Transient stability boundary evaluation method for hybrid power supply system of unmanned aerial vehicle

Abstract

The invention relates to the technical field of electrical control, and discloses a transient stability boundary evaluation method, system and device for a hybrid power system of an unmanned aerial vehicle and a medium. The method comprises the following steps: establishing a state-space equation of the unmanned aerial vehicle hybrid power supply system under each working condition, and obtaining a balance point of the unmanned aerial vehicle hybrid power supply system under each working condition; dividing the nonlinear state-space equation under each working condition into a plurality of linear state-space equations, and establishing a global T-S fuzzy model of the unmanned aerial vehicle hybrid power system under each working condition; constructing a Lyapunov function of the global T-S fuzzy model under each working condition, and estimating an attraction domain of the unmanned aerial vehicle hybrid power system at a balance point under each working condition; according to the attraction domain corresponding to the unmanned aerial vehicle hybrid power system under each working condition, the transient stability boundary of the unmanned aerial vehicle hybrid power system is evaluated, and system stability analysis under the large disturbance condition is achieved.

Description

Technical Field

[0001] The present invention relates to the field of electrical control technology, and in particular to a transient stability boundary assessment method, system, device and medium for a hybrid power system of an unmanned aerial vehicle (UAV). Background Art

[0002] In recent years, with the rapid development of energy storage technology, UAV power systems are gradually shifting from traditional centralized power supply using a single power source to distributed power supply using multiple power sources. New power supply units, such as fuel cells, lithium batteries, flywheels, and supercapacitors, have been gradually integrated into UAV power systems, forming multi-source hybrid power systems. Currently, this multi-source hybrid power system, consisting of fuel cells, lithium batteries, and supercapacitors, has been successfully applied to various integrated reconnaissance and strike UAVs. Multi-source hybrid power systems are typical cascade power electronic systems and are highly susceptible to source-load interaction instability. Therefore, evaluating the stability boundaries of multi-source hybrid power systems and preventing instability is key to ensuring the safe and stable operation of UAVs.

[0003] Currently, there have been numerous studies on the stability analysis of hybrid power systems for drones, but the focus has primarily been on small-signal stability analysis. Small-signal stability analysis is a method used to assess the stability of a system when subjected to small disturbances. Typical small-signal stability analysis methods include the Nyquist stability criterion, the eigenvalue method, the impedance ratio criterion, and its improved versions. Because small-signal stability analysis methods ignore higher-order disturbance terms in the system model, small-signal stability theory is only applicable to analyzing the stability of a system when subjected to small disturbances at a certain steady-state operating point. It cannot be used to study the stability of a system under large disturbances such as startup, fault response, and heavy load switching.

[0004] Commonly used large-signal stability analysis methods—those used to assess the stability of a system under large disturbances—mainly include the phase plane method, Lyapunov stability theory, mixed potential function theory, and sum-of-squares programming. However, existing research results on large-signal stability analysis methods remain incomplete, making it difficult to effectively address the challenges of large-signal stability analysis for multi-source hybrid power systems in drones. For example, when large-signal disturbances (strong pulse loads, large load switching) are present in the system, existing large-signal stability analysis methods still have application limitations. The analysis either ignores the dynamic regulation of differentiated power sources, resulting in highly conservative results, or suffers from the curse of dimensionality, making the analysis difficult to perform. Therefore, the transient instability mechanism of drone hybrid power systems that consider the dynamics of the control system under large disturbances remains unclear, and quantitative transient stability assessment metrics are still lacking. Summary of the Invention

[0005] The purpose of the present invention is to provide a transient stability boundary assessment method, system, device and medium for a hybrid power system of an unmanned aerial vehicle (UAV), which can solve the problem that the existing system cannot effectively deal with large-signal stability analysis of a multi-source hybrid power system of an UAV.

[0006] To solve the above technical problems, an embodiment of the present invention provides a transient stability boundary assessment method for a hybrid power system of a UAV, comprising the following steps: Based on the architecture of the drone hybrid power system and the control strategy adopted by the drone hybrid power system under different operating conditions, the state space equation of the drone hybrid power system under each operating condition is established, and the equilibrium point of the drone hybrid power system under each operating condition is obtained based on the state space equation; wherein, the operating conditions include normal operation, short circuit fault, energy feedback mode, and failure of any power supply; Based on the theory of TS fuzzy model, the nonlinear state space equation under each working condition is divided into multiple linear state space equations. Then, a global TS fuzzy model of the UAV hybrid power system under each working condition is established through multiple linear state space equations. Construct a Lyapunov function for the global TS fuzzy model under each operating condition, and use the Lyapunov function to estimate the attraction domain of the UAV hybrid power system at the equilibrium point under each operating condition; wherein the attraction domain is used to characterize the tolerance range of the UAV hybrid power system to large signal disturbances at the equilibrium point; According to the corresponding attraction domain of the UAV hybrid power system under each working condition, the transient stability boundary of the UAV hybrid power system is evaluated.

[0007] Optionally, when the operating condition is normal operation, the global TS fuzzy model of the UAV hybrid power system is established by the following steps: Obtain the nonlinear terms in the state space equations of the UAV hybrid power system during normal operation; All nonlinear terms belong to x 1 and x 2. Based on the TS fuzzy model, the state variables in the state space equation to which the nonlinear terms belong are divided into fuzzy subspaces corresponding to the state variables to construct a global TS fuzzy model; Among them, the global TS fuzzy model of the UAV hybrid power system during normal operation is: ; Where, is the state variable, x 1 and x 2 is the state variable in the state space equation to which the nonlinear term belongs; for To translate, , Indicates status The balance point, for The derivative of , for The maximum and minimum values of ; is the preset coefficient matrix.

