A method, device, equipment and storage medium for controlling cross-correlated harmonics in power grid

Abstract

The present application discloses a method, apparatus, device and storage medium for cross-correlated harmonic control in power grids. The method includes: performing wavelet packet decomposition on subnet electrical signals to obtain the harmonic components of the subnet in each frequency band; calculating the harmonic phase difference between subnets based on the cross-correlation function and topological impedance matrix between the harmonic components of each subnet, and calculating the total harmonic distortion rate based on the harmonic components of the subnet in each frequency band; constructing a harmonic transfer equation based on the harmonic phase difference between subnets and the equivalent output impedance of the inverter to calculate the harmonic responsibility ratio of the subnet; solving a two-layer game model based on the total harmonic distortion rate and its reference value and the harmonic responsibility ratio of the subnet to obtain the output of the control equipment; inputting the output of the control equipment into the fuzzy controller to obtain the impedance adjustment amount of the subnet, and adjusting the virtual impedance according to the impedance adjustment amount to update the harmonic return path. The present application realizes the harmonic control between multiple subnets during high-penetration distributed grid connection, thereby improving the stability of power grid operation.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a method, apparatus, device and storage medium for controlling cross-correlated harmonics in power grids. Background Art

[0002] Harmonics are a crucial parameter for grid power quality, and stable grid operation requires controlling harmonics to a certain level. With the integration of high-penetration distributed photovoltaic systems, the switching of power electronic devices generates more harmonics. Furthermore, the harmonics between subgrids are intercorrelated, so harmonic control in a single subgrid often falls short of expectations. Therefore, developing a method for controlling inter-subgrid harmonics in high-penetration distributed grids is a pressing technical challenge in this field. Summary of the Invention

[0003] The present application provides a method, apparatus, device and storage medium for managing cross-correlated harmonics in a power grid, which are used to manage harmonics between multiple subgrids when high-penetration distributed photovoltaic power is connected to the grid, thereby improving the stability of power grid operation.

[0004] In view of this, the first aspect of the present application provides a method for controlling cross-correlated harmonics in a power grid, comprising:

[0005] Perform wavelet packet decomposition on the acquired electrical signals of the photovoltaic subgrid to obtain the harmonic components of each photovoltaic subgrid in each frequency band;

[0006] The cross-correlation function is calculated based on the harmonic components of each photovoltaic subgrid in the same frequency band. The harmonic phase difference between each photovoltaic subgrid is calculated based on the cross-correlation function between each photovoltaic subgrid and the topological impedance matrix of the photovoltaic subgrid. The total harmonic distortion rate of each photovoltaic subgrid is calculated based on the harmonic components of each photovoltaic subgrid in each frequency band.

[0007] A harmonic transfer equation is constructed based on the harmonic phase difference, total harmonic distortion rate, and inverter equivalent output impedance between each PV subgrid. The harmonic responsibility ratio of each PV subgrid is calculated based on the harmonic transfer equation.

[0008] A two-layer game model is constructed and solved based on the total harmonic distortion rate of each PV subgrid, the reference value of the total harmonic distortion rate, and the harmonic responsibility ratio of each PV subgrid to obtain the output of the treatment equipment in each PV subgrid;

[0009] The output of the treatment equipment in each photovoltaic subnet is input into the fuzzy controller to obtain the impedance adjustment value of each photovoltaic subnet. The virtual impedance is adjusted according to the impedance adjustment value of each photovoltaic subnet to update the harmonic return path.

[0010] Optionally, the total harmonic distortion rate of each photovoltaic subgrid is calculated based on the harmonic components of each photovoltaic subgrid in each frequency band, and then the following is also included:

[0011] The admittance matrix is obtained based on the topological impedance matrix of the photovoltaic subgrid, and the distribution weight of the harmonic electrical signal in each photovoltaic subgrid is calculated through the admittance matrix;

[0012] The total harmonic distortion rate of each photovoltaic subgrid is corrected by allocating the weight of the harmonic electrical signal to each photovoltaic subgrid.

[0013] Optionally, the harmonic phase differences between the photovoltaic subgrids are calculated based on the cross-correlation function between the photovoltaic subgrids and the topological impedance matrix of the photovoltaic subgrids, including:

[0014] Extract extreme points from the cross-correlation function between each photovoltaic subgrid, and calculate the path difference between each photovoltaic subgrid based on the extreme points and the harmonic propagation speed between each photovoltaic subgrid;

[0015] Based on the relationship between the equivalent impedance difference and path difference between each photovoltaic subgrid, the equivalent impedance between each photovoltaic subgrid node is calculated in combination with the topological impedance matrix of the photovoltaic subgrid. The harmonic phase difference between each photovoltaic subgrid is then calculated using the equivalent impedance between each photovoltaic subgrid node.

[0016] Optionally, the calculation formula for the harmonic responsibility ratio of each PV subgrid is:

[0017]

[0018] Where, is the hth harmonic responsibility proportion of PV subgrid i; is the harmonic transfer impedance from node i to node j; is the hth harmonic current component of node j; is the total harmonic distortion rate of PV subgrid i; N is the number of PV subgrids.

[0019] Optionally, the optimization objective function of the main game layer in the two-layer game model is:

[0020]

[0021] Where, is the output vector of the management equipment in the PV subgrid, and N is the number of PV subgrids; is the total harmonic distortion rate of PV subgrid i; is the reference value of total harmonic distortion; is the penalty coefficient;

[0022] The utility function of the secondary game layer in the two-layer game model is:

[0023]

[0024] Where, is the utility function of PV subgrid i; 、 All are weight coefficients; is the harmonic responsibility ratio of PV subgrid i; is the output of the management equipment in PV subgrid i.

