Photovoltaic power station assembly online health evaluation method and device based on parameter identification

Abstract

The invention provides an online health evaluation method and device for a photovoltaic power station module based on parameter identification, and relates to the technical field of photovoltaic monitoring control, and the method comprises the steps: collecting the actual measurement backboard temperature, actual measurement working voltage, actual measurement working current and actual measurement environment irradiance of a target photovoltaic module at each sampling moment; the method comprises the following steps: acquiring component parameters of a target photovoltaic component, constructing a first I-V curve of the target photovoltaic component, determining short-circuit current corresponding to actually measured environmental irradiance at each sampling moment through a preset fitting function, and determining the temperature of the target photovoltaic component based on the short-circuit current at each sampling moment, the actually measured backboard temperature and the component parameters of the target photovoltaic component. The first I-V curve is converted into a first I-V correction curve under a preset standard through a preset correction model, and the component parameters of the target photovoltaic component are obtained by optimizing a pre-constructed photovoltaic component current model through a particle swarm algorithm and the test photovoltaic component; constructing a first target data point set of the first I-V correction curve and a second target data point set corresponding to the standard I-V correction curve; and determining a grey correlation degree between the first target data point set and the second target data point set, and determining a health index of the target photovoltaic module under the grey correlation degree through a preset health evaluation model. According to the invention, quantitative analysis of the health state of the photovoltaic module is realized.

Description

Technical Field

[0001] The present application relates to the field of photovoltaic monitoring and control technology, and specifically to an online health evaluation method for photovoltaic power station components based on parameter identification, an online health evaluation device for photovoltaic power station components based on parameter identification, and a terminal device. Background Art

[0002] As the operation time goes by, the operation and maintenance cost of photovoltaic power stations increases year by year, especially the early photovoltaic power stations, which occupy the location with good light resources, but due to lack of maintenance, many component and system problems cannot be discovered and handled in time, resulting in reduced power generation and great waste of light resources. Therefore, the performance evaluation and health assessment of photovoltaic power stations have gradually attracted people's attention. According to the results of health assessment, potential faults and abnormal conditions of photovoltaic power stations can be monitored, and maintenance cycles can be determined, thereby greatly reducing operation and maintenance costs.

[0003] At present, the health evaluation of photovoltaic power stations mainly uses the performance ratio (PR value) to evaluate the performance of photovoltaic arrays or systems. The PR value is a ratio calculated by the power generation, rated power, absorbed radiation and reference radiation of the photovoltaic power generation system. The higher the PR, the better the performance of the photovoltaic array. However, PR can only evaluate the performance of the photovoltaic array from a macro perspective, and it is not easy to identify slight power losses or abnormalities in the photovoltaic array. For example, when a few photovoltaic modules in the photovoltaic array are partially blocked, the PR value is still within the normal range, and it is not possible to sensitively detect abnormal conditions in the photovoltaic array, which ultimately causes continuous energy loss. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a method for online health evaluation of photovoltaic power station components based on parameter identification, an apparatus for online health evaluation of photovoltaic power station components based on parameter identification, and a terminal device to solve the above problems.

[0005] In order to achieve the above-mentioned purpose, the first aspect of the present application provides a method for online health evaluation of photovoltaic power station components based on parameter identification, comprising: Within the specified sampling period, the measured backplane temperature, measured working voltage, measured working current of the target photovoltaic module at each sampling time are collected, and the measured ambient irradiance at each sampling time is obtained; Acquire the component parameters of the target photovoltaic component, construct a first IV curve of the target photovoltaic component based on the collected measured working voltage and measured working current, determine the short-circuit current corresponding to the measured ambient irradiance at each sampling moment through a preset fitting function, and convert the first IV curve into a first IV correction curve under a preset standard through a preset correction model based on the short-circuit current at each sampling moment, the measured backplane temperature and the component parameters of the target photovoltaic component. The component parameters of the target photovoltaic component are obtained by optimizing the pre-constructed photovoltaic component current model through a particle swarm algorithm and a test photovoltaic component; Determine at least one first target data point on the first IV correction curve to construct a first target data point set, obtain at least one second target data point corresponding to each first target data point on a preset standard IV correction curve, and construct a second target data point set; The grey correlation degree between the first target data point set and the second target data point set is determined by a grey correlation analysis method, and the health index of the target photovoltaic component under the grey correlation degree is determined by a preset health evaluation model.

[0006] Optionally, the target photovoltaic assembly is composed of a plurality of photovoltaic cells connected in series and parallel, and the assembly parameters of the target photovoltaic assembly include the photocurrent of the photovoltaic cell, the reverse saturation current of the diode in the photovoltaic cell, the equivalent series resistance of the photovoltaic cell, the equivalent parallel resistance of the photovoltaic cell and the diode ideal factor, and the photovoltaic assembly current model includes: ; in, and are the output current and voltage of the photovoltaic cell, is the photogenerated current, is the reverse saturation current of the diode in the photovoltaic cell, is the charge constant, is the diode ideality factor, is the Boltzmann constant, is the back panel temperature of the photovoltaic module, is the equivalent series resistance, is the equivalent parallel resistance, is the number of photovoltaic cells connected in parallel, is the number of photovoltaic cells connected in series.

