Optical storage and charging system voltage regulation and control method based on preset time observer

Abstract

The invention discloses an optical storage and charging system voltage regulation and control method based on a preset time observer, and the method comprises the steps: observing the total disturbance of a system through introducing a preset time expansion state observer, and feeding back a disturbance observation value to a compensator for elimination; optimal robust sliding mode control is designed for optimization, high and low frequency components are respectively compensated by a storage battery and a super capacitor by utilizing high and low frequency characteristics of elements of the hybrid energy storage system, and fluctuating power is distributed between the storage battery and the super capacitor by utilizing a low-pass filter. According to the invention, convergence of the system state within the preset time can be ensured, the dynamic response performance of the system is improved, and the anti-disturbance capability of the system is also improved; power frequency division distribution can be rapidly carried out, the dynamic response characteristic of the optical storage and charging direct current micro-grid system is optimized, and meanwhile effective operation of the photovoltaic micro-grid system is coordinated; the method is superior to PI control and ADRC control under various working conditions.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic storage and charging DC microgrids, and particularly relates to a voltage control method for a photovoltaic storage and charging system based on a preset time observer. Background Art

[0002] With the energy crisis and environmental issues becoming increasingly prominent, the continued growth in electricity demand, the shortage of traditional energy sources, and the opening up of the electricity market are driving the development of microgrids towards efficient, flexible, intelligent, and sustainable approaches. Sustainability is a fundamental characteristic of future microgrids, as evidenced by the widespread adoption of new distributed generation technologies such as solar, fuel cells, and wind power, and the widespread integration of distributed power sources. The influx of distributed power sources has altered the power flow distribution in DC microgrids. The rapid fluctuations in intermittent primary energy sources can cause rapid fluctuations in distribution network voltage, which, if not addressed, can lead to overvoltage and undervoltage. To address this issue, energy storage systems are often added to compensate for intermittent photovoltaic power fluctuations. Hybrid energy storage control systems consisting of supercapacitors and batteries have been widely adopted in photovoltaic-storage-charged DC microgrids. These photovoltaic-storage-charged DC microgrids are susceptible to uncertainties such as load and sunlight, which can reduce the control performance of bus voltage. Given that hybrid energy storage systems currently require distributed power sources to adopt a maximum power point tracking (MPPT) operation strategy, significant research has focused on leveraging existing hybrid energy storage system voltage control equipment to address voltage stability issues associated with the integration of distributed power sources. The traditional PI control algorithm in the control of DC microgrid bus voltage makes the hybrid energy storage control system only able to passively respond to and handle load changes, parameter perturbations or other conditions in the system. Its energy absorption efficiency is low and the system convergence speed is still slow, which limits the development of new energy access to the grid.

[0003] To further optimize the dynamic and anti-interference performance of hybrid energy storage systems, some literature uses extended state observers to estimate disturbances in the battery's voltage and current loops, respectively. These disturbance observations are then fed into an active disturbance rejection controller as feedforward terms to compensate for system disturbances. This effectively suppresses DC bus voltage fluctuations and improves the hybrid energy storage system's disturbance estimation and rejection capabilities. However, traditional active disturbance rejection controllers have too many parameters, making parameter adjustment more complex. To address the problems of poor bus voltage stability and slow system response in photovoltaic-storage-charged DC microgrids caused by photovoltaic output fluctuations and load power disturbances, some literature proposes an intelligent algorithm-based energy storage control system consisting of supercapacitors and batteries. This system achieves adaptive adjustment of controller parameters, thereby ensuring stable operation of the microgrid hybrid energy storage system. Literature has addressed the issues of poor bus voltage stability and performance in photovoltaic-storage-charged DC microgrids, which can arise from fluctuations in photovoltaic output and large-scale parameter perturbations. By employing a finite-time extended state observer (DSO), the authors improve the estimation speed of system state variables and total disturbances, ensuring that the system state converges within a finite time. An improved sliding mode controller is then used to enhance the system's anti-interference capability, while also demonstrating good robustness in the face of parameter perturbations. For practical microgrid hybrid energy storage systems, optimizing system control performance is also a key consideration. Using optimal robust control techniques to adjust the matching between intermittent energy output and power loads, thereby improving power quality and reliability and ensuring the safe and efficient operation of microgrid networks, is currently a key focus of microgrid technology research.

