Photovoltaic MPPT optimization method and system

Abstract

The invention relates to the technical field of energy utilization optimization, in particular to a photovoltaic MPPT optimization method and system. The method comprises the following steps: acquiring an environment dynamic factor set, analyzing the environment dynamic factor set, and determining an environment influence factor; acquiring an equipment operation parameter set, analyzing the equipment operation parameter set, and determining an internal influence factor; based on the equipment operation parameter set, according to the environment influence factor and the internal influence factor, determining a dynamic initial adjustment node; and performing disturbance adjustment on the photovoltaic parameters according to the dynamic initial adjustment node, dynamically adjusting the disturbance amplitude according to an adjustment result, and determining and outputting a convergence power node. According to the method, the flexibility and the response speed of MPPT are improved, a guarantee is provided for effective utilization of clean energy, and the photovoltaic system is enabled to quickly approach the maximum power point, so that energy collection is maximized, and the power generation efficiency of the photovoltaic system is maintained at a high level.

Description

Technical Field

[0001] The present application relates to the technical field of energy utilization optimization, and in particular to a photovoltaic MPPT optimization method and system. Background Art

[0002] The rapid development of photovoltaic (solar photovoltaic) technology has made solar energy an increasingly important renewable energy source. Since the output of photovoltaic power generation systems is affected by many factors, how to effectively maximize the energy output of photovoltaic systems has become an important research topic. In this context, MPPT (Maximum Power Point Tracking) technology came into being.

[0003] However, the adaptive capabilities of existing MPPT technologies are unable to meet the needs of photovoltaic power generation systems with increasingly complex deployment scenarios, resulting in the process of determining the maximum power point of photovoltaic power generation systems being easily affected by the cross-influence of internal and external factors of photovoltaic modules, making it difficult for photovoltaic power generation systems to maintain a high level of power generation efficiency for a long time. Summary of the invention

[0004] The present application provides a photovoltaic MPPT optimization method and system to solve the above technical problems.

[0005] In a first aspect, the present application provides a photovoltaic MPPT optimization method, the method comprising: Acquire a set of environmental dynamic factors, analyze the set of environmental dynamic factors, and determine environmental influencing factors; Acquire a device operating parameter set, analyze the device operating parameter set, and determine internal influencing factors; Based on the equipment operation parameter set, according to the environmental influencing factors and the internal influencing factors, determining a dynamic starting adjustment node; According to the dynamic starting adjustment node, the photovoltaic parameters are disturbed and adjusted, and the disturbance amplitude is dynamically adjusted according to the adjustment result, and the convergence power node is determined and output.

[0006] Through this solution, the environmental dynamic factor set and the equipment operation parameter set are obtained and analyzed, and the environmental influencing factors and internal influencing factors that affect the power output are accurately identified. This process ensures the real-time response of the MPPT process to the changes in the external environment and internal equipment, so that the maximum power point analysis process can quickly track the maximum power point under different conditions. On this basis, by determining the dynamic starting adjustment node and adjusting the disturbance of the photovoltaic output power, combined with the strategy of dynamically adjusting the disturbance amplitude, the photovoltaic system can quickly approach the maximum power point, thereby maximizing energy collection and maintaining the power generation efficiency of the photovoltaic system at a high level. Furthermore, by confirming and outputting the convergent power node, it is ensured that the photovoltaic system operates stably and efficiently after optimization, and the power generation efficiency is significantly improved. The above optimization method improves the flexibility and response speed of MPPT, and provides a guarantee for the effective use of clean energy.

[0007] Optionally, the environmental dynamic factor set includes light intensity monitoring information, component sun-facing surface image set, air particulate monitoring information and climate monitoring information, and the analyzing the environmental dynamic factor set to determine the environmental impact factor includes: Analyze the image set of the sun-facing side of the component to determine a static shadow ratio, a dynamic shadow ratio and a shadow intensity data set; Based on the light intensity monitoring information, and according to the shadow intensity data set, the static shadow proportion and the dynamic shadow proportion are analyzed to determine basic light intensity distribution information; Analyzing the air particulate monitoring information based on the climate monitoring information to determine an attachment index; According to the attachment index, the basic illumination intensity distribution information is fine-tuned for attachment influence, effective illumination intensity distribution information is determined, and the effective illumination intensity distribution information is used as the environmental impact factor.

[0008] Through this scheme, starting from the two key aspects of shadow and attachment, the various environmental factors that affect the actual light intensity received by the photovoltaic modules are analyzed, and the static shadow ratio, dynamic shadow ratio and shadow intensity data sets are obtained. The basic light intensity distribution information that can characterize the light intensity received by the photovoltaic modules under the influence of shadows is quantified, and the amount of particle attachment on the surface of the photovoltaic panel is further characterized by quantitatively analyzing the attachment index. The basic light intensity distribution information is adjusted based on the attachment index to obtain the effective light intensity distribution information that can reflect the actual light intensity received by the photovoltaic panel. This is used as an environmental influencing factor to effectively ensure the sensitivity of the subsequent maximum power point tracking analysis process to environmental changes.

[0009] Optionally, the analyzing the static shadow proportion and the dynamic shadow proportion based on the light intensity monitoring information and according to the shadow intensity data set to determine basic light intensity distribution information includes: Determining an ambient light intensity benchmark according to the light intensity monitoring information; Determining static shadow intensity and dynamic shadow intensity according to the shadow intensity data set; Based on the ambient light intensity benchmark, the basic light intensity on the surface of each photovoltaic module is determined according to the shadow intensity data set, the static shadow ratio and the dynamic shadow ratio, specifically the following formula: ; in, is the basic light intensity, is the ambient light intensity benchmark, is the static shadow intensity, is the static shadow ratio, To preset static adjustment parameters, is the dynamic shadow intensity, To dynamically adjust the parameters for the preset, is the dynamic shadow ratio; The basic light intensity distribution information is constructed according to the basic light intensity corresponding to each photovoltaic component.

[0010] Through this scheme, mathematical analysis methods are used, based on the ambient light intensity benchmark, according to the shadow intensity data set, static shadow ratio and dynamic shadow ratio, and a mathematical formula is designed to quantitatively describe the joint impact of static shadows and dynamic shadows on light intensity, and the basic light intensity corresponding to the photovoltaic modules is obtained, so that the basic light intensity conforms to the actual situation of the photovoltaic modules under the influence of shadows, thereby improving the accuracy and scientificity of the subsequent maximum power point analysis process.

[0011] Optionally, the climate monitoring information includes air humidity, average wind speed and ambient temperature, the air particle monitoring information includes air particle content, and the air particle monitoring information is analyzed based on the climate monitoring information to determine the adhesion index, which is specifically the following formula: ; in, is the adhesion index, is the preset impact adjustment coefficient, is the air humidity, is the air particulate matter content, is the preset adjustment index, is the average wind speed, is the preset temperature influence coefficient, is the ambient temperature, is the preset reference conversion temperature; The step of fine-tuning the basic illumination intensity distribution information for the attachment effect according to the attachment index to determine the effective illumination intensity distribution information includes: According to the attachment index, the basic illumination intensity corresponding to each photovoltaic module is adjusted to determine the effective illumination intensity corresponding to each photovoltaic module, which is specifically the following formula: ; in, is the effective light intensity, is the basic light intensity, is the adhesion index; The effective light intensity distribution information is constructed according to the effective light intensity corresponding to each photovoltaic component.

[0012] Through this scheme, mathematical analysis methods are used, and mathematical formulas are designed based on climate monitoring information and air particle monitoring information to describe the effects of air humidity, average wind speed and ambient temperature on particle attachment, so as to quantify the attachment index that can reflect the amount of particle attachment on the surface of photovoltaic modules. On this basis, through further mathematical analysis process, according to the attachment index, the basic light intensity is adjusted to obtain the effective light intensity received by the photovoltaic modules under the influence of attachment and shadow, so as to improve the accuracy and scientificity of the effective light intensity, and thus improve the comprehensiveness of the subsequent maximum power point analysis process.

