Photovoltaic module hot spot development stage prediction method

Abstract

The invention provides a method for predicting the hot spot development stage of a photovoltaic module, and belongs to the field of photovoltaic technologies. The method comprises the following steps of: substituting a calibration value of the photovoltaic module into a volt-ampere characteristic curve equation to obtain a calculation formula of unknown parameters, solving the unknown parameters by adopting a genetic algorithm, and calculating a current temperature value of the photovoltaic module by measuring a current irradiation intensity value and current and voltage values of the photovoltaic module; and further, by observing the change condition of the temperature value of the photovoltaic module for a relatively long period of time, a temperature change curve along with time is drawn, so that different development stages of the hot spots under different change modes are identified, whether the photovoltaic module has the hot spots or not can be judged, and which development stage the hot spots are in at present can be intuitively obtained. According to the method, the precision is improved, the generating capacity can be ensured, and meanwhile, the method has guiding significance for operation and maintenance management of the photovoltaic power station.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaics, and particularly relates to a method for predicting the development stage of hot spots in photovoltaic modules. Background Art

[0002] Photovoltaic modules are often covered by bird droppings, dust, shadows, fallen leaves, snow, etc. Due to local shading, the current and voltage of some solar cells change, resulting in a local temperature increase on these battery modules, that is, the hot spot effect occurs, greatly reducing the power generation. Since the hot spots in large-area photovoltaic grid-connected power stations cannot be identified by the human eye, currently, people use drones equipped with thermal imagers to obtain the temperature images of photovoltaic cells, which can realize the identification of hot spots. However, different methods are required for different development stages of hot spots. For example, for minor hot spots with local heating and no permanent damage, the problem can be solved by removing the shading object, disconnecting the wiring of the module where the hot spot is located, and enabling the bypass diode to temporarily isolate the faulty area. However, for serious hot spots that have caused component burnout, glass fragmentation, or obvious discoloration, the damaged components need to be disassembled and replaced. Therefore, it is of great significance to predict the development stage of hot spots. Summary of the Invention

[0003] In view of the problems in the background art, the present invention provides a method for predicting the development stage of hot spots in photovoltaic modules, which can determine whether there are hot spots in the photovoltaic module and which development stage the hot spot is currently in, so as to give corresponding treatment measures.

[0004] The technical solution of the present invention is as follows:

[0005] A method for predicting the development stage of hot spots in photovoltaic modules includes the following steps:

[0006] Step S1: Obtain the irradiance intensity values, ambient temperature values, current values, and voltage values at different time periods every day within a certain period.

[0007] Step S2: Classify the data with the same irradiance intensity value and ambient temperature value within this period as the same type of sample data.

[0008] Step S3: Calculate the temperature values of each time period of the photovoltaic module according to the same type of sample data.

[0009] Step S4: Based on the temperature values of each time period, draw a curve of temperature change over time, and predict the development state of the photovoltaic hot spot according to the curve change.

[0010] Further, in the step S1, obtaining the sample data includes: selecting a fine weather, and obtaining the irradiance intensity values, ambient temperature values, current values, and voltage values at different time periods between 9 am and 3 pm.

[0011] Further, in step S3, calculating the temperature values of the photovoltaic module in each time period includes:

[0012] Among the same type of sample data, respectively form sequences of all irradiation intensity values, current values, and voltage values in chronological order to obtain the irradiation intensity sequence value Q s (t), the current sequence value I s (t) of the photovoltaic module, and the voltage sequence value U s (t), where t is a time variable; assume the standard irradiation intensity is Q0, the photo-generated current under standard conditions is I ph0 , the reverse saturation current is I0, and let I pv0 = I ph0 + I0. Since the photo-generated current is proportional to the irradiation intensity, the photo-generated current sequence value of the photovoltaic module is obtained as:

[0013]

[0014] Based on the volt-ampere characteristic curve, calculate the temperature value T(t) in each time period:

[0015]

[0016] where R s is the series resistance; G = nk / q, n is the P-N junction material characteristic coefficient, k is the Boltzmann constant, and q is the electron charge.

