Optimization method and device for configuration of shunt active power filter of power distribution network

Abstract

The application relates to a power distribution network shunt active power filter configuration optimization method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: obtaining each harmonic impedance of each branch of a power distribution network, each harmonic mutual impedance between nodes, an association matrix formed between all nodes and all branches, and each harmonic current of each branch when no shunt active power filter is configured; determining a target function of the shunt active power filter configuration of the power distribution network, and taking the target function as a harmonic network loss suppression model, wherein the target function comprises a harmonic network loss function of the power distribution network and a configuration quantity function of the shunt active power filter; and solving the harmonic network loss suppression model in a preset solution range through a multi-objective particle swarm optimization algorithm to obtain a global platonic optimal solution set of the harmonic network loss suppression model. The method can improve the use value and efficiency of the shunt active power filter.

Description

Technical Field

[0001] This application relates to the field of power electronics technology, and in particular to a method and apparatus for optimizing the configuration of parallel active power filters in power distribution networks. Background Technology

[0002] In modern power distribution networks, the number of nonlinear loads, primarily power electronic converters, connected to the network has increased significantly. While providing flexible and efficient power, these loads also inject a large amount of harmonic current into the distribution network. When harmonic currents flow through line impedance or resistive loads, they generate branch harmonic voltages and cause harmonic power losses, leading to harmonic network losses. In terms of harmonic mitigation, shunt active power filters (SAPFs) can quickly and accurately compensate for harmonics, and their widespread application makes real-time harmonic control possible.

[0003] In related technologies, a local compensation configuration method is often adopted, where SAPF (Active Power Filter) is configured on the user side, connected in parallel next to the problematic load. However, with the increase in the number of nonlinear loads and their relatively dispersed distribution, traditional local compensation methods lead to an excessive number of SAPFs installed, reducing compensation efficiency. Furthermore, configuring multiple SAPFs in the distribution network reduces system stability. In addition, when some nonlinear loads are disconnected, the corresponding SAPFs cannot be effectively utilized, resulting in wasted resources and increased costs. Therefore, there is an urgent need for a parallel active power filter configuration method for suppressing harmonic losses in distribution networks. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for optimizing the configuration of parallel active power filters in power distribution networks, in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a method for optimizing the configuration of parallel active power filters in a distribution network. The method includes:

[0006] Obtain the harmonic impedance of each branch of the distribution network, the harmonic mutual impedance between each node, the correlation matrix formed between all nodes and all branches, and the harmonic current of each branch when no parallel active power filter is configured.

[0007] The objective function for configuring parallel active power filters in the distribution network is determined and used as a harmonic network loss suppression model. The objective function includes the harmonic network loss function of the distribution network and the configuration quantity function of parallel active power filters.

[0008] Based on the harmonic impedance of each branch, the harmonic mutual impedance between each node, all elements corresponding to each branch in the correlation matrix, and the harmonic current of each branch when no parallel active power filter is configured, the harmonic network loss suppression model is solved by a multi-objective particle swarm optimization algorithm within a preset solution range to obtain the global Pareto optimal solution set of the harmonic network loss suppression model. The global Pareto optimal solution set includes the alternative configuration schemes of the number of parallel active power filters in the distribution network.

[0009] In one embodiment, the objective function for determining the configuration of parallel active power filters in the distribution network includes:

[0010] Obtain the harmonic currents injected into all nodes by the parallel active power filter;

[0011] Based on the harmonic impedance of each branch, the harmonic mutual impedance between each node, the correlation matrix formed between all nodes and all branches, the harmonic current of each branch when no parallel active power filter is configured, and the harmonic current injected into each node by the parallel active power filter, the harmonic network loss function of each branch is determined.

[0012] The harmonic network loss functions of all branches are integrated to obtain the harmonic network loss function of the distribution network.

[0013] The configuration quantity function of the parallel active power filter is determined based on the harmonic currents injected into all nodes by the parallel active power filter.

[0014] In one embodiment, the harmonic network loss function of each branch is determined based on the harmonic impedance of each branch, the harmonic mutual impedance between each node, the correlation matrix formed between all nodes and all branches, the harmonic current of each branch when no parallel active power filter is configured, and the harmonic current injected into each node by the parallel active power filter, including:

[0015] Based on the harmonic impedance of each branch, the harmonic mutual impedance between each node, the correlation matrix formed between all nodes and all branches, the harmonic current of each branch when no parallel active power filter is configured, and the harmonic current injected into each node by the parallel active power filter, determine the harmonic current of each branch after configuring the parallel active power filter.

[0016] The harmonic network loss of each branch is determined based on the harmonic current and harmonic impedance of each branch.

[0017] In one embodiment, the configuration quantity function of the parallel active power filter includes:

[0018]

[0019]

[0020] Where, N SAPF This indicates the number of parallel active power filters configured. This represents the h-th harmonic current injected by the parallel active power filter at node m, where m represents the node, N represents the total number of nodes in the distribution network, H represents the highest order of the harmonic current, and k represents the highest order of the harmonic current. m This indicates the number of parallel active power filters configured at node m.

[0021] In one embodiment, the process of determining the preset solution range includes:

[0022] Determine the minimum output capacity of the parallel active power filter;

[0023] Based on the harmonic currents injected into each node by the parallel active power filter and the minimum output capacity of the parallel active power filter, it is determined whether the corresponding node needs to be configured with a parallel active power filter. Based on the determination result of each node, the preset solution range is determined.

[0024] In one embodiment, a method for optimizing the configuration of a parallel-type active power filter in a distribution network further includes:

[0025] Based on the target harmonic network loss suppression ratio and the global Pareto optimal solution set, the global Pareto optimal solution of the multi-objective particle swarm optimization algorithm is determined.

[0026] The optimal output of the active power filter is determined based on the global Pareto optimal solution of the multi-objective particle swarm optimization algorithm.

[0027] Secondly, this application also provides a configuration optimization device for parallel active power filters in a power distribution network. The device includes:

[0028] The acquisition module is used to acquire the harmonic impedance of each branch of the distribution network, the harmonic mutual impedance between each node, the correlation matrix formed between all nodes and all branches, and the harmonic current of each branch when no parallel active power filter is configured.

