Voltage and current double-loop adaptive robust control method for parallel inverter system

Abstract

The invention provides a voltage and current double-loop adaptive robust control method for a parallel inverter system, and the method comprises the steps: firstly, designing an adaptive gain-based extended state observer capable of reducing an initial estimation peak value for a multivariable and strong-coupling inverter dynamic model, so as to estimate the lumped disturbance of the system; thirdly, designing inverter system outer loop voltage sliding mode control based on observer compensation, and enabling the output voltage to track a set reference value at high precision; and finally, designing an inner loop current adaptive proportional-integral control law based on a nonlinear # imgabs0 # function to meet the inner loop control requirement. According to the method, a double-loop self-adaptive control strategy of the outer loop voltage SMC based on AGESO and the inner loop current self-adaptive PI based on # imgabs1 # is adopted, so that the parallel inverter system can operate stably, the capacity of resisting disturbance of loads, filtering parameters and the like is enhanced, the dynamic performance of the system is improved, and the load power can be quickly equally divided on each parallel inverter.

Description

Technical Field

[0001] The invention belongs to the field of microgrid system control based on new energy, and relates to a voltage-current dual-loop adaptive robust control method for a parallel inverter system. Background Art

[0002] Distributed renewable energy is usually connected to the microgrid through an inverter, but the capacity of a single inverter is limited, and it is difficult and economical to design a single large-capacity inverter. In order to meet the expansion needs of the microgrid, parallel inverters are widely used in actual projects. Therefore, in order to improve the reliability and power quality of the microgrid, studying the control strategy of the parallel inverter system has important theoretical significance and engineering value.

[0003] The control strategy of parallel inverter systems currently generally adopts single-loop voltage or current control. The single-loop control law is simple, but its dynamic response is slow and it is difficult to cope with various disturbances such as load fluctuations and changes in filter parameters. In order to overcome this shortcoming and realize power sharing of parallel inverter systems at the same time, it is necessary to control the outer loop voltage and inner loop current of the system at the same time, which is referred to as a dual-loop control strategy.

[0004] For the voltage outer loop in the dual-loop control strategy, most of them use a linear proportional integral (PI) controller, but the system dynamic performance of this control method is poor, especially when external disturbances occur, the distortion rate of the system output voltage waveform is high. Therefore, nonlinear control methods such as repetitive control, fuzzy logic control, model-free predictive control, and sliding mode control (SMC) have emerged to overcome the above shortcomings. Due to its strong robustness, SMC has been widely used in voltage outer loop control.

[0005] Conventional SMC strategies can only make the system robust in sliding mode, but are still sensitive to disturbances suffered in the arrival stage before entering the sliding mode. Therefore, it is necessary to study how to enhance the global robustness of SMC. To this end, the present invention first constructs an adaptive observer to estimate the lumped disturbance during system operation in real time, and then proposes an outer loop voltage SMC strategy based on compensating for the lumped disturbance.

[0006] As the core of the ADRC, the Extended State Observer (ESO) can estimate the system state variables and disturbances in real time and has been widely used in control engineering. Due to the large deviation between its estimated initial value and the actual value, the traditional ESO has the "initial estimation peak" phenomenon; to solve this problem, the adaptive gain ESO (AGESO) can be used. Therefore, this paper proposes an AGESO-based outer loop voltage SMC control strategy to enhance the global robustness of the system and improve the system dynamic performance.

[0007] For the current inner loop in the dual-loop control strategy, a linear PI controller is generally used in actual engineering applications. Existing technicians have proposed a current loop control method that combines PI with repetitive control to enhance the dynamic characteristics of the system. However, due to the periodic delay of the repetitive control itself, it leads to large system error fluctuations during actual operation, and has the defects of poor dynamic performance and high sensitivity to parameter disturbances. In addition, existing technicians have proposed a current loop control method that combines PI with model predictive control, and compensates for the disturbance caused by parameter changes by adding a control structure, but its design process is complicated. Therefore, the present invention proposes a voltage and current dual-loop adaptive robust control method for a parallel inverter system to solve the above-mentioned technical defects. Summary of the invention

[0008] The present invention proposes a voltage-current dual-loop adaptive robust control method for a parallel inverter system, which makes the parallel inverter system have stronger robustness and anti-disturbance ability to better adapt to load disturbances and filter inductance parameter perturbations, and can also solve the problem of large deviation between the traditional ESO estimated initial value and the actual value.

