Local shadow photovoltaic array MPP positioning method considering PSO
The particle swarm optimization algorithm is used to adjust the irradiance parameters of the photovoltaic array and reconstruct the PV characteristic curve, which solves the accuracy problem of MPP positioning under local shadow conditions of traditional methods and realizes the MPP positioning accuracy and power accuracy measurement of the photovoltaic array.
Patent Information
- Application Number
- CN202510735748.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-04
- Publication Date
- 2025-10-17
AI Technical Summary
The traditional photovoltaic array MPP positioning method has difficulty in accurately locating the maximum power point under local shadow conditions, and the intelligent algorithm optimization has problems with convergence speed and steady-state oscillation, which cannot meet the accuracy requirements of the test instrument.
The particle swarm optimization (PSO) algorithm is used to dynamically adjust the irradiance parameters of the photovoltaic array. By building a refined mathematical model and fitting it with the measured power values, the PV characteristic curve is reconstructed to locate the MPP position.
The MPP positioning accuracy of photovoltaic arrays in partial shadow scenarios is significantly improved, and the measurement accuracy and output power of test instruments are improved.
Smart Images
Figure CN120811280A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of power electronics, in particular to a local shadow photovoltaic array MPP positioning method considering PSO. BACKGROUND
[0002] In recent years, photovoltaic power generation, as one of the most potential sustainable energy technologies, has been widely included in the energy structure optimization scheme. However, in the process of maximizing the efficiency of the photovoltaic system, there are still many technical challenges, among which the accurate positioning of the MPP (global extreme value analysis of the maximum power point) of the photovoltaic array curve testing instrument under the condition of local shadow is particularly prominent. The fixed step voltage scanning strategy adopted by the traditional testing method is easy to cause the characteristic point to be missed, and its determination criterion is difficult to accurately identify the global maximum power point. Although the existing improved scheme introduces intelligent algorithms to optimize the global search capability, it is still limited by the convergence speed and steady-state oscillation problem, and cannot meet the accuracy requirements of the testing instrument.
[0003] In view of the above problems, a local shadow photovoltaic array MPP positioning method considering PSO (particle swarm optimization) is proposed. SUMMARY
[0004] The MPP positioning method reconstructs the photovoltaic array characteristic curve by dynamically adjusting the irradiance parameter, significantly improving the MPP positioning accuracy of the photovoltaic array under the local shadow scene.
[0005] The technical solution to achieve the above purpose is: A local shadow photovoltaic array MPP positioning method considering PSO, comprising: Step S1, according to the single diode photovoltaic cell model, a refined mathematical model of the photovoltaic array is constructed, and the power of the photovoltaic array is calculated; Step S2, the measured power value is obtained by uniform sampling of the photovoltaic array curve testing instrument, the fitting voltage point distribution is determined, and after transmission and processing by the upper computer software, it is used as the fitting data set; Step S3, the PSO algorithm is used to solve the optimal irradiance intensity of each part of the photovoltaic array surface, and the voltage fitting point power value is calculated; Step S4, taking the least square error of the measured power and the model power as the objective function, the optimal combination of the irradiance parameter of the photovoltaic array is determined; Step S5, the P-V characteristic curve of the photovoltaic array is reconstructed based on the optimal combination of the irradiance parameter, and the MPP position is located by global extreme value analysis; Step S6, the coordinates of the repositioned maximum power point are imported into the upper computer of the photovoltaic array curve testing instrument, and set as the maximum power point reference value corresponding to the measured curve.
