An intelligent photovoltaic junction box testing method and system

Abstract

This application relates to the technical field of fault detection, and specifically relates to an intelligent photovoltaic junction box testing method and system, which specifically includes: constructing a voltage-current difference index based on the data changes of the current and voltage of the intelligent photovoltaic junction box at adjacent moments, and combining the correlation between the voltage and the ambient temperature to construct an electrical parameter anomaly coefficient; based on the correlation between the internal temperature of the photovoltaic junction box and the time series of other parameters, and combining the electrical parameter anomaly coefficient to construct the fault confidence of the photovoltaic junction box; performing anomaly detection on the photovoltaic junction box based on the fault confidence, reducing the influence of environmental factors on the relevant data of the photovoltaic junction box, avoiding the problem of easy misdetection caused by unreasonable threshold setting when the existing photovoltaic junction box detection technology detects through temperature thresholds, improving the detection accuracy of the photovoltaic junction box, and avoiding misdetection.

Description

Technical Field

[0001] This application relates to the technical field of fault detection, and specifically relates to an intelligent photovoltaic junction box testing method and system. Background Art

[0002] With the growing global demand for renewable energy, as an important part of clean energy, the application scope and scale of solar photovoltaic power generation systems are constantly expanding. As a key component connecting solar cell modules to the external circuit, the photovoltaic junction box not only undertakes the task of current transmission, but also plays a role in protecting the photovoltaic power generation components from electrical faults. Since the photovoltaic junction box is exposed to the natural environment for a long time, its performance is easily affected by the environment and thus fails. Therefore, it is necessary to test the performance of the photovoltaic junction box to ensure the stable operation of the photovoltaic power generation system.

[0003] The existing methods for detecting photovoltaic junction boxes usually involve simple temperature detection. By comparing the temperature of the photovoltaic junction box with a set threshold, the state of the photovoltaic junction box is determined. However, it does not consider the influence of environmental factors on the relevant data of the photovoltaic junction box during the photovoltaic power generation of the photovoltaic power generation components, resulting in poor detection accuracy and easy false detection. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide an intelligent photovoltaic junction box testing method and system, and the specific technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of this application provides an intelligent photovoltaic junction box testing method, which includes the following steps:

[0006] Collect the current, voltage flowing through the intelligent photovoltaic junction box, as well as the internal temperature of the intelligent photovoltaic junction box and the ambient temperature in real time, and construct a current sequence, a voltage sequence, an internal temperature sequence, and an ambient temperature sequence;

[0007] Based on the data changes in the neighborhood of the data points in the first-order difference sequences of the current sequence and the voltage sequence, construct the change fluctuation coefficients of the current sequence and the voltage sequence respectively; based on the change fluctuation coefficients, construct the voltage-current dissimilarity index of the intelligent photovoltaic junction box.

[0008] Based on the difference between the data fluctuation characteristics of the voltage sequence and the ambient temperature sequence, construct the voltage change index of the photovoltaic junction box; analyze the correlation between the voltage sequence and the ambient temperature sequence, and combine the voltage-current dissimilarity index and the voltage change index to construct the electrical parameter abnormality coefficient of the photovoltaic junction box.

[0009] Construct a temperature response index of the photovoltaic junction box based on the correlations between the internal temperature sequence and the current sequence, voltage sequence, and ambient temperature sequence respectively; construct a temperature difference sequence based on the difference between the internal temperature sequence and the ambient temperature sequence, and based on the change trends of the data in the internal temperature, ambient temperature, and temperature difference sequences, construct a temperature normal coefficient of the photovoltaic junction box in combination with the temperature response index; construct a fault confidence level of the photovoltaic junction box based on the electrical parameter anomaly coefficient and the temperature normal coefficient;

[0010] Conduct a fault analysis on the photovoltaic junction box based on the fault confidence level of the photovoltaic junction box.

[0011] In one embodiment, the process of obtaining the change fluctuation coefficient is as follows:

[0012] Obtain each extreme point in the first-order difference sequence of the current sequence through an extreme point detection algorithm, calculate the coefficient of variation of the sequence composed of all data within the neighborhood window of each extreme point, calculate the variance of the coefficients of variation of all extreme points, denoted as B; calculate the ratio of the number of extreme points to the number of elements in the first-order difference sequence of the current sequence, denoted as D; denote the change fluctuation coefficient of the current sequence as , The expression of ;

[0013] Based on the voltage sequence, obtain the change fluctuation coefficient of the voltage sequence by using the same calculation method as that of the change fluctuation coefficient of the current sequence.