[0007] Optionally, a pre-built photovoltaic module current model is optimized by a particle swarm algorithm, including: S1. Initialize the particle swarm algorithm parameters, which include at least the maximum number of iterations, the population size, the weight of the particles, and the learning factor; S2, converting the photocurrent of the photovoltaic cell, the reverse saturation current of the diode in the photovoltaic cell, the equivalent series resistance of the photovoltaic cell, the equivalent parallel resistance of the photovoltaic cell and the ideal factor of the diode into the position vector of each particle, and randomly generating the initial position of each particle; S3, taking the average relative error between the predicted output current value and the actual output current value of the tested photovoltaic assembly as the fitness function of the particle swarm algorithm, and calculating the fitness value of each particle based on the fitness function; S4. Determine the individual extreme value and population extreme value of each particle according to the fitness value of each particle, wherein the individual extreme value is the optimal position searched by a single particle up to the present moment, and the population extreme value is the optimal position searched by the entire particle group up to the present moment; S5. Update the current speed of each particle according to the weight of each particle, the individual extreme value of each particle and the population extreme value, and update the current position of each particle according to the current speed of each particle; S6. Determine whether the maximum number of iterations has been reached. If so, output the larger fitness value between the current optimal individual extreme value and the population extreme value as the optimal photovoltaic cell's photocurrent, the reverse saturation current of the diode in the photovoltaic cell, the photovoltaic cell's equivalent series resistance, the photovoltaic cell's equivalent parallel resistance and the diode ideal factor. Otherwise, return to step S3.

[0008] Optionally, the fitness function includes: MRE=1 / N∑|predicted output current value-actual output current value| / actual output current value; Where N is the number of samples; Calculating the fitness value of each particle based on the fitness function includes: Fi = 1 / MRE; Among them, Fi represents the fitness value of the i-th particle.

[0009] Optionally, the fitting function includes: ; Among them, I sc is the short-circuit current, C and b are fitting coefficients.

[0010] Optionally, the modified model includes: I2=I1+I sc (G2 / G1-1)+α(T2-T1); ; in, is the operating current of the target photovoltaic module under the preset standard, is the operating voltage of the target photovoltaic module under the preset standard, is the measured working current of the target photovoltaic module, is the measured operating voltage of the target photovoltaic module, is the short-circuit current, is the ambient irradiance under the preset standard, is the measured ambient irradiance, is the backplane temperature of the target component under the preset standard, is the measured backplane temperature of the target photovoltaic module, is the current temperature coefficient of the target photovoltaic module, is the voltage temperature coefficient of the target photovoltaic module, is the internal resistance of the target photovoltaic module, is the curve correction factor.

[0011] Optionally, determining at least one first target data point on the first IV correction curve and constructing a first target data point set comprises: determining a maximum power point of the target photovoltaic assembly based on the first IV correction curve; Determine each data point within a target interval including a maximum power point of the target photovoltaic assembly on the first IV correction curve as a first target data point; constructing a first target data point set based on all first target data points; Acquiring at least one second target data point corresponding to each first target data point on a preset standard IV correction curve, and constructing a second target data point set, comprising: Determine a data point on the standard IV correction curve corresponding to the operating voltage of each first target data point as a second target data point corresponding to each first target data point; A second target data point set is constructed based on all acquired second target data points.

[0012] Optionally, determining the health index of the target photovoltaic component under the grey correlation degree by a preset health evaluation model includes: According to the grey correlation degree, the fuzzy membership of the target photovoltaic assembly to each preset health status level is calculated by a triangular membership function; Performing a weighted summation on the obtained fuzzy memberships to obtain a health index of the target photovoltaic module; The triangular membership function comprises: ; ; ; ; Among them, u1, u2, u3, and u4 are the fuzzy memberships of each health status level respectively, and a, b, c, and d are the membership boundaries of the triangular membership function.

[0013] Optionally, the standard IV correction curve is constructed by the following steps: Acquire, by an IV scanner, second IV curves of a plurality of different photovoltaic modules at different ambient irradiances and / or at different backplane temperatures; Converting each second IV curve into a second IV correction curve under a preset standard by using the correction model; For all the second IV correction curves obtained: Fixing each operating voltage of each second IV correction curve, determining the operating current corresponding to each fixed operating voltage on each second IV correction curve, taking the average of all the obtained operating currents as the operating current of each fixed operating voltage under the preset standard, and obtaining a standard IV correction curve; The fitting function is constructed by the following steps: Determine the short-circuit current of all second IV curves, and obtain the ambient irradiance corresponding to each second IV curve; Based on all the obtained short-circuit currents and their corresponding ambient irradiances, the correlation between the ambient irradiances and the short-circuit currents is fitted by the least squares method to obtain a fitting function of the short-circuit current and the ambient irradiance.