[0004] Compared with the prior art, the differences are as follows:

[0005] Technical comparison with patent CN117096846B "Complementary sliding mode control method for bus voltage of photovoltaic storage system based on finite time observer"

[0006] Patent CN117096846B describes a method for busbar voltage control in a photovoltaic (PV) energy storage system. This method primarily designs a finite-time extended state observer to observe the total disturbance to the system, then designs a complementary sliding mode controller. The disturbance observations are then fed into the complementary sliding mode controller as feedforward terms to compensate for the system disturbance and ensure that the system state converges within a finite time. This method primarily considers the accuracy and stability of closed-loop voltage control, but does not consider the optimal control strategy for each parameter in the control algorithm, thus neglecting the time and efficiency required to achieve control stability.

[0007] The patent focuses on the voltage optimization control method of the light storage charging system based on the preset time observer. The core is to improve the disturbance estimation ability of the observer using the predetermined time convergence theory, and to estimate and compensate the disturbance using the preset time extended state observer. Based on the optimal guaranteed performance robust tracking control method, the tracking error of the DC bus voltage is quickly reduced, the power difference between photovoltaic power generation and power load is automatically compensated, and the performance index is guaranteed to reach a certain upper bound in the presence of external disturbances and uncertainties. The influence of parameter uncertainty on the system can be suppressed, and the robustness of the system is further improved. The patent focuses on the predetermined time estimation and compensation of external disturbances, and the optimal control of various parameters is controlled to meet the rapidity requirements of the voltage optimization control of the light storage charging system.

[0008] There are essential differences between the two in system architecture, scheduling target and technical path.

[0009] Comparison with patent CN114825312B "High stability control method for bus voltage of light storage direct current power distribution system"

[0010] Patent CN114825312B invents a high stability control method for bus voltage of light storage direct current power distribution system. This method mainly designs an extended state observer to observe the total disturbance of the system, designs a disturbance rejection sliding mode controller, and inputs the disturbance observation value as a feedforward item into the disturbance rejection sliding mode controller to compensate for the system disturbance. This method mainly considers the accuracy and stability of voltage closed-loop control, and does not consider the optimal control strategy change of various parameters for the control algorithm, ignoring the time and efficiency required to achieve control stability.

[0011] The patent focuses on the light storage direct current microgrid hybrid energy storage system affected by external disturbances and uncertainties. The core is to use the predetermined time convergence theory, use the preset time extended state observer to estimate and compensate the disturbance, so that the system state converges within the preset time, and guarantees the preset time convergence of the overall control system. At the same time, an optimal sliding mode surface function is designed, and the selection of the sliding mode surface function ensures that the system state moves along the time-optimal trajectory, meeting the rapidity requirements of the system. And on this basis, an optimal guaranteed performance robust sliding mode controller is designed according to the state quantity and disturbance quantity estimation information. The patent focuses on the predetermined time estimation and compensation of external disturbances, and guarantees that the performance index can reach a certain upper bound in the presence of external disturbances and uncertainties, which can suppress the influence of parameter uncertainty on the system and further improve the robustness of the system.

[0012] There are essential differences between the two in system architecture, scheduling target and technical path.

[0013] Technical comparison with patent CN117081032B "Improved Active Disturbance Rejection Control Method for DC Microgrid Bus Voltage Based on Third-Order ST-ESO"

[0014] Patent CN117081032B describes an improved active disturbance rejection control method for DC microgrid bus voltage. This method primarily employs a third-order super-spiral sliding mode extended state observer (ST-ESO) to observe the system's state and disturbance variables, a fast non-singular adaptive super-spiral sliding mode controller, and the disturbance observations as feedforward inputs into the sliding mode controller to compensate for system disturbances. This method primarily considers compensating for uncertain disturbances such as photovoltaic output power fluctuations and load disturbances. However, the control algorithm does not consider changes in the optimal control strategy for each parameter, and thus neglects the time and efficiency required to achieve control stability.

