Multi-inverter equipment control parameter collaborative optimization method and device

Abstract

The invention provides a multi-inverter equipment control parameter collaborative optimization method and device, and relates to the field of electrical equipment safety control, and the method comprises the steps: constructing a control parameter optimization model containing an inverter power supply fault process according to the electrical parameters of multi-inverter equipment; performing control parameter discretization processing on the control parameter optimization model to obtain a control parameter simplified model; and inputting the electrical parameters of the multi-inverter equipment into the control parameter simplified model to obtain control optimization parameters of the multi-inverter equipment. According to the method, the multi-inverter power supply control parameter collaborative optimization model considering transient voltage safety can be constructed, and the model solving efficiency is improved through control parameter discretization processing and a feasible solution obtaining method.

Description

Technical Field

[0001] The present application relates to the field of electrical equipment safety control, and specifically to a method and device for collaborative optimization of control parameters of a multi-inverter device. Background Art

[0002] In recent years, with the continuous increase in the installed capacity of new energy, the proportion of asynchronous machine power sources in the power grid has gradually increased, and the power grid shows an increasingly obvious weak grid form lacking the support of synchronous machine power sources. Moreover, the characteristics of asynchronous machine power sources relying on inverters to access the power grid, such as poor anti-voltage disturbance and overcurrent capabilities and difficulty in effectively coordinating between multiple power sources, have significantly increased the risk of large-area disconnection of photovoltaic and energy storage under large disturbances in the power grid.

[0003] To solve the above problems, the method of developing the voltage support ability of the inverter power source itself can provide guarantee for the further development of new energy. However, in practical applications, there may be phenomena of "competition" and "mutual cancellation" of voltage support for a certain weak point between multiple inverter power sources, thus reducing the transient voltage security of the system. To solve such problems, it is necessary to introduce the algebraic-differential equation system (DAEs) describing the dynamic characteristics of the inverter and design a multi-device collaborative optimization model. However, the complex characteristics of DAEs make the solution efficiency of the model low and difficult to be applied in practice. Therefore, it is crucial to propose a method for efficiently solving the multi-device collaborative optimization model.

[0004] This section aims to provide background or context for the embodiments of the present invention stated in the claims. The description herein is not admitted to be prior art merely because it is included in this section. Summary of the Invention

[0005] Aiming at the problems in the prior art, the present application provides a method and device for collaborative optimization of control parameters of a multi-inverter device, which can construct a collaborative optimization model of control parameters of a multi-inverter power source considering transient voltage safety, and improve the model solution efficiency through the method of control parameter discretization processing and obtaining feasible solutions.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a method for collaborative optimization of control parameters of a multi-inverter device, including:

[0008] Constructing a control parameter optimization model including the fault process of the inverter power source according to the electrical parameters of the multi-inverter device;

[0009] Performing control parameter discretization processing on the control parameter optimization model to obtain a control parameter simplified model;

[0010] Input the electrical parameters of the multi-inverter device into the control parameter simplification model to obtain the control optimization parameters of the multi-inverter device.

[0011] Further, the control parameter optimization model includes the output voltage equation of the network-forming equipment under steady state, the output current equation of the grid-following equipment under steady state, and the coordinate transformation equation under steady state; the construction of the control parameter optimization model including the inverter power supply fault process according to the electrical parameters of the multi-inverter device includes:

[0012] Construct the output voltage equation of the network-forming equipment under steady state according to the voltage set value of the network-forming equipment under steady state, the internal voltage reference value of the network-forming equipment under steady state, and the current set value of the network-forming equipment under steady state;

[0013] Construct the output current equation of the grid-following equipment under steady state according to the current set value of the grid-following equipment under steady state, the internal current reference value of the grid-following equipment under steady state, and the voltage set value of the grid-following equipment under steady state;

[0014] Construct the coordinate transformation equation under steady state according to the power angle of the network-forming equipment under steady state, the phase-locked loop output angle of the grid-following equipment, the voltage value of the network-forming equipment under steady state, and the current value of the grid-following equipment under steady state;

[0015] Determine the value range of the control variables of the grid-following equipment and the network-forming equipment; among them, the control variable range includes the value range of the virtual admittance and the value range of the virtual impedance.

[0016] Further, the control parameter optimization model includes the output voltage equation of the network-forming equipment during the transient process, the output current equation of the grid-following equipment during the transient process, the coordinate transformation equation during the transient process, the network equation constraint during the transient process, the capacity limit of the inverter power supply and the grid connection point voltage safety constraint during the transient process, the boundary conditions of the network-forming equipment at different critical moments during the transient process, the boundary conditions of the grid-following equipment at different critical moments during the transient process, and the current setting value constraint under each fault during the transient process; the construction of the control parameter optimization model including the inverter power supply fault process according to the electrical parameters of the multi-inverter device includes:

[0017] Construct the output voltage equation of the network-forming equipment during the transient process according to the voltage value of the network-forming equipment during the transient process, the internal voltage reference value of the network-forming equipment during the transient process, and the current value of the network-forming equipment during the transient process;

[0018] Construct the output current equation of the grid-following equipment during the transient process according to the current value of the grid-following equipment during the transient process, the internal current reference value of the grid-following equipment during the transient process, and the voltage value of the grid-following equipment during the transient process;

[0019] Construct a coordinate transformation equation during the transient process based on the power angle of the network-forming equipment and the phase-locked loop output angle of the network-following equipment, the voltage value of the network-forming equipment during the transient process, and the current value of the network-following equipment during the transient process;

[0020] Construct network equation constraints during the transient process based on the network node current value and network node voltage value during the transient process, and the real and imaginary parts of the network node admittance matrix during the transient process;

[0021] Construct the capacity limit of the inverter power supply and the grid connection point voltage safety constraint during the transient process based on the upper limit of the current, low-voltage disconnection threshold value, and high-voltage disconnection threshold value of the network-forming equipment and the network-following equipment during the transient process;

[0022] Construct boundary conditions of the network-forming equipment at different critical moments during the transient process based on the internal voltage reference value and internal voltage setting value of the network-forming equipment;

[0023] Construct boundary conditions of the network-following equipment at different critical moments during the transient process based on the internal current reference value of the network-following equipment;

[0024] Determine the current setting value constraint under each fault during the transient process.

[0025] Furthermore, the discretization process of the control parameters for the control parameter optimization model is carried out to obtain a simplified control parameter model, including:

[0026] Discretize the gain value and current reference value of the inner loop feedback term in the output voltage equation of the network-forming equipment during the transient process, the output current equation of the network-following equipment during the transient process, and the current setting value constraint under each transient fault to obtain a stepped adjustment amount;

[0027] Use the stepped adjustment amount to convert the bilinear term in the output voltage equation of the network-forming equipment during the transient process, the output current equation of the network-following equipment during the transient process, and the current setting value constraint under each transient fault into a product term of a continuous variable and a discrete variable;

[0028] Generate the simplified control parameter model according to the product term of the continuous variable and the discrete variable transformed from the bilinear term.

