Photovoltaic power control method and system based on improved sparrow search algorithm and variable step size perturbation observation method

Abstract

The present invention provides a photovoltaic power control method and system based on an improved sparrow search algorithm and a variable step size perturbation observation method. The method comprises: taking the collected voltage and current as input, transmitting them to the improved sparrow search algorithm, and iteratively training, judging whether the number of iterations has been reached, and outputting the optimal fitness value f g and the best sparrow position X best , i.e., the maximum power and the maximum power point voltage, while terminating the iterative training of the improved sparrow search algorithm; detecting the voltage and current after the iterative training of the improved sparrow search algorithm, and obtaining the power difference, while also calculating the voltage difference; and finally, completing the search for the maximum power point using the variable step-size perturbation observation method. The present invention first utilizes the improved sparrow search algorithm to quickly track to the vicinity of the maximum power point, and then utilizes the variable step-size perturbation observation method to perform a fine search for the maximum power point, thereby improving tracking speed and tracking accuracy while taking into account the requirements of MPPT search speed and search accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power control, and in particular to a photovoltaic power control method and system based on an improved sparrow search algorithm and a variable step-size perturbation observation method. Background Art

[0002] In today's society, people pay more and more attention to environmental protection and resource conservation. The deteriorating ecological environment and gradually depleted resources have made people pay more and more attention to renewable resources. Vigorously developing clean energy represented by photovoltaics is an important means to deal with energy and environmental crises at home and abroad.

[0003] In practical applications of photovoltaic power generation technology, the PU characteristic curve of a photovoltaic array exhibits multimodal characteristics due to factors such as cloud cover and dust accumulation, with multiple local maximum power points and a single global maximum power point. Under these conditions, traditional photovoltaic power control methods, such as the perturbation-and-observe method and the conductance increment method, are unable to track the true maximum power point of the photovoltaic cell. While existing intelligent photovoltaic power control methods can track the global maximum power point, they suffer from slow tracking speeds and large power oscillations. These drawbacks severely impact the efficiency of photovoltaic power generation systems and hinder the development of the photovoltaic industry. Therefore, a new photovoltaic power control method is needed that can quickly and accurately track the global maximum power point under partial shadows, minimizing power oscillations and energy losses. This approach would not only improve the efficiency of photovoltaic power generation systems and promote the development of the photovoltaic industry, but also contribute positively to addressing energy and environmental crises. Summary of the Invention

[0004] Purpose of the invention: In order to solve the problems arising from the above-mentioned prior art, the present invention provides a photovoltaic power control method based on an improved sparrow search algorithm and a variable step-size perturbation observation method, which solves the problem of low control accuracy caused by poor local search capability of the sparrow search algorithm when applied to the field of power system power optimization control. The present invention also provides a photovoltaic power control system based on the improved sparrow search algorithm and the variable step-size perturbation observation method.

[0005] Technical solution: According to a first aspect of the present invention, a photovoltaic power control method based on an improved sparrow search algorithm and a variable step-size perturbation observation method is provided, the method comprising:

[0006] The collected voltage and current are used as input and transmitted to the improved sparrow search algorithm, and iterative training is performed to determine whether the number of iterations has been reached. If the number of iterations has been reached, the optimal fitness value f is output. g and the best sparrow position X best , i.e., the maximum power and the maximum power point voltage, while terminating the iterative training of the improved sparrow search algorithm;

[0007] Detecting the voltage and current after iterative training of the improved sparrow search algorithm, and calculating the power at the previous moment and the power at the current moment, respectively, to obtain the power difference dP, and simultaneously calculating the voltage difference dU, and finally, completing the search for the maximum power point according to the variable step size perturbation observation method;

[0008] Among them, the improved sparrow search algorithm is based on the drunkard's walk strategy to update the position of the joiner in the population, and the variable step size perturbation observation method is used to automatically adjust the perturbation step size according to the distance between the current working point of the photovoltaic power generation system and the maximum power point. When the working point of the photovoltaic power generation system is at the maximum power point, that is, The photovoltaic power generation system will no longer apply disturbances.

[0009] Further, including:

[0010] The process of iterative training of the improved sparrow search algorithm comprises the following steps:

[0011] S1 inputs the detected voltage and current, sets the population size, maximum number of iterations, number of discoverers, and number of joiners;

[0012] S2 initializes the position of the sparrow population, and uses voltage and current as the individual sparrows, calculates the fitness value of each sparrow according to the objective function, and finally sorts to determine the current global optimal solution X best and the worst solution X worst ;

[0013] S3 updates the position of the population discoverer and combines the global optimal solution X based on the drunkard's walk strategy best and the worst solution X worst Update the position of the joiners in the population; finally, update the position of the early warning persons in the population who are aware of the danger.

