Fault diagnosis method and device of photovoltaic system, electronic equipment and storage medium

Abstract

The invention discloses a fault diagnosis method and device for a photovoltaic system, electronic equipment and a storage medium. The method comprises the following steps: calculating similarity deviation of each PV string in a group of PV strings with similarity relationship in the photovoltaic system according to a similarity deviation index algorithm; continuously obtaining similarity deviation data of each PV string, and obtaining a similarity deviation index of each PV string according to the similarity deviation data; and when the similarity deviation index of a certain PV string is outside a first preset threshold value, judging that the PV string has a fault, and waiting for field check. A corresponding photovoltaic system fault diagnosis device, electronic equipment and a storage medium are prepared according to the method. Therefore, the defects of traditional photovoltaic system diagnosis are overcome, the fault diagnosis speed is improved, normal and reliable operation of the photovoltaic system is ensured, and possible potential safety hazards are eradicated.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic systems, and in particular to a fault diagnosis method, device, electronic equipment and storage medium for a photovoltaic system. Background Art

[0002] Industrial and commercial photovoltaic power stations mainly use centralized inverters or string inverters. PV modules are connected in series to form photovoltaic strings. For string inverters, photovoltaic strings are generally directly connected to the inverter, and the inverter can support multiple photovoltaic strings. For centralized inverters, photovoltaic strings will first be connected to the combiner box. For example, some combiner boxes can connect 16 photovoltaic strings, and then the combiner box is connected to the PV strings of the centralized inverter.

[0003] Generally, the power configuration and azimuth of each photovoltaic string are basically the same, that is, the number of components and the inclination angle of each photovoltaic string are basically the same, so that under the same climate (irradiance, weather conditions), the power generation trend and power generation of each photovoltaic string are basically the same. If the photovoltaic string is abnormally aged, shadowed, etc., a single photovoltaic string may be significantly different from other photovoltaic strings, so it is necessary to provide a method to screen out abnormalities in a timely manner.

[0004] Under normal circumstances, the service life of photovoltaic modules is 20 to 25 years. However, during the years of operation of photovoltaic systems, exposure to harsh outdoor environmental conditions may cause different types of failures to continue to occur. As the core component of the photovoltaic power generation system, the reliability of photovoltaic modules is the key to the power generation efficiency of the entire photovoltaic system. Common faults in photovoltaic modules include shadows, hot spots, potential induced degradation (PID), diode failure, hidden cracks, and cold solder joints. Among them, the failure rate of hot spots is 25%, which is the highest and most serious. The occurrence of these faults often reduces the service life of the components and accelerates the aging process of the components. In more serious cases, it will cause a series of safety problems, such as fire and leakage, so it is necessary to screen out abnormalities in a timely manner.

[0005] There have been many studies on the failure of photovoltaic modules; the current fault diagnosis methods of photovoltaic arrays mainly include infrared detection, time domain reflection, ground capacitance measurement, IV measurement, intelligent fault diagnosis based on BP neural network and fault tree, and fault diagnosis based on signal processing, etc. However, the analysis data used in the above methods are not easy to obtain under the normal operation of the photovoltaic system, resulting in slow fault diagnosis speed, affecting the normal and reliable operation of the photovoltaic system. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a photovoltaic system fault diagnosis method, device, electronic device and storage medium to overcome the defects of traditional photovoltaic system diagnosis, improve the fault diagnosis speed, ensure the normal and reliable operation of the photovoltaic system, and eliminate possible safety hazards.

[0007] To solve the above technical problems, the fault diagnosis method of the photovoltaic system of the present invention comprises the following steps: Step 1: Calculate the similarity deviation of each PV string in a group of PV strings having a similarity relationship in the photovoltaic system according to a similarity deviation index algorithm; Step 2: Continuously obtain the similarity deviation data of each PV string, and obtain the similarity deviation index of each PV string according to the similarity deviation data; Step 3: When the similarity deviation index of a PV string is outside the first preset threshold, it is determined that the PV string has a fault and needs to be inspected on site.

