Measurement method for describing random influence factors of dust deposition of photovoltaic module

Abstract

The invention discloses a measurement method for describing random influence factors of dust deposition of a photovoltaic module, which comprises the following steps of: qualitatively identifying the influence factors of the dust deposition of the photovoltaic module, and determining the quantitatively described influence factors of the dust deposition of the photovoltaic module; the method comprises the following specific steps of: providing a random process envelope for describing dynamic nature and volatility of environmental factors along with time change and a section distribution rule of the random process envelope, respectively providing probability distribution models of the dust characteristic factors according to the characteristic conditions of the dust characteristic factors, and establishing random simulation to generate dust deposition influence factors. The photovoltaic module dust deposition influence factor randomness can be quantified, the accuracy and reliability of dust particle deposition rate prediction are improved, the method can be used for predicting the generating capacity of a photovoltaic power generation system under the dust deposition condition, and technical support is provided for operation and maintenance decision making of a photovoltaic power station.

Description

Technical Field

[0001] The present invention relates to a measurement method for factors affecting dust deposition on photovoltaic modules, which is applicable to quantifying factors affecting randomness of dust deposition, and in particular to a measurement method for describing factors affecting randomness of dust deposition on photovoltaic modules. Background Art

[0002] To increase photovoltaic module power generation and extend their service life, timely cleaning of dust from their surfaces is essential. Developing effective cleaning strategies requires studying the factors that influence dust deposition. This process, however, is a dynamic and stochastic process influenced by a variety of uncertainties. Therefore, studying the stochastic nature of factors influencing dust deposition is not only of great theoretical significance but also holds broad application prospects in engineering applications.

[0003] Traditionally, deterministic measurement methods have been used to study the factors affecting dust deposition. However, since the factors affecting dust deposition on the surface of photovoltaic modules are random, it is necessary to study and propose more scientific measurement methods to accurately predict the dust deposition results. Summary of the Invention

[0004] In view of the above problems in formulating cleaning strategies, the present invention provides a measurement method for describing the factors affecting the randomness of dust deposition on photovoltaic modules, so as to improve the accuracy of dust deposition result prediction and help decision makers better formulate cleaning strategies.

[0005] The object of the present invention is achieved by adopting the following technical solution: A measurement method for describing the factors affecting the randomness of dust deposition on photovoltaic modules, the steps of which are as follows:

[0006] 1) Qualitative identification of factors affecting dust deposition on photovoltaic modules:

[0007] Including: environmental factors, dust characteristic factors, photovoltaic module characteristics and installation parameters; the environmental factors include wind speed, rainfall, relative humidity, wind direction, temperature and dust concentration; the installation parameters include installation height, installation inclination and installation direction; the dust characteristic factors include dust particle size and dust type; the photovoltaic module characteristics include photovoltaic module materials, structure and process;

[0008] 2) Determine the quantitative factors affecting dust deposition on photovoltaic modules:

[0009] Considering the controllability of PV module characteristics and installation parameters and the feasibility of quantitative analysis, the factors affecting dust deposition are determined for stochastic description.

[0010] 3) Quantitative description of factors affecting dust deposition on photovoltaic modules:

[0011] Including: environmental factors and dust characteristics factors;

[0012] A collection of time series can be represented by a vector:

[0013]

[0014] Where: is the value of the ωth dust deposition influencing factor on the tth day of the i-th year; ω=1,2,…,ω y ;i=1,2,…,m; t=1,2,…,T;

[0015] Constructing factors affecting dust deposition Panel data in matrix form:

[0016]

[0017] Factors affecting dust deposition on photovoltaic modules:

[0018] {y1,y2,…,y ω ,…,y8}(ω=1,2,…,8);

[0019] It consists of six environmental factors and two dust characteristic factors;

[0020] 4) Description of random environmental factors:

[0021] The environmental factors can all be described by random processes. Therefore, the random values ​​of each environmental factor can be represented by a random vector:

[0022] {y1,y2,…y ω ,…,y6}(ω=1,2,…,6);

[0023] The random vector of environmental factors is expressed as:

