Fault detection method for photovoltaic micro inverter

Abstract

The invention discloses a fault detection method for a photovoltaic micro inverter, which relates to the technical field of inverter fault detection, and comprises the following steps: acquiring battery state information of a plurality of photovoltaic cell panels forming a photovoltaic array, and according to the arrangement position of the photovoltaic micro inverter connected with the photovoltaic cell panels, determining the fault of the photovoltaic micro inverter according to the arrangement position of the photovoltaic micro inverter connected with the photovoltaic cell panels; the method comprises the following steps: dividing a photovoltaic array into different array partitions, monitoring inverter operation parameters of a photovoltaic micro inverter in each array partition, combining battery state information of a photovoltaic cell panel in each array partition to generate battery pack state information, performing data processing and feature extraction on the battery pack state information and the inverter operation parameters, and obtaining the battery pack state information and the inverter operation parameters. Generating partition feature information of each array partition, inputting the partition feature information into a preset fault preliminary screening library, marking the partition feature information with the suspected fault by the fault preliminary screening library, and performing pattern recognition on the partition feature information of each suspected fault array partition; and the photovoltaic micro-inverters under all the array partitions with the faults finally are positioned.

Description

Technical Field

[0001] The invention relates to the technical field of inverter fault detection, and in particular to a fault detection method for a photovoltaic micro inverter. Background Art

[0002] A photovoltaic micro-inverter is a power electronic device used in a photovoltaic power generation system. The photovoltaic power generation system includes but is not limited to a photovoltaic array. Its main function is to convert the direct current (DC) generated by the photovoltaic panels into alternating current (AC) for subsequent power supply or grid connection. The photovoltaic micro-inverter is a key component in the photovoltaic power generation system, and its operation affects the efficiency of photovoltaic power generation.

[0003] However, during long-term operation, photovoltaic micro-inverters are prone to failures due to various internal or external factors, which directly affect the operation of the photovoltaic micro-inverter and indirectly affect the normal operation of the photovoltaic power generation system. At present, the fault detection method for photovoltaic micro-inverters often has poor detection effect. Therefore, an effective detection method is urgently needed to comprehensively consider the impact of various influencing factors on photovoltaic micro-inverters and efficiently and accurately locate the faults of photovoltaic micro-inverters. Summary of the invention

[0004] In order to solve the above problems, an object of the present invention is to provide a fault detection method for a photovoltaic micro-inverter.

[0005] The object of the present invention can be achieved by the following technical solution: A fault detection method for a photovoltaic micro-inverter comprises the following steps: Step S1: collecting battery status information of a number of photovoltaic panels constituting a photovoltaic array, dividing the photovoltaic array into different array partitions according to the arrangement positions of the photovoltaic micro-inverters connected to the photovoltaic panels in the photovoltaic array, and monitoring the inverter operating parameters of the photovoltaic micro-inverters corresponding to each array partition; Step S2: combining the battery status information of all photovoltaic panels under each array partition to generate battery pack status information, performing data processing and feature extraction according to the battery pack status information and inverter operating parameters to generate partition feature information of each array partition; Step S3: inputting the partition characteristic information of each array partition into a preset fault preliminary screening library, and then marking the partition characteristic information of the array partition suspected of having a fault in the fault preliminary screening library; Step S4: Perform pattern recognition on the partition feature information of each array partition suspected of having a fault through a machine learning algorithm, and then locate the photovoltaic micro-inverters under all array partitions that ultimately have a fault, as well as the specific fault types of the photovoltaic micro-inverters.