[0008] Furthermore, when the PV strings in the photovoltaic system are consistent with the channel power configuration, the method for continuously obtaining the similarity deviation data of each PV string includes: Obtain the current curve of each PV string of the inverter; Select a reference curve or reference point from the PV string current curve in combination with the irradiance conditions; The deviation percentage of each feature point in the reference curve or reference point is calculated by using the feature point weighted average deviation comparison method, and a warning signal is issued when the cumulative deviation percentage of each feature point is greater than a second preset threshold.

[0009] Furthermore, similarity deviation data is obtained based on current, voltage, irradiance information, component temperature information and data measurement, and the similarity deviation index is the sum of the absolute values ​​of several deviations.

[0010] Furthermore, the temperature, current, voltage, and irradiance information of each PV string in the photovoltaic system are obtained, and a daily PV string power generation database of each PV string is accumulated and established; The current temperature, current, voltage and irradiance information are combined with the aging factor of the photovoltaic system to obtain the normalized value of the actual daily PV string power generation. The normalized value of the actual daily PV string power generation is compared with the most matching power generation in the PV string power generation database. When the difference between the two is greater than the third preset threshold, it is determined that the current PV string is faulty.

[0011] Furthermore, the aging factor of the PV string is the aging rate of the PV string. The aging rate of the PV string in the first year is set to 2%, and then increases by 0.5% every year.

[0012] A fault diagnosis device for a photovoltaic system based on the above-mentioned diagnosis method comprises: A similarity deviation data acquisition module is used to acquire similarity deviation data of each PV string in the photovoltaic system according to a preset similarity deviation index; A similarity deviation degree calculation module is used to calculate the similarity deviation of each PV string in a group of PV strings having a similarity relationship in the photovoltaic system according to a similarity deviation index algorithm, and is used to continuously obtain similarity deviation data of each PV string, and obtain a similarity deviation index of each PV string according to the similarity deviation data; The judgment module is used to judge that a PV string has a fault and needs to be inspected on site when the similarity deviation index of a PV string is outside a first preset threshold.

[0013] Furthermore, the similarity deviation data acquisition module includes: A current curve acquisition unit, used to acquire a current curve of each PV string of the inverter; A reference curve or reference point selection unit, used to select a reference curve or reference point from a PV string current curve in combination with irradiance conditions; The calculation unit calculates the deviation percentage of each feature point by using the feature point weighted average deviation comparison method; The warning signal sending unit is used to send a warning signal when the cumulative deviation percentage of each feature point is greater than a second preset threshold.

[0014] A fault diagnosis electronic device for a photovoltaic system comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned diagnosis method are implemented.

[0015] A photovoltaic system fault diagnosis storage medium is disclosed, wherein the storage medium is a computer-readable storage medium and stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned diagnosis method are implemented. Since the fault diagnosis method, device, electronic device and storage medium of the photovoltaic system of the present invention adopt the above-mentioned technical scheme, that is, the method calculates the similarity deviation of each PV string in a group of PV strings with similarity relationship in the photovoltaic system according to the similarity deviation index algorithm; continuously obtains the similarity deviation data of each PV string, and obtains the similarity deviation index of each PV string according to the similarity deviation data; when the similarity deviation index of a certain PV string is outside the first preset threshold, it is judged that the PV string has a fault and is to be inspected on site. And the corresponding photovoltaic system fault diagnosis device, electronic device and storage medium are prepared according to this method. Thereby overcoming the defects of traditional photovoltaic system diagnosis, improving the speed of fault diagnosis, ensuring the normal and reliable operation of the photovoltaic system, and eliminating possible safety hazards. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments: Figure 1 It is a flowchart of the fault diagnosis method of the photovoltaic system of the present invention; Figure 2 A flowchart of the process of continuously obtaining similarity deviation data of each PV string in this method; Figure 3 This is a schematic diagram of the fault diagnosis device of the photovoltaic system; Figure 4 This is a structural block diagram of the similarity deviation data acquisition module in the device; Figure 5 Schematic diagram of electronic equipment for fault diagnosis of photovoltaic system; Figure 6 This is the PV current branch curve in the photovoltaic system. DETAILED DESCRIPTION