[0024]

[0025] Where: is the random variable taking the value of the ωth (ω=1,2,…,6)th environmental influencing factor on the tth day of the i-th year;

[0026] The random variables of the environmental factors on the tth day of the i-th year form the following random sequence:

[0027]

[0028] According to the characteristics of environmental factor data, they are divided into two categories for description: one is the case without extreme values; the other is the case with extreme values;

[0029] The characteristic environmental factors of the no extreme value condition include wind speed, wind direction, relative humidity, rainfall and dust concentration;

[0030] The random process of environmental factors can be expressed as Since environmental factors have a property of changing over a period of years, the envelope of the random process is used to describe the dynamics and volatility of their random changes over time.

[0031] There are two functions and So that for any random variable y in the random sequence ω (t), in the entire time domain Established, called Y max (t) and Y min (t) are the upper envelope and lower envelope of the random process of environmental factors;

[0032] Each random variable y of the random sequence ω (t) are independent of each other, and for the random process with the same influencing factor, its cross-sectional data at time t obey the same “cut-off” distribution law;

[0033] Fit the distribution of the "cut-off" probability distribution law of the random process of environmental factors;

[0034] The goodness-of-fit test was used to determine the optimal distribution pattern;

[0035] The maximum likelihood method is used to estimate the parameters of the "truncation" distribution for the sample points;

[0036] The characteristic environmental factor with extreme value is temperature;

[0037] The temperature random process can be expressed as and Where i represents the i-th year, t represents the t-th day, i=1,2,…,m; t=1,2,…,T;

[0038] There are two functions like this and So that for any temperature random variable y1(t) in the random sequence, in the entire time domain If established, then it is called Y max (t) and Y min (t) are the upper envelope and lower envelope of the temperature random process;

[0039] The cross-sectional data of the temperature random process obeys a certain distribution law;

[0040] Determine the value of the section distribution parameter according to the distribution law obeyed by the temperature section data;

[0041] 5) Description of random dust characteristics:

[0042] Includes: description of randomness of dust types and dust particle sizes;

[0043] The set of dust types is:

[0044] Y7(t)={A1,A2,…,A k};

[0045] The dust type random variable y7(t) obeys a discrete probability distribution;

[0046] The mass composition of dust types is {B1, B2, ···, B7}, and the probability distribution of the random variable y7(t) can be defined as:

[0047]

[0048] There is only one dust type per day, and each y7(t) represents the dust type of a particular day;

[0049] The random variable R of the dust particle size obeys a certain distribution law;

[0050] Determine the distribution parameter value according to the distribution law obeyed by the dust particle size data;

[0051] 6) The Monte Carlo method is used to generate random influencing factors:

[0052] Including: random environmental factors, random dust types and random dust particle size generation;

[0053] The specific steps of generating the random environmental factors include the following:

[0054] Variable and parameter assignment: i, t, ω;

[0055] Read the dataset:

[0056] Fitting the optimal probability distribution: Fit the {m×T} sample data of the entire random process time series of each influencing factor to the probability distribution, perform a goodness of fit test, and determine the optimal probability distribution, that is, the cut-off distribution law;

[0057] Parameter estimation: For each random variable cut-off sample function Y(t), according to the determined optimal probability distribution, the maximum likelihood method is used to estimate the parameters of the distribution law;

[0058] Determine the distribution model: Determine the distribution model based on the distribution law and its parameters;

[0059] Randomly generate sample sets: Use the Monte Carlo method to randomly generate enough sample sets that conform to the distribution model;

[0060] The specific steps of generating the random dust type include the following:

[0061] Variable and parameter assignment: ω, B1, B2, B3, B4, B5, B6, B7;

[0062] Randomly generate sample sets: Use the Monte Carlo method to generate random numbers ξ, and use conditional judgment to select the corresponding dust type as the dust type for the day;

[0063] Conditional judgment is determined by comparing ξ with the cumulative values ​​of each probability;

[0064] The specific steps of generating the random dust particle size include the following:

[0065] Variable and parameter assignment: ω, R min , R max ;

