A control method, device, computer equipment and storage medium for a power grid system

Abstract

The present application provides a power grid system control method, device, computer equipment and storage medium in the field of power grid system control. First, the dynamic inertia coefficient is determined by integrating the node frequency deviation, energy storage charge state and voltage deviation, and the inertia compensation power is calculated using the frequency deviation to accurately capture the dynamic characteristics of the system. Then, the objective function is constructed based on the equivalent impedance matrix and power deviation, and the target control sequence is solved under the constraint condition to achieve the optimal distribution of system power. The node power output is further adjusted from multiple aspects by determining the composite inertia, synthetic inertia power, regulating the power vector and inertia matrix. Finally, the control instructions for each node are generated based on the energy function construction and partial derivative calculation. These steps can effectively deal with system power disturbances, quickly compensate for insufficient inertia, optimize power distribution, maintain system frequency stability, improve the safety, reliability and stability of power system operation, and enhance the system's adaptability to complex working conditions.

Description

Technical Field

[0001] The present application relates to the field of power grid system control technology, and in particular to a power grid system control method, device, computer equipment and storage medium. Background Art

[0002] During grid operation, traditional generators provide rotational inertia, helping to maintain grid stability. However, with the integration of renewable energy (such as wind power and photovoltaics) and energy storage systems, traditional generators are gradually being phased out, resulting in a decrease in grid rotational inertia. Virtual rotational inertia typically involves using power electronics to simulate the inertial response of traditional synchronous generators, helping to maintain grid stability during frequency fluctuations. AC / DC grids may consist of AC and DC subgrids, such as a VSC-HVDC interconnected grid. Grid-connected energy storage can be considered a voltage source converter (VSC)-based system that actively supports and regulates the grid's frequency and voltage. The power electronics included in the grid-connected energy storage system can simulate the inertial response of traditional synchronous generators, providing virtual rotational inertia to determine whether conduction performance is adequate. Therefore, a solution is needed that can effectively utilize the virtual inertia provided by grid-connected energy storage to provide effective support for the grid system. Summary of the Invention

[0003] The purpose of this application is to solve at least one of the above-mentioned technical deficiencies, and in particular to provide a solution that can reasonably utilize the virtual inertia provided by grid-type energy storage.

[0004] In a first aspect, the present application provides a control method for a power grid system, comprising:

[0005] The dynamic inertia coefficient of each node is obtained based on the frequency deviation, energy storage charge state and voltage deviation of each node. The inertia compensation power of each node is obtained based on the frequency deviation of each node.

[0006] The objective function is obtained based on the equivalent impedance matrix of the target system and the power deviation of each node. The objective function is minimized under the set constraints to obtain the target control sequence.

[0007] According to the current control instructions corresponding to each node in the target control sequence, the composite inertia corresponding to each node is determined, and according to the composite inertia and the frequency change rate of the node, the synthetic inertia power corresponding to each node is determined;

[0008] The control power of each node is obtained by summing the inertia compensation power and the composite inertia power of each node. The control power vector is obtained based on the control power of each node. The inertia matrix of the target system is obtained based on the dynamic inertia coefficient and composite inertia of each node.

[0009] According to the inertia matrix and the angular velocity deviation vector of the target system, the kinetic energy term of the frequency deviation is obtained, and according to the curl of the control power vector, the spatial eddy current effect term of the power deviation is obtained;

[0010] An energy function is constructed based on the sum of the kinetic energy term and the spatial eddy current effect term. The control instructions of each node are obtained based on the partial derivative of the energy function with respect to the state variables of each node.

[0011] In a second aspect, the present application provides a control device for a power grid system, comprising:

[0012] A first processing module is configured to obtain a dynamic inertia coefficient of each node based on the frequency deviation, energy storage charge state, and voltage deviation of each node, and to obtain an inertia compensation power of each node based on the frequency deviation of each node;

[0013] The second processing module is used to obtain an objective function based on the equivalent impedance matrix of the target system and the power deviation of each node, and solve the minimized objective function under set constraints to obtain a target control sequence;

[0014] The third processing module is used to determine the composite inertia corresponding to each node according to the current control instruction corresponding to each node in the target control sequence, and determine the synthetic inertia power corresponding to each node according to the composite inertia and the frequency change rate of the node;

[0015] The fourth processing module is used to obtain the control power of each node based on the sum of the inertia compensation power and the composite inertia power of each node, obtain the control power vector based on the control power of each node, and obtain the inertia matrix of the target system based on the dynamic inertia coefficient and composite inertia of each node;

[0016] a fifth processing module, configured to obtain a kinetic energy term of the frequency deviation based on the inertia matrix and the angular velocity deviation vector of the target system, and to obtain a spatial eddy current effect term of the power deviation based on the curl of the control power vector;

[0017] The sixth processing module is used to construct an energy function according to the sum of the kinetic energy term and the spatial eddy current effect term, and obtain the control instruction of each node according to the partial derivative of the energy function with respect to the state variable of each node.

[0018] In a third aspect, the present application provides a computer device comprising one or more processors and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the steps of the power grid system control method in any of the above embodiments are executed.

[0019] In a fourth aspect, the present application provides a computer-readable storage medium, which stores computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the control method of the power grid system in any of the above embodiments.

[0020] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0021] Based on the control method of the power grid system in this embodiment, the dynamic inertia coefficient is first determined by integrating the node frequency deviation, energy storage charge state and voltage deviation, and the inertia compensation power is calculated to accurately capture the dynamic characteristics of the system. Then, the objective function is constructed based on the equivalent impedance matrix and power deviation. The target control sequence is solved under the constraint conditions to achieve optimal distribution of system power. The node power output is further adjusted from multiple aspects by determining the composite inertia, synthetic inertia power, regulating the power vector and inertia matrix. Finally, based on the energy function construction and partial derivative calculation, the control instructions for each node are generated. These steps are closely linked and can effectively respond to system power disturbances, quickly compensate for insufficient inertia, optimize power distribution, maintain system frequency stability, improve the safety, reliability and stability of power system operation, and enhance the system's adaptability to complex working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0023] Figure 1 A flowchart of a method for controlling a power grid system according to an embodiment of the present application is provided;

[0024] Figure 2 This is a flow chart of constructing an objective function in one embodiment of the present application;

[0025] Figure 3 This is a schematic diagram of a process for filtering currents at each node in one embodiment of the present application;

[0026] Figure 4 A schematic diagram of a process for implementing a tensor manifold fault traversal in one embodiment of the present application;

[0027] Figure 5 A diagram of the internal structure of a computer device provided for one embodiment of the present application. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application and do not belong to all the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0029] See also Figure 1 , the present application provides a control method for a power grid system, including steps S102 to S112.

[0030] S102 , obtaining the dynamic inertia coefficient of each node according to the frequency deviation, energy storage charge state and voltage deviation of each node, and obtaining the inertia compensation power of each node according to the frequency deviation of each node.

[0031] It can be understood that the nodes in this application can be subnets containing multiple devices, but they are abstracted and equivalent to one node. The frequency deviation reflects the difference between the actual frequency of the node and the nominal frequency. The energy storage charge state reflects the ratio of the remaining capacity of the energy storage device contained in the node to the rated capacity, which will affect the available inertia support capacity. The voltage deviation reflects the difference between the actual voltage of the node and the nominal voltage. The dynamic inertia coefficient characterizes the inertial support capability of each node in the target system for frequency fluctuations during dynamic operation, and reflects the upper limit of the node inertia support capability. Traditional algorithms are usually statically allocated based on a single factor (such as SOC or frequency deviation). This application dynamically adjusts by integrating multi-dimensional real-time data (frequency deviation, energy storage charge state, voltage deviation), and is the core parameter for achieving grid inertia balance. The inertia compensation power is the compensation power generated by the proportional integral differential adaptive control algorithm, which is used to dynamically correct the grid frequency deviation.

[0032] The frequency stability of the power system is closely related to the system inertia. When the system experiences a power disturbance, the frequency of each node will change. The frequency deviation, energy storage charge state, and voltage deviation of each node jointly affect the dynamic inertia coefficient of the node. The frequency deviation reflects the degree of system power imbalance. The energy storage charge state determines the energy storage device can participate in regulation. The voltage deviation affects the operating performance and output capacity of the equipment. By comprehensively evaluating these factors, the role of the node in frequency regulation can be more accurately determined. Calculating the inertia compensation power based on the frequency deviation can capture the dynamic details of the frequency change. By establishing a mathematical model and combining the frequency deviation adaptive control algorithm, the inertia compensation power of each node is obtained. This power is used to compensate for the insufficient inertia of the system caused by power disturbances to maintain system frequency stability.

[0033] S104: Obtain an objective function based on the equivalent impedance matrix of the target system and the power deviation of each node, and solve the minimized objective function under set constraints to obtain a target control sequence.

[0034] It can be understood that the equivalent impedance matrix of the target system is a mathematical abstraction of the power system network structure and component parameters. It describes the electrical connections between nodes in the system. The elements in the matrix reflect the impedance characteristics between nodes and serve as important fundamental data for analyzing power flow, short-circuit current, and other issues in the power system. Power deviation refers to the difference between the injected power at a node and the power reference value for stable system operation, reflecting the degree of system power imbalance. In power system optimization control, the objective function is a mathematical expression constructed based on the system's operating objectives and constraints to measure control effectiveness. Constraints are set to ensure safe and reliable system operation. The operating state of a power system can be described by the equivalent impedance matrix and the power deviation at each node. The equivalent impedance matrix reflects the system's electrical topology and component characteristics, while the power deviation reflects the system's power balance. By establishing an objective function that incorporates the system's power deviation, the optimization goal is to minimize the objective function and solve the problem while satisfying the set constraints. The existence of constraints ensures that the system operates within a safe and feasible range. By solving the objective function through the optimization algorithm, the obtained target control sequence can guide the control operations of each node in the system at multiple consecutive time points, realize the optimal distribution of system power, and enable the system to gradually recover from a power imbalance state to a stable operating state.