[0014] In one embodiment, the expression of the voltage-current difference index is:

[0015] , where A is the voltage-current difference index of the photovoltaic junction box, , are the change fluctuation coefficients of the current sequence and the voltage sequence respectively.

[0016] In one embodiment, the process of obtaining the voltage change index is as follows:

[0017] Denote the first-order difference sequence of the voltage sequence as the voltage change sequence, and denote the first-order difference sequence of the ambient temperature sequence as the ambient change sequence; use the metric distance between the voltage change sequence and the ambient change sequence as the voltage change index of the photovoltaic junction box.

[0018] In one embodiment, the expression of the electrical parameter anomaly coefficient is:

[0019] , where K is the electrical parameter anomaly coefficient of the photovoltaic junction box; A and F are the voltage-current difference index and the voltage change index of the photovoltaic junction box respectively; X is the correlation degree between the voltage sequence and the ambient temperature sequence; is a preset extremely small positive number.

[0020] In one embodiment, the process of obtaining the temperature response index is as follows:

[0021] Denote the correlation between the internal temperature sequence and the current sequence as the first correlation; denote the correlation between the internal temperature sequence and the ambient temperature sequence as the second correlation; take the sum value of the first correlation and the second correlation as the temperature response index of the photovoltaic junction box.

[0022] In one embodiment, the process of obtaining the temperature normal coefficient is as follows:

[0023] Denote the mean value of all elements in the first-order difference sequence of the internal temperature sequence as ; denote the mean value of all elements in the first-order difference sequence of the ambient temperature sequence as ; denote the temperature difference index of the photovoltaic junction box as W, and the expression of W is: ;

[0024] Perform linear fitting on the temperature difference sequence through a fitting algorithm to obtain a fitting straight line, and denote the absolute value of the slope of the fitting straight line as H;

[0025] Denote the temperature normal coefficient of the photovoltaic junction box as G, and the expression of G is: , where P is the temperature response index of the photovoltaic junction box, is a preset extremely small positive number.

[0026] In one embodiment, the expression of the fault confidence level is:

[0027] , where L is the fault confidence level of the photovoltaic junction box, K is the abnormal coefficient of the electrical parameters of the photovoltaic junction box, G is the temperature normal coefficient of the photovoltaic junction box, is a preset extremely small positive number.

[0028] In one embodiment, the fault analysis of the photovoltaic junction box based on the fault confidence level of the photovoltaic junction box is specifically as follows:

[0029] For any photovoltaic junction box in the photovoltaic power generation system, calculate the absolute value of the difference between the fault confidence level of the any photovoltaic junction box and the fault confidence levels of each of the remaining photovoltaic junction boxes, and denote it as the first absolute value of the difference; calculate the mean value of all the first absolute values of the difference of the any photovoltaic junction box, and denote it as the first mean value; denote the product of the first mean value and the fault confidence level of the any photovoltaic junction box as the outlier fault confidence level of the any photovoltaic junction box;

[0030] Take the outlier fault confidence of all photovoltaic junction boxes as the input of the anomaly detection algorithm, and the output is the outlier points among all the outlier fault confidences. Determine the photovoltaic junction box corresponding to the outlier point as the faulty junction box.

[0031] In a second aspect, an embodiment of the present application further provides an intelligent photovoltaic junction box testing system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the method described in any one of the above are implemented.

[0032] The embodiments of the present application have at least the following beneficial effects:

[0033] By analyzing the data change amounts of the current and voltage of the intelligent photovoltaic junction box at adjacent moments, the present application constructs a voltage-current dissimilarity index of the intelligent photovoltaic junction box to preliminarily evaluate the performance of the photovoltaic junction box, avoiding the problem that the change of light intensity affects the abnormal detection of voltage and current; based on the difference between the data fluctuation characteristics of voltage and ambient temperature, and the correlation between the time series of voltage and ambient temperature, an electrical parameter anomaly coefficient is constructed in combination with the voltage-current dissimilarity index to analyze the change relationship between voltage and ambient temperature, and further evaluate the equipment state of the photovoltaic junction box; considering that if an anomaly occurs in the photovoltaic junction box, it will cause abnormal temperature rise, based on the correlation between the internal temperature of the photovoltaic junction box and the time series of other parameters, a fault confidence of the photovoltaic junction box is constructed in combination with the electrical parameter anomaly coefficient to evaluate whether the temperature change of the photovoltaic junction box conforms to the normal law, improving the accuracy of fault analysis of the photovoltaic junction box; based on the difference between the fault confidences of each photovoltaic junction box and those of other photovoltaic junction boxes, the outlier fault confidence of each photovoltaic junction box is constructed, and then the anomaly detection of each photovoltaic junction box is carried out, reducing the influence of environmental factors on the relevant data of the photovoltaic junction box, avoiding the problem that when the existing photovoltaic junction box detection technology detects through a temperature threshold, an unreasonable threshold setting is likely to cause false detection, improving the detection accuracy of the photovoltaic junction box and avoiding false detection. Description of the Drawings

