Method and apparatus for obtaining cause information of abnormality in power generation amount of photovoltaic module

Abstract

An apparatus according to one aspect comprises at least one memory, and at least one processor, wherein the at least one processor inputs information of a photovoltaic module and information related to the sun into a power generation abnormality cause prediction model as input data of the power generation abnormality cause prediction model, determines whether the power generation of the photovoltaic module is abnormal on the basis of the information of the photovoltaic module and the information related to the sun, derives power generation abnormality cause information of the photovoltaic module on the basis of a result of determining whether the power generation is abnormal, and obtains the power generation abnormality cause information as output data of the power generation abnormality cause prediction model.

Description

Method and apparatus for obtaining information on the cause of abnormal power generation of a solar module

[0001] The present disclosure relates to a method and apparatus for obtaining information on the cause of abnormal power generation of a photovoltaic module.

[0002] Recently, as interest in eco-friendly energy technology increases, the installation of solar power generation systems is also on the rise.

[0003] However, due to the nature of solar power generation systems, there were often cases where unnecessary manpower and time were wasted trying to determine whether there was a problem with the solar modules, and if so, to identify the cause.

[0004] In addition, when the power generation of a solar module temporarily decreased, it was difficult for the user to analyze the cause of the decrease.

[0005] The invention provides a method and apparatus for obtaining information on the cause of abnormal power generation of a solar module. Additionally, the invention provides a computer-readable recording medium storing a program for executing the above method on a computer. The technical problems to be solved are not limited to those described above, and other technical problems may exist.

[0006] According to one aspect of the present disclosure, a method for obtaining information on the cause of an abnormal power generation of a solar module may be provided, comprising: inputting information on a solar module and information related to the sun into a model for predicting the cause of an abnormal power generation as input data for the model for predicting the cause of an abnormal power generation; determining whether there is an abnormal power generation of the solar module based on the information on the solar module and the information related to the sun; deriving information on the cause of the abnormal power generation of the solar module based on the result of determining whether there is an abnormal power generation; and obtaining the information on the cause of the abnormal power generation as output data for the model for predicting the cause of an abnormal power generation.

[0007] An apparatus according to another aspect of the present disclosure comprises: a memory storing at least one program; and at least one processor executing said at least one program, wherein the at least one processor inputs information of a solar module and information related to the sun as input data of a model for predicting the cause of an abnormal power generation, determines whether there is an abnormal power generation of said solar module based on the information of said solar module and the information related to the sun, derives information on the cause of the abnormal power generation of said solar module based on the result of determining whether there is an abnormal power generation, and obtains the information on the cause of the abnormal power generation of said solar module as output data of the model for predicting the cause of the abnormal power generation.

[0008] A computer-readable recording medium according to another aspect of the present disclosure includes a recording medium that records a program for executing the above-described method on a computer.

[0009] Based on hourly power generation data of solar modules, the cause of a decrease in power generation can be accurately analyzed and predicted by utilizing the position of the sun, the installation angle of the solar modules, and environmental information of the region where the solar modules are installed.

[0010] In addition, the cause of the decrease in solar module power generation can be identified remotely.

[0011] However, the effects of the embodiments are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art from the description of the present invention.

[0012] FIG. 1 is a drawing for explaining an example of a power supply system according to one embodiment.

[0013] FIG. 2 is a configuration diagram illustrating an example of a device for obtaining information on the cause of abnormal power generation of a solar module according to one embodiment.

[0014] FIG. 3 is a flowchart illustrating an example of a method for obtaining information on the cause of abnormal power generation of a solar module according to one embodiment.

[0015] FIG. 4 is a diagram illustrating an example of a method for obtaining information on the cause of changes in power generation of a solar module using a power generation abnormality cause prediction model according to one embodiment.

[0016] FIG. 5 is a diagram illustrating an example of information about a solar module and information related to the sun according to one embodiment.

[0017] FIG. 6 is a diagram illustrating an example of a method for determining whether there is an abnormal amount of power generation of a solar module according to one embodiment.

[0018] FIG. 7 is a diagram illustrating an example of a method for deriving the cause of an abnormal power generation amount of a solar module according to one embodiment.

[0019] FIG. 8 is a diagram illustrating an example of a method for providing a user with a notification regarding the cause of an abnormal power generation amount of a solar module according to one embodiment.

[0020] A device according to one aspect comprises at least one memory; and at least one processor; wherein the at least one processor inputs information of a solar module and information related to the sun as input data of a model for predicting the cause of an abnormal power generation, determines whether there is an abnormal power generation of the solar module based on the information of the solar module and the information related to the sun, derives information on the cause of the abnormal power generation of the solar module based on the result of determining whether there is an abnormal power generation, and obtains the information on the cause of the abnormal power generation as output data of the model for predicting the cause of the abnormal power generation.

[0021] The terms used in the embodiments have been selected to be as close as possible to currently widely used general terms; however, these may vary depending on the intent of those skilled in the art, case law, the emergence of new technologies, etc. Additionally, in specific cases, terms have been selected at the applicant's discretion, and in such cases, their meanings will be described in detail in the relevant description section. Therefore, terms used in the specification must be defined not merely by their names, but based on their meanings and the content throughout the specification.

