WO2022252621A1 - 光伏组件故障检测方法及装置 - Google Patents

光伏组件故障检测方法及装置 Download PDF

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Publication number
WO2022252621A1
WO2022252621A1 PCT/CN2022/070941 CN2022070941W WO2022252621A1 WO 2022252621 A1 WO2022252621 A1 WO 2022252621A1 CN 2022070941 W CN2022070941 W CN 2022070941W WO 2022252621 A1 WO2022252621 A1 WO 2022252621A1
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Prior art keywords
curve
detected
fault
circuit point
photovoltaic module
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PCT/CN2022/070941
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English (en)
French (fr)
Inventor
黄腾飞
蔡寰
韩志平
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厦门科灿信息技术有限公司
科华数据股份有限公司
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Publication of WO2022252621A1 publication Critical patent/WO2022252621A1/zh

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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
    • H02S50/00—Monitoring or testing of PV systems, e.g. load balancing or fault identification
    • H02S50/10—Testing of PV devices, e.g. of PV modules or single PV cells
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy

Definitions

  • the present application relates to the technical field of operation and maintenance, and in particular to a photovoltaic module fault detection method and device.
  • Photovoltaic module array is an important part of photovoltaic power generation system. Since photovoltaic power generation needs to be carried out in an outdoor environment, each photovoltaic module in the photovoltaic module array needs to be exposed to a relatively harsh environment for a long time, which is easy to cause open circuit and aging of photovoltaic modules. , dust and hot spots and other faults, which will affect the power generation efficiency of the photovoltaic power generation system.
  • the technical problem to be solved in this application is to provide a photovoltaic module fault detection method and a photovoltaic module fault detection device, so as to realize the fault detection of the photovoltaic module, thereby improving the power generation efficiency of the photovoltaic power generation system.
  • the present application provides a photovoltaic module fault detection method, including:
  • Collection step collecting multiple data groups of photovoltaic modules in the photovoltaic module array
  • the first obtaining step obtaining a reference curve
  • the first fitting step performing normalization processing on each of the data groups according to the reference curve, and performing the first fitting processing on each of the normalized data groups to obtain the photovoltaic module The curve to be detected;
  • the second obtaining step obtaining characteristic information of the reference curve and the curve to be detected
  • Detection step performing fault detection on the photovoltaic module according to the characteristic information of the curve to be detected and the characteristic information of the reference curve;
  • each of the data sets includes current and voltage;
  • the reference curve is the IV curve corresponding to the photovoltaic module in a fault-free state;
  • the characteristic information includes maximum power, short-circuit point current, open-circuit point voltage, a The order derivative trend, the second order derivative trend, the number of coordinate points where the current is 0;
  • the first order derivative trend is used to characterize the sign order of the first order derivative of the curve to be detected, and the second order derivative trend is used to characterize the described The order of the signs of the second derivatives of the curve to be detected.
  • the collecting step includes:
  • the collection step includes:
  • the reference curve is obtained by fitting.
  • the second obtaining step further includes:
  • a second fitting process is performed on each of the selected coordinate points.
  • the detection step includes:
  • the first detection sub-step judging whether the number of coordinate points where the current of the curve to be detected is 0 is greater than a preset first threshold;
  • the second detection sub-step if the number of coordinate points where the current of the curve to be detected is 0 is greater than the preset first threshold, then determine that the fault type of the photovoltaic module is a component open-circuit fault;
  • the third detection sub-step if the number of coordinate points where the current of the curve to be detected is 0 is not greater than the preset first threshold, then judge whether the open circuit point voltage of the curve to be detected is less than the open circuit point of the reference curve Voltage;
  • the fourth detection sub-step if the open circuit point voltage of the curve to be detected is lower than the open circuit point voltage of the reference curve, it is determined that the fault type of the photovoltaic module is the first fault type, and the first fault type includes broken faults, diode short faults, component misconfiguration faults and potential induced decay PID faults;
  • the fifth detection sub-step if the open circuit point voltage of the curve to be detected is not less than the open circuit point voltage of the reference curve, then when the maximum power of the curve to be detected is less than the maximum power of the reference curve, determine the The fault type of the photovoltaic module is the second fault type, and the second fault type includes dust accumulation fault, component aging fault, shadow fault and hot spot fault.
  • the fourth detection sub-step further include:
  • the short-circuit point current of the curve to be detected is smaller than the short-circuit point current of the reference curve, it is determined that the fault type of the photovoltaic module is the broken fault in the first fault type
  • the short-circuit point current of the curve to be detected is not less than the short-circuit point current of the reference curve, it is judged whether the difference between the open-circuit point voltage of the reference curve and the open-circuit point voltage of the curve to be detected is greater than a preset second threshold;
  • the fault type of the photovoltaic module is one of the first fault types The diode short circuit fault
  • the difference between the open circuit point voltage of the reference curve and the open circuit point voltage of the curve to be detected is not greater than the preset second threshold, then judge the open circuit point voltage of the reference curve and the open circuit point of the curve to be detected Whether the point voltage difference is greater than a preset third threshold, and the preset third threshold is smaller than the preset second threshold;
  • the fault type of the photovoltaic module is one of the first fault types misconfigured failure of said component
  • the fault type of the photovoltaic module is one of the first fault types The Potential Induced Decay PID malfunctions.
  • the fifth detection sub-step further include:
  • the short-circuit point current of the curve to be detected is smaller than the short-circuit point current of the reference curve, it is determined that the fault type of the photovoltaic module is the dust accumulation fault in the second fault type;
  • the short-circuit point current of the curve to be detected is not less than the short-circuit point current of the reference curve, it is judged whether the first-order derivative trend of the curve to be detected is a preset first trend;
  • the fault type of the photovoltaic module is the shadow fault in the second fault type
  • first-order derivative trend of the curve to be detected is not the preset first trend, then judging whether the second-order derivative trend of the curve to be detected is a preset second trend;
  • the fault type of the photovoltaic module is the hot spot fault in the second fault type
  • the fault type of the photovoltaic component is the component aging fault in the second fault type.
  • the present application also provides a photovoltaic module fault detection device, including:
  • the collection unit is used to collect multiple data groups of photovoltaic modules in the photovoltaic module array, each of which includes current and voltage;
  • a first acquisition unit configured to acquire a reference curve, the reference curve being the corresponding IV curve when the photovoltaic module is in a fault-free state;
  • the first fitting unit is configured to perform normalization processing on each of the data groups according to the reference curve, and perform a first fitting processing on each of the normalized data groups to obtain the The curve to be tested of the photovoltaic module;
  • the second acquisition unit is used to acquire the characteristic information of the reference curve and the curve to be detected, the characteristic information includes maximum power, short-circuit point current, open-circuit point voltage, first-order derivative trend, second-order derivative trend, current The number of coordinate points of 0, the first-order derivative trend is used to characterize the sign order of the first-order derivative of the curve to be detected, and the second-order derivative trend is used to characterize the sign order of the second-order derivative of the curve to be detected;
  • the detection unit is configured to perform fault detection on the photovoltaic module according to the characteristic information of the curve to be detected and the characteristic information of the reference curve.
  • the first acquisition unit is a curve acquisition unit
  • the curve acquisition unit is used for:
  • the first acquisition unit is a nameplate parameter acquisition unit
  • the nameplate parameter acquisition unit is used for:
  • the reference curve is obtained by fitting.
  • the photovoltaic module fault detection device further includes:
  • a selection unit is used to uniformly select a plurality of coordinate points from the curve to be detected
  • the second fitting unit is configured to perform a second fitting process on each of the selected coordinate points.
  • the detection unit includes:
  • the first detection subunit is used to determine whether the number of coordinate points where the current of the curve to be detected is 0 is greater than a preset first threshold;
  • the second detection subunit is used to confirm that if the number of coordinate points where the current of the curve to be detected is 0 is greater than the preset first threshold, then it is determined that the fault type of the photovoltaic module is a component open circuit fault;
  • the third detection subunit is used to confirm that if the number of coordinate points where the current of the curve to be detected is 0 is not greater than the preset first threshold, then judge whether the open circuit point voltage of the curve to be detected is less than the reference curve The open circuit point voltage;
  • the fourth detection subunit is used to confirm that if the open circuit point voltage of the curve to be detected is lower than the open circuit point voltage of the reference curve, it is determined that the fault type of the photovoltaic module is the first fault type, and the first fault Types include broken faults, diode short faults, component misconfiguration faults and potential induced decay PID faults;
  • the fifth detection subunit is used to confirm that if the open circuit point voltage of the curve to be detected is not less than the open circuit point voltage of the reference curve, when the maximum power of the curve to be detected is less than the maximum power of the reference curve, It is determined that the fault type of the photovoltaic module is a second fault type, and the second fault type includes a dust accumulation fault, a component aging fault, a shadow fault, and a hot spot fault.
  • the present application further provides a storage medium, the storage medium includes stored instructions, wherein when the instructions are executed, the device where the storage medium is located is controlled to execute any of the photovoltaic module fault detection methods described above.
  • the present application also provides an electronic device, including a memory, and one or more instructions, wherein one or more instructions are stored in the memory, and the electronic device is also configured with one or more The above processor, the processor is configured to execute any one of the photovoltaic module fault detection methods described above.
  • the present application includes the following advantages:
  • This application provides a photovoltaic module fault detection method and device. Since the characteristics of the IV curves corresponding to different faulty photovoltaic modules are different, by collecting multiple data groups of photovoltaic modules in the photovoltaic module array, each data group includes current and Voltage, to obtain a reference curve, the reference curve is the corresponding IV curve of the photovoltaic module in a fault-free state, according to the reference curve, each data group is normalized, and each data group after the normalization processing is first Fitting processing to obtain the curve to be detected of the photovoltaic module, and obtain the characteristic information of the reference curve and the curve to be detected; the characteristic information includes maximum power, short-circuit point current, open-circuit point voltage, first-order derivative trend, second-order derivative trend, and current is 0 The number of coordinate points; the first-order derivative trend is used to characterize the symbol order of the first-order derivative of the curve to be detected, and the second-order derivative trend is used to characterize the symbol order of the second-order derivative of the curve to be detected
  • Fig. 1 is a method flowchart of a photovoltaic module fault detection method provided by an embodiment of the present application
  • Fig. 2 is a method flowchart of detection steps in a photovoltaic module fault detection method provided by another embodiment of the present application;
  • FIG. 3 is a schematic diagram of IV curves corresponding to different faults in a photovoltaic module fault detection method provided by an embodiment of the present application;
  • Fig. 4 is a partial method flowchart of detection steps in a photovoltaic module fault detection method provided by another embodiment of the present application.
