Real-time monitoring system for photovoltaic power generation facility

Abstract

The present invention relates to a real-time monitoring system for a photovoltaic power generation facility. More particularly, the present invention relates to a real-time monitoring system for a photovoltaic power generation facility, the system allowing diagnosis of a type of failure by using an area on an I–V coordinate plane while comparing a predicted power generation amount and a current power generation amount for each string of the photovoltaic power generation facility. Accordingly, the system enables quick identification of the location and type of a failure even in a remote environment, and allows visual confirmation of whether a failure has occurred and the type of the failure, thereby facilitating prompt response to the failure. In addition, the system allows diagnosis of an unbalanced output state of a photovoltaic module according to the degree of voltage imbalance and voltage fluctuation between modules within a string, thereby enabling accurate diagnosis of the unbalanced state.

Description

Real-time monitoring system for solar power plants

[0001] The present invention relates to a real-time monitoring system for a solar power generation facility, and more particularly, to a real-time monitoring system for a solar power generation facility that diagnoses the type of failure by using the area on the IV coordinate plane while comparing the predicted and current power generation for each string of the solar power generation facility, thereby enabling the location and type of failure to be quickly identified even remotely, and enabling the presence and type of failure to be visually confirmed, thereby enabling a rapid response to the failure, and diagnosing the imbalance of the solar module output according to the degree of imbalance and the degree of voltage fluctuation in the voltage between modules in the string, thereby enabling an accurate diagnosis of the imbalance.

[0002] Solar power, a renewable energy source, has seen a surge in demand recently due to its numerous advantages, and technologies to improve power generation efficiency have also been advancing rapidly. In particular, solar power generation systems are being installed in various forms, including rooftops, floating structures, and even building-integrated photovoltaic (BIPV) systems that are integrated into buildings.

[0003] In the case of solar power generation devices, multiple solar modules are connected in series to form a string, and multiple strings are connected in parallel to form a single panel (array). However, if the generated power of a string decreases, the power deviation between strings increases, which significantly reduces the overall power generation efficiency.

[0004] Accordingly, a system for monitoring the power generation of a string has been developed and is being used, but in the case of the existing monitoring system, as in the patent document below, the power generation according to the environment is predicted and a failure is diagnosed only based on the difference from the predicted power generation, so there is a problem in that the type of failure cannot be determined.

[0005] (Patent Document) Patent Publication No. 10-2646725 (registered on March 7, 2024) "Edge device including power generation prediction and fault diagnosis functions of solar panels and solar power generation system including the same"

[0006] The present invention has been devised to solve the above problems.

[0007] The purpose of the present invention is to provide a real-time monitoring system for solar power generation facilities that enables rapid identification of the location and type of a fault even remotely by comparing the predicted and current power generation for each string of a solar power generation facility and diagnosing the type of fault using the area on the IV coordinate plane, and enables rapid response to a fault by visually confirming the presence and type of fault.

[0008] The purpose of the present invention is to provide a real-time monitoring system for solar power generation facilities that enables accurate diagnosis of an imbalance state by diagnosing an imbalance state in solar module output according to the degree of imbalance in voltage between modules in a string and the degree of voltage fluctuation.

[0009] The purpose of the present invention is to provide a real-time monitoring system for a solar power generation facility that can detect a voltage imbalance condition in a simple manner without measuring the voltage of all solar modules by diagnosing the voltage imbalance condition by comparing the voltage of a specific module multiplied by the number of modules with the voltage of the entire string.

[0010] The purpose of the present invention is to provide a real-time monitoring system for solar power generation facilities that calculates an imbalance index indicating the degree of output imbalance between modules using the degree of voltage imbalance and the degree of voltage fluctuation within a string, and detects the cause of the imbalance according to the imbalance index, thereby enabling a quick and accurate response to the imbalance.

[0011] In order to achieve the above-mentioned purpose, the present invention is implemented by an embodiment having the following configuration.

[0012] According to one embodiment of the present invention, a real-time monitoring system for a solar power generation facility according to the present invention is characterized by including a power generation prediction unit that predicts the power generation amount for each string of a solar power generation device, a power generation measurement unit that measures the current power generation amount of a string in real time, and a fault diagnosis unit that diagnoses a fault type of a string using an area on an IV coordinate plane according to the predicted power generation amount and the measured power generation amount.

[0013] According to another embodiment of the present invention, in the real-time monitoring system for solar power generation facilities according to the present invention, the fault diagnosis unit is characterized by including a power generation decline diagnosis module that diagnoses a decline in power generation when the measured power generation falls short of the predicted power generation by a certain degree or more, an Isc area calculation module that calculates the area of ​​the Isc area formed by a line connecting points on an IV coordinate plane according to voltage and current values ​​of the predicted and 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 an IV coordinate plane according to voltage and current values ​​of the predicted and measured power generation and a line connecting points indicating open-circuit voltage, an area area comparison module that compares the areas of the Isc area and the Voc area, and a fault cause detection module that detects the type of fault based on the difference in the compared areas.

