Method for selecting a photovoltaic power plant mast array

Abstract

The application discloses a kind of photovoltaic power station standard pole photovoltaic array selection method, and its steps are as follows: collection photovoltaic array operating data and meteorological data;Photovoltaic array efficiency PR and power generation are calculated respectively;The distribution of photovoltaic array efficiency index is modeled;Based on the power generation sorting and probability model of photovoltaic array, the standard pole photovoltaic array is obtained.This method simultaneously considers the power generation capacity and output stability of photovoltaic array, establishes photovoltaic array efficiency calculation index PR, establishes the probability density distribution model of state index using statistical means, and the standard pole photovoltaic array can be screened out through state position, as the reference model of fault diagnosis and performance analysis of photovoltaic array.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation technology, and in particular to a method for selecting a benchmark array for a photovoltaic power station. Background Technology

[0002] With the depletion of fossil fuels, the development and utilization of renewable energy are becoming increasingly important. Photovoltaic power generation, as one of the most mainstream renewable energy generation methods globally, boasts numerous advantages such as wide energy distribution and sustainability. The continuous growth in installed capacity of photovoltaic power plants also places higher demands on photovoltaic technology. As a critical component of photovoltaic power plants, frequent failures in photovoltaic arrays directly impact the efficiency of the plant. Therefore, selecting benchmark strings can be used to evaluate photovoltaic arrays and serve as reference arrays for fault diagnosis, which is of great significance for the assessment and fault diagnosis of photovoltaic power plants. Summary of the Invention

[0003] To address the aforementioned problems, this invention provides a method for selecting benchmark arrays for photovoltaic power plants, which can serve as a reference model for photovoltaic array fault diagnosis and performance analysis.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for selecting a benchmark array for a photovoltaic power station, characterized by comprising the following steps:

[0005] S1. Collect and clean photovoltaic array operation data and meteorological data;

[0006] S2. Calculate the photovoltaic array efficiency PR and power generation respectively;

[0007] S3. Model the distribution of photovoltaic array efficiency indicators;

[0008] S4. A benchmark photovoltaic array is obtained based on the power generation ranking and probability model of the photovoltaic array.

[0009] Furthermore, the specific method for step S1 is as follows: collect the output power from the photovoltaic array operation data and the irradiance value from the meteorological data, and delete values ​​with irradiance below 30W / m. 2 The corresponding power and irradiance data.

[0010] Furthermore, the specific method of step S2 is as follows: the ratio of the actual power generation of the photovoltaic array to the theoretical power generation at the same moment is used as the photovoltaic array efficiency calculation index, and the calculation formulas (1)-(3) are:

[0011] U=∫Pdt (1)

[0012] U t =P Installed ·H T (2)

[0013]

[0014] In the formula: the actual power generation U is the integral of the power over the time period T; the theoretical power generation U t The power generation of the photovoltaic array under STC conditions during time period T is the nominal installed capacity P of the photovoltaic array. Installed Peak solar hours H of the array during time period T T The product of.

[0015] Furthermore, the specific method for modeling the distribution of photovoltaic array efficiency index in step S3 is based on formulas (4)-(6):

[0016]

[0017]

[0018] M a =d·m (6)

[0019] Obtain the statistical modeling results corresponding to the a-th photovoltaic array when PR is set to x.

[0020] In the formula: π is a constant; exp represents an exponential function with the natural constant e as the base; h is the bandwidth; M a This represents the total PR index of the a-th photovoltaic array in day d; m is the total PR in a day; σ is the M corresponding to the a-th photovoltaic array. a The standard deviation of each state index; the modeling results corresponding to the photovoltaic array as x changes. Follow the changes.