[0008] Optionally, when the operating condition is normal operation, the Lyapunov function of the global TS fuzzy model is constructed by the following steps: The linear time-invariant system of the hybrid power system of the UAV during normal operation is obtained as follows: ; For the global TS fuzzy model, a real symmetric positive definite matrix M is preset so that the candidate Lyapunov function is constructed as: ; If the matrix If is negative definite, then according to the linear time-invariant system and the global TS fuzzy model, the Lyapunov function of the TS fuzzy model is constructed as the following optimization problem: ; Where, is the preset coefficient matrix, is the transposed matrix of M, for The transposed matrix of Optionally, when the operating condition is normal operation, the attraction domain of the hybrid power system of the UAV at the equilibrium point is estimated by the following steps, including: make and Starting from the equilibrium point of the UAV hybrid power system, and increasing the and reduce ; Check whether the optimization problem has a feasible solution. If so, continue to increase the default step size δ and reduce , until there is no feasible solution to the optimization problem; According to the obtained x i The extreme value of and M matrix determine the attraction domain.

[0009] Optionally, the transient stability boundary of the UAV hybrid power system is evaluated according to the attraction domain corresponding to the UAV hybrid power system under each operating condition, including: Under each working condition, the influence of the system parameters of the UAV hybrid power system on the attraction domain is obtained to evaluate the transient stability boundary of the UAV hybrid power system; Among them, the system parameters include: the main circuit parameters of the power supply side of the UAV hybrid power system, the virtual droop parameters of the power supply side, the PI regulator parameters and the load power level.

[0010] Optionally, the hybrid power system of the UAV includes three power sources: a fuel cell, a lithium battery, and a supercapacitor; wherein the fuel cell, the lithium battery, and the supercapacitor are connected to the DC bus through respective converters, the converter for the fuel cell is a unidirectional Boost converter, and the converters for the lithium battery and the supercapacitor are bidirectional Buck-Boost converters; The load of the hybrid power system of the UAV includes at least a constant power load, a resistive load and a pulse load; The output voltages of the individual converters are equal, and the sum of the output currents of the individual converters is equal to the total current of the load, as follows: ; Where, v ofc , v obat , v osc are the output voltages of fuel cells, lithium batteries, and supercapacitor converters respectively; v bus is the voltage of the DC bus; i ofc , i obat , i osc are the output currents of fuel cells, lithium batteries, and supercapacitor converters respectively; P is the load power.

[0011] Optionally, according to the architecture of the UAV hybrid power system and the control strategy adopted by the UAV hybrid power system under different working conditions, the state space equation of the UAV hybrid power system under each working condition is established, including: When the hybrid power system of the drone is operating normally, when a short circuit fault occurs, when the fuel cell fails / is in energy feedback mode, when the lithium battery fails, and when the supercapacitor fails, the following parameters are obtained as corresponding state variables: Load power; filter inductors and filter capacitors for fuel cells, lithium batteries, and supercapacitor converters; input voltage, input current, and output voltage of fuel cell converters, lithium battery converters, and supercapacitor converters; maximum input current of fuel cell converters, lithium battery converters, and supercapacitor converters; reference value of the voltage loop of the dual closed-loop PI controller of the UAV hybrid power system after applying virtual impedance droop control; nominal bus voltage; output current of the fuel cell converter, lithium battery converter, and supercapacitor converter; resistive droop coefficient of the fuel cell converter, lithium battery converter, and supercapacitor converter; inductive droop coefficient of the fuel cell converter and capacitive droop coefficient of the supercapacitor converter; virtual current of the fuel cell converter and virtual voltage of the supercapacitor converter; bus voltage; proportional gain and integral gain of the voltage loop and current loop of the PI controller in the fuel cell converter, lithium battery converter, and supercapacitor converter; current loop reference value of the fuel cell converter, lithium battery converter, and supercapacitor converter; Based on the obtained state variables, the state space equations of the UAV hybrid power system are established respectively when it is in normal operation, when a short circuit fault occurs, when the fuel cell fails / is in energy feedback mode, when the lithium battery fails, and when the supercapacitor fails.

[0012] An embodiment of the present invention further provides a transient stability boundary assessment system for a hybrid power system of an unmanned aerial vehicle, comprising: A state acquisition module is used to establish a state space equation for the hybrid power system of the drone under each operating condition based on the architecture of the hybrid power system of the drone and the control strategy adopted by the hybrid power system of the drone under different operating conditions, and to obtain the equilibrium point of the hybrid power system of the drone under each operating condition based on the state space equation; wherein the operating conditions include normal operation, short circuit fault, energy feedback mode, and failure of any power supply; The model building module is used to divide the nonlinear state space equation under each working condition into multiple linear state space equations based on the theory of TS fuzzy model, and then establish the global TS fuzzy model of the UAV hybrid power system under each working condition through multiple linear state space equations; A function construction module is used to construct a Lyapunov function of the global TS fuzzy model under each operating condition, and estimate the attraction domain of the hybrid power system of the UAV at the equilibrium point under each operating condition through the Lyapunov function; wherein the attraction domain is used to characterize the tolerance range of the hybrid power system of the UAV to large signal disturbances at the equilibrium point; The stability evaluation module is used to evaluate the transient stability boundary of the UAV hybrid power system according to the corresponding attraction domain of the UAV hybrid power system under each working condition.

[0013] An embodiment of the present invention also provides a computer device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above-mentioned transient stability boundary assessment method of the hybrid power system of the unmanned aerial vehicle.

[0014] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the transient stability boundary assessment method of the hybrid power system of a UAV.

