Photovoltaic system dynamic state estimation method and system based on solar irradiance change

Abstract

The invention discloses a photovoltaic system dynamic state estimation method and a photovoltaic system dynamic state estimation system based on solar irradiance change, which are characterized in that the randomness of solar irradiance is regarded as unknown input of DSE, and the unknown input is merged into an unscented Kalman filtering algorithm in an unbiased minimum variance form to realize state estimation of a photovoltaic system. According to the method, the dynamic state of the photovoltaic system can be timely and accurately estimated, the problem that the estimation precision is influenced by illumination volatility is solved, and the operation state of the power system is better described.

Description

Technical Field

[0001] The present invention belongs to the technical field of dynamic state estimation of power systems, and mainly relates to a method and system for dynamic state estimation of photovoltaic systems based on solar irradiance changes. Background Art

[0002] In recent years, the global photovoltaic industry has expanded rapidly due to its environmentally friendly characteristics. However, since the photovoltaic system is significantly affected by external climatic factors, it exhibits obvious volatility and randomness. These fluctuations in turn can cause problems for the power system, such as reduced power quality and misoperation of relay protection. In addition, numerous power electronic equipment devices have also fundamentally changed the dynamic response of the power system, which will undoubtedly lead to increased complexity in the operation and control of the power system. Therefore, it is necessary to estimate the dynamic state of the photovoltaic system in a timely and accurate manner in order to better describe the operating state of the power system.

[0003] Dynamic state estimation (DSE) has the ability to track real-time states, and many studies have focused on developing tools for DSE in power systems and applying them to traditional synchronous generators. However, when it comes to state estimation of photovoltaic systems, most existing works adopt static estimation or oversimplified dynamic models. In addition, previous works do not take into account the volatility of solar irradiance, thus posing a threat to the accuracy of the estimation. Summary of the invention

[0004] The present invention aims at the fact that the existing technology does not take into account the influence of solar irradiance fluctuation on the dynamic state estimation of photovoltaic system, and proposes a method and system for dynamic state estimation of photovoltaic system based on solar irradiance change, which regards the randomness of solar irradiance as the unknown input of DSE and incorporates it into the unscented Kalman filter algorithm in the form of unbiased minimum variance to realize the state estimation of photovoltaic system. The method of the present invention can timely and accurately estimate the dynamic state of photovoltaic system, solve the problem that the light fluctuation affects the estimation accuracy, and better describe the operation state of power system.

[0005] In order to achieve the above-mentioned purpose, the technical solution adopted by the present invention is: a dynamic state estimation method of a photovoltaic system based on changes in solar irradiance, which regards the randomness of solar irradiance as an unknown input of DSE, and incorporates it into the unscented Kalman filtering algorithm in the form of unbiased minimum variance to realize state estimation of the photovoltaic system.

[0006] As an improvement of the present invention, a method for estimating the dynamic state of a photovoltaic system based on changes in solar irradiance includes the following steps:

[0007] S1: Establish a photovoltaic system dynamic state estimation model, which includes a nonlinear photovoltaic system process equation and a measurement equation; the process equation describes the dynamic behavior of the photovoltaic system, and the measurement equation associates the system's measurement value with the state prediction value, specifically:

[0008] x k =f(x k-1 ,u k )+G k d k +w k

[0009] z k =h(x k ,u k )+v k

[0010] in, and They are photovoltaic state variables and measurement variables, and are known inputs and unknown inputs respectively, f(·) and h(·) are nonlinear functions, and w k and v k are process noise and measurement noise with mean 0, G k is a known matrix representing the effect of unknown input on state variables;

[0011] S2: Construct an unscented Kalman filter algorithm based on unbiased minimum variance. The algorithm is an extension of the traditional unscented Kalman filter algorithm. Its goal is to optimize the state estimation process in an unbiased and minimum variance manner. Combined with the above dynamic state estimation model, it provides a more accurate and reliable estimation result.