[0006] Furthermore, the process of collecting battery status information of a plurality of photovoltaic panels constituting the photovoltaic array and dividing the photovoltaic array into different array partitions according to the arrangement positions of photovoltaic micro-inverters connected to the photovoltaic panels in the photovoltaic array includes: The photovoltaic array is composed of a number of photovoltaic panels, which are numbered and recorded as i, i = 1, 2, 3, ..., n, where n is a natural number greater than 0, and the battery status information corresponding to each of the photovoltaic panels is collected. The battery status information includes battery temperature, operating voltage, operating current and battery internal resistance; A number of photovoltaic micro-inverters are arranged on the photovoltaic array, each photovoltaic micro-inverter is used to connect to R photovoltaic panels, and the value of R is an integer from 1 to 3. The photovoltaic micro-inverter is used to convert the direct current of the photovoltaic panel into alternating current. According to the arrangement positions of the several photovoltaic micro-inverters connected to the photovoltaic panels in the photovoltaic array, the photovoltaic panels connected to the corresponding photovoltaic micro-inverters are divided into the same array partition on the photovoltaic array, and then several array partitions corresponding to the several photovoltaic micro-inverters on the photovoltaic array are divided.

[0007] Furthermore, the process of monitoring the inverter operating parameters of the photovoltaic micro-inverter corresponding to each array partition includes: Arrange different types of sensors and power monitoring equipment in each array partition. The different types of sensors include temperature sensors, humidity sensors, and air pressure sensors. The inverter operating parameters of the corresponding photovoltaic micro-inverter are obtained through the sensors and power monitoring equipment in each array partition. The inverter operating parameters include a first operating parameter and a second operating parameter; The first operating parameter includes the ambient temperature collected by the temperature sensor, the ambient humidity collected by the humidity sensor, and the ambient atmospheric pressure collected by the pressure sensor; The second operating parameters include the inverter DC input voltage, inverter AC output voltage, inverter DC input current, inverter AC output current, inverter DC input power and inverter AC output power collected by the power monitoring equipment.

[0008] Furthermore, the process of combining the battery status information of all photovoltaic panels under each array partition to generate battery group status information includes: The battery status information of each photovoltaic battery group is classified according to the timestamp, and then the battery timing status information corresponding to each photovoltaic battery group at several timestamps is generated. The battery timing status information consists of the battery temperature, operating voltage, operating current and battery internal resistance at the same timestamp; The battery timing status information of all photovoltaic battery groups in the same array partition is combined according to the same timestamp to generate the battery group timing status information corresponding to the photovoltaic arrays at several timestamps, and the battery group timing status information corresponding to all timestamps of each photovoltaic array is combined to generate the battery group status information corresponding to each photovoltaic array.

[0009] Furthermore, the process of performing data processing and feature extraction according to the battery pack status information and the inverter operating parameters to generate partition feature information of each array partition includes: By performing data processing on the battery pack status information, the battery pack status information is disassembled into battery pack timing status information corresponding to a plurality of timestamps, and the first operating parameter and the second operating parameter included in the inverter operating parameter are processed, and the inverter operating parameter is disassembled into inverter timing operating parameters corresponding to a plurality of timestamps; Construct a one-dimensional data analysis time axis, and perform feature extraction on the battery group timing state information and inverter timing operation parameters corresponding to each timestamp on the one-dimensional data analysis time axis, thereby obtaining the battery group timing features corresponding to the battery group timing state information of all photovoltaic panels under the array partition corresponding to each timestamp, and obtaining the inverter timing features corresponding to the inverter timing operation parameters of the photovoltaic micro-inverter under the array partition corresponding to each timestamp; The battery group timing characteristics and inverter timing characteristics corresponding to each array partition at the same timestamp are integrated to generate the superimposed timing characteristics of the corresponding timestamps. The superimposed timing characteristics of all timestamps of each array partition are summarized to generate the partition characteristic information corresponding to each array partition.

[0010] Furthermore, the partition characteristic information of each array partition is input into a preset fault preliminary screening library, and then the process of marking the partition characteristic information of the array partition suspected of having a fault by the fault preliminary screening library includes: A fault screening library is preset. The fault screening library is established based on a machine learning model. The machine learning model is generated by performing data processing, data mining and feature processing on the historical data of the photovoltaic array, and then combining data modeling technology. The machine learning model is used to analyze the characteristic information of all partitions corresponding to several array partitions of the photovoltaic array when a fault is suspected to have occurred at a historical time; The partition characteristic information of each array partition is input into the fault screening library, and the partition characteristic information of the array partition suspected of having a fault is marked by the fault screening library. The partition characteristic information of the array partition suspected of having a fault is associated with a suspected fault identifier, which is recorded as doubt-Er-Sign. The partition characteristic information of other array partitions not suspected of having a fault is associated with a safe operation identifier, which is recorded as safe-Sign.