[0034] To more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0035] Figure 1 It is a flowchart of the steps of an intelligent photovoltaic junction box testing method provided by an embodiment of the present application;

[0036] Figure 2Schematic diagram of the acquisition process of the temperature response index. Specific embodiments

[0037] In order to further elaborate on the technical means and effects adopted by this application to achieve the intended invention purpose, the following will combine the accompanying drawings and preferred embodiments to detail the specific embodiments, structures, features and effects of an intelligent photovoltaic junction box testing method and system proposed according to this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0038] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs.

[0039] The following will specifically describe the specific solutions of an intelligent photovoltaic junction box testing method and system provided by this application with reference to the accompanying drawings.

[0040] Please refer to Figure 1 , which shows a flowchart of the steps of an intelligent photovoltaic junction box testing method provided by an embodiment of this application. The method includes the following steps:

[0041] Step S1, collect the current, voltage flowing through the intelligent photovoltaic junction box, as well as the internal temperature of the intelligent photovoltaic junction box and the ambient temperature in real time, and construct a current sequence, a voltage sequence, an internal temperature sequence and an ambient temperature sequence.

[0042] Both ends of the photovoltaic junction box are respectively connected to a photovoltaic power generation component and an electrical device. The photovoltaic power generation component transmits the generated direct current to the electrical device through the photovoltaic junction box. In this application, through the control center of the intelligent photovoltaic junction box, the current data, voltage data and internal temperature data of the intelligent photovoltaic junction box are collected in real time. At the same time, a temperature sensor is placed 5 cm away from the photovoltaic junction box to collect the ambient temperature data of the intelligent photovoltaic junction box in real time.

[0043] Preferably, for the collection of various relevant parameters of the intelligent photovoltaic junction box, in the embodiments of this application, the data collection time interval is set to 1 second. As other embodiments of this application, the implementer can set the data collection time interval according to the actual situation.

[0044] And the time series of the collected current data is recorded as the current sequence, the time series of the voltage data is recorded as the voltage sequence, the time series of the temperature data of the photovoltaic junction box is recorded as the internal temperature sequence, and the time series of the ambient temperature data is recorded as the ambient temperature sequence. In order to eliminate the influence of the dimension between different parameter data, the data in each sequence is processed by Z-score standardization.

[0045] Step S2: respectively construct the change fluctuation coefficients of the current sequence and the voltage sequence based on the data changes in the neighborhoods of the data points in the first-order difference sequences of the current sequence and the voltage sequence; construct the voltage-current dissimilarity index of the intelligent photovoltaic junction box based on the change fluctuation coefficients.

[0046] The power generation of a photovoltaic power generation component is greatly affected by the solar irradiation intensity. Since the solar irradiation intensity shows a characteristic of first rising and then falling during a day, the power generation generated by the photovoltaic power generation component also shows a change characteristic of first rising and then falling.

[0047] Since the photovoltaic junction box leads out the direct current generated by the photovoltaic power generation component, and the power generation of the photovoltaic power generation component changes relatively stably at adjacent acquisition moments. Therefore, if the operating state of the photovoltaic junction box is good, the change degree of the current flowing through the photovoltaic junction box at adjacent moments is also relatively stable; at the same time, although the voltage of the photovoltaic power generation component is greatly affected by the ambient temperature, the higher the temperature, the smaller the voltage, but since the change of the ambient temperature at adjacent acquisition moments is relatively stable, the change of the voltage of the photovoltaic junction box at adjacent moments is also relatively stable.

[0048] Thus, when problems such as diode burnout, heat dissipation failure, and material aging occur in the photovoltaic junction box, it will cause the current path to be blocked, resulting in drastic fluctuations in the current and the voltage becoming unstable, causing the change degree of the current at two adjacent moments to be inconsistent, and the change degree of the voltage at two adjacent moments to be inconsistent; at the same time, the failure of the photovoltaic junction box will also cause the temperature to rise, further affecting the safety and service life of the junction box and its surrounding components.