[0014] In a second aspect of the present application, a device for online health evaluation of photovoltaic power station components based on parameter identification is provided, comprising: The data acquisition module is configured to collect the measured backplane temperature, measured working voltage, measured working current of the target photovoltaic module at each sampling time within a specified sampling period, and obtain the measured ambient irradiance at each sampling time; An IV curve conversion module is configured to obtain component parameters of the target photovoltaic component, construct a first IV curve of the target photovoltaic component based on the collected measured working voltage and measured working current, determine the short-circuit current corresponding to the measured ambient irradiance at each sampling moment through a preset fitting function, and convert the first IV curve into a first IV correction curve under a preset standard through a preset correction model based on the short-circuit current at each sampling moment, the measured backplane temperature and the component parameters of the target photovoltaic component. The component parameters of the target photovoltaic component are obtained by optimizing a pre-constructed photovoltaic component current model through a particle swarm algorithm and a test photovoltaic component; a correlation degree building module, configured to determine at least one first target data point on the first IV correction curve, build a first target data point set, obtain at least one second target data point corresponding to each first target data point on a preset standard IV correction curve, and build a second target data point set; The health index calculation module is configured to determine the grey correlation degree between the first target data point set and the second target data point set through a grey correlation analysis method, and determine the health index of the target photovoltaic component under the grey correlation degree through a preset health evaluation model.

[0015] In a third aspect of the present application, a terminal device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned online health assessment method for photovoltaic power station components based on parameter identification when executing the computer program.

[0016] The embodiments provided in this application have the following beneficial effects: The present application collects the current, voltage, backplane temperature and other data of the photovoltaic module in real time, constructs the IV curve of the photovoltaic module and corrects it to the standard test conditions, compares the corrected IV curve with the predetermined photovoltaic module standard IV correction curve, and obtains the photovoltaic module health index, thereby realizing quantitative analysis of the health status of the photovoltaic module.

[0017] Other features and advantages of the embodiments or implementations of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings: Figure 1 A method flow chart schematically shows a method for online health evaluation of photovoltaic power station components based on parameter identification according to an embodiment of the present application; Figure 2 The schematic diagram of the online health evaluation logic of the photovoltaic module of the embodiment of the present application is shown schematically; Figure 3 The IV characteristic curve diagram of the embodiment of the present application is schematically shown; Figure 4 A schematic block diagram of an online health evaluation device for photovoltaic power station components based on parameter identification according to an embodiment of the present application is schematically shown; Figure 5 A schematic diagram of the structure of a terminal device according to an embodiment of the present application is shown schematically.

[0019] Description of Reference Numerals 10-terminal device, 100-processor, 101-memory, 102-computer program. DETAILED DESCRIPTION

[0020] The specific implementation of the embodiment of the present application is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present application, and is not used to limit the embodiment of the present application.

[0021] In order to solve the above problems, Figure 1 As shown, the first aspect of the present application provides an online health evaluation method for photovoltaic power station components based on parameter identification, characterized in that it includes: S100, within a specified sampling period, collecting the measured backplane temperature, measured operating voltage, and measured operating current of the target photovoltaic module at each sampling moment, and obtaining the measured ambient irradiance at each sampling moment; S200, obtaining the component parameters of the target photovoltaic component, constructing a first IV curve of the target photovoltaic component based on the collected measured working voltage and measured working current, determining the short-circuit current corresponding to the measured ambient irradiance at each sampling moment through a preset fitting function, converting the first IV curve into a first IV correction curve under a preset standard through a preset correction model based on the short-circuit current at each sampling moment, the measured backplane temperature and the component parameters of the target photovoltaic component, and obtaining the component parameters of the target photovoltaic component by optimizing the pre-constructed photovoltaic component current model through a particle swarm algorithm and a test photovoltaic component; S300, determining at least one first target data point on the first IV correction curve, constructing a first target data point set, obtaining at least one second target data point corresponding to each first target data point on the preset standard IV correction curve, and constructing a second target data point set; S400, determining the grey correlation degree between the first target data point set and the second target data point set by a grey correlation analysis method, and determining the health index of the target photovoltaic module under the grey correlation degree by a preset health evaluation model.

[0022] In this way, the present application collects the current, voltage, backplane temperature and other data of the photovoltaic module in real time, constructs the IV curve of the photovoltaic module and corrects it under standard test conditions, compares the corrected IV curve with the predetermined photovoltaic module standard IV correction curve, and obtains the photovoltaic module health index, thereby realizing quantitative analysis of the health status of the photovoltaic module.

[0023] In the present application, data collection can be realized by the data collection device configured by the photovoltaic power station inverter itself. For example, the current, voltage data, backplane temperature of each photovoltaic module under the inverter can be collected by current, voltage sensors and backplane temperature sensors connected in parallel with the photovoltaic modules, and the real-time meteorological data of the environment in which the photovoltaic modules are located can be collected by the meteorological station. Then the collected IV data, meteorological data and backplane temperature data are transmitted to the data analysis unit through the communication unit. Among them, the communication unit includes a communication network composed of 485 communication lines, serial port servers, switches, network cables, optical cables, etc. It can be understood that the communication network is usually configured when the photovoltaic power station is built. The data analysis unit can be a server deployed in the monitoring center of the photovoltaic power station for data aggregation and analysis.