[0022] When a part of the specification is described as "comprising" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components. Furthermore, terms such as "~ unit" or "~ module" as used in the specification refer to a unit that processes at least one function or operation, and this may be implemented in hardware or software, or as a combination of hardware and software.

[0023] Additionally, terms including ordinal numbers, such as "first" or "second," used in the specification may be used to describe various components, but said components should not be limited by said terms. Such terms may be used for the purpose of distinguishing one component from another.

[0024] The present disclosure will be described in detail below with reference to the attached drawings. However, embodiments may be implemented in various different forms and are not limited to the examples described herein.

[0025] FIG. 1 is a drawing for explaining an example of a power supply system according to one embodiment.

[0026] Hereinafter, an example of a power supply system will be described with reference to FIG. 1.

[0027] Referring to FIG. 1, the power supply system (10) may include a solar module (11), a device (12), a load (14), and / or a distribution device (15). The power supply system (10) may be connected to an external power grid (16).

[0028] At least one solar module (11) can be installed on the roof or exterior wall of a building to generate power. Multiple solar modules (11) can be connected to form a solar module array.

[0029] A solar module (11) can be connected to a device (12). For example, at least one device (12) can be connected to each solar module (11). As an example, if one device (12) is connected to each solar module (11), the number of devices (12) constituting the power supply system (10) can be equal to the number of solar modules (11).

[0030] The device (12) may be a Power Conditioning System (or Power Conversion System) that performs power conversion for power generated from a solar module (11). For example, the device (12) may perform a predetermined conversion for power generated from a solar module (11) and supply it to other components of the power supply system (10) (e.g., a power grid (16) and / or a load (14), etc.).

[0031] Additionally, the device (12) may be a Module Level Power Electronics (MLPE). For example, the device (12) may be an optimizer or a Micro Inverter (MI).

[0032] As an example, if the device (12) is an optimizer, the device (12) can regulate the power produced by the solar module (11) and output it to an inverter (e.g., a string inverter). The current converted by the inverter (e.g., converting direct current into alternating current) can be output to a power grid (16) or a load (14).

[0033] As another example, if the device (12) is a micro inverter, the device (12) can convert power generated from the solar module (11) (e.g., converting direct current into alternating current). The current converted by the device (12) can be output to the power grid (16) or the load (14).

[0034] If necessary, the power supply system (10) may further include a combiner (13). At least some of the devices (12) may be connected to a distribution device (15) through the combiner (13). For example, power output from a plurality of devices (12) may be combined into a single output at the combiner (13) and supplied to the distribution device (15).

[0035] Meanwhile, the device (12) and the distribution device (15) may be connected via a power path that does not include a combiner (13), and at least one device (12) may be connected to the distribution device (15) via a power path that does not include a combiner (13), and at least one other device (12) may be connected to the distribution device (15) through a combiner (13).

[0036] The combiner (13) can control the voltage, current, and / or power output from the device (12) according to the power supply status of the solar module (11), the device (12), and / or the power system (16), and can set the operating mode of the combiner (13) to a diagnostic mode or an operating mode.

[0037] Additionally, the combiner (13) may include an Energy Management System (EMS) that controls the operation of the combiner (13). The EMS can control the voltage, current, and / or power supplied to or output from the combiner (13) depending on the power supply status of the solar module (11), the device (12), and / or the power grid (16), and can set the operating mode of the combiner (13) to a diagnostic mode or an operating mode.

[0038] A load (14) refers to an object that operates by receiving at least one of the following: energy generated by a solar module (11), energy stored in an energy storage device (17), and / or energy supplied from a power grid (16), installed in an electric consumer such as a house, commercial facility, or factory. For example, if the electric consumer receiving the power is a house, the load (14) may include home appliances such as a washing machine, a refrigerator, or a TV.

[0039] The power system (16) may include infrastructure systems for generating, transmitting, and distributing power. For example, the power system (16) may include infrastructure systems such as power plants, substations, and power grids. Meanwhile, the power system (16) may transmit electrical energy generated at a power plant to a power supply system (10), or transmit surplus power generated at the power supply system (10) to the outside of the power supply system (10).

[0040] For example, commercial power transmitted from the power system (16) through a utility pole can be supplied to a power consumer through a transformer. Meanwhile, the power supply system (10) may be implemented as an off-grid system that is not connected to the power system (16).

[0041] Meanwhile, the power supply system (10) may further include at least one energy storage device (17). If necessary, the power supply system (10) may include a plurality of energy storage devices (17). The energy storage device (17) may receive and store power generated by the solar module (11) and / or power delivered from the power grid (16). The energy storage device (17) can efficiently supply power by storing power and supplying power to the load (14) when the load (14) requires it.

[0042] The energy storage device (17) may include a battery for storing power and a power conversion module. The battery may be equipped with a Battery Management System (BMS) that monitors the battery's SOC, SOH, voltage and / or current, performs diagnostics on the battery, and performs safety functions such as current cutoff.

[0043] Additionally, the power conversion module may be a PCS that performs conversion between battery-side power and opposite-side power. For example, the PCS may perform conversion between battery-side DC current and opposite-side AC current. As an example, the PCS may include a bidirectional DC-DC converter connected to the battery to convert the voltage, and a bidirectional inverter connecting the DC-DC converter and the outside of the energy storage device (17).

