Distributed active filtering system improved by filtering algorithm

Abstract

The invention discloses a distributed active filtering system improved by adopting a filtering algorithm, which relates to the technical field of edge computing, and comprises the following steps: acquiring harmonic impedance information of an access point to cooperatively generate a real-time harmonic impedance distribution diagram reflecting the harmonic voltage sensitivity of the whole network; constructing a local revenue function of each node according to the harmonic impedance distribution diagram and the local state information; on the basis of the revenue function, limited information interaction and iterative gaming are carried out between adjacent nodes; and executing an optimal compensation strategy determined by the game, and performing feedback correction on the harmonic impedance distribution diagram. According to the method, the real-time harmonic impedance distribution diagram is generated cooperatively, so that each node has global perception capability; a comprehensive optimal decision is realized by constructing a multi-target revenue function fusing a local effect, a capacity utilization rate and a system contribution degree; and online correction is carried out on the model through actual effect feedback, the adaptability of long-term operation is ensured, and the contradiction between local optimization and a global target is fundamentally solved.

Description

A distributed active filtering system with improved filtering algorithm Technical Field

[0001] This invention relates to the field of power quality optimization technology, and in particular to a distributed active filter system using an improved filtering algorithm. Background Technology

[0002] With the widespread integration of power electronic equipment, nonlinear loads, and distributed energy resources, harmonic pollution in distribution networks and microgrids is becoming increasingly prominent. Harmonics not only cause overheating, reduced efficiency, and shortened lifespan of electrical equipment, but may also trigger systemic risks such as protection malfunctions and communication interference. Active power filters (APFs), as an effective means of harmonic mitigation, have evolved from centralized, large-capacity devices to distributed, modular, small-capacity devices to more flexibly and reliably address dispersed harmonic sources. Traditional distributed active power filter control strategies are mostly based on independent compensation using local detection information. To improve coordination, existing technologies have developed centralized optimization methods based on a central coordinator. The method of collecting information from the entire network and solving the optimization model, and issuing instructions to each APF, can achieve global optimization. However, it relies on high-speed and reliable central communication and powerful computing capabilities, which leads to problems such as single-point failure risk, heavy communication burden, and poor scalability. It is difficult to adapt to distributed application scenarios with variable topologies and plug-and-play functionality. In recent years, distributed optimization algorithms have been introduced to overcome the shortcomings of centralized control. However, in the context of harmonic mitigation, simple consistency control does not fully consider the differences in compensation capabilities of each node, the unevenness of system impedance distribution, and the mutual sensitivity of harmonic voltages between nodes. It is difficult to maximize the overall harmonic suppression benefits of the system while ensuring the local compensation effect.

[0003] However, the common solutions currently available have many drawbacks, including: existing distributed active filtering technologies are mainly based on centralized control architectures, which have the risk of single point of failure and poor scalability; fully distributed independent control is prone to suboptimal compensation, harmonic resonance, or uneven equipment utilization due to the lack of global coordination; collaborative solutions are often accompanied by high communication and computing overhead; and most methods are based on fixed models, which cannot adapt to the time-varying power grid parameters and have insufficient robustness in long-term operation. Summary of the Invention

[0004] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0005] In view of the problems existing in the current distributed active filtering system with improved filtering algorithm, the present invention is proposed.

[0006] Therefore, the purpose of this invention is to provide a distributed active filter system with an improved filtering algorithm, which is suitable for solving the problems of existing distributed active filter technologies, which are mainly characterized by centralized control architecture with single-point failure risk and poor scalability; fully distributed independent control, due to lack of global coordination, is prone to suboptimal compensation, harmonic resonance or uneven equipment utilization; cooperative schemes are often accompanied by high communication and computing overhead; and most methods are based on fixed models, which cannot adapt to the time-varying power grid parameters and have insufficient robustness in long-term operation.

