Method and apparatus for obtaining information about the cause of power generation abnormalities of a photovoltaic module

Abstract

Provided is an apparatus for obtaining information on a cause of power generation abnormality of a photovoltaic module, the apparatus including a memory storing at least one program and a processor configured to execute the at least one program, wherein the processor is configured to input information on the photovoltaic module and information related to the sun as inputs to a power generation abnormality cause prediction model, determine whether a power generation abnormality of the photovoltaic module has occurred based on the information on the photovoltaic module and the information related to the sun, derive information on a cause of the power generation abnormality of the photovoltaic module based on a result of the determination of whether the power generation abnormality has occurred, and obtain the information on the cause of the power generation abnormality from the power generation abnormality cause prediction model.

Description

Technical Field

[0001] This disclosure relates to methods and apparatus for obtaining information about the causes of abnormal power generation in photovoltaic modules. Background Technology

[0002] Recently, with increasing attention to eco-friendly energy technologies, the number of photovoltaic power generation systems has also been increasing.

[0003] However, due to the characteristics of photovoltaic power generation systems, it often takes unnecessary labor and time to determine whether an anomaly has occurred in the photovoltaic module and, if so, to determine the cause of the anomaly.

[0004] In addition, when the power output of the photovoltaic module temporarily decreases, it is difficult for users to analyze the reason for the decrease in power output. Summary of the Invention

[0005] Technical issues

[0006] This disclosure aims to provide a method and apparatus for obtaining information about the causes of abnormal power generation in photovoltaic modules. This disclosure also aims to provide a computer-readable recording medium having a program recorded thereon for causing a computer to execute the method. The technical problems to be solved are not limited to those described above, and other technical problems may exist.

[0007] Technical solution

[0008] According to one aspect of this disclosure, a method is provided for obtaining information about the cause of power generation anomalies in photovoltaic modules. The method includes: inputting information about the photovoltaic module and solar-related information as input data to a power generation anomaly cause prediction model; determining whether a power generation anomaly has occurred based on the information about the photovoltaic module and the solar-related information; deriving information about the cause of the power generation anomaly based on the result of determining whether a power generation anomaly has occurred; and obtaining the information about the cause of the power generation anomaly as output data of the power generation anomaly cause prediction model.

[0009] According to another aspect of this disclosure, an apparatus is provided for obtaining information about the cause of a power generation anomaly in a photovoltaic module. The apparatus includes a memory storing at least one program and at least one processor configured to execute the at least one program. The at least one processor is configured to: input information about the photovoltaic module and solar-related information as input data to a power generation anomaly cause prediction model; determine whether a power generation anomaly has occurred based on the information about the photovoltaic module and the solar-related information; derive information about the cause of the power generation anomaly based on the result of determining whether a power generation anomaly has occurred; and obtain the information about the cause of the power generation anomaly as output data of the power generation anomaly cause prediction model.

[0010] According to another aspect of this disclosure, a computer-readable recording medium is provided having a program recorded thereon for causing a computer to perform the method.

[0011] Beneficial effects

[0012] By using information about the sun's position, the installation angle of the photovoltaic module, and environmental information about the area where the photovoltaic module is installed, the reasons for reduced power generation of the photovoltaic module can be accurately analyzed and predicted based on the time-based power generation data of the photovoltaic module.

[0013] It can remotely identify the reasons for reduced power generation from photovoltaic modules.

[0014] However, the effects of the embodiments are not limited to those mentioned above, and those skilled in the art can clearly understand other effects not mentioned based on the description of this disclosure. Attached Figure Description

[0015] Figure 1 This is a diagram used to illustrate an example of a power supply system according to an implementation method;

[0016] Figure 2 This is a block diagram illustrating an example of an apparatus for obtaining information about the cause of power generation anomalies in a photovoltaic module, according to an embodiment.

[0017] Figure 3 This is a flowchart illustrating an example of a method for obtaining information about the cause of power generation anomalies in a photovoltaic module according to an embodiment;

[0018] Figure 4 This is a diagram illustrating an example of a method for obtaining information about the causes of power generation anomalies in photovoltaic modules using a power generation anomaly cause prediction model according to an implementation method;

[0019] Figure 5This is a diagram illustrating examples of information about photovoltaic modules and solar-related information according to an embodiment;

[0020] Figure 6 This is a diagram illustrating an example of a method for determining whether a power generation anomaly has occurred in a photovoltaic module according to an embodiment;

[0021] Figure 7 This is a diagram illustrating an example of a method for determining the cause of abnormal power generation in a photovoltaic module according to an embodiment; and

[0022] Figure 8 This is a diagram illustrating an example of a method for providing a user with a notification based on the cause of a power generation anomaly in a photovoltaic module, according to an embodiment. Detailed Implementation

[0023] Best way to implement this disclosure

[0024] According to one aspect, an apparatus is provided for obtaining information about the cause of a power generation anomaly in a photovoltaic module. The apparatus includes a memory storing at least one program and at least one processor configured to execute the at least one program, wherein the at least one processor is configured to: input information about the photovoltaic module and solar-related information as input data to a power generation anomaly cause prediction model; determine, based on the information about the photovoltaic module and the solar-related information, whether a power generation anomaly has occurred in the photovoltaic module; derive information about the cause of the power generation anomaly based on the result of determining whether a power generation anomaly has occurred; and obtain the information about the cause of the power generation anomaly as output data of the power generation anomaly cause prediction model.