[0014] According to another embodiment of the present invention, in the real-time monitoring system for a solar power generation facility according to the present invention, the failure cause detection module is characterized in that it determines that a failure is caused by an increase in series resistance when the area of ​​the Isc area is larger than the area of ​​the Voc area.

[0015] According to another embodiment of the present invention, in the real-time monitoring system for a solar power generation facility according to the present invention, the failure cause detection module is characterized in that it determines that a failure is due to a decrease in the module parallel resistance when the area of ​​the Voc area is larger than the area of ​​the Isc area.

[0016] According to another embodiment of the present invention, in the real-time monitoring system for a solar power generation facility according to the present invention, the power generation prediction unit is characterized by including a specification information collection module that collects specification information of solar modules included in a string, a number information collection module that collects information on the number of solar modules included in the string, a power generation information collection module that collects information on the number of days of power generation of the string, an environmental information collection module that collects surrounding environment information, an irradiance information collection module that collects irradiance information, a temperature information collection module that collects solar module temperature information, a deterioration rate calculation module that calculates a deterioration rate according to the specifications of the solar modules and the number of days of power generation, and a predicted power generation generation module that predicts the power generation by using IV curve data for each module specification according to irradiance and module temperature, environmental information, and a deterioration rate.

[0017] According to another embodiment of the present invention, a real-time monitoring system for a solar power generation facility according to the present invention includes a module imbalance diagnosis unit that diagnoses imbalance between modules in a string, and the module imbalance diagnosis unit is characterized by including a voltage imbalance calculation unit that calculates the degree of imbalance in 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, and an imbalance detection unit that detects imbalance between modules of the string according to the degree of voltage imbalance and the degree of fluctuation abnormality.

[0018] According to another embodiment of the present invention, in the real-time monitoring system for a solar power generation facility according to the present invention, the voltage imbalance calculation unit is characterized by including a string voltage measurement module that measures the voltage of power output from a string, a module voltage measurement module that measures the voltage of a specific module in the string, and a voltage imbalance coefficient calculation module that calculates a voltage imbalance coefficient indicating the degree of voltage imbalance between modules by subtracting a value obtained by multiplying the number of solar modules included in the string by the voltage of the specific module from the string voltage.

[0019] According to another embodiment of the present invention, in the real-time monitoring system for solar power generation facilities according to the present invention, the voltage imbalance calculation unit is characterized by including a voltage imbalance diagnosis module that compares the voltage imbalance coefficient calculated by the imbalance coefficient calculation module with a reference value, diagnoses the voltage imbalance between modules when the reference value is exceeded, and executes a fluctuation abnormality calculation unit.

[0020] According to another embodiment of the present invention, in the real-time monitoring system for a solar power generation facility according to the present invention, the fluctuation abnormality calculation unit is characterized by including 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 value of a ratio of voltage change to current change for a ratio of voltage to current per unit time for a certain period of time and calculates a power fluctuation coefficient representing the degree of voltage and current fluctuation by the average value thereof.

[0021] According to another embodiment of the present invention, in the real-time monitoring system for a solar power generation facility according to the present invention, the fluctuation abnormality calculation unit is characterized in that it includes a coefficient adjustment module that increases the scale while changing the standard for the normal state of the power fluctuation coefficient to 0.

[0022] According to another embodiment of the present invention, in the real-time monitoring system for solar power generation facilities according to the present invention, the coefficient adjustment module is characterized in that it calculates the adjusted power variation coefficient by adjusting the power variation coefficient by mathematical expression 2. (Mathematical expression 2) Pf = (1-Pd)*10 (where, Pf is the adjusted power variation coefficient, and Pd is the initial power variation coefficient)

[0023] According to another embodiment of the present invention, in the real-time monitoring system for a solar power generation facility according to the present invention, the imbalance detection unit is characterized by including a voltage imbalance coefficient loading module for loading a voltage imbalance coefficient, a power variation coefficient loading module for loading a power variation coefficient, an imbalance index calculation module for calculating an imbalance index indicating the degree of output imbalance between modules by multiplying the voltage imbalance coefficient and the power variation coefficient, and an abnormality information diagnosis module for diagnosing an abnormality due to output imbalance between modules of a string according to the calculated imbalance index.

[0024] According to another embodiment of the present invention, in the real-time monitoring system for solar power generation facilities according to the present invention, the abnormal information diagnosis module is characterized in that, when the imbalance index is positive, it diagnoses damage, shading, or contamination of the module, and when the imbalance index is negative, it diagnoses PID, cell cracking, or reduced insulation resistance.

