Inverter mppt voltage abnormality diagnosis method and device, electronic equipment and storage medium

Abstract

This invention discloses a method, apparatus, electronic device, and storage medium for diagnosing inverter MPPT voltage anomalies. The method includes: calculating the component temperature during the diagnostic time period based on the component temperature expression; calculating the theoretical MPPT voltage value during the diagnostic time period based on the component temperature and a preset voltage model; and determining whether the inverter MPPT voltage is abnormal based on the theoretical MPPT voltage value and the actual MPPT voltage value of the inverter during the diagnostic time period. This invention can promptly detect whether the inverter MPPT voltage is abnormal.

Description

Technical Field

[0001] This invention relates to the field of inverter technology, and in particular to a method, apparatus, electronic device, and storage medium for diagnosing MPPT voltage anomalies in inverters. Background Technology

[0002] An inverter is a converter that transforms direct current (DC) into fixed-frequency or frequency- and voltage-regulated alternating current (AC). Inverters have important applications in fields such as photovoltaic (PV) systems. The MPPT (Maximum Power Point Tracking) voltage of an inverter refers to how the inverter adjusts the output power of the PV array according to different external environmental characteristics such as temperature and light intensity, ensuring that the PV array always outputs maximum power. In the absence of external power limiting commands, the inverter typically operates at its maximum power point, and its efficiency is optimal when operating at the maximum power point.

[0003] When an inverter deviates from its maximum power point, it indicates an abnormal MPPT voltage, which leads to a reduction in active power. However, inverter MPPT voltage abnormalities generally do not trigger an alarm, increasing the difficulty for maintenance personnel to detect these anomalies and resulting in significant power generation losses. In other words, current technology cannot detect MPPT voltage abnormalities in a timely manner. Summary of the Invention

[0004] This invention provides a method, device, electronic device, and storage medium for diagnosing inverter MPPT voltage anomalies, so as to detect whether the inverter MPPT voltage is abnormal in a timely manner.

[0005] According to one aspect of the present invention, a method for diagnosing MPPT voltage anomalies in an inverter is provided, comprising:

[0006] Calculate the component temperature during the time period to be diagnosed based on the component temperature expression;

[0007] Calculate the theoretical MPPT voltage value during the diagnostic time period based on the component temperature and preset voltage model during the diagnostic time period.

[0008] The inverter's MPPT voltage is determined to be abnormal based on the theoretical MPPT voltage value and the actual MPPT voltage value of the inverter during the diagnostic time period.

[0009] Optionally, determining whether the inverter's MPPT voltage is abnormal based on the theoretical MPPT voltage value and the inverter's actual MPPT voltage value during the diagnostic time period includes:

[0010] Calculate the deviation rate between the theoretical MPPT voltage and the actual MPPT voltage. If the absolute value of the deviation rate is greater than a preset value during the diagnostic time period, then the inverter MPPT voltage is determined to be abnormal.

[0011] Optionally, determining whether the inverter's MPPT voltage is abnormal based on the theoretical MPPT voltage value and the inverter's actual MPPT voltage value during the diagnostic time period includes:

[0012] The deviation rate between the theoretical MPPT voltage value and the actual MPPT voltage is calculated. If the absolute value of the deviation rate is greater than a preset value during the diagnostic time period, a preset condition is judged. If the preset condition is met, the inverter MPPT voltage is determined to be abnormal. Here, n is a positive integer, and the preset condition includes: the absolute value of the deviation rate is less than the preset value during each extended preset time period before and after the diagnostic time period of the previous n days.

[0013] Optionally, the preset conditions further include: in the time periods before and after the diagnosis period and the n-day diagnosis period, the start time is greater than the inverter grid connection time by a first preset value, and the end time is greater than the inverter grid disconnection time by a second preset value.

[0014] Optionally, the preset conditions also include: during the time period to be diagnosed and the time periods before and after the n-day period to be diagnosed, the inverter apparent power is less than the maximum apparent power, the AC current is less than the maximum current, and there is no external power limiting command.