[0029] The determination module is used to determine the objective function for configuring parallel active power filters in the distribution network, and to serve as a harmonic network loss suppression model. The objective function includes the harmonic network loss function of the distribution network and the configuration quantity function of parallel active power filters.

[0030] The solution module is used to solve the harmonic network loss suppression model within a preset solution range using a multi-objective particle swarm optimization algorithm, based on the harmonic impedance of each branch, the harmonic mutual impedance between each node, all elements corresponding to each branch in the correlation matrix, and the harmonic current of each branch when no parallel active power filter is configured. The global Pareto optimal solution set of the harmonic network loss suppression model is obtained, where the global Pareto optimal solution set includes the alternative configuration schemes of the number of parallel active power filters in the distribution network.

[0031] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0032] Obtain the harmonic impedance of each branch of the distribution network, the harmonic mutual impedance between each node, the correlation matrix formed between all nodes and all branches, and the harmonic current of each branch when no parallel active power filter is configured.

[0033] The objective function for configuring parallel active power filters in the distribution network is determined and used as a harmonic network loss suppression model. The objective function includes the harmonic network loss function of the distribution network and the configuration quantity function of parallel active power filters.

[0034] Based on the harmonic impedance of each branch, the harmonic mutual impedance between each node, all elements corresponding to each branch in the correlation matrix, and the harmonic current of each branch when no parallel active power filter is configured, the harmonic network loss suppression model is solved by a multi-objective particle swarm optimization algorithm within a preset solution range to obtain the global Pareto optimal solution set of the harmonic network loss suppression model. The global Pareto optimal solution set includes the alternative configuration schemes of the number of parallel active power filters in the distribution network.

[0035] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0036] Obtain the harmonic impedance of each branch of the distribution network, the harmonic mutual impedance between each node, the correlation matrix formed between all nodes and all branches, and the harmonic current of each branch when no parallel active power filter is configured.

[0037] The objective function for configuring parallel active power filters in the distribution network is determined and used as a harmonic network loss suppression model. The objective function includes the harmonic network loss function of the distribution network and the configuration quantity function of parallel active power filters.

[0038] Based on the harmonic impedance of each branch, the harmonic mutual impedance between each node, all elements corresponding to each branch in the correlation matrix, and the harmonic current of each branch when no parallel active power filter is configured, the harmonic network loss suppression model is solved by a multi-objective particle swarm optimization algorithm within a preset solution range to obtain the global Pareto optimal solution set of the harmonic network loss suppression model. The global Pareto optimal solution set includes the alternative configuration schemes of the number of parallel active power filters in the distribution network.

[0039] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0040] Obtain the harmonic impedance of each branch of the distribution network, the harmonic mutual impedance between each node, the correlation matrix formed between all nodes and all branches, and the harmonic current of each branch when no parallel active power filter is configured.

[0041] The objective function for configuring parallel active power filters in the distribution network is determined and used as a harmonic network loss suppression model. The objective function includes the harmonic network loss function of the distribution network and the configuration quantity function of parallel active power filters.

[0042] Based on the harmonic impedance of each branch, the harmonic mutual impedance between each node, all elements corresponding to each branch in the correlation matrix, and the harmonic current of each branch when no parallel active power filter is configured, the harmonic network loss suppression model is solved by a multi-objective particle swarm optimization algorithm within a preset solution range to obtain the global Pareto optimal solution set of the harmonic network loss suppression model. The global Pareto optimal solution set includes the alternative configuration schemes of the number of parallel active power filters in the distribution network.

[0043] The aforementioned method, apparatus, computer equipment, storage medium, and computer program product for optimizing the configuration of parallel active power filters in distribution networks obtain the harmonic impedances of each branch, the harmonic mutual impedances between each node, the correlation matrix formed between all nodes and all branches, and the harmonic currents of each branch when no parallel active power filter is configured; determine the objective function for configuring parallel active power filters in distribution networks, and use it as a harmonic network loss suppression model; based on the harmonic impedances of each branch, the harmonic mutual impedances between each node, and the harmonic currents of each branch without a parallel active power filter, the method further optimizes the configuration of parallel active power filters in distribution networks. The harmonic network loss suppression model is solved using a multi-objective particle swarm optimization algorithm within a preset solution range, taking into account all elements corresponding to a branch in the correlation matrix and the harmonic currents of each branch without a parallel active power filter. This yields the global Pareto optimal solution set of the harmonic network loss suppression model. This method reduces the number of parallel active power filters required and decreases power losses caused by harmonics in the distribution network, thereby improving the utilization value and efficiency of the parallel active power filters. Attached Figure Description

[0044] Figure 1 This is an application environment diagram of the method for optimizing the configuration of parallel active power filters in a distribution network in one embodiment;

[0045] Figure 2 This is a flowchart illustrating a method for optimizing the configuration of parallel active power filters in a distribution network, as shown in one embodiment.

[0046] Figure 3 This is a flowchart illustrating the method for optimizing the configuration of parallel active power filters in a distribution network in yet another embodiment.

[0047] Figure 4 This is a topology diagram of an IEEE 18-node system built in one embodiment;

[0048] Figure 5 This is a schematic diagram of the capacity of a nonlinear load in one embodiment;

[0049] Figure 6 This is a schematic diagram of the optimal Pareto edge of the MOPSO algorithm when the number of iterations is 1 in one embodiment;

[0050] Figure 7 This is a schematic diagram of the optimal Pareto edge of the MOPSO algorithm when the number of iterations is 150 in one embodiment;

[0051] Figure 8 This is a schematic diagram of the optimal Pareto edge of the MOPSO algorithm when the number of iterations is 300 in one embodiment;

[0052] Figure 9This is a schematic diagram of the optimal Pareto edge of the MOPSO algorithm when the number of iterations is 450 in one embodiment;

[0053] Figure 10 This is a schematic diagram of the optimal Pareto edge of the MOPSO algorithm when the number of iterations is 600 in one embodiment;

[0054] Figure 11 This is a schematic diagram of the optimal Pareto edge of the MOPSO algorithm when the number of iterations is 750 in one embodiment;

[0055] Figure 12 This is a schematic diagram of the optimal Pareto edge of the MOPSO algorithm when the number of iterations is 900 in one embodiment;

[0056] Figure 13 This is a diagram showing the optimal output of SAPF in one embodiment;

[0057] Figure 14 This is a structural block diagram of a distribution network parallel-type active power filter configuration optimization device in one embodiment;

[0058] Figure 15 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0060] The method for optimizing the configuration of parallel active power filters in distribution networks provided in this application can be applied to, for example... Figure 1 The application environment shown is illustrated. Terminal 101 communicates with server 102 via a network. A data storage system can store the data that server 102 needs to process. The data storage system can be integrated onto server 102, or it can be located in the cloud or on another network server.

