An operating optimization method and system for a generator set based on feedback analysis

Abstract

The present invention discloses a method and system for optimizing the operation of a generator set based on feedback analysis, which relates to the technical field of generator sets. The method includes: obtaining a first operating circuit of the generator set, performing real-time fault signal feedback on the first operating circuit to determine a faulty circuit component; filtering and marking the faulty circuit component in the initial operating circuit to obtain the remaining operating circuit after filtering, and performing connected loop reconstruction optimization on the remaining operating circuit after filtering through the Kruskal algorithm to obtain a second operating circuit; performing transient operation on the generator set according to the second operating circuit until the faulty circuit component is repaired, and then restoring to operate the generator set with the first operating circuit. It solves the technical problem in the prior art that the entire circuit needs to be disconnected for maintenance when a fault occurs in the generator set, resulting in a long downtime, and achieves the technical effect of realizing partial continuous operation, reducing the downtime, and improving the operation efficiency through real-time fault feedback and circuit reconstruction optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of generator sets, and particularly to an operation optimization method and system for generator sets based on feedback analysis. Background Art

[0002] With the continuous expansion of the scale of the power system, the operation efficiency and stability of generator sets have become increasingly important in ensuring power supply. A generator set usually consists of multiple circuits and components. Once a fault occurs, traditional handling methods often require disconnecting the entire circuit for repair. This approach not only results in a relatively long downtime of the generator set, affecting the stability of power supply, but also significantly reduces the overall operation efficiency of the system. Especially in large-scale power generation systems, the impact of downtime is more prominent. Summary of the Invention

[0003] The present application provides an operation optimization method and system for generator sets based on feedback analysis, which solves the technical problem in the prior art that when a generator set fails, the entire circuit needs to be disconnected for repair, resulting in a long downtime.

[0004] In the first aspect of the present application, an operation optimization method for generator sets based on feedback analysis is provided. The method includes:

[0005] Obtain the first operating circuit of the generator set, perform real-time fault signal feedback on the first operating circuit to determine the faulty circuit components, where the first operating circuit is the initial operating circuit; filter and mark the faulty circuit components in the initial operating circuit to obtain the remaining operating circuit after filtering, and perform connected loop reconstruction optimization on the remaining operating circuit after filtering through the Kruskal algorithm to obtain the second operating circuit, where the second operating circuit is the reconstructed operating circuit; perform transient operation on the generator set according to the second operating circuit until the faulty circuit components are repaired, and then resume operating the generator set on the first operating circuit.

[0006] In the second aspect of the present application, an operation optimization system for generator sets based on feedback analysis is provided. The system includes:

[0007] A signal feedback module is used to obtain the first operating circuit of the generator set, perform real-time fault signal feedback on the first operating circuit, and determine the faulty circuit components, where the first operating circuit is the initial operating circuit; A reconstruction and optimization module is used to filter out the identification of the faulty circuit components in the initial operating circuit, obtain the remaining operating circuit after filtering, and perform connected loop reconstruction and optimization on the remaining operating circuit after filtering through the Kruskal algorithm to obtain the second operating circuit, where the second operating circuit is the reconstructed operating circuit; A recovery module is used to perform transient operation on the generator set according to the second operating circuit until the faulty circuit components are repaired, and then resume operating the generator set with the first operating circuit.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] First, obtain the first operating circuit of the generator set, perform real-time fault signal feedback on the first operating circuit, and determine the faulty circuit components, where the first operating circuit is the initial operating circuit. Then, filter out the identification of the faulty circuit components in the initial operating circuit, obtain the remaining operating circuit after filtering, and perform connected loop reconstruction and optimization on the remaining operating circuit after filtering through the Kruskal algorithm to obtain the second operating circuit, where the second operating circuit is the reconstructed operating circuit. Finally, perform transient operation on the generator set according to the second operating circuit until the faulty circuit components are repaired, and then resume operating the generator set with the first operating circuit. This solves the technical problem in the prior art that when the generator set fails, the entire circuit needs to be disconnected for maintenance, resulting in a long downtime, and achieves the technical effect of realizing partial continuous operation, reducing downtime, and improving operation efficiency through real-time fault feedback and circuit reconstruction and optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for description in the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0011] Figure 1 It is a schematic flowchart of a method for optimizing the operation of a generator set based on feedback analysis provided by an embodiment of this application;

[0012] Figure 2 It is a schematic structural diagram of a system for optimizing the operation of a generator set based on feedback analysis provided by an embodiment of this application.

