Spectrum mismatch calculation method for photovoltaic device

Abstract

The invention provides a spectral mismatch calculation method for a photovoltaic device, which comprises the following steps: acquiring spectral data and spectral response characteristic data of the photovoltaic device, converting the spectral response characteristic data and the spectral data into probability distribution, calculating information entropy of the probability distribution of the spectral data, and calculating the spectral mismatch of the photovoltaic device. Calculating the information entropy of the probability distribution of the spectral response characteristic data of the photovoltaic device, calculating the difference between the information entropy of the spectral data and the information entropy of the spectral response characteristic data of the photovoltaic device, and evaluating the spectrum mismatch degree according to the difference; the method can simplify spectrum mismatch evaluation, improve evaluation accuracy, enhance adaptive capacity, optimize photovoltaic system design, reduce energy loss, and provide basis for design, installation and maintenance of the photovoltaic system.

Description

Technical Field

[0001] The invention relates to the technical field of photovoltaic power generation, and in particular to a spectrum mismatch calculation method for photovoltaic devices. Background Art

[0002] The performance evaluation of photovoltaic devices is one of the key factors to ensure their efficient operation. When evaluating the performance of photovoltaic devices, an important consideration is spectral mismatch (SM), which is the difference between the actual working spectrum of the photovoltaic device and the spectrum under standard test conditions; this difference will lead to a decrease in the output power of the photovoltaic device, thereby affecting the energy conversion efficiency of the entire photovoltaic system;

[0003] The development of photovoltaic device mismatch calculation has gone through the following key stages. Initially, the research focused on theoretical models and explored the performance changes of photovoltaic devices under different spectral conditions. These studies mainly focused on theoretical analysis and lacked verification in practical applications. Subsequently, the International Organization for Standardization began to formulate relevant standards, such as GB / T6495.7-2006, which specifies the calculation method for the spectral response mismatch between the test sample and the standard device during the measurement of photovoltaic devices. This marks the beginning of the standardization stage of photovoltaic device mismatch calculation. In recent years, with the widespread application of bifacial photovoltaic modules and the advancement of technology, new For example, an invention patent granted by Trina Solar (CN201910603397.6) proposed a method for calculating the mismatch loss on the back of a bifacial photovoltaic module; this shows that the mismatch calculation of photovoltaic devices needs to take into account more complex practical application scenarios; research on the mismatch phenomenon has also gradually deepened, for example, a power boost method based on photovoltaic array reconstruction (Master's thesis of Zhejiang University, 2021) was proposed to reduce the power loss caused by mismatch; at the same time, there are also studies exploring detailed engineering models of mismatch effects (Renewable Energy, 2016, Issue 07) in order to more accurately predict mismatch conditions;

[0004] Although there are many methods to deal with the mismatch problem of photovoltaic devices, the existing technology still has some shortcomings: 1. Difficulty in quantitative evaluation: Existing mismatch calculation methods often rely on complex mathematical models, which makes it difficult to intuitively evaluate the degree of spectral mismatch; 2. Limited adaptability: Most mismatch calculation methods assume that the spectral distribution is known, but in reality, due to factors such as weather changes and geographical location differences, the actual received spectral distribution may be significantly different from the standard spectral distribution; 3. Inefficiency: Existing methods cannot fully consider the impact of different wavelength ranges on the performance of photovoltaic devices, resulting in inaccurate evaluation results; 4. Lack of flexibility: Existing calculation methods usually do not consider the weights of specific wavelength ranges, which means that they cannot accurately reflect the performance of photovoltaic devices at specific wavelengths. Summary of the invention

[0005] The present invention provides a spectral mismatch calculation method for photovoltaic devices, which evaluates the spectral mismatch degree by calculating the information entropy difference between the spectral distribution and the response characteristic distribution of the photovoltaic device. This method can not only provide a simple and intuitive evaluation method, but also flexibly adjust the importance of different wavelength ranges to more accurately reflect the performance of photovoltaic devices in practical applications.