[0044] Additionally, the energy storage device (17) may further include an EMS that controls the operation of the energy storage device (17). The EMS may control the voltage, current, and / or power supplied to or output from the energy storage device (17) according to the power supply status of the battery and / or power grid (16), and may set the operating mode of the energy storage device (17) to a diagnostic mode or an operating mode.

[0045] If necessary, an EMS coupled to a specific component of the power supply system (10) can not only control the operation of the specific component but also further control the operation of other components of the power supply system (10). For example, an EMS coupled to a combiner (13) or an EMS coupled to an energy storage device (17) can control both the operation of the combiner (13) and the operation of the energy storage device (17).

[0046] Meanwhile, the distribution device (15) can provide electrical connections between components of the power supply system (10) and control the power flow of the power supply system (10). For example, the distribution device (15) can electrically connect a solar module (11) and a load (14). As an example, the distribution device (15) can electrically connect the solar module (11) and the load (14) by connecting to a device (12) connected to the solar module (11). If necessary, the distribution device (15) can be further connected to at least one of an energy storage device (17) and a power grid (16).

[0047] For example, the distribution device (15) may be a distribution board that distributes power within the power supply system (10). As an example, the distribution device (15) may be a Master Service Panel (MSP) that distributes power generated from a solar module (11) to a load (14), etc.

[0048] As another example, the distribution device (15) may be a main controller that performs power distribution within a power supply system and controls each device (12). As an example, the main controller may include a switch, a circuit breaker, and a control unit. The switch, the circuit breaker, and the control unit may each be implemented as independent devices, or at least some of the switch, the circuit breaker, and the control unit may be included in a single device.

[0049] The main controller may include a switch that controls the electrical connection between components connected to the main controller, such as a device (12) and a load (14). For example, the main controller may include a relay or power semiconductor, etc., that provides or blocks the electrical connection to the device (12) and / or energy storage device (17) depending on the operating state of each component of the power supply system (10).

[0050] The main controller can perform a rapid shutdown to stop the power generation of the solar module (11) in the event of an emergency situation, such as an overcurrent occurring in the power supply system (10). To this end, the main controller may include a circuit breaker that cuts off the connection between the device (12) and the load (14).

[0051] The main controller may include a control unit that controls the overall operation of the main controller. In addition to the main controller, the control unit may control the operation of other components of the power supply system (10) (e.g., a device (12) or an energy storage device (17), etc.).

[0052] The control unit can control the voltage, current, and / or power output from or supplied to each component according to the power supply status of the solar module (11), device (12), combiner (13), load (14), power grid (16), and / or energy storage device (17). Additionally, the control unit can set the operating mode of the main controller, device (12), and / or energy storage device (17) to a diagnostic mode or an operating mode.

[0053] For example, the control unit may control a photovoltaic module (11), a device (12), a combiner (13), and / or an energy storage device (17) based on the state of the power supply system (10). As an example, the control unit may control other components of the power supply system (10) by causing the main controller to communicate with other components of the power supply system (10) (e.g., a device (12), etc.). Communication between the main controller and other components of the power supply system (10) may be performed via Power Line Communication (PLC), but is not limited thereto.

[0054] As an example, the control unit can control the device (12) according to the power generation status of the solar module (11). For example, the main controller can receive a control command from a server that monitors the power generation status of the solar module (11), and the control unit can control the device (12) according to the control command.

[0055] The main controller can supply power to at least some of the loads (14) when power supply from the power system (16) is not smooth (e.g., off-grid situation). For example, when power supply from the power system (16) is not smooth, the main controller can preferentially supply power generated from the solar module (11) and / or power stored in the energy storage device (17) to backup loads that have a relatively high need for stable power supply.

[0056] Meanwhile, the power supply system (10) may further include an auxiliary power generation device (e.g., a diesel generator) that generates power in a manner separate from solar power generation. For example, an auxiliary power generation device may be further connected to the distribution device (15). If the main controller cannot respond to the backup load using only the solar modules (11) and the energy storage device (17) due to environmental factors such as time of day or weather, it can supply power generated by the auxiliary power generation device to the backup load.

[0057] The control unit may be implemented by at least one processor. The processor may process instructions of a computer program by performing basic arithmetic, logic, and input / output operations. Here, the instructions may be provided from the internal memory of the main controller or from an external device. Additionally, the processor may control the overall operation of other components included in the main controller.

[0058] Meanwhile, the processor may perform at least some of the data analysis, processing, and result information generation for performing the aforementioned operations using at least one of machine learning, neural network, or deep learning algorithms as a rule-based or artificial intelligence algorithm. Examples of neural networks may include neural network models based on architectures such as Convolutional Neural Network (CNN), Deep Neural Network (DNN), and Recurrent Neural Network (RNN).

[0059] For example, a processor may be implemented as an array of multiple logic gates, or as a combination of a general-purpose microprocessor and memory storing a program that can be executed on the microprocessor. For example, the processor may 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.

[0060] In some environments, the processor may include an Application-Specific Integrated Circuit (ASIC), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), etc. For example, the processor may refer to a combination of processing devices, such as a combination of a digital signal processor (DSP) and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors combined with a digital signal processor (DSP) core, or any other combination of such configurations.

[0061] By combining at least some of the components described above, the power supply system (10) can be implemented in various forms.

