Photovoltaic power station comprehensive diagnosis and intelligent energy management method and system

Abstract

The invention discloses a photovoltaic power station comprehensive diagnosis and intelligent energy management method and system, and the method comprises the steps: collecting the real-time operation data of a photovoltaic power station, and analyzing the collected data through a preset diagnosis algorithm, so as to recognize the operation state of the photovoltaic power station; making a corresponding energy management strategy according to the identified operation state; and finally, executing the formulated energy management strategy to realize intelligent management of the photovoltaic power station. According to the method, the operation efficiency of the photovoltaic power station can be improved, the service life of equipment is prolonged, the energy utilization rate is improved, and intelligent management of the photovoltaic power station is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic power station operation management, and specifically relates to a photovoltaic power station comprehensive diagnosis and intelligent energy management method and system. Background Art

[0002] Solar energy is a clean, renewable energy source. As the main facility for solar power generation, the operating status of photovoltaic power stations directly affects the power generation efficiency and the service life of the equipment. In order to ensure the efficient operation of photovoltaic power stations, it is usually necessary to monitor and manage the operating status of the power stations in real time. This usually involves collecting real-time operating data of the power station, such as voltage, current, temperature, etc., and analyzing these data to identify the operating status of the power station.

[0003] However, there are some problems with existing methods for identifying the operating status of photovoltaic power plants. First, existing identification methods often rely on empirical rules or simple threshold judgments, which often have low recognition accuracy when processing complex or noisy data. Second, existing identification methods often cannot handle dynamic changes in the operating status of power plants. For example, when the operating status of a power plant suddenly changes from normal operation to equipment failure, existing identification methods may not be able to make accurate judgments in a timely manner.

[0004] In addition, there are some problems with the existing energy management strategies for photovoltaic power plants. For example, the existing energy management strategies are often static and cannot be dynamically adjusted according to the real-time operating status of the power plant. This results in the existing energy management strategies being unable to respond effectively in a timely manner when the operating status of the power plant changes, thus affecting the power generation efficiency of the power plant and the service life of the equipment.

[0005] Therefore, the technical problems existing in the existing photovoltaic power station operation status identification methods and energy management strategies mainly include: 1) The accuracy of the identification method is not high, and the operating status of the power station cannot be accurately identified; 2) The energy management strategy has weak response capabilities and cannot effectively respond to dynamic changes in power plant operating conditions; 3) It is impossible to realize intelligent management of power plants, such as real-time transmission and processing of data, dynamic adjustment of strategies, etc.

[0006] These problems limit the operating efficiency of photovoltaic power stations and the service life of equipment, and also affect the utilization rate of energy. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide a method and system for comprehensive diagnosis and intelligent energy management of photovoltaic power plants in view of the deficiencies in the above-mentioned prior art, so as to solve the technical problems of comprehensive diagnosis and energy management of photovoltaic power plants.

[0008] The present invention adopts the following technical solutions: A photovoltaic power station comprehensive diagnosis and intelligent energy management method, comprising the following steps: S1. Collect real-time operation data of photovoltaic power stations; S2. Analyze the collected real-time operation data using diagnostic algorithms to identify the operation status of the photovoltaic power station; S3, formulate corresponding energy management strategies according to the identified operating status of the photovoltaic power station; S4. Implement energy management strategies to achieve intelligent management of photovoltaic power plants.

[0009] Preferably, the real-time operation data of the photovoltaic power station is collected as follows: Collect real-time operation data of the photovoltaic power station, including the voltage, current, temperature of the photovoltaic panels, the working status of the inverter, and the total power generation of the power station; Sending real-time operation data to the data processing center through data transmission equipment; The data processing center cleans and preprocesses the collected real-time operation data.

[0010] More preferably, cleaning and preprocessing include removing outliers, filling missing values ​​and data normalization.

[0011] Preferably, identifying the operating status of the photovoltaic power station is specifically: Use support vector machine algorithm to analyze the collected real-time operation data; The input data is mapped to a high-dimensional space through a support vector machine model, and then a hyperplane is found in the high-dimensional space to separate data in different operating states; According to the prediction results of the support vector machine model, the operating status of the photovoltaic power station is identified.

