A method and apparatus for power tracking of a photovoltaic system

Abstract

The application discloses a power tracking method and device of a photovoltaic system, and the method comprises the following steps: firstly, when a variation corresponding to environment information of the photovoltaic system at a current moment meets a preset condition, the environment information is processed by using a photovoltaic system output model to obtain a first theoretical power; secondly, first parameter information of a first control signal corresponding to the first theoretical power is determined, and position constraint information of particles contained in a particle swarm is determined according to the first parameter information and an error range of the photovoltaic system output model, wherein the position of the particles corresponds to the parameter information of the control signal; finally, a position optimization of the particles is performed according to the position constraint information to maximize the output power of the photovoltaic system as an optimization target, and target parameter information corresponding to a second control signal is obtained, wherein the target parameter information is used to support a controller of the photovoltaic system to regulate and control the output power of the photovoltaic system. In this way, the tracking of the power of the photovoltaic system can be realized, and the photovoltaic system can continuously output the maximum power.

Description

Technical Field

[0001] This application relates to the field of photovoltaic power generation technology, and specifically to a power tracking method and apparatus for a photovoltaic system. Background Technology

[0002] Photovoltaic energy, as a clean energy source, has been widely and flexibly applied. Typically, a photovoltaic system consists of several photovoltaic cells, which are then connected in series and parallel to achieve higher voltage and power. To prevent hot spot effects and improve the power generation efficiency of the photovoltaic system, photovoltaic modules usually have bypass diodes connected in series to allow for varying photocurrents. When the photovoltaic system receives uneven sunlight, the less irradiated areas will generate smaller photocurrents, limiting the maximum current flowing through the system. Therefore, to improve the efficiency of a photovoltaic system, it is necessary to find the maximum power point on the PV curve. By controlling the system voltage at the voltage corresponding to the maximum power point, the voltage at the maximum power point of the photovoltaic system can be controlled, thus maintaining maximum power output. Summary of the Invention

[0003] This application discloses a power tracking method and apparatus for a photovoltaic system.

[0004] Firstly, a power tracking method for a photovoltaic system is provided, the method comprising:

[0005] Determine whether the changes in the environmental information of the photovoltaic system at the current moment meet the preset conditions;

[0006] If so, the environmental information is processed through the photovoltaic system output model to obtain the first theoretical power;

[0007] The first parameter information of the first control signal corresponding to the first theoretical power is determined, and the position constraint information of N particles contained in the particle swarm is determined based on the first parameter information and the error range of the output model of the photovoltaic system, wherein the positions of the N particles correspond to the parameter information of the N control signals.

[0008] With the goal of maximizing the output power of the photovoltaic system, the positions of the N particles are optimized according to the position constraint information to obtain the target parameter information corresponding to the second control signal. The target parameter information is used to support the controller of the photovoltaic system to regulate the output power of the photovoltaic system with the second control signal.

[0009] In one possible implementation, the environmental information includes at least the first average irradiance received by the photovoltaic system within the time interval corresponding to the current moment; wherein,

[0010] The step of determining whether the change in environmental information corresponding to the photovoltaic system at the current moment meets the preset conditions includes:

[0011] Determine whether the absolute value of the difference between the first average irradiance and the second average irradiance received by the photovoltaic system in the previous time interval is greater than a preset threshold.

[0012] If so, then the change in the environmental information of the photovoltaic system at the current moment is determined to meet the preset conditions.

[0013] In one possible implementation, the parameter information includes: the duty cycle corresponding to the first control signal.

[0014] In one possible implementation, the step of optimizing the positions of the N particles based on the position constraint information to obtain the target parameter information corresponding to the second control signal, with the goal of maximizing the output power of the photovoltaic system, includes:

[0015] Based on the position constraint information, perform K rounds of position optimization on the N particles to obtain the optimal fitness among the N*K fitness values ​​corresponding to the N*K positions of the N particles;

[0016] Based on the particle position corresponding to the optimal fitness, the target parameter information corresponding to the second control signal is obtained;

[0017] Wherein, for any fitness among the N*K fitnesss, the fitness is the actual output power of the photovoltaic system obtained by the controller of the photovoltaic system using the control signal corresponding to the particle position to regulate the photovoltaic system.

[0018] In one possible implementation, the step of performing K rounds of position optimization on the N particles based on the position constraint information to obtain the optimal fitness among the N*K fitness values ​​corresponding to the N*K positions of the N particles includes:

[0019] In any k-th execution round out of K execution rounds, based on the positions of the N particles in the k-th execution round, the controller of the photovoltaic system is adjusted using the corresponding N control signals to obtain the N actual output powers corresponding to the k-th execution round;

[0020] If the maximum value among the N true output powers is greater than the current optimal fitness value, then the maximum value among the N true output powers is updated as the optimal fitness.