[0025] The present invention can obtain the following effects through the combination and use of the configuration described above and the following examples.

[0026] The present invention compares the predicted power generation amount and the current power generation amount for each string of a solar power generation facility, and diagnoses the type of failure using the area on the IV coordinate plane, thereby enabling the location and type of failure to be quickly identified even remotely, and enabling the presence and type of failure to be visually confirmed, thereby enabling a rapid response to failure.

[0027] The present invention has the effect of enabling accurate diagnosis of an imbalance state by diagnosing an imbalance state in the output of a solar module according to the degree of imbalance in voltage between modules in a string and the degree of voltage fluctuation.

[0028] The present invention has the effect of enabling the detection of a voltage imbalance in a simple manner without having to measure the voltage of all solar modules, by diagnosing a voltage imbalance by comparing the voltage of a specific module multiplied by the number of modules with the voltage of the entire string.

[0029] The present invention has the effect of enabling a method of calculating an imbalance index indicating a degree of output imbalance between modules by using the degree of voltage imbalance and the degree of voltage fluctuation within a string, and enabling detection of the cause of an imbalance state based on the imbalance index, thereby enabling a quick and accurate response to an imbalance state.

[0030] Figure 1 is a block diagram showing the configuration of a real-time monitoring system for solar power generation facilities according to one embodiment of the present invention.

[0031] Figure 2 is a block diagram showing the configuration of the power generation prediction unit.

[0032] Figure 3 is a block diagram showing the configuration of the fault diagnosis unit.

[0033] Figure 4 is a reference diagram showing the IV graph used by the fault diagnosis unit.

[0034] Figure 5 is a block diagram showing the configuration of the module imbalance diagnosis unit.

[0035] Figure 6 is a reference diagram showing an example of voltage measurement by a voltage imbalance calculation unit.

[0036] Figure 7 is a graph showing an example of voltage change according to string status.

[0037] Figure 8 is a reference diagram showing the operating point on the IV curve according to the imbalance index.

[0038] Figure 9 is a block diagram showing the configuration of the insulation resistance diagnosis unit.

[0039] Figure 10 is a graph showing an example of insulation resistance measurement.

[0040] *Explanation of symbols used in drawings

[0041] 1: Power generation prediction unit 2: Power generation measurement unit

[0042] 3: Fault diagnosis section 4: Module imbalance diagnosis section

[0043] 41: Voltage imbalance calculation unit 42: Fluctuation abnormality calculation unit

[0044] 43: Unbalance detection unit 5: Insulation resistance diagnosis unit

[0045] Hereinafter, preferred embodiments of a real-time monitoring system for solar power generation facilities according to the present invention will be described in detail with reference to the attached drawings. In the following description of the present invention, if a detailed description of a known function or configuration is determined to unnecessarily obscure the gist of the present invention, the detailed description will be omitted. Throughout the specification, when a part is said to "include" a certain component, this does not mean that other components are excluded, but rather that other components can be further included, unless specifically stated otherwise. In addition, terms such as "... part", "... module", etc. described in the specification mean a unit that processes at least one function or operation, and this can be implemented by hardware, software, or a combination of hardware and software.

[0046] A real-time monitoring system for a solar power generation facility according to an embodiment of the present invention will be described with reference to FIGS. 1 to 10. The real-time monitoring system for a solar power generation facility includes a power generation prediction unit (1) that predicts the power generation amount for each string of a solar power generation device, a power generation measurement unit (2) that measures the power generation amount of a current string in real time, a fault diagnosis unit (3) that diagnoses a fault type of a string using an area on an IV coordinate plane according to the predicted power generation amount and the measured power generation amount, a module imbalance diagnosis unit (4) that diagnoses an imbalance between modules in a string, and an insulation resistance diagnosis unit (5) that precisely diagnoses whether insulation resistance has decreased.

[0047] When monitoring the status of a solar power plant using the power generation amount, the power generation amount in a normal state is predicted, and the measured current power generation amount is compared with the predicted power generation amount to diagnose a fault. However, due to the various characteristics and different environments of solar power plants, it is difficult to accurately predict the power generation amount, which reduces the accuracy of fault diagnosis. In addition, even if a fault is diagnosed, the cause of the fault is completely unknown, making it difficult to respond quickly. Therefore, the present invention diagnoses a fault using the difference between the predicted and measured power generation amounts, and in particular, identifies the type of fault using the difference in area according to the power generation amount on the IV coordinate plane, thereby enabling a faster response to a fault. In addition, the real-time monitoring system for solar power plants enables comprehensive monitoring by diagnosing the imbalance between solar modules within a string and the status of insulation resistance.

