Ai-based solar power generation module monitoring system and method

Abstract

An artificial intelligence-based solar power generation module monitoring system and method are disclosed. The present invention can provide convenience during the operation, maintenance, protection, and inspection of a solar power generation module by monitoring the structural and electrical safety of the solar power generation module using ICT, and can provide an optimal management state by predicting structural safety and analyzing the causes of problems and reductions in power generation through the monitoring of the solar power generation module.

Description

Technology Field

[0001] The present invention relates to an artificial intelligence-based solar power generation module monitoring system and method, and more specifically, to an artificial intelligence-based solar power generation module monitoring system and method that provides monitoring of the structural safety and electrical safety of a solar power generation module using ICT. Background Technology

[0002] A solar power generation system is a method of generating electricity that directly converts sunlight into electrical energy using solar cells.

[0003] To this end, the solar power generation system is composed of solar power modules, an inverter, and a monitoring unit that monitors the power generation process, and is configured to convert the direct current produced by the solar power modules into alternating current for use within the home.

[0004] In addition, the photovoltaic power generation system is generally configured to reduce the cost and effort required to build the system by linking multiple photovoltaic power generation modules, each consisting of multiple solar cells, into a string for power generation efficiency, and then linking multiple strings into an array.

[0005] In such conventional photovoltaic power generation systems, the monitoring unit monitors leakage current, abnormal voltage and current per string, etc., generated during photovoltaic power generation, and inspects or repairs the photovoltaic power generation module where an abnormality has occurred to ensure stable photovoltaic power generation. To this end, a measurement sensor can be installed for each individual photovoltaic power generation module.

[0006] In addition, it is configured to monitor the power generation status by receiving environmental data using sensors, such as a solar irradiance determination device installed on the exterior of the solar power generation module.

[0007] However, conventional monitoring methods are primarily configured to identify fluctuations in power generation due to current and voltage anomalies, so there is a problem in that monitoring of the solar power generation system facilities is not performed when stable power generation is achieved.

[0008] In other words, outdoor solar power generation systems have a problem in that if vibrations occur due to snow, rain, wind, or other weather conditions, individual solar power generation modules, strings connecting multiple modules, and fastening means of structures that fix and support the array may become loose and the fastening state may be released.

[0009] In addition, there is a structural problem in that corrosion can easily occur due to strong winds and rain or over time after installation, leading to reduced durability or damage, which cannot be predicted in advance. Prior art literature

[0010] Korean Registered Patent No. 10-1558106 (Title of Invention: Solar Power Generation Device with Solar Module Vibration Detection Function) The problem to be solved

[0011] To solve these problems, the present invention aims to provide an artificial intelligence-based solar power module monitoring system and method that utilizes ICT to provide monitoring of the structural safety and electrical safety of the solar power module. means of solving the problem

[0012] To achieve the above objective, one embodiment of the present invention is an artificial intelligence-based photovoltaic power generation module monitoring system comprising: a sensing module that causes an arbitrary vibration pattern to occur in the photovoltaic power generation module, which is composed of a photovoltaic power generation panel and a photovoltaic power generation structure, for safety diagnosis of the photovoltaic power generation module, and detects one or more of current, voltage, leakage current, vibration, and sound generated from the photovoltaic power generation module; and a monitoring server that monitors the operating state of the photovoltaic power generation module, determines whether a defect has occurred by comparing the data detected by the sensing module with preset reference data, and generates management data by performing defect analysis of the photovoltaic power generation module and predicting the occurrence of a defect based on the vibration pattern detected by the sensing module.

[0013] In addition, the solar power generation module monitoring system according to the above embodiment is characterized by further including a manager terminal that receives management data generated from a monitoring server and displays it, and controls the operation of the solar power generation module unit and the detection module unit.

[0014] In addition, the sensing module according to the above embodiment is characterized by generating a preset vibration pattern in a photovoltaic power generation structure and detecting vibrations generated in the photovoltaic power generation structure according to the vibration pattern and sounds generated according to the vibration.

[0015] In addition, the sensing module according to the above embodiment is characterized by transmitting unique data and installation location data generated according to the structure assigned to the photovoltaic power generation module to a monitoring server along with the data detected by the photovoltaic power generation module.

[0016] In addition, the monitoring server according to the above embodiment is characterized by comprising: a data collection unit that receives current, voltage, leakage current, vibration, and sound data of the photovoltaic power generation module transmitted from the detection module to monitor the operating status of the detection module; a defect determination unit that analyzes whether a defect has occurred in the photovoltaic power generation module due to voltage / current abnormalities, arc occurrence, or structural damage by comparing the data received from the data collection unit with preset reference data; a modeling unit that generates monitoring data and management data by analyzing defects in the photovoltaic power generation module and predicting defect occurrence therefrom, by simulating the vibration and sound patterns of the photovoltaic power generation module, in which external vibrations and sounds generated from the installation location, terrain, climate, and specific noise sources of the photovoltaic power generation module are filtered, through an artificial intelligence prediction analysis model; and a data communication unit that transmits the data output from the defect determination unit and the modeling unit to an administrator terminal.