[0007] To address the aforementioned technical problems, the present invention provides the following technical solution: Firstly, embodiments of the present invention provide a distributed active filtering method employing an improved filtering algorithm. This method includes acquiring harmonic impedance information of its access points and collaboratively generating a real-time harmonic impedance distribution map reflecting the harmonic voltage sensitivity of the entire network through inter-node communication; constructing a local revenue function for each node based on the harmonic impedance distribution map and its local state; performing limited information interaction and iterative game theory among adjacent nodes based on the revenue function, dynamically converging to the optimal harmonic compensation output strategy for each node; executing the optimal compensation strategy determined through the game theory, and performing feedback correction on the harmonic impedance distribution map based on the deviation between the actual compensation effect and the expected effect.

[0008] As a preferred embodiment of the distributed active filtering method improved by the filtering algorithm described in this invention, the local revenue function is related to the following factors: the harmonic compensation effect of this node, the remaining capacity utilization rate of this node, and the contribution of the compensation behavior of this node to the improvement of the harmonic voltage distortion of the whole network, calculated based on the harmonic impedance distribution diagram; the local status information includes: the current local harmonic current, the local harmonic voltage, and the remaining available compensation capacity of the distributed active filtering device.

[0009] As a preferred embodiment of the distributed active filtering method using an improved filtering algorithm described in this invention, the following steps are taken: First, the harmonic impedance information of the access point is obtained, and a real-time harmonic impedance distribution map reflecting the harmonic voltage sensitivity of the entire network is generated collaboratively through inter-node communication. Specifically, this involves: real-time measurement of the harmonic voltage and harmonic current at the access point; calculation of the local harmonic impedance information at the node based on the measurement data using a preset estimation algorithm; transmission of the calculated local harmonic impedance information, node identifier, and timestamp information to adjacent nodes via an inter-node communication network, and reception of corresponding information from adjacent nodes; based on the local and received harmonic impedance information from adjacent nodes, a distributed collaborative estimation algorithm is used to iteratively update and ultimately achieve a consistent estimate of the equivalent harmonic impedance model or harmonic voltage sensitivity matrix for the entire network, thereby generating the real-time harmonic impedance distribution map; the generated harmonic impedance distribution map is represented in the form of a matrix, a weighted topology diagram, or a data table, where its elements or connection weights are used to quantify the impact of any node injecting a unit harmonic current on the harmonic voltage of all nodes in the network, i.e., the harmonic voltage sensitivity.

[0010] As a preferred embodiment of the distributed active filtering method improved by the filtering algorithm described in this invention, the local revenue function of each node is constructed based on the harmonic impedance distribution map and local state information. Specifically, the local state information of the node is obtained; revenue functions are calculated based on the real-time harmonic impedance distribution map and the local state information; the calculated revenue items are weighted and combined with the preset or adaptively adjusted weight coefficients of the node to generate the final local revenue function value; the constructed local revenue function is used to quantitatively evaluate and compare the advantages and disadvantages of different compensation output strategies during the iterative game process.

[0011] As a preferred embodiment of the distributed active filtering method improved by the filtering algorithm described in this invention, the following steps are taken: Based on the payoff function, limited information interaction and iterative game are conducted between adjacent nodes to dynamically converge to the optimal harmonic compensation output strategy of each node. Specifically, each node generates an initial harmonic compensation output strategy as its first proposal in the game; in each iteration, each node sends its current strategy proposal only to its adjacent nodes and receives strategy proposals from all adjacent nodes; based on the received adjacent node proposals, its own local payoff function, and the system coupling relationship reflected by the harmonic impedance distribution diagram, each node independently calculates and generates a new compensation processing strategy that can improve its own payoff as its next round proposal; when the change in the strategy proposals of all nodes in consecutive iterations is less than a preset threshold, or when the preset maximum number of iterations is reached, the iteration terminates; at this time, the final proposal held by each node is the dynamically converged optimal harmonic compensation processing strategy.