[0015] Optionally, the deviation rate is calculated using the following formula:

[0016] ΔU=(U 实际MPPT -U 理论MPPT ) / U 理论MPPT Where ΔU is the deviation rate, U 实际MPPT U is the actual MPPT voltage. 理论MPPT This is the theoretical MPPT voltage.

[0017] Optionally, before calculating the component temperature within the diagnostic time period based on the component temperature expression, the method further includes:

[0018] Obtain the module temperature, tilt surface irradiance, total module area in the string, string voltage, string current, module temperature, air temperature, and wind speed for the first historical time period to fit the module temperature expression.

[0019] Optionally, obtaining the module temperature, tilt surface irradiance, total module area in the string, string voltage, string current, module temperature, air temperature, and wind speed for the first historical time period to fit the module temperature expression includes:

[0020] Starting from time t0 in the first historical time period, the component temperature T is obtained. 组件t0 and the tilt surface irradiance f, total module area A in the string, string voltage u, string current i, and module temperature T at each time after time t0. 组件 Temperature T 空气 And wind speed v;

[0021] Substituting into the heat balance equation from time t0 to time t1,

[0022] Fit the values ​​of a, b, and c.

[0023] Optionally, before calculating the theoretical MPPT voltage value for the time period to be diagnosed based on the component temperature and a preset voltage model during the time period to be diagnosed, the method further includes:

[0024] Using the irradiance, module temperature, module voltage temperature coefficient, and module nameplate maximum power point voltage in the second historical time period as input parameters, and the actual MPPT voltage of the inverter during normal operation in the second historical time period as output parameters, the preset voltage model is trained using a deep learning algorithm.

[0025] Optionally, the deep learning algorithm includes: an unsupervised pre-trained network, a convolutional neural network, a recurrent neural network, or a recurrent neural network.

[0026] According to another aspect of the present invention, an inverter MPPT voltage anomaly diagnostic device is provided, comprising:

[0027] The first calculation module is configured to calculate the component temperature during the diagnostic time period based on the component temperature expression.

[0028] The second calculation module is configured to calculate the theoretical MPPT voltage value during the time period to be diagnosed based on the component temperature and a preset voltage model during the time period to be diagnosed.

[0029] The judgment module is configured to determine whether the inverter MPPT voltage is abnormal based on the theoretical MPPT voltage value and the actual MPPT voltage value of the inverter during the diagnostic time period.

[0030] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0031] At least one processor; and

[0032] A memory communicatively connected to the at least one processor; wherein,

[0033] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the MPPT voltage anomaly diagnosis method described above.

[0034] According to another aspect of the present invention, a storage medium is provided that stores computer instructions for causing a processor to execute the above-described MPPT voltage anomaly diagnosis method.

[0035] The technical solution of this invention calculates the theoretical MPPT voltage of the inverter during the diagnostic period by adding the component temperature factor. The calculation of the theoretical MPPT voltage is more accurate, and it is compared with the actual MPPT voltage during the diagnostic period. This allows for timely detection of whether the inverter's MPPT voltage is abnormal, enabling maintenance personnel to perform maintenance in a timely manner, thereby ensuring that the inverter is always in the best working condition and preventing energy waste.

[0036] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a flowchart of an inverter MPPT voltage anomaly diagnosis method provided in Embodiment 1 of the present invention;

[0039] Figure 2 This is a flowchart of an inverter MPPT voltage anomaly diagnosis method provided in Embodiment 2 of the present invention;

[0040] Figure 3 This is a flowchart of an inverter MPPT voltage anomaly diagnosis method provided in Embodiment 3 of the present invention;

[0041] Figure 4 This is a flowchart of an inverter MPPT voltage anomaly diagnosis method provided in Embodiment 4 of the present invention;

[0042] Figure 5 This is a schematic diagram of the structure of an inverter MPPT voltage anomaly diagnostic device provided in Embodiment 4 of the present invention;

