Virtual synchronous generator parameter adaptive control method

Abstract

The invention discloses a virtual synchronous generator parameter adaptive control method. The method comprises the following steps: firstly, establishing a VSG control model, and calculating upper and lower limits of rotational inertia and a damping coefficient; and then, parameters are optimized in combination with an improved whale optimization algorithm, and steady-state values of rotational inertia and a damping coefficient of the VSG system in a steady state and a threshold value of an adaptive control law are obtained. And finally, designing a proper adaptive control law, realizing reasonable matching of rotational inertia and damping coefficient adjustment strategies by setting threshold subdivision frequency deviation and frequency change rate change conditions, and outputting the adaptive rotational inertia and damping coefficient of the system. According to the scheme, the rotational inertia and the damping coefficient output by the system can be dynamically adjusted according to the frequency change rate and the frequency deviation monitored in real time so as to improve the damping ratio of the system, accurate control over frequency and voltage response is ensured, and the dynamic response of the system is effectively improved.

Description

Technical Field

[0001] The invention relates to the field of virtual synchronous generator control, and in particular to a virtual synchronous generator parameter adaptive control method. Background Art

[0002] With the rapid development of new energy power generation, wind and solar power generation have attracted more and more attention and applications. Given that new energy power generation needs to be connected to the large power grid through an inverter, and the proportion of traditional synchronous generators installed is gradually decreasing, it is particularly important to introduce virtual synchronous generator (VSG) control based on traditional control methods. The essence of VSG control is an inverter control method that simulates the characteristics of synchronous generators. By introducing virtual inertia and damping, it aims to enhance the inertia of microgrids and improve their operating stability.

[0003] Although traditional VSG control has improved the system frequency and output power, it has not yet fully utilized the virtual inertia and adjustable damping characteristics of the virtual synchronous motor. In order to further improve the dynamic adjustment capability of the system and make the inverter control more flexible to cope with different working conditions, a VSG control method with adaptive moment of inertia and damping coefficient is proposed. In this method, when the VSG system responds dynamically, the advantages of the flexible and adjustable moment of inertia and damping coefficient are used to jointly adjust the two to obtain a better frequency response effect.

[0004] On this basis, in order to better suppress frequency fluctuations, the parameter adaptive control law is designed using the functional relationship between the frequency offset and the frequency change rate and the moment of inertia and the damping coefficient. Therefore, designing a reasonable adaptive control law and simplifying the fixed parameters and threshold calculations in the control law are crucial to achieving a better dynamic response of the system in a stable state. Summary of the invention

[0005] Purpose of the invention: In order to meet the adaptive requirements of virtual moment of inertia and damping coefficient under different working conditions, the present invention discloses a virtual synchronous generator parameter adaptive control method, and designs an adaptive control law based on angular velocity offset and angular velocity change rate to enhance the dynamic response of the system. On this basis, an improved whale algorithm is used to optimize the parameters and thresholds of the adaptive control law to achieve more accurate parameter output.

[0006] Technical solution: The present invention discloses a virtual synchronous generator parameter adaptive control method, comprising the following steps:

[0007] Step 1: According to the filter inductor current and filter capacitor voltage of the three-phase full-bridge inverter, the output active power P and reactive power Q are calculated through abc-dq coordinate transformation;

[0008] Step 2: Establish a small signal model of the VSG control strategy, determine the transfer function, optimize the active control link according to the second-order transfer function of the VSG, and obtain the functional relationship between the damping ratio and the second-order natural angular frequency and the damping coefficient D and the moment of inertia J;

[0009] Step 3: According to the functional relationship between the damping ratio and the second-order natural angular frequency obtained in step 2, considering the damping ratio range of the system, as well as the stability and adjustment time of the active power loop, determine the value range of the damping coefficient D and the moment of inertia J;

[0010] Step 4: Based on the value ranges of the moment of inertia J and the damping coefficient D obtained in step 3, the whale optimization algorithm is used to optimize the moment of inertia J0 and the damping coefficient D0 in the steady state and the threshold for distinguishing the change in the angular velocity rate of change as the whale individuals to be optimized;