[0015] This patent focuses on the voltage optimization and control method of the photovoltaic charging system based on the preset time observer. The core is to use the preset time convergence theory to improve the disturbance estimation ability of the observer, and use the preset time expanded state observer to perform preset time estimation and compensation for the disturbance. The method based on the optimal performance guaranteed robust tracking control quickly reduces the tracking error of the DC bus voltage, automatically compensates for the power difference between photovoltaic power generation and power load, and ensures that the performance index can still reach a certain upper limit in the presence of external disturbances and uncertainties, which can suppress the impact of parameter uncertainty on the system and further improve the robust performance of the system. This patent focuses on the preset time estimation and compensation of external interference, and optimizes and controls various parameters to meet the rapidity requirements of the voltage optimization and control of the photovoltaic charging system. Summary of the Invention

[0016] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0017] A voltage control method for a photovoltaic energy storage and charging system based on a preset time observer is disclosed. The method comprises the following steps: observing the total disturbance to the system by introducing a preset time expanded state observer, and feeding the disturbance observation value back to a compensator for elimination; designing an optimal robust sliding mode control for optimization, and utilizing the high and low frequency characteristics of the hybrid energy storage system components. The high and low frequency components are compensated by batteries and supercapacitors respectively, and the fluctuating power is distributed between the two using a low-pass filter, thereby improving the control response speed of the system.

[0018] Furthermore, the specific method is as follows: S1, the current equivalent distribution of the difference power of the photovoltaic storage and charging DC microgrid energy storage system is established to establish a mathematical model of the photovoltaic storage and charging microgrid system, and the power distribution of the photovoltaic storage and charging microgrid system is equivalent to current distribution to obtain the battery reference current; the uncompensated battery current is added to the supercapacitor current reference value to obtain the supercapacitor reference current value; S2, a preset time state observer is designed to define the hybrid energy storage control system as a second-order system, as follows:

[0019] In the above formula, u is the system input; y is the system output; U dc is the DC bus voltage; ω is the external disturbance; a1 and a2 are system parameters; b is the system gain; a1, a2, and b are unknown; and b0≈b;

[0020] By separating the internal uncertainty disturbance of the system and putting the internal and external disturbances into the lumped disturbance, the above formula can be rewritten as

[0021]

[0022] In the above formula, b0 is the estimated value; F is the lumped disturbance; the lumped disturbance F is uniformly bounded, and its derivative with respect to time is bounded, that is, ;

[0023] The photovoltaic storage and charging DC microgrid system exchanges power with the DC bus through a bidirectional converter, and its control target is the bus voltage U dc To ensure the stability of the circuit, the voltage loop and current loop observers are designed respectively;

[0024] S3. Design of optimal robust sliding mode controller

[0025] The system bus voltage tracking error variable is defined as:

[0026]

[0027] Where U dcref is the bus voltage reference value, e1 is the bus voltage tracking error, and e2 is the derivative of the bus voltage tracking error;

[0028] Define the error variable matrix as e=[e1 e2], then the differential equation of the system error variable is:

[0029]

[0030] Where, ;

[0031] In order to obtain the second-order optimal sliding surface, the following integral quadratic performance index J is selected:

[0032]

[0033] Where, is a positive definite symmetric matrix, p 11 , p 12 and p 22 are the coefficients of the positive definite symmetric matrix P.

[0034] Define the auxiliary variable λ1 as follows:

[0035]

[0036] We can further obtain the following set of standard equations:

[0037]

[0038] Where, ;

[0039] According to the LQR optimal feedback control strategy, the following optimal feedback control law can be obtained by using the minimization principle and the optimal control law:

[0040]

[0041] Where R1 is a positive constant and satisfies the following Riccati equation:

[0042]

[0043] Obtain the unique solution of the Riccati equation from the Riccati differential equation .

[0044] Furthermore, the specific method of step S1 is as follows: a mathematical model of a photovoltaic-storage-charging microgrid system is established, wherein the photovoltaic-storage-charging microgrid system is composed of distributed photovoltaic power generation, batteries and supercapacitors, a DC bus, and a DC charging load. The hybrid energy storage system including distributed photovoltaic power generation, DC charging load, supercapacitors and batteries is connected to the DC bus through a bidirectional DC-DC converter, thereby achieving system power balance and bus voltage stability;

[0045] Since the battery and supercapacitor are connected in parallel, when the line impedance is not considered, the power distribution is equivalent to the current distribution; the current distribution is ; Among them, I ref is the total output current reference value; I sc is the actual current output value of the supercapacitor; I b Output current to the battery;

[0046] The battery reference current I can be obtained bref for:

[0047]

[0048] Where, Isc is the actual current value of the supercapacitor;

[0049] The uncompensated battery current is added to the supercapacitor current reference value to obtain the supercapacitor reference current I scref for:

[0050]

[0051] Where T is the filter time constant value; gain k=U b / U sc , U b 、U sc are the terminal voltages of the battery and supercapacitor respectively.