[0066] Calculate the mean and standard deviation: Based on the dust particle size data at the study site, calculate the mean and standard deviation;

[0067] Generate uniformly distributed random numbers: Generate uniformly distributed random numbers ξ between 0 and 1 using the Monte Carlo method;

[0068] Generate random numbers from a standard normal distribution: Use the Box-Muller transformation to convert the uniformly distributed random numbers into random numbers ζ from a standard normal distribution (mean 0, standard deviation 1);

[0069] Adjust the mean and standard deviation of the random number ζ:

[0070] ζ R =μ R +σ R ζ;

[0071] Randomly generate sample sets: randomly generate dust particle size sample sets based on the adjusted mean and standard deviation;

[0072] A measurement method for describing the influencing factors of random dust deposition on photovoltaic modules is used to measure the influencing factors of random dust deposition on photovoltaic modules.

[0073] The present invention can quantify the random influencing factors of dust deposition on photovoltaic modules, improve the accuracy and reliability of dust particle deposition rate prediction, and can be used to predict the power generation of photovoltaic power generation systems under dust deposition conditions, providing technical support for photovoltaic power station operation and maintenance decision-making. The standard deviation of the relative error of the predicted power generation of the deterministic influencing factor model is 10.7, while the standard deviation of this indicator of the random influencing factor model is 8.85, which is 52% lower than the former. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 This is a step-by-step diagram of factors affecting dust deposition on photovoltaic modules;

[0075] Figure 2 This is a correlation diagram of factors affecting dust deposition on photovoltaic modules;

[0076] Figure 3 It is the envelope diagram of the random process of environmental factors; DETAILED DESCRIPTION

[0077] The present invention is further described below with reference to examples of factors affecting dust deposition on photovoltaic modules.

[0078] A measurement method for describing the factors affecting the randomness of dust deposition on photovoltaic modules is implemented as follows (e.g. Figure 1 shown):

[0079] Step 1: Qualitatively identify factors affecting dust deposition on photovoltaic modules (e.g. Figure 2 shown):

[0080] Factors affecting dust deposition on PV modules include environmental factors, dust characteristics, PV module characteristics, and installation parameters;

[0081] Environmental factors include: wind speed, rainfall, relative humidity, wind direction, temperature, and dust concentration;

[0082] Installation parameters include: installation height, installation angle and installation direction;

[0083] Dust characteristic factors include: dust particle size and dust type;

[0084] PV module characteristics include: PV module materials, structure and process;

[0085] Step 2: Determine the quantitative factors affecting dust deposition on photovoltaic modules:

[0086] Considering the controllability of photovoltaic module characteristics and installation parameters and the feasibility of quantitative analysis, it was determined that six environmental factors affecting dust deposition, namely wind speed, rainfall, relative humidity, wind direction, temperature and dust concentration, as well as two dust characteristic factors affecting dust deposition, namely dust particle size and dust type, should be stochastically described.

[0087] Step 3: Quantitative description of factors affecting dust deposition on photovoltaic modules:

[0088] Including: environmental factors and dust characteristics factors;

[0089] Constructing factors affecting dust deposition Panel data in matrix form:

[0090]

[0091] Factors affecting dust deposition on photovoltaic modules:

[0092] {y1,y2,…,y ω ,…,y8}(ω=1,2,…,8);

[0093] It consists of two categories: six environmental factors and two dust characteristic factors;

[0094] Step 4: Description of random environmental factors:

[0095] The random vector of environmental factors is expressed as:

[0096]

[0097] Where: is the random variable taking the value of the ωth (ω=1,2,…,6)th environmental influencing factor on the tth day of the i-th year;

[0098] The random variables of the environmental factors on the tth day of the i-th year form the following random sequence:

[0099]

[0100] Environmental factors without extreme value characteristics include wind speed, wind direction, relative humidity, rainfall and dust concentration;

[0101] The random process of environmental factors can be expressed as There are two functions and So that for any random variable y in the random sequence ω (t), in the entire time domain Established, called Y max (t) and Y min (t) are the upper and lower envelopes of the random process of environmental factors (e.g. Figure 3 shown);

[0102] Five probability distribution models are used: normal distribution, lognormal distribution, gamma distribution, Weibull distribution and exponential distribution to fit the probability distribution law of the "cut-off" of the random process of environmental factors.