[0061] The terminal acquires the harmonic impedance of each branch of the distribution network, the harmonic mutual impedance between each node, the correlation matrix formed between all nodes and all branches, and the harmonic current of each branch when no parallel active power filter is configured. It then determines the objective function for configuring the parallel active power filter of the distribution network and uses it as the harmonic network loss suppression model. Finally, it solves the harmonic network loss suppression model within a preset solution range using a multi-objective particle swarm optimization algorithm to obtain the global Pareto optimal solution set of the harmonic network loss suppression model.

[0062] The terminal 101 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle systems. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 102 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0063] In one embodiment, such as Figure 2 As shown, a method for optimizing the configuration of parallel active power filters in a distribution network is provided. This embodiment illustrates the method by applying it to a terminal. It is understood that this method can also be applied to a server, and to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0064] 201. Obtain the harmonic impedance of each branch of the distribution network, the harmonic mutual impedance between each node, the correlation matrix formed between all nodes and all branches, and the harmonic current of each branch when no parallel active power filter (SAPF) is configured.

[0065] 202. Determine the objective function for configuring parallel active power filters in the distribution network and use it as a harmonic network loss suppression model. The objective function includes the harmonic network loss function of the distribution network and the configuration quantity function of parallel active power filters.

[0066] 203. Based on the harmonic impedance of each branch, the harmonic mutual impedance between each node, all elements corresponding to each branch in the correlation matrix, and the harmonic current of each branch when no parallel active power filter is configured, the harmonic network loss suppression model is solved by the multi-objective particle swarm optimization algorithm within the preset solution range to obtain the global optimal solution set of the harmonic network loss suppression model. The global optimal solution set includes the alternative configuration schemes of the number of parallel active power filters in the distribution network.

[0067] Harmonics refer to a distortion of the normal current waveform. Generally, harmonic sources are mainly classified into three categories: semiconductor converters, nonlinear impedances, and saturated reactances. In step 201 above, the harmonic impedances of each branch, the mutual harmonic impedances between nodes, and the correlation matrix formed between all nodes and all branches are obtained from the distribution network line planning data manual. The harmonic impedances of each branch refer to the harmonic impedances between nodes in the distribution network.

[0068] In step 202 above, the objective function, also known as the fitness function, is the main optimization objective of the harmonic network loss suppression model. The objective function can consist of multiple functions. In this embodiment of the invention, the objective function mainly includes the harmonic network loss function of the distribution network and the function for the number of parallel active power filters. Harmonic network loss refers to the total harmonic power loss in the distribution network. The objective of the objective function is to minimize both the harmonic network loss of the distribution network and the number of parallel active power filters.

[0069] The harmonic network loss function of the distribution network includes the harmonic impedance of each branch of the distribution network, the harmonic mutual impedance between each node, the correlation matrix formed between all nodes and all branches, and the harmonic current of each branch when no parallel active power filter is configured.

[0070] In step 203 above, the multi-objective particle swarm optimization (MOPSO) algorithm applies the particle swarm optimization (PSO) algorithm, which was originally only applicable to single-objective tasks, to multi-objective tasks. The steps of the MOPSO algorithm include:

[0071] (1) Initialization: Set the number of particles P in the particle swarm, the upper and lower limits of the position and velocity of each particle, and then randomly select the initial position and initial velocity of each particle within the limit range.

[0072] (2) Calculate the fitness function of each particle at present;

[0073] (3) Determine the Pareto optimal solution set of the current particle swarm and individual. If there is a particle whose fitness function is greater than that of the target particle, then the target particle does not belong to the Pareto optimal solution set. Replace the original target particle with this particle as the new target particle. Otherwise, the target particle belongs to the Pareto optimal solution set.

[0074] (4) Update particle position and velocity. Randomly select a particle position from the individual Pareto optimal solution set and the global Pareto optimal solution set of the particle, respectively, as the "individual Pareto optimal solution" and "global Pareto optimal solution" of the current particle. Based on the iterative process of the multi-objective particle swarm algorithm, update the position X and velocity V of all particles according to formula (1) and formula (2) respectively:

[0075]

[0076]

[0077] In equations (1) and (2), r represents the number of iterations, p represents the particle number; ω represents the inertia coefficient inherited from the previous particle velocity, which is ω = 0.5 when the number of iterations is less than 50% of the upper limit of iterations, and ω = 0.8 when the number of iterations is greater than 50% of the upper limit of iterations; c1 and c2 represent the learning factors for the individual Pareto optimal solution and the global Pareto optimal solution, respectively, both equal to 2; rand() symbol represents the random generation of a random number between 0 and 1; Archivep and Archiveg are the dominant particles of the current particle, which are the individual Pareto optimal solution and the global Pareto optimal solution randomly selected from the individual Pareto optimal solution set of particle p and the global Pareto optimal solution set of the particle swarm, respectively.

[0078] (5) Iteration termination condition: The iteration terminates when the number of iterations reaches the specified upper limit or the global Pareto optimal solution no longer changes; otherwise, return to step (2) above.

[0079] Specifically, the first step is to optimize the configuration of parallel active power filters (APBs) in the distribution network, given the underlying problem. The objective is to reduce the number of APBs while minimizing harmonic losses. This leads to two objective functions: the harmonic loss function of the distribution network and the function for determining the number of parallel APBs. Then, a multi-objective particle swarm optimization algorithm is used to solve these two objective functions within a predefined solution range, yielding a global Pareto optimal solution set.