[0012] S3: Establish an expression for the effect of unknown input illumination on photovoltaic state variables. When the unknown input comes from illumination changes, the partial derivative of the state variable with respect to illumination is derived according to the photovoltaic model, and the relationship between illumination and photovoltaic system state variables is established, and G is obtained. k The expression of PV dynamic state estimation model is further improved.

[0013] As an improvement of the present invention, in step S1, the photovoltaic state estimation model relies on the process equation and measurement equation provided by the photovoltaic model. The two-stage photovoltaic system model used is composed of four parts: photovoltaic array, boost converter, inverter and filter, and its differential algebraic equation is as follows:

[0014] Differential Equations:

[0015]

[0016]

[0017]

[0018]

[0019]

[0020]

[0021]

[0022]

[0023]

[0024]

[0025] Algebraic equation:

[0026] D = k p (u pv - u m ) + k i D1

[0027]

[0028] u dc i dc = u id i d + u iq i q

[0029] u rd = u sd + L f i q -[k ip (i dref - i d ) + k ii u rd1 )

[0030] u rq = u sq - L f i d -[k ip (i qref - i q ) + k ii u rq1 )

[0031] i dref = k op (u dcref - u dc)+k oi i dref1

[0032] i qref =k op (q dcref -q)+k oi i qref1

[0033] Among them, D1, u rd1 、u rq1 、i dref1 and i qref1 is the intermediate state, i pv and u pv are the output current and output voltage of the photovoltaic array, u oc 、i sc 、u m and i m are the open circuit voltage, short circuit current, maximum power voltage and maximum power current of the photovoltaic module in actual conditions. C1 and C2 are coefficients. pv , L dc and C dc are the array capacitor, the inductor and capacitor of the boost converter, D is the duty cycle of the switch, i L is the inductor current of the boost converter, i dc and u dc are the output current and output voltage of the boost converter, k p and k i are the proportional and integral control gains of the controller, u id and u iq are the d-axis and q-axis components of the inverter output voltage, u rd and u rq are the d-axis and q-axis components of the modulated wave, U tri is the peak value of the carrier, u dcref and q dcref are the DC side reference voltage and the output side reference reactive power, k op and k oi are the proportional and integral control gains of the outer loop controller, k ip and k ii are the proportional and integral control gains of the inner loop controller, q is the actual output reactive power of the photovoltaic system, i d and i q are the d-axis and q-axis components of the actual output current of the photovoltaic system, i dref and i qref are the d-axis and q-axis components of the output reference current, u sd and u sq are the d-axis and q-axis components of the grid voltage, L fis the filter inductance, and ω is the grid angular frequency.

[0034] As another improvement of the present invention, the open circuit voltage u of the photovoltaic module in the actual situation is oc , short circuit current i sc , Maximum power voltage u m and the maximum power current i m They are:

[0035]

[0036]

[0037]

[0038]

[0039] Where S and T are the light radiation intensity and temperature respectively, u' oc 、i' sc 、u' m and i' m The corresponding measured values ​​under standard test conditions provided by the manufacturer.

[0040] As another improvement of the present invention, the step S2 specifically includes the following steps:

[0041] S21: given the state estimate at time k-1 Use traceless transformation to deal with the nonlinearity of the system and select 2n sigma points The weight of each point is w i =1 / 2n, i = 1,…, 2n; propagate the sigma point through the nonlinear function to obtain the updated sigma point

[0042]

[0043]

[0044] The predicted mean of the state vector at this time and the prediction covariance matrix The expression is:

[0045]

[0046]

[0047] Based on the updated sigma point, the predicted measurement vector The expression is:

[0048]

[0049]

[0050] Error update covariance matrix and the cross covariance matrix The calculation formula is:

[0051]

[0052]

[0053] in The covariance matrices are and

[0054] S22: Statistically linearize the nonlinear measurement equation, and the expression is:

[0055]