[0014] Further, including:

[0015] The drunkard's walk strategy combined with the global optimal solution X best and the worst solution X worst Update the position of the joiner in the population, including:

[0016] The drunkard's walk strategy is an irregular variation form, and each step in the variation process is random. Its expression is:

[0017] D i =step·sin(-r4·π / 2+π / 2);

[0018] Among them, step is the drunkard's step length; r4 is a random number in [0,1];

[0019] Therefore, the updated rule of the participant is improved by the drunkard's walk strategy, and the improved participant's position update formula is:

[0020]

[0021] in, The best position found for the current finder; is the position of the sparrow with the worst fitness value in the world; A is a matrix with 1 row and d columns, and its elements are randomly assigned to -1 or 1; when When , it means that the i-th participant with low fitness value gets very little food and needs to go to other areas to find food; when When , it means that the joiner jumps to the current best position, t is the current iteration number; i is the i-th sparrow; X i,j is the position information of the i-th sparrow in the j-th dimension, j = 1, 2, 3, ..., d; L is a matrix with 1 row and d columns, all elements of which are 1, and Q is a random number that obeys the normal distribution.

[0022] Further, including:

[0023] The improved sparrow search algorithm further includes: initializing the position of the sparrow population using Tent chaos mapping.

[0024] Further, including:

[0025] The position of the early warning person who is aware of the danger in the updated population is specifically represented as:

[0026]

[0027] in, is the position with the best global fitness at present; K is a random number in [-1,1]; β is a random number that obeys the standard normal distribution; f i is the fitness value of the current individual, and f g and f w is the current global optimal fitness value and the worst fitness value; ε is a minimum constant; when f i =f g When , it means that the sparrow is aware of the danger of being preyed upon and needs to get closer to other sparrows; when f i >f g When , it means that the sparrow has poor adaptability and is more likely to be preyed upon.

[0028] Further, including:

[0029] The variable step size perturbation observation method uses the derivative of the sigmoid function to update the perturbation step size of the variable step size perturbation observation method in real time, specifically including:

[0030] Transform the derivative of the sigmoid function, and the transformed function expression is:

[0031]

[0032] The range of the transformed function value is [0,1]. When the independent variable is zero, the function value is also zero. Replace x in the above formula with dP / dU, and then set the maximum perturbation step value F according to the actual situation. max , the proposed variable step size update formula is obtained as:

[0033]

[0034] in, P (k) is the output power value of the photovoltaic panel at the current moment, P (k-1) U is the output power value of the photovoltaic panel at the previous moment; (k) is the voltage value of the photovoltaic panel at the current moment, U (k-1) It is the voltage value of the photovoltaic panel at the previous moment.

[0035] In a second aspect, the present invention further provides a photovoltaic control system, which includes a sampling module, a photovoltaic array, a photovoltaic power control module, a PWM drive module, a Boost module, and a load module;

[0036] The input end of the sampling module is connected to the output end of the photovoltaic array, the output end of the sampling module is connected to the input end of the photovoltaic power control module, the output end of the photovoltaic power control module is connected to the input end of the PWM drive module, the output end of the PWM drive module is connected to the input end of the Boost boost module, and the output end of the Boost boost module is connected to the load module. The photovoltaic power control module is provided with the above-mentioned photovoltaic power control method based on the improved sparrow search algorithm and the variable step size perturbation observation method.

[0037] Further, including:

[0038] The sampling module includes a voltage sampling module and a current sampling module, which are respectively connected to the output end of the photovoltaic array and are used to collect the output voltage and output current of the photovoltaic array in real time and send them to the photovoltaic power control module.

[0039] In a third aspect, the present invention further provides a computer-readable storage medium having computer instructions stored thereon, which, when executed, executes the photovoltaic power control method based on the improved sparrow search algorithm and the variable step-size perturbation observation method.

[0040] In a fourth aspect, the present invention also provides a risk assessment device based on task indicators, characterized in that the risk assessment device includes: a memory, a processor, and a photovoltaic power control program based on an improved sparrow search algorithm and a variable step-size perturbation observation method stored in the memory and runnable on the processor. When the photovoltaic power control program based on the improved sparrow search algorithm and the variable step-size perturbation observation method is executed by the processor, the steps of the photovoltaic power control method based on the improved sparrow search algorithm and the variable step-size perturbation observation method as described above are implemented.

[0041] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0042] Because the output characteristics of photovoltaic cells are affected by both light intensity and ambient temperature, the maximum power point of photovoltaic cells varies under different lighting conditions and temperatures. In the presence of partial shadows, the output power of the photovoltaic array has multiple peaks. Traditional photovoltaic power control methods are prone to falling into local extremes and cannot track the maximum power point. To address this problem, the present invention proposes a photovoltaic power control method that improves the sparrow search algorithm and the variable step-size perturbation observation method. The tent chaotic map is introduced into the sparrow search algorithm to make the initial position of the population uniform, thereby improving the global search capability of the sparrow search algorithm.

[0043] The present invention improves the joiner update rule through the drunkard's walk strategy and enhances the local search capability of the sparrow search algorithm; the variable step size value of the variable step size perturbation and observation method is derived based on the derivative of the sigmoid function, which overcomes the problems of slow search speed and oscillation in the traditional perturbation and observation method.