[0080] It is worth mentioning that the preset solution range in step 203 above refers to determining a preset solution range before solving the two objective functions using the multi-objective particle swarm optimization algorithm. The solutions obtained after this process are all within the preset solution range.

[0081] The method provided in this invention determines the objective function for configuring parallel active power filters in a distribution network and solves it using a multi-objective particle swarm optimization algorithm. This yields the global Pareto optimal solution set for the harmonic network loss suppression model. By reducing the number of parallel active power filters installed, the method can reduce power losses caused by harmonics in the distribution network, thereby improving the utility and efficiency of the parallel active power filters.

[0082] In conjunction with the above embodiments, in one embodiment, determining the objective function for configuring parallel active power filters in a distribution network includes:

[0083] 301. Obtain the harmonic currents injected into all nodes by the parallel active power filter;

[0084] 302. Based on the harmonic impedance of each branch, the harmonic mutual impedance between each node, the correlation matrix formed between all nodes and all branches, the harmonic current of each branch when no parallel active power filter is configured, and the harmonic current injected by the parallel active power filter to each node, determine the harmonic network loss function of each branch.

[0085] 303. Integrate the harmonic network loss functions of all branches to obtain the harmonic network loss function of the distribution network;

[0086] 304. Determine the configuration quantity function of the parallel active power filter based on the harmonic currents injected into all nodes by the parallel active power filter.

[0087] In step 301 above, the harmonic currents injected into each node by the parallel active power filter are the particle positions in the multi-objective particle swarm optimization algorithm in step 203 above, which can be expressed as:

[0088]

[0089] In equation (3), X[m][n] represents the particle position in the multi-objective particle swarm optimization algorithm in step 203 above. This represents the h-th harmonic current injected by the SAPF installed at node m, where N is the total number of nodes in the distribution network, and H is the highest order of the harmonic current.

[0090] The particle's velocity is defined by a complex matrix V, which has the same dimension as the particle's position matrix X. The particle's velocity determines the iteration rate of its position and updates along with its position. The formulas for updating the particle's position and velocity are shown in equations (1) and (2), respectively.

[0091] Based on the above, it can be determined that the number of particles in the particle swarm of the multi-objective particle swarm optimization algorithm in step 203 is N*(H-1).

[0092] It is worth mentioning that when solving the harmonic network loss suppression model using the multi-objective particle swarm optimization algorithm within the preset solution range, it is assumed that each node in the distribution network is equipped with SAPF. During the solution process, at any node in the distribution network, the harmonic current injected by the parallel active power filter to the corresponding node may change, that is, the position of the particle will change during the iteration process.

[0093] Specifically, after each update of the harmonic current injected into all nodes by the parallel active power filter, that is, after each update of the position of each particle, the velocity of the corresponding particle is also updated. At the same time, after the position of the particle is updated, two objective values ​​of the particle at the new position are determined based on two objective functions. After all particles determine their respective two objective values, the individual Pareto optimal solution set and the global Pareto optimal solution set are determined based on the two objective values ​​of each particle.

[0094] The method provided in this embodiment of the invention can determine the harmonic network loss function of the distribution network and the configuration quantity function of the parallel active power filter, and solve the two objective functions through a multi-objective particle swarm optimization algorithm, thereby obtaining the global Pareto optimal solution set of the two objective functions, and further obtaining the global Pareto optimal solution of the two objective functions.

[0095] In conjunction with the above embodiments, in one embodiment, the harmonic network loss function of each branch is determined based on the harmonic impedance of each branch, the harmonic mutual impedance between each node, the correlation matrix formed between all nodes and all branches, the harmonic current of each branch when no parallel active power filter is configured, and the harmonic current injected into each node by the parallel active power filter, including:

[0096] 401. Based on the harmonic impedance of each branch, the harmonic mutual impedance between each node, the correlation matrix formed between all nodes and all branches, the harmonic current of each branch when no parallel active power filter is configured, and the harmonic current injected into each node by the parallel active power filter, determine the harmonic current of each branch after configuring the parallel active power filter.

[0097] 402. Determine the harmonic network loss of each branch based on the harmonic current and harmonic impedance of each branch.

[0098] Specifically, based on the harmonic impedance of each branch, the harmonic mutual impedance between each node, the correlation matrix formed between all nodes and all branches, the harmonic current of each branch without a parallel active power filter, and the harmonic current injected into each node by the parallel active power filter with a parallel active power filter, the harmonic current of each branch after configuring the parallel active power filter is determined, including:

[0099]

[0100] In equation (4), This represents the h-th harmonic current in branch l. This represents the h-th harmonic current in branch l when no parallel active power filter is configured. Let A[n,l] represent the h-th harmonic impedance of branch l, and let A[n,l] represent the element in the nth row and lth column of the correlation matrix A formed between all nodes and all branches. This represents the h-th harmonic current injected into the SAPF at node m. The impedance represents the h-th harmonic impedance between nodes m and n, and N is the total number of nodes in the distribution network.

[0101] Based on the harmonic currents and harmonic impedances of each branch, determine the harmonic network losses of each branch, including:

[0102]

[0103] In equation (5), This represents the h-th harmonic network loss of branch l. This represents the h-th harmonic impedance of branch l. This represents the h-th harmonic current in branch l. and They are a pair of conjugate complex numbers.

[0104] Finally, the harmonic network losses of each branch are integrated to obtain the harmonic network losses of each branch.

[0105] The method provided in this invention can obtain the total harmonic network loss of the distribution network by determining the harmonic network loss of each branch. Then, a smaller harmonic network loss can be obtained by solving the harmonic network loss suppression model through a multi-objective particle swarm optimization algorithm, thereby reducing the harmonic network loss of the distribution network.

[0106] In conjunction with the above embodiments, in one embodiment, the configuration quantity function of the parallel active power filter includes:

[0107]

[0108]

[0109] In equation (6), This represents the h-th harmonic current injected by the parallel active power filter at node m, where m represents the node, N represents the total number of nodes in the distribution network, H represents the highest order of the harmonic current, and k represents the highest order of the harmonic current. m This indicates the number of parallel active power filters configured at node m. In equation (7), N SAPF k represents the number of parallel active power filters configured. m This indicates the number of parallel active power filters configured at node m.