[0013] Optionally, the device operation parameter set includes load fluctuation information and electrical noise change information, and the analyzing the device operation parameter set to determine the internal influencing factors includes: Analyze the load fluctuation information to determine the load standard deviation and the load average; Determining a load fluctuation amplitude according to the load standard deviation and the load average value; Analyzing the electric noise variation information to determine the electric noise standard deviation and the electric noise average value; The load fluctuation amplitude, the electric noise standard deviation and the electric noise average value are used as the internal influencing factors.

[0014] Through this scheme, the load fluctuation information and the electric noise change information are analyzed, and the load standard deviation, load average value, electric noise standard deviation and electric noise average value that can reflect the load change characteristics and electric noise change characteristics in the photovoltaic system are obtained respectively. The load fluctuation amplitude is further determined according to the ratio of the load standard deviation to the load average value, and the load fluctuation amplitude, electric noise standard deviation and electric noise average value are used as internal influencing factors, which is helpful to comprehensively consider the influence of load changes and electric noise fluctuations in the subsequent maximum power point tracking process.

[0015] Optionally, the device operation parameter set further includes a current current and a current voltage, and the determining the dynamic start adjustment node based on the device operation parameter set and according to the environmental influencing factor and the internal influencing factor includes: Extracting load monitoring duration, angular frequency and phase constant according to the load fluctuation information; Based on the current current, determining a disturbance starting current according to the load monitoring duration, the angular frequency, the phase constant, the environmental influencing factor and the internal influencing factor; Based on the current voltage, determining a disturbance starting voltage according to the load monitoring duration, the angular frequency, the phase constant, the environmental influencing factor and the internal influencing factor; The dynamic start adjustment node is constructed according to the disturbance start current and the disturbance start voltage.

[0016] Through this scheme, the disturbance starting current and the disturbance starting voltage are quantitatively analyzed according to the load monitoring duration, angular frequency, phase constant, environmental influencing factors and internal influencing factors, and a dynamic starting adjustment node is constructed based on this, to ensure that the subsequent maximum power point tracking process can respond quickly to load changes under environmental and internal influences, and significantly shorten the distance between the dynamic starting adjustment node and the maximum power point, so as to significantly improve the tracking speed of the maximum power point.

[0017] Optionally, the disturbance starting current is determined based on the current current according to the load monitoring duration, the angular frequency, the phase constant, the environmental influencing factor and the internal influencing factor, specifically as the following formula: ; in, is the disturbance starting current, is the current, is the preset light-current relationship coefficient, is the effective light intensity, To preset the lighting benchmark, The preset current light impact adjustment index, is the preset current load relationship coefficient, is the load fluctuation amplitude, is the angular frequency, is the load monitoring duration, is the phase constant, is the Gaussian noise model, is the average value of the electrical noise, is the average value of the electrical noise.

[0018] Through this scheme, mathematical analysis methods are used to design mathematical formulas based on load monitoring duration, angular frequency, phase constant, environmental influencing factors and internal influencing factors to scientifically quantify the disturbance starting current, so that the analysis process of the disturbance starting current covers consideration of the environment, load changes and electrical noise fluctuations, thereby improving the accuracy of the disturbance starting current and thus improving the scientific nature of the dynamic starting adjustment node.

[0019] Optionally, the disturbance starting voltage is determined based on the current voltage according to the load monitoring duration, the angular frequency, the phase constant, the environmental influencing factor and the internal influencing factor, specifically as the following formula: ; in, is the disturbance starting voltage, is the current, is the preset light-voltage relationship coefficient, is the effective light intensity, To preset the lighting benchmark, The preset voltage light impact adjustment index, is the preset voltage-load relationship coefficient, is the load fluctuation amplitude, is the angular frequency, is the load monitoring duration, is the phase constant, is the Gaussian noise model, is the average value of the electrical noise, is the average value of the electrical noise.

[0020] Through this scheme, mathematical analysis methods are used to design mathematical formulas based on load monitoring duration, angular frequency, phase constant, environmental influencing factors and internal influencing factors to scientifically quantify the disturbance starting voltage, so that the analysis process of the disturbance starting voltage covers consideration of the environment, load changes and electrical noise fluctuations, improves the accuracy of the disturbance starting voltage, and further improves the scientific nature of the dynamic starting adjustment node.

[0021] Optionally, the step of performing disturbance adjustment on the photovoltaic output power according to the dynamic starting adjustment node, and dynamically adjusting the disturbance amplitude according to the adjustment result, and determining and outputting the convergence power node includes: According to the disturbance starting current and the disturbance starting voltage corresponding to the dynamic starting adjustment node, the real-time current and the real-time voltage are adjusted, and the real-time output power is determined accordingly; According to a preset starting adjustment amplitude, a disturbance adjustment is performed on the real-time current and the real-time voltage to determine the output power after the disturbance; Comparing the post-disturbance output power with the real-time output power to determine a dynamic power difference; Determine the subsequent disturbance direction according to the dynamic power difference, and perform binary reduction processing on the subsequent disturbance amplitude to determine the subsequent disturbance result; According to the subsequent disturbance result, the converged power node is determined and output.

[0022] Through this scheme, according to the analysis of the dynamic power difference corresponding to different disturbance adjustment stages, the disturbance direction and disturbance amplitude are dynamically adjusted during the disturbance adjustment process to improve the tracking speed of the disturbance observation algorithm for the maximum power point, and through the binary decreasing processing of the disturbance amplitude, the unpredictable nonlinear reaction caused by excessive disturbance is prevented, the reliability of the disturbance observation algorithm is improved, and the obtained converged power node can keep the photovoltaic system at a high-efficiency power generation level.

[0023] In a second aspect, the present application provides a photovoltaic MPPT optimization system, the system comprising: An environmental analysis module, used to obtain a set of environmental dynamic factors, analyze the set of environmental dynamic factors, and determine environmental influencing factors; An internal analysis module, used to obtain a device operation parameter set, analyze the device operation parameter set, and determine internal influencing factors; A node analysis module, configured to determine a dynamic start adjustment node based on the equipment operation parameter set, the environmental influencing factor and the internal influencing factor; The disturbance output module is used to perform disturbance adjustment on the photovoltaic parameters according to the dynamic starting adjustment node, dynamically adjust the disturbance amplitude according to the adjustment result, and determine and output the convergence power node. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0025] Figure 1 A schematic diagram of an application scenario provided for an embodiment of the present application; Figure 2 A flowchart of a photovoltaic MPPT optimization method provided in one embodiment of the present application; Figure 3 A schematic diagram of the structure of a photovoltaic MPPT optimization system provided in one embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0027] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0028] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0029] The adaptive capability of existing MPPT technology cannot meet the needs of photovoltaic power generation systems with increasingly complex deployment scenarios, resulting in the process of determining the maximum power point of photovoltaic power generation systems being easily affected by the cross-influence of internal and external factors of photovoltaic modules, making it difficult for the power generation efficiency of photovoltaic power generation systems to be maintained at a high level for a long time.

[0030] Based on this, the present application provides a method and system for optimizing MPPT for photovoltaics. The environmental dynamic factor set and the equipment operation parameter set are obtained and analyzed, and the environmental influencing factors and internal influencing factors that affect the power output are accurately identified. This process ensures the real-time response of the MPPT process to the changes in the external environment and internal equipment, so that the maximum power point analysis process can quickly track the maximum power point under different conditions. On this basis, by determining the dynamic starting adjustment node and adjusting the disturbance of the photovoltaic output power, combined with the strategy of dynamically adjusting the disturbance amplitude, the photovoltaic system can quickly approach the maximum power point, thereby maximizing energy collection and maintaining the power generation efficiency of the photovoltaic system at a high level. Further, by confirming and outputting the convergence power node, it is ensured that the photovoltaic system operates stably and efficiently after optimization, and the power generation efficiency is significantly improved. The above optimization method improves the flexibility and response speed of MPPT, and provides a guarantee for the effective use of clean energy.