[0044] Therefore, this method first uses the improved sparrow search algorithm to quickly track the maximum power point, and then uses the variable step size perturbation observation method to perform a fine search for the maximum power point, thereby improving the tracking speed and tracking accuracy, while taking into account the MPPT search speed and search accuracy requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a structural diagram of a photovoltaic control system according to an embodiment of the present invention;

[0046] Figure 2 This is a flow chart of a photovoltaic power control method based on an improved sparrow search algorithm and a variable step-size perturbation observation method according to an embodiment of the present invention;

[0047] Figure 3 is a graph of the derivative of the transformed sigmoid function according to an embodiment of the present invention;

[0048] Figure 4The photovoltaic array PU characteristic curve under different lighting conditions of the photovoltaic power control method based on the improved sparrow search algorithm and the variable step size perturbation observation method described in the embodiment of the present invention;

[0049] Figure 5 This is a comparison chart of photovoltaic power control results between the improved sparrow search algorithm and variable step-size perturbation and observation method (ISSA-IP&O) of the present invention and the traditional sparrow search algorithm (SSA) and traditional perturbation and observation method (P&O) under the standard condition (STC) described in an embodiment of the present invention;

[0050] Figure 6 This is a comparison chart of photovoltaic power control results under partial shadow conditions (PSC) described in an embodiment of the present invention, using an improved sparrow search algorithm and a variable step-size perturbation and observation method (ISSA-IP&O) and a traditional sparrow search algorithm (SSA) and a traditional perturbation and observation method (P&O);

[0051] Figure 7 It is a schematic diagram of the terminal hardware structure of each embodiment of the photovoltaic power control method based on the improved sparrow search algorithm and the variable step size perturbation observation method described in the embodiments of the present invention. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention and not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0053] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0054] Example 1

[0055] The present invention proposes a photovoltaic power control method based on an improved sparrow search algorithm and a variable step-size perturbation observation method, comprising: sampling the output voltage and current of a photovoltaic array; inputting the obtained voltage and current into a photovoltaic power control module using the improved sparrow search algorithm and the variable step-size perturbation observation method, and outputting a control signal to a switch tube of a Boost circuit.

[0056] The improved sparrow search algorithm and variable step-size perturbation observation method photovoltaic power control module include: introducing a tent chaotic map into the sparrow search algorithm to make the initial position of the population uniform, thereby improving the global search capability of the sparrow search algorithm; improving the joiner update rule through a drunkard's walk strategy, thereby improving the local search capability of the sparrow search algorithm; and using the derivative of a sigmoid function to update the perturbation step size of the variable step-size perturbation observation method in real time, thereby solving the problems of slow search speed and oscillation in the traditional perturbation observation method.

[0057] This method first uses the improved sparrow search algorithm to quickly track the maximum power point, and then uses the variable step size perturbation observation method to perform a fine search for the maximum power point, while taking into account the requirements of MPPT search speed and search accuracy.

[0058] like Figure 2 As shown, the specific steps include:

[0059] S10 takes the collected voltage and current as input, transmits them to the improved sparrow search algorithm, and iterates the training to determine whether the number of iterations has been reached. If the number of iterations has been reached, it outputs the optimal fitness value f g and the best sparrow position X best , i.e., the maximum power and the maximum power point voltage, and simultaneously terminate the iterative training of the improved sparrow search algorithm.

[0060] This step in this embodiment does not limit the specific method of collecting voltage and current, but usually a collection module can be provided to perform real-time collection.

[0061] The Sparrow Search Algorithm (SSA) is a novel swarm intelligence optimization algorithm, primarily inspired by the foraging and anti-predation behaviors of sparrows. During the foraging process, sparrows are divided into finders (explorers) and joiners (followers). Finders are responsible for finding food within the population and providing foraging areas and directions for the entire sparrow population, while joiners use finders to obtain food. To obtain food, sparrows can generally adopt two behavioral strategies: finder and joiner. Individuals in a population monitor the behavior of other individuals, and aggressors within the population will compete for food resources with high-intake peers to increase their predation rate. Furthermore, when a sparrow population senses danger, it will engage in anti-predation behaviors.

[0062] First, in this embodiment, the Tent chaotic map is introduced into the improved sparrow search algorithm. Since there is no local optimum in the chaotic optimization process, the Tent chaotic map is introduced to make the initial position of the population uniform and improve the global search capability of the sparrow algorithm. The expression is:

[0063]

[0064] Among them, x i is the population of the i-th dimension, u is a number between [0,1]. When u = 0.5, the sequence distribution is relatively uniform, and there is approximately consistent distribution density for different parameters.

[0065] Secondly, in this embodiment, the improved sparrow search algorithm also includes the introduction of a drunkard's walk strategy, which is an irregular variation form, and each step in the variation process is random. Its expression is:

[0066] D i =step·sin(-r4·π / 2+π / 2) (2)

[0067] Among them, step is the drunkard's step length, which can be fixed or variable; r4 is a random number in [0,1].