[0103] The Akaike Information Criterion (AIC) value is used to evaluate the goodness of fit of the probability distribution model. The model corresponding to the minimum AIC value is the relatively optimal model under the given data, and the optimal probability distribution model is selected accordingly;

[0104] According to the optimal probability distribution model, the maximum likelihood method is used to estimate the parameters of the "truncation" distribution of the sample points;

[0105] The environmental factor with extreme characteristic is temperature;

[0106] The temperature random process can be expressed as and

[0107] There are two functions like this and So that for any temperature random variable y1(t) in the random sequence, in the entire time domain If established, then it is called Y max (t) and Y min (t) are the upper envelope and lower envelope of the temperature random process;

[0108] The cross-sectional data of the temperature random process obeys the normal distribution law:

[0109]

[0110] According to the 3σ criterion, the cut-off distribution parameter values, namely the mean and standard deviation, are determined:

[0111]

[0112] Step 5. Description of random dust characteristic factors:

[0113] Includes: description of randomness of dust types and dust particle sizes;

[0114] The set of dust types is:

[0115] Y7(t)={SiO2,Al2O3,Fe2O3,CaMg(CO3)2,Ca(OH)2,CaO,CaCO3};

[0116] The physical property parameters of different types of dust are shown in Table 1;

[0117] Table 1 Physical property parameters of each dust type

[0118]

[0119] The dust type random variable y7(t) obeys a discrete probability distribution;

[0120] The probability distribution of each dust type in a certain area is shown in Table 2;

[0121] Table 2 Probability distribution of each dust type

[0122] Dust type Probability distribution <![CDATA[SiO2]]> 42％ <![CDATA[Al2O3]]> 12％ <![CDATA[Fe2O3]]> 4％ <![CDATA[CaMg(CO3)2]]> 8％ <![CDATA[Ca(OH)2]]> 2％ CaO 4％ <![CDATA[CaCO3]]> 28％

[0123] Define the probability distribution of the random variable y7(t) as:

[0124]

[0125] There is only one dust type per day, and each y7(t) represents the dust type of a particular day;

[0126] The random variable R of the dust particle size obeys the normal distribution:

[0127] Y8(t)~N(μ R ,σ R 2 );

[0128] Where: μ R and σ R is the mean and standard deviation of particle size in a specific geographical area;

[0129] The dust particle size range is related to the geographical region, as shown in Table 3;

[0130] Table 3 Dust particle size range in different regions

[0131]

[0132] The dust particle size in a certain area follows a normal distribution, with most dust particles ranging from 10 μm to 40 μm.

[0133] The mean and standard deviation are determined according to the 3σ criterion:

[0134]

[0135] Step 6: Use Monte Carlo method to generate random influencing factors:

[0136] Including: random environmental factors, random dust types and random dust particle size generation;

[0137] The specific steps for generating random environmental factors include the following:

[0138] Variable and parameter assignment: i, t, ω;

[0139] Read the dataset:

[0140] Fitting the optimal probability distribution: Fit the probability distribution of the {14×366} sample data of the entire random process time series of each influencing factor, calculate the AIC value of each fitted distribution, and determine the probability distribution with the smallest AIC value as the optimal probability distribution, that is, the cut-off distribution law;

[0141] Based on the optimal probability distribution fitted by the environmental factor data of a certain region, assuming that the temperature conforms to the normal distribution, Table 4 shows the fitting results of the distribution of each factor in the region. The bold font indicates the optimal probability distribution fitted;

[0142]

[0143] Parameter estimation: For each random variable cut-off sample function Y(t), according to the determined optimal probability distribution, the maximum likelihood method is used to estimate the parameters of the distribution law;

[0144] Determine the distribution model based on the distribution law and its parameters;

[0145] Using the Monte Carlo method, according to the distribution model, randomly generate enough sample sets that conform to the model;