[0009] The present invention specifically comprises the following steps:

[0010] Step 1: Based on the dq axis rotating coordinate system, construct the dynamic mathematical model of the inverter circuit. The dynamic mathematical model of the inverter circuit is shown in the following formula:

[0011] ;

[0012] ;

[0013] In the formula, For the inverter Inductor current under the axis; For the inverter Inductor current under the axis; is the inverter bridge arm midpoint voltage after coordinate transformation Axis component; is the q-axis component of the inverter bridge arm midpoint voltage after coordinate transformation; For the inverter Output voltage under the shaft; For the inverter Output voltage under the shaft; For the inverter Output current under the shaft; For the inverter Output current under the shaft; For time; is the inductance of the filter; is the capacitance of the filter; is the equivalent resistance of the filter; is the angular frequency of the alternating current in the stationary coordinate system.

[0014] Step 2: Design an SMC control law for the outer loop voltage of the inverter system based on AGESO compensation, so that the output voltage can track its set reference value with high precision; specifically, it includes:

[0015] The inverter dynamic model based on AGESO compensation is constructed. The inverter dynamic model is shown as follows:

[0016] ;

[0017] In the formula, is the first state variable The derivative of is the second state variable The derivative of; the first state variable ; The second state variable ; is the SMC control law for the outer loop voltage;

[0018] The outer loop voltage SMC control law based on AGESO is designed. The outer loop voltage SMC control law is shown as follows:

[0019]

[0020] In the formula, is the positive sliding mode coefficient; and are all positive real numbers; is the second state variable Observed value of is a symbolic function; is the sliding surface; is the first state variable Observed value of

[0021] in, The expression is as follows:

[0022] ;

[0023] ;

[0024] In the formula, for The error between the shaft reference voltage and its output voltage observation; for The derivative of For inverter system Reference voltage of the axis; AGESO observations Shaft voltage.

[0025] Stability analysis of the outer loop voltage SMC control law based on AGESO:

[0026] Construct the Lyapunov function as shown below:

[0027] ;

[0028] in, Refers to the synovial surface, which is a function of time;

[0029] In order to effectively suppress chattering, the exponential reaching law is selected :

[0030] ;

[0031] In the formula, and are all positive real numbers;

[0032] For the Lyapunov function The derivative is as follows:

[0033] ;

[0034] Obviously, is a positive definite function; since , are greater than 0, so It is a negative definite function. Therefore, according to the Lyapunov stability theory, the outer loop voltage SMC control law based on AGESO can ensure the stability of the outer loop voltage system.

[0035] Step 3: Design based on nonlinearity The inner loop current adaptive PI control law of the function is used to improve the system's adaptability to parameter perturbations and speed up system response;

[0036] Design based on The inner loop current adaptive PI control law of the function is as follows:

[0037] ;

[0038] In the formula, It is the inner loop current adaptive PI control law; for The error of shaft inductance current;

[0039] in, The expression is as follows:

[0040] ;

[0041] In the formula, For the inverter The inductor current reference value under the axis;

[0042] in, The expression is as follows:

[0043] ;

[0044] in, The expression is as follows:

[0045] ;

[0046] In the formula, is a constant proportional gain; is the piecewise nonlinear function in the proportional term; It is a constant in the proportional term, and its value range is 0-1; is a positive constant in the proportional term; is the constant integral gain; is the piecewise nonlinear function in the integral term; is a constant in the integral term, with a value range of 0-1; is a positive constant in the integral term.