[0024] like Figure 2 As shown, before step S100, the present application pre-establishes a standard IV correction curve for the photovoltaic module, and the standard IV correction curve is constructed by the following steps: S10, obtaining second IV curves of multiple different photovoltaic modules at different environmental irradiances and / or at different backplane temperatures through an IV scanner.

[0025] For example, in the irradiance range of 600W / m 2 Up to 1000W / m 2 , under the meteorological conditions of ambient temperature of about 15℃ to 30℃, use IV scanner to test IV curves of any 5-10 photovoltaic modules or module units in good operating condition of photovoltaic power station, and obtain multiple measured IV characteristic curves under different environmental conditions, such as Figure 3 As shown in the figure, the current value corresponding to the intersection of each measured IV characteristic curve and the vertical axis is the short-circuit current I sc Among them, each component unit can be composed of 4-5 components.

[0026] S20, fitting the irradiance G and the short-circuit current Isc of the photovoltaic module under different environmental conditions obtained in step S10 sc Specifically, the fitting function is constructed by the following steps: S21, determining the short-circuit current of all second IV curves, and obtaining the ambient irradiance corresponding to each second IV curve; S22, based on all the obtained short-circuit currents and their corresponding ambient irradiances, fitting the correlation between the ambient irradiances and the short-circuit currents by the least squares method to obtain a fitting function of the short-circuit current and the ambient irradiance; Among them, the fitting function can be expressed as: ; Among them, I scis the short-circuit current, C and b are fitting coefficients.

[0027] S30, converting each second IV curve into a second IV correction curve under a preset standard through a correction model; For all the second IV correction curves obtained: The working voltages of the second IV correction curves are fixed, and the working currents corresponding to the fixed working voltages on the second IV correction curves are determined, and the average of all the working currents obtained is used as the working current of each fixed working voltage under the preset standard to obtain a standard IV correction curve. For example, for the multiple corrected IV characteristic curves obtained, the horizontal coordinate V is fixed, the vertical coordinate I is averaged, and an IV characteristic curve is calculated and used as the standard IV correction curve of the photovoltaic module under the preset standard, i.e., the standard test condition (STC).

[0028] Among them, the revised model includes: I2=I1+I sc (G2 / G1-1)+α(T2-T1); ; in, is the operating current of the target PV module under the preset standard, is the operating voltage of the target PV module under the preset standard, is the measured working current of the target PV module, is the measured operating voltage of the target PV module, is the short-circuit current, is the ambient irradiance under the preset standard. In this application, The value is 1000W / m 2 , is the measured ambient irradiance, The backplane temperature of the target component under the preset standard, for example, can be 25°C. is the measured backplane temperature of the target PV module, is the current temperature coefficient of the target PV module, is the voltage temperature coefficient of the target PV module, is the internal resistance of the target PV module, is the curve correction coefficient. , and The reference values ​​provided by the manufacturer when the photovoltaic modules leave the factory are used. The value obtained by optimizing the photovoltaic module current model through particle swarm algorithm and testing photovoltaic modules in advance, I sc It is calculated based on the actual measured irradiance G through the fitting function.

[0029] That is, when the second IV curve is corrected and converted, the short-circuit current of the target photovoltaic module at the corresponding sampling moment is first calculated according to the ambient irradiance of the target photovoltaic module at each sampling moment, the measured backplane temperature at the current sampling moment is obtained, and the working voltage and working current at the current sampling moment are corrected to the working voltage and working current under STC through the correction model, and the working current and working voltage at each sampling moment are corrected in turn to obtain the second IV correction curve under STC. For the obtained multiple second IV correction curves, the working voltage is used as the reference, and each working voltage is fixed unchanged. The average of the working currents corresponding to each second IV correction curve under this working voltage is calculated, and the average is used as the working current corresponding to the current working voltage under STC. The calibration of the working currents corresponding to all working voltages is completed, that is, the standard IV correction curve of the photovoltaic module under standard test conditions (STC) is obtained.

[0030] After establishing the standard IV correction curve of the photovoltaic module, in step S100, with every 24 hours as a sampling cycle and every n minutes in a cycle as a timestamp, i.e., the sampling moment, for example, 1≦n≦5, the real-time IV data, irradiance data and module backplane temperature data corresponding to each timestamp are collected in real time.

