Photovoltaic power generation monitoring system using string characteristics

Abstract

The present invention relates to a photovoltaic power generation monitoring system and, more specifically, to a photovoltaic power generation monitoring system using string characteristics, wherein the system classifies strings in consideration of characteristics of photovoltaic modules included in the respective strings and characteristics of inverters connected to the strings, and detects abnormal states of the strings by using the pattern of change in a string state indicator in the classified strings, thus making it possible to increase the accuracy of photovoltaic power generation monitoring.

Description

Solar power generation monitoring system utilizing string characteristics

[0001] The present invention relates to a photovoltaic power generation monitoring system, and more specifically, to a photovoltaic power generation monitoring system utilizing string characteristics that can improve the accuracy of photovoltaic power generation monitoring by classifying strings by considering the characteristics of photovoltaic modules included in each string and the characteristics of inverters connected to the strings, and by detecting abnormal conditions of strings using the change pattern of indicators regarding string conditions within the classified strings.

[0002] Solar power generation, a sector of renewable energy, has recently seen a surge in demand due to its numerous advantages, and technologies aimed at increasing generation efficiency have also advanced significantly. In particular, solar power generation devices are currently being installed in various forms, including on building rooftops and on water, as well as in Building Integrated Photovoltaics (BIPV) systems that are integrated with the building itself.

[0003] Therefore, as the specifications and characteristics of each solar power generation device differ, it is difficult to accurately determine their status; consequently, it is difficult to accurately monitor failures or hazardous conditions of the solar power generation devices.

[0004] Previously, as shown in the patent document below, the condition of a solar power generation device was diagnosed only by methods such as comparing it with a reference value, which failed to reflect the specifications of various power generation devices and resulted in significantly lower accuracy, rendering it completely ineffective in preventing fires and other incidents.

[0005] (Patent Document) Registered Patent Publication No. 10-2292748 (Registered Aug. 17, 2021) "Solar photovoltaic power generation and control system, and method of operating a solar photovoltaic power generation and control system"

[0006] The present invention has been devised to solve the above-mentioned problems,

[0007] The present invention aims to provide a solar power generation monitoring system that can improve the accuracy of solar power generation monitoring by classifying strings by considering the characteristics of solar modules included in each string and the characteristics of inverters connected to the strings, and by detecting abnormal conditions of strings using the change pattern of indicators regarding string conditions within the classified strings.

[0008] The present invention is implemented by an embodiment having the following configuration to achieve the aforementioned objective.

[0009] According to one embodiment of the present invention, a photovoltaic power generation monitoring system utilizing string characteristics according to the present invention comprises a string classification unit that classifies strings according to device information regarding strings of a photovoltaic power generation facility, a pattern analysis unit that analyzes the change patterns of indicators affecting failure in each classification of strings, a pattern classification unit that classifies strings having similar change patterns of each indicator within strings of the same classification, and an abnormal string detection unit that detects strings with different change patterns as abnormal states by comparing the change patterns of indicators between strings of the same classification, wherein the string classification unit classifies strings according to the specifications and number of photovoltaic modules included in the string and the capacity of the inverter connected to the string.

[0010] According to another embodiment of the present invention, in a photovoltaic power generation monitoring system utilizing string characteristics according to the present invention, the string classification unit includes a solar radiation collection module that collects solar radiation information for a string and a module temperature collection module that collects temperature information of a photovoltaic module, and the string selection module is characterized by classifying strings in which the solar radiation and module temperature per unit period are within a similar range into the same string group.

[0011] According to another embodiment of the present invention, in a photovoltaic power generation monitoring system utilizing string characteristics according to the present invention, the pattern analysis unit includes an imbalance pattern analysis unit that analyzes a change pattern of the degree of imbalance of photovoltaic modules within the string, and the imbalance pattern analysis unit includes a voltage imbalance calculation unit that calculates the degree of imbalance regarding voltage between modules constituting the string, a fluctuation abnormality calculation unit that calculates the degree of abnormality according to voltage and current fluctuations of the string, an imbalance index calculation unit that calculates an imbalance index representing the degree of imbalance between modules according to the degree of voltage imbalance and the degree of fluctuation abnormality, and an imbalance index analysis unit that analyzes the change pattern of the imbalance index.

[0012] According to another embodiment of the present invention, in a photovoltaic power generation monitoring system utilizing string characteristics according to the present invention, the voltage imbalance calculation unit comprises a string voltage measurement module for measuring the voltage of power output from a string, a module voltage measurement module for measuring the voltage at one specific module within the string, and a voltage imbalance coefficient calculation module for calculating a voltage imbalance coefficient indicating the degree of voltage imbalance between modules by subtracting the value obtained by multiplying the number of photovoltaic modules included in the string by the voltage of the specific module from the string voltage.

[0013] According to another embodiment of the present invention, in a photovoltaic power generation monitoring system utilizing string characteristics according to the present invention, the voltage imbalance calculation unit is characterized by including a coefficient correction module that corrects the voltage imbalance coefficient according to solar irradiance and module temperature.

[0014] According to another embodiment of the present invention, in a photovoltaic power generation monitoring system utilizing string characteristics according to the present invention, the fluctuation anomaly calculation unit comprises a voltage measurement module that measures a voltage output from a string for a certain period of time, a current measurement module that measures a current output from a string for a certain period of time, and a power fluctuation coefficient calculation module that calculates a power fluctuation coefficient indicating the degree of fluctuation of voltage and current by calculating the value of the ratio of voltage change amount to current change amount for the ratio of voltage to current per unit time for a certain period of time and the average value.

[0015] According to another embodiment of the present invention, in a photovoltaic power generation monitoring system utilizing string characteristics according to the present invention, the fluctuation anomaly calculation unit is characterized by including a coefficient adjustment module that increases the scale while changing the standard for the steady state of the power fluctuation coefficient to 0.

[0016] According to another embodiment of the present invention, in a photovoltaic power generation monitoring system utilizing string characteristics according to the present invention, the coefficient adjustment module is characterized by adjusting the power fluctuation coefficient according to Equation 1 to calculate the adjusted power fluctuation coefficient. (Equation 1) Pf = (1-Pd)*10 (wherein Pf is the adjusted power fluctuation coefficient, Pd is the initial power fluctuation coefficient)

[0017] According to another embodiment of the present invention, in a photovoltaic power generation monitoring system utilizing string characteristics according to the present invention, the imbalance index calculation unit comprises a voltage imbalance coefficient loading module that retrieves a voltage imbalance coefficient, a power fluctuation coefficient loading module that retrieves a power fluctuation coefficient, and an imbalance index calculation module that calculates an imbalance index representing the degree of imbalance in output between modules by multiplying the voltage imbalance coefficient and the power fluctuation coefficient.