[0025] Method of implementing this disclosure

[0026] Where possible, the terminology used in the implementation is selected from currently widely used general terms, but may vary depending on the understanding and practice of those skilled in the art, judicial precedent (case law), or the emergence of new technologies. Furthermore, in certain cases, the applicant may choose any terminology, and in such cases, the meaning of the term will be described in detail in the corresponding section. Therefore, the terminology used herein should be interpreted based on its meaning and the content throughout the specification, rather than solely on its name.

[0027] Throughout this specification, when a section is referred to as "including" a certain component, this does not mean that other components are excluded, but rather that other components may be further included, unless otherwise specified. Additionally, terms such as "unit" or "module" described in this specification refer to a unit that performs at least one function or operation, which may be implemented in hardware or software, or a combination of hardware and software.

[0028] Furthermore, the terms used herein, including ordinal numbers such as "first" and "second," can be used to describe various components, but components are not limited by these terms. Rather, these terms are only used to distinguish one component from another. These terms can be used for the purpose of differentiating one component from another.

[0029] The present disclosure will be described in detail below with reference to the accompanying drawings. However, embodiments may be implemented in many different forms and are not limited to the examples described below.

[0030] Figure 1 This is a diagram used to describe an example of a power supply system according to an implementation method.

[0031] In the following text, reference will be made to Figure 1 An example describing a power supply system.

[0032] Reference Figure 1 The power supply system 10 may include photovoltaic modules 11, equipment 12, loads 14, and / or power distribution equipment 15. The power supply system 10 may be connected to an external power grid 16.

[0033] At least one photovoltaic module 11 can be installed on the roof or exterior wall of a building to generate electricity. Multiple photovoltaic modules 11 can be connected to form a photovoltaic module array.

[0034] Photovoltaic module 11 can be connected to device 12. For example, at least one device 12 can be connected to each photovoltaic module 11. As an example, if one device 12 is connected to each photovoltaic module 11, the number of devices 12 included in the power supply system 10 can be the same as the number of photovoltaic modules 11.

[0035] Device 12 may be a power regulation system (PCS) (also known as a power conversion system) configured to perform power conversion on the power generated by photovoltaic module 11. For example, device 12 may perform a predetermined conversion on the power generated by photovoltaic module 11 and supply the converted power to other components of power supply system 10 (e.g., power grid 16 and / or load 14).

[0036] In this implementation, device 12 may be a module-level power electronic device (MLPE). For example, device 12 may be an optimizer or a micro-inverter (MI).

[0037] As an example, when device 12 is an optimizer, device 12 can regulate the power generated by photovoltaic module 11 and output the regulated power to an inverter (e.g., a string inverter). The current converted by the inverter (e.g., from direct current (DC) to alternating current (AC)) can be supplied to the power grid 16 or the load 14.

[0038] As another example, when device 12 is a micro-inverter, device 12 can convert the power generated by photovoltaic module 11 (e.g., DC to AC). The current converted by device 12 can be output to power grid 16 or load 14.

[0039] As needed, the power supply system 10 may also include a combiner 13. At least some of the devices 12 can be connected to the distribution equipment 15 via the combiner 13. For example, power output from multiple devices 12 can be combined by the combiner 13 into a single output and supplied to the distribution equipment 15.

[0040] In some implementations, the device 12 and the power distribution device 15 can be connected via a power path excluding the combiner 13. For example, at least one device 12 can be connected to the power distribution device 15 via a power path excluding the combiner 13, and at least one other device 12 can be connected to the power distribution device 15 via the combiner 13.

[0041] The combiner 13 can control the voltage, current and / or power output from the device 12 based on the power supply status of the photovoltaic module 11, the device 12 and / or the power grid 16, and the operating mode of the combiner 13 can be set to diagnostic mode, operating mode, etc.

[0042] In an implementation, the combiner 13 may include an energy management system (EMS) configured to control the operation of the combiner 13. The EMS may control the voltage, current and / or power supplied to or output from the combiner 13 based on the power supply status of the photovoltaic module 11, the device 12 and / or the power grid 16, and may set the operating mode of the combiner 13 to a diagnostic mode, an operating mode, etc.

[0043] Load 14 may refer to an object installed at a power-consuming location such as a residential building, commercial facility, or factory, and configured to operate using at least one of the energy generated by the photovoltaic module 11, the energy stored in the energy storage system 17, and / or the energy supplied from the power grid 16. For example, in the case where the power-consuming location receiving electricity is a residential building, load 14 may include household appliances such as a washing machine, refrigerator, or television (TV).

[0044] The power grid 16 may include infrastructure systems for generating, transmitting, and distributing electricity. For example, the power grid 16 may include infrastructure systems such as power plants, substations, and power line networks. In some embodiments, the power grid 16 may deliver electrical energy generated at a power plant to the power supply system 10, or may deliver surplus electricity generated by the power supply system 10 to the outside of the power supply system 10.

[0045] For example, commercial electricity transmitted from the power grid 16 via utility poles can be supplied to locations where electricity is consumed via transformers. In some embodiments, the power supply system 10 can be implemented as an off-grid system not connected to the power grid 16.

[0046] In some embodiments, the power supply system 10 may further include at least one energy storage system 17. Depending on the needs, the power supply system 10 may include multiple energy storage systems 17. The energy storage system 17 can receive and store electricity generated by the photovoltaic module 11 and / or electricity delivered from the power grid 16. The energy storage system 17 can store electricity and supply the stored electricity to the load 14 when needed, thereby achieving efficient power supply.

[0047] The energy storage system 17 may include a power conversion module and a battery configured to store electrical energy. The battery may include a battery management system (BMS) configured to monitor the battery's state of charge (SOC), state of health (SOH), voltage and / or current, perform diagnostics on the battery, and perform safety functions such as current interruption.