[0011] Furthermore, the process of performing pattern recognition on the partition feature information of each array partition suspected of having a fault by using a machine learning algorithm, and then locating the photovoltaic micro-inverters under all the array partitions that ultimately have a fault includes: An initial pattern recognition model is constructed by a machine learning algorithm. The pattern recognition model is used to perform pattern recognition on partition feature information of array partitions suspected of having faults, and all partition feature information associated with suspected fault identifiers is used as input parameters of the pattern recognition model. Acquire a historical fault data set, which records several fault problems of photovoltaic micro-inverters at historical times, and synchronize the historical fault data set as an input parameter of a pattern recognition model. The pattern recognition model outputs corresponding recognition output results according to the received input parameters. The recognition output results are: the device numbers of photovoltaic micro-inverters under all array partitions that eventually have faults, and the specific fault types corresponding to the photovoltaic micro-inverters; When the photovoltaic micro-inverter under the corresponding array partition is located to have a final fault, the suspected fault mark doubt-Er-Sign of the partition characteristic information corresponding to the array partition is replaced with a fault mark, and the fault mark is recorded as sure-Er-Sign. Maintenance personnel are arranged to repair all photovoltaic micro-inverters located to a specific fault type. When the fault problem corresponding to the specific fault type of the photovoltaic micro-inverter is resolved, the fault mark sure-Er-Sign is replaced with a safety mark safe-Sign.

[0012] Furthermore, specific fault types of the photovoltaic micro-inverter include open circuit fault, short circuit fault, over-temperature fault, component aging fault and capacitor and inductor fault.

[0013] Compared with the prior art, the present invention has the following beneficial effects: The photovoltaic array is divided into different array partitions according to the layout position of the photovoltaic micro-inverter. According to the inverter operating parameters of the photovoltaic micro-inverter under each array partition and the battery group status information generated after integrating the battery status information of all photovoltaic panels under the array partition, data processing and feature extraction are performed to generate the partition feature information of each array partition. The partition feature information of each array partition is input into the fault initial screening library. The partition feature information of the array partition suspected of having a fault is first marked, and then the partition feature information of each array partition suspected of having a fault is pattern recognized to locate the photovoltaic micro-inverters under all array partitions that ultimately have a fault and their specific fault types. The relevant battery status information of the photovoltaic panel and the influence of the inverter operating parameters of the photovoltaic micro-inverter itself during operation on the operation of the photovoltaic micro-inverter are comprehensively considered. The corresponding partition feature information is obtained through feature extraction as the data basis for locating the specific fault type, thereby realizing efficient detection of the corresponding faults of several photovoltaic micro-inverters in the photovoltaic array and improving the accuracy of fault detection to a certain extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0015] like Figure 1 As shown, a fault detection method for a photovoltaic micro-inverter comprises the following steps: Step S1: collecting battery status information of a number of photovoltaic panels constituting a photovoltaic array, dividing the photovoltaic array into different array partitions according to the arrangement positions of the photovoltaic micro-inverters connected to the photovoltaic panels in the photovoltaic array, and monitoring the inverter operating parameters of the photovoltaic micro-inverters corresponding to each array partition; Step S2: combining the battery status information of all photovoltaic panels under each array partition to generate battery pack status information, performing data processing and feature extraction according to the battery pack status information and inverter operating parameters to generate partition feature information of each array partition; Step S3: inputting the partition characteristic information of each array partition into a preset fault preliminary screening library, and then marking the partition characteristic information of the array partition suspected of having a fault in the fault preliminary screening library; Step S4: Perform pattern recognition on the partition feature information of each array partition suspected of having a fault through a machine learning algorithm, and then locate the photovoltaic micro-inverters under all array partitions that ultimately have a fault, as well as the specific fault types of the photovoltaic micro-inverters.