[0018] According to another embodiment of the present invention, a photovoltaic power generation monitoring system utilizing string characteristics according to the present invention includes a module imbalance diagnosis unit that diagnoses the cause of imbalance when string imbalance is detected by comparing the change pattern of the imbalance index through the abnormal string detection unit, and the module imbalance diagnosis unit includes an imbalance index loading module that loads the imbalance index and an abnormal information diagnosis module that diagnoses the cause of imbalance according to the imbalance index, wherein the abnormal information diagnosis module diagnoses the module as damage, shading, or contamination when the imbalance index is positive, and diagnoses PID, cell cracking, or insulation resistance degradation when the imbalance index is negative.

[0019] According to another embodiment of the present invention, in a photovoltaic power generation monitoring system utilizing string characteristics according to the present invention, the pattern analysis unit includes a power generation pattern analysis unit that analyzes a change pattern regarding the error between the predicted power generation amount and the measured power generation amount of the string, and the power generation pattern analysis unit includes a power generation prediction unit that predicts the power generation amount for each string of a photovoltaic power generation device, a power generation measurement unit that measures the power generation amount of the current string in real time, and an error analysis unit that analyzes a change pattern regarding the error between the predicted and measured power generation amounts, and the error analysis unit is characterized by analyzing a change pattern regarding the area of ​​each region on the IV coordinate plane according to the predicted power generation amount and the measured power generation amount.

[0020] According to another embodiment of the present invention, in a photovoltaic power generation monitoring system utilizing string characteristics according to the present invention, the error analysis unit comprises: an Isc area calculation module that calculates the area of ​​the Isc area formed by a line connecting points on the IV coordinate plane according to the voltage and current values ​​of the predicted power generation and the measured power generation, and a line connecting points indicating short-circuit current; a Voc area calculation module that calculates the area of ​​the Voc area formed by a line connecting points on the IV coordinate plane according to the voltage and current values ​​of the predicted power generation and the measured power generation, and a change pattern analysis module that analyzes the change pattern of the area of ​​the Isc area or the area of ​​the Voc area.

[0021] According to another embodiment of the present invention, a photovoltaic power generation monitoring system utilizing string characteristics according to the present invention includes a power generation diagnosis unit that diagnoses the cause of an abnormality in power generation reduction when an abnormal string is detected because the change pattern of the area of ​​the Isc area or the area of ​​the Voc area is different. The power generation diagnosis unit is characterized by including an area area loading module that loads information regarding the areas of the Isc area and the Voc area, an area area comparison module that compares the loaded areas of the Isc area and the Voc area, and a reduction cause detection module that detects the cause of power generation reduction according to the result of the area comparison.

[0022] According to another embodiment of the present invention, in a photovoltaic power generation monitoring system utilizing string characteristics according to the present invention, the degradation cause detection module is characterized by determining an abnormality caused by an increase in series resistance when the area of ​​the Isc region is larger than the area of ​​the Voc region, and determining an abnormality caused by a decrease in module parallel resistance when the area of ​​the Voc region is larger than the area of ​​the Isc region.

[0023] According to another embodiment of the present invention, in a photovoltaic power generation monitoring system utilizing string characteristics according to the present invention, the pattern analysis unit includes an insulation resistance pattern analysis unit that analyzes a change pattern of an index regarding the insulation resistance of the string, and the insulation resistance pattern analysis unit includes a change pattern analysis unit that analyzes a change pattern regarding the degree of change in insulation resistance over a set period of time, and the change pattern analysis unit includes a first insulation resistance measurement module that measures the insulation resistance after an initial certain period of time has elapsed, a second insulation resistance measurement module that measures the insulation resistance after a set period of time has elapsed from the initial certain period of time, a change index calculation module that calculates a change index indicating the degree of change in insulation resistance according to the ratio of the second insulation resistance to the first insulation resistance, and an index pattern analysis module that analyzes the change pattern of the calculated change index.

[0024] According to another embodiment of the present invention, in a photovoltaic power generation monitoring system utilizing string characteristics according to the present invention, the pattern analysis unit includes an insulation resistance pattern analysis unit that analyzes a change pattern of an indicator regarding the insulation resistance of the string, and the insulation resistance pattern analysis unit includes a kick index analysis unit that analyzes a change pattern of a kick index indicating the degree of rapid change of insulation resistance per unit time during a set time period, and the kick index analysis unit includes a voltage application module that applies voltage to the string for a certain period of time, an insulation resistance measurement module that measures insulation resistance at unit time intervals according to the voltage application, a kick index calculation module that calculates a kick index indicating the degree of change of insulation resistance per unit time during a certain period of time, and a kick index change analysis module that analyzes a change pattern of the calculated kick index.

[0025] According to another embodiment of the present invention, in a photovoltaic power generation monitoring system utilizing string characteristics according to the present invention, the kick index calculation module is characterized by calculating a time kick index by the following mathematical formula 2.

[0026] (Mathematical Formula 2)

[0027]

[0028] According to another embodiment of the present invention, in a photovoltaic power generation monitoring system utilizing string characteristics according to the present invention, the kick index analysis unit is characterized by including a voltage adjustment module that allows the insulation resistance to be measured while changing the voltage in steps.

[0029] According to another embodiment of the present invention, in a photovoltaic power generation monitoring system utilizing string characteristics according to the present invention, the kick index calculation module is characterized by calculating the voltage kick index according to the following mathematical formula 3.

[0030] (Mathematical Formula 3)

[0031]

[0032] The present invention can achieve the following effects through the combination and usage relationship of the embodiments described above and the configuration described below.

[0033] The present invention has the effect of improving the accuracy of solar power generation monitoring by classifying strings by considering the characteristics of the photovoltaic modules included in each string and the characteristics of the inverter connected to the string, and by detecting abnormal conditions of the strings using the change pattern of indicators regarding the string condition within the classified strings.

[0034] FIG. 1 is a block diagram showing the configuration of a photovoltaic power generation monitoring system utilizing string characteristics according to an embodiment of the present invention.

[0035] FIG. 2 is a block diagram showing the configuration of the string classification unit.

[0036] Figure 3 is a block diagram showing the configuration of the pattern analysis unit.

[0037] Figure 4 is a block diagram showing the configuration of the unbalance pattern analysis unit.

[0038] Figure 5 is a block diagram showing the configuration of the voltage imbalance calculation unit.