[0031] Figure 1 A schematic diagram of an application scenario provided by the present application. In the photovoltaic power generation process, the method provided by the present application is applied to optimize the tracking and analysis process of the maximum power point in the photovoltaic power generation system, thereby achieving a comprehensive and efficient analysis of the maximum power point.

[0032] Specifically, the method of the present application is applied to any server, which communicates with the environmental parameter sensor and the parameter monitoring sensor respectively. The server obtains and analyzes the environmental dynamic factor set provided by the environmental parameter sensor and the equipment operating parameters provided by the parameter monitoring sensor, and accurately identifies the environmental influencing factors and internal influencing factors that affect the power output. This process ensures the real-time response of the MPPT process to the changes in the external environment and internal equipment, so that the maximum power point analysis process can quickly track the maximum power point under different conditions. On this basis, by determining the dynamic starting adjustment node and adjusting the disturbance of the photovoltaic output power, combined with the strategy of dynamically adjusting the disturbance amplitude, the photovoltaic system quickly approaches the maximum power point, thereby maximizing energy collection and maintaining the power generation efficiency of the photovoltaic system at a high level. Further, by confirming and outputting the convergence power node, it is ensured that the photovoltaic system operates stably and efficiently after optimization, and the power generation efficiency is significantly improved. The above optimization method improves the flexibility and response speed of MPPT, and provides a guarantee for the effective use of clean energy. The specific implementation method can refer to the following embodiments.

[0033] Figure 2 This is a flow chart of a photovoltaic MPPT optimization method provided in an embodiment of the present application. The method of this embodiment can be applied to the server in the above scenario. Figure 2 As shown, the method includes: S201. Obtain a set of environmental dynamic factors, analyze the set of environmental dynamic factors, and determine environmental influencing factors.

[0034] The environmental dynamic factor set can be a collection of factors in the environment where the photovoltaic system is located that affect the light intensity received by the photovoltaic panel, such as light, weather, and obstructions. The environmental dynamic factor set can be obtained through different types of environmental parameter sensors deployed in the photovoltaic power station.

[0035] The environmental impact factor may be specific quantitative data that can characterize the light intensity received by the photovoltaic panel under the influence of environmental dynamic factors.

[0036] Specifically, in a photovoltaic power generation system, changes in the external environment will directly affect the power generation efficiency. Specifically, the light intensity, weather changes and shadows caused by obstructions or attachments in the external environment will directly affect the light intensity received by the photovoltaic panel, thereby causing fluctuations in the photoelectric conversion efficiency. This fluctuation will directly affect the subsequent tracking of the maximum power point. Therefore, through mathematical analysis, a comprehensive analysis of the different factors in the environmental dynamic factor set is carried out to obtain the changing characteristics of the actual light intensity under the cross-influence of different factors, so as to provide data support for the subsequent tracking of the maximum power point, so that the subsequent maximum power point analysis process can maintain high sensitivity and high adaptability to environmental changes.

[0037] S202: Acquire a device operation parameter set, analyze the device operation parameter set, and determine internal influencing factors.

[0038] The equipment operation parameter set may be a set of real-time parameters during the operation of equipment in a photovoltaic system, such as current, voltage, load, and electrical noise. The equipment operation parameter set may be obtained through different types of parameter monitoring sensors deployed inside the photovoltaic equipment.

[0039] The internal influencing factors may be specific quantitative data within the equipment operating parameter set that may have a significant impact on the maximum power point tracking process.

[0040] Specifically, the operating status of the internal equipment of the photovoltaic system will also affect the overall efficiency of the photovoltaic system, and thus affect the analysis and tracking of the maximum power point. For example, changes in the load of the photovoltaic system will lead to changes in the power demand, and the photovoltaic system will need to readjust the working point that determines its output power to achieve maximum power output. Electrical noise will affect the accuracy of various basic data in the maximum power point analysis process, such as current and voltage, which will lead to a significant deviation between the maximum power point obtained by analysis and the actual maximum power point. Therefore, through mathematical analysis, the equipment operation parameter set is screened and analyzed, and then the data that will have a significant impact on the maximum power point tracking process is quantified, so as to improve the data support for the subsequent maximum power point tracking, so that the subsequent maximum power point analysis process can cope with the influence from the internal equipment of the photovoltaic system, and further improve the accuracy and adaptability of the maximum power point.

[0041] S203: Determine a dynamic start adjustment node based on the equipment operation parameter set, environmental influencing factors and internal influencing factors.

[0042] The dynamic start adjustment node may be a disturbance start node corresponding to a disturbance observation algorithm for tracking a maximum power point under the influence of the current environment and equipment.

[0043] Specifically, the existing technology usually analyzes and tracks the maximum power point in the photovoltaic system through a perturbation observation algorithm. Because the perturbation observation algorithm technology has a long sedimentation time and low hardware requirements, it can adapt to photovoltaic systems of different sizes and types. However, the perturbation observation algorithm is difficult to respond quickly when facing rapidly changing input conditions, resulting in the system being unable to lock the maximum power point in time. This is mainly determined by the underlying mechanism of the perturbation observation algorithm. In the process of the perturbation observation algorithm analyzing the maximum power point, it is necessary to first determine the starting disturbance node, and based on this node, adjust the current and voltage through a preset adjustment range, and receive feedback on the output power, and make the next disturbance adjustment based on the feedback. The existing starting disturbance node Usually, it is preset based on historical data or expert advice, which leads to a large fluctuation in the distance between the starting disturbance node and the maximum power point. When the distance between the starting disturbance node and the maximum power point is large, under frequent changes in input conditions, the disturbance observation algorithm needs to go through many disturbance adjustments to approach the maximum power point, resulting in obvious lag in the disturbance observation algorithm, which in turn causes increased efficiency loss of the photovoltaic system. Therefore, through mathematical analysis, a comprehensive pre-analysis of environmental influencing factors and internal influencing factors is carried out. Before the disturbance observation algorithm performs tracking analysis, a dynamic starting adjustment node close to the maximum power point is provided for it, which can significantly improve the real-time performance of the disturbance observation algorithm and help it quickly locate the maximum power point.

[0044] S204, according to the dynamic starting adjustment node, the photovoltaic parameters are disturbed and adjusted, and the disturbance amplitude is dynamically adjusted according to the adjustment result, and the convergence power node is determined and output.

[0045] Photovoltaic parameters can be parameter data in a photovoltaic system that has a direct impact on the photovoltaic output system, including current and voltage.

[0046] The disturbance amplitude may be the amplitude of the disturbance adjustment of the photovoltaic parameters through the disturbance observation algorithm.

[0047] The converged power node may be a photovoltaic system output power node that is finally determined after disturbance adjustment, and the node is extremely close to or reaches an actual maximum power point.

[0048] Specifically, after determining the dynamic starting adjustment node, starting from the underlying mechanism of the aforementioned disturbance observation algorithm, if the maximum power point is still tracked according to the preset disturbance amplitude, the convergence speed of the disturbance observation algorithm is difficult to guarantee, and it is necessary to dynamically adjust the next disturbance amplitude according to the result of each layer of disturbance adjustment to narrow the subsequent tracking range and improve the convergence speed of the disturbance observation algorithm, so as to obtain the convergence power node that can be used as the maximum power point of the current photovoltaic system, and provide the convergence power node to the photovoltaic system maintenance personnel, so that the photovoltaic system maintenance personnel can grasp the maximum power point in the photovoltaic system in real time, and use it as the basis for adjusting the photovoltaic system.