[0049] (1) Since the overall changes in temperature and light intensity within a day are both large, the overall change in the power generated by the photovoltaic power generation component is also large. Furthermore, under normal circumstances, the overall data of the current and voltage of the junction box also show large changes. Therefore, when analyzing the abnormal data fluctuations in the current and voltage sequences, it is necessary to avoid the influence of the normal fluctuation characteristics of the data on the analysis of abnormal data. Therefore, simple methods such as variance and standard deviation cannot be used to calculate the fluctuation degree of the current and voltage sequences.

[0050] Obtain the first-order difference sequence of the current sequence, and denote it as the current change sequence. The current change sequence can reflect the change degree of the current at adjacent moments.

[0051] Obtain each extreme point in the current change sequence through an extreme point detection algorithm. Respectively construct a neighborhood window for each extreme point with each extreme point as the center. Preferably, in an embodiment of the present application, the size of the neighborhood window is set to 。As other embodiments of the present application, the implementer can set the size of the neighborhood window according to the actual situation. Among them, the extreme point detection algorithm is a well-known technology, and the specific process will not be elaborated here.

[0052] Calculate the coefficient of variation of the sequence composed of all data within the neighborhood window of each extreme point as the local fluctuation index of each extreme point. Among them, the coefficient of variation is a well-known technology, and the specific process will not be elaborated here. It should be noted that when there is insufficient data in the neighborhood window, data filling is performed using the mean value of the existing data in the neighborhood window.

[0053] The smaller the local fluctuation index of each extreme point, the more consistent the data within the neighborhood window of each extreme point. During the time period corresponding to the neighborhood window, the degree of change in the current is relatively consistent, and the greater the possibility that the fluctuation at the corresponding extreme point is a normal fluctuation.

[0054] (2) Calculate the variance of the local fluctuation indices of all extreme points in the current change sequence, denoted as the extreme point fluctuation index of the current change sequence. The larger the extreme point fluctuation index, the greater the fluctuation difference between each extreme point, reflecting that in the current sequence, the data changes more violently.

[0055] (3) Calculate the change fluctuation coefficient of the current sequence based on the extreme point fluctuation index of the current change sequence and the proportion of extreme points in the current change sequence. The expression is:

[0056] , where in the formula, is the change fluctuation coefficient of the current sequence; B is the extreme point fluctuation index of the current change sequence; D is the ratio of the number of extreme points in the current change sequence to the number of all elements in the current change sequence.

[0057] If the extreme point fluctuation index is larger, it indicates that the difference in the local fluctuation degree between different extreme points in the current change sequence is greater, which is less in line with the normal current fluctuation characteristics of the photovoltaic junction box; if the proportion of extreme points is larger, it means that there are more extreme points in the current change sequence, reflecting that the number of sudden changes in the current data is more; thus, if the change fluctuation coefficient of the current sequence is larger, it indicates that the change degree of the current flowing through the photovoltaic junction box at adjacent moments is more inconsistent, and the greater the possibility that the photovoltaic junction box has a fault.

[0058] (4) Based on the voltage sequence, using the same calculation method as the change fluctuation coefficient of the current sequence, obtain the change fluctuation coefficient of the voltage sequence, denoted as .

[0059] (5) Further, based on the above analysis, construct the voltage-current dissimilarity index of the photovoltaic junction box, denoted as A, and the expression of A is: .

[0060] Under normal circumstances, the variation fluctuation coefficients of the voltage and current of the photovoltaic junction box are very small, so the absolute value of the difference between the two is small, and the sum value is also small; when the photovoltaic junction box is abnormal, both the current and voltage will fluctuate violently, and the variation fluctuation coefficients of the voltage and current are both large. Further, there is a difference in the degree of change between the current and the voltage, so the absolute value of the difference between the two is large; furthermore, the larger A is, the more likely the photovoltaic junction box is in an abnormal state.

[0061] Step S3, construct the voltage change index of the photovoltaic junction box based on the difference between the data fluctuation characteristics of the voltage sequence and the ambient temperature sequence; analyze the correlation between the voltage sequence and the ambient temperature sequence, and combine the voltage-current difference index and the voltage change index to construct the electrical parameter abnormality coefficient of the photovoltaic junction box.

[0062] If the operating state of the photovoltaic junction box is good, the voltage of the photovoltaic junction box will slowly decrease as the ambient temperature rises and slowly rise as the ambient temperature drops, and there is a certain proportional relationship between the degree of change of the voltage and the degree of change of the ambient temperature. If a fault occurs in the photovoltaic junction box, it will cause the voltage to fluctuate, thus changing the correlation characteristics between the voltage and the ambient temperature.

