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CN-121981544-A - Visual data monitoring method and system for generator group

CN121981544ACN 121981544 ACN121981544 ACN 121981544ACN-121981544-A

Abstract

The invention is suitable for the technical field of power generation monitoring, and provides a visualized data monitoring method and a visualized data monitoring system for a generator group, wherein the visualized data monitoring method and the visualized data monitoring system comprise the following steps of collecting operation data of all generators in the generator group, wherein the operation data comprise electrical parameters, mechanical vibration parameters, temperature parameters, equipment space position and topological connection relation data; the method comprises the steps of calculating a conduction intensity index between any two generators based on operation data, constructing a conduction relation matrix, determining potential conduction paths of faults, mapping each generator into a visual node, generating connection graphic elements representing the conduction intensity and the direction and a field effect graphic representing overall risk distribution based on the conduction relation matrix and the potential conduction paths, and generating a grading treatment plan according to the potential conduction paths and the conduction intensity index. By quantifying the hidden electric, mechanical and thermal coupling relations into visual conduction intensity and graphic elements, the potential propagation path of the fault is clear at a glance, and the state perception is more convenient.

Inventors

  • ZHOU YUAN
  • XIE DONG

Assignees

  • 湖北睿麟伟创动力科技有限公司

Dates

Publication Date
20260505
Application Date
20260123

Claims (10)