[0039] Figure 6 is a reference diagram showing an example of voltage measurement by the voltage imbalance calculation unit.

[0040] Figure 7 is a block diagram showing the configuration of the fluctuation anomaly calculation unit.

[0041] Figure 8 is a graph showing an example of voltage change according to string condition.

[0042] FIG. 9 is a block diagram showing the configuration of the imbalance index calculation unit

[0043] FIG. 10 is a block diagram showing the configuration of the power generation pattern analysis unit.

[0044] FIG. 11 is a block diagram showing the configuration of the power generation prediction unit.

[0045] FIG. 12 is a block diagram showing the configuration of the error analysis unit

[0046] FIG. 13 is a block diagram showing the configuration of the insulation resistance pattern analysis unit.

[0047] Figure 14 is a graph showing an example of a change index.

[0048] Figure 15 is a graph showing an example of insulation resistance measurement by the kick index analysis unit.

[0049] FIG. 16 is a block diagram showing the configuration of the module imbalance diagnosis unit.

[0050] Figure 17 is a reference diagram showing the operating point on the IV curve according to the unbalance index.

[0051] FIG. 18 is a block diagram showing the configuration of the power generation diagnosis unit.

[0052] FIG. 19 is a reference diagram showing an IV graph used by the power generation diagnosis unit.

[0053] Explanation of symbols used in drawings

[0054] 1: String Classification Unit 2: Pattern Analysis Unit

[0055] 21: Unbalance Pattern Analysis Unit 211: Voltage Unbalance Calculation Unit

[0056] 212: Fluctuation Anomaly Calculation Unit 213: Imbalance Index Calculation Unit

[0057] 214: Imbalance Index Analysis Department 22: Power Generation Pattern Analysis Department

[0058] 221: Power Generation Prediction Unit 222: Power Generation Measurement Unit

[0059] 223: Error Analysis Department 23: Insulation Resistance Pattern Analysis Department

[0060] 231: Change Pattern Analysis Department 232: Kick Index Analysis Department

[0061] 3: Pattern Classification Unit 4: Abnormal String Detection Unit

[0062] 5: Module Imbalance Diagnosis Unit 6: Power Generation Diagnosis Unit

[0063] Hereinafter, preferred embodiments of a photovoltaic power generation monitoring system utilizing string characteristics according to the present invention will be described in detail with reference to the attached drawings. In describing the present invention below, if it is determined that a detailed description of known functions or configurations may unnecessarily obscure the essence of the present invention, such detailed description will be omitted. Throughout the specification, when a part is described as "including" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components. Furthermore, terms such as "...part," "...module," etc., described in the specification refer to a unit that processes at least one function or operation, and this may be implemented in hardware, software, or a combination of hardware and software.

[0064] A solar power generation monitoring system utilizing string characteristics according to an embodiment of the present invention is described with reference to FIGS. 1 to 19. The solar power generation monitoring system comprises: a string classification unit (1) that classifies strings according to device information regarding strings of a solar power generation facility; a pattern analysis unit (2) that analyzes the change patterns of indicators affecting failure in each classification of strings; a pattern classification unit (3) that classifies strings having similar change patterns of each indicator within strings of the same classification; an abnormal string detection unit (4) that detects strings with different change patterns as abnormal by comparing change patterns of indicators between strings of the same classification; a module imbalance diagnosis unit (5) that diagnoses the cause of imbalance when string imbalance is detected by comparing the change patterns of the imbalance index through the abnormal string detection unit; and a power generation diagnosis unit (6) that diagnoses the cause of abnormality due to a decrease in power generation.

[0065] The present invention relates to a system for diagnosing abnormal conditions in photovoltaic power generation, more precisely, abnormal conditions of strings. First, strings having similar characteristics are classified according to the characteristics of each string, and then, strings in which abnormalities have occurred are detected using the change patterns of indicators within the classified strings. Here, "indicators" refer to factors that affect the failure of a photovoltaic power generation device and may refer to values ​​indicating an imbalance between modules within the string, a state of reduced power generation, or a state of reduced insulation resistance. Accordingly, the present invention enables accurate diagnosis of abnormal conditions and allows for the diagnosis of the causes of imbalance and reduced power generation, thereby enabling a rapid and accurate response to abnormal conditions.

[0066] The above-mentioned solar power generation monitoring system can enable remote monitoring of multiple solar power generation facilities, and can also enable monitoring by receiving voltage, current, insulation resistance, etc., measured through junction boxes, remotely.

[0067] The string classification unit (1) is configured to classify strings according to characteristics, and classifies strings with similar basic device information and status information into the same string group. The string classification unit (1) may include a module specification collection module (11), a module count collection module (12), a solar radiation collection module (13), a module temperature collection module (14), an inverter capacity collection module (15), and a string selection module (16).

[0068] The above module specification collection module (11) is configured to collect specification information of a solar module included in a string, and can collect and store information such as the manufacturer and item in advance.

[0069] The above module count collection module (12) is configured to collect information on the number of solar modules included in the string, and can collect and store information on the number of solar modules connected in series in advance.

[0070] The above solar radiation collection module (13) is configured to collect solar radiation information for a string, and can be measured and stored through a separate sensor.

[0071] The above module temperature collection module (14) is configured to collect temperature information of a solar module and can receive and store information from a separate sensor that measures the module temperature.

[0072] The above inverter capacity collection module (15) is configured to collect information regarding the capacity of the inverter to which the string is connected, and to collect and store information regarding the inverter to which the string of each solar power generation facility is connected in advance.

[0073] The string selection module (16) is configured to select strings with similar conditions and classify them into the same string group. First, it classifies similar strings within a certain range according to the specifications of the solar module, the number of modules, and the inverter capacity. Additionally, the string selection module (16) can classify strings with similar solar irradiance and module temperature per unit period into the same string group. For example, it can classify strings with similar daily average solar irradiance and module temperature within a certain range into the same string group.

[0074] The pattern analysis unit (2) is configured to analyze the change pattern of an indicator that affects the failure of the string, and can analyze the change pattern regarding the degree of imbalance between modules within the string, the change pattern regarding the degree of reduction in power generation, and the change pattern regarding the degree of change in insulation resistance. To this end, the pattern analysis unit (2) may include an imbalance pattern analysis unit (21), a power generation pattern analysis unit (22), and an insulation resistance pattern analysis unit (23).

