Power distribution network fault risk sensing method

Abstract

The invention discloses a power distribution network fault risk sensing method, which comprises the following steps: S1, data acquisition and monitoring: collecting operation data of a power grid in real time through a sensor, an intelligent electric meter, a fault indicator and a remote terminal unit which are installed in the power distribution network; s2, data preprocessing: cleaning the collected original data, removing abnormal values, processing missing data, and unifying data formats; s3, real-time analysis and monitoring: analyzing the real-time data flow by using a big data analysis technology, monitoring the operation state of the power grid, and identifying an abnormal condition; s4, fault early warning model establishment: based on the historical fault data and the current operation data, establishing a fault early warning model for predicting the possibility of fault occurrence and the possible fault type; and S5, risk assessment. According to the invention, potential faults can be sensed in advance, and key information is provided for preventive maintenance and emergency response, so that the stability and reliability of the power distribution network are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network fault risk perception, and in particular to a distribution network fault risk perception method. Background Art

[0002] The distribution network is an important part of the power system. It directly faces the end-user network and comprehensively undertakes distributed power sources, electric vehicles and other loads. Therefore, the reliability of the distribution network directly affects the user's evaluation of the use of electricity. With the acceleration of the construction of the power system, the distribution network has gradually connected to a high proportion of distributed power sources, which has brought huge benefits, but also brought problems such as passive acceptance, insufficient management and control, low degree of information digitization, and limited observable and controllable capabilities, which increased the risk of operation. The root cause of the risk of the distribution network lies in the probabilistic nature of its behavior. The impact of factors such as random failures of equipment, uncertainty of load, external weather and human construction are difficult to accurately predict. These factors may cause large-scale power outages in the system or even the entire network.

[0003] The existing publication number CN115564331A discloses a typical fault risk perception method for a distribution network, a distribution network, a device and a storage medium, which relate to the technical field of risk assessment of distribution networks. The present invention comprises the following steps: obtaining historical monitoring data and real-time operation data of a distribution network control background information system; performing wavelet transform on the acquired historical monitoring data and real-time operation data to extract fault features; creating a typical fault set based on the extracted fault features, and constructing a digital twin smart brain fault discriminator; inputting the measured data of the distribution network into the constructed digital twin smart brain fault discriminator, extracting features from the data, and obtaining fault diagnosis results.

[0004] Although the existing technology has achieved the perception of distribution network risks, it cannot accurately perceive the fault risks due to the limited scope of monitoring data. Therefore, we propose a distribution network fault risk perception method. Summary of the invention

[0005] The purpose of the present invention is to provide a distribution network fault risk perception method to solve the problems in the prior art.

[0006] To achieve the above object, the present invention provides the following technical solution: a method for sensing the risk of distribution network failure, comprising the following steps:

[0007] S1, data collection and monitoring: through the sensors, smart meters, fault indicators, and remote terminal units installed in the distribution network, the operation data of the power grid is collected in real time; the operation data of the power grid collected in S1 includes current, voltage, power, frequency, temperature, and switch status;

[0008] S2. Data preprocessing: clean the collected raw data, remove outliers, process missing data, unify data formats, ensure data quality, and lay a good foundation for subsequent analysis;

[0009] S3. Real-time analysis and monitoring: Use big data analysis technology to analyze real-time data streams, monitor the operation status of the power grid, and identify abnormal situations; this includes but is not limited to trend analysis, fluctuation detection, pattern recognition, etc.

[0010] S4. Establishment of fault warning model: Based on historical fault data and current operation data, a fault warning model is established to predict the possibility of fault occurrence and possible fault types;

[0011] S5. Risk assessment: Based on the results of the early warning model, the risk level of potential faults is assessed, taking into account factors such as the probability of fault occurrence, the scope of impact, the degree of impact on grid stability, and economic losses.

[0012] It also includes S6, visual presentation: using GIS and dashboards to present risk assessment results to operators in an intuitive manner, including fault warning maps and risk hotspot maps, overlaying monitoring data with geographic information to achieve a visual display of fault risks and help decision makers quickly understand risk distribution.