[0021] Furthermore, the specific method of step S4 includes the following sub-steps:

[0022] S41. Based on the modeling results obtained in S3, the mean value μ of the array efficiency PR is extracted, and the calculation formula (7) is as follows:

[0023]

[0024] The peak abscissa of the photovoltaic array efficiency index PR fitting curve is used as the state position feature of the photovoltaic array, and the range of the photovoltaic array PR value is 0.8-1;

[0025] S42. Normalize the power generation of the photovoltaic array. The calculation formula (8) is as follows:

[0026]

[0027] Cumulative power generation U total The calculation formula (9) is:

[0028]

[0029] In the formula: U a,normalized Let be the normalized power generation of the i-th photovoltaic array;

[0030] S43. Sort the cumulative power generation of all photovoltaic arrays in the photovoltaic power station from largest to smallest, calculate the fitting mean value corresponding to the array with the largest power generation, and determine whether the state position feature of the photovoltaic array is between 0.8 and 1. If so, select the photovoltaic array as the benchmark photovoltaic array; otherwise, the photovoltaic array will no longer participate in the power generation sorting and proceed to step S44.

[0031] S44. Calculate the fitted mean value corresponding to the photovoltaic array with the largest power generation, and determine whether the state position feature of the photovoltaic array is between 0.8 and 1. If so, select the photovoltaic array as the benchmark photovoltaic array; otherwise, the photovoltaic array will no longer participate in the power generation ranking. Repeat step S44 until a benchmark array that meets the requirements is selected.

[0032] The beneficial effects of this invention are:

[0033] 1. This method constructs an index PR that can reflect the comprehensive performance of photovoltaic arrays. Considering the unstable output of photovoltaic arrays, a probability density distribution model of the state index is established using statistical methods. Benchmark photovoltaic arrays can be screened through state position to serve as reference models for fault diagnosis and performance analysis of photovoltaic arrays.

[0034] 2. The established efficiency index takes into account the similarity between the power distribution characteristics and the irradiance distribution characteristics in the photovoltaic array, and effectively reduces the rate of change of the data.

[0035] 3. The probability density distribution model of the state index fits the distribution characteristics of the data based on the characteristics of the data itself. Considering the difficulty in applying the index due to the irregular fluctuation of the photovoltaic array efficiency index, it effectively solves the problem of inaccurate parameter fitting.

[0036] 4. The selected benchmark photovoltaic array takes into account both power generation performance and output stability. The benchmark photovoltaic array can serve as a reference model for fault diagnosis and performance analysis. Attached Figure Description

[0037] Figure 1 This is a flowchart of the photovoltaic power plant benchmark array selection method of the present invention.

[0038] Figure 2 This is the time series distribution of the PR of the photovoltaic array in the 31-day embodiment of the present invention.

[0039] Figure 3 This is a distribution diagram of the cumulative power generation calculation results of the entire station array in an embodiment of the present invention.

[0040] Figure 4 This is a distribution diagram showing the result of modeling the PR distribution of the entire station array in an embodiment of the present invention. Detailed Implementation

[0041] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0042] like Figure 1 As shown, the method for selecting a benchmark array for a photovoltaic power station includes the following steps:

[0043] S1. Collect and clean photovoltaic array operation data and meteorological data;

[0044] S2. Calculate the photovoltaic array efficiency PR and power generation respectively;

[0045] S3. Model the distribution of photovoltaic array efficiency indicators;

[0046] S4. A benchmark photovoltaic array is obtained based on the power generation ranking and probability model of the photovoltaic array.

[0047] The specific method for step S1 is as follows: collect the output power from the photovoltaic array operation data and the irradiance value from the meteorological data, and delete those with irradiance values ​​lower than 30W / m. 2 The corresponding power and irradiance data.

[0048] The specific method of step S2 is as follows: the ratio of the actual power generation of the photovoltaic array to the theoretical power generation at the same moment is used as the photovoltaic array efficiency calculation index, and the calculation formulas (1)-(3) are:

[0049] U=∫Pdt (1)

[0050] U t =P Installed ·H T (2)

[0051]

[0052] In the formula: the actual power generation U is the integral of the power over the time period T; the theoretical power generation U t The power generation of the photovoltaic array under STC conditions during time period T is the nominal installed capacity P of the photovoltaic array. Installed Peak solar hours H of the array during time period T T The product of.