[0146] The specific steps for generating random dust types include the following:

[0147] Variable and parameter assignment: ω = 7, B1, B2, B3, B4, B5, B6, B7;

[0148] Table 5 gives the conditional judgment and selection of dust types;

[0149] Table 5 Conditional judgment and selection of dust types

[0150] Conditional judgment choose 0≤ξ<42% <![CDATA[SiO2]]> 42%≤ξ<54% <![CDATA[Al2O3]]> 54%≤ξ<58% <![CDATA[Fe2O3]]> … … 72%≤ξ<100% <![CDATA[CaCO3]]>

[0151] Generate random number ξ using Monte Carlo method;

[0152] By comparing ξ with each probability cumulative value, the corresponding dust type is selected as the dust type for the day;

[0153] The specific steps for generating random dust particle size include the following:

[0154] Variable and parameter assignment: ω = 8, R min =10μm, R max =40μm;

[0155] We can get μ R 25μm, σ R 5μm;

[0156] Generate uniformly distributed random numbers: Generate uniformly distributed random numbers ξ between 0 and 1 using the Monte Carlo method;

[0157] Generate random numbers from a standard normal distribution: Use the Box-Muller transformation to convert the uniformly distributed random numbers into random numbers ζ from a standard normal distribution (mean 0, standard deviation 1);

[0158] Adjust the mean and standard deviation of the random number ζ:

[0159] ζ R =25+5ζ

[0160] A sample set of dust particle sizes is randomly generated based on the adjusted mean and standard deviation.

[0161] According to the calculation results of the above steps, a deterministic value sequence of the random sequence can be randomly generated

[0162]

[0163] Using the Monte Carlo analysis method, repeat the above solution process a sufficient number of times and integrate the above data series to determine Random panel data in matrix form.

[0164] 7) Application of the method:

[0165] The above method is applied to systematically measure the factors affecting the randomness of dust deposition on photovoltaic modules, and to predict the power generation of photovoltaic modules under dust accumulation conditions.