[0021] Secondly, a power tracking device for a photovoltaic system is provided, the device comprising:

[0022] The environmental judgment unit is used to determine whether the change in the environmental information of the photovoltaic system at the current moment meets the preset conditions;

[0023] The first calculation unit is used to process the environmental information through the photovoltaic system output model to obtain the first theoretical power when the change in the environmental information at the current moment meets the preset conditions.

[0024] The second calculation unit is used to determine the first parameter information of the first control signal corresponding to the first theoretical power, and to determine the position constraint information of N particles contained in the particle swarm based on the first parameter information and the error range of the output model of the photovoltaic system, wherein the positions of the N particles correspond to the parameter information of the N control signals.

[0025] The power optimization unit is used to optimize the positions of the N particles according to the position constraint information with the goal of maximizing the output power of the photovoltaic system, and obtain the target parameter information corresponding to the second control signal. The target parameter information is used to support the controller of the photovoltaic system to regulate the output power of the photovoltaic system with the second control signal.

[0026] In one possible implementation, the environmental information includes at least the first average irradiance received by the photovoltaic system within the time interval corresponding to the current moment; wherein,

[0027] The environmental prediction unit is specifically used to determine whether the absolute value of the difference between the first average irradiance and the second average irradiance received by the photovoltaic system in the previous time interval is greater than a preset threshold.

[0028] If so, then the change in the environmental information of the photovoltaic system at the current moment is determined to meet the preset conditions.

[0029] In one possible implementation, the parameter information includes: the duty cycle corresponding to the first control signal.

[0030] Thirdly, a computer-readable storage medium is provided having a computer program stored thereon, wherein when the computer program is executed in a computing device, the computing device performs the method described in any one of the first aspects.

[0031] Fourthly, a computing device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in any one of the first aspects.

[0032] In the technical solution provided in this application, when the change in environmental information corresponding to the photovoltaic system at the current moment meets preset conditions, the environmental information is first processed through the photovoltaic system output model to obtain a first theoretical power. Then, the first parameter information of the first control signal corresponding to the first theoretical power is determined. Based on the first parameter information and the error range of the photovoltaic system output model, the position constraint information of N particles in the particle swarm algorithm to be used is determined. The positions of these N particles correspond to the parameter information of the N control signals. Finally, with maximizing the output power of the photovoltaic system as the optimization objective, the positions of the N particles are optimized according to the position constraint information to obtain the target parameter information corresponding to the second control signal. This target parameter information is used to support the photovoltaic system controller in regulating the output power of the photovoltaic system using the second control signal. In this way, it is possible to quickly and efficiently track the maximum power point of the photovoltaic system even when the environmental information of the photovoltaic system is constantly changing, enabling the photovoltaic system to maintain or approach its maximum output power. Attached Figure Description

[0033] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 A schematic flowchart of a power tracking method for a photovoltaic system provided in an embodiment of this application is shown;

[0035] Figure 2 This paper presents a comparison chart showing the relationship between the actual output power of a photovoltaic system obtained by the method provided in this application and other methods, as exemplarily provided in an embodiment of this application, and the change over time.

[0036] Figure 3 A schematic diagram of the structure of a power tracking device for a photovoltaic system provided in an embodiment of this application is shown. Detailed Implementation

[0037] The solution provided in this specification will now be described with reference to the accompanying drawings.

[0038] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application will be described below with reference to the accompanying drawings. Obviously, the described embodiments are merely a part of the embodiments in this specification, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments in this specification without creative effort should fall within the scope of protection of this specification.

[0039] In the description of the embodiments of this application, the words "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the words "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a specific manner.

[0040] In the description of the embodiments of this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, B existing alone, and A and B existing simultaneously. Furthermore, unless otherwise stated, the term "multiple" means two or more.

[0041] Furthermore, the terms "first" and "first" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical feature. Thus, a feature defined with "first" or "first" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and their variations all mean "including but not limited to," unless otherwise specifically emphasized.

[0042] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above description is only a specific embodiment of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solution of this application should be included within the scope of protection of this application.

[0043] Photovoltaic energy, as a clean energy source, has been widely and flexibly applied. Typically, a photovoltaic system consists of several photovoltaic cells, which are then connected in series or parallel to achieve higher voltage and power. Photovoltaic systems heavily rely on solar irradiance, and uneven illumination, i.e., localized shading, often occurs during operation. For example, slowly moving clouds can affect the irradiance received by photovoltaic systems in large-scale power plants; the angle of solar radiation in mountainous areas can also cause uneven irradiance; urban photovoltaic systems inevitably experience dust accumulation; and the shading from buildings and trees can exacerbate insufficient sunlight exposure.