[0017] In addition, the modeling unit according to the above embodiment is characterized by comprising: a model learning unit that learns an artificial intelligence prediction analysis model that processes defect analysis and defect occurrence prediction data based on the defect analysis through machine learning of changes in vibration and sound patterns according to the normal structural state and the damaged structural state of the photovoltaic power generation module unit from learning data; and a model service unit that performs a simulation using the learned artificial intelligence analysis model, wherein the vibration and sound patterns of the photovoltaic power generation module unit are simulated after filtering external vibrations and sounds generated from the installation location, terrain, climate, and specific noise sources of the photovoltaic power generation module unit, and outputs management data generated through defect analysis of the photovoltaic power generation module unit and the prediction of defect occurrence based thereon.

[0018] In addition, the monitoring server according to the above embodiment is characterized by further including an external linkage unit that transmits monitoring and management data of the photovoltaic power generation module unit by linking with at least one of an EMS (Energy Management System), EPMS (Energy Power Management System), BMS (Battery Management System), ESS (Energy Storage System), and UES (Un-interrupted Energy Storage System) through a data communication unit.

[0019] In addition, one embodiment of the present invention is an artificial intelligence-based solar power generation module monitoring method comprising: a) a monitoring step in which a sensing module unit causes an arbitrary vibration pattern to occur in the solar power generation module unit, which is composed of a solar power generation panel and a solar power generation structure, for safety diagnosis of the solar power generation module unit, and detects one or more of current, voltage, leakage current, vibration, and sound generated from the solar power generation module unit; and b) a step in which a monitoring server collects data detected by the sensing module unit, determines whether a defect has occurred through comparison with preset reference data, and generates management data by performing defect analysis of the solar power generation module unit and predicting the occurrence of a defect based on the vibration pattern detected by the sensing module unit.

[0020] In addition, step a) according to the above embodiment is characterized by including the step of a sensing module generating a preset vibration pattern in a photovoltaic power generation structure and detecting vibrations generated in the photovoltaic power generation structure according to the vibration pattern and sounds generated according to the vibrations.

[0021] Additionally, step b) according to the above embodiment comprises: b-1) a step in which a monitoring server receives current, voltage, leakage current, vibration, and sound data of a photovoltaic power generation module transmitted from a detection module; b-2) a step in which the monitoring server compares the received data with preset reference data to analyze whether a defect has occurred in the photovoltaic power generation module due to voltage / current abnormalities, arc occurrence, or structural damage, and simulates the vibration and sound patterns of the photovoltaic power generation module, in which external vibrations and sounds generated from the installation location, terrain, climate, and specific noise sources of the photovoltaic power generation module are filtered, through an artificial intelligence prediction analysis model to analyze defects in the photovoltaic power generation module and predict the occurrence of defects accordingly; and

[0022] b-3) A step in which the monitoring server generates monitoring data and management data of the photovoltaic power generation module based on defect analysis and the resulting defect occurrence prediction results; characterized by including

[0023] In addition, step b-2) according to the above embodiment is characterized by further including the step of analyzing defects through machine learning of changes in vibration patterns and sound patterns according to the normal state and damaged state of the photovoltaic power generation module part from the learning data, and training an artificial intelligence predictive analysis model that processes defect occurrence prediction data based on the defect analysis.

[0024] In addition, the solar power generation module monitoring method according to the above embodiment further comprises the step of b-4) transmitting monitoring and management data of the solar power generation module part generated by the monitoring server to at least one device among an administrator terminal, an EMS (Energy Management System), an EPMS (Energy Power Management System), a BMS (Battery Management System), an ESS (Energy Storage System), and an UES (Un-interrupted Energy storage System). Effects of the invention

[0025] The present invention has the advantage of providing convenience during the stable operation, maintenance, protection, and inspection of a photovoltaic power generation module by monitoring the structural and electrical safety of the photovoltaic power generation module using ICT.

[0026] In addition, the present invention has the advantage of providing an optimal management state by predicting structural safety through monitoring of the photovoltaic power generation module, and analyzing the causes of problems and the causes of power generation reduction.

[0027] In addition, the present invention has the advantage of accurately determining the degree of defects in electrical equipment and structures through the automation of defect analysis using artificial intelligence, without relying on qualitative judgments such as visual inspection.

[0028] In addition, the present invention has the advantage of ensuring the safety of the electricity and structures of a photovoltaic power generation system by utilizing a multi-sensor capable of measuring vibration, sound, current, temperature, etc.