[0075] The above-mentioned imbalance pattern analysis unit (21) is configured to analyze a change pattern regarding the degree of imbalance of the solar modules within the string, and to analyze a comprehensive change pattern of the degree of imbalance by comprehensively considering the degree of voltage imbalance between modules and the degree of voltage fluctuation of the string. To this end, the above-mentioned imbalance pattern analysis unit (21) may include a voltage imbalance calculation unit (211), a fluctuation abnormality calculation unit (212), an imbalance index calculation unit (213), and an imbalance index analysis unit (214).

[0076] The above voltage imbalance calculation unit (211) is configured to calculate the degree of voltage imbalance between solar modules within a string, specifically using the voltage output from the string and the voltage measured from a specific module within the string. Conventionally, to detect voltage imbalance between modules within a string, it was necessary to measure and compare the voltages of each module, but this resulted in increased installation and maintenance costs. Therefore, the above voltage imbalance calculation unit (211) calculates the degree of voltage imbalance by comparing the voltage of the string with the value obtained by multiplying the voltage of a specific module by the number of modules, thereby simplifying installation and maintenance and reducing costs and time. In other words, if there is no imbalance between solar modules, the voltage of the string will be equal to the value obtained by multiplying the voltage of a specific module by the number of modules, so the degree of voltage imbalance can be calculated based on that difference. In addition, since the degree of voltage imbalance changes due to the influence of solar irradiance and module temperature, the degree of voltage imbalance is calculated by reflecting this. To this end, the voltage imbalance calculation unit (211) may include a string voltage measurement module (211a), a module voltage measurement module (211b), a voltage imbalance coefficient calculation module (211c), and a coefficient correction module (211d).

[0077] The string voltage measurement module (211a) is configured to measure the voltage output from the string, and as shown in FIG. 6, the voltage can be measured at the output terminal of the string.

[0078] The above module voltage measurement module (211b) is configured to measure the voltage of a specific module within a string, and measures the voltage of only one of the multiple solar modules included in the string, and, for example, can measure the voltage of the final module.

[0079] The above voltage imbalance coefficient calculation module (211c) is configured to calculate a voltage imbalance coefficient indicating the degree of voltage imbalance of modules within a string, and can calculate the voltage imbalance coefficient by subtracting the value obtained by multiplying the voltage of a specific module by the number of modules from the string voltage. Therefore, the greater the degree of voltage imbalance between modules within a string, the larger the voltage imbalance coefficient.

[0080] The above coefficient correction module (211d) is configured to correct the voltage imbalance coefficient according to standard conditions based on solar irradiance and module temperature, and the correction can be performed according to the following (Equation 1). Therefore, the voltage imbalance coefficient can be calculated for strings according to the same standard.

[0081] (Mathematical Formula 1)

[0082]

[0083] (Here, the module temperature coefficient varies by module and is generally 0.4% / 1 degree)

[0084] The above-mentioned fluctuation abnormality calculation unit (212) is configured to calculate the degree of abnormality regarding voltage fluctuation of the string, and represents the degree of voltage fluctuation as a numerical value to be reflected in the calculation of the degree of imbalance. As shown in FIG. 8, in the case of a normal state, the string continues to track the maximum power point while maintaining voltage fluctuations within a certain range as in ①, but if it shows a fluctuation range that is too large or too small, deviating from the voltage fluctuation range as in ② and ③, it is determined to be an abnormal state of fluctuation, and the degree thereof is calculated. In addition, the above-mentioned fluctuation abnormality calculation unit (212) adjusts the scale and reference value to calculate the degree of imbalance by linking the degree of abnormality of voltage fluctuation with the voltage imbalance coefficient. To this end, the above-mentioned fluctuation abnormality calculation unit (212) may include a voltage measurement module (212a), a current measurement module (212b), a power fluctuation coefficient calculation module (212c), and a coefficient adjustment module (212d).

[0085] The above voltage measurement module (212a) is configured to measure the voltage output from the string, and measures it for a certain period of time to calculate the degree of change per unit time.

[0086] The above current measurement module (212b) is configured to measure the current output from the string, and measures it for a certain period of time like voltage to calculate the degree of change with respect to voltage.

[0087] The power fluctuation coefficient calculation module (212c) is configured to calculate a power fluctuation coefficient that indicates the degree of fluctuation in voltage and current, and calculates the degree of change in voltage and current per unit time. For example, the power fluctuation coefficient calculation module (212c) can calculate the power fluctuation coefficient by dividing the ratio of the voltage and current change range by the ratio of the voltage and current magnitudes as shown in (Equation 2) below. If there is no imbalance between modules, the power fluctuation coefficient will have a value close to 1, if the voltage change range is large, it will have a value less than 1, and if the voltage change range is small, it will have a value greater than 1.

[0088] (Mathematical Formula 2)

[0089]

[0090] (I, V = Current, Voltage / dI, dV = Fluctuation range of current, voltage)

[0091] In this case, a large voltage fluctuation range indicates that a specific module has deteriorated or that the power generation has decreased due to odors, pollution, etc., and signifies a state in which the voltage is changed significantly as shown in ② of FIG. 8 to find the maximum power point by the MPPT algorithm of the inverter. Additionally, a small voltage fluctuation range resulting in a power fluctuation coefficient greater than 1 signifies a case in which the voltage change is almost negligible compared to the current change, as shown in ③ of FIG. 8, indicating a state in which the power generation performance of the module is significantly reduced. The power fluctuation coefficient calculation module (212c) can calculate the power fluctuation coefficient for unit time periods of, for example, 5 seconds or 10 seconds, and can determine the final power fluctuation coefficient using the average value of the power fluctuation coefficients for unit time periods over a certain period.

[0092] The coefficient adjustment module (212d) is configured to adjust the final power fluctuation coefficient calculated by the power fluctuation coefficient calculation module (212c) to be linked with the voltage imbalance coefficient, converting the standard of the steady state from 1 to 0 and increasing the scale, and can calculate the adjusted power fluctuation coefficient according to the following (Equation 3).

[0093] (Mathematical Formula 3)

[0094] Pf = (1-Pd)*10

[0095] (Here, Pf is the adjusted power change coefficient, and Pd is the final power change coefficient)

[0096] Therefore, in a steady state where there is no imbalance between modules, the adjusted power fluctuation coefficient becomes 0, and power fluctuation coefficients less than 1 are converted into positive numbers, and power fluctuation coefficients greater than 1 are converted into negative numbers.

[0097] The above-mentioned imbalance index calculation unit (213) is configured to calculate an imbalance index indicating the degree of output imbalance of a module within a string, and calculates the degree of imbalance between modules by reflecting the degree of voltage imbalance calculated by the above-mentioned voltage imbalance calculation unit (211) and the degree of voltage fluctuation abnormality calculated by the above-mentioned fluctuation abnormality calculation unit (212). To this end, the above-mentioned imbalance index calculation unit (213) may include a voltage imbalance coefficient loading module (213a), a power fluctuation coefficient loading module (213b), and an imbalance index calculation module (213c).