[0013] Description of the reference numerals: Signal feedback module 11, Reconstruction and optimization module 12, Recovery module 13. Detailed implementation mode

[0014] By providing an operation optimization method and system for a generator set based on feedback analysis, this application solves the technical problem in the prior art that when a generator set fails, the entire circuit needs to be disconnected for maintenance, resulting in a long downtime.

[0015] Next, the technical solutions in the embodiments of this application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts shall fall within the protection scope of this application.

[0016] It should be noted that the terms "include" and "have" are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products, or devices.

[0017] Embodiment 1, as Figure 1 shown, this application provides an operation optimization method for a generator set based on feedback analysis. Among them, the method includes:

[0018] Obtain the first operating circuit of the generator set, perform real-time fault signal feedback on the first operating circuit, and determine the faulty circuit element, where the first operating circuit is the initial operating circuit.

[0019] In the embodiments of this application, first, the first operating circuit of the generator set is obtained. The first operating circuit is a complete circuit structure currently in a normal working state, that is, the initial operating circuit. This circuit includes various main components (such as transformers, circuit breakers, busbars, capacitors, etc.) inside the generator set and their connection relationships, constituting the basic topological structure for the generator set to support load output under normal operating conditions.

[0020] During the operation of the generator set, the system continuously monitors the first operating circuit in real time, collects operation data including parameters such as current, voltage, temperature, and frequency, and uses sensors installed at key nodes and components to detect fault signals. When a certain component appears in an abnormal state, such as overload, short circuit, insulation breakdown, or abnormal temperature rise, the system will identify the fault state of the component through set fault criteria (such as exceeding the set threshold, severe current fluctuation, or frequent tripping, etc.). By matching the collected real-time signals with the preset fault characteristics, one or more circuit components in the current abnormal state are identified and marked as faulty circuit components.

[0021] Identify and filter out the faulty circuit element in the initial operating circuit to obtain the remaining operating circuit after filtering. Reconstruct and optimize the connected loop of the remaining operating circuit after filtering through the Kruskal algorithm to obtain a second operating circuit, where the second operating circuit is the reconstructed operating circuit.

[0022] After identifying the faulty circuit element, the system identifies and logically isolates the above-mentioned faulty circuit element in the initial operating circuit, that is, removes or sets to an unavailable state the faulty element and its directly connected edges (i.e., connecting wires, cables, etc.) from the circuit topology to generate the remaining operating circuit after filtering. On this basis, the system uses the Kruskal algorithm to reconstruct and optimize the connected loop of the remaining operating circuit after filtering; the Kruskal algorithm is a minimum spanning tree (MST) algorithm based on edge weight sorting, and its basic idea is to start from the edge with the smallest edge weight and sequentially select the edges that do not form a loop and add them to the spanning tree until all nodes are connected.

[0023] Reconstruct and optimize the connected loop of the remaining operating circuit after filtering through the Kruskal algorithm. Specifically, extract all component nodes and their connecting edges in the remaining circuit to construct a circuit diagram model; sort the connecting edges according to preset edge weight parameters (which may include connection current capacity, equivalent resistance, capacitance, etc.); initialize the union-find data structure and set the parent node of each node to itself; sequentially select the connecting edges in ascending order of edge weight. If the two nodes connected by the current edge do not belong to the same set, add the edge to the spanning tree and merge the sets; repeat the above process until a minimum spanning subgraph connecting all normal components is constructed, forming an optimal connected path set that does not include faulty components. Finally, the circuit obtained after reconstruction and optimization is the second operating circuit. Without passing through the faulty component, the second operating circuit maintains the basic connectivity and operating ability of the system circuit and can be used for the subsequent transient operation of the generator set to ensure that the power generation system can maintain partial or degraded operation during the period when the faulty component has not been repaired, significantly improving the fault tolerance and continuous power supply ability of the system.