  • Fig. 5 is a partial method flowchart of detection steps in a photovoltaic module fault detection method provided by another embodiment of the present application.
  • Fig. 6 is a schematic structural diagram of a photovoltaic module fault detection device provided by the present application.
  • FIG. 7 is a schematic structural diagram of an electronic device provided by the present application.
  • the application is applicable to numerous general purpose or special purpose computing device environments or configurations.
  • personal computer server computer, handheld or portable device, tablet type device, multiprocessor device, distributed computing environment including any of the above devices or devices, etc.
  • the inventor found that the characteristics of the IV curves corresponding to different faulty photovoltaic modules are different. It can be understood that the IV curve of the photovoltaic module refers to the relationship between the output current and the output voltage of the solar panel, specifically the output In the process of the voltage from zero to the open circuit voltage, the corresponding output current change curve.
  • the embodiment of the present application provides a photovoltaic module fault detection method, which can be applied to various system platforms, and the execution body of the method can be a computer terminal or a processor of various mobile devices.
  • the method flow of the method As shown in Figure 1, it specifically includes:
  • S101 Collect multiple data groups of photovoltaic modules in a photovoltaic module array.
  • each data group includes current and voltage, that is, multiple currents and multiple voltages of the photovoltaic components in the photovoltaic component array are collected.
  • the photovoltaic module array includes a plurality of photovoltaic modules arranged in a determinant. By collecting the current and voltage of multiple photovoltaic modules, multiple data sets can be obtained.
  • the IV curve corresponding to the photovoltaic module in the fault-free state is obtained, and the IV curve corresponding to the photovoltaic assembly in the fault-free state is used as the reference curve, that is, the reference curve is the corresponding IV curve in the fault-free state of the photovoltaic module. IV curve.
  • the abscissa of the IV curve is the voltage, the unit is V, and the ordinate is the current, the unit is A.
  • S102 specifically includes the following steps:
  • S102 specifically includes the following steps:
  • the reference curve is obtained by fitting.
  • the reference curve can be obtained in two ways.
  • the first way is: collect multiple IV curves of the photovoltaic modules in the photovoltaic module array.
  • the abscissa of the IV curve is the voltage, and the ordinate is the current.
  • the current at each coordinate point calculate the average current of each IV curve, determine the average current with the largest value from the calculated average current, determine the average current with the largest value as the maximum average current, and set the IV corresponding to the maximum average current
  • the curve is determined as a reference curve.
  • the second method is to obtain the nameplate parameters on the photovoltaic modules in the photovoltaic module array, where the nameplate parameters are the parameters set by the manufacturer, and the nameplate parameters include but are not limited to the maximum rated power, the voltage at the maximum power point, the current at the maximum power point, Open-circuit voltage, short-circuit current and module efficiency are fitted to obtain reference curves according to each nameplate parameter.
  • the photovoltaic modules mentioned in this embodiment can be photovoltaic modules that are being put into use by the photovoltaic power generation system, or photovoltaic modules that have not been put into use. This embodiment does not specifically limit the use status of photovoltaic modules. .
  • each data group is normalized according to the reference curve, specifically, for each data group, the voltage in the data group is divided by the maximum abscissa value in the coordinate system where the reference curve is located, Normalization of the data set is achieved by dividing the current in the data set by the maximum ordinate in the coordinate system where the reference curve is located.
  • the first fitting processing is performed on each data group after normalization processing.
  • the first fitting processing may be polynomial fitting processing, that is, polynomial fitting processing is performed on each data group after normalization processing. fitting process, Obtain the curve to be tested for each photovoltaic module.
  • the characteristic information of the reference curve is obtained, specifically, the open circuit point voltage, the short circuit point current and the maximum power of the reference curve are obtained. It can be understood that the characteristic information of each curve to be detected is acquired.
  • the characteristic information of the curve to be detected is acquired, specifically, the maximum power, short-circuit point current, open-circuit point voltage, first-order derivative trend, second-order derivative trend, and the number of coordinate points where the current is 0 of the curve to be detected are obtained.
  • the first-order derivative trend is used to characterize the sign order of the first-order derivative of the curve to be detected
  • the second-order derivative trend is used to characterize the sign order of the second-order derivative of the curve to be detected.
  • S105 Perform fault detection on the photovoltaic module according to the characteristic information of the curve to be detected and the characteristic information of the reference curve.
  • the inventor found that the characteristics of the IV curves of photovoltaic modules with different faults are different. Therefore, for the curves to be detected, according to the characteristic information of the reference curves, the characteristic information of the curves to be detected can be compared and identified, and each curve to be detected can be determined.
  • the photovoltaic component corresponding to the curve fails, and in case of failure, determine the fault type of the faulty photovoltaic component, wherein the fault type of the photovoltaic component includes component open circuit fault, the first fault type and the second fault type, the first The first fault type includes broken fault, diode short circuit fault, component misconfiguration fault and potential induced decay PID fault, and the second fault type includes dust accumulation fault, component aging fault, shadow fault and hot spot fault.
  • the module open-circuit fault refers to the phenomenon that the photovoltaic module is in a circuit break state.
  • Broken failure refers to the phenomenon that photovoltaic modules are in the state of cracks, cracks, and crushing.
  • Diode short circuit fault refers to the phenomenon that the diode of the photovoltaic module is in a short circuit state.
  • Component configuration error fault refers to the phenomenon that the parameters of accessories, the type of accessories or the relationship between different accessories in photovoltaic modules are wrong.
  • the full name of PID is Potential Induced Degradation, which means potential-induced decay, refers to the phenomenon that the output power drops when high voltage flows through photovoltaic modules in a high temperature and humid environment.
  • Dust accumulation failure refers to the phenomenon that the surface of photovoltaic modules is covered with dust.
  • Component aging failure refers to the phenomenon of aging and wear of accessories in photovoltaic modules.
  • Shadow failure refers to the phenomenon that the surface of the photovoltaic module has a shield to form a shadow or a shadow is formed between adjacent photovoltaic modules.
  • Hot spot failure refers to the phenomenon of localized temperature rise in photovoltaic modules.
  • the fault detection process of the photovoltaic module includes the following steps:
  • the preset first threshold is an empirical value, which can be adjusted according to requirements, for example, the preset first threshold can be It is 80.
  • IV curves of photovoltaic modules under different faults are shown in FIG. 3 .
  • the IV curve corresponding to the component open circuit fault has a large amount of current close to 0. If the number of coordinate points where the current of the curve to be detected is 0 is greater than the preset first threshold, the fault of the photovoltaic component is determined. Type is component open circuit fault.
  • the number of coordinate points where the current of the curve to be detected is 0 is not greater than the preset first threshold, it is determined that the fault type of the photovoltaic module is not an open circuit fault of the module, and it is judged whether the voltage of the open circuit point of the curve to be detected is less than The open circuit point voltage of the reference curve.
  • the open circuit point voltages of the IV curves corresponding to broken faults, diode short circuit faults, component misconfiguration faults and potential induced decay PID faults are all less than the open circuit point voltages of the reference curve, if the open circuit point voltage of the curve to be detected If the point voltage is less than the open-circuit point voltage of the reference curve, the fault type of the photovoltaic module is determined to be the first fault type, and the first fault type includes broken faults, diode short-circuit faults, component configuration error faults, and potential-induced decay PID faults.
  • S205 Determine whether the maximum power of the curve to be detected is smaller than the maximum power of the reference curve. If yes, perform S206. If not, perform S207.
  • the open circuit point voltage of the curve to be detected is not less than the open circuit point voltage of the reference curve, it is determined that the fault type of the photovoltaic module is not the first fault type, and it is judged whether the maximum power of the curve to be detected is less than that of the reference curve Maximum power.
  • the open circuit point voltage of the IV curve corresponding to the broken fault, diode short circuit fault, component configuration error fault and potential induced decay PID fault is not less than the open circuit point voltage of the reference curve, and the broken fault, diode short circuit
  • the maximum power of the IV curve corresponding to the fault, component configuration error fault and potential induced decay PID fault is less than the maximum power of the reference curve; when the open circuit point voltage of the curve to be detected is not less than that of the reference curve, if the If the maximum power of the detection curve is less than the maximum power of the reference curve, it is determined that the fault type of the photovoltaic module is the second fault type, and the second fault type includes dust accumulation fault, component aging fault, shadow fault and hot spot fault.
  • each data group includes current and voltage
  • Obtain the reference curve the reference curve is the IV curve corresponding to the photovoltaic module in a fault-free state, perform normalization processing on each data group according to the reference curve, and perform the first fitting on each data group after normalization processing Process to obtain the curve to be detected of the photovoltaic module, and obtain the characteristic information of the reference curve and the curve to be detected;
  • the characteristic information includes maximum power, short-circuit point current, open-circuit point voltage, first-order derivative trend, second-order derivative trend, and coordinates where the current is 0
  • the number of points the first-order derivative trend is used to characterize the symbol order of the first-order derivative of the curve to be detected, and the second-order derivative trend is used to characterize the symbol order of the second-order derivative of the
  • step S103 Before obtaining the characteristic information of the curve to be detected involved in step S103 disclosed in FIG. 1 of the above-mentioned embodiment of the present application, the following steps may also be included:
  • a second fitting process is performed on each selected coordinate point.
  • a plurality of coordinate points may be uniformly selected from the curve to be detected, and a second fitting process is performed on each selected coordinate point, for example, the second fitting
  • the processing may be polynomial fitting processing or piecewise fitting processing.
  • steps S104 and S105 are executed according to the curve to be detected after the second fitting process. That is to say, if a plurality of coordinate points are evenly selected for the curve to be detected, and the second fitting process is performed on the selected coordinate points, then in step S104, obtaining the characteristic information of the reference curve and the curve to be detected is obtaining the reference curve and the characteristic information of the curve to be detected after the second fitting process, in step S105, according to the characteristic information of the curve to be detected and the characteristic information of the reference curve, the fault detection of the photovoltaic module is based on the characteristic information of the curve after the second fitting process The characteristic information of the curve to be detected and the characteristic information of the reference curve are used to detect the fault of the photovoltaic module.
  • a plurality of coordinate points can be evenly selected from the curve to be detected, and a second fitting process can be performed on each selected coordinate point , so that the curve to be detected becomes smoother, thereby reducing the error caused in the process of collecting current and voltage.
