Controllable intelligent photovoltaic monitoring method, system, equipment, medium and program

Abstract

The invention discloses a controllable intelligent photovoltaic monitoring method, system, device, medium and program, and belongs to the technical field of photovoltaic power station fault monitoring. The method comprises the following steps: acquiring actually measured environmental parameters and actual output power of a photovoltaic power station; obtaining theoretical output power according to the photovoltaic array mathematical model; pre-processing the I-V characteristic data according to an actually measured I-V characteristic curve of the photovoltaic array to obtain pre-processed data; simulating the preprocessed data to extract fault features, and calculating a corrected power deviation according to the extracted fault features and a preset theoretical output power; and judging the fault type of the photovoltaic equipment based on the corrected power deviation. According to the method, real-time monitoring data is compared with a theoretical model estimation result, and an artificial intelligence algorithm is applied, so that fault diagnosis of the photovoltaic equipment is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power station fault monitoring, and in particular to a controllable intelligent photovoltaic monitoring method, system, equipment, medium and program. Background Art

[0002] Under the grand background of the in-depth implementation of the global energy transformation and sustainable development strategy, the importance of photovoltaic power stations as a key force in promoting the green energy revolution and reducing carbon emissions has become increasingly prominent. However, the operation and maintenance management of photovoltaic power stations is facing unprecedented challenges. On the one hand, photovoltaic power stations are usually deployed in complex and changeable outdoor environments and are exposed to harsh climatic conditions such as high temperature, strong wind, rain, snow, and dust for a long time. This makes the core components of the power station - the combiner box and inverter, especially the sensors on the DC and AC sides, very susceptible to interference from on-site environmental noise signals such as electromagnetic interference and drift caused by temperature changes when collecting key data such as operating voltage, current, and power factor, resulting in reduced data accuracy and bringing difficulties to subsequent fault diagnosis and performance evaluation. On the other hand, photovoltaic power stations are often widely distributed and geographically remote. How to realize the real-time collection and remote transmission of monitoring data and ensure the real-time and reliability of data has become a major problem in technical implementation. In addition, since different photovoltaic power stations may use different equipment and sensors, the format and standard of the collected data may be different, which not only increases the complexity of data processing, but also affects the comparability and consistency of the data, bringing inconvenience to the subsequent data analysis, mining, and formulation of operation and maintenance strategies. At the same time, faced with massive amounts of data, how to achieve efficient data storage, processing and analysis and ensure the security and integrity of data is also an issue that must be considered in technology implementation.

[0003] Due to the complexity and uncertainty of the operating status of photovoltaic power stations, how to achieve high-precision data preprocessing, effectively filter out noise signals, correct and supplement missing data, and perform intelligent diagnosis and prediction based on monitoring data to accurately identify fault types, evaluate equipment performance, and predict equipment life, thereby ensuring the optimization of power station equipment performance, timely discovering and handling potential faults, and extending the service life of equipment, has become a key technical problem that the industry needs to solve urgently. These technical difficulties are not only related to the operation and maintenance efficiency and cost of photovoltaic power stations, but also directly affect the long-term stable operation and energy efficiency improvement of photovoltaic power stations, and have a far-reaching impact on promoting high-quality development of the photovoltaic industry and promoting the transformation of the global energy structure. Therefore, how to ensure the accuracy and integrity of data collection, the real-time and reliability of data transmission, the standardization and efficiency of data processing in complex outdoor environments, and achieve high-precision data preprocessing and intelligent diagnosis and prediction, has become a major issue that must be faced and solved in the development and application of controllable intelligent photovoltaic monitoring methods. Summary of the invention

[0004] In view of the problem of how to achieve high-precision data preprocessing and intelligent diagnosis and prediction in photovoltaic monitoring under complex outdoor environments in the prior art, the present invention provides a controllable intelligent photovoltaic monitoring method, which combines the comparison of real-time monitoring data with the estimation results of theoretical models and uses artificial intelligence algorithms to realize photovoltaic equipment fault diagnosis.