[0006] Preferably, in the step S1, the photovoltaic cell as a basic component unit of the photovoltaic array adopts a single-diode photovoltaic cell model, which is expressed as follows: ; In the formula, and are the voltage and the current of the photovoltaic cell respectively, is the photocurrent, which is proportional to the solar irradiance of the photovoltaic cell, is the reverse saturation current of the diode, is the diode ideality constant, is the thermal voltage, and are the series resistance and the shunt resistance respectively; The photovoltaic submodule is formed by connecting photovoltaic cells of the same type in series. When the photovoltaic submodule is in an environment without local shadow effect, the current-voltage characteristic of the photovoltaic submodule is consistent with the characteristic described by the above formula. At this time, the current and the voltage of the photovoltaic submodule can be expressed as: ; ; In the formula, is the number of photovoltaic cells in the photovoltaic array; When the photovoltaic submodule is in a local shadow condition, the partial shadow effect in the current-voltage characteristic of the photovoltaic cell is considered. In view of this, a bypass diode is introduced into the model. When the current of the photovoltaic cell is greater than the photocurrent, the bypass diode will be turned on to provide an additional path for the current, thereby avoiding that the photovoltaic cell in the shadow part bears an excessively large reverse bias voltage. Based on the above principle, the voltage and current mathematical model of the photovoltaic submodule can be expressed as: ; In the formula, is the voltage of the bypass diode, is the parallel resistance current, and the calculation formula is as follows: ; The photovoltaic cells inside the photovoltaic submodule are connected in series. Based on this structural characteristic, each photovoltaic submodule in the photovoltaic assembly is also connected in series. In consideration of different shadow degrees of the photovoltaic submodule, the I-V characteristic of the photovoltaic assembly can be expressed as: ; In the formula, is the assembly voltage, is the number of submodules in the photovoltaic assembly, is the assembly current, is the current of the th submodule Output voltage under When a submodule is shaded, its voltage contribution is negative, causing the output voltage of the PV module to decrease. Based on the above formula, the voltage and current of the PV string are derived as follows: ; Where, is the photovoltaic string voltage, is the number of PV modules in the PV string, is the photovoltaic string current, For the Components in the current Output voltage under The photovoltaic array is composed of photovoltaic series and parallel connections. Therefore, the voltage and current expression formula of the photovoltaic array is: ; Where, is the PV array voltage, is the number of photovoltaic strings in the photovoltaic array, is the photovoltaic array current, For the PV strings at voltage Output current under Based on the above formula, the calculated power of the photovoltaic array is: ; Where, is the power of the photovoltaic array.
[0007] Preferably, in step S2, the fitting voltage points are selected at uniform intervals, and the measured power values are obtained by uniform sampling using a photovoltaic array curve tester. , and transmit it to the host computer for fitting processing as the basic data for optimization target.
[0008] Preferably, in step S3, the control variable of the PSO algorithm is the irradiance parameter of each submodule , randomly generate the initial particle swarm, the position vector of each particle represents a set of irradiance distributions, namely , the constraints that need to be satisfied are: ; Where, is the radiation of the unshaded part of the photovoltaic array surface; In the PSO algorithm, each particle has an independent speed and position, and searches independently within a fixed range to obtain the local optimal solution for a single particle. , compare the local optimal solutions and get the overall optimal solution , the position and velocity of the particle detection are expressed as: ; ; In the formula, is the position of the particle, is the motion rate of the particle, is time, is the inertia weight, and are a natural cognitive learning factor and a social learning factor, respectively, and are random numbers in [0, 1]; According to the irradiance parameter of each particle , the model calculation power value corresponding to each voltage fitting point is calculated by bringing the photovoltaic array power mathematical model .
[0009] Preferably, in the step S4, according to the actual power value of the photovoltaic array and the model calculation power value , a target function is designed, and then the optimal combination of the irradiance parameter is determined, and the target function is expressed as: ; In the formula, is the number of fitting points; The target function is expressed as the sum of squares of differences between the actual power value and the model calculation power value , when the fitness value corresponding to the detected particle is less than the historical value, the fitness value is updated as a local optimal solution; the local optimal solutions are compared to obtain a global optimal solution.
[0010] Preferably, in the step S5, the combination of the irradiance parameters corresponding to the global optimal solution is selected, the complete P-V characteristic curve is reconstructed through the photovoltaic array model, and the P-V curve data is traversed to find the voltage point corresponding to the maximum power value, that is, the global MPP position.