[0035] S106 , determining the composite inertia corresponding to each node according to the current control instruction corresponding to each node in the target control sequence, and determining the synthetic inertia power corresponding to each node according to the composite inertia and the frequency change rate of the node.

[0036] As can be understood, current control commands are determined based on the target control sequence and are used to control the current magnitude and phase of power equipment at a node (such as converters and generator excitation systems). Their purpose is to regulate the node's power output and system inertia characteristics. Composite inertia is the equivalent inertia derived by combining the node's inherent inertia with the additional inertia introduced by the control strategy (such as that provided by energy storage devices). It reflects the node's comprehensive inertial response capability during system frequency regulation. Composite inertia power is the power used to maintain system frequency stability, calculated using a physical formula based on the composite inertia and the node's frequency change rate. Its magnitude is proportional to the composite inertia and the frequency change rate. Current control commands within the target control sequence are used to adjust the operating state of node devices, thereby changing the node's inertia characteristics. Controlling node devices through current control commands enables the node to output or absorb more power, thereby changing its composite inertia. Composite inertia integrates the various inertia sources of a node and more comprehensively reflects the node's role in frequency regulation. Based on the relationship between inertia and power in physics, combined with the composite inertia and the frequency change rate of the node, a specific calculation formula is used to obtain the synthetic inertia power. This power is used to compensate for the energy demand during system frequency changes, maintain system frequency stability, and work together with the synthetic inertia power of other nodes to achieve frequency stability control of the entire power system.

[0037] S108, obtain the control power of each node based on the sum of the inertia compensation power and the composite inertia power of each node, and obtain the control power vector based on the control power of each node, and obtain the inertia matrix of the target system based on the dynamic inertia coefficient and composite inertia of each node.

[0038] It can be understood that control power is a control variable used to adjust node power output to achieve system frequency stability and power balance, taking into account the inertia compensation power and composite inertia power of each node. The control power vector is a vector composed of the control powers of each node arranged in a certain order. It describes the distribution of control power at each node in the system and is an important tool for analyzing the overall power regulation performance of the system. The inertia matrix, composed of the dynamic inertia coefficient and composite inertia of each node, reflects the inertia distribution in the system and is a key parameter for studying the dynamic characteristics and stability of the system frequency. The inertia compensation power and composite inertia power of each node adjust the system frequency from different perspectives. The inertia compensation power is mainly used to compensate for the inertia deficiency caused by power disturbances, while the composite inertia power is generated based on the node's composite inertia and the frequency change rate to maintain system frequency stability. The control power obtained by adding the two can more comprehensively reflect the role of the node in system frequency regulation. By forming the control power vector with the control power of each node, the overall power regulation of the system can be analyzed and studied. The dynamic inertia coefficient and composite inertia of each node together constitute the inertia matrix, which reflects the mutual correlation and influence of the inertia characteristics of each node in the system. By analyzing the inertia matrix, we can gain a deep understanding of the system frequency dynamic characteristics and stability, and provide a basis for system control.

[0039] S110 , obtaining a kinetic energy term of the frequency deviation based on the inertia matrix and the angular velocity deviation vector of the target system, and obtaining a spatial eddy current effect term of the power deviation based on the curl of the control power vector.

[0040] It can be understood that the angular velocity deviation vector is a vector that describes the deviation of the angular velocity of each node in the power system from the rated angular velocity. In power systems, frequency and angular velocity are closely related, and the angular velocity deviation vector can reflect changes in system frequency. The kinetic energy term of the frequency deviation is an energy term related to system frequency changes, calculated using a physical formula based on the inertia matrix and the angular velocity deviation vector. It reflects the change in kinetic energy of the system due to frequency changes. The spatial eddy current effect term of the power deviation is calculated based on the curl of the control power vector. It is a physical quantity used to describe the spatial distribution and flow characteristics of the power deviation in the system. It reflects the uneven distribution and interaction of power in the system network. The frequency stability of a power system is closely related to the system's energy state. The inertia matrix reflects the inertia characteristics of each node in the system, and the angular velocity deviation vector reflects the change in system frequency. The kinetic energy term of the frequency deviation, obtained by calculating the two, can describe the impact of system frequency changes on system energy from an energy perspective. The control power vector describes the control power of each node in the system and is obtained by integrating the control power of all nodes into a single vector. The spatial eddy current effect of the power deviation, calculated from the curl, reflects the spatial distribution and flow characteristics of the power deviation in the system, revealing the interaction between power within the system network. The kinetic energy term of the frequency deviation and the spatial eddy current effect of the power deviation reflect the system's operating status and energy characteristics from different perspectives. Combining the two provides a more comprehensive description of the system's energy state, providing a more accurate basis for system control.

[0041] S112, constructing an energy function based on the sum of the kinetic energy term and the spatial eddy current effect term, and obtaining a control instruction for each node based on the partial derivative of the energy function with respect to the state variable of each node.

[0042] It can be understood that the energy function is a mathematical function that describes the energy state of a power system by comprehensively considering the kinetic energy term of frequency deviation and the spatial eddy current effect term of power deviation. It can comprehensively reflect the dynamic characteristics and stability of the system and is an important basis for formulating power system control strategies. State variables are physical quantities used to describe the operating state of a power system, specifically including the frequency deviation and power deviation of each node. By taking the partial derivatives of the energy function with respect to each node's state variable, control instructions for each node can be obtained. These control instructions are used to adjust the node's operating state to achieve stable system operation. The energy function combines the kinetic energy term of frequency deviation and the spatial eddy current effect term of power deviation, modeling the system in manifold space using differential geometry methods, and comprehensively describes the operating state of the power system from an energy perspective. In a power system, stable operation is directly related to the state variables of each node. Taking the partial derivatives can obtain the rate of change of the energy function in the direction of each state variable. Based on these rates of change, the direction and magnitude of the adjustment of each node's state variable can be determined. Through negative gradient feedback, the system's energy dissipation path can be dynamically adjusted to obtain the control instructions for each node. These control instructions are used to adjust the node's operating state to achieve stable system operation and power balance.

[0043] Based on the control method of the power grid system in this embodiment, the dynamic inertia coefficient is first determined by integrating the node frequency deviation, energy storage charge state and voltage deviation, and the inertia compensation power is calculated using the frequency deviation to accurately capture the dynamic characteristics of the system. Then, the objective function is constructed based on the equivalent impedance matrix and power deviation. The target control sequence is solved under the constraint conditions to achieve optimal distribution of system power. The node power output is further adjusted from multiple aspects by determining the composite inertia, synthetic inertia power, regulating the power vector and inertia matrix. Finally, based on the energy function construction and partial derivative calculation, the control instructions for each node are generated. These steps are closely linked and can effectively respond to system power disturbances, quickly compensate for insufficient inertia, optimize power distribution, maintain system frequency stability, improve the safety, reliability and stability of power system operation, and enhance the system's adaptability to complex working conditions.

[0044] In one embodiment, the dynamic inertia coefficient of each node is obtained according to the frequency deviation, energy storage charge state and voltage deviation of each node according to the following expression:

[0045]

[0046] in, is the dynamic inertia coefficient of the i-th node, which is used to quantify the parameter of the i-th node's inertial support capability of the power system when the frequency, voltage and energy storage status change, and reflects the dynamic characteristics of the node's participation in frequency regulation. Frequency sensitivity factor, with a typical value between 0.5 and 3.0, measures the effect of node frequency deviation on the dynamic inertia coefficient and reflects the system's sensitivity to frequency changes. The larger the value, the more significant the effect of frequency deviation on the inertia coefficient. It can be set inversely proportional to the square root of the frequency deviation, so that when the frequency deviation increases, it can automatically attenuate to avoid overshoot. is the frequency deviation of the ith node. This factor can take into account the urgency of the frequency. is the energy storage charge state of the i-th node, which describes the ability boundary of the energy storage to participate in inertia compensation. Too high or too low energy storage charge state will limit the energy storage regulation effect. It is a fuzzy weight factor, which is used to fuzzy process the parameters of the influence of the energy storage state of charge on the dynamic inertia coefficient. Through nonlinear mapping, the relationship between the energy storage state of charge and the inertia coefficient is more in line with the complex characteristics of the actual system, avoiding the limitations of linear assumptions. When the fuzzy weight factor is greater than 1, more inertia is allocated to high SOC nodes to enhance the energy storage support capacity; when it is less than 1, the influence of SOC differences on allocation is reduced to improve balance. Its typical value is between 0.8-1.5. Therefore, is the weighted state of charge obtained by performing nonlinear weighted correction on the energy storage state of charge of the i-th node using the fuzzy weight factor, is the weighted state of charge after nonlinear weighted correction of the energy storage state of charge of the j-th node using the fuzzy weight factor. is the voltage deviation penalty factor for the i-th node. This factor quantifies the penalty effect of the i-th node voltage deviation on the dynamic inertia coefficient. The further the voltage deviates from the rated value, the stronger the suppression effect on the inertia coefficient, preventing inertia overload in the voltage-exceeding-limit region and enhancing system stability. is the voltage of the i-th node, is the rated voltage, - is the voltage deviation of the i-th node, n is the total number of nodes in the target system, The basic inertia coefficient is the static inertia reference value preset by the system. It is used to ensure the minimum inertia support and prevent the risk of zero inertia during dynamic distribution. It is generally preset according to the inertia time constant of the power grid.