[0052] Furthermore, the design method of the voltage loop and current loop observer in step S2 is as follows: First, the voltage loop preset time expansion state observer is designed, and the system state variables x1, x2 and x3 are defined as the bus voltage U dc and its first-order derivative, lumped disturbance F, then the state variable equation of the voltage loop system is expressed as:

[0053]

[0054] Where b0 is the known control gain;

[0055] definition and are the state reconstruction values ​​of the state variables x1, x2 and x3 respectively, and the observation error is and , design the preset time observer as:

[0056]

[0057] Where, ; ; l1, l2, l3 and ξ are the observer gain coefficients to be designed; ; ; k3 > 1; the switching function Γ(t) is defined as follows:

[0058]

[0059] Where, T E >0 is the preset convergence time.

[0060] Combining the above two equations, the dynamic equation of the observation error is:

[0061] .

[0062] Furthermore, step S3 also includes improving the dynamic response quality of the system, and the method is as follows: designing the optimal sliding surface as:

[0063]

[0064] When the optimal feedback control law is adopted, the closed-loop system reaches the desired sliding mode state, that is, reaches the optimal sliding mode surface, and s(t)=0, that is:

[0065]

[0066] For the second-order optimal sliding mode surface, the second-order optimal sliding mode controller is shown as follows:

[0067]

[0068] Where u a is the feedforward steady-state control term used to improve model compensation; u s is the robust optimal control term used to suppress the impact of uncertainty on system performance; m1 is a positive constant; ; are the state reconstruction values ​​and observation values ​​of state variables x1 and x2 respectively;

[0069] The adaptive law of variable parameter gain k1 is selected as:

[0070]

[0071] Where, ε>0 is a small constant; and is the upper and lower bounds of the variable parameter gain k1; the adaptive law can limit the variable gain k1(t) to the interval formula (19) and within, that is .

[0072] Compared with the prior art, the present invention mainly has the following advantages:

[0073] 1. By using the observer to quickly observe and compensate the lumped disturbance within a preset time, the system state can be guaranteed to converge within the preset time, which not only improves the dynamic response performance of the system, but also improves the system's anti-disturbance ability;

[0074] 2. An optimal sliding surface was designed based on optimal feedback theory. Combining sliding mode control with optimal guaranteed cost robust control, the proposed control strategy can rapidly distribute power frequency, optimize the dynamic response characteristics of the photovoltaic-storage-charging DC microgrid system, and coordinate the efficient operation of the photovoltaic microgrid system.

[0075] 3. The performance under various working conditions is better than that of PI control and ADRC control, indicating that the proposed control strategy can well resist load disturbances and improve the robustness of the system, reflecting the superiority of PTESO+ORSMC. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is a step diagram of the control method of the present invention;

[0077] Figure 2 This is a structural diagram of the photovoltaic storage and charging DC microgrid system in the present invention;

[0078] Figure 3 This is the control block diagram of the photovoltaic storage and charging DC microgrid system in the present invention;

[0079] Figure 4 This is a simplified block diagram of the PTESO+ORSMC controller for the voltage loop of the photovoltaic storage and charging DC microgrid system in the present invention;

[0080] Figure 5 It is a graph of the output power fluctuation of the photovoltaic power source in the present invention;

[0081] FIG6 is a bus voltage waveform diagram of different control strategies when photovoltaic power is disturbed in the present invention, wherein FIG6(a) is a bus voltage waveform diagram of PI control, FIG6(b) is a bus voltage waveform diagram of ADRC control, and FIG6(c) is a bus voltage waveform diagram of PTESO+ORSMC control;

[0082] Figure 7 is a simulation waveform diagram of the bus voltage when the capacitor parameters are perturbed in the present invention, wherein Figure 7(a) is a simulation waveform diagram of the bus voltage when the capacitor parameters are perturbed, Figure 7(b) is an enlarged view of range A in Figure 7(a), and Figure 7(c) is an enlarged view of range B in Figure 7(a). DETAILED DESCRIPTION

[0083] The following will describe the technical solutions in the embodiments of the present invention clearly and completely with reference to the accompanying drawings.