[0043] Figure 6 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. Detailed Implementation

[0044] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0045] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0046] Example 1

[0047] Figure 1 This is a flowchart of a method for diagnosing inverter MPPT voltage anomalies according to Embodiment 1 of the present invention. This embodiment is applicable to the diagnosis of inverter MPPT voltage anomalies. The method can be executed by an inverter MPPT voltage anomaly diagnosis device, which can be implemented in hardware and / or software, and can be configured in a processor. Figure 1 As shown, the method includes:

[0048] S130. Calculate the component temperature during the time period to be diagnosed based on the component temperature expression.

[0049] Specifically, the inverter can be, for example, a string inverter used in a photovoltaic system. The diagnostic time period represents the period during which the inverter needs to be monitored. For example, it can be a specific time period within the date on which MPPT voltage anomaly diagnosis is required, or it can be a specific time period after the inverter starts operating and begins timing. The current data of the inverter is the independent variable involved in the module temperature expression (which will be explained later). The current data at any time within the diagnostic time period can be substituted into the module temperature expression to calculate the module temperature at that time, which facilitates the subsequent calculation of the theoretical MPPT voltage.

[0050] S140. Calculate the theoretical MPPT voltage value during the diagnostic time period based on the component temperature and preset voltage model during the diagnostic time period.

[0051] Specifically, the theoretical MPPT voltage value at any given moment during the diagnostic period can be calculated by substituting the component temperature and other directly obtainable relevant data (which will be explained later) into a preset voltage model.

[0052] S150. Determine whether the inverter MPPT voltage is abnormal based on the theoretical MPPT voltage value and the actual MPPT voltage value of the inverter during the diagnostic period.

[0053] Specifically, during inverter operation, the actual MPPT voltage value at any given time is used as an operating parameter of the inverter and can be directly obtained. The method for obtaining this value is well known to those skilled in the art and will not be elaborated here. By comparing the theoretical MPPT voltage with the actual MPPT voltage during the diagnostic period, it can be determined whether the inverter's MPPT voltage is abnormal. If abnormal, maintenance personnel should be notified promptly for maintenance.

[0054] The technical solution in this embodiment employs an inverter MPPT voltage anomaly diagnosis method. By adding the component temperature factor, the theoretical MPPT voltage of the inverter during the diagnostic period is calculated. The calculation of the theoretical MPPT voltage is more accurate, and it is compared with the actual MPPT voltage during the diagnostic period. This allows for timely detection of whether the inverter MPPT voltage is abnormal, enabling maintenance personnel to perform timely maintenance and ensuring that the inverter is always in optimal working condition, thus preventing energy waste.

[0055] Example 2

[0056] Figure 2 This is a flowchart of a method for diagnosing inverter MPPT voltage anomalies according to Embodiment 2 of the present invention. Based on the above embodiments, this embodiment refines the method for determining whether the inverter MPPT voltage is abnormal. Figure 2 As shown, the method includes:

[0057] S1. Obtain the module temperature, tilt surface irradiance, total module area in the string, string voltage, string current, module temperature, air temperature, and wind speed for the first historical time period to fit the module temperature expression.

[0058] Specifically, the inverter's component temperature affects its theoretical MPPT voltage value, and the component temperature can be calculated using relevant parameters. For example, an expression for the inverter's component temperature can be fitted based on data from a first historical time period. An expression for the inverter's component temperature can be fitted using historical data, allowing the calculation of the inverter's component temperature within the diagnostic period. The first historical time period can be any period during which the inverter operates normally.

[0059] S130. Calculate the component temperature during the time period to be diagnosed based on the component temperature expression;

[0060] S140. Calculate the theoretical MPPT voltage value during the diagnostic time period based on the component temperature and preset voltage model during the diagnostic time period.

[0061] S151. Calculate the deviation rate between the theoretical MPPT voltage and the actual MPPT voltage. If the absolute value of the deviation rate is greater than the preset value within the diagnostic time period, then the inverter MPPT voltage is determined to be abnormal.