  • step S204 disclosed in FIG. 2 of the above-mentioned embodiment of the present application involves determining that the fault type of the photovoltaic module is the first fault type
  • the flow chart is shown in FIG. 4 , and may further include the following steps:
  • the short-circuit point current of the IV curve corresponding to the damage fault is less than the short-circuit point current of the reference curve; if the short-circuit point current of the curve to be detected is less than the short-circuit point current of the reference curve, then the failure of the photovoltaic module is determined
  • the type is a broken fault in the first fault type.
  • the short-circuit point current of the curve to be detected is not less than the short-circuit point current of the reference curve, then it is determined that the fault type of the photovoltaic module is not a broken fault in the first fault type, and the difference between the open-circuit point voltage of the reference curve and Whether the difference between the open circuit point voltages of the curves to be detected is greater than a preset second threshold.
  • the open circuit point voltage of the IV curve corresponding to the diode short circuit fault is less than the open circuit point voltage of the reference curve, if the difference between the open circuit point voltage of the reference curve and the open circuit point voltage of the curve to be detected is greater than the preset second threshold value, then It is determined that the fault type of the photovoltaic module is a diode short-circuit fault in the first fault type.
  • S405. Determine whether the difference between the open circuit point voltage of the reference curve and the open circuit point voltage of the curve to be detected is greater than a preset third threshold, if yes, perform S406, and if not, perform S407.
  • the difference between the open circuit point voltage of the reference curve and the open circuit point voltage of the curve to be detected is not greater than the preset second threshold, it is determined that the fault type of the photovoltaic module is not a diode short circuit fault in the first fault type , judging whether the difference between the open circuit point voltage of the reference curve and the open circuit point voltage of the curve to be detected is greater than a preset third threshold, and the preset third threshold is smaller than the preset second threshold.
  • the open circuit point voltage of the IV curve corresponding to the component configuration error fault is smaller than the open circuit point voltage of the reference curve, and greater than the open circuit point voltage of the IV curve corresponding to the diode short circuit fault.
  • the difference between the open circuit point voltage of the reference curve and the open circuit point voltage of the curve to be detected is not greater than the preset second threshold, if the difference between the open circuit point voltage of the reference curve and the open circuit point voltage of the curve to be detected is greater than the preset
  • the third threshold value determines that the fault type of the photovoltaic component is a component configuration error fault in the first fault type; wherein the preset third threshold value is smaller than the preset second threshold value.
  • the open circuit point voltage of the IV curve corresponding to the potential induced decay PID fault is smaller than the open circuit point voltage of the reference curve, and greater than the open circuit point voltage of the IV curve corresponding to the component configuration fault. If the difference between the open circuit point voltage of the reference curve and the open circuit point voltage of the curve to be detected is not greater than the preset third threshold, the fault type of the photovoltaic module is a potential induced decay PID fault in the first fault type.
  • the photovoltaic module fault detection method provided by the embodiment of the present application, after determining that the fault type of the photovoltaic module is the first fault type, can pass the open circuit point voltage and short circuit point current of the curve to be detected, and the open circuit point voltage and short circuit point voltage of the reference curve Point current to further determine the specific fault type of photovoltaic modules.
  • step S206 disclosed in FIG. 2 of the above-mentioned embodiment of the present application involves determining that the fault type of the photovoltaic module is the second fault type
  • the flow chart is shown in FIG. 5 , and may further include the following steps:
  • the short-circuit point current of the IV curve corresponding to the dust accumulation fault is less than the short-circuit point current of the reference curve; if the short-circuit point current of the curve to be detected is less than the short-circuit point current of the reference curve, the photovoltaic module
  • the fault type is a dust accumulation fault in the second fault type.
  • the short-circuit point current of the curve to be detected is not less than the short-circuit point current of the reference curve, it is determined that the fault type of the photovoltaic module is not a dust accumulation fault in the second fault type, and the first-order fault of the curve to be detected is determined.
  • the derivative trend is the default first trend.
  • the preset first trend is: the sign sequence of the first derivative is [0, ⁇ , 0, ⁇ ]. Judging whether the first-order derivative trend of the curve to be detected is the preset first trend, that is, judging whether the first-order derivative trend of the curve to be detected is [0, ⁇ , 0, ⁇ ].
  • the sign sequence of the first derivative of the IV curve corresponding to the shadow fault is [0, -, 0, -], that is to say, the curve trend of the IV curve corresponding to the shadow fault is first horizontal and then The trend of falling, leveling and falling again.
  • the first-order derivative trend of the curve to be detected is the preset first trend, that is, if the first-order derivative trend of the curve to be detected is [0, ⁇ , 0, ⁇ ]
  • determine the IV curve corresponding to the shadow fault is a trend of first level, then drop, then level and then drop. Therefore, it is determined that the fault type of the photovoltaic module is a shadow fault in the second fault type.
  • S505. Determine whether the second-order derivative trend of the curve to be detected is the preset second trend. If yes, execute S506. If not, execute S507.
  • the preset first trend it is determined that the fault type of the photovoltaic module is not a shadow fault in the second fault type, and it is judged whether the second-order derivative trend of the curve to be detected is the preset first Two trends, Wherein, the preset second trend is: the sign order of the second order derivative is [0, +, -, 0, +, -].
  • the second-order derivative trend of the curve to be detected is the preset second trend, that is, it is judged that the second-order derivative trend of the curve to be detected is [0, +, -, 0, +, -].
  • the trend of the first derivative of the IV curve corresponding to the hot spot fault is [0, -]
  • the trend of the second derivative is [0, +, -, 0, +, -] that is, the hot spot fault
  • the IV curve corresponding to the spot fault is both concave and convex. If the second-order derivative trend of the curve to be detected is the preset second trend, it is determined that the curve to be detected has concavo-convexity. Therefore, it is determined that the fault type of the photovoltaic module is a hot spot fault in the second fault type.
  • the first-order derivative trend of the IV curve corresponding to the component aging fault is [0, -]
  • the second-order derivative trend is [0, +] that is, the IV curve corresponding to the hot spot fault has only Concavity, no convexity
  • the second-order derivative trend of the curve to be detected is not the preset second trend, it is determined that the curve to be detected does not have both concavity and convexity at the same time. Therefore, it is determined that the fault type of the photovoltaic module is the first Component aging failure in the second failure type.
  • the photovoltaic module after determining that the fault type of the photovoltaic module is the second fault type, the photovoltaic module can be further determined by the short-circuit point current, the first-order derivative trend and the second-order derivative trend of the curve to be detected. Component specific failure type.
  • the embodiment of the present application also provides a photovoltaic module fault detection device, which is used to implement the method in Figure 1.
  • the structural diagram of the photovoltaic module fault detection device is shown in Figure 6, specifically including :
  • the collection unit 601 is used to collect multiple data groups of photovoltaic modules in the photovoltaic module array, each data group includes current and voltage;
  • the first acquisition unit 602 is configured to acquire a reference curve, and the reference curve is an IV curve corresponding to a photovoltaic module in a fault-free state;
  • the first fitting unit 603 is configured to perform normalization processing on each data group according to the reference curve, and perform first fitting processing on each data group after the normalization processing, so as to obtain the curve to be detected of the photovoltaic module;
  • the second acquisition unit 604 is used to acquire the characteristic information of the reference curve and the curve to be detected;
  • the characteristic information includes maximum power, short-circuit point current, open-circuit point voltage, first-order derivative trend, second-order derivative trend, and the number of coordinate points where the current is 0 ;
  • the first-order derivative trend is used to characterize the sign order of the first-order derivative of the curve to be detected, and the second-order derivative trend is used to characterize the sign order of the second-order derivative of the curve to be detected;
  • the detection unit 605 is configured to detect the fault of the photovoltaic module according to the characteristic information of the curve to be detected and the characteristic information of the reference curve.
  • the photovoltaic module fault detection device because the characteristics of IV curves corresponding to different faulty photovoltaic modules are different, by collecting multiple data groups of photovoltaic modules in the photovoltaic module array, each data group includes current and voltage, Obtain the reference curve, the reference curve is the IV curve corresponding to the photovoltaic module in a fault-free state, perform normalization processing on each data group according to the reference curve, and perform the first fitting on each data group after normalization processing Process to obtain the curve to be detected of the photovoltaic module, and obtain the characteristic information of the reference curve and the curve to be detected; the characteristic information includes maximum power, short-circuit point current, open-circuit point voltage, first-order derivative trend, second-order derivative trend, and coordinates where the current is 0 The number of points; the first-order derivative trend is used to characterize the symbol order of the first-order derivative of the curve to be detected, and the second-order derivative trend is used to characterize the symbol order of the second-order derivative of the curve to
  • the first acquisition unit 602 is used to acquire a reference curve
  • the first acquisition unit 602 is a curve acquisition unit
  • the curve acquisition unit is used for:
  • the first acquisition unit 602 is a nameplate parameter acquisition unit, and the nameplate parameter acquisition unit is used for:
  • the reference curve is obtained by fitting.
  • a selection unit is used to uniformly select a plurality of coordinate points from the curve to be detected
  • the second fitting unit is configured to perform a second fitting process on each selected coordinate point.
  • the detection unit 605 is used to detect the fault of the photovoltaic module according to the characteristic information of the curve to be detected and the characteristic information of the reference curve.
  • the detection unit 605 specifically includes:
  • the first detection subunit is used to determine whether the number of coordinate points where the current of the curve to be detected is 0 is greater than a preset first threshold;
  • the second detection subunit is used to confirm that if the number of coordinate points where the current of the curve to be detected is 0 is greater than the preset first threshold, then it is determined that the fault type of the photovoltaic module is an open-circuit fault of the module;
  • the third detection subunit is used to confirm that if the number of coordinate points where the current of the curve to be detected is 0 is not greater than the preset first threshold, then determine whether the open circuit point voltage of the curve to be detected is less than the open circuit point voltage of the reference curve;
  • the fourth detection subunit is used to confirm that if the open circuit point voltage of the curve to be detected is less than the open circuit point voltage of the reference curve, it is determined that the fault type of the photovoltaic module is the first fault type;
  • the first fault type includes broken faults and diode short circuit faults , Component configuration error faults and potential induced decay PID faults;
  • the fifth detection subunit is used to confirm that if the open circuit point voltage of the curve to be detected is not less than the open circuit point voltage of the reference curve, then when the maximum power of the curve to be detected is less than the maximum power of the reference curve, it is determined that the fault type of the photovoltaic module is A second fault type; the second fault type includes dust accumulation faults, component aging faults, shadow faults, and hot spot faults.