[0049] Through this solution, the environmental dynamic factor set and the equipment operation parameter set are obtained and analyzed, and the environmental influencing factors and internal influencing factors that affect the power output are accurately identified. This process ensures the real-time response of the MPPT process to the changes in the external environment and internal equipment, so that the maximum power point analysis process can quickly track the maximum power point under different conditions. On this basis, by determining the dynamic starting adjustment node and adjusting the disturbance of the photovoltaic output power, combined with the strategy of dynamically adjusting the disturbance amplitude, the photovoltaic system can quickly approach the maximum power point, thereby maximizing energy collection and maintaining the power generation efficiency of the photovoltaic system at a high level. Furthermore, by confirming and outputting the convergent power node, it is ensured that the photovoltaic system operates stably and efficiently after optimization, and the power generation efficiency is significantly improved. The above optimization method improves the flexibility and response speed of MPPT, and provides a guarantee for the effective use of clean energy.

[0050] In some embodiments, the component sun-facing side image set is analyzed to determine the static shadow ratio, dynamic shadow ratio and shadow intensity data set; based on the light intensity monitoring information, according to the shadow intensity data set, the static shadow ratio and the dynamic shadow ratio are analyzed to determine the basic light intensity distribution information; based on the climate monitoring information, the air particulate monitoring information is analyzed to determine the attachment index; according to the attachment index, the basic light intensity distribution information is fine-tuned for the attachment effect, the effective light intensity distribution information is determined, and the effective light intensity distribution information is used as an environmental impact factor.

[0051] The set of environmental dynamic factors includes light intensity monitoring information, component sun-facing side image set, air particle monitoring information and climate monitoring information.

[0052] The light intensity monitoring information may be light intensity monitoring information in the environment where the photovoltaic system is located, and the light intensity monitoring information may be obtained through a light sensor in the environmental parameter sensor.

[0053] The component sun-facing side image set may be a collection of images of the sun-facing side of a photovoltaic panel in a photovoltaic device component, and the component sun-facing side image set may be obtained by an image sensor in an environmental parameter sensor.

[0054] The air particle monitoring information may be information on the particle content in the air in the environment where the photovoltaic system is located. The air particle monitoring information may be obtained through a light scattering particle sensor in the environmental parameter sensor.

[0055] The climate monitoring information may be climate parameter information in the environment where the photovoltaic system is located, such as temperature, humidity, wind speed, etc. The climate monitoring information may be obtained through a meteorological sensor in the environmental parameter sensor.

[0056] The static shadow ratio may be the ratio of the static shadow on the sun-facing side of the photovoltaic panel, and the static shadow may be the shadow caused by the shading of a static object.

[0057] The dynamic shadow ratio may be the ratio of the dynamic shadow on the sun-facing side of the photovoltaic panel, and the dynamic shadow may be the shadow caused by the occlusion of a dynamic object.

[0058] The shadow intensity data set may be a data set including shadow intensities corresponding to static shadows and dynamic shadows, and the shadow intensity may be a quantitative value representing the degree of shadow shading.

[0059] The basic light intensity distribution information may be the light intensity received by each photovoltaic panel in the photovoltaic system under the influence of the external environment.

[0060] The adhesion index can be a quantitative value that characterizes the degree of particle adhesion on the surface of the photovoltaic panel. The larger the adhesion index, the more particles are attached to the surface of the photovoltaic panel.

[0061] The effective light intensity distribution information can be the effective light intensity received by each photovoltaic panel in the photovoltaic system, which is obtained based on the influence of the external environment and the adhesion of particulate matter on the photovoltaic panel itself.

[0062] Specifically, changes in various factors in the environment in which the photovoltaic panels in the photovoltaic system are located will directly affect the light intensity they receive, and thus affect the performance of the maximum power point. Therefore, the light intensity in the environment cannot be simply used as the actual light intensity received by the photovoltaic panels. The edge extraction algorithm in the image analysis technology is used to extract the shadow contours in the sun-facing image set, and the changes in each shadow contour are tracked and analyzed in time series to identify the static shadow contours and dynamic shadow contours, and obtain the static shadow ratio and dynamic shadow ratio. The memory cloth extracts the image brightness in the static shadow contours and dynamic shadow contours, and compares them with the image brightness in the non-shadow area. The greater the deviation, the greater the shadow intensity. The shadow intensity corresponding to the static shadow and dynamic shadow is determined to obtain the shadow intensity data set, and then through Through mathematical analysis, according to the static shadow ratio and the dynamic shadow ratio, combined with the corresponding shadow intensity data set, the basic light intensity of each photovoltaic panel under the influence of shadow is determined. At the same time, considering that the photovoltaic panels are outdoors, they are easily affected by the climate, causing impurities to adhere to the surface of the photovoltaic panels, affecting the actual light intensity received by the photovoltaic panels under the basic light intensity. Through mathematical analysis, the data directly affecting the amount of particle attachment to the photovoltaic panels in the climate monitoring information are integrated and analyzed, and the attachment index is quantified to characterize the amount of particle attachment on the surface of the photovoltaic panels. The basic light intensity is further adjusted numerically to obtain the effective light intensity distribution information that can accurately reflect the actual light intensity received by the photovoltaic panels under the influence of shadows and attachments, and the effective light intensity distribution information is used as an environmental influencing factor.

[0063] Through this scheme, starting from the two key aspects of shadow and attachment, the various environmental factors that affect the actual light intensity received by the photovoltaic modules are analyzed, and the static shadow ratio, dynamic shadow ratio and shadow intensity data sets are obtained. The basic light intensity distribution information that can characterize the light intensity received by the photovoltaic modules under the influence of shadows is quantified, and the amount of particle attachment on the surface of the photovoltaic panel is further characterized by quantitatively analyzing the attachment index. The basic light intensity distribution information is adjusted based on the attachment index to obtain the effective light intensity distribution information that can reflect the actual light intensity received by the photovoltaic panel. This is used as an environmental influencing factor to effectively ensure the sensitivity of the subsequent maximum power point tracking analysis process to environmental changes.

[0064] In some embodiments, an ambient light intensity benchmark is determined based on light intensity monitoring information; a static shadow intensity and a dynamic shadow intensity are determined based on a shadow intensity data set; and a basic light intensity on the surface of each photovoltaic module is determined based on the ambient light intensity benchmark and the shadow intensity data set, the static shadow ratio, and the dynamic shadow ratio, specifically as follows: (1) in, is the basic light intensity, is the ambient light intensity benchmark, is the static shadow intensity, is the static shadow ratio, To preset static adjustment parameters, is the dynamic shadow intensity, To dynamically adjust the parameters for the preset, is the dynamic shadow ratio; according to the basic light intensity corresponding to each photovoltaic component, the basic light intensity distribution information is constructed.

[0065] The ambient light intensity benchmark may be an average light intensity of the environment in which the photovoltaic system is located, and the ambient light intensity benchmark may be obtained by performing mean processing on a light intensity data set collected by an environmental parameter sensor.

[0066] The basic light intensity may be the light intensity on the surface of the photovoltaic panel under the influence of shadow.

[0067] The preset static adjustment parameter may be a parameter used to adjust the effect of the static shadow ratio on the basic illumination intensity at its corresponding shadow intensity. The preset static adjustment parameter may be obtained by fitting based on experimental data.

[0068] The preset dynamic adjustment parameter may be a parameter used to adjust the effect of the dynamic shadow ratio on the basic illumination intensity at its corresponding shadow intensity. The preset dynamic adjustment parameter may be obtained by fitting based on experimental data.

[0069] Specifically, through formula (1) Describe the effect of static shadow ratio on basic illumination intensity at its corresponding shadow intensity. Since the object causing the static shadow is in a non-moving state, the static shadow profile on the surface of the photovoltaic panel is only affected by the change in light irradiation angle. Therefore, a logarithmic function is used to describe the effect of static shadow on basic illumination intensity under smooth changes. The influence of dynamic shadow proportion on basic illumination intensity at its corresponding shadow intensity is described. Since the object causing dynamic shadow is in motion, the proportion of dynamic shadow changes rapidly in a short period of time. Therefore, an exponential function is used to simulate the characteristic that the influence of dynamic shadow increases rapidly when the proportion increases, so that the influence of dynamic shadow conforms to the actual situation. Through a comprehensive analysis of the influence of static shadow and dynamic shadow on basic illumination intensity, the basic illumination intensity corresponding to the photovoltaic module is obtained based on the ambient illumination intensity benchmark.