[0047] The frequency stability of a power system relies on the coordinated support of inertia at each node. When the system is disturbed (such as a sudden load change or power failure), the frequency, voltage, and energy storage state change dynamically, requiring precise quantification of the node inertia response capability. This step integrates multi-dimensional influencing factors by constructing a dynamic inertia coefficient calculation model: frequency deviation is used to reflect the degree of active power imbalance in the system, and the frequency sensitivity factor correlates the response relationship between frequency change and inertia regulation, allowing the inertia coefficient to adjust in real time with frequency fluctuations. The energy storage state of charge and fuzzy weight factors are introduced to account for the nonlinear charging and discharging characteristics of the energy storage, avoiding regulation deviations caused by simple linear fitting and adapting the energy storage's ability to participate in inertia compensation to the actual available capacity. A voltage offset penalty factor is used to incorporate voltage deviation into the constraint. When the node voltage exceeds the limit (such as a voltage sag caused by a fault), the dynamic inertia coefficient is reduced through an exponential term, limiting the node's excessive participation in inertia regulation and prioritizing voltage stability. The denominator term normalizes the influencing factors of each node, ensuring the coordination of the dynamic inertia coefficient at the system level and preventing global regulation errors caused by abnormal parameters of a single node.

[0048] In one embodiment, the voltage offset penalty factor is obtained according to the following expression:

[0049]

[0050] in, is the impedance proportional coefficient, which is a correction coefficient for the relationship between the node impedance characteristics and the voltage offset penalty. It reflects the weight requirements of the system design or operation scenario for the impedance-voltage constraint. It can be calibrated through offline simulation or system characteristic testing to adapt to power systems with different voltage levels and grid structures. R is the self-resistance of the i-th node, X is the self-reactance of the i-th node, is the maximum allowable voltage deviation. In power systems, node voltage deviation can affect the normal operation of equipment (e.g., abnormal motor speed or electronic equipment failure), necessitating control strategies to limit the risk of voltage overshoot. The voltage deviation penalty factor establishes a logical connection between "impedance characteristics, voltage constraints, and control penalties": a node's self-resistance and self-reactance determine its voltage-current characteristics, and the ratio of self-resistance to self-reactance reflects the resistive component of the impedance. When the self-resistance increases (e.g., due to overloaded line heating), the resistive voltage drop contributes more to the voltage loss at the same current, necessitating a stronger voltage deviation penalty. The maximum allowable voltage deviation, as the denominator, reflects the tolerance of the voltage constraint. The smaller the maximum allowable voltage deviation (e.g., nodes supplying precision loads), the larger the voltage deviation penalty factor for the same voltage deviation, and the stronger the penalty. This expression converts node physical parameters and operational constraints into penalty coefficients for the control strategy, automatically incorporating calculations of dynamic inertia coefficients and power compensation into voltage stability constraints. This, in conjunction with factors such as frequency deviation and energy storage status, ensures stable system operation under multiple constraints.

[0051] In one embodiment, the inertia compensation power of each node is obtained according to the frequency deviation of each node according to the following expression:

[0052]

[0053] in, is the inertia compensation power of the i-th node, which is used to indicate the amount of active power that the i-th node in the power system needs to actively adjust to output or absorb in order to compensate for the system frequency deviation and maintain system frequency stability, in order to cope with the frequency fluctuation caused by the system power disturbance. It is a key physical quantity for achieving inertia support and frequency regulation. is the proportional gain of the i-th node, which reflects the coefficient of the proportional relationship between the node frequency deviation and the inertia compensation power, and determines the intensity of the frequency deviation's control over the compensation power through the proportional link. The larger the value, the greater the compensation power contributed by the proportional link under the same frequency deviation, which is used to quickly respond to instantaneous changes in frequency deviation. is the frequency deviation of the i-th node, is the integral gain of the i-th node, For the i-th random The dynamic adjustment parameter of the gradient change, Γ(·) is the gamma function, From the initial time 0 to the current time in the time domain At any time in between, is the differential gain of the i-th node, which is used to measure the regulatory effect of the differential link on the inertia compensation power. It reflects the degree of response to the frequency deviation change trend (such as frequency deviation acceleration and deceleration). It can predict the frequency change in advance and output compensation power to suppress further frequency fluctuations.

[0054] The inertia compensation power at the node can also be constrained according to the dynamic inertia coefficient of the node, such as limiting the range of the inertia compensation power according to the expression ,in is the power reference value of the node, is the response time requirement of the node, To constrain the adjustment coefficient, its value is limited by the grid inertia requirements, equipment tolerance limits, and startup mode. For grids with a high proportion of renewable energy, the value should be increased to 0.3-0.5 to enhance inertia support. If the critical current limit of the superconducting coil is reached, the value should be reduced to 0.1-0.15. When the system is in black start mode, the value can be around 0.4.

[0055] In one embodiment, the objective function is obtained according to the equivalent impedance matrix of the target system and the power deviation of each node, see Figure 2 , including steps S202 to S208.

[0056] S202: Generate an equivalent impedance matrix of the target system according to the three-dimensional impedance tensor of the target system.

[0057] It can be understood that in the analysis and control of power systems, accurately describing the impedance characteristics of the system is the basis for achieving precise control. The three-dimensional impedance tensor is a mathematical tool that can comprehensively characterize the multi-dimensional impedance relationship of the target system. It takes into account the impedance coupling of different nodes in the system. The three dimensions of the three-dimensional impedance matrix tensor are node dimension, frequency dimension and time dimension. The equivalent impedance matrix is ​​a reasonable simplification of the three-dimensional impedance tensor, split into the form of the sum of the products of multiple groups of three-dimensional basis vectors. For example ,in, The basis vector representing the rth component in the node dimension, describing the impedance distribution characteristics between nodes, can be extracted from the FFT analysis of the node voltage spectrum. The basis vector representing the rth component in the frequency dimension, describing the frequency dependence, is generated by the harmonic characteristics of the current spectrum. Represents the basis vector of the rth component in the time dimension, reflecting the dynamic characteristics of the impedance over time, which can be obtained by sampling the sliding time window. R represents the rank of the tensor decomposition, which is dynamically adjusted according to the complexity of the power grid and reflects the independent dimension of the impedance characteristic.

[0058] S204, obtaining a quantum loss term according to the equivalent impedance matrix.

[0059] It is understandable that in the optimization of power system control strategies, the construction of loss terms is key to measuring control effectiveness and guiding optimization directions. The quantum loss term is a new type of loss term constructed using an equivalent impedance matrix, combining concepts related to quantum computing or quantum theory. It is used to introduce optimization considerations of quantum characteristics into the control objective function to adapt to the future development trend of the integration of quantum computing and power systems and improve the performance of control strategies. The quantum Hamiltonian can be constructed based on the inverse of the equivalent impedance matrix to explicitly encode the node impedance correlation and ensure that the control sequence meets the grid topology constraints. The quantum loss term is obtained based on the constructed quantum Hamiltonian.

[0060] S206: Obtain a classical loss term based on the power deviation of each node.

[0061] It can be understood that the classical loss term is a loss metric constructed based on traditional analytical methods in power system control, utilizing power deviations at each node. It is used to intuitively reflect the degree of system power imbalance and is a key component of the objective function to ensure the system's basic power balance requirements. In power system operation, power balance is fundamental to maintaining frequency and voltage stability, and the construction of the classical loss term is directly linked to the system's basic operating constraints. The power deviation at each node reflects the local imbalance in the system's power supply and demand. By constructing the classical loss term, local power deviations are integrated into a global loss metric.

[0062] S208, performing weighted summation of the classical loss term and the quantum loss term to obtain the objective function.

[0063] It can be understood that the objective function is the core guide for power system optimization control. Step S208 combines the basic requirements of traditional power system control for power balance and the optimization potential brought by quantum theory by performing a weighted summation of the classical loss term and the quantum loss term, thereby constructing an objective function that adapts to the characteristics of complex systems and the future development of computing technology.

[0064] In one embodiment, the dimensions of the three-dimensional impedance tensor include node dimension, frequency dimension and time dimension. Generating the equivalent impedance matrix of the target system according to the three-dimensional impedance tensor of the target system includes: decomposing the three-dimensional impedance tensor into the sum of the outer products of multiple groups of basis vectors of three dimensions. The basis vector is a vector with rank one in the corresponding dimension. ,in, The basis vector representing the rth component in the node dimension, describing the impedance distribution characteristics between nodes, can be extracted from the FFT analysis of the node voltage spectrum. The basis vector representing the rth component in the frequency dimension, describing the frequency dependence, is generated by the harmonic characteristics of the current spectrum. Represents the basis vector of the rth component in the time dimension, reflecting the dynamic characteristics of the impedance over time, which can be obtained by sliding time window sampling. R represents the rank of the tensor decomposition, which is dynamically adjusted according to the complexity of the power grid and reflects the independent dimension of the impedance characteristics. Each basis vector represents a potential characteristic pattern, for example, Characterize the node impedance distribution of a certain type of new energy cluster, Corresponding to the characteristic frequency of its grid-connected inverter, Reflects the time evolution of the mode in the transient process. For any dimension, all the basis vectors of the dimension are arranged into a factor matrix. That is, each dimension integrates the basis vectors of all components together to form a low-rank approximation of the original tensor. The arrangement here can be arranged in columns, that is, each column corresponds to the basis vector of a component of a rank. The three dimensions of the three-dimensional impedance tensor are I, J, and K, respectively, so The real number space of , The real number space of , The real number space of After the tensor decomposition of R, the real number domain space will be expanded to R if the rows are Corresponding node dimension, Corresponding to the frequency dimension, The corresponding time dimension can be obtained , so the real number domain space of the node dimension is expanded to I rows and R columns, the real number domain space of the frequency dimension is expanded to J rows and R columns, and the real number domain space of the time dimension is expanded to K rows and R columns.

[0065] According to each factor matrix and the following expression, the equivalent impedance matrix is ​​obtained:

[0066]

[0067] in, is the equivalent impedance matrix, is the factor matrix of the node dimension, B is the factor matrix of the frequency dimension, and C is the factor matrix of the time dimension. The factor matrices of these three dimensions are expanded into a two-dimensional matrix form according to the column Kronecker (Khatri-Rao) product to obtain the equivalent impedance matrix.