[0012] More preferably, analyzing the collected real-time operation data is specifically performed as follows: The real-time operation data of the photovoltaic power station is used as input data, and the operation status of the power station is used as the category to train the support vector machine model; the operation status includes normal operation, low efficiency or equipment failure.

[0013] Preferably, the corresponding energy management strategy is formulated as follows: Evaluate the current operating status of the PV power station based on the operating status identified by the support vector machine algorithm; Develop energy management strategies based on the assessment results; Implement the developed energy management strategy into the operation of the PV power plant, including adjusting equipment parameters and arranging equipment maintenance; Monitor the effects of energy management strategies after implementation, such as changes in equipment operating efficiency and equipment failure rates, and adjust energy management strategies based on monitoring results.

[0014] More preferably, the energy management strategy is specifically: If equipment efficiency decreases, strategies include adjusting equipment operating parameters, such as adjusting the angle of photovoltaic panels to improve lighting efficiency; if equipment fails, strategies include arranging equipment repair or replacement.

[0015] Preferably, the energy management strategy is implemented as follows: When the energy management strategy is to adjust the equipment operating parameters, the operating status of the equipment is changed through the control system; when the energy management strategy is to arrange equipment maintenance, the maintenance personnel are notified to perform the operation; After executing the energy management strategy, the operating data of the photovoltaic power station is collected, including the operating status of the equipment and the power generation of the power station, to evaluate the implementation effect of the energy management strategy; the effectiveness of the strategy is judged by comparing the equipment operating efficiency and power generation of the power station before and after the implementation of the strategy; and the energy management strategy is adjusted according to the results of the effect evaluation.

[0016] Preferably, the energy management strategy is adjusted as follows: When the energy management strategy is not implemented effectively, modify the strategy or formulate a new strategy; when the energy management strategy is implemented effectively, continue to implement it or use it in other photovoltaic power plants.

[0017] In a second aspect, an embodiment of the present invention provides a photovoltaic power station comprehensive diagnosis and intelligent energy management system, which is characterized by comprising: Collection module, collecting real-time operation data of photovoltaic power station; The identification module uses diagnostic algorithms to analyze the collected real-time operating data and identify the operating status of the PV power station; Strategy module, which formulates corresponding energy management strategies according to the identified operating status of the photovoltaic power station; Output module executes energy management strategies to achieve intelligent management of photovoltaic power plants.

[0018] In a third aspect, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned photovoltaic power station comprehensive diagnosis and intelligent energy management method when executing the computer program.

[0019] In a fourth aspect, an embodiment of the present invention provides an electronic device, including a computer program, which, when executed by the electronic device, implements the steps of the above-mentioned photovoltaic power station comprehensive diagnosis and intelligent energy management method.

[0020] Compared with the prior art, the present invention has at least the following beneficial effects: A comprehensive diagnosis and intelligent energy management method for photovoltaic power plants improves the accuracy of operating status identification: by using a support vector machine (SVM) algorithm to analyze the collected data, the present invention can more accurately identify the operating status of the photovoltaic power plant, including normal operation, low efficiency and equipment failure.

[0021] Furthermore, the flexibility of the energy management strategy is improved: the present invention formulates a corresponding energy management strategy according to the identified operating status, and can dynamically adjust the strategy according to subsequent changes in the operating status, so as to better cope with the dynamic changes in the operating status of the photovoltaic power station.

[0022] Furthermore, intelligent management of photovoltaic power stations is realized: the present invention realizes real-time transmission and processing of data through a cloud computing platform, and evaluates the effect through a machine learning algorithm, thereby realizing intelligent management of photovoltaic power stations.

[0023] Furthermore, the power generation efficiency and equipment service life of the photovoltaic power station are improved: through accurate operation status identification and flexible energy management strategies, the present invention can effectively improve the power generation efficiency of the photovoltaic power station and also extend the service life of the equipment.

[0024] Furthermore, the energy utilization rate is improved: through intelligent energy management, the present invention can more effectively utilize the energy generated by the photovoltaic power station, thereby improving the energy utilization rate.

