An off-grid micro-grid adaptive frequency and voltage secondary control method and device

Abstract

The application provides an off-grid micro-grid adaptive frequency and voltage secondary control method and device, and belongs to the technical field of power system operation and control. The method comprises the following steps: constructing frequency control variables and voltage control variables of a controlled inverter secondary control in an off-grid micro-grid; based on the frequency control variables and the voltage control variables, interference-related dynamic linearization models of the micro-grid frequency and voltage secondary control are constructed respectively; based on the interference-related dynamic linearization models, active power control instructions and reactive power control instructions of the inverter are calculated to realize the secondary control of the micro-grid frequency and voltage. The application can greatly improve the efficiency, safety and flexibility of the micro-grid voltage and frequency control in the scene of high renewable energy penetration and incomplete model, and improve the operation safety and stability of the micro-grid.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of power system operation and control, and particularly relates to a kind of off-grid microgrid adaptive frequency voltage secondary control method and device. BACKGROUND

[0002] Under the impetus of energy and environment problems, the proportion of renewable energy in power grid is increasing, and large-scale, high-penetration renewable energy generation grid-connected becomes the frontier and hotspot in international energy and power field. At the same time, the construction of microgrid has a good promoting effect on low-carbon emission reduction, development of renewable energy, improvement of energy efficiency and improvement of power supply reliability. Microgrid can operate in grid-connected and off-grid modes. When the large power grid has an accident or a special scene, the microgrid can switch to off-grid operation model to ensure the power supply of important loads or as a black start power source. For off-grid microgrid, secondary control is particularly important to maintain microgrid voltage and frequency.

[0003] In traditional microgrid research, voltage or frequency secondary control is often achieved based on accurate model. However, traditional model-based optimization control method depends on accurate system model parameters, and ideal model of microgrid is difficult to obtain, so this model-based optimization method is difficult to be applied in practice. In order to deal with the problem of incomplete model of microgrid, data-driven control method is proposed in recent years, which can learn system model by using measurement data of microgrid. However, existing data-driven methods are mostly not adaptive, that is, they cannot use the latest online measurement data to correct the parameter information learned from data in time under the scene of time-varying microgrid operation conditions. Moreover, existing data-driven methods are sensitive to abnormal values in measurement data. In addition, most of the existing microgrid secondary control methods only consider the scene that local primary control of controllable inverters in microgrid is based on droop control. For the scene that new adaptive meshing control method is used as primary control of controllable inverters in microgrid, there is a lack of related research on microgrid voltage and frequency secondary control. SUMMARY

[0004] The purpose of the present application is to overcome the shortcomings of the prior art, and provide a kind of off-grid micro-grid adaptive frequency voltage secondary control method and device.The present application considers that the primary control layer of inverter in off-grid micro-grid is based on new adaptive network control technology, can coordinate the active and reactive power instructions of each inverter in off-grid micro-grid to realize the voltage and frequency secondary control of micro-grid;The present application does not need accurate system model parameters, but uses online measurement data to learn the dynamic linearization model of micro-grid;The present application is also adaptive to the change of system operating conditions, when the system operating conditions change, the present application can use online measurement data to update control parameters in time, so as to maintain good control performance.The present application can greatly improve the voltage and frequency control quality of micro-grid, improve the operation safety and stability of micro-grid.

[0005] The first aspect embodiment of the present application provides a kind of off-grid micro-grid adaptive frequency voltage secondary control method, comprising:

[0006] The frequency control variable and voltage control variable of the controlled inverter secondary control in off-grid micro-grid are constructed;

[0007] Based on the frequency control variable and the voltage control variable, the disturbance related dynamic linearization model of the frequency and voltage secondary control of the micro-grid is respectively constructed;

[0008] Based on the disturbance related dynamic linearization model, the active power control instruction and reactive power control instruction of the inverter are calculated to realize the secondary control of the frequency and voltage of the micro-grid.

[0009] In one specific embodiment of the present application, the frequency control variable and voltage control variable of the controlled inverter secondary control in off-grid micro-grid are constructed, comprising:

[0010] 1) the model of the controlled inverter in off-grid micro-grid is established;

[0011] N i controlled inverters in off-grid micro-grid are recorded;

[0012] Wherein, the voltage and phase angle equation of the i inverter is respectively represented as:

[0013]

[0014] In the formula, V i , θ i Respectively, the voltage amplitude and phase angle of the i inverter node are V i r , Respectively, the voltage set point and angular frequency set point of the i inverter are ω i The actual angular frequency of the i inverter is ω V i θ i The derivative with respect to time; V i r2 V i 2 V i r V i The square of P; i Q i Let P be the actual active power and reactive power output of the i-th inverter, respectively. i * , For the active power setting command and reactive power setting command from the i-th inverter of the secondary control; V dc,i Let be the actual value of the DC voltage on the DC side of the i-th inverter. ΔP is the setpoint value of the DC voltage on the DC side of the i-th inverter; i ΔQ i These are functions related to the setpoint deviations of the active and reactive power of the i-th inverter, respectively; Δφ vi , Δφ ωi Let η be the voltage deviation function and frequency deviation function related to the DC-side voltage change of the i-th inverter, respectively; i α i κ i , λ i These are the primary control parameters for the i-th inverter;

[0015] 2) Based on the results of step 1), construct the frequency control variables and voltage control variables for the secondary control of the controlled inverter;

[0016] Wherein, at time k, the frequency control command and voltage control command of the i-th inverter are respectively expressed as:

[0017]

[0018] In the formula, This is the voltage control command for the i-th inverter at time k. This is the voltage control command for the i-th inverter at time k. P represents the square of the voltage amplitude setpoint of the i-th inverter node; i * (k) These are the active and reactive power setting commands from the secondary control unit for the i-th inverter at time k.

[0019] In a specific embodiment of the present invention, the step of constructing the interference-related dynamic linearization model for the secondary control of the microgrid frequency and voltage respectively includes:

[0020] 1) Construct the disturbance-dependent linearization model of the secondary frequency control of the microgrid, expressed as follows:

[0021] Δy F (k+1) = S F (k) T · Δx F (k) + ξ F (k) (7)

[0022] wherein,

[0023] Δy F (k+1) = y F (k+1) - y F (k), Δx F (k) = x F (k) - x F (k-1)

[0024] In the formula, y F (k+1), y F (k) respectively represent the frequency measurement vector in the microgrid at the k+1 time and the k time, which is an n F dimensional column vector, and n F is the number of frequency measurements in the microgrid; each element of y F (k+1) is the frequency measurement value of the key frequency node in the microgrid at the k+1 time; Δy F (k+1) represents the increment of the frequency measurement vector in the microgrid at the k+1 time, which is an n F dimensional column vector; x F (k), x F (k-1) respectively represent the vector composed of all frequency control command in the microgrid at the k time and the k-1 time, which is an N i dimensional column vector; the i-th element of x F (k) is S F (k) represents the frequency control pseudo partial derivative parameter at the k time, which is an N i row n F column matrix; S F (k) T represents the transpose of the matrix S F (k), which is an n F row N i column matrix; ξ F (k) represents the frequency control lumped disturbance parameter at the k time, which is an n F dimensional column vector;

[0025] 2) Construct the disturbance-dependent linearization model of the microgrid voltage secondary control, the expression is as follows:

[0026] Δy U (k+1) = S U (k) T · Δx U (k) + ξ U (k) (8)

[0027] wherein,

[0028] Δy U (k+1) = y U (k+1) - y U (k), Δx U (k) = x U (k) - x U (k-1)

[0029] In the formula, y U (k+1), y U (k) respectively represent the voltage square measurement vector in the k+1 time, the k time in the microgrid, which is an n U dimensional column vector, and n U is the number of measured voltages in the microgrid; each element of y U (k+1) is the square of the voltage amplitude measurement value of the key voltage node in the k+1 time microgrid; Δy U (k+1) represents the increment of the voltage square measurement vector in the k+1 time microgrid, which is an n U dimensional column vector; x U (k), x U (k-1) respectively represent the vector composed of all voltage control quantity instructions in the k time, the k-1 time microgrid, which is an N i dimensional column vector; the i-th element of x U (k) is S U (k) represents the voltage control pseudo-derivative parameter in the k time, which is an N i row n U column matrix; S U (k) T represents the transpose of the matrix S U (k), which is an n U row N i column matrix; ξ U (k) represents the voltage control lumped disturbance parameter in the k time, which is an n U dimensional column vector.

[0030] In one specific embodiment of the present application, the calculating the active power control instruction and the reactive power control instruction of the inverter comprises:

[0031] 1) At the kth moment, frequency measurement values of key frequency nodes in the microgrid are collected to obtain a frequency measurement vector y F (k), and squares of voltage amplitude measurement values of key voltage nodes in the microgrid are collected to obtain a voltage square measurement vector y U (k);

[0032] 2) The voltage control pseudo-derivative parameter estimation value and the frequency control pseudo-derivative parameter estimation value are updated;

[0033] Wherein, the robust recursive linear regression method based on the maximum correlation entropy criterion calculates the voltage control pseudo-derivative parameter estimation value at the kth moment in turn according to the following expression

[0034]

[0035] c U (k) = exp(-||e US (k) || 2 / (2σ 2 )) (10)

[0036]

[0037] Φ U (k) = β -1 (Φ U (k-1) - g U (k)x U (k-1) T Φ U (k-1)) (12)

[0038]

[0039] In the formula, Δy U (k) = y U (k) - y U (k-1) represents an increment of the voltage square measurement vector in the microgrid at the kth moment; Δx U (k-1) = x U (k-1) - x U (k-2) represents an increment of the voltage control vector in the microgrid at the k-1th moment, wherein, at the k=1th moment, Δx U (k-1) and Δx U (k-2) are values of corresponding control quantities of the device itself at the previous moment, and Δy U(k-1) represents the value actually measured at the previous time instant; is an n i row N U column matrix; represents the transpose of the matrix is an n U row N i column matrix; e US (k) is the output estimation error of the voltage pseudo partial derivative parameter at the kth time instant; c U (k) is the value of the correlation entropy function of the voltage output estimation error at the kth time instant; σ U is the bandwidth parameter in the maximum correlation entropy criterion in voltage control; ||e US (k) || 2 represents the square of the two-norm of the vector e US (k); g U (k) is the iteration gain vector in the robust recursive linear regression method based on the maximum correlation entropy criterion for voltage at the kth time instant, which is an n U column column vector; β U is the forgetting factor parameter in voltage control; Φ U (k-1) is the inverse information matrix in the robust recursive linear regression method for voltage at the k-1th time instant, Φ U (k) is the inverse information matrix in the robust recursive linear regression method for voltage at the kth time instant;