[0048] The above solar power generation facility real-time monitoring system can enable remote monitoring of multiple solar power generation facilities, and can enable monitoring by remotely receiving voltage, current, insulation resistance, etc. measured through a connection box, etc.

[0049] The above power generation prediction unit (1) 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 power generation prediction unit (1) 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 deterioration rate. To this end, the power generation prediction unit (1) may include a specification information collection module (11), a quantity information collection module (12), a power generation information collection module (13), an environmental information collection module (14), an irradiance information collection module (15), a temperature information collection module (16), a deterioration rate calculation module (17), and a predicted power generation generation module (18).

[0050] The above specification information collection module (11) is configured to collect specification information of solar modules included in a string, and can collect and store manufacturer and product information in advance.

[0051] The above-mentioned number information collection module (12) is configured to collect information on the number of solar modules included in a string, and can generate prediction information by adding the predicted power generation information for each solar module according to the number of solar modules.

[0052] The above power generation information collection module (13) is configured to collect information on the number of days of power generation of a solar power module, and can calculate and collect information on the number of days of power generation by accumulating and storing information on power generation from the time of installation.

[0053] The above environmental information collection module (14) is configured to collect environmental information around the solar module, and can collect information on temperature, humidity, etc.

[0054] The above-mentioned solar irradiance information collection module (15) is configured to collect solar irradiance information reaching a solar module, and can predict power generation using IV curve data according to the collected solar irradiance.

[0055] The above temperature information collection module (16) 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.

[0056] The above deterioration rate calculation module (17) is configured to calculate the deterioration rate that indicates the degree of deterioration of a solar module, and calculates the deterioration rate based on the number of days of power generation collected by the power generation information collection module (13) by reflecting the degree of deterioration by day determined by the characteristics of each specification of the solar module.

[0057] The above predicted power generation module (18) is configured to predict the power generation of a string, and predicts the power generation by considering IV curve data according to solar irradiance and module temperature, environmental information, and deterioration rate. The prediction of the power generation according to the IV curve data and environmental information can be made experimentally or by analyzing measurement information collected from a solar power generation facility. For example, the correlation can be derived by learning by reflecting environmental information and deterioration rate in the power generation according to the IV curve data provided by the manufacturer of each solar module, and the power generation can be predicted using the derived correlation.

[0058] The above power generation measuring unit (2) 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.

[0059] The above fault diagnosis unit (3) is configured to diagnose a fault due to a decrease in power generation, and diagnoses a decrease in power generation based on the difference between the predicted power generation and the measured power generation, and in particular, detects a fault type that causes a decrease in power generation based on the area of ​​an area connecting a location on the IV coordinate plane according to the power generation and a point indicating a short-circuit current and an open-circuit voltage. To this end, the fault diagnosis unit (3) may include a power generation decrease diagnosis module (31), an Isc area calculation module (32), a Voc area calculation module (33), an area comparison module (34), and a fault cause detection module (35).

[0060] The above power generation decline diagnosis module (31) is configured to diagnose a decline in power generation. If the measured power generation falls short of the predicted power generation by a certain degree or more, it diagnoses a decline in power generation and enables the cause of the decline in power generation to be detected using the area on the IV coordinate plane.

[0061] The above Isc area calculation module (32) is configured to calculate the area of ​​the Isc area formed by the point on the IV coordinate plane where the predicted power generation and measured power generation are indicated and the point (Isc) indicating the short-circuit current, and calculates the area ∆a in the graph shown in Fig. 4. The area ∆a is the area generated according to the difference between the predicted power generation and measured power generation, and a large area of ​​∆a means a decrease in current, i.e., an increase in the series resistance on the string.

[0062] The above Voc area calculation module (33) is configured to calculate the area of ​​the Voc area formed by the point on the IV coordinate plane indicated by the predicted power generation and the measured power generation and the point (Voc) indicating the open circuit voltage, and calculates the area of ​​∆b in the graph shown in Fig. 4. The area of ​​∆b is also an area generated 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, i.e., a decrease in parallel resistance in the string.

[0063] The above area comparison module (34) is configured to compare the areas of the Isc area (∆a) and the Voc area (∆b), and depending on which area has a larger area, it can be determined whether the cause of the decrease in power generation is an increase in series resistance or a decrease in parallel resistance.

[0064] The above-described fault cause detection module (35) is configured to detect the cause of the decrease in power generation based on the comparison result by the above-described area comparison module (34). If the area of ​​the Isc area (∆a) is larger than the area of ​​the Voc area (∆b), it can be seen that the decrease in power generation is caused by an increase in series resistance, such as a cable connection condition, a line condition, or shading within the module. If the area of ​​the Isc area (∆a) is smaller than the area of ​​the Voc area (∆b), it can be seen that the decrease in power generation is caused by a decrease in parallel resistance, such as a leakage current, cell cracks, or a decrease in insulation resistance. Therefore, it is possible to quickly inspect and respond to the decrease in power generation based on the result detected by the fault cause detection module (35).