[0006] The present invention provides a method for calculating spectral mismatch of a photovoltaic device, comprising the following steps:

[0007] Step 1, obtaining spectral data and spectral response characteristic data of a photovoltaic device;

[0008] Step 2, converting the spectral response characteristic data and the spectral data into probability distribution;

[0009] Step three, calculating the information entropy of the probability distribution of the spectral data;

[0010] Step 4, calculating the information entropy of the probability distribution of the spectral response characteristic data of the photovoltaic device;

[0011] Step 5, calculating the difference between the information entropy of the spectral data and the information entropy of the spectral response characteristic data of the photovoltaic device;

[0012] Step six, evaluating the degree of spectral mismatch based on the difference.

[0013] According to a spectral mismatch calculation method for a photovoltaic device provided by the present invention, the step of obtaining spectral data and spectral response characteristic data of the photovoltaic device comprises:

[0014] The spectrum data includes n preset wavelength intervals λ, the actually received spectrum distribution E corresponding to the wavelength intervals, and the standard AM1.5G spectrum distribution E corresponding to the wavelength intervals. st ;

[0015] The spectral response characteristic data is the photovoltaic device response characteristic distribution I corresponding to the wavelength range.

[0016] According to a spectral mismatch calculation method for a photovoltaic device provided by the present invention, converting spectral response characteristic data and spectral data into probability distribution comprises:

[0017] For the actual received spectral distribution E, calculate the normalized probability distribution P λ :

[0018]

[0019] Among them, P λis the normalized probability distribution of the actual spectral distribution, E i is the spectral irradiance corresponding to the ith wavelength interval in the actual spectral distribution, in units of w / m 2 / nm;

[0020] For standard AM1.5G spectrum distribution E st , calculate the normalized probability distribution P st :

[0021]

[0022] Among them, P st is the normalized probability distribution of the standard spectral distribution, E sti is the spectral irradiance corresponding to the i-th wavelength interval in the standard spectral distribution, in w / m 2 / nm;

[0023] For the photovoltaic device response characteristic distribution I, calculate the normalized probability distribution P IV :

[0024]

[0025] Among them, P iV is the normalized probability distribution of the photovoltaic device response characteristics, I i It is the ratio of the current to the radiation flux corresponding to the i-th wavelength interval in the response characteristic distribution, with the unit of A / W.

[0026] According to a spectral mismatch calculation method for a photovoltaic device provided by the present invention, the information entropy of the probability distribution of the spectral data is calculated including:

[0027] Normalized probability distribution P based on actual spectral distribution λ Calculate the actual spectral information entropy H G :

[0028]

[0029] Among them, H G It is used to reflect the uncertainty or information content of the actual received spectral distribution, H G The larger the value, the higher the uncertainty of the spectral distribution, that is, the more uniform the distribution of light intensity in different wavelength ranges;

[0030] Normalized probability distribution P based on standard spectral distribution st Calculate the standard spectral information entropy H B :

[0031]

[0032] Among them, HB As the definition of the actual spectral information entropy H G The threshold parameter.

[0033] According to a spectral mismatch calculation method for a photovoltaic device provided by the present invention, the information entropy of calculating the probability distribution of the spectral response characteristic data of the photovoltaic device includes:

[0034] Normalized probability distribution P based on the response characteristic distribution of photovoltaic devices IV Calculate the information entropy H of the spectral response characteristics Q :

[0035]

[0036] Among them, H Q It is used to reflect the uncertainty or information content of the response characteristic data of photovoltaic devices. Q The larger the value, the more uniform the response of the photovoltaic device's response effect data in different wavelength ranges.

[0037] According to a spectral mismatch calculation method for a photovoltaic device provided by the present invention, the difference between the information entropy of the spectral data and the information entropy of the spectral response characteristic data of the photovoltaic device is calculated including:

[0038] Based on the actual spectral information entropy H G and the spectral response characteristic information entropy H Q Calculate the first entropy difference;

[0039] ΔH1=|H G -H Q |

[0040] Among them, ΔH1 is the first entropy difference, which is used to reflect the matching degree between the actual spectrum step and the photovoltaic device response characteristic step; the smaller the first entropy difference ΔH1, the higher the matching degree between the spectrum and the photovoltaic device response characteristic.

[0041] A spectral mismatch calculation method for a photovoltaic device provided by the present invention further includes:

[0042] Based on the actual spectral information entropy H G and standard spectral information entropy H B Calculating the second entropy difference;

[0043] ΔH2=|H G -H B |

[0044] Among them, ΔH2 is the second entropy difference, which is used to reflect the matching degree between the actual spectrum step and the standard spectrum step; the smaller the second entropy difference ΔH2 is, the higher the matching degree between the actual spectrum and the standard in terms of distribution is.

