method simulation combining particle swarm optimization and adaptive step size adjustment coefficient proposed by the present invention, the maximum power point tracking method proposed by the present invention is used to output the simulation diagram as shown in FIG. Figure 3 As shown in Figure 2, after algorithm tracking, the respective simulation results are obtained, such as Figure 4 and Figure 5 The traditional fixed-step perturbation observation method is compared with the method proposed in this invention. The main results are as follows: 2 and 1000W / m 2 The calculation formula of the steady-state oscillation rate in the steady-state arrival time and steady-state oscillation rate is as follows:

[0180]

[0181] The following table shows the simulation results of the above two methods. It can be seen that the perturbation observation method based on the combination of particle swarm optimization and adaptive step-size adjustment coefficient proposed in the present invention takes less time to reach the steady state before and after the sudden change of light intensity than the traditional fixed step-size method, and also ensures that the oscillation is small after reaching the maximum steady state. Figure 4 and Figure 5 The table below shows that the first and second steady-state arrival times are 0.012s and 0.04s faster than the traditional fixed step length, respectively. At the same time, the steady-state oscillation rate is reduced by 0.342% and 0.27%, and the power impact of the sudden change is also reduced to a certain extent, and the transient performance is improved. Therefore, the proposed scheme not only speeds up the tracking speed but also increases the tracking accuracy.

[0182]

[0183] The above description is only a preferred embodiment of the present invention and does not constitute any other form of limitation to the present invention. Any modification or equivalent change made based on the technical essence of the present invention still falls within the scope of protection required by the present invention.

Claims

1. A perturbation observation method combining particle swarm optimization and an adaptive step size adjustment coefficient, characterized in that: It includes the following steps: Step 1: Obtain the voltage U(k) and current I(k) of the current control period; Step 2: Using the obtained voltage U(k) and current I(k), calculate the current power P(k), and subtract it from the power P(k-1) at the previous moment to obtain the power difference ΔP(k); at the same time, subtract the voltage U(k) at the current moment from the voltage U(k-1) at the previous moment to obtain the voltage difference ΔU(k); similarly, the current difference ΔI(k) can also be obtained. Then, determine the step size adjustment coefficient X(k) from the absolute value of the derivative of the power difference ΔP with respect to the voltage difference ΔU and the current value I(k) at the current moment; Step 3: Judge the relationship between the current step size adjustment coefficient X(k) and the lower limit value a and the upper limit value b. If a < X(k) < b, output the step size as the product value of the fixed reference step size and the step size adjustment coefficient; if X(k) < a, output the step size as the product value of the fixed reference step size and the lower limit value a of the step size adjustment coefficient; if X(k) > b, output the step size as the product value of the fixed reference step size and the upper limit value b of the step size adjustment coefficient. Define the step size output in Step 3 as Step, and then enter Step 4; Step 4: When judging the output step size, if ΔP(k) = 0 and ΔU(k) is 0, then output the step size as 0, and U(k) = U(k-1); if ΔU(k) > 0 under the premise that ΔP(k) > 0, then output the step size as Step, U(k) = U(k-1) + Step, and when ΔU(k) < 0, then output the step size as -Step, U(k) = U(k-1) - Step; if ΔU(k) > 0 under the premise that ΔP(k) < 0, then output the step size as -Step, U(k) = U(k-1) - Step. When ΔU(k) < 0, then output the step size as Step, U(k) = U(k-1) + Step, and then enter Step 5; Step 5: Update U(k-1) and I(k-1), U(k-1) = U(k), I(k-1) = I(k). Finally, send the updated values into the particle swarm optimization algorithm for secondary power tracking optimization. The updated voltage is input into the particle swarm optimization algorithm as part of the initial particle position. The position of each particle represents a voltage setting, and the velocity of the particle represents the change direction and velocity during the search process. Through iterative optimization, the particle swarm algorithm will continuously adjust the voltage setting until the maximum power is reached or the iteration number limit is reached.

2. The perturbation-observation method combining particle swarm optimization and adaptive step-size adjustment coefficient according to claim 1, characterized in that: In the said Step 1, the voltage U(k) and current I(k) of the current control period are the initial voltage and initial current.