  1. 1. A method for visual data monitoring of a group of power generators, the method comprising the steps of: Collecting operation data of all generators in the cluster, wherein the operation data comprise electrical parameters, mechanical vibration parameters, temperature parameters, equipment space positions and topological connection relation data; Calculating a conduction intensity index between any two generators based on the operation data, constructing a conduction relation matrix representing the internal association intensity of the whole group according to the conduction intensity index, and determining potential conduction paths of faults; mapping each generator into a visual node, generating connection graphic elements representing the conduction intensity and the direction and a field effect graph representing the overall risk distribution based on the conduction relation matrix and the potential conduction path, and forming a three-dimensional association network situation map; from the potential conduction path and the conduction intensity index, a hierarchical treatment plan is generated, different treatment plans should be for different risk types.
  2. 2. The method for visual data monitoring of a group of power generators of claim 1, wherein the step of calculating a conduction intensity index between any two power generators comprises: Determining an electrical coupling index based on the equivalent electrical impedance between the generators, the electrical coupling index being used to characterize the inherent tightness of the electrical connection; determining a mechanical correlation index based on the time sequence correlation of the real-time vibration signals and the physical distance between the devices, wherein the mechanical correlation index is used for representing the real-time intensity of mechanical vibration energy transmission; Determining a thermodynamic gradient index based on the real-time temperature difference and the physical distance between the devices, wherein the thermodynamic gradient index is used for representing the real-time potential of heat conduction; And carrying out weighted summation on the electric coupling index, the mechanical association index and the thermodynamic gradient index to obtain a conduction intensity index.
  3. 3. The method for visual data monitoring of a group of power generators of claim 1, wherein the step of constructing a conductivity relationship matrix from the conductivity intensity indices, determining potential conductivity paths for the fault, comprises: Constructing an N multiplied by N matrix M as a conduction relation matrix, wherein the group comprises N generators, and the value of matrix element M (i, j) is the conduction intensity index of generator i to generator j; When any generator k is monitored to have abnormal parameters, k is taken as a conduction source node, potential conduction path searching is carried out, namely, in a matrix M, all generators M meeting M (k, M) > theta are screened, theta is taken as a conduction activation threshold value, the generator M is marked as a first-stage risk node directly influenced by the generator k, each first-stage risk node M is taken as a new conduction source, a next-stage risk node n is searched, and the like until a new node meeting the condition cannot be found, so that a multi-stage linkage potential conduction path set starting from the source node k is generated; for each path P in the set of potential conductive paths, calculating an overall conductive strength S (P) for the path, and ordering all potential conductive paths in descending order according to S (P); counting the frequency of each generator passing through and the frequency of each connecting edge in all potential conduction paths, and identifying the generator with the highest passing frequency and the connecting edge as a key fragile link in the group network.
  4. 4. The method for monitoring visual data of a group of power generators of claim 3, wherein the step of generating connection graphic elements and field effect graphics to form a three-dimensional associated network situation map comprises: Traversing the conduction relation matrix M, and creating a dynamic connecting line between a generator node i and a node j for all M (i, j) > theta elements; Color is given to each dynamic connecting line according to the conduction dominant factors, and the visual thickness of the dynamic connecting line is set according to the value of M (i, j); Calculating a local risk value V for each generator i, wherein V is a comprehensive score of each operating parameter deviating from a normal range; Generating a continuous risk potential energy field covering the whole group area by using all generator nodes as discrete points and based on the V value of each node and the association strength determined by M (i, j) among the nodes through a spatial interpolation algorithm; Rendering the risk potential energy field into a semitransparent color equipotential surface, representing a high risk area by using a warm tone, and representing a low risk area by using a cold tone; and superposing and rendering the generator nodes, the dynamic connecting lines and the risk equipotential surfaces in the same scene to form a three-dimensional associated network situation map.
  5. 5. The method for monitoring visual data of a group of power generators of claim 4, wherein a particle animation flowing in the i to j direction is superimposed on each of the dynamic connection lines, the particle flow velocity being positively correlated with the value of (M (i, j) - θ).
  6. 6. The method for visual data monitoring of a group of power generators of claim 3, wherein the step of generating a hierarchical treatment plan comprises: Determining risk types according to the potential conduction path set, wherein the risk types are isolated risks, local conduction risks and cascade conduction risks; Determining a treatment plan for each risk type, marking an enhanced monitoring identifier on a corresponding device node for isolated risks, highlighting source devices and primary risk devices which recommend load adjustment in a network situation map for local conduction risks, and blocking and identifying key fragile links in the network situation map for cascade conduction risks.
  7. 7. A visual data monitoring system for a group of power generators, the system comprising: The operation data acquisition module is used for acquiring operation data of all the generators in the cluster, wherein the operation data comprise electrical parameters, mechanical vibration parameters, temperature parameters, equipment space position and topological connection relation data; the conduction relation determining module is used for calculating a conduction intensity index between any two generators based on the operation data, constructing a conduction relation matrix representing the internal association intensity of the whole group according to the conduction intensity index, and determining potential conduction paths of faults; the network situation map module is used for mapping each generator into a visual node, generating connection graphic elements representing the conduction intensity and the direction and field effect graphs representing the overall risk distribution based on the conduction relation matrix and the potential conduction path, and forming a three-dimensional association network situation map; A treatment plan generation module for generating a hierarchical treatment plan from the potential conduction path and the conduction intensity index, different treatment plans being for different risk types.
  8. 8. The visual data monitoring system for a group of power generators of claim 7, wherein the conductivity relationship determination module comprises: An electrical coupling index unit for determining an electrical coupling index based on an equivalent electrical impedance between the generators, the electrical coupling index being used to characterize an inherent tightness of the electrical connection; The mechanical association index unit is used for determining a mechanical association index based on time sequence correlation of the real-time vibration signals and physical distance between devices, and the mechanical association index is used for representing real-time intensity of mechanical vibration energy transmission; the thermodynamic gradient index unit is used for determining a thermodynamic gradient index based on the real-time temperature difference and the physical distance between the devices, and the thermodynamic gradient index is used for representing the real-time potential of heat conduction; and the conduction intensity index unit is used for carrying out weighted summation on the electric coupling index, the mechanical association index and the thermal gradient index to obtain a conduction intensity index.
  9. 9. The visual data monitoring system for a group of power generators of claim 7, wherein the conductivity relationship determination module further comprises: A matrix construction unit for constructing an n×n matrix M as a conduction relation matrix, the group comprising N generators, the matrix element M (i, j) having a value of the conduction intensity index of generator i to generator j; the system comprises a conduction path searching unit, a next-stage risk node n, a multi-stage linkage potential conduction path set, a transmission path searching unit and a transmission path searching unit, wherein the conduction path searching unit is used for taking k as a conduction source node when any generator k is monitored to be abnormal, screening all generators M meeting M (k, M) > theta in a matrix M, taking theta as a conduction activation threshold value, and marking the generators M as primary risk nodes directly influenced by the generators k; a path descending order sorting unit, configured to calculate, for each path P in the set of potential conductive paths, an overall conductive strength S (P) of the path, and sort all the potential conductive paths in descending order according to S (P); And the critical fragile link unit is used for counting the passed frequency of each generator and the used frequency of each connecting edge in all potential conduction paths, and identifying the generator and the connecting edge with the highest passed frequency as the critical fragile link in the group network.
  10. 10. The visual data monitoring system for a group of power generators of claim 9, wherein the network situational map module comprises: the dynamic connecting line unit is used for traversing the conduction relation matrix M, and for all M (i, j) > theta elements, a dynamic connecting line is established between the generator node i and the node j; A color thickness determining unit for giving color to each dynamic connection line according to the conduction dominant factor, and setting visual thickness of the dynamic connection line according to the value of M (i, j); The local risk value unit is used for calculating a local risk value V for each generator i, wherein V is a comprehensive score of each operation parameter deviating from a normal range; The risk potential energy field unit is used for generating a continuous risk potential energy field covering the whole group area through a spatial interpolation algorithm based on the V value of each node and the association strength determined by M (i, j) among the nodes by taking all generator nodes as discrete points; a tone determining unit for rendering the risk potential energy field into a semitransparent color equipotential surface, representing a high risk area with a warm tone and a low risk area with a cold tone; And the superposition rendering unit is used for performing superposition rendering on the generator nodes, the dynamic connecting lines and the risk equipotential surfaces in the same scene to form a three-dimensional associated network situation map.