[0017] Implementation example Figure 1 As shown, the fault diagnosis method of the photovoltaic system of the present invention comprises the following steps: Step 1: Calculate the similarity deviation of each PV string in a group of PV strings having a similarity relationship in the photovoltaic system according to a similarity deviation index algorithm; Step 2: Continuously obtain the similarity deviation data of each PV string, and obtain the similarity deviation index of each PV string according to the similarity deviation data; Step 3: When the similarity deviation index of a PV string is outside the first preset threshold, it is determined that the PV string has a fault and needs to be inspected on site.

[0018] The fault diagnosis method of the photovoltaic system mentioned above first obtains the similarity deviation data of each PV string in the photovoltaic system according to a preset similarity deviation index; then continuously obtains the similarity deviation data, and obtains the similarity deviation degree of each PV string according to the similarity deviation data, and when the similarity deviation degree is outside a first preset threshold, it is judged that the current PV string has a fault, and the similarity deviation data is relatively easy to obtain data such as temperature, current, voltage, irradiance information, etc., which improves the detection efficiency.

[0019] Specifically, first, a group of PV strings (PV string clusters) with similar power generation performance are found in the photovoltaic system, and similarity deviation data of each PV string in the group of PV strings is obtained according to the similarity deviation index algorithm; then the similarity deviation data is continuously obtained, and the similarity deviation index of each PV string is obtained according to the similarity deviation data. When the similarity deviation index is outside the first preset threshold, it is determined that the current PV string has a fault. In this method, the similarity deviation data is calculated based on information and data that are relatively easy to obtain, such as current, voltage, irradiance information, and component temperature, thereby improving detection efficiency.

[0020] Among them, there are elements in the photovoltaic system that have similar power generation performance relationships. For example, in an industrial and commercial photovoltaic system, a centralized photovoltaic inverter often has multiple PV input strings. These PV photovoltaic strings often have the same number of photovoltaic components and the same azimuth angle. This group of PV strings has similar power generation performance. For example, a junction box has several PV branches. These PV branches often have the same number of PV components and the same azimuth angle during design and construction. The PV branches of this group of junction boxes have similar power generation performance.

[0021] Preferably, Figure 2 As shown, when the PV strings in the photovoltaic system are consistent with the channel power configuration, the method for continuously obtaining the similarity deviation data of each PV string includes: Obtain the current curve of each PV string of the inverter; Select a reference curve or reference point from the PV string current curve based on the irradiance conditions; for example, select the values ​​at 9:00, 10:00, 11:00, 12:00, 13:00, 14:00, and 15:00 as reference points, and the current curve from 9:00 to 15:00 is the reference curve; The deviation percentage of each feature point in the reference curve or reference point is calculated by using the feature point weighted average deviation comparison method, and a warning signal is issued when the cumulative deviation percentage of each feature point is greater than the second preset threshold. For example, if the absolute value of the deviation of more than 5 of the 7 reference points is greater than 20%, a warning is issued.

[0022] Preferably, the similarity deviation data is obtained based on current, voltage, irradiance information, component temperature information and data measurement, and the similarity deviation index is the sum of the absolute values ​​of several deviations.

[0023] Preferably, the temperature, current, voltage, and irradiance information of each PV string in the photovoltaic system is obtained, and a daily PV string power generation database of each PV string is accumulated and established; The current temperature, current, voltage and irradiance information are combined with the aging factor of the photovoltaic system to obtain the normalized value of the actual daily PV string power generation. The normalized value of the actual daily PV string power generation is compared with the most matching power generation in the PV string power generation database. When the difference between the two is greater than the third preset threshold, it is determined that the current PV string is faulty.