  • the detection unit 605 further includes a sixth detection subunit, and the sixth detection subunit is used for:
  • the fault type of the photovoltaic module is a broken fault in the first fault type
  • the short-circuit point current of the curve to be detected is not less than the short-circuit point current of the reference curve, it is judged whether the difference between the open-circuit point voltage of the reference curve and the open-circuit point voltage of the curve to be detected is greater than a preset second threshold;
  • the fault type of the photovoltaic module is a diode short circuit fault in the first fault type
  • the preset second threshold is smaller than the preset second threshold
  • the fault type of the photovoltaic module is a component configuration error fault in the first fault type
  • the fault type of the photovoltaic module is a PID fault in the first fault type.
  • the detection unit 605 further includes a seventh detection subunit, and the seventh detection subunit is used for:
  • the fault type of the photovoltaic module is a dust accumulation fault in the second fault type
  • the short-circuit point current of the curve to be detected is not less than the short-circuit point current of the reference curve, it is judged whether the first-order derivative trend of the curve to be detected is a preset first trend;
  • the fault type of the photovoltaic module is a shadow fault in the second fault type
  • first-order derivative trend of the curve to be detected is not a preset first trend, then it is judged whether the second-order derivative trend of the curve to be detected is a preset second trend;
  • the fault type of the photovoltaic module is a hot spot fault in the second fault type
  • the fault type of the photovoltaic module is a component aging fault in the second fault type.
  • the embodiment of the present application also provides a storage medium, the storage medium includes stored instructions, wherein when the instructions are executed, the device where the storage medium is located is controlled to perform the following operations:
  • the fault detection of the photovoltaic module is carried out;
  • each data group includes current and voltage;
  • the reference curve is the IV curve corresponding to the photovoltaic module in a fault-free state;
  • characteristic information includes maximum power, short-circuit point current, open-circuit point voltage, first-order derivative trend, second-order derivative trend , the number of coordinate points where the current is 0;
  • the first-order derivative trend is used to characterize the sign order of the first-order derivative of the curve to be detected, and the second-order derivative trend is used to characterize the sign order of the second-order derivative of the curve to be detected.
  • the embodiment of the present application also provides an electronic device, as shown in FIG.
  • the device is also configured with one or more processors 703, and the processors 703 are used to execute one or more instructions 702, and specifically perform the following operations:
  • the fault detection of the photovoltaic module is carried out;
  • each data group includes current and voltage;
  • the reference curve is the IV curve corresponding to the photovoltaic module in a fault-free state;
  • characteristic information includes maximum power, short-circuit point current, open-circuit point voltage, first-order derivative trend, second-order derivative trend , the number of coordinate points where the current is 0;
  • the first-order derivative trend is used to characterize the sign order of the first-order derivative of the curve to be detected, and the second-order derivative trend is used to characterize the sign order of the second-order derivative of the curve to be detected.
  • each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments.
  • the description is relatively simple, and for the related parts, please refer to the part of the description of the method embodiment.

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Abstract

本申请提供了一种光伏组件故障检测方法及装置，由于不同故障的光伏组件对应的IV曲线的特征不同，通过采集光伏组件阵列中光伏组件的多个数据组，每个数据组均包括电流和电压，获取参考曲线，依据参考曲线，对每个数据组进行归一化处理，并对归一化处理后的各个数据组进行第一拟合处理，得到光伏组件的待检测曲线，获取参考曲线和待检测曲线的特征信息，特征信息包括最大功率、短路点电流、开路点电压、一阶导数趋势、二阶导数趋势、电流为0的坐标点数量，依据待检测曲线的特征信息和参考曲线的特征信息，对光伏组件进行故障检测，实现了对光伏组件进行故障检测。

Description

光伏组件故障检测方法及装置
本专利申请要求于2021年6月1日提交的中国专利申请No.CN202110608889.1的优先权。在先申请的公开内容通过整体引用并入本申请。
技术领域
本申请涉及运维技术领域，尤其涉及一种光伏组件故障检测方法及装置。
 背景技术
随着光伏技术的飞速发展，采用光伏发电的方式被广泛推广。光伏组件阵列作为光伏发电系统的重要组成部分，由于光伏发电需要在室外环境下进行，从而导致光伏组件阵列中的各个光伏组件需要长期暴露在比较恶劣的环境中，容易引发光伏组件的开路、老化、积尘和热斑等故障，进而影响光伏发电系统的发电效率。