[0098] The above voltage imbalance coefficient loading module (213a) is configured to load the voltage imbalance coefficient calculated by the above voltage imbalance calculation unit (211), and loads the voltage imbalance coefficient calculated by the above voltage imbalance coefficient calculation module (211c).

[0099] The power fluctuation coefficient loading module (213b) is configured to load the power fluctuation coefficient calculated by the fluctuation anomaly calculation unit (212), and loads the power fluctuation coefficient adjusted by the coefficient adjustment module (212d).

[0100] The above-mentioned imbalance index calculation module (213c) is configured to calculate an imbalance index indicating the degree of imbalance between modules, and can calculate the imbalance index by multiplying the voltage imbalance coefficient and the power fluctuation coefficient.

[0101] The above-mentioned imbalance index analysis unit (214) is configured to analyze the change pattern of the imbalance index and can analyze and store the flow of change of the imbalance index calculated at regular intervals.

[0102] The above power generation pattern analysis unit (22) is configured to analyze the change pattern regarding the error between the predicted power generation and the measured power generation of the string, and analyzes the change pattern of the area of ​​each region on the IV coordinate plane according to the predicted power generation and the measured power generation. The area on the IV coordinate according to the difference between the predicted power generation and the measured power generation signifies a different state for each region, and by analyzing the change pattern of the area of ​​each region, it is possible to accurately detect an abnormal state due to a decrease in power generation. The above power generation pattern analysis unit (22) may include a power generation prediction unit (221), a power generation measurement unit (222), and an error analysis unit (223).

[0103] The above power generation prediction unit (221) is configured to predict the power generation of a solar power generation facility, and can predict the power generation for each string. In particular, the above power generation prediction unit (221) can increase the accuracy by predicting the power generation by reflecting the specifications and characteristics of each solar module, IV curve data, environmental information, and degradation rate. To this end, the above power generation prediction unit (221) may include a specification information collection module (221a), a number information collection module (221b), a power generation information collection module (221c), an environmental information collection module (221d), a solar radiation information collection module (221e), a temperature information collection module (221f), a degradation rate calculation module (221g), and a predicted power generation generation module (221h).

[0104] The specification information collection module (221a) above is configured to collect specification information of a solar module included in a string, and can collect and store manufacturer and item information in advance.

[0105] The above count information collection module (221b) is configured to collect information on the number of solar modules included in the string, and can generate prediction information by summing the predicted power generation information for each solar module according to the number of solar modules.

[0106] The above-mentioned power generation information collection module (221c) is configured to collect information regarding the number of days of power generation of a solar power module, and can calculate and collect information regarding the number of days of power generation by accumulating and storing information on power generation since the time of installation.

[0107] The above environmental information collection module (221d) is configured to collect environmental information around the solar module, and can collect information regarding temperature, humidity, etc.

[0108] The above-mentioned solar radiation information collection module (221e) is configured to collect solar radiation information reaching the solar module, and can predict the amount of power generated using IV curve data based on the collected solar radiation.

[0109] The above temperature information collection module (221f) is configured to collect module temperature information of a solar module, and can collect temperature information measured by a sensor installed in each solar module.

[0110] The above degradation rate calculation module (221g) is configured to calculate a degradation rate indicating the degree of degradation of a solar module, and calculates the degradation rate according to the power generation day information collected by the above power generation information collection module (221c) by reflecting the degree of degradation per day determined by the characteristics of the specifications of the solar module.

[0111] The above-mentioned predicted power generation module (221h) is configured to predict the power generation of a string, and the prediction of power generation is made by considering IV curve data based on solar irradiance and module temperature, environmental information, and degradation rate. The prediction of power generation based on IV curve data and environmental information can be done experimentally or by analyzing measurement information collected from a photovoltaic power generation facility. For example, a correlation can be derived by learning by reflecting environmental information and degradation rate to the power generation based on IV curve data provided by the manufacturer of each photovoltaic module, and the power generation can be predicted using the derived correlation.

[0112] The above power generation measurement unit (222) is configured to measure the power generation of each string, and can calculate the power generation by measuring the voltage and current output from each string.

[0113] The above error analysis unit (223) is configured to analyze change patterns regarding errors in predicted and measured power generation amounts, and in particular, to analyze change patterns regarding the area of ​​the region connecting the location on the IV coordinate plane according to the power generation amount and the point representing the short-circuit current and open-circuit voltage. Since each region according to the error in power generation amount connecting the short-circuit current and open-circuit voltage will show a different area depending on the cause of the power generation decrease, it is possible to detect an accurate abnormal state by analyzing the change in the area of ​​each region. To this end, the above error analysis unit (223) may include an Isc area calculation module (223a), a Voc area calculation module (223b), and a change pattern analysis module (223c).

[0114] The Isc area calculation module (223a) is configured to calculate the area of ​​the Isc region formed by a point on the IV coordinate plane representing the predicted power generation and the measured power generation and a point (Isc) representing the short-circuit current, and calculates the area of ​​∆a in the graph shown in FIG. 19. The area of ​​∆a is an area that occurs according to the difference between the predicted power generation and the measured power generation, and a large area of ​​∆a means a decrease in current, that is, an increase in the series resistance on the string.

[0115] The above Voc area calculation module (223b) is configured to calculate the area of ​​the Voc region formed by a point on the IV coordinate plane representing the predicted power generation and the measured power generation and a point (Voc) representing the open-circuit voltage, and calculates the area of ​​∆b in the graph shown in FIG. 19. The area of ​​∆b is also an area that occurs according to the difference between the predicted power generation and the measured power generation, and a large area of ​​∆b means a decrease in voltage, that is, a decrease in parallel resistance in the string.

[0116] The above change pattern analysis module (223c) is configured to analyze the change pattern for the error between the predicted power generation amount and the measured power generation amount, and analyzes the change pattern for each of the areas of the Isc region (∆a) and the Voc region (∆b).