[0045] According to a spectral mismatch calculation method for a photovoltaic device provided by the present invention, the evaluating the spectral mismatch degree according to the difference comprises:

[0046] A first entropy difference coefficient K1 and a second entropy difference coefficient K2 are preset, K1+K2=1, and K1>K2;

[0047] The total entropy difference is calculated based on the first entropy difference ΔH1, the second entropy difference ΔH2, the first entropy difference coefficient K1 and the second entropy difference coefficient K2:

[0048] ΔH0=K1*ΔH1+K2*ΔH2

[0049] Among them, ΔH0 is the total entropy difference, which is used to comprehensively reflect the matching degree between the actual spectral distribution and the standard spectral distribution and the response characteristics of the photovoltaic device;

[0050] Preset the mismatch threshold ΔH based on the photovoltaic device model and working environment information x ;

[0051] When ΔH0≤ΔH x When , the output evaluation result is that the spectral mismatch degree of the current photovoltaic device is low;

[0052] When ΔH0>ΔH x When , the output evaluation result is that the spectral mismatch degree of the current photovoltaic device is high.

[0053] According to a spectral mismatch calculation method for photovoltaic devices provided by the present invention, a weight is also assigned to each wavelength interval to reflect the influence of different wavelength intervals on the performance of the photovoltaic device, specifically:

[0054] Normalized probability distribution P based on actual spectral distribution λ And interval weight W to calculate the actual spectral information entropy H G :

[0055] H G =-∑ λ W λ *P λ logP λ ;

[0056] Normalized probability distribution P based on standard spectral distribution st And the interval weight W calculates the standard spectral information entropy H B :

[0057] H B =-∑ st W st *P st logP st ;

[0058] Normalized probability distribution P based on the response characteristic distribution of photovoltaic devices IV And interval weight W to calculate the spectral response characteristic information entropy H Q :

[0059] H Q =-∑ IV W IV *P IV logP IV .

[0060] A spectral mismatch calculation method for a photovoltaic device provided by the present invention further includes:

[0061] When the output evaluation result is that the spectral mismatch of the current photovoltaic device is low, an alarm is triggered and the current total entropy difference ΔH0 and the preset mismatch threshold ΔH x Sent to the administrator's terminal device.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] 1. By calculating the information entropy difference between the actual spectral distribution and the response characteristic distribution of the photovoltaic device, a simple and intuitive method is provided to evaluate the degree of spectral mismatch, thereby simplifying the evaluation process.

[0064] 2. Using the concept of information entropy to quantify the degree of spectral mismatch can more accurately reflect the performance of photovoltaic devices in practical applications, especially in terms of the impact of different wavelength ranges on the performance of photovoltaic devices.

[0065] 3. By assigning weights to each wavelength range, this method can better adapt to the needs of different application scenarios, especially those that need to focus on specific wavelength ranges.

[0066] 4. Accurate evaluation of the degree of spectral mismatch can help designers optimize the selection of photovoltaic devices and system layout, thereby improving the energy conversion efficiency of the entire photovoltaic system.

[0067] 5. Improve the overall performance and economy of photovoltaic devices by reducing energy loss caused by spectral mismatch.

[0068] 6. This method is based on the analysis of existing spectral data and response characteristic data, does not require additional hardware equipment, and is easy to implement in a laboratory or field environment.

[0069] 7. Allow users to adjust the importance of wavelength ranges according to specific application scenarios, thereby achieving more refined performance evaluation.

[0070] 8. It provides a scientific basis for the design, installation and maintenance of photovoltaic systems, helps to optimize the layout and component selection of photovoltaic systems, and thus improves the economic benefits of the overall system.

[0071] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0072] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0074] Figure 1 It is a flow chart of a method for calculating spectral mismatch of a photovoltaic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0075] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0076] Embodiment 1:

[0077] The embodiment of the present invention provides a method for calculating spectral mismatch of photovoltaic devices. Figure 1 , including the following steps:

[0078] Step 1, obtaining spectral data and spectral response characteristic data of a photovoltaic device;

[0079] Step 2, converting the spectral response characteristic data and the spectral data into probability distribution;

[0080] Step 3, calculating the information entropy of the probability distribution of the spectral data;

[0081] Step 4, calculating the information entropy of the probability distribution of the spectral response characteristic data of the photovoltaic device;

[0082] Step 5, calculating the difference between the information entropy of the spectral data and the information entropy of the spectral response characteristic data of the photovoltaic device;

[0083] Step six, evaluate the degree of spectral mismatch based on the difference.