[0062]

[0063] FIG. 2 is a configuration diagram illustrating an example of a device for obtaining information on the cause of abnormal power generation of a solar module according to one embodiment.

[0064] Referring to FIG. 2, a device (hereinafter referred to as the 'device') (200) for obtaining information on the cause of an abnormal power generation of a solar module may include a communication unit (210), a processor (220), and a memory (230). Only the components related to the embodiment are shown in the device (200) of FIG. 2. Therefore, it is obvious to a person skilled in the art that other general-purpose components may be included in addition to the components shown in FIG. 2.

[0065] The communication unit (210) may include one or more components that enable wired / 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 communication with an external server or external device.

[0066] The processor (220) controls the overall operation of the device (200). For example, the processor (220) can control the input unit (not shown), display (not shown), communication unit (210), memory (230), etc., by executing programs stored in memory (230).

[0067] The processor (220) may be implemented using at least one of ASICs (application specific integrated circuits), DSPs (digital signal processors), DSPDs (digital signal processing devices), PLDs (programmable logic devices), FPGAs (field programmable gate arrays), controllers, microcontrollers, microprocessors, and other electrical units for performing functions.

[0068] The processor (220) can control the operation of the device (200) by executing programs stored in memory (230). For example, the processor (220) can perform at least part of the method for obtaining information on the cause of abnormal power generation of a solar module, as described with reference to FIGS. 3 to 8.

[0069] The memory (230) is hardware that stores various data processed within the device (200) and can store a program for processing and controlling the processor (220).

[0070] For example, the memory (230) may store various data such as information related to the sun, sun position information, solar radiation data, solar module installation location information, installation angle information, power generation amount, environmental information, temperature, current power amount, predicted power consumption, stored power amount, mobile power amount, power consumption amount, weather information such as wind speed, rainfall amount, snowfall amount, sunrise / sunset time, and data generated according to the operation of the processor (220). In addition, the memory (230) may store an operating system (OS) and at least one program (e.g., a program required for the processor (220) to operate).

[0071] The memory (230) may include RAM (random access memory), such as DRAM (dynamic random access memory) and SRAM (static random access memory), ROM (read-only memory), EEPROM (electrically erasable programmable read-only memory), CD-ROM, Blu-ray or other optical disc storage, HDD (hard disk drive), SSD (solid state drive), or flash memory.

[0072]

[0073] FIG. 3 is a flowchart illustrating an example of a method for obtaining information on the cause of abnormal power generation of a solar module according to one embodiment.

[0074] Referring to FIG. 3, a method for obtaining information on the cause of an abnormal power generation of a solar module may include steps 310 to 340. However, it is not limited thereto, and other general steps other than those shown in FIG. 3 may be further included in the method for obtaining information on the cause of an abnormal power generation of a solar module. Additionally, as described above with reference to FIG. 1 and FIG. 2, at least one of the steps of the flowchart shown in FIG. 3 may be processed by a processor.

[0075] First, in step 310, the processor may input information about the solar module and information related to the sun into the model for predicting the cause of anomalies in power generation as input data for the model. Here, the information about the solar module includes information about the installation location of the solar module, information about the installation angle, and power generation data, and the information related to the sun may include information about the sun's location and solar irradiance data.

[0076] For example, the processor can obtain installation location information of the solar module from the solar module and calculate sun position information and solar irradiance data based on the installation location information of the solar module.

[0077] For example, the training of a model for predicting the cause of power generation anomalies can be performed by conducting preprocessing to adjust information on solar modules and information related to the sun to the same scale, and then training the model to derive information on the cause of power generation anomalies using the preprocessed information on solar modules and information related to the sun as training data for the model.

[0078] In addition, the processor can input information on solar modules, sun position information, and solar irradiance data into a model for predicting the causes of power generation anomalies.

[0079] In step 320, the processor can determine whether there is an abnormal amount of power generated by the solar module based on information about the solar module and information related to the sun.

[0080] For example, the processor can derive a pattern of change in power generation over time of the solar module based on information about the solar module and information related to the sun, and use the pattern of change in power generation to determine whether there is an anomaly in the power generation.

[0081] In step 330, the processor can derive information on the cause of the abnormal power generation of the solar module based on the result of determining whether there is an abnormal power generation.

[0082] For example, the processor can derive the cause of the power generation anomaly based on the result of determining whether the power generation is anomaly, and obtain information on the cause of the power generation anomaly based on the derived cause of the power generation anomaly.

[0083] In addition, the processor can image the power generation data included in the information of the solar modules according to the arrangement of the solar modules, and derive information on the cause of the power generation anomaly based on the imaged power generation data.

[0084] In step 340, the processor can obtain information on the cause of power generation anomalies as output data of the power generation anomaly cause prediction model.

[0085] Additionally, the processor may provide a notification to the user regarding the cause of the abnormal power generation of the solar module based on information on the cause of the abnormal power generation. Here, the cause of the abnormal power generation of the solar module may include a first cause related to obstacles, a second cause related to weather, and a third cause related to errors in the solar module.

[0086]

[0087] FIG. 4 is a diagram briefly illustrating an example of a method for obtaining information on the cause of changes in power generation of a solar module using a power generation abnormality cause prediction model according to one embodiment.