[0011] Preferably, in the step S6, the voltage point coordinate corresponding to the maximum power value obtained by traversing in the step S5 is imported into the host computer of the photovoltaic array curve tester, and is set to display the maximum power point reference value corresponding to the measured curve on the curve, so that the test personnel reposition the selection of the photovoltaic array MPP position.
[0012] The beneficial effects of the present application are: the present application constructs a photovoltaic array refinement model, introduces a particle swarm optimization algorithm to dynamically adjust the irradiance parameters of the sub-modules, establishes an optimization criterion with the minimum variance of the measured power and the model calculated power as the target, realizes the dual functions of shadow pattern recognition and P-V curve reconstruction; this method effectively solves the performance limitations of traditional MPPT algorithms under complex lighting conditions, and provides an innovative solution for accurate measurement of photovoltaic array testers in non-uniform irradiation environment. BRIEF DESCRIPTION OF DRAWINGS
[0013] Figure 1 is a flowchart of a local shadow photovoltaic array MPP positioning method considering PSO of the present application; Figure 2 is a schematic diagram of a photovoltaic cell circuit model in the present application; Figure 3 is a schematic diagram of photovoltaic array output power before and after the P-V curve is reconstructed in the present application. DETAILED DESCRIPTION
[0014] The technical solutions of the present application will be described clearly and completely in combination with the drawings. In the description of the present application, it should be noted that the orientations or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer" and the like are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second", "third" are only for descriptive purposes and cannot be understood as indicating or implying opposite importance.
[0015] The present application will be further described in combination with the drawings.
[0016] The present application designs a local shadow photovoltaic array MPP positioning method considering PSO, which is used to position the MPP position of the photovoltaic array under local shadow conditions. By establishing a refined mathematical model of the photovoltaic array, the influence mechanism of the array topological structure and the local shadow distribution on the electrical characteristics is characterized. On this basis, the PSO algorithm is used to dynamically adjust the irradiance parameters of each sub-module, and the minimum mean square error of the measured power point and the model calculated power is minimized as the optimization target, the global optimal irradiance parameter combination is solved, and the P-V characteristic curve of the photovoltaic array is reconstructed. Finally, the MPP position is positioned based on the global extreme value analysis of the reconstructed curve, and the specific steps are as follows.
[0017] As shown in Figure 1 , a local shadow photovoltaic array MPP positioning method considering PSO includes: Step S1, according to the single-diode photovoltaic cell model, a refined mathematical model of the photovoltaic array is constructed, and the power of the photovoltaic array is calculated.
[0018] In the embodiment, the photovoltaic cell is used as the basic component unit of the photovoltaic array, and the single-diode photovoltaic cell model is adopted, the circuit model thereof is as shown in Figure 2 The expression is as follows: ; In the formula, and are the voltage and current of the photovoltaic cell respectively, is the photocurrent, which is proportional to the solar irradiance of the photovoltaic cell, is the reverse saturation current of the diode, is the diode ideality constant, is the thermal voltage, and are the series resistance and shunt resistance respectively; The photovoltaic sub-module is connected in series by photovoltaic cells of the same type, and when the photovoltaic sub-module is in an environment without local shadow effect, the current-voltage characteristic thereof is consistent with the characteristic described in the above formula, at this time, the current and the voltage of the photovoltaic sub-module can be expressed as: ; ; In the formula, is the number of photovoltaic cells in the photovoltaic array; When the photovoltaic sub-module is in a local shadow condition, the partial shadow effect in the current-voltage characteristic of the photovoltaic cell is considered, specifically, the photocurrent value in the photovoltaic cell model can be reduced, and when a plurality of photovoltaic cells are connected in series, the current flowing through each part is the same due to the characteristics of the series circuit, at this time, the photovoltaic cell in the non-shadow area will generate a relatively high photocurrent, forcing the photovoltaic cell in the shadow area to operate under the condition