[0017] Further, the solution methods for G, R s , I pv0 and I0 include:

[0018] 1) Obtain the system calibration values of the photovoltaic module, including: under the conditions of standard sunlight and temperature, the maximum output power P m , the maximum working voltage U m , the maximum working current I m , the open-circuit voltage U oc and the short-circuit current I sc ;

[0019] 2) According to the volt-ampere characteristic curve equation:

[0020]

[0021] In the formula: U is the voltage value of the photovoltaic module; I is the current value of the photovoltaic module; T is the absolute temperature of the photovoltaic module; I ph is the photo-generated current, I0 is the reverse saturation current, and let I pv = I ph + I0; R sis the series resistance; n is the P-N junction material characteristic coefficient; k is the Boltzmann constant; q is the electron charge;

[0022] Substitute the maximum working voltage U m and the maximum working current I m into formula (3), and we get:

[0023]

[0024] where: G = nk / q, T0 is the absolute temperature value of the photovoltaic module under standard conditions, I pv0 = I ph0 + I0, I ph0 is the photocurrent under standard conditions;

[0025] Substitute the open-circuit voltage U oc into formula (3), and we get:

[0026]

[0027] Substitute the short-circuit current I sc into formula (3), and we get:

[0028]

[0029] 3) Take the partial derivative of the current with respect to the voltage in formula (3), and then according to the calculation relationship between the current and voltage at the maximum power point, we get:

[0030]

[0031] 4) Eliminate the unknowns I pv0 and I0 in formulas (4) to (7), and we get a system of equations with only two unknowns G and R s :

[0032]

[0033]

[0034] 5) Use the genetic algorithm to solve formulas (8) and (9) to obtain the values of G and R s and substitute the values of G and R s into formulas (5) and (6) to calculate the values of I pv0 and I0.

[0035] Furthermore, in the above 5), the steps of using the genetic algorithm to solve are as follows:

[0036] 5.1) Randomly generate the initial population

[0037] parameters G and Rs The coding method adopts the binary multi-parameter cascade coding method, and the initial population is randomly generated according to this coding method;

[0038] 5.2) Calculation of fitness

[0039] According to formulas (8) - (9), let:

[0040]

[0041] The multi-objective optimization function of the genetic algorithm is obtained:

[0042]

[0043] Using the weight coefficient variation method, the above multi-objective optimization problem is transformed into a single-objective optimization problem:

[0044]

[0045] Since the objective function in formula (13) is positive, the solution result of the objective function is the fitness value of the individual;

[0046] 5.3) Design of genetic operators

[0047] Set the operating parameters of the algorithm including: population size M, termination generation T, crossover probability p c , distinct probability p m , generation gap G;

[0048] The selection operation in the genetic algorithm is carried out using the proportional selection operator combined with the elitist preservation strategy. First, calculate the fitness value of each individual based on the proportional selection operator and generate the corresponding selection probability accordingly; then obtain the selection times of each individual according to the selection probability. If the number of random selections is less than the population size M, then combined with the elitist preservation strategy, some of the best individuals are saved and do not participate in the crossover and mutation operations, but are directly inherited to the next generation population, and the remaining individuals perform normal crossover and mutation operations;

[0049] 5.4) When the objective function shown in formula (13) is less than the specified threshold or reaches the termination generation T, the genetic operation is terminated, and the G and R s values at this time are the final calculation results.

[0050] Further, in step S4, to predict the development stage of the hot spot according to the curve, the specific method is as follows: Based on the temperature values in each time period, a curve is plotted with time as the x-axis and temperature as the y-axis. When the curve is a straight line segment close to parallel to the x-axis, it is determined that there is no hot spot in the photovoltaic module; when the curve shows a linear increasing change, it is the early stage of hot spot formation and there is no need to replace the photovoltaic module; when the curve shows an exponential increasing change, it is the late stage of hot spot formation and the photovoltaic module needs to be replaced, otherwise the module will be burned or a fire will occur after a period of time; when the curve shows a linear change in the early stage and an exponential change in the late stage, the inflection point of the change is the optimal replacement time of the photovoltaic module.

