CN-121984213-A - Power grid equipment state degradation simulation and maintenance optimization method based on digital twin

CN121984213ACN 121984213 ACN121984213 ACN 121984213ACN-121984213-A

Abstract

The invention discloses a digital twin-based power grid equipment state degradation simulation and maintenance optimization method, which comprises the steps of constructing a digital twin model, establishing a real-time data interaction link, collecting multidimensional degradation influence parameters, simulating a degradation process through a dynamic collaborative quantization algorithm integrating a coupling relation of the multidimensional degradation influence parameters and a time accumulation effect, determining maintenance optimization priority through a priority calculation model by combining resource constraint parameters, and outputting a scheduling instruction. The method solves the problems of inaccurate degradation simulation and unreasonable maintenance decision in the prior art, and realizes accurate simulation and maintenance optimization of the state of the power grid equipment.

Inventors

TIAN JING
FENG BINJIE
Xu Caishen
LI SHENGWEI

Assignees

广州劲源科技发展股份有限公司

Dates

Publication Date: 20260505
Application Date: 20251231

Claims (10)

1. The digital twinning-based power grid equipment state degradation simulation and maintenance optimization method is characterized by comprising the following steps of: s1, constructing a digital twin model of power grid equipment, establishing a real-time data interaction link with physical power grid equipment, and synchronizing operation basic parameters of the physical power grid equipment; s2, acquiring a multi-dimensional degradation influence parameter which influences the state degradation of the physical power grid equipment, wherein the multi-dimensional degradation influence parameter is acquired based on real-time mapping data of the digital twin model and sensing monitoring data of the physical power grid equipment; S3, simulating a state degradation process of physical power grid equipment based on the multi-dimensional degradation influence parameters and the digital twin model by a dynamic collaborative quantization algorithm fusing the coupling relation of the multi-dimensional degradation influence parameters and a time accumulation effect to obtain a degradation state quantization result; and S4, combining the degradation state quantification result and the power grid maintenance resource constraint parameter, determining the maintenance optimization priority of the physical power grid equipment through a priority calculation model, and outputting a maintenance optimization scheduling instruction.
2. The digital twinning-based power grid equipment state degradation simulation and maintenance optimization method according to claim 1, wherein the digital twinning model in S1 comprises a geometric simulation model, a physical characteristic model and an operation behavior model, the geometric simulation model is used for re-etching three-dimensional structural parameters of the physical power grid equipment, the physical characteristic model is matched with material properties and electrical characteristic parameters of the physical power grid equipment, the operation behavior model is used for simulating start-stop states and load change rules of the physical power grid equipment, and the real-time data interaction link is used for realizing data transmission through an industrial Ethernet interface and an edge computing gateway.
3. The digital twin-based power grid equipment state degradation simulation and maintenance optimization method according to claim 1, wherein the multi-dimensional degradation influencing parameters in S2 comprise an electric field intensity parameter, an environment temperature parameter, a harmonic distortion rate parameter, a vibration acceleration parameter, a partial discharge pulse number parameter, a running time parameter and a load factor parameter, and the sensing monitoring data are acquired by an optical fiber sensor, a temperature sensor, a current sensor and a vibration sensor which are installed on the physical power grid equipment.
4. The digital twin-based power grid equipment state degradation simulation and maintenance optimization method according to claim 1, wherein the dynamic collaborative quantization algorithm in S3 establishes a degradation factor quantization model based on time-series data of the multi-dimensional degradation influence parameters, and the degradation state quantization result is calculated by the degradation factor quantization model.
5. The digital twin-based power grid equipment state degradation simulation and maintenance optimization method according to claim 4, wherein the degradation factor quantification model achieves comprehensive quantification of multi-dimensional degradation influence parameters through electric field-temperature coupling action, harmonic-load coupling action, operation time accumulation action and multi-factor cooperative amplification action, wherein the electric field-temperature coupling action reflects combined influence of actual electric field intensity and environment temperature on power grid equipment degradation, the harmonic-load coupling action reflects combined influence of harmonic distortion rate and load coefficient on power grid equipment degradation, the operation time accumulation action reflects progressive influence of operation time length on power grid equipment degradation, and the multi-factor cooperative amplification action reflects mutual promotion effect among different degradation influence parameters.
6. The digital twin-based power grid equipment state degradation simulation and maintenance optimization method according to claim 5, wherein the degradation state quantification result is accurately quantified through vibration correction action, partial discharge correction action and accumulated degradation action, the vibration correction action is determined based on the ratio of actual vibration acceleration to rated vibration acceleration, the partial discharge correction action is determined based on the ratio of the actual partial discharge pulse number to the rated partial discharge pulse number, and the accumulated degradation action is determined based on the integral result of degradation factors over time.
7. The digital twinning-based power grid equipment state degradation simulation and maintenance optimization method according to claim 6, wherein the priority calculation model in S4 realizes the quantification of maintenance optimization priority through degradation degree, degradation spreading risk coefficient and resource constraint action, wherein the resource constraint action is determined based on the ratio of the number of current schedulable maintenance equipment to the total number of power grid equipment to be maintained, and the degradation spreading risk coefficient is determined based on the node importance of the power grid equipment in the power grid topology, load bearing proportion and the number of power grid nodes with fault occurrence.
8. The digital twinning-based power grid equipment state degradation simulation and maintenance optimization method according to claim 5, wherein the actual electric field strength parameter is obtained by acquiring potential differences at two ends of an insulating layer of the physical power grid equipment and calculating the thickness of the insulating layer through an optical fiber sensor, and the rated electric field strength parameter is determined based on design specifications and material insulation grades of the physical power grid equipment.
9. The method for simulating and maintaining the state degradation of the power grid equipment based on the digital twin according to claim 6, wherein the integration calculation of the accumulated degradation effect adopts a trapezoidal integration method, the integration step length is consistent with the data transmission period of the real-time data interaction link, and the data transmission period is set by the configuration parameters of the edge calculation gateway.
10. The digital twinning-based power grid equipment state degradation simulation and maintenance optimization method according to claim 7, wherein the power grid topology is constructed by combining a geometric simulation model and an operation behavior model of the digital twinning model, the node importance is determined based on the level of the power grid equipment in a power supply link, and the load bearing proportion is determined based on the ratio of rated load to actual load bearing load of the power grid equipment.

