KR102051402B1 - A Diagnosis Syetem of Photovoltaic Generation Based on IoT - Google Patents

Abstract

The present invention relates to a photovoltaic power generation diagnosis system and, more specifically, to an IoT-based photovoltaic power generation diagnosis system which compares a difference of a power generation amount within grouped photovoltaic power generation devices through trend analysis of the power generation amount during the same period in the past through machine learning using artificial intelligence and the like rather than state diagnosis through comparison of an actual power generation amount with a calculated power generation predictive value of a specific photovoltaic power generation device so as to diagnose a state of the specific photovoltaic power generation device, thereby increasing accuracy of the state diagnosis. Collection of data for abnormality diagnosis is performed by using an IoT network so as to perform installation and operation of the system at a low cost and perform quick transmission and reception of the data. Moreover, the IoT-based photovoltaic power generation diagnosis system comprises a photovoltaic power generation device and a diagnosis server.

Description

IoT Diagnosis Syetem of Photovoltaic Generation Based on IoT

The present invention relates to a photovoltaic diagnosis system, and more particularly, through machine learning (Machine learning) using artificial intelligence, rather than state diagnosis through comparison of actual power generation with the calculated power generation prediction value of a specific photovoltaic device. Compare the difference of generation in the grouped photovoltaic devices through the analysis of power generation trend in the past period to diagnose the status of specific photovoltaic devices to improve the accuracy of status diagnosis and to diagnose abnormality using IoT network. By allowing the collection of data to be made, the installation and operation of the system at a low cost, and relates to the IoT-based photovoltaic diagnostic system to enable the rapid transmission and reception of data.

Photovoltaic power generation, a field of renewable energy, has been rapidly increasing in demand due to many advantages, and technologies for increasing power generation efficiency have been developed. These photovoltaic devices are increasing the importance of maintenance to quickly diagnose and respond to the normal power output to the solar module due to various reasons, such as shading, failure, aging in the operation process.

However, existing technologies for diagnosing photovoltaic failures, using various prediction techniques, calculate the expected generation amount that can be developed in consideration of various environmental factors in the photovoltaic facility (or module). However, if the actual power generation is out of a certain range compared to the estimated power generation, the concept is judged to be higher than that. Therefore, the expected power generation accurately reflects various factors affecting photovoltaic power generation. Due to the limitations that cannot be achieved, the accuracy of power generation is already greatly reduced. Therefore, the fault diagnosis technique based on the expected power generation with such a large error range also has a limit of increasing the error rate of judgment.

<Patent Document> Korean Patent Registration No. 10-1728692 "Failure prediction monitoring system and method of solar modules"

The prior art disclosed in the <Patent Document> also calculates the real-time estimated power generation of the photovoltaic module based on the existing change trend of the solar module and data such as solar radiation information and temperature, and then matches the estimated power generation with the actual power generation. That is, to determine the failure of the photovoltaic module based on the degree of difference, the existing problem that the error of diagnosis is large.

Accordingly, there is an increasing need for an apparatus and technology capable of accurately diagnosing and responding to photovoltaic conditions for the efficiency of photovoltaic maintenance.

In addition, the conventional photovoltaic monitoring system is to use a general IP network, there is a problem that a lot of cost is required to build and operate the system, it is difficult to quickly transmit and receive a large amount of data.

The present invention has been made to solve the above problems,

The present invention is a grouped photovoltaic through analysis of power generation trends of the same period in the past through machine learning using artificial intelligence, not state diagnosis by comparing actual power generation with the calculated power generation prediction value of a specific photovoltaic device. By comparing the difference in the amount of power generation within the power generation devices, it is possible to improve the status diagnosis accuracy by diagnosing the condition of a specific photovoltaic device and to collect data for abnormal diagnosis using the IoT network. Installation and operation of the is made and there is an effect to enable the rapid transmission and reception of data.

The present invention is a state diagnosis accuracy through precise grouping by forming a group after processing, refinement, correction of power generation data in consideration of the failure and maintenance history of each photovoltaic device, surrounding weather and environmental information, equipment characteristics There is an effect to enable the improvement of.

The present invention has the effect of enabling more accurate construction of the abnormal diagnosis system over time by changing the criteria of the degree of processing, refinement, correction by comparing the abnormal diagnosis and the actual abnormality to be applied to the grouping.

The present invention has the effect of enabling not only the failure of the photovoltaic device but also the prediction and failure diagnosis of the failure within a certain period.

The present invention is to increase the power generation efficiency by leveling the power output from each string, such that the leveling process is made through the charging and discharging of the power storage unit connected to each string to enable a simple configuration of the device, to diagnose the abnormality of each string It has the effect of making it possible.

The present invention is implemented by the embodiment having the following configuration to achieve the above object.

According to an embodiment of the present invention, the IoT-based photovoltaic diagnosis system according to the present invention includes a solar power generation device that generates power using sunlight; And a diagnosis server configured to receive power generation data of the photovoltaic device using an IoT network, and to diagnose a state of the photovoltaic device based on the generated power generation data. Grouping similar photovoltaic devices and diagnosing the status of a particular photovoltaic device by comparing power generation differences within the group.

According to another embodiment of the present invention, in the IoT-based photovoltaic diagnostic system according to the present invention, the diagnostic server is a grouping unit for grouping the photovoltaic devices, and the error range within the grouped photovoltaic devices An abnormality diagnosis unit for selecting a specific photovoltaic device indicative of the amount of generated power generation, wherein the grouping unit comprises: a power generation data collection module for collecting power generation information of the photovoltaic device; A power generation data processing module for processing power generation information through information such as failure history and maintenance history of each photovoltaic device; A power generation data purification module for purifying processed power generation information according to weather information and environmental information of regions where each photovoltaic device is located; A power generation data correction module for correcting purified power generation data according to facility characteristics of each solar power generation device; And a grouping module for grouping photovoltaic devices by applying a clustering algorithm to the corrected power generation data.

According to another embodiment of the present invention, in the IoT-based photovoltaic power generation diagnostic system according to the present invention, the power generation data processing module is a troubleshooting for receiving information on the failure, maintenance degree of each photovoltaic device A precision receiving module; A troubleshooting time information receiving module for receiving information regarding a time at which a failure and maintenance has been made; A failure maintenance index calculation module for calculating a failure maintenance index according to the failure, the degree of maintenance, and the time; And a processing operation module configured to calculate the processed power generation data by multiplying the power failure maintenance index by the power generation data.

According to another embodiment of the present invention, in the IoT-based photovoltaic power generation diagnostic system according to the present invention, the power generation data purification module is an environmental information receiving module for receiving information about the surrounding weather, environment of each photovoltaic device and; An environmental index calculation module for calculating an environmental index that quantifies the effect on power generation according to received weather and environmental information; And a refinement calculation module configured to calculate purified power generation data by multiplying the environmental index by the processed power generation data.