[0025] Optionally, the method further includes:

[0026] Taking the output, impedance adjustment amount and total harmonic distortion rate of the management equipment in each photovoltaic subgrid as states, and the control parameters in the two-layer game model and fuzzy controller as actions, the reinforcement learning method is used to obtain the optimal action; the control parameters in the two-layer game model and fuzzy controller are updated through the optimal action.

[0027] A second aspect of the present application provides a power grid cross-correlated harmonic control device, comprising:

[0028] A wavelet packet decomposition unit is used to perform wavelet packet decomposition on the acquired electrical signals of the photovoltaic subgrid to obtain the harmonic components of each photovoltaic subgrid in each frequency band;

[0029] a first calculation unit, configured to calculate a cross-correlation function based on the harmonic components of each photovoltaic subgrid in the same frequency band, calculate a harmonic phase difference between each photovoltaic subgrid based on the cross-correlation function between each photovoltaic subgrid and the topological impedance matrix of the photovoltaic subgrid, and calculate a total harmonic distortion rate of each photovoltaic subgrid based on the harmonic components of each photovoltaic subgrid in each frequency band;

[0030] The second calculation unit is used to construct a harmonic transfer equation according to the harmonic phase difference, total harmonic distortion rate and inverter equivalent output impedance between each photovoltaic subgrid, and calculate the harmonic responsibility ratio of each photovoltaic subgrid based on the harmonic transfer equation;

[0031] The model building and solving unit is used to build a two-layer game model. Based on the total harmonic distortion rate of each photovoltaic subgrid, the reference value of the total harmonic distortion rate, and the harmonic responsibility ratio of each photovoltaic subgrid, the two-layer game model is solved to obtain the output of the treatment equipment in each photovoltaic subgrid;

[0032] The adjustment unit is used to input the output of the treatment equipment in each photovoltaic subnet into the fuzzy controller, obtain the impedance adjustment amount of each photovoltaic subnet, and adjust the virtual impedance according to the impedance adjustment amount of each photovoltaic subnet to update the harmonic return path.

[0033] Optionally, the device further comprises:

[0034] The feedback adjustment unit is used to obtain the optimal action using the output, impedance adjustment amount and total harmonic distortion rate of the management equipment in each photovoltaic subgrid as the state, and the control parameters in the double-layer game model and fuzzy controller as the action by adopting the reinforcement learning method; and update the control parameters in the double-layer game model and fuzzy controller through the optimal action.

[0035] A third aspect of the present application provides an electronic device, the device comprising a processor and a memory;

[0036] The memory is used to store program code and transmit the program code to the processor;

[0037] The processor is used to execute the power grid cross-correlation harmonic control method described in any one of the first aspects according to the instructions in the program code.

[0038] In a fourth aspect, the present application provides a computer-readable storage medium for storing program code. When the program code is executed by a processor, the method for controlling cross-correlated harmonics in a power grid as described in any one of the first aspects is implemented.

[0039] It can be seen from the above technical solutions that this application has the following advantages:

[0040] The grid cross-correlation harmonic control method provided in this application realizes the precise separation of multi-band harmonic energy by performing wavelet packet decomposition on electrical signals, thereby avoiding the spectrum leakage problem of traditional FFT; performs cross-correlation analysis on wavelet packets instead of original electrical signals, thereby avoiding noise and frequency band aliasing interference in the original electrical signals and improving the accuracy of phase difference detection; constructs the inter-subgrid harmonic transfer equation based on the phase difference between subgrids, and then quantifies the harmonic responsibility weight of each subgrid; realizes dynamic coordination between subgrids by adopting a two-layer game model, obtains the control output of each subgrid, and then adjusts the virtual impedance, changes the equivalent impedance characteristics of the local power grid, forces the harmonic current to choose a low-impedance path, breaks the original harmonic propagation path, and forms a new return network with control equipment as the core, thereby realizing harmonic control between multiple subgrids during high-penetration distributed grid connection;

[0041] Furthermore, this application dynamically adjusts the game control strategy and virtual impedance parameters through a feedback mechanism, thereby improving the operating condition change problem caused by photovoltaic fluctuations, and achieving Pareto optimality among multiple objectives such as harmonic suppression, equipment loss, and stability, thereby improving the system's anti-interference ability and realizing adaptive management of harmonics between multiple subgrids during high-penetration distributed grid connection. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] 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 work.

[0043] Figure 1A flowchart of a method for controlling cross-correlated harmonics in a power grid provided in an embodiment of the present application;

[0044] Figure 2 A schematic structural diagram of a power grid cross-correlated harmonic control device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0045] In order to help those skilled in the art better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0046] For easier understanding, please refer to Figure 1 , an embodiment of the present application provides a method for controlling cross-correlated harmonics in a power grid, comprising:

[0047] Step 110: performing wavelet packet decomposition on the acquired electrical signals of the photovoltaic subgrid to obtain harmonic components of each photovoltaic subgrid in each frequency band;

[0048] Obtain the voltage signal or current signal of each sub-grid in the high-penetration distributed photovoltaic grid, perform wavelet packet decomposition on it, and obtain the harmonic components of each photovoltaic sub-grid in each frequency band.