[0065] The above module imbalance diagnosis unit (4) is configured to diagnose imbalance between solar modules in a string, and to diagnose imbalance in performance caused by failure, damage, aging, etc. The above module imbalance diagnosis unit (4) can be configured to diagnose in real time at regular intervals, or can be configured to be executed when a decrease in power generation is diagnosed by the above failure diagnosis unit (3). In particular, the above module imbalance diagnosis unit (4) comprehensively considers the degree of imbalance in voltage between solar modules and the degree of voltage fluctuation to diagnose the imbalance condition, thereby increasing the accuracy of the diagnosis. In other words, the module imbalance diagnosis unit (4) detects the degree to which the voltages between solar modules do not match, and in addition, considers the degree of voltage fluctuation in the diagnosis of the imbalance state. In the string inverter, the maximum power point tracking control (MPPT) is performed to find the maximum output point and fluctuate the voltage and current. In the normal state, the voltage and current fluctuate within a certain range, but when an imbalance occurs between solar modules, the fluctuation range of the voltage and current increases or decreases beyond a certain range. Therefore, by reflecting this and diagnosing the imbalance state, the accuracy of the diagnosis can be further increased. To this end, the module imbalance diagnosis unit (4) may include a voltage imbalance calculation unit (41), a fluctuation abnormality calculation unit (42), and an imbalance detection unit (43).

[0066] The voltage imbalance calculation unit (41) above is configured to calculate the degree of voltage imbalance between solar modules within a string, and in particular, uses the voltage output from the string and the voltage measured from a specific module within the string. Conventionally, in order to detect voltage imbalance between modules within a string, the voltage of each module had to be measured and compared, but in this case, there was a problem that installation and maintenance costs increased. Therefore, the voltage imbalance calculation unit (41) calculates the degree of voltage imbalance by comparing the voltage of the string with the voltage of a specific module multiplied by the number of modules, thereby simplifying installation and maintenance and reducing cost and time. In other words, if there is no imbalance between solar modules, the voltage of the string will be equal to the voltage of a specific module multiplied by the number of modules, and thus the degree of voltage imbalance can be calculated based on the difference. To this end, the voltage imbalance calculation unit (41) may include a string voltage measurement module (411), a module voltage measurement module (412), a voltage imbalance coefficient calculation module (413), and a voltage imbalance diagnosis module (414).

[0067] The above string voltage measurement module (411) is configured to measure the voltage output from the string, and can measure the voltage at the output terminal of the string as shown in FIG. 6.

[0068] The above module voltage measurement module (412) is configured to measure the voltage of a specific module within a string, and measures the voltage of only one of a number of solar modules included in the string. For example, it can measure the voltage of the module at the final stage.

[0069] The above voltage imbalance coefficient calculation module (413) is configured to calculate a voltage imbalance coefficient that indicates the degree of voltage imbalance of modules within a string, and can calculate the voltage imbalance coefficient by subtracting a value obtained by multiplying the voltage of a specific module by the number of modules from the string voltage. Accordingly, the greater the degree of voltage imbalance between modules within a string, the greater the voltage imbalance coefficient.

[0070] The above voltage imbalance diagnosis module (414) is configured to diagnose a voltage imbalance state of a string when the voltage imbalance coefficient exceeds a set reference value, and can be configured to execute a fluctuation abnormality calculation unit (42) when a voltage imbalance state is diagnosed.

[0071] The above fluctuation abnormality calculation unit (42) is configured to calculate the abnormality degree of the voltage fluctuation of the string, and reflects the voltage fluctuation degree in the calculation of the degree of imbalance by expressing it numerically. As described above, in the case of a normal state, as shown in FIG. 7, the string continues to fluctuate in a certain range of voltage as in ① and tracks the maximum power point. However, in the case where the voltage fluctuation range is too large or too small outside a certain 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 fluctuation abnormality calculation unit (42) adjusts the scale and reference value to calculate the degree of imbalance by linking the abnormality degree of voltage fluctuation with the voltage imbalance coefficient. To this end, the fluctuation abnormality calculation unit (422) may include a voltage measurement module (421), a current measurement module (422), a power fluctuation coefficient calculation module (423), and a coefficient adjustment module (424).

[0072] The above voltage measurement module (421) is configured to measure the voltage output from the string, and calculates the degree of change per unit time by measuring for a certain period of time.

[0073] The above current measurement module (422) is configured to measure the current output from the string, and calculates the degree of change with the voltage by measuring it for a certain period of time, similar to voltage.

