Method and device for identifying abnormal photovoltaic inverter by using IGBT (Insulated Gate Bipolar Translator) temperature

Abstract

The invention discloses a method and a device for identifying an abnormal photovoltaic inverter by using IGBT (Insulated Gate Bipolar Translator) temperature, aiming at a photovoltaic power station with m converters. The maximum value point of the probability density function of the converter IGBT temperature of the single converter and the converter IGBT temperature of all the converters is used for calculating the difference value or the offset value of the converter IGBT temperature of the single converter and the converter IGBT temperature of all the converters, and whether the single converter is abnormal or not is judged according to the difference value or the offset value of the converter IGBT temperature of the single converter and the converter IGBT temperature of all the converters. The device can be applied to a large-scale photovoltaic power station, and the condition of the temperature of the IGBT of the photovoltaic converter or the temperature of the IGBT radiator can be recorded in the photovoltaic power station.

Description

Technical Field

[0001] The present invention belongs to the field of photovoltaic converter operation abnormality diagnosis, and in particular relates to a method and device for identifying abnormal photovoltaic inverters by utilizing IGBT temperature. Background Art

[0002] PV converters are an important part of power transmission in PV power plants. Diagnosing abnormal PV converters and overhauling and modifying them is of great significance to improving the efficiency of PV power plants.

[0003] Currently, many methods for determining the normal operation of converters rely on determining whether the electrical operating parameters of individual converters exceed their design levels. Converter performance varies significantly across different environments, making it difficult to consistently assess converter anomalies using fixed values. Large-scale photovoltaic power plants often have a large number of converters, and current converter health assessment methods fail to fully utilize this advantage. Summary of the Invention

[0004] The purpose of the present invention is to address the deficiencies of the prior art and provide a method and device for identifying abnormal photovoltaic inverters using IGBT temperature.

[0005] The object of the present invention is achieved through the following technical solution: a method for identifying abnormal photovoltaic inverters using IGBT temperature, the method specifically comprising:

[0006] For a photovoltaic power station with m converters, during the daytime of the photovoltaic power station, the converter IGBT temperature is obtained by measuring the jth IGBT temperature of the ith converter directly or measuring the IGBT heat sink temperature as t i,j ;

[0007] Then, through the i-th converter, n i The converter IGBT temperature obtained by the acquisition is used to calculate the probability density function f of the converter IGBT temperature for the i-th converter within N days. i (t i,j ), i=1,…,i,…,m, j=1,…,j,…,n i ;

[0008] Then, the probability density function f of the converter IGBT temperature for all converters within N days is calculated by collecting and measuring the temperatures of all converter IGBTs within N days. S (t i,j );

[0009] Finally, the probability density function f of the converter IGBT temperature of the i-th converter is calculatedi (t i,j )The temperature at which the maximum value is obtained is And calculate the probability density function f of the converter IGBT temperature of all converters S (t i,j )The temperature at which the maximum value is obtained is By temperature and temperature The difference or offset between the two values is used to determine whether the i-th converter is abnormal.

[0010] Furthermore, the probability density function f of the temperature of the converter IGBT of the i-th converter within the N days is i (t i,j ) is calculated as:

[0011]

[0012] Where b = 1,…,b,…,n i ;h i represents the window width used when calculating the probability density function of the converter IGBT temperature of the i-th converter;

[0013] The window width h i The calculation formula is:

[0014]

[0015] Among them, σ i represents the standard deviation of all converter IGBT temperatures measured for the i-th converter within N days;

[0016] The standard deviation σ i The calculation formula is:

[0017]

[0018] in, represents the average temperature of all converter IGBTs measured in the i-th converter within N days;

[0019] The mean The calculation formula is:

[0020] Furthermore, the probability density function f of the converter IGBT temperature for all converters within N days is: S (t i,j ) is calculated as:

[0021]

[0022] in, a=1,…,a,…,n;h s represents the window width used when calculating the probability density function of the converter IGBT temperature for all converters;

[0023] The window width h s The calculation formula is:

[0024]

[0025] Among them, σ s represents the standard deviation of the IGBT temperatures of all converters measured over N days;

[0026] The standard deviation σ s The calculation formula is:

[0027]

[0028] in, represents the average temperature of all converter IGBTs measured over N days;

[0029] The mean The calculation formula is:

[0030] Furthermore, the passing temperature and temperature The difference between the values of and is used to determine whether the i-th converter is abnormal. Specifically:

[0031] when Then it is judged that the i-th converter is abnormal, otherwise it is judged that the i-th converter is normal, where T zd Indicates the difference threshold.

