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WO2021060772A1 - Système de gestion d'opération de génération d'énergie photovoltaïque en fonction d'un apprentissage automatique et procédé de gestion - Google Patents

Système de gestion d'opération de génération d'énergie photovoltaïque en fonction d'un apprentissage automatique et procédé de gestion Download PDF

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Publication number
WO2021060772A1
WO2021060772A1 PCT/KR2020/012536 KR2020012536W WO2021060772A1 WO 2021060772 A1 WO2021060772 A1 WO 2021060772A1 KR 2020012536 W KR2020012536 W KR 2020012536W WO 2021060772 A1 WO2021060772 A1 WO 2021060772A1
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WO
WIPO (PCT)
Prior art keywords
reference pattern
management server
machine learning
connection panel
parameter
Prior art date
Application number
PCT/KR2020/012536
Other languages
English (en)
Korean (ko)
Inventor
전형덕
김명환
이민규
Original Assignee
주식회사 아이팔
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from KR1020190116795A external-priority patent/KR102084784B1/ko
Priority claimed from KR1020190116765A external-priority patent/KR102084783B1/ko
Application filed by 주식회사 아이팔 filed Critical 주식회사 아이팔
Publication of WO2021060772A1 publication Critical patent/WO2021060772A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B17/00—Fire alarms; Alarms responsive to explosion
    • G08B17/12—Actuation by presence of radiation or particles, e.g. of infrared radiation or of ions
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/18—Status alarms
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
    • H02S40/00—Components or accessories in combination with PV modules, not provided for in groups H02S10/00 - H02S30/00
    • H02S40/30—Electrical components
    • H02S40/34—Electrical components comprising specially adapted electrical connection means to be structurally associated with the PV module, e.g. junction boxes
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
    • H02S50/00—Monitoring or testing of PV systems, e.g. load balancing or fault identification
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy

Definitions

  • the present invention relates to a machine learning-based solar power generation operation management system and management method.
  • solar cells and other green energy sources have great advantages in that they do not use fossil fuels, which are limited to the earth, and that they do not emit carbon dioxide gas, so they can minimize environmental pollution.
  • These advantages are global warming and fossil fuels. Considering the position of modern people who must prepare for the near future when exhaustion becomes serious, the importance of this is bound to be very high.
  • Korean Patent Registration No. 10-0999978 measures a plurality of solar cell modules that convert and output solar energy into electrical energy, an inverter that converts power output from the plurality of solar cell modules, and the generated voltage and current.
  • a photovoltaic power generation monitoring control device that monitors and controls the presence or absence of overvoltage, overcurrent, or reverse current in the circuit group to which each solar cell module is connected, and a switching unit that cuts off the power according to the monitoring result, and its information are received. It is characterized by consisting of a data collection and monitoring device that collects and monitors data.
  • the Applicant provides a machine learning-based solar power generation operation management system that predicts changes in parameters collected in real time using a reference pattern analyzed based on machine learning, and controls the access panel when an abnormality is detected through the predicted change. I want to.
  • a machine learning-based photovoltaic power generation operation management system for achieving the above object includes a solar cell including a plurality of solar cell modules, a connection panel, and the amount of sunlight of the solar cell, and It includes a management server that monitors current and humidity as parameters,
  • connection panel predicts a change trend of the parameter by comparing a reference pattern for each parameter previously analyzed in consideration of the installation location and environmental factors of the solar cell with a parameter collected in real time, and a predicted inflection point extracted from the predicted change trend. And parameter information used for predicting change trends may be used as change data.
  • the management server receives the change data for each parameter from the access panel, transmits a reference pattern for each parameter by analyzing the received change data for each parameter based on machine learning, to the access panel, and from the received change data
  • a notification and a control signal for abnormality control may be provided to the connection panel.
  • the access panel predicts the change trend for each parameter, but predicts the change trend in units of a preset interest section for the parameter collected in real time, and generates change data only when the change trend is predicted in the interest section and manages the above. Can be sent to the server.