[0016] It should be further explained that, in a specific implementation process, the process of collecting battery status information of a plurality of photovoltaic panels constituting a photovoltaic array and dividing the photovoltaic array into different array partitions according to the arrangement positions of photovoltaic micro-inverters connected to the photovoltaic panels in the photovoltaic array includes: The photovoltaic array is composed of a plurality of photovoltaic panels, and the plurality of photovoltaic panels are numbered and the number is recorded as i, i=1, 2, 3, ..., n, where n is a natural number greater than 0, and battery status information corresponding to each of the plurality of photovoltaic panels is collected, and the battery status information includes battery temperature, operating voltage, operating current and battery internal resistance; A plurality of photovoltaic micro-inverters are arranged on the photovoltaic array, each photovoltaic micro-inverter is used to access R photovoltaic panels, wherein R is an integer of 1-3, and the photovoltaic micro-inverter is used to convert the direct current of the photovoltaic panel into alternating current, and the converted alternating current is used for the subsequent power supply of the photovoltaic array or grid connection; According to the arrangement positions of several photovoltaic micro-inverters connected to photovoltaic panels in the photovoltaic array, the photovoltaic panels connected to the corresponding photovoltaic micro-inverters are divided into the same array partition on the photovoltaic array, and then several array partitions corresponding to the several photovoltaic micro-inverters on the photovoltaic array are divided.

[0017] It should be noted that there is a one-to-one correspondence between each photovoltaic micro-inverter and an array partition, and each photovoltaic micro-inverter is used to convert direct current into alternating current for 1 to 3 photovoltaic panels under the corresponding array partition.

[0018] It should be further explained that, in the specific implementation process, the process of monitoring the inverter operating parameters of the photovoltaic micro-inverter corresponding to each array partition includes: Arrange different types of sensors and power monitoring equipment in each array partition, wherein the different types of sensors include temperature sensors, humidity sensors, and air pressure sensors, and obtain inverter operating parameters of the corresponding photovoltaic micro-inverter through the sensors and power monitoring equipment in each array partition; The inverter operating parameters include a first operating parameter and a second operating parameter; The first operating parameter includes the ambient temperature collected by the temperature sensor, the ambient humidity collected by the humidity sensor, and the ambient atmospheric pressure collected by the pressure sensor; The second operating parameters include the inverter DC input voltage, the inverter AC output voltage, the inverter DC input current, the inverter AC output current, the inverter DC input power, and the inverter AC output power collected by the power monitoring device; The first operating parameter is marked as D1, and the ambient temperature, ambient humidity and ambient atmospheric pressure included in the first operating parameter are respectively marked as C 温度 , SD and Pa, and then the first operating parameter is expressed as: D1 = <C 温度 , SD, Pa>; The second operating parameter is marked as D2, and the inverter DC input voltage and inverter AC output voltage included in the second operating parameter are respectively marked as V 直流输入 and V 交流输出 The inverter DC input current and inverter AC output current are respectively denoted as A 直流输入 and A 交流输出 , The inverter DC input power and inverter AC output power are respectively denoted as W 直流输入 and W 交流输出 , and the second operating parameter is expressed as follows: D2= <V 直流输入 , V 交流输出 , A 直流输入 , A 交流输出 , W 直流输入 , W 交流输出 >.

[0019] It should be further explained that, in a specific implementation process, the process of combining the battery status information of all photovoltaic panels under each array partition to generate the battery group status information includes: Set the information combination period and record it as T 组合 , then T 组合 =[t1, t2], where t1 is the start time of the information combination period, and t2 is the end time of the information combination period. During the information combination period, the battery status information of all photovoltaic panels in each array partition is combined, as follows: Classifying the battery status information of each photovoltaic battery group according to the timestamp, and then generating the battery timing status information corresponding to a plurality of timestamps of each photovoltaic battery group, wherein the battery timing status information is composed of the battery temperature, the operating voltage, the operating current and the battery internal resistance at the same timestamp; The battery timing status information of all photovoltaic battery groups in the same array partition is combined according to the same timestamp to generate the battery group timing status information corresponding to the photovoltaic arrays at several timestamps, and the battery group timing status information corresponding to all timestamps of each photovoltaic array is combined to generate the battery group status information corresponding to each photovoltaic array.

