Photovoltaic power generation system reliability evaluation method considering influence of inclination angle and orientation of photovoltaic array

Abstract

The invention discloses a method for evaluating the reliability of a photovoltaic power generation system by considering the influence of the inclination angle and the orientation of a photovoltaic array, and belongs to the technical field of the operation reliability of the photovoltaic power generation system. The influence of the inclination angle and orientation of a photovoltaic array on a photovoltaic power generation system is considered, and the degradation rate of the photovoltaic array is different due to the fact that the solar irradiance received by the inclined plane of the photovoltaic array is different. And predicting the degradation rate of the photovoltaic array by using a statistical analysis method, and calculating the maximum output power of the photovoltaic array and predicting the service life of the photovoltaic array according to the degradation rate. Besides, the junction temperature profile of the IGBT is calculated by using an electric heating model, the IGBT junction temperature profile is obtained according to an IGBT junction temperature calculation online model based on XGBoost, the service life of the photovoltaic inverter is obtained by using an IGBT service life model, and the reliability of the photovoltaic power generation system is evaluated by using Monte Carlo simulation in consideration of parameter uncertainty brought by an IGBT manufacturing process.

Description

Technical Field

[0001] This invention belongs to the field of photovoltaic power generation system operation reliability technology, and particularly relates to a method for assessing the reliability of photovoltaic power generation systems that takes into account the influence of photovoltaic array tilt angle and orientation. Background Technology

[0002] Photovoltaic power generation, as one of the representatives of clean energy, is receiving increasing global attention. With the high-quality and rapid development of photovoltaic power generation, it will play an important role in the construction of my country's new energy system. The operational reliability of photovoltaic power generation systems has become a key indicator of whether these systems can operate efficiently and stably.

[0003] A photovoltaic (PV) power generation system consists of a PV array and a PV inverter, which influence each other. The reliability of the PV array is affected by its installation location. Different installation locations have different mission profiles, including solar irradiance and ambient temperature. Differences in mission profiles lead to variations in the maximum output power of the PV array, thus affecting its degradation rate and ultimately the lifespan of both the PV array and the PV inverter. In previous studies, the solar irradiance used to assess the reliability of PV inverters was measured based on fixed tilt angles and orientations. To achieve the maximum output power of the PV power generation system, a tilt angle equal to the latitude of the installation location was chosen, and the orientation was set to due south. However, the impact of the PV array's tilt angle and orientation on the reliability of the PV power generation system has not yet been considered.

[0004] To address the aforementioned issues, this invention proposes a reliability assessment method for photovoltaic power generation systems that considers the influence of photovoltaic array tilt angle and orientation. Based on different selections of the photovoltaic array tilt angle and orientation, the method analyzes the relationship between the photovoltaic array tilt angle and orientation and the reliability of the photovoltaic power generation system. Summary of the Invention

[0005] The purpose of this invention is to provide a reliability assessment method for photovoltaic power generation systems that takes into account the effects of photovoltaic array tilt angle and orientation. The overall flowchart of this invention is as follows: Figure 1 As shown in the attached diagram. The following is a detailed explanation of each step.

[0006] 1. Collect meteorological data for the photovoltaic power generation system installation area, including horizontal solar irradiance and ambient temperature, and preprocess the collected horizontal solar irradiance and ambient temperature data. Use Median Absolute Deviation (MAD) to remove outliers from the measured data. MAD is defined as the median absolute deviation of a data point from the median. For measured solar irradiance data of length n {S1, S2, S3...S...} n}, its expression is:

[0007]

[0008] To ensure that normal data falls within the middle 50% range and outliers fall within the two outer 50% ranges, the upper and lower thresholds for the measured data are set as follows:

[0009]

[0010] In the formula, S m φ is the median of the solar irradiance data; k is 1 / φ(3 / 4).

[0011] When solar irradiance exceeds a threshold, the value is defined as an outlier.

[0012] 2. The Klein-Hay model is used to convert the solar irradiance on the horizontal surface into that on the inclined surface. According to the Klein sky scattering isotropic model, the expression for the direct irradiance on the inclined surface is:

[0013]

[0014]

[0015] ω s =arccos(-tanφ·tanδ)

[0016] ω sT =min{ω s arccos[-tan(φ-β)tanδ]}

[0017] In the formula, H b Direct irradiance on the horizontal plane, MJ / m 2 ;R b ω is the direct sunlight tilt factor; β is the slope angle (°); φ is the local latitude (°); δ is the solar declination angle (°); n is the number of days in a year for the desired date; ω s and ω sT , respectively, represent the solar sunset angles on the horizontal and inclined planes, in degrees (°).

