Method and device for identifying abnormal operation of photovoltaic converter by using temperature difference

Abstract

The invention discloses a method and a device for identifying abnormal operation of a photovoltaic converter by using temperature difference, and aims at a photovoltaic power station with m converters. The maximum value point of the probability density function of the temperature difference between the radiator modules of the single converter and all the converters and the in-machine air is used for calculating the difference value or the offset value of the temperature difference between the single converter and 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 temperature difference. The system can be applied to a large-scale photovoltaic power station, and the temperature of the radiator module of the photovoltaic converter and the temperature of air in the photovoltaic power station can be recorded in the photovoltaic power station.

Description

Technical Field

[0001] The present invention belongs to the field of abnormal operation diagnosis of photovoltaic converters, and particularly relates to a method and device for identifying abnormal operation of photovoltaic converters by using temperature difference. Background Art

[0002] As an important link in the power transmission of a photovoltaic power station, diagnosing an abnormally operating photovoltaic converter and overhauling and transforming it are of great significance for improving the efficiency of the photovoltaic power station.

[0003] Currently, a large number of methods for judging whether the operation state of a converter is normal are realized by judging whether the electrical operation parameters of a single converter exceed the design level. The performance of the converter varies greatly in different environments, and the method of using a fixed value to judge whether the converter is abnormal is difficult to achieve the same effect in different regions. There are often a large number of converters in a large-scale photovoltaic power station, and the current method for evaluating the operation state of the converter does not make good use of this favorable condition. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and device for identifying abnormal operation of photovoltaic converters by using temperature difference in view of the deficiencies of the prior art.

[0005] The purpose of the present invention is achieved by the following technical solutions: A method for identifying abnormal operation of photovoltaic converters by using temperature difference, specifically:

[0006] For a photovoltaic power station with m converters, during the daytime of the photovoltaic power station, first, the temperature of the radiator module collected for the jth time by the ith converter is measured and the temperature of the air inside the machine to obtain the temperature difference ΔT between the radiator module and the air inside the machine measured for the jth time by the ith converter i,j which is

[0007] Subsequently, the probability density function f of the temperature difference between the radiator module and the air inside the machine for the ith converter within N days is calculated through the temperature differences between the radiator module and the air inside the machine collected and measured by the ith converter n times within N days i where i = 1,..., i,..., m, j = 1,..., j,..., n i (ΔT i,j ), i = 1,..., i,..., m, j = 1,..., j,..., n i ;

[0008] Then, the probability density function f of the temperature difference between the radiator module and the air inside the machine for all converters within N days is calculated through the temperature differences between the radiator module and the air inside the machine collected and measured by m converters within N days S (ΔTi,j )

[0009] Finally, the probability density function f of the temperature difference between the radiator module of the i-th converter and the air inside the machine is obtained i (ΔT i,j ) at the point where the maximum value is obtained is And the probability density function f of the temperature difference between the radiator module of all converters and the air inside the machine is obtained S (ΔT i,j ) at the point where the maximum value is obtained is Based on the magnitude of the difference or the offset of the temperature difference and the temperature difference to determine whether the i-th converter is abnormal

[0010] Furthermore, the probability density function f of the temperature difference of the i-th converter within N days i (ΔT i,j ) has the following calculation formula

[0011]

[0012] where b = 1, …, b, …, n i ; h i represents the window width used when calculating the probability density function of the temperature difference between the radiator module of the i-th converter and the air inside the machine

[0013] The window width h i has the following calculation formula

[0014]

[0015] where σ i represents the standard deviation of all the temperature differences between the radiator module of the i-th converter and the air inside the machine measured within N days

[0016] The standard deviation σ i has the following calculation formula

[0017]

[0018] where represents the mean value of all the temperature differences between the radiator module of the i-th converter and the air inside the machine measured within N days

[0019] The mean value has the following calculation formula

[0020] Furthermore, the probability density function f of the temperature difference between the radiator module of all converters and the air inside the machine within N days S (ΔT i,j) The calculation formula is:

[0021]