[0040] The robust recursive linear regression method based on the maximum correlation entropy criterion calculates the frequency control pseudo partial derivative parameter estimation value at the kth time instant in turn according to the following expressions

[0041]

[0042] c F (k) = exp (-||e FS (k) || 2 / (2σ 2 )) (15)

[0043]

[0044] Φ F (k) = β -1 (Φ F (k-1) - g F (k) x F (k-1) T Φ F (k-1)) (17)

[0045]

[0046] where Δy F (k) = y F (k) - y F (k - 1) represents the increment of the frequency vector in the microgrid at the kth moment; Δx F (k - 1) = x F (k - 1) - x F (k - 2) represents the increment of the frequency control vector at the k - 1th moment; is the estimated value of the pseudo-derivative parameter of the frequency control at the k - 1th moment, and is an n i row n F column matrix; represents the transpose of the matrix , which is an n F row N i column matrix; e FS (k) is the output estimation error of the frequency pseudo-derivative parameter at the kth moment; c F (k) is the value of the correlation entropy function of the frequency output estimation error at the kth moment; σ F is the bandwidth parameter in the maximum correlation entropy criterion in the frequency control; ||e FS (k) || 2 represents the square of the two-norm of the vector e FS (k); g F (k) is the iteration gain vector in the robust recursive linear regression method based on the maximum correlation entropy criterion of the frequency at the kth moment, which is an n F column vector; β F is the forgetting factor parameter in the frequency control; Φ F (k - 1) is the inverse information matrix in the robust recursive linear regression method of the frequency at the k - 1th moment, Φ F (k) is the inverse information matrix in the robust recursive linear regression method of the frequency at the kth moment;

[0047] 3) Calculate the voltage control and the frequency control;

[0048] where the voltage control at the kth moment is calculated, and the expression is as follows:

[0049]

[0050] where y U ref (k + 1) represents the vector composed of the squares of the voltage amplitude target reference values in the microgrid at the k + 1th moment; represents the estimated value of y U (k); and represents the estimated value of the voltage control lumped disturbance parameter of the microgrid at the kth moment; This represents the output estimation error of the voltage control lumped disturbance parameter at time k. Representation matrix The square of the norm; ρ Ux , λ Ux These represent the step size parameter and the suppression term parameter for voltage control, respectively; U1 For the gain parameters of the voltage-controlled interference observer;

[0051] The frequency control quantity at time k is calculated using the following expression:

[0052]

[0053] In the formula, y F ref (k+1) represents a vector consisting of the target frequency reference values ​​of the microgrid at time k+1; Indicates y F The estimated value of (k); This represents the estimated value of the frequency control lumped disturbance parameter of the microgrid at time k. This represents the output estimation error of the frequency control lumped disturbance parameter at time k. Representation matrix The square of the norm; ρ Fx , λ Fx These represent the step size parameter and the suppression term parameter for frequency control, respectively; F1 For the gain parameters of the frequency-controlled interference observer;

[0054] 4) Calculate the adaptive disturbance observer update for voltage control and frequency control respectively:

[0055] Based on the adaptive disturbance observer method, the adaptive disturbance observation update of the voltage control at time k is calculated sequentially according to the following expression:

[0056]

[0057] In the formula, Let L represent the adaptive gain coefficient matrices of the voltage adaptive interference observer at time k and time (k-1), respectively. e n U line n U A matrix of columns; L e is the length of the historical time window of the adaptive interference observer, and is a positive integer; For a dimension L e n U , where Δe Uy (k)=e Uy (k)-e Uy (k-1) represents a dimension of nU is a column vector of dimension n UO is a suppression term parameter of the voltage-controlled adaptive disturbance observer; is the square of the norm of is an estimation of the voltage square output vector at the (k+1)th time instant, is an estimation of the voltage-controlled lumped disturbance parameter at the (k+1)th time instant;

[0058] Based on the adaptive disturbance observer method, the frequency-controlled adaptive disturbance observer update at the kth time instant is calculated in turn according to the following expressions:

[0059]

[0060] wherein, respectively represent the adaptive gain coefficient matrix of the frequency adaptive disturbance observer at the kth time instant and the (k-1)th time instant, and is an L e n F n F n e is the history time window length of the adaptive disturbance observer, and is a positive integer; is an L e n F dimension column vector; wherein, Δe Fy (k) = e Fy (k) - e Fy (k-1) is an n F dimension column vector; λ FO is a suppression term parameter of the frequency-controlled adaptive disturbance observer; is the square of the norm of is an estimation of the frequency output vector at the (k+1)th time instant, is an estimation of the frequency-controlled lumped disturbance parameter at the (k+1)th time instant;

[0061] 5) The active power control instruction and the reactive power control instruction of each inverter are calculated respectively:

[0062] wherein, the active power control instruction and the reactive power control instruction of the ith inverter are respectively:

[0063]

[0064] wherein, is the ith element in the obtained voltage control vector x U (k), is the ith element in the obtained frequency control vector x F (k); ​​

[0065] 6) the active power control instruction and the reactive power control instruction of each inverter obtained in step 5) are respectively sent to the local control layer of the corresponding inverter, and the local control layer of the inverter performs primary control according to the sent power instruction.

[0066] The second aspect embodiment of the present application provides a kind of off-grid micro-grid adaptive frequency voltage secondary control device, comprising:

[0067] Control variable construction module, for constructing the frequency control variable and the voltage control variable of the secondary control of inverter in off-grid micro-grid;

[0068] Interference related dynamic linearization model construction module, for constructing the interference related dynamic linearization model of the frequency and voltage secondary control of micro-grid based on the frequency control variable and the voltage control variable;

[0069] Control module, for calculating the active power control instruction and the reactive power control instruction of the inverter based on the interference related dynamic linearization model, to realize the secondary control of the frequency and voltage of micro-grid.

[0070] In one specific embodiment of the present application, the frequency control variable and the voltage control variable of the secondary control of inverter in off-grid micro-grid are constructed, comprising:

[0071] 1) the model of inverter in off-grid micro-grid is established;

[0072] Record N i controlled inverter in off-grid micro-grid;

[0073] Wherein, the voltage and phase angle equation of the i inverter is respectively represented as:

[0074]

[0075] In the formula, V i , θ i The voltage amplitude and phase angle of the i inverter node are respectively V i r , The voltage set point and angular frequency set point of the i inverter are respectively V i ω The actual angular frequency of the i inverter is; The derivative of V i , θ i To time;V i r2 , V i 2 Indicate the square of V i r , V i Pi i Pi and Qi are the actual active power and reactive power output of the ith inverter, respectively, i * Pi* and Qi* are the active power and reactive power set point of the ith inverter from the secondary control, dc,i Vi is the actual value of the DC voltage of the DC side of the ith inverter, Vi* is the set value of the DC voltage of the DC side of the ith inverter; ΔPi and ΔQi i i ΔPi and ΔQi are functions related to the active power and reactive power set point deviation of the ith inverter, respectively; Δφi and Δφi vi ωi Δφi and Δφi are the voltage deviation function and frequency deviation function related to the DC side voltage change of the ith inverter, respectively; ηi and αi i i i i Ki and Kii are the primary control parameters of the ith inverter;

[0076] 2) Based on the results of step 1), construct the frequency control variable and voltage control variable of the secondary control of the controlled inverter;

[0077] Where, at the kth moment, the frequency control variable command and the voltage control variable command of the ith inverter are represented as:

[0078]

[0079] In the formula, Vk is the voltage control variable command of the ith inverter at the kth moment, Vk is the voltage control variable command of the ith inverter at the kth moment; Vk is the voltage control variable command of the ith inverter at the kth moment; i * (k) and (k) are the active power and reactive power set point of the ith inverter from the secondary control at the kth moment, respectively.

[0080] In one embodiment of the present application, the disturbance-related dynamic linearization model of the microgrid frequency and voltage secondary control is constructed as follows:

[0081] 1) Construct the disturbance-related dynamic linearization model of the microgrid frequency secondary control, the expression is as follows:

[0082] Δy F (k+1) = S F (k) T · Δx F (k) + ξ​​​​​​​​F (k) (7)

[0083] wherein,

[0084] Δy F (k+1) = y F (k+1) - y F (k), Δx F (k) = x F (k) - x F (k-1)

[0085] wherein, y F (k+1), y F (k) respectively represent the frequency measurement vector of the microgrid at the k+1 time, the k time, which is an n F dimensional column vector, n F is the number of measured frequencies in the microgrid; each element of y F (k+1) is the frequency measurement value of the key frequency node in the microgrid at the k+1 time; Δy F (k+1) represents the increment of the frequency measurement vector of the microgrid at the k+1 time, which is an n F dimensional column vector; x F (k), x F (k-1) respectively represent the vector composed of all frequency control quantity instructions in the microgrid at the k time, the k-1 time, which is an N i dimensional column vector; x F (k) is the i-th element of x S F (k) represents the frequency control pseudo partial derivative parameter at the k time, which is an N i row n F column matrix; S F (k) T represents the transpose of the matrix S F (k), which is an n F row N i column matrix; ξ F (k) represents the frequency control lumped disturbance parameter at the k time, which is an n F dimensional column vector;

[0086] 2) Construct an interference related dynamic linearization model of the microgrid voltage quadratic control, the expression is as follows:

[0087] Δy U (k+1) = S U (k) T · Δx U (k) + ξ U (k) (8)

[0088] wherein,

[0089] Δy U (k+1) = y U (k+1) - y U (k), Δx U (k) = x U (k) - x U (k-1)

[0090] wherein, y U (k+1), y U (k) respectively represent the voltage square measurement vector of the k+1th moment, the kth moment in the microgrid, which is an n U dimensional column vector, n U is the number of measured voltages in the microgrid; each element of y U (k+1) is the square of the voltage amplitude measurement value of the key voltage node in the k+1th moment of the microgrid; Δy U (k+1) represents the increment of the voltage square measurement vector of the k+1th moment in the microgrid, which is an n U dimensional column vector; x U (k), x U (k-1) respectively represent the vector composed of all voltage control quantity instructions in the kth moment, the k-1th moment in the microgrid, which is an N i dimensional column vector; the i-th element of x U (k) is S U (k) represents the voltage control pseudo-derivative parameter of the kth moment, which is an N i row n U column matrix; S U (k) T represents the transpose of the matrix S U (k), which is an n U row N i column matrix; ξ U (k) represents the voltage control lumped disturbance parameter of the kth moment, which is an n U dimensional column vector.

