Polynomial fuzzy modeling method and device for multi-source direct-current microgrid

Abstract

The application provides a polynomial fuzzy modeling method and device for a multi-source direct-current microgrid, and relates to the technical field of direct-current microgrid modeling and control. The method comprises the following steps: constructing a state vector of a target system, wherein the state vector comprises an inductance current of a constant-power load unit, a capacitance voltage of the constant-power load unit, an inductance current of a photovoltaic unit, an inductance current of an energy storage converter unit and a voltage of a direct-current bus unit; constructing a unit state equation by using the state vector, wherein the unit state equation comprises a first state equation and a second state equation corresponding to the constant-power load unit, a third state equation corresponding to the photovoltaic unit, a fourth state equation corresponding to the energy storage converter unit and a fifth state equation corresponding to the direct-current bus unit; obtaining a nonlinear state space equation of a single constant-power load unit based on the unit state equation, and constructing a global fuzzy model of the target system based on the nonlinear state space equation. The application can improve the accuracy and reliability of the model.

Description

Technical Field

[0001] This application relates to the field of DC microgrid modeling and control technology, and in particular to a polynomial fuzzy modeling method and apparatus for multi-source DC microgrids. Background Technology

[0002] DC microgrids, as a highly efficient and flexible distributed energy integration solution, have been widely used in recent years in fields such as ship power systems, data center power supply, electric vehicle charging stations, and renewable energy grid connection. Compared with AC microgrids, DC microgrids have significant advantages such as fewer energy conversion links, simpler control, natural adaptation to DC loads, and higher power quality. A typical DC microgrid system usually includes photovoltaic units, energy storage units, and various power electronic loads.

[0003] In DC microgrid systems, constant power loads (CPLs) are a special type of load whose power remains constant and exhibit negative impedance characteristics: when the load voltage increases, the current decreases; when the voltage decreases, the current increases. This negative impedance characteristic poses a serious threat to the stability of DC microgrids, mainly manifested in stability problems such as bus voltage oscillation, voltage collapse risk, and multiple CPL coupling effects.

[0004] In the field of DC microgrids, existing constant power load (CPL) modeling methods also face a trade-off between accuracy and complexity. Commonly used modeling methods include: Small-signal linearization modeling: This method performs a Taylor expansion of the nonlinear equations near the steady-state operating point, retaining first-order terms to obtain a linearized model. This method has a simple model and can directly apply linear system theory, but it is only suitable for analyzing small disturbances near the operating point and cannot describe dynamic behavior under large disturbances, thus limiting accuracy. It requires re-linearization when the operating point changes, making it unsuitable for wide-range operating scenarios. Precise switching modeling: This method establishes piecewise linear differential equations based on the actual switching states of power electronic devices. This method has the highest accuracy and can completely describe the nonlinear characteristics of the system, but it is computationally complex, requires tracking changes in switching states, has small simulation steps, high computational load, and a large model, making it inconvenient for controller design and resulting in significant simulation time consumption. TS fuzzy modeling: This method represents the nonlinear system as a fuzzy weighted sum of multiple linear subsystems. This method provides a global model, effective throughout the entire domain, facilitating controller design, and allowing the application of parallel distributed compensation methods. However, the selection of membership functions is highly empirical, often employing trigonometric or Gaussian functions, lacking theoretical basis; the approximation error is uncontrollable, failing to guarantee an accurate approximation of the original nonlinear system. Currently, regarding the characteristics of CPL... The precise fuzzy modeling methods are not perfect; moreover, existing research is mostly focused on single-type units, and there is insufficient research on fuzzy modeling of multi-source hybrid systems, making it difficult to conduct overall analysis and coordinated control. Summary of the Invention

[0005] This application provides a polynomial fuzzy modeling method and apparatus for multi-source DC microgrids to solve the problem of low accuracy in existing DC microgrid constant power load systems under high-precision scenarios.

[0006] Firstly, this application provides a polynomial fuzzy modeling method for multi-source DC microgrids, including:

[0007] Construct the state vector of the target system, which includes the inductor current of the constant power load unit, the capacitor voltage of the constant power load unit, the inductor current of the photovoltaic unit, the inductor current of the energy storage converter unit, and the voltage of the DC bus unit.

[0008] Using the state vector, a unit state equation is constructed, which includes a first state equation and a second state equation corresponding to the constant power load unit, a third state equation corresponding to the photovoltaic unit, a fourth state equation corresponding to the energy storage converter unit, and a fifth state equation corresponding to the DC bus unit.

[0009] Based on the unit state equation, the nonlinear state-space equation of a single constant power load unit is obtained, and based on the nonlinear state-space equation, a global fuzzy model of the target system is constructed.

[0010] Secondly, this application provides a polynomial fuzzy modeling device for multi-source DC microgrids, comprising:

[0011] The vector construction module is used to construct the state vector of the target system. The state vector includes the inductor current of the constant power load unit, the capacitor voltage of the constant power load unit, the inductor current of the photovoltaic unit, the inductor current of the energy storage converter unit, and the voltage of the DC bus unit.

[0012] The state equation construction module is used to construct unit state equations using the state vectors. The unit state equations include the first and second state equations corresponding to the constant power load unit, the third state equation corresponding to the photovoltaic unit, the fourth state equation corresponding to the energy storage converter unit, and the fifth state equation corresponding to the DC bus unit.

[0013] The fuzzy model construction module is used to obtain the nonlinear state-space equation of a single constant power load unit based on the unit state equation, and to construct the global fuzzy model of the target system based on the nonlinear state-space equation.