[0029] In a second aspect, the present application provides a multi-inverter device control parameter collaborative optimization device, including:

[0030] An optimization model construction unit for constructing a control parameter optimization model including the inverter power supply fault process according to the electrical parameters of the multi-inverter device;

[0031] A simplified model construction unit for performing discretization processing on the control parameters of the control parameter optimization model to obtain a simplified control parameter model;

[0032] A control parameter generation unit, configured to input the electrical parameters of the multi-inverter device into the control parameter simplification model to obtain the control optimization parameters of the multi-inverter device.

[0033] Further, the control parameter optimization model includes an output voltage equation of the network-forming equipment under steady state, an output current equation of the grid-following equipment under steady state, and a coordinate transformation equation under steady state; the optimization model construction unit includes:

[0034] A steady-state voltage equation construction module, configured to construct an output voltage equation of the network-forming equipment under steady state according to the voltage given value of the network-forming equipment under steady state, the internal voltage reference value of the network-forming equipment under steady state, and the current given value of the network-forming equipment under steady state;

[0035] A steady-state current equation construction module, configured to construct an output current equation of the grid-following equipment under steady state according to the current given value of the grid-following equipment under steady state, the internal current reference value of the grid-following equipment under steady state, and the voltage given value of the grid-following equipment under steady state;

[0036] A steady-state transformation equation construction module, configured to construct a coordinate transformation equation under steady state according to the power angle of the network-forming equipment and the phase-locked loop output angle of the grid-following equipment under steady state, the voltage value of the network-forming equipment under steady state, and the current value of the grid-following equipment under steady state;

[0037] A steady-state value range determination module, configured to determine the value range of the control variables of the grid-following equipment and the network-forming equipment; wherein, the control variable range includes the value range of the virtual admittance and the value range of the virtual impedance.

[0038] Further, the control parameter optimization model includes an output voltage equation of the network-forming equipment during the transient process, an output current equation of the grid-following equipment during the transient process, a coordinate transformation equation during the transient process, a network equation constraint during the transient process, a capacity limit of the inverter power supply and a grid connection point voltage safety constraint during the transient process, boundary conditions of the network-forming equipment at different critical moments during the transient process, boundary conditions of the grid-following equipment at different critical moments during the transient process, and a current setting value constraint for each fault during the transient process; the optimization model construction unit includes:

[0039] A transient voltage equation construction module, configured to construct an output voltage equation of the network-forming equipment during the transient process according to the voltage value of the network-forming equipment during the transient process, the internal voltage reference value of the network-forming equipment during the transient process, and the current value of the network-forming equipment during the transient process;

[0040] A transient current equation construction module, configured to construct an output current equation of the grid-following equipment during the transient process according to the current value of the grid-following equipment during the transient process, the internal current reference value of the grid-following equipment during the transient process, and the voltage value of the grid-following equipment during the transient process;

[0041] A transient transformation equation construction module, configured to construct a coordinate transformation equation during a transient process based on the power angle of a network-forming device and the phase-locked loop output angle of a network-following device, the voltage value of the network-forming device during the transient process, and the current value of the network-following device during the transient process;

[0042] An equation constraint construction module, configured to construct network equation constraints during a transient process based on the network node current value and the network node voltage value during the transient process, and the real part and the imaginary part of the network node admittance matrix during the transient process;

[0043] A safety constraint construction module, configured to construct capacity limits of an inverter power supply and grid connection point voltage safety constraints during a transient process based on the upper limits of currents of a network-forming device and a network-following device during the transient process, a low-voltage disconnection threshold value, and a high-voltage disconnection threshold value;

[0044] A network-forming boundary condition determination module, configured to construct boundary conditions of a network-forming device at different critical moments during a transient process based on an internal voltage reference value of the network-forming device and an internal voltage setting value of the network-forming device;

[0045] A network-following boundary condition determination module, configured to construct boundary conditions of a network-following device at different critical moments during a transient process based on an internal current reference value of the network-following device;

[0046] A transient current constraint determination module, configured to determine current setting value constraints for each fault during a transient process.

[0047] Further, the simplified model construction unit includes:

[0048] A discretization processing module, configured to discretize the gain value and the current reference value of the inner loop feedback term in the output voltage equation of the network-forming device during the transient process, the output current equation of the network-following device during the transient process, and the current setting value constraints for each fault during the transient process, to obtain a stepped adjustment amount;

[0049] A discrete product determination module, configured to use the stepped adjustment amount to convert the bilinear terms in the output voltage equation of the network-forming device during the transient process, the output current equation of the network-following device during the transient process, and the current setting value constraints for each fault during the transient process into product terms of continuous variables and discrete variables;

[0050] A simplified model determination module, configured to generate the control parameter simplified model according to the product terms of continuous variables and discrete variables obtained by converting the bilinear terms.

[0051] In a third aspect, the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where when the processor executes the program, the steps of the multi-inverter device control parameter collaborative optimization method are implemented.

[0052] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the multi-inverter device control parameter collaborative optimization method are implemented.

[0053] In a fifth aspect, the present application provides a computer program product, including a computer program / instructions. When the computer program / instructions are executed by a processor, the steps of the multi-inverter device control parameter collaborative optimization method are implemented.

[0054] Aiming at the problems in the prior art, the multi-inverter device control parameter collaborative optimization method and device provided by the present application can construct a multi-inverter power control parameter collaborative optimization model considering transient voltage safety, and then improve the solving efficiency of the model by means of control parameter discretization processing and obtaining feasible solutions, and perform collaborative optimization of the control parameters of the multi-inverter device. Specifically, according to the characteristics that the current and voltage inside the power supply cannot change suddenly, the variables in the constraint expression are replaced, simplifying the complexity of the model, and converting the non-linear programming problem in the optimization solution into a mixed-integer linear programming problem through steps such as the discretization method, reducing the difficulty of model solving. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0056] Figure 1 It is a flowchart of the multi-inverter device control parameter collaborative optimization method in the embodiment of the present application;

[0057] Figure 2 It is a flowchart of the steady-state model in the construction of the control parameter optimization model in the embodiment of the present application;

[0058] Figure 3 It is a flowchart of the transient model in the construction of the control parameter optimization model in the embodiment of the present application;

[0059] Figure 4 It is a flowchart of the construction of the control parameter simplified model in the embodiment of the present application;

[0060] Figure 5 It is a structural diagram of the multi-inverter device control parameter collaborative optimization device in the embodiment of the present application;

[0061] Figure 6 It is one of the structural diagrams of the optimization model construction unit in the embodiment of the present application;

[0062] Figure 7 This is the second structural diagram of the optimization model construction unit in the embodiments of the present application;

[0063] Figure 8 This is the structural diagram of the simplified model construction unit in the embodiments of the present application;

[0064] Figure 9 This is the schematic structural diagram of the electronic device in the embodiments of the present application;

[0065] Figure 10 This is the schematic flow diagram of the method for collaborative optimization of control parameters of a multi-inverter device in the embodiments of the present application. Detailed implementation manners

[0066] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer and more understandable, the following further describes the embodiments of the present invention in detail with reference to the accompanying drawings. Herein, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but do not limit the present invention.