[0020] It should be further explained that, in a specific implementation process, data processing and feature extraction are performed according to battery pack status information and inverter operating parameters to generate partition feature information of each array partition, including: By performing data processing on the battery pack status information, the battery pack status information is disassembled into battery pack timing status information corresponding to a plurality of timestamps, and the first operating parameter and the second operating parameter included in the inverter operating parameter are processed, and the inverter operating parameter is disassembled into inverter timing operating parameters corresponding to a plurality of timestamps; Construct a one-dimensional data analysis time axis, and perform feature extraction on the battery group timing state information and inverter timing operation parameters corresponding to each timestamp on the one-dimensional data analysis time axis, thereby obtaining the battery group timing features corresponding to the battery group timing state information of all photovoltaic panels under the array partition corresponding to each timestamp, and obtaining the inverter timing features corresponding to the inverter timing operation parameters of the photovoltaic micro-inverter under the array partition corresponding to each timestamp; The battery group timing characteristics and inverter timing characteristics corresponding to each array partition at the same timestamp are integrated to generate the superimposed timing characteristics of the corresponding timestamps. The superimposed timing characteristics of all timestamps of each array partition are summarized to generate the partition characteristic information corresponding to each array partition.

[0021] It should be noted that the battery pack timing characteristics corresponding to the battery pack timing status information and the inverter timing characteristics corresponding to the inverter timing operation parameters are obtained through feature extraction methods such as time domain feature extraction, frequency domain feature extraction and statistical feature extraction. The specific feature extraction operation is implemented based on historical data and big data technology and is carried out according to actual conditions.

[0022] It should be further explained that, in a specific implementation process, the partition feature information of each array partition is input into a preset fault screening library, and then the process of marking the partition feature information of the array partition suspected of having a fault by the fault screening library includes: A fault screening library is preset. The fault screening library is established based on a machine learning model. The machine learning model is generated by performing data processing, data mining and feature processing on the historical data of the photovoltaic array, and then combining data modeling technology. The machine learning model is used to analyze the characteristic information of all partitions corresponding to several array partitions of the photovoltaic array when a fault is suspected to have occurred at a historical time; The partition characteristic information of each array partition is input into the fault preliminary screening library, and the partition characteristic information of the array partition suspected of having a fault is marked by the fault preliminary screening library, and the partition characteristic information of the array partition suspected of having a fault is associated with a suspected fault identifier, and the suspected fault identifier is recorded as doubt-Er-Sign, and the partition characteristic information of other array partitions not suspected of having a fault is associated with a safe operation identifier, and the safe operation identifier is recorded as safe-Sign.