  • the management server generates a correlation model that analyzes the correlation between the sunlight amount reference pattern and the current amount reference pattern, and analyzes the correlation between the measured amount of sunlight and the measured amount of current from the change data collected in real time, and the degree of similarity with the correlation model. If is less than the preset rate, it can be judged as more than the current.
  • the management server may notify the manager terminal and transmit a control signal for turning off the switch of the corresponding solar cell module determined as the current abnormality to the connection panel.
  • the access panel includes a heat pump for controlling the internal humidity
  • the management server controls the driving of the heat pump so that the predicted humidity analyzed from the change data collected in real time becomes the pre-analyzed reference humidity.
  • a signal can be transmitted to the connection panel.
  • the access panel transmits a thermal image sensor that generates a thermal image for each area by dividing the interior of the access panel, and transmits a thermal image for each area generated in real time to the management server.
  • the management server may include a control unit for notifying the management server when the temperature increases compared to the reference pattern in a specific region.
  • control unit of the access panel may drive a fire extinguishing unit when a fire is expected due to a sudden increase in temperature in a specific area analyzed by the generated thermal image, and may notify the occurrence of a fire to the management server.
  • the management server may analyze the thermal image for each area based on machine learning, and generate a temperature reference pattern for each area in which environmental factors of the access panel are considered and transmit it to the access panel.
  • a solar cell including a plurality of solar cell modules; Connection panel; And a management server for monitoring the amount of sunlight of the solar cell, current and humidity of the connection panel as parameters, the method of operating and managing a system based on photovoltaic power generation, in the connection panel, collecting the parameters in real time, the In a connection panel, predicting a change trend of the parameter by comparing a reference pattern for each parameter previously analyzed in consideration of the installation location and environmental factors of the solar cell and the parameters collected in real time, and in the connection panel, the predicted change Generating the predicted inflection point extracted from the trend and parameter information used for predicting the change trend as change data and transmitting it to the management server, the management server analyzing the change data to detect an abnormal prediction, and the management server In the case of detecting an abnormality prediction, it may include providing a control signal for abnormality control to the connection panel.
  • the parameter collected in real time is predicted in units of a preset interest section, but change data can be generated and transmitted to the management server only when the change trend is predicted in the interest section. have.
  • the step of detecting the abnormal prediction may include generating a correlation model that analyzes the correlation between the reference pattern of the amount of sunlight and the reference pattern of the amount of current, and by analyzing the correlation between the amount of sunlight and the amount of current measured from the change data collected in real time. If the degree of similarity with the correlation model is less than or equal to a preset rate, it can be determined as more than the current.
  • a control signal for notifying the manager terminal and turning off the switch of the corresponding solar cell module determined as the current abnormality may be transmitted to the connection panel.
  • control signal includes the heat pump provided in the connection panel so that the predicted humidity analyzed from the change data collected in real time becomes a pre-analyzed reference humidity.
  • a control signal for controlling driving may be transmitted to the connection panel.
  • connection panel when a fire is expected due to a sudden increase in temperature in a specific area analyzed by the generated thermal image, the step of driving a fire extinguishing unit and notifying the occurrence of a fire to the management server may be further included.
  • the management server generating a sunlight amount reference pattern, a current reference pattern, and a humidity reference pattern by analyzing the amount of sunlight, current and humidity collected in real time by applying an environmental factor, in the management server, the thermal image for each area
  • the image is analyzed based on machine learning, but may further include generating a temperature reference pattern for each region in which environmental factors are considered, and transmitting a reference pattern for each parameter to the connection panel.
  • the machine learning-based photovoltaic power generation operation management system and management method of the present invention uses the reference pattern analyzed by big data in consideration of the installation location and environmental factors (time, season, shade, weather, etc.) of the solar cell. Based on this, it is possible to predict changes in parameters collected in real time and detect abnormalities.
  • a notification is provided to the manager terminal, and the operation of the access panel can be controlled using a control signal.
  • the fire extinguishing unit is immediately operated at the connection panel to prevent major damage from the fire, and the occurrence of reverse current can be prevented by turning off a switch in which an abnormal current is detected.