[0032] Furthermore, the temperature and temperature The offset of is used to determine whether the i-th converter is abnormal, specifically:

[0033] when It is determined that the i-th converter is abnormal, otherwise it is determined that the i-th converter is normal, where ε represents the offset threshold.

[0034] Furthermore, the number of days N is greater than or equal to 7, and the number of times each converter collects the converter IGBT temperature within N days is n. i ≥100, the number of converters m≥10; the daytime period of the photovoltaic power station is the period from sunrise to sunset at the location of the photovoltaic power station.

[0035] Furthermore, the difference threshold T zd ≥1℃.

[0036] Furthermore, the offset threshold ε is ≥ 0.1.

[0037] The present invention also includes a device for identifying abnormal photovoltaic inverters using IGBT temperature, comprising a memory and one or more processors, wherein the memory stores executable code, and when the one or more processors execute the executable code, it is used for the above-mentioned method of identifying abnormal photovoltaic inverters using IGBT temperature.

[0038] The present invention also includes a computer-readable storage medium having a program stored thereon. When the program is executed by a processor, the method of identifying an abnormal photovoltaic inverter by using IGBT temperature is implemented.

[0039] The beneficial effects of the present invention are as follows: compared with the prior art, the present invention utilizes the difference or offset rate between the temperature at the maximum point of the probability density of the temperature distribution of the IGBT of a single converter and the temperature at the maximum point of the probability density of the temperature distribution of the IGBT of all converters, so as to determine whether a single converter is in an abnormal working state; after adopting this method, the influence of the operating environment need not be considered separately, and when the number of daily sampling points is not fixed, the converter with abnormal operation in a large photovoltaic power station can be found very accurately, which provides a reference for timely inspection and modification of abnormal converters, and is of great significance to improving the efficiency of photovoltaic power stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A flow chart of a method for identifying abnormal photovoltaic inverters using IGBT temperature;

[0041] Figure 2 is the probability density function f of the converter IGBT temperature of the fourth converter in Example 2 4 (t 4,j )

[0042] Figure 3 is the probability density function f of the converter IGBT temperature of all converters in Example 2 S (t i,j )

[0043] Figure 4 The structure diagram of a device that uses IGBT temperature to identify abnormal photovoltaic inverters. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to illustrate the present invention, rather than to represent all embodiments. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments of the present invention without creative work are within the scope of protection of the present invention.

[0045] The present invention aims to provide a method for identifying abnormal photovoltaic converter operation using IGBT temperature, for large-scale photovoltaic power plants with numerous converters. This method utilizes the point with the highest probability density in the IGBT temperature distribution. By comparing the maximum probability density point of the IGBT temperature distribution for a single converter with the maximum probability density point for all converters, the offset ratio of the maximum probability density point is determined, thereby determining whether a single converter is abnormal. This method can accurately identify converters with abnormal operating conditions in large photovoltaic power plants.

[0046] Example 1

[0047] like Figure 1 As shown, the present invention provides a method for identifying abnormal photovoltaic inverters using IGBT temperature, and the method is specifically as follows:

[0048] For a photovoltaic power station with m converters, during the daytime of the photovoltaic power station, the converter IGBT temperature is obtained by measuring the jth IGBT temperature of the ith converter directly or measuring the IGBT heat sink temperature as t i,j .

[0049] Then, through the i-th converter, n i The converter IGBT temperature obtained by the acquisition is used to calculate the probability density function f of the converter IGBT temperature for the i-th converter within N days. i (t i,j ), i=1,…,i,…,m, j=1,…,j,…,n i .

[0050] The number of days N is greater than or equal to 7, and the number of times each converter collects the converter IGBT temperature within N days is n. i ≥100, the number of converters m≥10; the daytime period of the photovoltaic power station is the period from sunrise to sunset at the location of the photovoltaic power station.

[0051] Then, the probability density function f of the converter IGBT temperature for all converters within N days is calculated by collecting and measuring the temperatures of all converter IGBTs within N days. S (t i,j ).

[0052] Finally, the probability density function f of the converter IGBT temperature of the i-th converter is calculated i (t i,j )The temperature at which the maximum value is obtained is And calculate the probability density function f of the converter IGBT temperature of all converters S (t i,j )The temperature at which the maximum value is obtained is By temperature and temperature The difference or offset between the two values is used to determine whether the i-th converter is abnormal.