[0048] In an implementation, the power conversion module may be a power conversion system (PCS) configured to perform the conversion between battery-side power and grid-side power. For example, the PCS may perform the conversion between battery-side DC current and grid-side AC current. As an example, the PCS may include a bidirectional DC-DC converter connected to the battery and configured to perform voltage conversion, and a bidirectional inverter connecting the DC-DC converter to the external side of the energy storage system 17.

[0049] In an embodiment, the energy storage system 17 may further include an energy management system (EMS) configured to control the operation of the energy storage system 17. The EMS may control the voltage, current and / or power supplied to or output from the energy storage system 17 based on the power supply status of the battery and / or power grid 16, and may set the operating mode of the energy storage system 17 to a diagnostic mode, an operating mode, etc.

[0050] As needed, an EMS coupled to a specific component of the power supply system 10 can control the operation of that specific component, and can also control the operation of other components of the power supply system 10. For example, an EMS coupled to the combiner 13 or an EMS coupled to the energy storage system 17 can control the operation of both the combiner 13 and the energy storage system 17.

[0051] In some implementations, the power distribution device 15 can provide electrical connections between components of the power supply system 10 and can control the power flow of the power supply system 10. For example, the power distribution device 15 can electrically connect the photovoltaic module 11 to the load 14. As an example, the power distribution device 15 can be connected to device 12 (which is connected to the photovoltaic module 11), thereby electrically connecting the photovoltaic module 11 to the load 14. The power distribution device 15 can also be connected to at least one of the energy storage system 17 and the power grid 16, if needed.

[0052] For example, power distribution equipment 15 may be a distribution board that distributes power within the power supply system 10. As an example, power distribution equipment 15 may be a main service board (MSP) that distributes power generated by photovoltaic module 11 to loads such as 14.

[0053] In another example, power distribution equipment 15 may be a master controller that performs power distribution and controls each of the devices 12 within a power supply system. As an example, the master controller may include switches, circuit breakers, and control units. The switches, circuit breakers, and control units may each be implemented as independent devices, or at least some of them may be integrated into a single device.

[0054] The main controller may include a switch configured to control electrical connections between components connected to the main controller (e.g., device 12 and load 14). For example, the main controller may include relays, power semiconductor devices, etc., which provide or block electrical connections to device 12 and / or energy storage system 17 based on the operating state of components of the power supply system 10.

[0055] The main controller can perform a rapid shutdown to stop the photovoltaic module 11 from generating electricity in emergency situations such as an overcurrent in the power supply system 10. For this purpose, the main controller may include a circuit breaker configured to disconnect the device 12 from the load 14.

[0056] The main controller may include a control unit configured to control the overall operation of the main controller. In addition to the operation of the main controller, the control unit may control the operation of other components of the power supply system 10 (e.g., device 12 or energy storage system 17).

[0057] The control unit can control the voltage, current, and / or power output from or supplied to each component based on the power supply status of the photovoltaic module 11, device 12, combiner 13, load 14, power grid 16, and / or energy storage system 17. In an embodiment, the control unit can set the operating modes of the main controller, device 12, and / or energy storage system 17 to diagnostic mode, operating mode, etc.

[0058] For example, the control unit can control the photovoltaic module 11, device 12, combiner 13, and / or energy storage system 17 based on the state of the power supply system 10. As an example, the control unit can control other components of the power supply system 10 by enabling the main controller to communicate with other components of the power supply system 10 (e.g., device 12). Communication between the main controller and other components of the power supply system 10 can be performed using a power line communication (PLC) scheme, but this disclosure is not limited thereto.

[0059] As an example, the control unit can control the device 12 based on the power generation status of the photovoltaic module 11. For instance, the main controller can receive control commands from a server that monitors the power generation status of the photovoltaic module 11, and the control unit can control the device 12 according to the control commands.

[0060] When the power supply from the power grid 16 is unavailable or unstable (e.g., in an off-grid situation), the main controller can supply power to at least some of the loads 14. For example, when the power supply from the power grid 16 is unavailable or unstable, the main controller can prioritize supplying the power generated by the photovoltaic module 11 and / or the power stored in the energy storage system 17 to standby loads that have a relatively high demand for a stable power supply.

[0061] In some embodiments, the power supply system 10 may also include auxiliary power generation equipment configured to generate electricity via methods other than photovoltaic power generation (e.g., a diesel generator). For example, the auxiliary power generation equipment may also be connected to the power distribution equipment 15. When the photovoltaic module 11 and energy storage system 17 are unable to adequately power the standby load due to environmental factors such as time of day or weather, the main controller can supply power generated by the auxiliary power generation equipment to the standby load.

[0062] A control unit can be implemented using at least one processor. The processor processes instructions from a computer program by performing basic arithmetic, logic, and input / output operations. Instructions can be provided from the main controller's internal memory or from external devices. The processor can control the overall operation of other components included in the main controller.

[0063] In some implementations, the processor may use at least one of rule-based methods or artificial intelligence algorithms (such as machine learning, neural networks, or deep learning algorithms) to perform at least a portion of the data analysis, processing, and generation of resulting information for performing the operations described above. Examples of neural networks may include neural network models based on architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), and recurrent neural networks (RNNs).

[0064] For example, a processor can be implemented as an array of logic gates, or as a combination of a general-purpose microprocessor and a memory storing programs that can be executed by the microprocessor. For instance, a processor can include a general-purpose processor, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a controller, a microcontroller, a state machine, etc.

[0065] In some environments, a processor may include an application-specific integrated circuit (ASIC), a programmable logic device (PLD), a field-programmable gate array (FPGA), and so on. For example, a processor may be implemented as a combination of processing devices, such as a combination of a DSP and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors coupled to a DSP core, or any other such combination.