[0051] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention provides a method for predicting the development stage of hot spots in photovoltaic modules, which can determine whether there is a hot spot in the photovoltaic module and which development stage the hot spot is currently in, so as to give corresponding treatment measures. If it is the initial or early stage of the hot spot and the development is slow, there is no need to replace the photovoltaic module. If the hot spot has reached the middle or late stage, the rapid development stage, the photovoltaic module needs to be replaced as soon as possible, and the optimal inflection point for replacing the photovoltaic module is given; Selecting the correct time to replace the photovoltaic module can avoid the burning of the module or the occurrence of a fire, which has guiding significance for the operation and maintenance management of the photovoltaic power station. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flowchart of the method provided by an embodiment of the present invention;

[0053] Figure 2 It is a curve graph of calculating the values of parameters G and R by using the genetic algorithm provided by an embodiment of the present invention s value;

[0054] Figure 3 It is a graph of temperature change over time without hot spots provided by an embodiment of the present invention;

[0055] Figure 4 It is a graph of temperature change over time in the early stage of hot spots provided by an embodiment of the present invention;

[0056] Figure 5 It is a graph of temperature change over time in the late stage of hot spots provided by an embodiment of the present invention;

[0057] Figure 6 It is a graph of temperature change over time from the early stage to the late stage of hot spots provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0059] See Figure 1 As shown, an embodiment of the present invention provides a method for predicting the hot spot development stage of a photovoltaic module, which specifically includes the following steps:

[0060] Step 1: Obtain the system calibration values of the photovoltaic module, including: under the conditions of standard sunlight and temperature, the maximum output power P m 、maximum working voltage U m 、maximum working current I m 、open circuit voltage U oc and short circuit current I sc values;

[0061] Step 2: According to the volt-ampere characteristic curve equation:

[0062]

[0063] Where: U is the voltage value of the photovoltaic module, I is the current value of the photovoltaic module, T is the absolute temperature of the photovoltaic module, I ph is the photo-generated current, I0 is the reverse saturation current, let I pv =I ph +I0; R s is the series resistance; n is the P-N junction material characteristic coefficient; k is the Boltzmann constant, q is the electron charge;

[0064] Step 3: Substitute the values of the maximum working voltage U m and the maximum working current I m into formula (1), and obtain:

[0065]

[0066] Where: G = nk / q, T0 is the absolute temperature value of the photovoltaic module under the standard state, I pv0 =I ph0 +I0, I ph0 is the photo-generated current under the standard state;

[0067] Step 4: Substitute the value of the open circuit voltage U oc into formula (1), and obtain:

[0068]

[0069] Step 5: Substitute the value of the short circuit current I sc into formula (1), and obtain:

[0070]

[0071] Step 6: Take the partial derivative of the current with respect to the voltage in formula (1), and then according to the calculation relationship between the current and voltage at the maximum power point, it can be obtained:

[0072]

[0073] Step 7: Eliminate the unknowns I pv0 and I0 in the above formula, and obtain a system of equations with only two unknowns G and R s as follows:

[0074]

[0075] Step 8: Use the genetic algorithm to solve formulas (6) and (7) to obtain the values of G and R s and further calculate to obtain the values of I pv0 and I0; the specific process is as follows:

[0076] Step 8.1: Randomly generate the initial population

[0077] The parameters G and R s are encoded using the binary multi-parameter cascade encoding method, and the initial population is randomly generated according to this encoding method;

[0078] Step 8.2: Calculation of fitness

[0079] Let:

[0080]

[0081] The multi-objective optimization function of the genetic algorithm can be obtained:

[0082]

[0083] Using the weight coefficient variation method, transform the above multi-objective optimization problem into a single-objective optimization problem:

[0084]

[0085] Since the objective function of formula (11) is positive, the fitness of the individual is directly taken as the corresponding objective function;

[0086] Step 8.3: Design genetic operators

[0087] Set the operating parameters of the algorithm, including: population size M, termination generation T, crossover probability p c , distinct probability p m , generation gap G;

[0088] The selection operation in the genetic algorithm is carried out using the proportional selection operator combined with the elitist strategy. First, calculate the fitness value of each individual based on the proportional selection operator and generate the corresponding selection probability accordingly; then obtain the selection times of each individual according to the selection probability. If the number of random selections is less than the number M of the population, then combine with the elitist strategy to save some of the best individuals, which do not participate in the crossover and mutation operations and are directly inherited to the next generation population, and the remaining individuals perform normal crossover and mutation operations;

[0089] Step 8.4: When the objective function shown in formula (11) is less than the specified threshold or reaches the termination algebra T, then terminate the genetic operation, and the values of G and R at this time s are the final calculation results (see Figure 2 ), and then substitute the values of G and R s into formula (3) and formula (4) to find out and the value of I0.