[0005] In order to achieve the above object, the present invention provides the following technical solutions.

[0006] In a first aspect, the present invention provides a controllable intelligent photovoltaic monitoring method, comprising: Obtain the measured environmental parameters and actual output power of the photovoltaic power station; obtain the theoretical output power based on the mathematical model of the photovoltaic array; According to the measured IV characteristic curve of the photovoltaic array, the IV characteristic data is preprocessed to obtain preprocessed data; The preprocessed data is simulated to extract fault features, and the corrected power deviation is calculated based on the extracted fault features and the preset theoretical output power; The fault type of the photovoltaic equipment is judged based on the corrected power deviation.

[0007] As a further improvement of the present invention, the step of obtaining the measured environmental parameters of the photovoltaic power station includes: Measured environmental parameters obtained through meteorological data acquisition equipment in photovoltaic power plants.

[0008] As a further improvement of the present invention, the method of obtaining the theoretical output power according to the photovoltaic array mathematical model includes:

[0009] In the formula, is the photogenerated current; is the reverse saturation current of the PN junction in the photovoltaic module; q is the charge constant; Rs is the equivalent series resistance; Rsh is the equivalent parallel resistance; a is the diode ideal coefficient; K is the Boltzmann constant; T is the photovoltaic module temperature; I and V are the output current and voltage of the photovoltaic module respectively; The values ​​are:

[0010] Where, ISC,STC is the short-circuit current under standard test conditions (STC), α is the temperature coefficient of the short-circuit current point; G is the coplanar irradiance; TSTC is the reference temperature under STC, i.e. 25°C; GSTC is the reference irradiance under STC, i.e. 1000W / m2; The values ​​are:

[0011] in, is the bandgap width of the semiconductor material, is the equivalent reverse saturation current of the PN junction of the photovoltaic module under STC; Its values ​​are:

[0012] In the formula It is the open circuit voltage under STC.

[0013] As a further improvement of the present invention, the IV characteristic data is preprocessed according to the measured IV characteristic curve of the photovoltaic array to obtain the preprocessed data, including: The measured IV characteristic curve is collected by the photovoltaic monitoring system and is used to measure the voltage and current of the photovoltaic array under different conditions; Based on the electrical physics theoretical basis of photovoltaic arrays and the model established by analysis, the collected IV characteristic data are preprocessed to obtain preprocessed data.

[0014] As a further improvement of the present invention, the method of simulating the preprocessed data to extract fault features and calculating the corrected power deviation according to the extracted fault features and the preset theoretical output power includes: According to the meteorological data collection equipment of the photovoltaic station, the environmental parameters such as solar irradiance and temperature are obtained, and the theoretical output power of the photovoltaic array is estimated based on the environmental parameters. Then, the power generation power is obtained through the measured input voltage and current data of the DC side of the photovoltaic inverter and compared with the theoretical output power to calculate the power deviation Peer.

[0015] As a further improvement of the present invention, the method of judging the fault type of the photovoltaic device based on the corrected power deviation includes: If the corrected power deviation Peer is less than the fault detection threshold Pr, it is considered that there is no fault at present; otherwise, it is considered that a suspected fault occurs.

[0016] In a second aspect, the present invention provides a controllable intelligent photovoltaic monitoring system, comprising: Data acquisition module: used to obtain the measured environmental parameters and actual output power of the photovoltaic power station; obtain the theoretical output power according to the mathematical model of the photovoltaic array; Preprocessing data module: used to preprocess IV characteristic data according to the measured IV characteristic curve of the photovoltaic array to obtain preprocessed data; Deviation calculation module: used to simulate the preprocessed data to extract fault features, and calculate the corrected power deviation based on the extracted fault features and the preset theoretical output power; Fault judgment module: used to judge the fault type of photovoltaic equipment based on the corrected power deviation.