[0013] It also includes S7, generating specific maintenance, overhaul or adjustment operation strategy recommendations based on risk assessment and early warning results.

[0014] It also includes S8, feedback and iterative optimization: collect feedback information, evaluate the effectiveness of measures, continuously optimize early warning models and risk assessment mechanisms, form closed-loop management, and improve the accuracy and efficiency of risk perception.

[0015] Preferably, in S1, fault indicators and intelligent sensors are deployed at key nodes of the distribution network to quickly locate the fault area and feedback fault information; data is transmitted to the control center in real time through wireless communication technology to achieve remote monitoring and early warning.

[0016] Preferably, the topological structure information of the distribution network is used in S3 in combination with real-time measurement data to perform state estimation, identify deviations in the system operating state, and predict possible fault paths; active distribution network state estimation technology can provide a more accurate system state view and help identify potential risks.

[0017] Preferably, the fault warning model in S4 includes a machine learning model and a statistical analysis model.

[0018] Preferably, it also includes the integration of meteorological and environmental factors: considering external factors such as weather forecasts and natural disaster warnings, evaluating their impact on power grid operation, and pre-sensing the risk of failure caused by environmental factors.

[0019] Preferably, health index and aging management are also included: regular health assessments of distribution network assets are conducted, equipment aging models are established, and the probability of equipment failure is predicted; the remaining service life and risk level of equipment are evaluated through maintenance history, operating years, and performance degradation indicator information.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] 1. Identify and assess potential failure risks through continuous monitoring, data analysis and intelligent prediction, so as to take measures in advance to prevent or mitigate their impact; be able to perceive potential failures in advance and provide key information for preventive maintenance and emergency response, thereby improving the stability and reliability of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0023] Figure 1 It is a structural schematic diagram of the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention.

[0025] See also Figure 1 In an embodiment of the present invention, a method for sensing distribution network fault risk includes the following steps:

[0026] S1. Data collection and monitoring: Real-time collection of grid operation data through sensors, smart meters, fault indicators, and remote terminal units installed in the distribution network;

[0027] S2. Data preprocessing: clean the collected raw data, remove outliers, process missing data, and unify the data format;

[0028] S3. Real-time analysis and monitoring: Use big data analysis technology to analyze real-time data streams, monitor the operation status of the power grid, and identify abnormal situations;

[0029] S4. Establishment of fault warning model: Based on historical fault data and current operation data, a fault warning model is established to predict the possibility of fault occurrence and possible fault types;

[0030] S5. Risk assessment: Based on the results of the early warning model, the risk level of potential failures is assessed.

[0031] Preferably, it also includes S6, visual presentation: using GIS and dashboards to present risk assessment results to operators in an intuitive manner, including fault warning maps and risk hotspot maps, superimposing monitoring data with geographic information to achieve a visual display of fault risks and help decision makers quickly understand risk distribution.

[0032] Preferably, it also includes S7, generating specific maintenance, repair or adjustment operation strategy suggestions based on the risk assessment and warning results.

[0033] Preferably, it also includes S8, feedback and iterative optimization: collecting feedback information, evaluating the effectiveness of measures, continuously optimizing early warning models and risk assessment mechanisms, and forming a closed-loop management.

[0034] Preferably, the operation data of the power grid collected in S1 includes current, voltage, power, frequency, temperature, and switch status.

[0035] Preferably, in S1, fault indicators and intelligent sensors are deployed at key nodes of the distribution network to quickly locate the fault area and feedback fault information; data is transmitted to the control center in real time through wireless communication technology to achieve remote monitoring and early warning.

[0036] Preferably, the topological structure information of the distribution network is used in S3 in combination with real-time measurement data to perform state estimation, identify deviations in the system operating state, and predict possible fault paths; active distribution network state estimation technology can provide a more accurate system state view and help identify potential risks.