According to another embodiment of the present invention, in the IoT-based photovoltaic power generation diagnostic system according to the present invention, the power generation data correction module includes a facility information receiving module for receiving information on the facility characteristics of each photovoltaic device; A characteristic index calculation module for calculating a specific index that quantifies the effect on the amount of photovoltaic power generation according to facility characteristics; And a correction calculation module configured to multiply the characteristic index by the purified power generation data to calculate the corrected power generation data.

According to another embodiment of the present invention, in the IoT-based photovoltaic diagnosis system according to the present invention, the grouping unit includes a group optimization module for optimizing the group by feeding back a diagnosis result of the fault diagnosis unit, the group optimization The module includes a diagnostic verification module for verifying the accuracy of the diagnosis by comparing the information of the photovoltaic device diagnosed with the abnormality in the abnormality diagnosis unit and the photovoltaic device that actually occurred; Index control module for adjusting the degree of power generation data modified by the power generation data processing module, power generation data purification module, power generation data correction module so that the diagnosis and the actual abnormality for the photovoltaic device that does not match the error diagnosis and; And a group automatic change module for changing the group according to the degree adjusted by the index control module.

According to another embodiment of the present invention, in the IoT-based photovoltaic power generation diagnostic system according to the present invention, the group optimization module is the power generation data for the photovoltaic device that does not match the fault diagnosis by the fault diagnosis unit And an exponential comparison module for comparing the degree of power generation data modified by the processing module, the power generation data purification module, and the power generation data correction module, wherein the exponent adjustment module is configured to process the power generation data according to the result of comparison by the index comparison module. The module, the power generation data purification module, the generation data correction module, characterized in that the module to control the degree of modification to the priority of the module with a large degree of modification.

According to another embodiment of the present invention, in the IoT-based photovoltaic diagnosis system according to the present invention, the grouping unit includes an environmental reference grouping unit for classifying the same environmental area into the same group, the grouping unit is the environmental reference group The fire department may classify the same area into the same group first, and then analyze the power generation trend for the same group to further subdivide the group.

According to another embodiment of the present invention, in the IoT-based photovoltaic diagnosis system according to the present invention, the fault diagnosis unit diagnoses a specific photovoltaic device indicating a power generation out of a certain error range within the same group as a failure Fault diagnosis unit; If the amount of generation of a certain range of errors does not deviate from the error range diagnosed as the fault, but the amount of generation of the specific range of errors continues for a certain time, the risk index is calculated using the error degree and duration, and the estimated time to predict the failure will be reported according to the risk index. Failure risk prediction unit; It is characterized in that it comprises a; deterioration detection unit for detecting a deterioration of a specific photovoltaic device by analyzing the rate of change of power generation error, although the signal related to the failure is not output.

According to another embodiment of the present invention, in the IoT-based photovoltaic diagnostic system according to the present invention, the failure risk prediction unit and the error monitoring module for monitoring the error of the amount of power generation of the photovoltaic device having an error range of a certain degree or more; A time measurement module that measures the duration of the error, a risk index calculation module that calculates a risk index according to the degree and duration of the error, and a fault prediction information notification module that calculates and reports a failure prediction time according to the risk index. It is characterized by including.

According to another embodiment of the present invention, in the IoT-based photovoltaic diagnostic system according to the present invention, the degradation detection unit is a slope calculation module for calculating a slope according to the degree of change of the error for a predetermined period, the slope is set threshold Threshold checking module for measuring the degree of deviation, time information measuring module for measuring the time out of the threshold, and the degree of deterioration by calculating the degree of deterioration according to the degree and duration of the slope is out of the threshold, if the degree of deterioration Inform is characterized in that it comprises a degradation alarm module.

According to another embodiment of the present invention, in the IoT-based photovoltaic diagnostic system according to the present invention, the photovoltaic device is connected to each of the plurality of strings in the array of each photovoltaic device to each of the photovoltaic devices And a string leveling unit for minimizing power variation between strings due to shading or failure in a specific module when an abnormality occurs in the power generation device, wherein the string leveling unit includes a power measuring unit for measuring output current or voltage for each of a plurality of strings, A power storage unit for compensating or absorbing power for each string, and a control unit controlling an operation of the power storage unit based on data of the power measuring unit, wherein the control unit determines a state of charge of the power storage unit; And a storage control module for controlling charging and discharging of the power storage unit according to the state of charge of the power storage unit. And a connection control module that controls the connection between the power storage unit and the string according to whether each string is charged or discharged, wherein the storage control module supplies power to the string of which the output is reduced when the amount of charge of the power storage unit is sufficient. A discharge control module and a charge control module for absorbing power of a string having a high output when the charge amount of the power storage unit is insufficient to minimize power deviation between strings, and the connection control module discharges the power storage unit by the discharge control module. Comprising a compensation connection module for connecting the power storage string and the power storage in this case, and the absorption control module for connecting the power storage and the string to supply power when the charging of the power storage by the charge control module It is done.

According to another embodiment of the present invention, in the IoT-based photovoltaic diagnostic system according to the present invention, the string leveling unit includes a string diagnostic unit for detecting a string having an abnormality, the string diagnostic unit to the connection control module A connection string detection module detecting a string connected by the connection, a charge / discharge recognition module that recognizes charge / discharge of the string detected by the connection string detection module, and measure the degree of charge / discharge perceived by the charge / discharge recognition module An abnormality index calculation module for calculating an abnormality index of each string according to a charge / discharge measuring module, a charge and discharge degree measured by the charging and discharging measurement module, and an abnormality index of each string calculated by the abnormality index calculating module for a predetermined period of time. Abnormality accumulation module that accumulates the error, and if the accumulated abnormality index exceeds a certain value, an error occurs in the corresponding string. Characterized in that it comprises an abnormal string diagnostic module for diagnosing the string.

The present invention can obtain the following effects by the configuration, combination, and use relationship described above with the present embodiment.

The present invention is a grouped photovoltaic through analysis of power generation trends of the same period in the past through machine learning using artificial intelligence, not state diagnosis by comparing actual power generation with the calculated power generation prediction value of a specific photovoltaic device. By comparing the difference in the amount of power generation within the power generation devices, it is possible to improve the status diagnosis accuracy by diagnosing the condition of a specific photovoltaic device and to collect data for abnormal diagnosis using the IoT network. Its purpose is to provide an IoT-based photovoltaic diagnostics system that enables the installation and operation of the system and enables the rapid transmission and reception of data.

The present invention is a state diagnosis accuracy through precise grouping by forming a group after processing, refinement, correction of power generation data in consideration of the failure and maintenance history of each photovoltaic device, surrounding weather and environmental information, equipment characteristics The purpose of the present invention is to provide an IoT-based photovoltaic diagnostic system that enables improvement of the system.

The present invention compares the diagnosis of abnormality and the actual abnormality by changing the standard of the degree of processing, refinement, correction to be applied to the grouping, IoT-based photovoltaic diagnostics that enables the construction of a more accurate diagnosis system over time The purpose is to provide a system.