[0014] This application provides a polynomial fuzzy modeling method and apparatus for multi-source DC microgrids. Based on the nonlinear state-space equations of a single constant-power load unit, this application constructs a global fuzzy model of the target system. This model comprehensively considers the complexity and uncertainty of the entire multi-source DC microgrid. The global fuzzy model can better handle the fuzzy and uncertain factors existing in the microgrid, making the model more robust and adaptable, and enabling more accurate prediction and analysis of the microgrid's operating state and performance under different operating conditions. Furthermore, corresponding state equations are constructed for different units. This unit-based equation construction fully considers the characteristics and operating rules of each unit, enabling each equation to more accurately describe the dynamic behavior of the corresponding unit, improving the model's accuracy and reliability. Simultaneously, this application covers the states of various types of power sources (photovoltaic units), loads (constant-power load units), energy storage converter units, and DC bus units, fully considering the complexity and diversity of multi-source DC microgrids. This comprehensive state description allows the model to accurately reflect the interactions and influences between different units, providing strong support for the coordinated control and optimized operation of multi-source microgrids. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating the implementation of the polynomial fuzzy modeling method for multi-source DC microgrids provided in this application embodiment;

[0017] Figure 2 This is a schematic diagram of the circuit topology of the multi-source DC microgrid system provided in the embodiments of this application;

[0018] Figure 3 This is a schematic diagram of the sector nonlinear method provided in the embodiments of this application;

[0019] Figure 4 This is a system response diagram of the original model of the multi-source DC microgrid system provided in the embodiments of this application;

[0020] Figure 5 This is a system response diagram of a multi-source DC microgrid system provided in the embodiments of this application, based on polynomial fuzzy modeling.

[0021] Figure 6 This is a schematic diagram of the structure of the polynomial fuzzy modeling device for a multi-source DC microgrid provided in the embodiments of this application. Detailed Implementation

[0022] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0024] Figure 1 The implementation flowchart of the polynomial fuzzy modeling method for multi-source DC microgrids provided in the embodiments of this application is described in detail below:

[0025] In step 101, the state vector of the target system is constructed. The state vector includes the inductor current of the constant power load unit, the capacitor voltage of the constant power load unit, the inductor current of the photovoltaic unit, the inductor current of the energy storage converter unit, and the voltage of the DC bus unit.

[0026] In this embodiment, Kirchhoff's voltage law and Kirchhoff's current law for power electronic systems are used to define the state vector of the target system. See also: Figure 2 As shown, the target system includes a constant power load unit, a photovoltaic unit, an energy storage converter unit, and a DC bus unit. The corresponding state vectors include the inductor current of the constant power load unit, the capacitor voltage of the constant power load unit, the inductor current of the photovoltaic unit, the inductor current of the energy storage converter unit, and the voltage of the DC bus unit.

[0027] When the target system contains only one constant power load unit (i.e.) When ), the state vector can be defined as:

[0028]

[0029] in, The state variable is the inductor current of the constant power load unit. The state variable is the capacitor voltage of the constant power load unit. Let be the state variable of the inductor current of the photovoltaic unit. The state variable is the inductor current of the energy storage converter unit. The voltage state variable of the DC bus unit. The inductor current of the constant power load unit. The capacitor voltage of the constant power load unit. The inductor current of the photovoltaic unit. For the inductor current of the energy storage converter unit, This refers to the voltage of the DC bus unit. It is the transpose of the vector.

[0030] When the target system contains multiple constant power load power units with the same parameters (i.e. When ), the state vector can be defined as:

[0031]

[0032] in, For the first The state variable of the inductor current of a constant power load unit with identical parameters For the first The state variables of the capacitor voltage of two constant power load units with identical parameters. For example, when there are two constant power load units with identical parameters, the state vector can be defined as:

[0033]

[0034] in, , Let the inductor current be the state variable of two constant power load units with identical parameters. , These are the state variables of the capacitor voltages of two constant power load units with identical parameters. , Let the inductor currents of two constant power load power units with identical parameters be given. , The capacitor voltages are those of two constant power load units with identical parameters.

[0035] In step 102, the unit state equations are constructed using state vectors. The unit state equations include the first and second state equations corresponding to the constant power load unit, the third state equation corresponding to the photovoltaic unit, the fourth state equation corresponding to the energy storage converter unit, and the fifth state equation corresponding to the DC bus unit.

[0036] In this embodiment, based on the physical structure of each unit in the target system, switch state variables are introduced. Differential equations for each unit under different switch operating states are derived using Kirchhoff's laws. These state variables are then fused into a unified nonlinear state equation for each unit using a linear combination, namely, the first state equation, the second state equation, the third state equation, and the fourth state equation. Simultaneously, the core current relationship of the DC bus unit is determined by combining its capacitance-voltage characteristics, thus establishing its corresponding fifth state equation.

[0037] In one possible implementation, constructing the unit state equations using state vectors can include:

[0038] Based on the structural topology diagram of the target system, the topologies of the constant power load unit, photovoltaic unit, and energy storage converter unit are determined respectively. The topology of the constant power load unit includes a first switching transistor, a first diode, and an LC filter connected to it. The topology of the photovoltaic unit includes a first inductor, a second switching transistor, and a second diode. The topology of the energy storage converter unit includes a second inductor, a third switching transistor, and a fourth switching transistor.

[0039] Based on the topology of the constant power load unit, Kirchhoff's voltage law and Kirchhoff's current law are used to obtain the first and second switching differential equations of the constant power load unit. Based on the first and second switching differential equations and the first preset switching state variables, the first and second state equations are determined.

[0040] Based on the topology of the photovoltaic unit, Kirchhoff's voltage law and Kirchhoff's current law are used to obtain the third and fourth switch differential equations of the photovoltaic unit. Based on the third and fourth switch differential equations and the second preset switch state variables, the third state equation is determined.

[0041] Based on the topology of the energy storage converter unit, Kirchhoff's voltage law and Kirchhoff's current law are used to obtain the fifth and sixth switch differential equations of the energy storage converter unit. Based on the fifth and sixth switch differential equations and the third preset switch state variables, the fourth state equation is determined.

[0042] The charging and discharging current of the DC bus unit is determined based on the inductor current of the constant power load unit, the inductor current of the photovoltaic unit, the inductor current of the energy storage converter unit, the first preset switch state variable, the second preset switch state variable, and the third preset switch state variable.

[0043] Based on the capacitor's volt-ampere characteristics, the charging and discharging current of the DC bus unit is calculated, and the differential equation of the DC bus unit is obtained. Based on the differential equation of the DC bus unit, the fifth state equation is determined.

[0044] Optionally, refer to Figure 2 As shown, based on the structural topology diagram of the target system, the topology of each unit is clarified, namely, the constant power load unit CPL is composed of the first switching transistor. First diode It consists of a photovoltaic unit and its connected LC filter; the photovoltaic unit consists of a first inductor. Second switching transistor Second diode Composition; the energy storage converter unit consists of a second inductor Third switching transistor and the fourth switching transistor composition.