[0029] In addition, the present invention has the advantage of reducing equipment maintenance costs by extending the uninterrupted operation time of the solar power generation system through component replacement or maintenance using an artificial intelligence-based analysis and prediction model. Brief explanation of the drawing

[0030] FIG. 1 is an exemplary diagram schematically illustrating the configuration of an artificial intelligence-based solar power generation module monitoring system according to one embodiment of the present invention. FIG. 2 is a block diagram showing the configuration of the sensing module part of an artificial intelligence-based solar power generation module monitoring system according to the embodiment of FIG. 1. FIG. 3 is a block diagram showing the configuration of a monitoring server of an artificial intelligence-based solar power generation module monitoring system according to an embodiment of FIG. 1. FIG. 4 is a block diagram showing the configuration of the modeling section of a monitoring server according to an embodiment of FIG. 1. FIG. 5 is a flowchart illustrating an artificial intelligence-based solar power generation module monitoring method according to an embodiment of the present invention. FIG. 6 is a flowchart illustrating the process of predicting the occurrence of defects in an artificial intelligence-based solar power generation module monitoring method according to the embodiment of FIG. 5. Specific details for implementing the invention

[0031] Hereinafter, the present invention will be described in detail with reference to preferred embodiments of the invention and the accompanying drawings, under the premise that identical reference numerals in the drawings refer to identical components.

[0032] Before describing specific details for the implementation of the present invention, it should be noted that configurations not directly related to the technical essence of the present invention have been omitted to the extent that they do not detract from the technical essence of the present invention.

[0033] Furthermore, terms or words used in this specification and claims should be interpreted in a meaning and concept consistent with the technical spirit of the invention, based on the principle that the inventor may define the concept of appropriate terms to best describe their invention.

[0034] In this specification, the expression that a part "includes" a certain component means that it does not exclude other components but may include additional components.

[0035] In addition, terms such as "...part," "...unit," and "...module" refer to a unit that processes at least one function or operation, and this can be classified as hardware, software, or a combination of both.

[0036] In addition, the term "at least one" is defined as a term including both singular and plural forms, and it is self-evident that even if the term "at least one" does not exist, each component may exist in the singular or plural form and may mean the singular or plural.

[0037] Hereinafter, a preferred embodiment of an artificial intelligence-based photovoltaic power generation module monitoring system and method according to one embodiment of the present invention will be described in detail with reference to the attached drawings.

[0038] FIG. 1 is an exemplary diagram schematically illustrating the configuration of an artificial intelligence-based solar power generation module monitoring system according to an embodiment of the present invention, FIG. 2 is a block diagram illustrating the configuration of a sensing module part of an artificial intelligence-based solar power generation module monitoring system according to an embodiment of FIG. 1, FIG. 3 is a block diagram illustrating the configuration of a monitoring server of an artificial intelligence-based solar power generation module monitoring system according to an embodiment of FIG. 1, and FIG. 4 is a block diagram illustrating the configuration of a modeling part of a monitoring server according to an embodiment of FIG. 1.

[0039] Referring to FIGS. 1 to 4, an artificial intelligence-based solar power generation module monitoring system according to one embodiment of the present invention may be configured to include a solar power generation module (10), a sensing module unit (100), a monitoring server (200), and an administrator terminal (300).

[0040] The solar power generation module (10) may be composed of a solar power generation panel (11) and a solar power generation structure (12).

[0041] A solar power generation panel (11) is configured to generate power using sunlight, and a plurality of solar power generation panels (11) can be configured as an array.

[0042] The solar power generation structure (12) is configured to support the solar power generation panel (11) so that it is installed and fixed, and may be configured to include fastening means such as bolts and rivets that connect the structures, such as a frame.

[0043] The detection module (100) can generate any vibration pattern in the solar power module (10) for safety diagnosis of the solar power module (10) composed of a solar power panel (11) and a solar power structure (12).

[0044] Additionally, the detection module (100) can be installed in a connection board that manages the solar power generation module (10) and can detect one or more data among current, voltage, leakage current, vibration, and sound generated from the solar power generation module (10).

[0045] In addition, the detection module (100) can transmit the detected data to the monitoring server (200) using ICT (Information and Communication Technologies).

[0046] Additionally, the detection module (100) can transmit unique data generated according to the structure assigned to the solar power generation module (10) and location data where the solar power generation module (10) is installed to the monitoring server (200) along with the data detected by the solar power generation module (10).

[0047] To this end, the sensing module (100) may be configured to include a voltage / current sensor unit (110), a vibration sensor unit (120), an acoustic sensor unit (130), a vibration generating unit (140), a control unit (150), and a data communication unit (160).

[0048] Additionally, the sensing module (100) may be configured to further include a sensor that detects the temperature, humidity, solar radiation, etc., around the solar power generation module (10).

[0049] The voltage / current sensor unit (110) is individually installed on a plurality of solar power generation panels (11) and is configured to measure the voltage and current generated from the solar power generation panels (11), and measures the voltage of the solar power generation panels (11) and the output and input currents of the solar power generation panels (11).

[0050] In addition, the voltage / current sensor unit (110) may measure leakage current by comparing the difference between the measured outgoing current and the incoming current.

[0051] The vibration sensor unit (120) is installed in multiple places on the solar power generation structure (12) and can detect vibrations generated by wind, etc., and vibrations for diagnosing the structure generated by the vibration generating unit (140).

[0052] In addition, the vibration sensor unit (120) is installed at regular intervals on the solar power generation structure (12) to detect vibration patterns occurring in the structure where the solar power generation panel (11) is installed.

[0053] The acoustic sensor unit (130) is installed in the solar power generation structure (12) and can detect sounds generated in the solar power generation structure (12), for example, friction sounds between structures caused by the loosening of fastening means connecting structures, friction sounds between fastening means and structures, and any sounds generated in the structures.