[0024] Preferably, the aging factor of the PV string is the aging rate of the PV string, and the aging rate of the PV string is set to 2% in the first year and then increases by 0.5% every year.

[0025] By combining the current temperature, current, voltage and irradiance information with the aging factors of the photovoltaic system, the impact of equipment aging is eliminated, misjudgment is avoided, and the accuracy of fault diagnosis is improved.

[0026] like Figure 3 As shown, a fault diagnosis device for a photovoltaic system based on the above-mentioned diagnosis method comprises: A similarity deviation data acquisition module is used to acquire similarity deviation data of each PV string in the photovoltaic system according to a preset similarity deviation index; A similarity deviation degree calculation module is used to calculate the similarity deviation of each PV string in a group of PV strings having a similarity relationship in the photovoltaic system according to a similarity deviation index algorithm, and is used to continuously obtain similarity deviation data of each PV string, and obtain a similarity deviation index of each PV string according to the similarity deviation data; The judgment module is used to judge that a PV string has a fault and needs to be inspected on site when the similarity deviation index of a PV string is outside a first preset threshold.

[0027] Preferably, Figure 4 As shown, the similarity deviation data acquisition module includes: A current curve acquisition unit, used to acquire a current curve of each PV string of the inverter; A reference curve or reference point selection unit, used to select a reference curve or reference point from a PV string current curve in combination with irradiance conditions; The calculation unit calculates the deviation percentage of each feature point by using the feature point weighted average deviation comparison method; The warning signal sending unit is used to send a warning signal when the cumulative deviation percentage of each feature point is greater than a second preset threshold.

[0028] The fault diagnosis device of the photovoltaic system above obtains similarity deviation data of each PV string in the photovoltaic system according to a preset similarity deviation index; continuously obtains the similarity deviation data, and obtains the similarity deviation degree of each PV string according to the similarity deviation data, and when the similarity deviation degree is outside a first preset threshold, it is judged that the current PV string has a fault, and the similarity deviation data is relatively easy to obtain data such as temperature, current, voltage, irradiance information, etc., which improves the detection efficiency.

[0029] It should be noted that the irradiance benchmark value can be based on the project tracking of 15 days of operation, and the average of the total daily irradiance values ​​on non-rainy days is selected as the benchmark value. This value is for reference and does not need to be very accurate. It is only to exclude the analysis when the power generation efficiency of solar panels is low. In this case, the difference of strings is not easy to reflect. For a group of strings with similar attributes, the characteristic value analysis method is used to calculate the current similarity deviation of each string. Considering that the daily power generation of solar strings is related to multiple factors such as solar irradiation and temperature, it is not a linear relationship of a single factor, and the solar irradiance in the morning and evening is relatively low, so the similarity analysis of the strings needs to select the interval where the system power generation efficiency is relatively stable in the middle period.

[0030] Based on actual project experience, selecting 7 characteristic values ​​can meet the needs of screening and simplify the identification of similarity. It is recommended to select the 7 detection values ​​closest to 9:00, 10:00, 11:00, 12:00, 13:00, 14:00, and 15:00, and add the current deviation percentages of the 7 characteristic values ​​to get the average value to obtain the similarity deviation of the string.

[0031] like Figure 6 As shown in the example, the data of the actual project on February 13 contains the current curves of 12 similar valid strings (most of the current curves in the figure overlap). Taking the feature point closest to 9:00 as an example, first calculate the average current of all strings at that time: I 平均 =(Ipv 1-1 +IPv 2-1 +IPv 3-1 +IPv 4-1 +IPv 5-1 +IPv 6-1 +IPv 9-1 + Ipv 10-1 +IPv 11-1 +IPv 12-1 +IPv 13-1 +IPv 14-1 ) / 12; The current deviation of the characteristic point of the PV1 string: Dpv1-1 =(Ipv 1-1 -I 平均 ) / I 平均 ×100%; Similarly, the current deviation Dpv of the PV1 string at 10:00, 11:00, 12:00, 13:00, 14:00, and 15:00 is calculated. 1-2 、 Dpv 1-3 、Dpv 1-4 、Dpv 1-5 、Dpv 1-6 、Dpv 1-7 ; Similarity deviation of PV1 strings: Dpv1=(Dpv 1-1 +Dpv 1-2 +Dpv 1-3 +Dpv 1-4 +Dpv 1-5 +Dpv 1-6 +Dpv 1-7 ) / 7; Similarly, the similarity deviation of each string can be calculated.