[0018] In the Northern Hemisphere, the scattered irradiance of the sky is greater in the south than in the north, so the assumption of isotropic scattered irradiance cannot be used. The Hay anisotropic model of sky scattered irradiance considers that the scattered irradiance on the tilted surface towards the equator consists of two parts: the irradiance of the solar disk and the scattered irradiance uniformly distributed across the rest of the sky dome. The expression for the scattered irradiance on the tilted surface is:

[0019]

[0020]

[0021] In the formula, Hd Horizontal diffuse irradiance, MJ / m 2 H represents horizontal irradiance, in MJ / m². 2 H0 represents the horizontal irradiance outside the atmosphere, in MJ / m². 2 ;I sc is the solar constant.

[0022] When solar radiation strikes the ground, it is absorbed and reflected on an inclined surface. The intensity of this reflected radiation depends on the radiation angle coefficient between the ground and the inclined surface. The expression for surface irradiance is:

[0023]

[0024] In the formula, γ is the ground reflectivity.

[0025] Combining historical data on solar irradiance and diffuse irradiance on horizontal surfaces, the expression for solar irradiance on inclined surfaces is obtained as follows:

[0026]

[0027] In the formula, H T Solar irradiance on the inclined surface, MJ / m 2 .

[0028] 3. Calculate the maximum output power of the photovoltaic cell using an engineering mathematical model, based on I under standard test conditions. sc U oc I m U m The input to the photovoltaic cell engineering mathematical model is as follows:

[0029] P pv =VI=VI sc (1-C1{exp[U / (C2U oc )]-1})

[0030] C1=(1-I m / I sc )exp[-U m / (C2U oc )]

[0031] C2=(U m / U oc -1)[ln(1-I m / I sc )] -1

[0032] In the formula, P pv I represents the maximum output power of the photovoltaic cell under standard test conditions. sc and Im These are the short-circuit current and the current at the maximum power point of the photovoltaic cell under standard test conditions, respectively; U oc and U m These are the open-circuit voltage and the voltage corresponding to the maximum power point of the photovoltaic cell under standard test conditions, respectively.

[0033] Photovoltaic cells are connected in series and parallel to form a photovoltaic array. The expression for the maximum output power of the photovoltaic array is:

[0034] P pv-array =m·n·P pv

[0035] In the formula, P pv-array denoted as the maximum output power of the photovoltaic array; m represents the number of photovoltaic cells connected in parallel to the photovoltaic array; and n represents the number of photovoltaic cells connected in series to the photovoltaic array.

[0036] 4. The maximum output power of the photovoltaic array is calibrated according to standard test conditions. The maximum output power of the photovoltaic array under different environments is converted to the maximum output power of the photovoltaic array under standard test conditions (STC), eliminating the dependence of the photovoltaic array output characteristics on solar irradiance and ambient temperature. The converted power expression is as follows:

[0037]

[0038] In the formula, S STC and T STC These are solar irradiance and ambient temperature under standard test conditions, respectively; S t and T t , respectively, represent the actual solar irradiance and ambient temperature; r is the temperature coefficient of the photovoltaic array.

[0039] The per-unit value of the maximum output power of the photovoltaic array is calculated based on the initial maximum output power of the photovoltaic array. The expression for the per-unit value of the maximum output power of the photovoltaic array is as follows:

[0040]

[0041] In the formula, P represents the per-unit value of the maximum output power of the photovoltaic array. STC (t = initial) represents the initial maximum output power of the photovoltaic array.

[0042] The per-unit attenuation of the maximum output power of the photovoltaic array is taken as the change in the photovoltaic array degradation rate. The expression for the change in the photovoltaic array degradation rate is:

[0043]

[0044] In the formula, ΔY(t) is the change in the degradation rate of the photovoltaic array; Y(t) is the degradation rate of the photovoltaic array.