[0044] When a photovoltaic (PV) system receives uneven illumination, the area with lower irradiance will generate a smaller photocurrent, limiting the maximum current flowing through the same series connection. Significant differences in irradiance and prolonged uneven illumination not only reduce system efficiency but can also damage some PV system components due to hot spot effects. To prevent hot spot effects and improve the power generation efficiency of PV systems, PV modules typically use bypass diodes connected in series to handle varying photocurrents, and a controller is added to regulate the circuit. Consequently, the current-voltage characteristic curve (IV curve) of a PV system exhibits a stepped variation due to the varying photocurrents, meaning the power-voltage characteristic curve (PV curve) of the PV system has multiple extreme points and displays a multi-peak characteristic.

[0045] Therefore, in order to improve the efficiency of photovoltaic systems, it is necessary to find the maximum power point of the photovoltaic system in the PV curve. By controlling the voltage value of the system at the voltage value corresponding to the maximum power point, the voltage of the photovoltaic system at the maximum power point can be controlled, so that the photovoltaic system can maintain maximum power.

[0046] Most current power trackers (PSTs) are based on traditional algorithms, such as the perturbation-observation method and the incremental conductance method, to achieve maximum power point tracking (MPPT) for photovoltaic (PV) systems and improve output power. However, in these methods, the step size affects the convergence speed and steady-state oscillations. A larger step size results in faster convergence but also larger steady-state oscillations; a smaller step size results in smaller steady-state oscillations but slower convergence. Both larger steady-state oscillations and slower convergence speeds increase output power losses. Furthermore, traditional algorithms are only applicable to single-peak output power curves of PV systems under uniform irradiance conditions. For multi-peak output power curves of PV systems under partially shaded conditions, they often get trapped in local optima, meaning the peak point found is not the one with the maximum output power, thus significantly reducing output power.

[0047] Figure 1 This is an exemplary power tracking method for a photovoltaic system provided in an embodiment of this application. See also... Figure 1 As shown, the method may include, but is not limited to, some or all of the following steps S101 to S107.

[0048] Step S101: Determine whether the change in the environmental information of the photovoltaic system at the current moment meets the preset conditions.

[0049] If the change in the environmental information of the photovoltaic system at the current moment meets the preset conditions, then step S103 can be continued to process the environmental information through the photovoltaic system output model to obtain the first theoretical power.

[0050] Different environmental information can correspond to shading conditions. In other words, the ambient temperature and the radiation intensity of the photovoltaic system may be different under different shading conditions.

[0051] The ambient temperature at the current moment can be obtained using a temperature sensor; the irradiance of each photovoltaic panel in the photovoltaic system at the current moment can be obtained using an irradiance sensor. Correspondingly, the average ambient temperature and average irradiance of the photovoltaic system within the time interval to which the current moment belongs can be calculated.

[0052] The environmental information of the photovoltaic system at the current moment may include, but is not limited to, the average ambient temperature and / or average irradiance of the photovoltaic system within the time interval to which the current moment belongs. Correspondingly, a first preset value corresponding to the ambient temperature and / or a second preset value corresponding to the irradiance can be set.

[0053] In one possible implementation, it can be determined whether the absolute value of the difference between the average ambient temperature of the photovoltaic system in the current time interval and the average ambient temperature of the photovoltaic system in the previous time interval is greater than a first preset value. If so, it is determined that the change in the environmental information of the photovoltaic system at the current time meets the preset condition.

[0054] In one possible implementation, it can be determined whether the absolute value of the difference between the average irradiance of the photovoltaic system in the current time interval and the average irradiance of the photovoltaic system in the previous time interval is greater than a second preset value. If so, it is determined that the change in the environmental information of the photovoltaic system at the current time meets the preset condition.

[0055] The average ambient temperature and average irradiance of the photovoltaic system within the current time interval can be input into the photovoltaic system output model to obtain the first theoretical power of the photovoltaic system obtained by processing the photovoltaic system output model.

[0056] The output models of photovoltaic systems include: single quadratic term models, double quadratic term models, energy balance models, and neural network models. For example, the single quadratic term model models the photovoltaic system as a circuit model with components such as current sources, voltage sources, and diodes. Based on input parameters such as irradiance and temperature, the current, voltage, and output power of the photovoltaic system are calculated.

[0057] For example, a photovoltaic system contains three photovoltaic panels with the same parameters. During the time period of 0-15 seconds, there are three different shading conditions on the three photovoltaic panels of the photovoltaic system. Different shading conditions correspond to different environmental information. For example, different shading conditions can cause multiple photovoltaic panels in the photovoltaic system to have different irradiance or temperature at the same time.