[0032] From the example, we can see that the string similarity deviation of PV14 exceeds 20%, which is obviously abnormal. This can prompt the operation and maintenance personnel to conduct on-site investigation and further analysis.

[0033] The obviously abnormal strings are removed, and then the string similarity deviation is calculated for all the strings according to the above method and recorded as the similarity deviation value of the string on that day.

[0034] After obtaining the similarity deviation value of the day, the previous similarity deviation value recorded in the system can be subtracted to obtain the similarity deviation index of each group of strings on the day.

[0035] For example, the similarity deviation value of the system on February 12 has been recorded, and the similarity deviation of that day can be calculated: DDpv1=DPV1(February 13)-Dpv1(February 12); After long-term testing and tracking of actual projects, the similarity deviation is generally within 3% under normal circumstances.

[0036] In general, the calculation of similarity deviation helps to quickly find strings in abnormal working status; the calculation of similarity offset makes it easier to find abnormalities in the early stage of strings, which helps to find problems early. At the same time, the offset index takes into account the differences accumulated by each string in the early system operation process, so the combination of the two is used to find abnormalities and warn early from the data.

[0037] It should be noted that the selection of specific characteristic values ​​can also be flexibly implemented in the project.

[0038] In addition, for strings with similar attributes, the bus voltage is also basically the same. By analyzing the deviation of the bus voltage, it is also easy to first find abnormal strings. In actual projects, when the bus voltage is basically the same, the string current is more likely to deviate, so the string current similarity analysis is selected; if the string power is analyzed for similarity deviation, the result is similar to the current similarity deviation analysis.

[0039] like Figure 5 As shown, a fault diagnosis electronic device for a photovoltaic system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned diagnosis method are implemented.

[0040] The electronic device includes a memory, a processor and a network interface that are interconnected and communicated via a system bus. Figure 5 Only an electronic device having a component memory, a processor, and a network interface is shown, but it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead. Among them, those skilled in the art can understand that the electronic device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application-specific integrated circuit, a programmable gate array, a digital processor, an embedded device, etc.

[0041] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The electronic device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0042] The memory includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory can be an internal storage unit of an electronic device, such as a hard disk or memory of the electronic device. In other embodiments, the memory can also be an external storage device of an electronic device, such as a plug-in hard disk equipped on the electronic device, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Of course, the memory can also include both an internal storage unit of the electronic device and its external storage device. In this embodiment, the memory is generally used to store an operating system and various application software installed in the electronic device, such as a program code of a fault diagnosis method for a photovoltaic system, etc. In addition, the memory can also be used to temporarily store various types of data that have been output or are to be output.

[0043] In some embodiments, the processor may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor or other data processing chip. The processor is generally used to control the overall operation of the electronic device. In this embodiment, the processor is used to run the program code stored in the memory or process data, such as running the program code of the fault diagnosis method of the photovoltaic system.

[0044] The network interface may include a wireless network interface or a wired network interface, which is generally used to establish a communication connection between the electronic device and other electronic devices.

[0045] A photovoltaic system fault diagnosis storage medium is disclosed, wherein the storage medium is a computer-readable storage medium and stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned diagnosis method are implemented.