[0091] In one specific embodiment of the present application, the calculating the active power control instruction and the reactive power control instruction of the inverter comprises:

[0092] 1) at the kth moment, collecting the frequency measurement value of the key frequency node in the microgrid and obtaining the frequency measurement vector y F (k), collecting the square of the voltage amplitude measurement value of the key voltage node in the microgrid and obtaining the voltage square measurement vector y U (k);

[0093] 2) update the voltage control pseudo partial derivative parameter estimation value, the frequency control pseudo partial derivative parameter estimation value;

[0094] Wherein, the robust recursive linear regression method based on the maximum correlation entropy criterion calculates the voltage control pseudo partial derivative parameter estimation value of the k moment in turn according to the following expression

[0095]

[0096] c U (k) = exp (-||e US (k) || 2 / (2σ 2 )) (10)

[0097]

[0098] Φ U (k) = β -1 (Φ U (k-1) - g U (k) x U (k-1) T Φ U (k-1)) (12)

[0099]

[0100] In the formula, Δy U (k) = y U (k) - y U (k-1) represents the increment of the voltage square measurement vector in the microgrid at the k moment; Δx U (k-1) = x U (k-1) - x U (k-2) represents the increment of the voltage control vector in the microgrid at the k-1 moment, wherein, at the k = 1 moment, Δx U (k-1) and Δx U (k-2) are the values of the corresponding control quantities of the device itself at the initial moment, and Δy U (k-1) represents the value actually measured at the previous moment; is the estimation value of the voltage control pseudo partial derivative parameter at the k-1 moment, and is an n i row n U column matrix; represents the transpose of the matrix , and is an n U row N i column matrix; e US (k) is the output estimation error of the voltage pseudo partial derivative parameter at the k moment; c U(k) is the correlation entropy function value of the voltage output estimation error at the kth moment; σ U is the bandwidth parameter in the maximum correlation entropy criterion in voltage control; ||e US 2 represents the two-norm square of the vector e US (k); g U (k) is the iteration gain vector in the robust recursive linear regression method based on the maximum correlation entropy criterion for voltage at the kth moment, which is an n U dimension column vector; β U is the forgetting factor parameter in voltage control; Φ U (k-1) is the inverse information matrix in the robust recursive linear regression method for voltage at the k-1th moment, Φ U (k) is the inverse information matrix in the robust recursive linear regression method for voltage at the kth moment;

[0101] The robust recursive linear regression method based on the maximum correlation entropy criterion calculates the frequency control pseudo partial derivative parameter estimation value at the kth moment in turn according to the following expressions

[0102]

[0103] c F (k) = exp(-||e FS (k)| 2 / (2σ 2 )) (15)

[0104]

[0105] Φ F (k) = β -1 (Φ F (k-1) - g F (k) x F (k-1) T Φ F (k-1)) (17)

[0106]

[0107] In the formula, Δy F (k) = y F (k) - y F (k-1) represents the increment of the frequency vector in the microgrid at the kth moment; Δx F (k-1) = x F (k-1) - x F (k-2) represents the increment of the frequency control vector in the microgrid at the k-1th moment; ​is an n i row N F column matrix; denotes the transpose of matrix is an n F row N i column matrix; e FS (k) is the output estimation error of the frequency pseudo partial derivative parameter at the kth moment; c F (k) is the correlation entropy function value of the frequency output estimation error at the kth moment; σ F is the bandwidth parameter in the maximum correlation entropy criterion in frequency control; ||e FS (k) || 2 denotes the square of the two-norm of the vector e FS (k); g F (k) is the iteration gain vector in the robust recursive linear regression method based on the maximum correlation entropy criterion for frequency at the kth moment, which is an n F column vector; β F is the forgetting factor parameter in frequency control; Φ F (k-1) is the inverse information matrix in the robust recursive linear regression method for frequency at the k-1th moment, Φ F (k) is the inverse information matrix in the robust recursive linear regression method for frequency at the kth moment;

[0108] 3) Calculate the voltage control quantity and the frequency control quantity;

[0109] wherein the voltage control quantity at the kth moment is calculated, and the expression is as follows:

[0110]

[0111] In the formula, y U ref (k+1) represents a vector composed of the square of the voltage amplitude target reference value in the microgrid at the k+1th moment; denotes the estimation value of y U (k); denotes the estimation value of the voltage control lumped disturbance parameter of the microgrid at the kth moment; denotes the output estimation error of the voltage control lumped disturbance parameter at the kth moment; denotes the square of the norm of matrix ; ρ Ux , λ Ux respectively represent the step size parameter and the suppression term parameter of the voltage control; l U1 is the gain parameter of the disturbance observer of the voltage control;

[0112] The frequency control quantity at the kth moment is calculated, and the expression is as follows:

[0113]

[0114] where y F ref (k+1) denotes the vector of frequency target reference values of the microgrid at the k+1 time instant; denotes the estimate of y F (k); denotes the estimate of the frequency control lumped disturbance parameter of the microgrid at the k time instant; denotes the output estimation error of the frequency control lumped disturbance parameter at the k time instant; denotes the square of the norm of the matrix ; p Fx , l Fx denote the step size parameter and the suppression term parameter of the frequency control, respectively; l F1 is the gain parameter of the disturbance observer of the frequency control;

[0115] 4) Calculate the adaptive disturbance observer update of the voltage control and the frequency control, respectively:

[0116] Based on the adaptive disturbance observer method, the adaptive disturbance observer update of the voltage control at the k time instant is calculated in turn according to the following expressions:

[0117]

[0118] wherein, denote the adaptive gain coefficient matrix of the voltage adaptive disturbance observer at the k time instant and the k-1 time instant, respectively, and L e n U is an n U row n e column matrix; L e is the history time window length of the adaptive disturbance observer, and is a positive integer; is an L e n U dimension column vector; wherein, A Uy e Uy (k) = e Uy (k) - e U (k-1) is an n UO dimension column vector; l UO is the suppression term parameter of the adaptive disturbance observer of the voltage control; is the square of the norm of ; is the estimate of the voltage squared output vector at the k+1 time instant, is the estimate of the voltage control lumped disturbance parameter at the k+1 time instant;

[0119] Based on the adaptive interference observer method, the adaptive interference observer update of the frequency control at the kth moment is calculated in turn according to the following expressions:

[0120]

[0121] In the formula, respectively represent the adaptive gain coefficient matrix of the frequency adaptive interference observer at the kth moment and the k-1th moment, and L e n F is a matrix of n F rows and L e columns; L e is the historical time window length of the adaptive interference observer, and is a positive integer; is a column vector with a dimension of L F n Fy ; wherein, Δe Fy (k) = e Fy (k) - e F (k-1) is a column vector with a dimension of n FO ; λ U is a suppression term parameter of the frequency control adaptive interference observer; is the square of the norm of ; is the estimated value of the frequency output vector at the k+1th moment, is the estimated value of the frequency control lumped interference parameter at the k+1th moment;

[0122] 5) Calculate the active power control instruction and the reactive power control instruction of each inverter respectively:

[0123] Wherein, the active power control instruction and the reactive power control instruction of the i-th inverter are respectively:

[0124]

[0125] In the formula, is the i-th element in the obtained voltage control vector x U (k), is the i-th element in the obtained frequency control vector x F (k);

[0126] 6) The active power control instruction and the reactive power control instruction of each inverter obtained in step 5) are respectively issued to the local control layer of the corresponding inverter, and the local control layer of the inverter executes the primary control according to the issued power instruction.

[0127] The third aspect embodiment of the present application provides an electronic device, comprising:

[0128] at least one processor; and a memory communicatively connected with the at least one processor;

[0129] The memory stores instructions executable by the at least one processor, and the instructions are configured to execute the off-grid micro-grid adaptive frequency and voltage secondary control method.

[0130] The fourth aspect of the present application provides a computer readable storage medium, which stores computer instructions for causing the computer to execute the off-grid micro-grid adaptive frequency and voltage secondary control method.

[0131] The present application has the characteristics and benefits that:

[0132] The present application constructs a voltage control variable and a frequency control variable for the controlled inverter according to the characteristics of the primary control of the controllable inverter in the micro-grid, so as to decouple the voltage secondary control and the frequency secondary control. Then, the disturbance related dynamic linearization model of the micro-grid system is constructed for the micro-grid voltage secondary control and the frequency secondary control. In the real-time control process, the secondary coordination controller of the micro-grid continuously collects real-time measurement data of the system, updates the pseudo partial derivative parameters in the data-driven disturbance related dynamic linearization model in real time based on a robust recursive linear regression method, and estimates the lumped disturbance term in the data-driven disturbance related dynamic linearization model based on an adaptive disturbance observer; based on the real-time updated data-driven disturbance related dynamic linearization model, the secondary coordination controller of the micro-grid iteratively updates the control instructions of the voltage control variable and the frequency control variable, and converts the voltage control variable and the frequency control variable control instructions of each inverter into active and reactive power control instructions of the inverter. Each inverter in the micro-grid receives the active and reactive power control instructions and executes the local primary control, and finally realizes the micro-grid and voltage and frequency coordinated control, so that the voltage and frequency of the micro-grid can be maintained at the target reference value.

[0133] The present application can greatly improve the efficiency, safety and flexibility of the micro-grid voltage and frequency control under the condition of high renewable energy penetration and incomplete model, and is particularly suitable for micro-grids with serious incomplete model problem and strong uncertainty, which can maintain the voltage and frequency of the micro-grid to track the target reference value without constructing a system model, and has self-adaptability to the changes of system operation conditions, and is suitable for large-scale promotion.

[0134] 1) The application adopts a robust recursive linear regression method based on the maximum correlation entropy criterion, learns a data-driven dynamic linearization model of the microgrid by using online measurement data, learns the microgrid model from historical data, does not depend on accurate model parameters of the microgrid, can realize secondary control of voltage and frequency of the microgrid in the case of incomplete model, and can update the data-driven dynamic linearization model in real time when the system operating conditions change, so as to track the system changes;

[0135] 2) The application estimates the lumped disturbance parameter based on the adaptive disturbance observer, so as to improve the control performance in the case of unmodeled dynamics and uncertain disturbances of the system.

[0136] 3) The application does not depend on accurate model parameters of the microgrid, the control instruction update adopts a recursive calculation method, does not need complex calculation, has a simple structure easy to implement, small calculation amount, and strong robustness. BRIEF DESCRIPTION OF DRAWINGS

[0137] Figure 1 is a whole flow chart of a kind of off-grid microgrid adaptive frequency voltage secondary control method of the embodiment of the application. DETAILED DESCRIPTION

[0138] The application proposes a kind of off-grid microgrid adaptive frequency voltage secondary control method and device, as follows in further detail in conjunction with the drawings and specific embodiments.

[0139] The first aspect embodiment of the application proposes a kind of off-grid microgrid adaptive frequency voltage secondary control method, comprising:

[0140] The frequency control variable and the voltage control variable of the secondary control of the controlled inverter in the off-grid microgrid are constructed;

[0141] Based on the frequency control variable and the voltage control variable, the disturbance related dynamic linearization model of the frequency and voltage secondary control of the microgrid is respectively constructed;

[0142] Based on the disturbance related dynamic linearization model, the active power control instruction and the reactive power control instruction of the inverter are calculated, to realize the secondary control of the frequency and voltage of the microgrid.