因此，如何提供一种能够实现对光伏组件进行故障检测，以提升光伏发电系统的发电效率的技术方案，是目前本领域技术人员亟需解决的问题。
 技术问题
本申请所要解决的技术问题是提供一种光伏组件故障检测方法和一种光伏组件故障检测装置，以实现对光伏组件进行故障检测，从而提升光伏发电系统的发电效率。
 技术解决方案
在第一方面，本申请提供一种光伏组件故障检测方法，包括：
采集步骤：采集光伏组件阵列中光伏组件的多个数据组；
第一获取步骤：获取参考曲线；
第一拟合步骤：依据所述参考曲线，对每个所述数据组进行归一化处理，并对归一化处理后的各个所述数据组进行第一拟合处理，得到所述光伏组件的待检测曲线；
第二获取步骤：获取所述参考曲线和所述待检测曲线的特征信息；
检测步骤：依据所述待检测曲线的特征信息和所述参考曲线的特征信息，对所述光伏组件进行故障检测；
其中，每个所述数据组均包括电流和电压；所述参考曲线为所述光伏组件处于无故障状态下对应的IV曲线；所述特征信息包括最大功率、短路点电流、开路点电压、一阶导数趋势、二阶导数趋势、电流为0的坐标点数量；所述一阶导数趋势用于表征所述待检测曲线的一阶导数的符号顺序，所述二阶导数趋势用于表征所述待检测曲线的二阶导数的符号顺序。
结合第一方面，在一种可能的实现方式中，所述采集步骤，包括：
获取所述光伏组件阵列中所述光伏组件的多条所述IV曲线；
计算每一条所述IV曲线的平均电流，得到最大平均电流，所述最大平均电流为各个所述平均电流中数值最大的所述平均电流；
将所述最大平均电流对应的所述IV曲线确定为所述参考曲线；
或，所述采集步骤，包括：
获取所述光伏组件阵列中所述光伏组件的铭牌参数；
依据所述铭牌参数，拟合得到所述参考曲线。
结合第一方面，在一种可能的实现方式中，所述第二获取步骤之前，还包括：
从所述待检测曲线中均匀选取多个坐标点；
对所选取的各个所述坐标点进行第二拟合处理。
结合第一方面，在一种可能的实现方式中，所述检测步骤，包括：
第一检测子步骤：判断所述待检测曲线的电流为0的坐标点数量是否大于预设第一阈值；
第二检测子步骤：若所述待检测曲线的电流为0的坐标点数量大于所述预设第一阈值，则确定所述光伏组件的故障类型为组件开路故障；
第三检测子步骤：若所述待检测曲线的电流为0的坐标点数量不大于所述预设第一阈值，则判断所述待检测曲线的开路点电压是否小于所述参考曲线的开路点电压；
第四检测子步骤：若所述待检测曲线的开路点电压小于所述参考曲线的开路点电压，则确定出所述光伏组件的故障类型为第一故障类型，所述第一故障类型包括破碎故障、二极管短路故障、组件配置错误故障和电势诱导衰减PID故障；
第五检测子步骤：若所述待检测曲线的开路点电压不小于所述参考曲线的开路点电压，则当所述待检测曲线的最大功率小于所述参考曲线的最大功率时，确定出所述光伏组件的故障类型为第二故障类型，所述第二故障类型包括积尘故障、组件老化故障、阴影故障和热斑故障。
结合第一方面，在一种可能的实现方式中，所述第四检测子步骤之后，还包括：
判断所述待检测曲线的短路点电流是否小于所述参考曲线的短路点电流；
若所述待检测曲线的短路点电流小于所述参考曲线的短路点电流，则确定出所述光伏组件的故障类型为所述第一故障类型中的所述破碎故障；
若所述待检测曲线的短路点电流不小于所述参考曲线的短路点电流，则判断所述参考曲线的开路点电压与所述待检测曲线的开路点电压的差值是否大于预设第二阈值；
若所述参考曲线的开路点电压与所述待检测曲线的开路点电压的差值大于所述预设第二阈值，则确定出所述光伏组件的故障类型为所述第一故障类型中的所述二极管短路故障；
若所述参考曲线的开路点电压与所述待检测曲线的开路点电压的差值不大于所述预设第二阈值，则判断所述参考曲线的开路点电压与所述待检测曲线的开路点电压的差值是否大于预设第三阈值，所述预设第三阈值小于所述预设第二阈值；
若所述参考曲线的开路点电压与所述待检测曲线的开路点电压的差值大于所述预设第三阈值，则确定出所述光伏组件的故障类型为所述第一故障类型中的所述组件配置错误故障；
若所述参考曲线的开路点电压与所述待检测曲线的开路点电压的差值不大于所述预设第三阈值，则确定出所述光伏组件的故障类型为所述第一故障类型中的所述电势诱导衰减PID故障。
结合第一方面，在一种可能的实现方式中，所述第五检测子步骤之后，还包括：
判断所述待检测曲线的短路点电流是否小于所述参考曲线的短路点电流；
若所述待检测曲线的短路点电流小于所述参考曲线的短路点电流，则确定出所述光伏组件的故障类型为所述第二故障类型中的所述积尘故障；
若所述待检测曲线的短路点电流不小于所述参考曲线的短路点电流，则判断所述待检测曲线的一阶导数趋势是否为预设第一趋势；
若所述待检测曲线的一阶导数趋势为所述预设第一趋势，则确定出所述光伏组件的故障类型为所述第二故障类型中的所述阴影故障；
若所述待检测曲线的一阶导数趋势不为所述预设第一趋势，则判断所述待检测曲线的二阶导趋势是否为预设第二趋势；
若所述待检测曲线的二阶导数趋势为所述预设第二趋势，则确定出所述光伏组件的故障类型为所述第二故障类型中的所述热斑故障；
若所述待检测曲线的二阶导数趋势不为所述预设第二趋势，则确定出所述光伏组件的故障类型为所述第二故障类型中的所述组件老化故障。
在第二方面，本申请还提供一种光伏组件故障检测装置，包括：
采集单元，用于采集光伏组件阵列中光伏组件的多个数据组，每个所述数据组均包括电流和电压；
第一获取单元，用于获取参考曲线，所述参考曲线为所述光伏组件处于无故障状态下对应的IV曲线；
第一拟合单元，用于依据所述参考曲线，对每个所述数据组进行归一化处理，并对归一化处理后的各个所述数据组进行第一拟合处理，得到所述光伏组件的待检测曲线；
第二获取单元，用于获取所述参考曲线和所述待检测曲线的特征信息，所述特征信息包括最大功率、短路点电流、开路点电压、一阶导数趋势、二阶导数趋势、电流为0的坐标点数量，所述一阶导数趋势用于表征所述待检测曲线的一阶导数的符号顺序，所述二阶导数趋势用于表征所述待检测曲线的二阶导数的符号顺序；
检测单元，用于依据所述待检测曲线的特征信息和所述参考曲线的特征信息，对所述光伏组件进行故障检测。
结合第二方面，在一种可能的实现方式中，所述第一获取单元为曲线获取单元，所述曲线获取单元用于：
获取所述光伏组件阵列中所述光伏组件的多条所述IV曲线；
计算每一条所述IV曲线的平均电流，得到最大平均电流，所述最大平均电流为各个所述平均电流中数值最大的所述平均电流；
将所述最大平均电流对应的所述IV曲线确定为所述参考曲线；
或，所述第一获取单元为铭牌参数获取单元，所述铭牌参数获取单元用于：
获取所述光伏组件阵列中所述光伏组件的铭牌参数；
依据所述铭牌参数，拟合得到所述参考曲线。
结合第二方面，在一种可能的实现方式中，所述光伏组件故障检测装置还包括：
选取单元，用于从所述待检测曲线中均匀选取多个坐标点；
第二拟合单元，用于对所选取的各个所述坐标点进行第二拟合处理。
结合第二方面，在一种可能的实现方式中，所述检测单元包括：
第一检测子单元，用于判断所述待检测曲线的电流为0的坐标点数量是否大于预设第一阈值；
第二检测子单元，用于确认若所述待检测曲线的电流为0的坐标点数量大于所述预设第一阈值，则确定所述光伏组件的故障类型为组件开路故障；
第三检测子单元，用于确认若所述待检测曲线的电流为0的坐标点数量不大于所述预设第一阈值，则判断所述待检测曲线的开路点电压是否小于所述参考曲线的开路点电压；
第四检测子单元，用于确认若所述待检测曲线的开路点电压小于所述参考曲线的开路点电压，则确定出所述光伏组件的故障类型为第一故障类型，所述第一故障类型包括破碎故障、二极管短路故障、组件配置错误故障和电势诱导衰减PID故障；
第五检测子单元，用于确认若所述待检测曲线的开路点电压不小于所述参考曲线的开路点电压，则当所述待检测曲线的最大功率小于所述参考曲线的最大功率时，确定出所述光伏组件的故障类型为第二故障类型，所述第二故障类型包括积尘故障、组件老化故障、阴影故障和热斑故障。
在第三方面，本申请还提供一种存储介质，所述存储介质包括存储的指令，其中，在所述指令运行时控制所述存储介质所在的设备执行上述的任一光伏组件故障检测方法。
在第四方面，本申请还提供一种电子设备，包括存储器，以及一个或者一个以上的指令，其中一个或者一个以上的指令存储于所述存储器中，且所述电子设备还配置有一个或者一个以上的处理器，所述处理器用以执行上述的任一光伏组件故障检测方法。
 有益效果
与现有技术相比，本申请包括以下优点：
本申请提供了一种光伏组件故障检测方法及装置，由于不同故障的光伏组件对应的IV曲线的特征不同，通过采集光伏组件阵列中光伏组件的多个数据组，每个数据组均包括电流和电压，获取参考曲线，参考曲线为光伏组件处于无故障状态下对应的IV曲线，依据参考曲线，对每个数据组进行归一化处理，并对归一化处理后的各个数据组进行第一拟合处理，得到光伏组件的待检测曲线，获取参考曲线和待检测曲线的特征信息；特征信息包括最大功率、短路点电流、开路点电压、一阶导数趋势、二阶导数趋势、电流为0的坐标点数量；一阶导数趋势用于表征待检测曲线的一阶导数的符号顺序，二阶导数趋势用于表征待检测曲线的二阶导数的符号顺序，依据待检测曲线的特征信息和参考曲线的特征信息，对光伏组件进行故障检测，实现了对光伏组件进行故障检测。
 附图说明
为了更清楚地说明本申请实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据提供的附图获得其他的附图。
图1为本申请一实施例提供的一种光伏组件故障检测方法的方法流程图；
图2为本申请又一实施例提供的一种光伏组件故障检测方法中检测步骤的方法流程图；
图3为本申请一实施例提供的一种光伏组件故障检测方法中不同故障对应的IV曲线示意图；
图4为本申请再一实施例提供的一种光伏组件故障检测方法中检测步骤的部分方法流程图；
图5为本申请另一实施例提供的一种光伏组件故障检测方法中检测步骤的部分方法流程图；
图6为本申请提供的一种光伏组件故障检测装置的结构示意图；
图7为本申请提供的一种电子设备的结构示意图。
本申请的实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
本申请可用于众多通用或专用的计算装置环境或配置中。例如：个人计算机、服务器计算机、手持设备或便携式设备、平板型设备、多处理器装置、包括以上任何装置或设备的分布式计算环境等等。
发明人在研究过程中发现，不同故障的光伏组件对应的IV曲线的特征不同，可以理解的是，光伏组件的IV曲线指的是太阳能电池板的输出电流与输出电压的关系曲线，具体是输出电压从零到开路电压的过程中，对应的输出电流的大小变化曲线。
基于此，本申请实施例提供了一种光伏组件故障检测方法，该方法可以应用于多种系统平台，该方法的执行主体可以为计算机终端或各种移动设备的处理器，该方法的方法流程图如图1所示，具体包括：
S101、采集光伏组件阵列中光伏组件的多个数据组。
本实施例中，采集光伏组件阵列中光伏组件的多个数据组，其中，每个数据组均包括电流和电压，也就是采集光伏组件阵列中光伏组件的多个电流和多个电压。可以理解的是，光伏组件阵列包括呈行列式排布的多个光伏组件。通过采集多个光伏组件的电流和电压，可以得到多个数据组。