of reverse bias voltage, in view of this, a bypass diode is introduced in the model, when the photovoltaic cell current is greater than the photocurrent, the bypass diode will be turned on to provide an additional path for the current, avoiding the photovoltaic cell in the shadow part from bearing excessive reverse bias voltage, based on the above principle, the voltage and current mathematical model of the photovoltaic sub-module can be expressed as: ; In the formula, is the bypass diode voltage, is the parallel resistance current, and the calculation formula is as follows: ; The photovoltaic cells inside the photovoltaic submodule are connected in series. Based on this structural characteristic, the photovoltaic submodules in the photovoltaic assembly are also connected in series. In consideration of different shadow degrees of the photovoltaic submodules, the I-V characteristic of the photovoltaic assembly can be expressed as: ; In the formula, is the voltage of the assembly, is the number of submodules in the photovoltaic assembly, is the current of the assembly, is the output voltage of the first submodule at the current . When a submodule is shaded, its voltage contribution is negative, resulting in a decrease in the output voltage of the photovoltaic assembly. Based on the above formula, the voltage and current of the photovoltaic string are derived as: ; In the formula, is the voltage of the photovoltaic string, is the number of photovoltaic assemblies in the photovoltaic string, is the current of the photovoltaic string, is the output voltage of the first assembly at the current . The photovoltaic array is formed by connecting the photovoltaic strings in parallel. Therefore, the voltage and current expression formula of the photovoltaic array are: ; In the formula, is the voltage of the photovoltaic array, is the number of photovoltaic strings in the photovoltaic array, is the current of the photovoltaic array, is the output current of the first photovoltaic string at the voltage . Based on the above formula, the calculated power of the photovoltaic array is: ; In the formula, is the power of the photovoltaic array.
[0019] In step S2, the measured power values are obtained by uniform sampling through the photovoltaic array curve tester, the distribution of the fitting voltage points is determined, and after being transmitted and processed by the upper computer software, they are used as the fitting data set.
[0020] In the embodiment, the fitting voltage points are selected at uniform intervals, and the measured power values are obtained by uniform sampling through the photovoltaic array curve tester , and are transmitted to the upper computer for fitting processing as the basic data of the optimization target.
[0021] Step S3, the PSO algorithm is used to solve the optimal irradiance of each part of the photovoltaic array surface, and the voltage fitting point power value is calculated.
[0022] In the embodiment, the control variable of the PSO algorithm is the irradiance parameter of each sub-module , the initial particle swarm is randomly generated, and the position vector of each particle represents a set of irradiance distribution, that is , and the constraint condition to be met is: ; In the formula, is the radiation of the shadow-free part of the photovoltaic array surface; Each particle in the PSO algorithm has an independent speed and position, and is searched separately in a fixed range to obtain a local optimal solution of a single particle , the global optimal solution is obtained by comparing the local optimal solutions , and the position and speed of the particle are represented as: ; ; In the formula, is the position of the particle, is the motion rate of the particle, is the time, is the inertia weight, and are a natural cognitive learning factor and a social learning factor, respectively, and are random numbers in [0, 1]; According to the irradiance parameter of each particle , the model calculation power value corresponding to each voltage fitting point is calculated by bringing the photovoltaic array power mathematical model into the model calculation power value .
[0023] Step S4, taking the mean square error and minimization of the measured power and the model power as the objective function, the optimal combination of the irradiance parameter in the photovoltaic array is determined.
[0024] In the embodiment, the objective function is designed according to the actual power value of the photovoltaic array and the model calculation power value , and the optimal combination of the irradiance parameter is determined, and the objective function is represented as: ; In the formula, is the number of fitting points; The objective function is represented as the actual power value and the model calculation power value the sum of the squares of the differences, when the detected particle corresponds to the adaptive value less than the historical value, update it as a local optimal solution; compare the local optimal solutions to obtain the overall optimal solution.