[0088] Hereinafter, with reference to FIG. 4, an example of a method for a processor to obtain information on the cause of a change in power generation is briefly described.

[0089] Referring to FIG. 4, the processor can obtain information on the cause of the abnormal power generation of the solar module using a power generation abnormality cause prediction model (410).

[0090] For example, the processor can input information about the solar module and information related to the sun into the power generation abnormality cause prediction model (410) as input data for the power generation abnormality cause prediction model. Additionally, the processor can obtain information about the cause of the power generation abnormality of the solar module as output data for the power generation abnormality cause prediction model (410).

[0091] For example, the processor can identify the cause of an anomaly in the power generation of a solar module by utilizing information about the solar module and information related to the sun. That is, the processor determines whether an anomaly has occurred in the power generation of the solar module and can identify the cause of the anomaly based on the result of determining that an anomaly has occurred.

[0092] Meanwhile, the power generation abnormality cause prediction model (410) can be trained to derive information on the cause of the power generation abnormality of the solar module using information on the solar module and information related to the sun. Additionally, the training of the power generation abnormality cause prediction model (410) can be performed by a processor or another device.

[0093] For example, the learning of the power generation abnormality cause prediction model (410) can be performed by performing preprocessing to adjust the information of the solar module and the information related to the sun to the same scale, and by learning the power generation abnormality cause prediction model (410) to derive information on the cause of the power generation abnormality using the information of the solar module and the information related to the sun that has been preprocessed as the learning data of the power generation abnormality cause prediction model (410), and details will be described later.

[0094]

[0095] FIG. 5 is a diagram illustrating an example of information about a solar module and information related to the sun according to one embodiment.

[0096] Hereinafter, with reference to FIG. 5, an example of a method in which a processor acquires information about a solar module and information related to the sun and uses the same is described.

[0097] Referring to FIG. 5, the processor can generate information related to the sun (520) using information from the solar module (510).

[0098] For example, information of the solar module (510) may include installation location information of the solar module (510), installation angle information, arrangement information of a plurality of solar modules (510) included in the solar panel, and power generation data, and information related to the sun (520) may include location information of the sun (520) and solar radiation data.

[0099] First, the processor can obtain information about the solar module (510). As an example, the processor can obtain, in real time, installation location information, installation angle information, and arrangement information of multiple solar modules (510) included in the solar panel from the solar module (510). As another example, the processor can obtain installation location information, installation angle information, and arrangement information of multiple solar modules (510) included in the solar panel when the solar module (510) is installed, which is stored in memory.

[0100] For example, the processor may obtain power generation data of the solar module (510) from the solar module (510), and the power generation data may be real-time data or data at predetermined time intervals, but is not limited thereto.

[0101] For example, the processor can obtain information related to the sun (520) by using information from the solar module (510). That is, the processor can obtain information related to the sun (520) depending on the location where the solar module (510) is installed.

[0102] For example, the processor can obtain information related to the sun (520) based on the latitude and longitude of the region where the solar module (510) is installed and the date and time of obtaining information related to the sun (520). More specifically, the processor can obtain location information of the sun (520) and solar radiation data by time period using an astronomy library, a solar location API, a weather data API, etc., based on the latitude and longitude of the region where the solar module (510) is installed and the date and time of obtaining information related to the sun (520).

[0103] Here, an API (Application Programming Interface) may refer to an interface that provides different types of services through a connection between computers or programs. Accordingly, the processor can obtain position information of the sun (520) and solar radiation data by time period from other computer programs. Additionally, position information of the sun (520) can be obtained using altitude and azimuth.

[0104] Accordingly, the processor can input information about the solar module (510) and information related to the sun (520) into a model for predicting the cause of abnormal power generation.

[0105] Meanwhile, the learning of the model for predicting the cause of abnormal power generation can be performed using information about the solar module (510) and information related to the sun (520) as described above.

[0106] More specifically, the learning of the power generation abnormality cause prediction model can be performed by performing preprocessing to adjust the information of the solar module (510) and the information related to the sun (520) to the same scale, and by learning the power generation abnormality cause prediction model to derive information on the cause of the power generation abnormality using the information of the solar module (510) and the information related to the sun (520) that has been preprocessed as the learning data of the power generation abnormality cause prediction model.

[0107] For example, the data included in the information of the solar module (510) and the data included in the information of the sun (520) can be adjusted to the same scale through a normalization process. That is, since each data can be expressed in a different way, they can be adjusted to the same scale through a normalization process.

[0108] In addition, there may be missing parts in each data due to communication errors, sensor errors, etc., and such missing parts of each data can be supplemented through temporal interpolation. Here, temporal interpolation refers to a method of supplementing continuous temporal data, which means supplementing the data at a missing time by using data prior to and subsequent to the missing time. Therefore, missing parts of the data included in the information of the solar module (510) and the data included in the information of the sun (520) can be supplemented using temporal interpolation.

[0109] Additionally, each data can be adjusted into time-series data and integrated into a vector form to be used as training data for a model predicting the cause of power generation anomalies. That is, the model predicting the cause of power generation anomalies can be trained to determine whether there is an anomaly in the power generation of a solar module (510) by using the data preprocessed by the method described above as training data, and to derive the cause of the power generation anomaly based on the result of the determination. Here, the model predicting the cause of power generation anomalies may include an artificial intelligence model that analyzes time-series data and an artificial intelligence model that analyzes spatial data.