[0045] 5. The change in the degradation rate of the photovoltaic array, ΔY(t) = Y(t+Δt) - Y(t), follows a Gamma distribution. The probability density function (PDF) of the change in the degradation rate of the photovoltaic array following a Gamma distribution is expressed as follows:

[0046]

[0047] Δn(t)=n(t+Δt)-n(t)=k·(t+Δt) q -k·(t) q

[0048] In the formula, Δη(t) is the shape parameter of the Gamma distribution; q is the time coefficient; k is the proportionality coefficient; λ is the scale parameter of the Gamma distribution; and Γ(Δη(t)) is the Gamma function.

[0049] The expected value of the probability density function of the photovoltaic array degradation rate change ΔY(t) is used as the predicted degradation rate change of the photovoltaic array, and the variance is used as the reasonable prediction range from the expected value. The expressions for the expected value and variance of the probability density function of the photovoltaic array degradation rate change ΔY(t) are as follows:

[0050] E(t)=Δη(t)·λ

[0051] V(t)=Δη(t)·λ 2

[0052] In the formula, E(t) is the expectation of the probability density function of the change in the degradation rate of the photovoltaic array ΔY(t); V(t) is the variance of the probability density function of the change in the degradation rate of the photovoltaic array ΔY(t).

[0053] 6. The photovoltaic array degradation rate is obtained by summing up the predicted degradation rate changes of the photovoltaic array. Photovoltaic array manufacturers usually provide a warranty period of 20 to 25 years to ensure that the power generation performance of the photovoltaic array will not decrease by more than 20% under standard test conditions. That is, the photovoltaic array will fail when the degradation rate reaches 20%.

[0054] The actual maximum output power of a photovoltaic array is calculated based on its degradation rate, and the expression is as follows:

[0055] P pv_d =P pv-array ·(1-E(t))

[0056] In the formula, P pv_dE(t) represents the actual maximum output power of the photovoltaic array; E(t) represents the degradation rate of the photovoltaic array.

[0057] 7. Establish an electrothermal model including an IGBT power loss model and a thermal model. The power loss generated by the IGBT during operation is calculated by the power loss model, and the IGBT junction temperature profile is obtained based on the power loss model and the thermal model. IGBT power loss is divided into switching loss and conduction loss, and its expression is:

[0058]

[0059]

[0060]

[0061] In the formula, and These represent the conduction loss and switching loss of the IGBT, respectively; δ(t) is the duty cycle; i a T is the output current of the photovoltaic inverter. j V is the junction temperature of the IGBT; CE_25 ℃ represents the rated on-state voltage drop of the IGBT at 25℃; r CE_25 ℃ represents the rated on-state resistance of the IGBT at 25℃; K V_T K is the temperature influence coefficient of the IGBT on-state voltage drop. r_T f is the temperature effect coefficient of the IGBT on-state resistance; sw For the IGBT module's turn-on and turn-off frequencies; K sw K1(V) is the temperature influence coefficient of IGBT switching losses. dc K2(R) represents the voltage coefficient of the photovoltaic inverter; g ) represents the resistivity of the photovoltaic inverter.

[0062] The Foster model is used for thermal model analysis, transforming the IGBT power loss profile into an IGBT junction temperature profile, the expression of which is:

[0063]

[0064] In the formula: T j T represents the junction temperature of the IGBT. a Ambient temperature; and These represent the power losses of the IGBT and the anti-parallel diode, respectively; Z tjh and Z ha These are the thermal impedance parameters in the Foster model.

[0065] 8. Calculating IGBT junction temperature based on an electrothermal model is time-consuming and highly dependent on the IGBT electrothermal model parameters. When IGBTs experience aging or other phenomena, the calculated junction temperature error can be significant. To address this, this invention proposes an offline IGBT junction temperature calculation model based on XGBoost, taking a data-driven approach. The training data includes tilted solar irradiance, ambient temperature, maximum output power of the photovoltaic array, and time series data as features. The IGBT junction temperature profile calculated by the electrothermal model is used as a label to train the offline model, resulting in an online model for calculating IGBT junction temperature based on XGBoost. The XGBoost model is an additive expression composed of k decision trees, and its expression is:

[0066]

[0067] In the formula, f t (x i Let be the predicted value of the t-th decision tree for sample i; Let be the predicted value for the i-th sample.