[0053] The probability density function f of the converter IGBT temperature for the i-th converter within the N days is i (t i,j ) is calculated as:

[0054]

[0055] Where b = 1,…,b,…,n i ;h i represents the window width used when calculating the probability density function of the converter IGBT temperature of the i-th converter;

[0056] The window width h i The calculation formula is:

[0057]

[0058] Among them, σ i represents the standard deviation of all converter IGBT temperatures measured for the i-th converter within N days;

[0059] The standard deviation σ i The calculation formula is:

[0060]

[0061] in, represents the average temperature of all converter IGBTs measured in the i-th converter within N days;

[0062] The mean The calculation formula is:

[0063] The probability density function f of the converter IGBT temperature for all converters within the N days is: S (t i,j ) is calculated as:

[0064]

[0065] in, a=1,…,a,…,n;h s represents the window width used when calculating the probability density function of the converter IGBT temperature for all converters;

[0066] The window width h s The calculation formula is:

[0067]

[0068] Among them, σ s represents the standard deviation of the IGBT temperatures of all converters measured over N days;

[0069] The standard deviation σ s The calculation formula is:

[0070]

[0071] in, represents the average temperature of all converter IGBTs measured over N days;

[0072] The mean The calculation formula is:

[0073] The passing temperature and temperature The difference between the values of and is used to determine whether the i-th converter is abnormal. Specifically:

[0074] when Then it is judged that the i-th converter is abnormal, otherwise it is judged that the i-th converter is normal, where T zd Denotes the difference threshold. The difference threshold T zd ≥1℃.

[0075] The temperature and temperature The offset of is used to determine whether the i-th converter is abnormal, specifically:

[0076] when It is determined that the i-th converter is abnormal, otherwise it is determined that the i-th converter is normal, where ε represents an offset threshold. The offset threshold ε is ≥ 0.1.

[0077] Example 2

[0078] First, the converter IGBT temperature t of 96 converters in a large photovoltaic power station was measured from 5:00 am to 9:00 pm during the daytime. i,j; Among them, the fourth converter collects multiple times a day and obtains multiple converter IGBT temperatures. The total number of converter IGBT temperatures obtained within 40 days is n4 = 6704. The probability density function of the converter IGBT temperature for the fourth converter within 40 days can be calculated Among them, t 4,j represents the converter IGBT temperature collected for the jth time in the fourth converter, t 4,b Indicates the converter IGBT temperature collected for the bth time in the fourth converter, j = 1,…,j,…,n i ,b=1,…,b,…,n i .

[0079] The probability density function f of the converter IGBT temperature for the fourth converter within 40 days is obtained. 4 (t 4,j )like Figure 2 shown.

[0080] Putting together the air temperatures inside the converters for all converters within 40 days, a total of n = 647347 converter IGBT temperatures, the probability density function of the converter IGBT temperature for all converters within 40 days can be calculated: Among them, t a,b represents the IGBT temperature of the a-th converter collected for the b-th time; a=1,…,a,…,n.

[0081] The probability density function f of the converter IGBT temperature for all converters within 40 days is obtained S (t i,j )like Figure 3 shown.

[0082] Depend on Figure 2 and Figure 3 It can be seen that the probability density function f 4 (t 4,j The converter IGBT temperature corresponding to the maximum point of the probability density distribution of ) is Probability density function f S (t i,j The converter IGBT temperature corresponding to the maximum point of the probability density distribution of ) is The calculated offset rate of the converter IGBT temperature of the fourth converter and all converters is 0.4. In this embodiment, the offset threshold ε is set to 0.1. Since 0.32>0.1, it is determined that the fourth converter is abnormal.

[0083] Example 3

[0084] This embodiment relates to a device for identifying abnormal photovoltaic inverters using IGBT temperature, including a memory and one or more processors. The memory stores executable code. When the one or more processors execute the executable code, they are used for the method of identifying abnormal photovoltaic inverters using IGBT temperature in the above-mentioned embodiment 1. The device embodiment can be applied to any device with data processing capabilities, and any device with data processing capabilities can be a device or apparatus such as a computer.

[0085] like Figure 4 At the hardware level, the knowledge distillation device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, it may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 Of course, in addition to software implementation, the present invention does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0086] Improvements to a technology can be clearly distinguished as either hardware improvements (for example, improvements to circuit structures such as diodes, transistors, and switches) or software improvements (improvements to method flows). However, with the advancement of technology, many current method flow improvements can now be considered direct improvements to hardware circuit structures. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that a method flow improvement cannot be implemented using a hardware physical module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is an integrated circuit whose logical function is determined by user programming of the device. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to hire a chip manufacturer to design and produce a dedicated integrated circuit chip. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly done using "logic compiler" software. This is similar to the software compiler used when developing programs. Before compilation, the original code must also be written in a specific programming language, called a hardware description language (HDL). There is not just one HDL, but many, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art will also understand that by simply programming the method flow in one of these hardware description languages and then programming it into an integrated circuit, a hardware circuit that implements the logic method flow can be easily obtained.