[0031] In this application, multiple data samples are obtained by testing the same type of test photovoltaic modules as the target photovoltaic modules in advance. Based on the obtained data samples, the pre-built photovoltaic module current model is optimized by the particle swarm algorithm to obtain the module parameters of the target photovoltaic module. It can be understood that the target photovoltaic module is composed of multiple photovoltaic cells connected in series and parallel. In this application, the module parameters of the target photovoltaic module include the photocurrent of the photovoltaic cell, the reverse saturation current of the diode in the photovoltaic cell, the equivalent series resistance of the photovoltaic cell, the equivalent parallel resistance of the photovoltaic cell and the ideal factor of the diode. The photovoltaic module current model includes: ; in, and are the output current and voltage of the photovoltaic cell, is the photogenerated current, is the reverse saturation current of the diode in the photovoltaic cell, is the charge constant, is the diode ideality factor, is the Boltzmann constant, is the back panel temperature of the photovoltaic module, is the equivalent series resistance, is the equivalent parallel resistance, is the number of photovoltaic cells connected in parallel, is the number of photovoltaic cells connected in series.

[0032] Among them, I and V can be directly obtained by testing the test photovoltaic module, but since the photovoltaic module current model of this application is a nonlinear transcendental equation, The five parameters cannot be directly solved, so they need to be obtained by fitting data. In this application, the five parameters are calculated by particle swarm algorithm. Specifically, the pre-built photovoltaic module current model is optimized by particle swarm algorithm, including: S1. Initialize the parameters of the particle swarm algorithm. The parameters of the particle swarm algorithm include at least the maximum number of iterations, the population size, the weight of the particles, and the learning factor; S2, converting the photocurrent of the photovoltaic cell, the reverse saturation current of the diode in the photovoltaic cell, the equivalent series resistance of the photovoltaic cell, the equivalent parallel resistance of the photovoltaic cell and the ideal factor of the diode into the position vector of each particle, and randomly generating the initial position of each particle; S3. The average relative error between the predicted output current value and the actual output current value of the test photovoltaic module is used as the fitness function of the particle swarm algorithm, and the fitness value of each particle is calculated based on the fitness function; wherein the fitness function is MRE=1 / N∑|predicted output current value-actual output current value| / actual output current value; wherein N is the number of data samples, for example, the data samples are the output current and output voltage of the test photovoltaic module at multiple sampling times. In this application, the fitness value of each particle is calculated based on the fitness function, including: Fi=1 / MRE; wherein Fi represents the fitness value of the i-th particle.

[0033] S4. Determine the individual extreme value and population extreme value of each particle according to the fitness value of each particle, wherein the individual extreme value is the optimal position searched by a single particle up to the present moment, and the population extreme value is the optimal position searched by the entire particle group up to the present moment; S5. Update the current speed of each particle according to the weight of each particle, the individual extreme value of each particle and the population extreme value, and update the current position of each particle according to the current speed of each particle; The speed update formula is: v(i)=v(i)w+c1rand*(pbest(i)-x(i))+c2*rand(gbest(i)-x(i)); Location Updates: x(i) = x(i) + v(i); Among them, v(i) and x(i) are the velocity and position of particle i respectively, pbest(i) is the individual optimal position of particle i, i.e., the individual extreme value, gbest is the global optimal position, i.e., the group extreme value, rand is a random number between [0,1), w is the inertia weight, c1 is the learning factor of the individual particle, and c2 is the group learning factor of the particle, which represents the influence of individual optimality and group optimality.

[0034] S6, determine whether the maximum number of iterations has been reached. If so, output the current optimal individual extreme value and the population extreme value, whichever has the larger fitness value, as the optimal photovoltaic cell photocurrent, the reverse saturation current of the diode in the photovoltaic cell, the equivalent series resistance of the photovoltaic cell, the equivalent parallel resistance of the photovoltaic cell and the ideal factor of the diode. Otherwise, return to step S3. After the maximum number of iterations has been reached, the optimal value obtained is used as the optimal component parameter, and the optimal equivalent series resistance R obtained is used as the optimal component parameter. s As the internal resistance of the target PV module.

[0035] In this application, the root mean square error (RMSE) is also used as an evaluation index of the photovoltaic module parameters to perform error analysis on the prediction results. It describes the error between the calculated value and the actual current value of the photovoltaic module current, and its expression is: ; in, is the number of data samples, A set of parameters that need to be identified .

[0036] In step S200, for the measured operating voltage and measured operating current of the target photovoltaic component collected, the short-circuit current at each sampling moment is first calculated by a preset fitting function, and then the measured operating voltage and measured operating current on the first IV curve are converted to corresponding values ​​under STC through a correction model, and the first IV curve is converted into a correction curve under STC.

[0037] In step S300, at least one first target data point on the first IV correction curve is determined, and a first target data point set is constructed, including: S301 . Determine a maximum power point of a target photovoltaic assembly based on a first IV correction curve.

[0038] S302: Determine each data point within a target interval including a maximum power point of a target photovoltaic assembly on a first IV correction curve as a first target data point.

[0039] S303: Construct a first target data point set based on all first target data points.

[0040] For the obtained correction curve of the target photovoltaic assembly, a data point set (I1, V1) of a local IV characteristic curve of the target photovoltaic assembly near the optimum power point is obtained as a first target data point set.

