A method for predicting the development stage of hot spots in photovoltaic modules

Abstract

The present invention provides a method for predicting the development stage of hot spots in photovoltaic modules, which belongs to the field of photovoltaic technology. The method first substitutes the calibration value of the photovoltaic module into the volt-ampere characteristic curve equation to obtain the calculation formula of the unknown parameters, then uses a genetic algorithm to solve the unknown parameters, and then calculates the current temperature value of the photovoltaic module by measuring the current irradiation intensity value, photovoltaic module current and voltage value; further, by observing the change of the temperature value of the photovoltaic module over a long period of time, a temperature change curve with time is drawn, thereby identifying the different development stages of the hot spot under different change modes, and being able to judge whether the photovoltaic module has a hot spot, and intuitively obtaining the current development stage of the hot spot. The method of the present invention improves the accuracy, thereby being able to ensure the power generation, and at the same time has guiding significance for the operation and maintenance management of photovoltaic power stations.

Description

Technical Field

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

[0002] Photovoltaic panels are often covered by bird droppings, dust, shadows, fallen leaves, snow, and other debris. This localized obstruction can cause changes in the current and voltage of certain cells, leading to localized temperature increases in these panels. This is known as the hot spot effect, significantly reducing power generation. Because hot spots on panels in large-scale grid-connected photovoltaic power plants are invisible to the human eye, drones equipped with thermal imagers are currently being used to obtain temperature images of photovoltaic cells, enabling their identification. However, different hot spot development stages require different approaches. For example, minor hot spots that cause localized heating but do not cause permanent damage can be resolved by removing obstructions, disconnecting the wiring to the panel where the hot spot is located, and temporarily isolating the faulty area with bypass diodes. However, severe hot spots that have caused panel burns, glass shattering, or significant discoloration require removal and replacement of the damaged panels. Therefore, predicting the development stage of hot spots is crucial. Summary of the Invention

[0003] In response to the problems existing in the background technology, the present invention provides a method for predicting the development stage of hot spots in photovoltaic modules, which can determine whether a photovoltaic module has a hot spot and the current development stage of the hot spot, thereby providing corresponding treatment measures.

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

[0005] A method for predicting the development stage of a photovoltaic module hot spot comprises the following steps:

[0006] 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;

[0007] Step S2: classifying data with the same radiation intensity value and ambient temperature value within the period as the same type of sample data;

[0008] Step S3: Calculate the temperature value of the photovoltaic module in each time period based on similar sample data;

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

[0010] Furthermore, in 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 9:00 am and 3:00 pm.

[0011] Furthermore, in step S3, the temperature value of the photovoltaic module in each time period is calculated, including:

[0012] In the same sample data, all the irradiation intensity values, current values ​​and voltage values ​​are arranged into sequences in chronological order to obtain the irradiation intensity sequence value Q s (t), the current sequence value I of the photovoltaic module s (t) and 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, since the photocurrent is directly proportional to the irradiation intensity, the photocurrent sequence value of the photovoltaic module is:

[0013]

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

[0015]

[0016] 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.

[0017] Furthermore, the G, R s , I pv0 The solutions for and I0 include:

[0018] 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 ;

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

[0020]

[0021] 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;R sis the series resistance; n is the characteristic coefficient of the PN junction material; k is the Boltzmann constant; q is the electron charge;

[0022] The maximum operating voltage U m and maximum operating current I m Substituting the value of into formula (3), we get:

[0023]

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

[0025] The open circuit voltage U oc Substituting the value of into formula (3), we get:

[0026]

[0027] The short-circuit current I sc Substituting the value of into formula (3), 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 calculated relationship between the current and voltage at the maximum power point, we can obtain:

[0030]

[0031] 4) Eliminate the unknown number I from formula (4) to formula (7) pv0 and I0, we have only two unknowns G and R s The system of equations:

[0032]

[0033]

[0034] 5) Use genetic algorithm to solve formula (8) and formula (9) to obtain G and R s The value of G and R s Substitute the value into formula (5) and formula (6) to calculate I pv0 and the value of I0.