[0025] It can be understood that the beneficial effects of the second aspect mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0026] In summary, the present invention can improve the operating efficiency of photovoltaic power stations, extend the service life of equipment, improve energy utilization, and realize intelligent management of photovoltaic power stations.

[0027] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a schematic diagram of the process of the present invention; Figure 2 A schematic diagram of a computer device provided by an embodiment of the present invention.

[0029] Figure 3 The block diagram of a chip provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0032] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0033] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. 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 the present invention generally indicates that the associated objects are in an "or" relationship.

[0034] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0035] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0036] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0037] The present invention provides a comprehensive diagnosis and intelligent energy management method for photovoltaic power stations. First, the real-time operation data of the photovoltaic power station is collected, and then the collected data is analyzed using a pre-set diagnostic algorithm, such as a support vector machine (SVM) algorithm, to identify the operation status of the photovoltaic power station. According to the identified operation status, a corresponding energy management strategy is formulated; finally, the formulated energy management strategy is implemented to realize the intelligent management of the photovoltaic power station; it can improve the operation efficiency of the photovoltaic power station, extend the service life of the equipment, improve the energy utilization rate, and realize the intelligent management of the photovoltaic power station.

[0038] See also Figure 1 The present invention provides a photovoltaic power station comprehensive diagnosis and intelligent energy management method, comprising the following steps: S1. Collect real-time operation data of photovoltaic power stations; S101. Collect data through various sensors and devices installed in photovoltaic power plants, including but not limited to the voltage, current, temperature of photovoltaic panels, the working status of inverters, and the total power generation of power plants. These data are the basis for diagnosing the operating status of photovoltaic power plants and formulating energy management strategies; S102, sending the collected data to the data processing center through data transmission equipment. In this embodiment, the data transmission equipment includes but is not limited to wired network equipment, wireless network equipment, and optical fiber equipment to ensure real-time data transmission and data security. The purpose of this step is to ensure real-time data transmission and data security; S103. In the data processing center, the collected data is cleaned and preprocessed to facilitate subsequent data analysis.

[0039] Data cleaning and preprocessing include steps such as removing outliers, filling missing values, and data standardization, with the goal of improving data quality and the accuracy of subsequent data analysis.

[0040] S2, using a pre-set diagnostic algorithm to analyze the collected data to identify the operating status of the photovoltaic power station; The data analysis using the support vector machine (SVM) algorithm is as follows: The real-time operation data of the photovoltaic power station is used as input data, and the operation status of the power station is used as a category to train the SVM model; the operation status includes normal operation, low efficiency or equipment failure. The SVM algorithm is trained through online learning to adapt to the dynamic changes in the operation status of the photovoltaic power station. Through the machine learning algorithm, the operation status of the photovoltaic power station is automatically identified to improve the accuracy and efficiency of identification.

[0041] The voltage, current, temperature and other parameters of the photovoltaic power station are used as input data, and the operating status of the power station (such as normal operation, low efficiency, equipment failure, etc.) is used as a category to train an SVM model. During the training process, the SVM model finds a hyperplane that separates the data of different operating states as much as possible. For example, all data points marked as "normal operation" are on one side of the hyperplane, and all data points marked as "low efficiency" or "equipment failure" are on the other side of the hyperplane.

[0042] For the newly collected photovoltaic power plant operation data, these data are used as input and predicted by the SVM model. The SVM model maps these new data to the hyperplane found before, and then predicts the corresponding operating status of these new data based on the mapped position. For example, if the newly collected data is mapped to the "normal operation" side of the hyperplane, it is predicted that the power plant is operating normally; if the data is mapped to the "inefficiency" or "equipment failure" side of the hyperplane, it is predicted that the power plant has inefficiency or equipment failure.

[0043] If the newly collected data is mapped to one side of the hyperplane, the power plant is predicted to be operating normally; if the data is mapped to the other side of the hyperplane, the power plant is predicted to be faulty. The purpose of this step is to convert the prediction results of the machine learning algorithm into the actual operating status and provide a basis for the subsequent energy management strategy formulation.