[0056] in ε k represents the error with mean zero, and its covariance matrix expression is

[0057] S23: Derivation of unknown inputs based on the minimum unbiased variance estimation criterion State Vector and the covariance matrix The expression is:

[0058]

[0059] In the above expressions of unknown input, state vector and covariance matrix, the specific calculation formulas of some matrices are:

[0060]

[0061] As a further improvement of the present invention, step S3 specifically includes the following steps:

[0062] S31. Based on the nonlinear system process model and the photovoltaic model, the partial derivatives of the above state variables with respect to the unknown input light are calculated to establish the influence of light on the photovoltaic state variables, i.e., G k The expression is:

[0063] For the state quantity u rd1 ,u rq1 ,i d ,i q ,i dref1 ,i qref1 , there is no clear expression for solar irradiance, then the state variable x = [u pv,i L ,u dc ,D1,u rd1 ,u rq1 ,i d ,i q ,i dref1 ,i qref1 ] T , G in the above state estimation model k The specific form can be written as:

[0064] G k =[G1,G2,G3,G4,0,0,0,0,0,0] T

[0065] Among them, G1, G2, G3, G4 are state variables u pv ,i L ,u dc ,The partial derivative of D1 with respect to illumination describes the effect of illumination on photovoltaic state variables.

[0066] S32. Considering the relationship between parameters and illumination, we further obtain:

[0067]

[0068]

[0069]

[0070] in

[0071]

[0072] In order to achieve the above purpose, the present invention also adopts a technical solution: a photovoltaic system dynamic state estimation system based on solar irradiance changes, including a computer program, which implements the steps of any of the above methods when executed by a processor.

[0073] Compared with the prior art, the present invention has the following beneficial effects: in view of the background of the era when photovoltaic power is connected to the power grid in large quantities, the present invention proposes a method and system for estimating the dynamic state of a photovoltaic system taking into account changes in solar irradiance.

[0074] In terms of PV model selection, existing DSE work in the PV field is based on simplified PV models that only focus on PV power generation. This paper studies DSE for the first time in a detailed two-level PV model, where PV arrays, boost converters, inverters and filters are all included, which is conducive to better describing the dynamic characteristics and inferring the operating conditions of the PV system.

[0075] In terms of algorithms, the dynamic state estimation method provided by this technology takes photovoltaics as unknown inputs, derives the relationship between photovoltaic parameters and solar irradiance, and obtains an expression that reflects the impact of light fluctuations on state variables, which allows us to develop DSE based on unbiased minimum variance under unknown inputs. Even when solar irradiance changes dramatically, the photovoltaic state can be accurately and reliably estimated, solving the problem of light fluctuations affecting estimation accuracy and better describing the operating state of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is a flow chart of a method for estimating a dynamic state of a photovoltaic system based on changes in solar irradiance according to the present invention;

[0077] Figure 2 It is a schematic diagram of a photovoltaic power system in Embodiment 1 and Embodiment 2 of the present invention;

[0078] Figure 3 is a schematic diagram of the fluctuation of solar irradiance in Example 1 of the present invention;

[0079] Figure 4 is the verification result of Example 1 of the present invention;

[0080] Figure 5 is a schematic diagram of the fluctuation of solar irradiance in Example 2 of the present invention;

[0081] Figure 6 This is the verification result of Example 2 of the present invention. DETAILED DESCRIPTION

[0082] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention.

[0083] Example 1

[0084] This embodiment is applied to the New England 33 bus 1 generator system connected to photovoltaic. Figure 2 As shown, four two-stage photovoltaics are connected to nodes 8, 13, 28 and 32 respectively. The photovoltaic modules are initially under standard test conditions, that is, the solar irradiance is 1000W / (m2), the module temperature is 25℃, and the spectrum is AM1.5G. Interference is applied by setting load shedding at the 4th second of the simulation. The total simulation time is set to 10 seconds.