[0110] Accordingly, the processor can determine whether there is an abnormality in the power generation of the solar module (510) by using a model for predicting the cause of the abnormality in power generation. In addition, the processor can derive the cause of the abnormality in the power generation of the solar module (510) based on the result of determining whether there is an abnormality in the power generation of the solar module (510).

[0111]

[0112] FIG. 6 is a diagram illustrating an example of a method for determining whether there is an abnormal amount of power generation of a solar module according to one embodiment.

[0113] Hereinafter, with reference to FIG. 6, an example of a method for a processor to determine whether there is an abnormality in the power generation amount of a solar module will be described.

[0114] Referring to FIG. 6, the processor can determine whether there is an abnormality in the power generation of the solar module (610) by using the power generation data of the solar module (610) included in the plurality of solar modules (600).

[0115] For example, the processor can derive a pattern of change in the amount of power generated by the solar module (610) using information from the solar module (610).

[0116] For example, the processor can predict the pattern of change in power generation over time of the solar module (610) based on the solar module (610) power generation data by time period included in the information of the solar module (610), and the solar position data and solar radiation data included in the information related to the sun.

[0117] Additionally, the processor can determine whether there is an abnormality in the power generation of the solar module (610) by using the pattern of change in the power generation amount over time of the predicted solar module (610).

[0118] For example, the processor can determine whether there is an abnormality in the power generation of the solar module (610) by comparing the predicted pattern of change in power generation over time of the solar module (610) with the pattern of change in power generation prior to a preset period from when the pattern of change in power generation was predicted. That is, if the difference between the predicted pattern of change in power generation and the pattern of change in power generation prior to a preset period is greater than or equal to a preset value, the processor can determine that an abnormality has occurred in the power generation of the solar module (610).

[0119] As another example, the processor can determine whether there is an abnormality in the power generation of the solar module (610) by comparing the predicted pattern of change in power generation over time of the solar module (610) with the actual measured pattern of change in power generation over time of the solar module (610). That is, if the difference between the predicted pattern of change in power generation and the actual measured pattern of change in power generation is greater than a preset value, the processor can determine that an abnormality has occurred in the power generation of the solar module (610).

[0120] For example, the processor can determine the cause of the abnormality in the power generation of the solar module (610) based on the result of determining that an abnormality has occurred in the power generation of the solar module (610).

[0121]

[0122] FIG. 7 is a diagram illustrating an example of a method for deriving the cause of an abnormal power generation amount of a solar module according to one embodiment.

[0123] Hereinafter, with reference to FIG. 7, an example of a method for a processor to identify the cause of an abnormal power generation of a solar module will be described.

[0124] Referring to FIG. 7, the processor can determine the cause of the abnormal power generation of the solar module (710) based on the result of determining that an abnormality has occurred in the power generation of the solar module (710).

[0125] For example, if the power generation of at least some of the multiple solar modules (710) decreases even though there is no abnormality in the solar radiation by time period, the processor may determine that the cause of the abnormality in the power generation of the solar modules (710) is due to an obstacle (720). That is, even if there is no abnormality in the solar radiation by time period, the processor may determine that the cause of the abnormality in the power generation of the solar modules (710) is due to an obstacle, as the power generation of the solar modules (710) may decrease when sunlight is blocked by an obstacle.

[0126] More specifically, if at least one module among the plurality of solar modules (710) whose power generation has decreased is included in the shadow created by an obstacle (720), the processor may determine that the cause of the abnormal power generation of the solar module (710) is due to the obstacle. Here, for convenience of explanation, the obstacle (720) in FIG. 7 is depicted only as a cloud, but is not limited thereto, and the obstacle (720) may include all things that can affect the power generation of the solar module (710), such as trees, birds, and buildings.

[0127] Meanwhile, even if the power generation of at least some of the multiple solar modules (710) has decreased, if there are factors such as weather changes that reduce the power generation of the solar modules (710), the processor may determine that the cause of the abnormal power generation of the solar modules (710) is due to a decrease in sunlight.

[0128] Additionally, the processor can obtain information on the cause of the abnormal power generation based on the cause of the abnormal power generation of the derived solar module (710).

[0129] For example, the processor can image the power generation data included in the information of the solar module (710) according to the arrangement of the solar module (710).

[0130] For example, if the cause of the abnormal power generation of a solar module (710) is an obstacle (720), the shadow caused by the obstacle (720) may include at least one solar module (710) with reduced power generation. Accordingly, the processor can visualize the solar module (710) with reduced power generation as a shade, making it appear as if a shadow caused by the obstacle (720) has formed.

[0131] That is, the processor can visualize the power generation data of at least one solar module (710) whose power generation has decreased by displaying it as a shade according to the arrangement of the solar modules (710).

[0132] In addition, the processor can derive information on the cause of power generation anomalies based on imaged power generation data.

[0133] For example, the processor can derive information on the cause of abnormal power generation, such as the position of the sun at a given time and the direction of the shadow, by using the installation location, installation angle, and position data of the sun at each time and imaged power generation data of the solar module (710).

[0134] For example, the processor can obtain information on the cause of power generation anomalies as output data of a power generation anomaly cause prediction model.