[0068] S20 detects the voltage and current after iterative training of the improved sparrow search algorithm, and calculates the power at the previous moment and the power at the current moment respectively to obtain the power difference dP, and simultaneously calculates the voltage difference dU. Finally, according to the variable step size perturbation observation method, the maximum power point search is completed;

[0069] Among them, the improved sparrow search algorithm is based on the drunkard's walk strategy to update the position of the joiner in the population, and the variable step size perturbation observation method is used to automatically adjust the perturbation step size according to the distance between the current working point of the photovoltaic power generation system and the maximum power point. When the working point of the photovoltaic power generation system is at the maximum power point, that is, The photovoltaic power generation system will no longer apply disturbances. This embodiment does not limit the specific structure of the photovoltaic power generation system, and can be based on a conventional photovoltaic power generation system. Corresponding functions can also be added or removed based on the photovoltaic power generation system.

[0070] like Figure 2 As shown, combined with S10 and S20, it can be seen that the photovoltaic power control method based on the improved sparrow search algorithm and the variable step size perturbation observation method specifically includes the following steps:

[0071] Step 1: Set the population size, maximum number of iterations, number of discoverers, and number of joiners of the sparrow algorithm.

[0072] Step 2: Detect the voltage and current of the photovoltaic panel. Initialize the population based on the Tent chaos map, calculate the fitness value, and sort to obtain the global optimal solution X best and the worst solution X worst .

[0073] Step 3: Update the position of the population discoverer and calculate the fitness value to obtain the current global optimal solution; update the position of the joiner in the population based on the drunkard's walk strategy; update the position of the warning person in the population who is aware of the danger.

[0074] Step 4: Determine whether the number of iterations has been reached. If the number of iterations has been reached, output the optimal fitness value f g and the best sparrow position X best , that is, the maximum power and the maximum power point voltage, and at the same time terminate the sparrow algorithm and enter the variable step size perturbation observation method; otherwise, repeat steps 1 to 3.

[0075] Step 5: Detect the voltage and current obtained by the sparrow algorithm. Calculate the power at the previous moment and the current moment using the power calculation formula P = UI, obtaining the power difference dP and the voltage difference dU. Calculate the perturbation step size using Equation (9), and then complete the search for the maximum power point using the perturbation-observation method.

[0076] Step 6: Determine whether the algorithm restart condition is met. If so, restart the algorithm; otherwise, output the maximum power point voltage.

[0077] In step 3 above, the position of the population discoverer is updated, and the fitness value is calculated to obtain the current global optimal solution. This embodiment specifically includes:

[0078] In the sparrow algorithm, discoverers with better fitness values ​​will find food first in the process of searching for food, and provide foraging directions for other participants. Some individuals in the population conduct reconnaissance and early warning, and are called early warning detectors.

[0079] The formula for updating the discoverer's position is:

[0080]

[0081] Among them, t is the current iteration number; i is the i-th sparrow; X i,j is the position information of the i-th sparrow in the j-th dimension, j = 1, 2, 3, …, d; α is a random number in (0, 1]; R2∈(0, 1] is the warning value; S∈(0.5, 1] ​​is the safety value; M is the maximum number of iterations, Q is a random number that follows a normal distribution; L is a matrix with 1 row and d columns, all of which are 1.

[0082] When R2<S, the sparrow population is in a safe area and can conduct extensive searches; when R2≥S, the sparrows in the population have discovered predators and the sparrow population needs to go elsewhere to find food.

[0083] The joiner update formula in the prior art is as follows:

[0084]

[0085] in, The best position found for the current finder; is the position of the sparrow with the worst fitness value in the world; A is a matrix with 1 row and d columns, and its elements are randomly assigned to -1 or 1; when When , it means that the i-th participant with low fitness value gets very little food and needs to go to other areas to find food; when When , it means that the joiner jumps to the vicinity of the current best position.

[0086] In this embodiment, the position of the joiner in the population is updated based on the drunkard's wandering strategy; the position of the early warning person in the population who is aware of the danger is updated, specifically including:

[0087] The drunkard's walk is an irregular form of change, and each step in the change process is random. Its expression is shown in formula (2).

[0088] The drunkard's walk has strong randomness and uncertain direction. The local search ability of the sparrow algorithm is enhanced by improving the joiner update rule through the drunkard's walk strategy. The improved joiner position update formula is:

[0089]

[0090] The position update formula of the early warning person is:

[0091]

[0092] in, is the position with the best global fitness at present; K is a random number in [-1,1]; β is a random number that obeys the standard normal distribution; f i is the fitness value of the current individual, and f g and f w is the current global optimal fitness value and the worst fitness value; ε is a minimum constant; when f i =f g When , it means that the sparrow is aware of the danger of being preyed upon and needs to get closer to other sparrows; when f i >f g When , it means that the sparrow has poor adaptability and is more likely to be preyed upon.

[0093] In this embodiment, the perturbation step size is calculated according to formula (9), and then the maximum power point search is completed according to the perturbation rule of the perturbation-observation method, which specifically includes:

[0094] The variable step-size perturbation observation method utilizes the derivative of the sigmoid function to update the perturbation step size of the variable step-size perturbation observation method in real time, thereby solving the problems of slow search speed and oscillation in the traditional perturbation observation method.