[0045] Then, the first preset switch state variable is introduced. Second preset switch state variable and the third preset switch state variable The two switch state equations of each unit are fused into a single state equation through a linear combination of state variables. The three preset switch state variables each take values ​​of 0 or 1, corresponding to the two switch operating states of the unit, respectively. The first preset switch state variable... The second preset switching state variable is the switching state variable of the constant power load unit. The third preset switching state variable is the switching state variable of the photovoltaic unit. These are the switching state variables of the energy storage converter unit.

[0046] The process of constructing the first and second state equations for the constant power load cell is as follows:

[0047] Reference Figure 2 Based on Kirchhoff's voltage law and current law, two switching differential equations for the constant power load unit are obtained: the first switching differential equation and the second switching differential equation.

[0048] When the first switching transistor The first diode is conducting. When the switch is turned off, the first differential equation for the switch is obtained, namely:

[0049]

[0050] in, The inductor of the constant power load unit. This refers to the voltage of the DC bus unit. The capacitor voltage of the constant power load unit. For time.

[0051] When the first switching transistor Turn off, first diode When the circuit is turned on, the second switch differential equation is obtained, namely:

[0052]

[0053] Then, the first preset switch state variable is introduced. Combining the first and second switch differential equations, the first and second state equations of the constant power load unit are obtained, namely:

[0054] By inputting the first switch differential equation, the second switch differential equation, and the first preset switch state variable into the first formula, we obtain the first state equation and the second state equation. The first formula is as follows:

[0055]

[0056] in, Let be the first derivative of the state variable of the inductor current of the constant power load unit. Let be the first derivative of the state variable of the capacitor current of the constant power load unit. The state variable is the inductor current of the constant power load unit. The state variable is the capacitor current of the constant power load unit. The voltage state variable of the DC bus unit. This is the first preset switch state variable. The inductor of the constant power load unit. The power of the constant power load unit. The capacitor is for the constant power load unit. , , and These are all nonlinear coefficients corresponding to the switching states of the constant power load unit.

[0057] The process of constructing the third state equation for the photovoltaic unit is as follows:

[0058] Reference Figure 2 Based on Kirchhoff's voltage law and current law, two switching differential equations for the photovoltaic unit are obtained, namely the third switching differential equation and the fourth switching differential equation:

[0059] When the second switch The second diode is conducting. When the switch is turned off, the third switch differential equation is obtained, namely:

[0060]

[0061] in, For the inductance of the photovoltaic unit, This is the output voltage of the photovoltaic unit.

[0062] When the second switch Turn off, second diode When the circuit is turned on, the fourth switch differential equation is obtained, namely:

[0063]

[0064] Then, a second preset switch state variable is introduced. By combining the third and fourth switch differential equations, the third state equation of the photovoltaic unit is obtained, namely:

[0065] By inputting the third switch differential equation, the fourth switch differential equation, and the second preset switch state variable into the fifth formula, we obtain the third state equation. The fifth formula is as follows:

[0066]

[0067] in, Let be the first derivative of the state variable of the inductor current of the photovoltaic cell. This is the second preset switch state variable. Let be the state variable of the inductor current of the photovoltaic unit. The voltage state variable of the DC bus unit. , , , These are all nonlinear coefficients corresponding to the switching states of the photovoltaic unit.

[0068] The process of constructing the fourth state equation for the energy storage converter unit is as follows:

[0069] Reference Figure 2 Based on Kirchhoff's voltage law and current law, two switching differential equations for the energy storage converter unit are obtained, namely the fifth switching differential equation and the sixth switching differential equation:

[0070] When the third switch Turn on, fourth switch transistor When the switch is turned off, the fifth differential equation is obtained, namely:

[0071]

[0072] in, For the inductance of the energy storage converter unit, This represents the voltage of the energy storage converter unit.

[0073] When the third switch Turn off, fourth switch transistor When the circuit is turned on, the sixth switch differential equation is obtained, namely:

[0074]

[0075] Then, a third preset switch state variable is introduced. Combining the fifth and sixth switch differential equations, the fourth state equation of the energy storage converter unit is obtained, namely:

[0076] By inputting the fifth switch differential equation, the sixth switch differential equation, and the third preset switch state variable into the sixth formula, the fourth state equation is obtained. The sixth formula is:

[0077]

[0078] in, Let be the first derivative of the state variable of the inductor current in the energy storage converter unit. This is the third preset switch state variable. The state variable is the inductor current of the energy storage converter unit. The voltage state variable of the DC bus unit. , , , These are all nonlinear coefficients corresponding to the switching states of the energy storage converter unit.

[0079] The process of constructing the fifth state equation for the DC bus unit is as follows:

[0080] Due to the charging and discharging current of the DC bus unit The charging and discharging current of the DC bus unit is determined by the inductor currents of the energy storage converter unit, photovoltaic unit, and constant power load unit. The calculation process is as follows:

[0081] The inductor current of the constant power load unit, the inductor current of the photovoltaic unit, the inductor current of the energy storage converter unit, the first preset switch state variable, the second preset switch state variable, and the third preset switch state variable are input into the second formula to calculate the charging and discharging current of the DC bus unit. The second formula is as follows:

[0082]

[0083] in, This refers to the charging and discharging current of the DC bus unit.

[0084] Then, based on the capacitance-current characteristic This yields the differential equation for the DC bus unit expressed in terms of state vectors, namely:

[0085]

[0086] in, This is the supporting capacitor for the DC bus unit.

[0087] Transforming the differential equations of the above DC bus unit into state equation form yields the fifth state equation of the DC bus unit, namely:

[0088] The differential equations of the DC bus unit are transformed into state equations, resulting in the fifth state equation, which is:

[0089]

[0090] in, Let be the first derivative of the state variable of the voltage of the DC bus unit. The state variable is the inductor current of the constant power load unit. Let be the state variable of the inductor current of the photovoltaic unit. The state variable is the inductor current of the energy storage converter unit. This is the first preset switch state variable. This is the second preset switch state variable. This is the third preset switch state variable. This is the supporting capacitor for the DC bus unit.

[0091] In step 103, based on the unit state equation, the nonlinear state-space equation of a single constant power load unit is obtained, and based on the nonlinear state-space equation, a global fuzzy model of the target system is constructed.