[0063] (1) Denote the first-order difference sequence of the voltage sequence as the voltage change sequence, and denote the first-order difference sequence of the ambient temperature sequence as the ambient change sequence; calculate the DTW distance between the voltage change sequence and the ambient change sequence, and denote it as the voltage change index of the photovoltaic junction box. If the DTW distance is smaller, it means that the time of voltage change with the change of ambient temperature is closer, and the degree of change between the voltage and the ambient temperature is more similar, which is more in line with the voltage change characteristics under the normal operating state of the photovoltaic junction box; if the DTW distance is larger, it means that the change rules between the two sequences are more inconsistent, and the difference between the corresponding degrees of change is larger, indicating that the voltage is no longer only affected by temperature, and the possibility of multiple fluctuations is greater.

[0064] (2) Based on the above analysis, construct the electrical parameter abnormality coefficient of the photovoltaic junction box, and the expression is:

[0065] , where K is the electrical parameter abnormality coefficient of the photovoltaic junction box; A is the voltage-current difference index of the photovoltaic junction box; F is the voltage change index of the photovoltaic junction box; X is the correlation degree between the voltage sequence and the ambient temperature sequence, specifically the absolute value of the Pearson correlation coefficient between the voltage sequence and the ambient temperature sequence; is a very small positive number preset by humans, and its function is to avoid the denominator being 0. Preferably. In the embodiments of the present application, is set to 0.01. As other embodiments of the present application, the implementer can set according to the actual situation.The value. Among them, the Pearson correlation coefficient is a well-known technology, and the specific process will not be elaborated here.

[0066] It should be noted that for the calculation of the voltage-temperature correlation, this application only provides one way of calculating the correlation. There are many existing ways of calculating the correlation, and implementers can also use other correlation algorithms to calculate the voltage-temperature correlation. This application does not make specific restrictions.

[0067] If the voltage-current dissimilarity index is larger, it reflects that the change degrees of the voltage and the current at adjacent moments are more inconsistent, and it is less in line with the characteristics of a good photovoltaic junction box; if the voltage change index F is larger, it indicates that the possibility that there is no longer a proportional relationship between the voltage and the temperature is greater; if the voltage-temperature correlation is smaller, it means that the correlation between the voltage and the temperature is smaller, and the possibility that the voltage no longer changes with the temperature is greater. Therefore, if the electrical parameter abnormality coefficient K is larger, it reflects that the voltage and the current fluctuate violently, and the possibility that the voltage is no longer affected by the temperature is greater, and the possibility that the photovoltaic junction box has an abnormality is greater.

[0068] Step S4, construct the temperature response index of the photovoltaic junction box based on the correlations between the internal temperature sequence and the current sequence, the voltage sequence, and the ambient temperature sequence respectively; construct a temperature difference sequence based on the difference between the internal temperature sequence and the ambient temperature sequence, and based on the change trends of the data in the internal temperature, the ambient temperature, and the temperature difference sequence, combine the temperature response index to construct the temperature normal coefficient of the photovoltaic junction box; construct the fault confidence level of the photovoltaic junction box based on the electrical parameter abnormality coefficient and the temperature normal coefficient.

[0069] Furthermore, if a photovoltaic junction box fails, it will not only cause changes in related electrical parameters, but also cause an increase in the temperature inside the junction box. However, considering that the ambient temperature has a direct impact on the temperature of the photovoltaic junction box, an increase in the ambient temperature will also cause an increase in the temperature of the photovoltaic junction box. Therefore, it is necessary to comprehensively detect the temperature of the photovoltaic junction box according to the change of the ambient temperature to avoid misjudgment.

[0070] If the state of the photovoltaic junction box is good, with the increase of the irradiation intensity, the current flowing through the photovoltaic junction box increases with the increase of the power generation. According to Joule's law, an increase in the current will generate more heat, thereby increasing the temperature of the photovoltaic junction box; at the same time, an increase in the ambient temperature will also affect the temperature of the photovoltaic junction box. On the contrary, with the weakening of the irradiation intensity, the power generation decreases, the ambient temperature drops, and the temperature of the photovoltaic junction box will also decrease accordingly; there is a strong correlation between the temperature of the photovoltaic junction box and the current and the ambient temperature.

[0071] (1) Calculate the absolute value of the Pearson correlation coefficient between the internal temperature sequence and the current sequence, denoted as the first correlation; calculate the absolute value of the Pearson correlation coefficient between the internal temperature sequence and the ambient temperature sequence, denoted as the second correlation. Among them, the Pearson correlation coefficient is a well-known technology, and the specific process will not be elaborated.