[0143] In one specific embodiment of the application, the whole flow of the kind of off-grid microgrid adaptive frequency voltage secondary control method is as shown in Figure 1 The steps include:

[0144] 1) The frequency control variable and the voltage control variable of the secondary control of the controlled inverter in the off-grid microgrid are constructed;The specific steps are as follows:

[0145] 1-1) the model of the controlled inverter in the off-grid microgrid is established.

[0146] In this embodiment, there are N i The first control of the inverter device to be controlled adopts a new adaptive network configuration control technology. For the i-th inverter to be controlled, the voltage and phase angle equations are expressed as:

[0147]

[0148] In the formula, V i and θ i are the voltage amplitude and phase angle of the i-th inverter node, V i r , are the voltage set point and angular frequency set point of the i-th inverter, ω i is the actual angular frequency of the i-th inverter. are the derivatives of V i and θ i with respect to time. V i r2 , V i 2 represent the square of V i r , V i . P i and Q i are the actual active power and reactive power output by the i-th inverter, P i * , are the active power set point and reactive power set point of the i-th inverter from the secondary control. V dc,i is the actual value of the DC voltage on the DC side of the i-th inverter, is the set value of the DC voltage on the DC side of the i-th inverter. ΔP i and ΔQ i are functions related to the active power and reactive power set point deviations of the i-th inverter, respectively, and their definitions are shown in formula (3). Δφ vi and Δφ ωi are the voltage deviation function and frequency deviation function related to the change of the DC voltage on the DC side of the i-th inverter, respectively, and their definitions are shown in formula (4). η i , α i , κ i , λ i are the first control parameters of the i-th inverter, and their typical values can be taken as 0.01, 0.1, 1.1, 0.1, respectively.

[0149] 1-2) Based on the results of step 1-1), the frequency control variable and the voltage control variable of the secondary control of the inverter to be controlled are constructed.

[0150] In this embodiment, at the kth moment, in order to decouple the voltage secondary control and the frequency secondary control in the secondary control layer, the frequency control quantity instruction and the voltage control quantity instruction of the ith controlled inverter are defined as:

[0151]

[0152] In the formula, is the voltage control quantity instruction of the ith inverter at the kth moment, is the voltage control quantity instruction of the ith inverter at the kth moment. represents the square of the voltage amplitude setting value of the ith inverter node. P i * (k), are the active power and reactive power setting instructions of the ith inverter from the secondary control at the kth moment, respectively.

[0153] 2) Based on the results of step 1), the disturbance-related dynamic linearization model of the microgrid frequency and voltage secondary control is constructed, and the specific steps are as follows:

[0154] 2-1) Construct the disturbance-related dynamic linearization model of the microgrid frequency secondary control, and the expression is as follows:

[0155] Δy F (k+1) = S F (k) T · Δx F (k) + ξ F (k) (7)

[0156] wherein,

[0157] Δy F (k+1) = y F (k+1) - y F (k), Δx F (k) = x F (k) - x F (k-1)

[0158] In the formula, y F (k+1), y F (k) represent the frequency measurement vector in the microgrid at the k+1th moment and the kth moment, respectively, which is an n F dimensional column vector, and n F is the number of measured frequencies in the microgrid. y F Each element of y F (k+1) is the frequency measurement value of the key frequency node in the microgrid at the k+1th moment. Δy FA column vector of dimension x. F (k), x F (k-1) represents a vector consisting of all frequency control commands in the microgrid at time k and time (k-1), respectively, and is an N i A column vector of dimension N i This represents the number of controlled inverters in an off-grid microgrid. Specifically, x F The i-th element of (k) is S F (k) represents the frequency control pseudo-partial derivative parameter at time k, which is an N-order parameter. i line n F A column matrix. S F (k) T Representation matrix S F The transpose of (k) is an n F Line N i A matrix of columns. ξ F (k) represents the frequency control lumped disturbance parameter at time k, which is an n-valued parameter. F A column vector of dimension.

[0159] 2-2) Construct the disturbance-related dynamic linearization model for the secondary voltage control of the microgrid, with the following expression:

[0160] Δy U (k+1)=S U (k) T ·Δx U (k)+ξ U (k) (8)

[0161] in,

[0162] Δy U (k+1)=y U (k+1)-y U (k), Δx U (k)=x U (k)-x U (k-1)

[0163] In the formula, y U (k+1), y U (k) represents the voltage square measurement vector in the microgrid at time k+1 and time k, respectively, and is n U A column vector of dimension n U This refers to the number of voltage measurements taken in a microgrid. U Each element of (k+1) is the square of the voltage amplitude measurement of the critical voltage node in the microgrid at time k+1. Δy U (k+1) represents the increment of the voltage square measurement vector in the microgrid at time k+1, where n is the sum of the values ​​of n and n.U A column vector of dimension x. U (k), x U (k-1) represents a vector consisting of all voltage control commands in the microgrid at time k and time (k-1), respectively, and is an N i A column vector of dimension x. Specifically, x U The i-th element of (k) is S U (k) represents the voltage control pseudo-partial derivative parameter at time k, which is an N... i line n U A column matrix. S U (k) T Representation matrix S U The transpose of (k) is an n U Line N i A matrix of columns. ξ U (k) represents the voltage control lumped disturbance parameter at time k, which is an n U A column vector of dimension.

[0164] 3) Starting from k=1, take the kth time as the current control time.

[0165] 4) Based on the interference-related dynamic linearization model established in step 2), calculate the frequency and voltage secondary control commands for the microgrid.

[0166] For k≥1, the microgrid secondary coordination controller performs the following calculation steps at time k:

[0167] 4-1) At time k, collect real-time measurements of the microgrid.

[0168] In this embodiment, frequency measurements of key frequency nodes in the microgrid are collected and the frequency measurement vector y is obtained. F (k) collects the squares of the voltage amplitude measurements at key voltage nodes in the microgrid and obtains the voltage square measurement vector y. U (k).

[0169] 4-2) Update the estimated values ​​of the pseudo-partial derivative parameters for voltage control and frequency control.

[0170] In this embodiment, the robust recursive linear regression method based on the maximum correlation entropy criterion calculates the estimated value of the voltage control pseudo-partial derivative parameter at time k according to the following expression.

[0171]

[0172] c U (k)=exp(-||e US (k)||2 (2σ 2 )) (10)

[0173]

[0174] Φ U (k) = β -1 (Φ U (k-1) - g U (k) x U (k-1) T Φ U (k-1)) (12)

[0175]

[0176] where Δy U (k) = y U (k) - y U (k-1) represents the increment of the voltage squared measurement vector in the microgrid at the kth time instant. Δx U (k-1) = x U (k-1) - x U (k-2) represents the increment of the voltage control vector in the microgrid at the k-1th time instant, and Δx U (k-1) and Δx U (k-2) are the values of the corresponding control quantities of the device itself at the initial time, and Δy U (k-1) represents the value actually measured at the previous time instant. is the estimated value of the voltage control pseudo-derivative parameter at the k-1th time instant, and is an n i row n U column matrix. represents the transpose of the matrix , and is an n U row N i column matrix. e US (k) is the output estimation error of the voltage pseudo-derivative parameter at the kth time instant. c U (k) is the value of the correlation entropy function of the voltage output estimation error at the kth time instant, and is a scalar. σ U is the bandwidth parameter in the maximum correlation entropy criterion in voltage control, and a typical value can be taken as 0.01. ||e US (k) || 2 represents the square of the two-norm of the vector e US (k). g U (k) is the iteration gain vector in the robust recursive linear regression method based on the maximum correlation entropy criterion of the voltage at the kth time instant, and is an n U column column vector. β UFor the forgetting factor parameter in the voltage control, a typical value can be taken as 0.99. Φ U (k-1) is the inverse information matrix in the robust recursive linear regression method for the voltage at the (k-1)th moment, Φ U (k) is the inverse information matrix in the robust recursive linear regression method for the voltage at the kth moment. For the preceding moment when k = 1, a small initial value can be taken, and a typical value is to set all elements to 0.01.

[0177] The robust recursive linear regression method based on the maximum correlation entropy criterion calculates the frequency control pseudo partial derivative parameter estimate value at the kth moment in turn according to the following expression

[0178]

[0179] c F (k) = exp(-||e FS (k)| 2 / (2σ 2 )) (15)

[0180]

[0181] Φ F (k) = β -1 (Φ F (k-1) - g F (k)x F (k-1) T Φ F (k-1)) (17)

[0182]

[0183] In the formula, Δy F (k) = y F (k) - y F (k-1) represents the increment of the frequency vector in the microgrid at the kth moment. Δx F (k-1) = x F (k-1) - x F (k-2) represents the increment of the frequency control vector in the microgrid at the (k-1)th moment. is the estimate value of the frequency control pseudo partial derivative parameter at the (k-1)th moment, which is an n i row n F column matrix. represents the transpose of the matrix , which is an n F row N i column matrix. e FS (k) is the output estimation error of the frequency pseudo partial derivative parameter at the kth moment.F (k) is the correlation entropy function value of the frequency output estimation error at the kth moment, which is a scalar. F is the bandwidth parameter in the maximum correlation entropy criterion in frequency control, and a typical value can be taken as 0.01.||e FS (k)| 2 represents the square of the two-norm of the vector e FS (k). F (k) is the iteration gain vector in the robust recursive linear regression method based on the maximum correlation entropy criterion for the frequency at the kth moment, which is an n F column column vector. β F is the forgetting factor parameter in frequency control, and a typical value can be taken as 0.99. Φ F (k-1) is the inverse information matrix in the robust recursive linear regression method for the frequency at the k-1th moment, Φ F (k) is the inverse information matrix in the robust recursive linear regression method for the frequency at the kth moment.

[0184] 4-3) Calculate the voltage control amount and the frequency control amount.

[0185] In this embodiment, the voltage control amount at the kth moment is calculated, and the expression is as follows:

[0186]

[0187] In the formula, y U ref (k+1) represents a vector composed of the square of the voltage amplitude target reference value in the microgrid at the k+1th moment. represents the estimated value of y U (k). represents the estimated value of the voltage control lumped disturbance parameter of the microgrid at the kth moment. represents the output estimation error of the voltage control lumped disturbance parameter at the kth moment. represents the square of the norm of the matrix ρ Ux , λ Ux respectively represent the step size parameter and the suppression term parameter of the voltage control, and typical values can be taken as 0.1 and 0.01 respectively. l U1 is the gain parameter of the disturbance observer of the voltage control, and a typical value can be taken as 0.9.