[0041] Acquiring at least one second target data point corresponding to each first target data point on a preset standard IV correction curve, and constructing a second target data point set, comprising: S304 , determining data points on the standard IV correction curve corresponding to the operating voltages of the first target data points as second target data points corresponding to the first target data points.

[0042] S305: Construct a second target data point set based on all acquired second target data points.

[0043] The obtained standard IV correction curve is interpolated to obtain a second data point on the standard IV correction curve corresponding to the first target data point. Specifically, taking the working voltage V1 of the first data point as a reference, the working current corresponding to the working voltage V1 on the standard IV correction curve is obtained. , and obtain the second target data point set ( ,V1).

[0044] In step S400, the health index of the target photovoltaic module is determined under the grey correlation degree by using a preset health evaluation model, including: S401 , calculating the fuzzy membership of the target photovoltaic module to each preset health status level through a triangular membership function according to the grey correlation degree.

[0045] The calculation of the grey correlation degree between two data sets is a prior art and is not limited here.

[0046] In this application, the triangular membership function includes: ; ; ; ; Among them, u1, u2, u3, and u4 are the fuzzy memberships of each health status level, for example, u1, u2, u3, and u4 are the fuzzy memberships of the target PV module belonging to the first health status level, the second health status level, the third health status level, and the fourth health status level, respectively. a, b, c, and d are the membership boundaries of the triangular membership function, and the values ​​of a, b, c, and d are 0.5, 0.6, 0.8, and 0.9, respectively.

[0047] S402: Perform weighted summation on the obtained fuzzy membership degrees to obtain a health index of the target photovoltaic module.

[0048] ; Among them, v1, v2, v3, and v4 are coefficients, which are extracted from samples with determined health indexes. The values ​​of the coefficients can be determined by empirical methods and are not limited here. In this application, the value ranges of v1, v2, v3, and v4 are: 0.7≦v1≦0.9, 0.4≦v2≦0.5, 0.3≦v3≦0.4, and 0.1≦v4≦0.2.

[0049] like Figure 4 As shown, in a second aspect of the present application, a photovoltaic power station component online health evaluation device based on parameter identification is provided, comprising: The data acquisition module is configured to collect the measured backplane temperature, measured working voltage, measured working current of the target photovoltaic module at each sampling time within a specified sampling period, and obtain the measured ambient irradiance at each sampling time; An IV curve conversion module is configured to obtain component parameters of a target photovoltaic component, construct a first IV curve of the target photovoltaic component based on the collected measured working voltage and measured working current, determine the short-circuit current corresponding to the measured ambient irradiance at each sampling moment through a preset fitting function, and convert the first IV curve into a first IV correction curve under a preset standard through a preset correction model based on the short-circuit current at each sampling moment, the measured backplane temperature and the component parameters of the target photovoltaic component. The component parameters of the target photovoltaic component are obtained by optimizing a pre-constructed photovoltaic component current model through a particle swarm algorithm and a test photovoltaic component; A correlation building module is configured to determine at least one first target data point on the first IV correction curve, build a first target data point set, obtain at least one second target data point corresponding to each first target data point on a preset standard IV correction curve, and build a second target data point set; The health index calculation module is configured to determine the grey correlation between the first target data point set and the second target data point set through a grey correlation analysis method, and determine the health index of the target photovoltaic component under the grey correlation through a preset health evaluation model.

[0050] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0051] In a third aspect of the present application, a terminal device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned online health assessment method for photovoltaic power station components based on parameter identification when executing the computer program.

[0052] like Figure 5 FIG. 1 is a schematic diagram of a terminal device provided in an embodiment of the present application. Figure 5 As shown, the terminal device 10 of this embodiment includes: a processor 100, a memory 101, and a computer program 102 stored in the memory 101 and executable on the processor 100. When the processor 100 executes the computer program 102, the steps in the above method embodiment are implemented. Alternatively, when the processor 100 executes the computer program 102, the functions of each module / unit in the above device embodiments are implemented.

[0053] Exemplarily, the computer program 102 may be divided into one or more modules / units, one or more modules / units are stored in the memory 101, and are executed by the processor 100 to complete the present application. One or more modules / units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program 102 in the terminal device 10.

[0054] The terminal device 10 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal device 10 may include, but is not limited to, a processor 100 and a memory 101. Those skilled in the art will appreciate that Figure 5It is only an example of the terminal device 10 and does not constitute a limitation of the terminal device 10. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device may also include input and output devices, network access devices, buses, etc.

[0055] The processor 100 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0056] The memory 101 may be an internal storage unit of the terminal device 10, such as a hard disk or memory of the terminal device 10. The memory 101 may also be an external storage device of the terminal device 10, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card (FlashCard), etc. equipped on the terminal device 10. Further, the memory 101 may also include both an internal storage unit of the terminal device 10 and an external storage device. The memory 101 is used to store computer programs and other programs and data required by the terminal device 10. The memory 101 may also be used to temporarily store data that has been output or is to be output.