[0023] The following are the contents to determine whether an array partition is suspected of failure: Setting battery status information and inverter operating parameters, and a parameter determination set corresponding to partition feature information generated after data processing and feature extraction, wherein the parameter determination set includes a plurality of determination intervals for determining the battery status information and a plurality of determination thresholds for determining the inverter operating parameters; The several determination intervals for determining the battery status information include a battery temperature safety interval, a working voltage safety interval, a working current safety interval, and a battery internal resistance safety interval, and the battery temperature safety interval, the working voltage safety interval, the working current safety interval, and the battery internal resistance safety interval are respectively denoted as Ω 安全温度 ,Ω 安全电压 ,Ω 安全电流 and Ω 安全电阻 ; The battery temperature and battery internal resistance are respectively denoted as D 电池温度 and D 电池内阻 ; The values ​​of the operating voltage and the operating current are respectively denoted as D 工作电压 and D 工作电流 ; When any of the following conditions is met, it is determined that the photovoltaic panels in the current array partition may have a fault problem, and all of the conditions are as follows: D 电池温度 Ω 安全温度 ; D 电池内阻 Ω 安全电阻 ; D 工作电压 Ω 安全电压 ; D 工作电流 Ω 安全电流 ; Otherwise, it is determined that there is no fault problem with the photovoltaic panels under the current array partition; The several determination thresholds for determining the inverter operating parameters include a temperature threshold corresponding to the ambient temperature of the first operating parameter, a humidity threshold corresponding to the ambient humidity, and a pressure threshold included in the ambient atmospheric pressure; and also include a voltage loss threshold for the conversion of the inverter DC input voltage and the inverter AC output voltage, a current loss threshold for the conversion of the inverter DC input current and the inverter AC output current, and a power loss threshold for the conversion of the inverter DC input power and the inverter AC output power included in the second operating parameter; The temperature threshold, humidity threshold and pressure threshold are denoted as C 阈值 , SD 阈值 and Pa 阈值 ; The voltage loss threshold, current loss threshold and power loss threshold are denoted as V 损耗阈值 , A 损耗阈值 and W 损耗阈值 ; When any of the following conditions is met, it is determined that the photovoltaic micro-inverter under the current array partition may have a fault problem, and all of the conditions are as follows: C 温度 ≥C 阈值 ; SD≥SD 阈值 ; Pa≥Pa 阈值 ; |V 直流输入 -V 交流输出 |≥V 损耗阈值 ; |A 直流输入 -A 交流输出 |≥A 损耗阈值 ; |W 直流输入 -W 交流输出 |≥W 损耗阈值 ; Otherwise, it is determined that there is no fault problem in the photovoltaic micro-inverter under the current array partition; When it is determined that the photovoltaic panels under the current array partition may have a fault problem, or when it is determined that the photovoltaic micro-inverters under the current array partition may have a fault problem, the current array partition is determined to be suspected of having a fault, otherwise, it is determined that the current array partition is not faulty.

[0024] The judgment rule for determining whether an array partition is suspected of failure is used as a rule for the machine learning model to analyze the partition feature information.

[0025] It should be further explained that, in the specific implementation process, the partition feature information of each array partition suspected of having a fault is pattern recognized by a machine learning algorithm, and then the photovoltaic micro-inverters under all array partitions that ultimately have a fault are located, and the specific fault type of the photovoltaic micro-inverter is located. The process includes: An initial pattern recognition model is constructed by a machine learning algorithm, wherein the pattern recognition model is used to perform pattern recognition on partition feature information of array partitions suspected of having a fault, and all partition feature information associated with suspected fault identifiers is used as an input parameter of the pattern recognition model; Acquire a historical fault data set, wherein the historical fault data set records several fault problems of photovoltaic micro-inverters at historical times, and synchronize the historical fault data set as an input parameter of a pattern recognition model; The pattern recognition model outputs a corresponding recognition output result according to the received input parameters, wherein the recognition output result is: the device numbers of the photovoltaic micro-inverters under all the array partitions that ultimately have faults, and the specific fault types corresponding to the photovoltaic micro-inverters; When the photovoltaic micro-inverter under the corresponding array partition is located to be ultimately faulty, the suspected fault identifier doubt-Er-Sign of the partition characteristic information corresponding to the array partition is replaced with a fault identifier, and the fault identifier is recorded as sure-Er-Sign; Obtain the recognition accuracy of the initial pattern recognition model, and record the recognition accuracy as Sc, set the lower limit threshold corresponding to the recognition accuracy, and record the lower limit threshold as τ, when Sc≥τ, do not perform any operation, when Sc<τ, increase the data volume of the historical fault data set, and use it as a training set to train the pattern recognition model; Arrange maintenance and operation personnel to repair all photovoltaic micro-inverters that have been located to specific fault types, and generate corresponding maintenance logs. The maintenance logs record detailed fault information and maintenance and operation information of the photovoltaic micro-inverters. When the fault problem corresponding to the specific fault type of the photovoltaic micro-inverter is resolved, the fault mark sure-Er-Sign is replaced with the safety mark safe-Sign.