[0067] In the technical solutions of the present application, the information collected is information and data authorized by the user or fully authorized by all parties. Moreover, for the processing of relevant data such as collection, storage, use, processing, transmission, provision, disclosure, and application, all comply with the relevant laws, regulations, and standards of the relevant countries and regions, adopt necessary confidentiality measures, do not violate public order and good customs, and provide corresponding operation entrances for the user to choose to authorize or refuse.

[0068] Provide corresponding operation entrances for the user to choose to agree or refuse the automated decision-making results; if the user chooses to refuse, then enter the expert decision-making process.

[0069] In one embodiment, referring to Figure 1 , in order to be able to construct a collaborative optimization model for the control parameters of a multi-inverter power supply considering transient voltage safety, and improve the model solving efficiency through the method of control parameter discretization processing and obtaining feasible solutions, the present application provides a method for collaborative optimization of control parameters of a multi-inverter device, including:

[0070] S101: Construct a control parameter optimization model including the inverter power supply fault process according to the electrical parameters of the multi-inverter device;

[0071] S102: Perform control parameter discretization processing on the control parameter optimization model to obtain a control parameter simplified model;

[0072] S103: Input the electrical parameters of the multi-inverter device into the control parameter simplified model to obtain the control optimization parameters of the multi-inverter device.

[0073] It is understandable that this application proposes a fast solution method for the collaborative optimization model of multi-device control parameters and can use this model to collaboratively optimize the control parameters of multiple devices. See Figure 10 , this method first constructs a collaborative optimization model for the control parameters of multi-inverter power supplies considering transient voltage security, and then improves the model solution efficiency by discretizing the control parameters and using the MNDT algorithm to obtain feasible solutions for simple solution.

[0074] First, an optimization model including control parameters during the fault process of the inverter power supply is established. The specific optimization model can be seen in Formulas (1) to (30). Among them, Formulas (2) to (30) can be understood as the constraint conditions in this optimization model.

[0075] Among them, the objective function of the optimization model is as follows:

[0076]

[0077] The key control parameters in this optimization model are the virtual admittance g v,l , b v,l of the grid-connected (GFL) device, the current setting value I′ l,d,op , I′ l,q,op and the virtual impedance R v,m , x v,m of the grid-forming (GFM) equipment, the voltage setting value E' m,op , δ m,op . The objective function is to minimize the average value of the deviation of the inverter power supply voltage compared with the reference voltage V ref under all faults. This optimization model will determine the values of the control parameters of each inverter power supply in the network, so as to achieve the purpose of collaborative optimization of multi-inverter power supplies.

[0078] Finally, under the constraint conditions of Formulas (2) to (30), solve with Formula (1) as the objective function. The solution process can use existing optimization model solvers. Specifically, solve the relaxed problem to obtain the lower bound and initial solution of the original problem. According to the obtained initial solution, fix the integer variables and use the interior point method to solve the continuous variables, which can achieve the fast solution of the original problem.

[0079] During the specific solution, by obtaining the voltages V l 0 and V l s , currents and power angles and the output angle of the phase-locked loop and data such as the real and imaginary parts G and B of the admittance matrix of the system network nodes, and the key parameters g of the multi-inverter device v,l , b v,l , I′ l,d,op , I′ l,q,op R v,m , x v,m E' m,op , δ m,op (For the specific physical meaning, see the description below) are jointly optimized to output the set values of the key control parameters that can minimize the deviation between the node transient voltage and the voltage reference value. Thus, the control parameters of multiple devices are jointly optimized.

[0080] As can be seen from the above description, the method for jointly optimizing the control parameters of the multi-inverter device provided in this application can construct a joint optimization model of the control parameters of the multi-inverter power supply considering transient voltage safety, and then improve the solution efficiency of the model by discretizing the control parameters and obtaining feasible solutions, and jointly optimize the control parameters of the multi-inverter device. Specifically, based on the characteristic that the current and voltage inside the power supply cannot change suddenly, the variables in the constraint expression are replaced, simplifying the complexity of the model, and through steps such as the discretization method, the non-linear programming problem in the optimization solution is converted into a mixed-integer linear programming problem, reducing the difficulty of model solution.

[0081] In one embodiment, see Figure 2 , the control parameter optimization model includes the output voltage equation of the grid-forming equipment under steady state, the output current equation of the grid-following equipment under steady state, and the coordinate transformation equation under steady state; the control parameter optimization model constructed according to the electrical parameters of the multi-inverter device and including the inverter power supply fault process includes:

[0082] S201: Construct the output voltage equation of the grid-forming equipment under steady state according to the voltage given value of the grid-forming equipment under steady state, the internal voltage reference value of the grid-forming equipment under steady state, and the current given value of the grid-forming equipment under steady state;

[0083] It can be understood that the output voltage equation of the GFM equipment under steady state is:

[0084]

[0085] Among them, respectively represent the d-axis component and q-axis component of the voltage given value of the GFM equipment under steady state; represents the internal voltage reference value of the GFM equipment under steady state; respectively represent the d-axis component and q-axis component of the current given value of the GFM equipment under steady state; S m represents the set of GFM equipment; R v,m , x v,mRepresents the virtual impedance of the GFM equipment.

[0086] S202: Construct the output current equation of the grid-following equipment under steady state according to the current set value of the grid-following equipment under steady state, the internal current reference value of the grid-following equipment under steady state, and the voltage set value of the grid-following equipment under steady state;

[0087] It can be understood that the output current equation of the GFL equipment under steady state is:

[0088]

[0089] Wherein, respectively represent the d-axis component and q-axis component of the current set value of the GFL equipment under steady state; respectively represent the d-axis component and q-axis component of the internal current reference value of the GFL equipment under steady state; respectively represent the d-axis component and q-axis component of the voltage set value of the GFL equipment under steady state; S l represents the set of GFL equipment; g v,l ,b v,l represents the virtual admittance of the GFL equipment.

[0090] S203: Construct the coordinate transformation equation under steady state according to the power angle of the grid-forming equipment and the phase-locked loop output angle of the grid-following equipment under steady state, the voltage value of the grid-forming equipment under steady state, and the current value of the grid-following equipment under steady state;

[0091] It can be understood that the coordinate transformation equation under steady state is:

[0092]

[0093] Wherein, T l 0 respectively represent the coordinate transformation matrices of the GFM equipment and the GFL equipment under steady state; respectively represent the power angle of the GFM equipment and the phase-locked loop output angle of the GFL equipment under steady state; respectively represent the vector composed of the d-axis and q-axis components of the voltage of the GFM equipment under steady state, and the vector composed of the x-axis and y-axis components of the voltage of the GFM equipment under steady state; respectively represent the vector composed of the d-axis and q-axis components of the current of the GFL equipment under steady state, and the vector composed of the x-axis and y-axis components of the current of the GFL equipment under steady state; S m represents the set of GFM equipment; S l represents the set of GFL equipment.

[0094] S204: Determine the value range of the control variables of the grid-following equipment and the grid-forming equipment; wherein, the control variable range includes the value range of the virtual admittance and the value range of the virtual impedance.