[0090] Step 9: Obtain the current current value I s and voltage value U s of the current photovoltaic module; at the same time, obtain the current irradiance intensity value Q s . According to the relationship that the photocurrent I ph is proportional to the irradiance intensity, assuming the standard irradiance intensity is Q0, then combine with step 8 to calculate the current photocurrent I ph as:

[0091]

[0092] Substitute the current photocurrent I ph , current value I s and voltage value U s into formula (1) to obtain:

[0093]

[0094] Then obtain the current temperature T:

[0095]

[0096] Step 10: Predict the development state of the photovoltaic hot spot according to the calculation results of step 9, specifically as follows:

[0097] (1) Select a sunny day, obtain the irradiance intensity values, current values and voltage values at different time periods of the same day, select a certain fixed irradiance intensity and ambient temperature as the sample data of the day, and obtain the sample data of the same irradiance intensity and ambient temperature once every day in the future. After a long period of time, the irradiance intensity sequence value Q s (t), the current sequence value I s(t) and voltage sequence value U s (t). Since the photocurrent is proportional to the irradiation intensity, the current photocurrent sequence value is obtained as follows:

[0098]

[0099] According to formula (14), the temperature values T(t) for each time period are calculated:

[0100]

[0101] (2) Plot the curve of temperature versus time

[0102] See Figure 3 、 Figure 4 and Figure 5 and Figure 6 the temperature change diagrams shown; Observe the change of the curve: If the curve is a straight line segment close to parallel to the x-axis, it can be judged that there is no hot spot in the photovoltaic module, see Figure 3 ; If the curve approximately linearly increases, it is the initial or early stage of hot spot formation, and the photovoltaic module does not need to be replaced, see Figure 4 ; If the curve approximately exponentially increases, it is the later stage of hot spot formation, and the photovoltaic module needs to be replaced immediately, otherwise the module will be burned or a fire will occur after a period of time, see Figure 5 ; If the curve is approximately linearly changed in the early stage and approximately exponentially changed in the later stage, the inflection point of the change is the best replacement time of the photovoltaic module, see Figure 6 ; Specifically, it is shown as follows:

[0103]

[0104] Finally, it should be noted that the above embodiments are intended to illustrate the technical solutions of the present invention and do not constitute any form of limitation to the present invention. Those skilled in the art should fully understand that it is completely feasible to modify the technical solutions described in the foregoing embodiments or perform equivalent replacements on any part or all of the technical features therein. These modifications or replacements, as long as they do not deviate from the protection scope determined by the claims of the present invention, shall be regarded as reasonable extensions of the present invention.

Claims

1. A method for predicting the development stage of a photovoltaic module hot spot, characterized in that: The following steps are involved: Step S1: obtaining the radiation intensity value, ambient temperature value, current value and voltage value at different time periods of each day within a period; Step S2: classifying data with the same radiation intensity value and ambient temperature value within the period as sample data of the same type; Step S3: Calculate the temperature value of the photovoltaic module in each time period according to the sample data of the same type; Step S4: based on the temperature values ​​in each time period, a temperature variation curve over time is plotted, and the development state of the photovoltaic hot spot is predicted according to the curve variation.

2. A method for predicting the development stage of a photovoltaic module hot spot according to claim 1, characterized in that: In step S3, calculating the temperature value of the photovoltaic module in each time period includes: In the same sample data, all the irradiance intensity values, current values ​​and voltage values ​​are arranged into sequences in chronological order to obtain the irradiance intensity sequence value Q s (t), the current sequence value I of the photovoltaic module s (t) and the voltage sequence value U s (t), where t is the time variable; let the standard irradiation intensity be Q0, and the photocurrent under the standard state be I ph0 , the reverse saturation current is I0, let I pv0 =I ph0 +I0, calculate the photocurrent sequence value of the photovoltaic module: Calculate the temperature value T(t) for each time period: Among them, R s is the series resistance; G=nk / q, n is the characteristic coefficient of the PN junction material, k is the Boltzmann constant, and q is the electron charge.