[0049]

[0050] Where, Represents the voltage signal / current signal of the current photovoltaic subgrid; is the harmonic component of the rth photovoltaic node in the mth layer, is the wavelet basis function of the rth photovoltaic node in the mth layer, S is the number of wavelet decomposition layers, and M is the number of photovoltaic nodes in the current photovoltaic subnet.

[0051] The energy projection of the electrical signal in each frequency band can be extracted by inner product operation to obtain the harmonic components of each frequency band :

[0052]

[0053] Wavelet basis functions The mother wavelet is generated by scaling and translating it to cover a specific frequency range and time window. The mother wavelet can be selected according to the actual situation and is not specifically limited here.

[0054] Step 120: Calculate a cross-correlation function based on the harmonic components of each photovoltaic subgrid in the same frequency band, calculate the harmonic phase difference between each photovoltaic subgrid based on the cross-correlation function between each photovoltaic subgrid and the topological impedance matrix of the photovoltaic subgrid, and calculate the total harmonic distortion rate of each photovoltaic subgrid based on the harmonic components of each photovoltaic subgrid in each frequency band;

[0055] If the original electrical signal is directly subjected to cross-correlation analysis, it is susceptible to interference from noise and frequency band aliasing, which may cause phase difference detection errors due to the aliasing of multi-band harmonics in the electrical signal. Therefore, the embodiment of the present application performs cross-correlation analysis on the wavelet packet:

[0056]

[0057] in, is the cross-correlation function; is the integral variable in the time domain integral, representing the instantaneous moment on the time axis; and They are the electrical signals of the current photovoltaic subgrid Electrical signals with other photovoltaic subnets Harmonic components in the same frequency band. By suppressing non-target frequency band interference through wavelet packet decomposition, the phase difference detection error can be reduced by about 30%.

[0058] From the cross-correlation function between each photovoltaic subgrid Extract extreme points Determine the harmonic propagation delay, combined with the harmonic propagation speed v between the measurement points of each photovoltaic subnet (usually the electromagnetic wave speed m / s) to calculate the path difference between each photovoltaic subnet .

[0059] Through delay The calculated harmonic path difference can be used to locate the harmonic location, and after locating the harmonic source, it can be focused on treatment. This method can avoid testing all test points one by one.

[0060] In the embodiment of the present application, according to the relationship between the equivalent impedance difference and the path difference between each photovoltaic subnet, combined with the topological impedance matrix of the photovoltaic subnet, (Node admittance matrix The inverse matrix of the PV subgrid is used to calculate the equivalent impedance between the nodes of each PV subgrid, and the harmonic phase difference between the PV subgrids is calculated based on the equivalent impedance between the nodes of each PV subgrid. (i.e. node i) and PV subgrid (i.e. node j), its equivalent impedance difference Difference from path The relationship is:

[0061]

[0062] Where, It is the system benchmark impedance, which is determined by the rated voltage and capacity of the grid.

[0063] Using the topological impedance matrix The node impedance in , calculate the equivalent reactance between nodes (Imaginary part of impedance), and then derive the phase difference:

[0064]

[0065] Where, For nodes and nodes The phase difference between is the impedance between node i and harmonic source node k, is the impedance between node j and harmonic source node k, and Node To the harmonic source node The resistance and reactance values can be determined by solving the equations using the least squares method. location; and Node To the harmonic source node resistance and reactance values.

[0066] It should be noted that when 、 When it is 0, 、 It is also meaningful to be infinite or infinitesimal. For example, when When is infinite, Equal to 90°, when For infinite hours, Equal to -90°.

[0067] The process of determining the harmonic source location is as follows: According to the impedance matrix Harmonic transfer impedance from node i to node j , construct the equations for the harmonic current propagation path:

[0068]

[0069] in, is the node where the harmonic source is located, and is the measurement point node. By solving the equations by the least square method, we can determine location.

[0070] Latency It reflects the difference in the propagation paths of harmonics between sub-grids and is directly related to the topology of the power grid. Describes the impedance relationship between power grid nodes and is used to quantify the impedance difference of the propagation path of harmonic current. The signal after wavelet packet decomposition ( and frequency components) is used to suppress noise interference and help improve The embodiment of the present application captures the time difference of harmonic propagation through cross-correlation analysis and converts the physical path difference into electrical phase difference by combining the grid topology impedance matrix.

[0071] After obtaining the harmonic components of PV subgrid i, calculate the harmonic energy of each frequency band:

[0072]

[0073] Where, is the hth harmonic energy of PV subgrid i; is the hth harmonic component of the rth photovoltaic node in the mth layer, which can be filtered by a bandpass filter in the hth harmonic frequency band. Filter to obtain ; S is the number of wavelet decomposition layers, and M is the number of all photovoltaic nodes in photovoltaic subgrid i.

[0074] It should be noted that a single frequency band may contain multiple harmonics. For example, 200~300Hz includes the 4th, 5th, and 6th harmonics (the fundamental frequency is usually 50Hz). The hth harmonic component can be obtained by using a bandpass filter in the hth harmonic frequency band. This process belongs to the existing technology and its specific process will not be repeated here.

[0075] Then the total harmonic distortion rate of photovoltaic subgrid i is calculated based on the harmonic energy of each frequency band of photovoltaic subgrid i :

[0076]

[0077] Where, is the fundamental wave energy of PV subgrid i, and H is the highest harmonic order (usually 50th).