[0011] Step 5: According to the optimal moment of inertia J0 and damping coefficient D0 in the steady state output in step 4 and the threshold for distinguishing the change in the angular velocity rate of change, set the angular velocity change Δω and the angular velocity change rate The threshold value, subdivided Δω and Change range, determine the real-time moment of inertia J and damping coefficient D;

[0012] Step 6: Determine the moment of inertia J and the damping coefficient D according to step 5, obtain the output phase angle of the virtual speed regulator, and then generate a voltage given value with the voltage amplitude output by the excitation controller;

[0013] Step 7: The voltage given value obtained in step 6 is output through voltage and current double closed-loop control, and the full-bridge switch of the three-phase inverter is controlled by the PWM generator.

[0014] Furthermore, the functional relationship between the damping ratio and the second-order natural angular frequency obtained in step 2 and the damping coefficient D and the moment of inertia J is:

[0015]

[0016] Where: E is the voltage at the VSG input port; U is the voltage at the VSG output port; X is the equivalent impedance; ξ is the damping ratio; ω n is the second-order natural angular frequency; D is the damping coefficient; J is the moment of inertia; ω0 is the angular velocity on the grid side.

[0017] Furthermore, the value range of the damping coefficient D and the moment of inertia J determined in step 3 is:

[0018]

[0019] Where: f cmaxis the maximum cutoff frequency. According to the functional relationship between the damping ratio and the second-order natural angular frequency and the damping coefficient D and the moment of inertia J, the moment of inertia J and the damping coefficient D can be expressed with each other. After determining the range of the damping coefficient D, the value range of the moment of inertia J is also determined.

[0020] Furthermore, in step 4, the whale optimization algorithm uses the weighted function of the THD index and the ITAE index as the objective function, and the specific calculation formula of its fitness value y is:

[0021]

[0022] Where: U zon and U zol are the harmonic amplitude and fundamental amplitude of the output voltage respectively; e(t) is the error signal, that is, the frequency difference between the set frequency and the actual output frequency; a and b are the weighting coefficients of THD and ITAE respectively.

[0023] Furthermore, in step 4, the moment of inertia J0 and damping coefficient D0 in the steady state and the threshold for distinguishing the change in the angular velocity rate of change are regarded as individual whales to be optimized for optimization, which includes the following steps:

[0024] Step 4.1: Set initial parameters, including population size, maximum number of iterations, and upper and lower limits of the solution space;

[0025] Step 4.2: Initialize the whale population using KENT chaotic mapping;

[0026] Step 4.3: Calculate the fitness of the individual, record the moment of inertia J0 and damping coefficient D0 represented by the current optimal whale individual position in the steady state, and the threshold N for distinguishing the change in the angular velocity rate of change Jm ;

[0027] Step 4.4: When the current number of iterations is less than the maximum number of iterations, calculate the improved convergence factor And adaptive weight w, and update parameters A and C, generate a random number P;

[0028] Step 4.5: Update the position of the whale population, calculate the fitness value, and filter out the new optimal individuals:

[0029] Step 4.6: Introduce a greedy mechanism to decide whether to update the current optimal individual;

[0030] Step 4.7: Calculate the fitness of the best individuals and sort them, take the whale individual with the best fitness value as the global optimal solution, and select half of the individuals with better fitness to enter the next round of iteration;

[0031] Step 4.8: Determine whether the number of iterations reaches the maximum number of iterations, and whether the observed frequency error and total harmonic distortion meet the standards. If so, proceed to step 4.9; otherwise, return to step 4.4 to continue iterating.

[0032] Step 4.9: Output the moment of inertia J0 and damping coefficient D0 in steady state and the threshold N for distinguishing the change in angular velocity rate Jm , parameter optimization is completed.

[0033] Furthermore, in step 4.4, the improved convergence factor The specific calculation process of and adaptive weight w is:

[0034]

[0035] Where: t is the current iteration number; Max iter is the maximum number of iterations; k and d are adjustment coefficients.