[0068] In one embodiment, the quantum loss term is obtained according to the equivalent impedance matrix according to the following expression:

[0069]

[0070]

[0071]

[0072] in, is the quantum loss term. is the Hamiltonian, It is the element in the i-th row and j-th column of the Hamiltonian, and its elements are determined by the elements of the equivalent impedance matrix, reflecting the influence of the impedance characteristics between the nodes of the power system on the quantum state energy. The density matrix is ​​a mathematical tool for describing the state of a quantum system and is used to characterize the probability distribution of quantum states. In power system applications, the discrete states of control variables (such as energy storage output, generator excitation, etc.) are mapped to quantum pure states, and the transition probability is used to describe the probability distribution of quantum states. Describe the possibility of transition between different control states and reflect the uncertainty and diversity of the system control strategy. is the probability of transition from quantum state a to quantum state b, which corresponds to the possibility of the control strategy of the target system switching from one discrete state to another. It is the bridge connecting the control requirements of the power system and the evolution of quantum states. Tr() is the operation of finding the trace of the matrix, which reflects the average expectation of the matrix. Therefore, Represents the loss expectation value of the Hamiltonian in the quantum state corresponding to the density matrix. The state vectors of the ath and bth pure states are respectively, which are the vectors describing the quantum pure state in quantum mechanics. The column vector form used to represent the quantum state, the left vector is the row vector form of its conjugate transpose. The inner product of the two can calculate the degree of overlap between quantum states and reflect the similarity of the states.

[0073] Specifically, the equivalent impedance matrix reflects the electrical connection and impedance characteristics between the nodes of the power system, and is the basis of the system's power flow and voltage distribution. The Hamiltonian is constructed based on the elements of the equivalent impedance matrix, taking the inverse of the node impedance modulus as the matrix element, and converting the impedance characteristics of the power system into an energy description of the quantum system. The density matrix characterizes the distribution of the system's control strategy through the quantum pure state and transition probability of the control state. The quantum loss term uses matrix trace operations to calculate the product trace of the density matrix and the Hamiltonian, which is essentially to find the expected energy of the quantum system under the current control state distribution. This process integrates the impedance characteristics of the power system, the uncertainty of the control state and the energy optimization goal of the quantum system, so that the objective function can simultaneously consider the classical power balance constraints and the optimization potential of the quantized system characteristics.

[0074] During implementation, the power system control variables (such as energy storage charging and discharging power, distributed power output, etc.) can be discretized to determine a finite number of control state gears (such as 5 gears: -100%, -50%, 0%, +50%, +100% rated power), and each gear corresponds to a quantum pure state. For example, 0% energy storage output corresponds to a quantum pure state (Assume that the gear index starts from 1.) Based on historical operating data or expert experience, the transition frequency between each control state is counted and the transition probability is initialized. Under normal operating conditions, the probability of energy storage switching from 50% output (a=2) to 0% output (b=3) is 0.3, that is, = 0.3). Construct a density matrix and fill in the specific elements according to the above expression. Use a quantum computing simulation library (such as Qiskit or Cirq) to encode the quantum state and density matrix. The Hamiltonian can also be constructed by filling in the inverse of the equivalent impedance matrix based on the above expression. Finally, use the quantum computing simulation environment to load the encoded density matrix and Hamiltonian H and perform a matrix trace operation. In Qiskit, the trace can be approximated using the numpy.trace function (combined with a numerical representation of the quantum state) or a quantum algorithm to obtain the value of the quantum loss term. The calculated result is output as input to construct the objective function and is used in conjunction with the classical loss term to optimize the control strategy.

[0075] In one embodiment, a classical loss term is obtained based on the power deviation of each node, including:

[0076]

[0077]

[0078] in, This is a classic loss term used in power system control, based on traditional power balance theory, to quantify the degree of system power imbalance. By calculating the weighted sum of the squares of the power deviations at each node, the power supply and demand differences at local nodes are integrated into a global loss metric, which is used to guide control strategy optimization and ensure system power balance and stable operation. The weight of the i-th node is used to measure its importance in system power balance control. This weight can be set based on factors such as node type (such as whether it is a hub node or whether it connects to critical loads) and its impact on system frequency and voltage stability. This allows the classic loss term to focus more on power deviations at key nodes, prioritizing power quality in important power supply areas. is the power deviation of the i-th node. Its calculation integrates electrical quantities such as the equivalent impedance, voltage amplitude, and phase between nodes, reflecting the coupling relationship between power and electrical parameters in the power system. N is the total number of nodes in the target system. is the product of the voltage amplitudes of the i-th node and the j-th node, representing the potential difference driving capability between the two nodes. is the equivalent impedance between the i-th node and the j-th node, which quantifies the coupling strength between the nodes. The mutual coupling between these two points can represent the power transmission capability. is the impedance angle between the i-th node and the j-th node, 、 are the voltage phases of the i-th node and the j-th node respectively. The angle difference can affect the direction of power transmission. is the load power at the ith node, independent of the driving part of the potential difference, but with impedance correction to match the power distribution to the load demand.

[0079] In one embodiment, the objective function is obtained by weighted summing the classical loss term and the quantum loss term based on the following expression:

[0080]

[0081]

[0082] in, is the objective function, is the classic loss term, is the quantum loss term, is the dynamic weight factor, is the adjustment coefficient, is the frequency deviation of the node. This objective function uses a dynamic weight factor to coordinate the influence of the classical and quantum loss terms in the objective function. Its value changes dynamically with the system frequency deviation, allowing the objective function to adaptively adjust the weight of attention paid to classical constraints and quantum optimization based on the system operating state (such as the magnitude of frequency fluctuations).

[0083] In one embodiment, the composite inertia corresponding to each node is determined based on the current control instructions corresponding to each node in the target control sequence. This includes determining the contribution current and current change rate of the superconducting coil corresponding to each node based on the current control instructions corresponding to each node in the target control sequence. The composite inertia of the corresponding node is obtained based on the contribution current and current change rate of the superconducting coil. It can be understood that in power system optimization control, the optimal control instruction sequence obtained by minimizing the objective function (integrating classical and quantum loss terms) includes the current control instructions for each node, which are used to guide the operating status of equipment such as the superconducting coil and achieve optimal system inertia configuration. Composite inertia is the equivalent inertial response capability of the node and is composed of multiple inertia sources, including the inherent inertia of the energy storage component and the dynamic energy storage inertia of the superconducting coil. It reflects the node's comprehensive ability to provide inertial support when the system frequency changes. The larger the value, the stronger the node's ability to resist system frequency fluctuations. Contribution current is the effective current component provided by the superconducting coil to the system under the target control sequence and is a key physical quantity for achieving inertia control. The magnitude and direction of the contribution current directly affect the energy storage status of the superconducting coil and the system inertia distribution. The rate of change of the superconducting coil's contribution current over time, together with the inductance characteristics of the superconducting coil, determines its energy storage change rate. It is the core parameter for calculating the dynamic inertia contribution and reflects the superconducting coil's ability to quickly respond to system frequency changes.

[0084] In power systems, frequency stability is closely related to system inertia. Traditional synchronous generators provide frequency support through mechanical inertia, but the high integration of renewable energy sources leads to a decrease in overall system inertia, necessitating the introduction of dynamic inertia sources (such as superconducting coils). The current control instructions within the target control sequence directly determine the operating state of the superconducting coils. The contribution current is the current component of the superconducting coil that actually contributes to system inertia regulation under these instructions. Its value is related to the superconducting coil's internal resistance, external circuit impedance, and control strategy. The current rate of change reflects the dynamic characteristics of the contribution current over time. The product of the superconducting coil's inductance L and the current rate of change corresponds to the coil's energy storage change rate, which is directly related to the system inertia. The dynamic inertia of the superconducting coil is proportional to the square of the contribution current and the inductance, and is also affected by the current rate of change (a larger rate of change indicates a faster dynamic response). Through this relationship, the current control instructions can precisely control the node's composite inertia, achieving an optimized distribution of system inertia and improving frequency stability.

[0085] In one embodiment, the composite inertia of the corresponding node is obtained based on the contribution current and the current change rate of the superconducting coil based on the following expression:

[0086]

[0087] in, is the composite inertia, It is the inherent inertia of the energy storage corresponding to the node, which is generally related to the energy storage charge state of the battery energy storage. is the inductance of the superconducting coil at the node, is the contribution current of the superconducting coil at the node, is the current change rate of the superconducting coil at the node, is the nominal angular velocity of the node. In this expression, the inherent inertia of the energy storage device provides the base inertia, reflecting the static inertia capacity of the energy storage device. The sub-item to the right of the plus sign represents the dynamic inertia contributed by the superconducting coil. The rate of change of the superconducting coil's current determines the direction of the dynamic inertia contribution. A positive rate of change increases the inertia, while a negative rate of change decreases the inertia. This expression converts the superconducting coil's rapid dynamic response into an incremental composite inertia, giving the node inertia the dual characteristics of a static base and dynamic response. This provides more flexible and faster inertial support for system frequency control and adapts to the dynamic inertia requirements of power grids with a high proportion of new energy.

[0088] In one embodiment, the synthetic inertia power corresponding to each node is determined based on the composite inertia and the frequency change rate of the node based on the following expression:

[0089]

[0090] in, The composite inertia power is the active power output or absorbed by a node in the power system when it responds to frequency changes through composite inertia. is the composite inertia, is the frequency change rate of the node, is the nominal frequency of the target system. The frequency dynamics of the power system follows the "inertia-frequency" coupling relationship: the frequency change rate is directly related to the system inertia and unbalanced power. The calculation principle of the synthetic inertia power is based on the expansion of this coupling relationship, which associates the node composite inertia with the local frequency change rate. If it is less than 0, the frequency is rapidly decreasing, and the resulting synthetic inertia power is negative, which means that the superconducting coil needs to inject power into the system to suppress the frequency drop. Conversely, it needs to extract power from the system to suppress the frequency rise.