[0084] Example:

[0085] like Figure 1 As shown, this embodiment provides a voltage control method for a photovoltaic energy storage and charging system based on a preset time observer. The method is as follows: a preset time expanded state observer is introduced to observe the total disturbance received by the system, and the disturbance observation value is fed back to the compensator for elimination; an optimal robust sliding mode control is designed for optimization, and the high and low frequency characteristics of the hybrid energy storage system components are utilized. The high and low frequency components are compensated by the battery and supercapacitor respectively, and the fluctuating power is distributed between the two using a low-pass filter, so as to improve the control response speed of the system.

[0086] like Figure 1 The specific method is as follows: S1, current equivalent distribution is performed on the power difference of the energy storage system of the light storage and charging direct-current micro grid to establish a mathematical model of the light storage and charging micro grid system, as shown in Figure 2 As shown in the figure, the light storage and charging micro grid system is composed of distributed photovoltaic power generation, storage batteries and super capacitors, a direct-current bus, and direct-current charging loads. For the hybrid energy storage system containing distributed photovoltaic power generation, direct-current charging loads, and super capacitors and storage batteries, the system is connected to the direct-current bus through a bidirectional DC-DC converter, so as to realize power balance and stability of the bus voltage of the system.

[0087] Since the storage battery and the super capacitor are connected in parallel, when the line impedance is not considered, the power distribution is equivalent to current distribution. The current distribution is ; wherein I ref is a total output current reference value; I sc is an actual current output value of the super capacitor; and I b is a storage battery output current.

[0088] The reference current I bref of the storage battery can be obtained as follows:

[0089]

[0090] In the formula, I sc is an actual current value of the super capacitor.

[0091] The uncompensated storage battery current is added to the reference current value of the super capacitor, so that the reference current I scref of the super capacitor is obtained as follows:

[0092]

[0093] In the formula, T is a filter time constant value; the gain k = U b / U sc , U b and U sc are the terminal voltages of the storage battery and the super capacitor, respectively.

[0094] By maintaining the stability of the bus voltage in the light storage and charging direct-current micro grid, the power balance of the system can be maintained. The control block diagram of the HESS power distribution realized by using a low-pass filter LPF is shown in Figure 3 . Figure 3 In the formula, U b , i b , L b , S b1 , and S b2 are the voltage of the storage battery, the inductor current, the inductor, and the duty cycle of the switch tube, respectively; U sc , i sc , L sc , Ssc1 and S sc2 They are respectively the supercapacitor voltage, the circuit current of the supercapacitor, the inductance and the duty cycle of the switch tube; U pv , I pv , L pv 、C pv , D and S pv They are the photovoltaic power module voltage, inductor current, inductor, filter capacitor, diode and duty cycle of the switch tube; U dc , C are the voltage and filter capacitance on the DC bus side respectively. Based on the above LPF current frequency division control, the PTESO+ORSMC control strategy is adopted for the photovoltaic storage and charging DC microgrid system. The control scheme is shown in the figure below. Figure 3 shown. Figure 3 First, the expected value of bus voltage U dcref With bus voltage U dc The deviation is used as the input value of the voltage loop control, and the PTESO+ORSMC control amount is used as the total current reference value I of the current loop. ref The low frequency component battery current reference value I bref As the input signal of the battery inner loop current control, the duty cycle of the battery switch tube is obtained after PTESO+ORSMC control, and the same applies to the supercapacitor current loop. The voltage and current loops of this embodiment adopt the same control strategy, so in the subsequent design, the total disturbance received by the system is first observed through the preset time observer (PTESO), and the total disturbance is input to the control end for compensation; the optimal robust sliding mode controller (ORSMC) with strong robustness and fast convergence speed is used as the system controller to improve the bus voltage anti-interference ability and the system dynamic response ability. The simplified block diagram of the overall scheme structure of the PTESO+ORSMC control law of the present invention is shown as follows: Figure 4 S2. Design a preset time state observer to define the hybrid energy storage control system as a second-order system as follows:

[0095] In the above formula, u is the system input; y is the system output; U dc is the DC bus voltage; ω is the external disturbance; a1 and a2 are system parameters; b is the system gain; a1, a2, and b are unknown; and b0≈b;

[0096] By separating the internal uncertainty disturbance of the system and putting the internal and external disturbances into the lumped disturbance, the above formula can be rewritten as

[0097]

[0098] In the above formula, b0 is the estimated value; F is the lumped disturbance; the lumped disturbance F is uniformly bounded, and its derivative with respect to time is bounded, that is, ;

[0099] The light storage and charging DC micro-grid system exchanges power with the DC bus through a bidirectional converter, and the control target is the stability of the bus voltage U dc The voltage loop and current loop observers are designed respectively;