[0053] The specific method for modeling the distribution of photovoltaic array efficiency index in step S3 is based on formulas (4)-(6):

[0054]

[0055]

[0056] M a =d·m (6)

[0057] Obtain the statistical modeling results corresponding to the a-th photovoltaic array when PR is set to x.

[0058] In the formula: π is a constant; exp represents an exponential function with the natural constant e as the base; h is the bandwidth; M a This represents the total PR index of the a-th photovoltaic array in day d; m is the total PR in a day; σ is the M corresponding to the a-th photovoltaic array. a The standard deviation of each state index; the modeling results corresponding to the photovoltaic array as x changes. Follow the changes.

[0059] The specific method of step S4 also includes the following sub-steps:

[0060] S41. Based on the modeling results obtained in S3, the mean value μ of the array efficiency PR is extracted, and the calculation formula (7) is as follows:

[0061]

[0062] The peak abscissa of the photovoltaic array efficiency index PR fitting curve is used as the state position feature of the photovoltaic array, and the range of the photovoltaic array PR value is 0.8-1;

[0063] S42. Normalize the power generation of the photovoltaic array. The calculation formula (8) is as follows:

[0064]

[0065] Cumulative power generation U total The calculation formula (9) is:

[0066]

[0067] In the formula: U a,normalized Let be the normalized power generation of the i-th photovoltaic array;

[0068] S43. Sort the cumulative power generation of all photovoltaic arrays in the photovoltaic power station from largest to smallest, calculate the fitting mean value corresponding to the array with the largest power generation, and determine whether the state position feature of the photovoltaic array is between 0.8 and 1. If so, select the photovoltaic array as the benchmark photovoltaic array; otherwise, the photovoltaic array will no longer participate in the power generation sorting and proceed to step S44.

[0069] S44. Calculate the fitted mean value corresponding to the photovoltaic array with the largest power generation, and determine whether the state position feature of the photovoltaic array is between 0.8 and 1. If so, select the photovoltaic array as the benchmark photovoltaic array; otherwise, the photovoltaic array will no longer participate in the power generation ranking. Repeat step S44 until a benchmark array that meets the requirements is selected.

[0070] Example

[0071] The data for this example comes from a photovoltaic power station in China, which has 553 arrays. The system collects power data and meteorological data from the photovoltaic arrays every 10 minutes. A one-month time period was selected to clean the historical operating data of the photovoltaic arrays, removing data with irradiance below 30W / m² during both nighttime and daytime. 2 Hourly power and irradiance data were used to obtain the following: Figure 2 The time series distribution of the PR of the photovoltaic array over 31 days is shown.

[0072] from Figure 2 As can be seen, the PR of the photovoltaic array fluctuates with the date. Most arrays show the same fluctuation trend, but the fluctuation of the PR of the same array on different dates is irregular, and some arrays show abnormal fluctuation points in PR. There are two main reasons for this:

[0073] (1) Different weather conditions result in different photovoltaic array performances;

[0074] (2) For photovoltaic arrays located in the same photovoltaic power station, the weather conditions are similar, and the main difference lies in the performance of the photovoltaic arrays.

[0075] To eliminate the impact of photovoltaic array installation on cumulative power generation, the power generation is normalized and then integrated to obtain the cumulative power generation calculation result for the entire array, as shown below. Figure 3 As shown.

[0076] Figure 4 The result of modeling the PR distribution of the whole station array is shown in the distribution map. By comparing the whole station arrays, it can be found that the array PR is concentrated in a certain range, but the state position of different arrays varies greatly.

[0077] The arrays with the highest cumulative power generation were sorted from largest to smallest, and array number 291 was found to be ranked first. Its fitted mean was calculated to be 0.9494, which is within the range of 0.8-1. Therefore, array number 291 was selected as the benchmark array for this photovoltaic power station.