[0070] Through this scheme, mathematical analysis methods are used, based on the ambient light intensity benchmark, according to the shadow intensity data set, static shadow ratio and dynamic shadow ratio, and a mathematical formula is designed to quantitatively describe the joint impact of static shadows and dynamic shadows on light intensity, and the basic light intensity corresponding to the photovoltaic modules is obtained, so that the basic light intensity conforms to the actual situation of the photovoltaic modules under the influence of shadows, thereby improving the accuracy and scientificity of the subsequent maximum power point analysis process.

[0071] In some embodiments, the air particle monitoring information includes the air particle content. Based on the climate monitoring information, the air particle monitoring information is analyzed to determine the attachment index, which is specifically the following formula (2): (2) in, is the adhesion index, is the preset impact adjustment coefficient, is the air humidity, is the air particulate matter content, is the preset adjustment index, is the average wind speed, is the preset temperature influence coefficient, is the ambient temperature, is the preset reference conversion temperature; according to the attachment index, the basic light intensity corresponding to each photovoltaic module is adjusted to determine the effective light intensity corresponding to each photovoltaic module, which is specifically the following formula (3): (3) in, is the effective light intensity, is the basic light intensity, is the attachment index; according to the effective light intensity corresponding to each photovoltaic module, the effective light intensity distribution information is constructed.

[0072] Climate monitoring information includes air humidity, average wind speed and ambient temperature.

[0073] Air particle monitoring information includes air particle content.

[0074] The preset impact adjustment coefficient may be a numerical value used to characterize the degree of joint impact of air humidity and air particulate matter content on the adhesion index. The preset impact adjustment coefficient may be obtained by fitting the adhesion experimental data of the photovoltaic module.

[0075] The preset adjustment index may be a value used to adjust the degree of influence of wind speed on the attachment index. The preset adjustment index may be obtained by fitting the attachment experimental data of the photovoltaic module.

[0076] The preset temperature influence coefficient may be a value representing the influence of the ambient temperature on the adhesion index, and the preset temperature influence coefficient may be obtained by fitting the adhesion experimental data of the photovoltaic module.

[0077] The preset reference conversion temperature may be a reference temperature value for standardizing the ambient temperature, and the preset reference conversion temperature may be set according to actual needs.

[0078] Specifically, the adhesion index of photovoltaic modules is positively correlated with air humidity and air particulate matter content, so through formula (2) Describe the linear relationship between air humidity and air particle content and the adhesion index through an exponential decay function , describing the relationship between average wind speed and adhesion index. The greater the wind speed, the faster the particles move, and the smaller the chance of particles being retained and attached to the surface of the photovoltaic module. The influence of ambient temperature on the adhesion index is described. The ambient temperature is standardized using the preset reference conversion temperature, and a preset temperature influence coefficient is introduced to reflect the influence of ambient temperature on the kinetics and chemical reaction rate of air particles. The adhesion index of the PV module under the current external environmental conditions is obtained by combining the above different influences to reflect the amount of particles attached to the surface of the PV module. On this basis, the basic light intensity is adjusted using formula (3) to obtain the effective light intensity received by the PV module under the influence of adhesion and shadow.

[0079] Through this scheme, mathematical analysis methods are used, and mathematical formulas are designed based on climate monitoring information and air particle monitoring information to describe the effects of air humidity, average wind speed and ambient temperature on particle attachment, so as to quantify the attachment index that can reflect the amount of particle attachment on the surface of photovoltaic modules. On this basis, through further mathematical analysis process, according to the attachment index, the basic light intensity is adjusted to obtain the effective light intensity received by the photovoltaic modules under the influence of attachment and shadow, so as to improve the accuracy and scientificity of the effective light intensity, and thus improve the comprehensiveness of the subsequent maximum power point analysis process.

[0080] In some embodiments, load fluctuation information is analyzed to determine the load standard deviation and the load average value; the load fluctuation amplitude is determined based on the load standard deviation and the load average value; the electric noise change information is analyzed to determine the electric noise standard deviation and the electric noise average value; the load fluctuation amplitude, the electric noise standard deviation and the electric noise average value are used as internal influencing factors.

[0081] The equipment operation parameter set includes load fluctuation information and electrical noise change information.

[0082] The load fluctuation information may be load variation information of the photovoltaic system during a monitoring period, and the load fluctuation information may be obtained through a power meter.

[0083] The electric noise variation information may be variation information of the electric noise caused by each device in the photovoltaic system during a monitoring period, and the electric noise variation information may be obtained through a spectrum analyzer.

[0084] The load average value may be the arithmetic mean of the load of the photovoltaic system during the monitoring period, indicating a typical load level of the photovoltaic system during the monitoring period.

[0085] The load standard deviation can be the volatility of the PV system load level around the load average value, which can reflect the degree of load instability.

[0086] The load fluctuation range may be a ratio of a load standard deviation to a load average value, and is a parameter used to quantify an actual load fluctuation range.

[0087] The average value of the electrical noise may be the arithmetic average value of the electrical noise inside the photovoltaic system during the monitoring period, reflecting the overall noise level of the photovoltaic system during the monitoring period.

[0088] The electric noise standard deviation may be a numerical value used to reflect the degree of fluctuation of the electric noise intensity, and is used to characterize the instability of the electric noise.

[0089] Specifically, the output power of the photovoltaic system is directly affected by load changes. When the load fluctuates greatly in a short period of time, the photovoltaic inverter needs to continuously adjust the output to ensure that it matches the load power demand; if the load increases suddenly, the system may not be able to increase the output power quickly enough, resulting in energy loss; if the impact of load changes is not fully considered during the maximum power point tracking analysis process, the maximum power point obtained will not be able to meet the real-time load demand. At the same time, the electrical noise in the photovoltaic system will interfere with the accuracy of the sensor, resulting in errors in the measurement of basic parameters such as voltage and current, thereby affecting the accuracy of the maximum power point; if the impact of electrical noise is not fully considered during the maximum power point tracking analysis process, the tracking of the maximum power point will be unstable. The random impact of electrical noise on basic parameters will cause misjudgment in the maximum power point tracking process, resulting in frequent power fluctuations, and may even mistakenly regard noise fluctuations as real load changes.

[0090] Therefore, in the process of analyzing the internal influencing factors, by analyzing the load fluctuation information and electric noise change information in the photovoltaic system, the load standard deviation, load average value, electric noise standard deviation and electric noise average value that can reflect the load change characteristics and electric noise change characteristics in the photovoltaic system are obtained through the standard deviation calculation formula and the average value calculation formula respectively. Further, according to the ratio of the load standard deviation to the load average value, the load fluctuation amplitude is determined, and the load fluctuation amplitude, electric noise standard deviation and electric noise average value are used as internal influencing factors, which is helpful to comprehensively consider the influence of load changes and electric noise fluctuations in the subsequent maximum power point tracking process.

[0091] Through this scheme, the load fluctuation information and the electric noise change information are analyzed, and the load standard deviation, load average value, electric noise standard deviation and electric noise average value that can reflect the load change characteristics and electric noise change characteristics in the photovoltaic system are obtained respectively. The load fluctuation amplitude is further determined according to the ratio of the load standard deviation to the load average value, and the load fluctuation amplitude, electric noise standard deviation and electric noise average value are used as internal influencing factors, which is helpful to comprehensively consider the influence of load changes and electric noise fluctuations in the subsequent maximum power point tracking process.

[0092] In some embodiments, based on the load fluctuation information, the load monitoring duration, angular frequency and phase constant are extracted; based on the current current, the disturbance starting current is determined according to the load monitoring duration, angular frequency, phase constant, environmental influencing factors and internal influencing factors; based on the current voltage, the disturbance starting voltage is determined according to the load monitoring duration, angular frequency, phase constant, environmental influencing factors and internal influencing factors; and based on the disturbance starting current and the disturbance starting voltage, a dynamic starting adjustment node is constructed.