[0078] Furthermore, after calculating the total harmonic distortion rate of each photovoltaic subnet, the embodiment of the present application calculates the total harmonic distortion rate of each photovoltaic subnet according to the topological impedance matrix of the photovoltaic subnet. Get the admittance matrix ( is the admittance matrix The inverse matrix of the admittance matrix Calculate the distribution weight of harmonic electrical signals in each photovoltaic subgrid; and modify the total harmonic distortion rate of each photovoltaic subgrid by the distribution weight of harmonic electrical signals in each photovoltaic subgrid. The calculation formula is:

[0079]

[0080] Where, is the self-admittance of node i (the sum of the admittance to ground and the admittance of all branches), is the admittance between nodes i and j;

[0081] Corrected total harmonic distortion rate of PV subgrid i .

[0082] The grid topology impedance matrix determines the distribution characteristics of harmonic currents. High impedance nodes have a more significant inhibitory effect on harmonic currents. Correct the total harmonic distortion THD of PV subgrid i i actual impact.

[0083] This embodiment of the present application achieves precise separation of multi-band harmonic energy through wavelet packet decomposition, avoiding the spectrum leakage problem of traditional FFT (Fast Fourier Transform). By adopting a multi-scale cross-correlation harmonic detection method, the limitations of traditional FFT spectrum analysis for non-stationary harmonics are overcome. A composite detection model combining wavelet packet decomposition with cross-correlation functions is introduced to accurately locate harmonic phase differences and propagation paths between subnets.

[0084] Step 130: construct a harmonic transfer equation based on the harmonic phase difference, total harmonic distortion, and inverter equivalent output impedance between the photovoltaic subgrids, and calculate the harmonic responsibility ratio of each photovoltaic subgrid based on the harmonic transfer equation;

[0085] The harmonic phase difference between each photovoltaic subgrid can be Generate matrix form and get harmonic phase difference matrix According to the harmonic phase difference matrix , inverter equivalent output impedance (known parameters) and the harmonic voltage of each node to construct the harmonic transfer equation:

[0086]

[0087] Where, is the harmonic current vector, is the set of harmonic currents of each subnet; is the inverse matrix of the admittance matrix; is the harmonic voltage vector, is the set of harmonic voltages of each subgrid; is the inverse matrix of the inverter equivalent output impedance matrix; is the internal harmonic voltage source vector of the inverter.

[0088] Admittance Matrix in Harmonic Transfer Equation It needs to be adjusted according to the phase difference to reflect the time delay effect of the harmonic propagation path:

[0089]

[0090] in is the phase rotation factor, which corrects the phase relationship of the harmonic currents between nodes. Harmonic current vector The calculation of necessitates the introduction of a phase difference correction term:

[0091]

[0092] is the phase difference of the harmonic source node k (given by It is deduced that the phase difference directly affects the superposition effect of harmonic currents, and a high phase difference may lead to harmonic cancellation or enhancement.

[0093] By harmonic voltage amplitude Indirectly affects the harmonic transfer equation. Assume that the fundamental voltage is ,but:

[0094]

[0095] Where, is the hth harmonic current component of node i; is the fundamental current of node i; is the fundamental voltage of node i; is the hth harmonic voltage component of node i. Converted into harmonic voltage components and injected into the right side of the harmonic transfer equation vector.

[0096] The calculation formula for the harmonic responsibility ratio of PV subgrid i is:

[0097]

[0098] Where, is the hth harmonic responsibility proportion of PV subgrid i; is the harmonic transfer impedance from node i to node j; is the hth harmonic current component at node j; N is the number of PV subgrids. PV subgrids with higher THD values are assigned higher governance responsibilities.

[0099] After calculating the harmonic responsibility ratio of each photovoltaic subgrid, , ,..., Select a certain harmonic responsibility ratio as the final harmonic responsibility ratio of each PV subgrid ,Right now = , , and finally output the responsibility vector .

[0100] Step 140: Construct a two-layer game model, solve the two-layer game model based on the total harmonic distortion rate of each photovoltaic subgrid, the total harmonic distortion rate reference value, and the harmonic responsibility ratio of each photovoltaic subgrid, and obtain the output of the treatment equipment in each photovoltaic subgrid;

[0101] The embodiment of the present application adopts hierarchical collaborative game control and a two-layer game model (including master and slave game layers and Nash equilibrium strategy) to achieve dynamic coordination between subnets. The optimization objective function of the master game layer in the two-layer game model is:

[0102]

[0103] Where, is the output vector of the management equipment in the PV subgrid, and N is the number of PV subgrids; is the total harmonic distortion rate of PV subgrid i; is the reference value of total harmonic distortion; is the penalty coefficient;

[0104] The utility function of the secondary game layer in the two-layer game model is:

[0105]

[0106] Where, is the utility function of PV subgrid i; is the weight coefficient of harmonic responsibility ratio, is the weight coefficient of the marginal effect of equipment output on harmonic suppression; is the harmonic responsibility ratio of PV subgrid i; is the output of the management equipment in PV subgrid i; It represents the marginal effect of the control equipment output on harmonic suppression.

[0107] The main game layer sets the global optimization goal, and the slave game layer feeds back the local optimal solution through the Nash equilibrium strategy, forming a two-layer iterative optimization structure. The governance output vector P output by the main game layer serves as the constraint condition of the slave game layer, limiting the output range of each subnet. The utility function calculated by the slave game layer is Adjust the penalty coefficient by reacting to the objective function of the main game layer through the Lagrange multiplier method and weight coefficient .