[0072] It should be noted that for the calculation of the first correlation and the second correlation, this application only provides a correlation calculation method. There are many existing correlation algorithms, and implementers can also use other correlation algorithms to calculate the first correlation and the second correlation. This application does not make specific restrictions.

[0073] Take the sum of the first correlation and the second correlation as the temperature response index of the photovoltaic junction box. The larger the temperature response index, the greater the possibility that the temperature of the photovoltaic junction box conforms to the normal temperature change.

[0074] In summary, the steps to obtain the temperature response index are as follows: Step S401, denote the correlation between the internal temperature sequence and the current sequence as the first correlation; Step S402, denote the correlation between the internal temperature sequence and the ambient temperature sequence as the second correlation; Step S403, take the sum value of the first correlation and the second correlation as the temperature response index of the photovoltaic junction box. The schematic diagram of the process of obtaining the temperature response index is as Figure 2 shown.

[0075] (2) Further, by analyzing the temperature difference between the temperature of the photovoltaic junction box and the ambient temperature, the temperature change caused by natural conditions and the abnormal temperature rise caused by potential failures can be further distinguished.

[0076] Calculate the absolute value of the difference between each element in the internal temperature sequence and the element at the same moment in the ambient temperature sequence, denoted as the first absolute difference value, and denote the sequence composed of the first absolute difference values at all moments as the temperature difference sequence.

[0077] If the photovoltaic junction box is in good condition and the internal heat dissipation system operates normally and can dissipate the heat brought by the large current, the temperature difference sequence will be relatively stable with small fluctuations. If a fault occurs in the photovoltaic junction box and there is an abnormal temperature rise and the heat dissipation system is too late to dissipate the heat, it will cause the temperature difference sequence to fluctuate greatly, and as time goes by, the temperature difference will become larger and larger.

[0078] Fit the temperature difference sequence by the least squares method to obtain a fitting straight line, and denote the absolute value of the slope of this fitting straight line as the rising index of the temperature difference sequence. The larger the rising index, the greater the temperature difference between the temperature of the photovoltaic junction box and the ambient temperature over time, and the less it conforms to the characteristics of a good photovoltaic junction box.

[0079] (3) Obtain the first-order difference sequences of the internal temperature sequence and the ambient temperature sequence respectively, and calculate the mean value of all elements in the first-order difference sequence of the internal temperature sequence, denoted as the internal change index. ; Calculate the mean value of all elements in the first-order difference sequence of the ambient temperature sequence, denoted as the ambient change index. . The internal change index and the ambient change index reflect the change rates of the two temperature sequences over continuous time. The larger the value, the more intense the temperature rise rate is reflected.

[0080] Set the temperature difference index of the photovoltaic junction box based on the difference between the internal change index and the ambient change index. The expression is:

[0081] , where W is the temperature difference index of the photovoltaic junction box, is the internal change index of the internal temperature of the photovoltaic junction box, is the ambient change index of the ambient temperature.

[0082] The larger the temperature difference index, the greater the difference between the temperature rise rate of the photovoltaic junction box and the temperature rise rate of the ambient temperature, and the faster the overall temperature rise rate of the photovoltaic junction box.

[0083] (4) Based on the above analysis, construct the temperature normal coefficient of the photovoltaic junction box. The expression is:

[0084] , where G is the temperature normal coefficient of the photovoltaic junction box, P is the temperature response index of the photovoltaic junction box, H is the rising index of the temperature difference sequence, W is the temperature difference index of the photovoltaic junction box, is a very small positive number preset by humans, and its function is to prevent the denominator from being 0.

[0085] If the temperature response index P is larger, it indicates that the correlation between the internal temperature of the photovoltaic junction box and the current and ambient temperature is stronger and the change is more consistent; if the rising index H is smaller, it indicates that over time, the temperature difference between the internal temperature of the photovoltaic junction box and the ambient temperature still doesn't differ much; if the temperature difference index W is smaller, it indicates that the difference in the temperature rise rates of the internal temperature and ambient temperature of the photovoltaic junction box is smaller, and when W = 0, it indicates that the average temperature rise rate of the ambient temperature is larger, reflecting better heat dissipation performance of the photovoltaic junction box. Therefore, if the temperature normal coefficient G is larger, it indicates that the temperature change of the photovoltaic junction box more conforms to the characteristics of a good junction box, and the possibility that the photovoltaic junction box is in a normal state is greater.

[0086] (5) Construct the fault confidence level of the photovoltaic junction box, denoted as L, based on the electrical parameter abnormal coefficient K and the temperature normal coefficient G of the photovoltaic junction box. The expression of L is: .