[0024] Furthermore, before reconstructing and optimizing the connected loop of the remaining operating circuit after filtering through the Kruskal algorithm, the method includes:

[0025] Define node key parameters, where the node key parameters include the load power, reactive power compensation capacity, historical fault frequency, and reference capacity of each component; perform weighted quantization calculation on the key parameters of each node in the remaining operating circuit after filtering through the first dynamic weight formula, output a set of node weight coefficients, and define the node weights of the remaining operating circuit according to the set of node weight coefficients to update the remaining operating circuit.

[0026] Furthermore, the expression of the first dynamic weight formula includes:

[0027] is the node weight coefficient of the component corresponding to the i-th node, is the load power of the component corresponding to the i-th node, is the reactive power compensation capacity of the component corresponding to the i-th node, is the historical failure frequency of the component corresponding to the i-th node, is the base capacity, is the load importance weight of the component, is the reactive power support capacity weight of the component, is the reliability weight of the component.

[0028] Optionally, before performing the connected loop reconstruction optimization on the remaining operating circuit after filtering, by defining the node key parameters and weight quantification calculation, it is ensured that the operating importance and load capacity of each node (i.e., the internal components of the generator set) can be considered during the circuit reconstruction process. The node key parameters refer to the key factors affecting the circuit operation performance and stability, including but not limited to the load power, reactive power compensation capacity, historical failure frequency, and base capacity of each component. Among them, the base capacity usually takes 10 MVA.

[0029] After defining the node key parameters, by using the first dynamic weight formula, the weight quantification calculation of the key parameters of each node in the remaining operating circuit after filtering is performed. In the first dynamic weight formula, is the load power borne by the i-th node component under normal operation, and this parameter reflects the contribution degree of the component to the system load; is the reactive power compensation ability provided by the i-th node component, indicating the contribution of the component to the voltage stability in the power grid; is the failure frequency of the i-th node component in the past operation, reflecting the reliability of the component and its sensitivity to failure events; is the base capacity, which is used as a reference value for measuring the load capacity of the component. After calculating the weight coefficient of each node according to the first dynamic weight formula, the system will output the set of node weight coefficients, that is, the set composed of the weight coefficients of all nodes. The set of node weight coefficients contains the relative importance of all nodes in the circuit and is used for subsequent node weight definition of the remaining operating circuit. The updated remaining operating circuit will weight each node according to the weight coefficient of the node to form a new circuit structure.

[0030] Furthermore, before performing the connected loop reconstruction optimization on the remaining operating circuit after filtering by using the Kruskal algorithm, the method further includes:

[0031] Define the edge key parameters, which include the rated current and equivalent capacitance of the connection circuit between adjacent components; through the second dynamic weight formula, perform weight quantification calculation on each edge in the remaining operating circuit after filtering, output the edge weight coefficient set, and define the edge weights of the remaining operating circuit according to the edge weight coefficient set to update the remaining operating circuit.

[0032] Furthermore, the expression of the second dynamic weight formula includes:

[0033] is the edge weight coefficient of the connection circuit corresponding to adjacent nodes u and v, is the rated current of the connection circuit corresponding to adjacent nodes u and v, is the equivalent capacitance of the connection circuit corresponding to adjacent nodes u and v.

[0034] Optionally, before performing the connected loop reconstruction optimization on the remaining operating circuit after filtering, define the edge key parameters and perform weight quantification calculation to ensure that the electrical characteristics between each connected component are considered during the circuit reconstruction process. The edge key parameters refer to the key indicators describing the performance of the connection circuit between two adjacent components in the circuit, including but not limited to the rated current and equivalent capacitance of the connection circuit between adjacent components.