[0017] In a third aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of a controllable intelligent photovoltaic monitoring method when executing the computer program.

[0018] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the controllable intelligent photovoltaic monitoring method are implemented.

[0019] In a fifth aspect, the present invention provides a computer program product, characterized in that it includes computer instructions, which, when executed by a processor, implement the steps of the controllable intelligent photovoltaic monitoring method.

[0020] Compared with the prior art, the present invention has the following beneficial effects: The present invention can more accurately evaluate the actual operating status of the photovoltaic system by obtaining the measured environmental parameters and actual output power of the photovoltaic power station and comparing them with the theoretical output power calculated according to the mathematical model of the photovoltaic array. This comparison helps to find the difference between the actual output and the theoretical output, so as to more accurately locate potential problems. The present invention uses the measured IV characteristic curve of the photovoltaic array for data preprocessing, and extracts fault characteristics by simulating the preprocessed data. This method can capture the subtle changes of the photovoltaic equipment under the fault state, so as to achieve accurate judgment of the fault type. In addition, the fault judgment based on the corrected power deviation further improves the accuracy and reliability of the diagnosis. Through intelligent monitoring and diagnosis means, the present invention can significantly reduce the operation and maintenance costs of the photovoltaic power station. The operation and maintenance personnel can locate the fault point more accurately, reduce unnecessary inspections and maintenance work, and thus improve the operation and maintenance efficiency. Real-time monitoring and intelligent diagnosis help to timely discover and handle abnormal conditions in the photovoltaic system, so as to ensure the stable operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings described herein are for illustrative purposes only and are not intended to limit the scope of the present disclosure in any way. In the drawings: Figure 1 A flow chart of a controllable intelligent photovoltaic monitoring method of the present invention; Figure 2 This is the technical route for intelligent monitoring of photovoltaic equipment in the present invention; Figure 3 It is a schematic diagram of the process of the photovoltaic array modeling method of the present invention; Figure 4 It is a schematic diagram of the photovoltaic string intelligent monitoring and fault diagnosis algorithm based on the measured IV characteristics of the present invention; Figure 5 It is a schematic diagram of the process flow of the photovoltaic string intelligent monitoring and fault diagnosis algorithm of the present invention; Figure 6 It is a structural schematic diagram of a controllable intelligent photovoltaic monitoring system of the present invention; Figure 7 It is a schematic diagram of the electronic device of the present invention. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. The described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present invention.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0024] In view of the problem of how to achieve high-precision data preprocessing and intelligent diagnosis and prediction in photovoltaic monitoring under complex outdoor environments in the prior art, the present invention provides a controllable intelligent photovoltaic monitoring method, which includes: Obtain the measured environmental parameters and actual output power of the photovoltaic power station; obtain the theoretical output power based on the mathematical model of the photovoltaic array; According to the measured IV characteristic curve of the photovoltaic array, the IV characteristic data is preprocessed to obtain preprocessed data; The preprocessed data is simulated to extract fault features, and the corrected power deviation is calculated based on the extracted fault features and the preset theoretical output power; The fault type of the photovoltaic equipment is judged based on the corrected power deviation.

[0025] The present invention achieves life prediction and fault diagnosis of photovoltaic equipment by establishing a photovoltaic array mathematical model, combining real-time monitoring data with comparison of theoretical model estimation results, and applying artificial intelligence algorithms.

[0026] The present invention is further explained below in conjunction with the specific drawings.

[0027] Photovoltaic equipment intelligent monitoring system Figure 2The photovoltaic equipment intelligent monitoring application system includes equipment monitoring module, fault diagnosis module and life prediction module.

[0028] The system is connected to various devices in the photovoltaic power station, including batteries, inverters, inductors and controllers, through a sensor network, collects device data in real time, and transmits it to the central control unit for analysis and processing via wireless or wired means.