[0110] Specifically, during the process of solving the harmonic network loss suppression model within the preset solution range using the multi-objective particle swarm optimization algorithm, the positions of the particles in the particle swarm (i.e., the harmonic currents injected by the SAPF to each node) are updated. Therefore, after each update of the position of the particles in the particle swarm, the number of parallel active power filters configured at any node will also be updated, and thus the number of parallel active power filters will also be updated.

[0111] The method provided in this embodiment of the invention can accelerate the optimization of the number of SAPF configurations by determining the magnitude of the harmonic current that needs to be injected into each node by determining the magnitude of the harmonic current, thereby improving the iteration speed of the MOPSO algorithm and thus improving the solution speed of the MOPSO algorithm.

[0112] In conjunction with the above embodiments, in one embodiment, the process of determining the preset solution range includes:

[0113] 501. Determine the minimum output capacity of a parallel active power filter;

[0114] 502. Based on the harmonic currents injected into each node by the parallel active power filter and the minimum output capacity of the parallel active power filter, determine whether the corresponding node needs to be configured with a parallel active power filter. Based on the determination result of each node, determine the preset solution range.

[0115] In step 501 above, the minimum output capacity of the parallel active power filter refers to the minimum output current of the parallel active power filter. This embodiment of the invention does not specifically limit the minimum output capacity of the parallel active power filter, including but not limited to: it can be a set minimum capacity, or it can be a minimum capacity specified during the production of the parallel active power filter.

[0116] In step 502 above, the preset solution range refers to the nodes that need to be configured with parallel active power filters.

[0117] Specifically, before solving the harmonic network loss suppression model within a preset solution range using a multi-objective particle swarm optimization algorithm, the preset solution range is determined based on the harmonic currents of each branch when no parallel active power filter is configured. More specifically, when no parallel active power filter is configured at a certain node, the harmonic currents of that node are equal to the harmonic currents injected by other harmonic sources such as nonlinear loads. Once the harmonic currents injected by harmonic sources at that node are obtained, the node needs to be configured with a corresponding parallel active power filter.

[0118] Furthermore, if the harmonic current required by the parallel active power filter at this node is less than the minimum current capacity of the parallel active power filter output, then it is considered that no parallel active power filter needs to be configured at this node, and the harmonic current injected into the node by the parallel active power filter is set to 0, specifically as shown in equation (8).

[0119] when

[0120] In equation (8), I represents the h-th harmonic current injected into SAPF at node m. SAPF,max This indicates the minimum current capacity output of a parallel active power filter.

[0121] Therefore, each node is judged by equation (8), and the preset solution range is determined based on the judgment result of each node.

[0122] It is worth mentioning that, in the process of solving the harmonic network loss suppression model within the preset solution range using the multi-objective particle swarm optimization algorithm, each particle position update (corresponding to formula (8)) If an update is performed, the preset solution range will also be updated.

[0123] The method provided in this invention compares the SAPF capacity required to be configured through any node with the minimum output capacity of the parallel active power filter during the iterative solution of the harmonic network loss suppression model using a multi-objective particle swarm optimization algorithm. This enables accelerated optimization of the number of SAPFs, thereby accelerating the convergence speed of the multi-objective particle swarm optimization algorithm and improving the solution efficiency.

[0124] In conjunction with the above embodiments, in one embodiment, a method for optimizing the configuration of parallel active power filters in a distribution network further includes:

[0125] 601. Determine the global Pareto optimal solution of the multi-objective particle swarm optimization algorithm based on the target harmonic network loss suppression ratio and the global Pareto optimal solution set;

[0126] 602. Determine the optimal output of the active power filter based on the global Pareto optimal solution of the multi-objective particle swarm optimization algorithm.

[0127] In step 601 above, the target harmonic network loss suppression ratio refers to the requirement for suppressing harmonic network losses in the distribution network, that is, the harmonic power loss in the distribution network should not be greater than the preset harmonic power loss.

[0128] Specifically, after solving the harmonic network loss suppression model within a preset solution range using a multi-objective particle swarm optimization algorithm, a global Pareto optimal solution set for the harmonic network loss suppression model is obtained. This solution set consists of all Pareto optimal fronts of the objective function (the harmonic network loss function of the distribution network minus the number of parallel active power filters). From this global Pareto optimal solution set, the solution that minimizes the number of parallel active power filters and also minimizes harmonic network loss is selected as the optimal solution. In this optimal solution, the number and location of the parallel active power filters are determined, thus allowing the acquisition of the optimal output of the parallel active power filters corresponding to this optimal solution. Here, the output of the parallel active power filters refers to the current input to the distribution network by the parallel active power filters.

[0129] Given that the number of parallel active power filters corresponding to the optimal solution and the location of the nodes where parallel active power filters need to be configured are determined, the output of the parallel active power filters corresponding to the optimal solution is used as the initial output value, and the goal is to minimize the harmonic network loss of the distribution network. The gradient descent method is used to predict the optimal output, thereby obtaining the optimal output corresponding to the optimal solution.

[0130] Finally, based on the fact that the number of SAPFs can be minimized and the optimal output can be obtained by using the multi-objective particle swarm optimization algorithm and gradient descent method, parallel active power filters can be reasonably installed in the distribution network to suppress harmonic network losses in the distribution network with a small number of parallel active power filters.

[0131] The method provided in this invention can obtain a better output through a multi-objective particle swarm optimization algorithm, and use this better output as the initial output for gradient descent iteration, thereby reducing the number of iterations of the multi-objective particle swarm optimization algorithm and improving the speed of the multi-objective particle swarm optimization algorithm.

[0132] In conjunction with the above embodiments, in one embodiment, a method for optimizing the configuration of parallel active power filters in a distribution network is provided, such as... Figure 3 As shown, it includes:

[0133] 701. Obtain the harmonic impedance of each branch of the distribution network, the harmonic mutual impedance between each node, the correlation matrix formed between all nodes and all branches, and the harmonic current of each branch when no parallel active power filter is configured.

[0134] 702. Optimize modeling, including:

[0135] The optimization variables are determined as harmonic network losses in the distribution network and the number of SAPF configurations in the distribution network.