[0015] The transient stability boundary assessment method for a hybrid power system of a UAV provided by the present invention has at least the following beneficial effects: First, a state-space equation is established for a drone hybrid power system under various operating conditions (e.g., normal operation, short-circuit fault, energy feedback mode, and failure of any power supply). Based on the state-space equation, the equilibrium point of the drone hybrid power system under each operating condition is obtained. Then, based on the TS fuzzy model, the Lyapunov function is used to evaluate the stability of the drone hybrid power system under different operating conditions. Specifically, the attraction domain of the drone hybrid power system at the equilibrium point under different operating conditions is estimated. This fully covers the system state when the system is subjected to large disturbances. Since the attraction domain is used to describe the region where the system state trajectory converges to a specific attraction set (e.g., an equilibrium point or periodic orbit), the present invention can use the attraction domain of the drone hybrid power system at the equilibrium point under different operating conditions to characterize the tolerance range of the drone hybrid power system to large signal disturbances at the equilibrium point under each operating condition. Based on this, the transient instability mechanism of the drone hybrid power system under large signal disturbances can be analyzed. In other words, this attraction domain is used as an evaluation indicator for the transient stability boundary of the drone hybrid power system, thereby further improving the stability of the drone hybrid power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] One or more embodiments are exemplarily described by the figures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments.

[0017] Figure 1 This is a flow chart of a transient stability boundary assessment method for a hybrid power system of a UAV provided according to one embodiment of the present invention; Figure 2 This is a schematic diagram of the architecture of a hybrid power system for a drone provided according to an embodiment of the present invention; Figure 3 is a schematic diagram of a simplified equivalent circuit of a multi-source hybrid power system based on virtual impedance droop control according to an embodiment of the present invention; Figure 4 is a schematic diagram of a system bottom-layer operation control strategy provided according to an embodiment of the present invention; Figure 5 is a schematic diagram of a system short circuit fault provided according to an embodiment of the present invention; Figure 6 This is a schematic diagram of a fuel cell fault exit / energy feedback mode provided according to an embodiment of the present invention; Figure 7 This is a schematic diagram of a supercapacitor failure provided according to an embodiment of the present invention; Figure 8 This is a schematic diagram of a lithium battery failure provided according to an embodiment of the present invention; Figure 9 1 is a schematic diagram of the influence of a change in a passive parameter on a power supply side on an estimated attraction domain according to an embodiment of the present invention; Figure 10 Schematic diagram of the effect of a change in a power supply side droop coefficient on an estimated attraction domain provided by an embodiment of the present invention; Figure 11 Schematic diagram of the effect of a fuel cell converter PI regulator parameter change on an estimated attraction domain according to an embodiment of the present invention; Figure 12 Schematic diagram of the effect of parameter changes of a PI regulator of a lithium battery converter on the estimated attraction domain provided by one embodiment of the present invention; Figure 13 Schematic diagram of the effect of a change in a PI regulator parameter of a supercapacitor converter on an estimated attraction domain according to an embodiment of the present invention; Figure 14 The figure is a schematic diagram of the influence of a system with constant power loads of different power levels on the estimated attraction domain according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] To make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, it will be understood by those skilled in the art that in the embodiments of the present invention, many technical details are provided to enable the reader to better understand the present invention. However, even without these technical details and the various changes and modifications based on the following embodiments, the technical solutions claimed in the present invention can be implemented. The division of the following embodiments is for convenience of description and should not constitute any limitation on the specific implementation of the present invention. The various embodiments can be combined with each other and referenced to each other under the premise that there is no contradiction.

[0019] One embodiment of the present invention relates to a transient stability boundary assessment method for a hybrid power system of an unmanned aerial vehicle. The implementation details of the transient stability boundary assessment method for a hybrid power system of an unmanned aerial vehicle of this embodiment are described in detail below. The following content is only the implementation details provided for the convenience of understanding and is not necessary for the implementation of this solution.

[0020] The specific process of the transient stability boundary assessment method of the hybrid power system of the UAV in this embodiment can be as follows: Figure 1 Shown, including: Step 101: Based on the architecture of the drone hybrid power system and the control strategy adopted by the drone hybrid power system under different working conditions, a state space equation of the drone hybrid power system under each working condition is established, and based on the state space equation, the equilibrium point of the drone hybrid power system under each working condition is obtained; wherein the working conditions include normal operation, short circuit fault, energy feedback mode, and failure of any power supply.

[0021] Specifically, the architecture of the UAV hybrid power system is as follows: Figure 2 As shown in the figure, the system includes three power sources: fuel cell, lithium battery, and supercapacitor. The fuel cell, lithium battery, and supercapacitor are connected to the 270V DC bus through their own converters. The converter of the fuel cell is a unidirectional Boost converter, and the converters of the lithium battery and supercapacitor are bidirectional Buck-Boost converters.

[0022] The load of the UAV hybrid power system includes at least constant power load, resistive load and pulse load; the output voltage of each converter is equal, and the sum of the output current of each converter is equal to the total current of the load, as follows: ; Where, v ofc , v obat , v osc are the output voltages of fuel cells, lithium batteries, and supercapacitor converters respectively; v bus is the voltage of the DC bus; i ofc , i obat , i osc are the output currents of fuel cells, lithium batteries, and supercapacitor converters respectively; P is the load power.

[0023] The simplified equivalent circuit of the power supply system after the introduction of virtual impedance droop control is as follows: Figure 3As shown in the figure, the load power is automatically distributed to the supercapacitor, lithium battery and fuel cell through virtual impedance droop control, which provides the high-frequency power, medium-frequency power and low-frequency power. In steady state, the fuel cell provides all the load power, while the supercapacitor and lithium battery jointly provide dynamic power in the dynamic process. In order to quickly stabilize the bus voltage, a dual closed-loop PI regulator is introduced based on the virtual impedance droop control. The underlying operation control strategy of each power supply unit is as follows: Figure 4 shown.