[0058] In the example above, the three shading conditions change sequentially over time from 0 to 15 seconds. The first shading condition exists on the photovoltaic panel for 0 to 5 seconds. At the 5th second, the shading condition on the photovoltaic panel switches to the second shading condition, which exists for 5 to 10 seconds. At the 10th second, the shading condition on the photovoltaic panel switches to the third shading condition, which exists for 10 to 15 seconds.

[0059] When the first shading condition exists, the average irradiance of the three series-connected photovoltaic panels is 1000 W / m². 2 800W / m 2 700W / m 2 When the second shading condition exists, the average irradiance of the three series-connected photovoltaic panels is 1000 W / m². 2 700W / m 2 600W / m 2 When the third shading condition is present, the average irradiance of the three series-connected photovoltaic panels is 800 W / m². 2 600W / m 2 1000W / m 2 The average ambient temperature was 298K.

[0060] Based on the environmental information corresponding to the first shading condition existing from 0 to 5 seconds, the first theoretical power calculated by the output model of the photovoltaic system is 34W; based on the environmental information corresponding to the second shading condition existing from 5 to 10 seconds, the first theoretical power calculated by the output model of the photovoltaic system is 28W; based on the environmental information corresponding to the third shading condition existing from 10 to 15 seconds, the first theoretical power calculated by the output model of the photovoltaic system is 31W.

[0061] For example, the second preset value for irradiation intensity is 30 W / m. 2 During the 0-5s interval, the irradiance of the three series-connected photovoltaic panels was 1000W / m². 2 800W / m 2 700W / m 2 The average irradiance of the three series-connected photovoltaic panels is 833 W / m². 2 At 5 seconds, the shading conditions on the photovoltaic panels switched to the second shading condition, and the irradiance of the three series-connected photovoltaic panels was 1000 W / m². 2 700W / m 2 600W / m 2 The average irradiance of the three series-connected photovoltaic panels is 766 W / m². 2 The change compared to the first shaded condition is 67 W / m. 2Greater than the preset value of 30W / m 2 Therefore, the change in environmental information at 5 seconds satisfies the preset conditions. At 10 seconds, the shading condition on the photovoltaic panel switches to the third shading condition, and the irradiance of the three series-connected photovoltaic panels is 800W / m². 2 600W / m 2 1000W / m 2 The average irradiance of the three series-connected photovoltaic panels is 800W / m². 2 The change compared to the second shaded condition is 34 W / m. 2 Greater than the preset value of 30W / m 2 If the change in environmental information at the 10th second satisfies the preset condition.

[0062] Step S105: Determine the first parameter information of the first control signal corresponding to the first theoretical power, and determine the position constraint information of N particles contained in the particle swarm based on the first parameter information and the error range of the photovoltaic system output model. The positions of the N particles correspond to the parameter information of the N control signals.

[0063] In the particle swarm optimization algorithm, each particle represents a candidate solution in the search space of the optimization problem; that is, each particle represents the output power of a photovoltaic system. Each particle has its own state during the search process, including attributes such as position and velocity. The particle's position is the location of the candidate solution in the search space, and each particle's position corresponds to the parameter information of a single control signal; while the particle's velocity refers to the direction and step size of the particle's search for solutions in the search space.

[0064] In step S105, the voltage value of the photovoltaic system can be obtained based on the first theoretical power of the photovoltaic system. Then, the voltage value corresponding to the first theoretical power value is converted into the first parameter information of the first control signal of the controller in the photovoltaic system circuit. The parameter information of the control signal may include the duty cycle of the circuit.

[0065] The positional constraints of N particles refer to the positional range of N particles. After determining the positional range of the N particles in the particle swarm based on the first parameter information corresponding to the first theoretical power and the error range of the photovoltaic system output model, it is also necessary to initialize the optimal fitness of the N particles and the individual optimal fitness of the nth particle among the N particles. For example, the optimal fitness of the N particles and the individual optimal fitness of the nth particle among the N particles are both set to 0.

[0066] The error range of the photovoltaic system output model can be obtained by comparing the actual maximum power value of the photovoltaic system measured under various shading conditions with the first theoretical power value calculated by the photovoltaic system output model under various shading conditions.

[0067] For example, measuring the true maximum power values ​​W1, W2, W3...W of a photovoltaic system under m different shading conditions. m ; and the first theoretical power W corresponding to the existence of m different shadow stripes calculated through the photovoltaic system output model. j1 W j2 W j3 W j4 ...W jm The error between the m true maximum power and the m actual maximum power is calculated using the following formula 1. The error range (Mean Absolute Error, MAE) of the first theoretical power calculated by the photovoltaic system output model is determined based on the error, which is the error range of the photovoltaic system output model.