[0044] S3. Formulate corresponding energy management strategies based on the identified operating status; If the SVM model predicts that the power plant is operating normally, continue to implement the current energy management strategy; if the SVM model predicts that the power plant is inefficient, adjust the energy management strategy, such as optimizing equipment configuration or adjusting power plant operating parameters, to improve the operating efficiency of the power plant; if the SVM model predicts that the power plant has equipment failure, immediately start the equipment maintenance or replacement process to restore the normal operation of the power plant as soon as possible.

[0045] If equipment efficiency decreases, strategies include adjusting equipment operating parameters, such as adjusting the angle of the photovoltaic panels to improve light efficiency; If equipment fails, strategies include arranging for equipment repair or replacement.

[0046] In the process of formulating energy management strategies, environmental factors such as climate conditions and seasonal changes are also taken into account to optimize the power generation efficiency of photovoltaic power plants. The purpose of this step is to formulate the best energy management strategy based on the actual operating status of the photovoltaic power plant and environmental factors, so as to improve the operating efficiency of the photovoltaic power plant and the service life of the equipment.

[0047] S4. Implement the formulated energy management strategy to achieve intelligent management of photovoltaic power plants Implement the formulated energy management strategy into the operation of the photovoltaic power station, including adjusting equipment parameters and arranging equipment maintenance. The purpose of this step is to transform the energy management strategy from theory to practice and realize the intelligent management of photovoltaic power stations.

[0048] After executing the strategy, the operating data of the photovoltaic power station is collected, including the operating status of the equipment and the power generation of the power station, to evaluate the implementation effect of the strategy. The purpose of this step is to monitor the implementation effect of the strategy in real time by collecting real-time data, and provide data support for subsequent strategy adjustments.

[0049] Then, based on the collected data, the execution effect of the energy management strategy is evaluated. In this embodiment, the effect evaluation is performed through a machine learning algorithm to improve the accuracy and efficiency of the evaluation. The effectiveness of the strategy is judged by comparing the equipment operating efficiency and power generation of the power station before and after the execution of the strategy. The purpose of this step is to evaluate the effectiveness of the energy management strategy through data analysis and provide a basis for subsequent strategy adjustments.

[0050] Finally, the energy management strategy is adjusted based on the results of the effect evaluation. If the strategy execution effect is poor, the strategy is modified or a new strategy is formulated; if the strategy execution effect is good, the strategy is continued to be implemented or promoted in other photovoltaic power stations. The purpose of this step is to dynamically adjust the energy management strategy based on the execution effect of the strategy and realize the intelligent management of photovoltaic power stations.

[0051] Intelligent management is achieved through the cloud computing platform. The cloud computing platform provides powerful data processing capabilities and system scalability, can process large amounts of data, support complex data analysis and machine learning algorithms, and improve the management efficiency and intelligence level of photovoltaic power stations.

[0052] In another embodiment of the present invention, a photovoltaic power station comprehensive diagnosis and intelligent energy management system is provided, which can be used to implement the above-mentioned photovoltaic power station comprehensive diagnosis and intelligent energy management method. Specifically, the photovoltaic power station comprehensive diagnosis and intelligent energy management system includes a collection module, an identification module, a strategy module and an output module.

[0053] Among them, the collection module collects the real-time operation data of the photovoltaic power station; The identification module uses diagnostic algorithms to analyze the collected real-time operating data and identify the operating status of the PV power station; Strategy module, which formulates corresponding energy management strategies according to the identified operating status of the photovoltaic power station; Output module executes energy management strategies to achieve intelligent management of photovoltaic power plants.

[0054] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the comprehensive diagnosis and intelligent energy management method of photovoltaic power stations, including: Collect real-time operating data of the photovoltaic power station; use diagnostic algorithms to analyze the collected real-time operating data and identify the operating status of the photovoltaic power station; formulate corresponding energy management strategies based on the identified operating status of the photovoltaic power station; implement energy management strategies to achieve intelligent management of the photovoltaic power station.

[0055] See also Figure 2 , the terminal device is a computer device. The computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the method for calculating the composition of the fluid in the reservoir transformation wellbore in the embodiment is implemented. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the photovoltaic power station comprehensive diagnosis and intelligent energy management system in the embodiment are implemented. To avoid repetition, it is not described one by one here.