[0024] At this time, when the drone hybrid power system is operating normally, when a short circuit fault occurs, when the fuel cell fails / is in energy feedback mode, when the lithium battery fails, and when the supercapacitor fails, the following parameters are obtained as corresponding state variables: Load power; filter inductors and filter capacitors for fuel cells, lithium batteries, and supercapacitor converters; input voltage, input current, and output voltage of fuel cell converters, lithium battery converters, and supercapacitor converters; maximum input current of fuel cell converters, lithium battery converters, and supercapacitor converters; reference value of the voltage loop of the dual closed-loop PI controller of the UAV hybrid power system after applying virtual impedance droop control; nominal bus voltage; output current of the fuel cell converter, lithium battery converter, and supercapacitor converter; resistive droop coefficient of the fuel cell converter, lithium battery converter, and supercapacitor converter; inductive droop coefficient of the fuel cell converter and capacitive droop coefficient of the supercapacitor converter; virtual current of the fuel cell converter and virtual voltage of the supercapacitor converter; bus voltage; proportional gain and integral gain of the voltage loop and current loop of the PI controller in the fuel cell converter, lithium battery converter, and supercapacitor converter; current loop reference value of the fuel cell converter, lithium battery converter, and supercapacitor converter; Based on the obtained state variables, the state space equations of the UAV hybrid power system are established respectively when it is in normal operation, when a short circuit fault occurs, when the fuel cell fails / is in energy feedback mode, when the lithium battery fails, and when the supercapacitor fails.

[0025] Among them, when the UAV hybrid power system is operating normally, the state variables are selected as follows: ; Where, i Lfc , i Lbat , i Lsc are the input currents of fuel cell, lithium battery and supercapacitor converter respectively; v ofc , v obat , v oscare the output voltages of fuel cells, lithium batteries, and supercapacitor converters respectively; k vpfc , k vifc , k ipfc , k iifc , k vpbat , k vibat , k ipbat , k iibat , k vpsc , k visc , k ipsc , k iisc They are the proportional gain and integral gain of the voltage loop and current loop of the PI controller in the fuel cell, lithium battery and supercapacitor converter respectively; i Lfcref , i Lbatref , i Lscref These are the current loop reference values for fuel cells, lithium batteries, and supercapacitor converters respectively; v fcref , v batref , v scref are the reference values of the voltage loop of the dual closed-loop PI controller after applying virtual impedance droop control; i Lvfc and v Cvsc are the virtual current of the fuel cell converter and the virtual voltage of the supercapacitor converter respectively.

[0026] Then, based on the selected state variables, the state space equation of the UAV hybrid power system during normal operation is established as follows: ; The balance point of the drone hybrid power system during normal operation is as follows: .

[0027] make , translate the original system to the origin, and we have: .

[0028] When a short circuit occurs in the hybrid power system of the UAV, Figure 5As shown, the output current rises sharply and the control loop enters the current limiting operation mode. At this time, the droop control and voltage loop are both invalid. At this time, the state variables are selected as follows: .

[0029] It should be noted that at this time x 1~ x 7 and above x 1~ x 12 Different, according to the system architecture and control strategy at this time, the state space model and equilibrium point of the system at this time can be obtained.

[0030] The state space equation of the UAV hybrid power system when a short circuit fault occurs is as follows: .

[0031] The balance point of the drone hybrid power system in the event of a short circuit fault is as follows: .

[0032] make , translate the original system to the origin, and we have: .

[0033] When a fuel cell fails, the power supply system degenerates into a hybrid power supply system consisting of lithium batteries and supercapacitors, such as Figure 6 Therefore, when analyzing stability, only the stability of the hybrid power supply system consisting of lithium batteries and supercapacitors needs to be considered. In addition, when regenerative energy is fed back to the bus (i.e., energy feedback mode), since the fuel cell converter has unidirectional energy flow and cannot absorb the fed-back energy, the power supply system can also be considered to have degraded to a hybrid power supply system consisting of lithium batteries and supercapacitors. The stability analysis is the same as above.

[0034] When the system is in fuel cell failure / energy feedback mode, the state variables are selected as follows: .

[0035] It should be noted that at this time x 1~ x 8 and the above x 1~ x 7, according to the system architecture and control strategy at this time, the state space model and equilibrium point of the system at this time can be obtained.

[0036] The state space equation of the UAV hybrid power system in the event of a fuel cell failure / in energy regeneration mode is as follows: .

[0037] The balance point of the UAV hybrid power system in the event of fuel cell failure / energy regeneration mode is as follows: .

[0038] make , translate the original system to the origin, and we have: .

[0039] When the supercapacitor fails, the power supply system degenerates into a hybrid power supply system consisting of fuel cells and lithium batteries, such as Figure 7 Therefore, solving the attraction domain of the hybrid power supply system composed of fuel cells and lithium batteries after the fault can determine the stability of the power supply system after the fault.

[0040] When a supercapacitor fails, the state variables are selected as follows: .

[0041] It should be noted that at this time x 1~ x 8 and the above x 1~ x 8. According to the system architecture and control strategy at this time, the state space model and equilibrium point of the system at this time can be obtained.

[0042] The state space equation of the UAV hybrid power system when the supercapacitor fails is as follows: .

[0043] The balance point of the drone hybrid power system when the supercapacitor fails is as follows: .

[0044] make , translate the original system to the origin, and we have: .

[0045] When the lithium battery fails, the power supply system degenerates into a hybrid power supply system consisting of fuel cells and supercapacitors, such as Figure 8 Therefore, solving the attraction domain of the hybrid power supply system composed of fuel cells and supercapacitors after the fault can determine the stability of the power supply system after the fault.

[0046] When a lithium battery fails, the state variables are selected as follows: .

[0047] It should be noted that at this time x 1~ x 9 and the previous expressionx 1~ x 8. According to the system architecture and control strategy at this time, the state space model and equilibrium point of the system at this time can be obtained.