[0035] After defining the key parameters of the edge, use the second dynamic weight formula to perform weight quantification calculation on the key parameters of each edge in the remaining operating circuit after filtering. In the second dynamic weight formula, the rated current is the rated current of the connection circuit between adjacent nodes u and v, which reflects the maximum current load that this connection path can withstand under normal operating conditions; the equivalent capacitance is the equivalent capacitance of the connection circuit between adjacent nodes u and v, which reflects the energy storage capacity of this path and the response ability to voltage fluctuations. The second dynamic weight formula reflects the comprehensive electrical performance of the connection edges in the circuit. The larger the rated current and equivalent capacitance, the smaller the edge weight coefficient, indicating that the importance of this edge is relatively low during the reconstruction process, and vice versa, indicating that this edge has a higher importance.

[0036] After calculating the weight coefficient of each edge through the second dynamic weight formula, the system will output the edge weight coefficient set, that is, the set composed of the weight coefficients of all edges. The edge weight coefficient set contains the relative importance of all connection paths in the circuit, which is used to define the edge weights of the remaining operating circuit subsequently. The updated remaining operating circuit will weight each node and edge according to the weight coefficients of the nodes and edges to form a new circuit structure, ensuring that the reconstructed circuit retains those connection paths that are more important in terms of electrical performance while maintaining connectivity.

[0037] Furthermore, updating the remaining operating circuit according to the set of node weight coefficients and the set of edge weight coefficients, the method includes:

[0038] Establish a mapping relationship between the set of node weight coefficients and the set of edge weight coefficients, and output a set of node-edge weight coefficient groups; identify the node difference weight coefficients of each node-edge weight coefficient group, and calculate according to the corresponding edge weight coefficients within the node difference weight coefficient group to obtain the updated remaining operating circuit.

[0039] The system establishes a mapping relationship between the set of node weight coefficients and the set of edge weight coefficients according to the set of node weight coefficients and the set of edge weight coefficients. Specifically, the process of establishing the mapping relationship is to analyze each node in the circuit and the edges connected to it, and clarify the corresponding relationship between the weight coefficient corresponding to each node and the weight coefficients of each edge connected to the node. Based on this mapping relationship, the system outputs a set of node-edge weight coefficient groups, that is, the set of weight coefficients of each node and its adjacent edges. This set contains the weight relationships between all nodes and their adjacent edges, providing detailed information for subsequent circuit optimization.

[0040] After the set of node-edge weight coefficient groups is output, the system further identifies the node difference weight coefficients in each node-edge weight coefficient group. The node difference weight coefficient refers to evaluating the importance difference of the node in the circuit by comparing the weight differences between the same node and its different adjacent edges. Specifically, the node difference weight coefficient reflects the change in the relative importance of a certain node in different connection paths, usually caused by the weight coefficient differences of each connected edge.

[0041] After identifying the node difference weight coefficients, the system further calculates the weight coefficients of the edges connected to each node through these coefficients. The weight coefficients of the edges are calculated according to the influence of the node difference weight coefficients on each connection edge to ensure that the edges with larger weights in the circuit are preferentially retained. Specifically, the calculation process of the edge weight coefficients considers the importance differences between nodes and the electrical characteristics of each edge to ensure that the edges connecting key nodes are preferentially selected in circuit optimization.

[0042] According to the updated node weight coefficients and edge weight coefficients, the system performs weighted definition of nodes and edges on the remaining operating circuit, and updates the circuit topology accordingly, that is, rearranges the connection relationships in the circuit according to the weight information of nodes and edges, and filters or adjusts unimportant nodes and edges, so as to ensure that the optimized structure of the circuit can maintain the necessary connectivity and improve the operating efficiency and stability of the circuit to the greatest extent.

[0043] Furthermore, the Kruskal algorithm is used to reconstruct and optimize the connected loop of the remaining operating circuit after filtering to obtain the second operating circuit. The method includes:

[0044] The Kruskal algorithm creates a union-find data structure according to the updated remaining operating circuit; initializes the union-find data structure, where each node in the union-find data structure is used as a parent node; the updated remaining operating circuit is sorted in descending order of edges according to the union-find data structure to obtain an edge sorting result, and edge connections are made according to the edge sorting result to obtain a second operating circuit.