[0047] based on The inner loop current adaptive PI control law of the function can ensure that: when the current error is large, the proportional gain and integral gain in the PI control become smaller, thereby accelerating the dynamic response of the system; when the current error decreases, the proportional gain and integral gain in the PI control become larger, thereby reducing the system steady-state error and improving the system steady-state accuracy. The function's inner loop current adaptive PI control law makes the system well adaptable to load and parameter disturbances.

[0048] The beneficial effects of the present invention are:

[0049] The present invention has the ability to enhance the parallel inverter system's ability to resist load disturbances and filter parameter perturbations, specifically:

[0050] Firstly, for the multivariable and strongly coupled inverter dynamic model, the present invention designs an AGESO that can reduce the initial estimated peak value to estimate the lumped disturbance of the system;

[0051] Secondly, the present invention designs an inverter system outer loop voltage SMC based on AGESO compensation, so that the output voltage can track its set reference value with high precision;

[0052] Finally, the present invention designs a nonlinear The inner loop current adaptive PI control law of the function can meet the inner loop control requirements of rapidity, high precision and robustness.

[0053] Specifically:

[0054] 1. The present invention can speed up the response speed of the system, has a good control effect on the parallel inverter system, has high quality of output voltage and current waveforms, and has a fast power sharing speed;

[0055] 2. Compared with the traditional PI dual-loop control, the present invention makes the parallel inverter system have stronger robustness and anti-disturbance ability, and can better adapt to the load disturbance and filter inductance parameter perturbation;

[0056] 3. The present invention can quickly track the inverter output voltage, has a fast observation error convergence speed, and can reduce the initial estimation peak value, with a faster estimation speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 is a control block diagram of the parallel inverter system in this embodiment;

[0058] Figure 2 is a block diagram of the outer loop voltage SMC control based on AGESO in this embodiment;

[0059] Figure 3 In this embodiment, based on The inner loop current adaptive PI control block diagram of the function;

[0060] Figure 4 It is the three-phase voltage and three-phase current response waveform of the first inverter in system 1;

[0061] Figure 5 It is the three-phase voltage and three-phase current response waveform of the second inverter in system 1;

[0062] Figure 6 It is a schematic diagram comparing the active powers of the first inverter and the second inverter in system 1;

[0063] Figure 7 is a schematic diagram comparing the reactive power of the first inverter and the second inverter in system 1;

[0064] Figure 8 It is a schematic diagram comparing the d-axis output voltage and d-axis output current of system 1 and system 2 under load disturbance;

[0065] Fig. 9 It is a schematic diagram comparing the active power of system 1 and system 2 under load disturbance;

[0066] Fig.10 It is a comparative diagram of reactive power between system 1 and system 2 under load disturbance;

[0067] Fig.11 It is a schematic diagram comparing the output voltage and output current of phase a of system 1;

[0068] Fig.12 This is a schematic diagram comparing the output voltage and output current of phase a of system 2;

[0069] Fig.13 It is System 1 Estimates and System 3 Schematic diagram of comparison of estimated values;

[0070] Fig.14 It is System 1 Estimation Error and System 3 Schematic diagram of estimation error comparison;

[0071] Fig.15 It is a schematic diagram comparing the active power of system 1 and system 3;

[0072] Fig.16 It is a schematic diagram comparing the reactive power of system 1 and system 3;

[0073] Fig.17 It is a schematic diagram comparing the d-axis output voltage and d-axis output current of system 1 and system 2 under load disturbance;

[0074] Fig.18 It is a schematic diagram of the output voltage and output current of phase a of system 1 when the filter inductance parameters are perturbed;

[0075] Fig.19 It is a schematic diagram of the output voltage and output current of phase a of system 2 when the filter inductance parameters are perturbed;

[0076] Fig. 20 It is System 1 Estimates and System 3 Schematic diagram of comparison of estimated values;

[0077] Fig.21 It is System 1 Estimation Error and ESO Schematic diagram of estimation error comparison. DETAILED DESCRIPTION

[0078] The invention is described clearly and completely below in conjunction with the figures and implementation methods.