[0074] The above power variation coefficient calculation module (423) is configured to calculate the power variation coefficient that indicates the degree of voltage and current variation, and calculates the degree of voltage and current variation per unit time. For example, the above power variation coefficient calculation module (423) can calculate the power variation coefficient as the value obtained by dividing the change range ratio of voltage and current by the size ratio of voltage and current, as shown in the following (Mathematical Expression 1). If there is no imbalance between modules, the power variation coefficient will have a value close to 1, and if the voltage variation range is large, it will have a value less than 1, and if the voltage variation range is small, it will have a value greater than 1.

[0075] (Equation 1)

[0076]

[0077] (I, V = current, voltage / dI, dV = fluctuations in current, voltage)

[0078] At this time, a large voltage change range means that a specific module has deteriorated or the power generation has decreased due to negative radiation, contamination, etc., and it means a state in which the voltage is changed greatly as in ② of Fig. 7 in order to find the maximum power point by the MPPT algorithm of the inverter. In addition, a small voltage change range and a power variation coefficient greater than 1 means a state in which the power generation performance of the module has significantly decreased as in ③ of Fig. 7 in which the voltage variation amount is almost non-existent compared to the current variation amount. The power variation coefficient calculation module (423) may be configured to calculate the power variation coefficient per unit time, for example, 5 seconds or 10 seconds, and may be configured to determine the final power variation coefficient as the average value of the power variation coefficient per unit time for a certain period of time.

[0079] The above coefficient adjustment module (424) is configured to adjust the final power variation coefficient calculated by the power variation coefficient calculation module (423) to link it with the voltage imbalance coefficient, and can calculate the adjusted power variation coefficient by converting the standard of the normal state from 1 to 0 and increasing the scale, as shown below (Mathematical Formula 2).

[0080] (Equation 2)

[0081] Pf = (1-Pd)*10

[0082] (Here, Pf is the adjusted power variation coefficient, and Pd is the final power variation coefficient)

[0083] Therefore, in a normal state where there is no imbalance between modules, the adjusted power variation coefficient becomes 0, a power variation coefficient less than 1 is converted to a positive number, and a power variation coefficient greater than 1 is converted to a negative number.

[0084] The above-described imbalance detection unit (43) is configured to detect the imbalance state of modules within a string, and calculates the degree of imbalance between modules by reflecting the degree of voltage imbalance calculated by the voltage imbalance calculation unit (41) and the degree of voltage fluctuation abnormality calculated by the fluctuation abnormality calculation unit (42), and in particular, the cause of module imbalance can be diagnosed based on the calculated degree of imbalance. To this end, the above-described imbalance detection unit (433) may include a voltage imbalance coefficient loading module (431), a power fluctuation coefficient loading module (432), an imbalance index calculation module (433), and an abnormality information diagnosis module (434).

[0085] The above voltage imbalance coefficient loading module (431) is configured to load the voltage imbalance coefficient calculated by the voltage imbalance calculation unit (41), and loads the voltage imbalance coefficient calculated by the voltage imbalance coefficient calculation module (413).

[0086] The above power variation coefficient loading module (432) is configured to load the power variation coefficient calculated by the variation anomaly calculation unit (42), and loads the power variation coefficient adjusted by the coefficient adjustment module (424).

[0087] The above-mentioned imbalance index calculation module (433) is configured to calculate an imbalance index indicating the degree of imbalance between modules, and can calculate the imbalance index by multiplying a voltage imbalance coefficient and a power variation coefficient.

[0088] The above abnormal information diagnosis module (434) is configured to diagnose an imbalance state between modules according to an imbalance index, and can diagnose an imbalance state when the imbalance index exceeds a set value. Since the imbalance index is a value obtained by multiplying the degree of voltage imbalance and the degree of abnormal fluctuation, a larger absolute value means a more severe degree of imbalance, and since the power fluctuation coefficient is calculated as a positive or negative number depending on the power fluctuation state, a positive imbalance index means a state in which the voltage change is large, and a negative imbalance index means a state in which the voltage change is small and the current change is large. Therefore, as illustrated in FIG. 8, when the imbalance index is positive, it means that it is operating to the left (ⓐ) of the normal maximum power point, which means that the series resistance is large, and damage, shade, or contamination of the module may be suspected. In addition, when the imbalance index is negative, it means that it is operating to the right (ⓑ) of the normal maximum power point, which means that the parallel resistance is small, and leakage current, cell cracking, or reduced insulation resistance may be suspected. Accordingly, it is possible to accurately diagnose the imbalance between modules, simultaneously identify the cause, and enable rapid response.