[0012] As a preferred embodiment of the distributed active filtering method improved by the filtering algorithm described in this invention, the following steps are taken: The optimal compensation strategy determined through game theory is executed, and the harmonic impedance distribution map is corrected based on the deviation between the actual compensation effect and the expected effect. Specifically, each node executes the optimal harmonic compensation output strategy and monitors and records the actual harmonic voltage and harmonic current of the local access point in real time after execution; based on the actual monitoring data and the harmonic improvement effect predicted based on the harmonic impedance distribution map before execution, the deviation between the actual compensation effect and the expected effect is calculated; based on the deviation, the relevant parameters or weights in the harmonic impedance distribution map are locally updated and corrected using a preset correction algorithm to reduce the model prediction error of the harmonic impedance distribution map; the corrected harmonic impedance distribution map is used as the input for the next round of collaborative generation and game decision-making to achieve online adaptation and continuous optimization of the system model.

[0013] As a preferred embodiment of the distributed active filtering method improved by the filtering algorithm described in this invention, the correction algorithm for locally updating and correcting the harmonic impedance distribution map through a preset correction algorithm is specifically an online parameter estimation algorithm based on the least squares method or a state update algorithm based on Kalman filtering.

[0014] Secondly, to further address the aforementioned technical problems, this invention provides a distributed active filter system employing an improved filtering algorithm. The system includes: an information acquisition module for acquiring harmonic impedance information of its access points and collaboratively generating a real-time harmonic impedance distribution map reflecting the overall network's harmonic voltage sensitivity through inter-node communication; a function construction module for constructing a local revenue function for each node based on the harmonic impedance distribution map and its local state; an information interaction module for performing limited information interaction and iterative game theory among adjacent nodes based on the revenue function, dynamically converging to the optimal harmonic compensation output strategy for each node; and a feedback correction module for executing the optimal compensation strategy determined through game theory and performing feedback correction on the harmonic impedance distribution map based on the deviation between the actual compensation effect and the expected effect.

[0015] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the computer program, when executed by the processor, implements any step of the distributed active filtering method using an improved filtering algorithm as described in the first aspect of the present invention.

[0016] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of a distributed active filtering method using an improved filtering algorithm as described in the first aspect of the present invention.

[0017] The beneficial effects of this invention are as follows: By collaboratively generating a real-time harmonic impedance distribution map, this invention enables each node to have global awareness; by constructing a multi-objective benefit function that integrates local effects, capacity utilization, and system contribution, it achieves comprehensive optimal decision-making; by utilizing an iterative game mechanism that exchanges strategy proposals only with adjacent nodes, it dynamically converges to a globally coordinated compensation strategy in a distributed manner, combining optimization effect with system robustness; and by using actual effect feedback to perform online correction of the model, it ensures long-term adaptability, ultimately forming a complete, self-learning, plug-and-play distributed harmonic governance scheme, fundamentally solving the contradiction between local optimization and global objectives. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Among them: Figure 1 is a flowchart of the implementation of the present invention in Embodiment 1.

[0019] Figure 2 is a dynamic adjustment diagram of the present invention in Example 1. Detailed Implementation

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0023] Referring to Figures 1 and 2, Example 1 is the first embodiment of the present invention. This embodiment provides a distributed active filtering method with an improved filtering algorithm, including the following steps: S1: Obtain the harmonic impedance information of its access point, and through inter-node communication, collaboratively generate a real-time harmonic impedance distribution map reflecting the harmonic voltage sensitivity of the entire network.