[0093] The device operating parameter set also includes the current current and the current voltage.

[0094] Angular frequency can be a physical quantity that describes the frequency of load change and can be obtained through a frequency conversion formula.

[0095] The phase constant may be a value reflecting the timing characteristics of load changes, and the phase constant may be obtained by analyzing the load signal through a fast Fourier transform algorithm.

[0096] The disturbance start current may be a required corresponding current value when the disturbance observation algorithm starts to perform disturbance adjustment.

[0097] Specifically, in the process of determining the starting disturbance node of the disturbance observation algorithm, the current value and voltage value required for the starting disturbance stage of the disturbance observation algorithm are mainly analyzed and determined. The output power corresponding to the current value and voltage value is close to the maximum power point. In this process, in addition to considering the environmental influence factors and internal influence factors obtained in the aforementioned embodiments, it is also necessary to consider the load monitoring time, angular frequency and phase constant during the load change process, which is helpful to comprehensively analyze the marginal fluctuation characteristics of the load fluctuation. The above parameters can realize trend prediction, thereby helping the maximum power point tracking process to respond in advance when the load changes, reduce energy loss, and improve the response speed of the disturbance observation algorithm. Through mathematical analysis, the load monitoring time, angular frequency, phase constant, environmental influence factors and internal influence factors are integrated and analyzed to quantify the disturbance starting current and disturbance starting voltage required for the starting disturbance stage of the disturbance observation algorithm, so as to construct a dynamic starting adjustment node.

[0098] Through this scheme, the disturbance starting current and the disturbance starting voltage are quantitatively analyzed according to the load monitoring duration, angular frequency, phase constant, environmental influencing factors and internal influencing factors, and a dynamic starting adjustment node is constructed based on this, to ensure that the subsequent maximum power point tracking process can respond quickly to load changes under environmental and internal influences, and significantly shorten the distance between the dynamic starting adjustment node and the maximum power point, so as to significantly improve the tracking speed of the maximum power point.

[0099] In some embodiments, based on the current current, the disturbance starting current is determined according to the load monitoring duration, angular frequency, phase constant, environmental influence factors and internal influence factors, specifically the following formula (4): (4) in, is the disturbance starting current, is the current, is the preset light-current relationship coefficient, is the effective light intensity, To preset the lighting benchmark, The preset current light impact adjustment index, is the preset current load relationship coefficient, is the load fluctuation amplitude, is the angular frequency, is the load monitoring duration, is the phase constant, is the Gaussian noise model, is the average value of electrical noise, is the average value of electrical noise.

[0100] The preset light-current relationship coefficient may be a value used to characterize the degree of influence of light intensity change on the disturbance starting current, and the preset light-current relationship coefficient may be obtained by fitting experimental data.

[0101] The preset illumination reference may be a reference illumination value for standardizing the effective illumination intensity, and the preset illumination reference may be set according to specific requirements.

[0102] The preset current illumination influence adjustment index may be a value used to adjust the degree of influence of illumination on the disturbance starting current. The preset current illumination influence adjustment index may be obtained by fitting experimental data.

[0103] The preset current-load relationship coefficient may be a value used to characterize the degree of influence of load change on the disturbance starting current, and the preset current-load relationship coefficient may be obtained by fitting experimental data.

[0104] Specifically, through formula (4) Describe the nonlinear effect of light intensity changes on the perturbation starting current; and Describes the periodic effect of load changes on the disturbance starting current. The sine function has a smooth periodic characteristic and is suitable for simulating load fluctuations that change periodically over time. Gaussian distribution is used to describe the distribution characteristics of electrical noise. In actual power systems, the sources of electrical noise may be very complex and random, so it is reasonable to use Gaussian noise to simulate the characteristics of electrical noise. The above different influences are integrated and analyzed through formula (4), and the disturbance starting current is quantified based on the current current.

[0105] Through this scheme, mathematical analysis methods are used to design mathematical formulas based on load monitoring duration, angular frequency, phase constant, environmental influencing factors and internal influencing factors to scientifically quantify the disturbance starting current, so that the analysis process of the disturbance starting current covers consideration of the environment, load changes and electrical noise fluctuations, thereby improving the accuracy of the disturbance starting current and thus improving the scientific nature of the dynamic starting adjustment node.

[0106] In some embodiments, based on the current voltage, the disturbance starting voltage is determined according to the load monitoring duration, angular frequency, phase constant, environmental influence factors and internal influence factors, specifically the following formula (5): (5) in, is the disturbance starting voltage, is the current, is the preset light-voltage relationship coefficient, is the effective light intensity, To preset the lighting benchmark, The preset voltage light impact adjustment index, is the preset voltage-load relationship coefficient, is the load fluctuation amplitude, is the angular frequency, is the load monitoring duration, is the phase constant, is the Gaussian noise model, is the average value of electrical noise, is the average value of electrical noise.

[0107] The preset light-voltage relationship coefficient may be a value used to characterize the degree of influence of changes in light intensity on the disturbance starting voltage, and the preset light-voltage relationship coefficient may be obtained by fitting experimental data.

[0108] The preset voltage illumination influence adjustment index may be a value used to adjust the degree of influence of illumination on the disturbance starting voltage. The preset voltage illumination influence adjustment index may be obtained by fitting experimental data.

[0109] The preset voltage-load relationship coefficient may be a value used to characterize the degree of influence of load change on the disturbance starting voltage, and the preset voltage-load relationship coefficient may be obtained by fitting experimental data.

[0110] Specifically, through formula (5) Describe the nonlinear effect of light intensity changes on the perturbation starting voltage; and Describe the periodic effect of load change on the disturbance starting voltage. Usually, there is a phase difference between voltage and current. Therefore, using the cosine function (which is 90 degrees off-phase with the sine function) to describe the voltage change can better reflect the phase relationship between current and voltage. The Gaussian distribution is used to describe the distribution characteristics of electrical noise. The above different influences are integrated and analyzed by formula (5), and the disturbance starting voltage is quantified based on the current voltage.

[0111] Through this scheme, mathematical analysis methods are used to design mathematical formulas based on load monitoring duration, angular frequency, phase constant, environmental influencing factors and internal influencing factors to scientifically quantify the disturbance starting voltage, so that the analysis process of the disturbance starting voltage covers consideration of the environment, load changes and electrical noise fluctuations, improves the accuracy of the disturbance starting voltage, and further improves the scientific nature of the dynamic starting adjustment node.

[0112] In some embodiments, the real-time current and real-time voltage are adjusted according to the disturbance starting current and disturbance starting voltage corresponding to the dynamic starting adjustment node, and the real-time output power is determined accordingly; the real-time current and real-time voltage are subjected to disturbance adjustment according to a preset starting adjustment amplitude, and the output power after the disturbance is determined; the output power after the disturbance is compared with the real-time output power to determine the dynamic power difference; according to the dynamic power difference, the subsequent disturbance direction is determined, and the subsequent disturbance amplitude is subjected to binary reduction processing to determine the subsequent disturbance result; according to the subsequent disturbance result, the convergence power node is determined and output.

[0113] The real-time output power may be the current output power of the photovoltaic system obtained according to a power formula after adjusting the real-time current and the real-time voltage based on the disturbance starting current and the disturbance starting voltage.

[0114] The preset starting adjustment amplitude may be an adjustment amplitude corresponding to the first time the disturbance observation algorithm performs disturbance adjustment. The preset starting adjustment amplitude may be obtained by performing a disturbance cost minimization analysis based on historical disturbance data.

[0115] The post-disturbance output power may be the output power of the photovoltaic system obtained after the current and voltage are disturbed and adjusted by a disturbance observation algorithm.

[0116] The dynamic power difference may be the difference between the output power before and after the disturbance. If the difference is a negative number, it means that the last disturbance adjustment caused the output power to decrease. Otherwise, it means that the last disturbance adjustment caused the output power to increase.