[0089] The above insulation resistance diagnosis unit (5) is configured to diagnose a decrease in the insulation resistance of a string, and can preferably be run at night when the solar power generation facility is not in operation. In solar power generation facilities, a decrease in the insulation resistance is a major cause of fire, and thus, the insulation resistance status has been measured periodically in the past. However, since the conventional method was limited to checking whether the insulation resistance, which varies depending on the environment such as humidity, exceeded a reference value, there was a problem in that it could not diagnose local decrease in the insulation resistance or the deterioration status in various environments. Therefore, the insulation resistance diagnosis unit (5) monitors the change in the insulation resistance while applying voltage for a certain period of time, and diagnoses the decrease in the insulation resistance according to the change, thereby enabling accurate diagnosis of insulation resistance decrease even in various environments and local defects. To this end, the insulation resistance diagnosis unit (5) may include a diagnosis time setting module (51), a voltage application module (52), a voltage adjustment module (53), an insulation resistance measurement module (54), a kick index calculation module (55), and an abnormality detection module (56).

[0090] The above diagnosis time setting module (51) is configured to set the time to measure the insulation resistance, and can be configured to measure at night when no power generation is generated. For example, the diagnosis time setting module (51) can be configured to set a specific time, or can be configured to automatically initiate measurement by monitoring the amount of power generation. In addition, the diagnosis time setting module (51) can be configured to operate at a set time only when a decrease in insulation resistance is suspected by the fault diagnosis unit (3) or the module imbalance diagnosis unit (4), thereby enabling efficient measurement and diagnosis. In other words, the diagnosis time setting module (51) can initiate diagnosis of the insulation resistance when the area of ​​the Voc area (∆b) is larger than the area of ​​the Isc area (∆a) in the fault diagnosis unit (3), or when the imbalance index calculated by the imbalance index calculation module (433) is negative.

[0091] The above voltage application module (52) is configured to apply voltage to a string to measure insulation resistance, and can apply voltage for a certain period of time, for example, 10 minutes.

[0092] The voltage regulation module (53) is configured to regulate the voltage applied by the voltage application module (52), and can measure changes in insulation resistance in various voltage situations by increasing the voltage in steps. For example, the voltage regulation module (53) can increase the voltage in steps from 100 V to 500 V.

[0093] The above insulation resistance measurement module (54) is configured to measure insulation resistance for a certain period of time while voltage is applied. For example, when voltage is applied for 10 minutes, insulation resistance can be measured every second. In addition, when insulation resistance is measured while increasing voltage in stages by the voltage adjustment module (53), insulation resistance can be measured every time the voltage is increased.

[0094] The kick index calculation module (55) above is configured to calculate a kick index indicating the degree of change in insulation resistance. If the insulation resistance measured by the insulation resistance measurement module (54) in units of time or voltage does not change linearly but has a large change range or a rapid change as shown in FIG. 10, it can be determined that the insulation resistance has decreased. Accordingly, the kick index calculation module (55) can calculate the degree to which the insulation resistance changes on average as a kick index, and can calculate a time kick index regarding the degree of change per unit time when a constant voltage is applied for a certain period of time, and a voltage kick index regarding the degree of change each time the voltage is changed when the voltage is applied while being adjusted in steps for a certain period of time. At this time, the time kick index can be calculated by the following (Mathematical Formula 3), and the voltage kick index can be calculated by the following (Mathematical Formula 4).

[0095] (Equation 3)

[0096]

[0097] (Equation 4)

[0098]

[0099] The above abnormality detection module (56) is configured to detect an abnormal state due to a decrease in insulation resistance, and can be configured to determine that a state of danger has occurred due to a decrease in insulation resistance when the time kick index or voltage kick index calculated by the kick index calculation module (55) exceeds a set value.

[0100] In the above, the applicant has described various embodiments of the present invention, but such embodiments are only examples of implementing the technical idea of ​​the present invention, and any change or modification that implements the technical idea of ​​the present invention should be interpreted as falling within the scope of the present invention.

Claims

1. A power generation prediction unit that predicts the power generation amount for each string of a solar power generation device, A power generation measuring unit that measures the current string's power generation in real time, A real-time monitoring system for a solar power generation facility, characterized by including a fault diagnosis unit that diagnoses the type of fault in a string by using the area of ​​each region on the IV coordinate plane according to the predicted power generation amount and the measured power generation amount.

2. In the first paragraph, the fault diagnosis unit A real-time monitoring system for a solar power generation facility, characterized by including a power generation decline diagnosis module that diagnoses a decline in power generation when the measured power generation falls below the predicted power generation by a certain degree or more, an Isc area calculation module that calculates the area of ​​the Isc area formed by a line connecting points on an IV coordinate plane according to voltage and current values ​​of the predicted and measured power generation and a line connecting a point indicating a short-circuit current, a Voc area calculation module that calculates the area of ​​the Voc area formed by a line connecting points on an IV coordinate plane according to voltage and current values ​​of the predicted and measured power generation and a line connecting a point indicating an open circuit voltage, an area area comparison module that compares the areas of the Isc area and the Voc area, and a fault cause detection module that detects the type of fault based on the difference in the compared areas.