[0024] Preferably, the harmonic impedance information of the access point is obtained, and a real-time harmonic impedance distribution map reflecting the harmonic voltage sensitivity of the entire network is generated collaboratively through inter-node communication. Specifically, the harmonic voltage and harmonic current at the access point are measured in real time, and based on the measurement data, the local harmonic impedance information at that node is calculated using a preset estimation algorithm. The specific formula is as follows: In the formula, Local harmonic impedance represents the complex ratio of voltage to current at a specific harmonic frequency at the node connection point. The harmonic voltage instantaneous value sequence refers to the discrete data of the harmonic voltage measured at the node as a function of time after removing the fundamental component. It is obtained through the node's local voltage sensor and signal conditioning circuit. The harmonic current instantaneous value sequence refers to the discrete data of the harmonic current (or the harmonic current generated by the load) measured at the node as a function of time after removing the fundamental frequency component. It is obtained through the node's local current sensor and signal conditioning circuit.

[0025] Through the inter-node communication network, the calculated local harmonic impedance information, node identifier and timestamp information are sent to neighboring nodes, and corresponding information is received from neighboring nodes.

[0026] Based on local and received harmonic impedance information from neighboring nodes, a distributed collaborative estimation algorithm is used to iteratively update and ultimately achieve a consistent estimate of the equivalent harmonic impedance model or harmonic voltage sensitivity matrix for the entire network, thereby generating a real-time harmonic impedance distribution map. The specific formula is as follows: In the formula, For nodes In the number of iterations The estimated value of the harmonic impedance distribution diagram of the entire network held at that time; For nodes In the number of iterations The estimated value of the harmonic impedance distribution map of the entire network held at that time is the current state of each node's global perception. Iteratively, it continuously approaches the real and consistent global distribution map. , The consistency weighting coefficient determines the speed and stability of information fusion; For nodes The set of adjacent nodes defines the scope of information interaction for each node and is the foundation for realizing "limited information interaction" and "distributed" computing. This is the index for the number of iterations.

[0027] The generated harmonic impedance distribution map is represented in the form of a matrix, a weighted topology map, or a data table. Its elements or connection weights are used to quantify the degree of influence of any node injecting a unit harmonic current on the harmonic voltage of all nodes in the network, i.e., harmonic voltage sensitivity.

[0028] Preferably, a distributed consensus algorithm is used to enable each node to collaboratively construct and update an impedance distribution map reflecting the harmonic voltage sensitivity of the entire network in real time based on limited information exchange with neighboring nodes. This mechanism gives each local node a global perspective, enabling it to quantify the impact of its own compensation behavior on the propagation of the network voltage. This lays an accurate model foundation for resolving the conflict between local and global optimization objectives. At the same time, its fully distributed architecture eliminates the dependence on the central node, has low communication overhead, and has excellent topology adaptability and scalability.

[0029] For example, in an industrial park power distribution network containing multiple frequency converters and precision manufacturing equipment, each distributed active filter node first estimates the harmonic impedance of its respective access point in real time through local measurement and FFT analysis. Subsequently, the nodes communicate with each other via power line carrier, exchanging this local impedance information only with electrically adjacent nodes. After iteration based on the consensus algorithm, all nodes collaboratively generate a harmonic impedance distribution map (Z_map) of the entire network. This map clearly reveals that when node #3 (connected to a large stamping machine) at the end of the power distribution network injects harmonic current, it will significantly raise the harmonic voltage of node #8 (connected to a precision measuring instrument) in the middle of the power distribution trunk line through the network impedance, quantifying the voltage sensitivity relationship between them.

[0030] S2: Construct the local revenue function for each node based on the harmonic impedance distribution diagram and local state information.

[0031] Preferably, the local revenue function is related to the following factors: the harmonic compensation effect of this node, the remaining capacity utilization rate of this node, and the contribution of the compensation behavior of this node to the improvement of harmonic voltage distortion of the whole network, which is calculated based on the harmonic impedance distribution map.

[0032] Specifically, the local status information includes: the current local harmonic current, the local harmonic voltage, and the remaining available compensation capacity of the distributed active filter.

[0033] Furthermore, based on the harmonic impedance distribution diagram and local state information, a local revenue function for each node is constructed, specifically as follows: Obtain the local state information of the node.