[0062] Specifically, the formula for calculating the deviation rate is: ΔU=(U 实际MPPT -U 理论MPPT ) / U 理论MPPT Where ΔU is the deviation rate, U 实际MPPT U is the actual MPPT voltage. 理论MPPT The theoretical MPPT voltage is used. A preset value could be, for example, ±5%. In this embodiment, the deviation rate between the inverter's theoretical MPPT voltage and the actual MPPT voltage can be compared in real time to determine whether the inverter is abnormal at any given moment. To eliminate the influence of random shading (e.g., an obstruction blocking the inverter at a certain moment), the average absolute value of the deviation rate during the diagnostic time period can be calculated. If this average value is greater than the preset value, it indicates that the inverter is malfunctioning; if the average value is less than or equal to the preset value, it indicates that the inverter is functioning normally. This embodiment determines whether the inverter's MPPT voltage is abnormal by comparing the deviation rate between the theoretical and actual MPPT voltage during the diagnostic time period. The determination method is simple and requires minimal computation.

[0063] Example 3

[0064] Figure 3 This is a flowchart of an inverter MPPT voltage anomaly diagnosis method provided in Embodiment 3 of the present invention, as shown below. Figure 3 As shown, the method includes:

[0065] S1. Obtain the module temperature, tilt surface irradiance, total module area in the string, string voltage, string current, module temperature, air temperature, and wind speed for the first historical time period to fit the module temperature expression.

[0066] S130. Calculate the component temperature during the time period to be diagnosed based on the component temperature expression;

[0067] S140. Calculate the theoretical MPPT voltage value during the diagnostic time period based on the component temperature and preset voltage model during the diagnostic time period.

[0068] S152. Calculate the deviation rate between the theoretical MPPT voltage value and the actual MPPT voltage. If the absolute value of the deviation rate is greater than the preset value within the diagnostic time period, then perform a judgment based on the preset conditions. If the preset conditions are met, then determine that the inverter MPPT voltage is abnormal. Wherein, n is a positive integer, and the preset conditions include that the absolute value of the deviation rate is less than the preset value within each extended preset time period before and after the diagnostic time period of the previous n days.

[0069] Specifically, the diagnostic time period refers to a specific time segment of the date for which diagnosis is needed, such as 9:00 AM to 10:00 AM. To rule out fixed shading (an object obstructing the inverter during a certain time segment) affecting the inverter's MPPT voltage, the deviation rate can be calculated within the n days preceding the date of the diagnostic time period, extending by a preset time interval before and after the diagnostic time period. If the absolute value of the deviation rate is less than the preset value, it indicates that no fixed object is obstructing the inverter during the diagnostic time period. Therefore, the inverter's MPPT voltage abnormality is not due to fixed shading. In this case, a diagnosis of inverter MPPT voltage abnormality can be made, thus improving the accuracy of the inverter MPPT voltage abnormality diagnosis. The preset time can be 15 minutes.

[0070] Preferably, based on the above embodiments, the preset conditions further include: in the time period before and after the diagnosis period and the time period before and after the diagnosis period of the previous n days, the time between the start time and the grid connection time of the inverter is greater than a first preset value, and the time between the end time and the grid disconnection time of the inverter is greater than a second preset value.

[0071] Specifically, the first preset value and the second preset value can be the same. The MPPT voltage of the inverter may be unstable for a period of time after grid connection and for a period of time before grid disconnection. By judging the time period to be diagnosed and the time periods before and after the time period to be diagnosed, if the start time is greater than the time of grid connection and the end time is greater than the time of grid disconnection, it can be ensured that the abnormality of the inverter MPPT voltage is not caused by the grid connection or disconnection of the inverter, thereby improving the accuracy of the diagnosis of abnormal inverter MPPT voltage.