[0188] The frequency control amount at the kth moment is calculated, and the expression is as follows:

[0189]

[0190] In the formula, y F ref(k+1) represents a vector of frequency target reference values of the microgrid at the k+1 time. represents y F (k) is an estimated value of the k time. represents an estimated value of the k time of the frequency control lumped disturbance parameter of the microgrid. represents an output estimation error of the k time of the frequency control lumped disturbance parameter. represents the square of the norm of the matrix . Fx , λ Fx respectively represent the step size parameter and the suppression term parameter of the frequency control, and typical values can be taken as 0.1 and 0.01 respectively. F1 is a gain parameter of the disturbance observer of the frequency control, and a typical value can be taken as 0.9.

[0191] 4-4) Calculate the adaptive disturbance observer update of the voltage control and the frequency control respectively:

[0192] Based on the adaptive disturbance observer method, the adaptive disturbance observer update of the k time of the voltage control is calculated in turn according to the following expression:

[0193]

[0194] In the formula, respectively represent the adaptive gain coefficient matrix of the k time, the k-1 time of the voltage adaptive disturbance observer, and L e n U is a matrix of n U rows and n e columns. L e ≥1 is the length of the historical time window of the adaptive disturbance observer, which is a positive integer, and a typical value can be taken as 3. is a column vector of L e n U dimensions. Among them, Δe Uy (k) = e Uy (k) - e Uy (k-1) is a column vector of n U dimensions. λ UO is a suppression term parameter of the adaptive disturbance observer of the voltage control, and a typical value can be taken as 0.1. is the square of the norm of . is the estimated value of the voltage square output vector at the k+1 time, is the estimated value of the k+1 time of the voltage control lumped disturbance parameter.

[0195] Based on the adaptive disturbance observer method, the adaptive disturbance observer update of the k time of the frequency control is calculated in turn according to the following expression:

[0196]

[0197] wherein, respectively represent the adaptive gain coefficient matrix of the frequency adaptive interference observer at the kth moment and the (k-1)th moment, and is an n e n F row n F column matrix. L e ≥1 is the length of the history time window of the adaptive interference observer, and is a positive integer, and a typical value can be taken as 3. is an n e n F dimension column vector. Wherein, Δe Fy (k)=e Fy (k)-e Fy (k-1) is an n F dimension column vector. λ FO is the suppression term parameter of the frequency control adaptive interference observation, and a typical value can be taken as 0.1. is the square of the norm of . is the estimated value of the frequency output vector at the k+1th moment, is the estimated value of the frequency control lumped interference parameter at the k+1th moment.

[0198] 4-5) Calculate the active power control instruction and the reactive power control instruction of each inverter respectively:

[0199] Based on the obtained voltage and frequency control amount, the active power control instruction and the reactive power control instruction of the i-th inverter are calculated respectively as:

[0200]

[0201] In the formula, is the i-th element in the obtained voltage control vector x U (k), is the i-th element in the obtained frequency control vector x F (k).

[0202] 4-6) The secondary coordination controller sends the active power control instruction and the reactive power control instruction of each inverter obtained in step 4-5) to the local control layer of the corresponding inverter respectively, and the inverter local control layer executes primary control according to the sent power instruction.

[0203] The method described in the embodiment sends the secondary control instruction, and the inverter itself executes local primary control adjustment according to the secondary control instruction.

[0204] 5) Let k = k + 1, then return to step 3).

[0205] To achieve the above embodiments, a second aspect of the present invention proposes an adaptive frequency and voltage secondary control device for off-grid microgrids, comprising:

[0206] The control variable construction module is used to construct the frequency control variables and voltage control variables for the secondary control of the controlled inverter in an off-grid microgrid.

[0207] The interference-related dynamic linearization model construction module is used to construct interference-related dynamic linearization models for the secondary control of frequency and voltage of the microgrid based on the frequency control variables and the voltage control variables, respectively.

[0208] The control module is used to calculate the active power control command and reactive power control command of the inverter based on the interference-related dynamic linearization model, so as to realize the secondary control of the frequency and voltage of the microgrid.

[0209] In a specific embodiment of the present invention, the frequency control variables and voltage control variables for constructing the secondary control of the controlled inverter in the off-grid microgrid include:

[0210] 1) Establish a model of the controlled inverter in the off-grid microgrid;

[0211] Let N be the total number of off-grid microgrids. i Taiwan is accused of using inverters;

[0212] The voltage and phase angle equations for the i-th inverter are expressed as follows:

[0213]

[0214]

[0215] In the formula, V i θ i V represents the voltage amplitude and phase angle of the i-th inverter node, respectively. i r , These are the voltage setpoint and angular frequency setpoint of the i-th inverter, respectively, ω i Let be the actual angular frequency of the i-th inverter; V i θ i The derivative with respect to time; V i r2 V i 2 V i r V i The square of P; i Qi respectively, are the actual active power and reactive power output of the i-th inverter, i * 、 are the active power and reactive power set point commands of the i-th inverter from the secondary control; V dc,i is the actual value of the DC voltage at the DC side of the i-th inverter, is the set value of the DC voltage at the DC side of the i-th inverter; ΔP i , ΔQ i respectively, are functions related to the active power and reactive power set point deviation of the i-th inverter; Δφ vi , Δφ ωi respectively, are the voltage deviation function and frequency deviation function related to the i-th inverter and the DC side voltage variation of the inverter; η i , α i , κ i , λ i are the primary control parameters of the i-th inverter;

[0216] 2) based on the results of step 1), construct the frequency control variable and the voltage control variable of the secondary control of the controlled inverter;

[0217] wherein at the k-th moment, the frequency control variable command and the voltage control variable command of the i-th inverter are respectively represented as:

[0218]

[0219] wherein, is the voltage control variable command of the i-th inverter at the k-th moment, is the voltage control variable command of the i-th inverter at the k-th moment; represents the square of the voltage amplitude set value of the i-th inverter node; P i * (k), are respectively the active power and reactive power set point commands of the i-th inverter from the secondary control at the k-th moment.

[0220] In one specific embodiment of the present application, the disturbance-related dynamic linearization model of the microgrid frequency and voltage secondary control is respectively constructed, comprising:

[0221] 1) constructing a disturbance-related dynamic linearization model of the microgrid frequency secondary control, the expression being as follows:

[0222] Δy F (k+1) = S F (k) T · Δx F (k) + ξ F(k) (7)

[0223] wherein,

[0224] Δy F (k+1) = y F (k+1) - y F (k), Δx F (k) = x F (k) - x F (k-1)

[0225] wherein, y F (k+1), y F (k) respectively represent the frequency measurement vector of the microgrid at the k+1 time, the k time, which is an n F dimensional column vector, n F is the number of measured frequencies in the microgrid; each element of y F (k+1) is the frequency measurement value of the key frequency node in the microgrid at the k+1 time; Δy F (k+1) represents the increment of the frequency measurement vector of the microgrid at the k+1 time, which is an n F dimensional column vector; x F (k), x F (k-1) respectively represent the vector composed of all frequency control quantity instructions in the microgrid at the k time, the k-1 time, which is an N i dimensional column vector; x F (k) is the i-th element of S F (k) represents the frequency control pseudo partial derivative parameter at the k time, which is an N i row n F column matrix; S F (k) T represents the transpose of the matrix S F (k), which is an n F row N i column matrix; ξ F (k) represents the frequency control lumped disturbance parameter at the k time, which is an n F dimensional column vector;

[0226] 2) Construct the disturbance related dynamic linearization model of the microgrid voltage secondary control, the expression is as follows:

[0227] Δy U (k+1) = S U (k) T · Δx U (k) + ξ U (k) (8)

[0228] wherein,

[0229] Δy U (k+1) = y U (k+1) - y U (k), Δx U (k) = x U (k) - x U (k-1)

[0230] wherein y U (k+1), y U (k) represent the voltage square measurement vector at the k+1th moment, the kth moment in the microgrid respectively, which is an n U dimensional column vector, n U is the number of measured voltages in the microgrid; each element of y U (k+1) is the square of the voltage amplitude measurement value of the key voltage node in the microgrid at the k+1th moment; Δy U (k+1) represents the increment of the voltage square measurement vector in the microgrid at the k+1th moment, which is an n U dimensional column vector; x U (k), x U (k-1) represent the vector composed of all voltage control quantity instructions in the microgrid at the kth moment, the k-1th moment respectively, which is an N i dimensional column vector; x U (k) is the i-th element of S U (k) represents the voltage control pseudo-derivative parameter at the kth moment, which is an N i row n U column matrix; S U (k) T represents the transpose of the matrix S U (k), which is an n U row N i column matrix; ξ U (k) represents the voltage control lumped disturbance parameter at the kth moment, which is an n U dimensional column vector.

[0231] In one specific embodiment of the present application, the calculating the active power control instruction and the reactive power control instruction of the inverter comprises:

[0232] 1) at the kth moment, collecting the frequency measurement value of the key frequency node in the microgrid and obtaining the frequency measurement vector y F (k), collecting the square of the voltage amplitude measurement value of the key voltage node in the microgrid and obtaining the voltage square measurement vector y U (k);

[0233] 2) updating the voltage control pseudo partial derivative parameter estimation value, the frequency control pseudo partial derivative parameter estimation value;

[0234] wherein, the robust recursive linear regression method based on the maximum correlation entropy criterion calculates the voltage control pseudo partial derivative parameter estimation value of the k moment in sequence according to the following expression

[0235]

[0236] c U (k) = exp (-||e US (k) || 2 / (2σ 2 )) (10)

[0237]

[0238] Φ U (k) = β -1 (Φ U (k-1) - g U (k)x U (k-1) T Φ U (k-1)) (12)

[0239]

[0240] In the formula, Δy U (k) = y U (k) - y U (k-1) represents the increment of the voltage square measurement vector in the microgrid at the k moment; Δx U (k-1) = x U (k-1) - x U (k-2) represents the increment of the voltage control vector in the microgrid at the k-1 moment, wherein, at the k = 1 moment, Δx U (k-1) and Δx U (k-2) are the values of the corresponding control quantities of the device itself at the initial moment, and Δy U (k-1) represents the value actually measured at the previous moment; is the estimation value of the voltage control pseudo partial derivative parameter at the k-1 moment, and is an n i row n U column matrix; represents the transpose of the matrix , and is an n U row N i column matrix; e US (k) is the output estimation error of the voltage pseudo partial derivative parameter at the k moment; and c U(k) is the correlation entropy function value of the voltage output estimation error at the kth moment; σ U is the bandwidth parameter in the maximum correlation entropy criterion in voltage control; ||e US 2 represents the two-norm square of the vector e US U (k) is the iteration gain vector in the robust recursive linear regression method based on the maximum correlation entropy criterion for voltage at the kth moment, which is an n U column column vector; β U is the forgetting factor parameter in voltage control; Φ U (k-1) is the inverse information matrix in the robust recursive linear regression method for voltage at the k-1th moment, Φ U (k) is the inverse information matrix in the robust recursive linear regression method for voltage at the kth moment;