[0084] The principle of the above embodiment is that: spectral data refers to the spectral composition when solar radiation reaches the surface of the photovoltaic device, usually in units of wavelength or energy; spectral response characteristic data refers to the absorption efficiency or photoelectric conversion efficiency of the photovoltaic device for light of different wavelengths; the conversion to probability distribution is normalized to convert the spectral intensity and response efficiency into a probability density function (PDF), which is helpful for statistical analysis; calculating information entropy is an indicator to measure the uncertainty of random variables, which is used here to quantify the "uniformity" or "diversity" of spectral distribution; the higher the information entropy, the wider and more uniform the spectral distribution; conversely, it means that the spectrum is more concentrated in certain specific wavelengths; calculating the difference in information entropy between two probability distributions reflects the degree of mismatch between the actual spectrum and the device's optimal response spectrum; the greater the degree of mismatch, it means that the photovoltaic device cannot fully utilize light of all bands, resulting in reduced efficiency.

[0085] The beneficial effects of the above embodiments are: using the concept of information entropy can more accurately evaluate the efficiency loss caused by spectral mismatch; by calculating the degree of mismatch under different spectral conditions, it can help optimize the design of photovoltaic devices so that they can achieve higher conversion efficiency in specific application environments; considering the impact of spectral mismatch in the integrated design stage of photovoltaic systems can better match system components and improve the performance of the entire system; by analyzing the response characteristics of devices under specific spectral conditions, it can guide the research and development of new photovoltaic materials to reduce the negative impact of spectral mismatch.

[0086] In order to further optimize the above embodiment, obtaining the spectral data and spectral response characteristic data of the photovoltaic device includes:

[0087] The spectrum data includes the preset n wavelength intervals λ, the actually received spectrum distribution E corresponding to the wavelength intervals, and the standard AM1.5G spectrum distribution E corresponding to the wavelength intervals. st ;

[0088] The spectral response characteristic data is the photovoltaic device response characteristic distribution I corresponding to the wavelength range.

[0089] It should be noted that the wavelength interval λ is obtained by dividing the visible light to near infrared spectrum into n wavelength intervals; each interval represents a specific wavelength range, for example, from 400nm to 1100nm, which can be divided into multiple smaller intervals, such as an interval of every 100nm;

[0090] The actual received spectral distribution E is the spectral intensity distribution measured at a specific geographical location and meteorological conditions; it reflects the actual intensity of solar radiation in these wavelength ranges;

[0091] Standard AM1.5G spectral distribution E stIt is a standard solar spectrum distribution, usually used to simulate the solar spectrum received by the earth's surface; AM1.5G refers to the solar spectrum under the condition of atmospheric mass 1.5;

[0092] The photovoltaic device response characteristic distribution I is the response efficiency of the photovoltaic device to light of different wavelengths; it describes the photoelectric conversion efficiency of the photovoltaic device in each wavelength range;

[0093] Specifically, the division of wavelength intervals should take into account the response characteristics of photovoltaic devices and actual application requirements; for example, if a certain wavelength interval does not contribute much to the efficiency of the device, the weight of this interval can be appropriately reduced or merged into other intervals; the difference between the actual received spectral distribution E and the standard AM1.5G spectral distribution Est can help understand the impact of local meteorological conditions on the performance of photovoltaic devices; for example, in cloudy weather, short-wavelength light may be scattered, while long-wavelength light may be relatively more; the response characteristic distribution I of photovoltaic devices is the key to evaluating device performance; by understanding which wavelengths of light are efficiently utilized, the device design can be optimized or more suitable materials can be selected to improve the overall efficiency.