[0095] It can be understood that the control variable ranges of the GFL equipment and the GFM equipment are as follows:

[0096]

[0097] For the meanings of the symbols, please refer to the foregoing.

[0098] From the above description, it can be seen that the multi-inverter equipment control parameter collaborative optimization method provided by this application can construct a control parameter optimization model including the inverter power supply fault process according to the electrical parameters of the multi-inverter equipment.

[0099] In one embodiment, referring to Figure 3 , the control parameter optimization model includes the output voltage equation of the grid-forming equipment in the transient process, the output current equation of the grid-following equipment in the transient process, the coordinate transformation equation in the transient process, the network equation constraint in the transient process, the capacity limit of the inverter power supply and the grid connection point voltage safety constraint in the transient process, the boundary conditions of the grid-forming equipment at different critical moments in the transient process, the boundary conditions of the grid-following equipment at different critical moments in the transient process, and the current setting value constraint under each fault in the transient process; the construction of the control parameter optimization model including the inverter power supply fault process according to the electrical parameters of the multi-inverter equipment includes:

[0100] S301: Construct the output voltage equation of the grid-forming equipment in the transient process according to the voltage value of the grid-forming equipment in the transient process, the internal voltage reference value of the grid-forming equipment in the transient process, and the current value of the grid-forming equipment in the transient process;

[0101] It can be understood that the output voltage equation of the GFM equipment in the transient process is:

[0102]

[0103] Where respectively represent the d-axis component and the q-axis component of the voltage of the GFM equipment in the transient process; represents the internal voltage reference value of the GFM equipment in the transient process; respectively represent the d-axis component and the q-axis component of the current of the GFM equipment in the transient process; S flt represents the set of fault numbers; τ 1+ / τ 2- / τ 2+ respectively represent the moment of fault occurrence, the steady state during the fault, and the moment of fault clearing; for other symbol explanations, please refer to the foregoing.

[0104] S302: Construct the output current equation of the grid-following equipment in the transient process according to the current value of the grid-following equipment in the transient process, the internal current reference value of the grid-following equipment in the transient process, and the voltage value of the grid-following equipment in the transient process;

[0105] It can be understood that the output current equation of the GFL equipment during the transient process is:

[0106]

[0107] wherein, respectively represent the d-axis component and the q-axis component of the current of the GFL equipment during the transient process; represents the d-axis component and the q-axis component of the internal current reference value of the GFL equipment during the transient process; respectively represent the d-axis component and the q-axis component of the voltage of the GFL equipment during the transient process; τ 1+ / τ 2- / τ 2+ respectively represent the moment when the fault occurs, the steady state during the fault, and the moment when the fault is cleared.

[0108] S303: Construct a coordinate transformation equation during the transient process according to the power angle of the network-forming equipment and the phase-locked loop output angle of the grid-following equipment, the voltage value of the network-forming equipment during the transient process, and the current value of the grid-following equipment during the transient process;

[0109] It can be understood that the coordinate transformation equation during the transient process is:

[0110]

[0111] wherein, T l s respectively represent the coordinate transformation matrices of the GFM equipment and the GFL equipment during the transient process; respectively represent the power angle of the GFM equipment and the phase-locked loop output angle of the GFL equipment during the transient process; respectively represent the vector composed of the d-axis and q-axis components of the voltage of the GFM equipment during the transient process, and the vector composed of the x-axis and y-axis components of the voltage of the GFM equipment during the transient process; respectively represent the vector composed of the d-axis and q-axis components of the current of the GFL equipment during the transient process, and the vector composed of the x-axis and y-axis components of the current of the GFL equipment during the transient process; For other compliance descriptions, refer to the foregoing.

[0112] S304: Construct network equation constraints during the transient process according to the network node current value and the network node voltage value, and the real part and the imaginary part of the network node admittance matrix during the transient process;

[0113] It can be understood that the network equation constraints during the transient process are:

[0114]

[0115] wherein, G s,B s respectively represent the real part and the imaginary part of the nodal admittance matrix in the transient process network; respectively represent the components of the nodal current and the nodal voltage on the x-axis and y-axis during the transient process; for other symbol explanations, refer to the foregoing.

[0116] S305: Construct the capacity limit of the inverter power supply and the safety constraint of the grid connection point voltage during the transient process according to the upper limit of the current, the low-voltage disconnection threshold, and the high-voltage disconnection threshold during the transient process of the network-forming equipment and the grid-following equipment;

[0117] It can be understood that the capacity limit of the inverter power supply and the safety constraint of the grid connection point voltage during the transient process are:

[0118]

[0119] Among them, respectively represent the d-axis component and the q-axis component of the current during the transient process of the GFL equipment; respectively represent the d-axis component and the q-axis component of the current during the transient process of the GFM equipment; respectively represent the x-axis component and the y-axis component of the voltage during the transient process of the GFL equipment; the x-axis component and the y-axis component of the voltage during the transient process of the GFM equipment; respectively represent the upper limits of the currents during the transient processes of the GFM equipment and the GFL equipment; V LVRT,th ,V HVRT,th respectively represent the low-voltage disconnection threshold and the high-voltage disconnection threshold; for other symbol explanations, refer to the foregoing.

[0120] S306: Construct the boundary conditions of the network-forming equipment at different critical moments during the transient process according to the internal voltage reference value of the network-forming equipment and the internal voltage setting value of the network-forming equipment;

[0121] It can be understood that during the transient process, due to the sudden change of the power angle and the phase angle of the phase-locked loop cannot change suddenly, and the dq-axis current reference values given by the outer loop and the voltage reference value also cannot change suddenly. Therefore, the xy-axis components of the internal current and voltage reference values of the power supply cannot change suddenly. In order to solve this non-convex NLP (Nonlinear programming) problem more efficiently, the xy-axis components of the injected current and the output voltage reference value can be directly regarded as optimization variables, and all the dq-axis components of the variables in the constraint expression are replaced with xy-axis components to obtain the boundary condition formulas (14) and (15) at different critical moments during the transient process.

[0122] The boundary conditions of the GFM equipment at different critical moments during the transient process are:

[0123]

[0124] Among them, the superscript (·) s represents the variable under fault s, and the superscript (·)0 represents the variable under steady state; E' m,x , E' m,y respectively represent the x-axis component and the y-axis component of the internal voltage reference value of the GFM equipment; E' m,x,op , E' m,y,op represent the x-axis component and the y-axis component of the internal voltage set value of the GFM equipment; for other symbol descriptions, refer to the foregoing.

[0125] S307: Construct the boundary conditions of the grid-following equipment at different critical moments during the transient process according to the internal current reference value of the grid-following equipment;

[0126] It can be understood that the boundary conditions of the GFL equipment at different critical moments during the transient process are:

[0127]

[0128] Among them, I′ l,x , I′ l,y respectively represent the x-axis component and the y-axis component of the internal current reference value of the GFL equipment; for other symbol descriptions, refer to the foregoing.

[0129] S308: Determine the current set value constraints under each fault during the transient process.

[0130] It can be understood that the current set value constraints under each transient fault are:

[0131]

[0132] Among them, for symbol descriptions, refer to the foregoing.