[0108] The two-layer game model can be solved by the Lagrange multiplier method to obtain the Pareto optimal solution. The main game layer decomposes the global optimization problem into multiple sub-problems, and each sub-network solves:

[0109]

[0110] are the minimum and maximum outputs of the management equipment in PV subgrid i respectively. , so that the system reaches a Nash equilibrium state, that is, any sub-grid can not improve its own utility by changing its output alone. of When it is higher, Increase, driving the PV subgrid to allocate more governance resources ( increase), thereby reducing the global .

[0111] This application adopts a global-local collaborative control method. Compared with centralized control (such as PID parameter adjustment), the two-layer game model reduces the computational complexity from Reduce to .

[0112] The generation process of the output vector P of the treatment equipment is:

[0113] 1. Initial solution of the main game layer:

[0114] Responsibility Vector and system constraints, preliminary calculations The benchmark value .

[0115] 2. Fine-tuning the game layer:

[0116] Each photovoltaic sub-grid is based on Adjust output:

[0117] like (i.e. increase Can reduce ), then the ;

[0118] Otherwise it will decrease To avoid wasting resources.

[0119] 3. Dynamic convergence process:

[0120] The main game layer adjusts the penalty coefficient Control total output , from the game layer through the utility function Optimize local parameters until the system reaches a stable state, and finally output the output vector P of the governance equipment.

[0121] Step 150: Input the output of the treatment equipment in each photovoltaic subnet into the fuzzy controller to obtain the impedance adjustment value of each photovoltaic subnet, and adjust the virtual impedance according to the impedance adjustment value of each photovoltaic subnet to update the harmonic return path;

[0122] This application adopts a virtual impedance adaptive adjustment method, which uses power electronic equipment to adjust the virtual impedance characteristics in real time and change the harmonic return path. Specifically, the dynamic virtual impedance equation in this application is:

[0123]

[0124] Where, are proportional gain, integral gain, and differential gain, which are adjusted online by the fuzzy controller. s is a complex frequency variable (Laplace domain variable). F is the cutoff frequency coefficient of the differential filter, which is used to suppress high-frequency noise.

[0125] The corresponding stability constraints are:

[0126]

[0127] In the formula, Re( ) represents taking the real part of a complex number; is the grid impedance.

[0128] Each Corresponding photovoltaic subnet For example, if is the compensation current amplitude of the active filter, and its size directly affects the adjustment strength of the virtual impedance.

[0129] As the input variable of the fuzzy controller, it drives the gain parameter of the fuzzy controller. Dynamic adjustment of the output impedance For example: when When it increases, the fuzzy rule will improve , in order to quickly respond to harmonic suppression needs. When fluctuations continue, Increase to suppress overshoot and ensure stability. Output impedance adjustment amount For the current adjustment period t The increment relative to the previous adjustment period t-1, that is:

[0130]

[0131] According to the impedance adjustment Update the harmonic return path, such as by adjusting the impedance Adjusting virtual impedance , changing the equivalent impedance characteristics of the local power grid, forcing harmonic currents to choose low-impedance paths. Through dynamic adjustment, the original harmonic propagation path is broken, forming a new return network with the treatment equipment as the core.

[0132] In another embodiment, after step 150, the method further includes:

[0133] Step 160: Taking the output, impedance adjustment amount, and total harmonic distortion rate of the treatment equipment in each photovoltaic subgrid as states, and the control parameters in the two-layer game model and fuzzy controller as actions, a reinforcement learning method is used to obtain the optimal action; and the control parameters in the two-layer game model and fuzzy controller are updated through the optimal action.

[0134] In this embodiment of the application, the output vector P of the treatment equipment and the impedance adjustment amount and total harmonic distortion THD as the state, and the penalty coefficient in the two-layer game model , weight coefficient and impedance adjustment parameters in fuzzy controller For actions, a DQN network (Deep Q-Network) is constructed to optimize control parameters in real time and solve multi-objective conflicts.

[0135] The Q value function in the DQN network is:

[0136]

[0137] Reward Function for:

[0138]

[0139] The policy network update process is:

[0140]

[0141] The loss function is:

[0142]

[0143] Where, Is the Q value function, indicating that in state Next action The expected cumulative reward of ; E represents the expected value; is the reinforcement learning discount factor at time t, which is used to balance the weight of current rewards and future rewards; T is the termination time; It is the weight parameter of the total harmonic distortion suppression effect in the reward function, which is used to improve the THD suppression priority; It is the weight parameter of the energy consumption of the governance equipment in the reward function, which is used to limit the output energy consumption of the governance equipment; is the weight parameter of the impedance adjustment in the reward function, which is used to enhance the system stability constraint; are the parameters of the policy network, used to generate action strategies; is the learning rate, which controls the step size of the policy network parameter update; is the loss function, which measures the difference between the predicted Q value and the target Q value ( ), the network parameters are updated by minimizing the gap between the predicted Q value and the target Q value. is the state vector, including the current impedance adjustment value ΔZ v , THD value, output vector P of treatment equipment and other parameters; is the action vector, which represents the weight parameter The adjustment amount.

[0144] DQN network based on real-time rewards Calculate Q value and update policy network parameters , and finally output the optimal action, that is, the optimized parameters , the optimized parameters Acting on the master-slave game layer to adjust the objective function and utility function respectively, The fuzzy rule base of virtual impedance adjustment is modified on the virtual impedance adjustment layer to form a closed-loop control. The updated control parameters act on the harmonic control equipment to generate new and , re-enter the DQN network for iterative optimization.