[0054] Additionally, the acoustic sensor unit (130) can detect sound generated from the solar power generation structure (12) through the vibration generating unit (140) depending on whether the solar power generation structure (12) is in a normal or abnormally connected state.

[0055] That is, the detection module (100) generates a preset vibration pattern in the solar power generation structure (12) through the vibration sensor unit (120), and detects the vibration generated in the solar power generation structure (12) according to the vibration pattern and the sound generated according to the vibration through the vibration sensor unit (120) and the acoustic sensor unit (130).

[0056] A vibration generating unit (140) is installed in a solar power generation structure (12) and causes vibrations for safety diagnosis of the structure to be generated in the solar power generation structure (12), in addition to vibrations caused by natural phenomena, such as wind, external falling objects, etc.

[0057] That is, through the vibration generated by the vibration generating unit (140), the vibration pattern generated when the solar power generation structure (12) is in a normal state and the vibration pattern is deformed or an additional vibration pattern is generated due to the abnormal state of the solar power generation structure (12), for example, twisting, tilting of the structure, loosening or detachment of the fastening means.

[0058] The control unit (150) is configured to control the operation of the detection module unit (100), and at regular intervals, collects data detected by the voltage / current sensor unit (110), the vibration sensor unit (120), and the acoustic sensor unit (130) and transmits it to the monitoring server (200) through the data communication unit (160).

[0059] In addition, the control unit (150) outputs an arbitrary vibration pattern signal for safety diagnosis of the structure to the vibration generating unit (140) at regular intervals, so that vibration is generated by the vibration generating unit (140) in the photovoltaic power generation structure (12).

[0060] In addition, the control unit (150) can manage the voltage / current sensor unit (110), vibration sensor unit (120), acoustic sensor unit (130), and vibration generating unit (140) by assigning unique information to each.

[0061] In addition, the control unit (150) can store characteristic information set in the solar power generation module (10), such as unique information, location information, and characteristic information according to climate.

[0062] Additionally, when the control unit (150) transmits the detected information to the monitoring server (200), it can also transmit unique information set for each of the detection module unit (100), voltage / current sensor unit (110), vibration sensor unit (120), acoustic sensor unit (130), and vibration generating unit (140).

[0063] The data communication unit (160) can perform data communication with the monitoring server (200) via a wired or wireless network, and preferably performs data communication using LoRa, a low-power wireless communication modulation method.

[0064] The monitoring server (200) monitors the operating status of the solar power generation module (10) and can determine whether a defect has occurred by comparing the data detected by the detection module (100) with preset reference data.

[0065] Additionally, the monitoring server (200) can generate management data by performing a defect analysis of the photovoltaic power generation module (10) and predicting the occurrence of defects based on the vibration pattern detected by the detection module (100).

[0066] To this end, the monitoring server (200) may be configured to include a data collection unit (210), a defect determination unit (220), a modeling unit (230), a data communication unit (240), and an external linkage unit (250).

[0067] The data collection unit (210) can receive current, voltage, leakage current, vibration, and sound data of the solar power generation module unit (10) transmitted from the detection module unit (100) so that the monitoring server (200) can monitor the operating status of the detection module unit (100).

[0068] The defect determination unit (220) compares the data received from the data collection unit (210) with preset reference data to analyze whether voltage / current abnormalities, arc generation, or structural damage have occurred, and determines whether a defect has occurred in the photovoltaic power generation module unit (10).

[0069] That is, the defect determination unit (220) can determine whether a defect has occurred in the solar power generation module (10) by comparing the voltage and current values ​​detected in the solar power generation module (10) with preset reference data to determine whether a leakage current, overvoltage, overcurrent, or arc has occurred.

[0070] Additionally, when the defect judgment unit (220) receives vibration and sound data generated from the solar power generation module unit (10), it can analyze the amount of power generated by the solar power generation module unit (10) due to the occurrence of impact and compare it with preset reference data.

[0071] Additionally, the defect judgment unit (220) can determine that if the analyzed power generation amount is less than or equal to the reference value based on the comparison result, a defect has occurred in the solar power generation module unit (10) due to the occurrence of an impact, and can generate management data based on the judgment result.

[0072] The modeling unit (230) simulates the vibration and sound patterns of the solar power generation module (10) filtered from the installation location, terrain, climate, and specific noise sources of the solar power generation module (10) through an artificial intelligence prediction analysis model.

[0073] In addition, the modeling unit (230) can generate monitoring data and management data by analyzing defects in the photovoltaic power generation module unit (10) and predicting the occurrence of defects therefrom through simulation.

[0074] To this end, the modeling unit (230) may be configured to include a model learning unit (231) and a model service unit (232).

[0075] The model learning unit (231) learns an artificial intelligence prediction analysis model using data on vibration patterns and sound patterns that can occur in a normal state solar power generation structure (12) and an abnormal state solar power generation structure (12).

[0076] Here, the vibration pattern data and sound pattern data may be regular vibration pattern data and sound pattern data generated in the solar power generation structure (12) in response to a certain pattern of vibration provided to the solar power generation structure (12) for safety diagnosis of the structure when the solar power generation structure (12) is in a normal state.