[0034] Based on the real-time harmonic impedance distribution map and local state information, at least two of the following sub-terms constituting the revenue function are calculated: Local compensation effect sub-term: Based on the current proposed output compensation current, calculate its predicted cancellation rate for local harmonic current or predicted reduction rate for local harmonic voltage, as shown in the following formula: In the formula, To evaluate the effectiveness of local compensation, the suppression effect of the compensation strategy on local harmonics is quantified. The larger the value (the less negative), the better the local compensation effect. The compensation current command to be output is the harmonic current that the node controller plans to inject into the power grid. For the detected local harmonic current; The detected local harmonic voltage; , Reference current and reference voltage, used as reference values ​​for normalization; , This represents the weighting coefficient for the local compensation effect.

[0035] Capacity utilization sub-item: Calculates the ratio of the current planned output compensation current amplitude to the remaining available compensation capacity of the node. The details are as follows: In the formula, For capacity utilization evaluation; The amplitude of the proposed output compensation current; Remaining available compensation capacity refers to the maximum harmonic compensation current capability that the active filter can safely output under the current state; This is the weighting coefficient for capacity utilization.

[0036] System contribution sub-item: Based on the harmonic voltage sensitivity information of the entire network provided by the harmonic impedance distribution diagram, calculate the weighted sum of the estimated improvement of the harmonic voltage level of all other nodes in the entire network by the compensation current currently proposed to be output by this node. The specific content is as follows: In the formula, Evaluation of system contribution; In the harmonic impedance distribution diagram, from node To the node Transmission impedance; For nodes Voltage quality weighting; The system contribution weighting coefficient; This is the reference voltage.

[0037] The calculated revenue items are weighted and combined with the preset or adaptively adjusted weight coefficients of the node to generate the final local revenue function value, as detailed below: In the formula, The total revenue of the node; For the evaluation of local compensation effectiveness; For capacity utilization evaluation; Evaluation of system contribution.

[0038] The constructed local payoff function is used to quantitatively evaluate and compare the merits of different compensation output strategies during iterative game development.

[0039] Preferably, this step innovatively constructs a local benefit function that integrates multiple dimensions of objectives. Through mathematical modeling, it unifies three potentially conflicting objectives—local harmonic suppression effect, device capacity utilization rate, and contribution to the overall network voltage quality—into a comprehensive evaluation index with consistent dimensions and weighted adjustment. This function serves as the core of node value judgment, successfully decomposing the complex system-level multi-objective optimization problem into a local optimization problem that each node can solve autonomously, providing a clear and quantifiable optimization guide for achieving distributed collaborative decision-making.

[0040] For example, in the same industrial park network mentioned above, the active filter located at node #8 constructs its benefit function based on the obtained Z_map and local state. In the function, the local compensation effect sub-item (f_local) has a high weight to ensure strict protection of local precision instruments; the capacity utilization sub-item (f_capacity) encourages the equipment to make full use of its capacity within safe limits; and the system contribution sub-item (f_global) calculates the expected voltage improvement of its compensation behavior on the entire network (especially other nodes that are greatly affected by node #3) based on the Z_map. By setting adjustable weights, this function enables node #8 to realize its potential value in suppressing harmonic propagation across the entire network while pursuing the purification of its own bus.

[0041] S3: Based on the payoff function, limited information interaction and iterative game are conducted between adjacent nodes to dynamically converge to the optimal harmonic compensation processing strategy of each node.

[0042] Preferably, based on the payoff function, limited information interaction and iterative game are conducted between adjacent nodes to dynamically converge to the optimal harmonic compensation processing strategy of each node. The specific content is as follows: Each node generates an initial harmonic compensation processing strategy as the first proposal for participating in the game.

[0043] In each iteration, each node sends its current policy proposal only to its neighboring nodes and receives policy proposals from all neighboring nodes.