[0117] The insulation resistance pattern analysis unit (23) is configured to analyze the change pattern of an indicator regarding the insulation resistance of a string, and analyzes the change pattern of the degree of change in insulation resistance measured over a set period of time. Although periodic checks are conducted as the decrease in insulation resistance of a photovoltaic power generation facility is a major cause of fire, conventionally, this was done by comparing the measured insulation resistance with a reference value. Therefore, there was a problem in that it could not reflect the change in insulation resistance due to environmental changes and could not detect local defects or minor deterioration states of insulation resistance. Accordingly, the insulation resistance pattern analysis unit (23) analyzes the change pattern of insulation resistance, and in particular, rather than simply analyzing the change pattern of the insulation resistance value, it analyzes the change pattern of an indicator indicating the degree of change in insulation resistance, thereby increasing the accuracy of the diagnosis. The insulation resistance pattern analysis unit (23) may analyze the change pattern of the degree of change in insulation resistance over a certain period of time or the phenomenon of rapid increase, and to this end, it may include a change pattern analysis unit (231) and a kick index analysis unit (232).

[0118] The change pattern analysis unit (231) is configured to analyze the change pattern regarding the degree of change in insulation resistance over a set period of time, and analyzes the change pattern regarding the degree of change in insulation resistance after a set period of time has elapsed after the initial period of time has elapsed following the application of voltage. Normally, insulation resistance should remain constant during the set period of time when voltage is applied, but if it becomes higher or lower, it means that an abnormality has occurred in the insulation resistance. Accordingly, the change pattern analysis unit (231) can recognize that an abnormality has occurred in the insulation resistance by comparing the pattern of change in the degree of change in insulation resistance, that is, the degree of abnormality, for strings of the same string group. To this end, the change pattern analysis unit (231) may include a first insulation resistance measurement module (231a), a second insulation resistance measurement module (231b), a change index calculation module (231c), and an index pattern analysis module (231d).

[0119] The first insulation resistance measuring module (231a) is configured to measure the insulation resistance after an initial period of time has elapsed following the application of voltage. For example, when measuring the insulation resistance for 10 minutes, it can measure the insulation resistance after an initial period of 1 minute has elapsed.

[0120] The second insulation resistance measuring module (231b) is configured to measure the insulation resistance after a set period has elapsed. For example, when measuring the insulation resistance for 10 minutes, it can measure the insulation resistance after 10 minutes have elapsed.

[0121] The above change index calculation module (231c) is configured to calculate a change index that indicates the degree of change in insulation resistance over a set period of time, and can calculate the change index according to the ratio of the second insulation resistance to the first insulation resistance. At this time, when the change index is 1 as in (1) of FIG. 14, it indicates a normal state where there is no change in insulation resistance; when it is less than 1 as in (2), the insulation resistance gradually decreases, indicating the degree of insulation deterioration due to cable moisture absorption and contamination; and when it is greater than 1 as in (3), the insulation resistance gradually increases, so corrosion due to cable drying can be suspected.

[0122] The index pattern analysis module (231d) is configured to analyze the change pattern of the change index, and since the change index indicates the degree of abnormality regarding insulation resistance, it is configured to analyze and compare the change pattern regarding insulation resistance between identical string groups so that strings having abnormal change patterns can be detected.

[0123] The above kick index analysis unit (232) is configured to analyze the kick index, which indicates the degree of rapid change in insulation resistance. It calculates the degree of rapid change per unit time over a certain period as the kick index, thereby enabling the analysis of the change pattern. When an abnormality occurs in insulation resistance, the insulation resistance not only changes between the set time intervals as the change index, but also undergoes frequent rapid changes. Therefore, the above kick index analysis unit (232) calculates the degree of rapid change as the kick index and compares the change pattern, thereby enabling more accurate abnormality detection regardless of surrounding environmental conditions. To this end, the above kick index analysis unit (232) may include a voltage application module (232a), a voltage adjustment module (232b), an insulation resistance measurement module (232c), a kick index calculation module (232d), and a kick index change analysis module (232e).

[0124] The above voltage application module (232a) is configured to apply voltage to a string for measuring insulation resistance, and can apply voltage for a certain period of time, for example, 10 minutes.

[0125] The voltage adjustment module (232b) is configured to adjust the voltage applied by the voltage application module (232a), and can measure changes in insulation resistance under various voltage conditions by increasing the voltage in steps. For example, the voltage adjustment module (232b) can increase the voltage in steps from 100V to 500V.

[0126] The insulation resistance measuring module (232c) is configured to measure insulation resistance during a certain period of time when voltage is applied. For example, if voltage is applied for 10 minutes, insulation resistance can be measured at 1-second intervals. Additionally, when insulation resistance is measured while increasing the voltage stepwise by the voltage adjustment module (232b), insulation resistance can be measured each time the voltage is increased.

[0127] The above kick index calculation module (232d) is configured to calculate a kick index indicating the degree of rapid change in insulation resistance. If the insulation resistance measured in units of time or voltage by the insulation resistance measurement module (232c) does not change linearly, but rather has a large range of change or rapid change as shown in FIG. 15, it can be determined that the insulation resistance has decreased. Accordingly, the above kick index calculation module (232d) can calculate the degree of change in insulation resistance on average as a kick index. It can also calculate a time kick index regarding the degree of change per unit time when a constant voltage is applied for a certain period, and a voltage kick index regarding the degree of change each time the voltage is changed when the voltage is applied while adjusting it step by step for a certain period. At this time, the time kick index can be calculated by the following (Equation 4), and the voltage kick index can be calculated by the following (Equation 5).

[0128] (Mathematical Formula 4)

[0129]

[0130] (Mathematical Formula 5)

[0131]

[0132] The above kick index change analysis module (232e) is configured to analyze the change pattern of the kick index, and can analyze the change pattern of the time kick index or the voltage kick index.

[0133] The pattern classification unit (3) is configured to classify strings according to the change pattern of an indicator affecting string failure, and classifies strings according to the change pattern of the unbalance index, the area of ​​the Isc region (∆a) and Voc region (∆b), the time kick index, and the voltage kick index analyzed by the unbalance pattern analysis unit (21), the power generation pattern analysis unit (22), and the insulation resistance pattern analysis unit (23). Accordingly, the pattern classification unit (3) classifies strings with similar patterns of each indicator within the string group primarily classified by the string classification unit (1) into string groups for each indicator, and detects abnormal strings by analyzing the change pattern for each indicator within the same classification. As an example, the pattern classification unit (3) may use an algorithm that classifies according to the change pattern.

[0134] The above abnormal string detection unit (4) is configured to detect strings in which abnormalities have occurred through pattern analysis via the above pattern analysis unit (2), and can detect strings in which imbalance between modules, power generation reduction, and insulation resistance reduction have occurred. Accordingly, the above abnormal string detection unit (4) can detect strings in which abnormalities have occurred for each indicator by comparing the change pattern of the imbalance index analyzed by the above imbalance pattern analysis unit (21), the change pattern of each area of ​​the Isc region (∆a) and Voc region (∆b) analyzed by the above power generation pattern analysis unit (22), and the change pattern of the insulation resistance change index or kick index among strings, and can detect strings with different change patterns as strings in which abnormalities have occurred for each indicator, and can increase the accuracy of abnormal string detection by making comparisons between strings within the same string group classified by the above pattern classification unit (3).