It is an object of the present invention to provide an IoT-based photovoltaic diagnosis system that enables not only failure of a photovoltaic device but also diagnosis and degradation of a failure within a predetermined period of time.

The present invention is to increase the power generation efficiency by leveling the power output from each string, such that the leveling process is made through the charging and discharging of the power storage unit connected to each string to enable a simple configuration of the device, to diagnose the abnormality of each string The purpose is to provide an IoT-based PV diagnostic system.

1 is a conceptual diagram of a photovoltaic diagnosis system according to an embodiment of the present invention
Figure 2 is a state diagram for performing a diagnosis by grouping photovoltaic devices in the present invention
3 is a block diagram showing the configuration of the diagnostic server of FIG.
4 is a block diagram showing the configuration of the trend reference grouping unit of FIG.
FIG. 5 is a block diagram showing the configuration of the environmental standard grouping unit of FIG.
6 is a block diagram showing the configuration of the abnormality diagnosis unit of FIG.
7 is a conceptual view in which the string leveling unit is included in the photovoltaic device of FIG.
FIG. 8 is a detailed configuration diagram of the string leveling unit of FIG. 7.
9 is a block diagram illustrating a configuration of a controller of FIG. 8.
FIG. 10 is a block diagram illustrating a configuration of a string diagnosis unit of FIG. 8. FIG.

Hereinafter, preferred embodiments of the IoT-based photovoltaic diagnosis system according to the present invention will be described in detail with reference to the accompanying drawings. In the following description of the present invention, if it is determined that a detailed description of a known function or configuration may unnecessarily obscure the subject matter of the present invention, the detailed description thereof will be omitted. Throughout the specification, when a part is said to "include" any component, which means that it may further include other components, except to exclude other components unless specifically stated otherwise, also described in the specification The terms "... unit", "... module" and the like refer to a unit for processing at least one function or operation, which may be implemented by hardware or software or a combination of hardware and software.

Referring to FIGS. 1 to 10, the IoT-based photovoltaic diagnostic system according to an embodiment of the present invention will be described. The photovoltaic diagnostic system includes a photovoltaic device 3 for generating power using sunlight; And a diagnosis server 1 for receiving the power generation data of the photovoltaic device 3 using an IoT network and diagnosing the state of the photovoltaic device based on the transmitted power generation data.

In particular, the photovoltaic diagnosis system according to the present invention diagnoses the state of a particular photovoltaic device 3 by grouping photovoltaic devices having similar power generation trends during the same period in the past and comparing the difference in power generation within the group. By doing so, a more accurate state diagnosis can be made.

Here, the photovoltaic device 3 does not mean only a photovoltaic power plant installed on the ground, but generates power by solar light such as a building integrated photovoltaic device (BIPV) installed on a roof of a building, a water surface, or an outer wall of a building. It may include a variety of devices, by measuring the power generated in each of the photovoltaic device (3) so that the abnormal diagnosis of the photovoltaic device (3) can be made.

In this case, the amount of power produced by the photovoltaic device 3 may be measured by a voltage or current sensor installed in the inverter 34 or the like. Information about the measured amount of power generated is transmitted to the diagnostic server 1 through the IoT network. To be delivered. In particular, things that do not have a large amount of data to be exchanged through a network among the things constituting the Internet of Things (IoT) are called a small thing, and a network composed of the things is called a small internet network. It is possible to collect the generation amount information by using a small Internet network such as LoRa network, LTE-M of KT. Therefore, the present invention can allow a faster data collection at a lower cost than the existing IP network.

The diagnostic server 1 is a configuration for diagnosing the state of the photovoltaic device 3 on the basis of the power generation data transmitted from the photovoltaic device 3, the photovoltaic device 3 with a trend in generating power for a certain period of time. ) By grouping them into the same group to diagnose the abnormality when the generation amount error occurs in the same group, thereby improving the accuracy of the diagnosis. In the system for diagnosing the abnormality of the conventional photovoltaic device, the predicted power generation that can be generated in the photovoltaic facility is calculated by using various prediction techniques, and then, when the actual power generation out of a certain range is compared with the estimated power generation. Judging from the above, we are approaching the concept that precision diagnosis or maintenance can be performed. However, this projected power generation is not accurate enough in the forecasted power generation due to the limitation that various factors affecting photovoltaic power cannot be accurately reflected. Therefore, the fault diagnosis technique based on the expected generation amount having such a large error range also has a limit in that the error rate of judgment is increased. Accordingly, in the present invention, the grouping unit 11 for grouping the photovoltaic devices 3 having a similar trend in the past generation period through the diagnostic server 1, and the group of photovoltaic devices of the same group ( 3) includes an abnormality diagnosis portion 13 for selecting a specific photovoltaic device 3 representing the amount of power out of the error range.

The grouping unit 11 is configured to group the photovoltaic devices 3 (see FIG. 2) having similar trends in the amount of power generation in the past during the photovoltaic devices 3. Through the machine learning (Machine learning) to implement such as in the past, the clustering for each photovoltaic device (3) having a similar trend based on the power generation data of the same period. The grouping unit 11 may set the group only by the trend of the amount of power generation. In some cases, the photovoltaic devices 3 having similar weather environments and the like are first grouped into the same group and again according to the amount of power generation trend in the same group. It can also be configured to subdivide groups. Therefore, the grouping unit 11 basically includes a trend-based grouping unit 111 for setting a group according to the generation amount trend, and optionally the first group of photovoltaic devices 3 having a similar environment such as a weather environment. Environmental criteria grouping unit 113 to be grouped by may be further included.

The trend reference grouping unit 111 is configured to group the photovoltaic devices 3 according to the power generation trend, and analyzes the power generation trend by collecting power generation information from each photovoltaic device 3. In particular, the trend reference grouping unit 111 generates power generation data of each photovoltaic device 3 in consideration of failure history, maintenance history, surrounding weather information, environmental information, facility characteristics, etc. of each photovoltaic device 3. Process, refine, and calibrate, and use this revised power generation data to more accurately classify groups. In addition, the trend reference grouping unit 111 compares the result diagnosed by the abnormal diagnosis unit 13 with the information on the actual occurrence of the abnormality by the set group to verify the accuracy, and according to the verified result By automatically updating and optimizing the group, the accuracy of fault diagnosis can be further improved over time. To this end, the trend reference grouping unit 111 is a power generation data collection module 111a, power generation data processing module 111b, power generation data purification module 111c, power generation data correction module 111d as shown in FIG. , Grouping module 111e and group optimization module 111f.

The power generation data collection module 111a is configured to collect information such as the amount of power generation and the amount of power generation deviation from each photovoltaic device 3, and may be collected using an IoT network, preferably a small internet network. In order to group the photovoltaic devices 3, basic information is collected.