[0087] The controller can be implemented in any suitable manner. For example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also know that in addition to implementing the controller in a purely computer-readable program code format, the controller can be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules that implement the method and structures within the hardware component.

[0088] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0089] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0090] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0091] The present invention may be described in the general context of computer-executable instructions, such as program modules, executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media, including storage devices.

[0092] Example 4

[0093] An embodiment of the present invention further provides a computer-readable storage medium having a program stored thereon. When the program is executed by a processor, the method of identifying an abnormal photovoltaic inverter using IGBT temperature according to the first embodiment is implemented.

[0094] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for identifying abnormal photovoltaic inverters using IGBT temperature, characterized in that: The method is specifically as follows: For a photovoltaic power station with m converters, during the daytime of the photovoltaic power station, the converter IGBT temperature is obtained by measuring the jth IGBT temperature of the ith converter directly or measuring the IGBT heat sink temperature as t i,j ; Then, through the i-th converter, n i The converter IGBT temperature obtained by the acquisition is used to calculate the probability density function f of the converter IGBT temperature for the i-th converter within N days. i (t i,j ), i=1,…,i,…,m, j=1,…,j,…,n i ; Then, the probability density function f of the converter IGBT temperature for all converters within N days is calculated by collecting and measuring the temperatures of all converter IGBTs within N days. S (t i,j ); Finally, the probability density function f of the converter IGBT temperature of the i-th converter is calculated i (t i,j )The temperature at which the maximum value is obtained is And calculate the probability density function f of the converter IGBT temperature of all converters S (t i,j )The temperature at which the maximum value is obtained is By temperature and temperature The difference or offset between the two values is used to determine whether the i-th converter is abnormal.

2. The method for identifying abnormal photovoltaic inverters using IGBT temperature according to claim 1, characterized in that: The probability density function f of the converter IGBT temperature for the i-th converter within the N days is i (t i,j ) is calculated as: Where b = 1,…,b,…,n i ;h i represents the window width used when calculating the probability density function of the converter IGBT temperature of the i-th converter; The window width h i The calculation formula is: Among them, σ i represents the standard deviation of all converter IGBT temperatures measured for the i-th converter within N days; The standard deviation σ i The calculation formula is: in, represents the average temperature of all converter IGBTs measured in the i-th converter within N days; The mean The calculation formula is:

3. The method for identifying abnormal photovoltaic inverters using IGBT temperature according to claim 1, characterized in that: The probability density function f of the converter IGBT temperature for all converters within the N days is: S (t i,j ) is calculated as: in, h s represents the window width used when calculating the probability density function of the converter IGBT temperature for all converters; The window width h s The calculation formula is: Among them, σ s represents the standard deviation of the IGBT temperatures of all converters measured over N days; The standard deviation σ s The calculation formula is: in, represents the average temperature of all converter IGBTs measured over N days; The mean The calculation formula is:

4. The method for identifying abnormal photovoltaic inverters using IGBT temperature according to claim 1, characterized in that: The passing temperature and temperature The difference between the values of and is used to determine whether the i-th converter is abnormal. Specifically: when Then it is judged that the i-th converter is abnormal, otherwise it is judged that the i-th converter is normal, where T zd Indicates the difference threshold.

5. The method for identifying abnormal photovoltaic inverters using IGBT temperature according to claim 1, characterized in that: The temperature and temperature The offset of is used to determine whether the i-th converter is abnormal, specifically: when It is determined that the i-th converter is abnormal, otherwise it is determined that the i-th converter is normal, where ε represents the offset threshold.

6. The method for identifying abnormal photovoltaic inverters using IGBT temperature according to claim 1, characterized in that: The number of days N is greater than or equal to 7, and the number of times each converter collects the converter IGBT temperature within N days is n. i ≥100, the number of converters m≥10; the daytime period of the photovoltaic power station is the period from sunrise to sunset at the location of the photovoltaic power station.

7. The method for identifying abnormal photovoltaic inverters using IGBT temperature according to claim 4, characterized in that: The difference threshold T zd ≥1℃.

8. The method for identifying abnormal photovoltaic inverters using IGBT temperature according to claim 5, characterized in that: The offset threshold ε is ≥ 0.

1.

9. A device for identifying abnormal photovoltaic inverters using IGBT temperature, characterized in that: The invention comprises a memory and one or more processors, wherein the memory stores executable code, and when the one or more processors execute the executable code, they are used to implement the method of identifying an abnormal photovoltaic inverter by using IGBT temperature as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that A program is stored thereon, and when the program is executed by the processor, the method of identifying an abnormal photovoltaic inverter by using IGBT temperature as described in any one of claims 1 to 8 is implemented.

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