[0068]

[0069] Where MAE is the error of the first theoretical power calculated by the photovoltaic system output model; m is the total number of shading conditions; W i W represents the true maximum power of the photovoltaic system under the i-th shading condition. ji Let be the first theoretical power of the photovoltaic system under the i-th shading condition.

[0070] For example, the calculated output model error range for the photovoltaic system is ±15%.

[0071] Duty cycle refers to the proportion of a periodic signal's high level within the entire cycle of a photovoltaic (PV) system circuit, usually expressed as a percentage or decimal. A duty cycle of 0 indicates the signal is entirely low, while a duty cycle of 1 indicates the signal is entirely high. In a PV system, the duty cycle is converted into a PWM (Pulse Width Modulation) signal and output to a diode using the system's controller. This controls the conduction time of the diode's regulating circuit, thereby controlling the PV system's voltage.

[0072] Continuing with the previous example, the photovoltaic system uses a Boost circuit. During the first shading condition (0-5s), the voltage corresponding to the first theoretical power calculated by the photovoltaic system's output model is 43.8V, and the duty cycle of the corresponding first control signal is 0.35. During the second shading condition (5-10s), the voltage corresponding to the first theoretical power calculated by the photovoltaic system's output model is 36.6V, and the duty cycle of the corresponding first control signal is 0.67. During the third shading condition (10-15s), the voltage corresponding to the first theoretical power calculated by the photovoltaic system's output model is 40.2V, and the duty cycle of the corresponding first control signal is 0.52.

[0073] In the example above, the error range (±15%) of the photovoltaic system output model is as follows: when the first shadow condition exists in the first 0-5s, the duty cycle of the first control signal is 0.35, and the particle range is set to [0.35*(1-15%), 0.35*(1+15%)]; when the second shadow condition exists in the 5-10s, the duty cycle of the first control signal is 0.67, and the particle range is set to [0.67*(1-15%), 0.67*(1+15%)]; when the third shadow condition exists in the 10-15s, the duty cycle of the first control signal is 0.52, and the particle range is set to [0.52*(1-15%), 0.52*(1+15%)].

[0074] Step S107: With maximizing the output power of the photovoltaic system as the optimization objective, the positions of N particles are optimized according to the position constraint information to obtain the target parameter information corresponding to the second control signal. The target parameter information is used to support the controller of the photovoltaic system to regulate the output power of the photovoltaic system with the second control signal.

[0075] In the particle swarm optimization algorithm, the nth particle out of N particles updates its velocity based on its optimal position and the optimal positions among the N particles. It then uses this updated velocity to search for the optimal position of the nth particle and the optimal position among the N particles again in the search space. After all execution rounds, the optimal positions among the N particles are obtained, and the particle corresponding to the optimal position represents the maximum power of the photovoltaic system. Subsequently, the parameter information of the control signal corresponding to the optimal position among the N particles is input into the photovoltaic system's controller, which can regulate the photovoltaic system to maintain its maximum output power.

[0076] In one possible implementation, the N particles are optimized for position in K rounds based on the position constraint information to obtain the optimal fitness among the N*K fitness values ​​corresponding to the N*K positions of the N particles.

[0077] Based on the particle position corresponding to the optimal fitness, the target parameter information corresponding to the second control signal is obtained;

[0078] Wherein, for any fitness among the N*K fitnesss, the fitness is the actual output power of the photovoltaic system obtained by the controller of the photovoltaic system using the control signal corresponding to the particle position to regulate the photovoltaic system.

[0079] Based on the position constraints of the particles, K rounds of position optimization are performed on N particles, resulting in N*K updated positions for the N particles. Then, based on the parameter information of the N*K control signals corresponding to the N*K positions of the N particles, the parameter information of the control signals is input into the controller of the photovoltaic system to obtain N*K actual output powers of the photovoltaic system. These N*K actual output powers of the photovoltaic system are used as the N*K fitness values ​​corresponding to the N*K positions of the N particles in the K execution rounds. By comparing the magnitudes of the N*K fitness values ​​of the N particles, the optimal fitness among the fitness values ​​of the N particles is obtained, where the particle position of the optimal fitness corresponds to the optimal position among the N particles. Based on the optimal position among the particles corresponding to the optimal fitness of the N particles, the target information parameters corresponding to the second control signal are obtained.

[0080] In one possible implementation, in any kth execution round out of K execution rounds, the controller of the photovoltaic system is adjusted using the corresponding N control signals based on the positions of the N particles in the kth execution round, so as to obtain the N actual output powers corresponding to the kth execution round.

[0081] If the maximum value among the N true output powers is greater than the current optimal fitness value, then the maximum value among the N true output powers is updated as the optimal fitness.