[0095] When the derivative of the sigmoid function approaches infinity, the dependent variable can be limited to [0, 0.25]. Its function expression is:

[0096]

[0097] However, the derivative of the sigmoid function is not zero when the independent variable is 0, and the function value is zero when the independent variable approaches infinity. Therefore, the derivative of the sigmoid function is transformed, and the function expression after the transformation is:

[0098]

[0099] like Figure 3 As shown, the transformed function range is [0,1]. When the independent variable is zero, the function value is also zero. Replace x in formula (8) with dP / dU, and then set the maximum perturbation step value F according to the actual situation. max , the proposed variable step size update formula is:

[0100]

[0101] in, P (k) is the output power value of the photovoltaic panel at the current moment, P (k-1) U is the output power value of the photovoltaic panel at the previous moment; (k) is the voltage value of the photovoltaic panel at the current moment, U (k-1) It is the voltage value of the photovoltaic panel at the previous moment.

[0102] The variable step size perturbation observation method can automatically adjust the perturbation step size according to the distance between the system operating point and the maximum power point. When the system is working at the maximum power point, The system will no longer be disturbed.

[0103] like Figure 2 As shown, in this embodiment, after calculating the adaptive compensation F, it is determined whether there is P (k) =P (k-1) If they are equal, continue to judge U (k) =U (k-1) Otherwise, if P (k) <P (k-1) , then judge U (k) >U (k-1) , if U (k) >U (k-1) , then set U (k+1) =U (k) -F, otherwise, if U (k) ≤U (k-1) , then U (k+1) =U (k) +F;

[0104] If P (k) ≥P (k-1) , then determine whether there is U (k) >U (k-1) , if so, then U (k+1) =U (k) +F, otherwise, U (k+1) =U (k) -F;

[0105] Determine whether the restart condition is met. If so, re-adopt the improved sparrow algorithm for iterative training. Otherwise, maintain the current voltage output and end.

[0106] Example 2

[0107] On the basis of Example 1, Figure 1 As shown, the present invention proposes a system structure corresponding to a photovoltaic power control method based on an improved sparrow algorithm and a variable step-size perturbation observation method, comprising: a photovoltaic array, a sampling module, a photovoltaic power control module, a PWM drive module, a boost module, and a load module. The input end of the sampling module is connected to the output end of the photovoltaic array, the output end of the sampling module is connected to the input end of the photovoltaic power control module, the output end of the photovoltaic power control module is connected to the input end of the PWM drive module, the output end of the PWM drive module is connected to the input end of the boost module, and the output end of the boost module is connected to the load module.

[0108] The sampling module includes a voltage sampling module and a current sampling module. The voltage sampling module and the current sampling module are respectively connected to the output end of the photovoltaic array to collect the output voltage and output current of the photovoltaic array in real time and send them to the photovoltaic power control module.

[0109] This embodiment does not limit the specific components of the PWM drive module and the Boost boost module, which are customized according to the specific needs of the inventor.

[0110] The photovoltaic power control module in this embodiment integrates the improved sparrow search algorithm and the variable step size perturbation observation method photovoltaic power control method described in the embodiment, including:

[0111] Step 10: Set the population size, maximum number of iterations, number of discoverers, and number of joiners of the sparrow algorithm.

[0112] Step 20: Detect the voltage and current of the photovoltaic panel. Initialize the population based on the Tent chaotic map, calculate the fitness value, and sort to obtain the global optimal solution X best and the worst solution X worst .

[0113] Step 30: Update the position of the population discoverer and calculate the fitness value to obtain the current global optimal solution; update the position of the joiner in the population based on the drunkard's walk strategy; update the position of the warning person in the population who is aware of the danger.

[0114] Step 40: Determine whether the number of iterations has been reached. If the number of iterations has been reached, output the optimal fitness value f g and the best sparrow position X best , that is, the maximum power and the maximum power point voltage, and at the same time terminate the sparrow algorithm and enter the variable step size perturbation observation method; otherwise, repeat steps 1 to 3.

[0115] Step 50: Detect the voltage and current obtained by the sparrow algorithm. Calculate the power at the previous moment and the current moment using the power calculation formula P = UI, obtaining the power difference dP and the voltage difference dU. Calculate the perturbation step size using equation (9), and then complete the search for the maximum power point using the perturbation-observation method.

[0116] Step 60: Determine whether the algorithm restart condition is met. If so, restart the algorithm; otherwise, output the maximum power point voltage.

[0117] Other technical features of the photovoltaic power control system based on the improved sparrow algorithm and the variable step-size perturbation observation method described in the present invention are similar to those of the corresponding photovoltaic power control method based on the improved sparrow algorithm and the variable step-size perturbation observation method, and are not repeated here.

[0118] In order to verify the effectiveness of this application, the following examples are provided.