[0085] The dynamic state estimation method of photovoltaic system based on solar irradiance changes, such as Figure 1 As shown, the following steps are included:

[0086] S1: Establish a photovoltaic system dynamic state estimation model, which includes a nonlinear photovoltaic system process equation and a measurement equation. The process equation describes the dynamic behavior of the photovoltaic system, and the measurement equation associates the system's measurement value with the state prediction value, specifically:

[0087] x k =f(x k-1 ,u k )+G k d k +w k

[0088] z k =h(x k ,u k )+v k

[0089] in, and They are photovoltaic state variables and measurement variables, and are known inputs and unknown inputs respectively, f(·) and h(·) are nonlinear functions, and w k and v k They are process noise and measurement noise with mean 0, and the covariance matrices are and G k is a known matrix representing the effect of unknown input on state variables;

[0090] S11: The photovoltaic state estimation model relies on the process equations and measurement equations provided by the photovoltaic model. The two-stage photovoltaic system model used consists of four parts: photovoltaic array, boost converter, inverter and filter. Its differential algebraic equation is as follows:

[0091] Differential Equations:

[0092]

[0093]

[0094]

[0095]

[0096]

[0097]

[0098]

[0099]

[0100]

[0101]

[0102] Algebraic equation:

[0103] D = k p (u pv -u m )+k i D1

[0104]

[0105] u dc i dc =u id i d +u iq i q

[0106] u rd =u sd +L f i q -[k ip (i dref -i d )+k ii u rd1 ]

[0107] u rq =u sq -L f i d -[k ip (i qref -i q )+k ii u rq1 ]

[0108] i dref = k op (u dcref -u dc )+k oi i dref1

[0109] i qref = k op (q dcref -q)+k oi i qref1

[0110] Among them, D1, u rd1 , u rq1 , i dref1 Japanese qref1 This is the middle situation, i pv Japanese pvare the output current and output voltage of the photovoltaic array, u oc 、i sc 、u m and i m are the open circuit voltage, short circuit current, maximum power voltage and maximum power current of the photovoltaic module in actual conditions. C1 and C2 are coefficients. pv , L dc and C dc are the array capacitor, the inductor and capacitor of the boost converter, D is the duty cycle of the switch, i L is the inductor current of the boost converter, i dc and u dc are the output current and output voltage of the boost converter, k p and k i are the proportional and integral control gains of the controller, u id and u iq are the d-axis and q-axis components of the inverter output voltage, u rd and u rq are the d-axis and q-axis components of the modulated wave, U tri is the peak value of the carrier, u dcref and q dcref are the DC side reference voltage and the output side reference reactive power, k op and k oi are the proportional and integral control gains of the outer loop controller, k ip and k ii are the proportional and integral control gains of the inner loop controller, q is the actual output reactive power of the photovoltaic system, i d and i q are the d-axis and q-axis components of the actual output current of the photovoltaic system, i dref and i qref are the d-axis and q-axis components of the output reference current, u sd and u sq are the d-axis and q-axis components of the grid voltage, L f is the filter inductance, and ω is the grid angular frequency.

[0111] S12: The open circuit voltage u of the photovoltaic module in the actual situation oc , short circuit current i sc , Maximum power voltage u m and the maximum power current i m They are:

[0112]

[0113]

[0114]

[0115]

[0116] Where S and T are light radiation intensity (W / m2) and temperature (℃), u' oc 、i' sc 、u' m and i' m The corresponding measured values ​​under standard test conditions provided by the manufacturer.

[0117] S2: Construct an unscented Kalman filter algorithm based on unbiased minimum variance. The algorithm is an extension of the traditional unscented Kalman filter algorithm. Its goal is to optimize the state estimation process in an unbiased and minimum variance manner. Combined with the above dynamic state estimation model, it provides a more accurate and reliable estimation result.