S102、获取参考曲线。
本实施例中，获取光伏组件处于无故障状态下对应的IV曲线，将光伏组件处于无故障状态下对应的IV曲线作为参考曲线，也就是说，参考曲线为光伏组件处于无故障状态下对应的IV曲线。
其中，IV曲线的横坐标为电压，单位为V，纵坐标为电流，单位为A。
本实施例中，S102，具体包括以下步骤：
获取光伏组件阵列中光伏组件的多条IV曲线；
计算每一条IV曲线的平均电流，得到最大平均电流，最大平均电流为各个平均电流中数值最大的平均电流；
将最大平均电流对应的IV曲线确定为参考曲线；
或，S102，具体包括以下步骤：
获取光伏组件阵列中光伏组件的铭牌参数；
依据铭牌参数，拟合得到参考曲线。
本实施例中，可以通过两种方式获取参考曲线，方式一为：采集光伏组件阵列中光伏组件的多条IV曲线，IV曲线的横坐标为电压，纵坐标为电流，依据每一条IV曲线上各个坐标点的电流，计算每一条IV曲线的平均电流，从所计算的平均电流中，确定数值最大的平均电流，将数值最大的平均电流确定为最大平均电流，并将最大平均电流对应的IV 曲线确定为参考曲线。方式二为：获取光伏组件阵列中光伏组件上的铭牌参数，其中，铭牌参数为厂家出厂所设置的参数，铭牌参数包括但不限于最大额定功率、最大功率点的电压、最大功率点的电流、开路电压、短路电流和组件效率，依据各个铭牌参数，拟合得到参考曲线。
需要说明的是，本实施例所提及的光伏组件可以是光伏发电系统正在投入使用的光伏组件，也可以是未投入使用的光伏组件，本实施例对光伏组件的使用状态不做具体的限定。
S103、依据参考曲线，对每个数据组进行归一化处理，并对归一化处理后的各个数据组进行第一拟合处理，得到光伏组件的待检测曲线。
本实施中，依据参考曲线，对每个数据组进行归一化处理，具体的，针对每一个数据组，将该数据组中的电压除以参考曲线所处坐标系中的最大横坐标值，将该数据组中的电流除以该参考曲线所处坐标系中的最大纵坐标，从而实现对该数据组的归一化。
本实施例中，对归一化处理后的各个数据组进行第一拟合处理，例如，第一拟合处理可以是多项式拟合处理，也就是对归一化处理后的各个数据组进行多项式拟合处理， 得到每个光伏组件的待检测曲线。
S104、获取参考曲线和待检测曲线的特征信息。
本实施例中，获取参考曲线的特征信息，具体的，获取参考曲线的开路点电压、短路点电流和最大功率。可以理解的是，获取每个待检测曲线的特征信息。
本实施例中，获取待检测曲线的特征信息，具体的，获取待检测曲线的最大功率、短路点电流、开路点电压、一阶导数趋势、二阶导数趋势、电流为0的坐标点数量。其中， 一阶导数趋势用于表征待检测曲线的一阶导数的符号顺序，二阶导数趋势用于表征待检测曲线的二阶导数的符号顺序。
S105、依据待检测曲线的特征信息和参考曲线的特征信息，对光伏组件进行故障检测。
发明人在研究过程中发现，不同故障的光伏组件的IV曲线的特征不同，从而针对待检测曲线，依据参考曲线的特征信息，对待检测曲线的特征信息进行比对识别，可以确定每一条待检测曲线对应的光伏组件是否发生故障，以及在发生故障的情况下，确定发生故障的光伏组件的故障类型，其中，光伏组件的故障类型包括组件开路故障、第一故障类型和第二故障类型，第一故障类型包括破碎故障、二极管短路故障、组件配置错误故障和电势诱导衰减PID故障，第二故障类型包括积尘故障、组件老化故障、阴影故障和热斑故障。
其中，组件开路故障指的是光伏组件处于电路断路状态的现象。破碎故障指的是光伏组件处于裂纹、破裂、粉碎状态的现象。二极管短路故障指的是光伏组件的二极管处于电路短路状态的现象。组件配置错误故障指的是光伏组件中配件参数、配件类型或不同配件之间关系有误的现象。在电势诱导衰减PID故障中，PID的全称为Potential Induced Degradation，意思为电势诱导衰减，指的是在高温多湿环境下，高电压流经光伏组件时导致输出功率下降的现象。积尘故障指的是光伏组件的表面覆盖有灰尘的现象。组件老化故障指的是光伏组件中的配件老化磨损的现象。阴影故障指的是光伏组件的表面有遮挡物而形成阴影或者相邻光伏组件之间形成阴影的现象。热斑故障指的是光伏组件中局部温度升高的现象。
参阅图2，依据待检测曲线的特征信息和参考曲线的特征信息，对光伏组件进行故障检测的过程，具体包括以下步骤：
S201、判断待检测曲线的电流为0的坐标点数量是否大于预设第一阈值，若是，执行S202，若否，执行S203。
本实施例中，判断待检测曲线的电流为0的坐标点数量是否大于预设第一阈值，其中，预设第一阈值为经验值，可以根据需求进行调整，例如，预设第一阈值可以是80。
S202、确定光伏组件的故障类型为组件开路故障。
本实施例中，不同故障下光伏组件的IV曲线如图3所示。
本实施例中，参阅图3，组件开路故障对应的IV曲线有大量的电流接近于0，若待检测曲线的电流为0的坐标点数量大于预设第一阈值，则确定出光伏组件的故障类型为组件开路故障。
S203、判断待检测曲线的开路点电压是否小于参考曲线的开路点电压。
本实施例中，若待检测曲线的电流为0的坐标点数量不大于预设第一阈值，则确定出光伏组件的故障类型不为组件开路故障，并判断待检测曲线的开路点电压是否小于参考曲线的开路点电压。
S204、确定出光伏组件的故障类型为第一故障类型。
本实施例中，参阅图3，破碎故障、二极管短路故障、组件配置错误故障和电势诱导衰减PID故障对应的IV曲线的的开路点电压均小于参考曲线的开路点电压，若待检测曲线的开路点电压小于参考曲线的开路点电压，则确定出光伏组件的故障类型为第一故障类型，第一故障类型包括破碎故障、二极管短路故障、组件配置错误故障和电势诱导衰减PID故障。
S205、判断待检测曲线的最大功率是否小于参考曲线的最大功率，若是，执行S206、若否，执行S207。
本实施例中，若待检测曲线的开路点电压不小于参考曲线的开路点电压，则确定出光伏组件的故障类型不为第一故障类型，并判断待检测曲线的最大功率是否小于参考曲线的最大功率。
S206、确定出光伏组件的故障类型为第二故障类型。
本实例中，参阅图3，破碎故障、二极管短路故障、组件配置错误故障和电势诱导衰减PID故障对应的IV曲线的的开路点电压均不小于参考曲线的开路点电压，且破碎故障、二极管短路故障、组件配置错误故障和电势诱导衰减PID故障对应的IV曲线的的最大功率均小于参考曲线的最大功率；在待检测曲线的开路点电压不小于参考曲线的开路点电压的情况下，若待检测曲线的最大功率小于参考曲线的最大功率，则确定出光伏组件的故障类型为第二故障类型，第二故障类型包括积尘故障、组件老化故障、阴影故障和热斑故障。
S207、确定出光伏组件未发生故障。
本实施例中，在待检测曲线的开路点电压不小于参考曲线的开路点电压的情况下，若待检测曲线的最大功率不小于参考曲线的最大功率，则确定出光伏组件未发生故障。
本申请实施例提供的光伏组件故障检测方法，由于不同故障的光伏组件对应的IV 曲线的特征不同，通过采集光伏组件阵列中光伏组件的多个数据组，每个数据组均包括电流和电压，获取参考曲线，参考曲线为光伏组件处于无故障状态下对应的IV曲线，依据参考曲线，对每个数据组进行归一化处理，并对归一化处理后的各个数据组进行第一拟合处理，得到光伏组件的待检测曲线，获取参考曲线和待检测曲线的特征信息；特征信息包括最大功率、短路点电流、开路点电压、一阶导数趋势、二阶导数趋势、电流为0的坐标点数量；一阶导数趋势用于表征待检测曲线的一阶导数的符号顺序，二阶导数趋势用于表征待检测曲线的二阶导数的符号顺序，依据待检测曲线的特征信息和参考曲线的特征信息， 对光伏组件进行故障检测。实现了对光伏组件进行故障检测，并降低了故障检测的成本，以及提高了故障检测的效率。
上述本申请实施例图1公开的步骤S103涉及到的获取待检测曲线的特征信息之前，还可以包括以下步骤：
从待检测曲线中均匀选取多个坐标点；
对所选取的各个坐标点进行第二拟合处理。
本实施例中，在获取待检测曲线的特征信息之前，还可以从待检测曲线中均匀选取多个坐标点，并对所选取的各个坐标点进行第二拟合处理，例如，第二拟合处理可以是多项式拟合处理，也可以是分段拟合处理。
本实施例中，依据第二拟合处理后的待检测曲线，执行步骤S104和S105。也就是说，若对待检测曲线进行均匀选取多个坐标点，并对所选取的多个坐标点进行第二拟合处理，则步骤S104中获取参考曲线和待检测曲线的特征信息为获取参考曲线和经过第二拟合处理后的待检测曲线的特征信息，步骤S105中依据待检测曲线的特征信息和参考曲线的特征信息，对光伏组件进行故障检测为，依据经过第二拟合处理后的待检测曲线的特征信息和参考曲线的特征信息，对光伏组件进行故障检测。
本申请实施例提供的光伏组件故障检测方法，在获取待检测曲线的特征信息之前，还可以从待检测曲线中均匀选取多个坐标点，并对所选取的各个坐标点进行第二拟合处理，以使待检测曲线变得更光滑，从而减小采集电流和电压的过程中所带来的误差。
上述本申请实施例图2公开的步骤S204涉及到的确定出光伏组件的故障类型为第一故障类型之后，流程图如图4所示，还可以包括以下步骤：
S401、判断待检测曲线的短路点电流是否小于参考曲线的短路点电流，若是，执行S402，若否，执行S403。
S402、确定出光伏组件的故障类型为第一故障类型中的破碎故障。
本实施例中，参阅图3，破损故障对应的IV曲线的短路点电流小于参考曲线的短路点电流；若待检测曲线的短路点电流小于参考曲线的短路点电流，则确定出光伏组件的故障类型为第一故障类型中的破碎故障。
S403、判断参考曲线的开路点电压与待检测曲线的开路点电压的差值是否大于预设第二阈值，若是，执行S404，若否，执行S405。
本实施例中，若待检测曲线的短路点电流不小于参考曲线的短路点电流，则确定出光伏组件的故障类型不为第一故障类型中的破碎故障，并判断参考曲线的开路点电压与待检测曲线的开路点电压的差值是否大于预设第二阈值。
S404、确定出光伏组件的故障类型为第一故障类型中的二极管短路故障。
本实施例中，二极管短路故障对应的IV曲线的开路点电压小于参考曲线的开路点电压，若参考曲线的开路点电压与待检测曲线的开路点电压的差值大于预设第二阈值，则确定出光伏组件的故障类型为第一故障类型中的二极管短路故障。
S405、判断参考曲线的开路点电压与待检测曲线的开路点电压的差值是否大于预设第三阈值，若是，执行S406，若否，执行S407。
本实施例中，若参考曲线的开路点电压与待检测曲线的开路点电压的差值不大于预设第二阈值，则确定出光伏组件的故障类型不为第一故障类型中的二极管短路故障，判断参考曲线的开路点电压与待检测曲线的开路点电压的差值是否大于预设第三阈值，预设第三阈值小于预设第二阈值。
S406、确定出光伏组件的故障类型为第一故障类型中的组件配置错误故障。
本实施例中，参阅图3，组件配置错误故障对应的IV曲线的开路点电压小于参考曲线的开路点电压，大于二级管短路故障对应的IV曲线的开路点电压。在参考曲线的开路点电压与待检测曲线的开路点电压的差值不大于预设第二阈值的情况下，若参考曲线的开路点电压与待检测曲线的开路点电压的差值大于预设第三阈值，则确定出光伏组件的故障类型为第一故障类型中的组件配置错误故障；其中预设第三阈值小于预设第二阈值。