  • FIG. 1 is a block diagram showing a schematic configuration of a machine learning-based solar power generation operation management system according to an embodiment of the present invention.
  • FIG. 2 is a block diagram showing the configuration of the control unit of FIG. 1.
  • FIG. 3 is a graph for explaining prediction of a parameter change trend according to an embodiment of the present invention.
  • FIG. 4 is a block diagram showing the configuration of the management server of FIG. 1.
  • FIG. 5 is a graph for explaining a correlation between a reference pattern of a change in the amount of sunlight and the amount of current according to an embodiment of the present invention.
  • FIG. 6 is a graph for explaining an abnormality in a pattern of changes in the amount of sunlight and the amount of current collected in real time according to an embodiment of the present invention.
  • FIG. 7 is a flowchart illustrating a method of operating and managing photovoltaic power generation based on machine learning according to an embodiment of the present invention.
  • a machine learning-based photovoltaic operation management system (hereinafter referred to as a system) according to an embodiment of the present invention includes a solar cell 100, a sunlight sensor 110, a connection panel 200, a management server 300, and a DB It may include (400).
  • the system of the present invention provides visually by monitoring the amount of sunlight, current amount, humidity and temperature of the connection panel 200 of the solar cell 100 as parameters during solar power generation operation, and a manager terminal when a risk factor is predicted during monitoring. You can notify (not shown).
  • the system of the present invention predicts a change trend by comparing the parameter collected in real time with a reference pattern analyzed based on big data machine learning, and periodically updates the reference pattern based on this, thereby accurately predicting the change trend.
  • the solar cell 100 converts light energy incident from the sun into electrical energy and outputs it.
  • the solar cell 100 includes a plurality of solar cell arrays 100a to 100n.
  • the solar cell array includes a plurality of solar cell modules. A number of solar cell modules, which are components of each solar cell array, are configured by being connected in series with each other.
  • the amount of sunlight sensor 110 is installed on the panel of the solar cell 100 to sense the amount of sunlight at the location where the solar cell 100 is installed in real time, and the sensed amount of sunlight is controlled through the communication unit 240 of the connection panel 200. It can be transmitted to 230.
  • connection panel 200 compares the amount of sunlight received in real time from the sunlight amount sensor 110, the output current received from the solar cell 100, and the humidity and temperature in the connection panel 200 with a corresponding reference pattern to determine the change trend for each parameter. It is predictable.
  • the connection panel 200 includes a switch 210, a sensor unit 220, a control unit 230 (MCU), a communication unit 240, a control circuit unit 250, a heat pump 260, and a fire extinguishing unit ( 270, and the sensor unit 220 may include a current sensor 221, a humidity sensor 222, and a thermal image sensor 223.
  • the connection board 200 may be packaged using a functional fiber such as Gore-Tex, and a bolt to which Gore-Tex is applied may be used even when the enclosure is combined.
  • connection panel 200 when a differential pressure occurs due to a temperature change, pressure is balanced inside and outside the connection panel 200 by packaging and coupling bolts of the connection panel 200 using Gore-Tex to prevent external fluids (e.g., rain, etc.). It can prevent penetration and prevent condensation from occurring.
  • Gore-Tex to prevent external fluids (e.g., rain, etc.). It can prevent penetration and prevent condensation from occurring.
  • the switch 210 may receive the output power of the solar cell 100 in units of strings.
  • the control circuit unit 250 may merge output power in units of strings received under the control of the controller 230 and provide them to the inverter.
  • the current sensor 222 may sense the current in each string unit from the output power received in the string unit of the solar cell 100 and provide it to the controller 230.
  • the humidity sensor 222 may sense the humidity in the connection panel 300 in real time and provide it to the control unit 230.
  • the thermal image sensor 223 may provide the controller 230 with a thermal image obtained by capturing a thermal image in the connection panel 300 in real time.
  • the thermal image may be obtained by dividing the area within the connection panel 300 into a plurality of areas and photographing each area.
  • the control circuit unit 250 having a high heat generation temperature may be intensively sensed.
  • the thermal image sensor 220 may divide the area of the control circuit unit 250 by circuit configuration or by a unit area to obtain a real-time thermal image for each area location of the control circuit unit 250.