[0136] The objective functions are determined based on the optimization variables, namely the harmonic network loss function and the SAPF configuration quantity function.

[0137] 703. Solving using the MOPSO algorithm, including:

[0138] 7031. Initialization: Set the number of particles in the population, the upper and lower limits of the position and velocity of each particle, and then randomize the initial position and initial velocity of the particles within the limit range; where the position of the particle refers to the harmonic current input by SAPP to each node when configuring SAPP.

[0139] 7032. Calculate the objective function; calculate the objective function for each particle at the current time.

[0140] 7033. Determine the current individual Pareto optimal solution set and the global Pareto optimal solution set. If there exists a particle whose objective function is greater than the target particle, then the target particle does not belong to the Pareto optimal solution set; otherwise, it belongs to the Pareto optimal solution set.

[0141] 7034. Update the particle position and velocity. Randomly select a particle position from the individual Pareto optimal solution set and the population Pareto optimal solution set of the particle, respectively, as the "individual optimal" and "population optimal" of the current particle. Based on the iterative process of the particle swarm algorithm, update the position and velocity of all particles according to the above formulas (1) and (2).

[0142] 7035. Accelerated Optimization of SAPF Quantity: In the MOPSO algorithm, the objective function of each particle includes the harmonic network loss of the distribution network and the number of SAPFs to be configured. The harmonic network loss is a continuous function, while the number of SAPFs is a discrete function. The conventional MOPSO algorithm struggles to directly handle discrete objective functions, resulting in a slow optimization rate for the number of SAPFs. Therefore, an accelerated optimization step for the number of SAPFs is added during the iteration process to increase the speed of SAPF configuration optimization in the MOPSO algorithm. Specifically, during the iteration process, when the required SAPF capacity for node m is ultimately less than the specified minimum SAPF output capacity, the node is considered to not need to configure SAPFs, thus achieving the objective function's requirement of minimizing the number of nodes requiring SAPFs.

[0143] 7036. Termination condition: The iteration terminates when the number of iterations reaches the specified upper limit or the global Pareto optimal solution no longer changes. Otherwise, return to step 7032 above.

[0144] 704. After multiple iterations of the MOPSO algorithm, a set of Pareto optimal solutions is obtained. This set is the Pareto optimal frontier of "harmonic network loss of distribution network when configuring SAPF - number of configured SAPF". In the global Pareto optimal solution set, based on the requirements for the harmonic network loss suppression ratio, the optimal solution obtained by the MOPSO algorithm is determined, that is, the minimum number of SAPF required, the installation node location, and the optimal output.

[0145] 705. Based on the number and location of SAPF installations obtained from the MOPSO algorithm, and with the goal of minimizing harmonic network loss, the optimal output of the SAPF is predicted using the gradient descent method. At the same time, the optimal output obtained from the MOPSO algorithm is used as the initial value for the gradient descent iteration.

[0146] 706. Based on the above results of the number of SAPF configurations and optimal output obtained by the MOPSO algorithm and gradient descent method, SAPFs should be reasonably installed in the distribution network to suppress harmonic network losses in the distribution network with a small number of SAPFs.

[0147] The method provided in this invention, through the MOPSO algorithm and gradient descent method, can obtain the optimized number of SAPFs and the optimal output prediction results, thereby reducing harmonic network losses in the distribution network and reducing the installation cost of SAPFs, thus improving economic efficiency.

[0148] To verify the feasibility and effectiveness of the above-mentioned SAPF configuration optimization and optimal output prediction method in suppressing harmonic network losses in the distribution network, in one embodiment, an IEEE-18 node standard distribution system with 14 distributed nonlinear loads was designed in MATLAB. The SAPF was configured and the optimal output was predicted according to the above method. The harmonic network loss status of the distribution network before and after configuration and the required number of SAPFs were analyzed.

[0149] Figure 4 For the constructed IEEE 18-node system topology, nonlinear loads are configured at nodes 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, and 16. In the simulation, the nonlinear loads are six-pulse rectifier bridges, with the following harmonic current ratios: the 5th harmonic current accounts for 37.60% of the fundamental current, and the 7th harmonic current accounts for 16.77% of the fundamental current. The capacities of all nonlinear loads are within... Figure 5 The information is provided in the text.

[0150] Before the addition of the SAPF configuration number acceleration optimization, the MOPSO algorithm struggled to rapidly reduce the number of configurations, and convergence was difficult even after approximately 3000 iterations. After the addition of the SAPF configuration number acceleration optimization, convergence was achieved in about 1000 iterations. Figures 6-12The figures show the Pareto optimal frontier and objective function of each particle obtained by the MOPSO algorithm at iterations of 1, 150, 300, 450, 600, 750, and 900, respectively. "+" in the figures represents the objective function value of a particle in the population, and "□" represents the Pareto optimal frontier of the current iteration. It can be seen that as the number of iterations increases, the Pareto optimal frontier moves towards reducing harmonic losses and the required number of SAPFs, gradually forming a two-dimensional curve. Ultimately, the optimal configuration scheme obtained by the MOPSO algorithm requires only 3 SAPFs, installed on buses 2, 12, and 14 respectively. Before installing the SAPFs, the harmonic network loss in the distribution network was 16710W; after installing the SAPFs, the harmonic network loss was 3305W, suppressed by approximately 80%.

[0151] Finally, the output of SAPF was further optimized using the gradient descent method, yielding an optimal harmonic network loss of 2803W. The optimal output of SAPF is as follows: Figure 13 As shown:

[0152] The method provided in this embodiment of the invention can achieve approximately 84% harmonic network loss suppression by configuring only 3 SAPF units in a distribution network with 18 nodes (14 nodes connected to nonlinear loads). Therefore, it shows that the method has a significant effect on the suppression of harmonic network losses and the optimization of the number of SAPF units in the distribution network. It is practical, feasible, and has strong engineering application value, and can generate good economic benefits.

[0153] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0154] Based on the same inventive concept, this application also provides a distribution network parallel active power filter configuration optimization device for implementing the above-mentioned distribution network parallel active power filter configuration optimization method. The solution provided by this device is similar to the implementation scheme described in the above method. Therefore, the specific limitations of one or more distribution network parallel active power filter configuration optimization device embodiments provided below can be found in the limitations of the distribution network parallel active power filter configuration optimization method described above, and will not be repeated here.