[0087] If the abnormal coefficient K of the electrical parameters is larger and the temperature normal coefficient G is smaller, then the fault confidence level L is larger, indicating a greater possibility that all of the voltage, current, and temperature are abnormal, and a greater possibility that the photovoltaic junction box fails.

[0088] Step S5: Perform a fault analysis on the photovoltaic junction box based on the fault confidence level of the photovoltaic junction box.

[0089] Since there are multiple photovoltaic junction boxes in the same photovoltaic power generation system, for any one photovoltaic junction box in the photovoltaic power generation system, calculate the absolute value of the difference between the fault confidence level of the any one photovoltaic junction box and the fault confidence levels of each of the remaining photovoltaic junction boxes, which is denoted as the first absolute difference value; calculate the mean value of all the first absolute difference values of the any one photovoltaic junction box, which is denoted as the first mean value; and denote the product of the first mean value and the fault confidence level of the any one photovoltaic junction box as the outlier fault confidence level of the any one photovoltaic junction box. The outlier fault confidence level can reflect the difference between the fault confidence level of each photovoltaic junction box and the fault confidence levels of all the remaining photovoltaic junction boxes, thereby further avoiding the occurrence of false detection phenomena.

[0090] Use the outlier fault confidence levels of all the photovoltaic junction boxes as the input of the LOF outlier detection algorithm, obtain the outliers among all the outlier fault confidence levels through the LOF outlier detection algorithm, determine the photovoltaic junction boxes corresponding to the outliers as faulty junction boxes, and promptly perform maintenance on the faulty junction boxes. Among them, the LOF outlier detection algorithm is a well-known technology, and the specific process will not be elaborated here.

[0091] It should be noted that for the detection of outliers of the outlier fault confidence levels of all the photovoltaic junction boxes, this application only provides one outlier detection method. There are many existing outlier detection methods, and implementers can also use other outlier detection algorithms to obtain the outliers of the outlier fault confidence levels of all the photovoltaic junction boxes. This application does not make specific restrictions.

[0092] Based on the same inventive concept as the above method, an embodiment of this application also provides an intelligent photovoltaic junction box test system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the methods in the above-mentioned intelligent photovoltaic junction box test method are implemented.

[0093] In summary, the embodiment of the present application provides an intelligent photovoltaic junction box testing method. By analyzing the data change amounts of the current and voltage of the intelligent photovoltaic junction box at adjacent moments, a voltage-current dissimilarity index of the intelligent photovoltaic junction box is constructed to preliminarily evaluate the performance of the photovoltaic junction box, avoiding the problem that the change of light intensity affects the abnormal detection of voltage and current; based on the difference between the data fluctuation characteristics of voltage and ambient temperature, and the correlation between the time series of voltage and ambient temperature, an electrical parameter anomaly coefficient is constructed in combination with the voltage-current dissimilarity index to analyze the change relationship between voltage and ambient temperature, and further evaluate the equipment state of the photovoltaic junction box; considering that if an abnormality occurs in the photovoltaic junction box, it will cause abnormal temperature rise, based on the correlation between the internal temperature of the photovoltaic junction box and the time series of other parameters, a fault confidence level of the photovoltaic junction box is constructed in combination with the electrical parameter anomaly coefficient to evaluate whether the temperature change of the photovoltaic junction box conforms to the normal law, improving the accuracy of fault analysis of the photovoltaic junction box; based on the difference between the fault confidence levels of each photovoltaic junction box and those of other photovoltaic junction boxes, an outlier fault confidence level of each photovoltaic junction box is constructed, and then the abnormality detection of each photovoltaic junction box is carried out, reducing the influence of environmental factors on the relevant data of the photovoltaic junction box, avoiding the problem that when the existing photovoltaic junction box detection technology detects through a temperature threshold, an unreasonable threshold setting is likely to cause false detection, improving the detection accuracy of the photovoltaic junction box and avoiding false detection.

[0094] It should be noted that the above sequence of embodiments of the present application is only for description and does not represent the advantages or disadvantages of the embodiments. And the above specific embodiments of the present application have been described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0095] The embodiments in the present application are all described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