[0045] During the application of the Kruskal algorithm, a union-find data structure is created according to the updated remaining operating circuit. The union-find is an efficient data structure that can be used to manage the connectivity between various nodes in a circuit. In the union-find data structure, each node is initially an independent set; through the union-find data structure, it is possible to efficiently determine whether two nodes are in the same connected component and to merge different connected components into a larger connected component.

[0046] After creating the union-find data structure, the system initializes each node as its own parent node, that is, each node is first regarded as an independent set. By recording the parent node of each node, the union-find data structure can quickly check whether two nodes belong to the same connected component.

[0047] After initializing the union-find data structure, the Kruskal algorithm sorts the edges in the updated remaining operating circuit. Specifically, the system sorts all edges in descending order according to their weights (i.e., the weight coefficients of the edges), and gives priority to edges with larger weights. After obtaining the edge sorting result, the system sequentially performs connection operations on the edges sorted in descending order; for each edge, the Kruskal algorithm checks whether the two nodes it connects belong to the same connected component; if they do not belong to the same connected component, the edge is added to the spanning tree, and the two nodes are merged into the same connected component through the union-find. In this way, during the process of adding edges, the system gradually constructs a circuit diagram containing all connectable nodes. After the edge connection operation, the finally obtained circuit structure is the second operating circuit. The second operating circuit maintains the connectivity of the system and optimizes the circuit connection path according to the weight information of the nodes and edges. While ensuring the integrity of the circuit, the second operating circuit preferentially retains those nodes and edges with higher weights, further improving the operating efficiency and stability of the generator set under fault conditions.

[0048] Furthermore, when making edge connections according to the edge sorting result to obtain a second operating circuit, the method further includes:

[0049] Connect the edges according to the sorted edge results to obtain an initial reconstructed operating circuit; perform connectivity verification on the initial reconstructed operating circuit. When the connectivity verification passes, output the initial reconstructed operating circuit as the second operating circuit, where the connectivity verification includes the coverage rate of critical nodes and the MST connectivity of the initial reconstructed operating circuit.

[0050] Furthermore, the critical nodes are the nodes in the node weight coefficient set whose node weight coefficients are greater than the preset weight coefficient.

[0051] According to the Kruskal algorithm, sort the edges of the remaining operating circuit in descending order and connect them one by one. The system obtains an initial reconstructed operating circuit, which consists of all connection paths. After the edge sorting and merging operations of the Kruskal algorithm, a circuit topology containing all nodes and connection edges is constructed.

[0052] After obtaining the initial reconstructed operating circuit, the system performs connectivity verification on this circuit to ensure that all nodes in the circuit can remain connected and there are no isolated nodes or parts that cannot be reached through other nodes. Among them, the connectivity verification includes the coverage rate of critical nodes and the MST (Minimum Spanning Tree) connectivity of the initial reconstructed operating circuit.

[0053] Specifically, critical nodes refer to the nodes whose node weight coefficients are greater than the preset weight coefficient. These nodes are of higher importance in the system operation and usually carry a larger load or reactive power compensation capacity. The system ensures that all critical nodes can be retained and effectively connected in the reconstructed circuit by checking whether the initial reconstructed operating circuit contains all critical nodes.

[0054] Specifically, verify whether the initial reconstructed operating circuit satisfies the properties of the minimum spanning tree, that is, all nodes can form a connected network through the minimum number of connection edges. The MST connectivity ensures the efficiency and stability of the circuit while ensuring the minimum number of connection edges. The system further ensures the connectivity of the circuit by checking whether the initial reconstructed circuit conforms to the definition of the MST.

[0055] When the connectivity verification passes, the system outputs the initial reconstructed operating circuit as the second operating circuit. At this time, the second operating circuit has passed the connectivity verification, ensuring the stability, operation efficiency, and coverage of critical nodes of the circuit.