[0082] For example, the number of particles N=3, the number of execution rounds K=90, the individual factor c1=0.5, the group factor c2=2, and the maximum inertia weight w. max =1.2, minimum inertia weight w min =0.8, the initial positions of N particles are initialized to random positions within the range of the positions of N particles.

[0083] Subsequently, the inertia weight of the particle swarm is updated for the kth time; for example, the inertia weight is updated according to the linearly decreasing inertia weight strategy, i.e., Formula 2 below.

[0084]

[0085] Among them, w k It is the inertial weight of the k-th particle, w max It is the maximum inertia weight, w min It is the minimum inertia weight, w k It is the inertia weight after the kth update, where k is the number of the current execution round, and K is the total number of execution rounds.

[0086] Next, based on the individual optimal fitness of the nth particle, the optimal fitness of the Nth particle, and other parameters, the position of the nth particle in the kth iteration is updated; where the individual optimal fitness of the nth particle is initialized to the actual output power corresponding to the initial position of the nth particle.

[0087] For example, the position and velocity of the nth particle in the kth iteration are updated using Equations 3 and 4 as follows.

[0088]

[0089]

[0090] Where v is the particle's velocity, x is the particle's position, n refers to the nth particle, where 1 ≤ n ≤ N, N is the total number of particles, k is the current execution round, w is the inertia weight, c1 is the individual factor, c2 is the group factor, r1 and r2 are two random numbers ranging from [0,1], and P best,n G is the individual optimal fitness of the nth particle. best It is the optimal fitness of N particles.

[0091] Subsequently, if the position of the nth particle at the kth time does not conform to the position constraint information of the N particles, that is, the position of the nth particle at the kth time exceeds the position range of the N particles, the position of the nth particle is immediately updated again.

[0092] If the position of the nth particle exceeds the set range, the methods to immediately update the position of the nth particle include: position restriction method, position reversal method, and random initialization method. For example, using the position reversal method, when the position of the nth particle exceeds the search range for the kth time, the velocity direction of the nth particle can be reversed, that is, the velocity in that dimension is reversed, causing it to move in the opposite direction, so that the nth particle returns to the set range.

[0093] Then, based on the position of the nth particle at the kth time, the duty cycle of the control signal corresponding to the position of the nth particle at the kth time is obtained. The duty cycle of the control signal is input into the controller of the photovoltaic system, and the actual output power corresponding to the photovoltaic system is measured as the actual output power corresponding to the position of the nth particle at the kth time.

[0094] Subsequently, if the actual output power corresponding to the position of the nth particle at the kth time is greater than the individual optimal fitness of the nth particle, then the individual optimal fitness of the nth particle is updated to the actual output power of the nth particle at the kth time.

[0095] If the actual output power corresponding to the position of the nth particle at the kth time is greater than the optimal fitness of the N particles, then the individual optimal fitness of the nth particle is updated to the actual output power of the nth particle at the kth time.

[0096] Finally, when all execution rounds K of the nth particle have ended, the target parameter information corresponding to the second control signal is obtained based on the particle position corresponding to the optimal fitness among the N particles. The target parameter information is used to support the controller of the photovoltaic system to regulate the output power of the photovoltaic system with the second control signal.

[0097] At this time, the output power of the photovoltaic system is regulated by the second control signal by having the controller receive the target parameter information corresponding to the second control signal.

[0098] In the example above, Figure 2 The diagram illustrates the relationship between the actual output power of a photovoltaic system and time. Specifically, the particle swarm optimization (PSO) curve represents the relationship between the actual output power of a photovoltaic system based on a traditional algorithm and time, while the finite random particle swarm optimization (FSO) curve represents the relationship between the actual output power of a photovoltaic system and time obtained using the method provided in this application.

[0099] Obviously, through Figure 2 The comparison results show that, under the same shading conditions—namely, the first shading condition of 0-5s, the second shading condition of 5-10s, and the third shading condition of 10-15s—the method provided in this application exhibits smaller fluctuations and a shorter time to obtain the maximum power value. Its convergence speed is 51.39% faster than traditional algorithms, power loss is reduced by 38.71%, and energy loss is reduced by 69.35%. Therefore, in practical applications, it can significantly reduce the power loss of photovoltaic systems and improve their performance.

[0100] Corresponding to the method provided in this application, a power tracking device 300 for a photovoltaic system is also provided. For example... Figure 3 As shown, the device includes: an environment judgment unit 301, a first calculation unit 303, a second calculation unit 305, and a power optimization unit 307.

[0101] The environmental judgment unit 301 is used to determine whether the change in the environmental information of the photovoltaic system at the current moment meets the preset conditions.