[0096] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for testing an intelligent photovoltaic junction box, characterized in that: The method comprises the following steps: Real-time collection of the current and voltage flowing through the smart photovoltaic junction box, as well as the internal temperature of the smart photovoltaic junction box and the ambient temperature, to construct a current sequence, a voltage sequence, an internal temperature sequence and an ambient temperature sequence; Based on the data changes of the neighborhood of the data points in the first-order difference sequence of the current sequence and the voltage sequence, the change fluctuation coefficients of the current sequence and the voltage sequence are respectively constructed; based on the change fluctuation coefficients, the voltage and current dissimilarity index of the intelligent photovoltaic junction box is constructed; The voltage variation index of the photovoltaic junction box is constructed based on the difference between the data fluctuation characteristics of the voltage sequence and the ambient temperature sequence; the electrical parameter anomaly coefficient of the photovoltaic junction box is calculated, and the expression is: , where K is the electrical parameter anomaly coefficient of the photovoltaic junction box; A and F are the voltage and current difference index and voltage change index of the photovoltaic junction box respectively; X is the correlation between the voltage sequence and the ambient temperature sequence; It is an artificially preset extremely small positive number; The temperature response index of the photovoltaic junction box is constructed based on the correlation between the internal temperature sequence and the current sequence, voltage sequence and ambient temperature sequence; the temperature difference sequence is constructed based on the difference between the internal temperature sequence and the ambient temperature sequence. The mean of all elements in the first-order difference sequence of the internal temperature series is recorded as ; The mean of all elements in the first-order difference sequence of the ambient temperature series is recorded as ; The temperature difference index of the photovoltaic junction box is recorded as W, and the expression of W is: ; The temperature difference series is fitted with a straight line, and the absolute value of the slope of the fitted straight line is recorded as H; The temperature normal coefficient of the photovoltaic junction box is recorded as G, and the expression of G is: , where P is the temperature response index of the photovoltaic junction box, is a very small positive number preset manually; the fault confidence of the photovoltaic junction box is constructed based on the electrical parameter abnormal coefficient and the temperature normal coefficient; For any photovoltaic junction box in the photovoltaic power generation system, calculate the absolute value of the difference between the fault confidence of any photovoltaic junction box and each of the remaining photovoltaic junction boxes, and record it as the first absolute value of the difference; calculate the mean of all the first absolute values ​​of the difference of any photovoltaic junction box, and record it as the first mean; record the product of the first mean and the fault confidence of any photovoltaic junction box as the outlier fault confidence of any photovoltaic junction box; The outlier fault confidence of all photovoltaic junction boxes is used as the input of the anomaly detection algorithm, and the output is the abnormal point among all the outlier fault confidences. The photovoltaic junction box corresponding to the abnormal point is determined as a faulty junction box.

2. A method for testing an intelligent photovoltaic junction box according to claim 1, characterized in that: The process of obtaining the variation fluctuation coefficient is as follows: The extreme point detection algorithm is used to obtain the extreme points in the first-order difference sequence of the current sequence, and the coefficient of variation of the sequence composed of all data in the neighborhood window of each extreme point is calculated. The variance of the coefficient of variation of all extreme points is calculated, which is recorded as B; the ratio of the number of extreme points to the number of elements in the first-order difference sequence of the current sequence is calculated, which is recorded as D; the change fluctuation coefficient of the current sequence is recorded as , The expression is: ; Based on the voltage sequence, the variation fluctuation coefficient of the voltage sequence is obtained by adopting the same calculation method as the variation fluctuation coefficient of the current sequence.

3. A method for testing an intelligent photovoltaic junction box according to claim 1, characterized in that: The expression of the voltage-current dissimilarity index is: , where A is the voltage and current difference index of the photovoltaic junction box, , are the fluctuation coefficients of the current series and voltage series respectively.

4. A method for testing an intelligent photovoltaic junction box according to claim 1, characterized in that: The process of obtaining the voltage change index is as follows: The first-order difference sequence of the voltage sequence is recorded as the voltage change sequence, and the first-order difference sequence of the ambient temperature sequence is recorded as the ambient temperature change sequence; the metric distance between the voltage change sequence and the ambient temperature change sequence is taken as the voltage change index of the photovoltaic junction box.

5. A method for testing an intelligent photovoltaic junction box according to claim 1, characterized in that: The process of obtaining the temperature corresponding index is as follows: The correlation between the internal temperature sequence and the current sequence is recorded as the first correlation; the correlation between the internal temperature sequence and the ambient temperature sequence is recorded as the second correlation; and the sum of the first correlation and the second correlation is used as the temperature response index of the photovoltaic junction box.

6. A method for testing an intelligent photovoltaic junction box according to claim 1, characterized in that: The expression of the fault confidence is: , where L is the fault confidence of the photovoltaic junction box, K is the electrical parameter abnormality coefficient of the photovoltaic junction box, and G is the temperature normality coefficient of the photovoltaic junction box. It is an extremely small positive number preset artificially.

7. An intelligent photovoltaic junction box testing system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

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