[0100] First, the voltage loop preset time expansion state observer is designed, and the system state variables x1, x2 and x3 are defined as the bus voltage U dc and its first-order derivative, and the lumped disturbance F, then the state variable equation of the voltage loop system is expressed as:

[0101]

[0102] In the formula, b0 is a known control gain;

[0103] Let and be the state reconstruction value observations of state variables x1, x2 and x3 respectively, and the observation error is and , the preset time observer is designed as:

[0104]

[0105] In the formula, ; ; l1, l2, l3 and ξ are observer gain coefficients to be designed; ; ; k3 >1; the switching function Γ(t) is defined as follows:

[0106]

[0107] In the formula, T E >0 is the preset convergence time.

[0108] By combining the above two formulas, the kinetic equation of the observation error is:

[0109] ;

[0110] S3, design the optimal robust sliding mode controller

[0111] Define the system bus voltage tracking error variable as:

[0112]

[0113] In the formula, U dcref is the bus voltage reference value, e1 is the bus voltage tracking error, and e2 is the derivative of the bus voltage tracking error;

[0114] Define the error variable matrix as e=[e1 e2], then the differential equation of the system error variable is:

[0115]

[0116] Where, ;

[0117] In order to obtain the second-order optimal sliding surface, the following integral quadratic performance index J is selected:

[0118]

[0119] Where, is a positive definite symmetric matrix, p 11 , p 12 and p 22 are the coefficients of the positive definite symmetric matrix P.

[0120] Define the auxiliary variable λ1 as follows:

[0121]

[0122] We can further obtain the following set of standard equations:

[0123]

[0124] Where, ;

[0125] According to the LQR optimal feedback control strategy, the following optimal feedback control law can be obtained by using the minimization principle and the optimal control law:

[0126]

[0127] Where R1 is a positive constant and satisfies the following Riccati equation:

[0128]

[0129] Obtain the unique solution of the Riccati equation from the Riccati differential equation .

[0130] It also includes improving the dynamic response quality of the system by:

[0131] The optimal sliding surface is designed as:

[0132]

[0133] When the optimal feedback control law is adopted, the closed-loop system reaches the desired sliding mode state, that is, reaches the optimal sliding mode surface, and s(t)=0, that is:

[0134]

[0135] For the second-order optimal sliding mode surface, the second-order optimal sliding mode controller is shown as follows:

[0136]

[0137] Where u a is the feedforward steady-state control term used to improve model compensation; u s is the robust optimal control term used to suppress the impact of uncertainty on system performance; m1 is a positive constant; ; are the state reconstruction values ​​and observation values ​​of state variables x1 and x2 respectively;

[0138] The adaptive law of variable parameter gain k1 is selected as:

[0139]

[0140] Where, ε>0 is a small constant; and is the upper and lower bounds of the variable parameter gain k1; the adaptive law can limit the variable gain k1(t) to the interval formula (19) and within, that is .

[0141] Simulation Verification

[0142] In order to verify the effectiveness of the control strategy (PTESO+ORSMC) proposed in the above embodiment, a simulation system of PI control, ADRC control and PTESO+ORSMC strategy was built for a DC microgrid with hybrid energy storage in Matlab / Simulink simulation software, and the three different control strategies were compared. The initial conditions of the system are as follows: the light intensity is 1000W / m 2 The ambient temperature is 25°C and the low-pass filter TLPF is 0.005. The system model parameters are shown in Table 1, and the controller simulation parameters are shown in Table 2.

[0143] Table 1 System model parameters

[0144] Subsystem parameter Numerical PV array side <![CDATA[开路电压V oc / V Short-circuit current Isc / A Filter capacitor C pv / mF energy storage inductor L pv / mH]]> 45.25.620.4735 Battery side <![CDATA[初始电压U b / V energy storage inductor L b / mH State of Charge SOC / %]]> 240480 Supercapacitor side <![CDATA[额定电压U sc / V Initial voltage U sc / V Rated capacitance C sc / F Energy storage inductor L sc / mH]]> 300240294 DC bus side DC bus reference voltage Bus capacitance C / F Switching frequency f / kHz 7000.00410