[0057] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0058] In summary, this application uses the measured IV data near the maximum power point (mppt) of the photovoltaic module for analysis and extracts the local features of the IV characteristic curve. It can perform health evaluation on photovoltaic modules or module units online and in large quantities. Compared with the existing photovoltaic power station health evaluation method, this application does not require the inverter to have a real-time IV curve scanning function, and also avoids the errors caused by the simulation modeling of the photovoltaic power station. It has great application value for photovoltaic power stations with older inverter models. At the same time, this application establishes a photovoltaic module current model, and optimizes the module parameters of the photovoltaic module based on the particle swarm algorithm to determine the optimal module parameters of the photovoltaic module, so that when converting the measured IV curve, the pre-calculated module parameters can be directly obtained to ensure the consistency of the module parameters of different photovoltaic modules; at the same time, due to the short-circuit current I sc Affected by environmental conditions, the STC correction of real-time IV data has a large error. This application has determined the I sc The main influencing factor is irradiance G, which is obtained through experiments. sc The relationship curve between the irradiance G and the current irradiance G can be used to derive the corresponding short-circuit current I during online operation. sc , effectively improving the accuracy of STC correction.

[0059] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0060] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for online health assessment of photovoltaic power station components based on parameter identification, characterized in that: include: Within the specified sampling period, the measured backplane temperature, measured working voltage, measured working current of the target photovoltaic module at each sampling time are collected, and the measured ambient irradiance at each sampling time is obtained; Acquire the component parameters of the target photovoltaic component, construct a first IV curve of the target photovoltaic component based on the collected measured working voltage and measured working current, determine the short-circuit current corresponding to the measured ambient irradiance at each sampling moment through a preset fitting function, and convert the first IV curve into a first IV correction curve under a preset standard through a preset correction model based on the short-circuit current at each sampling moment, the measured backplane temperature and the component parameters of the target photovoltaic component. The component parameters of the target photovoltaic component are obtained by optimizing the pre-constructed photovoltaic component current model through a particle swarm algorithm and a test photovoltaic component; Determine at least one first target data point on the first IV correction curve to construct a first target data point set, obtain at least one second target data point corresponding to each first target data point on a preset standard IV correction curve, and construct a second target data point set; The grey correlation degree between the first target data point set and the second target data point set is determined by a grey correlation analysis method, and the health index of the target photovoltaic component under the grey correlation degree is determined by a preset health evaluation model.

2. The method for online health assessment of photovoltaic power station components based on parameter identification according to claim 1 is characterized in that: The target photovoltaic assembly is composed of a plurality of photovoltaic cells connected in series and parallel. The assembly parameters of the target photovoltaic assembly include the photocurrent of the photovoltaic cell, the reverse saturation current of the diode in the photovoltaic cell, the equivalent series resistance of the photovoltaic cell, the equivalent parallel resistance of the photovoltaic cell and the diode ideal factor. The photovoltaic assembly current model includes: ； in, and are the output current and voltage of the photovoltaic cell, is the photogenerated current, is the reverse saturation current of the diode in the photovoltaic cell, is the charge constant, is the diode ideality factor, is the Boltzmann constant, is the back panel temperature of the photovoltaic module, is the equivalent series resistance, is the equivalent parallel resistance, is the number of photovoltaic cells connected in parallel, is the number of photovoltaic cells connected in series.

3. The method for online health assessment of photovoltaic power station components based on parameter identification according to claim 2 is characterized in that: The pre-built photovoltaic module current model is optimized by particle swarm algorithm, including: S1. Initialize the particle swarm algorithm parameters, which include at least the maximum number of iterations, the population size, the weight of the particles, and the learning factor; S2, converting the photocurrent of the photovoltaic cell, the reverse saturation current of the diode in the photovoltaic cell, the equivalent series resistance of the photovoltaic cell, the equivalent parallel resistance of the photovoltaic cell and the ideal factor of the diode into the position vector of each particle, and randomly generating the initial position of each particle; S3, taking the average relative error between the predicted output current value and the actual output current value of the tested photovoltaic assembly as the fitness function of the particle swarm algorithm, and calculating the fitness value of each particle based on the fitness function; S4. Determine the individual extreme value and population extreme value of each particle according to the fitness value of each particle, wherein the individual extreme value is the optimal position searched by a single particle up to the present moment, and the population extreme value is the optimal position searched by the entire particle group up to the present moment; S5. Update the current speed of each particle according to the weight of each particle, the individual extreme value of each particle and the population extreme value, and update the current position of each particle according to the current speed of each particle; S6. Determine whether the maximum number of iterations has been reached. If so, output the larger fitness value between the current optimal individual extreme value and the population extreme value as the optimal photovoltaic cell's photocurrent, the reverse saturation current of the diode in the photovoltaic cell, the photovoltaic cell's equivalent series resistance, the photovoltaic cell's equivalent parallel resistance and the diode ideal factor. Otherwise, return to step S3.