[0091] In one embodiment, the kinetic energy term of the frequency deviation is obtained based on the inertia matrix and the angular velocity deviation vector of the target system, and the spatial eddy current effect term of the power deviation is obtained based on the curl of the control power vector based on the following expression:

[0092]

[0093]

[0094] in, is the kinetic energy term, is the angular velocity deviation vector, consisting of the difference between the point angular velocity of all nodes and the rated angular velocity, representing the dynamic imbalance of each node. M is the inertia matrix, which reflects the network inertia distribution of the entire target system. It is a positive definite symmetric matrix derived from the dynamic inertia coefficient and the dynamic inertia of each node. The kinetic energy term is essentially the rotational kinetic energy deviation of the power system. It quantifies the kinetic energy changes caused by frequency fluctuations in the system and reflects the passive support characteristics of inertia with respect to frequency. is the spatial eddy current effect term, To control the curl of the power vector, The curl is a three-dimensional volume element that can capture local inhomogeneities in the power distribution and globalize them through the integral term. This step is based on the concept of Lyapunov-differential geometry stability control. Its core function is to ensure the global stability of the system under dynamic disturbances, especially to quickly suppress oscillations and restore equilibrium in large power fluctuations or complex coupling scenarios. Compared to traditional PID or eigenvalue analysis methods based on linearized models, this step models the system in manifold space through differential geometry methods.

[0095] In one embodiment, the partial derivative of the control instruction of each node is obtained based on the energy function with respect to the state variable of each node based on the following expression:

[0096]

[0097] in, is the control instruction of the node, is the control gain coefficient, is the partial derivative of the node's state variable. By taking the partial derivative of each node's state variable, negative gradient feedback can be used to dynamically adjust the system's energy dissipation path and determine the final control command for each node. The control gain coefficient can be used to control the convergence speed.

[0098] In one embodiment, the energy storage state of charge of a node is updated according to the following expression:

[0099]

[0100]

[0101] in, is the equilibrium charge and discharge rate of the i-th node, Represents the profit evaluation function of the i-th node in the The benefits of charging and discharging at the same rate, is the covariance adjustment coefficient, Represents the relationship between the charge and discharge rate between node i and non-node i, is the energy storage capacity constraint of the i-th node, is the energy storage charge state of the i-th node at time t, is the preset update interval. This expression dynamically coordinates the charging and discharging strategies of each energy storage node through asymmetric game SOC equilibrium to solve the problem of local overload or resource waste caused by traditional uniform distribution. The core formula parameter design reflects the following physical meanings and logical connections The marginal utility represents the node's own benefits and reflects the impact of the energy storage unit's SOC status on the frequency regulation economy; A covariance penalty term is introduced to quantify the mutual constraints between different energy storage node strategies, avoiding grid shocks caused by simultaneous charging / discharging of multiple nodes. The Bayesian Nash equilibrium model processes incomplete information (such as delayed feedback on the status of some energy storage capacity) through probability distribution, enabling strategy optimization to adapt to the dynamic grid environment. Compared with traditional average distribution or fixed priority algorithms, this innovation uses an asymmetric game mechanism to consider the SOC attenuation characteristics and power limit differences of different energy storage types (such as batteries and superconducting magnetic storage), avoiding high loss problems. Covariance constraints are also used to prevent the convergence of multi-node strategies, thereby improving robustness in scenarios with topology changes or communication interruptions.

[0102] In one embodiment, see Figure 3 The control method further includes steps S302 to S310.

[0103] S302 : Determine the equivalent stiffness of the phononic crystal according to the equivalent impedance matrix.

[0104] It can be understood that phononic crystals are functional materials with periodic structures. Parameters such as elastic constants and mass density are periodically distributed. By regulating the structural parameters, elastic waves within a specific frequency range (analogous to harmonic fluctuations in power systems) cannot propagate, forming a band gap. This allows for filtering and waveguiding elastic waves, and can be used in power systems for scenarios such as harmonic control. Equivalent stiffness is a parameter used to describe the ability of a phononic crystal to resist elastic deformation when its complex periodic structure is equated to a continuous medium model. In scenarios related to power systems, conversion is required based on the power system's equivalent impedance matrix, so that the mechanical characteristic parameters of the phononic crystal are adapted to the power system's electrical characteristic analysis and a parameter mapping across physical domains is established.

[0105] Phenomena such as harmonic propagation and power fluctuations in power systems are physically comparable to elastic wave propagation in phononic crystals. The equivalent impedance matrix reflects the impedance constraints on the transmission of electrical quantities (such as voltage and current) in power systems, while the equivalent stiffness of phononic crystals determines the mechanical constraints on elastic wave propagation. The core logic of step S302 is to establish a parameter mapping relationship between the electrical and mechanical domains. First, the equivalent impedance between power system nodes affects the transmission characteristics of electrical quantities, similar to how the periodic structure in a phononic crystal hinders elastic waves, which is determined by the equivalent stiffness. By analyzing the effect of the equivalent impedance matrix on current distribution when harmonic currents (analogous to elastic waves in phononic crystals) propagate in power systems, combined with the wave equations for elastic wave propagation in phononic crystals (such as the Helmholtz equation in its periodic form), a mathematical conversion relationship between impedance and stiffness is established.

[0106] S304 : Determine the number of periodic units of the phononic crystal according to the main harmonic frequency and the reference frequency of the target system.

[0107] It can be understood that the main harmonic frequency is the frequency of the harmonic component with the greatest impact on power quality and the highest amplitude in a power system. It is typically generated by nonlinear loads (such as power electronic converters and arc furnaces). The main harmonic frequency may vary under different power system operating conditions and must be determined through harmonic detection and analysis. It is the frequency target that must be suppressed in phononic crystal bandgap design. The reference frequency is the rated frequency of the power system and serves as the basic frequency reference for normal operation. The number of periodic units in a phononic crystal refers to the number of repeating basic structural units (unit cells) in the phononic crystal. The arrangement of periodic units constitutes the periodic structure of the phononic crystal, and the number of periodic units directly affects the bandgap characteristics of the phononic crystal, including the position and width of the bandgap. This number of units directly affects the bandgap characteristics of the phononic crystal, including the position and width of the bandgap, and must be precisely designed based on the harmonic frequency characteristics of the power system. In power system applications, the goal is to ensure that the phononic crystal bandgap covers the main harmonic frequency and suppresses the propagation of harmonics of this frequency. The relationship between the main harmonic frequency and the reference frequency determines the frequency selectivity required of the phononic crystal periodic structure. Step S304 is to determine the number of periodic units by analyzing the multiple relationship between the main harmonic frequency and the reference frequency, combining the frequency response characteristics of the periodic structure of the phononic crystal, so as to match the band gap center frequency of the phononic crystal with the main harmonic frequency and realize dynamic filtering.

[0108] S306 , determining the quality parameter of the phononic crystal according to the main harmonic frequency.

[0109] The mass parameter, as it is understood, describes the mass distribution characteristics within a periodic unit cell of a phononic crystal. These parameters include unit mass, mass density, and mass distribution (e.g., lumped mass, distributed mass). The mass parameter directly influences the propagation characteristics of elastic waves within the phononic crystal. Together with the equivalent stiffness, it determines the location and width of the bandgap frequency, making it a key parameter in phononic crystal structure design. The bandgap frequency of a phononic crystal is determined by both its equivalent stiffness and mass parameter. Within a periodic phononic crystal structure, elastic wave propagation can be equated to the coupled vibrations of multiple resonators. The relationship between the bandgap frequency, the equivalent stiffness, and the mass parameter can be precisely described by the dispersion relation. The mass parameter is determined by inversely matching the primary harmonic frequency with the phononic crystal bandgap frequency. Starting from the wave equation and considering the dispersion characteristics of the periodic phononic crystal structure, the mass parameter influences the inertial properties of elastic waves, thereby altering the distribution of the bandgap frequency. Once the primary harmonic frequency is determined, the mass parameter must be adjusted to ensure that the bandgap frequency coincides with the primary harmonic frequency, thereby suppressing the harmonics of that frequency.

[0110] S308 , obtaining the band gap frequency of the target system according to the equivalent stiffness, the number of periodic units, and the quality parameter.

[0111] It can be understood that the band gap frequency is the frequency range in which elastic waves cannot propagate in phononic crystals, also known as the band gap frequency. In power system applications, the band gap frequency needs to cover the main harmonic frequency, thereby preventing the propagation of harmonics of this frequency in the system and achieving harmonic control. The calculation of the band gap frequency requires comprehensive consideration of the structural and material parameters of the phononic crystal, such as the equivalent stiffness, number of periodic units, and quality parameters. It is a key quantitative indicator of the characteristics of phononic crystals. The band gap frequency of a phononic crystal is determined by its periodic structure and material parameters. Its physical essence is the frequency band gap caused by the interference and scattering effects when elastic waves propagate in a periodic structure. From the perspective of wave theory, the propagation of elastic waves in phononic crystals must satisfy Bloch's theorem. Its dispersion relation describes the relationship between wave number and frequency, and the band gap frequency corresponds to the discontinuous frequency interval in the dispersion relation.

[0112] S310 , filtering the current of each node according to the bandgap frequency.

[0113] It's understandable that the band gap properties of phononic crystals give them the ability to block elastic waves of specific frequencies. In power systems, the propagation of harmonic currents can be analogized to the propagation of elastic waves in phononic crystals (a connection established through electromechanical analogy). When the frequency of a harmonic current falls within the band gap frequency range of a phononic crystal, its corresponding equivalent elastic wave cannot propagate within the periodic structure of the phononic crystal, thereby filtering the harmonic current at that frequency.