[0072] Preferably, based on the above embodiments, the preset conditions further include: during the time period to be diagnosed and the time periods before and after the time period to be diagnosed n days prior, the inverter apparent power is less than the maximum apparent power, the AC current is less than the maximum current, and there is no external power limiting command.

[0073] Specifically, when the inverter's apparent power equals the maximum apparent power, i.e., when the inverter is at full power, the MPPT voltage will deviate from the maximum power point; when the inverter's AC current equals the maximum current, i.e., when the inverter is at full current, the MPPT voltage will also deviate from the maximum power point; and when the inverter is under an external power limiting command, the MPPT voltage will also deviate from the maximum power point. In this embodiment, through the above settings, the abnormal MPPT voltage of the inverter under full power, full current, and external power limiting command conditions can be eliminated, further improving the accuracy of the inverter's MPPT voltage abnormality diagnosis.

[0074] Example 4

[0075] Figure 4 This is a flowchart of an inverter MPPT voltage anomaly diagnosis method provided in Embodiment 4 of the present invention, referred to... Figure 4 The preset voltage model is the MPPT voltage model; the inverter's MPPT voltage model can be used to calculate the theoretical MPPT voltage of the inverter during operation. For example, the inverter's MPPT voltage model can be trained based on data from the second historical time period.

[0076] Specifically, the second historical time period can be any period during which the inverter operates normally. The data within the second historical time period includes the component temperature of the inverter during that period. In some implementations, the second historical time period can be the same as the first historical time period, for example, the hour preceding the period to be diagnosed. The data in the second historical time period includes the actual MPPT voltage during normal inverter operation. Since the inverter operates normally, its actual MPPT voltage is the same as the theoretical MPPT voltage. Therefore, the actual MPPT voltage can be used as the output, and the data affecting the MPPT voltage (described later) can be used as the input. By training, an MPPT voltage model can be obtained, and subsequently, the theoretical MPPT voltage of the inverter during operation can be calculated using the MPPT voltage model.

[0077] To obtain the module temperature, tilt surface irradiance, total module area in the string, string voltage, string current, module temperature, air temperature, and wind speed for the first historical time period, and to fit the module temperature expression, the following data is used:

[0078] S111. Taking time t0 in the first historical time period as the starting time, obtain the component temperature T at time t0. 组件t0and the tilt surface irradiance f, total module area A in the string, string voltage u, string current i, and module temperature T at each time after time t0. 组件 Temperature T 空气 And wind speed v;

[0079] Specifically, the above data can be obtained from the corresponding monitoring module of the inverter. The acquisition method is well known to those skilled in the art and will not be described in detail here. The above data can be used to fit the component temperature expression.

[0080] S112. Substitute the heat balance equation from time t0 to time t1.

[0081] Specifically, in the above heat balance equation, The radiant energy received by the component. The energy that is radiated and converted into electrical energy. The heat transferred from the component to the air, c(T) 组件 -T 组件t0 ) represents the actual heat added by the component; a, b, and c are unknown quantities, and T represents the heat added in the data of the first historical time period. 组件 Since these are known quantities, the values ​​of a, b, and c can be fitted using historical data. Then, T can be further... 组件 As an unknown quantity, it is used to calculate the component temperature during the time period to be diagnosed.

[0082] S113. Fit the values ​​of a, b, and c.

[0083] Before calculating the theoretical MPPT voltage value for the diagnostic time period based on the component temperature and preset voltage model during the diagnostic time period, the following steps are also included:

[0084] S121. Using the irradiance, module temperature, module voltage temperature coefficient, and module nameplate maximum power point voltage in the second historical time period as input parameters, and the actual MPPT voltage of the inverter when it is working normally in the second historical time period as output parameters, the preset voltage model is trained using a deep learning algorithm.

[0085] Specifically, irradiance and module temperature can be obtained directly from the second historical time period, while module voltage temperature coefficient and module nameplate maximum power point voltage can be obtained from the module parameters. All of the above data affect the inverter's MPPT voltage. However, since there is no direct formula for calculating the MPPT voltage, a model of input and output parameters, i.e., the MPPT voltage model, can be trained using a deep learning algorithm.