[0241] The robust recursive linear regression method based on the maximum correlation entropy criterion calculates the frequency control pseudo partial derivative parameter estimation value at the kth moment in turn according to the following expressions

[0242]

[0243] c F (k) = exp (-||e FS (k) || 2 / (2σ 2 )) (15)

[0244]

[0245] Φ F (k) = β -1 (Φ F (k-1) - g F (k) x F (k-1) T Φ F (k-1)) (17)

[0246]

[0247] In the formula, Δy F (k) = y F (k) - y F (k-1) represents the increment of the frequency vector in the microgrid at the kth moment; Δx F (k-1) = x F (k-1) - x F (k-2) represents the increment of the frequency control vector in the microgrid at the k-1th moment; ​​is the estimation value of the pseudo-derivative parameter of frequency control at the k-1 th moment, which is an n i row N F column matrix; denotes the transpose of the matrix , which is an n F row N i column matrix; e FS (k) is the output estimation error of the frequency pseudo-derivative parameter at the k th moment; c F (k) is the correlation entropy function value of the frequency output estimation error at the k th moment; σ F is the bandwidth parameter in the maximum correlation entropy criterion in frequency control; ||e FS (k) || 2 denotes the square of the two-norm of the vector e FS (k); g F (k) is the iteration gain vector in the robust recursive linear regression method based on the maximum correlation entropy criterion at the k th moment, which is an n F column vector; β F is the forgetting factor parameter in frequency control; Φ F (k-1) is the inverse information matrix in the robust recursive linear regression method of frequency at the k-1 th moment, Φ F (k) is the inverse information matrix in the robust recursive linear regression method of frequency at the k th moment;

[0248] 3) Calculate the voltage control amount and the frequency control amount;

[0249] wherein the voltage control amount at the k th moment is calculated, and the expression is as follows:

[0250]

[0251] In the formula, y U ref (k+1) denotes a vector composed of the square of the voltage amplitude target reference value in the microgrid at the k+1 th moment; denotes the estimation value of y U (k); denotes the estimation value of the voltage control lumped disturbance parameter of the microgrid at the k th moment; denotes the output estimation error of the voltage control lumped disturbance parameter at the k th moment; denotes the square of the norm of the matrix ; p Ux , l Ux respectively denote the step parameter and the suppression term parameter of voltage control; l U1 is the gain parameter of the disturbance observer of voltage control;

[0252] The frequency control amount at the k th moment is calculated, and the expression is as follows:

[0253]

[0254] Among them, y F ref (k+1) represents a vector consisting of the target frequency reference values ​​of the microgrid at time k+1; Indicates y F The estimated value of (k); This represents the estimated value of the frequency control lumped disturbance parameter of the microgrid at time k. This represents the output estimation error of the frequency control lumped disturbance parameter at time k. Representation matrix The square of the norm; ρ Fx , λ Fx These represent the step size parameter and the suppression term parameter for frequency control, respectively; F1 For the gain parameters of the frequency-controlled interference observer;

[0255] 4) Calculate the adaptive disturbance observer update for voltage control and frequency control respectively:

[0256] Based on the adaptive disturbance observer method, the adaptive disturbance observation update of the voltage control at time k is calculated sequentially according to the following expression:

[0257]

[0258] in, Let L represent the adaptive gain coefficient matrices of the voltage adaptive interference observer at time k and time (k-1), respectively. e n U line n U A matrix of columns; L e is the length of the historical time window of the adaptive interference observer, and is a positive integer; For a dimension L e n U , where Δe Uy (k)=e Uy (k)-e Uy (k-1) represents a dimension of n U Column vectors; λ UO For the suppression term parameters of adaptive disturbance observation in voltage control; for The square of the norm; This is an estimate of the squared voltage output vector at time k+1. This is an estimate of the voltage control lumped disturbance parameter at time k+1;

[0259] Based on the adaptive disturbance observer method, the adaptive disturbance observer update of the frequency control at the kth moment is calculated in turn according to the following expressions:

[0260]

[0261] wherein, respectively represent the adaptive gain coefficient matrix of the frequency adaptive disturbance observer at the kth moment and the (k-1)th moment, L e n F is a matrix of L F n e columns; L e is the historical time window length of the adaptive disturbance observer, and is a positive integer; is a column vector of L F n Fy dimensions; wherein, Δe Fy (k) = e Fy (k) - e F (k-1) is a column vector of n FO dimensions; λ U is a suppression term parameter of the frequency control adaptive disturbance observer; is the square of the norm of ; is the estimated value of the frequency output vector at the (k+1)th moment, is the estimated value of the frequency control lumped disturbance parameter at the (k+1)th moment;

[0262] 5) Calculate the active power control instruction and the reactive power control instruction of each inverter respectively:

[0263] wherein, the active power control instruction and the reactive power control instruction of the ith inverter are respectively:

[0264]

[0265] In the formula, is the ith element in the obtained voltage control vector x U (k), is the ith element in the obtained frequency control vector x F (k);

[0266] 6) The active power control instruction and the reactive power control instruction of each inverter obtained in step 5) are respectively issued to the local control layer of the corresponding inverter, and the local control layer of the inverter executes one-time control according to the issued power instruction.

[0267] To achieve the above embodiment, a third aspect embodiment of the present application provides an electronic device, comprising:

[0268] at least one processor; and a memory connected with the at least one processor in communication;

[0269] The memory stores instructions executable by the at least one processor, and the instructions are configured to perform the off-grid micro-grid adaptive frequency and voltage secondary control method.

[0270] To implement the above-mentioned embodiments, the fourth aspect of the present application provides a computer readable storage medium, which stores computer instructions for causing the computer to perform the off-grid micro-grid adaptive frequency and voltage secondary control method.

[0271] It should be noted that the computer readable medium of the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination of the above. More specific examples of computer readable storage media can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or apparatus. In the present disclosure, the computer readable signal medium can include a data signal carried in a baseband or as a part of a carrier wave, which carries computer readable program code. Such a propagated data signal can take many forms, including but not limited to an electromagnetic signal, an optical signal or any suitable combination of the above. The computer readable signal medium can also be any computer readable medium other than the computer readable storage medium, which can send, propagate or transmit a program for use by or in conjunction with an instruction execution system, device or apparatus. The program code contained in the computer readable medium can be transmitted by any suitable medium, including but not limited to a wire, a cable, an RF (radio frequency) or the like, or any suitable combination of the above.

[0272] The computer readable medium described above can be included in the electronic device described above; or can exist separately and not be assembled into the electronic device. The computer readable medium described above carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the off-grid micro-grid adaptive frequency and voltage secondary control method of the above-mentioned embodiments.

[0273] Computer program code for carrying out operations of the present disclosure can be written in any one or more programming languages or combinations of languages including object oriented programming languages such as Java, Smalltalk, C++ or conventional procedural programming languages such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0274] In the description of the specification, the description of the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Also, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in one or more embodiments or examples. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the specification and the features of the different embodiments or examples, without contradiction.

[0275] In addition, the terms "first", "second", etc. are used only for the purpose of description and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "a plurality of" is at least two, for example, two, three, etc., unless otherwise specifically limited.

[0276] Any process or method descriptions or descriptions of the flow diagrams in the specification or elsewhere in this document, can be understood as representing the steps of the code of the modules, segments or portions of the code for implementing specific logic functions or steps in the process, and the scope of the preferred embodiments of the present application includes additional implementation in which the steps are performed in different order, including essentially simultaneously or in reverse order, and additional implementation in which the functions are performed by different entities or in combination with other functions, as will be understood by those skilled in the art of the embodiments of the present application.

[0277] The logic and / or steps represented in the flowcharts and / or described herein, for example, can be considered as a sequence of instructions to implement logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device, such as a computer-based system, processor- based system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For purposes of this specification, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be a computer- readable storage medium or a computer-readable signal medium. The computer- readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include the following: an electrical connection having one or more wires (electrical connections), a portable computer diskette (a magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). Note that the computer-readable medium can even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

[0278] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, the various steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, and in another embodiment, any of the following technologies, known in the art, or their combinations can be used: discrete logic circuitry having logic gates for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and the like.

[0279] Those skilled in the art can understand that all or part of the steps carried out by the above-mentioned embodiment methods can be completed by programs instructing related hardware, and the programs can be stored in a computer-readable storage medium. When the programs are executed, they include one or a combination of the steps of the method embodiments.

[0280] In addition, each of the functional units in the various embodiments of the present application can be integrated in one processing module, or each of the units can be physically present separately, or two or more units can be integrated in one module. The integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0281] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A method for adaptive frequency and voltage secondary control of off-grid microgrids, characterized in that, include: Construct the frequency control variables and voltage control variables for the secondary control of the controlled inverter in an off-grid microgrid; Based on the frequency control variables and the voltage control variables, interference-related dynamic linearization models for the secondary control of frequency and voltage of the microgrid are constructed respectively. Based on the aforementioned interference-related dynamic linearization model, the active power control command and reactive power control command of the inverter are calculated to achieve secondary control of the microgrid frequency and voltage. The frequency control variables and voltage control variables for the secondary control of the controlled inverter in the off-grid microgrid include: 1) Establish a model of the controlled inverter in the off-grid microgrid; Let N be the total number of off-grid microgrids. i Taiwan is accused of using inverters; The voltage and phase angle equations for the i-th inverter are expressed as follows: In the formula, V i θ i V represents the voltage amplitude and phase angle of the i-th inverter node, respectively. i r , These are the voltage setpoint and angular frequency setpoint of the i-th inverter, respectively, ω i Let be the actual angular frequency of the i-th inverter; V i θ i The derivative with respect to time; V i r2 V i 2 V i r V i The square of P; i Q i Let P be the actual active power and reactive power output of the i-th inverter, respectively. i * , For the active power setting command and reactive power setting command from the i-th inverter of the secondary control; V dc,i Let be the actual value of the DC voltage on the DC side of the i-th inverter. ΔP is the setpoint value of the DC voltage on the DC side of the i-th inverter; i ΔQ i These are functions related to the setpoint deviations of the active and reactive power of the i-th inverter, respectively; Δφ vi , Δφ ωi Let η be the voltage deviation function and frequency deviation function related to the DC-side voltage change of the i-th inverter, respectively; i α i κ i , λ i These are the primary control parameters for the i-th inverter; 2) Based on the results of step 1), construct the frequency control variables and voltage control variables for the secondary control of the controlled inverter; Wherein, at time k, the frequency control command and voltage control command of the i-th inverter are respectively expressed as: In the formula, This is the voltage control command for the i-th inverter at time k. This is the frequency control command for the i-th inverter at time k. This represents the square of the voltage amplitude setpoint of the i-th inverter node; These are the active and reactive power setting commands from the secondary control unit for the i-th inverter at time k.