[0056] Perform transient operation on the generator set according to the second operating circuit until the faulty circuit element is repaired, and then resume operating the generator set with the first operating circuit.

[0057] After the faulty components are filtered out and the remaining operating circuit is reconstructed and optimized using the Kruskal algorithm, the second operating circuit is used as the temporary operating circuit of the generator set. The system conducts transient operation of the generator set according to the second operating circuit, that is, on the premise of ensuring the basic operating ability of the system and power supply to the load, it continues to support the operation of the generator set. The transient operation mode is mainly to ensure that the generator set can maintain partial operation during the repair of faulty circuit components, reduce the downtime, and avoid additional losses caused by power supply interruption.

[0058] During the transient operation, the system continuously monitors the repair progress of the faulty circuit components to ensure that the repair work is completed as planned. The system obtains the signal indicating the completion of component repair in a timely manner through the fault recovery module according to the repair status of the faulty components; once the faulty circuit components are repaired, the system is ready to restore the circuit to its original operating state.

[0059] When the repair of the faulty circuit components is completed, the system replaces the part that temporarily replaces the faulty components in the second operating circuit back to the original first operating circuit. Specifically, the system reconnects all the components in the first operating circuit, restores the initial topological structure of the circuit, and gradually transitions to the fully restored operating state. During this process, the system ensures that there will be no interruption of power supply or load fluctuation during the restoration process, and ensures a smooth transition of the generator set to the normal operating state.

[0060] In summary, the embodiments of the present application have at least the following technical effects:

[0061] First, obtain the first operating circuit of the generator set, conduct real-time fault signal feedback on the first operating circuit, and determine the faulty circuit components. Here, the first operating circuit is the initial operating circuit. Then, filter out and identify the faulty circuit components in the initial operating circuit, obtain the remaining operating circuit after filtering, and conduct connected loop reconstruction and optimization on the remaining operating circuit after filtering using the Kruskal algorithm to obtain the second operating circuit. Here, the second operating circuit is the reconstructed operating circuit. Finally, conduct transient operation of the generator set according to the second operating circuit until the repair of the faulty circuit components is completed, and restore the generator set to operate with the first operating circuit. This solves the technical problem in the prior art that the entire circuit needs to be disconnected for repair when the generator set fails, resulting in a long downtime, and achieves the technical effect of realizing partial continuous operation, reducing the downtime, and improving the operating efficiency through real-time fault feedback and circuit reconstruction and optimization.

[0062] Embodiment 2, based on the same inventive concept as the method for optimizing the operation of a generator set based on feedback analysis in the foregoing embodiment, as Figure 2 shown, the present application provides a system for optimizing the operation of a generator set based on feedback analysis, wherein the system includes:

[0063] The signal feedback module 11 is used to obtain the first operating circuit of the generator set, perform real-time fault signal feedback on the first operating circuit, and determine the faulty circuit components, where the first operating circuit is the initial operating circuit; the reconstruction and optimization module 12 is used to filter out the faulty circuit components in the initial operating circuit, obtain the remaining operating circuit after filtering, and perform connected loop reconstruction and optimization on the remaining operating circuit after filtering through the Kruskal algorithm to obtain the second operating circuit, where the second operating circuit is the reconstructed operating circuit; the recovery module 13 is used to perform transient operation on the generator set according to the second operating circuit until the faulty circuit components are repaired, and then resume operating the generator set on the first operating circuit.

[0064] Further, the reconstruction and optimization module 12 is used to execute the following method:

[0065] Define the node key parameters, where the node key parameters include the load power, reactive power compensation capacity, historical fault frequency, and reference capacity of each component; through the first dynamic weight formula, perform weighted quantification calculation on the key parameters of each node in the remaining operating circuit after filtering, output the set of node weight coefficients, and define the node weights of the remaining operating circuit according to the set of node weight coefficients to update the remaining operating circuit.