[0102] The first calculation unit 303 is used to process the environmental information through the photovoltaic system output model to obtain the first theoretical power when the change in the environmental information at the current moment meets the preset conditions.

[0103] The second calculation unit 305 is used to determine the first parameter information of the first control signal corresponding to the first theoretical power, and to determine the position constraint information of N particles contained in the particle swarm based on the first parameter information and the error range of the photovoltaic system output model. The positions of the N particles correspond to the parameter information of the N control signals.

[0104] The power optimization unit 307 is used to optimize the position of N particles based on the position constraint information with the goal of maximizing the output power of the photovoltaic system, and obtain the target parameter information corresponding to the second control signal. The target parameter information is used to support the controller of the photovoltaic system to regulate the output power of the photovoltaic system with the second control signal.

[0105] In one possible implementation, the environmental information includes at least the first average irradiance received by the photovoltaic system within the time interval corresponding to the current moment; wherein...

[0106] The environmental prediction unit 301 is specifically used to determine whether the absolute value of the difference between the first average irradiance intensity and the second average irradiance intensity received by the photovoltaic system in the previous time interval is greater than a preset threshold.

[0107] If so, then the change in the environmental information of the photovoltaic system at the current moment is determined to meet the preset conditions.

[0108] In one possible implementation, the parameter information includes: the duty cycle corresponding to the first control signal.

[0109] According to another embodiment, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed in a computer, causes the computer to perform a combination Figure 1 The method described is executed by the environment judgment unit 301, the first calculation unit 303, the second calculation unit 305, and the power optimization unit 307.

[0110] According to another embodiment, a computing device is also provided, including a memory and a processor, wherein executable code is stored in the memory, and when the processor executes the executable code, it implements a combination... Figure 1 The method described is executed by the environment judgment unit 301, the first calculation unit 303, the second calculation unit 305, and the power optimization unit 307.

[0111] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in this invention can be implemented using hardware, software, firmware, or any combination thereof. When implemented in software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium.

[0112] The above specific embodiments further illustrate the purpose, technical solution and beneficial effects of this application. It should be understood that the above are only specific embodiments of this application and are not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solution of this application should be included within the scope of protection of this application.

Claims

1. A power point tracking method for a photovoltaic system, characterized in that, The method includes: The average ambient temperature at the current moment is obtained through the corresponding temperature sensor; the average irradiance of each photovoltaic panel in the photovoltaic system at the current moment is obtained through the irradiance sensor. Determining whether the change in the environmental information of the photovoltaic system at the current moment meets the preset conditions includes: judging whether the absolute value of the difference between the average irradiance of the photovoltaic system in the time interval to which the current moment belongs and the average irradiance of the photovoltaic system in the previous time interval is greater than a second preset value; if so, it is determined that the change in the environmental information of the photovoltaic system at the current moment meets the preset conditions. If the preset conditions are met, the average ambient temperature and average irradiance of the photovoltaic system within the time interval to which the current moment belongs are input into the photovoltaic system output model. The photovoltaic system output model processes the environmental information to obtain the first theoretical power of the photovoltaic system at the current moment. The photovoltaic system output model includes a single quadratic term model, a double quadratic term model, an energy balance model, or a neural network model. The first parameter information of the first control signal corresponding to the first theoretical power of the photovoltaic system at the current moment is determined. Based on the first parameter information and the error range of the output model of the photovoltaic system, the position constraint information of N particles in the particle swarm is determined, and the positions of the N particles correspond to the parameter information of the N control signals. Each particle represents the output power of a photovoltaic system. Each particle has its own state during the search process, including position and velocity. The position of the particle is the position of the candidate solution in the search space, and the position of each particle corresponds to the parameter information of a single control signal. The velocity of the particle includes the direction and step size of the particle searching for solutions in the search space. The voltage value of the photovoltaic system is obtained according to the first theoretical power of the photovoltaic system, and the voltage value corresponding to the first theoretical power value is converted into the first parameter information of the first control signal of the controller in the photovoltaic system circuit; wherein, the first parameter information of the first control signal includes the duty cycle of the circuit; The error range of the photovoltaic system output model is obtained by comparing the true maximum power value of the photovoltaic system measured under various shading conditions with the first theoretical power value calculated by the photovoltaic system output model under various shading conditions. With maximizing the output power of the photovoltaic system as the optimization objective, the positions of the N particles are optimized according to the position constraint information to obtain the target parameter information corresponding to the second control signal. The target parameter information is used to support the controller of the photovoltaic system to regulate the output power of the photovoltaic system with the second control signal. The target parameter information corresponding to the second control signal is input into the controller of the photovoltaic system to obtain the output power of the photovoltaic system.