[0022] Wherein, h s represents the window width used when calculating the probability density function of the temperature difference between the radiator modules of all converters and the air inside the machine;

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

[0024]

[0025] Wherein, σ s represents the standard deviation of the temperature differences between all radiator modules and the air inside the machine measured for all converters within N days;

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

[0027]

[0028] Wherein, represents the mean value of the temperature differences between all radiator modules and the air inside the machine measured for all converters within N days;

[0029] The said mean value The calculation formula is:

[0030] Furthermore, it is determined whether the i-th converter is abnormal by the magnitude of the difference between the temperature difference and the temperature difference , specifically:

[0031] When it is determined that the i-th converter is abnormal, otherwise it is determined that the i-th converter is normal, where T zd represents the difference threshold.

[0032] Furthermore, it is determined whether the i-th converter is abnormal by the magnitude of the offset between the temperature difference and the temperature difference , 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≥7, and the number of times n of collecting the temperature difference for each converter within N days i≥100, the number m of converters ≥ 10; the daytime period of the photovoltaic power station is the period from the sunrise time to the sunset time at the location of the photovoltaic power station.

[0035] Further, the difference threshold T zd ≥ 1 °C.

[0036] Further, the offset threshold ε ≥ 0.1.

[0037] The present invention also includes a device for identifying abnormal operation of a photovoltaic converter using temperature difference, including a memory and one or more processors. Executable code is stored in the memory. When the one or more processors execute the executable code, it is used for the method of identifying abnormal operation of a photovoltaic converter using temperature difference as described above.

[0038] The present invention also includes a computer-readable storage medium, on which a program is stored. When the program is executed by a processor, it implements the method of identifying abnormal operation of a photovoltaic converter using temperature difference as described above.

[0039] The beneficial effects of the present invention are as follows: Compared with the prior art, the present invention utilizes the offset rate of the maximum point of the probability density of the temperature difference distribution between a single converter and all converters to determine whether a single converter is in an abnormal working state. After adopting this method, the influence of the operating environment does not need to be considered separately, and when the number of daily sampling points is not fixed, the converters with abnormal operation in a large photovoltaic power station can be accurately found, providing a reference for timely overhaul and transformation of abnormal converters, which is of great significance for improving the efficiency of the photovoltaic power station. Description of the Drawings

[0040] Figure 1 It is a flowchart of a method for identifying abnormal operation of a photovoltaic converter using temperature difference;

[0041] Figure 2 It is a schematic diagram of the probability density function f 1 (ΔT 1,j ) of the temperature difference of the first converter in Embodiment 2;

[0042] Figure 3 It is a schematic diagram of the probability density function f S (ΔT i,j ) of the temperature difference of all converters in Embodiment 2;

[0043] Figure 4 It is a structural diagram of a device for identifying abnormal operation of a photovoltaic converter using temperature difference. Detailed Embodiments

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

[0045] Embodiment 1

[0046] As Figure 1 shown, the present invention provides a method for identifying abnormal operation of a photovoltaic converter using temperature difference, and the method is specifically as follows:

[0047] For a photovoltaic power station with m converters, during the daytime of the photovoltaic power station, first measure the temperature of the radiator module and the air temperature inside the machine collected for the j-th time by the i-th converter. and the air temperature inside the machine to obtain the temperature difference ΔT between the radiator module and the air inside the machine measured for the j-th time by the i-th converter. i,j is

[0048] Subsequently, calculate the probability density function f of the temperature difference between the radiator module and the air inside the machine for the i-th converter within N days through the temperature differences between the radiator module and the air inside the machine measured n times by the i-th converter within N days. i (ΔT i ), where i = 1,..., i,..., m, and j = 1,..., j,..., n. i,j . i .

[0049] The number of days N ≥ 7, the number of times n of collecting temperature differences for each converter within N days i ≥ 100, and the number of converters m ≥ 10; the daytime of the photovoltaic power station is the period from the sunrise time to the sunset time at the location of the photovoltaic power station.