[0036] Furthermore, in step 5, Δω and Change range, determine the real-time moment of inertia J and damping coefficient D as follows:

[0037]

[0038] Where: J0, D0 are the moment of inertia and damping coefficient in steady state respectively; k1, k2, k3 and m1, m2 are the proportional coefficient and exponential coefficient respectively; N Js and N Ds are the minimum thresholds of the angular velocity change rate and the minimum threshold of the angular velocity change size respectively; N Jm N is the threshold for distinguishing the change in angular velocity after optimization; Jl and N Dl are the maximum thresholds of the angular velocity change rate and the maximum threshold of the angular velocity change size, respectively; J b and D b are the boundary values ​​of the moment of inertia and damping coefficient respectively, and sign(x) is the sign function.

[0039] The beneficial effects of the present invention are as follows: 1) When the parameter range of the moment of inertia and the damping coefficient is determined, the improved whale algorithm is used to simplify the traditional parameter calculation, and the moment of inertia and the damping coefficient in the steady state and the threshold value for distinguishing the magnitude of the angular velocity change rate are obtained. 2) According to the different changes in the angular velocity when the system is connected to different loads or subjected to small disturbances, the angular velocity change interval is subdivided, and a more suitable adaptive function is selected to improve the dynamic response of the system while ensuring the stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1The schematic diagram of the VSG parameter adaptive system structure based on algorithm improvement;

[0041] Figure 2 This is the flowchart of the improved whale algorithm;

[0042] Figure 3 It is a schematic diagram of subdividing the angular velocity change and change rate interval;

[0043] Figure 4 This is the change in frequency in steady state after the algorithm optimizes the parameters. DETAILED DESCRIPTION

[0044] The present invention discloses a virtual synchronous generator parameter adaptive control method, comprising the following steps:

[0045] Step 1: Establish the main circuit of the three-phase full-bridge inverter. According to the filter inductor current and filter capacitor voltage of the three-phase full-bridge inverter, calculate the output active power P and reactive power Q through abc-dq coordinate transformation:

[0046]

[0047] Where: V d and V q are the AC side capacitor voltages on the d-axis and q-axis, I d and I q are the AC side load current on the d-axis and q-axis respectively, v a ,v b ,v c are the capacitor voltage on the AC side of the abc axis, i a ,i b ,i c are the AC side load currents on the abc axes respectively, and P and Q are the active power and reactive power respectively.

[0048] Step 2: Based on the P and Q values ​​calculated in step 1, the functional relationship between the moment of inertia and damping coefficient and the mechanical torque and electromagnetic torque is obtained through the VSG droop characteristics under the second-order model:

[0049]

[0050] θ=∫(ω-ω0)dt

[0051] Where: T m is the mechanical torque; T e is the electromagnetic torque; P m is the virtual mechanical power; P e is the inverter output active power; D is the damping coefficient; J is the moment of inertia; ω is the mechanical angular velocity; ω0 is the grid side angular velocity; θ is the output power angle.

[0052] According to the power-frequency characteristic curve of the synchronous generator, the droop characteristic equation can be obtained:

[0053] P m =P ref +k p (ω-ω0)

[0054] U m =U ref+ k q (Q ref -Q)

[0055] Where: P ref is the given value of active power; k p is the active power droop coefficient; U ref is the given value of VSG voltage; k q is the reactive power droop coefficient; Q ref is the reactive power rating.

[0056] Perform small signal analysis on the VSG power circuit to determine the transfer function of active power and the functional expressions of damping ratio and second-order natural angular frequency:

[0057]

[0058] Where: K p is the coefficient; E is the voltage at the VSG input port and the output voltage; U is the voltage at the VSG output port; X is the equivalent impedance; ξ is the damping ratio; ω n is the second-order natural angular frequency.

[0059] Step 3: According to the functional relationship between the damping ratio and the second-order natural angular frequency obtained in step 2, determine the expressions of the moment of inertia and the damping coefficient. Considering the damping ratio range of the system, as well as the stability and adjustment time of the active power loop, determine the value range of the damping coefficient D and the moment of inertia J.

[0060] Step 3.1: First consider the damping ratio range of the system:

[0061] h(dB)>0

[0062]

[0063] Where: h is the amplitude margin; γ is the phase margin.