[0046] Through the description of the above implementation, those skilled in the art can clearly understand that the above implementation can be implemented by means of software and necessary general hardware platforms, and of course by hardware, but in many cases the former is a better implementation. Based on this understanding, the storage medium in essence or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD), including several instructions for a terminal device (which can be a mobile phone, computer, server or network device, etc.) to execute a fault diagnosis method for a photovoltaic system.

Claims

1. A fault diagnosis method for a photovoltaic system, characterized in that The steps include: Step 1: Calculate the similarity deviation of each PV string in a group of PV strings having a similarity relationship in the photovoltaic system according to a similarity deviation index algorithm; Step 2: Continuously obtain the similarity deviation data of each PV string, and obtain the similarity deviation index of each PV string according to the similarity deviation data; Step 3: When the similarity deviation index of a PV string is outside the first preset threshold, it is determined that the PV string has a fault and needs to be inspected on site.

2. The fault diagnosis method of a photovoltaic system according to claim 1, characterized in that: When the PV strings in the photovoltaic system are consistent with the channel power configuration, the method of continuously obtaining the similarity deviation data of each PV string includes: Obtain the current curve of each PV string of the inverter; Select a reference curve or reference point from the PV string current curve in combination with the irradiance conditions; The deviation percentage of each feature point in the reference curve or reference point is calculated by using the feature point weighted average deviation comparison method, and a warning signal is issued when the cumulative deviation percentage of each feature point is greater than a second preset threshold.

3. The fault diagnosis method of a photovoltaic system according to claim 1, characterized in that: The similarity deviation data is obtained based on current, voltage, irradiance information, component temperature information and data measurement. The similarity deviation index is the sum of the absolute values ​​of several deviations.

4. The fault diagnosis method of a photovoltaic system according to claim 3, characterized in that: Obtain the temperature, current, voltage, and irradiance information of each PV string in the photovoltaic system, and accumulate and establish a daily PV string power generation database for each PV string; The current temperature, current, voltage and irradiance information are combined with the aging factor of the photovoltaic system to obtain the normalized value of the actual daily PV string power generation. The normalized value of the actual daily PV string power generation is compared with the most matching power generation in the PV string power generation database. When the difference between the two is greater than the third preset threshold, it is determined that the current PV string is faulty.

5. The photovoltaic system fault diagnosis method according to claim 4, characterized in that: The aging factor of the PV string is the aging rate of the PV string. The aging rate of the PV string is set to be 2% in the first year and then increased by 0.5% each year.

6. A fault diagnosis device for a photovoltaic system based on the diagnosis method according to any one of claims 1 to 5, characterized in that include: A similarity deviation data acquisition module is used to acquire similarity deviation data of each PV string in the photovoltaic system according to a preset similarity deviation index; A similarity deviation degree calculation module is used to calculate the similarity deviation of each PV string in a group of PV strings having a similarity relationship in the photovoltaic system according to a similarity deviation index algorithm, and is used to continuously obtain similarity deviation data of each PV string, and obtain a similarity deviation index of each PV string according to the similarity deviation data; The judgment module is used to judge that a PV string has a fault and needs to be inspected on site when the similarity deviation index of a PV string is outside a first preset threshold.

7. The fault diagnosis device for a photovoltaic system according to claim 6, characterized in that: The similarity deviation data acquisition module includes: A current curve acquisition unit, used to acquire a current curve of each PV string of the inverter; A reference curve or reference point selection unit, used to select a reference curve or reference point from a PV string current curve in combination with irradiance conditions; The calculation unit calculates the deviation percentage of each feature point by using the feature point weighted average deviation comparison method; The warning signal sending unit is used to send a warning signal when the cumulative deviation percentage of each feature point is greater than a second preset threshold.

8. An electronic device for fault diagnosis of a photovoltaic system, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the diagnostic method according to any one of claims 1 to 5 when executing the computer program.

9. A photovoltaic system fault diagnosis storage medium, characterized in that: The storage medium is a computer-readable storage medium and stores a computer program, and when the computer program is executed by a processor, the steps in the diagnostic method according to any one of claims 1 to 5 are implemented.

Images