[0048] The state space equation of the UAV hybrid power system when the lithium battery fails is as follows: .

[0049] The balance point of the drone hybrid power system when the lithium battery fails is as follows: .

[0050] make , translate the original system to the origin, and we have: ; Where, P is the load power; L fc , L bat , L sc , C fc , C bat , C sc They are filter inductors and filter capacitors for fuel cells, lithium batteries, and supercapacitor converters; v fc , v bat , v sc , i Lfc , i Lbat , i Lsc , v ofc , v obat , v osc They are the input voltage, input current (inductor current) and output voltage of the fuel cell, lithium battery and supercapacitor converter respectively; i Lfcmax , i Lbatmax , i Lscmax These are the maximum input currents of fuel cells, lithium batteries, and supercapacitor converters respectively; v fcref , v batref , v screfare the reference values of the voltage loop of the dual closed-loop PI controller of the UAV hybrid power system after applying the virtual impedance droop control; v nom is the nominal value of bus voltage; i ofc , i obat , i osc are the output currents of fuel cells, lithium batteries, and supercapacitor converters respectively; R vfc , R vbat , R vsc They are the resistive droop coefficients of fuel cells, lithium batteries, and supercapacitor converters respectively; L vfc , C vsc They are the inductive droop coefficient of the fuel cell converter and the capacitive droop coefficient of the supercapacitor converter respectively; i Lvfc and v Cvsc are the virtual current of the fuel cell converter and the virtual voltage of the supercapacitor converter respectively; v bus is the bus voltage; k vpfc , k vifc , k ipfc , k iifc , k vpbat , k vibat , k ipbat , k iibat , k vpsc , k visc , k ipsc , k iisc They are the proportional gain and integral gain of the voltage loop and current loop of the PI controller in the fuel cell, lithium battery and supercapacitor converter respectively; i Lfcref , i Lbatref , i Lscref These are the current loop reference values for fuel cells, lithium batteries, and supercapacitor converters respectively; Status The derivative of Indicates status The balance point, i is an integer greater than or equal to 1.

[0051] Step 102, based on the theory of TS fuzzy model, the nonlinear state space equation under each working condition is divided into multiple linear state space equations, and a global TS fuzzy model of the UAV hybrid power system under each working condition is established through multiple linear state space equations.

[0052] According to the TS fuzzy model theory, when the system operates normally, the following nonlinear terms exist in the system state equation: ; From the above formula, we can see that although there are many nonlinear terms (non-constant matrix coefficients) in the system model, they all belong to the state x 1 and x 2, that is, by dividing the state x 1 and x 2 fuzzy subspace to construct the global TS fuzzy model.

[0053] That is, the original system can be expressed by the following four local linear systems through the weighted summation of membership functions, and the stability of the original system can be analyzed by the stability of these four local linear systems: .

[0054] Where, for To translate, , for The derivative of , for The maximum and minimum values of ; is the preset coefficient matrix.

[0055] After constructing the TS fuzzy model, the system model shown in the above formula can be obtained.

[0056] Step 103: construct a Lyapunov function of the global TS fuzzy model under each working condition, and estimate the attraction domain of the UAV hybrid power system at the equilibrium point under each working condition through the Lyapunov function; wherein the attraction domain is used to characterize the tolerance range of the UAV hybrid power system to large signal disturbances at the equilibrium point.

[0057] Specifically, consider the following linear time-invariant system: ; The origin is the equilibrium point. For the above local linear system, assume that there exists a real symmetric positive definite matrix M , so that the candidate Lyapunov function is constructed as: ; If the matrix is negative definite, then the origin of the above linear time-invariant system is asymptotically stable.

[0058] Considering that the TS fuzzy model is composed of multiple local models weighted by membership functions, the Lyapunov function of the TS fuzzy model is V ( x ) can be formulated as the following optimization problem: ; Where, is the preset coefficient matrix, is the transposed matrix of M, for The transposed matrix of .

[0059] Since the local linear system model will change with the boundary of the fuzzy subspace, that is, when and When the system matrix changes A will also change accordingly. and After reaching a certain limit, there will be no feasible solution to the above optimization problem. Therefore, the specific implementation method of the attraction domain estimation is: let and Starting from the equilibrium point (after translation, it is the origin), and increasing by a certain step size δ and reduce After each operation, check whether the optimization problem has a feasible solution. If there is a feasible solution, continue to increase the step size δ and reduce , until there is no feasible solution to the above optimization problem, the algorithm terminates and the result obtained in the previous step is used x i The extreme value and M Matrix, draw the corresponding attraction domain.

[0060] Once the attraction domain of the equilibrium point is drawn, the stability of the operating point can be intuitively seen. At the same time, the amplitude range of large signal disturbances that the system can tolerate at this operating point can be known.

[0061] Step 104 : evaluating the transient stability boundary of the UAV hybrid power system according to the attraction domain corresponding to the UAV hybrid power system under each operating condition.

[0062] Specifically, under each working condition, the influence of the system parameters of the UAV hybrid power system on the attraction domain is obtained to evaluate the transient stability boundary of the UAV hybrid power system; among them, the system parameters mainly include: main circuit parameters on the power supply side; virtual droop parameters on the power supply side; PI regulator parameters; load power level.

[0063] Figure 9 The influence of the changes of the inductance and capacitance parameters on the estimated attraction domain during normal system operation is shown. Figure 9 (a) It can be seen that as the filter inductance of the fuel cell boost converter decreases, the estimated attraction domain decreases and rotates to the left, indicating that the anti-interference ability of the system is attenuating. Figure 9 (b) and Figure 9 (c) It can be seen that reducing the filter inductance of the lithium battery and supercapacitor Boost converter has little effect on the estimated attraction domain, that is, the anti-interference ability of the system has no significant change. Figure 9 (d) It can be seen that as the filter capacitance of the fuel cell Boost converter decreases, the estimated attraction domain shrinks along the horizontal axis and the system's anti-interference ability weakens.