[0079] Example

[0080] like Figure 1 As shown, for a parallel inverter system formed by connecting two identical three-phase voltage source inverters in parallel, in order to overcome load disturbances and filter parameter changes, effectively improve the robustness of system output voltage and output current control, and realize rapid balancing of load power on each inverter, this embodiment proposes a voltage and current dual-loop adaptive robust control method for a parallel inverter system, including the following steps:

[0081] Step 1: Based on the dq axis rotating coordinate system, construct the dynamic mathematical model of the inverter circuit. The dynamic mathematical model of the inverter circuit is shown in the following formula:

[0082] ;

[0083] ;

[0084] In the formula, For the inverter Inductor current under the axis; For the inverter Inductor current under the axis; is the inverter bridge arm midpoint voltage after coordinate transformation Axis component; is the q-axis component of the inverter bridge arm midpoint voltage after coordinate transformation; For the inverter Output voltage under the shaft; For the inverter Output voltage under the shaft; For the inverter Output current under the shaft; For the inverter Output current under the shaft; For time; is the inductance of the filter; is the capacitance of the filter; is the equivalent resistance of the filter; is the angular frequency of the alternating current in the stationary coordinate system.

[0085] Step 2: Figure 2As shown in the figure, an SMC control law for the outer loop voltage of the inverter system based on AGESO compensation is designed to make the output voltage track its set reference value with high precision; specifically, it includes:

[0086] The inverter dynamic model based on AGESO compensation is constructed. The inverter dynamic model is shown as follows:

[0087] ;

[0088] is the first state variable The derivative of is the second state variable The derivative of; the first state variable ; The second state variable ; is the SMC control law for the outer loop voltage;

[0089] The outer loop voltage SMC control law based on AGESO is designed. The outer loop voltage SMC control law is shown as follows:

[0090] ;

[0091] In the formula, is the positive sliding mode coefficient; and are all positive real numbers; is the second state variable Observed value of is a symbolic function; is the sliding surface; is the first state variable Observed value of

[0092] in, The expression is as follows:

[0093] ;

[0094] ;

[0095] In the formula, for The error between the shaft reference voltage and its output voltage observation; for The derivative of For inverter system Reference voltage of the axis; AGESO observations Shaft voltage.

[0096] Step 3: Figure 3 As shown, the design is based on nonlinear The inner loop current adaptive PI control law of the function is used to improve the system's adaptability to parameter perturbations and speed up the system response.

[0097] In step 3, based on The inner loop current adaptive PI control law of the function is as follows:

[0098] ;

[0099] In the formula, It is the inner loop current adaptive PI control law; for The error of shaft inductance current;

[0100] in, The expression is as follows:

[0101] ;

[0102] In the formula, For the inverter The inductor current reference value under the axis;

[0103] in, The expression is as follows:

[0104] ;

[0105] in, The expression is as follows:

[0106] ;

[0107] In the formula, is a constant proportional gain; is the piecewise nonlinear function in the proportional term; It is a constant in the proportional term, and its value range is 0-1; is a positive constant in the proportional term; is the constant integral gain; is the piecewise nonlinear function in the integral term; is a constant in the integral term, with a value range of 0-1; is a positive constant in the integral term.

[0108] In order to verify the effectiveness and superiority of this embodiment, three parallel inverter systems were built on the MATLAB / Simulink simulation platform, respectively called system 1, system 2 and system 3, and experiments 1 to 4 were completed.

[0109] For the outer loop voltage and inner loop current, system 1 adopts the control method provided in this embodiment, system 2 adopts the conventional proportional-integral and proportional-integral control methods, and system 3 adopts sliding mode control based on a conventional extended state observer and adaptive proportional-integral based on Nfal.

[0110] System 1, System 2 and System 3 all include two inverters with the same capacity and filter parameters, and their line impedances are and , and a droop controller with exactly the same parameters is used. The remaining system parameters are shown in Table 1 and the controller parameters are shown in Table 2.