[0066] By combining at least some of the above components, the power supply system 10 can be implemented in various forms.

[0067] Figure 2 This is a block diagram illustrating an example of an apparatus for obtaining information about the cause of power generation anomalies in a photovoltaic module, according to an embodiment.

[0068] Reference Figure 2 A device 200 (hereinafter referred to as "device 200") for obtaining information about the cause of abnormal power generation of a photovoltaic module may include a communication unit 210, a processor 220, and a memory 230. Figure 2 In this embodiment, only components relevant to the implementation are shown in device 200. Therefore, it will be apparent to those skilled in the art that, in addition to… Figure 2 In addition to the components shown, other general-purpose components may also be included.

[0069] The communication unit 210 may include one or more components configured to enable wired and / or wireless communication with an external server or external device. For example, the communication unit 210 may include a short-range communication unit (not shown) and a mobile communication unit (not shown) for communicating with an external server or external device.

[0070] The processor 220 can control the overall operation of the device 200. For example, by executing a program stored in the memory 230, the processor 220 can control the overall operation of the input unit (not shown), the display (not shown), the communication unit 210, the memory 230, etc.

[0071] The processor 220 may be implemented using at least one of the following: application-specific integrated circuit (ASIC), digital signal processor (DSP), digital signal processing device (DSPD), programmable logic device (PLD), field-programmable gate array (FPGA), controller, microcontroller, microprocessor and / or other electronic components configured to perform functions.

[0072] The processor 220 can control the operation of the device 200 by executing a program stored in the memory 230. For example, the processor 220 can execute a program that is referenced in the memory 230. Figures 3 to 8 The method described is at least a part of the method for obtaining information about the cause of abnormal power generation of a photovoltaic module.

[0073] The memory 230 can be hardware that stores various types of data processed in the device 200, and can store programs for processing and control executed by the processor 220.

[0074] For example, memory 230 can store various types of data, such as sun-related information, sun position information, solar irradiance data, photovoltaic module installation location and angle information, power generation, environmental information, temperature, current power, predicted power consumption, stored power, transmitted power, consumed power and other power-related information, weather information such as wind speed, rainfall, snowfall and sunrise / sunset times, and data generated during the operation of processor 220. For example, memory 230 can store an operating system (OS) and at least one program (e.g., a program required for the operation of processor 220).

[0075] The memory 230 may include random access memory (RAM) (such as dynamic random access memory (DRAM) and static random access memory (SRAM)), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM), Blu-ray disc or other optical disc storage media, hard disk drive (HDD), solid-state drive (SSD), or flash memory.

[0076] Figure 3 This is a flowchart illustrating an example of a method for obtaining information about the cause of power generation anomalies in a photovoltaic module, according to an embodiment.

[0077] Reference Figure 3Methods for obtaining information about the cause of abnormal power generation in photovoltaic modules may include operations 310 to 340. However, this disclosure is not limited thereto, and includes, in addition to... Figure 3 In addition to the operations shown, other general operations may also be included in the method for obtaining information about the cause of abnormal power generation from photovoltaic modules. In the implementation, as referred to above... Figure 1 and Figure 2 As mentioned above, Figure 3 At least one of the operations shown in the flowchart can be executed by a processor.

[0078] First, in operation 310, the processor can input information about the photovoltaic module and solar-related information as input data to the power generation anomaly cause prediction model. Here, the information about the photovoltaic module may include its installation location information, installation angle information, and power generation data, while the solar-related information may include the sun's position information and solar irradiance data.

[0079] For example, the processor can obtain the installation and location information of the photovoltaic module, and can calculate the sun's location information and solar irradiance data based on the installation and location information of the photovoltaic module.

[0080] For example, training a power generation anomaly cause prediction model can be performed by: preprocessing the information about photovoltaic modules and solar-related information to be scaled to the same level; and using the preprocessed information about photovoltaic modules and solar-related information as training data for the power generation anomaly cause prediction model to derive information about the causes of power generation anomalies.

[0081] In this implementation, the processor can input information about the photovoltaic module, the sun's location, and solar irradiance data into the power generation anomaly prediction model.

[0082] In operation 320, the processor can determine whether an abnormal power generation has occurred in the photovoltaic module based on information about the photovoltaic module and information related to the sun.

[0083] For example, the processor can derive the power generation variation pattern of the photovoltaic module over time based on information about the photovoltaic module and information related to the sun, and can use the power generation variation pattern to determine whether an abnormal power generation has occurred in the photovoltaic module.

[0084] In operation 330, the processor can derive information about the cause of the power generation anomaly based on the result of determining whether a power generation anomaly has occurred.

[0085] For example, the processor can determine the cause of the power generation anomaly based on the result of determining whether a power generation anomaly has occurred, and can obtain information about the cause of the power generation anomaly based on the determined cause.

[0086] In one implementation, the processor can visualize the power generation data included in the information about the photovoltaic modules based on the arrangement of the photovoltaic modules, and can derive information about the cause of power generation anomalies based on the visualized power generation data.

[0087] In operation 340, the processor can obtain information about the cause of the power generation anomaly as output data for the power generation anomaly cause prediction model.

[0088] In this implementation, the processor can provide the user with a notification regarding the cause of the power generation anomaly from the photovoltaic module, based on information about the cause. Here, the cause of the photovoltaic module's power generation anomaly may include a first cause related to obstacles, a second cause related to weather, and a third cause related to a malfunction of the photovoltaic module.