[0035] Furthermore, in 5), the steps of solving the problem using a genetic algorithm are as follows:

[0036] 5.1) Randomly generate the initial population

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

[0038] 5.2) Calculation of fitness

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

[0040]

[0041]

[0042] The multi-objective optimization function of the genetic algorithm is obtained:

[0043]

[0044] The weight coefficient change method is used to transform the multi-objective optimization problem into a single-objective optimization problem:

[0045]

[0046] Since the objective function in formula (13) is a positive value, the solution of the objective function is the fitness value of the individual;

[0047] 5.3) Design of genetic operators

[0048] The operating parameters of the algorithm include: population size M, termination number T, crossover probability p c , mutual difference probability p m , generation gap G;

[0049] The selection operation in the genetic algorithm uses the proportional selection operator combined with the optimal preservation strategy. First, the fitness value of each individual is calculated based on the proportional selection operator, and the corresponding selection probability is generated accordingly. The number of selections for each individual is then determined based on the selection probability. If the number of random selections is less than the number of individuals in the population M, the optimal preservation strategy is used to preserve the best individuals. These individuals do not participate in crossover and mutation operations and are directly inherited to the next generation of the population. The remaining individuals undergo normal crossover and mutation operations.

[0050] 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.

[0051] Furthermore, in step S4, the stage of development of the hot spot is predicted according to 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, otherwise the module will burn out or a fire will occur after a period of time; 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.

[0052] Compared with the prior art, the present invention has the following beneficial effects: 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 photovoltaic modules and at which development stage the hot spots are currently in, thereby providing corresponding treatment measures. If the hot spot is in the early or early stages and develops slowly, the photovoltaic module does not need to be replaced. If the hot spot has reached the middle or late stages and is developing rapidly, the photovoltaic module needs to be replaced as soon as possible, and the optimal inflection point for replacing the photovoltaic module is provided. Choosing the right time to replace the photovoltaic module can avoid module burning or fire, which has guiding significance for the operation and maintenance management of photovoltaic power stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A flow chart of a method provided by an embodiment of the present invention;

[0054] Figure 2 The embodiment of the present invention provides a genetic algorithm for calculating the parameters G and R s A graph of the values ​​of ;

[0055] Figure 3 A time-temperature variation diagram of no hot spot provided by an embodiment of the present invention;

[0056] Figure 4 A diagram showing the temperature change during the early stages of a hot spot according to an embodiment of the present invention;

[0057] Figure 5 A graph showing the late time-temperature variation of hot spots provided by an embodiment of the present invention;

[0058] Figure 6 This is a time temperature change diagram from the early stage to the late stage of the hot spot provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0060] See also Figure 1 As shown, an embodiment of the present invention provides a method for predicting the development stage of a hot spot in a photovoltaic module, which specifically includes the following steps:

[0061] Step 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 the short-circuit current I sc The value of

[0062] Step 2: According to the volt-ampere characteristic curve equation:

[0063]

[0064] 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;R s is the series resistance; n is the characteristic coefficient of the PN junction material; k is the Boltzmann constant, and q is the electron charge;

[0065] Step 3: Set the maximum operating voltage U m and the maximum operating current I m Substituting the value of into formula (3), we get:

[0066]

[0067] Where: G = nk / q, T0 is the absolute temperature of the photovoltaic module under standard conditions, I pv0 =I ph0 +I0,I ph0 is the photocurrent under standard conditions;

[0068] Step 4: Set the open circuit voltage U oc Substituting the value of into formula (3), we get:

[0069]

[0070] Step 5: Set the short-circuit current I sc Substituting the value of into formula (3), we get:

[0071]

[0072] Step 6: Take the partial derivative of the current with respect to the voltage in formula (3), and then based on the calculated relationship between the current and voltage at the maximum power point, we can obtain:

[0073]

[0074] Step 7: Eliminate the unknown number I from the above formula pv0 and I0, we have only two unknowns G and R s The system of equations:

[0075]

[0076] Step 8: Use genetic algorithm to solve formula (8) and formula (9) to obtain G and R s The value of I is further calculated pv0 and the value of I0; the specific process is as follows:

[0077] Step 8.1: Randomly generate the initial population

[0078] 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;

[0079] Step 8.2: Calculation of fitness

[0080] set up:

[0081]

[0082]

[0083] The multi-objective optimization function of the genetic algorithm can be obtained:

[0084]

[0085] The weight coefficient change method is used to transform the multi-objective optimization problem into a single-objective optimization problem:

[0086]

[0087] Since the objective function of formula (13) is positive, the individual fitness is directly taken as the corresponding objective function;

[0088] Step 8.3: Design genetic operators

[0089] Set the algorithm's operating parameters, including: population size M, termination number T, crossover probability p c , mutual difference probability p m , generation gap G;

[0090] The selection operation in the genetic algorithm uses the proportional selection operator combined with the optimal preservation strategy. First, the fitness value of each individual is calculated based on the proportional selection operator, and the corresponding selection probability is generated accordingly. The number of selections for each individual is then determined based on the selection probability. If the number of random selections is less than the number of individuals in the population M, the optimal preservation strategy is used to preserve the best individuals. These individuals do not participate in crossover and mutation operations and are directly inherited to the next generation of the population. The remaining individuals undergo normal crossover and mutation operations.