2. The method according to claim 1, characterized in that, The construction of interference-related dynamic linearization models for the secondary control of frequency and voltage in the microgrid includes: 1) Construct a disturbance-related dynamic linearization model for the secondary frequency control of the microgrid, with the following expression: Δy F (k+1)=S F (k) T ·Δx F (k)+ξ F (k) (7) in, Δy F (k+1)=y F (k+1)-y F (k),Δx F (k)=x F (k)-x F (k-1) In the formula, y F (k+1), y F (k) represents the frequency measurement vectors in the microgrid at time k+1 and time k, respectively, and is n F A column vector of dimension n F The number of measurement frequencies in a microgrid; y F Each element of (k+1) is a frequency measurement value of a key frequency node in the microgrid at time k+1; Δy F (k+1) represents the increment of the frequency measurement vector in the microgrid at time k+1, where n is the value of n. F A column vector of dimension x; F (k), x F (k-1) represents a vector consisting of all frequency control commands in the microgrid at time k and time (k-1), respectively, and is an N i A column vector of dimension x; F The i-th element of (k) is S F (k) represents the frequency control pseudo-partial derivative parameter at time k, which is an N-order parameter. i line n F A column matrix; S F (k) T Representation matrix S F The transpose of (k) is an n F Line N i A matrix of columns; ξ F (k) represents the frequency control lumped disturbance parameter at time k, which is an n-valued parameter. F A column vector of dimension; 2) Construct a disturbance-related dynamic linearization model for the secondary voltage control of the microgrid, with the following expression: Δy U (k+1)=S U (k) T ·Δx U (k)+ξ U (k) (8) in, Δy U (k+1)=y U (k+1)-y U (k),Δx U (k)=x U (k)-x U (k-1) In the formula, y U (k+1), y U (k) represents the voltage square measurement vector in the microgrid at time k+1 and time k, respectively, and is n U A column vector of dimension n U The number of voltage measurements in a microgrid; y U Each element of (k+1) is the square of the voltage amplitude measurement of the critical voltage node in the microgrid at time k+1; Δy U (k+1) represents the increment of the voltage square measurement vector in the microgrid at time k+1, where n is the sum of the values ​​of n and n. U A column vector of dimension x; U (k), x U (k-1) represents a vector consisting of all voltage control commands in the microgrid at time k and time (k-1), respectively, and is an N i A column vector of dimension x; U The i-th element of (k) is S U (k) represents the voltage control pseudo-partial derivative parameter at time k, which is an N... i line n U A column matrix; S U (k) T Representation matrix S U The transpose of (k) is an n U Line N i A matrix of columns; ξ U (k) represents the voltage control lumped disturbance parameter at time k, which is an n U A column vector of dimension.

3. The method according to claim 2, characterized in that, The calculation of the active power control command and reactive power control command of the inverter includes: 1) At time k, collect the frequency measurements of key frequency nodes in the microgrid and obtain the frequency measurement vector y. F (k) collects the squares of the voltage amplitude measurements at key voltage nodes in the microgrid and obtains the voltage square measurement vector y. U (k); 2) Update the estimated values ​​of the pseudo-partial derivative parameters for voltage control and frequency control; Among them, the robust recursive linear regression method based on the maximum correlation entropy criterion calculates the estimated value of the voltage control pseudo-partial derivative parameter at time k according to the following expression. : c U (k)=exp(-||e US (k)|| 2 / (2σ U 2 )) (10) F U (k)=β U -1 (F U (k-1)-g U (k)x U (k-1) T F U (k-1)) (12) In the formula, Δy U (k)=y U (k)-y U (k-1) represents the increment of the squared voltage measurement vector in the microgrid at time k; Δx U (k-1)=x U (k-1)-x U (k-2) represents the increment of the voltage control vector in the microgrid at time k-1, where Δx is the voltage control vector at time k=1. U (k-1) and Δx U (k-2) represents the initial value of the corresponding control variable of the device itself at the preceding time step, Δy. U (k-1) represents the value actually measured at the preceding time step; The estimated value of the voltage control pseudo-partial derivative parameter at time k-1 is an N i line n U A matrix of columns; Representation matrix The transpose of is an n U Line N i A matrix of columns; e US (k) represents the output estimation error of the voltage pseudo-partial derivative parameter at time k; c U (k) represents the correlation entropy function value of the voltage output estimation error at time k; σ U For the bandwidth parameter in the maximum correlation entropy criterion in voltage control; ||e US (k)|| 2 Represents vector e US The square of the second norm of (k); g U (k) is the iterative gain vector of the voltage at time k in the robust recursive linear regression method based on the maximum correlation entropy criterion, and is an n U The column vector of a column; β U Forgetting factor parameter in voltage control; Φ U (k-1) is the inverse information matrix in the robust recursive linear regression method for the voltage at time k-1, Φ U (k) is the inverse information matrix in the robust recursive linear regression method of the voltage at time k; The robust recursive linear regression method based on the maximum correlation entropy criterion calculates the estimated value of the frequency control pseudo-partial derivative parameter at time k according to the following expression. : c F (k)=exp(-||e FS (k)|| 2 / (2σ F 2 )) (15) F F (k)=β F -1 (F F (k-1)-g F (k)x F (k-1) T F F (k-1)) (17) In the formula, Δy F (k)=y F (k)-y F (k-1) represents the increment of the frequency vector in the microgrid at time k; Δx F (k-1)=x F (k-1)-x F (k-2) represents the increment of the frequency control vector in the microgrid at time k-1; The estimated value of the frequency control pseudo-partial derivative parameter at time k-1 is an N i line n F A matrix of columns; Representation matrix The transpose of is an n F Line N i A matrix of columns; e FS (k) represents the output estimation error of the frequency pseudo-partial derivative parameter at time k; c F (k) represents the correlation entropy function value of the frequency output estimation error at time k; σ F For frequency control, the bandwidth parameter is the maximum correlation entropy criterion; ||e FS (k)|| 2 Represents vector e FS The square of the second norm of (k); g F (k) is the iterative gain vector in the robust recursive linear regression method based on the maximum correlation entropy criterion at time k, and is an n F The column vector of a column; β F Forgetting factor parameters in frequency control; Φ F (k-1) is the inverse information matrix in the robust recursive linear regression method for the frequency at time k-1, Φ F (k) is the inverse information matrix in the robust recursive linear regression method for the frequency at time k; 3) Calculate the voltage control quantity and frequency control quantity; The voltage control quantity at time k is calculated using the following expression: In the formula, y U ref (k+1) represents a vector composed of the squares of the target reference values ​​of voltage amplitude in the microgrid at time k+1; Indicates y U The estimated value of (k); This represents the estimated value of the voltage control lumped disturbance parameter of the microgrid at time k. This represents the output estimation error of the voltage control lumped disturbance parameter at time k. Representation matrix The square of the norm; ρ Ux , λ Ux These represent the step size parameter and the suppression term parameter for voltage control, respectively; U1 For the gain parameters of the voltage-controlled interference observer; The frequency control quantity at time k is calculated using the following expression: In the formula, y F ref (k+1) represents a vector consisting of the target frequency reference values ​​of the microgrid at time k+1; Indicates y F The estimated value of (k); This represents the estimated value of the frequency control lumped disturbance parameter of the microgrid at time k. This represents the output estimation error of the frequency control lumped disturbance parameter at time k. Representation matrix The square of the norm; ρ Fx , λ Fx These represent the step size parameter and the suppression term parameter for frequency control, respectively; F1 For the gain parameters of the frequency-controlled interference observer; 4) Calculate the adaptive disturbance observer update for voltage control and frequency control respectively: Based on the adaptive disturbance observer method, the adaptive disturbance observation update of the voltage control at time k is calculated sequentially according to the following expression: In the formula, Let L represent the adaptive gain coefficient matrices of the voltage adaptive interference observer at time k and time (k-1), respectively. e n U line n U A matrix of columns; L e is the length of the historical time window of the adaptive interference observer, and is a positive integer; For a dimension L e n U , where Δe Uy (k)=e Uy (k)-e Uy (k-1) represents a dimension of n U Column vectors; λ UO For the suppression term parameters of adaptive disturbance observation in voltage control; The square of the norm; This is an estimate of the squared voltage output vector at time k+1. This is an estimate of the voltage control lumped disturbance parameter at time k+1; Based on the adaptive disturbance observer method, the adaptive disturbance observation update of the frequency control at time k is calculated sequentially according to the following expression: In the formula, Let L represent the adaptive gain coefficient matrices of the frequency adaptive interference observer at time k and time (k-1), respectively. e n F line n F A matrix of columns; L e is the length of the historical time window of the adaptive interference observer, and is a positive integer; For a dimension L e n F , where Δe Fy (k)=e Fy (k)-e Fy (k-1) represents a dimension of n F Column vectors; λ FO For frequency-controlled adaptive interference observation suppression parameters; for The square of the norm; This is the estimated value of the frequency output vector at time k+1. This is an estimate of the frequency control lumped disturbance parameter at time k+1. 5) Calculate the active power control command and reactive power control command for each inverter separately: The active power control command and reactive power control command for the i-th inverter are as follows: In the formula, The obtained voltage control vector x U The i-th element in (k), The obtained frequency control vector x F The i-th element in (k); 6) The active power control command and reactive power control command obtained in step 5) are sent to the local control layer of the corresponding inverter, and the local control layer of the inverter performs one control operation according to the sent power command.