[0111] Table 1 System parameter settings

[0112]

[0113] Table 2 Controller parameter settings

[0114]

[0115] Figure 1 The load 1 and load 2 in the figure are both resistive and inductive loads. The active and reactive power of load 1 are 20kW and 20kVar respectively, and the active and reactive power of load 2 are 10kW and 10kVar respectively. The simulation time is set to 3s. In the time period of 0s-1s, load 1 is connected to the system. In the time period of 1s-2s, Figure 1 The middle switch S is closed, and load 2 is connected to the system; in the 2s-3s period, Figure 1 The middle switch S is disconnected, and the load 2 exits the system. Experiments 1 to 7 are all carried out under this condition.

[0116] Experiment 1: Verify the three-phase voltage and current control effect of system 1:

[0117] like Figure 4 and 5 As shown in the figure, when loading for 1s or reducing load for 2s, the three-phase voltage waveforms of the two inverters did not change significantly, and their amplitudes were both stable at 311V. The three-phase output current amplitudes of the two inverters increased rapidly from 30.5A to 45.6A when loading for 1s, with a response time of about 0.03s, and quickly recovered to 30.5A before loading when reducing load for 2s, with a response time of about 0.03s. The above data show that system 1 has strong adaptability to load disturbances and enables the parallel inverter system to output higher quality voltage and current waveforms.

[0118] Depend on Figure 6 and 7As shown in the figure, when system 1 starts running for 0.15s and 0.25s respectively, its active power and reactive power reach their steady-state values. The steady-state active power and reactive power of each inverter are 10kW and 10kVar respectively. When loading for 1s or reducing load for 2s, after about 0.12s, the active power and reactive power of the two inverters reach exactly the same steady-state value again. The above data show that the droop control of system 1 can ensure that the load power is evenly distributed on the two inverters.

[0119] Experiment 2: Comparison of control effects between system 1 and system 2 under load disturbance:

[0120] The outer loop voltage and inner loop current of system 1 and system 2 use exactly the same PI control parameters. The proportional gain and integral gain of the inner loop current PI controller of system 2 are the same as those of the inner loop current adaptive controller of system 1. and Exactly the same.

[0121] Depend on Figure 8 As shown in the figure, regardless of loading or unloading, the d-axis output voltage of system 1 and system 2 is basically stable at 311V, and the d-axis output current of system 1 reaches a new steady-state value faster than that of system 2. For example, when the d-axis output current increases from 21.2A to a steady-state value of 31.6A at 1s loading, system 1 takes about 0.02s, while system 2 takes 0.1s, and the d-axis output current jitter of system 2 is very obvious. The above data show that system 1 is more robust than system 2.

[0122] Depend on Fig. 9 and 10 As shown in the figure, regardless of loading or unloading, the output power of system 1 reaches the new steady-state value faster than that of system 2. For example, when loading for 1s, the time it takes for the active power of system 1 to reach the new steady-state value of 15000W is 0.15s, and the time it takes for the reactive power of system 1 to reach the new steady-state value of 15000Var is 0.12s. The time it takes for the active power of system 2 to reach the new steady-state value of 15000W is 0.3s, and the time it takes for the reactive power of system 2 to reach the new steady-state value of 15000Var is 0.32s. The above data show that system 1 is more able to resist load interference, enabling the parallel inverter to achieve faster load power balancing, thereby avoiding the circulation between the two inverters and improving the power quality of the parallel inverter system.

[0123] Experiment 3: Comparison of control effects between system 1 and system 2 when filter inductance parameters are perturbed:

[0124] During the simulation process, the filter inductance parameter is 0.7L in the time period of 0.1s-0.2s, L in the time period of 0.2s-0.3s, and 1.3L in the time period of 0.3s-0.4s.

[0125] Depend on Fig.11 and 12 As shown in Figure 1, when the inductance parameter increases or decreases, the The phase output voltage can almost completely track its reference value, while system 2 has a certain degree of jitter around its reference value, so system 1 is more resistant to interference caused by changes in inductor parameters.

[0126] Experiment 4: Comparison of initial dynamic responses of system 1 and system 3:

[0127] The parameters of the outer loop voltage SMC and inner loop current adaptive PI controllers of system 1 and system 3 are exactly the same.