[0092] In this embodiment, constant power load sub-blocks and source terminal blocks are extracted using matrix block technology, and the state equations of each unit constructed in step 102 are systematically integrated to obtain the nonlinear state space equation of a single constant power load unit. Then, the negative impedance nonlinear characteristics (i.e., the nonlinear term of capacitor voltage) of the constant power load are processed based on the sector nonlinear method. By calculating the vertex values ​​of the nonlinear term at the boundary of the finite operating range, a normalized membership function and corresponding fuzzy rules are constructed. Finally, the complex global nonlinear system is accurately transformed into a polynomial fuzzy model of weighted summation of multiple local linear subsystems.

[0093] In one possible implementation, the nonlinear state-space equation of a single constant power load unit is obtained based on the unit state equation, which may include:

[0094] The system matrix and input matrix are determined using the unit state equations;

[0095] The constant vector matrix is ​​determined using the output voltage of the photovoltaic unit, the inductance of the photovoltaic unit, the input voltage of the energy storage converter unit, and the inductance of the energy storage converter unit.

[0096] The nonlinear state-space equations of a single constant power load unit are determined using the system matrix, input matrix, and constant vector matrix.

[0097] Optionally, the system matrix The calculation formula is:

[0098]

[0099] Input matrix The calculation formula is:

[0100]

[0101] constant vector matrix The calculation formula is:

[0102]

[0103] Then, the system matrix obtained from the above calculation is used. Input matrix and constant vector matrix Determine the nonlinear state-space equations for a single constant power load unit, namely:

[0104] By inputting the system matrix, input matrix, and constant vector matrix into the third formula, the nonlinear state-space equation for a single constant power load unit is obtained. The third formula is:

[0105]

[0106] in, The first derivative of the system state vector. For the system matrix, For the input matrix, It is a constant vector matrix. To control the input vector, This is the system state vector.

[0107] Among them, the control input vector It consists of a first preset switch state variable, a second preset switch state variable, and a third preset switch state variable, namely:

[0108]

[0109] In one possible implementation, after obtaining the nonlinear state-space equation of a single constant-power load element based on the element state equation, the method may further include:

[0110] The system matrix is ​​partitioned using matrix partitioning techniques. The process is divided into blocks to obtain constant power load sub-blocks. Heyuan terminal block ,Right now:

[0111]

[0112] Among them, constant power load sub-block The calculation formula is:

[0113]

[0114] Source terminal block The calculation formula is:

[0115]

[0116] When there are multiple constant power load units with the same parameters in the target system, an independent state vector is configured for each constant power load unit to obtain the block diagonal state space equation in the scenario of multiple constant power load units.

[0117] It has If there are 1 constant power load unit, then the system state equation is:

[0118]

[0119] in, For the first Preset switching state variables corresponding to each constant power load unit.

[0120] Let the system matrix It presents a block-diagonal structure, thus allowing for the processing of the input matrix. and constant vector matrix To expand, that is:

[0121] System Matrix for:

[0122]

[0123] in:

[0124]

[0125]

[0126] Input matrix for:

[0127]

[0128] in:

[0129]

[0130]

[0131] constant vector matrix for:

[0132]

[0133] in,

[0134] For example, refer to Figure 2 As shown, the correlation coefficient in the constant power load unit is: , In the first switching transistor When conducting: , When the first diode is turned on hour: , The correlation coefficient in the photovoltaic unit is: , In the second switching transistor When conducting: , When the second diode is turned on hour: , The correlation coefficient in the energy storage converter unit is: , In the third switching transistor When conducting: , In the fourth switching transistor hour: , The correlation coefficient in the DC bus unit is: .

[0135] When there is a constant power load unit in the target system (i.e.) )hour:

[0136] The system matrix is:

[0137]

[0138] The nonlinear characteristic of the capacitor voltage of the constant power load unit is expressed as follows:

[0139]

[0140] The input matrix is:

[0141]

[0142] The constant vector matrix is:

[0143]

[0144] When there are two constant power load units in the target system (i.e.) When the constant power load unit 1 and the constant power load unit 2 are in the same state, that is:

[0145] The control input vector is extended to:

[0146]

[0147] The system matrix is ​​extended to:

[0148]

[0149] Among them, the source terminal block for:

[0150] The two constant power load sub-blocks each contain their own nonlinear terms:

[0151]

[0152] The nonlinear terms are as follows:

[0153]

[0154] The input matrix is ​​expanded to:

[0155]

[0156] In one possible implementation, constructing a global fuzzy model of the target system based on nonlinear state-space equations can include:

[0157] Under the constraint of the capacitor voltage of the constant power load unit, calculate the vertex value of the nonlinear term at the boundary. The vertex value includes the upper vertex value and the lower vertex value. The nonlinear term is the nonlinear term of the capacitor voltage of the constant power load unit.

[0158] Using the values ​​of the upper and lower vertices, a membership function is constructed, which includes a first membership function and a second membership function.

[0159] Under the constraints of the preset fuzzy rules, the rule activation weights corresponding to the preset fuzzy rules are calculated using the upper vertex value, lower vertex value, and membership function.

[0160] A global fuzzy model of the target system is constructed by using nonlinear state-space equations and rule-based activation weights.

[0161] Optionally, a polynomial fuzzy model of the capacitor voltage of the constant power load unit is constructed using the sector nonlinearity method, i.e., nonlinear term identification and vertex value calculation are performed:

[0162] The nonlinear term is the nonlinear term of the capacitor voltage of the constant power load unit, that is:

[0163]

[0164] The constraint condition for the capacitor voltage of the constant power load unit is:

[0165]

[0166] in, This is the lower limit of the capacitor voltage of the constant power load unit. This represents the upper limit of the capacitor voltage of the constant power load unit.

[0167] Calculate the vertex value of the nonlinear term at the boundary, i.e.:

[0168]

[0169] in, The value of the upper vertex. This is the value of the lower vertex. Because... ,but .

[0170] Then, we construct the membership functions, defining two membership functions: the first membership function and the second membership function.

[0171]

[0172]

[0173] in, The first membership function, This is the second membership function.

[0174] The membership function must satisfy: .