4. The method for online health assessment of photovoltaic power station components based on parameter identification according to claim 3 is characterized in that: The fitness function comprises: MRE=1 / N∑|predicted output current value-actual output current value| / actual output current value; Where N is the number of samples; Calculating the fitness value of each particle based on the fitness function includes: Fi = 1 / MRE; Among them, Fi represents the fitness value of the i-th particle.

5. The method for online health assessment of photovoltaic power station components based on parameter identification according to claim 1 is characterized in that: The fitting function comprises: ； Among them, I sc is the short-circuit current, C and b are fitting coefficients.

6. The method for online health assessment of photovoltaic power station components based on parameter identification according to claim 1 is characterized in that: The modified model comprises: I2=I1+I sc (G2 / G1-1)+α(T2-T1); ； in, is the operating current of the target photovoltaic module under the preset standard, is the operating voltage of the target photovoltaic module under the preset standard, is the measured working current of the target photovoltaic module, is the measured operating voltage of the target photovoltaic module, is the short-circuit current, is the ambient irradiance under the preset standard, is the measured ambient irradiance, is the backplane temperature of the target component under the preset standard, is the measured backplane temperature of the target photovoltaic module, is the current temperature coefficient of the target photovoltaic module, is the voltage temperature coefficient of the target photovoltaic module, is the internal resistance of the target photovoltaic module, is the curve correction factor.

7. The method for online health assessment of photovoltaic power station components based on parameter identification according to claim 1 is characterized in that: Determining at least one first target data point on the first IV correction curve and constructing a first target data point set includes: determining a maximum power point of the target photovoltaic assembly based on the first IV correction curve; Determine each data point within a target interval including a maximum power point of the target photovoltaic assembly on the first IV correction curve as a first target data point; constructing a first target data point set based on all first target data points; Acquiring at least one second target data point corresponding to each first target data point on a preset standard IV correction curve, and constructing a second target data point set, comprising: Determine a data point on the standard IV correction curve corresponding to the operating voltage of each first target data point as a second target data point corresponding to each first target data point; A second target data point set is constructed based on all acquired second target data points.

8. The method for online health assessment of photovoltaic power station components based on parameter identification according to claim 1 is characterized in that: Determining the health index of the target photovoltaic component under the grey correlation degree through a preset health evaluation model includes: According to the grey correlation degree, the fuzzy membership of the target photovoltaic assembly to each preset health status level is calculated by a triangular membership function; The obtained fuzzy memberships are weighted and summed to obtain the health index of the target photovoltaic module; the triangular membership function includes: ； ； ； ； Among them, u1, u2, u3, and u4 are the fuzzy memberships of each health status level respectively, and a, b, c, and d are the membership boundaries of the triangular membership function.

9. The method for online health assessment of photovoltaic power station components based on parameter identification according to claim 1, characterized in that: The standard IV correction curve is constructed by the following steps: Acquire, by an IV scanner, second IV curves of a plurality of different photovoltaic modules at different ambient irradiances and / or at different backplane temperatures; Converting each second IV curve into a second IV correction curve under a preset standard by using the correction model; For all the second IV correction curves obtained: Fixing each operating voltage of each second IV correction curve, determining the operating current corresponding to each fixed operating voltage on each second IV correction curve, taking the average of all the obtained operating currents as the operating current of each fixed operating voltage under the preset standard, and obtaining a standard IV correction curve; The fitting function is constructed by the following steps: Determine the short-circuit current of all second IV curves, and obtain the ambient irradiance corresponding to each second IV curve; Based on all the obtained short-circuit currents and their corresponding ambient irradiances, the correlation between the ambient irradiances and the short-circuit currents is fitted by the least squares method to obtain a fitting function of the short-circuit current and the ambient irradiance.

10. An online health evaluation device for photovoltaic power station components based on parameter identification, characterized in that: include: The data acquisition module is configured to collect the measured backplane temperature, measured working voltage, measured working current of the target photovoltaic module at each sampling time within a specified sampling period, and obtain the measured ambient irradiance at each sampling time; An IV curve conversion module is configured to obtain component parameters of the target photovoltaic component, construct a first IV curve of the target photovoltaic component based on the collected measured working voltage and measured working current, determine the short-circuit current corresponding to the measured ambient irradiance at each sampling moment through a preset fitting function, and convert the first IV curve into a first IV correction curve under a preset standard through a preset correction model based on the short-circuit current at each sampling moment, the measured backplane temperature and the component parameters of the target photovoltaic component. The component parameters of the target photovoltaic component are obtained by optimizing a pre-constructed photovoltaic component current model through a particle swarm algorithm and a test photovoltaic component; a correlation degree building module, configured to determine at least one first target data point on the first IV correction curve, build a first target data point set, obtain at least one second target data point corresponding to each first target data point on a preset standard IV correction curve, and build a second target data point set; The health index calculation module is configured to determine the grey correlation degree between the first target data point set and the second target data point set through a grey correlation analysis method, and determine the health index of the target photovoltaic component under the grey correlation degree through a preset health evaluation model.

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