[0094] In order to further optimize the above embodiment, converting the spectral response characteristic data and the spectral data into a probability distribution includes:

[0095] For the actual received spectral distribution E, calculate the normalized probability distribution P λ :

[0096]

[0097] Among them, P λ is the normalized probability distribution of the actual spectral distribution, E i is the spectral irradiance corresponding to the ith wavelength interval in the actual spectral distribution, in units of w / m 2 / nm;

[0098] For standard AM1.5G spectrum distribution E st , calculate the normalized probability distribution P st :

[0099]

[0100] Among them, P st is the normalized probability distribution of the standard spectral distribution, E sti is the spectral irradiance corresponding to the i-th wavelength interval in the standard spectral distribution, in w / m 2 / nm;

[0101] For the photovoltaic device response characteristic distribution I, calculate the normalized probability distribution P IV :

[0102]

[0103] Among them, P IV is the normalized probability distribution of the photovoltaic device response characteristics, I i It is the ratio of the current to the radiation flux corresponding to the i-th wavelength interval in the response characteristic distribution, with the unit of A / W.

[0104] It should be noted that normalization is to make the sum of all probability distributions equal to 1. This allows these probability distributions to be directly used in subsequent information entropy calculations and also facilitates comparisons. Ensure that the units used in the calculations are consistent. For example, all spectral irradiances should use the same unit (w / m 2 / nm) for calculation.

[0105] When performing division operations, special attention should be paid to the case where the denominator is not zero. In actual operations, a very small positive number (e.g. ∈=10) can be added. -16 ) to avoid numerical instability problems.

[0106] Normalized probability distribution P λ , P st and P IV They respectively represent the probability of occurrence of each wavelength interval in the actual received spectrum, the standard spectrum and the distribution of the photovoltaic device response characteristics.

[0107] Through the above steps, we can obtain three probability distributions, which will be used for subsequent information entropy calculation and spectral mismatch evaluation.

[0108] In order to further optimize the above embodiment, calculating the information entropy of the probability distribution of spectral data includes:

[0109] Normalized probability distribution P based on actual spectral distribution λ Calculate the actual spectral information entropy H G :

[0110]

[0111] Among them, H G It is used to reflect the uncertainty or information content of the actual received spectral distribution, H G The larger the value, the higher the uncertainty of the spectral distribution, that is, the more uniform the distribution of light intensity in different wavelength ranges;

[0112] Normalized probability distribution P based on standard spectral distribution st Calculate the standard spectral information entropy H B :

[0113]

[0114] Among them, H B As the definition of the actual spectral information entropy H G The threshold parameter.

[0115] It should be noted that the information entropy H G and H B They respectively reflect the uncertainty of the actual received spectral distribution and the standard spectral distribution; the higher the information entropy, the more uniform the spectral distribution, that is, the closer the distribution of light intensity in different wavelength ranges is to being equal;

[0116] If the natural logarithm ln is used, the unit of information entropy will be nat; if the logarithm with base 2 is used, the unit of information entropy will be bit;

[0117] Standard spectral information entropy H B As a benchmark, it can be used to evaluate the actual spectral information entropy H G If H G Close to H B , it means that the actual received spectrum distribution is similar to the standard spectrum distribution; if H G Significantly smaller than H B , it indicates that the uncertainty of the actual spectral distribution is low, that is, the light intensity is concentrated in certain specific wavelength ranges;

[0118] When P λ or P st When it is close to zero, its logarithm may approach negative infinity, resulting in numerical instability in the calculation. In this case, a numerically stable calculation method can be used, such as using log(x+∈), where ∈ is a very small positive value (for example, ∈=10 -16 );

[0119] By calculating the actual spectral information entropy H G and standard spectral information entropy H B , we can further evaluate the difference between the actual received spectrum and the standard spectrum and lay the foundation for the evaluation of the degree of spectral mismatch in the subsequent steps.

[0120] In order to further optimize the above embodiment, calculating the information entropy of the probability distribution of the spectral response characteristic data of the photovoltaic device includes:

[0121] Normalized probability distribution P based on the response characteristic distribution of photovoltaic devices IV Calculate the information entropy H of the spectral response characteristics Q :

[0122]

[0123] Among them, H Q It is used to reflect the uncertainty or information content of the response characteristic data of photovoltaic devices. Q The larger the value, the more uniform the response of the photovoltaic device's response effect data in different wavelength ranges.