S407、确定出光伏组件的故障类型为第一故障类型中的电势诱导衰减PID故障。
本实施例中，参阅图3，电势诱导衰减PID故障对应的IV曲线的开路点电压小于参考曲线的开路点电压，大于组件配置故障对应的IV曲线的开路点电压。若参考曲线的开路点电压与待检测曲线的开路点电压的差值不大于预设第三阈值，则光伏组件的故障类型为第一故障类型中的电势诱导衰减PID故障。
本申请实施例提供的光伏组件故障检测方法，在确定出光伏组件的故障类型为第一故障类型后，可以通过待检测曲线的开路点电压和短路点电流，以及参考曲线的开路点电压和短路点电流，进一步确定光伏组件具体的故障类型。
上述本申请实施例图2公开的步骤S206涉及到的确定出光伏组件的故障类型为第二故障类型之后，流程图如图5所示，还可以包括以下步骤：
S501、判断待检测曲线的短路点电流是否小于参考曲线的短路点电流，若是，执行S502，若否，执行S503。
S502、确定出光伏组件的故障类型为第二故障类型中的积尘故障。
本实施例中，参阅图3，积尘故障对应的IV曲线的短路点电流小于参考曲线的短路点电流；若待检测曲线的短路点电流小于参考曲线的短路点电流，则确定出光伏组件的故障类型为第二故障类型中的积尘故障。
S503、判断待检测曲线的一阶导数趋势是否为预设第一趋势，若是，执行S504，若否，执行S505。
本实施例中，若待检测曲线的短路点电流不小于参考曲线的短路点电流，则确定出光伏组件的故障类型不为第二故障类型中的积尘故障，并判断待检测曲线的一阶导数趋势是否为预设第一趋势。其中，预设第一趋势为：一阶导数的符号顺序为[0，‑，0，‑]。判断待检测曲线的一阶导数趋势是否为预设第一趋势，也就是判断待检测曲线的一阶导数趋势是否为[0，‑，0，‑]。
S504、确定出光伏组件的故障类型为第二故障类型中的阴影故障。
本实施例中，参阅图3，阴影故障对应的IV曲线的一阶导数的符号顺序为[0 ，‑，0,‑]，也就是说，阴影故障对应的IV曲线的曲线趋势为先水平再下降再水平再下降的趋势。若待检测曲线的一阶导数趋势为预设第一趋势，也就是说，若待检测曲线的一阶导数趋势为[0，‑，0，‑]，则确定出阴影故障对应的IV曲线的曲线趋势为先水平再下降再水平再下降的趋势，因此，确定出光伏组件的故障类型为第二故障类型中的阴影故障。
S505、判断待检测曲线的二阶导数趋势是否为预设第二趋势，若是，执行S506，若否，执行S507。
若待检测曲线的一阶导数趋势为预设第一趋势，则确定出光伏组件的故障类型不为第二故障类型中的阴影故障，并判断待检测曲线的二阶导数趋势是否为预设第二趋势， 其中，预设第二趋势为：二阶导数的符号顺序为[0，+，‑，0，+，‑]。
本实施例中，判断待检测曲线的二阶导数趋势是否为预设第二趋势，也就是判断待检测曲线的二阶导数趋势为[0，+，‑，0，+，‑]。
S506、确定出光伏组件的故障类型为第二故障类型中的热斑故障。
本实施例中，参阅图3，热斑故障对应的IV曲线的一阶导数趋势为[0，‑]，二阶导数趋势为[0，+，‑，0，+，‑]，也就是热斑故障对应的IV曲线即具有凹性，又具有凸性。若待检测曲线的二阶导数趋势为预设第二趋势，则确定出待检测曲线具有凹凸性，因此，确定出光伏组件的故障类型为第二故障类型中的热斑故障。
S507、确定出光伏组件的故障类型为第二故障类型中的组件老化故障。
本实施例中，参阅图3，组件老化故障对应的IV曲线的一阶导数趋势为[0，‑]，二阶导数趋势为[0，+]，也就是热斑故障对应的IV曲线只具有凹性，没有凸性；若待检测曲线的二阶导数趋势不为预设第二趋势，则确定出待检测曲线没有同时均凹性和凸性，因此，确定出光伏组件的故障类型为第二故障类型中的组件老化故障。
本申请实施例提供的光伏组件故障检测方法，在确定出光伏组件的故障类型为第二故障类型后，可以通过待检测曲线的短路点电流、一阶导数趋势和二阶导数趋势，进一步确定光伏组件具体的故障类型。
与图1所述的方法相对应，本申请实施例还提供了一种光伏组件故障检测装置，用于实现图1中的方法，光伏组件故障检测装置的结构示意图如图6所示，具体包括：
采集单元601，用于采集光伏组件阵列中光伏组件的多个数据组，每个数据组均包括电流和电压；
第一获取单元602，用于获取参考曲线，参考曲线为光伏组件处于无故障状态下对应的IV曲线；
第一拟合单元603，用于依据参考曲线，对每个数据组进行归一化处理，并对归一化处理后的各个数据组进行第一拟合处理，得到光伏组件的待检测曲线；
第二获取单元604，用于获取参考曲线和待检测曲线的特征信息；特征信息包括最大功率、短路点电流、开路点电压、一阶导数趋势、二阶导数趋势、电流为0的坐标点数量；一阶导数趋势用于表征待检测曲线的一阶导数的符号顺序，二阶导数趋势用于表征待检测曲线的二阶导数的符号顺序；
检测单元605，用于依据待检测曲线的特征信息和参考曲线的特征信息，对光伏组件进行故障检测。
本申请实施例提供的光伏组件故障检测装置，由于不同故障的光伏组件对应的IV曲线的特征不同，通过采集光伏组件阵列中光伏组件的多个数据组，每个数据组均包括电流和电压，获取参考曲线，参考曲线为光伏组件处于无故障状态下对应的IV曲线，依据参考曲线，对每个数据组进行归一化处理，并对归一化处理后的各个数据组进行第一拟合处理，得到光伏组件的待检测曲线，获取参考曲线和待检测曲线的特征信息；特征信息包括最大功率、短路点电流、开路点电压、一阶导数趋势、二阶导数趋势、电流为0的坐标点数量；一阶导数趋势用于表征待检测曲线的一阶导数的符号顺序，二阶导数趋势用于表征待检测曲线的二阶导数的符号顺序，依据待检测曲线的特征信息和参考曲线的特征信息，对光伏组件进行故障检测，实现了对光伏组件进行故障检测，并降低了故障检测的成本，以及提高了故障检测的效率。
在本申请的一个实施例中，基于前述方案，第一获取单元602用于获取参考曲线，第一获取单元602为曲线获取单元，曲线获取单元用于：
获取光伏组件阵列中光伏组件的多条IV曲线；
计算每一条IV曲线的平均电流，得到最大平均电流，最大平均电流为各个平均电流中数值最大的平均电流；
将最大平均电流对应的IV曲线确定为参考曲线；
或，第一获取单元602为铭牌参数获取单元，铭牌参数获取单元用于：
获取光伏组件阵列中光伏组件的铭牌参数；
依据铭牌参数，拟合得到参考曲线。
在本申请的一个实施例中，基于前述方案，还可以包括：
选取单元，用于从待检测曲线中均匀选取多个坐标点；
第二拟合单元，用于对所选取的各个坐标点进行第二拟合处理。
在本申请的一个实施例中，基于前述方案，检测单元605用于依据待检测曲线的特征信息和所述参考曲线的特征信息，对光伏组件进行故障检测，检测单元605具体包括：
第一检测子单元，用于判断待检测曲线的电流为0的坐标点数量是否大于预设第一阈值；
第二检测子单元，用于确认若待检测曲线的电流为0的坐标点数量大于预设第一阈值，则确定光伏组件的故障类型为组件开路故障；
第三检测子单元，用于确认若待检测曲线的电流为0的坐标点数量不大于预设第一阈值，则判断待检测曲线的开路点电压是否小于参考曲线的开路点电压；
第四检测子单元，用于确认若待检测曲线的开路点电压小于参考曲线的开路点电压，则确定出光伏组件的故障类型为第一故障类型；第一故障类型包括破碎故障、二极管短路故障、组件配置错误故障和电势诱导衰减PID故障；
第五检测子单元，用于确认若待检测曲线的开路点电压不小于参考曲线的开路点电压，则当待检测曲线的最大功率小于参考曲线的最大功率时，确定出光伏组件的故障类型为第二故障类型；第二故障类型包括积尘故障、组件老化故障、阴影故障和热斑故障。
在本申请的一个实施例中，基于前述方案，检测单元605还包括第六检测子单元，第六检测子单元用于：
判断待检测曲线的短路点电流是否小于参考曲线的短路点电流；
若待检测曲线的短路点电流小于参考曲线的短路点电流，则确定出光伏组件的故障类型为第一故障类型中的破碎故障；
若待检测曲线的短路点电流不小于参考曲线的短路点电流，则判断参考曲线的开路点电压与待检测曲线的开路点电压的差值是否大于预设第二阈值；
若参考曲线的开路点电压与待检测曲线的开路点电压的差值大于预设第二阈值，则确定出光伏组件的故障类型为第一故障类型中的二极管短路故障；
若参考曲线的开路点电压与待检测曲线的开路点电压的差值不大于预设第二阈值，则判断参考曲线的开路点电压与待检测曲线的开路点电压的差值是否大于预设第三阈值；预设第三阈值小于预设第二阈值；
若参考曲线的开路点电压与待检测曲线的开路点电压的差值大于预设第三阈值，则确定出光伏组件的故障类型为第一故障类型中的组件配置错误故障；
若参考曲线的开路点电压与待检测曲线的开路点电压的差值不大于预设第三阈值，则确定出光伏组件的故障类型为第一故障类型中的电势诱导衰减PID故障。
在本申请的一个实施例中，基于前述方案，检测单元605还包括第七检测子单元，第七检测子单元用于：
判断待检测曲线的短路点电流是否小于参考曲线的短路点电流；
若待检测曲线的短路点电流小于参考曲线短路点电流，则确定出光伏组件的故障类型为第二故障类型中的积尘故障；
若待检测曲线的短路点电流不小于参考曲线的短路点电流，则判断待检测曲线的一阶导数趋势是否为预设第一趋势；
若待检测曲线的一阶导数趋势为预设第一趋势，则确定出光伏组件的故障类型为第二故障类型中的阴影故障；
若待检测曲线的一阶导数趋势不为预设第一趋势，则判断待检测曲线的二阶导趋势是否为预设第二趋势；
若待检测曲线的二阶导数趋势为预设第二趋势，则确定出光伏组件的故障类型为第二故障类型中的热斑故障；
若待检测曲线的二阶导数趋势不为预设第二趋势，则确定出光伏组件的故障类型为第二故障类型中的组件老化故障。
可以理解的是，光伏组件故障检测装置所包括的各个单元用于实现与上述各个方法实施例中的步骤相同的功能。
本申请实施例还提供了一种存储介质，存储介质包括存储的指令，其中，在指令运行时控制存储介质所在的设备执行以下操作：
采集光伏组件阵列中光伏组件的多个数据组；
获取参考曲线；
依据参考曲线，对每个数据组进行归一化处理，并对归一化处理后的各个数据组进行第一拟合处理，得到光伏组件的待检测曲线；
获取参考曲线和待检测曲线的特征信息；
依据待检测曲线的特征信息和参考曲线的特征信息，对光伏组件进行故障检测；
其中，每个数据组均包括电流和电压；参考曲线为光伏组件处于无故障状态下对应的IV曲线；特征信息包括最大功率、短路点电流、开路点电压、一阶导数趋势、二阶导数趋势、电流为0的坐标点数量；一阶导数趋势用于表征待检测曲线的一阶导数的符号顺序，二阶导数趋势用于表征待检测曲线的二阶导数的符号顺序。
本申请实施例还提供了一种电子设备，结构示意图如图7所示，具体包括存储器701，以及一个或者一个以上的指令702，其中一个或者一个以上的指令702存储于存储器701中，且电子设备还配置有一个或者一个以上的处理器703，处理器703用以执行一个或者一个以上的指令702，具体进行以下操作：
采集光伏组件阵列中光伏组件的多个数据组；
获取参考曲线；
依据参考曲线，对每个数据组进行归一化处理，并对归一化处理后的各个数据组进行第一拟合处理，得到光伏组件的待检测曲线；