[0114] In one embodiment, according to the equivalent impedance matrix, the equivalent stiffness of the phononic crystal is determined based on the following expression:

[0115]

[0116] in, is the equivalent stiffness at time t, The frequency is the main harmonic frequency The equivalent impedance matrix at this time is calculated for each node separately. The main harmonic frequency here is the main harmonic frequency of the corresponding node. Re() is a real part operation. Impedance is generally in complex form, and the real part represents the resistive component. Main harmonic frequency The resistive component of the equivalent impedance matrix when . is the phase delay characteristic of the unit of the phononic crystal at the spatial position x. The phase change law of the elastic wave caused by the structural periodicity and material properties of the periodic unit of the phononic crystal at the spatial position x reflects the phase accumulation effect of the elastic wave when propagating in the phononic crystal, which is directly related to the dispersion characteristics and band gap formation of the phononic crystal. Description) and the mechanical constraints of elastic wave propagation in phononic crystals (given by the equivalent stiffness Description) exhibits a coupling mechanism across physical domains. By utilizing the equivalent impedance matrix of the power system at the main harmonic frequency, combined with the phase delay characteristics of the phononic crystal unit, and coupling mathematical transformations with physical quantities, the equivalent stiffness is derived, adapting the mechanical properties of the phononic crystal to the harmonic management requirements of the power system. At the main harmonic frequency in the power system, the equivalent impedance matrix determines the energy dissipation characteristics of the propagation of harmonic currents. In phononic crystals, the propagation of elastic waves (analogous to harmonic currents) is modulated by the phase delay characteristics, and their second-order spatial derivative reflects the "curvature" of the phase change, which is related to the dispersion and band gap of the elastic waves. is the phase delay variation curvature of the unit cell of the phononic crystal. The above expression couples the impedance constraint of the electrical domain with the phase delay characteristics of the mechanical domain.

[0117] In one embodiment, the number of periodic units of the phononic crystal is determined based on the main harmonic frequency and the reference frequency of the target system based on the following expression:

[0118]

[0119] Among them, round() is the rounding function, is the number of periodic units, is the main harmonic frequency, The band gap characteristics of a phononic crystal are closely related to the number of periodic units. Its physical essence is the scattering effect of elastic waves propagating through a periodic structure. When the elastic wave frequency meets specific conditions, destructive interference occurs due to multiple scattering from the periodic structure, forming a band gap.

[0120] In one embodiment, the quality parameter of the phononic crystal is determined based on the main harmonic frequency based on the following expression:

[0121]

[0122] in, is the quality parameter, is the number of phonon units in the phononic crystal, is the material density of the kth phonon unit, is the unit volume of the kth phonon unit, is the modal participation factor related to the main harmonic frequency, and is related to the main harmonic frequency ( ) reflects the energy distribution weight of the elastic wave mode corresponding to the main harmonic frequency on the phonon unit. Different frequencies will cause different vibration modes of the phonon unit excited by elastic waves, and the participation factor quantifies the contribution of each unit to the target frequency filtering. The mass parameter of the phononic crystal determines its inertial properties and synergistically affects the band gap frequency with the equivalent stiffness. The modal participation factor associated with the main harmonic frequency is used to weight and aggregate the mass of each phonon unit (calculated from density and volume), so that the phononic crystal mass distribution adapts to the elastic wave mode corresponding to the main harmonic frequency, and the band gap frequency is precisely controlled.

[0123] In one embodiment, the band gap frequency of the target system is obtained based on the equivalent stiffness, the number of periodic units, and the quality parameter based on the following expression:

[0124]

[0125] in, is the bandgap frequency at time t, is the equivalent stiffness at time t, is the quality parameter, is the number of periodic units at time t. It can be understood that the band gap frequency of a phononic crystal is essentially the destructive interference effect of elastic waves propagating through a periodic structure. The equivalent stiffness reflects the elastic restoring force constraint, the mass parameter reflects the inertial resistance characteristics, and the number of periodic units determines the spatial modulation period of elastic wave scattering. These three factors are coupled through wave theory to derive the band gap frequency.

[0126] In one embodiment, filtering the current of each node according to the bandgap frequency is based on the following expression:

[0127]

[0128] in, is the angular frequency of the node current, is the filtered current, is the current before filtering, To set the band gap width.

[0129] In one embodiment, after filtering the current at each node according to the bandgap frequency, see Figure 4 , also including steps S402 to S406.

[0130] S402: Construct a fault feature vector according to the filtered current.

[0131] It's easy to understand that the core of constructing a fault feature vector is to extract characteristic parameters from the filtered current that can effectively distinguish between normal and faulty states. The filtered current has suppressed harmonic interference, and the remaining time-frequency characteristics of the current can better highlight fault information. When a power system fault (such as a short circuit or open circuit) occurs, parameters such as the current amplitude, phase, and frequency will undergo sudden changes. Therefore, the corresponding fault characteristics can be extracted from these parameters to construct the fault feature vector.

[0132] S404 , solving the Riemann manifold energy minimization of the target system according to the fault feature vector to obtain the optimal fault-tolerant trajectory.

[0133] It can be understood that a Riemannian manifold is a smooth manifold with a Riemannian metric, which is used to describe the geometric space of fault eigenvectors. Each point on the manifold represents a state of the system. Based on the Riemannian manifold metric, its energy function can be constructed to reflect the energy consumption of system state transitions, which is negatively correlated with system stability. The fault-tolerant trajectory is the path that minimizes the energy function and represents the optimal recovery path from the faulty state to the stable state. After a power system fault, it is necessary to find an optimal trajectory to smoothly restore the system from the faulty state to the stable state while minimizing energy consumption and the impact of disturbances. The Riemannian manifold provides a geometric representation of the fault eigenvectors, and the geodesics (shortest paths) on the manifold correspond to the state transition trajectories with the minimum energy.

[0134] S406: Control the circuit breaker to operate according to the fault-tolerant trajectory.

[0135] It can be understood that the circuit breaker control signal that can be generated according to the fault-tolerant trajectory contains information such as the opening and closing time and sequence, thereby guiding the fault isolation and system reconstruction, and guiding the system to recover along the optimal trajectory.

[0136] In one embodiment, constructing a fault feature vector according to the filtered current is based on the following expression:

[0137]

[0138]

[0139] in, is the fault feature vector. The fault feature vector is a multidimensional parameter set used to characterize the fault state of the power system. It integrates key information such as the amplitude, phase, and harmonics of the filtered current, providing a basis for subsequent fault diagnosis and fault-tolerant control. is the filtered current, and the root mean square value of the filtered current quantifies the average amplitude of the current and is the basic parameter for steady-state analysis of the power system. is the phase angle of the filtered current. Its phase changes with time, reflecting the periodic phase characteristics of the current. The derivative of the phase angle with respect to time reflects the dynamic change rate of the current phase. Phase mutation during a fault will cause this value to change significantly. The fault characteristic vector combined with the current phase mutation rate after filtering of each node can well determine the node fault. To extract the Hilbert phase of the filtered current, is the RMS value of the filtered current, is the rate of change of the phase angle, is the total harmonic distortion of the filtered current. The total distortion of the harmonic components in the filtered current at each node is integrated to measure the purity of the current at each node. The core of constructing the fault feature vector is to extract key parameters from the filtered current that accurately reflect the system fault state. While the filtered current has suppressed harmonic interference, its residual amplitude, phase, and harmonic characteristics still contain fault information. The fault feature vector integrates this fault information.

[0140] In one embodiment, the Riemann manifold energy minimization of the target system is solved according to the fault feature vector to obtain the optimal fault-tolerant trajectory based on the following expression:

[0141]

[0142]

[0143] in, is a fault-tolerant trajectory, representing the optimal path for the system to transfer from a faulty state to a safe and stable state. It must satisfy the energy minimization constraint and guide the system to recover. is the fault feature vector corresponding to the fault-tolerant trajectory, Calculate the expected value, where T represents the end time of the optimization time window. The Riemannian manifold metric tensor is a second-order tensor defined on the Riemannian manifold. It quantifies the coupling relationship between the changes in electrical dynamic characteristics in different dimensions and reflects the geometric resistance of the system state transition. Respectively The rate of change of the electrical dynamic characteristics of the first and second dimensions, that is, the The rate of change of the electrical dynamic characteristics of the first and vth dimensions (such as the fault feature vector dimensions such as current amplitude, phase change rate, harmonic distortion rate, etc.) over time is used to quantify the dynamic evolution trend of electrical state parameters. It is the core variable in the Riemannian manifold energy functional that describes the speed and direction of system state transfer. The safe space is the set of states in which the system operates stably. The fault-tolerant trajectory must always be within this space to ensure the safety of the system recovery process. Respectively The electrical dynamic characteristics of the first and vth dimensions, is the steady-state reference value corresponding to the electrical dynamic characteristics. After a power system fault, it is necessary to find a fault-tolerant trajectory with the minimum energy on the Riemann manifold formed by the fault characteristics to transition the system from the faulty state to a safe and stable state while minimizing the energy consumption of the state transition. The Riemann manifold is constructed by the coordinates of the manifold formed by the dimensions of the fault feature vector. The metric tensor is defined by the statistical covariance of the electrical dynamic characteristics, reflecting the degree of correlation between the characteristics and the geometric constraints of the state transition. The trajectory energy is described by the arc-length integral. Solving for the minimum value of this functional yields the energy-optimal fault-tolerant trajectory, ensuring that the system recovers to a safe state with minimal geometric cost. The above expression can be solved using variational methods on the Riemann manifold (such as the geodesic equation), transforming the energy functional minimization problem into the Euler-Lagrange equation. A numerical optimization algorithm is used to iteratively search for the optimal trajectory under the manifold constraints.

[0144] In one embodiment, setting the constraint conditions includes:

[0145]

[0146] in, represents the actual power of the i-th node, represents the composite inertia power of all nodes, represents the load power of all nodes, Represents the network loss of all nodes.

[0147]

[0148] in, represents the energy storage charge state of the i-th node, Represents the lower limit of the energy storage charge state, represents the lower limit of the energy storage charge state, and N represents the total number of nodes.

[0149]

[0150] in, represents the frequency change rate of the i-th node, is the upper limit of the frequency change rate. It can be converted into a second-order cone constraint .

[0151] In one embodiment, the corresponding first heat loss can be obtained according to the synthetic inertia power of a certain node, as shown in the following formula:

[0152]

[0153] in, is the synthetic inertia power of the i-th node, is the power-to-heat conversion efficiency (typical value 0.05~0.1), is the first heat loss of the i-th node.