3. In the second paragraph, the fault cause detection module A real-time monitoring system for solar power generation facilities characterized by determining that a failure is due to an increase in series resistance when the area of ​​the Isc region is larger than the area of ​​the Voc region.

4. In the second paragraph, the fault cause detection module A real-time monitoring system for solar power generation facilities characterized in that when the area of ​​the Voc region is larger than the area of ​​the Isc region, it is determined that the failure is due to a decrease in the module parallel resistance.

5. In the first paragraph, the power generation prediction unit A real-time monitoring system for a solar power generation facility, characterized in that it includes a specification information collection module that collects specification information of solar modules included in a string, a quantity information collection module that collects information on the number of solar modules included in a string, a power generation information collection module that collects information on the number of days of power generation of the string, an environmental information collection module that collects surrounding environment information, an irradiance information collection module that collects irradiance information, a temperature information collection module that collects solar module temperature information, a deterioration rate calculation module that calculates a deterioration rate according to the specifications of the solar modules and the number of days of power generation, and a predicted power generation generation module that predicts power generation using IV curve data by module specifications according to irradiance and module temperature, environmental information, and deterioration rate.

6. In paragraph 1, the real-time monitoring system for the solar power generation facility Includes a module imbalance diagnosis unit that diagnoses imbalance between modules within a string, The above module imbalance diagnosis unit is, A voltage imbalance calculation unit that calculates the degree of voltage imbalance between modules that make up the string, A fluctuation abnormality calculation unit that calculates the abnormality according to the voltage and current fluctuations of the string, A real-time monitoring system for a solar power generation facility, characterized by including an imbalance detection unit that detects imbalance between modules of a string according to the degree of voltage imbalance and the degree of abnormal fluctuation.

7. In paragraph 6, the voltage imbalance calculation unit A real-time monitoring system for a solar power generation facility, characterized by including a string voltage measurement module that measures the voltage of power output from a string, a module voltage measurement module that measures the voltage of a specific module within the string, and a voltage imbalance coefficient calculation module that calculates a voltage imbalance coefficient that indicates the degree of voltage imbalance between modules by subtracting a value obtained by multiplying the number of solar modules included in the string by the voltage of the specific module from the string voltage.

8. In paragraph 7, the voltage imbalance calculation unit A real-time monitoring system for a solar power generation facility, characterized in that it includes a voltage imbalance diagnosis module that compares the voltage imbalance coefficient calculated by the above-mentioned imbalance coefficient calculation module with a reference value, diagnoses a voltage imbalance between modules if the reference value is exceeded, and executes a fluctuation abnormality calculation module.

9. In paragraph 7, the above fluctuation abnormality calculation unit A real-time monitoring system for a solar power generation facility, characterized by including a voltage measurement module that measures the voltage output from a string for a certain period of time, a current measurement module that measures the current output from the string for a certain period of time, and a power fluctuation coefficient calculation module that calculates the value of the ratio of the voltage change amount to the current change amount for the ratio of the voltage to the current per unit time for a certain period of time and calculates a power fluctuation coefficient that indicates the degree of voltage and current fluctuation by the average value thereof.

10. In paragraph 9, the above fluctuation abnormality calculation unit A real-time monitoring system for a solar power generation facility, characterized by including a coefficient adjustment module that increases the scale while changing the standard for the normal state of the power fluctuation coefficient to 0.

11. In the 10th paragraph, the coefficient adjustment module A real-time monitoring system for a solar power generation facility characterized in that it calculates an adjusted power variation coefficient by adjusting the power variation coefficient by mathematical expression 2. (Equation 2) Pf = (1-Pd)*10 (Here, Pf is the adjusted power variation coefficient, and Pd is the initial power variation coefficient) 12. In the 10th paragraph, the imbalance detection unit A real-time monitoring system for a solar power generation facility, characterized by including a voltage imbalance coefficient loading module for loading a voltage imbalance coefficient, a power variation coefficient loading module for loading a power variation coefficient, an imbalance index calculation module for calculating an imbalance index indicating the degree of output imbalance between modules by multiplying the voltage imbalance coefficient and the power variation coefficient, and an abnormality information diagnosis module for diagnosing an abnormality due to output imbalance between modules of a string based on the calculated imbalance index.

13. In the 12th paragraph, the abnormal information diagnosis module A real-time monitoring system for solar power generation facilities characterized by diagnosing module damage, shading, and contamination when the imbalance index is positive, and diagnosing PID, cell cracks, and insulation resistance degradation when the imbalance index is negative.

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