Claims

1. A measurement method for describing the factors affecting the randomness of dust deposition on photovoltaic modules, characterized in that: The steps are as follows: 1) Qualitative identification of factors affecting dust deposition on photovoltaic modules: Including: environmental factors, dust characteristic factors, photovoltaic module characteristics and installation parameters; the environmental factors include wind speed, rainfall, relative humidity, wind direction, temperature and dust concentration; the installation parameters include installation height, installation inclination and installation direction; the dust characteristic factors include dust particle size and dust type; the photovoltaic module characteristics include photovoltaic module materials, structure and process; 2) Determine the quantitative factors affecting dust deposition on photovoltaic modules: Considering the controllability of PV module characteristics and installation parameters and the feasibility of quantitative analysis, the factors affecting dust deposition are determined for stochastic description. 3) Quantitative description of factors affecting dust deposition on photovoltaic modules: Including: environmental factors and dust characteristics factors; A collection of time series can be represented by a vector: Where: is the value of the ωth dust deposition influencing factor on the tth day of the i-th year; ω=1,2,…,ω y ;i=1,2,…,m; t=1,2,…,T; Constructing factors affecting dust deposition Panel data in matrix form: Factors affecting dust deposition on photovoltaic modules: {y1,y2,…,y ω ,…,y8}(ω=1,2,…,8); It consists of two categories: six environmental factors and two dust characteristic factors; 4) Description of random environmental factors: The environmental factors can all be described by random processes. Therefore, the random values ​​of each environmental factor can be represented by a random vector: {y1,y2,…y ω ,…,y6}(ω=1,2,…,6); The random vector of environmental factors is expressed as: Where: is the random variable taking the value of the ωth (ω=1,2,…,6)th environmental influencing factor on the tth day of the i-th year; The random variables of the environmental factors on the tth day of the i-th year form the following random sequence: According to the characteristics of environmental factor data, they are divided into two categories for description: one is the case without extreme values; the other is the case with extreme values; The characteristic environmental factors of the no extreme value condition include wind speed, wind direction, relative humidity, rainfall and dust concentration; The random process of environmental factors can be expressed as Since environmental factors have a property of changing over a period of years, the envelope of the random process is used to describe the dynamics and volatility of their random changes over time. There are two functions and So that for any random variable y in the random sequence ω (t), in the entire time domain Established, called Y max (t) and Y min (t) are the upper envelope and lower envelope of the random process of environmental factors; Each random variable y of the random sequence ω (t) are independent of each other, and for the random process with the same influencing factor, its cross-sectional data at time t obey the same "cut-off" distribution law; Fit the distribution of the "cut-off" probability distribution law of the random process of environmental factors; The goodness-of-fit test was used to determine the optimal distribution pattern; The maximum likelihood method is used to estimate the parameters of the "truncation" distribution for the sample points; The characteristic environmental factor with extreme value is temperature; The temperature random process can be expressed as and Where i represents the i-th year, t represents the t-th day, i = 1, 2, ..., m, t = 1, 2, ..., T; There are two functions like this and So that for any temperature random variable y1(t) in the random sequence, in the entire time domain If established, then it is called Y max (t) and Y min (t) are the upper envelope and lower envelope of the temperature random process; The cross-sectional data of the temperature random process obeys a certain distribution law; Determine the value of the section distribution parameter according to the distribution law obeyed by the temperature section data; 5) Description of random dust characteristics: Includes: description of randomness of dust types and dust particle sizes; The set of dust types is: <h2 style=";text-align:left;direction:ltr">Y7(t) = {A1,A2,…,A<h2 style=";text-align:left;direction:ltr"> k <h2 style=";text-align:left;direction:ltr">}; The dust type random variable y7(t) obeys a discrete probability distribution; The mass composition of dust types is {B1, B2, ···, B7}, and the probability distribution of the random variable y7(t) can be defined as: There is only one dust type per day, and each y7(t) represents the dust type of a particular day; The random variable R of the dust particle size obeys a certain distribution law; Determine the distribution parameter value according to the distribution law obeyed by the dust particle size data; 6) The Monte Carlo method is used to generate random influencing factors: Including: random environmental factors, random dust types and random dust particle size generation; The specific steps of generating the random environmental factors include the following: Variable and parameter assignment: i, t, ω; Read the dataset: Fitting the optimal probability distribution: Fit the {m×T} sample data of the entire random process time series of each influencing factor to the probability distribution, perform a goodness of fit test, and determine the optimal probability distribution, that is, the cut-off distribution law; Parameter estimation: For each random variable cut-off sample function Y(t), according to the determined optimal probability distribution, the maximum likelihood method is used to estimate the parameters of the distribution law; Determine the distribution model: Determine the distribution model based on the distribution law and its parameters; Randomly generate sample sets: Use the Monte Carlo method to randomly generate enough sample sets that conform to the distribution model; The specific steps of generating the random dust type include the following: Variable and parameter assignment: ω, B1, B2, B3, B4, B5, B6, B7; Randomly generate sample sets: Use the Monte Carlo method to generate random numbers ξ, and use conditional judgment to select the corresponding dust type as the dust type for the day; Conditional judgment is determined by comparing ξ with the cumulative values ​​of each probability; The specific steps of generating the random dust particle size include the following: Variable and parameter assignment: ω, R min , R max ; Calculate the mean and standard deviation: Based on the dust particle size data at the study site, calculate the mean and standard deviation; Generate uniformly distributed random numbers: Generate uniformly distributed random numbers ξ between 0 and 1 using the Monte Carlo method; Generate random numbers from a standard normal distribution: Use the Box-Muller transformation to convert the uniformly distributed random numbers into random numbers ζ from a standard normal distribution (mean 0, standard deviation 1); Adjust the mean and standard deviation of the random number ζ: g R =μ R +s R g; Randomly generate sample sets: Randomly generate dust particle size sample sets based on the adjusted mean and standard deviation.

2. An application of the measurement method according to claim 1, characterized in that: Used to measure the factors affecting the randomness of dust deposition on photovoltaic modules.

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