[0064] Step 3.2: Next, consider the stability and adjustment time of the active power loop:

[0065]

[0066] Where: f cmaxis the maximum cutoff frequency. According to the functional relationship between the damping ratio and the second-order natural angular frequency and the damping coefficient D and the moment of inertia J, the moment of inertia J and the damping coefficient D can be expressed with each other. After determining the range of the damping coefficient D, the value range of the moment of inertia J is also determined.

[0067] Step 4: According to the value range of the moment of inertia and damping coefficient obtained in step 3, the improved whale algorithm is used to optimize the moment of inertia and damping coefficient in steady state and the threshold for distinguishing the change of angular velocity rate. In order to consider the error and power quality of the power system at the same time, the weighted function of the THD index and the ITAE index is used as the objective function, and the specific calculation formula of the fitness value y is:

[0068]

[0069] Where: U zon and U zol are the harmonic amplitude and fundamental amplitude of the output voltage respectively; e(t) is the error signal, that is, the frequency difference between the set frequency and the actual output frequency; a and b are the weighting coefficients of THD and ITAE respectively.

[0070] The above steps 1 to 3 are mainly to determine the range of change of parameters J and D, because the improved whale algorithm in step 4 requires a parameter range. This algorithm does not directly realize parameter adaptation, but is to know the steady-state parameters of the system to reach a steady state. The parameters optimized by the algorithm are to obtain the values ​​of J0 and D0 of the system in the steady state without the need for adaptive changes in the parameters. Based on the optimal values ​​in this steady state, the adaptive control strategy is introduced to obtain the real-time parameters J and D when changes occur. The threshold N Jm It is the value that will have the same control effect under two different control strategies. Subdividing the control strategy to achieve adaptive parameters is more accurate.

[0071] The parameter tuning algorithm process is as follows: Figure 2 As shown, the steps are as follows:

[0072] Step 4.1: Set the initial parameters, including population size, maximum number of iterations, and upper and lower limits of the solution space.

[0073] Step 4.2: Initialize the population using KENT chaotic map:

[0074]

[0075] x=x min +Chaos×(x max -x min )

[0076] Where: a is the KENT mapping tuning parameter; x min and xmax are the lower and upper bounds of the independent variable value; Chaos is the chaotic factor generated by KENT mapping; x is the position of the individual mapped to the search space.

[0077] Step 4.3: Extract the frequency error signal and THD of each individual of the whale population, input the frequency error signal and THD into the fitness function expression, calculate the fitness of each individual whale, and record the VSG parameters in the steady state represented by the current optimal individual whale position, namely the moment of inertia J0 and damping coefficient D0, as well as the threshold N for distinguishing the change in the angular velocity rate of change Jm .

[0078] Step 4.4: When the current number of iterations is less than the maximum number of iterations, calculate the improved convergence factor And adaptive weight w, and update parameters A and C, generate a random number P;

[0079]

[0080] Where: t is the current iteration number; Max iter is the maximum number of iterations; k and d are adjustment coefficients; A and C are coefficients.

[0081] Step 4.5: Update the individual's position under different random numbers;

[0082]

[0083] Where: and The distance between a single whale and its prey is determined by using different individual position update mechanisms in different situations (according to the judgment condition of the random number P); The best current position for the whale; is the current position vector of the whale; b is a constant; l is a random number in the range of [-1 1]; Represents the random individual whale positions.

[0084] Step 4.6: According to the position of each whale individual in the updated population, compare and filter out the new optimal whale individual, calculate the fitness value, and introduce a greedy mechanism to decide whether the current optimal whale individual and its position need to be updated;

[0085]

[0086] Where: is the position component of the ith individual in the dth dimension in the tth generation; X * is the current optimal position; r3 is a random number between [02π]; r4 is a random number between [0 2]; r5 is a random number between

[01] ;

[0087] Step 4.7: Calculate the fitness of all individuals and sort them, and take the individual with the best fitness value as the global optimal solution; select half of the individuals with better fitness to enter the next iterative cycle.

[0088] Step 4.8: Determine whether the maximum number of iterations has been completed and whether the observation error meets the standard. If completed, proceed to step 4.9; otherwise, return to step 4.4 to continue iterating.