[0124] It should be noted that its calculation principle is different from the actual spectral information entropy H G and standard spectral information entropy H B The calculation principle of is similar, which reflects the uncertainty of the response characteristic data of photovoltaic devices; the higher the information entropy, the more uniform the response of photovoltaic devices in different wavelength ranges; in other words, photovoltaic devices have similar response capabilities to light of different wavelengths; by calculating the spectral response characteristic information entropy H Q , the effectiveness of photovoltaic device design can be evaluated; if H Q It is higher, indicating that the device can respond better to a wide wavelength range, which is very beneficial for improving the overall efficiency of photovoltaic devices.

[0125] In order to further optimize the above embodiment, the difference between the information entropy of the spectral data and the information entropy of the spectral response characteristic data of the photovoltaic device is calculated as follows:

[0126] Based on the actual spectral information entropy H G and the spectral response characteristic information entropy H Q Calculate the first entropy difference;

[0127] ΔH1=|H G -H Q |

[0128] Among them, ΔH1 is the first entropy difference, which is used to reflect the matching degree between the actual spectrum step and the photovoltaic device response characteristic step; the smaller the first entropy difference ΔH1, the higher the matching degree between the spectrum and the photovoltaic device response characteristic;

[0129] Based on the actual spectral information entropy H G and standard spectral information entropy H B Calculating the second entropy difference;

[0130] ΔH2=|H G -H B |

[0131] Among them, ΔH2 is the second entropy difference, which is used to reflect the matching degree between the actual spectrum step and the standard spectrum step; the smaller the second entropy difference ΔH2 is, the higher the matching degree between the actual spectrum and the standard in terms of distribution is.

[0132] It should be noted that the smaller the first entropy difference is, the higher the matching degree between the actually received spectrum distribution and the response characteristic distribution of the photovoltaic device is; this means that the photovoltaic device can more effectively utilize the received spectrum, thereby improving the conversion efficiency;

[0133] The absolute value is used to ensure that the result is non-negative, even if H G and H B The difference between them may be negative. The first entropy difference ΔH1 obtained by taking the absolute value is always non-negative, which makes it easy to compare the matching degree in different situations. The same is true for the second entropy difference.

[0134] The first entropy difference ΔH1 and the second entropy difference ΔH2 provide a quantitative indicator to evaluate the degree of spectral mismatch.

[0135] To further optimize the above embodiment, evaluating the degree of spectral mismatch according to the difference includes:

[0136] A first entropy difference coefficient K1 and a second entropy difference coefficient K2 are preset, K1+K2=1, and K1>K2;

[0137] The total entropy difference is calculated based on the first entropy difference ΔH1, the second entropy difference ΔH2, the first entropy difference coefficient K1 and the second entropy difference coefficient K2:

[0138] ΔH0=K1*ΔH1+K2*ΔH2

[0139] Among them, ΔH0 is the total entropy difference, which is used to comprehensively reflect the matching degree between the actual spectral distribution and the standard spectral distribution and the response characteristics of the photovoltaic device;

[0140] Preset the mismatch threshold ΔH based on the photovoltaic device model and working environment information x ;

[0141] When ΔH0≤ΔH x When , the output evaluation result is that the spectral mismatch degree of the current photovoltaic device is low;

[0142] When ΔH0>ΔH x When , the output evaluation result is that the spectral mismatch degree of the current photovoltaic device is high.

[0143] It should be noted that the values ​​of K1 and K2 depend on the working environment and application requirements of the photovoltaic device; for example, if the spectrum in the working environment varies greatly, the value of K1 can be set higher to take into account the matching degree between the actual spectrum and the response characteristics of the photovoltaic device;

[0144] Mismatch threshold ΔH xIt should be set according to the performance requirements of the photovoltaic device and the actual application environment. Generally speaking, if the photovoltaic device is expected to maintain a high conversion efficiency under a wider spectrum, then ΔH x The value of can be set higher;

[0145] The evaluation results can be used to guide the design optimization of photovoltaic devices, such as selecting appropriate materials or structures to improve the responsiveness of photovoltaic devices to spectra within a specific wavelength range;

[0146] By calculating the total entropy difference ΔH0 and comparing it with the preset mismatch threshold ΔH x By comparison, we can quantitatively evaluate the degree of spectral mismatch of photovoltaic devices and make corresponding optimization designs or adjustments accordingly.