[0064] Figure 10 The influence of the droop coefficient of fuel cell, lithium battery and supercapacitor boost converter on the estimated attraction domain of the power supply system during normal system operation is demonstrated. Figure 10 (a) It can be seen that the resistive droop coefficient of the fuel cell boost converter has a significant impact on the change of the attraction domain, which is manifested as the increase of the resistive droop coefficient within a certain range, and the size of the estimated attraction domain decreases significantly. Figure 10 (c) and Figure 10 (d) It can be seen that within a certain range, the change of the resistive droop coefficient of the lithium battery and supercapacitor Boost converter has no obvious effect on the estimated attraction domain. Figure 10 (b) and Figure 10 (e) It can be seen that within a certain range, the changes in the inductive droop coefficient and the capacitive droop coefficient have little effect on the estimated size of the attraction domain.

[0065] Figure 11 The influence of the changes of proportional gain and integral gain of voltage loop and current loop of PI regulator of fuel cell converter on the estimated attraction domain during normal system operation is shown. Figure 11 (a) and Figure 11 (c) It can be seen that the proportional gain has a significant effect on the size of the estimated attraction domain. Within a certain range, as the proportional gain of the voltage loop increases, the size of the estimated attraction domain decreases significantly. Within a certain range, as the proportional gain of the current loop increases, the attraction domain shows a trend of first decreasing, then rotating, and then decreasing again. Figure 11 (b) and Figure 11(d) It can be seen that within a certain range, the change of the integral gain (voltage loop and current loop) has little effect on the estimated size of the attraction domain.

[0066] Figure 12 The influence of the changes of the proportional gain and integral gain of the voltage loop and current loop of the lithium battery converter PI regulator on the estimated attraction domain during normal system operation is shown. Figure 12 (a) It can be seen that within a certain range, as the voltage loop proportional gain increases, the estimated attraction domain tends to decrease slightly; Figure 12 (c) It can be seen that within a certain range, as the current loop proportional gain increases, the attraction domain shows a trend of first increasing, then decreasing, and then rotating. Figure 12 (b) and Figure 12 (d) It can be seen that within a certain range, the change of the integral gain (voltage loop and current loop) has little effect on the estimated size of the attraction domain.

[0067] Figure 14 The influence of the changes of the proportional gain and integral gain of the voltage loop and current loop of the supercapacitor converter PI regulator on the estimated attraction domain during normal system operation is shown. Figure 13 (a), Figure 13 (b) and Figure 13 (d) It can be seen that within a certain range, with the increase of voltage loop proportional gain, voltage loop integral gain, and current loop integral gain, the estimated attraction domain shows a significant decreasing trend. Figure 13 (c) It can be seen that within a certain range, as the current loop proportional gain increases, the estimated attraction domain increases and rotates to the right. After increasing to a certain value, the attraction domain will no longer change significantly.

[0068] Figure 14 The estimated attraction region of the system with constant power loads of different power levels is shown. In the figure, P1, P2 and P3 represent 0kW, 3kW and 6kW operating points respectively. Figure 14 It can be seen from the figure that as the power level increases, the attraction domain gradually decreases, and the center of the attraction domain continues to shift to the right; in addition, Figure 14 It can also be seen from (a) that P1 is in the attraction domain of the 6kW operating point and P3 is in the attraction domain of the 0kW operating point. Therefore, according to the definition of the attraction domain, jumping directly from the 0kW operating point to the 6kW operating point and from the 6kW operating point to the 0kW operating point are both stable. Figure 14 (b) It can be seen that when the load power is 30kW (pulse load loading), the 0kW, 3kW and 6kW operating points are not included in the estimated attraction domain of 30kW, and jumping from 0kW, 3kW and 6kW to 30kW is unstable.

[0069] From the above analysis, we can know that when the system is operating normally, among the main circuit parameters, the fuel cell converter inductance L fc and capacitors C fc is the dominant stability parameter; among the virtual droop parameters, the resistive virtual droop parameter of the fuel cell converter is R vfc It is the dominant stability parameter; among the PI regulator parameters, the proportional gains of the voltage loop and the current loop are the dominant stability parameters; in addition, the power level change is also the dominant stability parameter.

[0070] It can be understood that this embodiment only describes the establishment of the global TS fuzzy model and the estimation of the attraction domain when the drone hybrid power system is operating normally. The TS fuzzy model establishment and attraction domain estimation methods when the drone hybrid power system is in other working conditions are similar and will not be repeated here.

[0071] In this embodiment, a state-space equation for a drone hybrid power system is first established under various operating conditions (e.g., normal operation, short-circuit fault, energy feedback mode, and failure of any power supply). Based on the state-space equation, the equilibrium point of the drone hybrid power system under each operating condition is obtained. Then, based on the TS fuzzy model, a Lyapunov function is used to evaluate the stability of the drone hybrid power system under different operating conditions. Specifically, the attraction domain of the drone hybrid power system at the equilibrium point under different operating conditions is estimated. This fully covers the system state when subjected to large disturbances. Because the attraction domain describes the region where the system state trajectory converges to a specific attraction set (e.g., an equilibrium point or periodic orbit), this embodiment uses the attraction domain of the drone hybrid power system at the equilibrium point under different operating conditions to characterize the tolerance range of the drone hybrid power system to large signal disturbances at the equilibrium point under each operating condition. This allows analysis of the transient instability mechanism of the drone hybrid power system under large signal disturbances. This attraction domain is then used as an evaluation indicator for the transient stability boundary of the drone hybrid power system, thereby further improving the stability of the drone hybrid power system.