[0029] The equipment monitoring module is used by the system to collect the voltage, current, temperature, humidity and other parameters of photovoltaic power station equipment in real time through the sensor network, and transmit the data synchronously to the central control unit. At the same time, the system supports multi-source data fusion and standardized processing to ensure the accuracy and reliability of the data.

[0030] The fault diagnosis module is used by the central control unit to receive data from various devices and analyze them using advanced algorithms to detect potential equipment failures. Once abnormal data is found, the system will immediately alert relevant personnel and provide fault location and possible cause analysis so that timely measures can be taken to repair it.

[0031] The life prediction module is used to predict the remaining life and maintenance cycle of equipment through long-term evaluation of equipment performance, combined with technical means such as IV curve and LTV curve. This will help photovoltaic power stations to formulate scientific maintenance plans, reduce operation and maintenance costs, and improve equipment reliability and stability.

[0032] This system uses online monitoring of the operating voltage, current or IV characteristic data of the DC side of the combiner box or inverter in the photovoltaic power station, the operating voltage, current, power factor and other data on the AC side, as well as meteorological data such as solar irradiance and temperature that are closely related to the power generation performance of the photovoltaic power station, and uses technical means such as the power Internet of Things and modern communication technology to achieve the transmission of photovoltaic power station monitoring data information, standardizes the monitoring data with reference to existing data specifications at home and abroad, and synchronously stores and backs up the measured data to facilitate the next step of in-depth analysis of the historical operating data of the photovoltaic power station. Since the data measurement process of sensors in the combiner box and inverter is easily interfered by the on-site environmental noise signal, in order to ensure the accuracy of fault diagnosis and life prediction of photovoltaic power station equipment, it is also necessary to use online filtering technology to pre-process the data and correct and supplement the missing data.

[0033] The life prediction module is used for life prediction and fault diagnosis of photovoltaic station equipment. Its implementation process depends on the accurate establishment of the corresponding mathematical model. The photovoltaic array modeling steps mainly include: Based on the theoretical foundation and analysis of photovoltaic array electrical physics: In order to describe the nonlinear IV characteristics of the photovoltaic array, it is necessary to analyze the theoretical model of the photovoltaic array. At present, the single diode model is commonly used in the modeling of solar cells. The output current of the photovoltaic module is: (1) Where Iph is the photocurrent; I0 ​​is the reverse saturation current of the PN junction in the photovoltaic module; q is the charge constant; Rs is the equivalent series resistance; Rsh is the equivalent parallel resistance; a is the diode ideal coefficient; K is the Boltzmann constant; T is the photovoltaic module temperature; I and V are the output current and voltage of the photovoltaic module respectively; the value of Iph is: (2) Where ISC,STC is the short-circuit current under standard test conditions (STC), and α is the temperature coefficient of the short-circuit current point. G is the coplanar irradiance. TSTC is the reference temperature under STC, i.e. 25°C. GSTC is the reference irradiance under STC, i.e. 1000W / m2. The value of I0 is: (3) Where Eg is the bandgap width of the semiconductor material, and I0,STC is the equivalent reverse saturation current of the PN junction of the photovoltaic module under STC. Its value is: (4) Where VOC,STC is the open circuit voltage at STC.

[0034] In order to establish a mathematical model of a photovoltaic array, the calculation speed and accuracy of the modeling should be guaranteed first.