[0133] As can be seen from the above description, the multi-inverter device control parameter collaborative optimization method provided by this application can construct a control parameter optimization model including the inverter power supply fault process according to the electrical parameters of the multi-inverter device.

[0134] In one embodiment, referring to Figure 4 , performing control parameter discretization processing on the control parameter optimization model to obtain a control parameter simplified model, including:

[0135] S401: Respectively perform discretization processing on the gain value and the current reference value of the inner loop feedback term in the output voltage equation of the grid-forming equipment, the output current equation of the grid-following equipment, and the current set value constraints under each transient fault during the transient process to obtain the step adjustment amount;

[0136] S402: Use the grading adjustment amount to convert the bilinear terms in the output voltage equation of the network-forming equipment during the transient process, the output current equation of the grid-following equipment during the transient process, and the current setting value constraints under each transient fault into the product terms of continuous variables and discrete variables;

[0137] S403: Generate the simplified model of the control parameters according to the product terms of continuous variables and discrete variables formed by the bilinear terms.

[0138] It can be understood that the control parameters are discretized next: To prevent the introduction of too many bilinear terms, the gains g v,l , b v,l , R v,m , x v,m of the inner-loop feedback term and the given current reference commands I′ l,d,op , I′ l,q,op are discretized to make them into grading adjustment amounts, so as to convert the bilinear terms in the constraint expressions (6), (7), and (16) corresponding to different faults and different critical moments into the product terms of continuous variables and discrete variables. Then, with the help of the big M method (a mathematical method for solving linear programming problems), the formula obtained in the previous step is equivalently transformed into the form of linear constraints, thereby reducing the model complexity.

[0139] Taking the bilinear term in Equation (6) as an example, first discretize the continuous variable R v,m as shown in Equation (17).

[0140]

[0141] where represents the integer variable introduced by the discretization of the variable R v,m , ΔR dec represents the discretization interval, R min represents the lower limit of the variable value, and N d represents the number of discrete points. At this time, the bilinear term becomes the product term of continuous variable and discrete variable as shown in Equation (18).

[0142]

[0143] Convert to the linear constraints shown in Equations (19) and (20) through the big M method.

[0144]

[0145] Using the same method, the constraint expressions (6), (7), and (16) are respectively converted into equations (21), (22), and (23). It should be noted that, in order to ensure the feasibility of the optimization result, only the control parameters are discretized in the embodiments of the present application, and the bilinear terms and square terms on the right side of the equal sign in the constraint expression (23) do not participate in the discretization. After the discretization process, the problem is transformed into a quadratic constraint programming problem with integer variables.

[0146]

[0147] Next, the NMDT algorithm (Normalized multiparametric disaggregation technique) is used to quickly obtain a feasible solution: select a single continuous variable in the bilinear term and square term for discretization. Taking in equation (23) as an example, select for discretization. In order to reduce the number of integer variables, binary expansion is used, and equation (24) can be obtained. Since the discretized variables cannot take on the values between adjacent "discrete points", in order to ensure the feasibility of the optimization result, a new continuous variable needs to be introduced, and its value range is equation (25).

[0148]

[0149]

[0150] Among them, represents the auxiliary 01 variable and continuous variable introduced by discretization, ΔV dec represents the discretization interval, V min represents the lower limit of the value of, N d represents the number of discrete intervals. At this time, the bilinear term will be transformed into the product term of discrete variables and continuous variables and the new bilinear term as shown in equation (26). The big M method is used to process the product term and it can be transformed into linear constraints, such as equations (27) and (28).

[0151]

[0152] For the new bilinear term McCormick relaxation is used for processing, so that the constraints are converted into linear constraints, such as equations (29) and (30). Among them, I min and I max represent the lower limit and upper limit of the value of the variable .

[0153]

[0154] After the above processing, the original problem is relaxed into a MILP (Mixed-integer linear programming) problem. The parameters to be optimized in the simplified model include: the variables introduced by the discretization of the virtual impedance of the GFM equipment The voltage setting value E' of the GFM equipment m,op , δ m,op And the variables introduced by the discretization of the virtual admittance of the GFL equipment The variables introduced by the discretization of the current setting value of the GFL equipment And the variables introduced by the discretization of the transient voltage of the GFL equipment

[0155] During the specific solution, the formulas (2) to (30) need to be input into the following optimization model:

[0156]

[0157] The key control parameters in this optimization model are the virtual admittance g of the GFL equipment v,l , b v,l The current setting value I′ l,d,op , I′ l,q,op And the virtual impedance R of the GFM equipment v,m , x v,m The voltage setting value E' m,op , δ m,op . The objective function is to minimize the average value of the deviation of the inverter power supply voltage compared with the reference voltage V ref . This optimization model will determine the values of the control parameters of each inverter power supply in the network, so as to achieve the purpose of collaborative optimization of multiple inverter power supplies. Among them, the GFM equipment refers to the grid-forming equipment, and the GFL equipment refers to the grid-following equipment.

[0158] From the multi-inverter device control parameter collaborative optimization method provided in the present application above, the control parameter discretization process can be performed on the control parameter optimization model to obtain a control parameter simplified model.

[0159] Based on the same inventive concept, an embodiment of the present application further provides a multi-inverter device control parameter collaborative optimization device, which can be used to implement the method described in the above embodiment, as described in the following embodiment. Since the principle of the multi-inverter device control parameter collaborative optimization device for solving problems is similar to that of the multi-inverter device control parameter collaborative optimization method, the implementation of the multi-inverter device control parameter collaborative optimization device can refer to the implementation of the method for determining software performance benchmarks, and the repeated parts will not be described again. As used hereinafter, the term "unit" or "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the systems described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0160] In one embodiment, referring to Figure 5 , in order to be able to construct a multi-inverter power control parameter collaborative optimization model considering transient voltage safety, and improve the model solving efficiency through the methods of control parameter discretization processing and obtaining feasible solutions, the present application provides a multi-inverter device control parameter collaborative optimization device, including: an optimization model construction unit 501, a simplified model construction unit 502, and a control parameter generation unit 503.

[0161] The optimization model construction unit 501 is used to construct a control parameter optimization model including the inverter power failure process according to the electrical parameters of the multi-inverter device;

[0162] The simplified model construction unit 502 is used to perform control parameter discretization processing on the control parameter optimization model to obtain a control parameter simplified model;

[0163] The control parameter generation unit 503 is used to input the electrical parameters of the multi-inverter device into the control parameter simplified model to obtain the control optimization parameters of the multi-inverter device.

[0164] In one embodiment, referring to Figure 6 , the control parameter optimization model includes a grid-forming equipment output voltage equation under steady state, a grid-following equipment output current equation under steady state, and a coordinate transformation equation under steady state; the optimization model construction unit 501 includes: a steady-state voltage equation construction module 601, a steady-state current equation construction module 602, a steady-state transformation equation construction module 603, and a steady-state value range determination module 604.