[0089] Step 4.9: Output the steady-state VSG parameters J0 and D0 and the threshold N for distinguishing the magnitude of the angular velocity change rate Jm , parameter optimization is completed.

[0090] Step 5: According to the steady-state VSG parameters J0 and D0 output in step 4 and the threshold for distinguishing the change in the angular velocity rate of change, set the thresholds for the angular velocity change and the angular velocity change rate, and subdivide Δω and The schematic diagram of the change range is as follows Figure 3 As shown, the adaptive control strategy is optimized:

[0091]

[0092] In summary, the selection of the moment of inertia is determined by the change amount and change rate of the virtual rotor angular velocity. The adjustment strategy of the damping coefficient follows the change trend of the moment of inertia, and is supplemented by appropriate calibration to achieve a more refined control effect. Therefore, the following adaptive adjustment formula for the moment of inertia and damping coefficient is obtained:

[0093]

[0094] Where: J0, D0 are the moment of inertia and damping coefficient in steady state respectively; k1, k2, k3 and m1, m2 are the proportional coefficient and exponential coefficient respectively; N Js and N Ds are the minimum thresholds of the angular velocity change rate and the minimum threshold of the angular velocity change size respectively; N Jm N is the threshold for distinguishing the change in angular velocity after optimization; Jl and N Dl are the maximum thresholds of the angular velocity change rate and the maximum threshold of the angular velocity change size, respectively; J b and D b are the boundary values ​​of the moment of inertia and damping coefficient respectively, and sign(x) is the sign function.

[0095] Step 6: Determine the VSG system parameters according to step 5, obtain the virtual speed regulator output phase angle, and then generate a voltage given value with the voltage amplitude output by the excitation controller;

[0096] Step 7: The voltage given value obtained in step 6 is output through voltage and current double closed-loop control, and the full-bridge switch of the three-phase inverter is controlled by the PWM generator.

[0097] Figure 4 After the algorithm optimizes the parameters, the steady-state moment of inertia and the steady-state damping coefficient are optimized by the improved whale optimization algorithm, which can effectively maintain the system frequency stable near the set value when the load is connected. On this basis, the adaptive adjustment formula is further used to adjust the moment of inertia and the damping coefficient in real time, so that the system can reach the expected target value faster and more smoothly.

[0098] The technical means disclosed in the scheme of the present invention are not limited to the technical means disclosed in the above-mentioned implementation mode, but also include technical schemes composed of any combination of the above-mentioned technical features. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications are also regarded as the protection scope of the present invention.

Claims

1. A virtual synchronous generator parameter adaptive control method, characterized in that: The steps include: Step 1: According to the filter inductor current and filter capacitor voltage of the three-phase full-bridge inverter, the output active power P and reactive power Q are calculated through abc-dq coordinate transformation; Step 2: Establish a small signal model of the VSG control strategy, determine the transfer function, optimize the active control link according to the second-order transfer function of the VSG, and obtain the functional relationship between the damping ratio and the second-order natural angular frequency and the damping coefficient D and the moment of inertia J; Step 3: According to the functional relationship between the damping ratio and the second-order natural angular frequency obtained in step 2, considering the damping ratio range of the system, as well as the stability and adjustment time of the active power loop, determine the value range of the damping coefficient D and the moment of inertia J; Step 4: Based on the value ranges of the moment of inertia J and the damping coefficient D obtained in step 3, the whale optimization algorithm is used to optimize the moment of inertia J0 and the damping coefficient D0 in the steady state and the threshold for distinguishing the change in the angular velocity rate of change as the whale individuals to be optimized; Step 5: According to the optimal moment of inertia J0 and damping coefficient D0 in the steady state output in step 4 and the threshold for distinguishing the change in the angular velocity rate of change, set the angular velocity change Δω and the angular velocity change rate The threshold value, subdivided Δω and Change range, determine the real-time moment of inertia J and damping coefficient D; Step 6: Determine the moment of inertia J and the damping coefficient D according to step 5, obtain the output phase angle of the virtual speed regulator, and then generate a voltage given value with the voltage amplitude output by the excitation controller; Step 7: The voltage given value obtained in step 6 is output through voltage and current double closed-loop control, and the full-bridge switch of the three-phase inverter is controlled by the PWM generator.