[0044] Each node independently calculates and generates a new compensation strategy that can improve its own revenue based on the proposals received from neighboring nodes, its own local revenue function, and the system coupling relationship reflected by the harmonic impedance distribution map, as a proposal for the next round.

[0045] When the change in the strategy proposals of all nodes is less than the preset threshold or the preset maximum number of iterations is reached, the iteration terminates; at this time, the final proposal held by each node is the dynamically converged optimal harmonic compensation processing strategy.

[0046] Preferably, the harmonic compensation problem is modeled as a distributed non-cooperative game process, where each node only exchanges strategy proposals with its neighbors and independently and iteratively optimizes its own strategy based on the system coupling relationship reflected by the payoff function. This mechanism enables the system to spontaneously converge to a Nash equilibrium state, in which no node can benefit by unilaterally changing its strategy. Thus, under completely decentralized conditions, a coordinated compensation effect that approaches the global optimum emerges. This method combines optimization efficiency and system robustness, and naturally supports plug-and-play and self-organization.

[0047] For example, based on the above payoff function, all filtering nodes in the park (including #3 and #8) begin distributed game theory. Node #3's initial proposal is to fully compensate for the harmonics of the local press, while node #8's initial proposal is to optimize only the local voltage. Through multiple rounds of iterative exchange of strategy proposals with only adjacent nodes, node #8 calculates that if it appropriately adjusts the phase and amplitude of its own compensation current, it can significantly offset the harmonic voltage propagation from node #3 (significantly increasing f_global) through the coupling path revealed by Z_map with very small f_local loss, thereby increasing the total payoff. Finally, all node strategies converge to an equilibrium, forming a global optimization pattern in which node #3 focuses on source suppression and node #8 takes into account both local and collaborative compensation.

[0048] S4: Execute the optimal compensation strategy determined by the game and perform feedback correction on the harmonic impedance distribution diagram based on the deviation between the actual compensation effect and the expected effect.

[0049] Preferably, the optimal compensation strategy determined by game theory is executed, and the harmonic impedance distribution diagram is corrected by feedback based on the deviation between the actual compensation effect and the expected effect. The specific content is as follows: each node executes the optimal harmonic compensation output strategy, and monitors and records the actual harmonic voltage and harmonic current of the local access point in real time after execution.

[0050] Based on actual monitoring data and the harmonic improvement effect predicted based on the harmonic impedance distribution diagram before implementation, the deviation between the actual compensation effect and the expected effect is calculated.

[0051] Based on the deviation, the relevant parameters or weights in the harmonic impedance distribution map are locally updated and corrected using a preset correction algorithm to reduce the model prediction error of the harmonic impedance distribution map.

[0052] The corrected harmonic impedance distribution map is used as the input for the next round of collaborative generation and game decision-making, realizing online adaptation and continuous optimization of the system model.

[0053] Specifically, the correction algorithm that locally updates and corrects the harmonic impedance distribution map through a preset correction algorithm is either an online parameter estimation algorithm based on the least squares method or a state update algorithm based on Kalman filtering.

[0054] Preferably, a closed-loop learning mechanism is introduced. By comparing the actual effect of the compensation strategy with the prediction effect based on the impedance distribution map, the impedance distribution map is corrected online using deviation-driven methods (such as using RLS or Kalman filtering algorithms). This enables the system to continuously track the time-varying characteristics of the power grid impedance, such as changes caused by topology changes and load switching, thereby effectively combating model mismatch and ensuring the long-term accuracy of the aforementioned perception and decision-making links. This adaptive learning capability significantly improves the system's robustness and continuous optimization performance in dynamic and uncertain environments.

[0055] For example, after the above system has been running for a period of time, a production line in the park is upgraded and a new set of nonlinear loads is switched on, which causes a change in the actual network harmonic impedance. After executing the original game strategy, each node monitors and finds that the actual harmonic voltage deviates systematically from the prediction based on the original Z_map. The system then triggers online correction based on the recursive least squares method, uses the deviation data to update the relevant impedance parameters in the Z_map, and the updated Z_map more accurately reflects the new network coupling relationship and is immediately used for the next round of game decision-making. This allows the system to quickly adapt without manual intervention after changes in the power grid topology and load characteristics, and restore the optimal collaborative compensation performance.