[0118] S21: given the state estimate at time k-1 Use traceless transformation to deal with the nonlinearity of the system and select 2n sigma points The weight of each point is w i =1 / 2n, i = 1,…, 2n. The sigma point is propagated through the nonlinear function to obtain the updated sigma point

[0119]

[0120] The predicted mean of the state vector at this time and the prediction covariance matrix The expression is:

[0121]

[0122]

[0123] Based on the updated sigma point, the predicted measurement vector The expression is:

[0124]

[0125]

[0126] Error update covariance matrix and the cross covariance matrix The calculation formula is:

[0127]

[0128]

[0129] in

[0130] S22: Statistically linearize the nonlinear measurement equation, and the expression is:

[0131]

[0132] in ε k represents the error with mean zero, and its covariance matrix expression is

[0133] S23: Derivation of unknown inputs based on the minimum unbiased variance estimation criterion State Vector and the covariance matrix The expression is:

[0134]

[0135] As another improvement of the present invention, in the expressions of unknown input, state vector and covariance matrix formed in step S23, the specific calculation formula of some matrices is:

[0136]

[0137] S3: Establish an expression for the effect of unknown input illumination on photovoltaic state variables. When the unknown input comes from illumination changes, the partial derivative of the state variable with respect to illumination is derived according to the photovoltaic model, and the relationship between illumination and photovoltaic system state variables is established, that is, G is obtained. k The expression of is further improved, which further improves the photovoltaic dynamic state estimation model;

[0138] S31: Based on the nonlinear system process model and photovoltaic model, calculate the partial derivatives of the above state variables with respect to the unknown input light, and establish the influence of light on the photovoltaic state variables, i.e., G k The expression is:

[0139] For the state quantity u rd1 ,u rq1 ,i d ,i q ,i dref1 ,i qref1 , there is no clear expression for solar irradiance, then the state variable x = [u pv ,i L ,u dc ,D1,u rd1 ,u rq1 ,i d ,i q ,i dref1 ,i qref1 ]T , G in the above state estimation model k The specific form can be written as:

[0140] G k =[G1,G2,G3,G4,0,0,0,0,0,0] T

[0141] Among them, G1, G2, G3, G4 are state variables u pv ,i L ,u dc ,The partial derivative of D1 with respect to illumination describes the effect of illumination on photovoltaic state variables.

[0142] S32: Considering the relationship between parameters and illumination, we can further obtain:

[0143]

[0144]

[0145]

[0146] in:

[0147]

[0148]

[0149]

[0150]

[0151]

[0152]

[0153] According to the solar irradiance fluctuation model based on stochastic differential equations in this embodiment, the solar irradiance change is simulated and used as an example scenario to perform simulation verification on the proposed dynamic state estimation method.

[0154] The stochastic differential equation is:

[0155] dξ t =μ(ξ t ,t)dt+σ(ξ t ,t)dW t

[0156] where ξ t is a random excitation, W t represents the standard Wiener process, μ(·) and σ(·) represent the drift and diffusion terms, respectively.

[0157] Simulating the beta distribution of illumination, the solar irradiance fluctuation model is:

[0158]

[0159]

[0160] The simulated beta distribution probability density function is:

[0161]

[0162] Where a and b are shape parameters, and B(a,b) is a function that ensures that the integral of the probability density function is 1.

[0163] Set a=4, b=1, select a lighting fluctuation scene, and verify the estimation performance of the proposed method in this lighting scene.

[0164] The solar irradiance fluctuation selected in this simulation verification is as follows Figure 3 shown. Figure 4 The simulation results of photovoltaic state variables obtained by the method proposed by the present invention are shown. Figure 4 It can be seen that the results of this method are very similar to the true value. Therefore, the proposed method can quickly and accurately track the dynamic changes of the photovoltaic system when the solar irradiance changes.