The power generation data processing module 111b is configured to process information collected by the power generation data collection module 111a through information such as failure history and maintenance history of each photovoltaic device 3. That is, even when information in which the amount of generation of the specific solar power generator 3 is sharply lowered is collected, a part or most of the solar power generator 3 is maintained for a periodic inspection or failure during the period. In the repaired state, the power generation data processing module 111b reflects this to correct and process power generation information of the photovoltaic device 3 to obtain accurate power generation trend information on the photovoltaic device 3. It becomes possible. To this end, the power generation data processing module 111b includes a failure repair accuracy receiving module 111b-1, a failure repair time information receiving module 111b-2, a failure repair index calculation module 111b-3, and a machining operation module 111b. -4).

The faulty repair accuracy receiving module 111b-1 is configured to receive information on the fault or the degree of maintenance for each of the photovoltaic devices 3, and breaks down all of the photovoltaic devices 3. Or to receive information about whether maintenance has been performed, and at what rate the failure or maintenance has caused the operation to stop. For example, the photovoltaic device 3 may include a plurality of arrays 33, etc., in one unit, and to receive information on a ratio of a part of the photovoltaic device 3 when a failure or maintenance work is performed on only some of them. can do. The failure repair accuracy receiving module 111b-1 may receive information by a separate input of an operator when a failure or maintenance is performed.

The troubleshooting time information receiving module 111b-2 may be configured to receive information regarding a time when a failure or maintenance is performed, and may also receive information by a separate input of an operator.

The troubleshooting index calculation module 111b-3 uses the degree and time information on the failure or maintenance received by the troubleshooting level receiving module 111b-1 and the troubleshooting time information receiving module 111b-2. By calculating the index for failure repair (hereinafter referred to as 'failure maintenance index'), where the failure repair index is the degree of inoperability due to failure or maintenance for each photovoltaic device (3) Means.

The processing operation module 111b-4 processes power generation data using the failure repair index calculated by the failure maintenance index calculation module 111b-3, and generates power generation data as it is not operated by failure or maintenance. To increase the modification. For example, when power generation is stopped due to failure or maintenance for one-third of the entire array of the specific photovoltaic device 3 for 24 hours, the failure index may be calculated as 1/3. The processed power generation data can be calculated by multiplying the power generation data of the photovoltaic device 3 by 3/2 (1.5 times).

The power generation data purification module 111c is configured to minimize error information on power generation trends through information such as local weather information and environmental information where each photovoltaic device 3 is located. That is, even if the power generation of the photovoltaic devices 3 of a particular period is similar, if the environment where each of the photovoltaic devices 3 is located, that is, the situation where the solar radiation information, temperature, humidity, etc. is different, When grouping the same power generation group, the reliability of the grouping result is reduced. Therefore, in addition to the information provided by the power generation data collection module 111a to the power generation data processing module 111b, the power generation data purification module 111c includes local weather information and environmental information where each of the photovoltaic devices 3 is located. By reflecting the same information, it is possible to minimize error information on the amount of power generation of each of the photovoltaic devices 3, thereby allowing reliable grouping to be performed in the grouping module 111e to be described later. To this end, the power generation data purification module 111c may include an environmental information receiving module 111c-1, an environmental index calculation module 111c-2, and a refinement calculation module 111c-3.

The environmental information receiving module 111c-1 is configured to collect weather and environmental information of an area in which each photovoltaic device 3 is located, so that information such as solar radiation, temperature, and humidity can be received.

The environmental index calculation module 111c-2 is configured to calculate an environmental index according to environmental information received by the environmental information receiving module 111c-1, and includes information such as insolation, temperature, and humidity in a predetermined section. According to the environmental index can be calculated. For example, the environmental index calculation module 111c-2 may be set to have a high environmental index as the solar radiation amount is high or the temperature is high, and the environmental index is analyzed by analyzing the correlation between each environmental information and the generation amount based on past generation amount data. Can be determined. In addition, in the present invention, since the environmental index is continuously updated by the verification of the abnormal diagnosis result, the value of the environmental index is continuously updated according to the use of the system, and the accuracy thereof is increased.

The refining calculation module 111c-3 is configured to purify the processed power generation data by applying the environmental index calculated by the environmental index calculation module 111c-2. For example, the purified power generation data may be the same environmental condition. The reciprocal of each environmental index can be multiplied by the processed power generation data so that it can be compared under the assumption so that the purified power generation data can be produced.

The power generation data correction module 111d is configured to correct refined power generation data according to facility characteristics of each photovoltaic device, and reflects facility characteristics according to the number, installation angle, type of module, etc. of the photovoltaic module 31. To correct it. To this end, the power generation data correction module 111d may include a facility information receiving module 111d-1, a characteristic index calculation module 111d-2, and a correction calculation module 111d-3.

The facility information receiving module 111d-1 collects facility information for each photovoltaic device 3 and collects information such as the number of solar modules 31, installation angles and directions, types of modules, and the like. In addition, the information registered in the diagnostic server 1 can be recalled. Specifically, each photovoltaic device 3 cannot be said to have the same installation direction, and the types and number of modules may be different, so that the amount of power generation is different even if other conditions are the same. Therefore, it is possible to correct the amount of power generation data using the facility information of each photovoltaic device (3).

The characteristic index calculation module 111d-2 is configured to calculate an index according to facility characteristics of the photovoltaic device 3 affecting the amount of power generation, for example, the number of modules, the ratio of the amount of power generation according to the installation direction, and the like. To be set. For example, if the number of modules is 100, the power generation data for the photovoltaic device 3 with 400 solar modules 31 are multiplied by 0.25, and when north is south, north is south. When the amount of electricity generated is only 50%, the power generation data for the north-side photovoltaic device 3 can be doubled, and 400 solar modules 31 are provided for the north-side photovoltaic device 3. It can be calculated that the characteristic index of 0.25 X 2 = 0.5 for

The correction calculation module 111d-3 is configured to correct the amount of power generation data according to the characteristic index calculated by the characteristic index calculation module 111d-2, and calculates the characteristic index calculated in consideration of the number of modules, the installation direction, and the like. Apply to power generation data. Therefore, when the characteristic index is calculated as 0.5, the correction data may be calculated by multiplying the power generation data by 0.5, and in addition, the power generation data may be corrected according to various equations and criteria.

The grouping module 111e applies the clustering algorithm (programming based on a clustering criterion) to the data calculated by the power generation data correction module 111d to group the photovoltaic devices 3 into groups showing similar generation trends. It is a constitution.

For example, the grouping module 111e may perform power plant grouping for each photovoltaic device 3 by the photovoltaic devices 3 having the most similar daily deviations based on the daily cumulative generation data for a predetermined period. Can be. This grouping is performed by grouping the photovoltaic devices 3 showing the most similar work generation pattern by reflecting the relevant environmental information, maintenance history information, facility characteristics, etc. of each photovoltaic device 3, thereby If any one of the generators 3 exhibits one power generation pattern out of an error range unlike other photovoltaic generators 3, the abnormality diagnosis unit 13 will diagnose the abnormality.