[0175] Define preset fuzzy rules: for Each constant power load unit has two options (i.e., corresponding vertex values). or Therefore, the preset number of fuzzy rules is: Each preset fuzzy rule corresponds to a tuple. .in, :when When selecting the next vertex value ;when When selecting the upper vertex value Then the first The standard form of a pre-defined fuzzy rule is:

[0176] IF is AND is AND ... AND is ;

[0177] THEN .

[0178] in, For a fuzzy set, its membership function is... , For the first The local system matrix under a pre-defined fuzzy rule.

[0179] In the Under the pre-defined fuzzy rules, nonlinear terms Replaced with constant term vertex value ,Right now:

[0180]

[0181] Therefore, the local system matrix is:

[0182]

[0183] in:

[0184]

[0185]

[0186] Then, calculate the first... Rule activation weights of preset fuzzy rules ,Right now:

[0187] Finally, using the nonlinear state-space equations and rule-based activation weights, a global fuzzy model of the target system is constructed, namely:

[0188] The nonlinear state-space equation and the regular activation weights are input into the fourth formula to construct the global fuzzy model of the target system. The fourth formula is:

[0189]

[0190] in, The first derivative of the system state vector. For the first The system matrix under the pre-defined fuzzy rules, For the input matrix, It is a constant vector matrix. To control the input vector, Let be the system state vector. This represents the total number of constant power load units. For the first The activation weight of the pre-defined fuzzy rules. The sequence number of the preset fuzzy rule.

[0191] This application's embodiments are designed for multi-source DC microgrid systems. By defining appropriate fuzzy sets, the nonlinear microgrid model containing multiple constant power loads, photovoltaic units, and energy storage units is transformed into a polynomial fuzzy model with linear subsystems. This allows the theoretical methods based on linear control to be directly applied to DC microgrids to achieve control objectives such as bus voltage stability and coordinated power distribution. The sector nonlinear method is used to construct the premise variables and fuzzy set membership functions, making the resulting polynomial fuzzy model equivalent to the original nonlinear model in analytical form, thus improving the accuracy of the fuzzy model. By dividing the state-space equations into constant power load sub-blocks and source terminal blocks, the dynamic connections between modules are effectively reduced. At the same time, the expansion is carried out in the form of a block diagonal matrix, successfully realizing uncoupled expansion in the scenario of multiple constant power load units, significantly reducing the complexity of multi-load grid-connected controller design.

[0192] For example, when there is a constant power load unit in the target system, the constructed global fuzzy model is as follows:

[0193] The capacitor voltage operating range of the constant power load unit is set to... Calculate nonlinear terms Vertex values ​​at the boundary: Upper vertex value Lower vertex value Based on this, the normalized membership function is defined as follows: , Based on the two vertices extracted by the sector nonlinear method, two preset fuzzy rules are constructed, namely:

[0194] Rule 1: IF is ,

[0195] THEN

[0196] Rule 2:IF is ,

[0197] THEN

[0198] The global fuzzy model is then represented as:

[0199]

[0200] in, , ; Corresponding vertex values fuzzy sets, Corresponding vertex value The fuzzy set. At this point, the local system matrix... Elements in: At this time, the local system matrix Elements in: .

[0201] Furthermore, experiments have verified that this application can accurately identify the nonlinear model of the original DC microgrid system. Figure 3 This is a schematic diagram illustrating the principle of the sector nonlinear method used in polynomial modeling. Figure 4 This represents the system response of the DC microgrid system under the original model. Figure 5 This table shows the system response of the DC microgrid system under the polynomial fuzzy model. Table 1 also presents the inductor current of the constant power load unit in the system. The capacitor voltage of the constant power load unit The inductor current of the photovoltaic unit Inductor current of energy storage converter unit and the voltage of the DC bus unit The standard deviations under the two models. As can be seen from the data in Table 1, the standard deviations of the modeling accuracy are 0.5805, 4.0110, 0.0442, 0.1430, and 2.9014, respectively, all within acceptable ranges. (Reference) Figure 4 and Figure 5 ,from Figure 4 and Figure 5 This also demonstrates that the present application can effectively approximate the nonlinear dynamic model of a DC microgrid system and provide an accurate model for subsequent control design.

[0202] Table 1 Error parameters of the original model and the polynomial fuzzy model

[0203]

[0204] When there are two constant power load units in the target system, the constructed global fuzzy model is as follows:

[0205] Based on the sector nonlinearity method, rules are combined. Since there are two nonlinear terms, four fuzzy rules are constructed:

[0206] Rule 1:IF is AND is ,

[0207] THEN

[0208] Rule 2:IF is AND is ,

[0209] THEN

[0210] Rule 3:IF is AND is ,

[0211] THEN

[0212] Rule 4:IF is AND is ,

[0213] THEN

[0214] The global fuzzy model is then represented as:

[0215]

[0216] in, For the corresponding fuzzy set. Local system matrix. to The corresponding diagonal sub-block elements are as follows:

[0217] Local system matrix (Select the upper vertex value) and ):

[0218]

[0219] Local system matrix (Select the upper vertex value) With lower vertex value ):

[0220]

[0221] Local system matrix (Select the lower vertex value) With the value of the upper vertex ):

[0222]

[0223] Local system matrix (Select the lower vertex value) With lower vertex value ):

[0224]

[0225] This application provides a polynomial fuzzy modeling method for multi-source DC microgrids. Based on the nonlinear state-space equations of a single constant-power load unit, a global fuzzy model of the target system is constructed. This method comprehensively considers the complexity and uncertainty of the entire multi-source DC microgrid. The global fuzzy model can better handle the fuzzy and uncertain factors existing in the microgrid, making the model more robust and adaptable, and enabling more accurate prediction and analysis of the microgrid's operating state and performance under different operating conditions. Furthermore, corresponding state equations are constructed for different units. This unit-based equation construction fully considers the characteristics and operating rules of each unit, enabling each equation to more accurately describe the dynamic behavior of the corresponding unit, improving the model's accuracy and reliability. Simultaneously, this application covers the states of various types of power sources (photovoltaic units), loads (constant-power load units), energy storage converter units, and DC bus units, fully considering the complexity and diversity of multi-source DC microgrids. This comprehensive state description allows the model to accurately reflect the interactions and influences between different units, providing strong support for the coordinated control and optimized operation of multi-source microgrids.