[0165] Example 2

[0166] This embodiment is also applicable to Figure 2 The New England 33 bus 1 generator system with photovoltaic access is shown. Among them, four two-level photovoltaics are connected to nodes 8, 13, 28 and 32 respectively. The photovoltaic modules are initially under standard test conditions, that is, the solar irradiance is 1000W / (m2), the module temperature is 25℃, and the spectrum is AM1.5G. The interference is imposed by setting the load shedding at the 4th second of the simulation. At the same time, in addition to the proposed method, the UKF algorithm is also used to estimate the state variables of the photovoltaic for comparison. The total simulation time is set to 10 seconds.

[0167] According to the steps of the present invention, the dynamic state of the photovoltaic system is estimated, and a scene with severe light fluctuation is set, such as Figure 5 shown. Figure 6The simulation results of the photovoltaic state variables obtained by the two methods are shown. It can be seen that the estimated results obtained by the proposed method are almost the same as the estimated results of the real state, while the results given by the UKF method are greatly deviated from the real values, especially when the solar irradiance fluctuates violently, because the parameters related to the solar irradiance have non-negligible mutations, so they can no longer be regarded as constant parameters, resulting in a high deviation of the UKF estimation results. In contrast, the proposed method uses solar irradiance as an unknown input, and then jointly estimates the dynamic state of the photovoltaic system and the unknown input, ensuring that a fairly accurate estimation result can be obtained, verifying the estimation performance of the proposed algorithm.

[0168] In summary, the photovoltaic system dynamic state estimation method based on solar irradiance changes of the present invention provides support for mastering the real-time operation state of the new power system; it includes the following steps: S1, establishing a photovoltaic system dynamic state estimation model; S2, constructing an unscented Kalman filter algorithm based on unbiased minimum variance (UKF-UMV);

[0169] S3. Establish an expression for the influence of unknown input illumination on photovoltaic state variables. The present invention fully considers the volatility and randomness of the photovoltaic system caused by the influence of external factors such as illumination, takes illumination volatility as an unknown input, estimates the dynamic state of the photovoltaic system, and timely and accurately estimates the dynamic state of the photovoltaic system, solving the problem of illumination volatility affecting the estimation accuracy, so as to better describe the operating state of the power system.

[0170] It should be noted that the above content only illustrates the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. For ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications all fall within the protection scope of the claims of the present invention.

Claims

1. A method for estimating the dynamic state of a photovoltaic system based on changes in solar irradiance, characterized in that: The randomness of solar irradiance is regarded as an unknown input of DSE and incorporated into the unscented Kalman filter algorithm in an unbiased minimum variance form to achieve state estimation of the photovoltaic system.

2. The method for estimating the dynamic state of a photovoltaic system based on solar irradiance changes according to claim 1, characterized in that: The steps include: S1: Establish a photovoltaic system dynamic state estimation model. The model is based on a two-level model of the photovoltaic system and establishes nonlinear photovoltaic system process equations and measurement equations, specifically: x k =f(x k-1 ,u k )+G k d k +w k z k =h(x k ,u k )+v k in, and They are photovoltaic state variables and measurement variables, and are known inputs and unknown inputs respectively, f(·) and h(·) are nonlinear functions, and w k and v k are process noise and measurement noise with mean 0, G k is a known matrix representing the effect of unknown input on state variables, and the subscript k represents the kth moment; S2: construct an unscented Kalman filter algorithm based on unbiased minimum variance, which optimizes the state estimation process in an unbiased and minimum variance manner; S3: Establish an expression for the influence of unknown input illumination on photovoltaic state variables. The expression derives the partial derivative of the state variable with respect to illumination based on the photovoltaic model, establishes the relationship between illumination and photovoltaic system state variables, and realizes the dynamic state estimation of the photovoltaic system.