As another example, the grouping module 111e may be configured for each photovoltaic device 3 having the most similar average power generation rate based on a trend of average power generation rate (data) for a period of time. Power plant grouping can proceed. This is grouped by photovoltaic devices (3) showing the most similar generation pattern over a period of time, so that one of the grouped photovoltaic devices (3) averages differently from other photovoltaic devices (3). If the amount of power generation out of the error range compared to the power generation rate is shown in the abnormality diagnosis unit 13 will be diagnosed as abnormal.

As another example, the grouping module 111e may group power plants by photovoltaic power generation devices 3 having the largest daily deviation of maximum generation amount considering the installed capacity based on a daily maximum generation amount trend (data) relative to the installed capacity for a certain period of time. You can proceed. The abnormality diagnosis unit 13 is a case where the deviation of the maximum power generation amount from the other solar power generators 3 in the group is out of the error range based on the data of the shortest period compared to the cumulative data or the average data. By diagnosing whether or not, a diagnosis can be made relatively quickly.

The group optimization module 111f is configured to update and optimize the group set by the grouping module 111e according to use, and includes abnormality information of the photovoltaic device 3 diagnosed by the abnormality diagnosis unit 13. The accuracy of the abnormality is compared to verify the accuracy, and the indexes are modified accordingly to allow regrouping. The group optimization module 111f modifies the failure repair index, the environmental index, and the characteristic index if the diagnosis result does not match the actual, so that the diagnosis result is consistent with the actual, and the correction of each index reflects the generation amount data. Can be adjusted from a large exponent. To this end, the group optimization module 111f may include a diagnostic verification module 111f-1, an index comparison module 111f-2, an index adjustment module 111f-3, and a group automatic change module 111f-4. Can be.

The diagnostic verification module 111f-1 is configured to verify the accuracy of the abnormality diagnosis, and diagnoses the abnormality of the photovoltaic device 3 by the abnormality diagnosis unit 13, and more specifically, the failure diagnosis unit 131 to be described later. By comparing the failure of the photovoltaic device (3) and the actual failure of the photovoltaic device (3) by) to verify. Therefore, the diagnostic verification module 111f-1 adjusts the indexes and proceeds to regrouping the results so that the result may match when the diagnosed failure does not coincide with the actual occurrence of the failure.

The index comparison module 111f-2 is configured to compare respective indices when the diagnosis result does not match the actual, index for changing the generation amount data to the largest ratio by comparing the failure repair index, the environmental index, and the characteristic index. From the index control module (111f-3) to be adjusted so that the index can be made.

The index control module 111f-3 is configured to adjust the respective indexes so that the diagnosis result of the fault by the abnormal diagnosis unit 13 matches the actual failure result, and is compared by the index comparison module 111f-2. Based on the results obtained, adjustments should be made starting with the index that has the greatest impact on the power generation data. However, the exponent adjustment module 111f-3 enables to predefine a range that can be adjusted for each index, and if the failure results do not coincide with the adjustment up to the corresponding range, the index having the next greatest influence is obtained. Adjust to match the results. Therefore, the index control module 111f-3 may adjust the indexes sequentially, and may adjust the range by changing the set range until the results match.

The group automatic change module 111f-4 automatically changes and regroups the group according to the index adjusted by the index control module 111f-3. Therefore, the group automatic change module 111f-4 can increase the accuracy of the abnormal diagnosis by automatically improving the accuracy of grouping as time passes.

The environmental reference grouping unit 113 is configured to set the same environmental region to the same group, and as described above, the grouping unit 11 groups the photovoltaic devices 3 in accordance with the power generation trend. In regions with severe differences or changes in weather, environment, and the like, priorities of power generation are prioritized, and regions with the same weather and environmental conditions are first grouped into the same group, and the group is segmented by the trend-based grouping unit 111 within the same group. You can also proceed. However, when the grouping is first performed by the environmental reference grouping unit 113, the power generation data purification module 111c may not proceed. As illustrated in FIG. 5, the environmental reference grouping unit 113 may include an environment information collecting module 113a and an environment area setting module 113b.

The environmental information collection module 113a is a configuration for collecting environmental information of a region where the photovoltaic device 3 is installed, and may include a fraction module 113a-1 and an environmental information extraction module 113a-2. .

The fraction module 113a-1 is configured to form a plurality of unit grids by dividing the area in which the photovoltaic device 3 is installed horizontally and vertically, and collecting environmental information for each unit grid. Allows you to collect accurate weather information at the point where) is located.

The environmental information extraction module 113a-2 is configured to extract environmental information for each unit grid, and extracts information on solar radiation, temperature, humidity, wind strength, and the like, which affect photovoltaic power generation. The environmental information extraction module 113a-2 may use an environmental sensor installed in each unit grid or use data of the Meteorological Agency, and environmental information may also be collected using an IoT network, preferably a small internet network. .

The environmental zone setting module 113b is configured to set the photovoltaic generators 3 in the same region as the group by the collected environmental information, and the same environmental zone is the same photovoltaic generator. When used, it means that the environmental factors such as the amount of sunshine, temperature, and wind strength are the same to produce the same amount of electricity. In addition, since the environmental information continuously changes, the environmental information is continuously changed according to the environment that is changed into a group set by the environmental zone setting module 113b.

The abnormality diagnosis unit 13 is configured to select a specific photovoltaic device 3 that represents an amount of power out of an error range within the grouped photovoltaic devices 3, and in addition to simply diagnosing the occurrence of a failure. Although a failure does not occur, the risk of failure may be diagnosed and predicted within a certain period of time, and even if not the risk of failure, the output of the photovoltaic device 3 may be detected and notified. To this end, the abnormality diagnosis unit 13 may include a failure diagnosis unit 131, a failure risk prediction unit 133, and a degradation detection unit 135 as shown in FIG. 6.

The failure diagnosis unit 131 is a configuration for diagnosing the failure of the photovoltaic device 3, the deviation of the amount of power generation within the photovoltaic devices 3 assigned to the same group by the grouping unit 11 is a certain error It is to be diagnosed as a failure for the photovoltaic device (3) out of range. Accordingly, the failure diagnosis unit 131 compares the total amount of power generation and today's power generation amount according to the grouping standard, and when any one of the daily cumulative generation amount, the average generation rate, and the maximum generation amount is out of an error range, the corresponding photovoltaic device 3 Should be diagnosed as a fault.

The failure risk prediction unit 133 does not occur to the extent that the error range is diagnosed as a failure, but if the error of a certain range lasts for a predetermined time to predict that there is a risk of failure within a certain period to inform it. To this end, the failure risk prediction unit 133 may calculate the risk index according to the error range and time, it may be set to notify the period by setting a high risk of failure according to the risk index. The failure risk prediction unit 133 includes an error monitoring module 133a, a duration measurement module 133b, a risk index calculation module 133c, and a failure prediction information notification module 133d.