[0056] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 2 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0057] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0058] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the computer device 60.

[0059] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0060] See also Figure 3 , the terminal device is a chip, and the chip 600 of this embodiment includes a processor 622, which may be one or more, and a memory 632 for storing a computer program executable by the processor 622. The computer program stored in the memory 632 may include one or more modules, each corresponding to a set of instructions. In addition, the processor 622 may be configured to execute the computer program to execute the above-mentioned photovoltaic power station comprehensive diagnosis and intelligent energy management method.

[0061] In addition, the chip 600 may further include a power supply component 626 and a communication component 650, wherein the power supply component 626 may be configured to perform power management of the chip 600, and the communication component 650 may be configured to implement communication, for example, wired or wireless communication, of the chip 600. In addition, the chip 600 may further include an input / output interface 658. The chip 600 may operate based on an operating system stored in the memory 632.

[0062] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs. It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory (Non-VolatileMemory), such as at least one disk memory.

[0063] The processor may load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the photovoltaic power station comprehensive diagnosis and intelligent energy management method in the above embodiment; the processor may load and execute the following steps: Collect real-time operating data of the photovoltaic power station; use diagnostic algorithms to analyze the collected real-time operating data and identify the operating status of the photovoltaic power station; formulate corresponding energy management strategies based on the identified operating status of the photovoltaic power station; implement energy management strategies to achieve intelligent management of the photovoltaic power station.

[0064] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0065] Application examples: Photovoltaic power stations are composed of thousands of photovoltaic panels, each of which is equipped with sensors to collect real-time operating data such as voltage, current and temperature. In addition, the power station also has inverters and other equipment, and the working status of these devices is also monitored in real time.

[0066] First, we collect real-time operating data of the photovoltaic power station through sensors and equipment installed in the photovoltaic power station. These data are sent to the data processing center through wired network equipment. In the data processing center, we clean and pre-process the collected data to facilitate subsequent data analysis.

[0067] Then, the collected data is analyzed using a pre-set support vector machine (SVM) algorithm. The real-time operating data of the photovoltaic power station is used as input data, and the operating status of the power station is used as a category to train the SVM model. The operating status of the photovoltaic power station is identified through the SVM model.

[0068] According to the identified operating status, the corresponding energy management strategy is formulated. For example, if the efficiency of a certain solar panel is found to be lower than expected, its angle is adjusted to improve the lighting efficiency; if the inverter is found to be faulty, it is arranged for repair or replacement.

[0069] Then, the energy management strategy is executed, for example, by changing the angle of the solar panels through the control system or notifying maintenance personnel to perform equipment maintenance.

[0070] After the strategy is implemented, the operating data of the photovoltaic power station is collected again to evaluate the implementation effect of the strategy. The effect evaluation is performed through machine learning algorithms to improve the accuracy and efficiency of the evaluation.

[0071] Finally, the energy management strategy is adjusted based on the results of the effect evaluation. If the strategy is found to be ineffective, the strategy is modified or a new strategy is formulated; if the strategy is found to be effective, the strategy is continued to be implemented or promoted in other photovoltaic power plants.

[0072] The method of the present invention realizes the intelligent management of photovoltaic power stations, improves the operating efficiency of power stations, prolongs the service life of equipment, and improves the utilization rate of energy.

[0073] In summary, the present invention provides a photovoltaic power station comprehensive diagnosis and intelligent energy management method and system, which collects real-time operation data of the photovoltaic power station, uses machine learning algorithms to identify the operation status of the power station, formulates energy management strategies according to the operation status, implements the strategies and evaluates the execution effect of the strategies, thereby realizing intelligent management of the photovoltaic power station. This method not only improves the operation efficiency of the photovoltaic power station and extends the service life of the equipment, but also improves the utilization rate of energy, which is conducive to promoting the development of the photovoltaic industry.