[0035] Since equation (1) is an implicit equation, iterative calculation is required to obtain an approximate solution. Commonly used iterative algorithms include Newton iteration method, parabola method, Stephenson method, etc. However, different iterative methods have different convergence speeds when solving the above equations. Usually, the photovoltaic module is the smallest unit analyzed in the photovoltaic array. Considering that the photovoltaic array may be affected by local shading, the equivalent absorbed irradiance of each photovoltaic module may be different. Therefore, it is necessary to calculate the output characteristics of the photovoltaic module under different environmental conditions, and finally obtain the IV curve of the entire photovoltaic array by superimposing the IV curves of each photovoltaic module. By analyzing the IV curve of the photovoltaic array, for example, when a photovoltaic module is partially shaded, the short-circuit current of the abnormal photovoltaic module is less than the short-circuit current of other normal photovoltaic modules. Because the photovoltaic modules in the string are connected in series, when the current of the normal photovoltaic module in the string is greater than the short-circuit current of the locally shaded photovoltaic module, the shaded photovoltaic module will become a load, causing a negative bias voltage. At this time, the bypass diode of the photovoltaic module with local shadow is turned on. The above-mentioned photovoltaic module mathematical model and photovoltaic array modeling method can be combined to use the Stephenson iteration method to quickly calculate the theoretical IV curve of the photovoltaic array under different environmental conditions. The corresponding photovoltaic array rapid modeling method flow chart is as follows: Figure 3 shown.

[0036] Through equations (1)-(4), the PV array model parameters series resistance Rs, parallel resistance Rsh and ideal factor a can be extracted. The model parameters Iph and I0 can be further estimated from the above three parameters and the ambient irradiation and temperature. However, these model parameters will change with the degradation of the PV array, so the above parameters need to be calibrated and preprocessed before analysis. The output IV curve of the PV array is also affected by the ambient irradiance and temperature, so it is also necessary to calibrate the parameters under different environmental conditions and finally obtain the corrected parameters relative to the environmental parameters. After establishing the above PV array mathematical model, the PV equipment monitoring data can be compared with the above theoretical model estimation results, combined with the application of artificial intelligence algorithms, to achieve PV equipment life prediction and fault diagnosis.

[0037] The photovoltaic module intelligent monitoring module and fault diagnosis module mainly include the steps of collecting monitoring data, establishing reference mathematical models, and fault diagnosis. The collected DC side data of photovoltaic equipment are the operating voltage and current data of photovoltaic strings or the IV characteristic data of photovoltaic arrays, as well as the meteorological data such as environmental irradiance and temperature collected synchronously. If the IV characteristics of photovoltaic arrays can be measured online, the flow chart of the proposed photovoltaic string intelligent monitoring and fault diagnosis algorithm is as follows: Figure 4 shown.

[0038] First, the solar irradiance, temperature and other environmental parameters are obtained according to the meteorological data collection equipment of the photovoltaic station, and the environmental parameters are substituted into the above-mentioned photovoltaic array mathematical model to estimate the theoretical output power of the photovoltaic array. Then, the power generation power is obtained through the measured input voltage and current data of the DC side of the photovoltaic inverter and compared with the theoretical output power to calculate the power deviation Peer. If Peer is less than the fault detection threshold Pr, it is considered that there is no fault at present. Otherwise, it is considered that a suspected fault occurs, and the fault record number k is increased by 1. When the suspected fault is detected for more than 3 times in a row, it is considered that the photovoltaic array fault is detected, and the photovoltaic array IV special function is started. The system has a characteristic acquisition function, pre-processes the measured IV characteristic data, and uses a machine learning algorithm to extract IV characteristic fault feature data, analyzes the fault feature data, and finally obtains the diagnosis results of different fault types. The diagnosable faults or abnormalities include: PV string open circuit, PV string short circuit, local shadowing of PV modules, small short-circuit current, bypass diode short circuit, abnormal series impedance in PV strings, abnormal parallel impedance in PV strings, PV string performance degradation, MPPT tracking abnormality, unexplained power loss, abnormal data communication status, abnormal electrical data measurement results, and at least 12 fault types.