[0037] Preferably, the fault warning model in S4 includes a machine learning model and a statistical analysis model.

[0038] Preferably, it also includes the integration of meteorological and environmental factors: consider external factors such as weather forecasts and natural disaster warnings, evaluate their impact on power grid operation, and perceive the risk of failure caused by environmental factors in advance. For example, strong winds and heavy rains may increase the risk of line damage, and high temperatures may cause equipment overheating and failure.

[0039] Preferably, health index and aging management are also included: regular health assessments of distribution network assets are conducted, equipment aging models are established, and the probability of equipment failure is predicted; the remaining service life and risk level of equipment are evaluated through maintenance history, operating years, and performance degradation indicator information.

[0040] Through continuous monitoring, data analysis and intelligent prediction, potential failure risks can be identified and assessed, so that measures can be taken in advance to prevent or mitigate their impact; potential failures can be sensed in advance, providing key information for preventive maintenance and emergency response, thereby improving the stability and reliability of the distribution network.

[0041] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A distribution network fault risk perception method, characterized in that: The following steps are involved: S1. Data collection and monitoring: Real-time collection of grid operation data through sensors, smart meters, fault indicators, and remote terminal units installed in the distribution network; S2. Data preprocessing: clean the collected raw data, remove outliers, process missing data, and unify the data format; S3. Real-time analysis and monitoring: Use big data analysis technology to analyze real-time data streams, monitor the operation status of the power grid, and identify abnormal situations; S4. Establishment of fault warning model: Based on historical fault data and current operation data, a fault warning model is established to predict the possibility of fault occurrence and possible fault types; S5. Risk assessment: Based on the results of the early warning model, the risk level of potential failures is assessed.

2. A distribution network fault risk perception method according to claim 1, characterized in that: It also includes S6, visual presentation: using GIS and dashboards to present risk assessment results to operators in an intuitive manner, including fault warning maps and risk hotspot maps, overlaying monitoring data with geographic information to achieve a visual display of fault risks and help decision makers quickly understand risk distribution.

3. A distribution network fault risk perception method according to claim 1 or 2, characterized in that: It also includes S7, generating specific maintenance, overhaul or adjustment operation strategy recommendations based on risk assessment and early warning results.

4. A distribution network fault risk perception method according to claim 3, characterized in that: It also includes S8, feedback and iterative optimization: collect feedback information, evaluate the effectiveness of measures, continuously optimize early warning models and risk assessment mechanisms, and form a closed-loop management.

5. A distribution network fault risk perception method according to claim 1, characterized in that: The operation data of the power grid collected in S1 include current, voltage, power, frequency, temperature, and switch status.

6. A distribution network fault risk perception method according to claim 1, characterized in that: In the S1, fault indicators and intelligent sensors are deployed at key nodes of the distribution network to quickly locate the fault area and feedback fault information; data is transmitted to the control center in real time through wireless communication technology to achieve remote monitoring and early warning.

7. A distribution network fault risk perception method according to claim 1, characterized in that: The S3 uses the topological structure information of the distribution network in combination with real-time measurement data to perform state estimation, identify deviations in the system operating state, and predict possible fault paths; active distribution network state estimation technology can provide a more accurate system state view and help identify potential risks.

8. A distribution network fault risk perception method according to claim 1, characterized in that: The fault warning model in S4 includes a machine learning model and a statistical analysis model.

9. A distribution network fault risk perception method according to claim 1, characterized in that: It also includes the integration of meteorological and environmental factors: considering external factors such as weather forecasts and natural disaster warnings, evaluating their impact on power grid operations, and pre-sensing the risk of failures caused by environmental factors.

10. A distribution network fault risk perception method according to claim 1, characterized in that: It also includes health index and aging management: regular health assessment of distribution network assets, establishment of equipment aging models, and prediction of equipment failure probability; and evaluation of the remaining service life and risk level of equipment through maintenance history, years of operation, and performance degradation indicator information.

Images