[0119] Taking into account that the PU output characteristic curve of the photovoltaic array under PSC will have multiple local maximum power points, the traditional photovoltaic power control method cannot accurately identify the local extreme value and the global maximum value, and is prone to failure when tracking the maximum power point, resulting in reduced tracking efficiency. The introduction of intelligent algorithms solves the defect that traditional photovoltaic power control methods are prone to falling into local optimality. Although intelligent algorithms can solve the disadvantage of traditional algorithms falling into local peaks under multi-peak conditions, there is still room for improvement in tracking speed, accuracy and stability. In order to further verify that the output characteristic curve of the photovoltaic array under different conditions presents a multi-peak phenomenon, a simulation model of the photovoltaic array is established, and different lighting conditions are set to prepare for the verification of the photovoltaic power control method. S1, S2, and S3 represent the light intensity on photovoltaic cells PV1, PV2, and PV3, respectively. Table 1 shows the different environmental parameters set in the example of the photovoltaic power control method based on the improved sparrow algorithm and the variable step-size perturbation observation method of the present invention, which are divided into STC and PSC.

[0120] Table 1 Irradiance under different lighting conditions

[0121]

[0122] Figure 4 This is the PV array PU characteristic curve under different lighting conditions using the improved sparrow algorithm and variable step-size perturbation observation method. Under STC, the PV array's PU curve has a single peak, while under PSC, it has multiple peaks. The more complex the lighting conditions, the more complex the output characteristic curve, and the more likely traditional PV power control methods are to fall into a local solution.

[0123] Figure 5 This is a comparison chart of photovoltaic power control results under STC between the improved sparrow algorithm and variable step size perturbation and observation method (ISSA-IP&O) of the present invention and the traditional sparrow algorithm (SSA) and traditional perturbation and observation method (P&O); Figure 6 The following chart compares the photovoltaic power control results of the proposed method under PSC with those of SSA and P&O. The results demonstrate that under STC and PSC, the proposed method achieves faster tracking speed and higher efficiency than other methods, preventing the system from falling into local maximum power points and effectively improving photovoltaic power generation efficiency.

[0124] Example 3

[0125] Reference Figure 7 , Figure 7 This is a schematic diagram of the terminal structure of the hardware operating environment involved in the embodiment of the present invention.

[0126] like Figure 7 As shown, the terminal may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a network interface 1003, and a memory 1004. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The network interface 1003 may optionally include a standard wired interface, a wireless interface (such as a wireless fidelity (WIreless-FIdelity, WI-FI) interface). The memory 1004 may be a high-speed RAM memory (Random Access Memory, RAM) or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. The memory 1004 may also be a storage device independent of the aforementioned processor 1001.

[0127] Those skilled in the art will understand that Figure 7 The terminal structure shown in the figure does not constitute a limitation to the terminal, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0128] like Figure 7 As shown, the memory 1004 as a computer storage medium may include an operating system, a data storage module, a network communication module, and a photovoltaic power control program based on an improved sparrow algorithm and a variable step size perturbation and observation method.

[0129] exist Figure 7 In the terminal shown, the network interface 1003 is mainly used to connect to the backend server and communicate data with the backend server; the processor 1001 can call the photovoltaic power control program based on the improved sparrow algorithm and the variable step size perturbation observation method stored in the memory 1004 and perform the following operations:

[0130] The collected voltage and current are used as input and transmitted to the improved sparrow search algorithm, and iterative training is performed to determine whether the number of iterations has been reached. If the number of iterations has been reached, the optimal fitness value f is output. g and the best sparrow position X best , i.e., the maximum power and the maximum power point voltage, while terminating the iterative training of the improved sparrow search algorithm;

[0131] Detecting the voltage and current after iterative training of the improved sparrow search algorithm, and calculating the power at the previous moment and the power at the current moment, respectively, to obtain the power difference dP, and simultaneously calculating the voltage difference dU, and finally, completing the search for the maximum power point according to the variable step size perturbation observation method;

[0132] Among them, the improved sparrow search algorithm is based on the drunkard's walk strategy to update the position of the joiner in the population, and the variable step size perturbation observation method is used to automatically adjust the perturbation step size according to the distance between the current working point of the photovoltaic power generation system and the maximum power point. When the working point of the photovoltaic power generation system is at the maximum power point, that is, The photovoltaic power generation system will no longer apply disturbances.

[0133] Furthermore, the processor 1001 may call the photovoltaic power control program based on the improved sparrow algorithm and the variable step size perturbation and observation method stored in the memory 1004, and further perform the following operations:

[0134] The process of iterative training of the improved sparrow search algorithm comprises the following steps:

[0135] S1 inputs the detected voltage and current, sets the population size, maximum number of iterations, number of discoverers, and number of joiners;

[0136] S2 initializes the position of the sparrow population, and uses voltage and current as the individual sparrows, calculates the fitness value of each sparrow according to the objective function, and finally sorts to determine the current global optimal solution X best and the worst solution X worst ;

[0137] S3 updates the position of the population discoverer and combines the global optimal solution X based on the drunkard's walk strategy best and the worst solution X worst Update the position of the joiners in the population; finally, update the position of the early warning persons in the population who are aware of the danger.