[0025] Step S5, reconstruct the P-V characteristic curve of the photovoltaic array based on the optimal combination of irradiance parameters, and locate the MPP position through global extreme value analysis.
[0026] In the embodiment, the overall optimal solution corresponding to the irradiance parameter combination is selected, the complete P-V characteristic curve is reconstructed through the photovoltaic array model, and the P-V curve data is traversed to find the voltage point corresponding to the maximum power, that is, the global MPP position.
[0027] Step S6, import the repositioned maximum power point coordinates into the photovoltaic array curve tester host computer, and set it as the maximum power point reference value corresponding to the measured curve.
[0028] In the embodiment, the voltage point coordinates corresponding to the maximum power obtained by traversing step S5 are imported into the photovoltaic array curve tester host computer, and set as the maximum power point reference value corresponding to the measured curve displayed on the curve, and the selection of the repositioning of the photovoltaic array MPP position is performed by the tester.
[0029] To verify the effectiveness of the method, an experiment is performed in the photovoltaic array tester, and the experimental results are shown in Figure 3 As shown in the figure, when the photovoltaic array is subjected to local shadow shielding, the output power is limited to 320W at 1.2s due to the traditional MPPT algorithm falling into a local extreme value; and after using the method, the P-V curve reconstructed by dynamically adjusting the irradiance parameters of the sub-modules accurately reflects the global power characteristics, the MPP positioning accuracy is significantly improved, and the system output power can be increased to 370W, which is increased by 15.6% compared with the shielding scene, verifying the feasibility of the method under complex non-uniform irradiation conditions.
[0030] The above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.
Claims
1. A method for MPP positioning of partially shadowed photovoltaic arrays taking into account PSO, characterized in that: include: Step S1, constructing a refined mathematical model of the photovoltaic array based on a single-diode photovoltaic cell model, and calculating the power of the photovoltaic array; Step S2, obtaining the measured power value by uniform sampling through the photovoltaic array curve tester, determining the fitting voltage point distribution, and transmitting and processing it through the host computer software as the fitting data set; Step S3, using the PSO algorithm to solve the optimal irradiation intensity of each part of the photovoltaic array surface, and using this to calculate the power value of the voltage fitting point; Step S4, determining the optimal combination of irradiance parameters in the photovoltaic array by minimizing the mean square error between the measured power and the model power as the objective function; Step S5, reconstructing the PV characteristic curve of the photovoltaic array based on the optimal combination of irradiance parameters, and locating the MPP position through global extreme value analysis; Step S6: import the relocated maximum power point coordinates into the host computer of the photovoltaic array curve tester and set them as the maximum power point reference values of the corresponding measurement curve.
2. The method for MPP positioning of a partially shadowed photovoltaic array taking into account PSO according to claim 1, characterized in that: In step S1, the photovoltaic cell is used as the basic component unit of the photovoltaic array, and a single-diode photovoltaic cell model is used, which is expressed as follows: ； Where, and are the voltage and current of the photovoltaic cell, is the photocurrent, which is proportional to the solar radiation intensity of the photovoltaic cell. is the diode reverse saturation current, is the ideal constant of the diode, is the thermal voltage, and are the series resistance and the shunt resistance respectively; The photovoltaic sub-module is composed of photovoltaic cells of the same type connected in series. When the photovoltaic sub-module is located in an environment where there is no local shadow effect, its current-voltage characteristics are consistent with the characteristics described by the above formula. At this time, the current of the photovoltaic sub-module is and voltage It can be expressed as: ； ； Where, is the number of photovoltaic cells in the photovoltaic array; When the photovoltaic sub-module is in partial shadow, the partial shadow effect in the photovoltaic cell current-voltage characteristics is considered. In view of this, a bypass diode is introduced into the model. When the photovoltaic cell current is greater than the photocurrent, the bypass diode will be turned on, providing an additional path for the current to prevent the photovoltaic cell in the shadowed part from being subjected to excessive reverse bias voltage. Based on the above principle, the