[0056] In summary, this invention enables each node to have global awareness by collaboratively generating a real-time harmonic impedance distribution map; it achieves comprehensive optimal decision-making by constructing a multi-objective benefit function that integrates local effects, capacity utilization, and system contribution; it dynamically converges to a globally coordinated compensation strategy in a distributed manner using an iterative game mechanism that exchanges strategy proposals only with adjacent nodes, achieving both optimization effectiveness and system robustness; and it ensures long-term adaptability by online calibration of the model through actual effect feedback. Ultimately, this invention forms a complete, self-learning, plug-and-play distributed harmonic governance scheme that fundamentally solves the contradiction between local optimization and global objectives.

[0057] Example 2, an embodiment of the present invention, provides a distributed active filter system with an improved filtering algorithm, comprising: an information acquisition module for acquiring harmonic impedance information of its access points and collaboratively generating a real-time harmonic impedance distribution map reflecting the harmonic voltage sensitivity of the entire network through inter-node communication; a function construction module for constructing a local revenue function for each node based on the harmonic impedance distribution map and local state; an information interaction module for performing limited information interaction and iterative game between adjacent nodes based on the revenue function, dynamically converging to the optimal harmonic compensation output strategy of each node; and a feedback correction module for executing the optimal compensation strategy determined by the game and performing feedback correction on the harmonic impedance distribution map based on the deviation between the actual compensation effect and the expected effect.

[0058] Example 3 is an embodiment of the present invention, which differs from the previous embodiment in that: if the function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0059] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0060] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0061] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0062] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A distributed active filtering method employing an improved filtering algorithm, characterized in that: include: The harmonic impedance information of its access point is obtained, and a real-time harmonic impedance distribution map reflecting the harmonic voltage sensitivity of the entire network is generated collaboratively through inter-node communication; based on the harmonic impedance distribution map and local status information, a local revenue function for each node is constructed. Based on the aforementioned payoff function, limited information interaction and iterative game are conducted between adjacent nodes to dynamically converge to the optimal harmonic compensation strategy for each node; the optimal compensation strategy determined by the game is executed, and the harmonic impedance distribution map is corrected by feedback based on the deviation between the actual compensation effect and the expected effect.

2. The distributed active filtering method using an improved filtering algorithm as described in claim 1, characterized in that: The local revenue function is related to the following factors: the harmonic compensation effect of this node, the remaining capacity utilization rate of this node, and the contribution of the compensation behavior of this node to the improvement of harmonic voltage distortion of the whole network, calculated based on the harmonic impedance distribution diagram. The local status information includes: the current local harmonic current, the local harmonic voltage, and the remaining available compensation capacity of the distributed active filter.

3. The distributed active filtering method using an improved filtering algorithm as described in claim 1, characterized in that: The process of acquiring harmonic impedance information at the access point and collaboratively generating a real-time harmonic impedance distribution map reflecting the harmonic voltage sensitivity of the entire network through inter-node communication is as follows: Real-time measurement of harmonic voltage and harmonic current at the access point, and calculation of local harmonic impedance information at the node based on the measurement data using a preset estimation algorithm; sending the calculated local harmonic impedance information, node identifier, and timestamp information to adjacent nodes through the inter-node communication network, and receiving corresponding information from adjacent nodes. Based on the local and received harmonic impedance information of neighboring nodes, a distributed collaborative estimation algorithm is used to iteratively update and finally achieve a consistent estimate of the equivalent harmonic impedance model or harmonic voltage sensitivity matrix of the entire network, thereby generating the real-time harmonic impedance distribution map. The generated harmonic impedance distribution map is represented in the form of a matrix, a weighted topology map, or a data table. Its elements or connection weights are used to quantify the degree of influence of any node injecting a unit harmonic current on the harmonic voltage of all nodes in the entire network, i.e., the harmonic voltage sensitivity.