  • the thermal image generated in real time may be transmitted to the controller 230.
  • the controller 230 may include a memory 231 and a processor 232.
  • the memory 231 may receive a sensing value for each parameter sensed from each of the sensors 110 and 220. That is, the memory 231 may store the amount of sunlight, the amount of current for each string, the amount of humidity, and the thermal image for each region, and may store a reference pattern for each parameter that is updated from the management server 300 at a preset period.
  • the reference pattern for each parameter is a pattern in which the amount of change of each parameter is analyzed based on big data machine learning in the management server 300, and environmental conditions such as the installation location (location, shade, etc.) of the solar cell, weather, season, time, etc. It can be analyzed in consideration of factors.
  • the installation location of the solar cell may be divided into a region and a plurality of locations installed within the region, and the system of the present invention may analyze and manage parameters for each location and region for monitoring. Meanwhile, the analysis of the reference pattern for each parameter will be described in detail when the management server 300 is described.
  • the processor 232 may compare the sensing value for each parameter stored in the memory 231 in real time with a reference pattern of a corresponding parameter to predict a change trend of the real-time parameter sensing value. With reference to FIG. 3, the prediction of the parameter change trend of the processor 232 will be described.
  • the processor 232 may compare a pattern of parameters collected in real time (actual measurement) with a reference pattern previously stored in the memory 231.
  • the pattern of the measured parameter and the reference pattern may represent a degree of change in the sensing value of the parameter sensing value over time.
  • the processor 232 samples the sensing values of the parameters collected in real time in units of a preset time interval, sets an interest interval (sampled unit interval) for predicting the trend of sensing values, and sets the sensing values of the parameters in the interest interval and
  • the trend of sensing values may be predicted based on the current point t1 by comparing pattern values in the corresponding interest section of the reference pattern.
  • the sensing value trend prediction may use a short-term load forecasting (STLF) analysis technique.
  • STLF short-term load forecasting
  • the processor 232 may extract the predicted inflection point P from the predicted sensing value trend pattern through the STLF analysis method.
  • the processor 232 When extracting the predicted inflection point (P), the processor 232 generates metadata including the amount of change in the sensing value and the predicted inflection point (P) in the interest section used for predicting the predicted inflection point (P) as change data, and the management server 300 ). That is, the related information may be transmitted to the management server 300 only when a change trend of a parameter according to the reference pattern is predicted.
  • the parameter may be an amount of sunlight, an amount of current, an amount of humidity, and the like, and may be a vector including time information.
  • the processor 232 may transmit a thermal image for each area collected in real time from the thermal image sensor 222 to the management server 300 through the communication unit 240. At this time, the processor 232 compares the thermal image for each region generated in real time with a temperature reference pattern for each region in which a pre-stored environmental factor is considered, and if the temperature increases compared to the reference pattern, the temperature for the region increases. Can be notified to the management server 300.
  • the processor 232 immediately drives the fire extinguishing unit 270 to extinguish the fire when a spark occurs in a specific area or the temperature in a specific area rapidly rises above a preset temperature and a fire is expected, and the management server 300 Fire occurrence can be notified by.
  • the communication unit 240 may receive a sensing value received from the sunlight amount sensor 110 and the sensor unit 120 in real time, and may perform data transmission/reception between the control unit 230 and the management server 300.
  • the control circuit unit 250 may control driving of the switch 210, the heat pump 260, and the fire extinguishing unit 270 according to the control of the controller 230.
  • the control unit 230 may control the driving of each component with the control circuit unit 250 according to the control signal received from the management server 300.
  • control signal may be a signal for controlling off of a switch in which an abnormal current is detected, driving of a heat pump for humidity control, and the like.
  • the heat pump 260 may prevent the occurrence of condensation in the connection panel 200.
  • a heat pump 260 of an air heat source type is provided in the connection panel 200 to control cooling and heating inside the connection panel, thereby controlling humidity.
  • the reference for cooling and heating control may be a reference humidity for preventing condensation.