[0068] The objective function of the XGBoost model consists of the loss function l and the regularization term Ω that reduces the complexity of the XGBoost model. Therefore, the expression for the objective function of the XGBoost model is:

[0069]

[0070] In the formula, f t (x i Let t be the decision tree for sample x. i The predicted value; Ω(f) is the predicted value for the i-th sample. i Let be the model complexity of the i-th decision tree.

[0071] Translate the function ft(xi) at point x i Performing a second-order Taylor series expansion at this point, the expression obtained is:

[0072]

[0073] In the formula, g i and h i Loss function pairs The first and second derivatives; I j Let w be the sample set of the j-th leaf node; j γ is the weight corresponding to leaf node j; γ is the regularization term weight coefficient; T is the number of leaf nodes.

[0074] To find the minimum of the objective function of the XGBoost model, we need to ignore the constants in the objective function. Its expression is:

[0075]

[0076] In the formula, G j for H j for

[0077] G j and H j This is the result obtained in the first t-1 steps, and its value is considered a constant. Only the leaf node w of the last tree... j Unknown, apply the objective function to w j Find the first derivative and set it equal to 0 to obtain the weight corresponding to leaf node j:

[0078]

[0079] Therefore, the minimum value of the objective function is:

[0080]

[0081] Solve the objective function of the offline model for calculating IGBT junction temperature based on XGBoost to obtain the online model for calculating IGBT junction temperature based on XGBoost.

[0082] 9. The irregular heat load cycles in the IGBT junction temperature profile output by the online model for calculating IGBT junction temperature based on XGBoost are converted into regular heat load cycles using the rainflow counting method. The junction temperature cycles exhibit different patterns in both the mean junction temperature and the junction temperature fluctuation values. Therefore, the rainflow counting method is applied to obtain the regular heat load cycles, thereby determining the mean junction temperature T for each cycle. jm and junction temperature fluctuation value ΔT j .

[0083] 10. To predict the lifespan of power electronic devices as a function of thermal load cycling parameters, accelerated power cycling tests were conducted on the power electronic devices. The resulting expression for the IGBT lifespan model is as follows:

[0084]

[0085] In the formula, N fi T is the number of failure cycles; jm The junction temperature is the average value; ΔT j This represents the junction temperature fluctuation value; t on For the cycle period; E a For activation energy; ar is the aspect ratio of the bond line; k b is the Boltzmann constant; the other parameters are empirical constants.

[0086] Calculate the number of failure cycles N using the IGBT lifetime model fi The lifetime consumption (LC) of IGBTs in photovoltaic inverters is calculated using the Miner linear cumulative damage criterion. The Miner linear cumulative damage criterion assumes that each temperature cycle causes damage to the device, and that the damage has a linear cumulative characteristic. The expression for the lifetime consumption (LC) of IGBTs in photovoltaic inverters is as follows:

[0087]

[0088] In the formula, n i N represents the number of loops per unit of time. fi This represents the number of failure cycles obtained from the IGBT lifetime model.

[0089] IGBTs are the core components of photovoltaic inverters, enabling power conversion and control. The fault distribution of various components in a photovoltaic inverter is as follows: Figure 5 As shown, IGBT damage accounts for up to 34% of the failure rate in photovoltaic inverters, the highest proportion among all components. Therefore, IGBT damage is considered as photovoltaic inverter damage.

[0090] 11. To overcome the parameter uncertainties introduced by the IGBT manufacturing process, Monte Carlo simulations were performed. In the Monte Carlo simulation, the thermal stress parameter, the average IGBT junction temperature T, was considered due to variations in IGBT damage caused by the manufacturing process. jm and junction temperature fluctuation value ΔT j Introducing a change, due to the average junction temperature T of the IGBT jm and junction temperature fluctuation ΔT j To maintain a dynamic state, the parameters cannot be directly converted into pseudo-random numbers conforming to a certain probability distribution. Therefore, the dynamic parameters need to be converted into equivalent static parameters. These equivalent static parameters can produce the same degradation effect on the device. According to the central limit theorem, when the sample size is sufficiently large, the thermal stress parameter variation tends to follow a normal distribution. Therefore, in this invention, the parameters are modeled using a normal distribution to obtain a set of static parameters. The expression for the probability density function of the normal distribution is:

[0091]

[0092] In the formula, μ and σ 2 These represent the expectation and variance of the normal distribution, respectively.