4. An adaptive frequency and voltage secondary control device for off-grid microgrids, characterized in that, include: The control variable construction module is used to construct the frequency control variables and voltage control variables for the secondary control of the controlled inverter in an off-grid microgrid. The interference-related dynamic linearization model construction module is used to construct interference-related dynamic linearization models for the secondary control of frequency and voltage of the microgrid based on the frequency control variables and the voltage control variables, respectively. The control module is used to calculate the active power control command and reactive power control command of the inverter based on the interference-related dynamic linearization model, so as to realize the secondary control of the microgrid frequency and voltage. The voltage and phase angle equations for the i-th inverter are expressed as follows: In the formula, V i θ i V represents the voltage amplitude and phase angle of the i-th inverter node, respectively. i r , These are the voltage setpoint and angular frequency setpoint of the i-th inverter, respectively, ω i Let be the actual angular frequency of the i-th inverter; V i θ i The derivative with respect to time; V i r2 V i 2 V i r V i The square of P; i Q i Let P be the actual active power and reactive power output of the i-th inverter, respectively. i * , For the active power setting command and reactive power setting command from the i-th inverter of the secondary control; V dc,i Let be the actual value of the DC voltage on the DC side of the i-th inverter. ΔP is the setpoint value of the DC voltage on the DC side of the i-th inverter; i ΔQ i These are functions related to the setpoint deviations of the active and reactive power of the i-th inverter, respectively; Δφ vi , Δφ ωi Let η be the voltage deviation function and frequency deviation function related to the DC-side voltage change of the i-th inverter, respectively; i α i κ i , λ i These are the primary control parameters for the i-th inverter; 2) Based on the results of step 1), construct the frequency control variables and voltage control variables for the secondary control of the controlled inverter; Wherein, at time k, the frequency control command and voltage control command of the i-th inverter are respectively expressed as: In the formula, This is the voltage control command for the i-th inverter at time k. This is the frequency control command for the i-th inverter at time k. P represents the square of the voltage amplitude setpoint of the i-th inverter node; i * (k) These are the active and reactive power setting commands from the secondary control unit for the i-th inverter at time k.

5. The apparatus according to claim 4, characterized in that, The construction of interference-related dynamic linearization models for the secondary control of frequency and voltage in the microgrid includes: 1) Construct a disturbance-related dynamic linearization model for the secondary frequency control of the microgrid, with the following expression: Δy F (k+1)=S F (k) T ·Δx F (k)+ξ F (k) (7) in, Δy F (k+1)=y F (k+1)-y F (k),Δx F (k)=x F (k)-x F (k-1) In the formula, y F (k+1), y F (k) represents the frequency measurement vectors in the microgrid at time k+1 and time k, respectively, and is n F A column vector of dimension n F The number of measurement frequencies in a microgrid; y F Each element of (k+1) is a frequency measurement value of a key frequency node in the microgrid at time k+1; Δy F (k+1) represents the increment of the frequency measurement vector in the microgrid at time k+1, where n is the value of n. F A column vector of dimension x; F (k), x F (k-1) represents a vector consisting of all frequency control commands in the microgrid at time k and time (k-1), respectively, and is an N i A column vector of dimension x; F The i-th element of (k) is S F (k) represents the frequency control pseudo-partial derivative parameter at time k, which is an N-order parameter. i line n F A column matrix; S F (k) T Representation matrix S F The transpose of (k) is an n F Line N i A matrix of columns; ξ F (k) represents the frequency control lumped disturbance parameter at time k, which is an n-valued parameter. F A column vector of dimension; 2) Construct a disturbance-related dynamic linearization model for the secondary voltage control of the microgrid, with the following expression: Δy U (k+1)=S U (k) T ·Δx U (k)+ξ U (k) (8) in, Δy U (k+1)=y U (k+1)-y U (k),Δx U (k)=x U (k)-x U (k-1) In the formula, y U (k+1), y U (k) represents the voltage square measurement vector in the microgrid at time k+1 and time k, respectively, and is n U A column vector of dimension n U The number of voltage measurements in a microgrid; y U Each element of (k+1) is the square of the voltage amplitude measurement of the critical voltage node in the microgrid at time k+1; Δy U (k+1) represents the increment of the voltage square measurement vector in the microgrid at time k+1, where n is the sum of the values ​​of n and n. U A column vector of dimension x; U (k), x U (k-1) represents a vector consisting of all voltage control commands in the microgrid at time k and time (k-1), respectively, and is an N i A column vector of dimension x; U The i-th element of (k) is S U (k) represents the voltage control pseudo-partial derivative parameter at time k, which is an N... i line n U A column matrix; S U (k) T Representation matrix S U The transpose of (k) is an n U Line N i A matrix of columns; ξ U (k) represents the voltage control lumped disturbance parameter at time k, which is an n U A column vector of dimension.

6. The apparatus according to claim 5, characterized in that, The calculation of the active power control command and reactive power control command of the inverter includes: 1) At time k, collect the frequency measurements of key frequency nodes in the microgrid and obtain the frequency measurement vector y. F (k) collects the squares of the voltage amplitude measurements at key voltage nodes in the microgrid and obtains the voltage square measurement vector y. U (k); 2) Update the estimated values ​​of the pseudo-partial derivative parameters for voltage control and frequency control; Among them, the robust recursive linear regression method based on the maximum correlation entropy criterion calculates the estimated value of the voltage control pseudo-partial derivative parameter at time k according to the following expression. : c U (k)=exp(-||e US (k)|| 2 / (2σ U 2 )) (10) In the formula, Δy U (k)=y U (k)-y U (k-1) represents the increment of the squared voltage measurement vector in the microgrid at time k; Δx U (k-1)=x U (k-1)-x U (k-2) represents the increment of the voltage control vector in the microgrid at time k-1, where Δx is the voltage control vector at time k=1. U (k-1) and Δx U (k-2) represents the initial value of the corresponding control variable of the device itself at the preceding time step, Δy. U (k-1) represents the value actually measured at the preceding time step; The estimated value of the voltage control pseudo-partial derivative parameter at time k-1 is an N i line n U A matrix of columns; Representation matrix The transpose of is an n U Line N i A matrix of columns; e US (k) represents the output estimation error of the voltage pseudo-partial derivative parameter at time k; c U (k) represents the correlation entropy function value of the voltage output estimation error at time k; σ U For the bandwidth parameter in the maximum correlation entropy criterion in voltage control; ||e US (k)|| 2 Represents vector e US The square of the second norm of (k); g U (k) is the iterative gain vector of the voltage at time k in the robust recursive linear regression method based on the maximum correlation entropy criterion, and is an n U The column vector of a column; β U Forgetting factor parameter in voltage control; Φ U (k-1) is the inverse information matrix in the robust recursive linear regression method for the voltage at time k-1, Φ U (k) is the inverse information matrix in the robust recursive linear regression method of the voltage at time k; The robust recursive linear regression method based on the maximum correlation entropy criterion calculates the estimated value of the frequency control pseudo-partial derivative parameter at time k according to the following expression. : c F (k)=exp(-||e FS (k)|| 2 / (2σ F 2 )) (15) F F (k)=β F -1 (F F (k-1)-g F (k)x F (k-1) T F F (k-1)) (17) In the formula, Δy F (k)=y F (k)-y F (k-1) represents the increment of the frequency vector in the microgrid at time k; Δx F (k-1)=x F (k-1)-x F (k-2) represents the increment of the frequency control vector in the microgrid at time k-1; The estimated value of the frequency control pseudo-partial derivative parameter at time k-1 is an N i line n F A matrix of columns; Representation matrix The transpose of is an n F Line N i A matrix of columns; e FS (k) represents the output estimation error of the frequency pseudo-partial derivative parameter at time k; c F (k) represents the correlation entropy function value of the frequency output estimation error at time k; σ F For frequency control, the bandwidth parameter is the maximum correlation entropy criterion; ||e FS (k)|| 2 Represents vector e FS The square of the second norm of (k); g F (k) is the iterative gain vector in the robust recursive linear regression method based on the maximum correlation entropy criterion at time k, and is an n F The column vector of a column; β F Forgetting factor parameters in frequency control; Φ F (k-1) is the inverse information matrix in the robust recursive linear regression method for the frequency at time k-1, Φ F (k) is the inverse information matrix in the robust recursive linear regression method for the frequency at time k; 3) Calculate the voltage control quantity and frequency control quantity; The voltage control quantity at time k is calculated using the following expression: In the formula, y U ref (k+1) represents a vector composed of the squares of the target reference values ​​of voltage amplitude in the microgrid at time k+1; Indicates y U The estimated value of (k); This represents the estimated value of the voltage control lumped disturbance parameter of the microgrid at time k. This represents the output estimation error of the voltage control lumped disturbance parameter at time k. Representation matrix The square of the norm; ρ Ux , λ Ux These represent the step size parameter and the suppression term parameter for voltage control, respectively; U1 For the gain parameters of the voltage-controlled interference observer; The frequency control quantity at time k is calculated using the following expression: In the formula, y F ref (k+1) represents a vector consisting of the target frequency reference values ​​of the microgrid at time k+1; Indicates y F The estimated value of (k); This represents the estimated value of the frequency control lumped disturbance parameter of the microgrid at time k. This represents the output estimation error of the frequency control lumped disturbance parameter at time k. Representation matrix The square of the norm; ρ Fx , λ Fx These represent the step size parameter and the suppression term parameter for frequency control, respectively; F1 For the gain parameters of the frequency-controlled interference observer; 4) Calculate the adaptive disturbance observer update for voltage control and frequency control respectively: Based on the adaptive disturbance observer method, the adaptive disturbance observation update of the voltage control at time k is calculated sequentially according to the following expression: In the formula, Let L represent the adaptive gain coefficient matrices of the voltage adaptive interference observer at time k and time (k-1), respectively. e n U line n U A matrix of columns; L e is the length of the historical time window of the adaptive interference observer, and is a positive integer; Δe Uy (k) T ,...,Δe Uy (kL e +2) T ] T For a dimension L e n U , where Δe Uy (k)=e Uy (k)-e Uy (k-1) represents a dimension of n U Column vectors; λ UO For the suppression term parameters of adaptive disturbance observation in voltage control; for The square of the norm; This is an estimate of the squared voltage output vector at time k+1. This is an estimate of the voltage control lumped disturbance parameter at time k+1; Based on the adaptive disturbance observer method, the adaptive disturbance observation update of the frequency control at time k is calculated sequentially according to the following expression: In the formula, Let L represent the adaptive gain coefficient matrices of the frequency adaptive interference observer at time k and time (k-1), respectively. e n F line n F A matrix of columns; L e is the length of the historical time window of the adaptive interference observer, and is a positive integer; Δe Fy (k) T ,...,Δe Fy (kL e +2) T ] T For a dimension L e n F , where Δe Fy (k)=e Fy (k)-e Fy (k-1) represents a dimension of n F Column vectors; λ FO For frequency-controlled adaptive interference observation suppression parameters; The square of the norm; This is the estimated value of the frequency output vector at time k+1. This is an estimate of the frequency control lumped disturbance parameter at time k+1. 5) Calculate the active power control command and reactive power control command for each inverter separately: The active power control command and reactive power control command for the i-th inverter are as follows: In the formula, The obtained voltage control vector x U The i-th element in (k), The obtained frequency control vector x F The i-th element in (k); 6) The active power control command and reactive power control command obtained in step 5) are sent to the local control layer of the corresponding inverter, and the local control layer of the inverter performs one control operation according to the sent power command.

7. An electronic device, characterized in that, include: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, the instructions being configured to perform the method described in any one of claims 1-3.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the method according to any one of claims 1-3.

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