[0226] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0227] The following are device embodiments of this application. For details not described in detail, please refer to the corresponding method embodiments described above.

[0228] Figure 6 A schematic diagram of the polynomial fuzzy modeling device for a multi-source DC microgrid provided in an embodiment of this application is shown. For ease of explanation, only the parts relevant to the embodiment of this application are shown, and are described in detail below:

[0229] like Figure 6 As shown, the polynomial fuzzy modeling device 6 for multi-source DC microgrids includes:

[0230] The vector construction module 61 is used to construct the state vector of the target system. The state vector includes the inductor current of the constant power load unit, the capacitor voltage of the constant power load unit, the inductor current of the photovoltaic unit, the inductor current of the energy storage converter unit, and the voltage of the DC bus unit.

[0231] The state equation construction module 62 is used to construct the unit state equation using the state vector. The unit state equation includes the first and second state equations corresponding to the constant power load unit, the third state equation corresponding to the photovoltaic unit, the fourth state equation corresponding to the energy storage converter unit, and the fifth state equation corresponding to the DC bus unit.

[0232] The fuzzy model construction module 63 is used to obtain the nonlinear state-space equation of a single constant power load unit based on the unit state equation, and to construct a global fuzzy model of the target system based on the nonlinear state-space equation.

[0233] This application provides a polynomial fuzzy modeling device for multi-source DC microgrids. Based on the nonlinear state-space equations of a single constant-power load unit, this application constructs a global fuzzy model of the target system. This model comprehensively considers the complexity and uncertainty of the entire multi-source DC microgrid. The global fuzzy model can better handle the fuzziness and uncertainty factors existing in the microgrid, making the model more robust and adaptable, and enabling more accurate prediction and analysis of the microgrid's operating state and performance under different operating conditions. Furthermore, corresponding state equations are constructed for different units. This unit-based equation construction method fully considers the characteristics and operating rules of each unit, enabling each equation to more accurately describe the dynamic behavior of the corresponding unit, improving the model's accuracy and reliability. Simultaneously, this application covers the states of various types of power sources (photovoltaic units), loads (constant-power load units), energy storage converter units, and DC bus units, fully considering the complexity and diversity of multi-source DC microgrids. This comprehensive state description allows the model to accurately reflect the interactions and influences between different units, providing strong support for the coordinated control and optimized operation of multi-source microgrids.

[0234] In one possible implementation, the state equation building module can specifically be used for:

[0235] Based on the structural topology diagram of the target system, the topologies of the constant power load unit, photovoltaic unit, and energy storage converter unit are determined respectively. The topology of the constant power load unit includes a first switching transistor, a first diode, and an LC filter connected to it. The topology of the photovoltaic unit includes a first inductor, a second switching transistor, and a second diode. The topology of the energy storage converter unit includes a second inductor, a third switching transistor, and a fourth switching transistor.

[0236] Based on the topology of the constant power load unit, Kirchhoff's voltage law and Kirchhoff's current law are used to obtain the first and second switching differential equations of the constant power load unit. Based on the first and second switching differential equations and the first preset switching state variables, the first and second state equations are determined.

[0237] Based on the topology of the photovoltaic unit, Kirchhoff's voltage law and Kirchhoff's current law are used to obtain the third and fourth switch differential equations of the photovoltaic unit. Based on the third and fourth switch differential equations and the second preset switch state variables, the third state equation is determined.

[0238] Based on the topology of the energy storage converter unit, Kirchhoff's voltage law and Kirchhoff's current law are used to obtain the fifth and sixth switch differential equations of the energy storage converter unit. Based on the fifth and sixth switch differential equations and the third preset switch state variables, the fourth state equation is determined.

[0239] The charging and discharging current of the DC bus unit is determined based on the inductor current of the constant power load unit, the inductor current of the photovoltaic unit, the inductor current of the energy storage converter unit, the first preset switch state variable, the second preset switch state variable, and the third preset switch state variable.

[0240] Based on the capacitor's volt-ampere characteristics, the charging and discharging current of the DC bus unit is calculated, and the differential equation of the DC bus unit is obtained. Based on the differential equation of the DC bus unit, the fifth state equation is determined.

[0241] In one possible implementation, the state equation building module can also be used for:

[0242] By inputting the first switch differential equation, the second switch differential equation, and the first preset switch state variable into the first formula, we obtain the first state equation and the second state equation. The first formula is as follows:

[0243]

[0244] in, Let be the first derivative of the state variable of the inductor current of the constant power load unit. Let be the first derivative of the state variable of the capacitor current of the constant power load unit. The state variable is the inductor current of the constant power load unit. The state variable is the capacitor current of the constant power load unit. The voltage state variable of the DC bus unit. This is the first preset switch state variable. The inductor of the constant power load unit. The power of the constant power load unit. The capacitor is for the constant power load unit. , , and These are all nonlinear coefficients corresponding to the switching states of the constant power load unit.

[0245] In one possible implementation, the state equation building module can also be used for:

[0246] The inductor current of the constant power load unit, the inductor current of the photovoltaic unit, the inductor current of the energy storage converter unit, the first preset switch state variable, the second preset switch state variable, and the third preset switch state variable are input into the second formula to calculate the charging and discharging current of the DC bus unit. The second formula is as follows:

[0247]

[0248] in, The charging and discharging current of the DC bus unit. For the inductor current of the energy storage converter unit, The inductor current of the photovoltaic unit. The inductor current of the constant power load unit. This is the first preset switch state variable. This is the second preset switch state variable. This is the third preset switch state variable.

[0249] In one possible implementation, the state equation building module can also be used for:

[0250] The differential equations of the DC bus unit are transformed into state equations, resulting in the fifth state equation, which is:

[0251]

[0252] in, Let be the first derivative of the state variable of the voltage of the DC bus unit. The state variable is the inductor current of the constant power load unit. Let be the state variable of the inductor current of the photovoltaic unit. The state variable is the inductor current of the energy storage converter unit. This is the first preset switch state variable. This is the second preset switch state variable. This is the third preset switch state variable. This is the supporting capacitor for the DC bus unit.

[0253] In one possible implementation, the fuzzy model building module can be used for:

[0254] The system matrix and input matrix are determined using the unit state equations;

[0255] The constant vector matrix is ​​determined using the output voltage of the photovoltaic unit, the inductance of the photovoltaic unit, the input voltage of the energy storage converter unit, and the inductance of the energy storage converter unit.