[0145] Table 2 Controller parameters

[0146] Controller Voltage loop Current loop PI <![CDATA[K pv =0.5K iv =15]]> K pi = 100 K ii = 0.5 ADRC <![CDATA[β1=100; β2=200; β3=300; b0 = 240; k pv =0.5;k d =0.5;]]> <![CDATA[β1=100; β2=200; β3=300; b0=240; k pv =0.5; k d =0.5;]]> PTESO+ORSMC <![CDATA[l1=l2=l3=0.01,v=0.05,T E =1s,L=0.1,k3=1.01,k2=5. 33,k1 =3. 35]]> <![CDATA[l1=l2=l3=0.01,v=0.05,T E =1s,L=0.1,k3=1.01,k2=5. 33,k1 =3. 35]]>

[0147] (1) Simulation comparison of constant load power and light intensity changes

[0148] When the load is running at a constant power of 2kW and the light intensity changes, the output power of the photovoltaic power supply is as follows: Figure 5As shown in Figure 2, for photovoltaic power fluctuations, different control strategies are compared and analyzed as follows, with the circuit parameters unchanged.

[0149] Figure 6 is a comparison of bus voltage waveforms during PV power disturbance. Analyzing the simulation results in Figure 6, we can see that the light intensity suddenly changes to 500W / m at 0.7s. 2 At this time, the photovoltaic output power is insufficient to meet the load demand, and the bus voltage drops. At this time, the hybrid energy storage unit compensates for the difference in power and stabilizes the bus voltage. 2 Increased to 1500 W / m 2 , the photovoltaic output power is greater than the load demand, and the bus voltage rises instantaneously. At this time, the hybrid energy storage unit absorbs the difference in power and stabilizes the bus voltage. Compared with other control strategies, the control method of the present invention is superior to other control strategies in terms of bus voltage overshoot and adjustment time. The control method of the present invention can realize the rapid frequency division and distribution of the power of the energy storage equipment in the hybrid energy storage, and its bus voltage overshoot and adjustment time are low, and it has a good suppression effect on disturbances. The control method of the present invention can effectively suppress bus voltage fluctuations, improve system stability, and verify the effectiveness of the control strategy of the present invention.

[0150] (2) Circuit parameter perturbation simulation comparison

[0151] Under actual operating conditions, system component parameters often change with increasing operating time and changes in factors such as system temperature. To verify the effectiveness of the control strategy of this invention, a simulation comparison of the hybrid energy storage system operating at different capacitance values ​​was conducted to simulate parameter perturbations under actual operating conditions.

[0152] The bus capacitor was operated at its normal value of 4mF, 3mF, and 5mF, respectively, to simulate actual capacitance parameter perturbations. The simulated bus voltage waveforms during capacitance parameter perturbations are shown in Figure 7. The simulation conditions were: at 0.5 s, the bus voltage reference value dropped to 660 V; at 1.0 s, the bus voltage reference value rose to 715 V. Figures 7(b) and 7(c) show that when the bus voltage reference value changes, the control strategy of the present invention can stably track the desired voltage under different capacitance parameters, demonstrating the dynamic and robust nature of the strategy.

[0153] The above simulation results show that the control method of the present invention performs better than other strategies under various working conditions, has a faster response speed and better anti-interference performance, which reflects the superiority of the control method of the present invention.

[0154] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the concept of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A voltage control method for a photovoltaic storage and charging system based on a preset time observer, characterized by: The method is as follows: by introducing a preset time-expanded state observer to observe the total disturbance received by the system, and feeding the disturbance observation value back to the compensator for elimination; designing an optimal robust sliding mode control for optimization, and utilizing the high and low frequency characteristics of the hybrid energy storage system components, the high and low frequency components are compensated by the battery and supercapacitor respectively, and the fluctuating power is distributed between the two using a low-pass filter to improve the system's regulation response speed.