[0050] Then, calculate the probability density function f of the temperature difference between the radiator module and the air inside the machine for all converters within N days through the temperature differences between the radiator module and the air inside the machine measured by m converters within N days. S (ΔT i,j ).

[0051] Finally, calculate that the temperature difference at the maximum point of the probability density function f of the temperature difference between the radiator module and the air inside the machine for the i-th converter is i (ΔT i,j ) is and calculate the probability density function f of the temperature difference between the radiator module and the air inside the machine for all converters S(ΔT i,j ) The temperature difference at the maximum point is Based on the temperature difference and the temperature difference to determine whether the i-th converter is abnormal by the magnitude of the difference or the magnitude of the offset.

[0052] The probability density function f of the temperature difference of the i-th converter within N days i (ΔT i,j ) is calculated as follows:

[0053]

[0054] where b = 1, …, b, …, n i ; h i represents the window width used when calculating the probability density function of the temperature difference between the radiator module and the air inside the machine of the i-th converter.

[0055] The window width h i is calculated as follows:

[0056]

[0057] where σ i represents the standard deviation of all the temperature differences between the radiator module and the air inside the machine of the i-th converter measured within N days.

[0058] The standard deviation σ i is calculated as follows:

[0059]

[0060] where represents the mean value of all the temperature differences between the radiator module and the air inside the machine of the i-th converter measured within N days.

[0061] The mean value is calculated as follows:

[0062] The probability density function f of the temperature differences between the radiator modules and the air inside the machine of all converters within N days S (ΔT i,j ) is calculated as follows:

[0063]

[0064] where h s represents the window width used when calculating the probability density function of the temperature differences between the radiator modules and the air inside the machine of all converters.

[0065] The window width hs The calculation formula is:

[0066]

[0067] where σ s represents the standard deviation of the temperature differences between all radiator modules and the internal air of the machine measured by all converters within N days.

[0068] For the standard deviation σ s the calculation formula is:

[0069]

[0070] where represents the mean value of the temperature differences between all radiator modules and the internal air of the machine measured by all converters within N days.

[0071] For the mean value the calculation formula is:

[0072] To determine whether the i-th converter is abnormal by the magnitude of the difference between the temperature difference and the temperature difference specifically:

[0073] When it is determined that the i-th converter is abnormal, otherwise it is determined that the i-th converter is normal, where T zd represents the difference threshold. The difference threshold T zd ≥1 °C.

[0074] To determine whether the i-th converter is abnormal by the magnitude of the offset between the temperature difference and the temperature difference specifically:

[0075] 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. The offset threshold ε ≥ 0.1.

[0076] Embodiment 2

[0077] First, from 5 am to 9 pm during the day, the temperatures of the radiator modules of 96 converters in a large-scale photovoltaic power station are measured within 40 days and the internal air temperature so that the temperature difference ΔT between the radiator module of the converter and the internal air of the machine measured by the i-th converter for the j-th acquisition i,j : Among them, the first converter collects multiple times every day and obtains the temperature differences between multiple radiator modules and the air inside the machine. The total number of temperature differences between the radiator modules and the air inside the machine obtained within 40 days is n1 = 6702. Then, the probability density function f of the temperature difference between the radiator modules and the air inside the machine for the first converter within 40 days can be calculated. 1 (ΔT 1,j ) is where ΔT 1,j represents the temperature difference between the radiator module and the air inside the machine for the j-th collection of the first converter, and ΔT 1,b represents the temperature difference between the radiator module and the air inside the machine for the b-th collection of the first converter, j = 1, …, j, …, n i , b = 1, …, b, …, n i .

[0078] The obtained probability density function f of the temperature difference between the radiator module and the air inside the machine for the first converter 1 (ΔT 1,j ) is as Figure 2 shown.

[0079] Putting together all the temperature differences between the radiator modules and the air inside the machine of all converters within 40 days, a total of n = 647347 temperature differences between the radiator modules and the air inside the machine, the probability density function f of the temperature difference between the radiator modules and the air inside the machine for all converters within 40 days can be calculated. S (ΔT i,j ) is where ΔT a,b represents the temperature difference between the radiator module and the air inside the machine for the b-th collection of the a-th converter; a = 1, ..., a, ..., n.