2. The method according to claim 1, characterized in that, The first parameter information includes: the duty cycle corresponding to the first control signal.

3. The method according to claim 1, characterized in that, The optimization objective is to maximize the output power of the photovoltaic system. Based on the position constraint information, the positions of the N particles are optimized to obtain the target parameter information corresponding to the second control signal, including: Based on the position constraint information, perform K rounds of position optimization on the N particles to obtain the optimal fitness among the N*K fitness values ​​corresponding to the N*K positions of the N particles; Based on the particle position corresponding to the optimal fitness, the target parameter information corresponding to the second control signal is obtained; Wherein, for any fitness among the N*K fitnesss, the fitness is the actual output power of the photovoltaic system obtained by the controller of the photovoltaic system using the control signal corresponding to the particle position to regulate the photovoltaic system.

4. The method according to claim 3, characterized in that, The step of performing K rounds of position optimization on the N particles based on the position constraint information to obtain the optimal fitness among the N*K fitness values ​​corresponding to the N*K positions of the N particles includes: In any k-th execution round out of K execution rounds, based on the positions of the N particles in the k-th execution round, the controller of the photovoltaic system is adjusted using the corresponding N control signals to obtain the N actual output powers corresponding to the k-th execution round; If the maximum value among the N true output powers is greater than the current optimal fitness, then the maximum value among the N true output powers is updated as the optimal fitness.

5. A power tracking device for a photovoltaic system, characterized in that, include: The environmental assessment unit is used to obtain the average ambient temperature at the current moment through the corresponding temperature sensor; and to obtain the average irradiance of each photovoltaic panel in the photovoltaic system at the current moment through the irradiance sensor. Determine whether the change in the environmental information of the photovoltaic system at the current moment meets the preset conditions; including: judging whether the absolute value of the difference between the average irradiance of the photovoltaic system in the time interval to which the current moment belongs and the average irradiance of the photovoltaic system in the previous time interval is greater than a second preset value. If so, determine that the change in the environmental information of the photovoltaic system at the current moment meets the preset conditions. The first calculation unit is used to input the average ambient temperature and average irradiance of the photovoltaic system within the time interval to which the current moment belongs into the photovoltaic system output model, and to process the environmental information through the photovoltaic system output model to obtain the first theoretical power of the photovoltaic system at the current moment, provided that preset conditions are met; the photovoltaic system output model includes a single quadratic term model, a double quadratic term model, an energy balance model, or a neural network model; The second calculation unit is used to determine the first parameter information of the first control signal corresponding to the first theoretical power of the photovoltaic system at the current moment, and to determine the position constraint information of N particles contained in the particle swarm based on the first parameter information and the error range of the output model of the photovoltaic system. The positions of the N particles correspond to the parameter information of the N control signals. Each particle represents the output power of a photovoltaic system. Each particle has its own state during the search process, including position and velocity. The position of the particle is the position of the candidate solution in the search space, and the position of each particle corresponds to the parameter information of a single control signal. The velocity of the particle includes the direction and step size of the particle searching for solutions in the search space. The voltage value of the photovoltaic system is obtained according to the first theoretical power of the photovoltaic system, and the voltage value corresponding to the first theoretical power value is converted into the first parameter information of the first control signal of the controller in the photovoltaic system circuit; wherein, the parameter information of the control signal includes the duty cycle of the circuit; The error range of the photovoltaic system output model is obtained by comparing the true maximum power value of the photovoltaic system measured under various shading conditions with the first theoretical power value calculated by the photovoltaic system output model under various shading conditions. The power optimization unit is used to optimize the positions of the N particles according to the position constraint information with the goal of maximizing the output power of the photovoltaic system, and obtain the target parameter information corresponding to the second control signal. The target parameter information is used to support the controller of the photovoltaic system to regulate the output power of the photovoltaic system with the second control signal. The target parameter information corresponding to the second control signal is input into the controller of the photovoltaic system to obtain the output power of the photovoltaic system.

6. The apparatus according to claim 5, characterized in that, The environmental information includes at least the first average irradiance received by the photovoltaic system within the time interval corresponding to the current moment; wherein, The environmental judgment unit is specifically used to determine whether the absolute value of the difference between the first average irradiance and the second average irradiance received by the photovoltaic system in the previous time interval is greater than a preset threshold. If so, then the change in the environmental information of the photovoltaic system at the current moment is determined to meet the preset conditions.

7. The apparatus according to claim 5, characterized in that, The parameter information includes: the duty cycle corresponding to the first control signal.

8. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed in a computing device, the computing device performs the method of any one of claims 1-4.

9. A computing device comprising a memory and a processor, wherein the memory stores a computer program, and the processor, when executing the computer program, implements the method of any one of claims 1-4.

Images