  • the control circuit unit 250 may control driving of the heat pump 260 by receiving a heat pump driving control signal for controlling the humidity inside the connection panel 200 to become the reference humidity from the analysis server 300. That is, the heat pump 260 is driven according to the control signal of the control circuit unit 250 to adjust the humidity inside the connection panel 200.
  • connection panel 200 prevents condensation from occurring by a control signal that controls the packaging of the connection panel 200 using Gore-Tex and the bolt coupling structure and driving of the heat pump 260. Can be prevented.
  • the management server 300 may include a data collection unit 310, a reference pattern generation unit 320, a correlation analysis unit 330, an abnormality prediction unit 340, and a monitoring unit 350. have.
  • the data collection unit 310 may collect parameter sensing values transmitted from at least one connection panel 200 and store them in the DB 400. That is, change data for each amount of sunlight, amount of current, and amount of humidity collected in real time, and thermal image images for each region may be stored based on time. In this case, each of the pieces of information may be classified for each connection panel 200.
  • the data collection unit 310 may collect information such as weather, temperature, humidity, rainfall, and amount of sunlight for each location of the solar cell 100 connected to the connection panel 200 from the meteorological office and store it in the DB 400.
  • the reference pattern generation unit 320 may generate a model for each parameter, that is, a reference pattern, using data collected by the data collection unit 310 and stored in the DB 400.
  • the reference pattern generation unit 320 generates a reference pattern for each connection panel 200, but considers environmental factors (solar cell installation location, shade, season, weather, etc.), and parameters for a preset period (for example, a day). You can create patterns. Accordingly, the reference pattern for each parameter of the amount of sunlight, current, and humidity is not a pattern having a fixed threshold, but may be generated in different models even at the same time according to environmental factors.
  • the reference pattern generation unit 320 analyzes the thermal image for each region based on machine learning, but generates a temperature reference pattern for each region in which environmental factors of the connection panel are considered and transmits it to the connection panel.
  • the reference pattern generation unit 320 may transmit and update the generated basic pattern to the connection panel 200 at a preset period.
  • the correlation analysis unit 330 may generate a correlation model that analyzes the correlation between the analyzed basic pattern of sunlight amount and the basic pattern of the amount of current analyzed based on change data of the collected amount of sunlight based on the collected sunlight amount change data. .
  • a correlation model may also be generated in consideration of environmental factors.
  • FIG. 5 is a graph for explaining the correlation between the amount of sunlight and the amount of current reference pattern according to an embodiment of the present invention. Referring to FIG. 5, the amount of sunlight and the amount of electric current can be checked for a change over time. Through this, it is possible to grasp the correlation between the reference pattern of the amount of sunlight and the reference pattern of the amount of current according to the time trend.
  • the abnormality prediction unit 340 predicts the current abnormality through analysis of the sunlight change data and current change data collected in real time, predicts the humidity abnormality through analysis of the humidity change data, and analyzes the thermal image collected in real time for each area.
  • the temperature abnormality can be predicted through
  • the abnormality prediction unit 340 analyzes the correlation between the measured amount of sunlight and the measured amount of current from the change data collected in real time, and determines the degree of similarity with the correlation model that analyzed the correlation between the amount of sunlight reference pattern and the current amount reference pattern. If is less than the preset rate, it can be judged as more than the current.
  • the measured sunlight amount pattern may be a pattern generated by analyzing sunlight amount change data based on machine learning.
  • the measured current amount pattern may be a pattern generated by analyzing current amount change data based on machine learning.
  • the abnormality predictor 340 may determine a degree of similarity between the correlation between the measured sunlight amount pattern and the measured current amount pattern with the correlation between the sunlight amount reference pattern and the current amount reference pattern of FIG. 5. Referring to FIG. 6, when comparing the correlation between the reference pattern of the amount of sunlight and the reference pattern of the amount of current, the occurrence of an abnormality is predicted at a location where the correlation between the measured amount of sunlight pattern and the measured amount of current pattern is different, that is, a portion with low correlation similarity (abnormality prediction). Can be.