[0165] The steady-state voltage equation construction module 601 is used to construct a grid-forming equipment output voltage equation under steady state according to the grid-forming equipment voltage given value under steady state, the grid-forming equipment internal voltage reference value under steady state, and the grid-forming equipment current given value under steady state;

[0166] The steady-state current equation construction module 602 is used to construct the output current equation of the grid-following equipment under steady state according to the current given value of the grid-following equipment under steady state, the internal current reference value of the grid-following equipment under steady state, and the voltage given value of the grid-following equipment under steady state;

[0167] The steady-state transformation equation construction module 603 is used to construct the coordinate transformation equation under steady state according to the power angle of the grid-forming equipment under steady state, the phase-locked loop output angle of the grid-following equipment, the voltage value of the grid-forming equipment under steady state, and the current value of the grid-following equipment under steady state;

[0168] The steady-state value range determination module 604 is used to determine the value range of the control variables of the grid-following equipment and the grid-forming equipment; wherein, the control variable range includes the virtual admittance value range and the virtual impedance value range.

[0169] In one embodiment, referring to Figure 7 , the control parameter optimization model includes the output voltage equation of the grid-forming equipment in the transient process, the output current equation of the grid-following equipment in the transient process, the coordinate transformation equation in the transient process, the network equation constraint in the transient process, the capacity limit of the inverter power supply and the grid connection point voltage safety constraint in the transient process, the boundary conditions of the grid-forming equipment at different critical moments in the transient process, the boundary conditions of the grid-following equipment at different critical moments in the transient process, and the current setting value constraint under each fault in the transient process; the optimization model construction unit 502 includes: a transient voltage equation construction module 701, a transient current equation construction module 702, a transient transformation equation construction module 703, an equation constraint construction module 704, a safety constraint construction module 705, a grid-forming boundary condition determination module 706, a grid-following boundary condition determination module 707, and a transient current constraint determination module 708.

[0170] The transient voltage equation construction module 701 is used to construct the output voltage equation of the grid-forming equipment in the transient process according to the voltage value of the grid-forming equipment in the transient process, the internal voltage reference value of the grid-forming equipment in the transient process, and the current value of the grid-forming equipment in the transient process;

[0171] The transient current equation construction module 702 is used to construct the output current equation of the grid-following equipment in the transient process according to the current value of the grid-following equipment in the transient process, the internal current reference value of the grid-following equipment in the transient process, and the voltage value of the grid-following equipment in the transient process;

[0172] The transient transformation equation construction module 703 is used to construct the coordinate transformation equation in the transient process according to the power angle of the grid-forming equipment in the transient process, the phase-locked loop output angle of the grid-following equipment, the voltage value of the grid-forming equipment in the transient process, and the current value of the grid-following equipment in the transient process;

[0173] An equation constraint construction module 704, configured to construct network equation constraints during a transient process according to the network node current values and network node voltage values during the transient process, and the real part and imaginary part of the network node admittance matrix during the transient process;

[0174] A safety constraint construction module 705, configured to construct capacity limitations of inverter power supplies and grid connection point voltage safety constraints during the transient process according to the upper limits of currents, low-voltage disconnection thresholds, and high-voltage disconnection thresholds of network-forming equipment and grid-following equipment during the transient process;

[0175] A network-forming boundary condition determination module 706, configured to construct boundary conditions of network-forming equipment at different critical moments during the transient process according to the internal voltage reference value and internal voltage setting value of the network-forming equipment;

[0176] A grid-following boundary condition determination module 707, configured to construct boundary conditions of grid-following equipment at different critical moments during the transient process according to the internal current reference value of the grid-following equipment;

[0177] A transient current constraint determination module 708, configured to determine current setting value constraints for each fault during the transient process.

[0178] In one embodiment, referring to Figure 8 , the simplified model construction unit 502 includes:

[0179] A discretization processing module 801, configured to discretize the gain values and current reference values of the inner-loop feedback terms in the output voltage equation of network-forming equipment during the transient process, the output current equation of grid-following equipment during the transient process, and the current setting value constraints for each fault during the transient process, to obtain step adjustment amounts;

[0180] A discrete product determination module 802, configured to use the step adjustment amounts to convert the bilinear terms in the output voltage equation of network-forming equipment during the transient process, the output current equation of grid-following equipment during the transient process, and the current setting value constraints for each fault during the transient process into product terms of continuous variables and discrete variables;

[0181] A simplified model determination module 803, configured to generate the control parameter simplified model according to the product terms of continuous variables and discrete variables obtained by converting the bilinear terms.

[0182] From a hardware level, in order to be able to construct a collaborative optimization model for control parameters of multiple inverter power supplies considering transient voltage safety, and improve the model solving efficiency through the method of control parameter discretization processing and obtaining feasible solutions, this application provides an embodiment of an electronic device for implementing all or part of the content in the collaborative optimization method for control parameters of the multiple inverter devices. The electronic device specifically includes the following content:

[0183] A processor, a memory, a communications interface, and a bus; wherein, the processor, the memory, and the communications interface complete communication with each other through the bus; the communications interface is used to implement information transmission between the multi-inverter device control parameter collaborative optimization device and related devices such as a core business system, a user terminal, and a related database; the logic controller can be a desktop computer, a tablet computer, a mobile terminal, etc., and this embodiment is not limited thereto. In this embodiment, the logic controller can be implemented with reference to the embodiments of the multi-inverter device control parameter collaborative optimization method and the embodiments of the multi-inverter device control parameter collaborative optimization device in the embodiments, the content of which is incorporated herein, and the repeated parts will not be elaborated.

[0184] It can be understood that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Among them, the smart wearable device may include smart glasses, a smart watch, a smart bracelet, etc.

[0185] In practical applications, part of the multi-inverter device control parameter collaborative optimization method can be executed on the electronic device side as described above, or all operations can be completed in the client device. Specifically, it can be selected according to the processing capacity of the client device and the limitations of the user usage scenario, etc. This application does not make any limitations in this regard. If all operations are completed in the client device, the client device may further include a processor.

[0186] The above-mentioned client device may have a communication module (i.e., a communication unit), and can be communicatively connected to a remote server to achieve data transmission with the server. The server may include a server on the task scheduling center side, and may also include a server on an intermediate platform in other implementation scenarios, such as a server on a third-party server platform communicatively linked to the task scheduling center server. The server may include a single computer device, or may include a server cluster composed of multiple servers, or a server structure of a distributed device.

[0187] Figure 9 This is a schematic block diagram of the system composition of the electronic device 9600 according to an embodiment of the present application. As Figure 9 shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It should be noted that this Figure 9 is exemplary; other types of structures can also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0188] In one embodiment, the function of the collaborative optimization method for the control parameters of the multi-inverter device can be integrated into the central processing unit 9100. Among them, the central processing unit 9100 can be configured to perform the following control:

[0189] S101: Construct a control parameter optimization model including the inverter power failure process based on the electrical parameters of the multi-inverter device;

[0190] S102: Perform discretization processing on the control parameters of the control parameter optimization model to obtain a simplified control parameter model;

[0191] S103: Input the electrical parameters of the multi-inverter device into the simplified control parameter model to obtain the control optimization parameters of the multi-inverter device.