[0077] Additionally, the vibration pattern data may be irregular or additional vibration pattern data generated in the solar power generation structure (12) in response to a certain pattern of vibration provided to the solar power generation structure (12) for safety diagnosis of the structure when the solar power generation structure (12) is in an abnormal state, for example, when a fastening means or part of the structure is damaged, and may be specific sound patterns and sound data of a specific acoustic band.

[0078] That is, the model learning unit (231) learns based on data of changes in vibration patterns and sound patterns in the solar power generation structure (12) caused by defects in the solar power generation structure (12) constituting the solar power generation module (10), such as loosening, bending, cracking, or damage of bolts, rivets, fasteners, supports, etc.

[0079] Additionally, the model learning unit (231) uses the learning data to learn an artificial intelligence prediction analysis model that processes defect analysis and defect occurrence prediction data based on the defect analysis, through machine learning, changes in vibration patterns and sound patterns according to the normal state and damaged state of the solar power generation module unit (10).

[0080] Here, artificial intelligence predictive analysis models can be seen as a type of analysis model created through a method called deep learning among machine learning.

[0081] Therefore, artificial intelligence predictive analysis models may also be used as representations of deep learning models or deep learning analysis models.

[0082] Furthermore, machine learning is an application of artificial intelligence that enables complex systems to automatically learn and improve from experience without being explicitly programmed.

[0083] In addition, the accuracy and validity of machine learning models can partially depend on the data used to train them.

[0084] In addition, the artificial intelligence predictive analysis model may iteratively learn using multiple input vibration and sound pattern data and the resulting structural defect data of the solar power generation module as training data, based on the results of comparing multiple training data.

[0085] The model service unit (232) can perform a simulation using a learned artificial intelligence analysis model and, through the simulation, perform a defect analysis of the solar power generation module unit (10) and predict the occurrence of defects accordingly.

[0086] In addition, the model service unit (232) can generate management data based on the results of performing a defect analysis of the photovoltaic power generation module unit (10) and a prediction of the occurrence of defects accordingly.

[0087] Additionally, the model service unit (232) can simulate the vibration pattern and sound pattern of the photovoltaic power generation module unit (10) that are filtered from external vibrations and sounds generated from the installation location, terrain, climate, and specific noise sources of the photovoltaic power generation module unit (10).

[0088] In addition, the model service unit (232) can generate management data by analyzing defects in the photovoltaic power generation module unit (10) and predicting the occurrence of defects based on the filtered vibration pattern and sound pattern.

[0089] Here, external vibrations and sounds generated from the installation location, terrain, climate, and specific noise sources of the solar power generation module (10) may be sounds including unique vibrations and frequencies of different frequency ranges generated from mountain peaks, the sea, building rooftops, aircraft, ships, railways, roads, construction sites, wild animals, etc.

[0090] In addition, the installation location, terrain, climate, and external vibrations and sounds generated from specific noise sources of the solar power generation module (10), and the external vibration and sound data can be used as training data in the model learning unit (231).

[0091] Additionally, the model service unit (232) performs a simulation based on data detected by the detection module unit (100) using a learned artificial intelligence analysis model, and predicts the occurrence of defects through the analysis of potential power generation reduction factors in the solar power generation module unit (10) and defects based on the simulation results.

[0092] Additionally, the model service unit (232) can generate management data for maintaining, managing, and repairing the solar power generation module (10) based on the results of predicting the occurrence of defects.

[0093] Here, the management data may include monitoring data and management data such as the power generation status of the solar power generation module (10), whether a defect or failure has occurred, factors causing a decrease in power generation and the results of the defect analysis therefrom, the location of the defect, information on the prediction of defect occurrence, the timing of equipment and parts replacement, and the next maintenance date.

[0094] The data communication unit (240) transmits management data output from the defect determination unit (220) and the modeling unit (230) to the manager terminal (300).

[0095] The external linkage unit (250) links with the data communication unit (240) to transmit monitoring and management data of the photovoltaic power generation module unit (10) to at least one of the following: EMS (Energy Management System), EPMS (Energy Power Management System), BMS (Battery Management System), ESS (Energy Storage System), and UES (Un-interrupted Energy storage System).

[0096] The administrator terminal (300) receives management data generated by the monitoring server (200) and displays it, and outputs an operation control signal that controls the operation of the solar power generation module unit (10) and the detection module unit (100) according to the setting information and management information entered by the administrator.

[0097] That is, the administrator terminal (300) displays monitoring data and management data received from the monitoring server (200), such as the power generation status of the solar power generation module (10), whether a defect or failure has occurred, factors causing a decrease in power generation and the resulting defect analysis results, defect location, defect occurrence prediction information, next maintenance date, and equipment and parts replacement timing, on the screen to provide an alarm to the administrator.

[0099] The following describes an artificial intelligence-based solar power generation module monitoring method according to one embodiment of the present invention.