[0135] For example, the processor can determine that the cause of the decrease in the power generation of the solar module (710) at a specific time is shadows or obstacles, and obtain the direction of the obstacles and the predicted value of the decrease in the power generation of the solar module (710) as information on the cause of the power generation anomaly. Additionally, the processor can obtain information on the cause of the power generation anomaly that the cause of the decrease in the power generation of the solar module (710) is due to obstacles, weather, or an error in the solar module itself.

[0136] In addition, the processor can provide notifications to the user based on the acquired information on the cause of the power generation anomaly.

[0137]

[0138] FIG. 8 is a diagram illustrating an example of a method for providing a user with a notification regarding the cause of an abnormal power generation amount of a solar module according to one embodiment.

[0139] Hereinafter, with reference to FIG. 8, an example of a method for a processor to provide a notification to a user regarding the cause of an abnormal power generation amount of a solar module is described.

[0140] Referring to Fig. 8, the processor can provide a notification to the user regarding the cause of the abnormal power generation of the solar module.

[0141] For example, the processor may provide a notification to the user regarding the cause of the abnormal power generation of the solar module based on information regarding the cause of the abnormal power generation. Here, the cause of the abnormal power generation of the solar module may include a first cause (810) related to obstacles, a second cause (820) related to weather, and a third cause (830) related to errors in the solar module.

[0142] For example, the processor can visualize the acquired information on the causes of power generation anomalies and provide it to the user via the web or an application.

[0143] For example, the processor can use information on the cause of abnormal power generation in solar modules to determine that a decrease in power generation during a specific time period is due to an obstacle, a change in weather, or an internal error of the solar module, and provide a related notification to the user. In other words, if the cause of the abnormal power generation is not due to an obstacle or a change in weather, but rather to an internal error of the solar module such as a mechanical defect, the processor can provide a notification to the user stating that direct action is required.

[0144] For example, if the cause of the abnormal power generation of the solar module is a first cause (810) related to an obstacle, the processor may 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 regarding the removal of the obstacle and a notification regarding the predicted location information of the obstacle, but is not limited thereto and may include all notifications related to the obstacle.

[0145] For example, the processor may provide the user with a notification that induces the user to remove an obstacle located around a specific solar module with reduced power generation as a first notification (811) related to the obstacle. More specifically, the processor may induce the user to check for the presence of the obstacle and remove the obstacle directly by providing the user with a notification regarding the location of the specific solar module with reduced power generation, the expected location or direction of the obstacle.

[0146] As another example, if the cause of the abnormal power generation of the solar module is a second cause (820) related to the weather, the processor may 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 such as cloud cover, solar radiation, rainfall, and snowfall, but is not limited thereto and may include all weather-related notifications.

[0147] For example, the processor can provide the user with a notification regarding the cause of the decrease in the power generation of the solar module by visualizing current weather information, such as the position of the sun, the position of clouds, and precipitation, as a second weather-related notification (821).

[0148] Meanwhile, if the power generation of the solar module decreases due to causes such as snow, rain, clouds, or insufficient solar radiation, and unnecessary notifications occur repeatedly within a short period, the processor may not provide notifications to the user after the first second weather-related notification (821). That is, if weather conditions such as rain, snow, or cloudiness persist for a certain period, the power generation of the solar module may continuously decrease, and accordingly, the second weather-related notification (821) may be continuously provided to the user, but the user may find notifications after the first second weather-related notification (821) unnecessary. Therefore, if the power generation of the solar module due to weather persists, the processor may provide the second weather-related notification (821) to the user only at the beginning or at preset time intervals.

[0149] As another example, if the cause of the abnormal power generation of the solar module is a third cause (830) related to an error in the solar module, the processor may provide the user with a third notification (831) related to an error in the solar module. For example, the third notification (831) related to an error in the solar module may include a notification related to an error in the solar module, such as a cause of a mechanical defect in the solar module.

[0150] For example, if the processor determines that the cause of the abnormal power generation of the solar module is not a first cause (810) related to an obstacle or a second cause (820) related to weather, the cause of the abnormal power generation of the solar module is a third cause (830) related to an error in the solar module. Accordingly, the processor can identify the cause of the error in the solar module and provide a notification to the user regarding the cause of the error in the solar module, such as the solar module, gateway, connector, etc.

[0151] Furthermore, the processor can quantitatively and qualitatively analyze the causes of power generation reduction in solar modules by accumulating and analyzing information on the causes of abnormal power generation, and can visualize the analysis results and provide them to the user. Additionally, by providing users with such analysis results, the efficiency of operation and maintenance of the solar power generation system can be improved.

[0152] Meanwhile, the above-described method can be written as a program executable on a computer and can be implemented on a general-purpose digital computer that operates the program using a computer-readable recording medium. In addition, the structure of the data used in the above-described method can be recorded on a computer-readable recording medium through various means. The computer-readable recording medium includes storage media such as magnetic storage media (e.g., ROM, RAM, USB, floppy disk, hard disk, etc.) and optical reading media (e.g., CD-ROM, DVD, etc.).

[0153] A person skilled in the art related to the present embodiment will understand that it may be implemented in modified forms without departing from the essential characteristics of the description above. Therefore, the disclosed methods should be considered in an illustrative rather than a restrictive sense, and the scope of rights is defined in the claims rather than the description above, and should be interpreted to include all differences within the scope of equivalence.