[0192] As can be seen from the above description, the collaborative optimization method for the control parameters of the multi-inverter device provided by this application can construct a collaborative optimization model for the control parameters of the multi-inverter power supply considering transient voltage safety, and then improve the solution efficiency of the model through control parameter discretization processing and the method of obtaining feasible solutions, and perform collaborative optimization of the control parameters of the multi-inverter device. Specifically, based on the characteristics that the current and voltage inside the power supply cannot change suddenly, it replaces the variables in the constraint expression, simplifies the complexity of the model, and converts the non-linear programming problem in the optimization solution into a mixed integer linear programming problem through steps such as the discretization method, reducing the difficulty of model solution.

[0193] In another embodiment, the collaborative optimization device for the control parameters of the multi-inverter device can be separately configured from the central processing unit 9100. For example, the collaborative optimization device for the control parameters of the data composite transmission device multi-inverter device can be configured as a chip connected to the central processing unit 9100, and the function of the collaborative optimization method for the control parameters of the multi-inverter device can be realized through the control of the central processing unit.

[0194] As Figure 9 shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It should be noted that the electronic device 9600 does not necessarily have to include Figure 9 all the components shown in Figure 9 ; in addition, the electronic device 9600 may further include

[0195] As Figure 9 shown, the central processing unit 9100 is sometimes also called a controller or an operation control, and may include a microprocessor or other processor devices and / or logic devices. The central processing unit 9100 receives inputs and controls the operations of the various components of the electronic device 9600.

[0196] Among them, the memory 9140 can be, for example, one or more of a buffer, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory, or other suitable devices. It can store the above-mentioned failure-related information, and can also store a program for executing relevant information. And the central processing unit 9100 can execute the program stored in the memory 9140 to implement information storage or processing, etc.

[0197] The input unit 9120 provides an input to the central processing unit 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to supply power to the electronic device 9600. The display 9160 is used to display display objects such as images and texts. The display can be, for example, an LCD display, but is not limited thereto.

[0198] The memory 9140 can be a solid-state memory. For example, it can be a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It can also be a memory that stores information even when power is off, can be selectively erased, and has more data. An example of this memory is sometimes referred to as an EPROM, etc. The memory 9140 can also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 can include an application / function storage unit 9142, which is used to store application programs and function programs or the processes for operating the electronic device 9600 through the central processing unit 9100.

[0199] The memory 9140 can also include a data storage unit 9143, which is used to store data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 can include various drivers for the communication function of the electronic device and / or for executing other functions of the electronic device (such as a messaging application, an address book application, etc.).

[0200] The communication module 9110 is a transmitter / receiver 9110 that transmits and receives signals via the antenna 9111. The communication module (transmitter / receiver) 9110 is coupled to the central processing unit 9100 to provide an input signal and receive an output signal, which can be the same as the case of a conventional mobile communication terminal.

[0201] Based on different communication technologies, in the same electronic device, multiple communication modules 9110 can be provided, such as a cellular network module, a Bluetooth module, and / or a wireless local area network module, etc. The communication module (transmitter / receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide an audio output via the speaker 9131 and receive an audio input from the microphone 9132, so as to implement normal telecommunication functions. The audio processor 9130 may include any suitable buffers, decoders, amplifiers, etc. In addition, the audio processor 9130 is also coupled to a central processor 9100, so that recording can be performed on the local device through the microphone 9132, and the sound stored on the local device can be played through the speaker 9131.

[0202] Embodiments of the present application also provide a computer-readable storage medium capable of implementing all steps of the multi-inverter device control parameter collaborative optimization method in which the execution subject in the above embodiments is a server or a client. A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, all steps of the multi-inverter device control parameter collaborative optimization method in which the execution subject in the above embodiments is a server or a client are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0203] S101: Construct a control parameter optimization model including the inverter power failure process according to the electrical parameters of the multi-inverter device;

[0204] S102: Perform control parameter discretization processing on the control parameter optimization model to obtain a control parameter simplified model;

[0205] S103: Input the electrical parameters of the multi-inverter device into the control parameter simplified model to obtain the control optimization parameters of the multi-inverter device.

[0206] As can be seen from the above description, the multi-inverter device control parameter collaborative optimization method provided by the present application can construct a multi-inverter power control parameter collaborative optimization model considering transient voltage safety, and then improve the solution efficiency of the model through control parameter discretization processing and the method of obtaining feasible solutions, and perform collaborative optimization of the control parameters of the multi-inverter device. Specifically, based on the characteristics that the current and voltage inside the power supply cannot change suddenly, the variables in the constraint expression are replaced, simplifying the complexity of the model, and converting the non-linear programming problem in the optimization solution into a mixed integer linear programming problem through steps such as the discretization method, reducing the difficulty of model solution.

[0207] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, apparatus, or computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0208] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (apparatus), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of blocks.

[0209] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that implements the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of blocks.

[0210] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are performed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of blocks.

[0211] Specific embodiments are applied in the present invention to elaborate on the principles and implementation manners of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for collaborative optimization of control parameters of multiple inverter devices, characterized in that: include: A control parameter optimization model including the inverter power failure process is constructed based on the electrical parameters of the multi-inverter equipment; Performing control parameter discretization processing on the control parameter optimization model to obtain a simplified control parameter model; The electrical parameters of the multi-inverter device are input into the control parameter simplified model to obtain the control optimization parameters of the multi-inverter device.

2. The method for collaborative optimization of control parameters of multiple inverter devices according to claim 1, characterized in that: The control parameter optimization model includes an output voltage equation of the grid-forming equipment under steady state, an output current equation of the grid-following equipment under steady state, and a coordinate transformation equation under steady state; The control parameter optimization model including the inverter power failure process is constructed according to the electrical parameters of the multi-inverter equipment, including: According to the voltage given value of the grid-forming equipment in the steady state, the internal voltage reference value of the grid-forming equipment in the steady state and the current given value of the grid-forming equipment in the steady state, the output voltage equation of the grid-forming equipment in the steady state is constructed; According to the current given value of the grid-following equipment in steady state, the internal current reference value of the grid-following equipment in steady state and the voltage given value of the grid-following equipment in steady state, the output current equation of the grid-following equipment in steady state is constructed; According to the power angle of the grid-forming equipment and the phase-locked loop output angle of the grid-following equipment, the voltage value of the grid-forming equipment and the current value of the grid-following equipment in the steady state, a coordinate transformation equation in the steady state is constructed; Determine the control variable value range of the network-following equipment and the network-building equipment; wherein the control variable value range includes the virtual admittance value range and the virtual impedance value range.