3. A method for predicting the development stage of a photovoltaic module hot spot according to claim 2, characterized in that: The G, R s ,I pv0 The solutions for and I0 include: 1) Obtain the system calibration value of the photovoltaic module, including: the maximum output power P of the photovoltaic module under standard sunshine and temperature conditions m , Maximum operating voltage U m , Maximum operating current I m , open circuit voltage U oc and short-circuit current I sc ; 2) According to the volt-ampere characteristic curve equation: Where: U is the voltage value of the photovoltaic module; I is the current value of the photovoltaic module; T is the absolute temperature of the photovoltaic module; I ph is the photocurrent, I0 is the reverse saturation current, let I pv =I ph +I0; The maximum operating voltage U m and maximum operating current I m Substituting the value of into formula (3), we get: Where: T0 is the absolute temperature value of the photovoltaic module under standard conditions, I pv0 =I ph0 +I0,I ph0 is the photocurrent under standard conditions; The open circuit voltage U oc Substituting the value of into formula (3), we get: The short-circuit current I sc Substituting the value of into formula (3), we get: 3) Take the partial derivative of the current with respect to the voltage in formula (3), and then according to the calculated relationship between the current and voltage at the maximum power point, we get: 4) Eliminate the unknown number I in formulas (4) to (7) pv0 and I0, we have only two unknowns G and R s The system of equations: 5) Use genetic algorithm to solve formula (8) and formula (9) to obtain G and R s and set G and R s Substituting the value into formula (5) and formula (6) to calculate I pv0 and the value of I0.

4. A method for predicting the development stage of a photovoltaic module hot spot according to claim 3, characterized in that: In the above 5), the steps of solving the problem using genetic algorithm are as follows: 5.1) Randomly generate the initial population Parameters G and R s The encoding method adopts binary multi-parameter cascade encoding method, and the initial population is randomly generated according to this encoding method; 5.2) Calculation of fitness set up: The multi-objective optimization function of the genetic algorithm is obtained: The weight coefficient change method is used to transform the above multi-objective optimization problem into a single-objective optimization problem: Since the objective function in formula (13) is a positive value, the solution of the objective function is the fitness value of the individual; 5.3) Design of genetic operators The algorithm's operating parameters include: population size M, termination number T, crossover probability p c , the probability of being different from each other p m , generation gap G; First, the fitness value of each individual is calculated based on the proportional selection operator, and the corresponding selection probability is generated accordingly; then the number of selections for each individual is obtained based on the selection probability. If the number of random selections is less than the number of populations M, the optimal preservation strategy is combined to preserve some of the best individuals without participating in crossover and mutation operations, and directly inherit them to the next generation population. The remaining individuals undergo normal crossover and mutation operations; 5.4) When the objective function shown in formula (13) is less than the specified threshold or reaches the termination generation T, the genetic operation is terminated. At this time, G and R s The value is the final calculation result.

5. A method for predicting the development stage of a photovoltaic module hot spot according to any one of claims 1 to 4, characterized in that: In step S4, the stage of hot spot development is predicted based on the curve. The specific method is: based on the temperature values ​​of each time period, a curve is drawn with time as the x-axis and temperature as the y-axis. When the curve is a straight line segment close to parallel to the x-axis, it is judged that no hot spot has appeared in the photovoltaic module; when the curve increases linearly, it is the early stage of hot spot formation and the photovoltaic module does not need to be replaced; when the curve increases exponentially, it is the late stage of hot spot formation and the photovoltaic module needs to be replaced; when the curve changes linearly in the early stage and changes exponentially in the later stage, the inflection point of the change is the optimal replacement time for the photovoltaic module.

6. A method for predicting the development stage of a photovoltaic module hot spot according to claim 5, characterized in that: In the step S1, obtaining sample data includes: selecting sunny weather, and obtaining radiation intensity values, ambient temperature values, current values, and voltage values ​​in different time periods between 9 am and 3 pm.

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