[0080] The obtained probability density function f of the temperature difference between the radiator modules and the air inside the machine for all converters S (ΔT i,j ) is as Figure 3 shown.

[0081] From Figure 2 and Figure 3 , it can be seen that the temperature difference corresponding to the maximum point of the probability density distribution of the probability density function f 1 (ΔT 1,j ) is The temperature difference corresponding to the maximum point of the probability density distribution of the probability density function f S (ΔT i,j ) is The calculated temperature difference deviation rate between the first converter and all converters is 1.2. In this embodiment, the offset threshold ε is set to 0.1. Since 1.2 > 0.1, it is determined that the first converter is abnormal.

[0082] Embodiment 3

[0083] This embodiment relates to a device for identifying abnormal operation of a photovoltaic converter by using temperature difference, which includes a memory and one or more processors. Executable code is stored in the memory. When the one or more processors execute the executable code, it is used for the method of identifying abnormal operation of a photovoltaic converter by using temperature difference in Embodiment 1 above. The device embodiment can be applied to any device with data processing capabilities, and the device with data processing capabilities can be a device or apparatus such as a computer.

[0084] Such as 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, there may also be other hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the above Figure 1 shown method. Of course, in addition to the software implementation method, the present invention does not exclude other implementation methods, such as logical devices or a combination of software and hardware, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, and can also be hardware or logical devices.

[0085] Improvements to a technology can be clearly distinguished as being hardware improvements (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software improvements (improvements to method flows). However, with the development of technology, many improvements to method flows today can be regarded as 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 an improvement to a method flow cannot be implemented using a hardware entity module. For example, a Programmable Logic Device (PLD) (e.g., a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logical function is determined by the user programming the device. Designers can program themselves to "integrate" a digital system onto a single PLD, without having to ask a chip manufacturer to design and fabricate a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly implemented using "logic compiler" software, which is similar to the software compiler used in program development and writing. The original code before compilation also has to be written in a specific programming language, which is called a Hardware Description Language (HDL), and there is not just one type of HDL, but many types, 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 currently are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also be aware that by simply performing a little logical programming on the method flow using the above-mentioned several hardware description languages and programming it into the integrated circuit, it is easy to obtain the hardware circuit that implements the logical method flow.

[0086] The controller can be implemented in any suitable manner. For example, the controller can take the form of, for example, a microprocessor or a processor and a computer-readable medium storing computer-readable program code (such as software or firmware) executable by the (micro)processor, logic gates, switches, an application specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of the controller 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 also know that in addition to implementing the controller in the form of pure computer-readable program code, it is entirely possible to implement the same functions by logically programming method steps so that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be regarded as the structures within the hardware component. Or even, the devices for implementing various functions can be regarded as either software modules for implementing the method or the structures within the hardware component.

[0087] The systems, devices, modules, or units illustrated in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0088] It should also be noted that the term "comprising", "including", or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, commodity, or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, commodity, or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the existence of additional identical elements in the process, method, commodity, or device comprising the said element.

[0089] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0090] The present invention can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention can also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0091] Embodiment 4

[0092] The embodiments of the present invention also provide a computer-readable storage medium, on which a program is stored. When the program is executed by a processor, it implements the method for identifying abnormal operation of a photovoltaic converter by using temperature difference in Embodiment 1 above.