  • the abnormality predicting unit 340 When determining a current abnormality, notifies the manager terminal (not shown), and transmits a control signal for turning off the switch of the corresponding solar cell module determined as the current abnormality to the connection panel 200, and By controlling 210, reverse current can be prevented.
  • the abnormality prediction unit 340 is a heat pump for controlling the estimated humidity analyzed from the humidity change data collected in real time to become a pre-analyzed reference humidity by determining it as the abnormal humidity when the analyzed predicted humidity is outside the error range of the previously analyzed reference humidity
  • the driving control signal may be transmitted to the connection panel 200.
  • the monitoring unit 350 uses the monitoring unit 350 to monitor the heat of the corresponding area. Temperature patterns according to image images and abnormalities due to temperature rise can be notified to the manager terminal.
  • the monitoring unit 350 visually provides a change trend predicted based on the change data collected in real time and a reference pattern, and may provide a mark and a notification sound that can be recognized when a notification occurs.
  • connection panel 200 in the connection panel 200, the amount of sunlight, the amount of current, the humidity of the connection panel 200, and the thermal image for each area may be collected in real time in the connection panel 200 (S10). ).
  • connection panel 200 may determine whether a change trend is predicted by comparing an interest section of each collected parameter with a pre-analyzed reference pattern (S15).
  • abnormal prediction can be made according to the parameter.
  • the current abnormality prediction analyzes the correlation between the measured amount of sunlight and the measured amount of current from the amount of sunlight change data and current change data collected in real time, and the degree of similarity with the correlation model that analyzes the correlation between the amount of sunlight reference pattern and the current amount reference pattern. By judging, if the degree of similarity is less than or equal to a preset rate, it can be determined as more than the current.
  • the humidity abnormality prediction may be detected as abnormal humidity when the predicted humidity analyzed from the humidity change data collected in real time is out of the error range of the previously analyzed reference humidity.
  • a control signal of the access panel 200 according to the abnormality prediction may be generated and transmitted to the access panel 200 (S35).
  • the connection panel 200 receiving this may control the switch 210 and the heat pump 260 according to the control signal.
  • the monitoring unit 350 when a thermal image abnormality is predicted, that is, when a notification by temperature rise is received from the thermal image of a specific area, the monitoring unit 350 enables the monitoring unit 350 to monitor the temperature in a temperature pattern according to the thermal image of the corresponding area. And an abnormality due to temperature rise may be notified to the manager terminal.
  • the reference pattern may be updated at a preset period by parameters collected in real time, and the updated reference pattern may be transmitted to the access panel 200.
  • FIGS. 1 to 6 what has been described with reference to FIGS. 1 to 6 above is a description of only the main matters of the present invention, and the present invention is limited to the configuration of FIGS. 1 to 6 as various designs are possible within the technical scope. It is self-evident that it is not.

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Abstract

La présente invention concerne un système de gestion d'opération de génération d'énergie photovoltaïque en fonction d'un apprentissage automatique. Plus précisément, la présente invention concerne un système de gestion d'opération de génération d'énergie photovoltaïque, en fonction d'un apprentissage automatique, qui comprend : des cellules solaires ; une plaquette de raccordement qui compare des paramètres des cellules solaires rassemblés en temps réel à un motif de référence précédemment analysé compte tenu de la position de l'installation et des facteurs environnementaux des cellules solaires, de façon à prédire une tendance de variation des paramètres, et qui génère, sous forme de données de variation, des informations de paramètre utilisées pour prédire la tendance de variation et un point prédit d'inflexion, extrait de la tendance prédite de variation ; un serveur de gestion qui reçoit, de la plaquette de raccordement, des données de variation pour chaque paramètre, qui transmet, à la plaquette de raccordement, un motif de référence pour chaque paramètre, obtenu par analyse des données reçues de variation pour chaque paramètre en fonction de l'apprentissage automatique et, lorsqu'une anomalie est détectée parmi les données reçues de variation, qui produit une alarme et qui fournit un signal de commande permettant de maîtriser l'anomalie au niveau de la plaquette de raccordement.