Claims

1. A step of inputting information on solar modules and information related to the sun into the power generation anomaly cause prediction model as input data; A step of determining whether the power generation amount of the solar module is abnormal based on the information of the solar module and the information related to the sun; A step of deriving information on the cause of the abnormal power generation of the solar module based on the result of determining whether the power generation is abnormal; and A step of obtaining the cause of the abnormal power generation as output data of the above-mentioned power generation abnormality cause prediction model; comprising, Method for obtaining information on the cause of abnormal power generation of solar modules.

2. In Paragraph 1, The information of the above solar module includes installation location information, installation angle information, and power generation data of the above solar module, and The above-mentioned information related to the sun includes sun position information and solar radiation data, a method.

3. In Paragraph 2, The above input step is, A step of obtaining installation location information of the solar module from the solar module; A step of calculating the sun's position information and the solar radiation data based on the installation location information of the solar module; and A method comprising the step of inputting the information of the solar module, the position information of the sun, and the solar radiation data into the power generation abnormality cause prediction model.

4. In Paragraph 1, The above-mentioned judgment step is, A step of deriving a pattern of change in power generation over time of the solar module based on the information of the solar module and the information related to the sun; and A method comprising the step of determining whether the power generation amount is abnormal using the power generation amount change pattern above.

5. In Paragraph 1, The above-derived step is, A step of deriving the cause of the abnormal power generation amount based on the result of determining whether the above power generation amount is abnormal; and A method comprising the step of obtaining information on the cause of the abnormal power generation based on the cause of the abnormal power generation derived above.

6. In Paragraph 5, The above-derived step is, A step of imaging power generation data included in the information of the solar module according to the arrangement of the solar module; and A method comprising the step of deriving information on the cause of the abnormal power generation based on the above-mentioned imaged power generation data.

7. In Paragraph 1, The training of the above-mentioned power generation anomaly cause prediction model is, A step of performing preprocessing to adjust the information of the solar module and the information related to the sun to the same scale; and A method comprising the step of training a power generation abnormality cause prediction model to derive information on the cause of the abnormal power generation using information on the solar module that has undergone the preprocessing and information related to the sun as training data for the power generation abnormality cause prediction model.

8. In Paragraph 1, The above method is, The method further includes the step of providing a notification to a user regarding the cause of the abnormal power generation of the solar module based on the above-mentioned information on the cause of the abnormal power generation; A method comprising a cause of abnormal power generation of the above-mentioned solar module, a first cause related to an obstacle, a second cause related to weather, and a third cause related to an error of the above-mentioned solar module.

9. A computer-readable recording medium storing a program for executing the method of claim 1 on a computer.

10. Memory in which at least one program is stored; and It includes at least one processor that executes the above at least one program, and The above at least one processor is, Inputting information of a solar module and information related to the sun into the predicted model for the cause of an abnormal power generation as input data for the predicted model for the cause of an abnormal power generation, determining whether there is an abnormal power generation of the solar module based on the information of the solar module and the information related to the sun, deriving information on the cause of the abnormal power generation of the solar module based on the result of determining whether there is an abnormal power generation, and obtaining the information on the cause of the abnormal power generation as output data for the predicted model for the cause of an abnormal power generation. A device for obtaining information on the cause of abnormal power generation of a solar module.

11. In Paragraph 10, The information of the above solar module includes installation location information, installation angle information, and power generation data of the above solar module, and The above-mentioned information related to the sun includes the sun's position information and solar radiation data, in a device.

12. In Paragraph 11, The above at least one processor is, A device that obtains installation location information of the solar module from the solar module, calculates the sun's position information and solar irradiance data based on the installation location information of the solar module, and inputs the information of the solar module, the sun's position information, and the solar irradiance data into a model for predicting the cause of abnormal power generation.

13. In Paragraph 10, The above at least one processor is, A device that derives a pattern of change in power generation over time of the solar module based on information of the solar module and information related to the sun, and determines whether there is an abnormality in the power generation using the pattern of change in power generation.

14. In Paragraph 10, The above at least one processor is, A device for deriving the cause of the abnormal power generation based on the result of determining whether the power generation is abnormal, and obtaining information on the cause of the abnormal power generation based on the derived cause of the abnormal power generation.

15. In Paragraph 14, The above at least one processor is, A device that images power generation data included in the information of a solar module according to the arrangement of the solar module, and derives information on the cause of the abnormal power generation based on the imaged power generation data.

16. In Paragraph 10, The training of the above-mentioned power generation anomaly cause prediction model is, An apparatus that performs preprocessing to adjust the information of the solar module and the information related to the sun to the same scale, and trains the power generation abnormality cause prediction model to derive the information on the cause of the power generation abnormality using the information of the solar module and the information related to the sun that has undergone preprocessing as training data for the power generation abnormality cause prediction model.

17. In Paragraph 10, The above at least one processor is, Based on the above information on the cause of the abnormal power generation, a notification is provided to the user regarding the cause of the abnormal power generation of the solar module, and A device comprising a first cause related to an obstacle, a second cause related to weather, and a third cause related to an error in the solar module, wherein the cause of the abnormal power generation of the solar module is a first cause related to an obstacle, a second cause related to weather, and a third cause related to an error in the solar module.

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