[0117] The subsequent disturbance direction can be the direction of the next disturbance adjustment determined according to the dynamic power difference. If the dynamic power difference is a negative number, the next disturbance adjustment direction should be opposite to this direction. If the dynamic power difference is a positive number, the next disturbance adjustment direction should be the same as this direction.

[0118] The subsequent disturbance result may be result-directed information for characterizing whether the subsequent disturbance adjustment causes the output power to increase or decrease.

[0119] Specifically, after determining the disturbance starting current and the disturbance starting voltage, the current and voltage in the current photovoltaic system are adjusted to the disturbance starting current and the disturbance starting voltage through the inverter, and the current real-time output power is calculated through the power formula, and then the disturbance observation algorithm is used to adjust the current current and the current voltage by a fixed amplitude according to the preset starting adjustment amplitude, and the adjusted output power is calculated through the power formula. By comparing the output power after the disturbance with the real-time output power and calculating the dynamic power difference, the effect of the disturbance can be accurately identified, and a reference can be provided for subsequent disturbances to ensure the disturbance observation algorithm. The method tends to converge. In order to prevent unpredictable nonlinear reactions caused by excessive disturbances, the binary decreasing process can smoothly adjust the amplitude, that is, the amplitude of each disturbance is half of the amplitude of the previous disturbance, so as to reduce the risk brought by drastic disturbance changes and accelerate the convergence of the algorithm. The power difference caused by subsequent disturbances remains in the positive range and continues to decrease, indicating that the output power is tending to the maximum power point at this time. By setting the convergence threshold, the convergence degree of the algorithm can be controlled. When the convergence degree of the algorithm meets the requirements, the corresponding real-time current and real-time voltage are the converged power nodes.

[0120] Through this scheme, according to the analysis of the dynamic power difference corresponding to different disturbance adjustment stages, the disturbance direction and disturbance amplitude are dynamically adjusted during the disturbance adjustment process to improve the tracking speed of the disturbance observation algorithm for the maximum power point, and through the binary decreasing processing of the disturbance amplitude, the unpredictable nonlinear reaction caused by excessive disturbance is prevented, the reliability of the disturbance observation algorithm is improved, and the obtained converged power node can keep the photovoltaic system at a high-efficiency power generation level.

[0121] Figure 3 A schematic diagram of a photovoltaic MPPT optimization system provided in one embodiment of the present application is shown in FIG. Figure 3 As shown, a photovoltaic MPPT optimization system 300 of this embodiment includes: an environmental analysis module 301 , an internal analysis module 302 , a node analysis module 303 and a disturbance output module 304 .

[0122] The environment analysis module 301 is used to obtain an environment dynamic factor set, analyze the environment dynamic factor set, and determine the environment influencing factor; An internal analysis module 302, used to obtain a device operation parameter set, analyze the device operation parameter set, and determine internal influencing factors; A node analysis module 303 is used to determine a dynamic start adjustment node based on the device operation parameter set, the environmental impact factor and the internal impact factor; The disturbance output module 304 is used to perform disturbance adjustment on the photovoltaic parameters according to the dynamic starting adjustment node, dynamically adjust the disturbance amplitude according to the adjustment result, and determine and output the convergence power node.

[0123] Optionally, the environment analysis module 301 is specifically used for: Analyze the image set of the sun-facing side of the component to determine a static shadow ratio, a dynamic shadow ratio and a shadow intensity data set; Based on the light intensity monitoring information, and according to the shadow intensity data set, the static shadow proportion and the dynamic shadow proportion are analyzed to determine basic light intensity distribution information; Analyzing the air particulate monitoring information based on the climate monitoring information to determine an attachment index; According to the attachment index, the basic illumination intensity distribution information is fine-tuned for attachment influence, effective illumination intensity distribution information is determined, and the effective illumination intensity distribution information is used as the environmental impact factor.

[0124] Optionally, when the environment analysis module 301 analyzes the static shadow proportion and the dynamic shadow proportion based on the light intensity monitoring information and the shadow intensity data set to determine the basic light intensity distribution information, it is specifically used to: Determining an ambient light intensity benchmark according to the light intensity monitoring information; Determining static shadow intensity and dynamic shadow intensity according to the shadow intensity data set; Based on the ambient light intensity benchmark, the basic light intensity on the surface of each photovoltaic module is determined according to the shadow intensity data set, the static shadow ratio and the dynamic shadow ratio, specifically the following formula: ; in, is the basic light intensity, is the ambient light intensity benchmark, is the static shadow intensity, is the static shadow ratio, To preset static adjustment parameters, is the dynamic shadow intensity, To dynamically adjust the parameters for the preset, is the dynamic shadow ratio; The basic light intensity distribution information is constructed according to the basic light intensity corresponding to each photovoltaic component.

[0125] Optionally, when the environment analysis module 301 analyzes the air particulate monitoring information based on the climate monitoring information to determine the adhesion index, it is specifically the following formula: ; in, is the adhesion index, is the preset impact adjustment coefficient, is the air humidity, is the air particulate matter content, is the preset adjustment index, is the average wind speed, is the preset temperature influence coefficient, is the ambient temperature, is the preset reference conversion temperature; The step of fine-tuning the basic illumination intensity distribution information for the attachment effect according to the attachment index to determine the effective illumination intensity distribution information includes: According to the attachment index, the basic illumination intensity corresponding to each photovoltaic module is adjusted to determine the effective illumination intensity corresponding to each photovoltaic module, which is specifically the following formula: ; in, is the effective light intensity, is the basic light intensity, is the adhesion index; The effective light intensity distribution information is constructed according to the effective light intensity corresponding to each photovoltaic component.

[0126] Optionally, the internal analysis module 302 is specifically used for: Analyze the load fluctuation information to determine the load standard deviation and the load average; Determining a load fluctuation amplitude according to the load standard deviation and the load average value; Analyzing the electric noise variation information to determine the electric noise standard deviation and the electric noise average value; The load fluctuation amplitude, the electric noise standard deviation and the electric noise average value are used as the internal influencing factors.

[0127] Optionally, the node analysis module 303 is specifically used to: Extracting load monitoring duration, angular frequency and phase constant according to the load fluctuation information; Based on the current current, determining a disturbance starting current according to the load monitoring duration, the angular frequency, the phase constant, the environmental influencing factor and the internal influencing factor; Based on the current voltage, determining a disturbance starting voltage according to the load monitoring duration, the angular frequency, the phase constant, the environmental influencing factor and the internal influencing factor; The dynamic start adjustment node is constructed according to the disturbance start current and the disturbance start voltage.

[0128] Optionally, when the node analysis module 303 determines the disturbance starting current based on the current current, according to the load monitoring duration, the angular frequency, the phase constant, the environmental impact factor and the internal impact factor, it is specifically the following formula: ; in, is the disturbance starting current, is the current, is the preset light-current relationship coefficient, is the effective light intensity, To preset the lighting benchmark, The preset current light impact adjustment index, is the preset current load relationship coefficient, is the load fluctuation amplitude, is the angular frequency, is the load monitoring duration, is the phase constant, is the Gaussian noise model, is the average value of the electrical noise, is the average value of the electrical noise.

[0129] Optionally, when the node analysis module 303 determines the disturbance starting voltage based on the current voltage, according to the load monitoring duration, the angular frequency, the phase constant, the environmental impact factor and the internal impact factor, it is specifically the following formula: ; in, is the disturbance starting voltage, is the current, is the preset light-voltage relationship coefficient, is the effective light intensity, To preset the lighting benchmark, The preset voltage light impact adjustment index, is the preset voltage-load relationship coefficient, is the load fluctuation amplitude, is the angular frequency, is the load monitoring duration, is the phase constant, is the Gaussian noise model, is the average value of the electrical noise, is the average value of the electrical noise.