PCT/KR2020/012536 2019-09-23 2020-09-17 Système de gestion d'opération de génération d'énergie photovoltaïque en fonction d'un apprentissage automatique et procédé de gestion WO2021060772A1 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
KR1020190116795A KR102084784B1 (ko) 2019-09-23 2019-09-23 머신러닝기반 태양광발전운영 관리방법
KR1020190116765A KR102084783B1 (ko) 2019-09-23 2019-09-23 머신러닝기반 태양광발전운영 관리시스템
KR10-2019-0116765 2019-09-23
KR10-2019-0116795 2019-09-23

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116131755A (zh) * 2023-02-23 2023-05-16 呼和浩特市语能科技有限责任公司 一种太阳能发电设备智能控制系统及方法
CN116388685A (zh) * 2023-04-07 2023-07-04 江苏新之阳新能源科技有限公司 一种太阳能电池板运行控制系统

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101462247B1 (ko) * 2014-03-10 2014-11-21 삼성영상보안주식회사 적외선 열영상 기반의 스마트 화재감지 시스템 및 자동소화장치 인터페이스 플랫폼
KR101761686B1 (ko) * 2017-03-31 2017-07-31 (주)하모니앤유나이티드 머신 러닝을 이용한 태양광 발전량 실시간 예측 시스템
KR101825725B1 (ko) * 2017-06-29 2018-02-06 (주)위너스엔지니어링 태양광 전지모듈의 열화 진단 방법 및 태양광 전지모듈의 열화 진단 장치
KR101922016B1 (ko) * 2018-07-19 2018-11-27 이우식 결로 예측 및 예방 기능을 구비한 태양광 발전 장치
KR20190007250A (ko) * 2017-07-12 2019-01-22 (주)유에너지 현장 고장 진단이 가능한 이동형 태양광 발전설비 고장 진단시스템
KR102084783B1 (ko) * 2019-09-23 2020-03-04 주식회사 아이팔 머신러닝기반 태양광발전운영 관리시스템
KR102084784B1 (ko) * 2019-09-23 2020-05-26 주식회사 아이팔 머신러닝기반 태양광발전운영 관리방법

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101462247B1 (ko) * 2014-03-10 2014-11-21 삼성영상보안주식회사 적외선 열영상 기반의 스마트 화재감지 시스템 및 자동소화장치 인터페이스 플랫폼
KR101761686B1 (ko) * 2017-03-31 2017-07-31 (주)하모니앤유나이티드 머신 러닝을 이용한 태양광 발전량 실시간 예측 시스템
KR101825725B1 (ko) * 2017-06-29 2018-02-06 (주)위너스엔지니어링 태양광 전지모듈의 열화 진단 방법 및 태양광 전지모듈의 열화 진단 장치
KR20190007250A (ko) * 2017-07-12 2019-01-22 (주)유에너지 현장 고장 진단이 가능한 이동형 태양광 발전설비 고장 진단시스템
KR101922016B1 (ko) * 2018-07-19 2018-11-27 이우식 결로 예측 및 예방 기능을 구비한 태양광 발전 장치
KR102084783B1 (ko) * 2019-09-23 2020-03-04 주식회사 아이팔 머신러닝기반 태양광발전운영 관리시스템
KR102084784B1 (ko) * 2019-09-23 2020-05-26 주식회사 아이팔 머신러닝기반 태양광발전운영 관리방법

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116131755A (zh) * 2023-02-23 2023-05-16 呼和浩特市语能科技有限责任公司 一种太阳能发电设备智能控制系统及方法
CN116131755B (zh) * 2023-02-23 2024-04-26 青岛中筑新能源有限公司 一种太阳能发电设备智能控制系统及方法
CN116388685A (zh) * 2023-04-07 2023-07-04 江苏新之阳新能源科技有限公司 一种太阳能电池板运行控制系统
CN116388685B (zh) * 2023-04-07 2024-04-09 江苏新之阳新能源科技有限公司 一种太阳能电池板运行控制系统
CN116388685B8 (zh) * 2023-04-07 2024-04-26 青岛中筑新能源有限公司 一种太阳能电池板运行控制系统

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