[0100] FIG. 5 is a flowchart illustrating an artificial intelligence-based solar power generation module monitoring method according to an embodiment of the present invention, and FIG. 6 is a flowchart illustrating a defect occurrence prediction process of the artificial intelligence-based solar power generation module monitoring method according to the embodiment of FIG. 5.

[0101] Referring to FIGS. 1 to 6, an artificial intelligence-based solar power generation module monitoring method according to one embodiment of the present invention performs monitoring (S100) in which a sensing module (100) generates an arbitrary vibration pattern in a solar power generation module (10) composed of a solar power generation panel (11) and a solar power generation structure (12), and collects sensing data including current, voltage, leakage current, vibration, and sound generated in the solar power generation module (10).

[0102] Additionally, in step S100, the detection module (100) generates a preset vibration pattern on the solar power generation structure (12) for safety diagnosis of the solar power generation module (10), and can detect vibrations generated in the solar power generation structure (12) according to the vibration pattern and sounds generated according to the vibrations.

[0103] Additionally, the detection module (100) collects information including detection data monitored in step S100 and vibration pattern and sound pattern data detected in response to vibration generated for safety diagnosis of the photovoltaic power generation module (10) at regular intervals (S200).

[0104] The monitoring server (200) can determine whether a defect has occurred by comparing the data collected by the detection module (100) in step S200 with preset reference data, and can generate management data by performing a defect analysis of the photovoltaic power generation module (10) and a prediction of defect occurrence based on the vibration pattern detected by the detection module (100).

[0105] That is, when the monitoring server (200) receives current, voltage, leakage current, vibration, and sound data of the photovoltaic power generation module (10) transmitted from the detection module (100), it compares the received data with preset reference data to analyze (S300) whether a defect has occurred in the photovoltaic power generation module (10) due to voltage / current abnormalities, arc generation, or structural damage.

[0106] Additionally, in step S300, when the monitoring server (200) receives vibration and sound data generated from the solar power generation module (10), it can analyze the amount of power generated by the solar power generation module (10) due to the occurrence of shock and compare it with preset reference data.

[0107] Additionally, the monitoring server (200) can determine, based on the comparison result, that if the analyzed power generation amount is below a reference value, a defect has occurred in the solar power generation module (10) due to the occurrence of an impact, and can generate management data based on the determination result.

[0108] In addition, at the S300 stage, the monitoring server (200) can simulate the vibration pattern and sound pattern of the solar power generation module (10) through an artificial intelligence prediction analysis model to analyze defects in the solar power generation module (10) and predict the occurrence of defects accordingly.

[0109] In step S300, the monitoring server (200) can learn an artificial intelligence prediction analysis model using data on vibration patterns and sound patterns that may occur in a normal state solar power generation structure (12) and an abnormal state solar power generation structure (12).

[0110] That is, the monitoring server (200) can perform learning based on data of changes in vibration patterns and sound patterns in the solar power generation structure (12) caused by defects in the solar power generation structure (12) constituting the solar power generation module (10), such as loosening, bending, cracking, or damage of bolts, rivets, fasteners, supports, etc.

[0111] Additionally, the monitoring server (200) can perform defect analysis and process defect occurrence prediction data based on the defect analysis through machine learning of changes in vibration patterns and sound patterns according to the normal state and damaged state of the solar power generation module (10) using learning data.

[0112] Additionally, the monitoring server (200) enables an artificial intelligence prediction analysis model to learn the vibration pattern and sound pattern of the solar power generation module (10), which are filtered from the installation location, terrain, climate, and specific noise sources of the solar power generation module (10).

[0113] In addition, the monitoring server (200) can generate monitoring data and management data (S400) by analyzing defects in the solar power generation module (10) and predicting the occurrence of defects accordingly through the simulation of an artificial intelligence prediction analysis model.

[0114] That is, in the S400 stage, the monitoring server (200) predicts the occurrence of a defect by analyzing the factors that may reduce the amount of power generated in the solar power generation module (10) and the defects resulting therefrom.

[0115] In addition, at step S400, the monitoring server (200) can predict the occurrence of defects by simulating the vibration pattern and sound pattern of the photovoltaic power generation module (10) filtered from external vibrations and sounds generated from the installation location, terrain, climate, and specific noise sources of the photovoltaic power generation module (10), and analyzing the factors that may reduce the amount of power generated in the photovoltaic power generation module (10) and the defects that may occur accordingly.

[0116] Additionally, in step S400, the monitoring server (200) may generate management data that predicts the occurrence of defects by analyzing potential factors for power generation reduction in the photovoltaic power generation module (10) and defects based on the analysis of defects, using vibration patterns and sound patterns that include external vibrations and sounds.

[0117] Additionally, at step S400, the monitoring server (200) can generate monitoring data of the solar power generation module (10) and management data for maintaining, managing, and repairing the solar power generation module (10) based on the result of predicting the occurrence of a defect.

[0118] Subsequently, the monitoring server (200) transmits the monitoring and management data of the solar power generation module (10) generated in step S400 to the manager terminal (300) (S500).

[0119] That is, step S500 displays monitoring data and management data, such as the power generation status of the solar power generation module (10), whether a defect or failure has occurred, factors causing a decrease in power generation and the resulting defect analysis results, defect location, defect occurrence prediction information, next maintenance date, and equipment and parts replacement timing, on the screen of the manager terminal (300) so that an alarm can be provided to the manager.