[0155] In one embodiment, such as Figure 14 As shown, a distribution network parallel active power filter configuration optimization device is provided, including: an acquisition module 1401, a first determination module 1402, and a solution module 1403, wherein:

[0156] The acquisition module 1401 is used to acquire the harmonic impedance of each branch of the distribution network, the harmonic mutual impedance between each node, the correlation matrix formed between all nodes and all branches, and the harmonic current of each branch when no parallel active power filter is configured.

[0157] The first determining module 1402 is used to determine the objective function for configuring parallel active power filters in the distribution network and to serve as a harmonic network loss suppression model. The objective function includes the harmonic network loss function of the distribution network and the configuration quantity function of the parallel active power filters.

[0158] The solution module 1403 is used to solve the harmonic network loss suppression model within a preset solution range using a multi-objective particle swarm optimization algorithm based on the harmonic impedance of each branch, the harmonic mutual impedance between each node, all elements corresponding to each branch in the correlation matrix, and the harmonic current of each branch when no parallel active power filter is configured. The global Pareto optimal solution set of the harmonic network loss suppression model is obtained. The global Pareto optimal solution set includes the alternative configuration schemes of the number of parallel active power filters in the distribution network.

[0159] In one embodiment, the first determining module 1402 includes:

[0160] The acquisition submodule is used to acquire the harmonic currents injected into all nodes by the parallel active power filter.

[0161] The first determining submodule is used to determine the harmonic network loss function of each branch based on the harmonic impedance of each branch, the harmonic mutual impedance between each node, the correlation matrix formed between all nodes and all branches, the harmonic current of each branch when no parallel active power filter is configured, and the harmonic current injected by the parallel active power filter to each node.

[0162] The integration submodule is used to integrate the harmonic network loss functions of all branches to obtain the harmonic network loss function of the distribution network.

[0163] The second determining submodule is used to determine the configuration quantity function of the parallel active power filter based on the harmonic currents injected into all nodes by the parallel active power filter.

[0164] In one embodiment, the first determining submodule includes:

[0165] The first determining unit is used to determine the harmonic current of each branch after configuring a parallel active power filter based on the harmonic impedance of each branch, the harmonic mutual impedance between each node, the correlation matrix formed between all nodes and all branches, the harmonic current of each branch when no parallel active power filter is configured, and the harmonic current injected by the parallel active power filter to each node.

[0166] The second determining unit is used to determine the harmonic network loss of each branch based on the harmonic current and harmonic impedance of each branch.

[0167] In one embodiment, the first determining module 1402 further includes:

[0168] The configuration quantity function of parallel active power filters,

[0169]

[0170]

[0171] In equations (9) and (10), N SAPF This indicates the number of parallel active power filters configured. This represents the h-th harmonic current injected by the parallel active power filter at node m, where m represents the node, N represents the total number of nodes in the distribution network, H represents the highest order of the harmonic current, and k represents the highest order of the harmonic current. m This indicates the number of parallel active power filters configured at node m.

[0172] In one embodiment, the solving module 1403 includes:

[0173] The third determining submodule is used to determine the minimum capacity of the output of the parallel active power filter;

[0174] The fourth determination submodule is used to determine whether a parallel active power filter needs to be configured for a corresponding node based on the harmonic current injected into each node by the parallel active power filter and the minimum output capacity of the parallel active power filter. Based on the determination result of each node, the preset solution range is determined.

[0175] In one embodiment, a distribution network parallel-type active power filter configuration optimization device further includes:

[0176] The second determining module is used to determine the global Pareto optimal solution of the multi-objective particle swarm optimization algorithm based on the target harmonic network loss suppression ratio and the global Pareto optimal solution set;

[0177] The third determination module is used to determine the optimal output of the active power filter based on the global Pareto optimal solution of the multi-objective particle swarm optimization algorithm.

[0178] Each module in the aforementioned power distribution network parallel-type active power filter configuration optimization device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0179] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 15 As shown, the computer device includes a processor, memory, and communication interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a method for optimizing the configuration of parallel active power filters in a power distribution network.

[0180] Those skilled in the art will understand that Figure 15 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0181] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0182] Obtain the harmonic impedance of each branch of the distribution network, the harmonic mutual impedance between each node, the correlation matrix formed between all nodes and all branches, and the harmonic current of each branch when no parallel active power filter is configured.

[0183] The objective function for configuring parallel active power filters in the distribution network is determined and used as a harmonic network loss suppression model. The objective function includes the harmonic network loss function of the distribution network and the configuration quantity function of parallel active power filters.

[0184] Based on the harmonic impedance of each branch, the harmonic mutual impedance between each node, all elements corresponding to each branch in the correlation matrix, and the harmonic current of each branch when no parallel active power filter is configured, the harmonic network loss suppression model is solved by a multi-objective particle swarm optimization algorithm within a preset solution range to obtain the global Pareto optimal solution set of the harmonic network loss suppression model. The global Pareto optimal solution set includes the alternative configuration schemes of the number of parallel active power filters in the distribution network.

[0185] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0186] Obtain the harmonic currents injected into all nodes by the parallel active power filter;

[0187] Based on the harmonic impedance of each branch, the harmonic mutual impedance between each node, the correlation matrix formed between all nodes and all branches, the harmonic current of each branch when no parallel active power filter is configured, and the harmonic current injected into each node by the parallel active power filter, the harmonic network loss function of each branch is determined.

[0188] The harmonic network loss functions of all branches are integrated to obtain the harmonic network loss function of the distribution network.

[0189] The configuration quantity function of the parallel active power filter is determined based on the harmonic currents injected into all nodes by the parallel active power filter.

[0190] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0191] Based on the harmonic impedance of each branch, the harmonic mutual impedance between each node, the correlation matrix formed between all nodes and all branches, the harmonic current of each branch when no parallel active power filter is configured, and the harmonic current injected into each node by the parallel active power filter, determine the harmonic current of each branch after configuring the parallel active power filter.