2. The voltage control method for a photovoltaic storage and charging system based on a preset time observer according to claim 1, characterized in that: The specific method is as follows: S1. Perform current equivalent distribution on the differential power of the photovoltaic-storage-charging DC microgrid energy storage system to establish a mathematical model of the photovoltaic-storage-charging microgrid system, and convert the power distribution of the photovoltaic-storage-charging microgrid system into current distribution to obtain a battery reference current; add the uncompensated battery current to the supercapacitor current reference value to obtain a supercapacitor reference current value; S2. Design a preset time state observer to define the hybrid energy storage control system as a second-order system as follows: ; In the above formula, u is the system input; y is the system output; U dc is the DC bus voltage; ω is the external disturbance; a1 and a2 are system parameters; b is the system gain; a1, a2, and b are unknown; and b0≈b; Separating the internal uncertainty disturbance of the system and putting the internal and external disturbances into the lumped disturbance, the above formula can be rewritten as ; In the above formula, b0 is the estimated value; F is the lumped disturbance; the lumped disturbance F is uniformly bounded, and its derivative with respect to time is bounded, that is, ; The photovoltaic storage and charging DC microgrid system exchanges power with the DC bus through a bidirectional converter, and its control target is the bus voltage U dc To ensure the stability of the circuit, the voltage loop and current loop observers are designed respectively; S3. Design of optimal robust sliding mode controller The system bus voltage tracking error variable is defined as: ; Where U dcref is the bus voltage reference value, e1 is the bus voltage tracking error, and e2 is the derivative of the bus voltage tracking error; Define the error variable matrix as e=[e1 e2], then the differential equation of the system error variable is: ; Where, ; In order to obtain the second-order optimal sliding surface, the following integral quadratic performance index J is selected: ; Where, is a positive definite symmetric matrix, p 11 , p 12 and p 22 are the coefficients of the positive definite symmetric matrix P; Define the auxiliary variable λ1 as follows: ; We can further obtain the following set of standard equations: ; Where, ; According to the LQR optimal feedback control strategy, the following optimal feedback control law can be obtained by using the minimization principle and the optimal control law: ; Where R1 is a positive constant and satisfies the following Riccati equation: ; Obtain the unique solution of the Riccati equation from the Riccati differential equation .

3. The voltage control method for a solar-to-storage system based on a preset time observer according to claim 2, characterized in that: The specific method of step S1 is as follows: establishing a mathematical model of a photovoltaic-storage-charging microgrid system, wherein the photovoltaic-storage-charging microgrid system is composed of distributed photovoltaic power generation, batteries and supercapacitors, a DC bus, and a DC charging load. For a hybrid energy storage system composed of distributed photovoltaic power generation, DC charging load, supercapacitors, and batteries, a bidirectional DC-DC converter is connected to the DC bus to achieve system power balance and bus voltage stability; Since the battery and supercapacitor are connected in parallel, when the line impedance is not considered, the power distribution is equivalent to the current distribution; the current distribution is ; Among them, I ref is the total output current reference value; I sc is the actual current output value of the supercapacitor; I b Output current to the battery; The battery reference current I can be obtained bref for: ; Where, I sc is the actual current value of the supercapacitor; The uncompensated battery current is added to the supercapacitor current reference value to obtain the supercapacitor reference current I scref for: Where T is the filter time constant value; gain k=U b / U sc , U b 、U sc are the terminal voltages of the battery and supercapacitor respectively.

4. The voltage control method for a photovoltaic storage and charging system based on a preset time observer according to claim 2, characterized in that: The design method of the voltage loop and current loop observer in step S2 is as follows: First, the voltage loop preset time expansion state observer is designed, and the system state variables x1, x2 and x3 are defined as the bus voltage U dc and its first-order derivative, lumped disturbance F, the state variable equation of the voltage loop system is expressed as: Where b0 is the known control gain; definition and are the state reconstruction values ​​of the state variables x1, x2 and x3 respectively, and the observation error is and , design the preset time observer as: Where, ; ; l1, l2, l3 and ξ are the observer gain coefficients to be designed; ; ; k3 > 1; the switching function Γ(t) is defined as follows: Where, T E >0 is the preset convergence time; Combining the above two equations, the dynamic equation of the observation error is: 。 5. The voltage control method for a photovoltaic storage and charging system based on a preset time observer according to claim 2, characterized in that: The step S3 also includes improving the dynamic response quality of the system, and the method is as follows: designing the optimal sliding surface as: When the optimal feedback control law is adopted, the closed-loop system reaches the desired sliding mode state, that is, reaches the optimal sliding mode surface, and s(t)=0, that is: For the second-order optimal sliding mode surface, the second-order optimal sliding mode controller is shown as follows: Where u a is the feedforward steady-state control term used to improve model compensation; u s is the robust optimal control term used to suppress the impact of uncertainty on system performance; m1 is a positive constant; ; are the state reconstruction values ​​and observation values ​​of state variables x1 and x2 respectively; The adaptive law of variable parameter gain k1 is selected as: Where, ε>0 is a small constant; and is the upper and lower bounds of the variable parameter gain k1; the adaptive law can limit the variable gain k1(t) to the interval formula (19) and within, that is .

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