[0089] Figure 4 This is a diagram illustrating an example of a method for briefly describing how to obtain information about the causes of power generation anomalies in photovoltaic modules using a power generation anomaly cause prediction model according to an implementation method.

[0090] In the following text, reference will be made to Figure 4 Briefly describe an example of a method by which the processor obtains information about the cause of the power generation anomaly.

[0091] Reference Figure 4 The processor can use the power generation anomaly cause prediction model 410 to obtain information about the cause of power generation anomalies in the photovoltaic module.

[0092] For example, the processor can input information about the photovoltaic module and solar-related information as input data to the power generation anomaly cause prediction model 410. In this implementation, the processor can obtain information about the cause of the photovoltaic module's power generation anomaly as output data of the power generation anomaly cause prediction model 410.

[0093] For example, a processor can use information about the photovoltaic module and solar-related information to determine the cause of abnormal power generation from the photovoltaic module. In other words, the processor can determine whether abnormal power generation has occurred in the photovoltaic module, and can deduce the cause of the abnormality based on this determination.

[0094] In some implementations, the power generation anomaly cause prediction model 410 can be trained to derive information about the causes of power generation anomalies from the photovoltaic module using information about the photovoltaic module and solar-related information. The training of the power generation anomaly cause prediction model 410 can be performed by a processor or other device.

[0095] For example, training the power generation anomaly cause prediction model 410 can be performed by: preprocessing to adjust the information about the photovoltaic module and the sun-related information to the same scale; and using the preprocessed information about the photovoltaic module and the preprocessed sun-related information as training data for the power generation anomaly cause prediction model 410 to derive information about the causes of power generation anomalies, as will be described in detail below.

[0096] Figure 5 This is a diagram used to illustrate examples of information about photovoltaic modules and solar-related information according to an implementation method.

[0097] In the following text, reference will be made to Figure 5 An example of a method is described, through which the processor obtains information about the photovoltaic module and solar-related information and uses the obtained information.

[0098] Reference Figure 5 The processor can use information about photovoltaic module 510 to calculate information related to sun 520.

[0099] For example, information about photovoltaic module 510 may include installation and positioning information of photovoltaic module 510, installation angle information of photovoltaic module 510, arrangement information of multiple photovoltaic modules 510 included in photovoltaic panel, and power generation data, and information related to sun 520 may include location information of sun 520 and solar irradiance data.

[0100] First, the processor can obtain information about the photovoltaic module 510. For example, the processor can obtain in real time the installation positioning information of the photovoltaic module 510, the installation angle information of the photovoltaic module 510, and the arrangement information of the multiple photovoltaic modules 510 included in the photovoltaic panel. As another example, the processor can obtain from memory the installation positioning information of the photovoltaic module 510, the installation angle information of the photovoltaic module 510, and the arrangement information of the multiple photovoltaic modules 510 included in the photovoltaic panel, stored during the installation of the photovoltaic module 510.

[0101] For example, the processor can obtain power generation data from the photovoltaic module 510, and the power generation data can be real-time data or data obtained at predetermined time intervals, but this disclosure is not limited thereto.

[0102] For example, the processor can use information about the photovoltaic module 510 to obtain information related to the sun 520. That is, the processor can obtain information related to the sun 520 based on the location where the photovoltaic module 510 is installed.

[0103] For example, the processor can obtain information related to the Sun 520 based on the latitude and longitude of the area where the photovoltaic module 510 is installed, as well as the date and time when information related to the Sun 520 is obtained. More specifically, the processor can obtain the location information of the Sun 520 and time-related solar irradiance data based on the latitude and longitude of the area where the photovoltaic module 510 is installed, as well as the date and time when information related to the Sun 520 is obtained, by using an astronomical database, a solar position application programming interface (API), a weather data API, etc.

[0104] Here, API can refer to the interface through which a program provides services or functions to another program. Therefore, a processor can obtain the position information and time-related solar irradiance data of the sun 520 from another computer program. For example, the position information of the sun 520 can be obtained using altitude and azimuth.

[0105] Therefore, the processor can input information about the photovoltaic module 510 and information related to the sun 520 into the power generation anomaly cause prediction model.

[0106] In some implementations, as described above, information about the photovoltaic module 510 and information related to the sun 520 can be used to train a model for predicting the causes of power generation anomalies.

[0107] In some implementations, training the power generation anomaly cause prediction model can be performed by: preprocessing to adjust the information about photovoltaic module 510 and the information related to the sun 520 to the same scale; and using the preprocessed information about photovoltaic module 510 and the preprocessed information related to the sun 520 as training data for the power generation anomaly cause prediction model to derive information about the cause of the power generation anomaly.

[0108] For example, normalization can be used to adjust the data included in the information about photovoltaic module 510 and the data included in the information related to the sun 520 to the same scale. In other words, since the data can be represented in different ways, normalization can be used to adjust the data to the same scale.

[0109] For example, due to communication errors, sensor errors, etc., missing values ​​may exist in the data. In this case, time interpolation can be used to fill in the missing values. Here, time interpolation can refer to a method of supplementing continuous time series data, in which data before and after the missing time are used to estimate the value at the missing time. Therefore, time interpolation can be used to fill in missing values ​​in the data included in the information about photovoltaic module 510 and the information related to sun 520.

[0110] In this implementation, the data can be organized as time-series data and combined into vectors for use as training data for a power generation anomaly cause prediction model. The data preprocessed as described above can be used as training data to train the power generation anomaly cause prediction model to determine whether a power generation anomaly has occurred in the photovoltaic module 510, and to deduce the cause of the anomaly based on the determination result. Here, the power generation anomaly cause prediction model may include an artificial intelligence model for analyzing time-series data and an artificial intelligence model for analyzing spatial data.