[0154] Further considering the power device loss of the node (such as insulated gate bipolar transistors, silicon carbide and other modules), the second heat loss can be obtained:

[0155]

[0156] in, is the second heat loss of the i-th node, is the thermal resistance of the kth power device in the i-th node, is the current of the kth power device in the i-th node, is the switching frequency of the kth power device in the i-th node.

[0157] Based on the topological photonics correction, the heat flow directional equation can be constructed by combining the heat source term composed of the first heat loss and the second heat loss:

[0158]

[0159] in, is the topological thermal conductivity tensor, which is obtained based on the photonic crystal band design. is the volume heat capacity, is the material property parameter, is the material density and specific heat capacity The product of . is the temperature field The gradient, is the rate of change of the temperature field. The topological thermal conductivity tensor is obtained according to the following expression:

[0160]

[0161] It is the reciprocal lattice vector of the photonic crystal, reflecting the spatial frequency of its periodic structure; It is the photonic crystal structure factor obtained through topological band design (such as Berry phase and boundary state regulation), and the anisotropy of thermal conductivity is determined by the topological band design. The fundamental thermal conductivity is determined by the intrinsic heat transport properties of the material. The diagonal elements of the topological thermal conductivity tensor represent the principal thermal conductivities along the x / y / z coordinate axes, describing the ability of heat flow to conduct along those axes. The off-diagonal elements represent the anisotropic coupling of heat conduction, describing the cross-conduction of heat flow between different coordinate axes (e.g., a temperature gradient in the x-direction induces a heat flow in the y-direction).

[0162] Thermal management goals of heat flow directional equations and , that is, the temperature does not exceed the rated temperature , the temperature gradient is less than the upper limit of the temperature gradient The boundary conditions of the heat flow orientation equation are:

[0163]

[0164] Represents the boundary of the system ( is the spatial domain of the system, is the surface of the domain), for example: consider the power device + heat sink as a system , then the device housing and radiator surface are .

[0165] Represents the partial derivative of the temperature field along the boundary normal vector direction n, which expresses the rate of change of the temperature field along the boundary normal direction after the thermal energy passes through the boundary. represents the ambient temperature field, represents the convective heat transfer coefficient, represents the temperature field of the system, Represents the coolant temperature field.

[0166] The approximate solution of the temperature field under the above conditions can be:

[0167]

[0168] is the (m,n,p)th order modal heat source intensity, is the eigenvalue of the topological photonic structure, reflecting the thermal mode frequency under the (m, n, p)th order thermal mode, 、 、 is the characteristic length of the system in the xyz directions. m represents the harmonic order of the thermal mode in the x direction, which determines the number of terms in the Fourier expansion of the temperature field in the x direction (reflecting the spatial frequency of the heat flow). n represents the harmonic order of the thermal mode in the y direction, which controls the harmonic component of the temperature distribution in the y direction. p represents the harmonic order of the thermal mode in the z direction, which describes the heat conduction characteristics in the vertical direction (such as the PCB stacking layer). This approach utilizes the non-trivial band structure of photonic crystals to guide heat flow, breaking through the isotropic limitations of traditional heat dissipation. It integrates electromagnetic, thermal, and material properties to accurately predict the three-dimensional temperature field. The three-dimensional temperature field plays a vital role in multiple of the above steps.

[0169] In one embodiment, a three-dimensional temperature field can limit the operation of superconducting coils. When determining the superconducting coil's contribution current and current change rate based on current control instructions, the current control instructions need to be modified based on the superconducting coil's three-dimensional temperature field to ensure that the superconducting coil temperature is below the quench temperature and avoid the risk of quenching. Coil regions with temperatures above the set value require a reduced current change rate, thereby reducing the composite inertia corresponding to that node. This allows the inertia output to be dynamically adjusted based on the thermal load, balancing the thermal load and grid demand.

[0170] In one embodiment, during the process of deriving the fault-tolerant trajectory, the impedance-related component of the Riemannian manifold metric tensor can be changed from a fixed quantity to a variable whose resistivity is affected by the temperature field. This can guide the fault current away from high-temperature areas and prevent thermal breakdown. After obtaining the optimal fault-tolerant trajectory, if a local hot spot in the temperature field exceeds a set value, the manifold path can be recalculated.

[0171] In one embodiment, the Hamiltonian can be constructed by adding a temperature term, such as , thereby correcting the target control sequence to avoid high-temperature nodes being allocated too much power, is the Boltzmann constant, which characterizes the conversion relationship between energy and temperature. is the temperature field, and Z is the partition function, which is used to describe the probability of quantum state distribution. The coupling method with the original Hamiltonian is to combine the original power grid Hamiltonian H and the thermodynamic term through the tensor direct sum: .

[0172] In one embodiment, when performing asymmetric balancing of the charge states of various energy storages, the temperature rise history of each node can be recorded, and the game weight of each node can be adjusted according to the temperature rise. The higher the temperature rise, the lower the game weight of the node, which helps to extend the life of the energy storage.

[0173] In one embodiment, when constructing an energy function based on the sum of the kinetic energy term and the spatial eddy current effect term, the energy function can be added with This temperature-related curl term is used to suppress thermal-electric coupling oscillations and improve transient stability.

[0174] The present application provides a control device for a power grid system, comprising: a first processing module for obtaining the dynamic inertia coefficient of each node based on the frequency deviation, energy storage state of charge, and voltage deviation of each node, and obtaining the inertia compensation power of each node based on the frequency deviation of each node. A second processing module for obtaining an objective function based on the equivalent impedance matrix of the target system and the power deviation of each node, and solving the minimized objective function under set constraints to obtain a target control sequence. A third processing module for determining the composite inertia corresponding to each node based on the current control instructions corresponding to each node in the target control sequence, and determining the synthetic inertia power corresponding to each node based on the composite inertia and the frequency change rate of the node. A fourth processing module for obtaining the control power of each node based on the sum of the inertia compensation power and the synthetic inertia power of each node, obtaining a control power vector based on the control power of each node, and obtaining the inertia matrix of the target system based on the dynamic inertia coefficient and the composite inertia of each node. A fifth processing module for obtaining the kinetic energy term of the frequency deviation based on the inertia matrix and the angular velocity deviation vector of the target system, and obtaining the spatial eddy current effect term of the power deviation based on the curl of the control power vector. The sixth processing module is used to construct an energy function according to the sum of the kinetic energy term and the spatial eddy current effect term, and obtain the control instruction of each node according to the partial derivative of the energy function with respect to the state variable of each node.

[0175] For the specific definition of the control device of the power grid system, please refer to the definition of the control method of the power grid system above, which will not be repeated here. The various modules in the control device of the above-mentioned power grid system can be implemented in whole or in part by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules. It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0176] The present application provides a computer device comprising one or more processors and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the one or more processors, the steps of the power grid system control method in any of the above embodiments are executed.

[0177] Schematically, as Figure 5 As shown, Figure 5 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. Figure 5Computer device 500 includes a processing component 502, which further includes one or more processors, and memory resources represented by memory 501 for storing instructions executable by processing component 502, such as application programs. The application programs stored in memory 501 may include one or more modules, each corresponding to a set of instructions. Furthermore, processing component 502 is configured to execute the instructions to perform the steps of the power grid system control method according to any of the above-described embodiments.

[0178] The computer device 500 may further include a power supply component 503 configured to perform power management of the computer device 500 , a wired or wireless network interface 504 configured to connect the computer device 500 to a network, and an input / output (I / O) interface 505 .

[0179] The present application provides a computer-readable storage medium, which stores computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the control method of the power grid system in any of the above embodiments.

[0180] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0181] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referenced to each other.

[0182] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A control method for a power grid system, characterized in that: include: Obtaining a dynamic inertia coefficient of each node according to the frequency deviation, energy storage charge state, and voltage deviation of each node, and obtaining an inertia compensation power of each node according to the frequency deviation of each node; Obtaining an objective function based on the equivalent impedance matrix of the target system and the power deviation of each node, and solving a method for minimizing the objective function under set constraints to obtain a target control sequence; determining, according to the current control instructions corresponding to the nodes in the target control sequence, the composite inertia corresponding to the nodes, and determining, according to the composite inertia and the frequency change rate of the nodes, the synthetic inertia power corresponding to the nodes; Obtaining a control power of each node according to the sum of the inertia compensation power and the composite inertia power of each node, obtaining a control power vector according to the control power of each node, and obtaining an inertia matrix of the target system according to the dynamic inertia coefficient and the composite inertia of each node; Obtaining a kinetic energy term of the frequency deviation according to the inertia matrix and the angular velocity deviation vector of the target system, and obtaining a spatial eddy current effect term of the power deviation according to the curl of the control power vector; An energy function is constructed according to the sum of the kinetic energy term and the spatial eddy current effect term, and a control instruction of each node is obtained according to a partial derivative of the energy function with respect to the state variable of each node.

2. The control method according to claim 1, characterized in that: The dynamic inertia coefficient of each node is obtained according to the frequency deviation, energy storage charge state and voltage deviation of each node according to the following expression: in, is the dynamic inertia coefficient of the i-th node, is the frequency sensitivity factor, is the frequency deviation of the i-th node, is the energy storage charge state of the i-th node, is the fuzzy weight factor, is the weighted state of charge obtained by performing nonlinear weighted correction on the energy storage state of charge of the i-th node using the fuzzy weight factor, is the weighted state of charge obtained by performing nonlinear weighted correction on the energy storage state of charge of the j-th node using the fuzzy weight factor, is the voltage offset penalty factor of the i-th node, is the voltage of the i-th node, is the rated voltage, - is the voltage deviation of the i-th node, n is the total number of nodes in the target system, is the basic inertia coefficient.

3. The control method according to claim 2, characterized in that: The voltage offset penalty factor is obtained according to the following expression: in, is the impedance proportional coefficient, R is the self-resistance of the i-th node, X is the self-reactance of the i-th node, is the maximum allowed voltage deviation.