[0091] Step 8.4: 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 (see Figure 2 ), then G and R s Substitute the value into formula (5) and formula (6) to obtain I pv0 and the value of I0.

[0092] Step 9: Get the current value I of the photovoltaic module s And voltage value U s ; At the same time, get the current irradiance intensity value Q s , according to the photocurrent I ph It is proportional to the irradiation intensity. Assuming the standard irradiation intensity is Q0, the current photocurrent I is calculated by combining step 8. ph for:

[0093]

[0094] The current photocurrent I ph , current value I s And voltage value U s Substituting into formula (3), we get:

[0095]

[0096] Then the current temperature T is obtained:

[0097]

[0098] Step 10: Predict the development status of photovoltaic hot spots based on the calculation results of step 9, as follows:

[0099] (1) Choose sunny weather and obtain the irradiation intensity, current and voltage values ​​at different time periods on the same day. Select a fixed irradiation intensity and ambient temperature as the sample data for the day. Then obtain the sample data of the same irradiation intensity and ambient temperature once a day. Continue for a long period of time to obtain the irradiation intensity sequence value Q. s (t), the current sequence value of the photovoltaic module I s(t) and voltage sequence value U s (t), since the photocurrent is directly proportional to the irradiation intensity, the current photocurrent sequence value is:

[0100]

[0101] According to formula (16), the temperature value T(t) of each time period is calculated:

[0102]

[0103] (2) Draw a curve of temperature change over time

[0104] See also Figure 3 、 Figure 4 as well as Figure 5 and Figure 6 Observe the curve changes: if the curve is a straight line segment close to the x-axis, it can be determined that there is no hot spot in the photovoltaic module. Figure 3 If the curve increases linearly, it is the early or early stage of hot spot formation and there is no need to replace the PV module. Figure 4 If the curve increases exponentially, it indicates the late stage of hot spot formation and the PV modules need to be replaced immediately. Otherwise, the modules may burn out or a fire may occur after a period of time. Figure 5 If the curve changes approximately linearly in the early stage and exponentially in the later stage, the inflection point of the change is the optimal replacement time for the photovoltaic modules, see Figure 6 ; The specific expressions are as follows:

[0105]

[0106] 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 of the present invention. Those skilled in the art should fully understand that it is entirely feasible to modify the technical solutions described in the above embodiments or to replace any or all of the technical features with equivalents. Such modifications or replacements, as long as they do not deviate from the scope of protection defined by the claims of the present invention, should be considered 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 the same type of sample data; Step S3: Calculate the temperature value of the photovoltaic module in each time period based on similar sample data; Step S4: based on the temperature values ​​in each time period, plotting a temperature change curve over time, and predicting the development state of the photovoltaic hot spot according to the curve change; In step S3, the temperature value of the photovoltaic module in each time period is calculated, including: In the same sample data, all the irradiation intensity values, current values ​​and voltage values ​​are arranged into sequences in chronological order to obtain the irradiation intensity sequence value Q s (t), the current sequence value I of the photovoltaic module s (t) and 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; The G, R s , I pv0 The solutions for 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 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 can obtain: 4) Eliminate the unknown number I from formula (4) to formula (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 The value of G and R s Substitute the value into formula (5) and formula (6) to calculate I pv0 and the value of I0; the steps of solving 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 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 operating parameters of the algorithm include: population size M, termination number T, crossover probability p c , mutual difference probability 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 calculated based on the selection probability. If the number of random selections is less than the number of individuals in the population M, the optimal preservation strategy is combined to preserve some of the best individuals. These individuals do not participate in crossover and mutation operations and are directly inherited 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.

2. A method for predicting the development stage of a photovoltaic module hot spot according to claim 1, 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.

3. A method for predicting the development stage of a photovoltaic module hot spot according to claim 2, characterized in that: In 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 9:00 am and 3:00 pm.

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