[0111] Therefore, the processor can determine whether a power generation anomaly has occurred in the photovoltaic module 510 by using a power generation anomaly cause prediction model. For example, the processor can deduce the cause of the power generation anomaly in the photovoltaic module 510 based on the result of determining whether a power generation anomaly has occurred.

[0112] Figure 6 This is a diagram illustrating an example of a method for determining whether a power generation anomaly has occurred in a photovoltaic module according to an embodiment.

[0113] In the following text, reference will be made to Figure 6 The description provides an example of a method by which the processor determines whether a power generation anomaly has occurred in the photovoltaic module.

[0114] Reference Figure 6 The processor can determine whether a power generation anomaly has occurred in each of the photovoltaic modules 610 included in the plurality of photovoltaic modules 600 by using the power generation data of the photovoltaic module 610.

[0115] For example, the processor can use information about the photovoltaic module 610 to determine the power generation variation pattern of the photovoltaic module 610.

[0116] For example, the processor can predict the power generation pattern of the photovoltaic module 610 over time based on the time-series power generation data of the photovoltaic module 610 included in the information about the photovoltaic module 610 and the solar position data and solar irradiance data included in the information related to the sun.

[0117] In one implementation, the processor can use the predicted power generation change pattern of the photovoltaic module 610 over time to determine whether a power generation anomaly has occurred in the photovoltaic module 610.

[0118] As an example, the processor can determine whether a power generation anomaly has occurred in the photovoltaic module 610 by comparing the predicted power generation change pattern of the photovoltaic module 610 over time with the power generation change pattern obtained during a preset period prior to the predicted power generation change pattern. In other words, the processor can determine that a power generation anomaly has occurred in the photovoltaic module 610 when the difference between the predicted power generation change pattern and the power generation change pattern obtained during a predetermined period prior to the predicted time is greater than or equal to a preset value.

[0119] As another example, the processor can determine whether a power generation anomaly has occurred in the photovoltaic module 610 by comparing the predicted power generation change pattern of the photovoltaic module 610 over time with the actual measured power generation change pattern of the photovoltaic module 610 over time. That is, when the difference between the predicted power generation change pattern and the actual measured power generation change pattern is greater than or equal to a preset value, the processor can determine that a power generation anomaly has occurred in the photovoltaic module 610.

[0120] For example, the processor can determine the cause of the power generation anomaly in the photovoltaic module 610 based on the result that a power generation anomaly has occurred in the photovoltaic module 610.

[0121] Figure 7 This is a diagram illustrating an example of a method for determining the cause of abnormal power generation in a photovoltaic module according to an embodiment.

[0122] In the following text, reference will be made to Figure 7 The description provides an example of a method by which the processor determines the cause of abnormal power generation from the photovoltaic module.

[0123] Reference Figure 7 The processor can determine the cause of the power generation anomaly in the photovoltaic module 710 based on the result that a power generation anomaly has occurred in the photovoltaic module 710.

[0124] For example, if there is no anomaly in the time-related solar irradiance but the power generation of at least some of the photovoltaic modules 710 is reduced, the processor can determine that the cause of the power generation anomaly in the photovoltaic module 710 can be attributed to the obstacle 720. That is, although there is no anomaly in the time-related solar irradiance, the power generation of the photovoltaic module 710 may be reduced when sunlight is blocked by an obstacle, and therefore the processor can determine that the cause of the power generation anomaly in the photovoltaic module 710 can be attributed to the obstacle.

[0125] For example, if the shadow formed by the obstacle 720 includes at least one photovoltaic module 710 among multiple photovoltaic modules 710 that experiences reduced power generation, the processor can determine that the abnormal power generation of the photovoltaic module 710 can be attributed to the obstacle. Here, although for ease of description, Figure 7The obstacle 720 is shown only as a cloud, but the obstacle 720 is not limited to this and may include any object (e.g., a tree, bird, or building) that can affect the power generation of the photovoltaic module 710.

[0126] For example, even if the power generation of at least some of the photovoltaic modules 710 is reduced, when there are factors that cause the power generation of the photovoltaic module 710 to be reduced (such as weather changes), the processor can determine that the cause of the abnormal power generation of the photovoltaic module 710 can be attributed to the reduction of solar irradiance.

[0127] In this implementation, the processor can obtain information about the cause of the power generation anomaly based on the obtained cause of the power generation anomaly in the photovoltaic module 710.

[0128] For example, the processor can visualize the power generation data included in the information about the photovoltaic module 710 based on the arrangement of the photovoltaic module 710.

[0129] For example, if the cause of abnormal power generation in photovoltaic module 710 can be attributed to obstacle 720, the shadow caused by obstacle 720 may include at least one photovoltaic module in photovoltaic module 710 that has reduced power generation. Therefore, the processor can display the photovoltaic module 710 with reduced power generation as a shadowed area, thereby visualizing the power generation data as if the shadow caused by obstacle 720 were present.

[0130] In other words, depending on the arrangement of the photovoltaic modules 710, the processor can visualize the power generation data by displaying at least one photovoltaic module 710 with reduced power generation as a shaded area.

[0131] For example, a processor can derive information about the cause of power generation anomalies based on visualized power generation data.

[0132] For example, the processor can use the installation location and angle of the photovoltaic module 710, time-related solar position data, and visualized power generation data to derive information about the cause of power generation anomalies (e.g., the solar position and direction of shadow at the corresponding time).

[0133] For example, the processor can obtain information about the causes of power generation anomalies as output data for a power generation anomaly cause prediction model.