[0072] The steps of the various methods above are divided only for the purpose of clear description. When implemented, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they include the same logical relationship, they are within the scope of protection of the present invention. Adding insignificant modifications or introducing insignificant designs to the algorithm or process without changing the core design of the algorithm and process are all within the scope of protection of the invention.

[0073] Another embodiment of the present invention relates to a transient stability boundary assessment system for a hybrid power system of an unmanned aerial vehicle. The implementation details of the transient stability boundary assessment system for the hybrid power system of an unmanned aerial vehicle of this embodiment are described in detail below. The following content is only provided for the convenience of understanding the implementation details and is not required for the implementation of this solution. The transient stability boundary assessment system for the hybrid power system of an unmanned aerial vehicle of this embodiment includes: Specifically, the state acquisition module is used to establish the state space equation of the drone hybrid power system under each working condition based on the architecture of the drone hybrid power system and the control strategy adopted by the drone hybrid power system under different working conditions, and obtain the equilibrium point of the drone hybrid power system under each working condition based on the state space equation; wherein, the working conditions include normal operation of the system, short circuit fault, energy feedback mode and failure of any power supply.

[0074] The model building module is used to divide the nonlinear state space equation under each working condition into multiple linear state space equations based on the theory of TS fuzzy model, and establish the global TS fuzzy model of the UAV hybrid power system under each working condition through multiple linear state space equations.

[0075] The function construction module is used to construct the Lyapunov function of the global TS fuzzy model under each working condition, and estimate the attraction domain of the UAV hybrid power system at the equilibrium point under each working condition through the Lyapunov function; among them, the attraction domain is used to characterize the tolerance range of the UAV hybrid power system to large signal disturbances at the equilibrium point.

[0076] The stability evaluation module is used to evaluate the transient stability boundary of the UAV hybrid power system according to the corresponding attraction domain of the UAV hybrid power system under each working condition.

[0077] It is not difficult to find that this embodiment is a system embodiment corresponding to the above-mentioned method embodiment, and this embodiment can be implemented in conjunction with the above-mentioned method embodiment. The relevant technical details and technical effects mentioned in the above-mentioned embodiment are still valid in this embodiment, and to reduce repetition, they are not repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the above-mentioned embodiment.

[0078] It is worth noting that all modules involved in this embodiment are logical modules. In actual applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, to highlight the innovations of the present invention, this embodiment does not include units that are not closely related to solving the technical problems proposed by the present invention. However, this does not mean that other units do not exist in this embodiment.

[0079] Another embodiment of the present invention relates to a computer device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the transient stability boundary assessment method of the hybrid power system of the unmanned aerial vehicle in the above-mentioned embodiments.

[0080] The memory and processor are connected using a bus, which can include any number of interconnected buses and bridges. The bus connects various circuits of one or more processors and memories. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits. These are all well known in the art and are therefore not described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single component or multiple components, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over a wireless medium via an antenna. Furthermore, the antenna receives data and transmits it to the processor.

[0081] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory can be used to store data used by the processor when performing operations.

[0082] Another embodiment of the present invention relates to a computer-readable storage medium storing a computer program, which implements the above method embodiment when executed by a processor.

[0083] That is, those skilled in the art will understand that all or part of the steps in the above-described method embodiments can be implemented by instructing the relevant hardware through a program. The program is stored in a storage medium and includes a number of instructions for causing a device (such as a microcontroller or chip) or a processor to execute all or part of the steps in the method embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0084] Those skilled in the art will appreciate that the above embodiments are specific embodiments for implementing the present invention, and that in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present invention.

Claims

1. A transient stability boundary assessment method for a hybrid power system of an unmanned aerial vehicle, characterized in that: include: Based on the architecture of the drone hybrid power system and the control strategy adopted by the drone hybrid power system under different operating conditions, the state space equation of the drone hybrid power system under each operating condition is established, and the equilibrium point of the drone hybrid power system under each operating condition is obtained based on the state space equation; wherein, the operating conditions include normal operation, short circuit fault, energy feedback mode, and failure of any power supply; Based on the theory of TS fuzzy model, the nonlinear state space equation under each working condition is divided into multiple linear state space equations. Then, a global TS fuzzy model of the UAV hybrid power system under each working condition is established through multiple linear state space equations. Construct a Lyapunov function for the global TS fuzzy model under each operating condition, and use the Lyapunov function to estimate the attraction domain of the UAV hybrid power system at the equilibrium point under each operating condition; wherein the attraction domain is used to characterize the tolerance range of the UAV hybrid power system to large signal disturbances at the equilibrium point; According to the corresponding attraction domain of the UAV hybrid power system under each working condition, the transient stability boundary of the UAV hybrid power system is evaluated.

2. The transient stability boundary assessment method for the hybrid power system of a UAV according to claim 1 is characterized in that: When the working condition is normal operation, the global TS fuzzy model of the UAV hybrid power system is established by the following steps: Obtain the nonlinear terms in the state space equations of the UAV hybrid power system during normal operation; All nonlinear terms belong to x 1 and x 2. Based on the TS fuzzy model, the state variables in the state space equation to which the nonlinear terms belong are divided into fuzzy subspaces corresponding to the state variables to construct a global TS fuzzy model; Among them, the global TS fuzzy model of the UAV hybrid power system during normal operation is: ； Where, is the state variable, x 1 and x 2 is the state variable in the state space equation to which the nonlinear term belongs; for To translate, , Indicates status The balance point, for The derivative of , for The maximum and minimum values of ; is the preset coefficient matrix.