[0120] In addition, the S500 stage can transmit the monitoring data and management data generated in the S400 stage to the EMS (Energy Management System), EPMS (Energy Power Management System), BMS (Battery Management System), ESS (Energy Storage System), and UES (Un-interrupted Energy storage System).

[0121] Therefore, by utilizing ICT to monitor the structural and electrical safety of the solar power generation module, convenience can be provided during the operation, maintenance, protection, and inspection of the solar power generation module.

[0122] In addition, by monitoring the solar power generation module, it is possible to provide an optimal management state through the prediction of structural safety and the analysis of the causes of problems and reductions in power generation.

[0123] In addition, the degree of defects in electrical equipment and structures can be accurately identified through the automation of defect analysis using artificial intelligence, without relying on qualitative judgments such as visual inspection.

[0124] In addition, the safety of the electrical and structural components of a photovoltaic power generation system can be ensured by using multi-sensors capable of measuring vibration, sound, current, temperature, etc.

[0125] In addition, facility maintenance costs can be reduced by extending uninterrupted operation time through component replacement or maintenance of the solar power generation system using an AI-based analysis and prediction model.

[0126] As described above, although the present invention has been explained with reference to preferred embodiments, those skilled in the art will understand that various modifications and changes can be made to the present invention without departing from the spirit and scope of the invention as set forth in the following claims.

[0127] In addition, the drawing numbers described in the claims of the present invention are provided for clarity and convenience of explanation only and are not limited thereto, and in the process of describing the embodiments, the thickness of lines or the size of components shown in the drawings may be exaggerated for clarity and convenience of explanation.

[0128] Furthermore, the terms described above are defined in consideration of their functions in the present invention, and since these may vary depending on the intentions or practices of the user or operator, the interpretation of these terms should be based on the content throughout this specification.

[0129] Furthermore, it is obvious that a person skilled in the art to which the present invention pertains may make various modifications including the technical concept according to the present invention from the description of the present invention, even if not explicitly stated or described, and such modifications still fall within the scope of the rights of the present invention.

[0130] Furthermore, the above embodiments described with reference to the attached drawings are described for the purpose of explaining the present invention, and the scope of the present invention is not limited to these embodiments. Explanation of the symbols

[0131] 10: Solar power generation module section 11: Solar power generation panel 12 : Solar power generation structure 100 : Sensing module section 110: Voltage / Current Sensor Unit 120: Vibration Sensor Unit 130: Acoustic sensor unit 140: Vibration generating unit 150: Control unit 160: Data communication unit 200: Monitoring Server 210: Data Collection Unit 220: Defect Judgment Unit 230: Modeling Unit 231 : Model Learning Department 232 : Model Service Department 240: Data Communication Unit 250: External Interfacing Unit 300 : Administrator Terminal