[0135] The above module imbalance diagnosis unit (5) is configured to diagnose the cause of module imbalance, and is configured to diagnose the cause of imbalance when an abnormal string is detected according to the comparison of the change pattern of the imbalance index in the above abnormal string detection unit (4), and can diagnose the cause according to the value of the imbalance index. To this end, the above module imbalance diagnosis unit (5) may include an imbalance index loading module (51) and an abnormal information diagnosis module (52).

[0136] The above-mentioned imbalance index loading module (51) is configured to retrieve imbalance index information of a string, and retrieves imbalance index information of a string that is detected as having an abnormality regarding the imbalance between modules by the above-mentioned abnormal string detection unit (4).

[0137] The above-mentioned abnormal information diagnosis module (52) is configured to diagnose the cause of the imbalance based on the imbalance index. The imbalance index is the value obtained by multiplying the degree of voltage imbalance and the degree of abnormal fluctuation. Since the power fluctuation coefficient is calculated as positive or negative depending on the power fluctuation state, a positive imbalance index indicates a state where the voltage change is large, and a negative imbalance index indicates a state where the voltage change is small and the current change is large. Therefore, as shown in FIG. 17, if the imbalance index is positive, it means that the module is operating to the left (ⓐ) of the normal maximum power point, indicating a state where the series resistance has increased, and damage, shading, or contamination of the module can be suspected. In addition, if the imbalance index is negative, it means that the module is operating to the right (ⓑ) of the normal maximum power point, indicating a state where the parallel resistance has decreased, and leakage current, cell cracking, or a decrease in insulation resistance can be suspected. Accordingly, it is possible to accurately diagnose the imbalance state between modules and simultaneously identify the cause, thereby enabling a rapid response.

[0138] The above power generation diagnosis unit (6) is configured to diagnose the cause of the decrease in power generation of a string. When the abnormal string detection unit (4) detects a string with a decrease in power generation because the change pattern of the area of ​​the Isc region (∆a) or Voc region (∆b) is different from that of other strings, the cause is diagnosed. To this end, the above power generation diagnosis unit (6) may include a region area loading module (61), a region area comparison module (62), and a decrease cause detection module (63).

[0139] The above area loading module (61) is configured to load area information for each of the Isc area (∆a) and Voc area (∆b), and loads area information for each area of ​​the string that is detected as having an abnormality.

[0140] The above area comparison module (62) is configured to compare the areas of the Isc area (∆a) and the Voc area (∆b), and depending on which area is larger, it is possible to determine whether the cause of the decrease in power generation is due to an increase in series resistance or a decrease in parallel resistance.

[0141] The above-mentioned depreciation cause detection module (63) is configured to detect the cause of the power generation reduction, and the cause can be detected based on the comparison result by the above-mentioned area comparison module (62). If the area of ​​the Isc region (∆a) is larger than the area of ​​the Voc region (∆b), the power generation reduction can be seen as caused by an increase in series resistance, such as cable connection status, line status, or shading within the module; if the area of ​​the Isc region (∆a) is smaller than the area of ​​the Voc region (∆b), the power generation reduction can be seen as caused by a decrease in parallel resistance, such as leakage current, cell cracking, or a decrease in insulation resistance. Accordingly, rapid inspection and response to the power generation reduction can be carried out based on the result detected by the depreciation cause detection module (35).

[0142] Although the applicant has described various embodiments of the present invention above, such embodiments are merely examples of implementing the technical concept of the present invention, and any modification or alteration that implements the technical concept of the present invention should be interpreted as falling within the scope of the present invention.

Claims

1. It includes a string classification unit that classifies strings according to device information regarding strings of a solar power generation facility, a pattern analysis unit that analyzes the change patterns of indicators affecting failure in each classification of strings, a pattern classification unit that classifies strings having similar change patterns of each indicator within strings of the same classification, and an abnormal string detection unit that detects strings with different change patterns as abnormal states by comparing the change patterns of indicators between strings of the same classification. The above string classification unit is, A solar power generation monitoring system utilizing string characteristics, characterized by classifying strings according to the specifications and number of solar modules included in the string and the capacity of the inverter connected to the string.

2. In claim 1, the string classification unit It includes a solar radiation collection module that collects solar radiation information for a string, and a module temperature collection module that collects temperature information of a solar module, and The above string sorting module is, A photovoltaic power generation monitoring system utilizing string characteristics, characterized by classifying strings with similar solar irradiance and module temperature per unit period into the same string group.

3. In claim 1, the pattern analysis unit It includes an imbalance pattern analysis unit that analyzes the change pattern of the degree of imbalance of photovoltaic modules within a string, and The above-mentioned unbalanced pattern analysis unit is, A voltage imbalance calculation unit that calculates the degree of voltage imbalance between modules constituting a string, and A fluctuation abnormality calculation unit that calculates the degree of abnormality based on voltage and current fluctuations of the string, and An unbalance index calculation unit that calculates an unbalance index indicating the degree of imbalance between modules according to the degree of voltage imbalance and the degree of abnormal fluctuation, and A photovoltaic power generation monitoring system utilizing string characteristics, characterized by including an imbalance index analysis unit that analyzes the change pattern of the imbalance index.

4. In Clause 3, the voltage imbalance calculation unit is A photovoltaic power generation monitoring system utilizing string characteristics, characterized by including a string voltage measurement module for measuring the voltage of power output from a string, a module voltage measurement module for measuring the voltage at a specific module within the string, and a voltage imbalance coefficient calculation module for calculating a voltage imbalance coefficient indicating the degree of voltage imbalance between modules by subtracting the value obtained by multiplying the number of photovoltaic modules included in the string by the voltage of the specific module from the string voltage.

5. In Clause 4, the voltage imbalance calculation unit is A photovoltaic power generation monitoring system utilizing string characteristics, characterized by including a coefficient correction module that corrects the voltage imbalance coefficient according to solar irradiance and module temperature.

6. In Paragraph 4, the above fluctuation abnormality calculation unit A photovoltaic power generation monitoring system utilizing string characteristics, characterized by including a voltage measuring module that measures a voltage output from a string for a certain period of time, a current measuring module that measures a current output from a string for a certain period of time, and a power fluctuation coefficient calculation module that calculates a power fluctuation coefficient indicating the degree of fluctuation of voltage and current by calculating the value of the ratio of voltage change amount to current change amount for the ratio of voltage to current per unit time for a certain period of time and the average value.