[0093] 12. Using Monte Carlo simulation, data is randomly extracted from the static parameter set, and the lifetime distribution of the photovoltaic inverter is fitted using the Weibull distribution to obtain the scale and shape parameters of the Weibull distribution. The expression for the probability density function of the Weibull distribution is:

[0094]

[0095] In the formula, x is a random variable representing the lifetime of the photovoltaic inverter; β is a shape parameter; and η is a scale parameter.

[0096] Integrating the probability density function of the Weibull distribution yields the Weibull cumulative distribution function (CDF), which is expressed as follows:

[0097]

[0098] The reliability of photovoltaic power generation systems is evaluated using the Weibull cumulative distribution function.

[0099] As can be seen, the reliability assessment method for photovoltaic power generation systems that considers the influence of the tilt angle and orientation of the photovoltaic array disclosed in this application takes into account the limitations of the installation location and other conditions of the photovoltaic power generation system, which lead to different selections of the tilt angle and orientation of the photovoltaic array. By analyzing the influence of the tilt angle and orientation of the selected photovoltaic array on the photovoltaic array and the photovoltaic inverter, the reliability assessment of the tilt angle and orientation of the photovoltaic array on the photovoltaic power generation system is obtained. Attached Figure Description

[0100] Figure 1 Flowchart of a reliability assessment method for photovoltaic power generation systems considering the effects of photovoltaic array tilt angle and orientation.

[0101] Figure 2 Photovoltaic array degradation rate curve

[0102] Figure 3 Online model for calculating IGBT junction temperature based on XGBoost

[0103] Figure 4 IGBT junction temperature profile after applying rainflow counting method

[0104] Figure 5 Fault distribution diagram of various components in a photovoltaic inverter

[0105] Figure 6 Normal distribution of junction temperature fluctuation values Detailed Implementation

[0106] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0107] (1) Collect meteorological data of the area where the photovoltaic power generation system is installed, including horizontal solar irradiance and ambient temperature, and preprocess the collected horizontal solar irradiance and ambient temperature data.

[0108] (2) The solar irradiance of the tilted surface of the photovoltaic array was calculated using the Klein-Hay model.

[0109] (3) Calculate the maximum output power of a single photovoltaic cell using the photovoltaic cell engineering mathematical model, and then calculate the maximum output power of the photovoltaic array by connecting the single photovoltaic cells in series and parallel.

[0110] (4) Calculate the maximum output power of the photovoltaic array and the per-unit value of the maximum output power of the photovoltaic array under standard test conditions, and use the attenuation of the per-unit value of the maximum output power of the photovoltaic array as the change in the degradation rate of the photovoltaic array.

[0111] (5) Use the Gamma distribution to fit the change in the degradation rate of the photovoltaic array, estimate the parameters of the fitted Gamma distribution, and use the expected value of the Gamma distribution as the predicted change in the degradation rate of the photovoltaic array.

[0112] (6) The photovoltaic array degradation rate is obtained by summing the predicted degradation rate changes of the photovoltaic array, the actual maximum output power of the photovoltaic array is calculated, and the life of the photovoltaic array is obtained according to the photovoltaic array degradation rate threshold.

[0113] (7) Train the offline model for calculating IGBT junction temperature based on XGBoost to obtain the online model for calculating IGBT junction temperature based on XGBoost, calculate the junction temperature profile of IGBT, and use the rainflow counting method to obtain the average junction temperature and junction temperature fluctuation value of IGBT.

[0114] (8) The failure cycle number of the IGBT is calculated using the IGBT life model, and the life damage of the IGBT is calculated using the cumulative fatigue criterion.

[0115] (9) Convert dynamic parameters, including the average junction temperature and junction temperature fluctuation of IGBT, into equivalent static parameters, and evaluate the reliability of the photovoltaic power generation system through Monte Carlo simulation.