[0026] The specific fault types of the photovoltaic micro inverter include open circuit fault, short circuit fault, over-temperature fault, component aging fault and capacitor and inductor fault.

[0027] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A fault detection method for a photovoltaic micro-inverter, characterized in that: The following steps are involved: Step S1: collecting battery status information of a number of photovoltaic panels constituting a photovoltaic array, dividing the photovoltaic array into different array partitions according to the arrangement positions of the photovoltaic micro-inverters connected to the photovoltaic panels in the photovoltaic array, and monitoring the inverter operating parameters of the photovoltaic micro-inverters corresponding to each array partition; Step S2: combining the battery status information of all photovoltaic panels under each array partition to generate battery pack status information, performing data processing and feature extraction according to the battery pack status information and inverter operating parameters to generate partition feature information of each array partition; Step S3: inputting the partition characteristic information of each array partition into a preset fault preliminary screening library, and then marking the partition characteristic information of the array partition suspected of having a fault in the fault preliminary screening library; Step S4: Perform pattern recognition on the partition feature information of each array partition suspected of having a fault through a machine learning algorithm, and then locate the photovoltaic micro-inverters under all array partitions that ultimately have a fault, as well as the specific fault types of the photovoltaic micro-inverters.

2. A fault detection method for photovoltaic micro-inverter according to claim 1, characterized in that: The process of collecting battery status information of several photovoltaic panels constituting a photovoltaic array and dividing the photovoltaic array into different array partitions according to the arrangement positions of photovoltaic micro-inverters connected to the photovoltaic panels in the photovoltaic array includes: The photovoltaic array is composed of a number of photovoltaic panels, which are numbered and recorded as i, i = 1, 2, 3, ..., n, where n is a natural number greater than 0, and the battery status information corresponding to each of the photovoltaic panels is collected. The battery status information includes battery temperature, operating voltage, operating current and battery internal resistance; A number of photovoltaic micro-inverters are arranged on the photovoltaic array, each photovoltaic micro-inverter is used to connect to R photovoltaic panels, and the value of R is an integer from 1 to 3. The photovoltaic micro-inverter is used to convert the direct current of the photovoltaic panel into alternating current. According to the arrangement positions of the several photovoltaic micro-inverters connected to the photovoltaic panels in the photovoltaic array, the photovoltaic panels connected to the corresponding photovoltaic micro-inverters are divided into the same array partition on the photovoltaic array, and then several array partitions corresponding to the several photovoltaic micro-inverters on the photovoltaic array are divided.

3. A fault detection method for photovoltaic micro-inverter according to claim 2, characterized in that: The process of monitoring the inverter operating parameters of the PV microinverters corresponding to each array partition includes: Arrange different types of sensors and power monitoring equipment in each array partition. The different types of sensors include temperature sensors, humidity sensors, and air pressure sensors. The inverter operating parameters of the corresponding photovoltaic micro-inverter are obtained through the sensors and power monitoring equipment in each array partition. The inverter operating parameters include a first operating parameter and a second operating parameter; The first operating parameter includes the ambient temperature collected by the temperature sensor, the ambient humidity collected by the humidity sensor, and the ambient atmospheric pressure collected by the pressure sensor; The second operating parameters include the inverter DC input voltage, inverter AC output voltage, inverter DC input current, inverter AC output current, inverter DC input power and inverter AC output power collected by the power monitoring equipment.

4. A fault detection method for photovoltaic micro-inverter according to claim 3, characterized in that: The process of combining the battery status information of all photovoltaic panels under each array partition to generate battery group status information includes: The battery status information of each photovoltaic battery group is classified according to the timestamp, and then the battery timing status information corresponding to each photovoltaic battery group at several timestamps is generated. The battery timing status information consists of the battery temperature, operating voltage, operating current and battery internal resistance at the same timestamp; The battery timing status information of all photovoltaic battery groups in the same array partition is combined according to the same timestamp to generate the battery group timing status information corresponding to the photovoltaic arrays at several timestamps, and the battery group timing status information corresponding to all timestamps of each photovoltaic array is combined to generate the battery group status information corresponding to each photovoltaic array.