[0128] Depend on Fig.13 and 14 As shown, the estimated values ​​of the d-axis output voltage of system 1 and system 3 both track its actual value 311V in steady state, but their initial estimated peak values ​​are approximately 330V and 360V respectively. Correspondingly, their initial estimated error peak values ​​are approximately 70V and 100V, and their dynamic response times are approximately 0.017s and 0.04s respectively. The above data show that compared with system 3, system 1 can not only reduce the initial estimated peak value, but also has a faster estimation speed.

[0129] Depend on Fig.15 and 16 As shown in the figure, it is precisely because of the initial estimated peak of the observer that the system has initial fluctuations in active and reactive power. Compared with system 3, the initial fluctuations in active and reactive power of system 1 are significantly smaller. This shows that system 1 based on AGESO has more satisfactory initial dynamic response characteristics.

[0130] In order to further verify the effectiveness and superiority of this embodiment, three parallel inverter systems identical to the above simulation experiments were built on the StarSim / hardware-in-the-loop simulation experiment platform, namely system 1, system 2 and system 3, and experiments five to seven were completed.

[0131] The experimental setup includes: a real-time simulator based on the NI FPGA board in the NI PXI chassis, StarSim software, a TMS320 F28335PGF DSP, an interface box, a personal computer, and an oscilloscope; the sampling period of the controller is set to 50 , and the other parameters are consistent with the above simulation experiments.

[0132] Experiment 5: Comparison of control effects between system 1 and system 2 under load disturbance:

[0133] Fig.17 The olive green line represents the , the blue line represents the ; Before and after the load change, the system 1 and system 2 All are always stable at about 1.55div×200V / div=310V;

[0134] Fig.17 The green line in the middle represents the , the purple line represents the ; Before loading, System 1 and System 2 Both are 1.06div×20A / div=21.2A. After loading, the system 1 and system 2 Both increase to 1.58div×20A / div=31.6A. When the load is reduced, The above data show that compared with system 2, system 1 has a higher The number of fluctuations is small and the time to reach the steady-state value is short, so system 1 has a stronger ability to resist load disturbances.

[0135] Experiment 6: Comparison of control effects between system 1 and system 2 when filter inductance parameters are perturbed:

[0136] Set working conditions: filter inductance decreases from L to 0.7L in 0.1s and increases to 1.3L in 0.14s;

[0137] Fig.18 and 19 The purple line in the figure represents the output voltage of phase a of the first inverter in system 1. and output current Waveform, the blue line represents the output voltage of phase a of the first inverter in system 2 and output current Waveform;

[0138] Depend on Fig.18 and 19 As shown in the figure, before and after the two step changes of the inductance parameters, the output voltage and output current amplitudes of the a phase of system 1 and system 2 are approximately 3.11div×100V / div=311V and 1.51div×20A / div=30.5A respectively; the above data show that although the output voltage and output current waveforms of the two are similar, before and after the two step changes of the filter inductance parameters, the output voltage and output current amplitudes of system 1 are approximately 3.11div×100V / div=311V and 1.51div×20A / div=30.5A respectively. and The ripple is significantly smaller than that of system 2 and , so system 1 has a stronger ability to resist filter inductance parameter disturbances.

[0139] Experiment 7: Comparison of initial dynamic responses of system 1 and system 3:

[0140] Fig. 20 Middle: The olive green line shows the system 1 about The blue line shows the estimated value of System 3. The purple line represents the estimated value of The actual value of .

[0141] Depend on Fig. 20 As shown, the estimated values ​​of the d-axis output voltage of system 1 and system 3 both track its actual value of 3.11div×100V / div=311V in steady state. However, the initial estimated peak value of system 1 is significantly smaller than the initial estimated peak value of system 3. The initial estimated peak values ​​of the two are approximately 3.22div×100V / div=322V and 3.56div×100V / div=356V, respectively.

[0142] Fig.21 Middle: The blue line shows the system 1 about The purple line represents the estimated error of system 3. The estimation error.