[0147] Example 2

[0148] Embodiment 2 of the present invention further includes assigning a weight to each wavelength interval to reflect the degree of influence of different wavelength intervals on the performance of the photovoltaic device, specifically:

[0149] Normalized probability distribution P based on actual spectral distribution λ And interval weight W to calculate the actual spectral information entropy H G :

[0150] H G =-∑ λ W λ *P λ logP λ ;

[0151] Normalized probability distribution P based on standard spectral distribution st And interval weight W to calculate the standard spectral information entropy H B :

[0152] H B =-∑ st W st *P st logP st ;

[0153] Normalized probability distribution P based on the response characteristic distribution of photovoltaic devices IV And interval weight W to calculate the spectral response characteristic information entropy H Q :

[0154] H Q =-∑ IV W IV *P IV logP IV .

[0155] It should be noted that the selection of weights should be based on the specific application environment and performance requirements of photovoltaic devices; for example, in some specific applications, short-wavelength light may be more important, while in other applications, long-wavelength light may be more important; in order to ensure that the sum of the weights is 1, the weights of all wavelength intervals can be normalized; this ensures that the calculation of information entropy is still meaningful;

[0156] By assigning weights to each wavelength range, we can more accurately reflect the impact of different wavelength ranges on the performance of photovoltaic devices, and thus more accurately evaluate the degree of spectral mismatch.

[0157] In order to further optimize the above embodiment, it also includes:

[0158] When the output evaluation result is that the spectral mismatch of the current photovoltaic device is low, an alarm is triggered and the current total entropy difference ΔH0 and the preset mismatch threshold ΔH x Sent to the administrator's terminal device.

[0159] It should be noted that the frequency of triggering alarms can be set according to actual needs; for example, you can choose to send an alarm immediately after each calculation, or set a fixed period (such as daily or weekly) to send a summary report; in the process of sending information, it is necessary to ensure the secure transmission of data to prevent the leakage of sensitive information; encryption technology and other means can be used to protect data security; by triggering an alarm when the degree of spectral mismatch is high and sending relevant information to management personnel, potential problems can be discovered and resolved in a timely manner, thereby improving the overall efficiency and reliability of the photovoltaic system.

[0160] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for calculating spectral mismatch of photovoltaic devices, characterized in that: The method comprises the following steps: Step 1, obtaining spectral data and spectral response characteristic data of a photovoltaic device; Step 2, converting the spectral response characteristic data and the spectral data into probability distribution; Step three, calculating the information entropy of the probability distribution of the spectral data; Step 4, calculating the information entropy of the probability distribution of the spectral response characteristic data of the photovoltaic device; Step 5, calculating the difference between the information entropy of the spectral data and the information entropy of the spectral response characteristic data of the photovoltaic device; Step six, evaluating the degree of spectral mismatch based on the difference.

2. A method for calculating spectral mismatch of photovoltaic devices according to claim 1, characterized in that: The obtaining of spectral data and spectral response characteristic data of the photovoltaic device comprises: The spectrum data includes n preset wavelength intervals λ, the actually received spectrum distribution E corresponding to the wavelength intervals, and the standard AM1.5G spectrum distribution E corresponding to the wavelength intervals. st ; The spectral response characteristic data is the photovoltaic device response characteristic distribution I corresponding to the wavelength range.

3. A spectral mismatch calculation method for photovoltaic devices according to claim 2, characterized in that: The converting of the spectral response characteristic data and the spectral data into a probability distribution comprises: For the actual received spectral distribution E, calculate the normalized probability distribution P λ : Among them, P λ is the normalized probability distribution of the actual spectral distribution, E i is the spectral irradiance corresponding to the ith wavelength interval in the actual spectral distribution, in units of w / m 2 / nm; For standard AM1.5G spectrum distribution E st , calculate the normalized probability distribution P st : Among them, P st is the normalized probability distribution of the standard spectral distribution, E sti is the spectral irradiance corresponding to the i-th wavelength interval in the standard spectral distribution, in w / m 2 / nm; For the photovoltaic device response characteristic distribution I, calculate the normalized probability distribution P IV : Among them, P IV is the normalized probability distribution of the photovoltaic device response characteristics, I i It is the ratio of the current to the radiation flux corresponding to the i-th wavelength interval in the response characteristic distribution, with the unit of A / W.