[0086] Preferably, the deep learning algorithm described above may include unsupervised pre-trained networks, convolutional neural networks, recurrent neural networks, or recurrent neural networks. Of course, other types of neural networks may also be used.

[0087] like Figure 4 As shown, the above method also includes:

[0088] S130. Calculate the component temperature during the time period to be diagnosed based on the component temperature expression;

[0089] S140. Calculate the theoretical MPPT voltage value during the diagnostic time period based on the component temperature and preset voltage model during the diagnostic time period.

[0090] S153. Calculate the deviation rate between the theoretical MPPT voltage value and the actual MPPT voltage. If the absolute value of the deviation rate is greater than the preset value within the diagnostic time period, then execute S154; otherwise, end.

[0091] S154. Determine whether the deviation rate is less than the preset value in each of the extended preset time periods before and after the diagnosis period of the previous n days; if yes, then execute S155; otherwise, end.

[0092] S155. Determine whether the time between the start time and the inverter grid connection time is greater than the first preset value, and whether the time between the end time and the inverter grid disconnection time is greater than the second preset value, in the time periods before and after the diagnosis period and the time periods before and after the diagnosis period n days prior. If yes, execute S156; otherwise, end.

[0093] S156. Determine whether the inverter's apparent power is less than the maximum apparent power, the AC current is less than the maximum current, and there is no external power limiting command during the time periods before and after the diagnosis period and the time periods before and after the diagnosis period n days prior. If yes, execute S157; otherwise, end.

[0094] S157 issues an alarm for abnormal inverter MPPT voltage.

[0095] Example 4

[0096] Figure 5 This is a schematic diagram of the structure of an inverter MPPT voltage anomaly diagnostic device provided in Embodiment 4 of the present invention. Figure 5 As shown, the device includes:

[0097] The first calculation module 103 is configured to calculate the component temperature during the time period to be diagnosed based on the component temperature expression.

[0098] The second calculation module 105 is configured to calculate the theoretical MPPT voltage value during the diagnostic time period based on the component temperature and the preset voltage model during the diagnostic time period.

[0099] The judgment module 104 is configured to determine whether the inverter MPPT voltage is abnormal based on the theoretical MPPT voltage value and the actual MPPT voltage value of the inverter during the diagnostic time period.

[0100] Specifically, the inverter MPPT voltage anomaly diagnosis device can be used to execute the inverter MPPT voltage anomaly diagnosis method provided in any embodiment of the present invention. Its working process and other details can be found in the description of the inverter MPPT voltage anomaly diagnosis method section of the embodiments of the present invention.

[0101] The technical solution in this embodiment employs an inverter MPPT voltage anomaly diagnostic device. By calculating the theoretical MPPT voltage of the inverter during the diagnostic time period and comparing it with the actual MPPT voltage during the diagnostic time period, it can promptly detect whether the inverter MPPT voltage is abnormal. Maintenance personnel can then perform timely maintenance, thereby ensuring that the inverter is always in optimal working condition and preventing energy waste.

[0102] Optionally, the judgment module 104 is specifically configured to calculate the deviation rate between the theoretical MPPT voltage and the actual MPPT voltage. If the absolute value of the deviation rate is greater than a preset value within the diagnostic time period, the inverter MPPT voltage is determined to be abnormal. In this embodiment, the deviation rate between the theoretical MPPT voltage and the actual MPPT voltage of the inverter can be compared in real time to determine whether the inverter is abnormal at any given time. To eliminate the influence of random shading (e.g., an obstruction blocking the inverter at a certain moment), the average value of the absolute value of the deviation rate within the diagnostic time period can be calculated. If the average value is greater than a preset value, it indicates that the inverter is malfunctioning; if the average value is less than or equal to the preset value, it indicates that the inverter is functioning normally. This embodiment can determine whether the inverter MPPT voltage is abnormal by comparing the deviation rate between the theoretical MPPT voltage and the actual MPPT voltage within the diagnostic time period. The judgment method is simple and requires less computation.