The error monitoring module 133a is configured to continuously monitor the deviation of the amount of generation of the specific photovoltaic device 3 within the same group in the set range, and calculate a risk index according to the error degree and time. To be able. Here, the deviation of the generation amount may be any one of the cumulative generation amount, the average generation rate, and the maximum generation amount, as in the case of failure diagnosis, and the set range is lower than the error range diagnosed as the failure, or the risk of failure occurs if it continues for a predetermined time. This means high range.

The duration measurement module 133b is configured to measure the duration of time when an error corresponding to a range set by the error monitoring module 133a is detected, so that a risk index can be calculated according to the duration.

The risk index calculation module 133c is configured to calculate the risk index according to the degree of error and the duration, and the risk index may have a higher value as the error is larger and the duration is longer. Therefore, the higher the risk index calculated by the risk index calculation module 133c, the higher the risk of failure occurring soon.

The failure prediction information notification module 133d is configured to provide information about a time when a failure is likely to occur according to the risk index calculated by the risk index calculation module 133c. A high time point is set in advance so that information can be provided accordingly. The point of time information according to the risk index may be set based on experimental or historical data. For example, the risk index is divided into 10 sections and within 10 days for the highest section, and within 20 days for the second section. Each section of the risk index can be predicted and provided at a high risk of failure.

The degradation detection unit 135 does not correspond to failure diagnosis or failure risk prediction, but if the rate of change of power generation error exceeds a predetermined value and lasts for a predetermined time, it detects that the output of the photovoltaic device 3 is deteriorated. It can alert you. To this end, the degradation detection unit 135 may include a slope calculation module 135a, a threshold check module 135b, a time information measuring module 135c, and a degradation warning module 135d.

The slope calculation module 135a is configured to calculate a slope according to the rate of change of the amount of power generation error of the photovoltaic device 3 and includes a specific mode within the same group as the failure diagnosis unit 131 and the failure risk prediction unit 133. The rate of change of the power generation error is calculated. In addition, the slope calculation module 135a also calculates the rate of change of the power generation error according to the grouping criterion, and when the slope exceeds the threshold value, the duration thereof is measured so that an alarm for deterioration can be made.

The threshold check module 135b is configured to check whether the slope calculated by the slope calculation module 135a exceeds a set threshold value, so that the duration can be measured when the threshold value is exceeded.

The time information measuring module 135c may measure the duration of the excessive slope when the rate of change of the error in the threshold checking module 135b exceeds the threshold.

The degradation alarm module 135d determines that the deterioration of the photovoltaic device 3 is in progress when a slope exceeding a threshold value lasts for a predetermined time, so as to notify a worker and the like, and to manage it. To be made. Therefore, the present invention can predict failures or detect deterioration even before failures occur, so that the operation and maintenance of the photovoltaic device 3 can be facilitated and the cost can be saved.

The photovoltaic device 3 is an apparatus for producing electrical energy by using sunlight (light energy), and the solar modules 31 of the minimum unit are gathered to form a string 32 and an array 33. In general, arrays 33, also called solar panels, are clustered to form a photovoltaic device 3. As described above, the photovoltaic device 3 does not only mean a photovoltaic power station installed on the ground, but is a building integrated photovoltaic device (BIPV) installed on a roof of a building, a water surface, or an outer wall of a building. It includes various types of photovoltaic device (3) including.

In particular, the photovoltaic device 3 is connected to a plurality of strings 32 in the array 33 of each photovoltaic device 3 to each of the photovoltaic devices 3 to identify when an abnormality occurs in the power generation device. It may further include a; string leveling unit 36 for minimizing the power deviation between the strings 32 due to the shading or failure in the photovoltaic module 31. Accordingly, the present invention allows the abnormal diagnosis of the photovoltaic device 3 by the abnormal diagnosis unit 13, and equalizes the power between the strings 32 until the corrective action is taken. By generating and supplying power, it is possible to minimize the damage caused by the abnormality of the photovoltaic device (3). (For reference, the photovoltaic module 31 is connected in series to form a string 32 to form a series circuit, the array 32 is formed by connecting the plurality of string 32 in parallel, the direct current generated using solar light The inverter 34 converts the power into AC power and supplies it to the customer, and the connection panel 35 for facilitating the connection between the array 33 and the inverter 34 and performing various protection functions includes solar light. The conventional configuration of the generator 3 will be omitted.)

The string leveling unit 36 is connected to the plurality of strings 32, respectively, to minimize the power variation between the strings 32 due to the shading or failure in the specific module 31, and thus, may efficiently output power. Do it. In particular, the string leveling unit 36 provides a power storage unit 363 connected to each string 32 to minimize the power deviation through charging and discharging with each string 32, the string between the strings with a simple configuration only The leveling of power can be made easy. In addition, the string leveling unit 36 may accumulate and store information about charge / discharge operation of each string 32 to detect the string 32 in which frequent output decreases. To this end, the string leveler 36 compensates or compensates the power of the plurality of strings 32 and the power measuring unit 361 for measuring output currents or voltages of the plurality of strings 32, as shown in FIG. 8. The power storage unit 363 absorbing electric power, the control unit 365 controlling the power storage unit 363 based on the data of the power measuring unit 361, and the string diagnosis detecting the string 32 having an abnormality. It may include a portion 367.

The power measuring unit 361 measures an output current or voltage of each of the plurality of strings 32, and for this purpose, each of the strings 32 of the plurality of strings 32 is provided through sensors 361a separately installed in the plurality of strings 32. The output current and / or voltage are measured to transmit information about the output power of each string 32 to the controller 365. As in the example shown in FIG. 8, the sensors 361a are connected to each string 32 stage to measure the current and / or voltage output from each string 32 for each of the plurality of strings 32. It may be formed of a current and / or a voltage sensor is installed, the power measuring unit 361 is the control unit 365 for information on the output power for each string 32 based on the information transmitted from the sensor (361a) ). Through the information measured and transmitted by the power measurement unit 361, whether or not the steady state power is produced and output from each string 32 or the failure or shadow of the specific photovoltaic module 31 in the specific string 32, etc. It is possible to check the output power amount for each string 32, such as whether the amount of power generated is reduced and the reduced power is output.

The power storage unit 363 is configured to compensate or absorb power for each of the plurality of strings 32. For this purpose, an energy storage device (ESS) capable of charging and discharging power may be connected to each of the plurality of strings 32. By compensating or absorbing power for a particular string 32, power variations between the paralleled strings 32 that make up the array 33 can be minimized or eliminated.