[0066] Further, the reconstruction and optimization module 12 is used to execute the following method:

[0067] The expression of the first dynamic weight formula includes:

[0068] is the node weight coefficient corresponding to the component of the i-th node, is the load power of the component corresponding to the i-th node, is the reactive power compensation capacity of the component corresponding to the i-th node, is the historical fault frequency of the component corresponding to the i-th node, is the reference capacity, is the weight of the component load importance, is the weight of the component reactive power support ability, is the weight of the component reliability.

[0069] Further, the reconstruction and optimization module 12 is used to execute the following method:

[0070] Define the edge key parameters, where the edge key parameters include the rated current and equivalent capacitance of the connecting circuit between adjacent components; through the second dynamic weight formula, perform weighted quantification calculation on each edge in the remaining operating circuit after filtering, output the set of edge weight coefficients, and define the edge weights of the remaining operating circuit according to the set of edge weight coefficients to update the remaining operating circuit.

[0071] Further, the reconstruction and optimization module 12 is used to execute the following method:

[0072] The expression of the second dynamic weight formula includes:

[0073] is the edge weight coefficient of the connection circuit corresponding to adjacent nodes u and v, is the rated current of the connection circuit corresponding to adjacent nodes u and v, is the equivalent capacitance of the connection circuit corresponding to adjacent nodes u and v.

[0074] Further, the reconstruction and optimization module 12 is used to execute the following method:

[0075] Establish a mapping relationship between the node weight coefficient set and the edge weight coefficient set, and output a set of node-edge weight coefficient groups; identify the node difference weight coefficient of each node-edge weight coefficient group, and calculate according to the corresponding edge weight coefficient in the node difference weight coefficient group to obtain the updated remaining operating circuit.

[0076] Further, the reconstruction and optimization module 12 is used to execute the following method:

[0077] The Kruskal algorithm creates and initializes a disjoint-set structure based on the updated remaining operating circuit; initializes the disjoint-set structure, with each node in the disjoint-set structure as the parent node; the updated remaining operating circuit is sorted in descending order of edges according to the disjoint-set structure to obtain an edge sorting result, and edge connections are made according to the edge sorting result to obtain a second operating circuit.

[0078] Further, the reconstruction and optimization module 12 is used to execute the following method:

[0079] Make edge connections according to the edge sorting result to obtain an initial reconstructed operating circuit; perform connectivity verification on the initial reconstructed operating circuit. When the connectivity verification passes, output the initial reconstructed operating circuit as the second operating circuit, where the connectivity verification includes the coverage rate of key nodes and the MST connectivity of the initial reconstructed operating circuit.

[0080] Further, the reconstruction and optimization module 12 is used to execute the following method:

[0081] The key node is a node in the node weight coefficient set whose node weight coefficient is greater than a preset weight coefficient.

[0082] It should be noted that the above-mentioned order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above description of specific embodiments of this specification has been made. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0083] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0084] This specification and the drawings are only exemplary descriptions of the present application and are considered to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. An operating optimization method for a generator set based on feedback analysis, characterized in that, The method includes: Obtain the first operating circuit of the generator set, perform real-time fault signal feedback on the first operating circuit, and determine the faulty circuit components, where the first operating circuit is the initial operating circuit; Filter and identify the faulty circuit components in the initial operating circuit, obtain the remaining operating circuit after filtering, and perform connected loop reconstruction optimization on the remaining operating circuit after filtering through the Kruskal algorithm to obtain the second operating circuit, where the second operating circuit is the reconstructed operating circuit; Perform transient operation on the generator set according to the second operating circuit until the faulty circuit components are repaired, and then resume operating the generator set with the first operating circuit; Perform connected loop reconstruction optimization on the remaining operating circuit after filtering through the Kruskal algorithm to obtain the second operating circuit. The method includes: The Kruskal algorithm creates a union-find structure based on the updated remaining operating circuit; Initialize the union-find structure to use each node in the union-find structure as the parent node; The updated remaining operating circuit is sorted in descending order of edges according to the union-find structure to obtain an edge sorting result, and edges are connected according to the edge sorting result to obtain the second operating circuit; Edges are connected according to the edge sorting result to obtain the second operating circuit. The method further includes: Edges are connected according to the edge sorting result to obtain an initial reconstructed operating circuit; Verify the connectivity of the initial reconstructed operating circuit. When the connectivity verification passes, output the initial reconstructed operating circuit as the second operating circuit, where the connectivity verification includes the coverage rate of key nodes and the MST connectivity of the initial reconstructed operating circuit.