4. The control method according to claim 1, wherein: The inertia compensation power of each node is obtained according to the frequency deviation of each node according to the following expression: in, is the inertia compensation power of the i-th node, is the proportional gain of the i-th node, is the frequency deviation of the i-th node, is the integral gain of the i-th node, Follow The dynamic adjustment parameter of the gradient change, Γ(·) is the gamma function, From the initial time 0 to the current time in the time domain At any time in between, is the differential gain of the i-th node.

5. The control method according to claim 1, characterized in that: Obtaining the objective function according to the equivalent impedance matrix of the target system and the power deviation of each node includes: generating an equivalent impedance matrix of the target system according to the three-dimensional impedance tensor of the target system; Obtaining a quantum loss term according to the equivalent impedance matrix; A classical loss term is obtained according to the power deviation of each of the nodes; The objective function is obtained by weighted summing the classical loss term and the quantum loss term.

6. The control method according to claim 5, characterized in that: The dimensions of the three-dimensional impedance tensor include a node dimension, a frequency dimension, and a time dimension. Generating an equivalent impedance matrix of the target system according to the three-dimensional impedance tensor of the target system includes: Decomposing the three-dimensional impedance tensor into the sum of outer products of multiple groups of three-dimensional basis vectors; the basis vectors are vectors of rank one in the corresponding dimensions; For any dimension, arrange all basis vectors of that dimension into a factor matrix; According to each of the factor matrices and the following expression, the equivalent impedance matrix is ​​obtained: in, is the equivalent impedance matrix, is the factor matrix of the node dimension, B is the factor matrix of the frequency dimension, and C is the factor matrix of the time dimension.

7. The control method according to claim 5, characterized in that: The quantum loss term obtained according to the equivalent impedance matrix is ​​obtained according to the following expression: in, is the quantum loss term, is the density matrix, is the Hamiltonian, represents the loss expectation value of the Hamiltonian in the quantum state corresponding to the density matrix, is the transition probability from the a quantum state to the b quantum state, are the state vectors of the ath and bth pure states respectively, is the element in the i-th row and j-th column of the Hamiltonian.

8. The control method according to claim 7, characterized in that: The classical loss term is obtained according to the power deviation of each node, include: in, is the classical loss term, is the weight of the i-th node, is the power deviation of the i-th node, N is the total number of nodes in the target system, is the product of the voltage amplitudes of the i-th node and the j-th node, is the element in the equivalent impedance matrix corresponding to the equivalent impedance between the i-th node and the j-th node, is the impedance angle between the i-th node and the j-th node, 、 are the voltage phases of the i-th node and the j-th node respectively, is the load power of the i-th node.

9. The control method according to claim 5, characterized in that: The objective function obtained by weighted summation of the classical loss term and the quantum loss term is obtained based on the following expression: in, is the objective function, is the classical loss term, is the quantum loss term, is the dynamic weight factor, is the adjustment coefficient, is the frequency deviation of the node.

10. The control method according to claim 1, characterized in that: The determining, according to the current control instructions corresponding to the nodes in the target control sequence, the composite inertia corresponding to the nodes includes: determining, according to the current control instructions corresponding to the nodes in the target control sequence, the contribution current and the current change rate of the superconducting coil corresponding to each node; The composite inertia corresponding to the node is obtained according to the contribution current and the current change rate of the superconducting coil.

11. The control method according to claim 10, characterized in that: The composite inertia corresponding to the node obtained according to the contribution current and current change rate of the superconducting coil is obtained based on the following expression: in, is the composite inertia, is the inherent inertia of the energy storage corresponding to the node, is the inductance of the superconducting coil at the node, is the contribution current of the superconducting coil at the node, is the current change rate of the superconducting coil at the node, is the nominal angular velocity of the node.

12. The control method according to claim 1, characterized in that: The determination of the synthetic inertia power corresponding to each node according to the composite inertia and the frequency change rate of the node is based on the following expression: in, is the synthetic inertia power, is the composite inertia, is the frequency change rate of the node, is the nominal frequency of the target system.

13. The control method according to claim 1, characterized in that: The kinetic energy term of the frequency deviation is obtained according to the inertia matrix and the angular velocity deviation vector of the target system, and the spatial eddy current effect term of the power deviation is obtained according to the curl of the control power vector based on the following expressions: in, is the kinetic energy term, is the angular velocity deviation vector, M is the inertia matrix, is the spatial eddy current effect term, is the curl of the control power vector, is a three-dimensional volume element.

14. The control method according to claim 1, characterized in that: The partial derivative of the control instruction of each node obtained by the energy function for the state variable of each node is obtained based on the following expression: in, is the control instruction of the node, is the control gain coefficient, is the partial derivative of the state variable of the node.

15. The control method according to claim 1, characterized in that: The energy storage state of charge of the node is updated according to the following expression: in, is the equilibrium charge and discharge rate of the i-th node, Represents the profit evaluation function of the i-th node in the The benefits of charging and discharging at the same rate, is the covariance adjustment coefficient, Represents the relationship between the charge and discharge rate between node i and non-node i, is the energy storage capacity constraint of the i-th node, is the energy storage charge state of the i-th node at time t, The preset update interval.

16. The control method according to claim 1, characterized in that: Also includes: Determining the equivalent stiffness of the phononic crystal according to the equivalent impedance matrix; Determining the number of periodic units of the phononic crystal according to the main harmonic frequency and the reference frequency of the target system; Determining a quality parameter of the phononic crystal according to the main harmonic frequency; Obtaining a bandgap frequency of the target system according to the equivalent stiffness, the number of periodic units, and the mass parameter; The current of each of the nodes is filtered according to the bandgap frequency.

17. The control method according to claim 16, characterized in that: The equivalent stiffness of the phononic crystal is determined based on the equivalent impedance matrix based on the following expression: in, is the equivalent stiffness at time t, The frequency is the main harmonic frequency The equivalent impedance matrix is, is the main harmonic frequency The resistive component of the equivalent impedance matrix is, is the phase delay characteristic of the unit cell of the phononic crystal at the spatial position x, is the phase delay variation curvature of the unit cell of the phononic crystal.

18. The control method according to claim 16, characterized in that: The determination of the number of periodic units of the phononic crystal according to the main harmonic frequency and the reference frequency of the target system is based on the following expression: in, is the number of periodic units, is the main harmonic frequency, is the reference frequency, and round() is the rounding function.

19. The control method according to claim 16, characterized in that: The quality parameter of the phononic crystal determined according to the main harmonic frequency is obtained based on the following expression: in, is the quality parameter, is the number of phonon units in the phononic crystal, is the material density of the kth phonon unit, is the unit volume of the kth phonon unit, is the modal participation factor related to the main harmonic frequency.

20. The control method according to claim 16, characterized in that: The bandgap frequency of the target system is obtained based on the equivalent stiffness, the number of periodic units, and the mass parameter based on the following expression: in, is the bandgap frequency at time t, is the equivalent stiffness at time t, is the quality parameter, is the number of periodic units at time t.

21. The control method according to claim 16, characterized in that: Filtering the current of each node according to the bandgap frequency is based on the following expression: in, is the angular frequency of the current at the node, is the filtered current, is the current before filtering, To set the band gap width.

22. The control method according to claim 16, characterized in that: After filtering the current of each node according to the bandgap frequency, the method further includes: Constructing a fault feature vector based on the filtered current; Minimizing the Riemannian manifold energy of the target system according to the fault feature vector to obtain an optimal fault-tolerant trajectory; The circuit breaker is controlled to operate according to the fault-tolerant trajectory.

23. The control method according to claim 22, characterized in that: The fault feature vector constructed according to the filtered current is obtained based on the following expression: in, is the phase angle of the filtered current, is the filtered current, To extract the Hilbert phase of the filtered current, is the RMS value of the filtered current, is the rate of change of the phase angle, is the total harmonic distortion of the filtered current, is the fault feature vector.

24. The control method according to claim 23, characterized in that: The Riemann manifold energy minimization of the target system according to the fault feature vector is solved to obtain the optimal fault-tolerant trajectory based on the following expression: in, is the fault-tolerant trajectory, is the fault feature vector corresponding to the fault-tolerant trajectory, Calculate the expectation, is the metric tensor of the Riemannian manifold, Respectively The rate of change of the electrical dynamic characteristics of the first and vth dimensions, T represents the end time of the optimization time window, For safe space, Respectively The electrical dynamic characteristics of the first and vth dimensions, is the steady-state reference value corresponding to the electrical dynamic characteristic.

25. A control device for a power grid system, characterized in that: include: A first processing module is configured to obtain a dynamic inertia coefficient of each node according to the frequency deviation, energy storage charge state, and voltage deviation of each node, and obtain an inertia compensation power of each node according to the frequency deviation of each node; A second processing module is configured to obtain an objective function based on the equivalent impedance matrix of the target system and the power deviation of each node, and solve a method for minimizing the objective function under set constraints to obtain a target control sequence; a third processing module, configured to determine a composite inertia corresponding to each node according to a current control instruction corresponding to each node in the target control sequence, and determine a synthetic inertia power corresponding to each node according to the composite inertia and a frequency change rate of the node; a fourth processing module, configured to obtain a control power of each node based on the sum of the inertia compensation power and the composite inertia power of each node, obtain a control power vector based on the control power of each node, and obtain an inertia matrix of the target system based on the dynamic inertia coefficient and the composite inertia of each node; a fifth processing module, configured to obtain a kinetic energy term of the frequency deviation based on the inertia matrix and the angular velocity deviation vector of the target system, and obtain a spatial eddy current effect term of the power deviation based on the curl of the control power vector; The sixth processing module is used to construct an energy function according to the sum of the kinetic energy term and the spatial eddy current effect term, and obtain a control instruction for each node according to a partial derivative of the energy function with respect to the state variable of each node.

26. A computer device, characterized in that: The system comprises one or more processors and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the control method according to any one of claims 1 to 24 are executed.

27. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the control method according to any one of claims 1 to 24.

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