The control unit 365 is configured to control the power storage unit 363 based on the data of the power measuring unit 361, and only targets a string whose power generation amount is reduced due to a shade or a failure of a specific module. In the conventional technology of supplying compensating power through a power compensator, a large amount of photovoltaic power generation is required because power must be stored in a separate power storage device (ESS) in advance to compensate for power in a string in which generated power is reduced. In the facility, the power storage device (ESS) for the power compensator is also provided in a large capacity (for example, when compensating for 1 hour with 10 kW of power generation, the 10 kWh battery capacity should be charged. In order to be prepared, a power storage device (ESS) with a larger battery capacity than the photovoltaic capacity must be provided for power compensation). The cost and economic efficiency is greatly reduced, as well as the need to provide a separate system connection or configuration (such as maintenance solar panel) for charging the power storage (ESS) bar, in the present invention simply output power is Rather than solving the problem with power compensation for the reduced string 32 alone, the solution is presented in such a way as to minimize or eliminate power variations between each string 32 when viewed throughout the array 33. To this end, the control unit 365 is connected to the storage state determination module 365a for determining the state of charge of the power storage unit 363 and the charge state of the power storage unit 363 determined by the storage state determination module 365a. According to the storage control module (365b) for determining whether to discharge or charge the power storage unit and the discharge or charge determined by the storage control module (365b) to determine the string 32 to compensate or absorb the power to determine It may include a connection control module 365c for connecting with the power storage unit 363. That is, depending on the state of the charge amount of the power storage unit 363 connected to each string 32 end in order to minimize the power deviation between each string 32, if the charge amount is sufficient, the power to the string 32, the output power is lowered Compensation power is supplied from the storage unit 363 (that is, discharge of the power storage unit 363) to increase the power of the corresponding string 32 to eliminate the power deviation between the entire strings 32, and if the charging amount is insufficient, the output Power is absorbed from the high-power strings 32 to the power storage unit 363 (ie, charging of the power storage unit 363) to lower the power of the string 32 to reduce the power variation between the entire strings 32. By applying the elimination method, since the power storage unit 363 is not a structure that must continue to supply the compensation power, the power storage unit 363 does not need to be provided with a large capacity, and also the charge of the power storage unit 363 Discharges within the array 33 It is so luer is not even have to be provided with a power storage unit (363) configured to separate system only for charging.

The storage state determination module 365a is configured to determine the state of the charge amount of the power storage unit 363, and is sufficient to supply the charge amount of the power storage unit 363 through the storage state determination module 365a to compensate power. When the information on whether the state is provided to determine whether to discharge or charge the power storage unit 363 in the storage control module 365b to be described later.

The storage control module 365b is configured to determine whether to discharge or charge the power storage unit 363 according to the state of charge of the power storage unit 363 determined by the storage state determination module 365a. The control module 365b may include a discharge control module 365b-1 for compensating power to the string 32 in which the output power is reduced through the discharge of the power storage unit when the amount of charge of the power storage unit 363 is sufficient; When the charge amount of the power storage unit 363 is insufficient, the charge control module 365b-2 to absorb the power of the string 32 having a high output power through the charge of the power storage unit to minimize the power deviation between the strings 32 ) May be included.

The discharge control module 365b-1 is based on the case where the charge amount of the power storage unit 363 determined by the storage state determination module 365a is sufficient. In the configuration to control the power to be compensated, the discharge control module (365b-1) by compensating the power to the string 32 output power is reduced through the discharge of the power storage unit to reduce the power deviation between the entire string 32 When the control is determined to be minimized, the connection control module 365c, which will be described later, determines the string 32 to compensate for power and connects the power storage unit 363 to control the discharge control module 365b-1. Should be made smoothly.

When the charge control module 365b-2 is insufficient in the amount of charge of the power storage unit 363 determined by the storage state determination module 365a, the power of the string 32 having a high output power through charging of the power storage unit is based on this. In this configuration to control the absorption, the charge control module (365b-2) through the charging of the power storage unit to absorb the power from the string 32 having a high output power to minimize the power deviation between the entire string (32) When the control is determined so that the connection control module 365c, which will be described later, determines the string 32 to absorb power and connects the power storage unit 363 to smoothly control the charging control module 365b-2. To make it happen.

The connection control module 365c is configured to determine a string 32 for compensating for power or absorbing power according to whether discharge or charging determined by the storage control module 365b is connected to the power storage unit 363. In addition, the amount of discharge or charge for each string 32 is determined so that the power output from each string 32 is the same, so that the connection can be made. To this end, the connection control module (365c) is the connection control module (365c) is the output power of the plurality of string 32 when the discharge control module (365b-1) discharges the power storage unit (363) Compensation connection module 365c-1 for identifying the degraded string 32 and connecting it to the power storage unit 363 and a plurality of cases when the charge control module 365b-2 charges the power storage unit 363. The string 32 may include an absorption connection module 365c-2 for connecting the string 32 having a high output power to the power storage 363.

The compensation connection module 365c-1 specifies the string 32 in which the output power is lowered among the plurality of strings 32 when the discharge control module 365b-1 discharges the power storage unit 363. For example, referring to FIG. 8, the output power is lowered due to shading or failure of a specific photovoltaic module 31 in the string 32, and the remaining string 32 is connected to the power storage unit 363. 2) In the case of normal output power in 2-4, when the amount of charge of the power storage unit 363 determined by the storage state determination module 365a is sufficient, the electric power storage in the discharge control module 365b-1 is based on this. When the control is determined to compensate for the power of the string 32 in which the output power is reduced through negative discharge, the compensating connection module 365c-1 specifies the string 32 in which the output power is reduced and then powers the power. The compensation power from the power storage unit 363 is connected to the storage unit 363 and the string 32 Provided as 1 to minimize power variation between the entire string 32.

The absorption connection module 365c-2 stores the string 32 having a high output power among the plurality of strings 32 when the charging control module 365b-2 charges the power storage 363. For example, referring to FIG. 8, the output power is lowered due to the shading or failure of the specific photovoltaic module 31 in the string 32, and the remaining strings 32 to 2-6 are connected to the 363. In the case of 4, when the output capacity of the power storage unit 363 determined by the storage state determination module 365a is insufficient, the charging control module 365b-2 charges the power storage unit based on this. When the control is determined to absorb power from the strings 32 to 2-4 having high output power, the absorption connection module 365c-2 specifies power strings 2 to 4 having high output power to store power. In connection with the unit 363, the power storage unit 363 absorbs power from the string 32 2-4 Charging minimizes power variation between the entire strings 32.

As described above, the string leveling unit 36 according to the present invention is different from the conventional one, according to the state of charge of the power storage unit 363 connected to each string 32 stage in order to minimize the power deviation between the strings 32. If the charging amount is sufficient, the compensating power is supplied from the power storage unit 363 to the string 32 in which the output power is lowered to increase the power of the corresponding string 32, thereby eliminating the power variation between the entire strings 32. In case of lack, the power is absorbed from the strings 32 having high output power to the power storage unit 363 to lower the power of the string 32 to eliminate the power variation between the entire strings 32. Since the storage unit 363 does not have to continuously supply compensation power, the power storage unit 363 does not need to be provided with a large capacity, and charging and discharging of the power storage unit 363 is performed by the array 33. of mine Therefore be made up power storage unit 363 does not have to be provided with a separate system to configure only for filling, without going through the power conversion efficiency is nopyige by charge and discharge is true.