[0078] In summary, the selection method disclosed in this application takes into account both the power generation capacity and output fluctuation of the photovoltaic array, establishes the PR index reflecting the performance of the photovoltaic array, and selects benchmark photovoltaic arrays by analyzing the probabilistic model characteristics of PR and power generation capacity, which can serve as a reference model for photovoltaic array fault diagnosis and performance analysis.

[0079] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Various changes and modifications can be made to the present invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method for selecting a benchmark array for a photovoltaic power station, characterized in that, Includes the following steps: S1. Collect and clean photovoltaic array operation data and meteorological data; S2. Calculate the photovoltaic array efficiency PR and power generation respectively; S3. Model the distribution of photovoltaic array efficiency indicators; S4. A benchmark photovoltaic array is obtained based on the power generation ranking and probability model of the photovoltaic array; The established efficiency index takes into account the similarity between the power distribution characteristics and the irradiance distribution characteristics in the photovoltaic array, and effectively reduces the rate of change of the data; The specific method for step S1 is as follows: collect the output power from the photovoltaic array operation data and the irradiance value from the meteorological data, and delete the power and irradiance data corresponding to when the irradiance is lower than 30W / m2; The specific method for modeling the distribution of photovoltaic array efficiency index in step S3 is based on formulas (4)-(6): (4) (5) Ma=d·m (6) Obtain the statistical modeling results corresponding to the a-th photovoltaic array when PR is set to x. x is a specific value of the photovoltaic array efficiency PR; In the formula: π is a constant; exp represents an exponential function with the natural constant e as the base; h is the bandwidth; Ma represents the total number of PR indicators for the a-th photovoltaic array in d days; m is the total number of PR indicators in a day; σ is the standard deviation of the Ma state indicators corresponding to the a-th photovoltaic array; when x changes, the modeling results corresponding to the photovoltaic array change accordingly. The specific method of step S4 includes the following sub-steps: S41. Based on the modeling results obtained in S3, the peak value μ of the array efficiency PR is extracted, and the calculation formula (7) is as follows: (7) The peak abscissa of the photovoltaic array efficiency index PR fitting curve is used as the state position feature of the photovoltaic array, and the range of the photovoltaic array PR value is 0.8-1; S42. Normalize the power generation of the photovoltaic array. The calculation formula (8) is as follows: ; Among them, P Installed The formula (9) for calculating the cumulative power generation Utotal, which is the nominal capacity, is as follows: ; In the formula: Ua,normalized is the normalized power generation of the i-th photovoltaic array; S43. Sort the cumulative power generation of all photovoltaic arrays in the photovoltaic power station from largest to smallest, calculate the fitting mean value corresponding to the array with the largest power generation, and determine whether the state position feature of the photovoltaic array is between 0.8 and 1. If so, select the photovoltaic array as the benchmark photovoltaic array; otherwise, the photovoltaic array will no longer participate in the power generation sorting and proceed to step S44. S44. Calculate the fitted mean value corresponding to the photovoltaic array with the largest power generation, and determine whether the state position feature of the photovoltaic array is between 0.8 and 1. If so, select the photovoltaic array as the benchmark photovoltaic array; otherwise, the photovoltaic array will no longer participate in the power generation ranking. Repeat step S44 until a benchmark array that meets the requirements is selected.

2. The method for selecting a benchmark array for a photovoltaic power station according to claim 1, characterized in that, The specific method of step S2 is as follows: the ratio of the actual power generation of the photovoltaic array to the theoretical power generation at the same moment is used as the photovoltaic array efficiency calculation index, and the calculation formulas (1)-(3) are: U=∫Pdt (1) As=P Installed ·H T (2) PR=U / Ut(3) In the formula: the actual power generation U is the integral of the power over the time period T; the theoretical power generation Ut is the array power generation under STC conditions within the time period T, i.e., the nominal capacity P of the photovoltaic array. Installed The product of the peak sunshine hours HT of the array during the time period T.

Images