[0256] The nonlinear state-space equations of a single constant power load unit are determined using the system matrix, input matrix, and constant vector matrix.

[0257] In one possible implementation, the fuzzy model building module can specifically be used for:

[0258] By inputting the system matrix, input matrix, and constant vector matrix into the third formula, the nonlinear state-space equation for a single constant power load unit is obtained. The third formula is:

[0259]

[0260] in, The first derivative of the system state vector. For the system matrix, For the input matrix, It is a constant vector matrix. To control the input vector, This is the system state vector.

[0261] In one possible implementation, the fuzzy model building module can also be used for:

[0262] Under the constraint of the capacitor voltage of the constant power load unit, calculate the vertex value of the nonlinear term at the boundary. The vertex value includes the upper vertex value and the lower vertex value. The nonlinear term is the nonlinear term of the capacitor voltage of the constant power load unit.

[0263] Using the values ​​of the upper and lower vertices, a membership function is constructed, which includes a first membership function and a second membership function.

[0264] Under the constraints of the preset fuzzy rules, the rule activation weights corresponding to the preset fuzzy rules are calculated using the upper vertex value, lower vertex value, and membership function.

[0265] A global fuzzy model of the target system is constructed by using nonlinear state-space equations and rule-based activation weights.

[0266] In one possible implementation, the fuzzy model building module can specifically be used for:

[0267] The nonlinear state-space equation and the regular activation weights are input into the fourth formula to construct the global fuzzy model of the target system. The fourth formula is:

[0268]

[0269] in, The first derivative of the system state vector. For the first The system matrix under the pre-defined fuzzy rules, For the input matrix, It is a constant vector matrix. To control the input vector, Let be the system state vector. This represents the total number of constant power load units. For the first The activation weight of the pre-defined fuzzy rules. The sequence number of the preset fuzzy rule.

[0270] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0271] Those skilled in the art will recognize that the templates, units, and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0272] If the module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the above embodiments of the polynomial fuzzy modeling method for various multi-source DC microgrids. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0273] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A polynomial fuzzy modeling method for multi-source DC microgrids, characterized in that, include: Construct the state vector of the target system, which includes the inductor current of the constant power load unit, the capacitor voltage of the constant power load unit, the inductor current of the photovoltaic unit, the inductor current of the energy storage converter unit, and the voltage of the DC bus unit. Using the state vector, a unit state equation is constructed, which includes a first state equation and a second state equation corresponding to the constant power load unit, a third state equation corresponding to the photovoltaic unit, a fourth state equation corresponding to the energy storage converter unit, and a fifth state equation corresponding to the DC bus unit. Based on the unit state equation, the nonlinear state space equation of a single constant power load unit is obtained, and based on the nonlinear state space equation, a global fuzzy model of the target system is constructed. The step of constructing the unit state equation using the state vector includes: Based on the structural topology diagram of the target system, the topologies of the constant power load unit, the photovoltaic unit, and the energy storage converter unit are determined respectively. The topology of the constant power load unit includes a first switching transistor, a first diode, and an LC filter connected to them. The topology of the photovoltaic unit includes a first inductor, a second switching transistor, and a second diode. The topology of the energy storage converter unit includes a second inductor, a third switching transistor, and a fourth switching transistor. Based on the topology of the constant power load unit, Kirchhoff's voltage law and Kirchhoff's current law are used to obtain the first and second switching differential equations of the constant power load unit. Based on the first and second switching differential equations and the first preset switching state variables, the first and second state equations are determined. Based on the topology of the photovoltaic unit, the third and fourth switching differential equations of the photovoltaic unit are obtained by using Kirchhoff's voltage law and Kirchhoff's current law. Based on the third switching differential equation, the fourth switching differential equation, and the second preset switching state variable, the third state equation is determined. Based on the topology of the energy storage converter unit, the fifth and sixth switch differential equations of the energy storage converter unit are obtained by using Kirchhoff's voltage law and Kirchhoff's current law. Based on the fifth and sixth switch differential equations and the third preset switch state variables, the fourth state equation is determined. The charging and discharging current of the DC bus unit is determined based on the inductor current of the constant power load unit, the inductor current of the photovoltaic unit, the inductor current of the energy storage converter unit, the first preset switch state variable, the second preset switch state variable, and the third preset switch state variable. Based on the capacitor's volt-ampere characteristics, the charging and discharging current of the DC bus unit is calculated to obtain the differential equation of the DC bus unit, and based on the differential equation of the DC bus unit, the fifth state equation is determined. The step of constructing a global fuzzy model of the target system based on the nonlinear state-space equation includes: Under the constraint of the capacitor voltage of the constant power load unit, the vertex value of the nonlinear term at the boundary is calculated. The vertex value includes the upper vertex value and the lower vertex value. The nonlinear term is the nonlinear term of the capacitor voltage of the constant power load unit. Using the upper vertex value and the lower vertex value, a membership function is constructed, which includes a first membership function and a second membership function; Under the constraint of a preset fuzzy rule, the rule activation weight corresponding to the preset fuzzy rule is calculated using the upper vertex value, the lower vertex value, and the membership function. A global fuzzy model of the target system is constructed using the nonlinear state-space equation and the rule activation weights. The step of constructing a global fuzzy model of the target system using the nonlinear state-space equation and the rule activation weights includes: The nonlinear state-space equation and the rule activation weights are input into the fourth formula to construct the global fuzzy model of the target system. The fourth formula is as follows: in, The first derivative of the system state vector. For the first The system matrix under the pre-defined fuzzy rules, For the input matrix, It is a constant vector matrix. To control the input vector, Let be the system state vector. This refers to the total number of the constant power load units. For the first The activation weight of the pre-defined fuzzy rules. The sequence number of the preset fuzzy rule.