[0138] Furthermore, the processor 1001 may call the photovoltaic power control program based on the improved sparrow algorithm and the variable step size perturbation and observation method stored in the memory 1004, and further perform the following operations:

[0139] The drunkard's walk strategy combined with the global optimal solution X best and the worst solution X worst Update the position of the joiner in the population, including:

[0140] The drunkard's walk strategy is an irregular variation form, and each step in the variation process is random. Its expression is:

[0141] D i =step·sin(-r4·π / 2+π / 2);

[0142] Among them, step is the drunkard's step length; r4 is a random number in [0,1];

[0143] Therefore, the updated rule of the participant is improved by the drunkard's walk strategy, and the improved participant's position update formula is:

[0144]

[0145] in, The best position found for the current finder; is the position of the sparrow with the worst fitness value in the world; A is a matrix with 1 row and d columns, and its elements are randomly assigned to -1 or 1; when When , it means that the i-th participant with low fitness value gets very little food and needs to go to other areas to find food; when When , it means that the joiner jumps to the current best position, t is the current iteration number; i is the i-th sparrow; X i,j is the position information of the i-th sparrow in the j-th dimension, j = 1, 2, 3, ..., d; L is a matrix with 1 row and d columns, all elements of which are 1, and Q is a random number that obeys the normal distribution.

[0146] Furthermore, the processor 1001 may call the photovoltaic power control program based on the improved sparrow algorithm and the variable step size perturbation and observation method stored in the memory 1004, and further perform the following operations:

[0147] The improved sparrow search algorithm further includes: initializing the position of the sparrow population using Tent chaos mapping.

[0148] Furthermore, the processor 1001 may call the photovoltaic power control program based on the improved sparrow algorithm and the variable step size perturbation and observation method stored in the memory 1004, and further perform the following operations:

[0149] The position of the early warning person who is aware of the danger in the updated population is specifically represented as:

[0150]

[0151] in, is the position with the best global fitness at present; K is a random number in [-1,1]; β is a random number that obeys the standard normal distribution; f i is the fitness value of the current individual, and f g and f w is the current global optimal fitness value and the worst fitness value; ε is a minimum constant; when f i =f g When , it means that the sparrow is aware of the danger of being preyed upon and needs to get closer to other sparrows; when f i >f g When , it means that the sparrow has poor adaptability and is more likely to be preyed upon.

[0152] Furthermore, the processor 1001 may call the photovoltaic power control program based on the improved sparrow algorithm and the variable step size perturbation and observation method stored in the memory 1004, and further perform the following operations:

[0153] The variable step size perturbation observation method uses the derivative of the sigmoid function to update the perturbation step size of the variable step size perturbation observation method in real time, specifically including:

[0154] Transform the derivative of the sigmoid function, and the transformed function expression is:

[0155]

[0156] The range of the transformed function value is [0,1]. When the independent variable is zero, the function value is also zero. Replace x in the above formula with dP / dU, and then set the maximum perturbation step value F according to the actual situation. max , the proposed variable step size update formula is obtained as:

[0157]

[0158] in, P (k) is the output power value of the photovoltaic panel at the current moment, P (k-1) U is the output power value of the photovoltaic panel at the previous moment; (k) is the voltage value of the photovoltaic panel at the current moment, U (k-1)It is the voltage value of the photovoltaic panel at the previous moment.

[0159] Furthermore, those skilled in the art will appreciate that all or part of the steps in the method of the above-described embodiment can be implemented by instructing the relevant hardware through a computer program. The computer program includes program instructions, which can be stored in a storage medium that is computer-readable. The program instructions are executed by at least one processor in the control terminal to implement the steps of the above-described method embodiment.

[0160] Therefore, the present invention also provides a computer-readable storage medium, which stores a photovoltaic power control program based on the improved sparrow algorithm and the variable step-size perturbation observation method. When the photovoltaic power control program based on the improved sparrow algorithm and the variable step-size perturbation observation method is executed by a processor, it implements the various steps of the photovoltaic power control method based on the improved sparrow algorithm and the variable step-size perturbation observation method as described in the above embodiment.

[0161] It should be noted that since the storage medium provided in the embodiments of this application is the storage medium used to implement the method of the embodiments of this application, based on the method described in the embodiments of this application, those skilled in the art will be able to understand the specific structure and deformation of the storage medium, and therefore will not be described in detail here. All storage media used in the method of the embodiments of this application fall within the scope of protection to be provided by this application.

[0162] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0163] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0164] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0165] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0166] It should be noted that, in the claims, any reference signs placed between parentheses shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claim. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The invention can be implemented by means of hardware comprising several distinct components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third etc. does not indicate any order. These words may be interpreted as names.

[0167] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0168] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

[0169] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A photovoltaic power control method based on an improved sparrow search algorithm and a variable step-size perturbation observation method, characterized in that: The method includes: The collected voltage and current are used as input and transmitted to the improved sparrow search algorithm, and iterative training is performed to determine whether the number of iterations has been reached. If the number of iterations has been reached, the optimal fitness value f is output. g and the best sparrow position X best , i.e., the maximum power and the maximum power point voltage, while terminating the iterative training of the improved sparrow search algorithm; Detecting the voltage and current after iterative training of the improved sparrow search algorithm, and calculating the power at the previous moment and the power at the current moment, respectively, to obtain the power difference dP, and simultaneously calculating the voltage difference dU, and finally, completing the search for the maximum power point according to the variable step size perturbation observation method; Among them, the improved sparrow search algorithm is based on the drunkard's walk strategy to update the position of the joiner in the population. The variable step size perturbation observation method is used to automatically adjust the perturbation step size according to the distance between the current working point of the photovoltaic power generation system and the maximum power point. When the working point of the photovoltaic power generation system is at the maximum power point, that is, The photovoltaic power generation system will no longer apply disturbances.