Claims

1. A method for reliability assessment of photovoltaic power generation system considering the effects of photovoltaic array tilt angle and orientation, characterized in that, The method comprises the following steps: S1: collecting meteorological data of the installation area of the photovoltaic power generation system, including horizontal plane solar irradiance and environmental temperature, and performing data preprocessing on the collected horizontal plane solar irradiance and environmental temperature; S2: converting the horizontal plane solar irradiance into inclined plane solar irradiance by using a Klein-Hay model; S3: calculating the maximum output power of the photovoltaic array by using an engineering mathematical model of a photovoltaic cell; S4: calibrating the maximum output power of the photovoltaic array according to standard test conditions, taking the initial maximum output power of the photovoltaic array as a reference to calculate the maximum output power standard value of the photovoltaic array, and taking the maximum output power standard value decay of the photovoltaic array as the change of the degradation rate of the photovoltaic array; S5: fitting the change of the degradation rate of the photovoltaic array by using a Gamma distribution, obtaining the shape parameter and the scale parameter of the Gamma distribution, and taking the product of the shape parameter and the scale parameter of the Gamma distribution as the predicted degradation rate change of the photovoltaic array; S6: accumulating the predicted degradation rate change of the photovoltaic array to obtain the degradation rate of the photovoltaic array, calculating the actual maximum output power of the photovoltaic array, and obtaining the service life of the photovoltaic array according to the degradation rate threshold of the photovoltaic array; S7: establishing an electro-thermal model including an IGBT power loss model and a thermal model to obtain an IGBT junction temperature profile; S8: taking the training data including the inclined plane solar irradiance, the environmental temperature, the maximum output power of the photovoltaic array and the time sequence as features, taking the IGBT junction temperature profile calculated by the electro-thermal model as a label, training an XGBoost-based IGBT junction temperature calculation offline model to obtain an XGBoost-based IGBT junction temperature calculation online model; S9: converting the IGBT junction temperature profile output by the XGBoost-based IGBT junction temperature calculation online model into the IGBT junction temperature mean value and the junction temperature fluctuation value by using the rain flow counting method; S10: calculating the IGBT failure cycle number by using an IGBT life model, and calculating the IGBT life damage based on the cumulative fatigue criterion to obtain the life damage of the photovoltaic inverter; S11: converting the dynamic parameters including the IGBT junction temperature mean value and the junction temperature fluctuation value into equivalent static parameters, and obtaining a static parameter set by changing the junction temperature mean value and the junction temperature fluctuation value in the equivalent static parameters according to the normal distribution respectively; S12: randomly extracting data in the static parameter set by using Monte Carlo simulation, fitting the life distribution of the photovoltaic inverter by using a Weibull distribution, obtaining the scale parameter and the shape parameter of the Weibull distribution, obtaining the Weibull probability density function of the life distribution of the photovoltaic inverter, integrating the Weibull probability density function to obtain the Weibull cumulative distribution function, and further evaluating the reliability of the photovoltaic power generation system.

2. The method for reliability assessment of photovoltaic power generation system considering the effects of the inclination and orientation of photovoltaic array according to claim 1, characterized in that, The data preprocessing uses the median absolute value (MAD) to remove outliers from the measured data. For measured solar irradiance data of length n {S1, S2, S3, ..., S...} n The expression for MAD is: In the formula, is the median of the solar irradiance data. 3.The method for reliability assessment of photovoltaic power generation system considering the influence of photovoltaic array inclination and orientation according to claim 1, characterized in that, The conversion of the horizontal plane solar irradiance into the inclined plane solar irradiance by using the Klein-Hay model requires calculation of the inclined plane direct irradiance, the ground irradiance and the scattering irradiance, the expression of the inclined plane direct irradiance is: where H b is the direct horizontal irradiance; R b is the tilt factor; β is the tilt angle of the surface; φ is the local latitude; δ is the solar declination; ω s and ω sT are the solar angles of sunset on the horizontal and tilted surfaces, respectively. the expression of the inclined plane ground irradiance is: in the formula, γ is the ground reflectivity; H is the horizontal plane irradiance; the expression of the inclined plane scattering irradiance is: In the formula, H d is the horizontal plane scattered irradiance; H0is the horizontal plane irradiance outside the atmosphere; the expression of the inclined plane solar irradiance is: H T = H bT + H dT + H γT (5) where H bT is the direct irradiance on the tilted surface; H dT is the diffuse irradiance on the tilted surface; and H γT is the global irradiance on the tilted surface. 4.The method for reliability assessment of photovoltaic power generation system considering the influence of photovoltaic array inclination and orientation according to claim 1, characterized in that, The engineering mathematical model of the photovoltaic cell is expressed as: P pv = VI = VI sc (1 - C1{exp[U / (C2U oc )] - 1}) (6) C1= (1 - I m / I sc ) exp[-U m / (C2U oc )] (7) C2 = (U m / U oc -1)[ln(1-I m / I sc )] -1 (8) where P pv is the maximum output power of the photovoltaic cell under standard test conditions; I sc and I m are the short-circuit current value and the current value corresponding to the maximum power point, respectively, of the photovoltaic cell under standard test conditions; U oc and U m are the open-circuit voltage value and the voltage value corresponding to the maximum power point, respectively, of the photovoltaic cell under standard test conditions; The expression of the maximum output power of the photovoltaic array is obtained by using the maximum output power of a single photovoltaic cell. P pv-array = m · n · P pv (9) In the formula, P pv-array is the maximum output power of the photovoltaic array; m is the number of photovoltaic cells in parallel in the photovoltaic array; and n is the number of photovoltaic cells in series in the photovoltaic array.