4. The distributed active filtering method using an improved filtering algorithm as described in claim 1, characterized in that: Based on the harmonic impedance distribution map and local state information, a local revenue function for each node is constructed, specifically as follows: The local state information of the node is obtained; based on the real-time harmonic impedance distribution map and the local state information, revenue functions are calculated respectively; the calculated revenue items are weighted and combined with the node's preset or adaptively adjusted weight coefficients to generate the final local revenue function value; the constructed local revenue function is used to quantitatively evaluate and compare the advantages and disadvantages of different compensation output strategies during the iterative game process.

5. The distributed active filtering method using an improved filtering algorithm as described in claim 1, characterized in that: Based on the aforementioned payoff function, limited information interaction and iterative game are conducted among adjacent nodes to dynamically converge to the optimal harmonic compensation output strategy for each node. Specifically: Each node generates an initial harmonic compensation strategy as its first proposal in the game; in each iteration, each node sends its current strategy proposal only to its adjacent nodes and receives strategy proposals from all adjacent nodes; based on the received adjacent node proposals, its own local payoff function, and the system coupling relationship reflected in the harmonic impedance distribution diagram, each node independently calculates and generates a new compensation strategy that improves its own payoff as its next proposal; when the change in the strategy proposals of all nodes between consecutive iterations is less than a preset threshold, or when the preset maximum number of iterations is reached, the iteration terminates; at this point, the final proposal held by each node is the dynamically converged optimal harmonic compensation strategy.

6. The distributed active filtering method using an improved filtering algorithm as described in claim 1, characterized in that: The process involves executing the optimal compensation strategy determined through game theory and performing feedback correction on the harmonic impedance distribution map based on the deviation between the actual compensation effect and the expected effect. Specifically, each node executes the optimal harmonic compensation output strategy and monitors and records the actual harmonic voltage and current at its local access point in real time after execution. Based on the actual monitoring data and the harmonic improvement effect predicted based on the harmonic impedance distribution map before execution, the deviation between the actual compensation effect and the expected effect is calculated. According to the deviation, a preset correction algorithm is used to locally update and correct the relevant parameters or weights in the harmonic impedance distribution map to reduce the model prediction error of the harmonic impedance distribution map. The corrected harmonic impedance distribution map is used as the input for the next round of collaborative generation and game decision-making, achieving online adaptation and continuous optimization of the system model.

7. A distributed active filtering method using an improved filtering algorithm as described in claim 6, characterized in that: The correction algorithm that locally updates and corrects the harmonic impedance distribution map using a preset correction algorithm is specifically an online parameter estimation algorithm based on the least squares method or a state update algorithm based on Kalman filtering.

8. A distributed active filtering system with an improved filtering algorithm, based on the distributed active filtering method with an improved filtering algorithm as described in any one of claims 1 to 7, characterized in that: This includes an information acquisition module, used to acquire harmonic impedance information of its access point and collaboratively generate a real-time harmonic impedance distribution map reflecting the harmonic voltage sensitivity of the entire network through inter-node communication; a function construction module, used to construct a local revenue function for each node based on the harmonic impedance distribution map and local status; and an information interaction module, used to conduct limited information interaction and iterative game between adjacent nodes based on the revenue function, dynamically converging to the optimal harmonic compensation processing strategy of each node. The feedback correction module is used to execute the optimal compensation strategy determined through game theory and to perform feedback correction on the harmonic impedance distribution diagram based on the deviation between the actual compensation effect and the expected effect.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the distributed active filtering method with improved filtering algorithm as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the distributed active filtering method with improved filtering algorithm as described in any one of claims 1 to 7.

Images