[0134] For example, the processor can determine that the reduction in power generation of the photovoltaic module 710 during a specific time period can be attributed to shadows and / or obstacles, and can obtain the predicted direction of the obstacles and the predicted amount of power generation reduction of the photovoltaic module 710 as information about the cause of the power generation anomaly. For example, the processor can obtain information about the cause of the reduced power generation of the photovoltaic module 710 being attributed to obstacles, weather, or errors in the photovoltaic module itself.

[0135] In one implementation, the processor can provide a notification to the user based on the information obtained regarding the cause of the power generation anomaly.

[0136] Figure 8 This is a diagram illustrating an example of a method for providing a user with a notification of the cause of a power generation anomaly based on a photovoltaic module, according to an embodiment.

[0137] In the following text, reference will be made to Figure 8 The description provides an example of a method by which the processor provides the user with notifications about the cause of power generation anomalies based on the photovoltaic module.

[0138] Reference Figure 8 The processor can provide users with notifications about the causes of power generation anomalies based on the photovoltaic module.

[0139] For example, the processor can provide the user with a notification corresponding to the cause of the power generation anomaly in the photovoltaic module, based on information about the cause. Here, the cause of the power generation anomaly in the photovoltaic module may include a first cause 810 related to obstacles, a second cause 820 related to weather, and a third cause 830 related to an error in the photovoltaic module.

[0140] For example, the processor can visualize the information obtained about the cause of power generation anomalies and provide the visualized information to users via the web or applications.

[0141] For example, the processor can use information about the cause of abnormal power generation from the photovoltaic module to determine whether the reduced power generation at a specific time period is due to obstacles, weather changes, or a fault in the photovoltaic module itself, and can provide the user with relevant notifications. In other words, when the cause of abnormal power generation from the photovoltaic module is not an obstacle or weather change but a fault in the photovoltaic module itself (such as a mechanical failure), the processor can provide the user with a notification indicating the need for direct action.

[0142] As an example, when the cause of abnormal power generation by the photovoltaic module is a first cause 810 related to an obstacle, the processor can provide the user with a first notification 811 related to the obstacle. For example, the first notification 811 related to the obstacle may include a warning notification about removing the obstacle and a notification providing predicted obstacle location information, but is not limited to these, and may include any notification related to the obstacle.

[0143] For example, the processor may provide the user with a notification as a first notification 811 related to an obstacle, prompting the user to remove the obstacle located around a specific photovoltaic module with reduced power generation. More specifically, the processor may provide the user with a notification including the location of the specific photovoltaic module with reduced power generation and the predicted location or direction of the obstacle, allowing the user to check the presence of the obstacle and remove it.

[0144] As another example, when the abnormal power generation of the photovoltaic module is caused by a second weather-related reason 820, the processor can provide the user with a second weather-related notification 821. For example, the second weather-related notification 821 may include notifications of current weather information (e.g., cloud cover, solar irradiance, rainfall, and snowfall), but is not limited to this, and may include any weather-related notifications.

[0145] For example, the processor can provide the user with a weather-related second notification 821 by visualizing current weather information (such as the position of the sun, the position of the clouds, and the amount of precipitation) as information about the reasons for the reduced power generation of the photovoltaic module.

[0146] In some implementations, when the photovoltaic module's power generation decreases due to simple reasons such as snow, rain, clouds, or insufficient solar irradiance, and unnecessary notifications are repeatedly generated within a short period, the processor may not provide the user with a notification following the initially provided weather-related second notification 821. That is, when weather conditions such as rain, snow, or cloudy weather persist for a specific period, the photovoltaic module's power generation may continue to decrease, and therefore, the user may be continuously provided with weather-related second notifications 821. However, the user may not need notifications following the initially provided weather-related second notification 821. Therefore, when the abnormal power generation of the photovoltaic module due to weather continues, the processor may initially provide only one weather-related second notification 821, or it may provide weather-related second notifications 821 at preset time intervals.

[0147] As another example, when the cause of abnormal power generation by the photovoltaic module is a third cause 830 related to an error in the photovoltaic module, the processor can provide the user with a third notification 831 related to the error in the photovoltaic module. For example, the third notification 831 related to the error in the photovoltaic module may include notifications related to the error in the photovoltaic module, such as the cause of a mechanical defect in the photovoltaic module.

[0148] For example, when the cause of the photovoltaic module's power generation anomaly does not correspond to either a first cause 810 related to obstacles or a second cause 820 related to weather, the processor can determine that the cause of the photovoltaic module's power generation anomaly corresponds to a third cause 830 related to a malfunction in the photovoltaic module. Therefore, the processor can deduce the cause of the photovoltaic module's malfunction and provide the user with a notification regarding the cause of the malfunction (e.g., a malfunction in the photovoltaic module, gateway, or connector).

[0149] For example, the processor can accumulate and analyze information about the causes of abnormal power generation, quantitatively and qualitatively analyzing the reasons for reduced power generation from photovoltaic modules, and can visualize and provide the analysis results to users. Furthermore, by receiving such analysis results, users can operate and maintain the photovoltaic power generation system more efficiently.

[0150] The methods described above can be embodied as programs executable on a computer and implemented in a general-purpose digital computer that runs the program using a computer-readable recording medium. Furthermore, the data structures used in the methods described above can be recorded on a computer-readable recording medium in various ways. Examples of computer-readable recording media include storage media such as ROM, RAM, USB memory, floppy disks, hard disks, etc., and optical reading media such as CD-ROMs and DVDs.