3. The method for collaborative optimization of control parameters of multiple inverter devices according to claim 1, characterized in that: The control parameter optimization model includes the output voltage equation of the grid-building equipment during the transient process, the output current equation of the grid-following equipment during the transient process, the coordinate transformation equation during the transient process, the network equation constraint during the transient process, the capacity limitation of the inverter power supply during the transient process and the grid connection point voltage safety constraint, the boundary conditions of the grid-building equipment at different critical moments during the transient process, the boundary conditions of the grid-following equipment at different critical moments during the transient process, and the current setting value constraint under each fault during the transient process; The control parameter optimization model including the inverter power failure process is constructed according to the electrical parameters of the multi-inverter equipment, including: The output voltage equation of the grid-forming equipment in the transient process is constructed according to the voltage value of the grid-forming equipment in the transient process, the internal voltage reference value of the grid-forming equipment in the transient process, and the current value of the grid-forming equipment in the transient process; The output current equation of the grid-following equipment in the transient process is constructed according to the current value of the grid-following equipment in the transient process, the internal current reference value of the grid-following equipment in the transient process, and the voltage value of the grid-following equipment in the transient process; The coordinate transformation equation in the transient process is constructed according to the power angle of the grid-forming equipment and the phase-locked loop output angle of the grid-following equipment, the voltage value of the grid-forming equipment in the transient process, and the current value of the grid-following equipment in the transient process; The network equation constraints in the transient process are constructed according to the network node current value and the network node voltage value in the transient process, and the real part and the imaginary part of the network node admittance matrix in the transient process; According to the upper limit of current, low-voltage grid-off threshold and high-voltage grid-off threshold of grid-building equipment and grid-following equipment during transient process, the capacity limit of inverter power supply and grid connection point voltage safety constraint during transient process are established; According to the internal voltage reference value of the networking equipment and the internal voltage setting value of the networking equipment, the boundary conditions of the networking equipment at different critical moments in the transient process are constructed; Construct boundary conditions of the grid-following equipment at different critical moments in the transient process according to the internal current reference value of the grid-following equipment; Determine the current setpoint constraints for each fault during the transient process.

4. The method for collaborative optimization of control parameters of multiple inverter devices according to claim 3, characterized in that: The step of performing control parameter discretization processing on the control parameter optimization model to obtain a simplified control parameter model includes: Discretize the output voltage equation of the grid-forming equipment in the transient process, the output current equation of the grid-following equipment in the transient process, and the gain value and current reference value of the inner loop feedback term in the current setting value constraint under each transient fault to obtain the graded regulation amount; The output voltage equation of the grid-forming equipment in the transient process, the output current equation of the grid-following equipment in the transient process, and the bilinear terms in the current setting value constraint under each transient fault are converted into product terms of continuous variables and discrete variables by using the graded adjustment amount; The control parameter simplified model is generated according to the transformation of the bilinear terms into product terms of continuous variables and discrete variables.

5. A device for collaborative optimization of control parameters of multiple inverter devices, characterized in that: include: An optimization model building unit, used for building a control parameter optimization model including an inverter power failure process according to electrical parameters of a multi-inverter device; A simplified model building unit, used for discretizing the control parameters of the control parameter optimization model to obtain a simplified control parameter model; The control parameter generating unit is used to input the electrical parameters of the multi-inverter device into the control parameter simplified model to obtain the control optimization parameters of the multi-inverter device.

6. The device for collaboratively optimizing control parameters of multiple inverters according to claim 5, characterized in that: The control parameter optimization model includes an output voltage equation of the grid-forming equipment under steady state, an output current equation of the grid-following equipment under steady state, and a coordinate transformation equation under steady state; The optimization model building unit comprises: A steady-state voltage equation building module is used to build a steady-state grid-type equipment output voltage equation according to a steady-state grid-type equipment voltage given value, a steady-state grid-type equipment internal voltage reference value, and a steady-state grid-type equipment current given value; A steady-state current equation building module is used to build a steady-state grid-type equipment output current equation according to a steady-state grid-type equipment current given value, a steady-state grid-type equipment internal current reference value, and a steady-state grid-type equipment voltage given value; A steady-state transformation equation construction module is used to construct a steady-state coordinate transformation equation according to the power angle of the grid-forming equipment and the phase-locked loop output angle of the grid-following equipment, the voltage value of the grid-forming equipment and the current value of the grid-following equipment; The steady-state value range determination module is used to determine the control variable value range of the network-following equipment and the network-building equipment; wherein the control variable value range includes the virtual admittance value range and the virtual impedance value range.

7. The multi-inverter equipment control parameter collaborative optimization device according to claim 5, characterized in that: The control parameter optimization model includes the output voltage equation of the grid-building equipment during the transient process, the output current equation of the grid-following equipment during the transient process, the coordinate transformation equation during the transient process, the network equation constraint during the transient process, the capacity limitation of the inverter power supply during the transient process and the grid connection point voltage safety constraint, the boundary conditions of the grid-building equipment at different critical moments during the transient process, the boundary conditions of the grid-following equipment at different critical moments during the transient process, and the current setting value constraint under each fault during the transient process; The optimization model building unit comprises: A transient voltage equation construction module is used to construct an output voltage equation of the grid-forming equipment in the transient process according to the voltage value of the grid-forming equipment in the transient process, the internal voltage reference value of the grid-forming equipment in the transient process, and the current value of the grid-forming equipment in the transient process; A transient current equation construction module is used to construct an output current equation of the grid-following equipment in the transient process according to the current value of the grid-following equipment in the transient process, the internal current reference value of the grid-following equipment in the transient process, and the voltage value of the grid-following equipment in the transient process; A transient transformation equation construction module is used to construct a coordinate transformation equation in a transient process according to the power angle of the grid-forming equipment and the phase-locked loop output angle of the grid-following equipment, the voltage value of the grid-forming equipment in a transient process, and the current value of the grid-following equipment in a transient process; An equation constraint building module is used to build network equation constraints in the transient process according to the current value and voltage value of the network nodes in the transient process and the real part and imaginary part of the admittance matrix of the network nodes in the transient process; The safety constraint construction module is used to construct the capacity limit of the inverter power supply and the grid connection point voltage safety constraint during the transient process according to the upper limit of the current of the grid-building equipment and the grid-following equipment, the low-voltage grid-off threshold value and the high-voltage grid-off threshold value; A network boundary condition determination module is used to construct the boundary conditions of the network-building equipment at different critical moments in the transient process according to the internal voltage reference value of the network-building equipment and the internal voltage setting value of the network-building equipment; A grid-following boundary condition determination module is used to construct boundary conditions of the grid-following equipment at different critical moments in the transient process according to the internal current reference value of the grid-following equipment; The transient current constraint determination module is used to determine the current setting value constraint under each fault in the transient process.

8. The device for collaboratively optimizing control parameters of multiple inverters according to claim 7, characterized in that: The simplified model building unit comprises: The discrete processing module is used to discretize the output voltage equation of the grid-forming equipment in the transient process, the output current equation of the grid-following equipment in the transient process, and the gain value and current reference value of the inner loop feedback item in the current setting value constraint under each transient fault, and obtain the graded adjustment amount; A discrete product determination module is used to convert the output voltage equation of the grid-forming equipment in the transient process, the output current equation of the grid-following equipment in the transient process, and the bilinear terms in the current setting value constraint under each transient fault into product terms of continuous variables and discrete variables using the graded adjustment amount; The simplified model determination module is used to generate the simplified model of the control parameters according to the product term of the continuous variable and the discrete variable converted into the bilinear term.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the method for collaborative optimization of control parameters of multiple inverter devices as described in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for collaborative optimization of control parameters of multiple inverter devices as described in any one of claims 1 to 6 are implemented.

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