3. The transient stability boundary assessment method for the hybrid power system of a UAV according to claim 2 is characterized in that: When the operating condition is normal operation, the Lyapunov function of the global TS fuzzy model is constructed by the following steps: The linear time-invariant system of the hybrid power system of the UAV during normal operation is obtained as follows: ； For the global TS fuzzy model, a real symmetric positive definite matrix M is preset so that the candidate Lyapunov function is constructed as: ； If the matrix If is negative definite, then according to the linear time-invariant system and the global TS fuzzy model, the Lyapunov function of the TS fuzzy model is constructed as the following optimization problem: ； Where, is the preset coefficient matrix, is the transposed matrix of M, for The transposed matrix of .

4. The transient stability boundary assessment method for a hybrid power system of a UAV according to claim 3 is characterized in that: When the operating condition is normal operation, the attraction domain of the hybrid power system of the UAV at the equilibrium point is estimated by the following steps, including: make and Starting from the equilibrium point of the UAV hybrid power system, and increasing the and reduce ; Check whether the optimization problem has a feasible solution. If so, continue to increase the default step size δ and reduce , until there is no feasible solution to the optimization problem; According to the obtained x i The extreme value of and M matrix determine the attraction domain.

5. The transient stability boundary assessment method for a hybrid power system of a UAV according to claim 1 is characterized in that: The transient stability boundary of the UAV hybrid power system is evaluated according to the attraction domain corresponding to the UAV hybrid power system under each working condition, including: Under each working condition, the influence of the system parameters of the UAV hybrid power system on the attraction domain is obtained to evaluate the transient stability boundary of the UAV hybrid power system; Among them, the system parameters include: the main circuit parameters of the power supply side of the UAV hybrid power system, the virtual droop parameters of the power supply side, the PI regulator parameters and the load power level.

6. The transient stability boundary assessment method for a hybrid power system of a UAV according to claim 1, characterized in that: The hybrid power system for the UAV includes three power sources: a fuel cell, a lithium battery, and a supercapacitor. The fuel cell, lithium battery, and supercapacitor are connected to the DC bus through their respective converters. The converter for the fuel cell is a unidirectional Boost converter, while the converters for the lithium battery and supercapacitor are bidirectional Buck-Boost converters. The load of the hybrid power system of the UAV includes at least a constant power load, a resistive load and a pulse load; The output voltages of the individual converters are equal, and the sum of the output currents of the individual converters is equal to the total current of the load, as follows: ； Where, v ofc , v obat , v osc are the output voltages of fuel cells, lithium batteries, and supercapacitor converters respectively; v bus is the voltage of the DC bus; i ofc , i obat , i osc are the output currents of fuel cells, lithium batteries, and supercapacitor converters respectively; P is the load power.

7. The transient stability boundary assessment method for a hybrid power system of a UAV according to claim 6, characterized in that: According to the architecture of the UAV hybrid power system and the control strategy adopted by the UAV hybrid power system under different working conditions, the state space equation of the UAV hybrid power system under each working condition is established, including: When the hybrid power system of the drone is operating normally, when a short circuit fault occurs, when the fuel cell fails / is in energy feedback mode, when the lithium battery fails, and when the supercapacitor fails, the following parameters are obtained as corresponding state variables: Load power; filter inductors and filter capacitors for fuel cells, lithium batteries, and supercapacitor converters; input voltage, input current, and output voltage of fuel cell converters, lithium battery converters, and supercapacitor converters; maximum input current of fuel cell converters, lithium battery converters, and supercapacitor converters; reference value of the voltage loop of the dual closed-loop PI controller of the UAV hybrid power system after applying virtual impedance droop control; nominal bus voltage; output current of the fuel cell converter, lithium battery converter, and supercapacitor converter; resistive droop coefficient of the fuel cell converter, lithium battery converter, and supercapacitor converter; inductive droop coefficient of the fuel cell converter and capacitive droop coefficient of the supercapacitor converter; virtual current of the fuel cell converter and virtual voltage of the supercapacitor converter; bus voltage; proportional gain and integral gain of the voltage loop and current loop of the PI controller in the fuel cell converter, lithium battery converter, and supercapacitor converter; current loop reference value of the fuel cell converter, lithium battery converter, and supercapacitor converter; Based on the obtained state variables, the state space equations of the UAV hybrid power system are established respectively when it is in normal operation, when a short circuit fault occurs, when the fuel cell fails / is in energy feedback mode, when the lithium battery fails, and when the supercapacitor fails.

8. A transient stability boundary assessment system for a hybrid power system of an unmanned aerial vehicle, characterized in that: include: A state acquisition module is used to establish a state space equation for the hybrid power system of the drone under each operating condition based on the architecture of the hybrid power system of the drone and the control strategy adopted by the hybrid power system of the drone under different operating conditions, and to obtain the equilibrium point of the hybrid power system of the drone under each operating condition based on the state space equation; wherein the operating conditions include normal operation, short circuit fault, energy feedback mode, and failure of any power supply; The model building module is used to divide the nonlinear state space equation under each working condition into multiple linear state space equations based on the theory of TS fuzzy model, and then establish the global TS fuzzy model of the UAV hybrid power system under each working condition through multiple linear state space equations; A function construction module is used to construct a Lyapunov function of the global TS fuzzy model under each operating condition, and estimate the attraction domain of the hybrid power system of the UAV at the equilibrium point under each operating condition through the Lyapunov function; wherein the attraction domain is used to characterize the tolerance range of the hybrid power system of the UAV to large signal disturbances at the equilibrium point; The stability assessment module is used to evaluate the transient stability boundary of the UAV hybrid power system according to the corresponding attraction domain of the UAV hybrid power system under each working condition.

9. A computer device, characterized in that: include: at least one processor; And, a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the transient stability boundary assessment method for the hybrid power system of a drone as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the transient stability boundary assessment method of the hybrid power system of a UAV according to any one of claims 1 to 7 is implemented.

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