2. The operation optimization method of a generator set based on feedback analysis according to claim 1, characterized in that Before performing connected loop reconstruction optimization on the remaining operating circuit after filtering through the Kruskal algorithm, the method includes: Define node key parameters, where the node key parameters include the load power, reactive power compensation capacity, historical fault frequency, and reference capacity of each component; Perform weighted quantization calculation on the key parameters of each node in the remaining operating circuit after filtering through the first dynamic weight formula, output a set of node weight coefficients, and define node weights for the remaining operating circuit according to the set of node weight coefficients to update the remaining operating circuit.

3. The operation optimization method for a generator set based on feedback analysis according to claim 2, characterized in that, The expression of the first dynamic weight formula includes: ； Among them, is the node weight coefficient of the component corresponding to the i-th node, is the load power of the component corresponding to the i-th node, is the reactive power compensation capacity of the component corresponding to the i-th node, is the historical failure frequency of the component corresponding to the i-th node, is the base capacity, is the weight of component load importance, is the weight of component reactive power support ability, is the weight of component reliability.

4. The operation optimization method of a generator set based on feedback analysis according to claim 2, wherein, Before performing connected loop reconstruction optimization on the remaining operating circuit after filtering through the Kruskal algorithm, the method further includes: Define edge key parameters, where the edge key parameters include the rated current and equivalent capacitance of the connecting circuit between adjacent components; Perform weighted quantization calculation on each edge in the remaining operating circuit after filtering through the second dynamic weight formula, output a set of edge weight coefficients, and define edge weights for the remaining operating circuit according to the set of edge weight coefficients to update the remaining operating circuit.

5. The operation optimization method of a generator set based on feedback analysis according to claim 4, wherein, The expression of the second dynamic weight formula includes: ； Among them, is the edge weight coefficient of the connection circuit corresponding to adjacent nodes u and v, is the rated current of the connection circuit corresponding to adjacent nodes u and v, is the equivalent capacitance of the connection circuit corresponding to adjacent nodes u and v.

6. The operation optimization method of a generator set based on feedback analysis according to claim 4, characterized in that Update the remaining operating circuit according to the set of node weight coefficients and the set of edge weight coefficients. The method includes: Establish a mapping relationship between the set of node weight coefficients and the set of edge weight coefficients, and output a set of node-edge weight coefficient groups; Identify the node difference weight coefficients of each node-edge weight coefficient group, and calculate according to the corresponding edge weight coefficients within the node difference weight coefficient group to obtain the updated remaining operating circuit.

7. The operation optimization method of a generator set based on feedback analysis according to claim 6, characterized in that The key nodes are the nodes in the node weight coefficient set whose node weight coefficients are greater than the preset weight coefficient.

8. An operating optimization system for a generator set based on feedback analysis, characterized in that For implementing a generator set operation optimization method according to any one of claims 1-7, the system includes: A signal feedback module, configured to obtain a first operating circuit of the generator set, perform real-time fault signal feedback on the first operating circuit, and determine a faulty circuit component, where the first operating circuit is an initial operating circuit; A reconstruction optimization module, configured to identify and filter out the faulty circuit component in the initial operating circuit, obtain the remaining operating circuit after filtering, and perform connected loop reconstruction optimization on the remaining operating circuit after filtering through the Kruskal algorithm to obtain a second operating circuit, where the second operating circuit is the reconstructed operating circuit; A recovery module, configured to perform transient operation on the generator set according to the second operating circuit until the faulty circuit component is repaired, and then resume operating the generator set on the first operating circuit.

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