2. A virtual synchronous generator parameter adaptive control method according to claim 1, characterized in that: The functional relationship between the damping ratio and the second-order natural angular frequency obtained in step 2 and the damping coefficient D and the moment of inertia J is: Where: E is the voltage at the VSG input port; U is the voltage at the VSG output port; X is the equivalent impedance; ξ is the damping ratio; ω n is the second-order natural angular frequency; D is the damping coefficient; J is the moment of inertia; ω0 is the angular velocity on the grid side.

3. A virtual synchronous generator parameter adaptive control method according to claim 1, characterized in that: The value ranges of the damping coefficient D and the moment of inertia J determined in step 3 are: Where: f cmax is the maximum cutoff frequency. According to the functional relationship between the damping ratio and the second-order natural angular frequency and the damping coefficient D and the moment of inertia J, the moment of inertia J and the damping coefficient D can be expressed with each other. After determining the range of the damping coefficient D, the value range of the moment of inertia J is also determined.

4. A virtual synchronous generator parameter adaptive control method according to claim 1, characterized in that: In step 4, the whale optimization algorithm uses the weighted function of the THD index and the ITAE index as the objective function, and the specific calculation formula of its fitness value y is: Where: U zon and U zol are the harmonic amplitude and fundamental amplitude of the output voltage respectively; e(t) is the error signal, that is, the frequency difference between the set frequency and the actual output frequency; a and b are the weighting coefficients of THD and ITAE respectively.

5. A virtual synchronous generator parameter adaptive control method according to claim 4, characterized in that: In step 4, the moment of inertia J0 and the damping coefficient D0 in the steady state and the threshold for distinguishing the magnitude of the change in the angular velocity change rate are regarded as individual whales to be optimized and optimized, including the following steps: Step 4.1: Set initial parameters, including population size, maximum number of iterations, and upper and lower limits of the solution space; Step 4.2: Initialize the whale population using KENT chaotic mapping; Step 4.3: Calculate the fitness of the individual, record the moment of inertia J0 and damping coefficient D0 represented by the current optimal whale individual position in the steady state, and the threshold N for distinguishing the change in the angular velocity rate of change Jm ; Step 4.4: When the current number of iterations is less than the maximum number of iterations, calculate the improved convergence factor And adaptive weight w, and update parameters A and C, generate a random number P; Step 4.5: Update the position of the whale population, calculate the fitness value, and filter out the new optimal individuals: Step 4.6: Introduce a greedy mechanism to decide whether to update the current optimal individual; Step 4.7: Calculate the fitness of the best individuals and sort them, take the whale individual with the best fitness value as the global optimal solution, and select half of the individuals with better fitness to enter the next round of iteration; Step 4.8: Determine whether the number of iterations reaches the maximum number of iterations, and whether the observed frequency error and total harmonic distortion meet the standards. If so, proceed to step 4.9; otherwise, return to step 4.4 to continue iterating. Step 4.9: Output the moment of inertia J0 and damping coefficient D0 in steady state and the threshold N for distinguishing the change in angular velocity rate Jm , parameter optimization is completed.

6. A virtual synchronous generator parameter adaptive control method according to claim 5, characterized in that: In step 4.4, the improved convergence factor The specific calculation process of and adaptive weight w is: Where: t is the current iteration number; Max iter is the maximum number of iterations; k and d are adjustment coefficients.

7. A virtual synchronous generator parameter adaptive control method according to claim 1, characterized in that: In step 5, Δω and Change range, determine the real-time moment of inertia J and damping coefficient D as follows: Where: J0, D0 are the moment of inertia and damping coefficient in steady state respectively; k1, k2, k3 and m1, m2 are the proportional coefficient and exponential coefficient respectively; N Js and N Ds are the minimum thresholds of the angular velocity change rate and the minimum threshold of the angular velocity change size respectively; N Jm N is the threshold for distinguishing the change in angular velocity after optimization; Jl and N Dl are the maximum thresholds of the angular velocity change rate and the maximum threshold of the angular velocity change size, respectively; J b and D b are the boundary values ​​of the moment of inertia and damping coefficient respectively, and sign(x) is the sign function.

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