3. The perturbation-observation method combining particle swarm optimization and adaptive step-size adjustment coefficient according to claim 1, characterized in that: In step 2, the current power P(k) is the product of the acquired voltage U(k) and current I(k), the power increment mentioned is the product of the voltage difference and the current difference, the voltage increment is the voltage difference, the current increment is the current difference, that is, the difference between the voltage value of the current control cycle and the voltage value of the previous control cycle, and the current difference is the difference between the current value of the current control cycle and the current value of the previous control cycle; X(k) is the adjustment coefficient of the step size, and the specific expression is as follows. ΔP(k) / ΔU(k) ​​is the derivative of power with respect to voltage in the current control cycle.

4. The perturbation-observation method combining particle swarm optimization and adaptive step-size adjustment coefficient according to claim 1, characterized in that: In the step 3, the values ​​of the lower limit a and the upper limit b of the step size adjustment coefficient are determined. The value of the lower limit a determines the tracking accuracy of the maximum power point. If the value of a is too large, the maximum power point tracking speed will become faster, but the tracking accuracy will decrease; if the value of a is too small, the tracking accuracy of the maximum power point is increased, but the tracking speed will slow down. Here, a is taken as 0.5 for comprehensive consideration; the value of the upper limit b determines the tracking speed of the maximum power point. If the value of b is too large, the maximum power point tracking speed will become faster, but the tracking accuracy will decrease; if the value of b is too small, the tracking accuracy of the maximum power point is increased, but the tracking speed will slow down. Here, b is taken as 2 for comprehensive consideration.

5. The perturbation-observation method combining particle swarm optimization and adaptive step-size adjustment coefficient according to claim 1, characterized in that: In step 4, Step is the adjusted step length, Step = X(k)U ref Among them, U ref is a fixed reference step size, U ref Take 0.

01.

6. The perturbation-observation method combining particle swarm optimization and adaptive step-size adjustment coefficient according to claim 1, characterized in that: In step 5, in PSO, a set of random solutions is first set, and then these solutions are continuously optimized through multiple iterations. Each solution can be represented as a position vector and a velocity vector in N-dimensional space. Each solution has a corresponding fitness value, which is determined by the function to be optimized. Assume that the position and velocity of the i-th particle in N-dimensional space are array X i =(x i1 ,x i2 ..x in) and V i =(v i1 ,v i2 ..v in ), in each iteration, the particle tracks the individual extreme value pbest, that is, Pi = (p i1 ,p i2 ..p in ), and track the global optimal solution gbest, i.e. G i After finding it, use the following formula to update your speed and position; vi(k+1)=ωvi(k)+c1r1[pi(k)-xi(k)]+c2r2[gi(k)-xi(k)] xi(k+1)=xi(k)+vi(k+1) P i With G i Defined as: Gi=max{f(p0),f(p1),...f(p n )} Among them, the control parameters that determine the performance of the particle swarm algorithm are: w is the inertia weight, which determines the influence of the previous iteration speed on the current iteration speed; k is the number of iterations; c1 and c2 are learning factors; the maximum speed is V max .

7. The perturbation-observation method combining particle swarm optimization with adaptive step-size adjustment coefficient according to claim 1, characterized in that: The updated value in step 5 is sent to the particle swarm optimization algorithm. The specific steps of power tracking secondary optimization are: Initialization: First, the parameters of the particle swarm algorithm are set, including the number of particles, number of iterations, inertia weight, cognitive coefficient and social coefficient. Then, the particle position, velocity, individual optimal position and fitness, as well as the global optimal position and fitness are initialized. Iterative optimization: In each iteration, the fitness of each particle is first calculated. The fitness function considers the product of the current voltage and current as power. Then, the individual optimal position and the global optimal position of each particle are updated. Then, according to the formula of the particle swarm algorithm, the speed and position of the particle are updated. Inertia weight adjustment: During the iteration process, the inertia weight is adjusted in a linear decreasing manner to reduce the power oscillation in the initial stage. The inertia weight determines the degree of inertia of the particle during movement. A high inertia weight can maintain a higher speed, while a low inertia weight can converge to the global optimal solution faster. Return the optimal solution: Finally, return the global optimal voltage as the optimized voltage setting.

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