5. A fault detection method for photovoltaic micro-inverter according to claim 4, characterized in that: The process of performing data processing and feature extraction according to battery pack status information and inverter operating parameters to generate partition feature information of each array partition includes: By performing data processing on the battery pack status information, the battery pack status information is disassembled into battery pack timing status information corresponding to a plurality of timestamps, and the first operating parameter and the second operating parameter included in the inverter operating parameter are processed, and the inverter operating parameter is disassembled into inverter timing operating parameters corresponding to a plurality of timestamps; Construct a one-dimensional data analysis time axis, and perform feature extraction on the battery group timing state information and inverter timing operation parameters corresponding to each timestamp on the one-dimensional data analysis time axis, thereby obtaining the battery group timing features corresponding to the battery group timing state information of all photovoltaic panels under the array partition corresponding to each timestamp, and obtaining the inverter timing features corresponding to the inverter timing operation parameters of the photovoltaic micro-inverter under the array partition corresponding to each timestamp; The battery group timing characteristics and inverter timing characteristics corresponding to each array partition at the same timestamp are integrated to generate the superimposed timing characteristics of the corresponding timestamps. The superimposed timing characteristics of all timestamps of each array partition are summarized to generate the partition characteristic information corresponding to each array partition.

6. A fault detection method for photovoltaic micro-inverter according to claim 5, characterized in that: The process of inputting the partition characteristic information of each array partition into a preset fault screening library, and then marking the partition characteristic information of the array partition suspected of having a fault by the fault screening library includes: A fault screening library is preset. The fault screening library is established based on a machine learning model. The machine learning model is generated by performing data processing, data mining and feature processing on the historical data of the photovoltaic array, and then combining data modeling technology. The machine learning model is used to analyze the characteristic information of all partitions corresponding to several array partitions of the photovoltaic array when a fault is suspected to have occurred at a historical time; The partition characteristic information of each array partition is input into the fault screening library, and the partition characteristic information of the array partition suspected of having a fault is marked by the fault screening library. The partition characteristic information of the array partition suspected of having a fault is associated with a suspected fault identifier, which is recorded as doubt-Er-Sign. The partition characteristic information of other array partitions not suspected of having a fault is associated with a safe operation identifier, which is recorded as safe-Sign.

7. A fault detection method for photovoltaic micro-inverter according to claim 6, characterized in that: The process of performing pattern recognition on the partition feature information of each array partition suspected of having a fault by using a machine learning algorithm, and then locating the photovoltaic micro-inverters under all the array partitions that ultimately have a fault includes: An initial pattern recognition model is constructed by a machine learning algorithm. The pattern recognition model is used to perform pattern recognition on partition feature information of array partitions suspected of having faults, and all partition feature information associated with suspected fault identifiers is used as input parameters of the pattern recognition model. Acquire a historical fault data set, which records several fault problems of photovoltaic micro-inverters at historical times, and synchronize the historical fault data set as an input parameter of a pattern recognition model. The pattern recognition model outputs corresponding recognition output results according to the received input parameters. The recognition output results are: the device numbers of photovoltaic micro-inverters under all array partitions that eventually have faults, and the specific fault types corresponding to the photovoltaic micro-inverters; When the photovoltaic micro-inverter under the corresponding array partition is located to have a final fault, the suspected fault mark doubt-Er-Sign of the partition characteristic information corresponding to the array partition is replaced with a fault mark, and the fault mark is recorded as sure-Er-Sign. Maintenance personnel are arranged to repair all photovoltaic micro-inverters located to a specific fault type. When the fault problem corresponding to the specific fault type of the photovoltaic micro-inverter is resolved, the fault mark sure-Er-Sign is replaced with a safety mark safe-Sign.

8. A fault detection method for photovoltaic micro-inverter according to claim 7, characterized in that: The specific fault types of the photovoltaic micro inverter include open circuit fault, short circuit fault, over-temperature fault, component aging fault and capacitor and inductor fault.

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