The string diagnosis unit 367 accumulates and stores information on charging and discharging operations of the strings 32 through the string leveling unit 36 so as to diagnose an abnormality of the string 32 where frequent output decreases. As shown in FIG. 10, the connection string detection module 367a, the charge / discharge recognition module 367b, the charge / discharge measurement module 367c, the abnormality index calculation module 367d, and the abnormality index accumulation module 367e. The abnormal string diagnosis module 367f may be included.

The connection string detection module 367a is configured to detect the string 32 connected to the power storage unit 363 by the connection control module 365c. Allow to be measured.

The charging / discharging recognition module 367b is configured to detect charging / discharging of the string sensed by the connection string sensing module 367a and to receive information on the operation of the storage control module 365b.

The charge / discharge measurement module 367c measures an amount of power charged and discharged with respect to the sensed string. The charge / discharge measurement module 367c is supplied to each string 32 from the power storage unit 363 or the power storage unit 363 at each string 32. Measure the amount of power supplied to the unit.

The abnormality index calculation module 367d is configured to calculate an abnormality index of each string 32 by measuring the amount of power charged and discharged from each string 32 to the power storage unit 363. The degree to which the output of each string 32 falls is shown. Therefore, when the abnormality index calculation module 367d supplies power to the specific string 32 from the power storage unit 363, that is, when the discharge occurs, the abnormal index calculation module 367d indicates that an abnormality has occurred in the corresponding string 32. The abnormality index for the specific string 32 may be calculated according to the amount of power. On the other hand, when the charging of the power storage unit 363 from the specific string 32 means that the abnormality has occurred in the remaining string 32 other than the string 32, so that the charge amount through the specific string 32 Accordingly, it is possible to calculate the abnormality index of the remaining string (32).

The abnormality index accumulation module 367e is configured to accumulate and store the abnormality index calculated by the abnormality index calculation module 367d, so that the abnormality index calculated for each charge / discharge of each string 32 is added. do.

The abnormal string diagnosis module 367f is configured to diagnose an abnormality of the corresponding string 32 when the accumulated abnormality index for each string 32 exceeds a set value. Allow action to be taken.

In the above, the Applicant has described various embodiments of the present invention, but these embodiments are merely one embodiment for implementing the technical idea of the present invention, and any changes or modifications may be made to the present invention as long as the technical idea of the present invention is implemented. It should be interpreted as falling within the scope of.

1: diagnostic server
11: grouping unit 111: trend-based grouping unit 111a: generation data collection module
111b: power generation data processing module 111c: power generation data purification module
111d: power generation data correction module 111e: grouping module 111f: group optimization module
123: environmental standard grouping unit 113a: environmental information collection module
133b: environmental area setting module
13: abnormal diagnosis
131: failure diagnosis unit 133: failure risk prediction unit 135: deterioration detection unit
3: solar power unit
31: solar module 32: string 33: array
34: inverter 35: connection board 36: string leveling unit
361: power measurement unit 361a: sensor
363: power storage unit 365: control unit 365a: storage status determination module
365b: storage control module 365b-1: discharge control module 365b-2: charge control module
365c: connection control module 365c-1: compensation connection module 365c-2: absorption connection module
367: string diagnosis unit 367a: connection string detection module 367b: charge and discharge recognition module
367c: Charge / discharge measurement module 367d: Abnormal index calculation module
367e: Abnormal index accumulation module 367f: Abnormal string diagnostic module

Claims (13)

Photovoltaic apparatus for generating power using solar light;
And a diagnostic server configured to receive power generation data of the photovoltaic device using an IoT network and diagnose a state of the photovoltaic device based on the transmitted power generation data.
The diagnostic server,
It is characterized by diagnosing the status of a specific photovoltaic device by grouping photovoltaic devices with similar power generation trends in the past and comparing the difference in power generation within the group.
The diagnostic server,
A grouping unit for grouping photovoltaic devices, and an abnormality diagnosis section for selecting a specific photovoltaic device representing a generation amount out of an error range within the grouped photovoltaic devices,
The grouping unit,
A power generation data collection module collecting power generation information of the photovoltaic device; A power generation data processing module for processing power generation information through information such as failure history and maintenance history of each photovoltaic device; A power generation data purification module for purifying processed power generation information according to weather information and environmental information of regions where each photovoltaic device is located; A power generation data correction module for correcting purified power generation data according to facility characteristics of each solar power generation device; And a grouping module for grouping photovoltaic devices by applying a clustering algorithm to the corrected power generation data.
The abnormal diagnosis portion,
A fault diagnosis unit for diagnosing a specific photovoltaic device as a fault indicating a generation amount outside a certain error range within the same group;
If the amount of generation of a certain range of errors does not deviate from the error range diagnosed as the fault, but the amount of generation of the specific range of errors continues for a certain time, the risk index is calculated using the error degree and duration, and the estimated time to predict the failure will be reported according to the risk index. Failure risk prediction unit;
It includes a deterioration detection unit for detecting a deterioration of a specific photovoltaic device by analyzing the rate of change of power generation error, although the signal about the failure is not output;
The failure risk prediction unit,
An error monitoring module that monitors errors in the amount of generation of photovoltaic devices having a certain error range or more, a duration measurement module that measures the duration of the error, and a risk index that calculates a risk index according to the degree and duration of the error. A calculation module and a failure prediction information notification module for calculating and informing a failure prediction time point according to a risk index,
The deterioration detection unit,
Gradient calculation module for calculating the slope according to the degree of change of the error over a certain period, a threshold check module for measuring the degree of inclination is out of the set threshold, time information measuring module for measuring the time out of the threshold, the slope is the threshold It includes a deterioration alarm module that calculates the degree of deterioration according to the degree and duration of deviation from the deterioration to notify if the degree of deterioration is out of a certain range,
The photovoltaic device includes a string leveling unit connected to each of the plurality of strings in each of the arrays of photovoltaic devices to minimize power deviation between strings when an abnormality occurs in the photovoltaic device.
The string leveling unit,
And a power measuring unit for measuring output currents or voltages of a plurality of strings, a power storage unit for compensating or absorbing power of a plurality of strings, and a controller for controlling the operation of the power storage unit based on data of the power measuring unit. and,
The control unit,
A storage state determination module for determining a state of charge of the power storage unit, a storage control module for controlling charging and discharging of the power storage unit according to the state of charge of the power storage unit, and connection of the power storage unit and the string according to whether each string is charged or discharged It includes a connection control module for adjusting the,
The storage control module,
A discharge control module for supplying power to the string of which the output is degraded when the amount of charge of the power storage unit is sufficient, and a charge control module for minimizing power deviation between strings by absorbing the power of the string with high output when the amount of charge of the power storage unit is insufficient. Including,
The connection control module,
Compensation connection module for connecting the string to receive power when the electric power storage unit is discharged by the discharge control module and the power storage unit, and the string and power storage unit to supply power when the power storage unit is charged by the charge control module IoT-based photovoltaic diagnostic system comprising an absorption connection module for connecting.

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