[0039] If it is difficult for existing photovoltaic stations to measure the IV characteristics of the photovoltaic array online, the flowchart of the proposed photovoltaic string intelligent monitoring and fault diagnosis algorithm is as follows: Figure 5As shown. First, the meteorological data such as irradiance and temperature measured at the photovoltaic station are substituted into the mathematical model of the photovoltaic array to obtain the model estimation results under the corresponding meteorological conditions, and the DC side voltage, current and other electrical data measured by the inverter or combiner box are normalized with the model estimation results to obtain the corresponding measured electrical data and simulated electrical data. Based on the data difference analysis method and fuzzy membership matching method, the fault state analysis is performed to obtain the current fault severity index, which can be divided into no fault, slight loss, abnormality, fault and other degrees.

[0040] The second object of the present invention is to provide a controllable intelligent photovoltaic monitoring system, such as Figure 6 As shown, including: Data acquisition module: used to obtain the measured environmental parameters and actual output power of the photovoltaic power station; obtain the theoretical output power according to the mathematical model of the photovoltaic array; Preprocessing data module: used to preprocess IV characteristic data according to the measured IV characteristic curve of the photovoltaic array to obtain preprocessed data; Deviation calculation module: used to simulate the preprocessed data to extract fault features, and calculate the corrected power deviation based on the extracted fault features and the preset theoretical output power; Fault judgment module: used to judge the fault type of photovoltaic equipment based on the corrected power deviation.

[0041] like Figure 7 As shown, the third object of the present invention is to provide an electronic device, the electronic device comprising: a processor 301, a memory 302 and a display screen 303. Among them, the memory 302 and the display screen 303 are connected to the processor 301, such as through a bus 304. Optionally, the electronic device may also include a transceiver 305. It should be noted that in actual applications, the transceiver 305 is not limited to one, and the structure of the electronic device does not constitute a limitation on the embodiments of the present application.

[0042] The processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0043] The bus 304 may include a path to transmit information between the above components. The bus 304 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus 304 may be divided into an address bus, a data bus, a control bus, etc.

[0044] The memory 302 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0045] The memory 302 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 302 to implement the contents shown in the above method embodiment.

[0046] Figure 7 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0047] The fourth object of the present invention is to provide a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program is stored thereon, and when the program is executed by a processor, the aforementioned Figure 1 The various processes of the method embodiment shown in the figure include, for example, a memory including instructions, and the instructions can be executed by a processor of an electronic device to complete the method.

[0048] A computer-readable storage medium may be a tangible device that holds and stores instructions used by an instruction execution device. A computer-readable storage medium may be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, a computer-readable storage medium may be a portable computer disk, a hard disk, a USB flash drive, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a lectern random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, an optical disk, a magnetic disk, a mechanical encoding device, and any combination thereof.

[0049] A fifth object of the present invention is to provide a computer program product, comprising computer instructions, which, when executed by a processor, implement the above Figure 1 The various processes of the method embodiment shown can achieve the same technical effect, and will not be described again here to avoid repetition.

[0050] Many embodiments and many applications beyond the examples provided will be apparent to those skilled in the art upon reading the above description. Therefore, the scope of the present teachings should not be determined with reference to the above description, but rather with reference to the preceding claims and the full scope of equivalents to which such claims are entitled. For the purpose of comprehensiveness, all articles and references, including disclosures of patent applications and publications, are incorporated herein by reference. The omission of any aspect of the subject matter disclosed herein in the preceding claims is not intended to be a disclaimer of such subject matter, nor should it be considered that the applicants did not consider such subject matter to be part of the disclosed inventive subject matter.

[0051] The above content is a further detailed description of the present invention. It cannot be determined that the specific implementation methods of the present invention are limited to these. For ordinary technicians in the technical field to which the present invention belongs, without departing from the concept of the present invention, they can also make several simple deductions or substitutions, which should be regarded as belonging to the protection scope of the present invention determined by the submitted claims.