Description

Visual data monitoring method and system for generator group Technical Field The invention relates to the technical field of power generation monitoring, in particular to a visual data monitoring method and a visual data monitoring system for a generator group. Background Large power plants typically consist of multiple generator sets in a co-operating group. The real-time and effective state monitoring and fault early warning of the generator groups are key to guaranteeing safe and stable operation of the power grid and improving operation and maintenance efficiency. The traditional generator group monitoring system mainly adopts a visual mode of an independent instrument panel or a two-dimensional overview chart, namely key operation parameters of each generator are displayed in parallel on a large screen of a control center. When a certain device parameter exceeds the limit, the system triggers an independent alarm of the device. However, this conventional approach has significant limitations in that it treats each generator as an island of information, failing to intuitively reveal the complex physical and electrical coupling relationships between the devices within the group. The generators are closely related by a common grid bus, a shared infrastructure, and similar electromagnetic or thermal environments. When one generator fails, the influence of the generator can be quickly transmitted to adjacent or even farther units through the coupling paths, so that chain reaction is initiated, and local or even whole-plant power failure can be caused when serious. The prior art can not predict and visualize the dynamic fault conduction process, so that operation and maintenance personnel can not easily and timely know the root cause of the fault. Accordingly, there is a need to provide a method and system for visual data monitoring for a group of power generators, which aims to solve the above-mentioned problems. Disclosure of Invention Aiming at the defects existing in the prior art, the invention aims to provide a visual data monitoring method and a visual data monitoring system for a generator group, so as to solve the problems existing in the background art. The invention is realized in that a method for visual data monitoring of a group of power generators, the method comprising the steps of: Collecting operation data of all generators in the cluster, wherein the operation data comprise electrical parameters, mechanical vibration parameters, temperature parameters, equipment space positions and topological connection relation data; Calculating a conduction intensity index between any two generators based on the operation data, constructing a conduction relation matrix representing the internal association intensity of the whole group according to the conduction intensity index, and determining potential conduction paths of faults; mapping each generator into a visual node, generating connection graphic elements representing the conduction intensity and the direction and a field effect graph representing the overall risk distribution based on the conduction relation matrix and the potential conduction path, and forming a three-dimensional association network situation map; from the potential conduction path and the conduction intensity index, a hierarchical treatment plan is generated, different treatment plans should be for different risk types. As a further aspect of the present invention, the step of calculating the conduction intensity index between any two generators specifically includes: Determining an electrical coupling index based on the equivalent electrical impedance between the generators, the electrical coupling index being used to characterize the inherent tightness of the electrical connection; determining a mechanical correlation index based on the time sequence correlation of the real-time vibration signals and the physical distance between the devices, wherein the mechanical correlation index is used for representing the real-time intensity of mechanical vibration energy transmission; Determining a thermodynamic gradient index based on the real-time temperature difference and the physical distance between the devices, wherein the thermodynamic gradient index is used for representing the real-time potential of heat conduction; And carrying out weighted summation on the electric coupling index, the mechanical association index and the thermodynamic gradient index to obtain a conduction intensity index. As a further aspect of the present invention, the step of constructing a conductive relation matrix according to the conductive intensity index, and determining a potential conductive path of the fault specifically includes: Constructing an N multiplied by N matrix M as a conduction relation matrix, wherein the group comprises N generators, and the value of matrix element M (i, j) is the conduction intensity index of generator i to generator j; When any generator k is monitored to have abn