Claims

Claim 1 A detection module (100) for a safety diagnosis of a solar power generation module (10) composed of a solar power generation panel (11) and a solar power generation structure (12), which outputs an arbitrary vibration pattern signal to the solar power generation module (10) at regular intervals to cause vibration, and detects one or more of current, voltage, leakage current, vibration, and sound generated from the solar power generation module (10); A monitoring server (200) that monitors the operating status of the solar power generation module (10) and determines whether a defect has occurred by comparing the data detected by the detection module (100) with preset reference data, and simulates the vibration pattern and sound pattern of the solar power generation module (10) filtered from the installation location, terrain, climate, and specific noise source of the solar power generation module (10) through an artificial intelligence prediction analysis model, and performs a defect occurrence prediction by analyzing structural defects and power generation reduction factors that may occur in the solar power generation module (10) and the defects therefrom based on the simulation results, thereby generating management data for maintaining, managing, and repairing the solar power generation module (10); an artificial intelligence-based solar power generation module monitoring system. Claim 2 The artificial intelligence-based solar power generation module monitoring system according to claim 1 further comprises: a manager terminal (300) that receives and displays management data generated from a monitoring server (200) and controls the operation of the solar power generation module unit (10) and the detection module unit (100). Claim 3 In claim 2, the detection module (100) is characterized by generating a preset vibration pattern in the photovoltaic power generation structure (12) and detecting vibrations generated in the photovoltaic power generation structure (12) and sounds generated according to the vibrations. Claim 4 In claim 3, the artificial intelligence-based solar power generation module monitoring system is characterized in that the sensing module (100) transmits unique data and installation location data generated according to the structure assigned to the solar power generation module (10) to the monitoring server (200) along with the data detected by the solar power generation module (10). Claim 5 In claim 2, the monitoring server (200) comprises: a data collection unit (210) that receives current, voltage, leakage current, vibration, and sound data of the photovoltaic power generation module (10) transmitted from the detection module (100) to monitor the operating status of the detection module (100); a defect determination unit (220) that analyzes whether a defect has occurred in the photovoltaic power generation module (10) due to voltage / current abnormalities, arc occurrence, or structural damage by comparing the data received by the data collection unit (210) with preset reference data; and a modeling unit (230) that simulates the vibration pattern and sound pattern of the photovoltaic power generation module (10), in which external vibrations and sounds generated from the installation location, terrain, climate, and specific noise sources of the photovoltaic power generation module (10) are filtered, through an artificial intelligence prediction analysis model, and performs a defect occurrence prediction based on the simulation results by analyzing structural defects and power generation reduction factors that may occur in the photovoltaic power generation module (10) and the defects accordingly, thereby generating management data for maintaining, managing, and repairing the photovoltaic power generation module (10). An artificial intelligence-based solar power generation module monitoring system characterized by including: a data communication unit (240) that transmits data output from the defect judgment unit (220) and the modeling unit (230) to a manager terminal (300). Claim 6 In claim 5, the modeling unit (230) performs machine learning on data regarding the change in vibration patterns and sound patterns in the solar power generation structure (12) due to defects occurring in the normal structural state and damaged structural state of the solar power generation module unit (10) from the learning data, thereby performing defect analysis and learning an artificial intelligence prediction analysis model to output defect occurrence prediction data based on the defect analysis; and a model service unit (232) that performs a simulation using the learned artificial intelligence analysis model, wherein the vibration patterns and sound patterns of the solar power generation module unit (10) are simulated with external vibrations and sounds generated from the installation location, terrain, climate, and specific noise sources of the solar power generation module unit (10) filtered, and performs defect occurrence prediction based on the simulation results through structural defects and power generation reduction factors that may occur in the solar power generation module unit (10) and defect analysis, and outputs management data for maintaining, managing, and repairing the solar power generation module (10). Claim 7 In claim 5, the artificial intelligence-based solar power generation module monitoring system is further characterized by including an external linkage unit (250) that transmits monitoring and management data of the solar power generation module unit (10) by linking with at least one of an EMS (Energy Management System), EPMS (Energy Power Management System), BMS (Battery Management System), ESS (Energy Storage System), and UES (Un-interrupted Energy storage System) through a data communication unit (240). Claim 8 a) A detection module (100) outputs an arbitrary vibration pattern signal at regular intervals to the solar power module (10) to cause vibration to occur for safety diagnosis of the solar power module (10) composed of a solar power panel (11) and a solar power structure (12), and a monitoring step of detecting one or more of current, voltage, leakage current, vibration, and sound generated from the solar power module (10); and b) a monitoring server (200) collects data detected by the detection module (100) and determines whether a defect has occurred by comparing it with preset reference data, wherein the vibration pattern and sound pattern of the solar power generation module (10) are filtered using an artificial intelligence prediction analysis model, and based on the simulation results, predicts the occurrence of a defect by analyzing structural defects and power generation reduction factors that may occur in the solar power generation module (10) and defects therefrom, and generates management data for maintaining, managing, and repairing the solar power generation module (10); comprising the step of: a method for monitoring an artificial intelligence-based solar power generation module. Claim 9 In claim 8, the above step a) comprises the step of the sensing module (100) generating a preset vibration pattern in the photovoltaic power generation structure (12) and detecting the vibration generated in the photovoltaic power generation structure (12) and the sound generated according to the vibration, characterized in that it is an artificial intelligence-based photovoltaic power generation module monitoring method. Claim 10 In claim 8, the above step b) comprises: b-1) a step in which a monitoring server (200) receives current, voltage, leakage current, vibration, and sound data of a photovoltaic power generation module (10) transmitted from a detection module (100); b-2) a step in which the monitoring server (200) compares the received data with preset reference data to analyze whether a defect has occurred in the photovoltaic power generation module (10) due to voltage / current abnormalities, arc occurrence, or structural damage, and simulates the vibration pattern and sound pattern of the photovoltaic power generation module (10) filtered from external vibrations and sounds generated from the installation location, terrain, climate, and specific noise sources of the photovoltaic power generation module (10) through an artificial intelligence prediction analysis model, and predicts the occurrence of a defect through an analysis of structural defects and power generation reduction factors that may occur in the photovoltaic power generation module (10) and the defects resulting therefrom based on the simulation results; and b-3) a step in which the monitoring server (200) generates monitoring data and management data of the solar power generation module (10) based on defect analysis and the resulting defect occurrence prediction results; an artificial intelligence-based solar power generation module monitoring method characterized by including Claim 11 In claim 10, the above step b-2) further comprises the step of training an artificial intelligence prediction analysis model to perform machine learning on data of fluctuations in vibration patterns and sound patterns that change in the solar power generation structure (12) due to defects occurring in the normal state of the solar power generation module part (10) and the damaged state of the structure from the learning data, and to output defect occurrence prediction data based on the defect analysis. Claim 12 In claim 10, b-4) the solar power generation module monitoring method further comprises the step of transmitting monitoring and management data of a generated solar power generation module part (10) to at least one device among a manager terminal (300), an EMS (Energy Management System), an EPMS (Energy Power Management System), a BMS (Battery Management System), an ESS (Energy Storage System), and an UES (Un-interrupted Energy storage System); characterized in that it is an artificial intelligence-based solar power generation module monitoring method.

Images