[0145] Among them, the parameters Feedback acts on the penalty coefficient in the optimization objective function of the main game layer , to update the penalty coefficient in the objective function, that is, ;

[0146] Feedback acts on the utility function from the game layer The weight parameter , update the weight parameters in the utility function ,Right now ;

[0147] Gain Tuning Rules for Fuzzy Controllers with Feedback , the gain adjustment in the fuzzy controller and Positive correlation, impedance matching conditions are met first: ,in, , used to quickly respond to impedance mutations; = , used to eliminate steady-state errors; = , used to suppress high-frequency oscillations; , 、 、 Both are learning rate coefficients (usually 0.1~1.0), which control the adjustment range to ensure the stability of the total gain. The larger the value, the higher the priority of virtual impedance adjustment for stability constraints.

[0148] This application optimizes the control weight parameters in real time through reinforcement learning, and dynamically adjusts the game control strategy and virtual impedance parameters through the feedback mechanism to achieve global harmonic suppression efficiency maximization and adaptive management. By adjusting the weight parameters in real time, the problem of working condition changes caused by photovoltaic fluctuations can be improved; The collaborative feedback mechanism achieves Pareto optimality among multiple objectives, including harmonic suppression, equipment loss, and stability. The parameter closed-loop mechanism in this application improves the system's tolerance to disturbances such as sudden grid impedance changes by 30%.

[0149] The grid cross-correlation harmonic control method provided in this application realizes the precise separation of multi-band harmonic energy by performing wavelet packet decomposition on electrical signals, thereby avoiding the spectrum leakage problem of traditional FFT; performs cross-correlation analysis on wavelet packets instead of original electrical signals, thereby avoiding noise and frequency band aliasing interference in the original electrical signals and improving the accuracy of phase difference detection; constructs the inter-subgrid harmonic transfer equation based on the phase difference between subgrids, and then quantifies the harmonic responsibility weight of each subgrid; realizes dynamic coordination between subgrids by adopting a two-layer game model, obtains the control output of each subgrid, and then adjusts the virtual impedance, changes the equivalent impedance characteristics of the local power grid, forces the harmonic current to choose a low-impedance path, breaks the original harmonic propagation path, and forms a new return network with control equipment as the core, thereby realizing harmonic control between multiple subgrids during high-penetration distributed grid connection;

[0150] Furthermore, this application dynamically adjusts the game control strategy and virtual impedance parameters through a feedback mechanism, thereby improving the operating condition change problem caused by photovoltaic fluctuations, and achieving Pareto optimality among multiple objectives such as harmonic suppression, equipment loss, and stability, thereby improving the system's anti-interference ability and realizing adaptive management of harmonics between multiple subgrids during high-penetration distributed grid connection.

[0151] The above is an embodiment of a method for controlling cross-correlated harmonics in a power grid provided by the present application, and the following is an embodiment of a device for controlling cross-correlated harmonics in a power grid provided by the present application.

[0152] Please refer to Figure 2, an embodiment of the present application provides a power grid cross-correlated harmonic control device, comprising:

[0153] The wavelet packet decomposition unit 210 is used to perform wavelet packet decomposition on the acquired electrical signals of the photovoltaic sub-grid to obtain the harmonic components of each photovoltaic sub-grid in each frequency band;

[0154] A first calculation unit 220 is configured to calculate a cross-correlation function based on the harmonic components of each photovoltaic subgrid in the same frequency band, calculate a harmonic phase difference between each photovoltaic subgrid based on the cross-correlation function between each photovoltaic subgrid and the topological impedance matrix of the photovoltaic subgrid, and calculate a total harmonic distortion rate of each photovoltaic subgrid based on the harmonic components of each photovoltaic subgrid in each frequency band;

[0155] A second calculation unit 230 is configured to construct a harmonic transfer equation based on the harmonic phase difference, total harmonic distortion, and inverter equivalent output impedance between the photovoltaic subgrids, and calculate the harmonic responsibility ratio of each photovoltaic subgrid based on the harmonic transfer equation;

[0156] The model building and solving unit 240 is used to build a two-layer game model, solve the two-layer game model based on the total harmonic distortion rate of each photovoltaic subgrid, the total harmonic distortion rate reference value, and the harmonic responsibility ratio of each photovoltaic subgrid, and obtain the output of the treatment equipment in each photovoltaic subgrid;

[0157] The adjustment unit is used to input the output of the treatment equipment in each photovoltaic subnet into the fuzzy controller, obtain the impedance adjustment amount of each photovoltaic subnet, and adjust the virtual impedance according to the impedance adjustment amount of each photovoltaic subnet to update the harmonic return path.

[0158] As a further improvement, the device further comprises:

[0159] The correction unit 250 is used to obtain an admittance matrix based on the topological impedance matrix of the photovoltaic subgrid, calculate the distribution weight of the harmonic electrical signal in each photovoltaic subgrid through the admittance matrix, and correct the total harmonic distortion rate of each photovoltaic subgrid based on the distribution weight of the harmonic electrical signal in each photovoltaic subgrid.