[0192] The harmonic network loss of each branch is determined based on the harmonic current and harmonic impedance of each branch.

[0193] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0194] The configuration quantity function for running parallel active power filters includes:

[0195]

[0196]

[0197] In equations (11) and (12), N SAPF This indicates the number of parallel active power filters configured. This represents the h-th harmonic current injected by the parallel active power filter at node m, where m represents the node, N represents the total number of nodes in the distribution network, H represents the highest order of the harmonic current, and k represents the highest order of the harmonic current. m This indicates the number of parallel active power filters configured at node m.

[0198] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0199] Determine the minimum output capacity of the parallel active power filter;

[0200] Based on the harmonic currents injected into each node by the parallel active power filter and the minimum output capacity of the parallel active power filter, it is determined whether the corresponding node needs to be configured with a parallel active power filter. Based on the determination result of each node, the preset solution range is determined.

[0201] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0202] Based on the target harmonic network loss suppression ratio and the global Pareto optimal solution set, the global Pareto optimal solution of the multi-objective particle swarm optimization algorithm is determined.

[0203] The optimal output of the active power filter is determined based on the global Pareto optimal solution of the multi-objective particle swarm optimization algorithm.

[0204] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0205] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0206] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0207] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0208] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0209] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A power distribution network shunt active power filter configuration optimization method, characterized by, The method comprises: obtaining each harmonic impedance of each branch of a power distribution network, each harmonic mutual impedance between nodes, an association matrix formed between all nodes and all branches, and each harmonic current of each branch when no shunt active power filter is configured; determining a target function of shunt active power filter configuration of the power distribution network as a harmonic network loss suppression model, the target function comprising a harmonic network loss function of the power distribution network and a configuration number function of the shunt active power filter; solving the harmonic network loss suppression model in a preset solution range by a multi-objective particle swarm optimization algorithm according to each harmonic impedance of each branch, each harmonic mutual impedance between nodes, all elements corresponding to each branch in the association matrix, and each harmonic current of each branch when no shunt active power filter is configured, to obtain a global plat optimal solution set of the harmonic network loss suppression model, the global plat optimal solution set comprising an alternative configuration number scheme of the shunt active power filter in the power distribution network; the determination process of the preset solution range comprises: determining a minimum capacity output by the shunt active power filter; judging whether a corresponding node needs to be configured with the shunt active power filter according to each harmonic current injected by the shunt active power filter into each node and the minimum capacity output by the shunt active power filter, and determining the preset solution range according to a judgment result of each node.

2. The method of claim 1, wherein, the determination of the target function of shunt active power filter configuration of the power distribution network comprises: obtaining each harmonic current injected by the shunt active power filter into all nodes; determining a harmonic network loss function of each branch according to each harmonic impedance of each branch, each harmonic mutual impedance between nodes, an association matrix formed between all nodes and all branches, each harmonic current of each branch when no shunt active power filter is configured, and each harmonic current injected by the shunt active power filter into each node; integrating the harmonic network loss functions of all branches to obtain the harmonic network loss function of the power distribution network; determining the configuration number function of the shunt active power filter according to each harmonic current injected by the shunt active power filter into all nodes.

3. The method of claim 2, wherein, the determination of the harmonic network loss function of each branch according to each harmonic impedance of each branch, each harmonic mutual impedance between nodes, an association matrix formed between all nodes and all branches, each harmonic current of each branch when no shunt active power filter is configured, and each harmonic current injected by the shunt active power filter into each node comprises: determining each harmonic current of each branch after the shunt active power filter is configured according to each harmonic impedance of each branch, each harmonic mutual impedance between nodes, an association matrix formed between all nodes and all branches, each harmonic current of each branch when no shunt active power filter is configured, and each harmonic current injected by the shunt active power filter into each node; determining the harmonic network loss of each branch according to each harmonic current of each branch and each harmonic impedance of each branch.

4. The method of claim 2, wherein, The configuration quantity function of the parallel active power filter comprises: wherein N SAPF represents the number of configurations of the shunt active power filter, represents the hth harmonic current injected by the shunt active power filter at node m, m represents a node, N represents the total number of nodes of the power distribution network, H represents the highest order of the harmonic current, k m represents the number of shunt active power filters configured at node m.

5. The method of claim 1, wherein, The method further comprises: According to the target harmonic network loss suppression ratio and the global Plat optimal solution set, a global Plat optimal solution of a multi-objective particle swarm optimization algorithm is determined. According to the global Plat optimal solution of the multi-objective particle swarm optimization algorithm, an optimal output of the active power filter is determined.

6. An apparatus for optimizing configuration of a shunt active power filter in a power distribution network, characterized by, The device comprises: An acquisition module is configured to acquire harmonic impedances of each branch of a power distribution network, harmonic mutual impedances between nodes, a correlation matrix formed between all nodes and all branches, and harmonic currents of each branch when no parallel active power filter is configured; A first determination module is configured to determine a target function of parallel active power filter configuration of the power distribution network, and use the target function as a harmonic network loss suppression model, wherein the target function comprises a harmonic network loss function of the power distribution network and a configuration quantity function of the parallel active power filter; A solving module is configured to solve the harmonic network loss suppression model in a preset solving range by a multi-objective particle swarm optimization algorithm according to the harmonic impedances of each branch, the harmonic mutual impedances between nodes, all elements corresponding to each branch in the correlation matrix, and the harmonic currents of each branch when no parallel active power filter is configured, to obtain a global Plat optimal solution set of the harmonic network loss suppression model, wherein the global Plat optimal solution set comprises a candidate configuration quantity scheme of the parallel active power filter in the power distribution network. The solving module comprises: A third determination submodule is configured to determine a minimum capacity of the parallel active power filter output; A fourth determination submodule is configured to determine whether a parallel active power filter needs to be configured for a corresponding node according to harmonic currents injected by the parallel active power filter for each node and the minimum capacity of the parallel active power filter output, and determine the preset solving range according to a determination result of each node. 7.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-6 when the computer program is executed by the processor. The processor executes the computer program to implement the steps of the method in any one of claims 1 to 5.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method in any one of claims 1 to 5.

9. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method in any one of claims 1 to 5.

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