获取参考曲线和待检测曲线的特征信息；
依据待检测曲线的特征信息和参考曲线的特征信息，对光伏组件进行故障检测；
其中，每个数据组均包括电流和电压；参考曲线为光伏组件处于无故障状态下对应的IV曲线；特征信息包括最大功率、短路点电流、开路点电压、一阶导数趋势、二阶导数趋势、电流为0的坐标点数量；一阶导数趋势用于表征待检测曲线的一阶导数的符号顺序，二阶导数趋势用于表征待检测曲线的二阶导数的符号顺序。
需要说明的是，本说明书中的各个实施例均采用递进的方式描述，每个实施例重点说明的都是与其他实施例的不同之处，各个实施例之间相同相似的部分互相参见即可。对于系统类实施例而言，由于其与方法实施例基本相似，所以描述的比较简单，相关之处参见方法实施例的部分说明即可。
最后，还需要说明的是，在本文中，诸如第一和第二等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间发生任何这种实际的关系或者顺序。而且，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、物品或者设备中还发生另外的相同要素。
为了描述的方便，描述以上系统时以功能分为各种单元分别描述。当然，在实施本申请时可以把各单元的功能在同一个或多个软件和/或硬件中实现。
通过以上的实施方式的描述可知，本领域的技术人员可以清楚地了解到本申请可借助软件加必需的通用硬件平台的方式来实现。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品可以存储在存储介质中，如ROM/RAM、磁碟、光盘等，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本申请各个实施例或者实施例的某些部分所述的方法。
以上对本申请所提供的一种光伏组件故障检测方法及装置进行了详细介绍，本文中应用了具体个例对本申请的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本申请的方法及其核心思想；同时，对于本领域的一般技术人员，依据本申请的思想，在具体实施方式及应用范围上均会有改变之处，综上所述，本说明书内容不应理解为对本申请的限制。

Claims (10)

  1. 一种光伏组件故障检测方法，其特征在于，包括：
    采集步骤：采集光伏组件阵列中光伏组件的多个数据组；
    第一获取步骤：获取参考曲线；
    第一拟合步骤：依据所述参考曲线，对每个所述数据组进行归一化处理，并对归一化处理后的各个所述数据组进行第一拟合处理，得到所述光伏组件的待检测曲线；
    第二获取步骤：获取所述参考曲线和所述待检测曲线的特征信息；
    检测步骤：依据所述待检测曲线的特征信息和所述参考曲线的特征信息，对所述光伏组件进行故障检测；
    其中，每个所述数据组均包括电流和电压；所述参考曲线为所述光伏组件处于无故障状态下对应的IV曲线；所述特征信息包括最大功率、短路点电流、开路点电压、一阶导数趋势、二阶导数趋势、电流为0的坐标点数量；所述一阶导数趋势用于表征所述待检测曲线的一阶导数的符号顺序，所述二阶导数趋势用于表征所述待检测曲线的二阶导数的符号顺序。
  2. 根据权利要求1所述的方法，其特征在于，所述采集步骤，包括：
    获取所述光伏组件阵列中所述光伏组件的多条所述IV曲线；
    计算每一条所述IV曲线的平均电流，得到最大平均电流，所述最大平均电流为各个所述平均电流中数值最大的所述平均电流；
    将所述最大平均电流对应的所述IV曲线确定为所述参考曲线；
    或，所述采集步骤，包括：
    获取所述光伏组件阵列中所述光伏组件的铭牌参数；
    依据所述铭牌参数，拟合得到所述参考曲线。
  3. 根据权利要求1所述的方法，其特征在于，所述第二获取步骤之前，还包括：从所述待检测曲线中均匀选取多个坐标点；
    对所选取的各个所述坐标点进行第二拟合处理。
  4. 根据权利要求1或3所述的方法，其特征在于，所述检测步骤，包括：
    第一检测子步骤：判断所述待检测曲线的电流为0的坐标点数量是否大于预设第一阈值；
    第二检测子步骤：若所述待检测曲线的电流为0的坐标点数量大于所述预设第一阈值，则确定所述光伏组件的故障类型为组件开路故障；
    第三检测子步骤：若所述待检测曲线的电流为0的坐标点数量不大于所述预设第一阈值，则判断所述待检测曲线的开路点电压是否小于所述参考曲线的开路点电压；
    第四检测子步骤：若所述待检测曲线的开路点电压小于所述参考曲线的开路点电压，则确定出所述光伏组件的故障类型为第一故障类型，所述第一故障类型包括破碎故障、二极管短路故障、组件配置错误故障和电势诱导衰减PID故障；
    第五检测子步骤：若所述待检测曲线的开路点电压不小于所述参考曲线的开路点电压，则当所述待检测曲线的最大功率小于所述参考曲线的最大功率时，确定出所述光伏组件的故障类型为第二故障类型，所述第二故障类型包括积尘故障、组件老化故障、阴影故障和热斑故障。
  5. 根据权利要求4所述的方法，其特征在于，所述第四检测子步骤之后，还包括：
    判断所述待检测曲线的短路点电流是否小于所述参考曲线的短路点电流；
    若所述待检测曲线的短路点电流小于所述参考曲线的短路点电流，则确定出所述光伏组件的故障类型为所述第一故障类型中的所述破碎故障；
    若所述待检测曲线的短路点电流不小于所述参考曲线的短路点电流，则判断所述参考曲线的开路点电压与所述待检测曲线的开路点电压的差值是否大于预设第二阈值；
    若所述参考曲线的开路点电压与所述待检测曲线的开路点电压的差值大于所述预设第二阈值，则确定出所述光伏组件的故障类型为所述第一故障类型中的所述二极管短路故障；
    若所述参考曲线的开路点电压与所述待检测曲线的开路点电压的差值不大于所述预设第二阈值，则判断所述参考曲线的开路点电压与所述待检测曲线的开路点电压的差值是否大于预设第三阈值，所述预设第三阈值小于所述预设第二阈值；
    若所述参考曲线的开路点电压与所述待检测曲线的开路点电压的差值大于所述预设第三阈值，则确定出所述光伏组件的故障类型为所述第一故障类型中的所述组件配置错误故障；
    若所述参考曲线的开路点电压与所述待检测曲线的开路点电压的差值不大于所述预设第三阈值，则确定出所述光伏组件的故障类型为所述第一故障类型中的所述电势诱导衰减PID故障。
  6. 根据权利要求4的方法，其特征在于，所述第五检测子步骤之后，还包括：
    判断所述待检测曲线的短路点电流是否小于所述参考曲线的短路点电流；
    若所述待检测曲线的短路点电流小于所述参考曲线的短路点电流，则确定出所述光伏组件的故障类型为所述第二故障类型中的所述积尘故障；
    若所述待检测曲线的短路点电流不小于所述参考曲线的短路点电流，则判断所述待检测曲线的一阶导数趋势是否为预设第一趋势；
    若所述待检测曲线的一阶导数趋势为所述预设第一趋势，则确定出所述光伏组件的故障类型为所述第二故障类型中的所述阴影故障；
    若所述待检测曲线的一阶导数趋势不为所述预设第一趋势，则判断所述待检测曲线的二阶导趋势是否为预设第二趋势；
    若所述待检测曲线的二阶导数趋势为所述预设第二趋势，则确定出所述光伏组件的故障类型为所述第二故障类型中的所述热斑故障；
    若所述待检测曲线的二阶导数趋势不为所述预设第二趋势，则确定出所述光伏组件的故障类型为所述第二故障类型中的所述组件老化故障。
  7. 一种光伏组件故障检测装置，其特征在于，包括：
    采集单元，用于采集光伏组件阵列中光伏组件的多个数据组，每个所述数据组均包括电流和电压；
    第一获取单元，用于获取参考曲线，所述参考曲线为所述光伏组件处于无故障状态下对应的IV曲线；
    第一拟合单元，用于依据所述参考曲线，对每个所述数据组进行归一化处理，并对归一化处理后的各个所述数据组进行第一拟合处理，得到所述光伏组件的待检测曲线；
    第二获取单元，用于获取所述参考曲线和所述待检测曲线的特征信息，所述特征信息包括最大功率、短路点电流、开路点电压、一阶导数趋势、二阶导数趋势、电流为0的坐标点数量，所述一阶导数趋势用于表征所述待检测曲线的一阶导数的符号顺序，所述二阶导数趋势用于表征所述待检测曲线的二阶导数的符号顺序；
    检测单元，用于依据所述待检测曲线的特征信息和所述参考曲线的特征信息，对所述光伏组件进行故障检测。
  8. 根据权利要求7所述的装置，其特征在于，所述第一获取单元为曲线获取单元，所述曲线获取单元用于：
    获取所述光伏组件阵列中所述光伏组件的多条所述IV曲线；
    计算每一条所述IV曲线的平均电流，得到最大平均电流，所述最大平均电流为各个所述平均电流中数值最大的所述平均电流；
    将所述最大平均电流对应的所述IV曲线确定为所述参考曲线；
    或，所述第一获取单元为铭牌参数获取单元，所述铭牌参数获取单元用于：
    获取所述光伏组件阵列中所述光伏组件的铭牌参数；
    依据所述铭牌参数，拟合得到所述参考曲线。
  9. 根据权利要求7所述的装置，其特征在于，还包括：
    选取单元，用于从所述待检测曲线中均匀选取多个坐标点；
    第二拟合单元，用于对所选取的各个所述坐标点进行第二拟合处理。
  10. 根据权利要求7或9所述的装置，其特征在于，所述检测单元包括：
    第一检测子单元，用于判断所述待检测曲线的电流为0的坐标点数量是否大于预设第一阈值；
    第二检测子单元，用于确认若所述待检测曲线的电流为0的坐标点数量大于所述预设第一阈值，则确定所述光伏组件的故障类型为组件开路故障；
    第三检测子单元，用于确认若所述待检测曲线的电流为0的坐标点数量不大于所述预设第一阈值，则判断所述待检测曲线的开路点电压是否小于所述参考曲线的开路点电压；
    第四检测子单元，用于确认若所述待检测曲线的开路点电压小于所述参考曲线的开路点电压，则确定出所述光伏组件的故障类型为第一故障类型，所述第一故障类型包括破碎故障、二极管短路故障、组件配置错误故障和电势诱导衰减PID故障；
    第五检测子单元，用于确认若所述待检测曲线的开路点电压不小于所述参考曲线的开路点电压，则当所述待检测曲线的最大功率小于所述参考曲线的最大功率时，确定出所述光伏组件的故障类型为第二故障类型，所述第二故障类型包括积尘故障、组件老化故障、阴影故障和热斑故障。
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CN117456371B (zh) * 2023-12-26 2024-04-12 浙江正泰智维能源服务有限公司 一种组串热斑检测方法、装置、设备及介质
CN118050588A (zh) * 2024-04-16 2024-05-17 锐曜石医疗科技(苏州)有限公司 一种用于超声切割刀的电故障探测方法

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