[0143] Depend on Fig.21 As shown, the initial estimation error peak of system 1 is significantly smaller than that of system 3. The initial estimation peaks of the two are approximately 2.05div×50A / div=102.5V and 1.32div×50A / div=66V, respectively, and system 1 has a faster convergence speed of estimation error.

[0144] The above data show that compared with System 3, System 1 can not only reduce the initial estimation peak, but also has a faster estimation speed.

[0145] Based on the verification results of Experiments 1 to 7, the following conclusions are drawn:

[0146] (1) This embodiment can speed up the response speed of the system, has a good control effect on the parallel inverter system, has high quality of output voltage and current waveforms, and has a fast power sharing speed;

[0147] (2) Compared with the traditional PI dual-loop control, this embodiment enables the parallel inverter system to have stronger robustness and anti-disturbance capability, and can better adapt to load disturbances and filter inductance parameter perturbations;

[0148] (3) This embodiment can quickly track the inverter output voltage, the observation error converges quickly, and can reduce the initial estimation peak, thus having a faster estimation speed.

Claims

1. A voltage and current dual-loop adaptive robust control method for a parallel inverter system, characterized in that: The following steps are involved: Step 1: Based on the dq axis rotating coordinate system, a dynamic mathematical model of the inverter circuit is constructed; Step 2: Design an SMC control law for the outer loop voltage of the inverter system based on AGESO compensation, so that the output voltage can track its set reference value with high precision; Step 3: Design based on nonlinearity The inner loop current adaptive PI control law of the function is used to improve the system's adaptability to parameter perturbations and speed up the system response.

2. The voltage and current dual-loop adaptive robust control method for a parallel inverter system according to claim 1, characterized in that: The circuit dynamic mathematical model of the inverter in the dq axis rotating coordinate system in step 1 is shown as follows: ； ； In the formula, For the inverter Inductor current under the axis; For the inverter Inductor current under the axis; is the inverter bridge arm midpoint voltage after coordinate transformation Axis component; is the q-axis component of the inverter bridge arm midpoint voltage after coordinate transformation; For the inverter Output voltage under the shaft; For the inverter Output voltage under the shaft; For the inverter Output current under the shaft; For the inverter Output current under the shaft; For time; is the inductance of the filter; is the capacitance of the filter; is the equivalent resistance of the filter; is the angular frequency of the alternating current in the stationary coordinate system.

3. The voltage-current dual-loop adaptive robust control method for a parallel inverter system according to claim 1, characterized in that: The step 2 comprises the following steps: An inverter dynamic model based on AGESO compensation is constructed, and the inverter dynamic model is shown in the following formula: ； In the formula, is the first state variable The derivative of is the second state variable The derivative of; the first state variable ; The second state variable ; is the outer loop voltage SMC control law; The outer loop voltage SMC control law based on AGESO is designed. The outer loop voltage SMC control law is shown as follows: ； In the formula, is the positive sliding mode coefficient; and are all positive real numbers; is the second state variable Observed value of is a symbolic function; is the sliding surface; is the first state variable Observed value of in, The expression is as follows: ； ； In the formula, for The error between the shaft reference voltage and its output voltage observation; for The derivative of For inverter system Reference voltage of the axis; Observed by AGESO Shaft voltage.

4. The voltage and current dual-loop adaptive robust control method for a parallel inverter system according to claim 1, characterized in that: In step 3, based on The inner loop current adaptive PI control law of the function is shown as follows: ； In the formula, It is the inner loop current adaptive PI control law; for The error of shaft inductance current; in, The expression is as follows: ； In the formula, For the inverter The inductor current reference value under the axis; in, The expression is as follows: ； in, The expression is as follows: ； In the formula, is a constant proportional gain; is the piecewise nonlinear function in the proportional term; It is a constant in the proportional term, and its value range is 0-1; is a positive constant in the proportional term; is the constant integral gain; is the piecewise nonlinear function in the integral term; is a constant in the integral term, with a value range of 0-1; is a positive constant in the integral term.

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