[0151] Those skilled in the art to which this embodiment pertains will understand that this disclosure can be implemented in modified forms without departing from the spirit and scope of this disclosure. Therefore, the disclosed methods should be considered illustrative rather than restrictive. The scope of this disclosure is defined by the appended claims rather than the foregoing description, and all differences within the scope of equivalent claims should be interpreted as including within this disclosure.

Claims

1. A method for obtaining information about the cause of power generation anomalies in photovoltaic modules, the method comprising: Information about photovoltaic modules and solar-related information are input into the power generation anomaly cause prediction model as input data. Based on the information about the photovoltaic module and the information related to the sun, it is determined whether an abnormality in the power generation of the photovoltaic module has occurred; Based on the determination of whether the power generation anomaly has occurred, information about the cause of the power generation anomaly of the photovoltaic module is obtained; as well as Information about the causes of the power generation anomalies is obtained as output data for the power generation anomaly cause prediction model.

2. The method according to claim 1, wherein, The information regarding the photovoltaic module includes its installation location information, installation angle information, and power generation data. The information related to the sun includes the sun's position and solar irradiance data.

3. The method according to claim 2, wherein, The inputs include: Obtain the installation and positioning information of the photovoltaic module from the photovoltaic module; The location information of the sun and the solar irradiance data are calculated based on the installation and positioning information of the photovoltaic module; and The information about the photovoltaic module, the location information of the sun, and the solar irradiance data are input into the power generation anomaly cause prediction model.

4. The method according to claim 1, wherein, The determination includes: Based on the information about the photovoltaic module and the information related to the sun, the power generation variation pattern of the photovoltaic module over time is derived; and The power generation change pattern is used to determine whether the power generation anomaly has occurred.

5. The method according to claim 1, wherein, The conclusions include: Based on the determination of whether the power generation anomaly occurred, the cause of the power generation anomaly is determined; and Information about the cause of the power generation anomaly is obtained based on the determined cause.

6. The method according to claim 5, wherein, The conclusions include: Based on the arrangement of multiple photovoltaic modules including the photovoltaic module, the power generation data included in the information about the photovoltaic modules is visualized; and Information about the cause of the power generation anomaly is derived based on visualized power generation data.

7. The method according to claim 1, wherein, The training of the power generation anomaly cause prediction model is performed as follows: Preprocessing is performed to adjust the information about the photovoltaic module and the solar-related information to the same scale; and By using preprocessed information about photovoltaic modules and preprocessed solar-related information as training data for the power generation anomaly cause prediction model, the power generation anomaly cause prediction model is trained to derive information about the cause of the power generation anomaly.

8. The method according to claim 1, further comprising providing the user with a notification corresponding to the cause of the power generation anomaly of the photovoltaic module based on information regarding the cause of the power generation anomaly. in, The causes of abnormal power generation by the photovoltaic module include a first cause related to obstacles, a second cause related to weather, and a third cause related to errors in the photovoltaic module.

9. A computer-readable recording medium having a program recorded thereon, the program being used to cause a computer to perform the method according to claim 1.

10. An apparatus for obtaining information about the cause of power generation anomalies in a photovoltaic module, the apparatus comprising: A memory, wherein at least one program is stored; as well as At least one processor, the at least one processor being configured to execute the at least one program. The at least one processor is configured to input information about the photovoltaic module and solar-related information as input data to the power generation anomaly cause prediction model, determine whether a power generation anomaly of the photovoltaic module has occurred based on the information about the photovoltaic module and the solar-related information, derive information about the cause of the power generation anomaly of the photovoltaic module based on the result of determining whether the power generation anomaly has occurred, and obtain information about the cause of the power generation anomaly as output data of the power generation anomaly cause prediction model.

11. The apparatus according to claim 10, wherein, The information regarding the photovoltaic module includes its installation location information, installation angle information, and power generation data. The information related to the sun includes the sun's position and solar irradiance data.

12. The apparatus according to claim 11, wherein, The at least one processor is configured to obtain the installation and positioning information of the photovoltaic module from the photovoltaic module, calculate the position information of the sun and the solar irradiance data based on the installation and positioning information of the photovoltaic module, and input the information about the photovoltaic module, the position information of the sun and the solar irradiance data into the power generation anomaly cause prediction model.

13. The apparatus according to claim 10, wherein, The at least one processor is configured to derive the power generation variation pattern of the photovoltaic module over time based on the information about the photovoltaic module and the information related to the sun, and to use the power generation variation pattern to determine whether the power generation anomaly has occurred.

14. The apparatus according to claim 10, wherein, The at least one processor is configured to determine the cause of the power generation anomaly based on the result of determining whether the power generation anomaly has occurred, and to obtain information about the cause of the power generation anomaly based on the determined cause.

15. The apparatus according to claim 14, wherein, The at least one processor is configured to visualize power generation data included in the information about the photovoltaic modules according to the arrangement of a plurality of photovoltaic modules including the photovoltaic module, and to derive information about the cause of the power generation anomaly based on the visualized power generation data.

16. The apparatus according to claim 10, wherein, The training of the power generation anomaly cause prediction model is performed by: preprocessing to adjust the information about the photovoltaic module and the information related to the sun to the same scale, and training the power generation anomaly cause prediction model by using the preprocessed information about the photovoltaic module and the preprocessed information related to the sun as training data to derive information about the cause of the power generation anomaly.

17. The apparatus according to claim 10, wherein, The at least one processor is further configured to provide the user with a notification corresponding to the cause of the power generation anomaly in the photovoltaic module, based on information about the cause of the anomaly. The causes of abnormal power generation by the photovoltaic module include a first cause related to obstacles, a second cause related to weather, and a third cause related to errors in the photovoltaic module.

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