5. The method for reliability assessment of photovoltaic power generation system considering the impacts of the tilt angle and orientation of photovoltaic array according to claim 1, characterized in that, The expression of the converted power is obtained by calibrating the maximum output power of the photovoltaic array under different environments to the maximum output power of the photovoltaic array under the standard test condition according to the standard test condition. In the formula, S STC and T STC These are solar irradiance and ambient temperature under standard test conditions, respectively; S t and T t These represent the actual solar irradiance and ambient temperature, respectively; r is the temperature coefficient of the photovoltaic array. The expression of the normalized maximum output power of the photovoltaic array is obtained by taking the initial maximum output power of the photovoltaic array as a reference. wherein and P STC P (t = initial) is the maximum output power of the photovoltaic array in units of power per unit area and P (t = initial) is the initial maximum output power of the photovoltaic array. The expression of the degradation rate change of the photovoltaic array is obtained by taking the normalized maximum output power decay of the photovoltaic array as the degradation rate change of the photovoltaic array. In the formula, ΔY(t) is the degradation rate change of the photovoltaic array; Y(t) is the degradation rate of the photovoltaic array.

6. The method for reliability assessment of photovoltaic power generation system considering the impacts of the tilt angle and orientation of photovoltaic array according to claim 1, characterized in that, The expression of the Gamma distribution is obtained by fitting the degradation rate change of the photovoltaic array with the Gamma distribution. ΔY(t)~Gamma(Δη(t),λ)=Gamma(η(t+Δt)-η(t),λ) (13) In the formula, ΔY(t) is the degradation rate change of the photovoltaic array; ΔY(t) is the shape parameter of the Gamma distribution; λ is the scale parameter of the Gamma distribution; t is the working time of the photovoltaic array; q is the time coefficient. The expression of the expectation of the Gamma distribution is obtained by taking the expectation of the Gamma distribution as the predicted degradation rate change of the photovoltaic array. E(t)=η(t)·λ (14) In the formula, E(t) is the expectation of the Gamma distribution.

7. The method for reliability assessment of photovoltaic power generation system considering the impacts of the tilt angle and orientation of photovoltaic array according to claim 1, characterized in that, The objective function of the XGBoost model is composed of a loss function l and a regular term Ω for inhibiting the complexity of the XGBoost model, so the expression of the objective function of the XGBoost model is: where ft(xi) is the predicted value of the tth decision tree for sample x i is the predicted value of the ith sample; Ω(fi) is the model complexity of the ith decision tree.​ According to the Taylor formula, the function ft(x) is expanded in a second order Taylor series at the point x i The resulting expression is: In the formula, g i and h i Loss function pairs The first and second derivatives.

8. The method for reliability assessment of photovoltaic power generation system considering the impacts of the tilt angle and orientation of photovoltaic array according to claim 1, characterized in that, The scale parameter and the shape parameter of the Weibull distribution are obtained by using the Monte Carlo simulation to randomly extract data in the static parameter set and fitting the lifetime distribution of the photovoltaic inverter with the Weibull distribution, and the Weibull probability density function of the lifetime distribution of the photovoltaic inverter is obtained. In the formula, x is a random variable of the lifetime of the photovoltaic inverter; β is the shape parameter; η is the scale parameter. The expression of the Weibull cumulative distribution function is obtained by integrating the probability density function of the Weibull distribution. The reliability of the photovoltaic power generation system is evaluated by using the Weibull cumulative distribution function.

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