[0074] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0075] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0076] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0077] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0078] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0079] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0080] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0081] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices, and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0082] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0083] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0084] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A photovoltaic power station comprehensive diagnosis and intelligent energy management method, characterized in that: The following steps are involved: S1. Collect real-time operation data of photovoltaic power stations; S2. Analyze the collected real-time operation data using diagnostic algorithms to identify the operation status of the photovoltaic power station; S3, formulate corresponding energy management strategies according to the identified operating status of the photovoltaic power station; S4. Implement energy management strategies to achieve intelligent management of photovoltaic power plants.

2. The photovoltaic power station comprehensive diagnosis and intelligent energy management method according to claim 1, characterized in that: The real-time operation data of the photovoltaic power station is collected as follows: Collect real-time operation data of the photovoltaic power station, including the voltage, current, temperature of the photovoltaic panels, the working status of the inverter, and the total power generation of the power station; Sending real-time operation data to the data processing center through data transmission equipment; The data processing center cleans and preprocesses the collected real-time operation data.

3. The photovoltaic power station comprehensive diagnosis and intelligent energy management method according to claim 2 is characterized in that: Cleaning and preprocessing include removing outliers, filling missing values, and data normalization.

4. The photovoltaic power station comprehensive diagnosis and intelligent energy management method according to claim 1, characterized in that: Identify the operating status of the photovoltaic power station as follows: Use support vector machine algorithm to analyze the collected real-time operation data; The input data is mapped to a high-dimensional space through a support vector machine model, and then a hyperplane is found in the high-dimensional space to separate data in different operating states; According to the prediction results of the support vector machine model, the operating status of the photovoltaic power station is identified.

5. The photovoltaic power station comprehensive diagnosis and intelligent energy management method according to claim 4 is characterized in that: The collected real-time operation data is analyzed as follows: The real-time operation data of the photovoltaic power station is used as input data, and the operation status of the power station is used as the category to train the support vector machine model; the operation status includes normal operation, low efficiency or equipment failure.

6. The photovoltaic power station comprehensive diagnosis and intelligent energy management method according to claim 1, characterized in that: The corresponding energy management strategies are as follows: Evaluate the current operating status of the PV power station based on the operating status identified by the support vector machine algorithm; Develop energy management strategies based on the assessment results; Implement the developed energy management strategy into the operation of the PV power plant, including adjusting equipment parameters and arranging equipment maintenance; Monitor the effects of energy management strategies after implementation, such as changes in equipment operating efficiency and equipment failure rates, and adjust energy management strategies based on monitoring results.

7. The photovoltaic power station comprehensive diagnosis and intelligent energy management method according to claim 6, characterized in that: The energy management strategy is as follows: If equipment efficiency decreases, strategies include adjusting equipment operating parameters, such as adjusting the angle of photovoltaic panels to improve lighting efficiency; if equipment fails, strategies include arranging equipment repair or replacement.

8. The photovoltaic power station comprehensive diagnosis and intelligent energy management method according to claim 1, characterized in that: The energy management strategy is implemented as follows: When the energy management strategy is to adjust the equipment operating parameters, the operating status of the equipment is changed through the control system; when the energy management strategy is to arrange equipment maintenance, the maintenance personnel are notified to perform the operation; After executing the energy management strategy, the operating data of the photovoltaic power station is collected, including the operating status of the equipment and the power generation of the power station, to evaluate the implementation effect of the energy management strategy; the effectiveness of the strategy is judged by comparing the equipment operating efficiency and power generation of the power station before and after the implementation of the strategy; and the energy management strategy is adjusted according to the results of the effect evaluation.

9. The photovoltaic power station comprehensive diagnosis and intelligent energy management method according to claim 1, characterized in that: The energy management strategy is adjusted as follows: When the energy management strategy is not implemented effectively, modify the strategy or formulate a new strategy; when the energy management strategy is implemented effectively, continue to implement it or use it in other photovoltaic power plants.

10. A photovoltaic power station comprehensive diagnosis and intelligent energy management system, characterized in that: include: Collection module, collecting real-time operation data of photovoltaic power station; The identification module uses diagnostic algorithms to analyze the collected real-time operating data and identify the operating status of the PV power station; Strategy module, which formulates corresponding energy management strategies according to the identified operating status of the photovoltaic power station; Output module executes energy management strategies to achieve intelligent management of photovoltaic power plants.

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