2. The polynomial fuzzy modeling method for multi-source DC microgrids according to claim 1, characterized in that, The step of determining the first state equation and the second state equation based on the first switch differential equation, the second switch differential equation, and the first preset switch state variable includes: The first switch differential equation, the second switch differential equation, and the first preset switch state variable are input into the first formula to obtain the first state equation and the second state equation. The first formula is: in, Let be the first derivative of the state variable of the inductor current of the constant power load unit. Let be the first derivative of the state variable of the capacitor current of the constant power load unit. Let be the state variable of the inductor current of the constant power load unit. Let be the state variable of the capacitor current of the constant power load unit. The voltage state variable of the DC bus unit. This is the first preset switch state variable. The inductance of the constant power load unit, The power of the constant power load unit. The capacitor of the constant power load unit. , , and All of these are nonlinear coefficients corresponding to the switching states of the constant power load unit.

3. The polynomial fuzzy modeling method for multi-source DC microgrids according to claim 1, characterized in that, The determination of the charging and discharging current of the DC bus unit based on the inductor current of the constant power load unit, the inductor current of the photovoltaic unit, the inductor current of the energy storage converter unit, the first preset switch state variable, the second preset switch state variable, and the third preset switch state variable includes: The inductor current of the constant power load unit, the inductor current of the photovoltaic unit, the inductor current of the energy storage converter unit, the first preset switch state variable, the second preset switch state variable, and the third preset switch state variable are input into the second formula to calculate the charging and discharging current of the DC bus unit. The second formula is: in, The charging and discharging current of the DC bus unit. The inductor current of the energy storage converter unit is... The inductor current of the photovoltaic unit is... The inductor current of the constant power load unit is... This is the first preset switch state variable. This is the second preset switch state variable. This refers to the third preset switch state variable.

4. The polynomial fuzzy modeling method for multi-source DC microgrids according to claim 1, characterized in that, The determination of the fifth state equation based on the differential equation of the DC bus unit includes: The differential equation of the DC bus unit is transformed into a state equation to obtain the fifth state equation, which is: in, Let be the first derivative of the state variable of the voltage of the DC bus unit. Let be the state variable of the inductor current of the constant power load unit. Let be the state variable of the inductor current of the photovoltaic unit. The state variable is the inductor current of the energy storage converter unit. This is the first preset switch state variable. This is the second preset switch state variable. The third preset switch state variable, This refers to the supporting capacitor of the DC bus unit.

5. The polynomial fuzzy modeling method for multi-source DC microgrids according to claim 1, characterized in that, The nonlinear state-space equation for a single constant power load unit, based on the unit state equation, includes: Using the aforementioned unit state equations, the system matrix and input matrix are determined; A constant vector matrix is ​​determined using the output voltage of the photovoltaic unit, the inductance of the photovoltaic unit, the input voltage of the energy storage converter unit, and the inductance of the energy storage converter unit. The nonlinear state-space equation of a single constant power load unit is determined using the system matrix, the input matrix, and the constant vector matrix.

6. A polynomial fuzzy modeling device for a multi-source DC microgrid, characterized in that, include: The vector construction module is used to construct the state vector of the target system. The state vector includes the inductor current of the constant power load unit, the capacitor voltage of the constant power load unit, the inductor current of the photovoltaic unit, the inductor current of the energy storage converter unit, and the voltage of the DC bus unit. The state equation construction module is used to construct unit state equations using the state vectors. The unit state equations include the first and second state equations corresponding to the constant power load unit, the third state equation corresponding to the photovoltaic unit, the fourth state equation corresponding to the energy storage converter unit, and the fifth state equation corresponding to the DC bus unit. The fuzzy model construction module is used to obtain the nonlinear state-space equation of a single constant power load unit based on the unit state equation, and to construct the global fuzzy model of the target system based on the nonlinear state-space equation. The state equation construction module is used for: Based on the structural topology diagram of the target system, the topologies of the constant power load unit, the photovoltaic unit, and the energy storage converter unit are determined respectively. The topology of the constant power load unit includes a first switching transistor, a first diode, and an LC filter connected to them. The topology of the photovoltaic unit includes a first inductor, a second switching transistor, and a second diode. The topology of the energy storage converter unit includes a second inductor, a third switching transistor, and a fourth switching transistor. Based on the topology of the constant power load unit, Kirchhoff's voltage law and Kirchhoff's current law are used to obtain the first and second switching differential equations of the constant power load unit. Based on the first and second switching differential equations and the first preset switching state variables, the first and second state equations are determined. Based on the topology of the photovoltaic unit, the third and fourth switching differential equations of the photovoltaic unit are obtained by using Kirchhoff's voltage law and Kirchhoff's current law. Based on the third switching differential equation, the fourth switching differential equation, and the second preset switching state variable, the third state equation is determined. Based on the topology of the energy storage converter unit, the fifth and sixth switch differential equations of the energy storage converter unit are obtained by using Kirchhoff's voltage law and Kirchhoff's current law. Based on the fifth and sixth switch differential equations and the third preset switch state variables, the fourth state equation is determined. The charging and discharging current of the DC bus unit is determined based on the inductor current of the constant power load unit, the inductor current of the photovoltaic unit, the inductor current of the energy storage converter unit, the first preset switch state variable, the second preset switch state variable, and the third preset switch state variable. Based on the capacitor's volt-ampere characteristics, the charging and discharging current of the DC bus unit is calculated to obtain the differential equation of the DC bus unit, and based on the differential equation of the DC bus unit, the fifth state equation is determined. The fuzzy model construction module is used for: Under the constraint of the capacitor voltage of the constant power load unit, the vertex value of the nonlinear term at the boundary is calculated. The vertex value includes the upper vertex value and the lower vertex value. The nonlinear term is the nonlinear term of the capacitor voltage of the constant power load unit. Using the upper vertex value and the lower vertex value, a membership function is constructed, which includes a first membership function and a second membership function; Under the constraint of a preset fuzzy rule, the rule activation weight corresponding to the preset fuzzy rule is calculated using the upper vertex value, the lower vertex value, and the membership function. A global fuzzy model of the target system is constructed using the nonlinear state-space equation and the rule activation weights. The fuzzy model construction module is used for: The nonlinear state-space equation and the rule activation weights are input into the fourth formula to construct the global fuzzy model of the target system. The fourth formula is as follows: in, The first derivative of the system state vector. For the first The system matrix under the pre-defined fuzzy rules, For the input matrix, It is a constant vector matrix. To control the input vector, Let be the system state vector. This refers to the total number of the constant power load units. For the first The activation weight of the pre-defined fuzzy rules. The sequence number of the preset fuzzy rule.

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