3. The method for estimating the dynamic state of a photovoltaic system based on solar irradiance changes according to claim 2, characterized in that: The two-stage model of the photovoltaic system in step S1 consists of four parts: photovoltaic array, boost converter, inverter and filter. Its differential algebraic equation is as follows: Differential Equations: Algebraic equation: D=k p (in pv -in m )+k i D1 in dc and dc =in id and d +in iq and q in rd =in sd +L f and q -[k ip (and dref -and d )+k ii in rd1 ] in rq =in sq -L f and d -[k ip (and qref -and q )+k ii in rq1 ] and dref =k op (in dcref -in dc )+k oi and dref1 i qref =k op (q dcref -q)+k oi i qref1 Among them, D1, u rd1 、u rq1 、i dref1 and i qref1 is the intermediate state, i pv and u pv are the output current and output voltage of the photovoltaic array, u oc 、i sc 、u m and i m are the open circuit voltage, short circuit current, maximum power voltage and maximum power current of the photovoltaic module in actual conditions. C1 and C2 are coefficients. pv , L dc and C dc are the array capacitor, the inductor and capacitor of the boost converter, D is the duty cycle of the switch, i L is the inductor current of the boost converter, i dc and u dc are the output current and output voltage of the boost converter, k p and k i are the proportional and integral control gains of the controller, u id and u iq are the d-axis and q-axis components of the inverter output voltage, u rd and u rq are the d-axis and q-axis components of the modulated wave, U tri is the peak value of the carrier, u dcref and q dcref are the DC side reference voltage and the output side reference reactive power, k op and k oi are the proportional and integral control gains of the outer loop controller, k ip and k ii are the proportional and integral control gains of the inner loop controller, q is the actual output reactive power of the photovoltaic system, i d and i q are the d-axis and q-axis components of the actual output current of the photovoltaic system, i dref and i qref are the d-axis and q-axis components of the output reference current, u sd and u sq are the d-axis and q-axis components of the grid voltage, L f is the filter inductance, and ω is the grid angular frequency.

4. The method for estimating the dynamic state of a photovoltaic system based on solar irradiance changes according to claim 3, characterized in that: The open circuit voltage u of the photovoltaic module oc , short circuit current i sc , Maximum power voltage u m and the maximum power current i m They are: Where S and T are the light radiation intensity and temperature respectively, u' oc , i′ sc 、u' m and i' m The corresponding measured values ​​under standard test conditions provided by the manufacturer.

5. The method for estimating the dynamic state of a photovoltaic system based on solar irradiance changes according to claim 2, characterized in that: The step S2 specifically includes the following steps: S21: given the state estimate at time k-1 Use traceless transformation to deal with the nonlinearity of the system and select 2n sigma points The weight of each point is w i =1 / 2n, i=1,…,2n, propagate the sigma point through the nonlinear function to obtain the updated sigma point The predicted mean of the state vector at this time and the prediction covariance matrix The expression is: Based on the updated sigma point, the predicted measurement vector The expression is: Error update covariance matrix and the cross covariance matrix The calculation formula is: in The covariance matrices are and S22: Statistically linearize the nonlinear measurement equation, and the expression is: in ε k represents the error with mean zero, and its covariance matrix expression is S23: Derivation of unknown inputs based on the minimum unbiased variance estimation criterion State Vector and the covariance matrix The expression is: The specific calculation formula of the matrix in the above expression is:

6. The method for estimating the dynamic state of a photovoltaic system based on solar irradiance changes according to claim 2, characterized in that: The step S3 specifically includes the following steps: S31. Based on the nonlinear system process model and the photovoltaic model, the partial derivatives of the above state variables with respect to the unknown input light are calculated to establish the influence of light on the photovoltaic state variables, i.e., G k The expression is: G k =[G1,G2,G3,G4,0,0,0,0,0,0] T Among them, G1, G2, G3, G4 are state variables u pv ,i L ,u dc , partial derivative of D1 with respect to illumination; S32. Considering the relationship between parameters and illumination, we can further obtain: in 7. A system for estimating the dynamic state of a photovoltaic system based on changes in solar irradiance, comprising a computer program, characterized in that: When the computer program is executed by a processor, the steps of any of the above methods are implemented.

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