[0103] Optionally, in some other embodiments, the judgment module 104 is specifically configured to calculate the deviation rate between the theoretical MPPT voltage value and the actual MPPT voltage. If the absolute value of the deviation rate is greater than a preset value within the diagnostic time period, a judgment based on preset conditions is performed. If the preset conditions are met, the inverter MPPT voltage is determined to be abnormal. Here, n is a positive integer, and the preset conditions include: the absolute value of the deviation rate is less than the preset value within each extended preset time period before and after the diagnostic time period of the previous n days.

[0104] The preset conditions may also include the following: during the period to be diagnosed, and during the extended preset time periods before and after the period to be diagnosed n days prior, the start time is greater than the time of inverter grid connection than the first preset value, and the end time is greater than the time of inverter grid disconnection than the second preset value.

[0105] The preset conditions also include: during the diagnostic period and the extended preset time periods before and after the diagnostic period of the previous n days, the inverter's apparent power is less than the maximum apparent power, the AC current is less than the maximum current, and there is no external power limiting command.

[0106] Optionally, the acquisition and fitting module 101 may specifically include: an acquisition unit configured to acquire the component temperature T at time t0, starting from time t0 in the first historical time period. 组件t0 and the tilt surface irradiance f, total module area A in the string, string voltage u, string current i, and module temperature T at each time after time t0. 组件 Temperature T 空气 And wind speed v;

[0107] The fitting unit is configured to substitute the above data into the heat balance equation from time t0 to time t1. And fit the values ​​of a, b, and c.

[0108] Optionally, the inverter MPPT voltage anomaly diagnostic device may also include a training unit. The training unit may be specifically configured to use the irradiance, module temperature, module voltage temperature coefficient, and module nameplate maximum power point voltage in the second historical time period as input parameters, and the actual MPPT voltage when the inverter is working normally in the second historical time period as output parameters, and use a deep learning algorithm to train the MPPT voltage model.

[0109] Example 5

[0110] Figure 6 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0111] like Figure 6As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0112] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0113] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above.

[0114] In some embodiments, the inverter MPPT voltage anomaly diagnosis method can be implemented as a computer program tangibly contained in a storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the inverter MPPT voltage anomaly diagnosis method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the inverter MPPT voltage anomaly diagnosis method by any other suitable means (e.g., by means of firmware).

[0115] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0116] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0117] In the context of this invention, a storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. Storage media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0118] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0119] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0120] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0121] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0122] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for diagnosing MPPT voltage anomalies in an inverter, characterized in that, include: Calculate the component temperature during the time period to be diagnosed based on the component temperature expression; Calculate the theoretical MPPT voltage value during the diagnostic time period based on the component temperature and preset voltage model during the diagnostic time period. Based on the theoretical MPPT voltage value and the actual MPPT voltage value of the inverter during the diagnostic time period, determine whether the inverter MPPT voltage is abnormal. Before calculating the component temperature within the diagnostic time period based on the component temperature expression, the following steps are also included: Obtain the module temperature, tilt surface irradiance, total module area in the string, string voltage, string current, module temperature, air temperature, and wind speed for the first historical time period to fit the module temperature expression; The process of obtaining the module temperature, tilt surface irradiance, total module area in the string, string voltage, string current, module temperature, air temperature, and wind speed for the first historical time period to fit the module temperature expression includes: Starting from time t0 in the first historical time period, obtain the component temperature T at time t0. 组件t0 and the tilt surface irradiance f, total module area A in the string, string voltage u, string current i, and module temperature T at each time after time t0. 组件 Temperature T 空气 And wind speed v; Substituting into the heat balance equation from time t0 to time t1, Fit the values ​​of a, b, and c.