4. A spectral mismatch calculation method for photovoltaic devices according to claim 3, characterized in that: The calculating the information entropy of the probability distribution of the spectral data comprises: Normalized probability distribution P based on actual spectral distribution λ Calculate the actual spectral information entropy H G : H G =-∑ λ P λ logP λ ; Among them, H G It is used to reflect the uncertainty or information content of the actual received spectral distribution, H G The larger the value, the higher the uncertainty of the spectral distribution, that is, the more uniform the distribution of light intensity in different wavelength ranges; Normalized probability distribution P based on standard spectral distribution st Calculate the standard spectral information entropy H B : H B =-∑ st P st logP st ; Among them, H B As the definition of the actual spectral information entropy H G The threshold parameter.

5. A method for calculating spectral mismatch for photovoltaic devices according to claim 4, characterized in that: The calculating of the information entropy of the probability distribution of the spectral response characteristic data of the photovoltaic device comprises: Normalized probability distribution P based on the response characteristic distribution of photovoltaic devices IV Calculate the information entropy H of the spectral response characteristics Q : H Q =-∑ IV P IV logP IV ; Among them, H Q It is used to reflect the uncertainty or information content of the response characteristic data of photovoltaic devices. Q The larger the value, the more uniform the response of the photovoltaic device's response effect data in different wavelength ranges.

6. A method for calculating spectral mismatch of photovoltaic devices according to claim 5, characterized in that: The difference between the information entropy of the spectral data and the information entropy of the spectral response characteristic data of the photovoltaic device is calculated as follows: Based on the actual spectral information entropy H G and the spectral response characteristic information entropy H Q Calculate the first entropy difference; ΔH1=|H G -H Q |; Wherein, ΔH1 is the first entropy difference, which is used to reflect the matching degree between the actual spectrum step and the photovoltaic device response characteristic step; the smaller the first entropy difference ΔH1, the higher the matching degree between the spectrum and the photovoltaic device response characteristic.

7. A spectrum mismatch calculation method for photovoltaic devices according to claim 6, characterized in that: Also includes: Based on the actual spectral information entropy H G and standard spectral information entropy H B Calculating the second entropy difference; ΔH2=|H G -H B |; Wherein, ΔH2 is the second entropy difference, which is used to reflect the matching degree between the actual spectrum step and the standard spectrum step; the smaller the second entropy difference ΔH2 is, the higher the matching degree between the actual spectrum and the standard in terms of distribution.

8. A method for calculating spectral mismatch of photovoltaic devices according to claim 7, characterized in that: The evaluating the degree of spectral mismatch according to the difference comprises: A first entropy difference coefficient K1 and a second entropy difference coefficient K2 are preset, K1+K2=1, and K1>K2; The total entropy difference is calculated based on the first entropy difference ΔH1, the second entropy difference ΔH2, the first entropy difference coefficient K1 and the second entropy difference coefficient K2: ΔH0=K1*ΔH1+K2*ΔH2 Among them, ΔH0 is the total entropy difference, which is used to comprehensively reflect the matching degree between the actual spectral distribution and the standard spectral distribution and the response characteristics of the photovoltaic device; Preset the mismatch threshold ΔH based on the photovoltaic device model and working environment information x ; When ΔH0≤ΔH x When , the output evaluation result is that the spectral mismatch degree of the current photovoltaic device is low; When ΔH0>ΔH x When , the output evaluation result is that the spectral mismatch degree of the current photovoltaic device is high.

9. A method for calculating spectral mismatch of photovoltaic devices according to claim 5, characterized in that: It also includes assigning a weight to each of the wavelength intervals to reflect the degree of influence of different wavelength intervals on the performance of the photovoltaic device, specifically: Normalized probability distribution P based on actual spectral distribution λ And interval weight W to calculate the actual spectral information entropy H G : H G =-∑ λ W λ *P λ logP λ ; Normalized probability distribution P based on standard spectral distribution st And interval weight W to calculate the standard spectral information entropy H B : H B =-∑ st W st *P tt logP st ; Normalized probability distribution P based on the response characteristic distribution of photovoltaic devices IV And interval weight W to calculate the spectral response characteristic information entropy H Q : H Q =-∑ IV W IV *P IV logP IV 。 10. A spectrum mismatch calculation method for photovoltaic devices according to claim 1, characterized in that: Also includes: When the output evaluation result is that the spectral mismatch of the current photovoltaic device is low, an alarm is triggered and the current total entropy difference ΔH0 and the preset mismatch threshold ΔH x Sent to the administrator's terminal device.

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