voltage and current mathematical model of the photovoltaic sub-module can be expressed as: ； Where, is the bypass diode voltage, is the parallel resistance current, and the calculation formula is as follows: ； The photovoltaic cells inside the photovoltaic sub-module are connected in series. Based on this structural characteristic, the photovoltaic sub-modules in the photovoltaic assembly are also connected in series. Considering the different shade levels of the photovoltaic sub-modules, the IV characteristics of the photovoltaic assembly can be expressed as: ； Where, is the component voltage, is the number of submodules in the PV panel, is the component current, For the The submodules are in current Output voltage under When a submodule is shaded, its voltage contribution is negative, causing the output voltage of the PV module to decrease. Based on the above formula, the voltage and current of the PV string are derived as follows: ； Where, is the photovoltaic string voltage, is the number of PV modules in the PV string, is the photovoltaic string current, For the Components in the current Output voltage under The photovoltaic array is composed of photovoltaic series and parallel connections. Therefore, the voltage and current expression formula of the photovoltaic array is: ； Where, is the PV array voltage, is the number of photovoltaic strings in the photovoltaic array, is the photovoltaic array current, For the PV strings at voltage Output current under Based on the above formula, the calculated power of the photovoltaic array is: ； Where, is the power of the photovoltaic array.
3. The method for MPP positioning of a partially shadowed photovoltaic array taking into account PSO according to claim 2, characterized in that: In step S2, the fitting voltage points are selected at uniform intervals, and the measured power values are obtained by uniform sampling using a photovoltaic array curve tester. , and transmit it to the host computer for fitting processing as the basic data for optimization target.
4. The method for MPP positioning of a partially shadowed photovoltaic array taking into account PSO according to claim 3, characterized in that: In step S3, the control variable of the PSO algorithm is the irradiance parameter of each submodule , randomly generate the initial particle swarm, the position vector of each particle represents a set of irradiance distributions, namely , the constraints that need to be satisfied are: ； Where, is the radiation of the unshaded part of the photovoltaic array surface; In the PSO algorithm, each particle has an independent speed and position, and searches independently within a fixed range to obtain the local optimal solution for a single particle. , compare the local optimal solutions and get the overall optimal solution , the position and velocity of the particle detection are expressed as: ； ； Where, is the position of the particle, is the particle's velocity, For time, is the inertia weight, and They are natural cognitive learning factors and social learning factors, and All are random numbers in [0,1]; , into the photovoltaic array power mathematical model, calculate the model calculation power value corresponding to each voltage fitting point According to the irradiance parameter of each particle .
5. The method for MPP positioning of a partially shadowed photovoltaic array taking into account PSO according to claim 4, characterized in that: In step S4, according to the actual power value of the photovoltaic array Calculate power value with model Design the objective function and then determine the optimal combination of irradiance parameters. The objective function is expressed as: ； Where, is the number of fitting points; The objective function is expressed as the actual power value Calculate power value with model The sum of the squares of the differences between the detected particles is the fitness value of the particle. If the value is smaller than the historical value, it is updated as the local optimal solution; the overall optimal solution is obtained by comparing the local optimal solutions.
6. The method for MPP positioning of a partially shadowed photovoltaic array taking into account PSO according to claim 5, characterized in that: In step S5, the irradiance parameter combination corresponding to the overall optimal solution is selected, the complete PV characteristic curve is reconstructed through the photovoltaic array model, and the PV curve data is traversed to find the voltage point corresponding to the maximum power, that is, the global MPP position.
7. The method for MPP positioning of a partially shadowed photovoltaic array taking into account PSO according to claim 6, characterized in that: In step S6, the voltage point coordinates corresponding to the maximum power value obtained in step S5 are imported into the host computer of the photovoltaic array curve tester, and set as the maximum power point reference value of the corresponding measurement curve to be displayed on the curve, and the tester repositions the selection of the photovoltaic array MPP position.