[0130] Optionally, the disturbance output module 304 is specifically used to: adjust the real-time current and the real-time voltage according to the disturbance starting current and the disturbance starting voltage corresponding to the dynamic starting adjustment node, and determine the real-time output power accordingly; perform disturbance adjustment on the real-time current and the real-time voltage according to a preset starting adjustment amplitude, and determine the output power after the disturbance; compare the output power after the disturbance with the real-time output power to determine the dynamic power difference; determine the subsequent disturbance direction according to the dynamic power difference, and perform binary reduction processing on the subsequent disturbance amplitude to determine the subsequent disturbance result; determine and output the convergence power node according to the subsequent disturbance result.

[0131] The system of this embodiment can be used to execute the method of any of the above embodiments. The implementation principles and technical effects are similar and will not be described in detail here.

Claims

1. A photovoltaic MPPT optimization method, characterized in that: include: Acquire a set of environmental dynamic factors, analyze the set of environmental dynamic factors, and determine environmental influencing factors; Acquire a device operating parameter set, analyze the device operating parameter set, and determine internal influencing factors; Based on the equipment operation parameter set, according to the environmental influencing factors and the internal influencing factors, determining a dynamic starting adjustment node; According to the dynamic starting adjustment node, the photovoltaic parameters are disturbed and adjusted, and the disturbance amplitude is dynamically adjusted according to the adjustment result, and the convergence power node is determined and output.

2. The method according to claim 1, characterized in that The environmental dynamic factor set includes light intensity monitoring information, component sun-facing surface image set, air particulate monitoring information and climate monitoring information. The analysis of the environmental dynamic factor set to determine the environmental impact factor includes: Analyze the image set of the sun-facing side of the component to determine a static shadow ratio, a dynamic shadow ratio and a shadow intensity data set; Based on the light intensity monitoring information, and according to the shadow intensity data set, the static shadow proportion and the dynamic shadow proportion are analyzed to determine basic light intensity distribution information; Analyzing the air particulate monitoring information based on the climate monitoring information to determine an attachment index; According to the attachment index, the basic illumination intensity distribution information is fine-tuned for attachment influence, effective illumination intensity distribution information is determined, and the effective illumination intensity distribution information is used as the environmental impact factor.

3. The method according to claim 2, characterized in that The step of analyzing the static shadow proportion and the dynamic shadow proportion based on the light intensity monitoring information and according to the shadow intensity data set to determine basic light intensity distribution information includes: Determining an ambient light intensity benchmark according to the light intensity monitoring information; Determining static shadow intensity and dynamic shadow intensity according to the shadow intensity data set; Based on the ambient light intensity benchmark, the basic light intensity on the surface of each photovoltaic module is determined according to the shadow intensity data set, the static shadow ratio and the dynamic shadow ratio, specifically the following formula: ； in, is the basic light intensity, is the ambient light intensity benchmark, is the static shadow intensity, is the static shadow ratio, To preset static adjustment parameters, is the dynamic shadow intensity, Dynamic adjustment parameters for presets, is the dynamic shadow ratio; The basic light intensity distribution information is constructed according to the basic light intensity corresponding to each photovoltaic component.

4. The method according to claim 3, characterized in that The climate monitoring information includes air humidity, average wind speed and ambient temperature, and the air particle monitoring information includes air particle content. Based on the climate monitoring information, the air particle monitoring information is analyzed to determine the adhesion index, which is specifically the following formula: ； in, is the adhesion index, is the preset impact adjustment coefficient, is the air humidity, is the air particulate matter content, is the preset adjustment index, is the average wind speed, is the preset temperature influence coefficient, is the ambient temperature, is the preset reference conversion temperature; The step of fine-tuning the basic illumination intensity distribution information for the attachment effect according to the attachment index to determine the effective illumination intensity distribution information includes: According to the attachment index, the basic light intensity corresponding to each photovoltaic module is adjusted to determine the effective light intensity corresponding to each photovoltaic module, which is specifically the following formula: ； in, is the effective light intensity, is the basic light intensity, is the adhesion index; The effective light intensity distribution information is constructed according to the effective light intensity corresponding to each photovoltaic component.

5. The method according to claim 4, characterized in that The device operation parameter set includes load fluctuation information and electrical noise change information, and the analyzing the device operation parameter set to determine the internal influencing factors includes: Analyze the load fluctuation information to determine the load standard deviation and the load average; Determining a load fluctuation amplitude according to the load standard deviation and the load average value; Analyzing the electric noise variation information to determine the electric noise standard deviation and the electric noise average value; The load fluctuation amplitude, the electric noise standard deviation and the electric noise average value are used as the internal influencing factors.

6. The method according to claim 5, characterized in that The device operation parameter set also includes a current current and a current voltage. The determining of the dynamic start adjustment node based on the device operation parameter set and according to the environmental influencing factors and the internal influencing factors includes: Extracting load monitoring duration, angular frequency and phase constant according to the load fluctuation information; Based on the current current, determining a disturbance starting current according to the load monitoring duration, the angular frequency, the phase constant, the environmental influencing factor and the internal influencing factor; Based on the current voltage, determining a disturbance starting voltage according to the load monitoring duration, the angular frequency, the phase constant, the environmental influencing factor and the internal influencing factor; The dynamic start adjustment node is constructed according to the disturbance start current and the disturbance start voltage.

7. The method according to claim 6, characterized in that The disturbance starting current is determined based on the current current according to the load monitoring duration, the angular frequency, the phase constant, the environmental influencing factor and the internal influencing factor, specifically as follows: ； in, is the disturbance starting current, is the current, is the preset light-current relationship coefficient, is the effective light intensity, To preset the lighting benchmark, The preset current light impact adjustment index, is the preset current load relationship coefficient, is the load fluctuation amplitude, is the angular frequency, is the load monitoring duration, is the phase constant, is the Gaussian noise model, is the average value of the electrical noise, is the average value of the electrical noise.

8. The method according to claim 6, characterized in that The disturbance starting voltage is determined based on the current voltage according to the load monitoring duration, the angular frequency, the phase constant, the environmental influencing factor and the internal influencing factor, specifically as follows: ； in, is the disturbance starting voltage, is the current, is the preset light-voltage relationship coefficient, is the effective light intensity, To preset the lighting benchmark, The preset voltage light impact adjustment index, is the preset voltage-load relationship coefficient, is the load fluctuation amplitude, is the angular frequency, is the load monitoring duration, is the phase constant, is the Gaussian noise model, is the average value of the electrical noise, is the average value of the electrical noise.

9. The method according to claim 8, characterized in that The method of performing disturbance adjustment on the photovoltaic output power according to the dynamic starting adjustment node, dynamically adjusting the disturbance amplitude according to the adjustment result, and determining and outputting the convergence power node includes: According to the disturbance starting current and the disturbance starting voltage corresponding to the dynamic starting adjustment node, the real-time current and the real-time voltage are adjusted, and the real-time output power is determined accordingly; According to a preset starting adjustment amplitude, a disturbance adjustment is performed on the real-time current and the real-time voltage to determine the output power after the disturbance; Comparing the post-disturbance output power with the real-time output power to determine a dynamic power difference; Determine the subsequent disturbance direction according to the dynamic power difference, and perform binary reduction processing on the subsequent disturbance amplitude to determine the subsequent disturbance result; According to the subsequent disturbance result, the converged power node is determined and output.

10. A photovoltaic MPPT optimization system, characterized in that: include: An environmental analysis module, used to obtain a set of environmental dynamic factors, analyze the set of environmental dynamic factors, and determine environmental influencing factors; An internal analysis module, used to obtain a device operation parameter set, analyze the device operation parameter set, and determine internal influencing factors; A node analysis module, used to determine a dynamic start adjustment node based on the equipment operation parameter set, the environmental influencing factor and the internal influencing factor; The disturbance output module is used to perform disturbance adjustment on the photovoltaic parameters according to the dynamic starting adjustment node, dynamically adjust the disturbance amplitude according to the adjustment result, and determine and output the convergence power node.

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