[0160] As a further improvement, the calculation formula for the harmonic responsibility ratio of each PV subgrid is:

[0161]

[0162] Where, is the hth harmonic responsibility proportion of PV subgrid i; is the harmonic transfer impedance from node i to node j; is the hth harmonic current component of node j; is the total harmonic distortion rate of PV subgrid i; N is the number of PV subgrids.

[0163] As a further improvement, the optimization objective function of the main game layer in the two-layer game model is:

[0164]

[0165] Where, is the output vector of the management equipment in the PV subgrid, and N is the number of PV subgrids; is the total harmonic distortion rate of PV subgrid i; is the reference value of total harmonic distortion; is the penalty coefficient;

[0166] The utility function of the secondary game layer in the two-layer game model is:

[0167]

[0168] Where, is the utility function of PV subgrid i; 、 All are weight coefficients; is the harmonic responsibility ratio of PV subgrid i; is the output of the management equipment in PV subgrid i.

[0169] As a further improvement, the device further comprises:

[0170] The feedback adjustment unit 260 is used to obtain the optimal action using the output, impedance adjustment amount and total harmonic distortion rate of the management equipment in each photovoltaic subgrid as the state, and the control parameters in the two-layer game model and fuzzy controller as the action by using the reinforcement learning method; and update the control parameters in the two-layer game model and fuzzy controller through the optimal action.

[0171] This application achieves accurate separation of multi-band harmonic energy by performing wavelet packet decomposition on electrical signals, avoiding the spectrum leakage problem of traditional FFT; performs cross-correlation analysis on wavelet packets instead of original electrical signals to avoid noise and frequency band aliasing interference in the original electrical signals, thereby improving the accuracy of phase difference detection; constructs an inter-subgrid harmonic transfer equation based on the phase difference between subgrids, and then quantifies the harmonic responsibility weight of each subgrid; achieves dynamic coordination between subgrids by adopting a two-layer game model, obtains the governance output of each subgrid, and then adjusts the virtual impedance, changes the equivalent impedance characteristics of the local power grid, forces the harmonic current to choose a low-impedance path, breaks the original harmonic propagation path, and forms a new return network with governance equipment as the core, thereby achieving harmonic governance between multiple subgrids during high-penetration distributed grid connection;

[0172] Furthermore, this application dynamically adjusts the game control strategy and virtual impedance parameters through a feedback mechanism, thereby improving the operating condition change problem caused by photovoltaic fluctuations, and achieving Pareto optimality among multiple objectives such as harmonic suppression, equipment loss, and stability, thereby improving the system's anti-interference ability and realizing adaptive management of harmonics between multiple subgrids during high-penetration distributed grid connection.

[0173] An embodiment of the present application further provides an electronic device, the device including a processor and a memory;

[0174] The memory is used to store program codes and transmit the program codes to the processor;

[0175] The processor is configured to execute the power grid cross-correlation harmonic control method in the aforementioned method embodiment according to instructions in the program code.

[0176] An embodiment of the present application also provides a computer-readable storage medium, which is used to store program code. When the program code is executed by a processor, it implements the power grid cross-correlation harmonic control method in the aforementioned method embodiment.

[0177] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0178] In the specification of this application and the above-mentioned drawings, the terms "first," "second," "third," "fourth," etc. (if any) are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. In addition, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus that includes a series of steps or elements is not necessarily limited to those steps or elements explicitly listed, but may include other steps or elements not explicitly listed or inherent to such process, method, product, or apparatus.

[0179] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships can exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or plural.

[0180] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0181] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0182] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0183] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the method described in each embodiment of the present application through a computer device (which can be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name: Read-Only Memory, English abbreviation: ROM), random access memory (full name: Random Access Memory, English abbreviation: RAM), disk or optical disk, and other media that can store program code.

[0184] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for controlling cross-correlated harmonics in a power grid, characterized in that: include: Perform wavelet packet decomposition on the acquired electrical signals of the photovoltaic subgrid to obtain the harmonic components of each photovoltaic subgrid in each frequency band; The cross-correlation function is calculated based on the harmonic components of each photovoltaic subgrid in the same frequency band. The harmonic phase difference between each photovoltaic subgrid is calculated based on the cross-correlation function between each photovoltaic subgrid and the topological impedance matrix of the photovoltaic subgrid. The total harmonic distortion rate of each photovoltaic subgrid is calculated based on the harmonic components of each photovoltaic subgrid in each frequency band. The harmonic transfer equation is constructed based on the harmonic phase difference, total harmonic distortion rate, and inverter equivalent output impedance between each photovoltaic subgrid. The harmonic responsibility ratio of each photovoltaic subgrid is calculated based on the harmonic transfer equation. The calculation formula for the harmonic responsibility ratio of each photovoltaic subgrid is: ； Where, is the hth harmonic responsibility proportion of PV subgrid i; is the harmonic transfer impedance from node i to node j; is the hth harmonic current component of node j; is the total harmonic distortion rate of PV subgrid i; N is the number of PV subgrids; A two-layer game model is constructed and solved based on the total harmonic distortion rate of each photovoltaic subgrid, the reference value of the total harmonic distortion rate, and the harmonic responsibility ratio of each photovoltaic subgrid to obtain the output of the treatment equipment in each photovoltaic subgrid. The optimization objective function of the main game layer in the two-layer game model is: ； Where, is the output vector of the management equipment in the PV subgrid, and N is the number of PV subgrids; is the total harmonic distortion rate of PV subgrid i; is the reference value of total harmonic distortion; is the penalty coefficient; The utility function of the secondary game layer in the two-layer game model is: ； Where, is the utility function of PV subgrid i; 、 All are weight coefficients; is the harmonic responsibility ratio of PV subgrid i; is the output of the management equipment in PV subgrid i; The output of the treatment equipment in each photovoltaic subnet is input into the fuzzy controller to obtain the impedance adjustment value of each photovoltaic subnet. The virtual impedance is adjusted according to the impedance adjustment value of each photovoltaic subnet to update the harmonic return path.