[0093] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for identifying abnormal operation of a photovoltaic inverter by using temperature difference, characterized in that, The specific method is as follows: For a photovoltaic power station with m inverters, during the daytime of the photovoltaic power station, first, measure the temperature of the radiator module collected for the j-th time by the i-th inverter and the temperature of the air inside the machine so as to obtain the temperature difference ΔT between the radiator module and the air inside the machine measured for the j-th time by the i-th inverter i,j which is Subsequently, the temperature difference between the radiator module and the air inside the machine measured by the i-th converter in N days for n i times of acquisitions is used to calculate the probability density function f i (ΔT i,j ) for the radiator module and the air inside the machine for the i-th converter within N days, where i = 1, …, i, …, m and j = 1, …, j, …, n i ; Then, the probability density function f of the temperature difference between the radiator modules and the air inside the machine for all converters within N days is calculated by collecting and measuring the temperature differences between all radiator modules and the air inside the machine through m converters within N days S (ΔT i,j ); Finally, the probability density function f of the temperature difference between the radiator module of the i-th converter and the air inside the machine is calculated. i (ΔT i,j ) at the point where it reaches the maximum value of the temperature difference is And the probability density function f of the temperature difference between the radiator modules of all converters and the air inside the machine is calculated. S (ΔT i,j ) at the point where it reaches the maximum value of the temperature difference is Based on the magnitude of the difference or the offset between the temperature difference and the temperature difference , it is determined whether the i-th converter is abnormal.

2. The method for identifying abnormal operation of a photovoltaic converter by using temperature difference according to claim 1, wherein The probability density function f of the temperature difference for the i-th converter within the N days i (ΔT i,j ) is calculated as follows: where \(b = 1,\ldots,b,\ldots,n\) i ; \(h\) i represents the window width used when calculating the probability density function of the temperature difference between the radiator module of the \(i\)-th converter and the air inside the machine; The window width h i The calculation formula is as follows: Among them, σ i represents the standard deviation of the temperature differences between all radiator modules and the air inside the machine measured by the i-th converter within N days; The standard deviation σ i has the following calculation formula: Among them, ΔT i represents the average value of the temperature differences between all radiator modules and the air inside the machine measured by the i-th converter within N days; The mean value The calculation formula is as follows:

3. A method for identifying abnormal operation of a photovoltaic converter using temperature difference according to claim 1, characterized in that, The probability density function f of the temperature difference between the radiator module and the internal air of all converters within the N days S (ΔT i,j ) is calculated by the following formula: Among them, a = 1, …, a, …, n; h s represents the window width used when calculating the probability density function of the temperature difference between the radiator modules of all converters and the air inside the machine; The window width h s The calculation formula is as follows: Among them, σ s represents the standard deviation of the temperature differences between all radiator modules and the air inside the machine measured by all converters within N days; The standard deviation σ s is calculated by the formula: Among them, ΔT s represents the average value of the temperature differences between all radiator modules and the air inside the machine measured by all converters within N days; The mean value The calculation formula is as follows:

4. A method for identifying abnormal operation of a photovoltaic converter using temperature difference according to claim 1, characterized in that, The above-mentioned by temperature difference and temperature difference to determine whether the i-th converter is abnormal according to the magnitude of the difference, specifically: When it is determined that the i-th converter is abnormal, otherwise it is determined that the i-th converter is normal, where T zd represents the difference threshold value.

5. A method for identifying abnormal operation of a photovoltaic converter by using temperature difference according to claim 1, characterized in that, Said by the temperature difference and the temperature difference to determine whether the i-th converter is abnormal according to the magnitude of the offset, 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 value.

6. The method for identifying abnormal operation of a photovoltaic inverter by using temperature difference according to claim 1, characterized in that The number of days N≥7, and the number of times n of collecting the temperature difference by each converter within N days i ≥100, and the number of converters m≥10; the daytime period of the PV power station is the period from the sunrise time to the sunset time at the location of the PV power station.

7. A method for identifying abnormal operation of a photovoltaic converter by using temperature difference according to claim 4, characterized in that, The difference threshold value T zd ≥ 1°C.

8. A method for identifying abnormal operation of a photovoltaic converter by using temperature difference according to claim 5, characterized in that, The offset threshold ε ≥ 0.

1.

9. A device for identifying abnormal operation of a photovoltaic converter by using temperature difference, characterized in that, It includes a memory and one or more processors. An executable code is stored in the memory. When the one or more processors execute the executable code, it is used to implement a method for identifying abnormal operation of a photovoltaic converter by using temperature difference according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, A program is stored thereon. When the program is executed by a processor, it implements a method for identifying abnormal operation of a photovoltaic converter by using temperature difference according to any one of claims 1-8.

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