2. The photovoltaic power control method based on the improved sparrow search algorithm and the variable step size perturbation observation method according to claim 1, characterized in that: The process of iterative training of the improved sparrow search algorithm comprises the following steps: S1 inputs the detected voltage and current, sets the population size, maximum number of iterations, number of discoverers, and number of joiners; S2 initializes the position of the sparrow population, and uses voltage and current as the individual sparrows, calculates the fitness value of each sparrow according to the objective function, and finally sorts to determine the current global optimal solution X best and the worst solution X worst ; S3 updates the position of the population discoverer and combines the global optimal solution X based on the drunkard's walk strategy best and the worst solution X worst Update the position of the joiners in the population; finally, update the position of the early warning persons in the population who are aware of the danger.

3. The photovoltaic power control method based on the improved sparrow search algorithm and the variable step size perturbation observation method according to claim 2, characterized in that: The drunkard's walk strategy combined with the global optimal solution X best and the worst solution X worst Update the position of the joiner in the population, including: The drunkard's walk strategy is an irregular variation form, and each step in the variation process is random. Its expression is: D i =step·sin(-r4·π / 2+π / 2); Among them, step is the drunkard's step length; r4 is a random number in [0,1]; Therefore, the updated rule of the participant is improved by the drunkard's walk strategy, and the improved participant's position update formula is: in, The best position found for the current finder; is the position of the sparrow with the worst fitness value in the world; A is a matrix with 1 row and d columns, and its elements are randomly assigned to -1 or 1; when When , it means that the i-th participant with low fitness value gets very little food and needs to go to other areas to find food; when When , it means that the joiner jumps to the current best position, t is the current iteration number; i is the i-th sparrow; X i,j is the position information of the i-th sparrow in the j-th dimension, j = 1, 2, 3, ..., d; L is a matrix with 1 row and d columns, all elements of which are 1, and Q is a random number that obeys the normal distribution.

4. The photovoltaic power control method based on the improved sparrow search algorithm and the variable step size perturbation observation method according to claim 1, characterized in that: The improved sparrow search algorithm further includes: initializing the position of the sparrow population using Tent chaos mapping.

5. The photovoltaic power control method based on the improved sparrow search algorithm and the variable step size perturbation observation method according to claim 2, characterized in that: The position of the early warning person who is aware of the danger in the updated population is specifically represented as: in, is the position with the best global fitness at present; K is a random number in [-1,1]; β is a random number that obeys the standard normal distribution; f i is the fitness value of the current individual, and f g and f w is the current global optimal fitness value and the worst fitness value; ε is a minimum constant; when f i =f g When , it means that the sparrow is aware of the danger of being preyed upon and needs to get closer to other sparrows; when f i >f g When , it means that the sparrow has poor adaptability and is more likely to be preyed upon.

6. The photovoltaic power control method based on the improved sparrow search algorithm and the variable step size perturbation observation method according to claim 1, characterized in that: The variable step size perturbation observation method uses the derivative of the sigmoid function to update the perturbation step size of the variable step size perturbation observation method in real time, specifically including: Transform the derivative of the sigmoid function, and the transformed function expression is: The range of the transformed function value is [0,1]. When the independent variable is zero, the function value is also zero. Replace x in the above formula with dP / dU, and then set the maximum perturbation step value F according to the actual situation. max , the proposed variable step size update formula is obtained as: in, P (k) is the output power value of the photovoltaic panel at the current moment, P (k-1) U is the output power value of the photovoltaic panel at the previous moment; (k) is the voltage value of the photovoltaic panel at the current moment, U (k-1) It is the voltage value of the photovoltaic panel at the previous moment.

7. A photovoltaic control system, characterized in that: The system includes a sampling module, a photovoltaic array, a photovoltaic power control module, a PWM drive module, a Boost module and a load module; The input end of the sampling module is connected to the output end of the photovoltaic array, the output end of the sampling module is connected to the input end of the photovoltaic power control module, the output end of the photovoltaic power control module is connected to the input end of the PWM drive module, the output end of the PWM drive module is connected to the input end of the Boost boost module, and the output end of the Boost boost module is connected to the load module. The photovoltaic power control module is provided with the photovoltaic power control method based on the improved sparrow search algorithm and the variable step size perturbation observation method according to any one of claims 1 to 6.

8. The photovoltaic control system according to claim 7, characterized in that: The sampling module includes a voltage sampling module and a current sampling module, which are respectively connected to the output end of the photovoltaic array and are used to collect the output voltage and output current of the photovoltaic array in real time and send them to the photovoltaic power control module.

9. A computer-readable storage medium having computer instructions stored thereon, which, when the computer instructions are executed, executes the photovoltaic power control method based on the improved sparrow search algorithm and the variable step size perturbation and observation method according to any one of claims 1 to 6.

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