7. In Clause 6, the above fluctuation abnormality calculation unit A photovoltaic power generation monitoring system utilizing string characteristics, characterized by including a coefficient adjustment module that increases the scale while changing the standard for the steady state of the power fluctuation coefficient to zero.

8. In claim 7, the coefficient adjustment module A photovoltaic power generation monitoring system utilizing string characteristics, characterized by calculating the adjusted power fluctuation coefficient by adjusting the power fluctuation coefficient according to mathematical formula 1. (Mathematical Formula 1) Pf = (1-Pd)*10 (Here, Pf is the adjusted power variation coefficient, and Pd is the initial power variation coefficient) 9. In claim 7, the above-mentioned imbalance index calculation unit A photovoltaic power generation monitoring system utilizing string characteristics, characterized by including a voltage imbalance coefficient loading module for retrieving a voltage imbalance coefficient, a power fluctuation coefficient loading module for retrieving a power fluctuation coefficient, and an imbalance index calculation module for calculating an imbalance index representing the degree of imbalance in output between modules by multiplying the voltage imbalance coefficient and the power fluctuation coefficient.

10. In claim 9, the solar power generation monitoring system It includes a module imbalance diagnosis unit that diagnoses the cause of imbalance when string imbalance is detected by comparing the change pattern of the imbalance index through the above abnormal string detection unit, The above module imbalance diagnosis unit is, It includes an imbalance index loading module that retrieves an imbalance index and an abnormal information diagnosis module that diagnoses the cause of imbalance according to the imbalance index, and The above abnormal information diagnosis module is, A photovoltaic power generation monitoring system utilizing string characteristics, characterized by diagnosing module damage, shading, or contamination when the imbalance index is positive, and diagnosing PID, cell cracking, or insulation resistance degradation when the imbalance index is negative.

11. In claim 1, the pattern analysis unit It includes a power generation pattern analysis unit that analyzes the change pattern regarding the error between the predicted power generation and the measured power generation of the string, and The above-mentioned power generation pattern analysis unit is, It includes a power generation prediction unit that predicts the power generation amount for each string of a photovoltaic power generation device, a power generation measurement unit that measures the power generation amount of the current string in real time, and an error analysis unit that analyzes change patterns regarding errors in the predicted and measured power generation amounts. The above error analysis unit is, A photovoltaic power generation monitoring system utilizing string characteristics, characterized by analyzing the pattern of change in area by region on an IV coordinate plane according to predicted power generation and measured power generation.

12. In claim 11, the error analysis unit A photovoltaic power generation monitoring system utilizing string characteristics, characterized by comprising: an Isc area calculation module that calculates the area of ​​the Isc region formed by a line connecting points on an IV coordinate plane according to voltage and current values ​​of predicted power generation and measured power generation and a line connecting points indicating short-circuit current; a Voc region calculation module that calculates the area of ​​the Voc region formed by a line connecting points on an IV coordinate plane according to voltage and current values ​​of predicted power generation and measured power generation and a line connecting points indicating open-circuit voltage; and a change pattern analysis module that analyzes the change pattern of the area of ​​the Isc region or the area of ​​the Voc region.

13. In Clause 12, the solar power generation monitoring system is It includes a power generation diagnosis unit that diagnoses the cause of an abnormality regarding a decrease in power generation when the change pattern of the area of ​​the Isc region or the area of ​​the Voc region is different and is detected as an abnormal string, The above-mentioned power generation diagnostic unit is, A photovoltaic power generation monitoring system utilizing string characteristics, characterized by including an area loading module that loads information regarding the areas of the Isc area and Voc area, an area comparison module that compares the loaded areas of the Isc area and Voc area, and a cause of reduction detection module that detects the cause of the reduction in power generation based on the result of the area comparison.

14. In claim 13, the degradation cause detection module is A photovoltaic power generation monitoring system utilizing string characteristics, characterized by determining an abnormality due to an increase in series resistance when the area of ​​the Isc region is larger than the area of ​​the Voc region, and determining an abnormality due to a decrease in module parallel resistance when the area of ​​the Voc region is larger than the area of ​​the Isc region.

15. In claim 1, the pattern analysis unit It includes an insulation resistance pattern analysis unit that analyzes the change pattern of an indicator regarding the insulation resistance of a string, and The insulation resistance pattern analysis unit includes a change pattern analysis unit that analyzes the change pattern regarding the degree of change in insulation resistance over a set period of time, and The above change pattern analysis unit is, A photovoltaic power generation monitoring system utilizing string characteristics, characterized by comprising: a first insulation resistance measurement module for measuring insulation resistance after an initial fixed time has elapsed; a second insulation resistance measurement module for measuring insulation resistance after a set time has elapsed from the initial fixed time; a change index calculation module for calculating a change index indicating the degree of change in insulation resistance according to the ratio of the second insulation resistance to the first insulation resistance; and an index pattern analysis module for analyzing the change pattern of the calculated change index.

16. In claim 1, the pattern analysis unit It includes an insulation resistance pattern analysis unit that analyzes the change pattern of an indicator regarding the insulation resistance of a string, and The insulation resistance pattern analysis unit above is, It includes a kick index analysis unit that analyzes the change pattern of the kick index, which indicates the degree of rapid change in insulation resistance per unit time during a set time, and The above-mentioned kick index analysis unit is, A photovoltaic power generation monitoring system utilizing string characteristics, characterized by including a voltage application module that applies voltage to a string for a set period of time, an insulation resistance measurement module that measures insulation resistance at unit time intervals according to the voltage application, a kick index calculation module that calculates a kick index indicating the degree of change in insulation resistance per unit time during a set period of time, and a kick index change analysis module that analyzes the change pattern of the calculated kick index.

17. In Clause 16, the above kick index calculation module is A solar power generation monitoring system utilizing string characteristics characterized by calculating a time kick index according to the mathematical formula 2 below. (Mathematical Formula 2) 18. In Clause 16, the above kick index analysis unit A photovoltaic power generation monitoring system utilizing string characteristics, characterized by including a voltage adjustment module that enables the measurement of insulation resistance while varying the voltage in steps.

19. In claim 18, the above kick index calculation module is A photovoltaic power generation monitoring system utilizing string characteristics, characterized by calculating the voltage kick index according to the mathematical formula 3 below. (Mathematical Formula 3)

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