2. The inverter MPPT voltage anomaly diagnosis method according to claim 1, characterized in that, The step of determining whether the inverter's MPPT voltage is abnormal based on the theoretical MPPT voltage value and the inverter's actual MPPT voltage value during the diagnostic time period includes: Calculate the deviation rate between the theoretical MPPT voltage and the actual MPPT voltage. If the absolute value of the deviation rate is greater than a preset value during the diagnostic time period, then the inverter MPPT voltage is determined to be abnormal.

3. The MPPT voltage anomaly diagnosis method according to claim 1, characterized in that, The step of determining whether the inverter's MPPT voltage is abnormal based on the theoretical MPPT voltage value and the inverter's actual MPPT voltage value during the diagnostic time period includes: The deviation rate between the theoretical MPPT voltage value and the actual MPPT voltage is calculated. If the absolute value of the deviation rate is greater than a preset value during the diagnostic time period, a preset condition is judged. If the preset condition is met, the inverter MPPT voltage is determined to be abnormal. Here, n is a positive integer, and the preset condition includes: the absolute value of the deviation rate is less than the preset value during each extended preset time period before and after the diagnostic time period of the previous n days.

4. The MPPT voltage anomaly diagnosis method according to claim 3, characterized in that, The preset conditions also include: in the time period to be diagnosed and the time periods before and after the n-day period to be diagnosed, the time between the start time and the inverter grid connection time is greater than a first preset value, and the time between the end time and the inverter grid disconnection time is greater than a second preset value.

5. The MPPT voltage anomaly diagnosis method according to claim 3 or 4, characterized in that, The preset conditions also include: during the time period to be diagnosed and the time periods before and after the n-day period to be diagnosed, the inverter apparent power is less than the maximum apparent power, the AC current is less than the maximum current, and there is no external power limiting command.

6. The MPPT voltage anomaly diagnosis method according to claim 2, characterized in that, The formula for calculating the deviation rate is: ΔU=(U 实际MPPT -U 理论MPPT ) / U 理论MPPT Where ΔU is the deviation rate, U 实际MPPT U is the actual MPPT voltage. 理论MPPT This is the theoretical MPPT voltage.

7. The MPPT voltage anomaly diagnosis method according to claim 1, characterized in that, Before calculating the theoretical MPPT voltage value for the diagnostic time period based on the component temperature and preset voltage model during the diagnostic time period, the following steps are also included: Using the irradiance, module temperature, module voltage temperature coefficient, and module nameplate maximum power point voltage in the second historical time period as input parameters, and the actual MPPT voltage of the inverter during normal operation in the second historical time period as output parameters, the preset voltage model is trained using a deep learning algorithm.

8. A device for diagnosing MPPT voltage anomalies in an inverter, characterized in that, include: The first calculation module is configured to calculate the component temperature during the diagnostic time period based on the component temperature expression. Before calculating the component temperature within the diagnostic time period based on the component temperature expression, the method further includes: obtaining the component temperature, tilt surface irradiance, total component area in the string, string voltage, string current, component temperature, air temperature, and wind speed for the first historical time period to fit the component temperature expression. The second calculation module is configured to calculate the theoretical MPPT voltage value during the time period to be diagnosed based on the component temperature and a preset voltage model during the time period to be diagnosed. The judgment module is configured to determine whether the inverter MPPT voltage is abnormal based on the theoretical MPPT voltage value and the actual MPPT voltage value of the inverter during the diagnostic time period. The acquisition unit is configured to acquire the component temperature T at time t0, starting from time t0 in the first historical time period. 组件t0 and the tilt surface irradiance f, total module area A in the string, string voltage u, string current i, and module temperature T at each time after time t0. 组件 Temperature T 空气 And wind speed v; The fitting unit is configured to substitute the above data into the heat balance equation from time t0 to time t1. Fit the values ​​of a, b, and c.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the MPPT voltage anomaly diagnosis method according to any one of claims 1-7.

10. A storage medium, characterized in that, The storage medium stores computer instructions that are used to cause the processor to execute the MPPT voltage anomaly diagnosis method according to any one of claims 1-7.

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