Claims

1. A controllable intelligent photovoltaic monitoring method, characterized in that: include: Obtain the measured environmental parameters and actual output power of the photovoltaic power station; The theoretical output power is obtained according to the mathematical model of the photovoltaic array; According to the measured IV characteristic curve of the photovoltaic array, the IV characteristic data is preprocessed to obtain preprocessed data; The preprocessed data is simulated to extract fault features, and the corrected power deviation is calculated based on the extracted fault features and the preset theoretical output power; The fault type of the photovoltaic equipment is judged based on the corrected power deviation.

2. A controllable intelligent photovoltaic monitoring method according to claim 1, characterized in that: The obtaining of the measured environmental parameters of the photovoltaic power station includes: Measured environmental parameters obtained through meteorological data acquisition equipment in photovoltaic power plants.

3. A controllable intelligent photovoltaic monitoring method according to claim 1, characterized in that: The method of obtaining the theoretical output power according to the photovoltaic array mathematical model includes: In the formula, is the photogenerated current; is the reverse saturation current of the PN junction in the photovoltaic module; q is the charge constant; Rs is the equivalent series resistance; Rsh is the equivalent parallel resistance; a is the diode ideal coefficient; K is the Boltzmann constant; T is the photovoltaic module temperature; I and V are the output current and voltage of the photovoltaic module respectively; The values ​​are: Where, ISC,STC is the short-circuit current under standard test conditions (STC), α is the temperature coefficient of the short-circuit current point; G is the coplanar irradiance; TSTC is the reference temperature under STC, i.e. 25°C; GSTC is the reference irradiance under STC, i.e. 1000W / m2; The values ​​are: in, is the bandgap width of the semiconductor material, is the equivalent reverse saturation current of the PN junction of the photovoltaic module under STC; Its values ​​are: In the formula It is the open circuit voltage under STC.

4. A controllable intelligent photovoltaic monitoring method according to claim 1, characterized in that: The step of preprocessing the IV characteristic data according to the measured IV characteristic curve of the photovoltaic array to obtain the preprocessed data includes: The measured IV characteristic curve is collected by the photovoltaic monitoring system and is used to measure the voltage and current of the photovoltaic array under different conditions; Based on the electrical physics theoretical basis of photovoltaic arrays and the model established by analysis, the collected IV characteristic data are preprocessed to obtain preprocessed data.

5. The controllable intelligent photovoltaic monitoring method according to claim 1, characterized in that: The simulating of the preprocessed data to extract fault features and calculating the corrected power deviation according to the extracted fault features and the preset theoretical output power includes: According to the meteorological data collection equipment of the photovoltaic station, the environmental parameters such as solar irradiance and temperature are obtained, and the theoretical output power of the photovoltaic array is estimated based on the environmental parameters. Then, the power generation power is obtained through the measured input voltage and current data of the DC side of the photovoltaic inverter and compared with the theoretical output power to calculate the power deviation Peer.

6. A controllable intelligent photovoltaic monitoring method according to claim 1, characterized in that: The determining of the fault type of the photovoltaic device based on the corrected power deviation includes: If the corrected power deviation Peer is less than the fault detection threshold Pr, it is considered that there is no fault at present; otherwise, it is considered that a suspected fault occurs.

7. A controllable intelligent photovoltaic monitoring system, characterized in that: include: Data acquisition module: used to obtain the measured environmental parameters and actual output power of the photovoltaic power station; The theoretical output power is obtained according to the mathematical model of the photovoltaic array; Preprocessing data module: used to preprocess IV characteristic data according to the measured IV characteristic curve of the photovoltaic array to obtain preprocessed data; Deviation calculation module: used to simulate the preprocessed data to extract fault features, and calculate the corrected power deviation based on the extracted fault features and the preset theoretical output power; Fault judgment module: used to judge the fault type of photovoltaic equipment based on the corrected power deviation.

8. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of a controllable intelligent photovoltaic monitoring method as described in any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the controllable intelligent photovoltaic monitoring method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions, which, when executed by a processor, implement the steps of a controllable intelligent photovoltaic monitoring method as described in any one of claims 1 to 6.

Images