Description

Power grid equipment state degradation simulation and maintenance optimization method based on digital twin Technical Field The invention relates to the technical field of power grid equipment state monitoring and maintenance optimization, in particular to a power grid equipment state degradation simulation and maintenance optimization method based on digital twinning. Background As a core component of the power system, the operating state of the power grid device directly influences the stability and safety of the power supply. As the scale of the power grid continues to expand and the operational years increase, power grid equipment faces the problem of state degradation caused by various factors. In the prior art, the state degradation simulation of the power grid equipment adopts a single factor analysis mode, only carries out independent evaluation on single parameters such as electric field intensity, environmental temperature and the like, does not consider the interaction relation among different degradation factors, and simultaneously ignores the time accumulation effect of the degradation process, so that the degradation simulation result has larger deviation from the actual state. Based on the maintenance strategy formulated by the inaccurate degradation simulation result, the power grid maintenance resource and the actual degradation requirement of equipment cannot be reasonably matched, so that the problem of insufficient maintenance or excessive maintenance is caused, and the reliability and the economy of the power grid operation are affected. Based on the above-mentioned problems, a technical solution capable of accurately simulating the state degradation process of the power grid equipment and optimizing the maintenance decision is needed. Disclosure of Invention The invention aims to solve the defects in the prior art, and provides a digital twinning-based power grid equipment state degradation simulation and maintenance optimization method, which comprises the following steps that S1, a digital twinning model of power grid equipment is constructed, a real-time data interaction link is established between the digital twinning model and physical power grid equipment, and operation basic parameters of the physical power grid equipment are synchronized; S2, acquiring a multi-dimensional degradation influence parameter which influences the state degradation of the physical power grid equipment, wherein the multi-dimensional degradation influence parameter is acquired based on real-time mapping data of the digital twin model and sensing monitoring data of the physical power grid equipment, S3 is based on the multi-dimensional degradation influence parameter and the digital twin model, a state degradation process of the physical power grid equipment is simulated through a dynamic collaborative quantization algorithm which fuses a coupling relation of the multi-dimensional degradation influence parameter and a time accumulation effect to obtain a degradation state quantization result, S4 is combined with the degradation state quantization result and a power grid maintenance resource constraint parameter, a maintenance optimization priority of the physical power grid equipment is determined through a priority calculation model, and a maintenance optimization scheduling instruction is output. Preferably, the digital twin model in S1 includes a geometric simulation model, a physical characteristic model and an operation behavior model, where the geometric simulation model is used for re-etching three-dimensional structural parameters of the physical power grid device, the physical characteristic model matches material properties and electrical characteristic parameters of the physical power grid device, the operation behavior model simulates start-stop states and load change rules of the physical power grid device, and the real-time data interaction link realizes data transmission through an industrial ethernet interface and an edge computing gateway. Further preferably, the multi-dimensional degradation influencing parameters in S2 include an electric field strength parameter, an ambient temperature parameter, a harmonic distortion rate parameter, a vibration acceleration parameter, a partial discharge pulse number parameter, a running time parameter and a load factor parameter, and the sensing monitoring data are acquired by an optical fiber sensor, a temperature sensor, a current sensor and a vibration sensor which are installed on the physical power grid equipment. Further preferably, in S3, the dynamic collaborative quantization algorithm establishes a degradation factor quantization model based on time-series data of the multi-dimensional degradation influence parameter, and the degradation state quantization result is calculated by the degradation factor quantization model. Further preferably, the degradation factor quantization model realizes comprehensive quantization of multi-dimensional d