2. The method for controlling cross-correlated harmonics in a power grid according to claim 1, wherein: The total harmonic distortion rate of each photovoltaic subgrid is calculated based on the harmonic components of each photovoltaic subgrid in each frequency band, and then it also includes: The admittance matrix is obtained based on the topological impedance matrix of the photovoltaic subgrid, and the distribution weight of the harmonic electrical signal in each photovoltaic subgrid is calculated through the admittance matrix; The total harmonic distortion rate of each photovoltaic subgrid is corrected by allocating the weight of the harmonic electrical signal to each photovoltaic subgrid.

3. The method for controlling cross-correlated harmonics in a power grid according to claim 1, wherein: The harmonic phase difference between each photovoltaic subgrid is calculated based on the cross-correlation function between each photovoltaic subgrid and the topological impedance matrix of the photovoltaic subgrid, including: Extract extreme points from the cross-correlation function between each photovoltaic subgrid, and calculate the path difference between each photovoltaic subgrid based on the extreme points and the harmonic propagation speed between each photovoltaic subgrid; Based on the relationship between the equivalent impedance difference and path difference between each photovoltaic subgrid, the equivalent impedance between each photovoltaic subgrid node is calculated in combination with the topological impedance matrix of the photovoltaic subgrid. The harmonic phase difference between each photovoltaic subgrid is then calculated using the equivalent impedance between each photovoltaic subgrid node.

4. The method for controlling cross-correlated harmonics in a power grid according to claim 1, wherein: The method further comprises: Taking the output, impedance adjustment amount and total harmonic distortion rate of the management equipment in each photovoltaic subgrid as states, and the control parameters in the two-layer game model and fuzzy controller as actions, the reinforcement learning method is used to obtain the optimal action; the control parameters in the two-layer game model and fuzzy controller are updated through the optimal action.

5. A device for controlling cross-correlated harmonics in a power grid, characterized in that: include: A wavelet packet decomposition unit is used to perform wavelet packet decomposition on the acquired electrical signals of the photovoltaic subgrid to obtain the harmonic components of each photovoltaic subgrid in each frequency band; a first calculation unit, configured to calculate a cross-correlation function based on the harmonic components of each photovoltaic subgrid in the same frequency band, calculate a harmonic phase difference between each photovoltaic subgrid based on the cross-correlation function between each photovoltaic subgrid and the topological impedance matrix of the photovoltaic subgrid, and calculate a total harmonic distortion rate of each photovoltaic subgrid based on the harmonic components of each photovoltaic subgrid in each frequency band; The second calculation unit is used to construct a harmonic transfer equation based on the harmonic phase difference, total harmonic distortion rate and inverter equivalent output impedance between each photovoltaic subgrid, and calculate the harmonic responsibility ratio of each photovoltaic subgrid based on the harmonic transfer equation; the calculation formula of the harmonic responsibility ratio of each photovoltaic subgrid is: ； Where, is the hth harmonic responsibility proportion of PV subgrid i; is the harmonic transfer impedance from node i to node j; is the hth harmonic current component of node j; is the total harmonic distortion rate of PV subgrid i; N is the number of PV subgrids; The model building and solving unit is used to build a two-layer game model. Based on the total harmonic distortion rate of each photovoltaic subgrid, the reference value of the total harmonic distortion rate and the harmonic responsibility ratio of each photovoltaic subgrid, the two-layer game model is solved to obtain the output of the treatment equipment in each photovoltaic subgrid. The optimization objective function of the main game layer in the two-layer game model is: ； Where, is the output vector of the management equipment in the PV subgrid, and N is the number of PV subgrids; is the total harmonic distortion rate of PV subgrid i; is the reference value of total harmonic distortion; is the penalty coefficient; The utility function of the secondary game layer in the two-layer game model is: ； Where, is the utility function of PV subgrid i; 、 All are weight coefficients; is the harmonic responsibility ratio of PV subgrid i; is the output of the management equipment in PV subgrid i; The adjustment unit is used to input the output of the treatment equipment in each photovoltaic subnet into the fuzzy controller, obtain the impedance adjustment amount of each photovoltaic subnet, and adjust the virtual impedance according to the impedance adjustment amount of each photovoltaic subnet to update the harmonic return path.

6. The power grid mutual-correlation harmonic control device according to claim 5, characterized in that: The device also includes: The feedback adjustment unit is used to obtain the optimal action using the output, impedance adjustment amount and total harmonic distortion rate of the management equipment in each photovoltaic subgrid as the state, and the control parameters in the double-layer game model and fuzzy controller as the action by adopting the reinforcement learning method; and update the control parameters in the double-layer game model and fuzzy controller through the optimal action.

7. An electronic device, characterized in that: The device includes a processor and a memory; The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the power grid cross-correlation harmonic control method according to any one of claims 1 to 4 according to the instructions in the program code.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium is used to store program code, and when the program code is executed by a processor, it implements the method for controlling cross-correlated harmonics in a power grid according to any one of claims 1 to 4.

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